From 136ba40b2047c96d45414b38f001d582f0e00e73 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Fri, 28 Jun 2024 20:21:33 -0700 Subject: [PATCH 01/39] Added a bunch of ELF stuff --- darklim/elf/__init__.py | 1 + darklim/elf/_elf.py | 228 +++++++++++++++++++++++++++++++ darklim/sensitivity/_sens_est.py | 87 ++++++++---- 3 files changed, 292 insertions(+), 24 deletions(-) create mode 100644 darklim/elf/__init__.py create mode 100644 darklim/elf/_elf.py diff --git a/darklim/elf/__init__.py b/darklim/elf/__init__.py new file mode 100644 index 0000000..b39441c --- /dev/null +++ b/darklim/elf/__init__.py @@ -0,0 +1 @@ +from ._elf import * diff --git a/darklim/elf/_elf.py b/darklim/elf/_elf.py new file mode 100644 index 0000000..18a13f1 --- /dev/null +++ b/darklim/elf/_elf.py @@ -0,0 +1,228 @@ +from IPython.utils import io +import numpy as np +import sys +sys.path.append('/Users/vetri/GitRepos/DarkELF/') +from darkelf import darkelf +from darklim import constants + +__all__ = [ + "get_dRdE_lambda_Al2O3_electron", + "get_dRdE_lambda_GaAs_electron", +] + +def get_dRdE_lambda_Al2O3_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, kcut=0, method='grid', withscreening=True, suppress_darkelf_output=False, gain=1.): + """ + Function to get an anonymous lambda function, which calculates dRdE + for DM-electron scattering in Al2O3 given only deposited energy. + + Parameters + ---------- + mX_eV : float + Dark matter mass in eV + mediator : str + Dark photon mediator mass. Must be "massive" (infinity) or + "massless" (zero). + sigmae : float + DM-electron scattering cross section in cm^2 + kcut : float + Maximum k value in the integration, in eV. If kcut=0 (default), the + integration is cut off at the highest k-value of the grid at hand. + method : str + Must be "grid" or "Lindhard". Choice to use interpolated grid of + epsilon, or Lindhard analytic epsilon + withscreening : bool + Whether to include the 1/|epsilon|^2 factor in the scattering rate + suppress_darkelf_output : bool + Whether to suppress the (useful but long) output that DarkELF gives + when loading a material's properties. + + Returns + ------- + fun : lambda function + A function to calculate dRdE in DRU given E + + """ + + # Set up DarkELF GaAs object + if suppress_darkelf_output: + print('WARNING: You are suppressing DarkELF output') + with io.capture_output() as captured: + sapphire = darkelf(target='Al2O3', filename="Al2O3_mermin.dat") + else: + sapphire = darkelf(target='Al2O3', filename="Al2O3_mermin.dat") + + # Create anonymous function to get rate with only deposited energy + # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV + # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) + sapphire.update_params(mX=mX_eV, mediator=mediator) + fun = lambda keV : np.heaviside(keV * 1000 / gain - 2 * constants.bandgap_Al2O3_eV, 1) * \ + sapphire.dRdomega_electron(keV * 1000 / gain, method=method, sigmae=sigmae, kcut=kcut, withscreening=withscreening) * \ + (1000 / 365.25) / gain + + return fun + + + +if False: + + def get_dRdE_lambda_Al2O3_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, kcut=0, method='grid', withscreening=True, suppress_darkelf_output=False, gain=1): + """ + Function to get an anonymous lambda function, which calculates dRdE + for DM-electron scattering in Al2O3 given only deposited energy. + + Parameters + ---------- + mX_eV : float + Dark matter mass in eV + mediator : str + Dark photon mediator mass. Must be "massive" (infinity) or + "massless" (zero). + sigmae : float + DM-electron scattering cross section in cm^2 + kcut : float + Maximum k value in the integration, in eV. If kcut=0 (default), the + integration is cut off at the highest k-value of the grid at hand. + method : str + Must be "grid" or "Lindhard". Choice to use interpolated grid of + epsilon, or Lindhard analytic epsilon + withscreening : bool + Whether to include the 1/|epsilon|^2 factor in the scattering rate + suppress_darkelf_output : bool + Whether to suppress the (useful but long) output that DarkELF gives + when loading a material's properties. + + Returns + ------- + fun : lambda function + A function to calculate dRdE in DRU given E + + """ + + # Set up DarkELF Al2O3 (sapphire) object + if suppress_darkelf_output: + print('WARNING: You are suppressing DarkELF output') + with io.capture_output() as captured: + sapphire = darkelf(target='Al2O3', filename="Al2O3_mermin.dat") + else: + sapphire = darkelf(target='Al2O3', filename="Al2O3_mermin.dat") + + # Create anonymous function to get rate with only deposited energy + # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV + # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) + sapphire.update_params(mX=mX_eV, mediator=mediator) + fun = lambda keV : np.heaviside(keV - 2 * constants.bandgap_Al2O3_eV, 1) * \ + (1000 / 365.25) * \ + sapphire.dRdomega_electron(keV * 1000, method=method, sigmae=sigmae, kcut=kcut, withscreening=withscreening) + + return fun + + +def get_dRdE_lambda_GaAs_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, kcut=0, method='grid', withscreening=True, suppress_darkelf_output=False, gain=1.): + """ + Function to get an anonymous lambda function, which calculates dRdE + for DM-electron scattering in GaAs given only deposited energy. + + Parameters + ---------- + mX_eV : float + Dark matter mass in eV + mediator : str + Dark photon mediator mass. Must be "massive" (infinity) or + "massless" (zero). + sigmae : float + DM-electron scattering cross section in cm^2 + kcut : float + Maximum k value in the integration, in eV. If kcut=0 (default), the + integration is cut off at the highest k-value of the grid at hand. + method : str + Must be "grid" or "Lindhard". Choice to use interpolated grid of + epsilon, or Lindhard analytic epsilon + withscreening : bool + Whether to include the 1/|epsilon|^2 factor in the scattering rate + suppress_darkelf_output : bool + Whether to suppress the (useful but long) output that DarkELF gives + when loading a material's properties. + + Returns + ------- + fun : lambda function + A function to calculate dRdE in DRU given E + + """ + + # Set up DarkELF GaAs object + if suppress_darkelf_output: + print('WARNING: You are suppressing DarkELF output') + with io.capture_output() as captured: + gaas = darkelf(target='GaAs', filename="GaAs_mermin.dat") + else: + gaas = darkelf(target='GaAs', filename="GaAs_mermin.dat") + + # Create anonymous function to get rate with only deposited energy + # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV + # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) + gaas.update_params(mX=mX_eV, mediator=mediator) + fun = lambda keV : np.heaviside(keV * 1000 / gain - 2 * constants.bandgap_GaAs_eV, 1) * \ + gaas.dRdomega_electron(keV * 1000 / gain, method=method, sigmae=sigmae, kcut=kcut, withscreening=withscreening) * \ + (1000 / 365.25) / gain + + return fun + + + + + +def get_dRdE_lambda_GaAs_phonon(mX_eV=1e8, mediator='massless', sigmae=1e-31, suppress_darkelf_output=False, gain=1.): + """ + Function to get an anonymous lambda function, which calculates dRdE + for DM-electron scattering in GaAs via phonons given only deposited + energy. We return the total rate from both single-photon and + multi-phonon interactions (by adding these together). + + Parameters + ---------- + mX_eV : float + Dark matter mass in eV + mediator : str + Dark photon mediator mass. Must be "massive" (infinity) or + "massless" (zero). + sigmae : float + DM-electron scattering cross section in cm^2 + suppress_darkelf_output : bool + Whether to suppress the (useful but long) output that DarkELF gives + when loading a material's properties. + + Returns + ------- + fun : lambda function + A function to calculate dRdE in DRU given E + + """ + + # Set up DarkELF Al2O3 (sapphire) object + if suppress_darkelf_output: + print('WARNING: You are suppressing DarkELF output') + with io.capture_output() as captured: + gaas_single = darkelf(target='GaAs',phonon_filename="GaAs_epsphonon_data10K.dat") + gaas_multi = darkelf(target='GaAs') + else: + gaas_single = darkelf(target='GaAs',phonon_filename="GaAs_epsphonon_data10K.dat") + gaas_multi = darkelf(target='GaAs') + + # Create anonymous function to get rate with only deposited energy + # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV + # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) + if mediator == 'massless': + gaas_single.update_params(mX=mX_eV, mMed=0) + gaas_multi.update_params(mX=mX_eV, mMed=0) + elif mediator == 'massive': + gaas_single.update_params(mX=mX_eV, mMed=1e11) + gaas_multi.update_params(mX=mX_eV, mMed=1e11) + fun = lambda keV : (1000 / 365.25) * \ + (gaas_single.dRdomega_phonon(keV * 1000, sigmae=sigmae) + + gaas_multi._dR_domega_multiphonons_no_single(keV * 1000, )) + + return fun + + + diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index 45599da..25ec800 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -4,10 +4,11 @@ import mendeleev from darklim import constants -from darklim.limit._limit import drde, drde_max_q, optimuminterval, fc_limits, get_fc_ul, get_signal_rate +from darklim.limit._limit import drde, optimuminterval, fc_limits, get_fc_ul, get_signal_rate from darklim.sensitivity._random_sampling import pdf_sampling from darklim.sensitivity._plotting import RatePlot +import darklim.elf._elf as elf __all__ = [ "calculate_substrate_mass", @@ -186,7 +187,8 @@ def __init__(self, m_det, time_elapsed, eff=1, tm="Si", gain=1):#, signal_name=' #self.signal = None #if self.signal_name=='SI-NR': # self.add_signal_model - + + def add_flat_bkgd(self, flat_rate): """ Method for adding a flat background to the simulation. @@ -332,7 +334,7 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, sigs = [] if m_dms is None: - m_dms = np.geomspace(0.5, 2, num=50) + m_dms = np.geomspace(0.01, 2, num=5) en_interp = np.geomspace(e_low, e_high, num=npts) @@ -411,7 +413,7 @@ def run_sim_fc(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms=None, uls = [] if m_dms is None: - m_dms = np.geomspace(0.5, 2, num=50) + m_dms = np.geomspace(0.01, 2, num=5) #if verbose: # print('Running over the following masses:',m_dms) @@ -428,6 +430,7 @@ def run_sim_fc(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms=None, drdefunction = None if use_drdefunction: drdefunction = [ lambda x,m: drde_wimp_obs( x, m, sigma0, self.tm, self.gain ) for m in m_dms ] + #drdefunction = [ drde_obs( lambda x: drde(x,m,sigma0,tm=self.tm), self.gain ) for m in m_dms ] #drdefunction = [drde_obs(en_interp,lambda x: drde(x,m,sigma0,tm=self.tm),self.gain) for m in m_dms] @@ -475,37 +478,71 @@ def run_sim_fc(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms=None, ax.axvline(ul,ls='--',color='red') ax.set_xlabel('Upper Limit [Events]') ax.set_xlim(0,max(np.asarray(uls))) - outdir = '/global/cfs/cdirs/lz/users/haselsco/TESSERACT_Limits/DarkLim/examples/' + outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' plt.savefig(outdir+pltname+'.png',dpi=300, facecolor='white',bbox_inches='tight') return m_dms, sig, ul def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, - plot_bkgd=False, res=None, verbose=False, sigma0=1e-41,use_drdefunction=False,pltname=None): + plot_bkgd=False, res=None, verbose=False, sigma0=1e-41,use_drdefunction=False,pltname=None, + elf_model=None, elf_params=None, elf_target=None, savedir=None, return_only_drde=False): """ Faster version of the above, avoiding repeat calculations of signal rates. """ if m_dms is None: - m_dms = np.geomspace(0.5, 2, num=50) - - #if e_high is None: # set upper energy range to max energy of DM recoil spectrum: - # e_uppers = np.asarray([ edep_to_eobs(drde_max_q(m, tm=self.tm))+5*res for m in m_dms ]) - # e_high = max(e_uppers) - - en_interp = np.geomspace(e_low, e_high, num=npts) - + m_dms = np.geomspace(0.01, 2, num=5) + + + + # create signal model functions at each mass + drdefunction = None + + if use_drdefunction and elf_model is None: + + drdefunction = [ lambda x,m: drde_wimp_obs( x, m, sigma0, self.tm, self.gain ) for m in m_dms ] + + elif elf_model == 'electron' and elf_target == 'GaAs': + + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_kcut = elf_params['kcut'] if 'kcut' in elf_params else 0 + elf_method = elf_params['method'] if 'method' in elf_params else 'grid' + elf_screening = elf_params['withscreening'] if 'withscreening' in elf_params else True + elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + + drdefunction = \ + [elf.get_dRdE_lambda_GaAs_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, + kcut=elf_kcut, method=elf_method, withscreening=elf_screening, + suppress_darkelf_output=elf_suppress, gain=self.gain) + for m in m_dms] + + elif elf_model == 'electron' and elf_target == 'Al2O3': + + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_kcut = elf_params['kcut'] if 'kcut' in elf_params else 0 + elf_method = elf_params['method'] if 'method' in elf_params else 'grid' + elf_screening = elf_params['withscreening'] if 'withscreening' in elf_params else True + elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + + drdefunction = \ + [elf.get_dRdE_lambda_Al2O3_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, + kcut=elf_kcut, method=elf_method, withscreening=elf_screening, + suppress_darkelf_output=elf_suppress, gain=self.gain) + for m in m_dms] + + if return_only_drde: + return drdefunction + + + # created summed 'known' background function: known_bkgd_func = lambda x: np.stack([bkgd(x) for ind,bkgd in enumerate(self._backgrounds) if ind in known_bkgs_list], axis=1,).sum(axis=1) print('Treating the following as known bkgs for FC limits: ') for idx in known_bkgs_list: print(' ',self._background_labels[idx]) - - # create signal model functions at each mass - drdefunction = None - if use_drdefunction: - drdefunction = [ lambda x,m: drde_wimp_obs( x, m, sigma0, self.tm, self.gain ) for m in m_dms ] - + + en_interp = np.geomspace(e_low, e_high, num=npts) + uls = np.zeros(nexp) obs = np.zeros(nexp) exp = np.zeros(nexp) @@ -532,7 +569,8 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= print('Median Exp. Bkg =\t {:0.2f} evts'.format(median_exp)) print('Median N Obs =\t {:0.2f} evts'.format(median_obs)) print('Median 90% CL UL =\t {:0.2f} evts'.format(median_ul)) - + + # get signal rates at the reference xsec for each DM mass: dm_rates, raw_dm_rates = get_signal_rate( en_interp, # efficiency curve energies @@ -545,9 +583,10 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= verbose=verbose, # print outs drdefunction=drdefunction, # hard_threshold=e_low, #threshold, - sigma0=sigma0 + sigma0=sigma0, + savedir=savedir ) - + if verbose: print('DM signal rates:',dm_rates) @@ -558,7 +597,7 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= ax.axvline(median_ul,ls='--',color='red') ax.set_xlabel('Upper Limit [Events]') ax.set_xlim(0,max(uls)) - outdir = '/global/cfs/cdirs/lz/users/haselsco/TESSERACT_Limits/DarkLim/examples/' + outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' plt.savefig(outdir+pltname+'.png',dpi=300, facecolor='white',bbox_inches='tight') # expected bkg rate, made to match m_dm len just to make analysis easier From 27b802de923ca6b2f24fc3ae6b29f5540ae00a38 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Fri, 28 Jun 2024 20:21:46 -0700 Subject: [PATCH 02/39] Added new existing limits --- .../ExistingLimits/DAMIC_M_ER_Massless.txt | 69 ++++++++++++++ .../ExistingLimits/Freeze_in_ER_Massless.txt | 86 ++++++++++++++++++ .../SENSEI_MINOS_ER_Massless.txt | 81 +++++++++++++++++ .../SENSEI_SNOLAB_ER_Massless.txt | 90 +++++++++++++++++++ ...EI_SNOLAB_Solar_Reflection_ER_Massless.txt | 66 ++++++++++++++ .../XENON1T_S2Only_ER_Massless.txt | 52 +++++++++++ .../protoSENSEI_MINOS_ER_Massless.txt | 20 +++++ 7 files changed, 464 insertions(+) create mode 100644 examples/ExistingLimits/DAMIC_M_ER_Massless.txt create mode 100644 examples/ExistingLimits/Freeze_in_ER_Massless.txt create mode 100644 examples/ExistingLimits/SENSEI_MINOS_ER_Massless.txt create mode 100644 examples/ExistingLimits/SENSEI_SNOLAB_ER_Massless.txt create mode 100644 examples/ExistingLimits/SENSEI_SNOLAB_Solar_Reflection_ER_Massless.txt create mode 100644 examples/ExistingLimits/XENON1T_S2Only_ER_Massless.txt create mode 100644 examples/ExistingLimits/protoSENSEI_MINOS_ER_Massless.txt diff --git a/examples/ExistingLimits/DAMIC_M_ER_Massless.txt b/examples/ExistingLimits/DAMIC_M_ER_Massless.txt new file mode 100644 index 0000000..db34b97 --- /dev/null +++ b/examples/ExistingLimits/DAMIC_M_ER_Massless.txt @@ -0,0 +1,69 @@ +# MeV DM-e cm^2 +1.6484 8.4983e-34 +1.6809 7.2954e-34 +1.7558 5.6973e-34 +1.7802 4.9354e-34 +1.7342 6.6726e-34 +1.8997 3.8009e-34 +1.8594 4.4985e-34 +1.9409 3.2672e-34 +2.0449 2.5783e-34 +1.9937 2.9526e-34 +2.1007 2.2012e-34 +2.1958 1.8998e-34 +2.2165 1.6621e-34 +2.3058 1.4605e-34 +2.4144 1.1619e-34 +2.3418 1.3492e-34 +2.5097 9.8813e-35 +2.6290 8.0406e-35 +2.7133 6.8605e-35 +2.9044 5.3292e-35 +2.7783 6.2321e-35 +3.0383 4.6151e-35 +3.2193 3.6498e-35 +3.0769 4.2583e-35 +3.3806 3.1549e-35 +3.6099 2.4783e-35 +3.4611 2.8786e-35 +4.0012 1.7336e-35 +4.1012 1.5072e-35 +4.4814 1.1169e-35 +4.8051 1.0017e-35 +5.4783 7.9592e-36 +5.7833 7.1675e-36 +6.5939 5.5131e-36 +7.0702 4.9503e-36 +8.7621 4.1557e-36 +9.6503 3.9048e-36 +12.219 3.4226e-36 +13.676 3.4410e-36 +18.786 3.4837e-36 +21.346 3.6306e-36 +26.454 4.1436e-36 +29.292 4.3716e-36 +37.289 5.0532e-36 +41.290 5.3600e-36 +52.562 6.2686e-36 +57.891 6.7589e-36 +71.745 8.1929e-36 +78.594 8.8369e-36 +96.659 1.0766e-35 +105.85 1.1696e-35 +128.05 1.3498e-35 +142.55 1.4962e-35 +180.01 1.8821e-35 +192.50 2.0003e-35 +206.03 2.1686e-35 +237.93 2.4689e-35 +256.25 2.6750e-35 +318.71 3.2423e-35 +347.27 3.4694e-35 +421.25 4.2211e-35 +459.00 4.5806e-35 +562.77 5.6338e-35 +613.20 6.1064e-35 +751.84 7.4064e-35 +823.62 7.9976e-35 +879.17 8.7470e-35 +957.10 9.4806e-35 diff --git a/examples/ExistingLimits/Freeze_in_ER_Massless.txt b/examples/ExistingLimits/Freeze_in_ER_Massless.txt new file mode 100644 index 0000000..ab595cd --- /dev/null +++ b/examples/ExistingLimits/Freeze_in_ER_Massless.txt @@ -0,0 +1,86 @@ +# MeV DM-e cm^2 +4.36998e-2 1.09120e-38 +4.87100e-2 1.22260e-38 +5.48110e-2 1.43430e-38 +6.16762e-2 1.68226e-38 +6.94013e-2 1.95726e-38 +7.80939e-2 2.26228e-38 +8.78754e-2 2.62825e-38 +9.88819e-2 3.03710e-38 +1.11267e-1 3.47972e-38 +1.25204e-1 3.93374e-38 +1.40886e-1 4.42861e-38 +1.58532e-1 4.97966e-38 +1.78115e-1 5.55544e-38 +1.98894e-1 6.29051e-38 +2.25874e-1 7.09495e-38 +2.54165e-1 7.68565e-38 +2.86000e-1 8.23268e-38 +3.21822e-1 8.69057e-38 +3.62130e-1 9.12485e-38 +4.07488e-1 9.63237e-38 +4.58527e-1 1.02928e-37 +5.15958e-1 1.12043e-37 +5.80583e-1 1.23220e-37 +6.53302e-1 1.29455e-37 +7.35130e-1 1.45050e-37 +8.25096e-1 1.62539e-37 +9.34389e-1 1.86388e-37 +1.04740e+0 2.07474e-37 +1.17859e+0 2.46986e-37 +1.31726e+0 2.82408e-37 +1.49232e+0 3.29001e-37 +1.67924e+0 3.53623e-37 +1.88957e+0 3.72927e-37 +2.12624e+0 3.92518e-37 +2.39256e+0 4.13744e-37 +2.69223e+0 4.30623e-37 +3.02944e+0 4.42544e-37 +3.40888e+0 4.56796e-37 +3.83585e+0 4.75432e-37 +4.31630e+0 4.94827e-37 +4.85692e+0 5.08278e-37 +5.46526e+0 5.18289e-37 +6.14980e+0 5.27983e-37 +6.92007e+0 5.37595e-37 +7.78682e+0 5.47383e-37 +8.76214e+0 5.56534e-37 +9.85962e+0 5.58437e-37 +1.10946e+1 5.58437e-37 +1.24842e+1 5.60073e-37 +1.40478e+1 5.70548e-37 +1.58074e+1 5.82354e-37 +1.77873e+1 5.96145e-37 +2.00152e+1 6.14144e-37 +2.25221e+1 6.52107e-37 +2.53430e+1 7.15761e-37 +2.85173e+1 7.84098e-37 +3.20891e+1 8.56032e-37 +3.64734e+1 9.19955e-37 +4.06310e+1 1.00475e-36 +4.52894e+1 1.12899e-36 +5.14467e+1 1.28750e-36 +5.78905e+1 1.38858e-36 +6.51414e+1 1.49687e-36 +7.33005e+1 1.56098e-36 +8.24815e+1 1.56403e-36 +9.28125e+1 1.56022e-36 +1.04437e+2 1.51450e-36 +1.17519e+2 1.45371e-36 +1.32238e+2 1.39469e-36 +1.48801e+2 1.34068e-36 +1.67439e+2 1.28499e-36 +1.88411e+2 1.23764e-36 +2.12010e+2 1.19377e-36 +2.38564e+2 1.15146e-36 +2.68445e+2 1.09720e-36 +3.02068e+2 1.03433e-36 +3.39903e+2 9.70323e-37 +3.82476e+2 9.14727e-37 +4.30382e+2 8.58540e-37 +4.84288e+2 8.05019e-37 +5.44947e+2 7.46780e-37 +6.13202e+2 6.92079e-37 +6.90007e+2 6.42950e-37 +7.76432e+2 5.97892e-37 +8.77596e+2 5.56273e-37 diff --git a/examples/ExistingLimits/SENSEI_MINOS_ER_Massless.txt b/examples/ExistingLimits/SENSEI_MINOS_ER_Massless.txt new file mode 100644 index 0000000..56fb0af --- /dev/null +++ b/examples/ExistingLimits/SENSEI_MINOS_ER_Massless.txt @@ -0,0 +1,81 @@ +# MeV DM-e cm^2 +1.6814 8.6240e-34 +1.6478 9.8202e-34 +1.7306 7.4013e-34 +1.7951 5.6973e-34 +1.8202 4.9628e-34 +1.7342 6.6726e-34 +1.8997 3.8009e-34 +1.8594 4.4985e-34 +1.9409 3.2672e-34 +2.0449 2.5783e-34 +1.9937 2.9526e-34 +2.1007 2.2012e-34 +2.1958 1.8998e-34 +2.2662 1.6621e-34 +2.3058 1.4605e-34 +2.4549 1.1658e-34 +2.3943 1.3197e-34 +2.5343 1.0219e-34 +2.7149 7.7358e-35 +2.5930 9.1576e-35 +2.8075 6.5800e-35 +2.9860 5.3292e-35 +3.0523 4.6350e-35 +3.3033 3.6446e-35 +3.1285 4.2583e-35 +3.4563 3.1901e-35 +3.7736 2.4804e-35 +3.5583 2.8786e-35 +3.9501 2.1674e-35 +4.1151 1.9060e-35 +4.4734 1.6244e-35 +4.7538 1.4644e-35 +5.0699 1.2889e-35 +5.3206 1.1366e-35 +5.5539 1.0128e-35 +5.8286 8.9019e-36 +6.3170 7.8266e-36 +7.0702 7.0195e-36 +7.9557 6.4296e-36 +8.9522 6.1716e-36 +10.073 6.0583e-36 +11.335 6.0376e-36 +12.755 6.0112e-36 +14.353 6.0112e-36 +16.150 6.0053e-36 +18.173 5.9790e-36 +20.449 6.0849e-36 +23.011 6.5403e-36 +25.893 7.0675e-36 +29.136 7.6224e-36 +32.785 8.2410e-36 +36.891 8.8923e-36 +41.512 9.6045e-36 +46.712 1.0374e-35 +52.562 1.1298e-35 +59.146 1.2467e-35 +66.554 1.3745e-35 +74.890 1.5153e-35 +84.270 1.6713e-35 +94.825 1.8452e-35 +106.70 2.0492e-35 +120.07 2.2980e-35 +135.11 2.5808e-35 +152.03 2.8828e-35 +171.07 3.2392e-35 +192.50 3.6289e-35 +216.61 4.0576e-35 +243.74 4.5548e-35 +274.27 5.0854e-35 +308.62 5.7112e-35 +347.27 6.3766e-35 +390.77 7.1613e-35 +439.72 7.9957e-35 +494.79 8.9752e-35 +553.79 1.0051e-34 +619.82 1.1364e-34 +697.45 1.2788e-34 +780.61 1.4459e-34 +873.68 1.6192e-34 +951.98 1.7605e-34 diff --git a/examples/ExistingLimits/SENSEI_SNOLAB_ER_Massless.txt b/examples/ExistingLimits/SENSEI_SNOLAB_ER_Massless.txt new file mode 100644 index 0000000..f780e47 --- /dev/null +++ b/examples/ExistingLimits/SENSEI_SNOLAB_ER_Massless.txt @@ -0,0 +1,90 @@ +# MeV DM-e cm^2 +1.2385 9.3552e-34 +1.2732 7.6629e-34 +1.2974 6.2098e-34 +1.3121 5.2491e-34 +1.3405 4.6283e-34 +1.3584 3.8363e-34 +1.3879 3.0852e-34 +1.4176 2.5682e-34 +1.4534 2.2283e-34 +1.4752 1.9512e-34 +1.4936 1.7361e-34 +1.5294 1.5282e-34 +1.5478 1.1340e-34 +1.5840 9.8968e-35 +1.6228 8.7558e-35 +1.6592 7.7790e-35 +1.6809 6.6757e-35 +1.5323 1.3335e-34 +1.7014 5.7641e-35 +1.7398 4.9991e-35 +1.7760 4.3248e-35 +1.8121 3.7641e-35 +1.8694 3.2392e-35 +1.9237 2.7858e-35 +1.9619 2.4458e-35 +2.0044 2.1926e-35 +2.0791 1.9000e-35 +2.1348 1.6578e-35 +2.1906 1.4653e-35 +2.2248 1.2944e-35 +2.3418 1.1124e-35 +2.4421 9.4743e-36 +2.5312 8.0415e-36 +2.6464 6.8591e-36 +2.7770 6.0507e-36 +2.9022 5.2945e-36 +3.0646 4.6199e-36 +3.2833 4.0166e-36 +3.5172 3.5807e-36 +3.7315 3.1592e-36 +4.0831 2.8965e-36 +4.5658 2.5632e-36 +5.0903 2.3790e-36 +5.7664 2.2371e-36 +6.4887 2.0966e-36 +7.3014 2.0233e-36 +8.2159 1.9988e-36 +9.2450 1.9775e-36 +10.403 1.9910e-36 +11.706 2.0885e-36 +13.172 2.1939e-36 +14.822 2.3047e-36 +16.678 2.4210e-36 +18.767 2.5408e-36 +21.118 2.7163e-36 +23.763 2.9554e-36 +26.740 3.2187e-36 +30.089 3.5020e-36 +33.857 3.8158e-36 +38.098 4.1517e-36 +42.870 4.5215e-36 +50.139 5.0712e-36 +56.242 5.6527e-36 +62.629 6.2078e-36 +71.514 6.8849e-36 +79.812 7.8055e-36 +89.392 8.4687e-36 +96.064 9.1168e-36 +105.66 9.8631e-36 +116.26 1.0868e-35 +134.60 1.2380e-35 +152.19 1.3902e-35 +180.13 1.6262e-35 +168.61 1.5034e-35 +207.51 1.8405e-35 +227.65 2.0437e-35 +251.88 2.2405e-35 +283.67 2.4943e-35 +319.57 2.7540e-35 +356.40 3.1114e-35 +400.68 3.3872e-35 +466.44 4.0224e-35 +524.86 4.4810e-35 +430.46 3.7621e-35 +600.19 5.1403e-35 +671.75 5.7350e-35 +746.48 6.3939e-35 +837.63 7.1710e-35 +914.25 8.3382e-35 diff --git a/examples/ExistingLimits/SENSEI_SNOLAB_Solar_Reflection_ER_Massless.txt b/examples/ExistingLimits/SENSEI_SNOLAB_Solar_Reflection_ER_Massless.txt new file mode 100644 index 0000000..45daca3 --- /dev/null +++ b/examples/ExistingLimits/SENSEI_SNOLAB_Solar_Reflection_ER_Massless.txt @@ -0,0 +1,66 @@ +# MeV DM-e cm^2 +0.010437 3.4757e-37 +0.011757 3.7892e-37 +0.013301 4.1719e-37 +0.014966 4.5858e-37 +0.016841 5.0704e-37 +0.018656 5.6306e-37 +0.019781 6.4685e-37 +0.020871 7.2372e-37 +0.022123 8.1291e-37 +0.024189 9.5355e-37 +0.025576 1.0815e-36 +0.027668 1.1848e-36 +0.030312 1.3552e-36 +0.032541 1.5259e-36 +0.035567 1.7260e-36 +0.038311 1.9528e-36 +0.041197 2.2321e-36 +0.044190 2.5397e-36 +0.047095 2.8310e-36 +0.050034 3.2788e-36 +0.053361 3.6895e-36 +0.056979 4.1585e-36 +0.061018 4.7080e-36 +0.067928 5.3006e-36 +0.072834 5.8962e-36 +0.076847 6.6847e-36 +0.081439 7.6410e-36 +0.086473 8.7504e-36 +0.092222 9.8288e-36 +0.098475 1.1061e-35 +0.10535 1.2528e-35 +0.11074 1.4206e-35 +0.11677 1.6094e-35 +0.12320 1.8344e-35 +0.12999 2.0842e-35 +0.13716 2.3680e-35 +0.14535 2.7044e-35 +0.15351 3.0815e-35 +0.16213 3.4816e-35 +0.17274 3.9450e-35 +0.18498 4.5415e-35 +0.19777 5.1675e-35 +0.21293 5.8683e-35 +0.22790 6.6405e-35 +0.25011 7.5596e-35 +0.28062 8.4715e-35 +0.30176 9.5977e-35 +0.32315 1.0968e-34 +0.34549 1.2506e-34 +0.37197 1.4122e-34 +0.40098 1.6037e-34 +0.42952 1.8309e-34 +0.45363 2.0768e-34 +0.47863 2.3457e-34 +0.50772 2.6487e-34 +0.54517 2.9738e-34 +0.58955 3.3505e-34 +0.63601 3.8120e-34 +0.69674 4.2725e-34 +0.77149 4.8234e-34 +0.85886 5.4014e-34 +0.95989 6.0347e-34 +1.0474 6.8356e-34 +1.1629 8.0668e-34 +1.2906 9.5699e-34 diff --git a/examples/ExistingLimits/XENON1T_S2Only_ER_Massless.txt b/examples/ExistingLimits/XENON1T_S2Only_ER_Massless.txt new file mode 100644 index 0000000..c4e4445 --- /dev/null +++ b/examples/ExistingLimits/XENON1T_S2Only_ER_Massless.txt @@ -0,0 +1,52 @@ +# MeV DM-e cm^2 +0.010056 3.9016e-37 +0.012345 5.1191e-37 +0.013869 6.2497e-37 +0.016560 7.0603e-37 +0.019022 8.1548e-37 +0.021489 9.6301e-37 +0.024546 1.1372e-36 +0.029310 1.3356e-36 +0.033667 1.6125e-36 +0.038033 1.9470e-36 +0.042728 2.3770e-36 +0.049627 2.6118e-36 +0.055572 3.0323e-36 +0.059086 3.2965e-36 +0.070150 3.9348e-36 +0.078108 4.4833e-36 +0.084516 5.0889e-36 +0.091638 5.7529e-36 +0.10659 6.6561e-36 +0.11307 7.1368e-36 +0.13497 8.3579e-36 +0.14317 8.9642e-36 +0.16372 1.1004e-35 +0.18422 1.1124e-35 +0.19732 1.2348e-35 +0.22831 1.5368e-35 +0.24612 1.6157e-35 +0.29377 1.9187e-35 +0.30996 2.0697e-35 +0.35360 2.3930e-35 +0.38004 2.4672e-35 +0.41699 2.7394e-35 +0.47099 3.1265e-35 +0.52339 3.6343e-35 +0.55630 4.1285e-35 +0.61477 4.3819e-35 +0.67467 4.4530e-35 +0.80101 5.7728e-35 +0.85426 6.0025e-35 +0.97163 7.4136e-35 +1.0530 7.7132e-35 +1.1807 9.6752e-35 +1.2502 1.0486e-34 +1.3356 1.1576e-34 +1.5312 1.2870e-34 +1.8297 1.6335e-34 +2.0353 1.8959e-34 +2.2221 2.1201e-34 +2.3926 2.2621e-34 +2.7952 2.7098e-34 +2.9493 2.9289e-34 diff --git a/examples/ExistingLimits/protoSENSEI_MINOS_ER_Massless.txt b/examples/ExistingLimits/protoSENSEI_MINOS_ER_Massless.txt new file mode 100644 index 0000000..f72eb25 --- /dev/null +++ b/examples/ExistingLimits/protoSENSEI_MINOS_ER_Massless.txt @@ -0,0 +1,20 @@ +# MeV DM-e cm^2 +6.0592 9.7797e-34 +6.4496 9.0492e-34 +6.9573 8.0452e-34 +7.7037 7.2035e-34 +8.6686 6.5629e-34 +9.7544 6.1718e-34 +10.976 6.1298e-34 +12.351 6.1898e-34 +13.898 6.2505e-34 +15.639 6.3148e-34 +17.597 6.3829e-34 +19.802 6.4802e-34 +22.282 6.9043e-34 +25.073 7.4355e-34 +28.339 8.0452e-34 +31.066 8.6021e-34 +33.806 9.1157e-34 +36.361 9.4111e-34 +39.099 9.8245e-34 From daeaf63f0e5dab2e5f7e2ae8d1218b35f6de384e Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Fri, 28 Jun 2024 20:22:24 -0700 Subject: [PATCH 03/39] Basic updates --- darklim/constants/_constants.py | 3 +++ darklim/limit/_limit.py | 16 +++++++++------- 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/darklim/constants/_constants.py b/darklim/constants/_constants.py index d23e0ca..495cc55 100644 --- a/darklim/constants/_constants.py +++ b/darklim/constants/_constants.py @@ -6,3 +6,6 @@ ve_orbital = 232e3 # mean orbital velocity of Earth [m/s] vesc_galactic = 544e3 # galactic escape velocity [m/s] rho0_dm = 0.3 # local DM density [GeV/cm^3] + +bandgap_GaAs_eV = 1.42 +bandgap_Al2O3_eV = 8.8 diff --git a/darklim/limit/_limit.py b/darklim/limit/_limit.py index 4194b72..7392201 100644 --- a/darklim/limit/_limit.py +++ b/darklim/limit/_limit.py @@ -16,6 +16,7 @@ import matplotlib.pyplot as plt + __all__ = [ "upper", "helmfactor", @@ -610,7 +611,7 @@ def fc_limits(known_bkg_func, eventenergies, effenergies, effs, masslist, exposu ax.set_xscale('log') ax.set_yscale('log') ax.legend() - outdir = '/global/cfs/cdirs/lz/users/haselsco/TESSERACT_Limits/DarkLim/examples/' + outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' plt.savefig(outdir+'testplot_{:0.3f}GeV.png'.format(mass),dpi=300, facecolor='white',bbox_inches='tight') sigma[ii] = (sigma0 / tot_rate) * ul @@ -640,7 +641,8 @@ def get_fc_ul(known_bkg_func, eventenergies, threshold, ehigh, exposure, verbose def get_signal_rate(effenergies, effs, masslist, exposure, tm="Si", res=None, gauss_width=10, verbose=False, - drdefunction=None, hard_threshold=0.0, sigma0=1e-41): + drdefunction=None, hard_threshold=0.0, sigma0=1e-41, + savedir=''): """ return the signal rate for each of the masses in masslist for the given reference cross section, exposure, and efficiency curve. @@ -665,7 +667,7 @@ def get_signal_rate(effenergies, effs, masslist, exposure, if drdefunction is None: init_rate = drde(en_interp, mass, sigma0, tm=tm) else: - init_rate = drdefunction[ii](en_interp,mass) # note here.. mass also an input + init_rate = drdefunction[ii](en_interp) if res is not None: init_rate = gauss_smear(en_interp, init_rate, res, gauss_width=gauss_width) @@ -691,7 +693,7 @@ def get_signal_rate(effenergies, effs, masslist, exposure, ax.set_yscale('log') ax.legend(loc='lower left',frameon=False) ax.set_title('m={:0.3f}GeV,\n rate over threshold={:0.3e} evts'.format(mass,signal_rates[ii])) - outdir = '/global/cfs/cdirs/lz/users/haselsco/TESSERACT_Limits/DarkLim/examples/' - plt.savefig(outdir+'testplot_{:0.3f}GeV.png'.format(mass),facecolor='white',bbox_inches='tight') - - return signal_rates, raw_signal_rates \ No newline at end of file + outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' + plt.savefig(outdir+savedir+'/testplot_{:0.3f}GeV.png'.format(mass),facecolor='white',bbox_inches='tight') + + return signal_rates, raw_signal_rates From 39ff2e42dc964cd5cecb6146cbdf212ec297793a Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Fri, 28 Jun 2024 20:22:58 -0700 Subject: [PATCH 04/39] New files for scanning --- examples/gaas_fc_scan.py | 177 ++++++++++++++++++++++++++ examples/sapphire_fc_scan.py | 240 +++++++++++++++++++++++++++++++++++ 2 files changed, 417 insertions(+) create mode 100644 examples/gaas_fc_scan.py create mode 100644 examples/sapphire_fc_scan.py diff --git a/examples/gaas_fc_scan.py b/examples/gaas_fc_scan.py new file mode 100644 index 0000000..95fb383 --- /dev/null +++ b/examples/gaas_fc_scan.py @@ -0,0 +1,177 @@ +import os +import sys +import numpy as np +import matplotlib.pyplot as plt +import scipy.stats as stats + +import darklim + +from multihist import Hist1d +import time + +################################################################## + +efficiency = 1.0 +tm = 'GaAs' # target name +energy_res = 0.373e-4 # energy resolution in keV + +gaas_gain = 0.40 * 0.40 + 0.60 * 0.05 + +################################################################## + +def plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=None): + + #print('Signal events at m={:0.3f} GeV & {:0.1e} cm2: {:0.3e} evts'.format(mass,sigma0,signal_rates[ii])) + + # plot the evt rate vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates) + #plt.plot(en_interp,curr_exp(en_interp),ls='--') + #ax.axvline(threshold,ls='--',color='red') + ax.set_ylabel('Events') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_title('Expected WIMP events at {:0.1e} cm2'.format(sigma0)) + + if savename is not None: + plt.savefig(savename+'_rate.png',facecolor='white',bbox_inches='tight') + + # plot the acceptance vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates/raw_dm_rates) + ax.set_ylabel('Signal Acceptance') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_ylim(1e-5,1) + #ax.set_title('Signal Acceptance') + + if savename is not None: + plt.savefig(savename+'_acceptance.png',facecolor='white',bbox_inches='tight') + + return + +def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None): + + if coinc==1: # if coinc is 1, LEE is 'unknown' + known_bkgs = [0] + else: + known_bkgs = [0,1] + + m_dms = np.geomspace(0.0001, 5, 50) + sigma0 = 1e-36 + + ehigh = 1 # keV + + if var_threshold: + nsigma = stats.norm.isf(stats.norm.sf(5)**(1/coinc)) + else: + nsigma = 5 + + per_device_threshold = nsigma * energy_res # threshold + threshold = coinc*per_device_threshold + + SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=gaas_gain) + SE.reset_sim() + SE.add_flat_bkgd(1) # flat background of 1 DRU + SE.add_nfold_lee_bkgd(m=n_devices,n=coinc,w=window) + + print('\nRunning with the following settings:') + print('Mass: {:0.4f} kg; Time: {:0.3f} d => Exposure: {:0.3f} kg-d'.format(SE.m_det,time_elapsed,SE.exposure)) + print('Coincidence: {:d}-fold in {:d} devices; {:0.1f} microsecond window'.format(coinc,n_devices,window/1e-6)) + print('Energy threshold: {:0.3f} eV; {:0.2f} sigma in each device'.format(threshold*1e3,nsigma)) + + # run + sig = np.zeros_like(m_dms) + ul = np.zeros_like(m_dms) + dm_rates = np.zeros_like(m_dms) + raw_dm_rates = np.zeros_like(m_dms) + exp_bkg = np.zeros_like(m_dms) + + for i, mass in enumerate(m_dms): + _, sig[i], ul[i], dm_rates[i], raw_dm_rates[i], exp_bkg[i] = SE.run_fast_fc_sim( + known_bkgs, + threshold, + ehigh, + e_low=1e-6, #threshold, + m_dms=[mass], + nexp=nexp, + npts=int(1e5), + plot_bkgd=True, +# res=np.sqrt(n_devices)*energy_res, + verbose=True, + sigma0=sigma0, + use_drdefunction=True, + pltname=savedir+'/ULs_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus'.format(time_elapsed,n_devices,coinc,window/1e-6) + #pltname=None + ) + + # save results to txt file + if save and savedir is not None: + outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) + tot = np.column_stack( (m_dms, sig, dm_rates/raw_dm_rates, exp_bkg) ) + np.savetxt(outname,tot,fmt=['%.5e','%0.5e','%0.5e','%0.5e'] ,delimiter=' ') + + # plot acceptance and DM evt rate: + savename = \ + './{:s}/dmrate_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) + plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=savename) + + return + +def gaas_scan(results_dir): + + if not os.path.exists(results_dir): + os.makedirs(results_dir) + + nexp = 200 # number of toys + + var_threshold = True # vary 5sigma requirement based on coinc level + + times = np.array([1]) # d + mass_det = 8.*5.32*1e-3 # mass in kg, = 8cc * 3.98g/cc + exposures = times*mass_det + + n_devices = 4 + coinc = np.arange(2,3) + #coinc = np.array([1]) + window = 100e-6 # s + + for t in times: + for n in coinc: + run_scan_point( + nexp, + t, + mass_det, + n_devices, + n, + window, + var_threshold=var_threshold, + save=True, + savedir=results_dir + ) + + return + +# ------------------------------------------------------ +# ------------------------------------------------------ +def main(): + + t_start = time.time() + try: + results_dir = sys.argv[1] + except: + print("Check inputs.\n") + return 1 + + gaas_scan(results_dir) + t_end = time.time() + print(f'Took {(t_end - t_start)/60} minutes.') + + return 0 + +if __name__ == "__main__": + main() diff --git a/examples/sapphire_fc_scan.py b/examples/sapphire_fc_scan.py new file mode 100644 index 0000000..c3b54ad --- /dev/null +++ b/examples/sapphire_fc_scan.py @@ -0,0 +1,240 @@ +import os +import sys +import numpy as np +import matplotlib.pyplot as plt +import scipy.stats as stats + +import darklim + +from multihist import Hist1d +import time +import datetime + +################################################################## + +efficiency = 1.0 +tm = 'Al2O3' # target name +energy_res = 0.373e-4 # energy resolution in keV + +det_gain = 0.40 + +################################################################## + +def plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=None): + + #print('Signal events at m={:0.3f} GeV & {:0.1e} cm2: {:0.3e} evts'.format(mass,sigma0,signal_rates[ii])) + + # plot the evt rate vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates) + #plt.plot(en_interp,curr_exp(en_interp),ls='--') + #ax.axvline(threshold,ls='--',color='red') + ax.set_ylabel('Events') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_title('Expected WIMP events at {:0.1e} cm2'.format(sigma0)) + + if savename is not None: + plt.savefig(savename+'_rate.png',facecolor='white',bbox_inches='tight') + + # plot the acceptance vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates/raw_dm_rates) + ax.set_ylabel('Signal Acceptance') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_ylim(1e-5,1) + #ax.set_title('Signal Acceptance') + + if savename is not None: + plt.savefig(savename+'_acceptance.png',facecolor='white',bbox_inches='tight') + + return + +def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None, + m_dms=np.geomspace(0.0001,5,50),sigma0=1e-36,elf_model=None, elf_target=None, elf_params=None): + + if coinc==1: # if coinc is 1, LEE is 'unknown' + known_bkgs = [0] + else: + known_bkgs = [0,1] + + if var_threshold: + nsigma = stats.norm.isf(stats.norm.sf(5)**(1/coinc)) + else: + nsigma = 5 + + per_device_threshold = nsigma * energy_res # threshold + threshold = coinc*per_device_threshold + + SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=det_gain) + SE.reset_sim() + SE.add_flat_bkgd(1) # flat background of 1 DRU + SE.add_nfold_lee_bkgd(m=n_devices,n=coinc,w=window) + + print('\nRunning with the following settings:') + print('Mass: {:0.4f} kg; Time: {:0.3f} d => Exposure: {:0.3f} kg-d'.format(SE.m_det,time_elapsed,SE.exposure)) + print('Coincidence: {:d}-fold in {:d} devices; {:0.1f} microsecond window'.format(coinc,n_devices,window/1e-6)) + print('Energy threshold: {:0.3f} eV; {:0.2f} sigma in each device'.format(threshold*1e3,nsigma)) + + # run + sig = np.zeros_like(m_dms) + ul = np.zeros_like(m_dms) + dm_rates = np.zeros_like(m_dms) + raw_dm_rates = np.zeros_like(m_dms) + exp_bkg = np.zeros_like(m_dms) + + ehigh_list = [] + for i, mass in enumerate(m_dms): + + ehigh = 1 # keV + + drdefunction = SE.run_fast_fc_sim( + known_bkgs, + threshold, + ehigh, + e_low=1e-6, + m_dms=[mass], + sigma0=sigma0, + use_drdefunction=True, + elf_model=elf_model, + elf_target=elf_target, + elf_params=elf_params, + return_only_drde=True + ) + drdefunction = drdefunction[0] + + ehigh_guesses = np.geomspace(1e-6, 1e3, 3000) + drdefunction_guesses = drdefunction(ehigh_guesses) + + indices = np.where(drdefunction_guesses > 0) + if len(indices[0]) == 0: + ehigh = 1. + else: + j = int(indices[0][-1]) + ehigh = ehigh_guesses[j] + ehigh_list.append(ehigh) + continue + + + _, sig[i], ul[i], dm_rates[i], raw_dm_rates[i], exp_bkg[i] = SE.run_fast_fc_sim( + known_bkgs, + threshold, + ehigh, + e_low=1e-6, #threshold, + m_dms=[mass], + nexp=nexp, + npts=int(1e5), + plot_bkgd=True, + # res=np.sqrt(n_devices)*energy_res, + verbose=True, + sigma0=sigma0, + use_drdefunction=True, + pltname=savedir+'/ULs_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus'.format(time_elapsed,n_devices,coinc,window/1e-6), + savedir=savedir, + elf_model=elf_model, + elf_target=elf_target, + elf_params=elf_params, + ) + + print(ehigh_list) + sys.exit() + + # save results to txt file + if save and savedir is not None: + outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) + tot = np.column_stack( (m_dms, sig, dm_rates/raw_dm_rates, exp_bkg) ) + np.savetxt(outname,tot,fmt=['%.5e','%0.5e','%0.5e','%0.5e'] ,delimiter=' ') + + # plot acceptance and DM evt rate: + savename = \ + './{:s}/dmrate_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) + plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=savename) + + return + +def sapphire_scan(results_dir): + + if not os.path.exists(results_dir): + os.makedirs(results_dir) + + nexp = 200 # number of toys + + var_threshold = True # vary 5sigma requirement based on coinc level + + times = np.array([1]) # d + mass_det = 8.*5.32*1e-3 # mass in kg, = 8cc * 3.98g/cc + exposures = times*mass_det + + n_devices = 4 + coinc = np.array([1]) + window = 100e-6 # s + + m_dms = np.geomspace(1e-4, 10, 20) # GeV + sigma0 = 1e-36 # cm2 (might be DM-n or DM-e) + + elf_model='electron' + elf_params={'mediator': 'massless', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} + + f = open(results_dir + '/info.txt', 'w') + f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') + f.write('Detector material: ' + tm + '\n') + f.write('Exposure time(s) (days): ' + str(times) + '\n') + f.write('Detector mass (kg): ' + '%.4e' % mass_det + '\n') + f.write('Number of devices: ' + str(n_devices) + '\n') + f.write('Coincidence level: ' + str(coinc) + '\n') + f.write('Time window (s): ' + str(window) + '\n') + f.write('Dark matter masses (GeV): ' + str(m_dms) + '\n') + f.write('Cross section (cm2): ' + str(sigma0) + '\n') + f.write('Baseline resolution (keV): ' + str(energy_res) + '\n') + f.write('Gain: ' + str(det_gain) + '\n') + f.write('Variable resolution: ' + str(var_threshold) + '\n') + f.write('ELF model: ' + str(elf_model) + '\n') + f.write('ELF params: ' + str(elf_params) + '\n') + f.close() + + + for t in times: + for n in coinc: + run_scan_point( + nexp, + t, + mass_det, + n_devices, + n, + window, + var_threshold=var_threshold, + save=True, + savedir=results_dir, + m_dms=m_dms, + sigma0=sigma0, + elf_target=tm, + elf_model=elf_model, + elf_params=elf_params, + ) + + return + +# ------------------------------------------------------ +# ------------------------------------------------------ +def main(): + + t_start = time.time() + try: + results_dir = sys.argv[1] + except: + print("Check inputs.\n") + return 1 + + sapphire_scan(results_dir) + t_end = time.time() + print(f'Took {(t_end - t_start)/60} minutes.') + + return 0 + +if __name__ == "__main__": + main() From efbc8baf0cba0c9ba6c9eff8a914dada2fbc601c Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sat, 29 Jun 2024 16:41:35 -0700 Subject: [PATCH 05/39] Added constant --- darklim/constants/_constants.py | 1 + 1 file changed, 1 insertion(+) diff --git a/darklim/constants/_constants.py b/darklim/constants/_constants.py index 495cc55..376f7b3 100644 --- a/darklim/constants/_constants.py +++ b/darklim/constants/_constants.py @@ -9,3 +9,4 @@ bandgap_GaAs_eV = 1.42 bandgap_Al2O3_eV = 8.8 +Al2O3_density = 3.98 From 109460b29a3752b697f3e8f11025e5a25b194872 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sat, 29 Jun 2024 16:42:41 -0700 Subject: [PATCH 06/39] Added ELF to run_sim() for optimum interval --- darklim/sensitivity/_sens_est.py | 48 +++++++++++++++++++++++++++----- 1 file changed, 41 insertions(+), 7 deletions(-) diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index 25ec800..9bb841c 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -292,7 +292,9 @@ def reset_sim(self): def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, - plot_bkgd=False, res=None, verbose=False, sigma0=1e-41): + plot_bkgd=False, res=None, verbose=False, sigma0=1e-41, + elf_model=None, elf_params=None, elf_target=None): + """ Method for running the simulation for getting the sensitivity estimate. @@ -337,9 +339,41 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, m_dms = np.geomspace(0.01, 2, num=5) en_interp = np.geomspace(e_low, e_high, num=npts) + + if elf_model is None: + + drdefunction = [ lambda x,m: drde_wimp_obs( x, m, sigma0, self.tm, self.gain ) for m in m_dms ] + + elif elf_model == 'electron' and elf_target == 'GaAs': - #drdefunction = [drde_obs(en_interp,lambda x: drde(x,m,sigma0,tm=self.tm),gain)*np.heaviside(en_interp-threshold,1) for m in m_dms] + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_kcut = elf_params['kcut'] if 'kcut' in elf_params else 0 + elf_method = elf_params['method'] if 'method' in elf_params else 'grid' + elf_screening = elf_params['withscreening'] if 'withscreening' in elf_params else True + elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + drdefunction = \ + [elf.get_dRdE_lambda_GaAs_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, + kcut=elf_kcut, method=elf_method, withscreening=elf_screening, + suppress_darkelf_output=elf_suppress, gain=self.gain) + for m in m_dms] + + elif elf_model == 'electron' and elf_target == 'Al2O3': + + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_kcut = elf_params['kcut'] if 'kcut' in elf_params else 0 + elf_method = elf_params['method'] if 'method' in elf_params else 'grid' + elf_screening = elf_params['withscreening'] if 'withscreening' in elf_params else True + elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + + drdefunction = \ + [elf.get_dRdE_lambda_Al2O3_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, + kcut=elf_kcut, method=elf_method, withscreening=elf_screening, + suppress_darkelf_output=elf_suppress, gain=self.gain) + for m in m_dms] + + + for ii in range(nexp): evts_sim = self._generate_background( en_interp, plot_bkgd=plot_bkgd and ii==0, @@ -353,10 +387,10 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, self.exposure, #exposure tm=self.tm, # target material cl=0.9, # C.L. - res=res, # include smearing of DM spectrum - gauss_width=10, # if smearing, number of sigma to go out to +# res=res, # include smearing of DM spectrum +# gauss_width=10, # if smearing, number of sigma to go out to verbose=verbose, # print outs - drdefunction=None, # + drdefunction=drdefunction, # hard_threshold=threshold, sigma0=sigma0 ) @@ -478,7 +512,7 @@ def run_sim_fc(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms=None, ax.axvline(ul,ls='--',color='red') ax.set_xlabel('Upper Limit [Events]') ax.set_xlim(0,max(np.asarray(uls))) - outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' + outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' plt.savefig(outdir+pltname+'.png',dpi=300, facecolor='white',bbox_inches='tight') return m_dms, sig, ul @@ -597,7 +631,7 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= ax.axvline(median_ul,ls='--',color='red') ax.set_xlabel('Upper Limit [Events]') ax.set_xlim(0,max(uls)) - outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' + outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' plt.savefig(outdir+pltname+'.png',dpi=300, facecolor='white',bbox_inches='tight') # expected bkg rate, made to match m_dm len just to make analysis easier From 5dd0666b5fd9d5076630f9f43bad6654956854fe Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sat, 29 Jun 2024 16:43:36 -0700 Subject: [PATCH 07/39] updated max energy to be 10% higher --- examples/sapphire_fc_scan.py | 27 ++++++++++++--------------- 1 file changed, 12 insertions(+), 15 deletions(-) diff --git a/examples/sapphire_fc_scan.py b/examples/sapphire_fc_scan.py index c3b54ad..010ace2 100644 --- a/examples/sapphire_fc_scan.py +++ b/examples/sapphire_fc_scan.py @@ -5,6 +5,7 @@ import scipy.stats as stats import darklim +from darklim import constants from multihist import Hist1d import time @@ -74,6 +75,7 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=det_gain) SE.reset_sim() SE.add_flat_bkgd(1) # flat background of 1 DRU + SE.add_nfold_lee_bkgd(m=n_devices,n=coinc,w=window) print('\nRunning with the following settings:') @@ -88,11 +90,11 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho raw_dm_rates = np.zeros_like(m_dms) exp_bkg = np.zeros_like(m_dms) - ehigh_list = [] for i, mass in enumerate(m_dms): ehigh = 1 # keV + # First, figure out what the maximum energy from this dRdE is drdefunction = SE.run_fast_fc_sim( known_bkgs, threshold, @@ -116,11 +118,9 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho ehigh = 1. else: j = int(indices[0][-1]) - ehigh = ehigh_guesses[j] - ehigh_list.append(ehigh) - continue - + ehigh = ehigh_guesses[j] * 1.1 + # Second, actually calculate the FC limit _, sig[i], ul[i], dm_rates[i], raw_dm_rates[i], exp_bkg[i] = SE.run_fast_fc_sim( known_bkgs, threshold, @@ -141,9 +141,6 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho elf_params=elf_params, ) - print(ehigh_list) - sys.exit() - # save results to txt file if save and savedir is not None: outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) @@ -162,23 +159,23 @@ def sapphire_scan(results_dir): if not os.path.exists(results_dir): os.makedirs(results_dir) - nexp = 200 # number of toys + nexp = 50 # number of toys - var_threshold = True # vary 5sigma requirement based on coinc level + var_threshold = False # vary 5sigma requirement based on coinc level times = np.array([1]) # d - mass_det = 8.*5.32*1e-3 # mass in kg, = 8cc * 3.98g/cc + mass_det = 8. * constants.Al2O3_density * 1e-3 # mass in kg, = 8cc exposures = times*mass_det - n_devices = 4 + n_devices = 1 coinc = np.array([1]) window = 100e-6 # s - m_dms = np.geomspace(1e-4, 10, 20) # GeV + m_dms = np.geomspace(1e-4, 10, 50) # GeV sigma0 = 1e-36 # cm2 (might be DM-n or DM-e) elf_model='electron' - elf_params={'mediator': 'massless', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} + elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} f = open(results_dir + '/info.txt', 'w') f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') @@ -237,4 +234,4 @@ def main(): return 0 if __name__ == "__main__": - main() + main() From 641f66f0c8897948c288084a95a75d64a5f995b2 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sat, 29 Jun 2024 19:36:40 -0700 Subject: [PATCH 08/39] Added multiphonons --- darklim/elf/_elf.py | 97 +++++++++++++++++++++------------------------ 1 file changed, 45 insertions(+), 52 deletions(-) diff --git a/darklim/elf/_elf.py b/darklim/elf/_elf.py index 18a13f1..f88413c 100644 --- a/darklim/elf/_elf.py +++ b/darklim/elf/_elf.py @@ -63,58 +63,51 @@ def get_dRdE_lambda_Al2O3_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, -if False: - - def get_dRdE_lambda_Al2O3_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, kcut=0, method='grid', withscreening=True, suppress_darkelf_output=False, gain=1): - """ - Function to get an anonymous lambda function, which calculates dRdE - for DM-electron scattering in Al2O3 given only deposited energy. - - Parameters - ---------- - mX_eV : float - Dark matter mass in eV - mediator : str - Dark photon mediator mass. Must be "massive" (infinity) or - "massless" (zero). - sigmae : float - DM-electron scattering cross section in cm^2 - kcut : float - Maximum k value in the integration, in eV. If kcut=0 (default), the - integration is cut off at the highest k-value of the grid at hand. - method : str - Must be "grid" or "Lindhard". Choice to use interpolated grid of - epsilon, or Lindhard analytic epsilon - withscreening : bool - Whether to include the 1/|epsilon|^2 factor in the scattering rate - suppress_darkelf_output : bool - Whether to suppress the (useful but long) output that DarkELF gives - when loading a material's properties. - - Returns - ------- - fun : lambda function - A function to calculate dRdE in DRU given E - - """ - - # Set up DarkELF Al2O3 (sapphire) object - if suppress_darkelf_output: - print('WARNING: You are suppressing DarkELF output') - with io.capture_output() as captured: - sapphire = darkelf(target='Al2O3', filename="Al2O3_mermin.dat") - else: - sapphire = darkelf(target='Al2O3', filename="Al2O3_mermin.dat") - # Create anonymous function to get rate with only deposited energy - # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV - # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) - sapphire.update_params(mX=mX_eV, mediator=mediator) - fun = lambda keV : np.heaviside(keV - 2 * constants.bandgap_Al2O3_eV, 1) * \ - (1000 / 365.25) * \ - sapphire.dRdomega_electron(keV * 1000, method=method, sigmae=sigmae, kcut=kcut, withscreening=withscreening) +def get_dRdE_lambda_Al2O3_phonon(mX_eV=1e8, mediator='massless', sigman=1e-31, dark_photon=False, suppress_darkelf_output=False, gain=1.): + """ + Function to get an anonymous lambda function, which calculates dRdE + for DM-nuclear scattering via phonons in Al2O3 given only deposited energy. + + Parameters + ---------- + mX_eV : float + Dark matter mass in eV + mediator : str + Dark photon mediator mass. Must be "massive" (infinity) or + "massless" (zero). + sigman : float + DM-nucleon scattering cross section in cm^2 + dark_photon : bool + Whether to treat this as a dark photon + suppress_darkelf_output : bool + Whether to suppress the (useful but long) output that DarkELF gives + when loading a material's properties. + + Returns + ------- + fun : lambda function + A function to calculate dRdE in DRU given E + + """ + + # Set up DarkELF GaAs object + if suppress_darkelf_output: + print('WARNING: You are suppressing DarkELF output') + with io.capture_output() as captured: + sapphire = darkelf(target='Al2O3', filename="Al2O3_mermin.dat", phonon_filename='Al2O3_epsphonon_o.dat') + else: + sapphire = darkelf(target='Al2O3', filename="Al2O3_mermin.dat", phonon_filename='Al2O3_epsphonon_o.dat') + + # Create anonymous function to get rate with only deposited energy + # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV + # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) + sapphire.update_params(mX=mX_eV, mediator=mediator) + fun = lambda keV : sapphire._dR_domega_multiphonons_no_single(keV * 1000 / gain, sigman=sigman, dark_photon=dark_photon) * \ + (1000 / 365.25) / gain + + return fun - return fun def get_dRdE_lambda_GaAs_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, kcut=0, method='grid', withscreening=True, suppress_darkelf_output=False, gain=1.): @@ -186,8 +179,8 @@ def get_dRdE_lambda_GaAs_phonon(mX_eV=1e8, mediator='massless', sigmae=1e-31, su mediator : str Dark photon mediator mass. Must be "massive" (infinity) or "massless" (zero). - sigmae : float - DM-electron scattering cross section in cm^2 + sigman : float + DM-nuclon scattering cross section in cm^2 suppress_darkelf_output : bool Whether to suppress the (useful but long) output that DarkELF gives when loading a material's properties. From ad2541ef722bc728bc8ca9a5508faacdff9c83fd Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sat, 29 Jun 2024 19:37:06 -0700 Subject: [PATCH 09/39] Updated to be consistent with multiphonons where you cant do dRdE(keV_arr) --- darklim/limit/_limit.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/darklim/limit/_limit.py b/darklim/limit/_limit.py index 7392201..ebc6a6e 100644 --- a/darklim/limit/_limit.py +++ b/darklim/limit/_limit.py @@ -667,7 +667,10 @@ def get_signal_rate(effenergies, effs, masslist, exposure, if drdefunction is None: init_rate = drde(en_interp, mass, sigma0, tm=tm) else: - init_rate = drdefunction[ii](en_interp) + try: + init_rate = drdefunction[ii](en_interp) + except ValueError: + init_rate = np.array([drdefunction[ii](en) for en in en_interp]) if res is not None: init_rate = gauss_smear(en_interp, init_rate, res, gauss_width=gauss_width) From e08ca23cc1bcc04dac4f20a34233c4c90c052fad Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sat, 29 Jun 2024 19:37:59 -0700 Subject: [PATCH 10/39] Added multiphonons and WIMPs --- darklim/sensitivity/_sens_est.py | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index 9bb841c..cc97d64 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -534,7 +534,8 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= if use_drdefunction and elf_model is None: - drdefunction = [ lambda x,m: drde_wimp_obs( x, m, sigma0, self.tm, self.gain ) for m in m_dms ] + drdefunction = [(lambda x: drde_wimp_obs( x, m, sigma0, self.tm, self.gain )) for m in m_dms ] + print('Using WIMPs') elif elf_model == 'electron' and elf_target == 'GaAs': @@ -564,6 +565,18 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] + elif elf_model == 'phonon' and elf_target == 'Al2O3': + + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + elf_darkphoton = elf_params['dark_photon'] if 'dark_photon' in elf_params else False + + drdefunction = \ + [elf.get_dRdE_lambda_Al2O3_phonon(mX_eV=m*1e9, sigman=sigma0, mediator=elf_mediator, + dark_photon=elf_darkphoton, + suppress_darkelf_output=elf_suppress, gain=self.gain) + for m in m_dms] + if return_only_drde: return drdefunction @@ -631,7 +644,7 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= ax.axvline(median_ul,ls='--',color='red') ax.set_xlabel('Upper Limit [Events]') ax.set_xlim(0,max(uls)) - outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' + outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' plt.savefig(outdir+pltname+'.png',dpi=300, facecolor='white',bbox_inches='tight') # expected bkg rate, made to match m_dm len just to make analysis easier From f5bf0ea8e696a41508159d1342ab99948f8a8088 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sat, 29 Jun 2024 19:38:47 -0700 Subject: [PATCH 11/39] Updated to be consistent with multiphonons where you cant do dRdE(keV_arr) --- examples/sapphire_fc_scan.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/examples/sapphire_fc_scan.py b/examples/sapphire_fc_scan.py index 010ace2..90a0359 100644 --- a/examples/sapphire_fc_scan.py +++ b/examples/sapphire_fc_scan.py @@ -111,8 +111,10 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho drdefunction = drdefunction[0] ehigh_guesses = np.geomspace(1e-6, 1e3, 3000) - drdefunction_guesses = drdefunction(ehigh_guesses) - + try: + drdefunction_guesses = drdefunction(ehigh_guesses) + except ValueError: + drdefunction_guesses = np.array([drdefunction(en) for en in ehigh_guesses]) indices = np.where(drdefunction_guesses > 0) if len(indices[0]) == 0: ehigh = 1. @@ -171,11 +173,13 @@ def sapphire_scan(results_dir): coinc = np.array([1]) window = 100e-6 # s - m_dms = np.geomspace(1e-4, 10, 50) # GeV + m_dms = np.geomspace(1, 100, 3) # GeV sigma0 = 1e-36 # cm2 (might be DM-n or DM-e) - elf_model='electron' - elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} +# elf_model='electron' +# elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} + elf_model='phonon' + elf_params={'mediator': 'massive', 'suppress_darkelf_output': False, 'dark_photon': False} f = open(results_dir + '/info.txt', 'w') f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') From 6b4e24755fb62b8f36cca40dd607cc2d8b21c44c Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sat, 29 Jun 2024 21:49:13 -0700 Subject: [PATCH 12/39] File paths for NERSC --- darklim/elf/_elf.py | 2 +- darklim/limit/_limit.py | 4 ++-- darklim/sensitivity/_sens_est.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/darklim/elf/_elf.py b/darklim/elf/_elf.py index f88413c..ed22f4a 100644 --- a/darklim/elf/_elf.py +++ b/darklim/elf/_elf.py @@ -1,7 +1,7 @@ from IPython.utils import io import numpy as np import sys -sys.path.append('/Users/vetri/GitRepos/DarkELF/') +sys.path.insert(0, '/global/cfs/cdirs/lz/users/vvelan/Test/DarkELF/') from darkelf import darkelf from darklim import constants diff --git a/darklim/limit/_limit.py b/darklim/limit/_limit.py index ebc6a6e..6250209 100644 --- a/darklim/limit/_limit.py +++ b/darklim/limit/_limit.py @@ -611,7 +611,7 @@ def fc_limits(known_bkg_func, eventenergies, effenergies, effs, masslist, exposu ax.set_xscale('log') ax.set_yscale('log') ax.legend() - outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' + outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' plt.savefig(outdir+'testplot_{:0.3f}GeV.png'.format(mass),dpi=300, facecolor='white',bbox_inches='tight') sigma[ii] = (sigma0 / tot_rate) * ul @@ -696,7 +696,7 @@ def get_signal_rate(effenergies, effs, masslist, exposure, ax.set_yscale('log') ax.legend(loc='lower left',frameon=False) ax.set_title('m={:0.3f}GeV,\n rate over threshold={:0.3e} evts'.format(mass,signal_rates[ii])) - outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' + outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/' plt.savefig(outdir+savedir+'/testplot_{:0.3f}GeV.png'.format(mass),facecolor='white',bbox_inches='tight') return signal_rates, raw_signal_rates diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index cc97d64..f0f7f89 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -644,7 +644,7 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= ax.axvline(median_ul,ls='--',color='red') ax.set_xlabel('Upper Limit [Events]') ax.set_xlim(0,max(uls)) - outdir = '/Users/vetri/GitRepos/Test/DarkLim/examples/' + outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' plt.savefig(outdir+pltname+'.png',dpi=300, facecolor='white',bbox_inches='tight') # expected bkg rate, made to match m_dm len just to make analysis easier From 2a3a74c6ce09686e4c7bd73cc6d6791bf2345797 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 07:26:43 -0700 Subject: [PATCH 13/39] added functionality for GaAs multiphonon --- darklim/constants/_constants.py | 1 + darklim/elf/_elf.py | 33 +++++++++++---------------------- 2 files changed, 12 insertions(+), 22 deletions(-) diff --git a/darklim/constants/_constants.py b/darklim/constants/_constants.py index 376f7b3..baeac7e 100644 --- a/darklim/constants/_constants.py +++ b/darklim/constants/_constants.py @@ -10,3 +10,4 @@ bandgap_GaAs_eV = 1.42 bandgap_Al2O3_eV = 8.8 Al2O3_density = 3.98 +GaAs_density = 5.32 diff --git a/darklim/elf/_elf.py b/darklim/elf/_elf.py index ed22f4a..68a8bf5 100644 --- a/darklim/elf/_elf.py +++ b/darklim/elf/_elf.py @@ -164,13 +164,10 @@ def get_dRdE_lambda_GaAs_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, - -def get_dRdE_lambda_GaAs_phonon(mX_eV=1e8, mediator='massless', sigmae=1e-31, suppress_darkelf_output=False, gain=1.): +def get_dRdE_lambda_GaAs_phonon(mX_eV=1e8, mediator='massless', sigman=1e-31, dark_photon=False, suppress_darkelf_output=False, gain=1.): """ Function to get an anonymous lambda function, which calculates dRdE - for DM-electron scattering in GaAs via phonons given only deposited - energy. We return the total rate from both single-photon and - multi-phonon interactions (by adding these together). + for DM-nuclear scattering via GaAs in Al2O3 given only deposited energy. Parameters ---------- @@ -180,7 +177,9 @@ def get_dRdE_lambda_GaAs_phonon(mX_eV=1e8, mediator='massless', sigmae=1e-31, su Dark photon mediator mass. Must be "massive" (infinity) or "massless" (zero). sigman : float - DM-nuclon scattering cross section in cm^2 + DM-nucleon scattering cross section in cm^2 + dark_photon : bool + Whether to treat this as a dark photon suppress_darkelf_output : bool Whether to suppress the (useful but long) output that DarkELF gives when loading a material's properties. @@ -192,30 +191,20 @@ def get_dRdE_lambda_GaAs_phonon(mX_eV=1e8, mediator='massless', sigmae=1e-31, su """ - # Set up DarkELF Al2O3 (sapphire) object + # Set up DarkELF GaAs object if suppress_darkelf_output: print('WARNING: You are suppressing DarkELF output') with io.capture_output() as captured: - gaas_single = darkelf(target='GaAs',phonon_filename="GaAs_epsphonon_data10K.dat") - gaas_multi = darkelf(target='GaAs') + gaas = darkelf(target='GaAs', filename="GaAs_mermin.dat", phonon_filename='GaAs_epsphonon_data10K.dat') else: - gaas_single = darkelf(target='GaAs',phonon_filename="GaAs_epsphonon_data10K.dat") - gaas_multi = darkelf(target='GaAs') + gaas = darkelf(target='GaAs', filename="GaAs_mermin.dat", phonon_filename='GaAs_epsphonon_data10K.dat') # Create anonymous function to get rate with only deposited energy # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) - if mediator == 'massless': - gaas_single.update_params(mX=mX_eV, mMed=0) - gaas_multi.update_params(mX=mX_eV, mMed=0) - elif mediator == 'massive': - gaas_single.update_params(mX=mX_eV, mMed=1e11) - gaas_multi.update_params(mX=mX_eV, mMed=1e11) - fun = lambda keV : (1000 / 365.25) * \ - (gaas_single.dRdomega_phonon(keV * 1000, sigmae=sigmae) - + gaas_multi._dR_domega_multiphonons_no_single(keV * 1000, )) + gaas.update_params(mX=mX_eV, mediator=mediator) + fun = lambda keV : gaas._dR_domega_multiphonons_no_single(keV * 1000 / gain, sigman=sigman, dark_photon=dark_photon) * \ + (1000 / 365.25) / gain return fun - - From b21cc1f46487c732185a66625c6e8f0ae0d9309b Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 07:27:00 -0700 Subject: [PATCH 14/39] Fixed typo --- darklim/limit/_limit.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/darklim/limit/_limit.py b/darklim/limit/_limit.py index 6250209..27715b0 100644 --- a/darklim/limit/_limit.py +++ b/darklim/limit/_limit.py @@ -696,7 +696,7 @@ def get_signal_rate(effenergies, effs, masslist, exposure, ax.set_yscale('log') ax.legend(loc='lower left',frameon=False) ax.set_title('m={:0.3f}GeV,\n rate over threshold={:0.3e} evts'.format(mass,signal_rates[ii])) - outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/' + outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' plt.savefig(outdir+savedir+'/testplot_{:0.3f}GeV.png'.format(mass),facecolor='white',bbox_inches='tight') return signal_rates, raw_signal_rates From ebc8ccd37313837028fefa9789d0fd1c0a73774a Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 13:10:22 -0700 Subject: [PATCH 15/39] Added some stuff for optimum interval --- darklim/limit/_limit.py | 7 +- darklim/sensitivity/_sens_est.py | 54 ++++++- examples/gaas_oi_scan.py | 241 +++++++++++++++++++++++++++++++ 3 files changed, 295 insertions(+), 7 deletions(-) create mode 100644 examples/gaas_oi_scan.py diff --git a/darklim/limit/_limit.py b/darklim/limit/_limit.py index 27715b0..44fc528 100644 --- a/darklim/limit/_limit.py +++ b/darklim/limit/_limit.py @@ -500,7 +500,11 @@ def optimuminterval(eventenergies, effenergies, effs, masslist, exposure, init_rate = gauss_smear(en_interp, init_rate, res, gauss_width=gauss_width) rate = init_rate * curr_exp(en_interp) else: - rate = drdefunction[ii](en_interp) * exposure + try: + rate = drdefunction[ii](en_interp) * exposure + except ValueError: + rate = np.array([drdefunction[ii](en) for en in en_interp]) * exposure + integ_rate = integrate.cumtrapz(rate, x=en_interp, initial=0) @@ -612,6 +616,7 @@ def fc_limits(known_bkg_func, eventenergies, effenergies, effs, masslist, exposu ax.set_yscale('log') ax.legend() outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' + plt.savefig(outdir+'testplot_{:0.3f}GeV.png'.format(mass),dpi=300, facecolor='white',bbox_inches='tight') sigma[ii] = (sigma0 / tot_rate) * ul diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index f0f7f89..3242bc7 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -293,7 +293,7 @@ def reset_sim(self): def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, plot_bkgd=False, res=None, verbose=False, sigma0=1e-41, - elf_model=None, elf_params=None, elf_target=None): + elf_model=None, elf_params=None, elf_target=None, return_only_drde=False): """ Method for running the simulation for getting the sensitivity @@ -340,18 +340,21 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, en_interp = np.geomspace(e_low, e_high, num=npts) + drdefunction = None + if elf_model is None: - - drdefunction = [ lambda x,m: drde_wimp_obs( x, m, sigma0, self.tm, self.gain ) for m in m_dms ] - + + drdefunction = [(lambda x: drde_wimp_obs( x, m, sigma0, self.tm, self.gain )) for m in m_dms ] + print('Using WIMPs') + elif elf_model == 'electron' and elf_target == 'GaAs': - + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' elf_kcut = elf_params['kcut'] if 'kcut' in elf_params else 0 elf_method = elf_params['method'] if 'method' in elf_params else 'grid' elf_screening = elf_params['withscreening'] if 'withscreening' in elf_params else True elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False - + drdefunction = \ [elf.get_dRdE_lambda_GaAs_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, kcut=elf_kcut, method=elf_method, withscreening=elf_screening, @@ -372,6 +375,33 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] + elif elf_model == 'phonon' and elf_target == 'Al2O3': + + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + elf_darkphoton = elf_params['dark_photon'] if 'dark_photon' in elf_params else False + + drdefunction = \ + [elf.get_dRdE_lambda_Al2O3_phonon(mX_eV=m*1e9, sigman=sigma0, mediator=elf_mediator, + dark_photon=elf_darkphoton, + suppress_darkelf_output=elf_suppress, gain=self.gain) + for m in m_dms] + + elif elf_model == 'phonon' and elf_target == 'GaAs': + + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + elf_darkphoton = elf_params['dark_photon'] if 'dark_photon' in elf_params else False + + drdefunction = \ + [elf.get_dRdE_lambda_GaAs_phonon(mX_eV=m*1e9, sigman=sigma0, mediator=elf_mediator, + dark_photon=elf_darkphoton, + suppress_darkelf_output=elf_suppress, gain=self.gain) + for m in m_dms] + + if return_only_drde: + return drdefunction + for ii in range(nexp): @@ -577,6 +607,18 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] + elif elf_model == 'phonon' and elf_target == 'GaAs': + + elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + elf_darkphoton = elf_params['dark_photon'] if 'dark_photon' in elf_params else False + + drdefunction = \ + [elf.get_dRdE_lambda_GaAs_phonon(mX_eV=m*1e9, sigman=sigma0, mediator=elf_mediator, + dark_photon=elf_darkphoton, + suppress_darkelf_output=elf_suppress, gain=self.gain) + for m in m_dms] + if return_only_drde: return drdefunction diff --git a/examples/gaas_oi_scan.py b/examples/gaas_oi_scan.py new file mode 100644 index 0000000..1e31784 --- /dev/null +++ b/examples/gaas_oi_scan.py @@ -0,0 +1,241 @@ +import os +import sys +import numpy as np +import matplotlib.pyplot as plt +import scipy.stats as stats + +import darklim +from darklim import constants + +from multihist import Hist1d +import time +import datetime + +################################################################## + +efficiency = 1.0 +tm = 'GaAs' # target name +energy_res = 0.373e-3 # energy resolution in keV + +det_gain = 0.40 + +################################################################## + +def plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=None): + + #print('Signal events at m={:0.3f} GeV & {:0.1e} cm2: {:0.3e} evts'.format(mass,sigma0,signal_rates[ii])) + + # plot the evt rate vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates) + #plt.plot(en_interp,curr_exp(en_interp),ls='--') + #ax.axvline(threshold,ls='--',color='red') + ax.set_ylabel('Events') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_title('Expected WIMP events at {:0.1e} cm2'.format(sigma0)) + + if savename is not None: + plt.savefig(savename+'_rate.png',facecolor='white',bbox_inches='tight') + + # plot the acceptance vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates/raw_dm_rates) + ax.set_ylabel('Signal Acceptance') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_ylim(1e-5,1) + #ax.set_title('Signal Acceptance') + + if savename is not None: + plt.savefig(savename+'_acceptance.png',facecolor='white',bbox_inches='tight') + + return + +def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None, + m_dms=np.geomspace(0.0001,5,50),sigma0=1e-36,elf_model=None, elf_target=None, elf_params=None): + + if coinc==1: # if coinc is 1, LEE is 'unknown' + known_bkgs = [0] + else: + known_bkgs = [0,1] + + if var_threshold: + nsigma = stats.norm.isf(stats.norm.sf(5)**(1/coinc)) + else: + nsigma = 5 + + per_device_threshold = nsigma * energy_res # threshold + threshold = coinc*per_device_threshold + + SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=det_gain) + SE.reset_sim() + SE.add_flat_bkgd(1) # flat background of 1 DRU + + SE.add_nfold_lee_bkgd(m=n_devices,n=coinc,w=window) + + print('\nRunning with the following settings:') + print('Mass: {:0.4f} kg; Time: {:0.3f} d => Exposure: {:0.3f} kg-d'.format(SE.m_det,time_elapsed,SE.exposure)) + print('Coincidence: {:d}-fold in {:d} devices; {:0.1f} microsecond window'.format(coinc,n_devices,window/1e-6)) + print('Energy threshold: {:0.3f} eV; {:0.2f} sigma in each device'.format(threshold*1e3,nsigma)) + + # run + sig = np.zeros_like(m_dms) + ul = np.zeros_like(m_dms) + dm_rates = np.zeros_like(m_dms) + raw_dm_rates = np.zeros_like(m_dms) + exp_bkg = np.zeros_like(m_dms) + + if not np.isscalar(sigma0): + sigma0_arr = np.copy(sigma0) + + for i, mass in enumerate(m_dms): + + ehigh = 1 # keV + sigma0 = sigma0_arr[i] + if sigma0 == np.inf: + print(f'Infinite, skipping mass {mass}') + continue + + # First, figure out what the maximum energy from this dRdE is + drdefunction = SE.run_fast_fc_sim( + known_bkgs, + threshold, + ehigh, + e_low=1e-6, + m_dms=[mass], + sigma0=sigma0, + use_drdefunction=True, + elf_model=elf_model, + elf_target=elf_target, + elf_params=elf_params, + return_only_drde=True + ) + drdefunction = drdefunction[0] + + ehigh_guesses = np.geomspace(1e-6, 1e3, 3000) + try: + drdefunction_guesses = drdefunction(ehigh_guesses) + except ValueError: + drdefunction_guesses = np.array([drdefunction(en) for en in ehigh_guesses]) + indices = np.where(drdefunction_guesses > 0) + if len(indices[0]) == 0: + ehigh = 1. + else: + j = int(indices[0][-1]) + ehigh = ehigh_guesses[j] * 1.1 + if ehigh < threshold: + ehigh = 1. + + _, sig[i] = SE.run_sim( + threshold, + ehigh, + e_low=1e-6, + m_dms=[mass], + nexp=nexp, + npts=100000, + plot_bkgd=True, +# res=None, + verbose=True, + sigma0=sigma0, + elf_model=elf_model, + elf_params=elf_params, + elf_target=elf_target, + return_only_drde=False) + + print(f'Done mass = {mass}, sigma = {sig[i]}') + + # save results to txt file + if save and savedir is not None: + outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) + tot = np.column_stack( (m_dms, sig) ) + np.savetxt(outname,tot,fmt=['%.5e','%0.5e'] ,delimiter=' ') + + return + +def gaas_scan(results_dir): + + if not os.path.exists(results_dir): + os.makedirs(results_dir) + + nexp = 5 # number of toys + + var_threshold = False # vary 5sigma requirement based on coinc level + + times = np.array([1]) # d + mass_det = 8. * constants.GaAs_density * 1e-3 # mass in kg, = 8cc + exposures = times*mass_det + + n_devices = 4 + coinc = np.array([2]) + window = 100e-6 # s + + m_dms, sigma0, _, _ = np.loadtxt('results_gaas_fc_phonon_massless_scalar_001_days/HeRALD_FC_1d_4device_2fold_100mus.txt').transpose() + +# elf_model='electron' +# elf_params={'mediator': 'massless', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} + elf_model='phonon' + elf_params={'mediator': 'massless', 'suppress_darkelf_output': False, 'dark_photon': False} + + f = open(results_dir + '/info.txt', 'w') + f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') + f.write('Detector material: ' + tm + '\n') + f.write('Exposure time(s) (days): ' + str(times) + '\n') + f.write('Detector mass (kg): ' + '%.4e' % mass_det + '\n') + f.write('Number of devices: ' + str(n_devices) + '\n') + f.write('Coincidence level: ' + str(coinc) + '\n') + f.write('Time window (s): ' + str(window) + '\n') + f.write('Dark matter masses (GeV): ' + str(m_dms) + '\n') + f.write('Cross section (cm2): ' + str(sigma0) + '\n') + f.write('Baseline resolution (keV): ' + str(energy_res) + '\n') + f.write('Gain: ' + str(det_gain) + '\n') + f.write('Variable resolution: ' + str(var_threshold) + '\n') + f.write('ELF model: ' + str(elf_model) + '\n') + f.write('ELF params: ' + str(elf_params) + '\n') + f.close() + + + for t in times: + for n in coinc: + run_scan_point( + nexp, + t, + mass_det, + n_devices, + n, + window, + var_threshold=var_threshold, + save=True, + savedir=results_dir, + m_dms=m_dms, + sigma0=sigma0, + elf_target=tm, + elf_model=elf_model, + elf_params=elf_params, + ) + + return + +# ------------------------------------------------------ +# ------------------------------------------------------ +def main(): + + t_start = time.time() + try: + results_dir = sys.argv[1] + except: + print("Check inputs.\n") + return 1 + + gaas_scan(results_dir) + t_end = time.time() + print(f'Took {(t_end - t_start)/60} minutes.') + + return 0 + +if __name__ == "__main__": + main() From 54a8b54e30994d4074a11e8afa95c9c96148169e Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 19:58:40 -0700 Subject: [PATCH 16/39] Functionality to convert energy deposited in GaAs to observed energy --- darklim/detector/_detector.py | 89 +++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 darklim/detector/_detector.py diff --git a/darklim/detector/_detector.py b/darklim/detector/_detector.py new file mode 100644 index 0000000..c99b46c --- /dev/null +++ b/darklim/detector/_detector.py @@ -0,0 +1,89 @@ +import numpy as np +import math +from darklim import constants +import time +from scipy import integrate, interpolate +from darklim import elf +import darklim.sensitivity._sens_est as sens_est +import matplotlib.pyplot as plt +import matplotlib as mpl + +def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincidence_light, threshold_eV, n_samples=1): + + E_light_eV = constants.GaAs_light_fraction * E_recoil_eV + + n_photons_generated_average = np.floor(E_light_eV / constants.bandgap_GaAs_eV) + if n_photons_generated_average == 0: + if n_samples == 1: + return 0. + else: + return np.full(n_samples, 0.) + + n_photons_detected_ch1 = np.random.binomial(n_photons_generated_average, lce_per_channel, n_samples) + n_photons_detected_ch2 = np.random.binomial(n_photons_generated_average, lce_per_channel, n_samples) + + E_ch1_eV = n_photons_detected_ch1 * constants.bandgap_GaAs_eV * np.random.normal(1, res, n_samples) + E_ch2_eV = n_photons_detected_ch2 * constants.bandgap_GaAs_eV * np.random.normal(1, res, n_samples) + + E_heat_eV = (1 - constants.GaAs_light_fraction) * E_recoil_eV + E_ch0_eV = np.full(n_samples, E_heat_eV * pce) + + if n_coincidence_light == 1: + E_det_eV = (E_ch0_eV + E_ch1_eV + E_ch2_eV) * (E_ch0_eV > threshold_eV) * ((E_ch1_eV > threshold_eV) + (E_ch2_eV > threshold_eV)) + elif n_coincidence_light == 2: + E_det_eV = (E_ch0_eV + E_ch1_eV + E_ch2_eV) * (E_ch0_eV > threshold_eV) * (E_ch1_eV > threshold_eV) * (E_ch2_eV > threshold_eV) + + if n_samples == 1: + return E_det_eV[0] + else: + return E_det_eV + + + +def convert_dRdE_dep_to_obs(E_dep_keV, dRdE_dep_DRU, pce=0.40, lce_per_channel=0.10, res=0.10, n_coincidence_light=1, calorimeter_threshold_eV=0.37, E_min_keV=None, E_max_keV=None, n_samples=int(1e6)): + + # Reduce data to the appropriate energy range + if E_min_keV is None or E_min_keV < E_dep_keV[0]: + E_min_keV = E_dep_keV[0] + if E_max_keV is None or E_max_keV > E_dep_keV[-1]: + E_max_keV = E_dep_keV[-1] + + E_pdf = E_dep_keV[(E_dep_keV >= E_min_keV) * (E_dep_keV <= E_max_keV)] + dRdE_pdf = dRdE_dep_DRU[(E_dep_keV >= E_min_keV) * (E_dep_keV <= E_max_keV)] + + # Draw samples from the distribution + cdf = integrate.cumtrapz(dRdE_pdf, x=E_pdf, initial=0.0) + cdf /= cdf[-1] + + inv_cdf = interpolate.interp1d(cdf, E_pdf) + + samples = np.random.rand(n_samples) + + energies_sim_keV = inv_cdf(samples) + energies_obs_keV = np.zeros_like(energies_sim_keV) + energies_obs_keV = np.copy(energies_sim_keV) + for i, E in enumerate(energies_sim_keV): + energies_obs_keV[i] = get_deposited_energy_gaas(E * 1000, pce, lce_per_channel, res, n_coincidence_light, calorimeter_threshold_eV) / 1000 + + # Convert to E vs dRdE that we can later interpolate from + # Normalize to the number of events that are detected + bins = np.geomspace(min(energies_obs_keV[energies_obs_keV > 0]) * 0.95, max(energies_obs_keV) * 1.05, 10000) + counts, bin_edges = np.histogram(energies_obs_keV, bins) + counts = counts * 1.0 / np.diff(bin_edges) + bin_centers = 0.5 * (bin_edges[1:] + bin_edges[:-1]) + + integral_original = sum(0.5 * (dRdE_pdf[1:] + dRdE_pdf[:-1]) * np.diff(E_pdf)) + fraction_surviving = sum(energies_obs_keV > 0) / len(energies_obs_keV) + integral_desired = integral_original * fraction_surviving + integral_observed = sum(counts * np.diff(bin_edges)) + + E_obs_keV = np.copy(bin_centers) + dRdE_obs_DRU = counts * integral_desired / integral_observed + + E_obs_keV = E_obs_keV[dRdE_obs_DRU > 0] + dRdE_obs_DRU = dRdE_obs_DRU[dRdE_obs_DRU > 0] + + # Return arrays and the list of energies + return E_obs_keV, dRdE_obs_DRU, energies_obs_keV + + From 95c5b4c85882d1afe428c7d4f747652fd0ef9ec1 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 20:01:59 -0700 Subject: [PATCH 17/39] Functionality to convert energy deposited in GaAs to observed energy --- darklim/constants/_constants.py | 1 + 1 file changed, 1 insertion(+) diff --git a/darklim/constants/_constants.py b/darklim/constants/_constants.py index baeac7e..7e3a1c3 100644 --- a/darklim/constants/_constants.py +++ b/darklim/constants/_constants.py @@ -11,3 +11,4 @@ bandgap_Al2O3_eV = 8.8 Al2O3_density = 3.98 GaAs_density = 5.32 +GaAs_light_fraction = 0.60 From 8a1909e3ab57c7fbb6ec5eba10f145e96f7a3afa Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 20:02:51 -0700 Subject: [PATCH 18/39] Functionality to convert energy deposited in GaAs to observed energy --- darklim/detector/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 darklim/detector/__init__.py diff --git a/darklim/detector/__init__.py b/darklim/detector/__init__.py new file mode 100644 index 0000000..54c6fb2 --- /dev/null +++ b/darklim/detector/__init__.py @@ -0,0 +1 @@ +from ._detector import * From 2b842891d0bf8a3f6b3f85079e7075d5667dacc0 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 20:04:30 -0700 Subject: [PATCH 19/39] Example for optimum interval scan --- examples/sapphire_oi_scan.py | 241 +++++++++++++++++++++++++++++++++++ 1 file changed, 241 insertions(+) create mode 100644 examples/sapphire_oi_scan.py diff --git a/examples/sapphire_oi_scan.py b/examples/sapphire_oi_scan.py new file mode 100644 index 0000000..be689c4 --- /dev/null +++ b/examples/sapphire_oi_scan.py @@ -0,0 +1,241 @@ +import os +import sys +import numpy as np +import matplotlib.pyplot as plt +import scipy.stats as stats + +import darklim +from darklim import constants + +from multihist import Hist1d +import time +import datetime + +################################################################## + +efficiency = 1.0 +tm = 'Al2O3' # target name +energy_res = 0.373e-3 # energy resolution in keV + +det_gain = 0.40 + +################################################################## + +def plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=None): + + #print('Signal events at m={:0.3f} GeV & {:0.1e} cm2: {:0.3e} evts'.format(mass,sigma0,signal_rates[ii])) + + # plot the evt rate vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates) + #plt.plot(en_interp,curr_exp(en_interp),ls='--') + #ax.axvline(threshold,ls='--',color='red') + ax.set_ylabel('Events') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_title('Expected WIMP events at {:0.1e} cm2'.format(sigma0)) + + if savename is not None: + plt.savefig(savename+'_rate.png',facecolor='white',bbox_inches='tight') + + # plot the acceptance vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates/raw_dm_rates) + ax.set_ylabel('Signal Acceptance') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_ylim(1e-5,1) + #ax.set_title('Signal Acceptance') + + if savename is not None: + plt.savefig(savename+'_acceptance.png',facecolor='white',bbox_inches='tight') + + return + +def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None, + m_dms=np.geomspace(0.0001,5,50),sigma0=1e-36,elf_model=None, elf_target=None, elf_params=None): + + if coinc==1: # if coinc is 1, LEE is 'unknown' + known_bkgs = [0] + else: + known_bkgs = [0,1] + + if var_threshold: + nsigma = stats.norm.isf(stats.norm.sf(5)**(1/coinc)) + else: + nsigma = 5 + + per_device_threshold = nsigma * energy_res # threshold + threshold = coinc*per_device_threshold + + SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=det_gain) + SE.reset_sim() + SE.add_flat_bkgd(1) # flat background of 1 DRU + + SE.add_nfold_lee_bkgd(m=n_devices,n=coinc,w=window) + + print('\nRunning with the following settings:') + print('Mass: {:0.4f} kg; Time: {:0.3f} d => Exposure: {:0.3f} kg-d'.format(SE.m_det,time_elapsed,SE.exposure)) + print('Coincidence: {:d}-fold in {:d} devices; {:0.1f} microsecond window'.format(coinc,n_devices,window/1e-6)) + print('Energy threshold: {:0.3f} eV; {:0.2f} sigma in each device'.format(threshold*1e3,nsigma)) + + # run + sig = np.zeros_like(m_dms) + ul = np.zeros_like(m_dms) + dm_rates = np.zeros_like(m_dms) + raw_dm_rates = np.zeros_like(m_dms) + exp_bkg = np.zeros_like(m_dms) + + if not np.isscalar(sigma0): + sigma0_arr = np.copy(sigma0) + + for i, mass in enumerate(m_dms): + + ehigh = 1 # keV + sigma0 = sigma0_arr[i] + if sigma0 == np.inf: + print(f'Infinite, skipping mass {mass}') + continue + + # First, figure out what the maximum energy from this dRdE is + drdefunction = SE.run_fast_fc_sim( + known_bkgs, + threshold, + ehigh, + e_low=1e-6, + m_dms=[mass], + sigma0=sigma0, + use_drdefunction=True, + elf_model=elf_model, + elf_target=elf_target, + elf_params=elf_params, + return_only_drde=True + ) + drdefunction = drdefunction[0] + + ehigh_guesses = np.geomspace(1e-6, 1e3, 3000) + try: + drdefunction_guesses = drdefunction(ehigh_guesses) + except ValueError: + drdefunction_guesses = np.array([drdefunction(en) for en in ehigh_guesses]) + indices = np.where(drdefunction_guesses > 0) + if len(indices[0]) == 0: + ehigh = 1. + else: + j = int(indices[0][-1]) + ehigh = ehigh_guesses[j] * 1.1 + if ehigh < threshold: + ehigh = 1. + + _, sig[i] = SE.run_sim( + threshold, + ehigh, + e_low=1e-6, + m_dms=[mass], + nexp=nexp, + npts=100000, + plot_bkgd=True, +# res=None, + verbose=True, + sigma0=sigma0, + elf_model=elf_model, + elf_params=elf_params, + elf_target=elf_target, + return_only_drde=False) + + print(f'Done mass = {mass}, sigma = {sig[i]}') + + # save results to txt file + if save and savedir is not None: + outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) + tot = np.column_stack( (m_dms, sig) ) + np.savetxt(outname,tot,fmt=['%.5e','%0.5e'] ,delimiter=' ') + + return + +def sapphire_scan(results_dir): + + if not os.path.exists(results_dir): + os.makedirs(results_dir) + + nexp = 1 # number of toys + + var_threshold = False # vary 5sigma requirement based on coinc level + + times = np.array([10]) # d + mass_det = 8. * constants.Al2O3_density * 1e-3 # mass in kg, = 8cc + exposures = times*mass_det + + n_devices = 1 + coinc = np.array([1]) + window = 100e-6 # s + + m_dms, sigma0, _, _ = np.loadtxt('results_sapphire_fc_electron_massive_001_days/HeRALD_FC_1d_1device_1fold_100mus.txt').transpose() + + elf_model='electron' + elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} +# elf_model='phonon' +# elf_params={'mediator': 'massive', 'suppress_darkelf_output': False, 'dark_photon': False} + + f = open(results_dir + '/info.txt', 'w') + f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') + f.write('Detector material: ' + tm + '\n') + f.write('Exposure time(s) (days): ' + str(times) + '\n') + f.write('Detector mass (kg): ' + '%.4e' % mass_det + '\n') + f.write('Number of devices: ' + str(n_devices) + '\n') + f.write('Coincidence level: ' + str(coinc) + '\n') + f.write('Time window (s): ' + str(window) + '\n') + f.write('Dark matter masses (GeV): ' + str(m_dms) + '\n') + f.write('Cross section (cm2): ' + str(sigma0) + '\n') + f.write('Baseline resolution (keV): ' + str(energy_res) + '\n') + f.write('Gain: ' + str(det_gain) + '\n') + f.write('Variable resolution: ' + str(var_threshold) + '\n') + f.write('ELF model: ' + str(elf_model) + '\n') + f.write('ELF params: ' + str(elf_params) + '\n') + f.close() + + + for t in times: + for n in coinc: + run_scan_point( + nexp, + t, + mass_det, + n_devices, + n, + window, + var_threshold=var_threshold, + save=True, + savedir=results_dir, + m_dms=m_dms, + sigma0=sigma0, + elf_target=tm, + elf_model=elf_model, + elf_params=elf_params, + ) + + return + +# ------------------------------------------------------ +# ------------------------------------------------------ +def main(): + + t_start = time.time() + try: + results_dir = sys.argv[1] + except: + print("Check inputs.\n") + return 1 + + sapphire_scan(results_dir) + t_end = time.time() + print(f'Took {(t_end - t_start)/60} minutes.') + + return 0 + +if __name__ == "__main__": + main() From dfa463dd8d2f75fdc82bd49c23ee57ac9d6d9b9c Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 20:16:26 -0700 Subject: [PATCH 20/39] Revert "Example for optimum interval scan" This reverts commit 7c92634b5d0b62d8ed16c87cdf75f1c273a3d71a. --- examples/sapphire_oi_scan.py | 241 ----------------------------------- 1 file changed, 241 deletions(-) delete mode 100644 examples/sapphire_oi_scan.py diff --git a/examples/sapphire_oi_scan.py b/examples/sapphire_oi_scan.py deleted file mode 100644 index be689c4..0000000 --- a/examples/sapphire_oi_scan.py +++ /dev/null @@ -1,241 +0,0 @@ -import os -import sys -import numpy as np -import matplotlib.pyplot as plt -import scipy.stats as stats - -import darklim -from darklim import constants - -from multihist import Hist1d -import time -import datetime - -################################################################## - -efficiency = 1.0 -tm = 'Al2O3' # target name -energy_res = 0.373e-3 # energy resolution in keV - -det_gain = 0.40 - -################################################################## - -def plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=None): - - #print('Signal events at m={:0.3f} GeV & {:0.1e} cm2: {:0.3e} evts'.format(mass,sigma0,signal_rates[ii])) - - # plot the evt rate vs mass: - fig, ax = plt.subplots(1,figsize=(6,4)) - plt.plot(m_dms,dm_rates) - #plt.plot(en_interp,curr_exp(en_interp),ls='--') - #ax.axvline(threshold,ls='--',color='red') - ax.set_ylabel('Events') - ax.set_xlabel('Dark Matter Mass [GeV]') - ax.set_xlim(m_dms[0],m_dms[-1]) - ax.set_xscale('log') - ax.set_yscale('log') - ax.set_title('Expected WIMP events at {:0.1e} cm2'.format(sigma0)) - - if savename is not None: - plt.savefig(savename+'_rate.png',facecolor='white',bbox_inches='tight') - - # plot the acceptance vs mass: - fig, ax = plt.subplots(1,figsize=(6,4)) - plt.plot(m_dms,dm_rates/raw_dm_rates) - ax.set_ylabel('Signal Acceptance') - ax.set_xlabel('Dark Matter Mass [GeV]') - ax.set_xlim(m_dms[0],m_dms[-1]) - ax.set_xscale('log') - ax.set_yscale('log') - ax.set_ylim(1e-5,1) - #ax.set_title('Signal Acceptance') - - if savename is not None: - plt.savefig(savename+'_acceptance.png',facecolor='white',bbox_inches='tight') - - return - -def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None, - m_dms=np.geomspace(0.0001,5,50),sigma0=1e-36,elf_model=None, elf_target=None, elf_params=None): - - if coinc==1: # if coinc is 1, LEE is 'unknown' - known_bkgs = [0] - else: - known_bkgs = [0,1] - - if var_threshold: - nsigma = stats.norm.isf(stats.norm.sf(5)**(1/coinc)) - else: - nsigma = 5 - - per_device_threshold = nsigma * energy_res # threshold - threshold = coinc*per_device_threshold - - SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=det_gain) - SE.reset_sim() - SE.add_flat_bkgd(1) # flat background of 1 DRU - - SE.add_nfold_lee_bkgd(m=n_devices,n=coinc,w=window) - - print('\nRunning with the following settings:') - print('Mass: {:0.4f} kg; Time: {:0.3f} d => Exposure: {:0.3f} kg-d'.format(SE.m_det,time_elapsed,SE.exposure)) - print('Coincidence: {:d}-fold in {:d} devices; {:0.1f} microsecond window'.format(coinc,n_devices,window/1e-6)) - print('Energy threshold: {:0.3f} eV; {:0.2f} sigma in each device'.format(threshold*1e3,nsigma)) - - # run - sig = np.zeros_like(m_dms) - ul = np.zeros_like(m_dms) - dm_rates = np.zeros_like(m_dms) - raw_dm_rates = np.zeros_like(m_dms) - exp_bkg = np.zeros_like(m_dms) - - if not np.isscalar(sigma0): - sigma0_arr = np.copy(sigma0) - - for i, mass in enumerate(m_dms): - - ehigh = 1 # keV - sigma0 = sigma0_arr[i] - if sigma0 == np.inf: - print(f'Infinite, skipping mass {mass}') - continue - - # First, figure out what the maximum energy from this dRdE is - drdefunction = SE.run_fast_fc_sim( - known_bkgs, - threshold, - ehigh, - e_low=1e-6, - m_dms=[mass], - sigma0=sigma0, - use_drdefunction=True, - elf_model=elf_model, - elf_target=elf_target, - elf_params=elf_params, - return_only_drde=True - ) - drdefunction = drdefunction[0] - - ehigh_guesses = np.geomspace(1e-6, 1e3, 3000) - try: - drdefunction_guesses = drdefunction(ehigh_guesses) - except ValueError: - drdefunction_guesses = np.array([drdefunction(en) for en in ehigh_guesses]) - indices = np.where(drdefunction_guesses > 0) - if len(indices[0]) == 0: - ehigh = 1. - else: - j = int(indices[0][-1]) - ehigh = ehigh_guesses[j] * 1.1 - if ehigh < threshold: - ehigh = 1. - - _, sig[i] = SE.run_sim( - threshold, - ehigh, - e_low=1e-6, - m_dms=[mass], - nexp=nexp, - npts=100000, - plot_bkgd=True, -# res=None, - verbose=True, - sigma0=sigma0, - elf_model=elf_model, - elf_params=elf_params, - elf_target=elf_target, - return_only_drde=False) - - print(f'Done mass = {mass}, sigma = {sig[i]}') - - # save results to txt file - if save and savedir is not None: - outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) - tot = np.column_stack( (m_dms, sig) ) - np.savetxt(outname,tot,fmt=['%.5e','%0.5e'] ,delimiter=' ') - - return - -def sapphire_scan(results_dir): - - if not os.path.exists(results_dir): - os.makedirs(results_dir) - - nexp = 1 # number of toys - - var_threshold = False # vary 5sigma requirement based on coinc level - - times = np.array([10]) # d - mass_det = 8. * constants.Al2O3_density * 1e-3 # mass in kg, = 8cc - exposures = times*mass_det - - n_devices = 1 - coinc = np.array([1]) - window = 100e-6 # s - - m_dms, sigma0, _, _ = np.loadtxt('results_sapphire_fc_electron_massive_001_days/HeRALD_FC_1d_1device_1fold_100mus.txt').transpose() - - elf_model='electron' - elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} -# elf_model='phonon' -# elf_params={'mediator': 'massive', 'suppress_darkelf_output': False, 'dark_photon': False} - - f = open(results_dir + '/info.txt', 'w') - f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') - f.write('Detector material: ' + tm + '\n') - f.write('Exposure time(s) (days): ' + str(times) + '\n') - f.write('Detector mass (kg): ' + '%.4e' % mass_det + '\n') - f.write('Number of devices: ' + str(n_devices) + '\n') - f.write('Coincidence level: ' + str(coinc) + '\n') - f.write('Time window (s): ' + str(window) + '\n') - f.write('Dark matter masses (GeV): ' + str(m_dms) + '\n') - f.write('Cross section (cm2): ' + str(sigma0) + '\n') - f.write('Baseline resolution (keV): ' + str(energy_res) + '\n') - f.write('Gain: ' + str(det_gain) + '\n') - f.write('Variable resolution: ' + str(var_threshold) + '\n') - f.write('ELF model: ' + str(elf_model) + '\n') - f.write('ELF params: ' + str(elf_params) + '\n') - f.close() - - - for t in times: - for n in coinc: - run_scan_point( - nexp, - t, - mass_det, - n_devices, - n, - window, - var_threshold=var_threshold, - save=True, - savedir=results_dir, - m_dms=m_dms, - sigma0=sigma0, - elf_target=tm, - elf_model=elf_model, - elf_params=elf_params, - ) - - return - -# ------------------------------------------------------ -# ------------------------------------------------------ -def main(): - - t_start = time.time() - try: - results_dir = sys.argv[1] - except: - print("Check inputs.\n") - return 1 - - sapphire_scan(results_dir) - t_end = time.time() - print(f'Took {(t_end - t_start)/60} minutes.') - - return 0 - -if __name__ == "__main__": - main() From 342697aa39457ca3f3a6022fd81690712a4a2ec7 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 30 Jun 2024 20:20:40 -0700 Subject: [PATCH 21/39] Example sapphire optimum interval scan --- examples/sapphire_oi_scan.py | 246 +++++++++++++++++++++++++++++++++++ 1 file changed, 246 insertions(+) create mode 100644 examples/sapphire_oi_scan.py diff --git a/examples/sapphire_oi_scan.py b/examples/sapphire_oi_scan.py new file mode 100644 index 0000000..c11a254 --- /dev/null +++ b/examples/sapphire_oi_scan.py @@ -0,0 +1,246 @@ +import os +import sys +import numpy as np +import matplotlib.pyplot as plt +import scipy.stats as stats + +import darklim +from darklim import constants + +from multihist import Hist1d +import time +import datetime + +################################################################## + +efficiency = 1.0 +tm = 'Al2O3' # target name +energy_res = 0.373e-3 # energy resolution in keV + +det_gain = 0.40 + +################################################################## + +def plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=None): + + #print('Signal events at m={:0.3f} GeV & {:0.1e} cm2: {:0.3e} evts'.format(mass,sigma0,signal_rates[ii])) + + # plot the evt rate vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates) + #plt.plot(en_interp,curr_exp(en_interp),ls='--') + #ax.axvline(threshold,ls='--',color='red') + ax.set_ylabel('Events') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_title('Expected WIMP events at {:0.1e} cm2'.format(sigma0)) + + if savename is not None: + plt.savefig(savename+'_rate.png',facecolor='white',bbox_inches='tight') + + # plot the acceptance vs mass: + fig, ax = plt.subplots(1,figsize=(6,4)) + plt.plot(m_dms,dm_rates/raw_dm_rates) + ax.set_ylabel('Signal Acceptance') + ax.set_xlabel('Dark Matter Mass [GeV]') + ax.set_xlim(m_dms[0],m_dms[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_ylim(1e-5,1) + #ax.set_title('Signal Acceptance') + + if savename is not None: + plt.savefig(savename+'_acceptance.png',facecolor='white',bbox_inches='tight') + + return + +def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None, + m_dms=np.geomspace(0.0001,5,50),sigma0=1e-36,elf_model=None, elf_target=None, elf_params=None): + + if coinc==1: # if coinc is 1, LEE is 'unknown' + known_bkgs = [0] + else: + known_bkgs = [0,1] + + if var_threshold: + nsigma = stats.norm.isf(stats.norm.sf(5)**(1/coinc)) + else: + nsigma = 5 + + per_device_threshold = nsigma * energy_res # threshold + threshold = coinc*per_device_threshold + + SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=det_gain) + SE.reset_sim() + SE.add_flat_bkgd(1) # flat background of 1 DRU + + SE.add_nfold_lee_bkgd(m=n_devices,n=coinc,w=window) + + print('\nRunning with the following settings:') + print('Mass: {:0.4f} kg; Time: {:0.3f} d => Exposure: {:0.3f} kg-d'.format(SE.m_det,time_elapsed,SE.exposure)) + print('Coincidence: {:d}-fold in {:d} devices; {:0.1f} microsecond window'.format(coinc,n_devices,window/1e-6)) + print('Energy threshold: {:0.3f} eV; {:0.2f} sigma in each device'.format(threshold*1e3,nsigma)) + + # run + sig = np.zeros_like(m_dms) + ul = np.zeros_like(m_dms) + dm_rates = np.zeros_like(m_dms) + raw_dm_rates = np.zeros_like(m_dms) + exp_bkg = np.zeros_like(m_dms) + + if not np.isscalar(sigma0): + sigma0_arr = np.copy(sigma0) + + for i, mass in enumerate(m_dms): + + ehigh = 1 # keV + sigma0 = sigma0_arr[i] + if sigma0 == np.inf: + print(f'Infinite, skipping mass {mass}') + continue + + # First, figure out what the maximum energy from this dRdE is + drdefunction = SE.run_fast_fc_sim( + known_bkgs, + threshold, + ehigh, + e_low=1e-6, + m_dms=[mass], + sigma0=sigma0, + use_drdefunction=True, + elf_model=elf_model, + elf_target=elf_target, + elf_params=elf_params, + return_only_drde=True + ) + drdefunction = drdefunction[0] + + ehigh_guesses = np.geomspace(1e-6, 1e3, 3000) + try: + drdefunction_guesses = drdefunction(ehigh_guesses) + except ValueError: + drdefunction_guesses = np.array([drdefunction(en) for en in ehigh_guesses]) + indices = np.where(drdefunction_guesses > 0) + if len(indices[0]) == 0: + ehigh = 1. + else: + j = int(indices[0][-1]) + ehigh = ehigh_guesses[j] * 1.1 + if ehigh < threshold: + ehigh = 1. + + _, sig[i] = SE.run_sim( + threshold, + ehigh, + e_low=1e-6, + m_dms=[mass], + nexp=nexp, + npts=100000, + plot_bkgd=True, +# res=None, + verbose=True, + sigma0=sigma0, + elf_model=elf_model, + elf_params=elf_params, + elf_target=elf_target, + return_only_drde=False) + + print(f'Done mass = {mass}, sigma = {sig[i]}') + + # save results to txt file + if save and savedir is not None: + outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) + tot = np.column_stack( (m_dms, sig) ) + np.savetxt(outname,tot,fmt=['%.5e','%0.5e'] ,delimiter=' ') + + return + +def sapphire_scan(results_dir): + + if not os.path.exists(results_dir): + os.makedirs(results_dir) + + nexp = 1 # number of toys + + var_threshold = False # vary 5sigma requirement based on coinc level + + times = np.array([1]) # d + mass_det = 8. * constants.Al2O3_density * 1e-3 # mass in kg, = 8cc + exposures = times*mass_det + + n_devices = 1 + coinc = np.array([1]) + window = 100e-6 # s + +# m_dms, sigma0, _, _ = np.loadtxt('results_sapphire_fc_electron_massive_001_days/HeRALD_FC_1d_1device_1fold_100mus.txt').transpose() + m_dms = np.array(list(np.geomspace(0.040, 10, 25)) + list(np.geomspace(11, 100, 8)) + list(np.geomspace(200, 1000, 3))) + print(m_dms) + sigma0 = np.full_like(m_dms, 1e-39) + + elf_model='electron' + elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} +# elf_model='phonon' +# elf_params={'mediator': 'massive', 'suppress_darkelf_output': False, 'dark_photon': False} +# elf_model = None +# elf_params = {} + + f = open(results_dir + '/info.txt', 'w') + f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') + f.write('Detector material: ' + tm + '\n') + f.write('Exposure time(s) (days): ' + str(times) + '\n') + f.write('Detector mass (kg): ' + '%.4e' % mass_det + '\n') + f.write('Number of devices: ' + str(n_devices) + '\n') + f.write('Coincidence level: ' + str(coinc) + '\n') + f.write('Time window (s): ' + str(window) + '\n') + f.write('Dark matter masses (GeV): ' + str(m_dms) + '\n') + f.write('Cross section (cm2): ' + str(sigma0) + '\n') + f.write('Baseline resolution (keV): ' + str(energy_res) + '\n') + f.write('Gain: ' + str(det_gain) + '\n') + f.write('Variable resolution: ' + str(var_threshold) + '\n') + f.write('ELF model: ' + str(elf_model) + '\n') + f.write('ELF params: ' + str(elf_params) + '\n') + f.close() + + + for t in times: + for n in coinc: + run_scan_point( + nexp, + t, + mass_det, + n_devices, + n, + window, + var_threshold=var_threshold, + save=True, + savedir=results_dir, + m_dms=m_dms, + sigma0=sigma0, + elf_target=tm, + elf_model=elf_model, + elf_params=elf_params, + ) + + return + +# ------------------------------------------------------ +# ------------------------------------------------------ +def main(): + + t_start = time.time() + try: + results_dir = sys.argv[1] + except: + print("Check inputs.\n") + return 1 + + sapphire_scan(results_dir) + t_end = time.time() + print(f'Took {(t_end - t_start)/60} minutes.') + + return 0 + +if __name__ == "__main__": + main() From a7440923617a3cf386da2a9c3ced1e101dad4b66 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 01:08:13 -0700 Subject: [PATCH 22/39] Consider the energy partioninig and deposition in GaAs --- darklim/detector/_detector.py | 14 ++++++- darklim/sensitivity/_sens_est.py | 72 ++++++++++++++++++++++++-------- 2 files changed, 68 insertions(+), 18 deletions(-) diff --git a/darklim/detector/_detector.py b/darklim/detector/_detector.py index c99b46c..c5e90ab 100644 --- a/darklim/detector/_detector.py +++ b/darklim/detector/_detector.py @@ -11,6 +11,9 @@ def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincidence_light, threshold_eV, n_samples=1): E_light_eV = constants.GaAs_light_fraction * E_recoil_eV + #print('E recoil eV', E_recoil_eV) + #print('GaAs light fraction', constants.GaAs_light_fraction) + #print('E light eV', E_light_eV) n_photons_generated_average = np.floor(E_light_eV / constants.bandgap_GaAs_eV) if n_photons_generated_average == 0: @@ -19,6 +22,9 @@ def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincide else: return np.full(n_samples, 0.) + #print('LCE per channel', lce_per_channel, type(lce_per_channel)) + #print('photons generated', n_photons_generated_average, type(n_photons_generated_average)) + #print('n samples', n_samples, type(n_samples)) n_photons_detected_ch1 = np.random.binomial(n_photons_generated_average, lce_per_channel, n_samples) n_photons_detected_ch2 = np.random.binomial(n_photons_generated_average, lce_per_channel, n_samples) @@ -40,7 +46,7 @@ def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincide -def convert_dRdE_dep_to_obs(E_dep_keV, dRdE_dep_DRU, pce=0.40, lce_per_channel=0.10, res=0.10, n_coincidence_light=1, calorimeter_threshold_eV=0.37, E_min_keV=None, E_max_keV=None, n_samples=int(1e6)): +def convert_dRdE_dep_to_obs_gaas(E_dep_keV, dRdE_dep_DRU, pce=0.40, lce_per_channel=0.10, res=0.10, n_coincidence_light=1, calorimeter_threshold_eV=0.37, E_min_keV=None, E_max_keV=None, n_samples=int(1e6)): # Reduce data to the appropriate energy range if E_min_keV is None or E_min_keV < E_dep_keV[0]: @@ -62,9 +68,15 @@ def convert_dRdE_dep_to_obs(E_dep_keV, dRdE_dep_DRU, pce=0.40, lce_per_channel=0 energies_sim_keV = inv_cdf(samples) energies_obs_keV = np.zeros_like(energies_sim_keV) energies_obs_keV = np.copy(energies_sim_keV) + #print(f'Out of {len(energies_sim_keV)} energies, {sum(np.isnan(energies_sim_keV))} are nan') + for i, E in enumerate(energies_sim_keV): energies_obs_keV[i] = get_deposited_energy_gaas(E * 1000, pce, lce_per_channel, res, n_coincidence_light, calorimeter_threshold_eV) / 1000 + # Perhaps no energy is ever observed + if sum(energies_obs_keV > 0) == 0: + return E_pdf, np.zeros_like(dRdE_pdf), np.array([]) + # Convert to E vs dRdE that we can later interpolate from # Normalize to the number of events that are detected bins = np.geomspace(min(energies_obs_keV[energies_obs_keV > 0]) * 0.95, max(energies_obs_keV) * 1.05, 10000) diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index 3242bc7..65d4fba 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -9,6 +9,7 @@ from darklim.sensitivity._plotting import RatePlot import darklim.elf._elf as elf +import darklim.detector._detector as detector __all__ = [ "calculate_substrate_mass", @@ -293,7 +294,8 @@ def reset_sim(self): def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, plot_bkgd=False, res=None, verbose=False, sigma0=1e-41, - elf_model=None, elf_params=None, elf_target=None, return_only_drde=False): + elf_model=None, elf_params=None, elf_target=None, + gaas_params=None, return_only_drde=False): """ Method for running the simulation for getting the sensitivity @@ -347,7 +349,7 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, drdefunction = [(lambda x: drde_wimp_obs( x, m, sigma0, self.tm, self.gain )) for m in m_dms ] print('Using WIMPs') - elif elf_model == 'electron' and elf_target == 'GaAs': + elif elf_model == 'electron' and elf_target == 'Al2O3': elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' elf_kcut = elf_params['kcut'] if 'kcut' in elf_params else 0 @@ -356,35 +358,35 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False drdefunction = \ - [elf.get_dRdE_lambda_GaAs_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, + [elf.get_dRdE_lambda_Al2O3_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, kcut=elf_kcut, method=elf_method, withscreening=elf_screening, suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] - elif elf_model == 'electron' and elf_target == 'Al2O3': + elif elf_model == 'phonon' and elf_target == 'Al2O3': elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' - elf_kcut = elf_params['kcut'] if 'kcut' in elf_params else 0 - elf_method = elf_params['method'] if 'method' in elf_params else 'grid' - elf_screening = elf_params['withscreening'] if 'withscreening' in elf_params else True elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False + elf_darkphoton = elf_params['dark_photon'] if 'dark_photon' in elf_params else False drdefunction = \ - [elf.get_dRdE_lambda_Al2O3_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, - kcut=elf_kcut, method=elf_method, withscreening=elf_screening, + [elf.get_dRdE_lambda_Al2O3_phonon(mX_eV=m*1e9, sigman=sigma0, mediator=elf_mediator, + dark_photon=elf_darkphoton, suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] - elif elf_model == 'phonon' and elf_target == 'Al2O3': + elif elf_model == 'electron' and elf_target == 'GaAs': elf_mediator = elf_params['mediator'] if 'mediator' in elf_params else 'massless' + elf_kcut = elf_params['kcut'] if 'kcut' in elf_params else 0 + elf_method = elf_params['method'] if 'method' in elf_params else 'grid' + elf_screening = elf_params['withscreening'] if 'withscreening' in elf_params else True elf_suppress = elf_params['suppress_darkelf_output'] if 'suppress_darkelf_output' in elf_params else False - elf_darkphoton = elf_params['dark_photon'] if 'dark_photon' in elf_params else False drdefunction = \ - [elf.get_dRdE_lambda_Al2O3_phonon(mX_eV=m*1e9, sigman=sigma0, mediator=elf_mediator, - dark_photon=elf_darkphoton, - suppress_darkelf_output=elf_suppress, gain=self.gain) + [elf.get_dRdE_lambda_GaAs_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, + kcut=elf_kcut, method=elf_method, withscreening=elf_screening, + suppress_darkelf_output=elf_suppress, gain=1.) for m in m_dms] elif elf_model == 'phonon' and elf_target == 'GaAs': @@ -399,11 +401,31 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] + + if self.tm == 'GaAs' and gaas_params is not None: + + for j, m in enumerate(m_dms): + E_deposited_keV_arr = np.geomspace(0.1e-3, 800, int(1e4)) + dRdE_deposited_DRU_arr = drdefunction[j](E_deposited_keV_arr) + + check = sum(dRdE_deposited_DRU_arr > 0) + if check == 0: + continue + + E_observed_keV_arr, dRdE_observed_DRU_arr, _ = \ + detector.convert_dRdE_dep_to_obs_gaas(E_deposited_keV_arr, dRdE_deposited_DRU_arr, + pce=gaas_params['pce'], + lce_per_channel=gaas_params['lce_per_channel'], + res=gaas_params['res'], + n_coincidence_light=gaas_params['n_coincidence_light'], + calorimeter_threshold_eV=gaas_params['calorimeter_threshold_eV']) + + drdefunction[j] = lambda E: np.interp(E, E_observed_keV_arr, dRdE_observed_DRU_arr) + + if return_only_drde: return drdefunction - - for ii in range(nexp): evts_sim = self._generate_background( en_interp, plot_bkgd=plot_bkgd and ii==0, @@ -549,7 +571,7 @@ def run_sim_fc(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms=None, def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, plot_bkgd=False, res=None, verbose=False, sigma0=1e-41,use_drdefunction=False,pltname=None, - elf_model=None, elf_params=None, elf_target=None, savedir=None, return_only_drde=False): + elf_model=None, elf_params=None, elf_target=None, savedir=None, return_only_drde=False, gaas_params=None): """ Faster version of the above, avoiding repeat calculations of signal rates. """ @@ -619,6 +641,22 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] + if self.tm == 'GaAs' and gaas_params is not None: + + for j, m in enumerate(m_dms): + E_deposited_keV_arr = np.geomspace(0.1e-3, 800, int(1e4)) + dRdE_deposited_DRU_arr = drdefunction[j](E_deposited_keV_arr) + + E_observed_keV_arr, dRdE_observed_DRU_arr, _ = \ + detector.convert_dRdE_dep_to_obs_gaas(E_deposited_keV_arr, dRdE_deposited_DRU_arr, + pce=gaas_params['pce'], + lce_per_channel=gaas_params['lce_per_channel'], + res=gaas_params['res'], + n_coincidence_light=gaas_params['n_coincidence_light'], + calorimeter_threshold_eV=gaas_params['calorimeter_threshold_eV']) + + drdefunction[j] = lambda E: np.interp(E, E_observed_keV_arr, dRdE_observed_DRU_arr) + if return_only_drde: return drdefunction From 4ac489f24214e789b5ca60e7ecb72391a0e01b7c Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 16:11:49 -0700 Subject: [PATCH 23/39] Added new existing limits --- .../ExistingLimits/DAMIC_M_ER_Massive.txt | 35 +++++++++++++ .../ExistingLimits/Darkside50_ER_Massive.txt | 30 +++++++++++ examples/ExistingLimits/SENSEI_Migdal_NR.txt | 30 +++++++++++ .../SENSEI_SNOLAB_ER_Massive.txt | 35 +++++++++++++ ...SEI_SNOLAB_Solar_Reflection_ER_Massive.txt | 13 +++++ .../XENON1T_S2Only_ER_Massive.txt | 34 ++++++++++++ ...ON1T_S2Only_Solar_Reflected_ER_Massive.txt | 20 +++++++ ...N1T_S2Only_Solar_Reflected_ER_Massless.txt | 52 +++++++++++++++++++ 8 files changed, 249 insertions(+) create mode 100644 examples/ExistingLimits/DAMIC_M_ER_Massive.txt create mode 100644 examples/ExistingLimits/Darkside50_ER_Massive.txt create mode 100644 examples/ExistingLimits/SENSEI_Migdal_NR.txt create mode 100644 examples/ExistingLimits/SENSEI_SNOLAB_ER_Massive.txt create mode 100644 examples/ExistingLimits/SENSEI_SNOLAB_Solar_Reflection_ER_Massive.txt create mode 100644 examples/ExistingLimits/XENON1T_S2Only_ER_Massive.txt create mode 100644 examples/ExistingLimits/XENON1T_S2Only_Solar_Reflected_ER_Massive.txt create mode 100644 examples/ExistingLimits/XENON1T_S2Only_Solar_Reflected_ER_Massless.txt diff --git a/examples/ExistingLimits/DAMIC_M_ER_Massive.txt b/examples/ExistingLimits/DAMIC_M_ER_Massive.txt new file mode 100644 index 0000000..42f713c --- /dev/null +++ b/examples/ExistingLimits/DAMIC_M_ER_Massive.txt @@ -0,0 +1,35 @@ +# MeV DM-e +1.77142e+0 4.32209e-34 +1.94353e+0 2.61897e-34 +2.06746e+0 1.53804e-34 +2.19929e+0 9.03249e-35 +2.33952e+0 5.14101e-35 +2.60680e+0 2.83590e-35 +2.73050e+0 1.66544e-35 +3.04244e+0 1.00917e-35 +3.28684e+0 6.11508e-36 +3.66234e+0 3.59121e-36 +3.89587e+0 2.31671e-36 +4.34094e+0 1.40381e-36 +4.76269e+0 1.05908e-36 +5.73310e+0 6.41749e-37 +6.19364e+0 5.07437e-37 +7.11785e+0 3.65263e-37 +8.43674e+0 2.93374e-37 +1.00000e+1 2.35634e-37 +1.20375e+1 1.98358e-37 +1.38337e+1 2.01488e-37 +1.69117e+1 2.01488e-37 +2.00453e+1 1.95277e-37 +2.37596e+1 2.21331e-37 +2.99578e+1 2.35634e-37 +3.83612e+1 2.67072e-37 +5.30679e+1 3.24799e-37 +7.22870e+1 4.13997e-37 +9.40057e+1 5.15444e-37 +1.32070e+2 7.04951e-37 +1.91372e+2 9.56614e-37 +2.77302e+2 1.38201e-36 +4.08073e+2 2.02807e-36 +6.09867e+2 2.99953e-36 +9.25642e+2 4.47121e-36 diff --git a/examples/ExistingLimits/Darkside50_ER_Massive.txt b/examples/ExistingLimits/Darkside50_ER_Massive.txt new file mode 100644 index 0000000..7120b9b --- /dev/null +++ b/examples/ExistingLimits/Darkside50_ER_Massive.txt @@ -0,0 +1,30 @@ +# MeV DM-e +1.31054e+1 8.36334e-36 +1.35168e+1 5.39525e-36 +1.38337e+1 2.88441e-36 +1.42680e+1 1.44846e-36 +1.47158e+1 7.74376e-37 +1.54141e+1 4.13997e-37 +1.61456e+1 2.07896e-37 +1.71089e+1 1.11146e-37 +1.79901e+1 6.32606e-38 +1.94353e+1 3.71511e-38 +2.06746e+1 2.25117e-38 +2.27710e+1 1.32205e-38 +2.52746e+1 7.06792e-39 +2.86006e+1 4.28280e-39 +3.23644e+1 3.01130e-39 +3.80660e+1 1.95787e-39 +4.47720e+1 1.39833e-39 +5.22542e+1 1.08001e-39 +6.29010e+1 8.88059e-40 +7.68964e+1 7.59370e-40 +9.54697e+1 7.18881e-40 +1.24154e+2 7.30224e-40 +1.63970e+2 7.71351e-40 +2.13235e+2 8.53974e-40 +2.81621e+2 1.00654e-39 +3.89587e+2 1.23372e-39 +5.22542e+2 1.59735e-39 +7.00870e+2 2.02014e-39 +9.25642e+2 2.57493e-39 diff --git a/examples/ExistingLimits/SENSEI_Migdal_NR.txt b/examples/ExistingLimits/SENSEI_Migdal_NR.txt new file mode 100644 index 0000000..5139c7f --- /dev/null +++ b/examples/ExistingLimits/SENSEI_Migdal_NR.txt @@ -0,0 +1,30 @@ +# MeV Dm-nucleon +1.10075e+0 7.01269e-28 +1.18511e+0 2.02526e-28 +1.31417e+0 8.34048e-29 +1.41489e+0 3.43480e-29 +1.59232e+0 1.11653e-29 +1.84573e+0 4.59812e-30 +2.01674e+0 1.78486e-30 +2.37247e+0 8.27348e-31 +2.59228e+0 4.31661e-31 +3.14095e+0 2.00090e-31 +3.91982e+0 9.83999e-32 +5.03846e+0 4.29924e-32 +6.47636e+0 1.99285e-32 +8.08232e+0 1.03975e-32 +1.03889e+1 5.42480e-33 +1.29650e+1 3.37984e-33 +1.71646e+1 1.87084e-33 +2.41073e+1 1.03557e-33 +3.28727e+1 6.45194e-34 +4.68559e+1 4.01979e-34 +6.98126e+1 2.36064e-34 +1.07135e+2 1.16091e-34 +1.43948e+2 7.67356e-35 +2.11329e+2 5.07219e-35 +3.19553e+2 2.64637e-35 +4.97684e+2 1.46484e-35 +6.49232e+2 8.10834e-36 +8.72315e+2 5.35957e-36 + diff --git a/examples/ExistingLimits/SENSEI_SNOLAB_ER_Massive.txt b/examples/ExistingLimits/SENSEI_SNOLAB_ER_Massive.txt new file mode 100644 index 0000000..8db38a4 --- /dev/null +++ b/examples/ExistingLimits/SENSEI_SNOLAB_ER_Massive.txt @@ -0,0 +1,35 @@ +# MeV DM-e +1.40492e+0 4.32209e-34 +1.47158e+0 2.70227e-34 +1.51778e+0 1.53804e-34 +1.61456e+0 9.61619e-35 +1.74426e+0 5.14101e-35 +1.85548e+0 2.83590e-35 +1.97379e+0 1.77307e-35 +2.09965e+0 1.10856e-35 +2.30365e+0 5.92659e-36 +2.45054e+0 4.19981e-36 +2.64740e+0 2.79550e-36 +2.90461e+0 1.91993e-36 +3.13794e+0 1.36054e-36 +3.77730e+0 8.24418e-37 +4.34094e+0 5.48753e-37 +4.83686e+0 4.84157e-37 +5.55861e+0 3.76881e-37 +6.90122e+0 3.05084e-37 +8.30736e+0 2.50861e-37 +1.04745e+1 2.26590e-37 +1.34127e+1 2.30165e-37 +1.91372e+1 2.31974e-37 +2.77302e+1 2.82114e-37 +3.60618e+1 3.22267e-37 +5.06638e+1 3.88868e-37 +7.22870e+1 5.23577e-37 +9.84665e+1 6.72608e-37 +1.44902e+2 9.49155e-37 +2.09965e+2 1.36054e-36 +3.04244e+2 1.89011e-36 +4.14429e+2 2.56487e-36 +5.55861e+2 3.39973e-36 +7.68964e+2 4.64967e-36 +9.69566e+2 5.92659e-36 diff --git a/examples/ExistingLimits/SENSEI_SNOLAB_Solar_Reflection_ER_Massive.txt b/examples/ExistingLimits/SENSEI_SNOLAB_Solar_Reflection_ER_Massive.txt new file mode 100644 index 0000000..1610556 --- /dev/null +++ b/examples/ExistingLimits/SENSEI_SNOLAB_Solar_Reflection_ER_Massive.txt @@ -0,0 +1,13 @@ +# MeV DM-e +5.06638e-1 7.25476e-35 +5.91306e-1 8.22269e-35 +6.90122e-1 9.03249e-35 +7.93102e-1 1.02376e-34 +9.40057e-1 1.27463e-34 +1.13160e+0 1.53804e-34 +1.34127e+0 1.68951e-34 +1.63970e+0 1.97584e-34 +1.94353e+0 2.31068e-34 +2.30365e+0 2.87689e-34 +2.64740e+0 3.47144e-34 +3.23644e+0 4.74774e-34 diff --git a/examples/ExistingLimits/XENON1T_S2Only_ER_Massive.txt b/examples/ExistingLimits/XENON1T_S2Only_ER_Massive.txt new file mode 100644 index 0000000..5cd0d6b --- /dev/null +++ b/examples/ExistingLimits/XENON1T_S2Only_ER_Massive.txt @@ -0,0 +1,34 @@ +# MeV DM-e +1.98911e+1 1.86075e-36 +2.03575e+1 1.12752e-36 +2.13235e+1 7.04951e-37 +2.23354e+1 4.40750e-37 +2.35767e+1 2.93374e-37 +2.52746e+1 1.72290e-37 +2.68862e+1 9.50394e-38 +2.81621e+1 6.52726e-38 +2.92714e+1 4.34471e-38 +3.08982e+1 2.80280e-38 +3.33803e+1 1.64600e-38 +3.60618e+1 1.02912e-38 +3.83612e+1 6.63890e-39 +4.20883e+1 3.54929e-39 +4.47720e+1 2.15069e-39 +4.85559e+1 1.38743e-39 +5.30679e+1 9.23505e-40 +6.00514e+1 6.34258e-40 +6.90122e+1 3.99668e-40 +8.17997e+1 2.72350e-40 +9.43696e+1 2.12005e-40 +1.09716e+2 1.65030e-40 +1.30045e+2 1.36766e-40 +1.47158e+2 1.24504e-40 +1.69117e+2 1.16947e-40 +1.97379e+2 1.15130e-40 +2.41296e+2 1.16035e-40 +3.04244e+2 1.20666e-40 +3.60618e+2 1.27463e-40 +4.78113e+2 1.45604e-40 +5.91306e+2 1.66327e-40 +7.11785e+2 1.92998e-40 +9.54697e+2 2.40290e-40 diff --git a/examples/ExistingLimits/XENON1T_S2Only_Solar_Reflected_ER_Massive.txt b/examples/ExistingLimits/XENON1T_S2Only_Solar_Reflected_ER_Massive.txt new file mode 100644 index 0000000..e714ca8 --- /dev/null +++ b/examples/ExistingLimits/XENON1T_S2Only_Solar_Reflected_ER_Massive.txt @@ -0,0 +1,20 @@ +# MeV DM-e +4.98869e-1 1.92495e-38 +5.82238e-1 2.04934e-38 +7.34128e-1 2.25117e-38 +8.97470e-1 2.57157e-38 +1.14922e+0 2.80280e-38 +1.42680e+0 2.98392e-38 +1.79901e+0 3.27779e-38 +2.26832e+0 4.08098e-38 +2.86006e+0 5.08100e-38 +3.77730e+0 6.32606e-38 +4.54693e+0 8.92706e-38 +5.64518e+0 1.14681e-37 +7.34128e+0 1.38381e-37 +9.25642e+0 1.59319e-37 +1.18529e+1 1.86318e-37 +1.47158e+1 2.28371e-37 +1.74426e+1 2.75567e-37 +1.74426e+1 2.82114e-37 +2.03575e+1 3.43092e-37 diff --git a/examples/ExistingLimits/XENON1T_S2Only_Solar_Reflected_ER_Massless.txt b/examples/ExistingLimits/XENON1T_S2Only_Solar_Reflected_ER_Massless.txt new file mode 100644 index 0000000..c4e4445 --- /dev/null +++ b/examples/ExistingLimits/XENON1T_S2Only_Solar_Reflected_ER_Massless.txt @@ -0,0 +1,52 @@ +# MeV DM-e cm^2 +0.010056 3.9016e-37 +0.012345 5.1191e-37 +0.013869 6.2497e-37 +0.016560 7.0603e-37 +0.019022 8.1548e-37 +0.021489 9.6301e-37 +0.024546 1.1372e-36 +0.029310 1.3356e-36 +0.033667 1.6125e-36 +0.038033 1.9470e-36 +0.042728 2.3770e-36 +0.049627 2.6118e-36 +0.055572 3.0323e-36 +0.059086 3.2965e-36 +0.070150 3.9348e-36 +0.078108 4.4833e-36 +0.084516 5.0889e-36 +0.091638 5.7529e-36 +0.10659 6.6561e-36 +0.11307 7.1368e-36 +0.13497 8.3579e-36 +0.14317 8.9642e-36 +0.16372 1.1004e-35 +0.18422 1.1124e-35 +0.19732 1.2348e-35 +0.22831 1.5368e-35 +0.24612 1.6157e-35 +0.29377 1.9187e-35 +0.30996 2.0697e-35 +0.35360 2.3930e-35 +0.38004 2.4672e-35 +0.41699 2.7394e-35 +0.47099 3.1265e-35 +0.52339 3.6343e-35 +0.55630 4.1285e-35 +0.61477 4.3819e-35 +0.67467 4.4530e-35 +0.80101 5.7728e-35 +0.85426 6.0025e-35 +0.97163 7.4136e-35 +1.0530 7.7132e-35 +1.1807 9.6752e-35 +1.2502 1.0486e-34 +1.3356 1.1576e-34 +1.5312 1.2870e-34 +1.8297 1.6335e-34 +2.0353 1.8959e-34 +2.2221 2.1201e-34 +2.3926 2.2621e-34 +2.7952 2.7098e-34 +2.9493 2.9289e-34 From 27d17e740491cb5dcb8eb2e486ef2b8fb07ef4e8 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 16:12:05 -0700 Subject: [PATCH 24/39] Plotting scripts --- examples/plot_gaas_ER_massive.py | 47 ++++++++++++++++++++++++ examples/plot_gaas_ER_massless.py | 51 +++++++++++++++++++++++++++ examples/plot_gaas_NR.py | 43 ++++++++++++++++++++++ examples/plot_sapphire_ER_massive.py | 44 +++++++++++++++++++++++ examples/plot_sapphire_ER_massless.py | 48 +++++++++++++++++++++++++ examples/plot_sapphire_NR.py | 46 ++++++++++++++++++++++++ 6 files changed, 279 insertions(+) create mode 100644 examples/plot_gaas_ER_massive.py create mode 100644 examples/plot_gaas_ER_massless.py create mode 100644 examples/plot_gaas_NR.py create mode 100644 examples/plot_sapphire_ER_massive.py create mode 100644 examples/plot_sapphire_ER_massless.py create mode 100644 examples/plot_sapphire_NR.py diff --git a/examples/plot_gaas_ER_massive.py b/examples/plot_gaas_ER_massive.py new file mode 100644 index 0000000..c521705 --- /dev/null +++ b/examples/plot_gaas_ER_massive.py @@ -0,0 +1,47 @@ +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import math + +curves_dir = 'ExistingLimits/' + +# Some figure setup +mpl.rcParams.update({'font.size': 20}) +mpl.rcParams.update({'axes.linewidth': 2}) +fig, ax = plt.subplots(1, 1, figsize=(11, 11)) +xmin = 1e-2; xmax = 1e4; +ymin = 1e-41; ymax = 1e-28; + +# Existing limits +m_limit, x_limit = np.loadtxt(curves_dir + 'DAMIC_M_ER_Massive.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='DAMIC-M') +m_limit, x_limit = np.loadtxt(curves_dir + 'Darkside50_ER_Massive.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='Darkside 50') +m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_ER_Massive.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI SNOLAB') +#m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_Solar_Reflection_ER_Massive.txt').transpose() +#ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI SNOLAB (Solar Refl.)') +m_limit, x_limit = np.loadtxt(curves_dir + 'XENON1T_S2Only_ER_Massive.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='XENON1T S2only') +#m_limit, x_limit = np.loadtxt(curves_dir + 'XENON1T_S2Only_Solar_Reflected_ER_Massive.txt').transpose() +#ax.plot(m_limit, x_limit, '--', lw=1.5, label='XENON1T S2only (Solar Refl.)') + +# Simulation +m_limit, x_limit = np.loadtxt('results_gaas_oi_scan_electron_massive_100days_2fold_lce10/HeRALD_FC_100d_2device_2fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'r-', lw=4, label=f'100 days, 1 light signal, 10% LCE per channel') +m_limit, x_limit = np.loadtxt('results_gaas_oi_scan_electron_massive_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'b-', lw=4, label=f'100 days, 2 light signal, 10% LCE per channel') +m_limit, x_limit = np.loadtxt('results_gaas_oi_scan_electron_massive_300days_3fold_lce10/HeRALD_FC_300d_3device_3fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'm-', lw=4, label=f'300 days, 2 light signal, 10% LCE per channel') + + +ax.set_xscale('log') +ax.set_xlim([xmin, xmax]) +ax.set_yscale('log') +ax.set_ylim([ymin, ymax]) +ax.set_xlabel('DM Mass [MeV]') +ax.set_ylabel('DM-electron cross-section massive [cm2]') +ax.legend(loc='best', fontsize=12) + +fig.tight_layout() +fig.savefig('gaas_ER_massive.png') diff --git a/examples/plot_gaas_ER_massless.py b/examples/plot_gaas_ER_massless.py new file mode 100644 index 0000000..e583ee5 --- /dev/null +++ b/examples/plot_gaas_ER_massless.py @@ -0,0 +1,51 @@ +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import math + +curves_dir = 'ExistingLimits/' + +# Some figure setup +mpl.rcParams.update({'font.size': 20}) +mpl.rcParams.update({'axes.linewidth': 2}) +fig, ax = plt.subplots(1, 1, figsize=(11, 11)) +xmin = 1e-2; xmax = 1e4; +ymin = 1e-41; ymax = 1e-28; + +# Existing limits +m_limit, x_limit = np.loadtxt(curves_dir + 'DAMIC_M_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='DAMIC-M') +m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_MINOS_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI MINOS') +m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI SNOLAB') +#m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_Solar_Reflection_ER_Massless.txt').transpose() +#ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI SNOLAB (Solar Refl.)') +#m_limit, x_limit = np.loadtxt(curves_dir + 'XENON1T_S2Only_Solar_Reflected_ER_Massless.txt').transpose() +#ax.plot(m_limit, x_limit, '--', lw=1.5, label='XENON1T S2 Only (Solar Refl.)') +m_limit, x_limit = np.loadtxt(curves_dir + 'protoSENSEI_MINOS_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='protoSENSEI') +m_limit, x_limit = np.loadtxt(curves_dir + 'Freeze_in_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '-', lw=8, label='Freeze-in') + + +# Simulation +for fold, ls in zip([2, 3], ['-', ':', '-.']): + for lce, color in zip([5, 10, 25], ['r', 'b', 'm', 'g', 'y']): + m_limit, x_limit = np.loadtxt(f'results_gaas_oi_scan_electron_massless_100days_{fold}fold_lce{lce:02d}/HeRALD_FC_100d_{fold}device_{fold}fold_100mus.txt').transpose() + ax.plot(m_limit*1e3, x_limit, lw=4, ls=ls, color=color, label=f'{fold}-fold, {lce/100:.02f} LCE per channel') + +m_limit, x_limit = np.loadtxt('results_gaas_oi_scan_electron_massless_300days_3fold_lce10/HeRALD_FC_300d_3device_3fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, lw=4, ls=':', color='k', label=f'300 days') + + +ax.set_xscale('log') +ax.set_xlim([xmin, xmax]) +ax.set_yscale('log') +ax.set_ylim([ymin, ymax]) +ax.set_xlabel('DM Mass [MeV]') +ax.set_ylabel('DM-electron cross-section massless [cm2]') +ax.legend(loc='best', fontsize=14) + +fig.tight_layout() +fig.savefig('gaas_ER_massless.png') diff --git a/examples/plot_gaas_NR.py b/examples/plot_gaas_NR.py new file mode 100644 index 0000000..d286ac4 --- /dev/null +++ b/examples/plot_gaas_NR.py @@ -0,0 +1,43 @@ +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import math + +curves_dir = 'ExistingLimits/' + +# Some figure setup +mpl.rcParams.update({'font.size': 20}) +mpl.rcParams.update({'axes.linewidth': 2}) +fig, ax = plt.subplots(1, 1, figsize=(11, 11)) +xmin = 1e1; xmax = 1e6; +ymin = 1e-48; ymax = 1e-35; + +# Existing limits +m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_Migdal_NR.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI Migdal') +m_limit, x_limit = np.loadtxt(curves_dir + 'CRESST_III_2019.txt').transpose() +ax.plot(m_limit*1e3, x_limit, '--', lw=1.5, label='CRESST III') +m_limit, x_limit = np.loadtxt(curves_dir + 'Darkside50_Migdal_2023.txt').transpose() +ax.plot(m_limit*1e3, x_limit, '--', lw=1.5, label='Darkside Migdal') +m_limit, x_limit = np.loadtxt(curves_dir + 'LZ_SI_2022.txt').transpose() +ax.plot(m_limit*1e3, x_limit, '--', lw=1.5, label='LZ') + +# Simulation +for fold, ls in zip([2, 3], ['-', ':', '-.']): + for lce, color in zip([5, 10, 25], ['r', 'b', 'm', 'g', 'y']): + m_limit, x_limit = np.loadtxt(f'results_gaas_oi_scan_phonon_massless_100days_{fold}fold_lce{lce:02d}_darkphoton/HeRALD_FC_100d_{fold}device_{fold}fold_100mus.txt').transpose() + ax.plot(m_limit*1e3, x_limit, lw=2, ls=ls, color=color, label=f'{fold}-fold, {lce/100:.02f} LCE per channel') + m_limit, x_limit = np.loadtxt(f'results_gaas_oi_scan_NR_100days_{fold}fold_lce{lce:02d}/HeRALD_FC_100d_{fold}device_{fold}fold_100mus.txt').transpose() + ax.plot(m_limit*1e3, x_limit, lw=2, ls='--', color=color, alpha=0.5) + +ax.set_xscale('log') +ax.set_xlim([xmin, xmax]) +ax.set_yscale('log') +ax.set_ylim([ymin, ymax]) +ax.set_xlabel('DM Mass [MeV]') +ax.set_ylabel('DM-nucleon [cm2]') +ax.legend(loc='lower left', fontsize=14) + +fig.tight_layout() +fig.savefig('gaas_NR.png') + diff --git a/examples/plot_sapphire_ER_massive.py b/examples/plot_sapphire_ER_massive.py new file mode 100644 index 0000000..3795975 --- /dev/null +++ b/examples/plot_sapphire_ER_massive.py @@ -0,0 +1,44 @@ +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import math + +curves_dir = 'ExistingLimits/' + +# Some figure setup +mpl.rcParams.update({'font.size': 20}) +mpl.rcParams.update({'axes.linewidth': 2}) +fig, ax = plt.subplots(1, 1, figsize=(11, 11)) +xmin = 1e-2; xmax = 1e4; +ymin = 1e-41; ymax = 1e-28; + +# Existing limits +m_limit, x_limit = np.loadtxt(curves_dir + 'DAMIC_M_ER_Massive.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='DAMIC-M') +m_limit, x_limit = np.loadtxt(curves_dir + 'Darkside50_ER_Massive.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='Darkside 50') +m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_ER_Massive.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI SNOLAB') +#m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_Solar_Reflection_ER_Massive.txt').transpose() +#ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI SNOLAB (Solar Refl.)') +m_limit, x_limit = np.loadtxt(curves_dir + 'XENON1T_S2Only_ER_Massive.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='XENON1T S2only') +#m_limit, x_limit = np.loadtxt(curves_dir + 'XENON1T_S2Only_Solar_Reflected_ER_Massive.txt').transpose() +#ax.plot(m_limit, x_limit, '--', lw=1.5, label='XENON1T S2only (Solar Refl.)') + +# Simulation +m_limit, x_limit = np.loadtxt('sapphire/results_sapphire_oi_electron_massive_001_days/HeRALD_FC_1d_1device_1fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'r-', lw=4, label=f'1 day, 40% PCE') +m_limit, x_limit = np.loadtxt('sapphire/results_sapphire_oi_electron_massive_010_days/HeRALD_FC_10d_1device_1fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'm-', lw=4, label=f'10 days, 40% PCE') + +ax.set_xscale('log') +ax.set_xlim([xmin, xmax]) +ax.set_yscale('log') +ax.set_ylim([ymin, ymax]) +ax.set_xlabel('DM Mass [MeV]') +ax.set_ylabel('DM-electron cross-section massive [cm2]') +ax.legend(loc='best', fontsize=12) + +fig.tight_layout() +fig.savefig('sapphire_ER_massive.png') diff --git a/examples/plot_sapphire_ER_massless.py b/examples/plot_sapphire_ER_massless.py new file mode 100644 index 0000000..ea20065 --- /dev/null +++ b/examples/plot_sapphire_ER_massless.py @@ -0,0 +1,48 @@ +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import math + +curves_dir = 'ExistingLimits/' + +# Some figure setup +mpl.rcParams.update({'font.size': 20}) +mpl.rcParams.update({'axes.linewidth': 2}) +fig, ax = plt.subplots(1, 1, figsize=(11, 11)) +xmin = 1e-2; xmax = 1e4; +ymin = 1e-41; ymax = 1e-28; + +# Existing limits +m_limit, x_limit = np.loadtxt(curves_dir + 'DAMIC_M_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='DAMIC-M') +m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_MINOS_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI MINOS') +m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI SNOLAB') +#m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_Solar_Reflection_ER_Massless.txt').transpose() +#ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI SNOLAB (Solar Refl.)') +#m_limit, x_limit = np.loadtxt(curves_dir + 'XENON1T_S2Only_Solar_Reflected_ER_Massless.txt').transpose() +#ax.plot(m_limit, x_limit, '--', lw=1.5, label='XENON1T S2 Only (Solar Refl.)') +m_limit, x_limit = np.loadtxt(curves_dir + 'protoSENSEI_MINOS_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='protoSENSEI') +m_limit, x_limit = np.loadtxt(curves_dir + 'Freeze_in_ER_Massless.txt').transpose() +ax.plot(m_limit, x_limit, '-', lw=8, label='Freeze-in') + + +# Simulation +m_limit, x_limit = np.loadtxt('sapphire/results_sapphire_oi_scan_electron_massless_001days/HeRALD_FC_1d_1device_1fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'b-', lw=4, label=f'1 day') +m_limit, x_limit = np.loadtxt('sapphire/results_sapphire_oi_scan_electron_massless_010days/HeRALD_FC_10d_1device_1fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'r-', lw=4, label=f'10 day') + + +ax.set_xscale('log') +ax.set_xlim([xmin, xmax]) +ax.set_yscale('log') +ax.set_ylim([ymin, ymax]) +ax.set_xlabel('DM Mass [MeV]') +ax.set_ylabel('DM-electron cross-section massless [cm2]') +ax.legend(loc='best', fontsize=14) + +fig.tight_layout() +fig.savefig('sapphire_ER_massless.png') diff --git a/examples/plot_sapphire_NR.py b/examples/plot_sapphire_NR.py new file mode 100644 index 0000000..79d121b --- /dev/null +++ b/examples/plot_sapphire_NR.py @@ -0,0 +1,46 @@ +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import math + +curves_dir = 'ExistingLimits/' + +# Some figure setup +mpl.rcParams.update({'font.size': 20}) +mpl.rcParams.update({'axes.linewidth': 2}) +fig, ax = plt.subplots(1, 1, figsize=(11, 11)) +xmin = 1e1; xmax = 1e6; +ymin = 1e-48; ymax = 1e-28; + +# Existing limits +m_limit, x_limit = np.loadtxt(curves_dir + 'SENSEI_Migdal_NR.txt').transpose() +ax.plot(m_limit, x_limit, '--', lw=1.5, label='SENSEI Migdal') +m_limit, x_limit = np.loadtxt(curves_dir + 'CRESST_III_2019.txt').transpose() +ax.plot(m_limit*1e3, x_limit, '--', lw=1.5, label='CRESST III') +m_limit, x_limit = np.loadtxt(curves_dir + 'Darkside50_Migdal_2023.txt').transpose() +ax.plot(m_limit*1e3, x_limit, '--', lw=1.5, label='Darkside Migdal') +m_limit, x_limit = np.loadtxt(curves_dir + 'LZ_SI_2022.txt').transpose() +ax.plot(m_limit*1e3, x_limit, '--', lw=1.5, label='LZ') + +# Simulation +m_limit, x_limit = np.loadtxt('sapphire/results_sapphire_oi_phonon_massless_001_days/HeRALD_FC_1d_1device_1fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'b--', lw=4, label='1 day, multiphonon') +m_limit, x_limit = np.loadtxt('sapphire/results_sapphire_oi_phonon_massless_010_days/HeRALD_FC_10d_1device_1fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'r--', lw=4, label='10 days, multiphonon') +# Also massive +m_limit, x_limit = np.loadtxt('sapphire/results_sapphire_oi_scan_NR_001days/HeRALD_FC_1d_1device_1fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'b:', lw=4, alpha=0.5, label='1 day, NR') +m_limit, x_limit = np.loadtxt('sapphire/results_sapphire_oi_scan_NR_010days/HeRALD_FC_10d_1device_1fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, 'r:', lw=4, alpha=0.5, label='1 day, NR') + +ax.set_xscale('log') +ax.set_xlim([xmin, xmax]) +ax.set_yscale('log') +ax.set_ylim([ymin, ymax]) +ax.set_xlabel('DM Mass [MeV]') +ax.set_ylabel('DM-nucleon [cm2]') +ax.legend(loc='lower left', fontsize=14) + +fig.tight_layout() +fig.savefig('sapphire_NR.png') + From b2b30fd47838e50c25bfacbd6cdeba22424e6632 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 16:13:20 -0700 Subject: [PATCH 25/39] Renamed existing file --- .../XENON1T_S2Only_ER_Massless.txt | 52 ------------------- 1 file changed, 52 deletions(-) delete mode 100644 examples/ExistingLimits/XENON1T_S2Only_ER_Massless.txt diff --git a/examples/ExistingLimits/XENON1T_S2Only_ER_Massless.txt b/examples/ExistingLimits/XENON1T_S2Only_ER_Massless.txt deleted file mode 100644 index c4e4445..0000000 --- a/examples/ExistingLimits/XENON1T_S2Only_ER_Massless.txt +++ /dev/null @@ -1,52 +0,0 @@ -# MeV DM-e cm^2 -0.010056 3.9016e-37 -0.012345 5.1191e-37 -0.013869 6.2497e-37 -0.016560 7.0603e-37 -0.019022 8.1548e-37 -0.021489 9.6301e-37 -0.024546 1.1372e-36 -0.029310 1.3356e-36 -0.033667 1.6125e-36 -0.038033 1.9470e-36 -0.042728 2.3770e-36 -0.049627 2.6118e-36 -0.055572 3.0323e-36 -0.059086 3.2965e-36 -0.070150 3.9348e-36 -0.078108 4.4833e-36 -0.084516 5.0889e-36 -0.091638 5.7529e-36 -0.10659 6.6561e-36 -0.11307 7.1368e-36 -0.13497 8.3579e-36 -0.14317 8.9642e-36 -0.16372 1.1004e-35 -0.18422 1.1124e-35 -0.19732 1.2348e-35 -0.22831 1.5368e-35 -0.24612 1.6157e-35 -0.29377 1.9187e-35 -0.30996 2.0697e-35 -0.35360 2.3930e-35 -0.38004 2.4672e-35 -0.41699 2.7394e-35 -0.47099 3.1265e-35 -0.52339 3.6343e-35 -0.55630 4.1285e-35 -0.61477 4.3819e-35 -0.67467 4.4530e-35 -0.80101 5.7728e-35 -0.85426 6.0025e-35 -0.97163 7.4136e-35 -1.0530 7.7132e-35 -1.1807 9.6752e-35 -1.2502 1.0486e-34 -1.3356 1.1576e-34 -1.5312 1.2870e-34 -1.8297 1.6335e-34 -2.0353 1.8959e-34 -2.2221 2.1201e-34 -2.3926 2.2621e-34 -2.7952 2.7098e-34 -2.9493 2.9289e-34 From 5581bdc53b3aecf30ce6cdc060650e74d4aec728 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 16:13:36 -0700 Subject: [PATCH 26/39] Fixed a bug --- darklim/sensitivity/_sens_est.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index 65d4fba..2b64035 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -406,7 +406,11 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, for j, m in enumerate(m_dms): E_deposited_keV_arr = np.geomspace(0.1e-3, 800, int(1e4)) - dRdE_deposited_DRU_arr = drdefunction[j](E_deposited_keV_arr) + try: + dRdE_deposited_DRU_arr = drdefunction[j](E_deposited_keV_arr) + except ValueError: + dRdE_deposited_DRU_arr = np.array([drdefunction[j](en) for en in E_deposited_keV_arr]) + check = sum(dRdE_deposited_DRU_arr > 0) if check == 0: From eae95ea3ffb68ed64e00bc065c1e9227f2683a47 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 16:14:21 -0700 Subject: [PATCH 27/39] updated for current workflow --- examples/gaas_fc_scan.py | 163 ++++++++++++++++++++++++++++++--------- examples/gaas_oi_scan.py | 39 ++++++---- 2 files changed, 152 insertions(+), 50 deletions(-) diff --git a/examples/gaas_fc_scan.py b/examples/gaas_fc_scan.py index 95fb383..e7dca91 100644 --- a/examples/gaas_fc_scan.py +++ b/examples/gaas_fc_scan.py @@ -5,17 +5,18 @@ import scipy.stats as stats import darklim +from darklim import constants from multihist import Hist1d import time +import datetime ################################################################## efficiency = 1.0 tm = 'GaAs' # target name -energy_res = 0.373e-4 # energy resolution in keV - -gaas_gain = 0.40 * 0.40 + 0.60 * 0.05 +energy_res = 0.373e-3 # energy resolution in keV +det_gain = 1. ################################################################## @@ -54,18 +55,14 @@ def plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=None): return -def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None): +def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None, + m_dms=np.geomspace(0.0001,5,50),sigma0=1e-36,elf_model=None, elf_target=None, elf_params=None, gaas_params=None): if coinc==1: # if coinc is 1, LEE is 'unknown' known_bkgs = [0] else: known_bkgs = [0,1] - - m_dms = np.geomspace(0.0001, 5, 50) - sigma0 = 1e-36 - - ehigh = 1 # keV - + if var_threshold: nsigma = stats.norm.isf(stats.norm.sf(5)**(1/coinc)) else: @@ -74,9 +71,10 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho per_device_threshold = nsigma * energy_res # threshold threshold = coinc*per_device_threshold - SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=gaas_gain) + SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm, gain=det_gain) SE.reset_sim() SE.add_flat_bkgd(1) # flat background of 1 DRU + SE.add_nfold_lee_bkgd(m=n_devices,n=coinc,w=window) print('\nRunning with the following settings:') @@ -91,34 +89,88 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho raw_dm_rates = np.zeros_like(m_dms) exp_bkg = np.zeros_like(m_dms) + if not np.isscalar(sigma0): + sigma0_arr = np.copy(sigma0) + for i, mass in enumerate(m_dms): + + ehigh = 1 # keV + sigma0 = sigma0_arr[i] + if sigma0 == np.inf: + print(f'Infinite, skipping mass {mass}') + continue + + # First, figure out what the maximum energy from this dRdE is + drdefunction = SE.run_fast_fc_sim( + known_bkgs, + threshold, + ehigh, + e_low=1e-6, + m_dms=[mass], + sigma0=sigma0, + use_drdefunction=True, + elf_model=elf_model, + elf_target=elf_target, + elf_params=elf_params, + return_only_drde=True, + gaas_params=None + ) + drdefunction = drdefunction[0] + + ehigh_guesses = np.geomspace(1e-6, 1e3, 3000) + try: + drdefunction_guesses = drdefunction(ehigh_guesses) + except ValueError: + drdefunction_guesses = np.array([drdefunction(en) for en in ehigh_guesses]) + indices = np.where(drdefunction_guesses > 0) + if len(indices[0]) == 0: + ehigh = 1. + else: + j = int(indices[0][-1]) + ehigh = ehigh_guesses[j] * 1.1 + if ehigh < threshold: + ehigh = 1. + + # Second, actually calculate the FC limit _, sig[i], ul[i], dm_rates[i], raw_dm_rates[i], exp_bkg[i] = SE.run_fast_fc_sim( - known_bkgs, - threshold, - ehigh, - e_low=1e-6, #threshold, - m_dms=[mass], - nexp=nexp, - npts=int(1e5), - plot_bkgd=True, -# res=np.sqrt(n_devices)*energy_res, - verbose=True, - sigma0=sigma0, - use_drdefunction=True, - pltname=savedir+'/ULs_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus'.format(time_elapsed,n_devices,coinc,window/1e-6) - #pltname=None - ) - + known_bkgs, + threshold, + ehigh, + e_low=1e-6, #threshold, + m_dms=[mass], + nexp=nexp, + npts=int(1e5), + plot_bkgd=True, + # res=np.sqrt(n_devices)*energy_res, + verbose=True, + sigma0=sigma0, + use_drdefunction=True, + pltname=savedir+'/ULs_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus'.format(time_elapsed,n_devices,coinc,window/1e-6), + savedir=savedir, + elf_model=elf_model, + elf_target=elf_target, + elf_params=elf_params, + return_only_drde=False, + gaas_params=gaas_params) + + print(f'Done mass = {mass}, sigma = {sig[i]}') + # save results to txt file if save and savedir is not None: outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) tot = np.column_stack( (m_dms, sig, dm_rates/raw_dm_rates, exp_bkg) ) np.savetxt(outname,tot,fmt=['%.5e','%0.5e','%0.5e','%0.5e'] ,delimiter=' ') - + # plot acceptance and DM evt rate: savename = \ './{:s}/dmrate_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=savename) + + # save results to txt file + if save and savedir is not None: + outname = './{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(savedir,time_elapsed,n_devices,coinc,window/1e-6) + tot = np.column_stack( (m_dms, sig) ) + np.savetxt(outname,tot,fmt=['%.5e','%0.5e'] ,delimiter=' ') return @@ -127,18 +179,51 @@ def gaas_scan(results_dir): if not os.path.exists(results_dir): os.makedirs(results_dir) - nexp = 200 # number of toys + nexp = 1 # number of toys - var_threshold = True # vary 5sigma requirement based on coinc level + var_threshold = False # vary 5sigma requirement based on coinc level times = np.array([1]) # d - mass_det = 8.*5.32*1e-3 # mass in kg, = 8cc * 3.98g/cc + mass_det = 8. * constants.Al2O3_density * 1e-3 # mass in kg, = 8cc exposures = times*mass_det - n_devices = 4 - coinc = np.arange(2,3) - #coinc = np.array([1]) + n_devices = 2 + coinc = np.array([2]) window = 100e-6 # s + +# m_dms = np.array(list(np.geomspace(0.040, 10, 25)) + list(np.geomspace(11, 100, 8)) + list(np.geomspace(200, 1000, 3))) + m_dms = np.geomspace(1e-3, 1e3, 7) + sigma0 = np.full_like(m_dms, 1e-37) + + elf_model='electron' + elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} +# elf_model='phonon' +# elf_params={'mediator': 'massive', 'suppress_darkelf_output': False, 'dark_photon': False} +# elf_model = None +# elf_params = {} + + gaas_params = {'pce': 0.4, 'lce_per_channel': 0.10, 'res': 0.10, + 'n_coincidence_light': (n_devices-1), + 'calorimeter_threshold_eV': (5 * energy_res / 1000)} + + f = open(results_dir + '/info.txt', 'w') + f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') + f.write('Detector material: ' + tm + '\n') + f.write('Exposure time(s) (days): ' + str(times) + '\n') + f.write('Detector mass (kg): ' + '%.4e' % mass_det + '\n') + f.write('Number of devices: ' + str(n_devices) + '\n') + f.write('Coincidence level: ' + str(coinc) + '\n') + f.write('Time window (s): ' + str(window) + '\n') + f.write('Dark matter masses (GeV): ' + str(m_dms) + '\n') + f.write('Cross section (cm2): ' + str(sigma0) + '\n') + f.write('Baseline resolution (keV): ' + str(energy_res) + '\n') + f.write('Gain: ' + str(det_gain) + '\n') + f.write('Variable resolution: ' + str(var_threshold) + '\n') + f.write('ELF model: ' + str(elf_model) + '\n') + f.write('ELF params: ' + str(elf_params) + '\n') + f.write('GaAs params: ' + str(gaas_params) + '\n') + f.close() + for t in times: for n in coinc: @@ -151,7 +236,13 @@ def gaas_scan(results_dir): window, var_threshold=var_threshold, save=True, - savedir=results_dir + savedir=results_dir, + m_dms=m_dms, + sigma0=sigma0, + elf_target=tm, + elf_model=elf_model, + elf_params=elf_params, + gaas_params=gaas_params ) return @@ -174,4 +265,4 @@ def main(): return 0 if __name__ == "__main__": - main() + main() diff --git a/examples/gaas_oi_scan.py b/examples/gaas_oi_scan.py index 1e31784..116ccf6 100644 --- a/examples/gaas_oi_scan.py +++ b/examples/gaas_oi_scan.py @@ -16,8 +16,7 @@ efficiency = 1.0 tm = 'GaAs' # target name energy_res = 0.373e-3 # energy resolution in keV - -det_gain = 0.40 +det_gain = 1. ################################################################## @@ -57,7 +56,7 @@ def plot_dm_rates(m_dms,dm_rates,raw_dm_rates,sigma0,savename=None): return def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_threshold=False,save=True,savedir=None, - m_dms=np.geomspace(0.0001,5,50),sigma0=1e-36,elf_model=None, elf_target=None, elf_params=None): + m_dms=np.geomspace(0.0001,5,50),sigma0=1e-36,elf_model=None, elf_target=None, elf_params=None, gaas_params=None): if coinc==1: # if coinc is 1, LEE is 'unknown' known_bkgs = [0] @@ -113,7 +112,8 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho elf_model=elf_model, elf_target=elf_target, elf_params=elf_params, - return_only_drde=True + return_only_drde=True, + gaas_params=None ) drdefunction = drdefunction[0] @@ -145,7 +145,8 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho elf_model=elf_model, elf_params=elf_params, elf_target=elf_target, - return_only_drde=False) + return_only_drde=False, + gaas_params=gaas_params) print(f'Done mass = {mass}, sigma = {sig[i]}') @@ -162,24 +163,32 @@ def gaas_scan(results_dir): if not os.path.exists(results_dir): os.makedirs(results_dir) - nexp = 5 # number of toys + nexp = 1 # number of toys var_threshold = False # vary 5sigma requirement based on coinc level - times = np.array([1]) # d - mass_det = 8. * constants.GaAs_density * 1e-3 # mass in kg, = 8cc + times = np.array([100]) # d + mass_det = 8. * constants.Al2O3_density * 1e-3 # mass in kg, = 8cc exposures = times*mass_det - n_devices = 4 + n_devices = 2 coinc = np.array([2]) window = 100e-6 # s - m_dms, sigma0, _, _ = np.loadtxt('results_gaas_fc_phonon_massless_scalar_001_days/HeRALD_FC_1d_4device_2fold_100mus.txt').transpose() +# m_dms = np.array(list(np.geomspace(0.040, 10, 25)) + list(np.geomspace(11, 100, 8)) + list(np.geomspace(200, 1000, 3))) + m_dms = np.geomspace(1e-3, 1e3, 7) + sigma0 = np.full_like(m_dms, 1e-37) + + elf_model='electron' + elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} +# elf_model='phonon' +# elf_params={'mediator': 'massive', 'suppress_darkelf_output': False, 'dark_photon': False} +# elf_model = None +# elf_params = {} -# elf_model='electron' -# elf_params={'mediator': 'massless', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} - elf_model='phonon' - elf_params={'mediator': 'massless', 'suppress_darkelf_output': False, 'dark_photon': False} + gaas_params = {'pce': 0.4, 'lce_per_channel': 0.10, 'res': 0.10, + 'n_coincidence_light': (n_devices-1), + 'calorimeter_threshold_eV': (5 * energy_res / 1000)} f = open(results_dir + '/info.txt', 'w') f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') @@ -196,6 +205,7 @@ def gaas_scan(results_dir): f.write('Variable resolution: ' + str(var_threshold) + '\n') f.write('ELF model: ' + str(elf_model) + '\n') f.write('ELF params: ' + str(elf_params) + '\n') + f.write('GaAs params: ' + str(gaas_params) + '\n') f.close() @@ -216,6 +226,7 @@ def gaas_scan(results_dir): elf_target=tm, elf_model=elf_model, elf_params=elf_params, + gaas_params=gaas_params ) return From 75c124140159475f2864ff9fe54be8c537cdce61 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 16:16:51 -0700 Subject: [PATCH 28/39] Removed some debugging lines --- darklim/detector/_detector.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/darklim/detector/_detector.py b/darklim/detector/_detector.py index c5e90ab..99eb188 100644 --- a/darklim/detector/_detector.py +++ b/darklim/detector/_detector.py @@ -11,9 +11,6 @@ def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincidence_light, threshold_eV, n_samples=1): E_light_eV = constants.GaAs_light_fraction * E_recoil_eV - #print('E recoil eV', E_recoil_eV) - #print('GaAs light fraction', constants.GaAs_light_fraction) - #print('E light eV', E_light_eV) n_photons_generated_average = np.floor(E_light_eV / constants.bandgap_GaAs_eV) if n_photons_generated_average == 0: @@ -22,9 +19,6 @@ def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincide else: return np.full(n_samples, 0.) - #print('LCE per channel', lce_per_channel, type(lce_per_channel)) - #print('photons generated', n_photons_generated_average, type(n_photons_generated_average)) - #print('n samples', n_samples, type(n_samples)) n_photons_detected_ch1 = np.random.binomial(n_photons_generated_average, lce_per_channel, n_samples) n_photons_detected_ch2 = np.random.binomial(n_photons_generated_average, lce_per_channel, n_samples) @@ -68,7 +62,6 @@ def convert_dRdE_dep_to_obs_gaas(E_dep_keV, dRdE_dep_DRU, pce=0.40, lce_per_chan energies_sim_keV = inv_cdf(samples) energies_obs_keV = np.zeros_like(energies_sim_keV) energies_obs_keV = np.copy(energies_sim_keV) - #print(f'Out of {len(energies_sim_keV)} energies, {sum(np.isnan(energies_sim_keV))} are nan') for i, E in enumerate(energies_sim_keV): energies_obs_keV[i] = get_deposited_energy_gaas(E * 1000, pce, lce_per_channel, res, n_coincidence_light, calorimeter_threshold_eV) / 1000 From 63f79101e7011d29e4428720bbb9d5f4c6212948 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 19:13:34 -0700 Subject: [PATCH 29/39] Added timing coincidence requirement --- darklim/constants/_constants.py | 1 + darklim/detector/_detector.py | 11 +++++++++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/darklim/constants/_constants.py b/darklim/constants/_constants.py index 7e3a1c3..513df25 100644 --- a/darklim/constants/_constants.py +++ b/darklim/constants/_constants.py @@ -12,3 +12,4 @@ Al2O3_density = 3.98 GaAs_density = 5.32 GaAs_light_fraction = 0.60 +GaAs_average_phonon_energy_eV = 0.022 diff --git a/darklim/detector/_detector.py b/darklim/detector/_detector.py index 99eb188..9d3a616 100644 --- a/darklim/detector/_detector.py +++ b/darklim/detector/_detector.py @@ -8,8 +8,9 @@ import matplotlib.pyplot as plt import matplotlib as mpl -def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincidence_light, threshold_eV, n_samples=1): +def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincidence_light, threshold_eV, coincidence_window_us=100., phonon_tau_us=100., n_samples=1): + # Get the light signal in each channel E_light_eV = constants.GaAs_light_fraction * E_recoil_eV n_photons_generated_average = np.floor(E_light_eV / constants.bandgap_GaAs_eV) @@ -25,8 +26,14 @@ def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincide E_ch1_eV = n_photons_detected_ch1 * constants.bandgap_GaAs_eV * np.random.normal(1, res, n_samples) E_ch2_eV = n_photons_detected_ch2 * constants.bandgap_GaAs_eV * np.random.normal(1, res, n_samples) + # Get the heat signal observed within coincidence window E_heat_eV = (1 - constants.GaAs_light_fraction) * E_recoil_eV - E_ch0_eV = np.full(n_samples, E_heat_eV * pce) + + n_phonons_generated_average = int(E_heat_eV / constants.GaAs_average_phonon_energy_eV) + phonon_arrival_times_us = np.random.exponential(phonon_tau_us, (n_phonons_generated_average, n_samples)) + n_phonons_detected = np.sum(phonon_arrival_times_us < coincidence_window_us, axis=0) + + E_ch0_eV = n_phonons_detected * pce * constants.GaAs_average_phonon_energy_eV * np.random.normal(1, res, n_samples) if n_coincidence_light == 1: E_det_eV = (E_ch0_eV + E_ch1_eV + E_ch2_eV) * (E_ch0_eV > threshold_eV) * ((E_ch1_eV > threshold_eV) + (E_ch2_eV > threshold_eV)) From 8ebb7afe177fd6a26293176c6e3bba9b930b12ea Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 19:31:02 -0700 Subject: [PATCH 30/39] Allow DM electron recoils between 1x and 2x the band gap --- darklim/elf/_elf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/darklim/elf/_elf.py b/darklim/elf/_elf.py index 68a8bf5..12b8cd9 100644 --- a/darklim/elf/_elf.py +++ b/darklim/elf/_elf.py @@ -55,7 +55,7 @@ def get_dRdE_lambda_Al2O3_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) sapphire.update_params(mX=mX_eV, mediator=mediator) - fun = lambda keV : np.heaviside(keV * 1000 / gain - 2 * constants.bandgap_Al2O3_eV, 1) * \ + fun = lambda keV : np.heaviside(keV * 1000 / gain - constants.bandgap_Al2O3_eV, 1) * \ sapphire.dRdomega_electron(keV * 1000 / gain, method=method, sigmae=sigmae, kcut=kcut, withscreening=withscreening) * \ (1000 / 365.25) / gain @@ -155,7 +155,7 @@ def get_dRdE_lambda_GaAs_electron(mX_eV=1e8, mediator='massless', sigmae=1e-31, # Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV # But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU) gaas.update_params(mX=mX_eV, mediator=mediator) - fun = lambda keV : np.heaviside(keV * 1000 / gain - 2 * constants.bandgap_GaAs_eV, 1) * \ + fun = lambda keV : np.heaviside(keV * 1000 / gain - constants.bandgap_GaAs_eV, 1) * \ gaas.dRdomega_electron(keV * 1000 / gain, method=method, sigmae=sigmae, kcut=kcut, withscreening=withscreening) * \ (1000 / 365.25) / gain From fb7db83ce0fb7f1a945f5085474581ca2decb663 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Mon, 1 Jul 2024 21:44:41 -0700 Subject: [PATCH 31/39] Add window details as optional input. Also force the energy deposition rate to be zero outside the defined region --- darklim/detector/_detector.py | 8 +++++--- darklim/sensitivity/_sens_est.py | 16 ++++++++++++---- 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/darklim/detector/_detector.py b/darklim/detector/_detector.py index 9d3a616..95e2eb9 100644 --- a/darklim/detector/_detector.py +++ b/darklim/detector/_detector.py @@ -8,7 +8,7 @@ import matplotlib.pyplot as plt import matplotlib as mpl -def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincidence_light, threshold_eV, coincidence_window_us=100., phonon_tau_us=100., n_samples=1): +def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincidence_light, threshold_eV, coincidence_window_us, phonon_tau_us, n_samples=1): # Get the light signal in each channel E_light_eV = constants.GaAs_light_fraction * E_recoil_eV @@ -47,7 +47,8 @@ def get_deposited_energy_gaas(E_recoil_eV, pce, lce_per_channel, res, n_coincide -def convert_dRdE_dep_to_obs_gaas(E_dep_keV, dRdE_dep_DRU, pce=0.40, lce_per_channel=0.10, res=0.10, n_coincidence_light=1, calorimeter_threshold_eV=0.37, E_min_keV=None, E_max_keV=None, n_samples=int(1e6)): +def convert_dRdE_dep_to_obs_gaas(E_dep_keV, dRdE_dep_DRU, pce=0.40, lce_per_channel=0.10, res=0.10, n_coincidence_light=1, + calorimeter_threshold_eV=0.37, coincidence_window_us=100., phonon_tau_us=100., E_min_keV=None, E_max_keV=None, n_samples=int(1e6)): # Reduce data to the appropriate energy range if E_min_keV is None or E_min_keV < E_dep_keV[0]: @@ -71,7 +72,8 @@ def convert_dRdE_dep_to_obs_gaas(E_dep_keV, dRdE_dep_DRU, pce=0.40, lce_per_chan energies_obs_keV = np.copy(energies_sim_keV) for i, E in enumerate(energies_sim_keV): - energies_obs_keV[i] = get_deposited_energy_gaas(E * 1000, pce, lce_per_channel, res, n_coincidence_light, calorimeter_threshold_eV) / 1000 + energies_obs_keV[i] = get_deposited_energy_gaas(E * 1000, pce, lce_per_channel, res, n_coincidence_light, calorimeter_threshold_eV, + coincidence_window_us, phonon_tau_us) / 1000 # Perhaps no energy is ever observed if sum(energies_obs_keV > 0) == 0: diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index 2b64035..d5432d3 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -422,9 +422,13 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, lce_per_channel=gaas_params['lce_per_channel'], res=gaas_params['res'], n_coincidence_light=gaas_params['n_coincidence_light'], - calorimeter_threshold_eV=gaas_params['calorimeter_threshold_eV']) + calorimeter_threshold_eV=gaas_params['calorimeter_threshold_eV'], + coincidence_window_us=gaas_params['coincidence_window_us'], + phonon_tau_us=gaas_params['phonon_tau_us']) - drdefunction[j] = lambda E: np.interp(E, E_observed_keV_arr, dRdE_observed_DRU_arr) + + print(f'In run_sim(). The integral is {sum(dRdE_observed_DRU_arr[1:] * np.diff(E_observed_keV_arr))} cts/kg/day') + drdefunction[j] = lambda E: np.interp(E, E_observed_keV_arr, dRdE_observed_DRU_arr, left=0., right=0.) if return_only_drde: @@ -657,9 +661,13 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= lce_per_channel=gaas_params['lce_per_channel'], res=gaas_params['res'], n_coincidence_light=gaas_params['n_coincidence_light'], - calorimeter_threshold_eV=gaas_params['calorimeter_threshold_eV']) + calorimeter_threshold_eV=gaas_params['calorimeter_threshold_eV'], + coincidence_window_us=gaas_params['coincidence_window_us'], + phonon_tau_us=gaas_params['phonon_tau_us']) + - drdefunction[j] = lambda E: np.interp(E, E_observed_keV_arr, dRdE_observed_DRU_arr) + print(f'In fast sim. The integral is {sum(dRdE_observed_DRU_arr[1:] * np.diff(E_observed_keV_arr))} cts/kg/day') + drdefunction[j] = lambda E: np.interp(E, E_observed_keV_arr, dRdE_observed_DRU_arr, left=0., right=0.) if return_only_drde: return drdefunction From 42d8bc86665ca81adaaa52d1508f81ff15fcedb6 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 7 Jul 2024 18:57:59 -0700 Subject: [PATCH 32/39] Add random seed --- darklim/sensitivity/_sens_est.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index d5432d3..07eb96f 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -10,6 +10,8 @@ import darklim.elf._elf as elf import darklim.detector._detector as detector +import time +np.random.seed(int(time.time())) __all__ = [ "calculate_substrate_mass", From 4fa880ae0c470bfe97b1c3d1d22bca22664cd0aa Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 7 Jul 2024 18:58:42 -0700 Subject: [PATCH 33/39] Basic plotting updates --- examples/plot_gaas_ER_massless.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/plot_gaas_ER_massless.py b/examples/plot_gaas_ER_massless.py index e583ee5..1ec1cb6 100644 --- a/examples/plot_gaas_ER_massless.py +++ b/examples/plot_gaas_ER_massless.py @@ -32,12 +32,15 @@ # Simulation for fold, ls in zip([2, 3], ['-', ':', '-.']): for lce, color in zip([5, 10, 25], ['r', 'b', 'm', 'g', 'y']): - m_limit, x_limit = np.loadtxt(f'results_gaas_oi_scan_electron_massless_100days_{fold}fold_lce{lce:02d}/HeRALD_FC_100d_{fold}device_{fold}fold_100mus.txt').transpose() + m_limit, x_limit = np.loadtxt(f'gaas/results_gaas_oi_scan_electron_massless_100days_{fold}fold_lce{lce:02d}/HeRALD_FC_100d_{fold}device_{fold}fold_100mus.txt').transpose() ax.plot(m_limit*1e3, x_limit, lw=4, ls=ls, color=color, label=f'{fold}-fold, {lce/100:.02f} LCE per channel') -m_limit, x_limit = np.loadtxt('results_gaas_oi_scan_electron_massless_300days_3fold_lce10/HeRALD_FC_300d_3device_3fold_100mus.txt').transpose() +m_limit, x_limit = np.loadtxt('gaas/results_gaas_oi_scan_electron_massless_300days_3fold_lce10/HeRALD_FC_300d_3device_3fold_100mus.txt').transpose() ax.plot(m_limit*1e3, x_limit, lw=4, ls=':', color='k', label=f'300 days') +m_limit, x_limit = np.loadtxt('results_gaas_oi_electron_massless_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, lw=6, ls='-', color='k', label=f'New') + ax.set_xscale('log') ax.set_xlim([xmin, xmax]) From 7d49bc2263786b9f1c8b79b30e6ebd15882633bf Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 7 Jul 2024 18:58:53 -0700 Subject: [PATCH 34/39] Basic plotting updates --- examples/plot_gaas_ER_massive.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/examples/plot_gaas_ER_massive.py b/examples/plot_gaas_ER_massive.py index c521705..d194313 100644 --- a/examples/plot_gaas_ER_massive.py +++ b/examples/plot_gaas_ER_massive.py @@ -27,13 +27,16 @@ #ax.plot(m_limit, x_limit, '--', lw=1.5, label='XENON1T S2only (Solar Refl.)') # Simulation -m_limit, x_limit = np.loadtxt('results_gaas_oi_scan_electron_massive_100days_2fold_lce10/HeRALD_FC_100d_2device_2fold_100mus.txt').transpose() +m_limit, x_limit = np.loadtxt('gaas/results_gaas_oi_scan_electron_massive_100days_2fold_lce10/HeRALD_FC_100d_2device_2fold_100mus.txt').transpose() ax.plot(m_limit*1e3, x_limit, 'r-', lw=4, label=f'100 days, 1 light signal, 10% LCE per channel') -m_limit, x_limit = np.loadtxt('results_gaas_oi_scan_electron_massive_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt').transpose() +m_limit, x_limit = np.loadtxt('gaas/results_gaas_oi_scan_electron_massive_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt').transpose() ax.plot(m_limit*1e3, x_limit, 'b-', lw=4, label=f'100 days, 2 light signal, 10% LCE per channel') -m_limit, x_limit = np.loadtxt('results_gaas_oi_scan_electron_massive_300days_3fold_lce10/HeRALD_FC_300d_3device_3fold_100mus.txt').transpose() +m_limit, x_limit = np.loadtxt('gaas/results_gaas_oi_scan_electron_massive_300days_3fold_lce10/HeRALD_FC_300d_3device_3fold_100mus.txt').transpose() ax.plot(m_limit*1e3, x_limit, 'm-', lw=4, label=f'300 days, 2 light signal, 10% LCE per channel') +m_limit, x_limit = np.loadtxt('results_gaas_oi_electron_massive_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt').transpose() +ax.plot(m_limit*1e3, x_limit, lw=6, ls='-', color='k', label=f'New') + ax.set_xscale('log') ax.set_xlim([xmin, xmax]) From 232ff6b237bd01fd4a9ddc4f33c202c9ee4d8f8d Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 7 Jul 2024 19:07:30 -0700 Subject: [PATCH 35/39] Updated to new energy resolution --- examples/gaas_oi_scan.py | 37 ++++++++++++++++++++++++------------- 1 file changed, 24 insertions(+), 13 deletions(-) diff --git a/examples/gaas_oi_scan.py b/examples/gaas_oi_scan.py index 116ccf6..d5ee4fc 100644 --- a/examples/gaas_oi_scan.py +++ b/examples/gaas_oi_scan.py @@ -15,7 +15,7 @@ efficiency = 1.0 tm = 'GaAs' # target name -energy_res = 0.373e-3 # energy resolution in keV +energy_res = 0.250e-3 # energy resolution in keV det_gain = 1. ################################################################## @@ -132,6 +132,8 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho ehigh = 1. _, sig[i] = SE.run_sim( +# _, sig[i], _, _, _, _ = SE.run_fast_fc_sim( +# known_bkgs, # remove threshold, ehigh, e_low=1e-6, @@ -141,6 +143,8 @@ def run_scan_point(nexp,time_elapsed,mass_det,n_devices,coinc,window,var_thresho plot_bkgd=True, # res=None, verbose=True, +# use_drdefunction=True, #remove +# savedir=savedir, # remove sigma0=sigma0, elf_model=elf_model, elf_params=elf_params, @@ -163,32 +167,39 @@ def gaas_scan(results_dir): if not os.path.exists(results_dir): os.makedirs(results_dir) - nexp = 1 # number of toys + nexp = 10 # number of toys - var_threshold = False # vary 5sigma requirement based on coinc level + var_threshold = True # vary 5sigma requirement based on coinc level times = np.array([100]) # d - mass_det = 8. * constants.Al2O3_density * 1e-3 # mass in kg, = 8cc + mass_det = 1. * constants.GaAs_density * 1e-3 # mass in kg, = 1 cc exposures = times*mass_det - n_devices = 2 - coinc = np.array([2]) + n_devices = 3 + coinc = np.array([3]) window = 100e-6 # s -# m_dms = np.array(list(np.geomspace(0.040, 10, 25)) + list(np.geomspace(11, 100, 8)) + list(np.geomspace(200, 1000, 3))) - m_dms = np.geomspace(1e-3, 1e3, 7) - sigma0 = np.full_like(m_dms, 1e-37) + m_dms = np.geomspace(1e-3, 300e-3, 25) + sigma0 = np.full_like(m_dms, 1e-35) elf_model='electron' - elf_params={'mediator': 'massive', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} + elf_params={'mediator': 'massless', 'kcut': 0, 'method': 'grid', 'withscreening': True, 'suppress_darkelf_output': False} # elf_model='phonon' -# elf_params={'mediator': 'massive', 'suppress_darkelf_output': False, 'dark_photon': False} +# elf_params={'mediator': 'massless', 'suppress_darkelf_output': False, 'dark_photon': True} # elf_model = None # elf_params = {} - gaas_params = {'pce': 0.4, 'lce_per_channel': 0.10, 'res': 0.10, + if var_threshold: + nsigma = stats.norm.isf(stats.norm.sf(5)**(1/coinc)) + else: + nsigma = 5 + gaas_params = {'pce': 0.4, + 'lce_per_channel': 0.10, + 'res': 0.17, 'n_coincidence_light': (n_devices-1), - 'calorimeter_threshold_eV': (5 * energy_res / 1000)} + 'calorimeter_threshold_eV': (nsigma * energy_res * 1000), + 'coincidence_window_us': (window*1e6), + 'phonon_tau_us': 100.} f = open(results_dir + '/info.txt', 'w') f.write(datetime.datetime.now().strftime('%m/%d/%Y, %H:%M:%S') + '\n\n') From 61f279133343a3e17b66931355540d5c4edce83d Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 7 Jul 2024 19:07:52 -0700 Subject: [PATCH 36/39] Plotting notebook for gaas ER scattering --- examples/plot_gaas_results_ER.ipynb | 1059 +++++++++++++++++++++++++++ 1 file changed, 1059 insertions(+) create mode 100644 examples/plot_gaas_results_ER.ipynb diff --git a/examples/plot_gaas_results_ER.ipynb b/examples/plot_gaas_results_ER.ipynb new file mode 100644 index 0000000..e2153c6 --- /dev/null +++ b/examples/plot_gaas_results_ER.ipynb @@ -0,0 +1,1059 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "if None != os.getenv('NERSC_HOST'):\n", + " import matplotlib.font_manager as font_manager\n", + " font_manager.fontManager.addfont('/global/cfs/cdirs/lz/physics/WS/SR1/msttcorefonts/Times_New_Roman.ttf')\n", + "\n", + "import matplotlib as mpl\n", + "import matplotlib\n", + "matplotlib.rcParams['font.family'] = 'serif'\n", + "matplotlib.rcParams['font.serif'] = 'Times New Roman'\n", + "plt.rcParams['mathtext.fontset'] = 'stix'\n", + "\n", + "plt.style.use('/global/cfs/cdirs/lz/users/haselsco/sr3/LZStyle/SetLZStyle.mplstyle')\n", + "\n", + "from multihist import Hist1d\n", + "\n", + "from scipy.interpolate import interp1d\n", + "\n", + "\n", + "%matplotlib inline\n", + "\n", + "def find_nearest(array, value):\n", + " array = np.asarray(array)\n", + " idx = (np.abs(array - value)).argmin()\n", + " return idx, array[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def extrapolate(data, mass=1e6):\n", + " m0, s0 = data[-1]\n", + " return np.concatenate([data,[[mass, s0*mass/m0]]], axis=0)\n", + "\n", + "def minbound(curves, masses=None):\n", + " if masses is None:\n", + " masses = np.logspace(-3, 4, 200)\n", + " result = np.array([np.nan]*len(masses))\n", + " for curve in curves:\n", + " if curve[-1][0] < masses[-1]:\n", + " curve = extrapolate(curve, masses[-1])\n", + " yinterp = 10**(interp1d(*(np.log10(curve).T), bounds_error=False)(np.log10(masses)))\n", + " #yinterp = interp1d(*(curve.T), bounds_error=False)(masses)\n", + " result = np.nanmin([result, yinterp], axis=0)\n", + " return np.array([masses, result]).T\n", + "\n", + "def load_and_plot_existing(ax, leg=True, lw=1, leg_params=None, ER_model='massive'):\n", + " \n", + " curves_dir = 'ExistingLimits/'\n", + " \n", + " if ER_model == 'massive':\n", + "\n", + " m_lim, x_lim = np.loadtxt(curves_dir + 'DAMIC_M_ER_Massive.txt').transpose()\n", + " l1, = ax.plot(m_lim, x_lim, '--', color='#CFC0E5', lw=lw, label='DAMIC-M')\n", + " \n", + " m_lim, x_lim = np.loadtxt(curves_dir + 'Darkside50_ER_Massive.txt').transpose()\n", + " l2, = ax.plot(m_lim, x_lim, '--', color='#CE977A', lw=lw, label='Darkside-50')\n", + "\n", + " m_lim, x_lim = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_ER_Massive.txt').transpose()\n", + " l3, = ax.plot(m_lim, x_lim, '--', color='#a4dbed', lw=lw, label='SENSEI SNOLAB')\n", + " \n", + " m_lim, x_lim = np.loadtxt(curves_dir + 'XENON1T_S2Only_ER_Massive.txt').transpose()\n", + " l4, = ax.plot(m_lim, x_lim, '--', color='#CA778C', lw=lw, label='XENON1T S2only')\n", + "\n", + " #m_lim, x_lim = np.loadtxt(curves_dir + 'XENON1T_S2Only_Solar_Reflected_ER_Massive.txt').transpose()\n", + " #ax.plot(m_lim, x_lim, '--', lw=1.5, label='XENON1T S2only (Solar Refl.)')\n", + " #m_lim, x_lim = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_Solar_Reflection_ER_Massive.txt').transpose()\n", + " #ax.plot(m_lim, x_lim, '--', lw=1.5, label='SENSEI SNOLAB (Solar Refl.)')\n", + "\n", + " #l5, = ax.plot(m_lim, x_lim, '--', color='#C3B02F', lw=lw, label='nu-cleus 2017')\n", + " #l5, = ax.plot(m_lim, x_lim, '--', color='darkgrey', lw=lw, label='PandaX-4T Migdal 2023') #color='#C3B02F'\n", + " #l6, = ax.plot(m_lim, x_lim, '--', color='#56872C', lw=lw, label='XENON1T S2-Only 2019')\n", + " #l7, = ax.plot(m_lim, x_lim, '--', color='#D7ADB7', lw=lw, label='EDELWEISS Migdal 2019')\n", + " #l8, = ax.plot(m_lim, x_lim, '--', color='#bcced4', lw=lw, label='LZ 2022')\n", + " #l9, = ax.plot(m_lim, x_lim, '--', color='#a4bd8e', lw=lw, label='DarkSide-50 2023')\n", + " \n", + " if leg:\n", + " leg1 = ax.legend(handles=[l1, l2, l3, l4], **leg_params)\n", + " ax.add_artist(leg1)\n", + "\n", + " elif ER_model == 'massless':\n", + " \n", + " m_lim, x_lim = np.loadtxt(curves_dir + 'DAMIC_M_ER_Massless.txt').transpose()\n", + " l1, = ax.plot(m_lim, x_lim, '--', color='#CFC0E5', lw=lw, label='DAMIC-M')\n", + "\n", + " m_lim, x_lim = np.loadtxt(curves_dir + 'SENSEI_MINOS_ER_Massless.txt').transpose()\n", + " l2, = ax.plot(m_lim, x_lim, '--', color='#CE977A', lw=lw, label='SENSEI MINOS')\n", + "\n", + " m_lim, x_lim = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_ER_Massless.txt').transpose()\n", + " l3, = ax.plot(m_lim, x_lim, '--', color='#a4dbed', lw=lw, label='SENSEI SNOLAB')\n", + "\n", + " #m_lim, x_lim = np.loadtxt(curves_dir + 'protoSENSEI_MINOS_ER_Massless.txt').transpose()\n", + " #l4, = ax.plot(m_lim, x_lim, '--', color='#CA778C', lw=lw, label='protoSENSEI')\n", + "\n", + " m_lim, x_lim = np.loadtxt(curves_dir + 'Freeze_in_ER_Massless.txt').transpose()\n", + " l4, = ax.plot(m_lim, x_lim, '-', color='#FF9039', lw=lw*2, label='Freeze-in')\n", + "\n", + " #m_lim, x_lim = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_Solar_Reflection_ER_Massless.txt').transpose()\n", + " #ax.plot(m_lim, x_lim, '--', lw=1.5, label='SENSEI SNOLAB (Solar Refl.)')\n", + " #m_lim, x_lim = np.loadtxt(curves_dir + 'XENON1T_S2Only_Solar_Reflected_ER_Massless.txt').transpose()\n", + " #ax.plot(m_lim, x_lim, '--', lw=1.5, label='XENON1T S2 Only (Solar Refl.)')\n", + "\n", + " if leg:\n", + " leg1 = ax.legend(handles=[l1, l2, l3, l4], **leg_params)\n", + " ax.add_artist(leg1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def plotexcludedmassive(ax, details=False, **kwargs):\n", + " \n", + " curves_dir = 'ExistingLimits/'\n", + " \n", + " damic = np.loadtxt(curves_dir + 'DAMIC_M_ER_Massive.txt')\n", + " ds50 = np.loadtxt(curves_dir + 'Darkside50_ER_Massive.txt')\n", + " sensei_snolab = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_ER_Massive.txt')\n", + " x1t_s2o = np.loadtxt(curves_dir + 'XENON1T_S2Only_ER_Massive.txt')\n", + "\n", + " data = minbound([damic, ds50, sensei_snolab, x1t_s2o], masses=None)#data.T[0])\n", + " kwargs.setdefault('edgecolor', None)\n", + " kwargs.setdefault('facecolor','#000000')\n", + " kwargs.setdefault('alpha',0.05)\n", + " #data = extrapolate(data, 1e5)\n", + " ax.fill_between(*(data.T), ax.get_ylim()[1], **kwargs)\n", + " if details:\n", + " load_and_plot_existing(ax,leg=False,lw=1,ER_model='massive')\n", + "\n", + " \n", + "def plotexcludedmassless(ax, details=False, **kwargs):\n", + " \n", + " curves_dir = 'ExistingLimits/'\n", + " \n", + " damic = np.loadtxt(curves_dir + 'DAMIC_M_ER_Massless.txt')\n", + " sensei_minos = np.loadtxt(curves_dir + 'SENSEI_MINOS_ER_Massless.txt')\n", + " sensei_snolab = np.loadtxt(curves_dir + 'SENSEI_SNOLAB_ER_Massless.txt')\n", + " proto_sensei = np.loadtxt(curves_dir + 'protoSENSEI_MINOS_ER_Massless.txt')\n", + " freeze_in = np.loadtxt(curves_dir + 'Freeze_in_ER_Massless.txt')\n", + "\n", + " data = minbound([damic, sensei_minos, sensei_snolab, proto_sensei], masses=None)#data.T[0])\n", + " kwargs.setdefault('edgecolor', None)\n", + " kwargs.setdefault('facecolor','#000000')\n", + " kwargs.setdefault('alpha',0.05)\n", + " #data = extrapolate(data, 1e5)\n", + " ax.fill_between(*(data.T), ax.get_ylim()[1], **kwargs)\n", + " if details:\n", + " load_and_plot_existing(ax,leg=False,lw=1,ER_model='massless')" + ] + }, + { + "cell_type": "raw", + "metadata": { + "tags": [] + }, + "source": [ + "# load results from .txt files\n", + "\n", + "acc_cut = 0.1\n", + "\n", + "window = 100e-6 # s\n", + "n_devices = 4\n", + "\n", + "#times = np.array([1,2,5,10,20,50,75,100,200,500]) # d\n", + "\n", + "#results_dir = 'fast_sim_250toys_finetimes_varthres'\n", + "#times = np.linspace(1,201,num=20, endpoint=True)\n", + "\n", + "results_dir = 'varbox_varthres_200toys'\n", + "times = np.concatenate( (np.array([1,2,5,10]), np.linspace(15,400,num=20, endpoint=True),np.array([30,183,365])) )\n", + "times = np.sort(times)\n", + "\n", + "mass_det = 8.*0.14*1e-3 # mass in kg, = 8cc * 0.14g/cc\n", + "exposures = times*mass_det\n", + "\n", + "#coinc = np.array([2])\n", + "coinc = np.arange(2,5)\n", + "\n", + "nexpo = len(exposures)\n", + "ncoinc = len(coinc)\n", + "\n", + "data = {}\n", + "\n", + "for i,t in enumerate(times):\n", + " for j,n in enumerate(coinc):\n", + " data['{:d}_{:d}'.format(i,j)] = np.loadtxt('./{:s}/HeRALD_FC_{:0.0f}d_{:d}device_{:d}fold_{:0.0f}mus.txt'.format(results_dir,t,n_devices,n,window/1e-6))" + ] + }, + { + "cell_type": "raw", + "metadata": { + "tags": [] + }, + "source": [ + "# money plot of sorts... selected run times \n", + "\n", + "t_sel = np.array([30,183,365])\n", + "t_idxs = []\n", + "for t in t_sel:\n", + " idx,_ = find_nearest(times, t)\n", + " t_idxs.append(idx)\n", + "\n", + "colors = ['blue','green','orange']\n", + "\n", + "n_lines = len(t_idxs)\n", + "print(n_lines)\n", + "cmaps = ['Blues','Oranges','Greens']\n", + "cmap = mpl.colormaps['Blues']\n", + "\n", + "#colors = cmap(np.linspace(0.5, 1, n_lines))\n", + "\n", + "lss = ['--','-.','-']\n", + " \n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "load_and_plot_existing(ax,leg=True,lw=1.5,leg_params={'loc':'upper right','frameon':False,'handlelength':1.5 } )\n", + "for ii,i in enumerate(t_idxs):\n", + " \n", + " leg_hands = []\n", + " for j,n in enumerate(coinc):\n", + " lab = '{:d}-fold coinc in {:0.0f}$\\mu$s'.format(n,window/1e-6)\n", + " #lh, = plt.plot(*(data['{:d}_{:d}'.format(i,j)].T), marker='.',ms=5,label=lab,color=colors[j])\n", + " \n", + " acc_mask = data['{:d}_{:d}'.format(i,j)][:,2]>acc_cut\n", + " masses = data['{:d}_{:d}'.format(i,j)][:,0][acc_mask]\n", + " sigs = data['{:d}_{:d}'.format(i,j)][:,1][acc_mask]\n", + " \n", + " lh, = plt.plot(masses,sigs,label=lab,color=colors[j],ls=lss[ii])\n", + " leg_hands.append(lh)\n", + " \n", + "#ax.set_title('{:0.1f} g LHe; {:0.0f} days; {:0.1f} g-day'.format(mass_det*1e3,times[i],mass_det*1e3*times[i]))\n", + "ax.set_yscale('log')\n", + "ax.set_xscale('log')\n", + "\n", + "ax.set_ylim(1e-45, 1e-36)\n", + "ax.set_xlim(0.01, 20)\n", + "ax.set_xlabel(\"Dark Matter Mass [GeV/c$^2$]\", fontsize=14)\n", + "ax.set_ylabel(\"Spin Indepedent Cross Section [cm$^2$]\", fontsize=14)\n", + "\n", + "#ax.grid(lw=0.3,ls='--',color='grey')\n", + "#ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "#ax.legend(handles=leg_hands,loc='upper left',frameon=True,ncol=1,title='{:d} Devices'.format(n_devices)) #bbox_to_anchor=(0.5, 1.05)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": false, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1167612/1832352967.py:14: RuntimeWarning: All-NaN axis encountered\n", + " result = np.nanmin([result, yinterp], axis=0)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = ['blue','green','darkorange']\n", + "\n", + "lss = ['-','-','-']\n", + " \n", + "fig, ax = plt.subplots(1, 1, figsize=(9.5,6))\n", + "\n", + "plotexcludedmassive(ax, details=False)\n", + "#leg_params = {'loc':'upper right',\n", + "# 'frameon':True,\n", + "# 'handlelength':1.5,\n", + "# 'fontsize':9,\n", + "# 'ncol':3 }\n", + "leg_params = {'loc':'upper left',\n", + " 'frameon':False,\n", + " 'handlelength':1.5,\n", + " 'fontsize':11,\n", + " 'ncol':1 }\n", + "load_and_plot_existing(ax,leg=True,lw=1.5,leg_params=leg_params, ER_model='massive')\n", + "\n", + "#ax.text(1.3e-2,1.5e-40,'HeRALD - {:0.1f} g \\n2x2 device array \\n {:0.0f} livedays'.format(mass_det*1e3,times[i]),fontsize=15)\n", + "#ax.text(1.25e0,2e-37,'SPICE\\n5.3 g GaAs', fontsize=15)\n", + "\n", + "leg_hands = []\n", + "\n", + "#m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_030days_2fold_lce10/HeRALD_FC_30d_2device_2fold_100mus.txt', unpack=True)\n", + "#lh, = plt.plot(m_limit*1e3, x_limit, color='magenta', ls='-', lw=2, label='30 d, Light coincident in 1 sensor')\n", + "#leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_030days_3fold_lce10/HeRALD_FC_30d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='magenta', ls='--', lw=2, label='30 days, 10% LCE per channel')\n", + "leg_hands.append(lh)\n", + "\n", + "#m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_100days_2fold_lce10/HeRALD_FC_100d_2device_2fold_100mus.txt', unpack=True)\n", + "#lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls='-', lw=2, label='100 d, Light coincident in 1 sensor')\n", + "#leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls='--', lw=2, label='100 days, 10% LCE per channel')\n", + "leg_hands.append(lh)\n", + "\n", + "#m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_300days_2fold_lce10/HeRALD_FC_300d_2device_2fold_100mus.txt', unpack=True)\n", + "#lh, = plt.plot(m_limit*1e3, x_limit, color='green', ls='-', lw=2, label='300 d, Light coincident in 1 sensor')\n", + "#leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_300days_3fold_lce10/HeRALD_FC_300d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='green', ls='--', lw=2, label='300 days, 10% LCE per channel')\n", + "leg_hands.append(lh)\n", + "\n", + "ax.text(1e2, 5e-36, r'F$_{DM}$ = 1', fontsize=18)\n", + "\n", + "#x = 6.5e-2\n", + "# if n<4:\n", + "# y = sigs[0]\n", + "# else: \n", + "# y = sigs[0]*0.75\n", + "# ax.text(x,y,lab,fontsize=14,color=colors[j],alpha=0.95)\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_xscale('log')\n", + "\n", + "ax.set_ylim(1e-40, 2e-35)\n", + "ax.set_xlim(1e-1, 1e3)\n", + "ax.set_xlabel(\"Dark Matter Mass [MeV/c$^2$]\", fontsize=14)\n", + "ax.set_ylabel(\"DM-Electron Cross Section [cm$^2$]\", fontsize=14)\n", + "\n", + "#ax.grid(lw=0.3,ls='--',color='grey')\n", + "#ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "ax.legend(handles=leg_hands, frameon=False, ncol=1, loc='lower left', fontsize=13)\n", + "\n", + "#plt.savefig('./pretty_plots/herald_limits_{:0.1f}g_{:0.0f}d.png'.format(mass_det*1e3,times[i]),facecolor='white',bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1167612/1832352967.py:14: RuntimeWarning: All-NaN axis encountered\n", + " result = np.nanmin([result, yinterp], axis=0)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = ['blue','green','darkorange']\n", + "\n", + "lss = ['-','-','-']\n", + " \n", + "fig, ax = plt.subplots(1, 1, figsize=(9.5,6))\n", + "\n", + "plotexcludedmassless(ax, details=False)\n", + "#leg_params = {'loc':'upper right',\n", + "# 'frameon':True,\n", + "# 'handlelength':1.5,\n", + "# 'fontsize':9,\n", + "# 'ncol':3 }\n", + "leg_params = {'loc':'upper left',\n", + " 'frameon':False,\n", + " 'handlelength':1.5,\n", + " 'fontsize':11,\n", + " 'ncol':1 }\n", + "load_and_plot_existing(ax,leg=True,lw=1.5,leg_params=leg_params, ER_model='massless')\n", + "\n", + "#ax.text(1.3e-2,1.5e-40,'HeRALD - {:0.1f} g \\n2x2 device array \\n {:0.0f} livedays'.format(mass_det*1e3,times[i]),fontsize=15)\n", + "#ax.text(1.25e0,2e-37,'SPICE\\n5.3 g GaAs', fontsize=15)\n", + "\n", + "leg_hands = []\n", + "\n", + "#m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_030days_2fold_lce10/HeRALD_FC_30d_2device_2fold_100mus.txt', unpack=True)\n", + "##lh, = plt.plot(m_limit*1e3, x_limit, color='magenta', ls='-', lw=2, label='30 d, Light coincident in 1 sensor')\n", + "#leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_030days_3fold_lce10/HeRALD_FC_30d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='magenta', ls='--', lw=2, label='30 days, 10% LCE per channel')\n", + "leg_hands.append(lh)\n", + "\n", + "#m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_100days_2fold_lce10/HeRALD_FC_100d_2device_2fold_100mus.txt', unpack=True)\n", + "#lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls='-', lw=2, label='100 d, Light coincident in 1 sensor')\n", + "#leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls='--', lw=2, label='100 days, 10% LCE per channel')\n", + "leg_hands.append(lh)\n", + "\n", + "#m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_300days_2fold_lce10/HeRALD_FC_300d_2device_2fold_100mus.txt', unpack=True)\n", + "#lh, = plt.plot(m_limit*1e3, x_limit, color='green', ls='-', lw=2, label='300 d, Light coincident in 1 sensor')\n", + "#leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_300days_3fold_lce10/HeRALD_FC_300d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='green', ls='--', lw=2, label='300 days, 10% LCE per channel')\n", + "leg_hands.append(lh)\n", + "\n", + "ax.text(1e1, 4e-35, r'F$_{DM}$ = ($\\alpha$ m$_e$ / q)$^2$', fontsize=18)\n", + "\n", + "#x = 6.5e-2\n", + "# if n<4:\n", + "# y = sigs[0]\n", + "# else: \n", + "# y = sigs[0]*0.75\n", + "# ax.text(x,y,lab,fontsize=14,color=colors[j],alpha=0.95)\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_xscale('log')\n", + "\n", + "ax.set_ylim(1e-38, 1e-34)\n", + "ax.set_xlim(1e-1, 1e3)\n", + "ax.set_xlabel(\"Dark Matter Mass [MeV/c$^2$]\", fontsize=14)\n", + "ax.set_ylabel(\"DM-Electron Cross Section [cm$^2$]\", fontsize=14)\n", + "\n", + "#ax.grid(lw=0.3,ls='--',color='grey')\n", + "#ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "ax.legend(handles=leg_hands, frameon=False, ncol=1, loc='lower right', fontsize=12)\n", + "\n", + "#plt.savefig('./pretty_plots/herald_limits_{:0.1f}g_{:0.0f}d.png'.format(mass_det*1e3,times[i]),facecolor='white',bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1167612/1832352967.py:14: RuntimeWarning: All-NaN axis encountered\n", + " result = np.nanmin([result, yinterp], axis=0)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JSkqiTZs2XHvttYwfP56ffvqJfv36AfDTTz/x008/cebMGWbPns3w4cP5+++/+eGHH0hMTOSDDz5g1KhR1K9fn8cee4wOHTrwzjvv8P7779OuXTvq169Ply5dChQIzc+mTZtYtGgR0dHRRERE0KNHDx5++GFOnTpF69atcbvdrFixgo0bN5KZmclXX33FzTffTEhIiJ/fdcGF5BpGLiWO3iBJNAw0cSLLxVmnRh1z+aytMCyBBixBMs5slew0D2F1/TiZQCAQCARl4J133mHmzJmVvQwfqm1xyQ0bNtC/f/8iN/T33Xcf8+bNY/369fTv39/b/vfff9OzZ0/uuOMOlixZUoErziO3uOSpU6dKFMxdlS0tAkFl8dln8PnnsGcPbN0Kbdte2njHM51sOZdNsAH6RV1a8oaLkXbORcppJ9ZgA43aXDyLnUAgEAgEFUlpLC3t27evkOKS1c7SksuFVdLzo6oqP/zwA0CBDF/dunVDlmVWrVqFoigYDAa/rrM4QkNDRQYqgaCMJCTowfgAa9deumhpFGRGJpssBTI9GiF+zCIWFG4k5bQTR5aC4lYxmISLmEAgEAiqDlXxhnmNvFImJCSQmJhIQEAA0dHRPseMRiOhoaHe9LsCgaB6cuON+s+gINix49LHM8kS9QJ1C8tZp3rpAxY3l0UmKMxIWF0T1dLULRAIBAJBBVNtLS3Fce7cOYAirRghISGkpaWRmppakcsqQM+ePQtYeqZOncrUqVMraUUCQfXBatV/ZmfD//4HqgrFGGBLRIdwKy2CjIThvvQFXoT6ok6LQCAQCKoRc+bMYe7cuT5t+RMg+ZsaKVpyffDM5sIDXHOzPRV1vKLYtm2bcA8TCMpI+/bQty/89hscPgy//AIDB17amPUDTYCJrCxFVK0XCAQCgSAfkydPZvLkyT5tGRkZNG7cuELmr5HuYbnpa202W6HHc1MN161b/lXgBQJBxZHfKPnxx+U3bkXFummahi3DgyNLFJqsDmTHH+H8/j/xOLIreykCgUBQ66iRoqVly5YEBweTmpqKw+HwOZaZmUl2djZRUVE0bNiwklYoEAjKg1tugdx/4x9+gKVLL31Mm0flUJbCvkz/C4m0sy7OxNo5LwpNVguSd/5Gyq7fcJ1PquylCAQCQa2jRooWg8HATTfdhKZp7N692+fY/v37ARg2bFixGcgqgp49e9KhQwefx8flebtYIKjhmExw//3675oG48eD3X5pY7pVjQPpLo7bVDyqf13EAsN0D117hgdVEe5oVR1zaAQA7szKjYcU1HxWr17N9OnTadKkiTfTaJMmTXwekZGR3mNDhw6t7CVXGVavXs2AAQP46quvKnspNY45c+bQs2dPn0ffvn0rbP5qG9Oiqnp2n6L8zqdPn86SJUtYsWIFV155pbd95cqVGI1Gpk+fXiHrLA4R0yIQXDqTJ8OLL+qixeGAL7/MEzJlIdQkE2KSyXSrJLo0Glr9l/rYHCBjski4nbqbWHCEf+vDCC4NU0gdAFwZQrQI/MsNN9zADTfcQKdOnXjwwQcBiIuLw2j03bYdOnSIJ554okKDoasq3333HR9++CHbt28HYMKECZW8opqHiGkpI6dOnQIgPj6+0OPdunXjpZde4tNPP/WmNt67dy8fffQRb731Fh06dKiwtQoEAv9Rvz4MGJD3PCvr0saTJInGQXqSjjN+Tn0sSRJB4bpQyTrv8etcgkvHFKqLFmFpEVQUTZo0KfZ4u3bt+PLLLwkLC6ugFVVdunfvzurVq2nZsmVlL0XgJ6qdaDl9+jS9evViypQpAMyfP5/u3buzbt26An2ffvpp3n33XUaNGsV1113H1KlT+fzzz5k2bVoFr1pQGjZs2MDQoUN56aWXSn1u9+7dWVpIYIOqqnzzzTdcdtll/Pbbb+WwSkFV4vnn837/9ttLH69xkC4kEp0aqp+ziAWF63dObRkeVD+7owkuDVNIjnuYsLQIKogLLSuFERER4bXG1GaaN2+OxWKha9eulb0UgZ+odu5hjRo14s8//yxx/wkTJggToZ/YsWMHb731Fl9//TV9+vShQ4cOuN1uDh06RMeOHXnqqado3rx5qcft2rUrhw8f5oorrij1udOnT/dxB8xF0zSio6PZuXNnqcfMT0ZGBs2aNeP8+fMADB8+nO+++w7Qs9U9/vjjhIeHk5CQwOOPP06nTp0uaT5ByejdG7p0gT17wGzWrS3BwWUfL8pqxGKQcCoaKS6NaIv/XMQsgTJGk4THrWHP8HgtL4KqhznHPcydnY6mKEgVlGVOILgYvXv3ruwlVBmqWhV3QflR7URLTaK6F5e87LLLuPfee/n666+ZP38+rVq1AvSN/YQJE+jUqRPr1q3j6quvLtW4kZGRZc7sNmbMmELbDQYD1113XZnGzM9HH33Ep59+Sp06+ualY8eO3mPjx49n2LBhTJgwgXPnznH11Vezfft2wsPDL3leQfFIEnz0EYSHQ+fOlz6enOMidjTDyVmnRrQfr4G6i5iR9CQ39ixFiJYqjCEgGMloQvO4cWenYQ6NrOwlCWo5//73v5k1a1ZlL6NKIUn+u8lU2xHFJWsxZQnEV9xFp0aVJAnZaCpzX4Op9MU2TaaCG6zQ0FAWLVpEly5dGDNmDMeOHSt13Qt/ZHa71Nob6enprFy5ktmzZxewoOzZs4fvv/+ezz//HIB69eoRExPD7NmzefLJJy9pXkHJ6NNH/6kosHIlZGbCmDHw99+wYgX06+cb+3IxGgeZOJnlQq6A619YXTMhkSbMAdXOY7dWIUkSDa4dgcEahCkovLKXI6jlLF682BuzW12YPXs2c+bMKdU5N910Ey+++KJ/FiQoFZUdiC9ESzVjz2f/KfJYaNM2tBx6t/f5vi9eQ/W4C+0b3KA5rW+Z5H1+4Ku36HzPM+W2TovFwqRJk5g+fTpr164lMTGR7du3ExwczLp16/jyyy9p3749X3zxBbNnz2bMmDEsW7aM+Ph4/vnnH5+x1q5dy/jx43nuuecYMWIEAO+88w5169Zl4cKFjB07lhkzZrBgwQL+7//+jylTpnD33fr7sG3bNj788ENatGjhTd6Qi6qqzJ492ytGOnfuzPvvv4/ZXLh4W7x4MXv37qVz58507dqVzz//nG7dugF6isW6desSnM8nqX379qxfv16IlgrE6YROnSA2Vg/Qr1MHcjOBnjxZOtHSMNDE7c3DcToceDz+DZI3WYRYqS4ENii9y6tAUB7kd31OSkoiLS2Na665phJXVHruv/9+7r+U9I6CWo24Ugr8Rq7r1N9//829997LE088wWuvvUZUVBSffvopALfccgsHDhxgw4YNzJs3jyeffNLHIpKRkcFXX33Fn3/+ydSpU2nQoAHvvfcel19+OdOnT2f16tVea8/w4cM5cOCANw12SkoKt912G2+99RYvvvgi9913n8/6PvzwQ1q2bMmMGTNYuXIlixcv5oMPPijy9UyZMoX09HS2bt1KWFgY11xzDbGxsQCcPHmSyEhfV5HQ0NACQkngXywWyI3BPHsW0tIg15j500/gLlzDF4osSciSVKJA2PKkqDTuAoGgdvPXX3+xfft2tm/fzokTJ3j99dcre0kCQYUiLC3VjC4TXyjy2IV+nJ3ufrrEfTuMe+LSFlbMHAEBAaxevZomTZrwv//9j+TkZOrXrw9AeHg4YWFh3HzzzbRq1cobFwO66BgzZgxz5871iXEJDQ3lscceQ1EURo8ezbhx4wAIDg72iR+ZNWsWbdu2pV69egD06tXLZ32zZ8/m3nvv5cCBAwAMGDAA+0UqE0qSRM+ePVm7di3XXnst7733Hh9//DGSJBEQEODTV1XVQt3nBP6le3dYtQpsNl2wDBsGixdDejr8/nvprC2guxVmeTSCjf71E1M8KsnxTpzZCk3aByFVhF+aoNR4bBlkHj8IQESHgkk/BIKKQJIk/vWvf7Ft27Zi+23ZsoU5c+awatUqLBYLAwcORJZlXC4Xp06dIjw8nBdffLFAxq3c89auXYvdbufAgQNFxpp+9913TJgwgRYtWtC1a1eee+45kXZY4BeEaKlmlCbuxF99S0quGOjatStWq5V//etfjB49mo4dO/rcTZYkqdDAuWPHjrF+/Xr++usvhg8f7m1/8sknyczMZOLEibz00kssWLDAK4Lyj7Nz505CQkKKXN/x48cZPXq01xczNxX2xo0bGTRokLffXXfdVSDwzGKx8PDDD7Nw4UJAz6W/Zs0anz7nz5+vMD9PQR4dO+qCBeCTT2D8eF20ACxfXjrRomgaK09lkOlW6R9pJMiPwkU2SNgzFRS3Rlaah5A6QvBWRTy2LFJ2/oohIFiIFkGlIkkSzZo1K7ZP7969ufrqq2nWrBljxoxh5syZPsfnzp3L4MGDWbduHZ3zZTHJPW/SpEksXbqU2NjYQkVLcnIyGzduRNM0vvjiC7p06VLsej755BOvp0VJueWWW8pUAkHgH1RN45RdI8Oj0Tm0YjMoCtFSiVT37GHF4Xa7mT9/Pm3btqVp06Zcc801nDhxgsDAQObPn1+iMXr27MnAgQOZMGECnTt39lphTpw4wRtvvMHUqVO55557GDlyJMePHy9wfnh4OH/99VeR4zdq1IiFCxcyY8YMb9vGjRu5/PLL2bVrl7etqKJdBoPB62M8dOhQnnvuObKzswkKCgIgNjaWW265pUSvVVB+3HgjNGkCp07Bzz/D66/raZBdLvj+e/jwQyhpngeDJBFklMl0q8Q7VNoG++8LWpIkwqJMpJ5xkZ7oEqKlipJbq0WxZ6G6Xch+uOEjEJSUkgSo79u3j/Pnz9MnN1tJPu677z6WLl3Kc889x4oVK3yO7d+/n6uuuor169cTFxfHtddeW+D8b775hgYNGhAREeEjeorigQce4IEHHrhoP0HVQ9U0vlr7O1qDFoTXawDAU5PuIiH2UIWtQcS0VCLbtm3jwIEDPo/qJlgKC1B2OBzce++9JCUl8fXXX7N//34yMzNJTEzk5MmTHDhwALvdzpEjR7znXJgyT9M0NE1j2rRp9O/fnxEjRpCdnQ3oKffOnj1L06ZNef/9932sNrnngZ7++PDhw950kLnxJ4mJiXg8HiZMmMDzzz/PCy+8wO+//87LL79MRkYGgYGBtGvXzvto0ED/5/zss8+8mVoSExNZsWIFTzyhu9V16tSJ/v378/PPPwNw5swZTpw4wcSJEy/9TRaUCqMRcmrPommwYEGedeX0aT2bWGloEarnOz7lUP0ebxISZQIJnDYVR3bFpZEUlByDJQDZoruCujNFkUlB1WfTpk3Islxk+YHLL7+c33//HafT6dO+efNm+vTpQ/PmzYmLiytw3m+//UafPn343//+R+/evUWq4RqKqmmcsKn8kuwhrFtvwus1wCJDx2CZFd98VaEFu4VoEZSZbdu2MXv2bAAee+wxnnzySR599FFuueUW6tevz549e+jWrRsDBw7ksssu44orruDTTz/l1ltvZdOmTZw6dYrFixdz5swZFi5cyN69ewE9E9e+ffv49ddf2b9/P+PHj2fv3r0MHTqUv/76i4yMDHr37s0rr7zCvHnzWLBgAQDLly8nISGBH3/8kfj4eIYMGcJHH33Em2++Sa9evVi9ejXR0dHs2LGD5ORkpk+fzhNPPMGcOXMYO3Ys4eHh3HjjjUW+3lWrVtGtWzeGDh3Ke++9x6effkpgYKD3+IIFC1i9ejWvvvoqL774IitXrvTJJiaoOCZN0q0rALNmQY7xC9DTH5eGJkFmjBLYFEhx+1e0GE0ywRG6ATw9qeiU5YLKJbfIpCvjfCWvRFCbKOtNk82bN9OpUyciIiKKHFdRFNLS0nzaDx8+TNu2bWnWrFkB0WKz2YiNjaV9+/Zs3bq12mUxE5SMDLfGL8kedmcq2FWwyNApRGZAlJGWQQaMFSxUJU2kqqlwMjIyCAsLIz09vdR1WgQCQckYMUKPYQGIiIDzOfvL9u0hJ9yqxPyZmM3RDCdNrBLdw/zrVevIVjj9jw0kiOkUhNEk7i1VNc7970cy4/YT2a0vER17XfwEgaCMrFmzhjvuuAPQXaOLEh5FoWkazZo1Y/To0bzxxhuF9rnjjjtYv349SUlJ3myJmqbxxBNP8Pbbb/PSSy+xfv16Nm7c6D1n7ty5jBs3jr179zJw4EA2b9580XiWimLSpEksWbKEjz76iPHjx1f2cqo1iqaxPln3qGkdJBMTIGO4QKjk1mmpiD2tuBoKBIIayRP5EuKlp0NOSR0OHoQLSgFdlJYhutkmwanhUf17n8caZMASKIMGGcmlyNEsqDAMVt10pzhtlbwSQU1GVVV+/PFH7/Ovv/661GMUF88CYLfb2bJlC7169fJJ737w4EHat28PUMDSsnv3btq0aUNgYCCbN28mPDy8QMHlysJut7N//36Ai2ZWE/iiahrHbQp/nvd4rXoGSaJXuJEBUUZaBBoKCJaKRogWgUBQI+nVC1q31n9XVcgfQ1paF7Eoq5EQk4yiwVmn/43TEfUtRDQwExolgvGrIoacmBbFWXyKdIGgrDz//PM0bdqUL7/80tv25JNP0qJFC7766qsSj7Np0yYkSSoynuXnn38mKyurgEUiN54FoHnz5qSnp5OSkoKiKGzatInrrrvOO37v3r2RS5rdxI/cc889NG/e3CtavvjiC2JiYvjss88qeWVVm1yx8kuyhz2ZKokujTP5rnNhJqnSxUouwj2sEsh1D2vTpk2NzR4mEFQFPvtMj28BuPXWPLFy5ZXw55+lGys+2wWKQihuEXBay/HYMlGcdowBwRisgRc/QSCoJMaOHcvx48f53//+V+jxwYMH43A42LBhg89+5IknnuCtt94C4NSpU3Ts2JENGzbwzz//MHjwYKKiovB4PDRt2pSnn36aBx98sEJej6D8UDSNU3aVI9kqdlVvs8hFu4GBngjpwhIQiqIQGxtbIe5hIuVxJbJt2zYR0yIQ+JExY+C552DwYHj4YTh6FPbuhb/+goQEKKJWWqE0DtJdxLKyFFG1vpZjDAzBGFh0DSiBoCqgaRpbtmxh1KhRhR7/5JNPiI2NZf369QVuoOanUaNGmM1mfv/9d1q2bElUVBQAO3bsICsrq0jXM0HVxaFobEr1lFis5DJ58mQmT57s05Yb01IRCNEiEAhqLIGBEBcHFj1rMbfcoosWgB9+yEuNXBqMRiNud8XEmmSneUhLdBHV2IIlsGKLeAkEgurN3r17C41nsdlsvPXWW/z888+sWbOG5s2b+xzfsmULMTEx3ueyLNO0aVO2bNnCY4895m3PjWcpSX0WQeWjahpyjiCxyGA1SGhotA6SaXoRsVJVEKJFIBDUaCwWyMyE+fPhiy/y2pcvL71ocSoq+zIVzmV76FPH4Hc3sazzbhxZCumJLuo2C/DrXIKSo7gcpB/egep2EdW9b2UvRyDw4dixY7z00kvs3r0bgPnz5/P9998D4HK5SEtLY+jQoWzatAmzOa84alxcHE8++SQbN24kODiYY8eO8e677wJ6LZcnn3wSgO+//54lS5awadMm3G43EyZMYPLkyfTu3buCX6mgJNgUjWM2ldMOlX6RRkyyhCRJ9AgzYJEpkVhxO1XSk1xYAgyERFZerKWIaakERMpjgaBiiY2FNm30YpMGAyiKXoQyKQnCw0s+jkfV+O54Gm5V46pwA9EW/waf5qY/lnLSHxtE+uMqgeKwEbdML1rbcsx0pCoQhCwQCAT5SXdrxNoUEhwauRv9LiEyzUphtXfaFNLOucg6r6c9NllkmnQI9LlhJ1IeCwQCQTnSqpUe1wJ5rmIeD/z8c+nGMcoSMcH6ncmTDrUcV1g4lkAZS6CMpkFGikh/XFWQzVbv74pLZBATCARVA03TSHKq/HHew++pHk7nCJYos0SvcAMxASXf9menuYk/ZPMKloAQA1GNLX5aeckQokUgENQKcotL2vKV1iht6mOAlqH6l/YZh4bLzzVbJEkiLFoXSRlJbpEAoIogybJXuKgi7bFAIKgiOFT4M00hyaVfKxpaJK6tY+TqCCN1LXKxLs2apuF25t2MCwg1YjBKBEcYadwukIatAwkMM1Zq9kwhWgQCQa0gfybxXDfuVavA4SjdOJEWA+FmAypwugKsLcERRmSjhMetkZ3m8ft8gpJhsOipjhWHKDApEAgqB4+mcTa/0DBINA2QaB4g0z/SyOXhRsJNxYsMVdFIO+fixL5szsTavTfHZFmiaccg6jUPqDKJYEQgfiXSs2dPUadFIKggRo6EJ56Ac+cgN/lXVhb88gsMG1bycSRJolWohb+TbRy3qzQLKP7u1aUiyRKhUSbSzrpIT3ITHCEKTlYFDJYA3JmiwKRAIKh4nKpGnE3luE3FpcH1kRIhRv061DW0ZFt7j0slPdFFRrIbNVf3GMHj0jBZ9LFkg++1rag6LRWFsLRUItu2bePAgQM+j+omWFJSUpg0aRL3338/t956K1arFUmSyMrKwuPx8P3339O8eXMaN27M448/zuOPP85jjz3GiBEjuP7660lISODtt98mNDSUfv364fH43knevHkzAwYMYMyYMezcuROAEydOMG7cOKZMmcLgwYMxGo3evPFZWVksWbKE8PBwOnTo4J3z0UcfZcCAAdxzzz0AbNy4keuvvx5JknjxxRc5fvx4oa9vz549jBo1ivvvv59rr70WSZK4/PLLAVizZg2DBg3CaDSydu1an/MSExOZO3cu4eHhLF68mKysLABUVWX27NmMGTOGhx56iHvuuYeRI0fyZ75Kh7nnyrJMy5YtWbp0KRkZGRf9W6xbt46PP/64QLvb7Wb58uU0atSIevXq8fDDDzNjxgxGjx7N0KFDiyw6VtOwWOBf/9J/z+9lVRYXseYhZgwSZHrgvNv/LlthUSasQTKhkSbhIlZFkC16NjchWgQCQUWR7dHYk6GwPsnD4WxdsAQadBFTUlwOlXPH7ZzYl01aoi5YTFaZ6KZWYjoFYSomwczkyZPZtm2bz+O3334rh1dWMkT2sEqgJmUPu/nmm7nzzjsZOXIkAAcPHqRPnz4cP36c4OBgAO68806OHz/O5s2bfc798MMPeeihhwD4+OOPefDBB5k8eTKzZ8/26Td37lyaN2/OgAEDAOjatStz587liiuuAHQBMnbsWOLj473nXHPNNTRr1oyFCxf6jPXRRx95K/d+9tlnTJo0CbvdjtVq5UJsNhsdOnRg06ZNNGnSBIBvvvmGd955h61btwL6HYbGjRtjt9v5448/aN++vc8Yo0aN4ptvvgHA4/Fwxx13YDKZWLBgAZaciPCDBw8yYMAA3njjDe68807vuY0aNeLOO+/kjTfeKOYvkMeQIUOIi4vj4MGDhd75HzduHCdOnPD5O/z3v/9lypQp7Nq1q8DaayLnzkGTJnmWFoDoaDhzRs8qVhq2J2WjqCoxFo1AQ9XPby8oX879sZLMY3up0/Va6nS6urKXIxAIajAORWNfpkKCM2/LHm6UaBUk08Ailcrab8vwcCZWv9liDTYQXs9MYGjZU/iL7GGCIvGoWpEP5QKlXVxfTyF9y8Ivv/xCWFiY93n79u2ZNm2aTx+jsXBT5cSJE72/BwUFMXLkSObMmcP777/v089kMnnHSEpKYs+ePT5zXnvttT6b/eLmvPfee72/57rmFdV3//79JCcnExQU5G0bNWoU/fr18xnjyiuvpH79+tx0002kpKT4jBEQkFdb44033mDVqlX83//9n1ewgP6ePf7440ycOJGjR4/6vG6TqWSuQAcPHsRut/PPP/+wZs2aQvsUNtYdd9yBy+Xi59Km0aqm1KsHN93k25aUBH/8UfqxekQH0bNusBAstZQ6na6mydB7CWtzWWUvRSAQ1HCMMiTnBNfXNUtcHWGgTx0DDa0XD67PTHWTnuTytgWE6EKlUdtAGrUJJKiSg+tLgxAt1Yxvjp0v8rHxbJZP32/jiu77a0KmT98Vx9PKtJ7evXszevRo5s+fj5rjFDlmzBifTXlhzJs3j8DAQJ+2Bx54gGnTpvH4448XuYmOjIykbdu29O/f31ssC+Duu+++6FoLm7M42rVrh9VqpXfv3mzatKnIucLDw1m5ciXp6emMGDGi0Grpmqbx8ccf07NnT68rW36GDBmCy+Uq4CtaUmbNmsV///tfevbsyaxZs0p83v/93/8B0KFDhzLNWx159tmCbWVxEQM9vqUo0esP3E7dB9meKQLyKxtTSASWiLoYzAWttAKBQFBWVE3jlF1lW5rH6w5slCS6hhroW8dIrwgjUebixYqqaKQluji5P5vE4w5SE5yoij6WJElENrJgDaoawfWlQYgWwSXx1Vdf0aNHDyZOnEi3bt1YsWIFLVu2LHBX/8SJE8yYMYMZM2Ywfvx4Xn/99ULHe+eddxg2bBijR49m3759BY7LsszKlSupV68et956K9dccw2///57oa5Nu3bt8s45YsQIvvrqq1K9tpCQEFavXo3b7ebaa6/lpptuKtKNqmXLlixfvpy//vqLKYWUWU9OTubMmTNeN7MLiYmJAWDv3r2lWmPu2BkZGTRv3px///vfrF69msOHDxfa959//uHRRx9l6tSptG3blv/85z+8/fbbDBkypNTzVle6d4devXzbli/3jXMpKZqmkeqR2J+pVEisSUaSi+R4p6jZIhAIBDUMt6oRm62wPtnDzgyFM06Nc66860oDq0zoRTKBedwqKaednNiXRUq8E49LQzZKhNU1+3v5FYLIHlbNGNUioshjF36Ub29edN8LubVZeJnWEx0dzfr161m6dClPPfUUw4cPZ/DgwSxdupSQkBBvv5iYGGbOnOl9/sILLxQ6nizLLFq0yCsScmNH8tOyZUu2bt3KvHnzeOGFF+jbty/jx4/ns88+87nr3a1bN++cmqbx0ksvlfr1XX755ezbt4/33nuPmTNn0qNHD6ZPn16o6LrmmmuYP38+48aNo127djzxxBMF+hS1sc1d94XZ5ErCJ598wv333w/AyJEjeeKJJ/jwww/58MMPC/Rt27Yt7777LgB2u50ZM2bw5ZdfMnjwYDp16lTquasrTzwBt90GkqSLlWPHYN8+6Ny5dOO4VI1NiTYUDRpaJSIuckG5VALDjKQlurGl6yKpupj0ayLurHQyj+9HNhgJb39FZS9HIBBUUxyKxjGbynG7iidni2CRoUWgTJ1SXFMyU90knnBAzhgmi0R4PTPBdUzIcs24VghLSzXDKEtFPgwXfCiL62sspG9ZiI2NBfTYiIMHD/LMM8+wZs2aiwqEC2NQ8hMUFMSPP/6I2+1m+PDhuFx5vpiKohAXF4fBYOD+++8nNjaWCRMm8OWXXxYI4M+PJEmMHTu2VK8tNTWV1NRUrFYrTz31FEePHuWGG25g5syZrFy5stBzxo4dy3/+8x9mzJjBjz/+6G2Pioqifv36xMXFFXpeQkICoAut0uByufjuu+/4+eefmTFjBs8//zwtW7bk888/v2jGsYCAAN577z1SU1MZP358qeat7txyC7z5JrzySl7b8uWlH8dikGkarN/BOmHzf80Wa7AB2aCb/p3Z/p9PUDQeeyapuzeSdnhHZS9FIBBUUzI9GuuSPcTadMESbICuIQYGRBlpHWTAXMzeTNM0r8sXoLt7aWANkqnfwkqTDkGERplrjGABIVoEl8gXX3zh/d1kMvHKK6/Qp0+fIt2TcmndujWgu3AVRsOGDfnpp5/YvXs3L774orfd7XazaNEi7/Pg4GA+++wzYmJiSjSny+XiwIEDF3lVOomJiaxatcr7PCoqiqVLl2K1Woud6/nnn2fcuHGMHTuW1NRUQBdNkyZN4u+///YKlPysWbOGgIAA7rvvvouu6+zZsxw5cgSARYsW8cQTTzBz5kzv45tvvsHpdDJ//vyLjiXLMpGRkV7xWVswGHRry1135bUtXVq2sVqF6vFbpx0a7jImtCgpkiQRmJODPztdxLVUJrnFJVWR8lggEJQQTdOw5RMawQYINUIdk8QVYQaujzQSEyhjuEhwfdZ5N6f/semWlRxMFpkmHYJo1DaIoHBTjbTEC9EiuCS++uortmzZ4n3ucDhITExk+PDh3jZFKdzff86cOd6gdUVRCtRo6datG19//TWJiYk+7bNmzeKff/7xPk9NTSU7O5tbbrnlonO+/PLL1KlTB8A7X3GFkV5++WXOnDnjfZ6QkIDBYOCGG27wec0XMm/ePLp37+5j7Xj22Wfp06cP999/v885R48e5dVXX+WLL77wiXnxeDwFgvoVReGll16iZcuWqKrKggULvOmmc2nUqBE333wz77//vs/5F76/AL/++iv79u1jzJgxRb4HNZmdO0HO+Rbctw9OnCj9GNFWI6EmGQU47fC/9SMwTBcttgwhWioTQ06dFtXtRKvA4moCgaD6oWkaCQ6VTakKv6V4vBlbJUni6ggj19QxUv8imcBUVSM9SQ+uPxfnwGlTsWV4UDx5ex2ztWZv60VMSyXSs2fPAjEMU6dOrVYFJm02G9dddx0DBgwgJiaG+Ph4pk2bxj333IPH4+Hbb79lw4YNpKenM23aNIxGI6qqEhcXx549ezh69CibNm1i0aJFREdHEx4e7i3eCDBs2DDee+89nzkTExPp2rUrN9xwA/Xr1+fEiRN8+umn9OvXj8zMTL799lt27drF4cOHefTRR5FlGUVR2Lt3L4qi8PLLL7Nx40avxeaVV15hwoQJtGjRosDr++eff2jbti1DhgwhPDycU6dO8dNPP9G+fXuSkpJYtmwZq1ev5quvvuKmm27y5ig3m80sX76cp556yjuWxWJh7dq1vP/++4wZM8YbfJ+cnMx3331Hjx49ADh37hzLli0jISGB+fPnc/LkSQwGAw6Hg507d3L99deTkpLC008/zd69e/nhhx8YMWKEd579+/eTlJTEiRMnuOeee3jxxRfZtWsXGzZswGazMWbMGOrXr8+5c+fYvXs306dPLzLGqKbTpYtvAP6yZfDoo6UbQ5IkWodZ2Z5s47hdJSag+AvPpZJraXHZVdwuFZO5Zl+kqiqy2eoNilJcdowBwZW9JIFAUMXw5GQCO2pTseXc25DRixJH51SdN13EfUvx6GIlPcmNmiNQZINEWLSJ0GgTBmPFWVTmzJlTIMtpcTd+yxtRXLISqEnFJQWC6oymQevWkFse57rroCzFfZ2KynfH01A1uLaOgXCTf4XE6X9sOGwK9ZtbCQovWS0fQfkT9+0sFKeNJkPvxRJRt7KXIxAIqgguNSe43qZXrQcwSdA8UKZ5oIylFHEmaYkuUuKdABjNenB9SGTVCa6vyOKSwtIiEAhqLZIErVrliZaNG+HwYWjTpnTjWAwyTYPMpDg9uCogPj46xorRJCGLwpaVisEaiOK0oThtlb0UgUBQhXCrcDgnWUqgAVoGyjQJkDGWwArvtCuoikZAsL5FD400YUv3EBJpIjii+hSC9AdCtAgEglrNtGmwZo3+u6bBlCnwyy+6oCkNV9QNwijpMU6FxQ+VJzXdb7m6YAgIgvRkFHt2ZS9FIBBUIufdKqkujZY5BRuDjBJtgmRCjBINLBLyRS4omqbhyFZIO+vClqFgtso0bm9AkvSbUw1bl7wwdk1GiBaBQFCrGTRIt6zkJoT79VdYsABKmwU61y/ZbDb7XbTkR9RrqTyiegwAwBQcVskrEQgEFY2m6cUfY7NVUt26D1g9i0xwToxJu+CL113TNA1buofz51w+aexNVhlV1TNdCvIQokUgENRqZBmee843/fFDD8HQoRAVVbYBj9s1Ik0Q4scAyex0D+fPOLEEGohuavXbPIKisYRHV/YSBAJBBaNqGvEOvXp9Vk4MugQ0tkqUJszElu4hOd6J26mLFUmCkEgT4XXNmIQ1vVDEuyIQCGo948ZB+/Z5zzMy9DouZeHvJBt7MjwcyvJ/RhWnTSU73VNoem+BQCAQlC/pbo1fkj3sytAFi1HS41UGRBnpHmYksBRxhhrgdqrIBgivZ6ZppyCim1qFYCkG8c4IBIJajyTBV1/5tn3+ue4qVlrahunFJs84NdLc/hMTASEGJBkUt4bLXgHR/4ICuDLPk7p3C2mH/q7spQgEAj+R/6ZQkAE8GlhkaB8sMzDKSMcQAwEXESset0rKaSdp51zetsBQA9FNLcR0CiaykQWjn7NO1gTEOyQQCARA9+5w882+bfffD4XUDi2WcIuRZiFmAL9aW2RZIiAkp9Bkuig0WRl4stJJ3bOJjNhdlb0UgUBQzjgUjf2ZClvO5xWrNsoSvSIMDIgy0jrIcNEaKy6HStJJByf3ZZN2zsX5s05UJa+wZGiUWWSBLAVCtAgEAkEOX38N+WuMHjkCr79e+nG6RAQgAYkujRQ/5kAOCtOjNLPShItYZWAICALA4xDZwwSCmkKWR2NXhof1yR6O2vQg+5R8VvMIk4zhIslPHNkKZ4/ZOXUgm4xkN5oGliCZujFWJLHzLjPirRMIBIIcAgLg44/1oPzcrC2vvw6HDpVunBCzgZahupvYoSzVb4IiKNyEJIPLrmLPrLiqxAIdo1UXLarTjlaBVaEFAkH5k+bW+DvNw4YUDyftGipQxyRxZbiBSFMpikGec3H6HxvZaboFPDDUQMM2ATRqE6h/Z4tsj2VGiBaBQCDIxw03wJdfwvTp+nO3W3cTU0tpMOkUYUUGUtwayS7/iBaDUSIk0gToVZMFFYtsCdDTzwGKU1hbBILqSqpLZWOqhwRnTupis0TvCAPX1DFSzyIXKzQ0TUPx5H3HB4YZQYLgOkYatw+kQatAAoJrd1HI8kKIFoGgBqMoCvPmzaNLly789ttvlb2cakNqKrhceQUmN27UA/NLQ5DJQOswC/WtBix+9FkOr2smMNRARD2z3+YQFI4kSV5ri0cUmBQIqg2appHlye/yJRFqhEZWievqGLkywkikufgtsqpqpCe5OLk/m+RTecGPZqtMs87B1GsWgCVAFFopT4RoEZSZP/74g7vuukuv2CrLfJ5vV7d06VICAgIYPXo027dvZ9euXYwfPx5Jkpg0aRKPP/44jz/+OFOnTqVOnTocP36cJUuW0LNnT0JCQtizZ4/PXCdOnOC1116jSZMmrFixwlu8z+VyMXPmTMaNG8e///1v7r77bsaNG8fBgwe955Zl3LNnzzJjxgxuueUWn/7Tpk2jY8eOjB8/nttvvx1Jkhg8eDB33303V111Fd26dSv0vTpx4gTjxo1jypQpDB48GKPRSFS+IiCapvH8889Tv359oqOjmTp1Kna7vdR/kwuRZZlevXqxd+/eSx6rNuFwwIcfQn6vrsceg8TE0o1zWVQg/RuHEebHrDAmi6zfyQsRZbcqA0OOaFHsWZW8EoFAcDFUTeOUXeW3FA+bUz14tLyg+GvrGOkRZiTsIq5gikcj9YyTE/uyST7lxOPSsGcq3gB70K3ggvJHXOUEZeaqq67iqquuon79+rz99ts0b97ce6xu3bpMnDiRjz76yNs2fvx4FixYwIwZM2jVqpW3/YYbbgBg5MiRDBs2jODgYG666Sa2bt1KvXr1AIiJieGJJ57gwIED3HrrrQBkZWUxcOBAevXqxYIFC5Bz3DQ2b95Mnz59+Oabb+jfv3+pxwWIjY1l7969ZGf73j1t3rw5O3bswGKxcPz4cZYtW8YTTzzBgAED0DSNF198sdD36uabb2bu3LlcccUVAGzcuJGxY8d6j3/22WcYjUbWrFnDn3/+ybRp07BYLLz77rul+ZMUQJIkOnbseElj1EYaNoQJE+Czz0BRdPGSlqYLlwULSj6OnGOqMZvNOJ1O/yxWUKmIYHyBoOrj0TRO2lWOZqvkZog3SnrdlUiz/j0tX8R9y+NSSUt06YH1uWOYJcLrmQmJNCGXprKkoEwI0VKJ9OzZE4PB13Q4depUpk6dWuQ5istd5DFJkpBNxhL2Bdlk8ulrMJuK7F8cM2fOZMuWLUyaNIndu3dz/vx5Pv74YxYtWuTTz2gs/OOWK1oAgoKC6Nu3r1dE/Prrr1iterVvk8nkM8YjjzzC8ePH2bhxo1ewAFxzzTWMGzeOUaNGERsbS3h4eKnGzR3jsssuY9OmTT7tY8aMwWKxFPo6JEkq9G+XlJTEnj17CAsL87Zde+213Hnnnd7njRs3ZtKkSQB07dqVI0eOsHr16kLnKS3Cj7ZsvPCCngL5xhvz2hYuhPHjYeDA0o3llgzszVSINEk09FPhsNwLqsEgEdGg8M+ooPyJ6t4Pul+PMTC0spciEAguwK1qxNlUjtlUckMLLTK0CJRpFiBfNGVxfjJT3aQn6vsqc4BMeD0zwRG1K1Zlzpw5zJ0716dNqcAkJEK0VCLbtm0jNLR0F7odz88q8lhY2+a0mTDC+3zXy/+H6i68fkNI88a0u3+U9/meN+bS/bl/lWotuRgMBr788ku6du3Ko48+SkpKCh9//HGRIiU/sbGxxMfH07dvX29bTEwMr7/+Otdffz333ntvAfEDkJmZyZdffsnIkSMxmQqKrSFDhjBr1iy+/vprpkyZUuJxL3xdF1K3bt1izynseGRkJG3btqV///58/PHHXpezu+++29snv3ADaNmyJc2aNfM+T09P59VXX8VqtbJ//34iIyN57733MJvNzJ8/nw8//JBXX32VpUuX8uOPPzJ69Ghmz55dYC3nz5/n7rvv5scff+Sdd97h0UcfBXQBeOTIERYuXEh4eHixr7G20KCB/vjXv/SMYrk88ADs3atnGispxzJdxNlUkg3QwCL55SLntCmkJ7qRDRBWV+T+ryjMYZGVvQSBQFAEDhUOZetmkUCDXr2+acDFUxaD/p2qqWAN1vcCYdFmHFkKodF6HGFtEiu5TJ48mcmTJ/u0ZWRk0Lhx4wqZX8S0CMqFVq1a8dZbbzF79mwGDhxY7Ob+7bffZsaMGTzyyCNcf/31hfbp1asXX3zxBV9//TUvvfRSgeOHDh3C5XLRpEmTQs+PiYkBKBDLcbFx/YEsy6xcuZJ69epx6623cs011/D777/Tvn37Is/ZtGkTTzzxhPf56NGjueaaa3jppZf49ttvOXjwIJMmTcJoNHLrrbeyf/9+1q1bx8cff8zatWuZM2cOR44cKTBuREQECxcuJCoqyusiB7oVbNasWUKwFMKrr0L9+nnPjx6Fp58u3RhtwywYJchU4JyfMokFhhkxWSRUBTJSirayCqon9tOJJG/ZSeY/xyt7KQJBlUTVNBIcKv/kFPXdsUPm+69NtAqUuSzUQL9II80DDRcVLPYsD2dibcQfspF0yuFNWS8bJBq0CiQorHZZV6oSwtJSzbjspX8XeezCf6JuxVhOLvx/6/LkfZe0LoDQ0FCaNm3K66+/zpgxYwgJCSm03+OPP+6Nabn5whLk+Rg5ciRHjx7lmWeeoV27dowcObJAn6LqX+RaeQqzlpRk3PKmZcuWbN26lXnz5vHCCy/Qt29fxo8f741lyc/evXupW7cu1113HQB79uxh9erVLFmyBND/zg8++CCjR4/m3XffpX7Ojvr2228nLCyMHj16AHDmzBlat25dYC2hoaFMmzaNN954g7Fjx+J0Ojlz5gwt8ldVFHgJC4P33oMxY/La3n9fdxEbOrRkY5gNMq3DrBxMc3AkW6WeufytLZIkEVbXTPIpJ+mJLsKiRT2AisCdlUZm3H4ko5GI9lf6bR7byTMk/7aN0E6tCGnbzG/zCATVDaeqccKuctymku2CbWvN/PWVlW1bjQQEaBwc6qFO4dsRL5qmYctQSDvnwpGV5+5ktspoKkgiCViVQFhaqhkGs6nIR/54lov3NRXoeyns3LmTv//+m99++42kpCSv29HFuOaaa3zcoC7kqaee4p577uGee+5h27Zt3vZ27dphNBqJi4sr9LyEhASAIrN5FTWuP1AUhbi4OAwGA/fffz+xsbFMmDCBL7/8soALV3p6OvPmzePtt9/2tuVmQnO78+6e54qRU6dOFdiY5oogtZjCIg8++CAnT57k+++/Z8mSJYwaNarIvrWdrCx4662C7ffcA2fOlHyc9uF63Zbzbo1Ut3+sLSGRJmSjhMelkX2+cNdQQfnisWWRumcT6Yd3+nUeY3CgPl+Wza/zCATVhfNulR3pHtYlefg7XmPpJ1Ye7RfGRw8Hs22rfh202yUWLix+f2PL8BB/yMbZo3ZdsEj6d2nTDkHUax4gXG2rEEK0CC6Z5ORkXn/9dd544w2aN2/OzJkzmTdvHqtWrbrouSaTiWbNmrFzZ9EX/NmzZ9OrVy+f9MMhISGMHj2adevWFZoaeM2aNdSrV69YK0ph4/oDt9vtEz8THBzMZ599RkxMDIcPH/a222w2Zs6cyWuvveZjfcm1gFyYrtlsNtOmTZsyrSksLIwHH3yQV155hVWrVjFs2LAyjVMbCA6GfNmpvSQl6UH5JS06GWCUaRGqB8gfyS5lpcoSIssSYdF5xSaLskQKyg9jQG7K42y/vt95ouXSU6ELBNWdeLvKplSFP/fKzH02kIf7hPPN24GknMkzibRvrzBrloNJk4p3l9VUDZddRZIhrK6JmI5B1I2xYvJT0hRB2Smze9ilupJIksTRo0cvaQxB5ZOdne1NbZybVWvq1KksWbKECRMmsHv3bm/sRG6GiQsv7PHx8fzwww90794dRVG8tVJyMZlMfPfdd1x11VU+7R988AE7d+5k2rRpfPLJJ94MYtu2beO///0vS5cuJTg42Dt3ScfNRdO0Yjchua/nwnELY9asWdx+++20bdsWgNTUVLKzs72CKTMzk0ceeYSHHnqIpKQkzp07x65duwgJCWHgwIH06dOHjz76yJuwYPPmzUyePJnw8PAi39fc5xf+zOWRRx7h/fff57bbbvPJviYoyDPPQEoKdOkC//1vXvv69fD22zB9esnG6RBu5WiGk0SXRrpbu2g9gLIQFmUi7awLp03FkaWI+i1+Jjflsaa40TwuJJN/MrcZg/XMD8LSIqiN2BUNp6oRbpJRVdj9m4k3ZwWyZ4uvFUWSNAYPVnjgARd9+yoFXOFVRSMj2a0LlGi9IG9gmJHIxhZC6phEfZUqTpmvZpqmcc8995T53C+//LKsUwuqCIsWLeKll14iOzubkydPeoPiDx06hCzLnDt3jgEDBvDaa69Rv359/u///g/QY1py3ZuysrL49ttvWbZsGSdPnuTLL7/k999/59tvv+WWW27xZgYLDw9n5cqVfPLJJ97569Spwx9//MEbb7zByJEjadq0KS6XC5vNxqZNm7xxM6UdF/RA+F9++YXDhw/z7bffMmLECJ+N/cGDB72vZ/78+URFRXH55ZcX+V4lJibStWtXbrjhBurXr8+JEyf49NNP6devHzabjeuvv57t27fz2Wefec8JDw/nTI7/0bfffssDDzzAHXfcQfv27dE0jXfffRdVVb1rX7p0Ka1bt/amSl6yZAmdO3f2xsIsWrSIdu3a0aBBA0DPanbzzTczceLEEv7Fay/XXgvbtun1WuLiYP9+3dICuqDp2xdySvAUS4jZQItQCwZNxSL75668wSQTmmNtMVmEGPU3stGMZDSjeVx47NmY/SZadEuL6nCiejzIJcjOKBBUZzRNI8Wtpyw+49QwOeDEKjOzZ1s4etT3uy04WOPOO93cf7+Lli0LfrcqHo30RBfpSS5UBWSj5K2tIkkS4XXNFfWyBJeApJXRnt2vXz82bNhQ5okv9fzqTEZGBmFhYaSnp5c65bFAUF5kZmYyffr0AoJNUDwJCRAYqMe5vPaa3taiBezcCSX9d9Y0jexs/7oTCSqOEz/Mxp15nkYDxhJQr6lf5tA0jcNvzEdTFFpMHYM5/CKRxQJBNcWjasQ7VOJsKpkKJJ6UWbvQysZvLdgyfS0hzZqpTJni4s473YV+/xZWENJkySkIWceEJApCXjK5KY8rYk97SZaWS0FcrAWCymHFihVkZGTw22+/8fDDD1f2cqodDRvqP6dPhw0b4M8/4dgxvX7LwoUFM/MVhiRJWCwWHA6HfxcrqBAM1iDcmefxOLL9NockSRiCA/CkZ6Fk2UCIFkEN5IRNZX+WgluFg38ZWfuFle2/mNA03y/W667z8MADLgYPVigkSSigp35POumAnO2mOUAmor6ZoHCRsri6UmbR8s4771zSxJd6vkAgKBsLFixgy5YtvPfee3Tt2rWyl1PtSEqCl16CZctg1Sro0wcyM2HRIhg8WA/OvxiappHqgX1pCl1CZKx+yE6jaRqObL3gZFQTC0aTcBXzF8aAnNg5u/9EC0Cj4QOQTEbMdYSFXlAz0DQNBTDmigg3/LLEzLovrRw/5LtFtVo1Ro1yM2WKm44dC09mommaV5BYA2XQ9OKQEfXMBNTSgpA1iTK7hwnKjnAPEwiqL/femxeMP3UqbN4Mu3frz4OCYMcOuFhSN03TWHs6k2SHh1aBMh1Cyr8IgKZpnD5sw5mtElHfTJ2G/om1EIArIxU0FWNgKLJJ+MYLBBfDpWqctKsct6s0tspEOYzMm2di7lwTSUm+N1gaNlS57z4399zjJjKy4JZV0zQcWQrnz7kwGCXqNQvIm8euYA4QRVb8SbVwDxMIBILayPPPw1dfgckEqal5ggUgO1svQvnHH2AuZu8qSRIdI6z8fiaL43aV1kEypnL2rc4NLj0X5yA90UVolAmjWVhb/IE5tE5lL0EgqBakuzXi7Aqn7bqFJeGozKdfWNm43ILD4fsd2LOnngXslls8mAoptaJpGrZ0hbRzThw5aeQlCZTGmjcLmBAsNYsSX8FSUlK48cYbCQ4Opnv37mzZsgWA2NhY3nzzzWIrm9cEkpKSuPHGGwkJCeHyyy9nd/6dSj7S09OZNWsWCxcu5Ndff63gVQoEAn/TrJnuCnbkiP7zwlJAO3bA009ffJxGgSbCzAY8GhzyU92WoHAjliA9RWjiCYeIJazmOM4mk7x5B+n7Yit7KQJBqUh2qfxx3sPvqR5O2DT2/mnkg/uDmX5DOGsXW72CxWDQGDHCzS+/ZPPLLzZuv72gYNE0jcxUN/EHbZw9ZtcFiwShUSaadAgSaYtrMCV2Dxs3bhzR0dEMGDCAEydOMHfuXF599VWGDRvG2bNnadSokbdeRE3kkUceYciQIZhMJqZOnYrBYGDv3r0+fY4ePcqzzz7LRx99RGRkZJFjCfcwgaDmkJwMnTrBuXO+7atWwQ03FH/u6WwXv53JAqBHmIFGfihm5nKoxB/KRlMhsrFFpPb0Ax57Fhmxu9E0lcguffw2T9rOQ5z9eSNBLZvQZPQQv80jEJQ3u9I9HMvU+PNnM+s/txK7z9fRJyRE4+673UyZ4qJp0+K3pWmJLlLinQB6vZUoE2H1zCJur5Koku5hDRo04O233/Y+v++++3jqqacICwvz1tyoqTgcDh577DEaN24MwH/+8x/uu+8+nz7nz59n/PjxfPfdd8UKFoFAULOIioK5c+FCY/Odd8Jff0HLlkWf2yjITMcIK/vPO9iVoRBqlAgp57uEZqtMZCMLyaecpJ52EhhiEC4T5YzqcpK6ZxOyyeJX0ZJbq0UUmBRUZRRN45RdpY5JJtQkcf48rPlvAHM+NZN01ldYNGmi8sADLu66y01YWOHjqYqGx61hzrmpE1LHpLu8RpoIjTYLy0otosSytGFOnk+nU1e3ZrOZd955h927d7N161b/rK6KYLVavYIFwO1288ADD/j0eeGFF2jRogWvv/46vXr1Yvr06SWqlC4QCKo3igIpKTBokG97SgrcdBOkpxd/fpc6AdQPMKJocMruHzex0CgTAaEGNA0ykt1+maM2YwzUs4epbieqx+W/eXJEi5Jt99scAkFZcakah7MU1iV72JOp8vtBjenTLXToEMyrL1p9BEv37grz59vZvTubBx8sXLAobpWUBCcn9mWReNzudW81GCWadgwiooFFCJZaRoktLa1bt+a9997jmWeeYcuWLXTv3h2AqVOnsnjxYp9q4TWZ+Ph4Fi5c6K0yDmCz2Zg3bx6vvvoq06ZN459//qFnz54oiiJSOwsENZikJBg4UA/Gb90amjSBU6fyjh88CKNGwU8/QVEFzGVJonf9YE5muYixSrhc5b/plSSJuk2tZKd5CI0uJKJVcEnIJguS0YzmceGxZfktMN9racm2o6kqUi257gqqNnZF46hN5YRdRdHg8A4ja/9rZetaE6qaJyokSWPYMA8PPujmqquUImtauZ0q6bkFIXM8xVQFVI+GwSTljCXESm2kxKLlpptu4sSJE7Rq1YouXbr4HBszZgzNmjUr77VVCI899liRQfUA06ZN48YbbwRg165dPPvss6xatYrrr7+ezZs3Y7FY+Ouvv7Db7YwbNw5JkmjXrh2jRo3ik08+4a233qo1gk4gqG1ERUGwfpOdI0fglVfg2Wd9+6xZA489Bh98UPQ4VoNMmzArAB6PB1Utf4uL0SwTJuJZ/IYxMBh3Rioee6bfRIshyKqnR9I0FJvDK2IEgspif6bCMZuKxwPb15lY898A/tnpu7UMDNQYN87NAw+4aNWq6HgVl0Ml7ayTzNQ8LxVLoEx4fTNBYaIgpKCUKY9jYmKIiYkp9NhVV11V7Lk2m43AwKr3BVsaS0i3bt346aefWL58ObfddhvLli1j7NixnD17FoCgoCBv3759+/LZZ59x+vRpmjRpUu7rFggElY8kwTvv6PVa3n4b+vbV3cLeew+GDoW1a8HjgVmzoH17mDLl4mMaLVb+dzaTZgESUX5KUawqGmnnXITXNyOXc6rl2ooxQBctii3Lb3NIsowh0IqSbceTZROiRVDh5Lpo5QqIzBSJ5fOt/Pa1leQL4lXq1VO5/343997rok4JdLzLrngFS0CIgfB6ZgJCREFIQR4VVqfliiuuYN++fRU1nV8ZPnw41113HQkJCQCEhIQAejB+rnCpW7cuABEREZWzSIFAUCFceSVs24bX1eGNN6B3bxgxAubPh0mT9PYHH9RdyPr3L368A2lOEhwqKS64ro6E1VD+F+wzR+04shQURSO6ibXcx6+NGAP064DH7j/RAmAMCULJtuNOz8JaP8qvcwkEuWiaxlmnRqxNpUWAzKndRubMMfH990bcbt/vqI4dFR580MXtt3uwFFHTVtM07JkKqqIRHKG7rAaFGwmNMhESacIaJJKFCApSbqIlMzOTN998kx07duBw+NYDSExM5ODBg+U1VZUgPDzc6yZ31VVXYTKZ2Lp1qzdgPzU1le7duxOc6zsiEAhqLPlvBJpMcNtt+u8TJ+pxLe+8owfs3367nlGsTZuix+pcJ4DTNjfpLoW/0xWujjAgl/Odxoj6Zs7E2slIchMUZiQwVNQZvlQMOcH4HnumX+epP7g3stWCObKIVEsCQTmiaBrxdo1Ym0JqFvzvRwu/fmXh2AHf7wxZ1hgyxMPkyW769i06XkXTNLLTPaSddeG0qRiMEoFhRmRZQpIkopuKmyiCoim3K9WoUaNYu3YtdevWxXKBtM7IyCivaSqFjIwMli9fzvDhwwkNDWXPnj24XC4G5aQLioyM5OGHH+aTTz5hxIgRACxfvpxnnnmmMpctEAgqiVOn9KB80C0v27fDb79BWhrceKMuXIoywhpliWvrB7M6PoNUt8aBLJVOIeV71zEw1EhotImMJDeJJxw0bhcoahxcIuFtexDaogvGwBC/zhPQuJ5fxxcIANyqxnG7yjGbyok4mV8WBbDxWwvZGb7fE5GRKnff7ebee93F1lfRC0J6SDvnwu3Iq14fFGFEUylFLltBbabExSUvRoMGDdiyZQstWrQocMzpdNKyZUvi4+PLYyoANmzYwLvvvssVV1zB888/X2S/n3/+mddeew1ZlnG73UydOpU777yzVHMdOXKEQYMGkZ2dTd++fWnZsiVPP/201y0M9ODZ6dOnY7PZCA0NJSYmhqlTpxY6niguKRDUTOLj4bnnYMEC2LoVLrsMVqyA8eMhKwtvJpz+/fXikxdWes7PqSwXG8/6r/CkqmrEH7LhdqhYgw00bB0gfMcFAgEAm5I9bFhvZN1CC3s2Fkzg0aOHwuTJLoYP92C9iHHEluEh6aQDj0v/ApQNEBplJqyuSdwsqQFUyeKSF2PQoEGFChYAi8XCypUry2We48ePs3DhQubPn09cXByXX355kX3nzp3Lww8/7E3RfOLECXr27El8fDwzZswo8ZytW7cmLi6u2D5Go5F33323xGNCyS1QFoulgPVKIBBUPb79Fj7/XP/9iSfgl19g9WrIzPEYkmVQVb393/+G//s/inSjaBJspkO4lQNp/ik8KcsS9VsEEH8oG0eWQsppJ1GNhWtGdeD83/uxnTpL3X5XYArzr2VHUDvI9GhYZcg4L7FwoYnZc4OIP+krKCwWjdtu83DffS569Ch5hkODUcLj0jAYJcLqmgiLNiP7IVZPUL44nU5vbcbiyMz0r0tsfspNtAwdOpQDBw7QoUOHQo8//fTT5SJcmjVrxrPPPku7du244447iuwXGxvLv//9b6ZMmeKtKRMTE8O0adN49tlnGTBgQLGCpyIoaVaxF154gRdffNG/ixEIBJfMAw/Ahx9Caqpev0VR9FTHu3fDn3/qgiUnYy2ffgodOsBDDxU9XtfIAFKcHtJdCh5NA8r3Qm+2ytSNsXIuzkFWqoeI+poo1lZGVI+btIN/4bFnE91zkF+tVul7DuM4k0RI22ZCtAjKjKZppLr1Git/bJfZ+o2VdSvMOBy+n92mTVUmTXJz111uIiOLd85xOVQykvVaU7k3QSyBBuq3CCAg1CCyFVYj3nnnHWbOnFnZy/Ch3ETLzTffzPjx4+ncuXOBY4mJiaxdu7a8pgKgzkXy573xxhs4HA5vjZVcBg0axDPPPMObb77pUyCyMjh16lSJTGnCyiIQVA8sFli2DJo1g/Bwvc1g0C0wPXrAuXN5LmIA06ZBdDSMHl34eLIkcU09PcBbczvxeDyFd7wEgiNMKB6NoDCjECyXgCTJpO7ZDEBklz4YrP5LR2xtGI3jTBKOhCRCO7T02zyCmomiaZx2aOxLVFj3g4Xfllg4urvgdrB/fw+TJ7sYNEjBUExYnapqZJ/3kJHixpGl6I0ShNc1Y8xJ2x4ULpJ9VDcee+wxHnzwwYv2y8zMpH379hWwonIULRMmTGDZsmUsW7as0OPlfdepuIKNqqryww8/ABQohNmtWzdkWWbVqlUoioKhuP9EPxMaGipiWgSCGka3bgXbGjWCpUuhXz+9bksuqgrjxulCZsyYwsezGvXvOs1gxWaz4fQomMr5bmVYtCg6ealIBgMGSyCK04bHnulf0dIgGgB7QqLf5hDUTA5mKqzaKPPLUitbV5lx2n2/S8LC9EKQEye6aN36IlYVu0J6spusVDeqktceGGogNNrsrV4vqJ5UxdCEchMtq1atYtasWQwcOBDrBVFZCQkJ3HzzzeU11UVJSEggMTGRgIAAoqOjfY4ZjUZCQ0NJS0vj6NGjtCku96hAIBCUE336wLvv6rEsAEajLmBUFe68UxcuY8cWfb4kSZz1GPg72cmV4QYi/VR4Muu8G1uGQnRTiwjMLyWGwGBdtNiysET4L8tXQEP9uuY4m4ymqkjF3MQT1G40TUOSJM6ckVi82MS8L4zExxW8Wdupk8J997kZOdJNvjrZxZKdoZCR5AbAaJYIjdRrrBj99N0kEJSbaBk2bFiRZqSYmBjefPPN8prqopw7dw6gSCtGSEgIaWlppKamVtiaCqNnz54FLD1Tp04tMuuYQCCoXrjdMG8e1K2r12558EG9EOWCBbpgCQ2FjAxduNx1V56AKQxN0zhr9+DR4O90xS+FJ91OlXNxDgAsATJhdYUFpjQYA0JwnU/0e4FJc2Q4ksmI5vbgSk7DUrcE5cYFtQqPqhGXobL0JwPbVlj5db0RVS1oVbn9dj1WpXt3tdjaKs5slYwUF4GhRm8xyJA6Rlw2hZBIk6hcX0uYM2cOc+fO9WlTFKWI3uVPuYmWgQMHkp6eTlhY4QWvcoVERZCb7cBsLvyCm+sXXtTximLbtm3CPUwgqKGkpkLPnnDsGMTE6PVZLBaYPRv27YOdO8Fmg6FD4eefdcFy9926xeWuuwqOJ0kSV9YN4rxLId2lsD1d4apyLjxpsshENraQEu8kOd6JJdCANVhUpi4pxpwCk4rNv9l0JFnG2iAa+8kz2M8kCdEi8JLl0diwCxYvNLFphYWM1IJWj+uu83DnnW5uvtlDQEDRYykelcxUDxnJbm9tFbczr4K90SRTr3kxAwhqHJMnT2by5Mk+bbkpjyuCchMtzZo145VXXuGmm24qcOzcuXO88cYbpUozfClERUUBYLPZCj2em2q4bt26FbIegUBQ+6hTR698f+wYnDgB69bpwiUgAJYv12u1fPCBLloefFBPf5xfuIwfX3DM3MKTq06lk+LW+CdLpX05F54MizbhzFbIOu/hbJxdFJ4sBYYAXbT429ICENAgGvups3gys/0+l6Bqo2kaR5M1vlhi5KfFFo7uKbi1a9xYZdw4N+PGuWnWrPhYFVumh8xkN1lpHsjpKkkQHGEkJKqY4lICgZ8pN9EyduxYEhMTC61VkutTWVG0bNmS4OBgUlNTcTgcPjE2mZmZZGdnExUVRcOGDStsTQKBoPbx2mu6G9irr8IVV+S1x8TAgQOQa+z96CO9hstHH+mC5Z579J93311wzFCzgV71gtl8NosjNpVoi0RUOfqQS5JEdFMrTrteePJcnEMUniwhxkA9/bDHz5YWgMje3Yi67nJkk8jKVFvRNNiyxcCXC4x8t9yE64JUxWazxo03erjrLjd9+xafASw/58+4vFnAzAEyoVEmguuYMIjaKoJKpty+7QYPHkzr1q1p0KBBgTiNU6dO8dprr5XXVBfFYDBw0003sXjxYnbv3s2VV17pPbZ//35Aj8EpLgNZRSBiWgSCmk337rqFpTDye6dKEsyaBSdPwg8/6JuRCRN0y8uECQXPjQk2cybUwtEMJzvSFfpGSpjLMaOYbJCo38JK/CEbjiyF1AQXkY2qVhaZqkhw4zYERDfxuon5E0OAKARaWzl+FuZ8aeTnRWaOHSuoRDp3VrjrLj2ovrjqEJqmYctQyEx2E9XU4rWohkWbMFt1sWIJFO6hgjxqTEzL2LFjGTRoUJHHS1r9vaSoqu5fqWmFmzmnT5/OkiVLWLFihY9oWblyJUajkenTp5fresqCiGkRCAS5vPuuLljat4eDB3XhMnGi/vPeewv27xEVSJLdTQOrjD/Kq5itBm/hSU3TKtxiXh0xWAP9mupYUHtRVY2ffpeZ95mZzatMeNy+/4vh4RojR+pB9V27Fl+t3u1UyUhxk5niRnHreyhLqoGIevqdlOAIkzduRSDIT42Jaenfvz8vvPACdrvdmyksPj6exYsXM3DgwHLPHnbq1CnvHIXRrVs3XnrpJd566y0mTZpEy5Yt2bt3Lx999BFvvfUWHTp0KNf1CAQCQUk4exbq1/dtO3oUnnpK//3gQejaFXbvzhMumZl6quT8msEkSwxpEoZB0uP3cm/klCfBESZMFlncba2inP97P+l7DhPevT3h3dtV9nIEfiApVeOTBUa+/sJCfGzB/8O+fT2MH+/mxhs9WIsxvmmqRnaaXgDSnpl3Z1w2SoTUMRIUJtwMBVUfSSvKVFFKnn76aWbOnEnjxo05efKkt13TNMaOHcvjjz9Ojx49Lnme06dPc9ttt7F7924cDj01Z7du3XjzzTcZOHBggf7//e9/+fjjjwkKCkLTNB577DFuueWWS17HpZCRkUFYWBjp6enC0iIQ1BIOHoTnnoNVq+DIEbgwpG7ZMhg5UncJAz0GZuvWvOMTJsAnn+gZyC5EURSysrNxqZR7GuT8aKqGBsjlXNyyJpERuxt7UjxhbXtgrVP/4idcAkm/bSNly07CurShwU19/TqXoOLQNNi+XWbOZya+W1YwVqVOHZW77vJwzz0uWrYs2RZO8Wic2JtF7o4vIMRAaJSJoDAjkvh/FlwCuZaWitjTlptoadWqFS+//DKDBg0iMjLS59jvv//OSy+9xC+//FIeU1V7hGgRCGofjz4K772n/z5lii5ALuSLL/Qg/Fz69YMNG/KeX3klfPddQcGT5VbYeCYTVVW5po4Rgx/cuDwulXNxdoxmmbrNrMJVrAgSfl2KLeEo0T0HE9amu1/nyjx8nNNL12KOjqDF5Dv8OpfA/2Rkaiz4xsTXX5jZvbugVeXq3h7uneDmlls8hd68yEXTNBzZCvYMhToN8zomxzuQZYmQSN2CWpNJTpZwOKBx43LZ4gqKoSJFS7nZAxs3bsyYMWMKPWYymdi2bVt5TVVjEIH4AkHt4amnYO5cCArS3b8K4+67dVewhx7Sn2/YoMezLF4Mdjv89RdcfrkuXHr1yjtPliRsHg2nCoeyVDqWcxpkALdLxZGtQraKNdhNWLQoPFkY5rBIbAlHcWWk+H2ugAZ62n5Xchqqy41sFnEI1ZG/dkl8Os/Ez9+ZsWf53gwIDdUYO9bFvfd6aNeueBdQTdXISvOQnujCadP7BoUbve6dUY1rfvKGI0ckPv7YzKJFJm65xcPcuY7KXlKNorID8cvN0nLDDTewYMECoqOjfdpVVWX48OHs27ePo0ePlsdU1R5haREIaiebNsFll+nCpThefx2eflr/XZLgP/+BefP07GKgZx775BPfAP1TWS42ntXrg1wVbiDaD3dS0865SDntBAkatQnEGiRiXS4kPXY3SX+tIrBBcxr2G+X3+WJnfYUnM5umd91EYNMGfp9PUD5k2jS+WGpkwX/NHNxR8P5xjx4KEye6GDHCQ+BFcjsoHo2MZBfpSXmB9ZIEwXVMRNQ313irCsC2bTLvvmvm55+NaJou/IxGjb17s2nUSFhb/Em1tLQ8+uij9OnTh2eeeYbu3bvjdrvZuXMns2bN8gbACwQCQW2mT5+S9XvqKUhPhzfe0P3bX3xRt9J8+SX8/ju4XHqA/s6detYxkwmaBJtpHWrhSIaTHRl6GmRLOfuqh9U14chWyE7zcC7OTuP2QaJ2wwWYQ/UcsxVhaQGwNowm659s7AlJQrRUcRQF/vzTwOLvDCxbYiI73VdMBAZq3HGHm4kT3XTrVrLEGo5shYTDNm+sisEoERZtIjTKhKEWFYXdtMnIypV5lsbgYI2773ZjEsbHGkW5iZZBgwbxwgsv8NBDD3H+/HlA96sMCAjg1Vdf5YEHHiivqQQCgaDG8/rrkJUFH38MderoFpq77tJjY3LvAX30EezbB0uWQHQ0XBYVyDm7mwy3yu4MhZ5hhnKNPZEkiboxVk7Zs/E4NZJPOajXLKDcxq8JmEP1mE5Pdgaqx41s9O+uKaBBNFn/HMdxJtGv8wjKhsMB6zYY+PFHI+tWG0lJKSgk2ndQmDRRr6sSFlb8eJqm4XFrmHIKyloCZGSDhMEkEV7XTHBEzQ+sz8rSb9zkr0EzYYKLt94yExam8cADLu6+2014eKUtUeAnyjXH3ZgxY7j11lvZsmULiYmJ1KlTh6uuuoqwi/0XCgQCQS3DbtdFx7XX6gH2F5JbcDIoCMaPh44d9fYPP9SLVj7wgH7h/u036NkTli+H7t0letcPZs2pDM46NU7YVZqVc7pi2SBRLyaA04dtZKV6CApzi5oO+TBYA5HNVlSXA3dmKpaIen6dz9owGmNoMMYgUR+mqpCWBmvWGFj2g4nffjHisBUUERaLxogRbu69180VV6hc7N6CqmpkprhJT3SjaRpNOwYhSRKSLNG4XSAGk1Tjk2OcPSsxe7aJzz4zM3asm5kznd5jERGwcqWNTp1Un8K9gppFucW0CEpObkxLmzZtRCC+QFALOXQI+veHhAS4/nrfDGEl5Y8/YMQIve4L6C5iTz+tu5Ydszs4nunkslADgbJ/vuJTTjuxZXio28yKJUDEtuQnfs0CHMmnqX/tCIKbtPHrXKLoZ9UgIUFi5Uoj3/9kZMsmA4qn4N8kMFBj4EAPw4Z5GDzYQ0TExcf1uFTSk9xkJLtQc+KdJRkatQ2sNf93Bw7IfPihmSVLjLhzimoGBWkcOJBVovdQUH4UFYgfGxtbtVMeP/zww3zwwQdlnvhSz6/OiEB8gaB243ZDhw4QG6tbVPbuzbOkXAxV1V3Ebr8dmjfXhUv+ei7t28PcuRpXXQ0SYLfb/ZLdRVNzAn5ruCtKWfDYMpDNAX53DRNULocPy/z0k5EffzSyfXvhAiIsQmXwEA+33eKhb1+FgBJ6U7rsCufPusg67/G2Gc0SYXXNhEaakGt4LJmmwe+/G5g1y8z69b5OQSaTxsiRHl54wUn9+uK+e2VTLQLx9+7de0kTX+r5AoFAUF0xmeCll/RYlJdfLrlg8Xj0APwvv4T582H9ej0w/+WX4c039eMHD0KfPhL/+he89hqEhgaSkJ5NAArGchQYF4oVRdFEUH4OxsCKvxmlaRqqy43BInxj/IWqwo4dulD56Scjhw8XLlTqNVYYPMzDyFs8XN1LxViGnZaq4BUs1mAD4XVNBIYZa7xVzeOBb7818tFHZvbs8X1/w8M1Jk50MXmymwYNhFipjZRrTItAIBAISsaYMfqjNCgKnDun/56ZCYMHw6+/wquvwqhRMGkSbNum36X8+GNYsULj8Zkuons5scgSbYMkmgbIyOW48dE0jfNnXaQnumjcLqhWpFetamQeiuPMzxsJbFyfxiMHV/ZyahSapguVZctMfPedkYSEwj/fnTsrDBnmYfBQN5d31S4ao5IfxaPHq2iqRkQDvRikJUgmor7Zp85KbeGllyzEx+e9zzExKv/6l4u77nITHFyJCxNUOmV2D5Nl+ZIVf0UWpKlKCPcwgUBQVmw2uPFGXawAREbq1paOHXVR8+GH8Mwzer9ceg9zMeaZbMKjNYIN0CHEQD1z+QTuappGwhE7jiwFa5CBhm0Cavzd4IuhuByk7PwNd1YaDfuN8vv7YTt1lpNf/oAxOJBWD9/p17lqA5qmx1EsW2bk229NHD9eUKhIskabyzwMGeZhwgiF5s1Lv5Vy2RXSk905gkWPVYnpFIzBWHv+f06ckIiJ8X3v3nvPzAsvWLjsMoV//9vFzTd7ymStElQM1cI97L///W95rkMgEAhqPefPc9HA0sBA+OEHuOEG2LIFUlL0oP6NG6FNG5g2DW69FaZMgTVr9HO2rDSzd7OJsTNsXD3CydY0hUiTRMcQA+GmS9sgedMgH8zGka374dfJuVtcW5GNJjKO7QFVxWPLwBTk3wya1vpRIEl4smy4M7IxhV6keqmgUGJjJZYtM7FsmZFDhwpaNwwmjc693fQZ4mL4UIXOTUpfC0lVNbLPe8hIduPIzrtxa7bKhNU1I9cCQ6WiwM8/G/nkExNbthjYuTObFi3yhMvdd7u48kqFq65SSmWxEtR8RPawSkBYWgQCQX62b4dnn9UD8w8coEQF0TIyYMAA3R0MoEEDWLlST4cM+t3ir77SRUxKvjqHLdup3Dg1mx6D3ESY4do65eMnn5HiJumEA4C6zayE1KndQegnfpyLOyOFhv1GEdigud/ni5v7Lc7EVBrdPoiQts38Pl9N4dQpieXLdYvKrl0FhYosa3Tv7aHHECe33OShU0OJcGPZrZTnzzhJPePyPg8MMxIWbSIgpHxrKlVF0tJgwQITc+aYOXEiT5098ICLN95wFn2ioEpTLSwtgkunZ8+eIuWxQCDgySfhl1/03z//HO677+LnhIbqlpR+/WDXLjhzBvr00YP7hw7Vs5Ldeace9/Loo7BwoX7e0UMyHzwUQssOCk89qyLf5ELTSlZ9u9j1RJp0d5dEN4knHBiMEoGhtfcSYw6LxJ2Rgis9pUJEi7VBNM7EVBwJiUK0XIRz5yRWrDCybJmRP/8s/DN6RS8Po+7wcMstHoIjVUwSmOTSxZaoqkbWeQ8ms0RAiD5PcKSJjBQ3oZEmQiJNGM0137Ry5IjEp5+aWbTIRHa2rzBr3VqhS5faGSpQHSkq5XFFISwtlYCwtAgEgvz8+SdcdRXExMA778Btt5X83JQUuPlm+N//9OfXXqsXnLzwpu2GDbo1548/fNsvuwyefdZNy6uyQYLmlxD0q2ka5+IcZKd5kGVo2jEIg6nmb8oKI3nnb6Qd+JOw1pcRfcUgv893fscBzq3aTGDzRjQdO8zv81U3UlPhxx9116+NGw2oakGrRvPOHq4a5uLqoS4GtJNoaC3bZ9dpU8hIcZOV6kZVIDDUQINWecU/a0ttna1bZd54w8K6dQWF4YABHh54wEX//kqtcImryQhLi0AgENQievXS41QGDQJLKcNBIiP11Md3361bXJYtKyhYQLfIbNmiW2eefz7PrWzHDhgxwkSLLsHc9rCdWwcrdAopW6IVSZKo28zK2aN2giNMtVawgG5pAXBlpFykZ/kQ0KAuAI4zybVmU3wxUlIkfv7ZwPffm/j1V4O3MGF+Grf2cNWNLq4c5qJtS5WYAJlGVhlTaWNVFI2s824ykt04bXmWS6NZwhps8Pmb1Ja/zcmTso9gCQzUGDvWzZQpbtq0uXTrrqD2ISwtlYCwtAgEgvJGVSEpCerVu3hfTdPjX154QRct+WnVzcOoSQ4euE0lNKhsmyuxaQZHcgLxa77EEBBM8xEP+n0+TVGJX7IaS71Iovr0QDbVznuS8fESP/6o11HZsqUIi0pzlRG3uanfz0HjNgqNrTIxAfIlJaU4fdiGIyu3ZD0EhRkJjaodsSoAcXESLpdE27Z5YsTths6dgzAaYfJkPWWxqGBf86hIS4sQLZWAEC0CgaAiSEmBRx6Bd9+FqKiCxzVNt/A8/zzs2eN7zBqoMXSoh1F3uOnfX8FcxpqFHrfK+bMuIhtZkMuxuGVVR3E5iFv6PoaAEGJunoxsrN2JCfzJkSMSP/xg4scfjezYUbh7Y2QDhWuGuZg2TuGyy/Q6KqkulVCjVOqiq7lWlaBwkzc9cUayi7RzLkKjzATXMWKsBVZGRYF16wzMm2dm3ToDQ4d6WLzY4dPnyBGJ5s01kbK4BlMjRcuTTz7JG2+8URFTVXmEaBEIBMWRkQGzZsG//gV16pRtDIcDBg6EzZuhVSv4+Wdo3brwvqoKy5frlpf9+wseDw/XuPlmN7ff7qFPHwVDCcNeNE3j9D82nDaVoHAj9Zpba8Vd51xUtxPZVLvTP/sDTYPdu2V++MHIjz8a+eefwj+QjZsrdB/oosdAFy26KJgNera84DLUQdE0DWe2qseqnNfrqkQ1sRAWbfYeh9rh+pWcLLFggYn5800+WcBkWWPv3myaNBH3wmsT1Va0HDlyhN27d2O328k/7JkzZ/jPf/6DLX+1s1qMEC0CgaAo1qyBceN0K8mMGfD662Ub58gRuO46PasY6LEv338PvXsXfY6q6kH8CxapfPstZKUXvFtct67K8OEe7rjDTc+e6kXrKNgzPSTE2kGDsGgTkY0ttWJjV1koDhe246cJbhODVIMinBUF/vzT4HX9Onmy8NfWvpNCt4FOug5006iVXucj3CgREyjTyFJ6q4rHpZKZqheAdDvz9jUmi0xEA3OtSe2taXpg/bx5ZpYvN+Jy+b6PTZqo3Huvm3vvdQkXsFpGtRQtb775Jk8//TSaplHYkJIkVWhatKqMEC0CgaAoTp7ULSIuF4SFwenTEFTGWoGnTunpj/ft059bLDB7th60fzEy7SoLv/ew6QcjP/wgFUhVCtCqlcq4cW5Gj3bTqFHRl5LMVDeJx3W3kchGFsLrldHXTFAsmqYR+8FClGw7MXffQkDjEgQ4VWGcTvj9dwM//aQLleTkQirTSxpX9FK45SYPN97oIbihwv/OKxglvLEqYWWMVVEUjeN7siDnoy3JEBxuJCTShDW4dsSqAKSnw7BhgezZU9CiNWCAh/vuczFoUMktsIKaRbUULZGRkTz44IP06tWLwMBAn2MpKSlMnDiR8+fPl8dU1Z5c0dKmTRtRp0UgEBTg0Uf1oPoXX4SWLS9trPR0uP12PcNYLg88AO+/T4njVLKzNb79zs0Xi+F/G0w4nb6bNUnSuP56hXHj3Nx4o4eAgIJjpJ1zkXJaLyBXW4pPZiccI+3gX5jD6xLdo3+FzHn6u/VkHjxG5DWXEX3d5RUyZ3mgqhAbK7N9u8yOHQZ27DCwd6+Mw1FQGBiNGj2v8XD5IBft+7ro3FSia6h+LdU0jdMOjfpWCWMpRIWmaTiyFRzZKhH5RPWZo3ZURSMk0kRwuBHZUDuEyoX06xfI33/r73FEhMZdd+lWlfyV7AU1n6LqtMTGxlYv0XLrrbeyYsWKIo+/+eabTJ8+vTymqvYIS4tAICgOTSs8bXFZcbvhoYd0K0suV10FS5dCo0YlWY/Gb2eySLC5IVvi2K8W1n5nZvOmgtG1oaEaI0a4GTfOzRVX5LmPaZpGSryT9CQ3SNCwVYC34F5NJevkIc5uWoElsiFNbhhfIXOm7TrE2ZUbsTasS7MJt1bInKVF0/QsX7o4kdm+3cCuXQYyMor+0FsDNC6/zk23gS4693UTFJq3dYk0SfSuU7bPktulkpXiJjM1z/2raccgTBY5Z621JxOexwM//2xk/XoDH3zg9PkOWrTIyNy5ZiZNcjFiROE3JgS1k2pZp+X6668nPT2dsLCwQo+73e7ymkogEAhqNOW9RzKZ4NNP4Yor9OB+p1MvMtmjh541rG7di61HonmImWSHB1eQRosbHTx8s4PpyUa2LDfzzddmjh/XN3kZGRKff27m88/NtGypcvPNboYO9XD55SqRjS143Bouu1IrKoGbQnTnfndWxXkZBLVoDIDjTBKKzYEh0FphcxdFcrLkY0HZsUMmKenif/8WLVSuvFLhpps8yF3teHIMICYJoswSUWaJaLNMUCndklRFIzvNQ2aqG3tmntu6JENwhO+2qDYIlrNnJT7/3MTnn5tISND/LqNHe7j66rz3ZswYD2PHeipriQIBUI6WlnXr1vHNN98wfnzBu0lnz57l/vvvF+5hOQhLi0AgKA2qCsnJFxcXJeHvv+G22/TYmSlT4JNPSn6uW9WIzXByKM2BzaPXYzBK0DJAJnmPma++MrF8ubHQ+JeoKJXBgxVuuMFN3+vchIXn3cl22VUsgTXPIV51uzi25F0Amt8+DYOlYgTEsTlLcSWdp+Hw/oR2uET/wlKgaXDmjMQ//8js2aOLlO3bDUUGzeenbn2Vtl0VmnZy07iTh1ZdFO5oZUDOEQ3HbAqKBtFmmTDjpYmJzBQ3iSfyUvNagw2ERpoIqkXuX5oGf/xhYM4cEz/8YMTj8X3d99/v4q23nJW0OkF1olrGtNSvX5/ExMRCv0hyzasiEF9HiBaBQFBStm3TXbtUFf78E8ojIVRysp6V7LXX9OD80qJoGicyXRxIc5DuUmgfYqR1oP7dn5UFP/xgZNEiE5s2GdC0gtcEi0XjuusUhgzx0LunHZPHTmiUiToNLd66FzWFuGUfojiyaXzD3VgjG1TInInr/yT1rz2EdWlDg5v6lvv4LhfExckcPizzzz95P48ckcnKuvjfLzxc47LLFFp28VC3vYf6Hd1E1PPdikSYJC4PMxBwiSLC7dSzfxnNMqGRehyVqmicPmwjKNxISB2T1xWsNpCdDUuWmJg718S+fb43CmRZY8gQD5Mmubn+eqVcvmsENZ9q6R42fPhwWrRoQVRUFHK+T7qmaZw8eZLXy5q3UyAQCGopmgaTJuUVfvz8c7j33ksfNyoK3nmnYPvq1XrmsosF/xskiRahFpqHmDltcxNlNWKRJVwuF3azk9ZDHSy4w4UnzcDatQZ+/tnIhg15FhinU2LtWiNr1xoBK00ahdC6pZu2rTxcfiX0uBKaNSsfgVbZmEIiUBzZuDPPV5hoCWrRmNS/9pB9LP6SYjLS0+HwYV2UHDmSJ1Di4uQCd+aLXEuQRpeuCh27KTTv7GFgL5W2LXQXyMNZCoeydYtdiAGizDLRZolIs4TpEgqR5rp/ZaS4vVXqTVaZkDpGJElCNkg0aV/GlHzVnMcft/LVV75JMKKjVe6+282ECW5RY0VQpSk30XLXXXdx9dVXF3k8KyurvKYSCASCWoEk6Vm++vWDDh2gRQv/zbV/v55lzGSCxYvhhhtKsj6JxkF5mZYsFgtHk5wkOjXOOBWizCoDb1cYO9aN0ymxcaOBVauMrFpl9PrOA5w6beTUaSMbNgLz9bagII2OHVU6dVLo2FGlY0eVDh0UwsPL93X7G1NIBI6keNyZFeceHdC0PvVuuIaglo0vKlg0DRISJB+rSa5AOXu25KpRkjRiYjTatlVp3VqhWRuVmM5uQpspnFc0PDl74ZAwA5Kkj9vQKhNgkIg2S1jLwS0r67ybrPMebBkeNDWvPSDEUCuy1V2IquqP/NXo777b7RUtPXsqTJ7s4tZbPWWyuAoEFU25FpdMTEzk2Wef5fvvvyctLY1WrVoxbtw4Hn30UazWyg8GrCoI9zCBQFAafvoJBg/WBYW/uOUW+OEH/XeDQc80NnFi6cdJc3o4kObgeKYrt7wFYUZoFWSggUVCliRvRfNVq4ysW2dk/34Zu71km9ZGjdQcEaPQoYP+e5s2aonTN1c0qfv+R/o/fxPWpgd1OhdT2dPPuFxw7JjstZzkFyglcenKJSBAo3VrldatVdq21d/7tm1VWrZUsVohyaWyLU3xipRccoPnmwfKRJVDEobcCvWWINkrzM4dt5OVqgeLmywSIXVMBEeaMNWCpA/5OX8evvrKxNy5ZqZPdzJuXF4AvabBK6+YGTbMw2WXqcWMIhCUjGoZ0xIfH0/v3r05deoUwcHBNGvWjNDQUI4dO0adOnXYsmVLkZnFahtCtAgEgqpGZqZedHL58ry2//wHnnuubNnMst0KB9McxGY4UXKuMvXMEldGFDTwKwocOyaxf7+BPbsldm6HQ/8YOX2mZM4ARqO+ke7QQaVrV5Vu3RS6dlWqRGXuik6Zm9+lK0+cGIiLk1CUkq8jKipPkOQXKE2aaKiSxnmXRopbI8Wl0cAq0SInkYJd0ViX7MEoQR2T7uoVbZYIM0qX/D543Cr2DAVbhm5NURVo3C7Qm8TBluHBkaUQGGbEEijXisxf+dm7V2bOHBNLlpi8NwEuu0zht99slbwyQU2mWoqWO++8k507dzJz5kxuuukmn2Nz585lz549fPjhh+UxVbVHiBaBQFAVUVV47DHdJS2XyZPh4499XUxKg1NROZzu5J80B93qWGho1ouRqTmXHrmI5C0uh4rTbeDgQZm9ew3s2gGHY40cPGggPb1km9FmzXQB061bnpCJjCzb66hKaBqcPi35CJNcl65z58rm0pUrUNq00d278r9PqqaR6NIFSqpbI82tkX/jcKEYTXdrhF5ihq9c3E6VzBQ3tgwPTpuvZUA2QHSMleDw2uf6lYvLpSe+mDvXxB9/FPwnvf56DwsX2gkJqYTFCWoF1VK0NGvWjK1bt1K3iJycgwcPZs2aNeUxVbUnV7S0adMGg8E3e8fUqVOZOnVqJa1MIBBUdZKS4JlnYMgQGD7cP3O88w48/nje85tugq+/hsDAso/pUTVkSRcpHo+Hg6k2jmR5aBNkoEmAVKh4ycVpV4g/aEOSIaKBhSyXmQMHDOzfb2D/fpkDB/TNu9t98U1y06a6S1nz5iotWug/mzdXiYnR/Op+V1o0Dc6dk4iLkzl2TP8ZFycTG6u/1sLSSheF1arSpo1WpEvXhTgUDYcK4SZ9DkXTWJXoIb9kCJAh0iwRaZKJNEsEl1PWN8Wtoml46/jYMz0kHLF7j5sDZILCjASGGn1cw2obKSnwySdmvvjCVECohoRojBvn5r77XLRuLQLrBeXHnDlzmDt3rk+boijExsZWL9HSv39/fvnllyKPd+jQgQMHDpTHVNUeYWkRCARl4fhx6N4d0tL07FoHDuC3ytSLF+vuYrl1gXv1gh9/1DOPXSqaprE6PoNUp57ZKVCG1sEGmlgLFy8uu0LiSQfOnExT5gCZ6KZWrPmqCrrd8M8/Mrt3y+zcqVdY37u35LEyBoNGkyaaV8w0aqRfGhVFrxSuKLolSlEkFAWfR27Ac+4x/fe8dmdyAgYti7DGjQkMDcBi0dM+6z/BbNaQJDh1SiYuThcox4+XfO25XOjSVTdjD1Fpu2nfrykNbig6UY5D0Uh2aaS4VZJdGtkKhBjh+sg8Fbcz3YMsQZ0ckRJYTvVMNE3DaVN1l6903ZoSFm0iqomupjRVI/GEg4BQI4GhBoym2hWfUhQpKRKtWwf5ZHFr105h8mQ3o0a5hWVFUGFUy5THQUFBHD9+nGbNmvm0u1wuHn/8cYKDg8trKoFAIKiVxMTA5ZfD+vX6ndZdu+Cqq/wz15gxejHL4cP1eJetW/XClCXJKnYxJEliYKNQjmQ4OXDejk3R2J2hcCQb2gQZaHyBeDEHGGjUJpDMFDcpp5247Cqn/7H51HYxmaBTJ5VOnVRv4LGi6PEdO3fK7NplYNcu3dWsMCuFokgcPy5x/LjMr79e+mv0JabcRpIkjWbNNNq0Kd6lCyDjkIWEZRnYT8QXOtahLIXTDpXsQkqoyegWFkPO36F7WLltF9A0zZvly5ahoF4Qte9x5T2XZIl6zf2kzKsBGRnw449GbDaJ++5ze9sjIzX691dYv97AsGEeJk9206ePUqb4M4GgulBulpY//viDW2+9lXHjxtG6dWuys7M5cuQIy5YtIy0tjRUrVnDjjTeWx1TVHmFpEQgEZeXAAXj7bb0wZP36/p9v1y4YOhReeAHuv7/8x/eoGkfSHRxIc+DIidiPCZDoGlr4Jllxq6ScdpKZkyXKaJZo2jGoxG5Cqqqn+M11t8p1vTp2TH+ekVH5uz6zWSMmRqV5c93y06xZriubRrNmhbt0FYZid3LkvS9B0wi6bzRp1iA6heS5VO1M93DKob/nYUaJqJwaKZGmS6uTUmAdHg23U/VaxjRN4+T+bK84kWQIzLGkBIYava5htRWnE9atM7J0qZ4e3OGQiIxUOXw428eF8dAhmZAQzWsVFAgqg2oZ0wKwYcMGpkyZQmxsrLetadOmvPvuu4wYMaK8pqn2CNEiEAiqE+npcGHyR1Ut3+KPHlXjcLqDg2kO+tQNJFhSUBTFJxYmP/ZMD0mnnIRGmgivVz75jjUNUlMljh6VSEqSkWUNWdZTQF/4kGWtkDbdzUyW8Tkv9cDfJO3bgal+B4La9sHlknA6yXnov3s80KiRLlIaNtQu6b11KHmZvfjmB8yJSZzt05uMtm24ro6RsJxYlfNuFadKuYsUj1vFkaXgyFKwZym47CqyDM26BnsF0/mzTlQFAkMNWIMMSOU4f3VEVWHLFgNLlxpZscJEWlrB9+O772wMGFCIWUwgqESqpXsYQL9+/Th8+DDbtm3j+PHjNG7cmB49emCuqgn0BQKBQHBRCstWf889ejzGyy+Xj8XHKEt0iAigbbjV65KkKArbkrI5a/fQxCoTbpIIN0mYZYmAECNN2hkg397OluHBnqkQ0cCMXIZNsCTpbjeRkRpQfjUsQt0BBCSmYI2Mo/Fl/q3VcsymsC8zb+2RjRoRmZhEWMJporq3I38oSkQ5x4eknXORkezC7Sx4L9RgklDcGkazvoCI+qKaIUBWFsycaeHbb30LruYSGakyYoSHkSPdXHGFqKsiqN2Uq2jJpWfPnvTs2dMfQwsEAoEgH4qiB82PHl32tMSl5a+/YMEC/fevv4annoJHHimfpACG/BYVSSbBrmBX4FB23oYtyKBntoo0STTLqdGhqRrJpxy4nRpZ591ENbESVI5xGJeCKUQvGOPOSiuX8ZyqbkVJcWkku1XaBRloYNU3vCE5WbxCjRBpkglv24Ts3buJNmg0DjEUN2yJ0DQNt0PFnmNJiWpswZAjflRF8woWc4BMQLABa85DBNAXTkAALFli5OzZvPcnKEjjxhs93HGHm+uvV6pUVjuBoDKpsG/0IUOGsGrVqoqaTiAQCGo827bBfffB7t16RrEHH6yYeePiICRED9DPytJTMM+eDTNn6uKpvIKBDbLETTHhHMtwkuTwkOL0kOXWA8ezFQ2HKtEiWEZVVZAgrZ4Fz3k3FruK+6id4DAjkY0tmCyVu2E2BeuiRbFnoXpcyMbSeR94NI0kp57hK9mtkunxPZ7i1miQE+cSaZIYHG3EkmNp0oLroT56NwZL2TweFI+G06bgcqhed6/8gfNBEUaCw/X3N7iOCUuQ7u5lKKcUyDWF7Gz4/nsjsbEyzz/v8rYbDHD77R4+/dTEgAEKd9zhZuhQD0FBlbhYgaCKUqaYlhkzZhAaGsrTTz/tbZs0aZJ+4SiEc+fOsXr1ahRF+GKCiGkRCATlw59/5mUPi46GEyf8lwL5QhIT9eD8OXN0f/xcrrwS3n0Xri46w+4l4VRUUhweUpwKgUaZlqEWPW2uR2HZiQxvP0nVCHBrBLhUwk0SjSKNhAfr9+k0TcOjgaLpTmCq93cNVQOLnFd3xK1qJDg0VDS9T75zNCDCJNEwx8rhUTX2Z6mAhpZzPPeRffoowbYUOrfvhCkoFEXT2JameI9zQf8os0T7YN0yku3R+CXFV6mEGCDSLHuD5y1lcIfTNA3FreF2qXicuT9VQiJNBITo71XWeTfn4hw+50kSWIIMBAQbCK5jwmwVVpTC0DT46y8DCxcaWb7cRGamhCxrHDiQTcOGeVuvc+ckjEZy3BIFgupFlQ/Eb968OXXq1GH79u3etkGDBrF+/fqiJ5IkIVpyEKJFIBCUF/fco6ci/uIL6NGj4uffvx8eewwurB08cqRueWnevGLW4VBUDp53kOL0kOLwcEEWXRoYoGeU7meT4dH47QIRkJ+WgTIdc1ypbIrG+uSi+8YEyHQN1fs6VY01SUX3bWyVuCzHZU3RNFYmFt23gUWiZ3ieyPojTSHYIJVapKiKhsuhYjRJSJoHg8WMPctD0gkHbleOUrqAyEYWb3IDp03h3HEHJouENchAQLARS6Bc6wPniyMhQWLxYhNffWUiNragoHv7bQeTJ7sLOVMgqH5UedFit9uRZRmLJS+QbunSpTidTkaPHo3xAsfqhIQErrrqKk6cOHHpK64BCNEiEAjKi/R0PSC+pGlw/cXq1bp4yV9D+OuvYdSoil+LpmlkuHWLzLksF0k2N+0iA4gJlHG73SScdbItJ4JfBgwSeoaynN+bBMi0zknP61I1dmUoyOh9DBLISEiSngMgv6VF0TRis1Uk8lzkpNyHBMEGibo5rmqapnHKoXmPk9Mn97nVUPZAeVXRyE73kHXejS1DQXO7kXb9hif1PK0euQuPYiD+kM3b32iWMFlkjGYZk1kiINToU7hTcHE0TXf/WrDAxC+/GFBVX1EXEqIxYoSbO+/UA+pFPZXyJysLREnAiqfKZw8LKMT/oHnz5jRs2LCAYAHdPWzhwoVlmUogEAgExVBYZq/K4IYbYMAAmDcPnn8eWrXSrS2VgSRJhJkNhJkNtAj1zVJlNJqwn82kvaKnFQ6NNBEebcZUhIuTWZa4Irxkl0qDJNE2uPjNvqZpSJKEJEk0DSi/naumadgyFLJS3WSne9DyuewZA8y4nU40RcF2IoGg/2fvvsOjKroADv+2pPdCSIOEXgVEQkdAAUFAQESK9bMgShUQVFCKjaIoCBYQsaAIohRFmoAoRaX3FiBACpDet+/3x8rCkgRC2Owm4bzPs0+Se2fvnGxIuGdn5kzNKMJreaB2VaJ2VRR7jxtRNIUC5s1z5Z9/bH/+7doZePxxPQ89JOtUSsvJk0rmznVh+XIX/v47l6pVZZpdRWW3iajjxo0jPDy80HP16tVji/23GBZCCFGILVts15k4iloNQ4bAqVOweHHBBfnTptmOxDiDQqEgsqYfnl5qMEFWsp7zR3NJOp1PfrYBO25dZiPvYhznVn9O4ualpXJ9kxEunsknJ92SsKhdFQSEulKlnifRd3njU6sKALln4lH+VzLaxU0pCUsJpKQoWLTIhev/qTzxhGXKV5UqJsaP13LgQA5r1uQzcKAkLKXp55/VfPWVKzk5Cj77TLbYqMhuq3rYn3/+af08IyODv/76q9A/+ImJiSxcuJA333zzdroTQghxA2lpMHw4fP89fPyx46qJXc/Pr+AI0ObNltLIb7wBI0daRmOcMTtWqVQQFOpJYGUPcjJ0JMfnkJWmJS/TQF6mgcBwV+seIto8I3qtCZPRjMkIJpP56udGM5WqulurZGWl6MhOK3qNip+PC/rsdEw6rbWtQgEqtQKVi8LyUa1E5aK4YfUts9mMLt9ETroBvdZEaHXLzAeVWoFvkItlGlqgi2XdyTUJiVe1SDL2HCX3bIK9Xso7ilYLmzap+P57F9auVaPXK6hd20SbNlfX6vbpo6dqVRP33mu068ar4iqdzvJmyLVloJ97Ts+sWa64uoK3t4yyVGS3lbQYjUYmT57Mtm3bAOjQoUORbXv16nU7XQkhhLiJ/fstCQvAuHHQpw9ERDg1JKsPPrB8NBgsn3/3HcyYAY8/br8SybdCoVDgE+CGT4Abmjw9yfG5pF/OJzjMB5SWG9HsNC2Zl3VFXiOsmgtu7moUCgWZRiOaHG2RbQPDg0ChwKjNQ5eTjSan6HeEI2p7ovpvmllWio6MSzpLQqNWoNOa0GuuDqPptSZrSedKVYte2OQZHQ5KBfq0THQZWbj6y3rKm8nPh99/V7NypZp169RkZ9v+Q1282MUmafHxgQ4dpOBQaUhLg0WLXPn8cxfefFPL449ffYMgONjMDz/k06yZ0SlvhAjHKdFC/GuZTCZefvllNmzYwKuvvlqwA4WCoKAgOnfujKurDNvB1YX4tWvXRqWynf86dOhQhg4d6qTIhBDl3bBhlqlZc+fCY485JyEoTH6+JUmZNg0011TQbd0aPvoIysJ+xEajCZXq6lvkyQm5ZCTnW0ZBVEqUVz6qLCMjASGe1k0T83P0aPIKjrQYjQb0Oj0evmoS1n2JLjOZ4FZ9UPtXx2y27INi1JswGswYDJYSxKHVPayJSGqCloxLtomTQgGefmq8A9R4+qlRFrOS17lvVpN/4SKVu7UloGn9kr5MFd769ZYRlQ0b1OTmFnxtK1c2MWCAZa1KnTqyS70j/Puvkk6dLHPs6tc3snNnXpn523YnmT9/PgsWLLA5ZjQaiY2NLbvVwwozd+5chjlrLkI5I9XDhBClJTcX0tMhMtLZkRQuLg5Gj4YVK2yPd+gAY8dCt25UuKk1eXl5GI1GLu34leyzhwls1JbAu9oW67kGvQm91oRRb8ZoMKNUKfD0U6NS3fodW8q2vaRs3Y13nWgiH+lyy8+/U4wZ48aCBbZvsvr7m3nwQQO9e+vp1MlIITWHhJ2YzZCXR4F1QJ07e/Lvv0p69jQwb56mzBQhudOV+ZLH4vZI0iKEuNOtXw8jRsDJk7bHDx+GBg2cE1NpMRqN5OXlkXF8Fyl7NuEVWYuw9n0dHofmYgqpO/fjUzsa3wY1Hd5/WZKeDr/9pmb1ahc+/lhDSMjVW6G//lLRvbsngYEmevY08NBDBtq3NyKTRUqXwWBZVP/JJ65ER5v46ivbTU3371fi62umenW5bS1LynzJ48KYTCamTJlCfn4+M2bMACA+Pp7vv/+eLl260KRJE3t1JYQQ4hYcPw41atguXnW2Bx6AQ4dg0SKYNcuSvHTpUjBhMZnK/8iLSqXCxcUFt8BQALRpl5wSh3toMBF9Ojml77IgNVXBr79a1qhs3arCYLCMVv36q5pnnrm62WPr1kZWr86jbVsZUXEkoxFef92Ny5eV7N+v5Nw5BVFRVxOUJk1kKt6dzm7/FUycOJG33nqLH374wXosMjKSV155henTp7N79257dSWEEKIYjEbLOpLGjeHdd50dTUGurvDCC3DsGKxaBVOn2p43maBZMxg6FGJjnROjvbi5ueEeWBnXgBA8wqIxm2TBdmkzm+HYMSWff+7CQw95ULOmF8OHu7Npk9qasAD8+aft2lKVyrKgXhKW0pWWZvu1mxs8+6wleWzY0ERqqixaEbbsNj2sZs2avPXWW3Tp0oWgoCCbc1u3bmXq1Kls2rTJHl2VezI9TAjhCIcPQ5MmluRFrYZduyxflxerV8OVwpMKhWW9y0svWTayVJXDDdt1Oh1abdEVxhzBbDKhTcnAkJWDd82qTo2ltA0Z4s733xc+vFiliomHHrKsUYmJMZX70bzyZMMGFfPmuXL4sJIjR3Jxv6boXUqKguPHlbRpY5SF9uWEI6eH2e3XNDIykoEDBxZIWABcXFzYtWuXvboSQghRDA0bwoQJlhv+l1+GunWdHdGtiY+/uhjXbIbffoMePaBWLcsIUkqKc+O7Va6uriidfHesvZxG3ILlJK7cXGobaTqKyQSHDyv59FMXnnzS3aYqHUCTJrajWdHRJkaN0rJlSy6HD+fy3ntaWrSQhMXRlixxYcsWNcnJSpYvtx3OCg4207atJCyicHb7VXV3dyc5ObnAcZPJxPTp06lUqZK9uhJCCFFMEybA339bbvLdi97Go0x66SW4cMFSJjkq6urxs2dh/HhLhbSnnrKMIJUXbm5umE1G9LmZzuk/JBCFWoVJq0OXmuGUGErKZIKDB5V88okLgwa5U62aN61bezF+vDsrV7qwe7ft8FuHDka6dTPwzjsatm3L5cCBXKZO1XHPPSa5KXaQxMSCL/RLL1lKeFerZsLT09ERifLMbjM2R48eTbt27ZgwYQJ33303er2effv2MWfOHA4dOsTcuXPt1ZUQQohicnWF5s2dHUXJBQRYEpSxYy0jLZ98AuvWWc5ptfDNN+DhUTb2eSkObWoiZ1Z9gdrLl6iHXnB4/wqlEvfwEPLPJ5GfcBm34ACHx3ArTCb49FMXtm1TsX27moyMorONnTtVtG17dXSlbl0TS5fmOyJMcZ29e5XMmuXKr7+q2bAhj+bNry6ij4kxsWZNHm3aGGWUS9wSuyUtXbp0YdKkSQwfPpz09HQAzGYzHh4evPPOO7z44ov26koIIcRtSEiA7OzyNV1MpYKePS2P2Fj47DP48ktL6dqXXrJtm5JiWdzftm3Z2VzzCje/YMtIS3Y6Jr0WpYubw2PwiLAkLZqES9C4jsP7L4rZDBkZlkT1CqUSvvjCldOnC97d+vubadPGQNu2Rtq1M9KggVSXKiuOHVOyerVlPdEnn7jSvLnt3L127aQQhbh1dq2NMXDgQHr37s22bdtITk4mMDCQVq1a4Sc7AAkhRJmwZQsMGAD+/pZpVeWxFkjNmvD++5ZqY7//Do0a2Z7/5hsYMwaqV7dMH3vySYiOdkqoBajdPXHx9kefk4E27RIelR2/GN4jPASA/ITLDu/7ehcvKvjjDxVbt6r54w8VgYFmtm/Ps2nTrp2B06ddCQgomKTIO/XOd+mSAhcXM4GBV4/17WvgzTdNqFTQuLEkk8I+SmVzydzcXLy8vDAYDKilZmABUj1MCOEMJhO0aAFXKtC/+KJlulVF07gxHDxoe6xDB3j6aejbF7y9nRHVVWfWLSYz7hjB99yPf13Hz2vTZ+dyes53oFBQa8xTqNwct2tiZiZs26b+L1FRcfx4wTJwZ87kEBx89dbk1CkFWq2C+vUlSSlLzp1T8Pbbbvz8s5oxY3S8/rrO5vyRI0pq1TLJppwVXLmsHgbwySefEBkZSePGjQE4e/Ysjz76KBs2bLBnN0IIIUpAqYSlSy2jLF26FNwXpSIwmWDcOOjc2XZq2B9/WJKW0FDo3x/mzIHz550To0dwOOC8TSZdfLxQ+3qD2YwmyTEl2GJjFdx3nydRUd4MHOjB55+7FkhYPDzM3HefgbQ02zl9tWqZadhQEpayRqWC5cvV6PUKvvzSheureTdoIAmLsC+7/Ql49913GTZsGFqt1lrSsVatWkyfPp0ePXrw448/2qsrp0hOTqZHjx74+PjQrFkzDhw4YHO+d+/eKBQKm0dh5Z+FEMKZqleHHTssi9qDg50djf0plfDYY7BhA5w7B++8YymRfEVuLixbBiNHWvaxuVZWFlx2wIwpz0rOTVoAQjq1JHJAN9zD7PePIC/Pkpz8+aeKw4dtby9CQ83s36/EZLqakKhUZmJijLzyipY1a/I4fz6HlSvzqV1bphOVNampCo4ds/2ZRkaaeeghAwEBZh5/XF8gaRHC3uw2PSwyMpL33nuPJ554go4dO7JlyxbruejoaDw9PTl69Kg9unKKl19+mW7duuHi4sLQoUNRqVQcOnQIsCQ0ffr0YeDAgXj9t6nA/v37yczMZNGiRQWuJdPDhBDCccxmS9nnr76yjDRl/ldtODUVm3n4ixbBM89YEruWLaFVK8ujUSNwKXyPwhLR52Zx+NvpoFBQ/dHRKNV2vHgpO3BAya5dKpKSFCQlKUlMVFg/v7ay16OP6vniC9vF1w884EF6uoIOHYx06GCgTRsjsuS1bMvLg3Hj3Fi2zIWGDU1s3my73ujiRQW+vmYpXXwHc+T0MLstOImKiuKJJ54AQHHNmLzBYODSJee9m2QPGo2GMWPGEBkZCcCUKVN4/vnnrefPnj3Lxo0b8fDwsB4bNWoUDz/8sMNjFUKIW6XTWda2DB1q35vzskKhuJqAzJtnWe9y+LBtwgKWESiAM2csj++/t3zt7g7NmlmefyWZCQsreTxqTx8C6zRF4eEDZueOKuTm8l/ioSw0EfnttzybKT6rVql5//2bVzzbulWF2Ww7RW/Vqvxyt1fQnc7DA/buVaHRKNi9W8WuXUpiYq7+mw0NLd8blIryxW5Ji7+/Pzk5OXh7e9vssjtnzhy0Wi133XWXvbpyOHd3d2vCAqDX621KODcvZBOETZs2MX36dIfEJ4QQJRUfD/36WUYiLlyADz5wdkSlS62Gpk0tj+s1bGgpk7x7Nza7q2s0sG2b5QHQowf88svV80YjfP45+Pldffj7X/3cxweb9RgKhYKojn3Jz8/HYDCUyvdpMkFysoLERAVhYWabm8uTJ5U8/rg7ifGQlVNwIfy1Ll5UULXq1eeGhRW8SXV3NxMWZiY83ERoqJnwcDM1apgwGGyTYElYyrb0dNi1S0WXLlfLESsUMGSIngkTlDzxhJ7wcElShPPYLWkZMWIEXbt25Y033kCj0bBz505++uknPvroIxQKBW+++aa9unKq+Ph4Fi9ezLJly4pss2fPHurXr4+bm+Pr7wshxK1ITIQ9eyyfz5sHw4eXnfLAjjZypOWh08H+/fDPP7BzpyWhO3v2aruWLW2fl5VlGaUqiloNEREQGQlz50KTJpbjKpWKtDQDWVkKQkPN3GqxzX/+URIXp7QZHUlMtIyYXLyowGCwDHPMnKnhhRf01ud5eZkLrdpVmEuXbJOW9u2NzJ2rISzMRHi4mbAwEwEBZW8/HHFrpkxx5dNPXTEY4NixXCpVuvoz799fz8MP651edU8IuyUtDzzwAPn5+bz00kucPXuWNm3aABAaGsr06dPp27evvbqyqzFjxhRYVH+tUaNG0aNHD8CyTmXixImsXbuWjh07sm3btkITkxUrVsjUMCFEudC8OXz0EcyYAcuX37kJy7VcXS2vS/PmliQO4NIlS/Ly99/Qtatt+4yMG1/PYLAUBTh3zlJxCSybLxtyMli1RMPQVyx7tahUlsRFpbIkOkolqNVmVCqoUcPEunW2u7uPGePOwYM3Tz6SkmwzipAQM15eZoK88whyTSMiSk31pkHWROTKiEloqJnr/4urXdskC+UrIINBQV6e5d/Jl1+6MH781fLFbm4U+HcghDOUyj4tsbGxXL58GT8/P+rVq2etJlZRrFixgr59+7J48WIGDRpU4HzTpk35888/8S7ibQlZiC+EKEvMZsjJsUxjErcuMxNWrrR8zMiwfLzyyMiA5GTLNLzUVEhLs+z4rs1K4+j3H/DVhvbMWdHlpn3Urm1k927bRdD9+nmwfn3B9x6Dg6+MglgSkM6djfToYTsNzWyGrCOnSFq1BffwEKL/17vkL4AoN5KSFCxZ4sKwYTqbtUrnzyto08aLRx/VM2SIjlq1ZBqYKJ5yuRD/Wi4uLsybN499+/ZRp04d3nrrLRo2bFgaXTlFnz59aN++PYmJiQXOnThxgsjIyCITFiGEKGsUisITFp0O2WehGPz84Kmnbt4uP//qug5XnwBUru5EBqfQ/YEcktM90Ost62MMhisfFRiNls8LW0vy2GN62rc3/Dc6YpmqVdjoSGEUCvCIqAyA9lIKJoMRpbp4U8ZE+TR3rgtvvOGG0aggOtrEww9fTWSrVjVz8mQO19QTEqLMKXHS4vrf/2R169Zl4MCBvPbaawAkJSXRunVrLl68iFqttn79999/U79+fftEXQb4+/vTqFGjAsd//vln+vTp44SIhBDCfj7+2FICeNMmy8iAuH3X3hAqFAo8gsLo1PQIg4ZG41v91ovV9O59e4v4Xfx9UHm6Y8zToL2UikdEyG1dT5Rtd91lwmi8OgXs2qQFkIRFlHklnrdlMBjo1asX+/btsyYsACNHjiQpKYmYmBjOnz9Peno6r7zyChMnTrRLwM6QlZXF119/TVZWFgAHDx5Ep9PRpUvBIf1ffvmFhx56yNEhCiGE3XzyCYwYAfv2WdZv5Off/Dni1nkEW+oma9Odsy2AQqHAPdySqOQnlO+tCcRVBw4oGTHCjf37bW/x7r3XSLt2Bl55Rctnn2mKeLYQZVeJR1pcXFz49NNPUamuDif//fffLF++HE9PT5YtW0blypah54kTJ9p9etjmzZuZNWsWzZs3v2Flst9++413330XpVKJXq9n6NChPP7447fU16VLl5g8eTKvvPIKHTp0oEaNGvzwww8F2sXHx+Pl5UVQUFCxrnslCboZNzc3qUQmhHCY++6DkBDL7vBdukip2tLiEWRJWnTpl50XQ0QIubHn0V5KdVoMwn5WrVLzxBOWIROTCebOvbpNvUIBa9bIOxCieLRaLVqt9qbtsrOzHRCNRYmTlrvuuovg4GCbY6+//joKhYJRo0ZRtWpV63GFQkGAneYXxMXFsXjxYr788kvOnj1Ls2bNimy7YMECRo4cyfbt27n77rs5d+4cMTExxMfH8+qrrxa7z1q1anH22nqXRYiMjGTjxo3Fvm6VKlWK1W7SpElMnjy52NcVQojbUbcu/P47bNwIo0c7O5qKyyM4FABt+mXMZrPNxsyO4t+4Lr71a+ASIEVhyqPrN/C8/34Dvr5msrIUrFunRqfTyro0USIffPAB06ZNc3YYNkqctFxfIWDlypX88ccfVK5cudCE4MKFCyXtykZ0dDQTJ06kbt269OvXr8h2sbGxjBgxgiFDhnD33XcDEBUVxahRo5g4cSKdOnW6YcLjCBcuXChWpQUZZRFCONpdd1keovS4B4SgUKow6TQY8rJw8fJzeAxqH0+H9yluj9EIGzao+PJLV9q0MTJq1NXyxN7eMH68Fk9PePRRvSQsosTGjBnDsGHDbtouOzubevXqOSCi20ha/Pz8WL16NQ899BDnz5/npZdeQqFQ8N577xWonLVlyxbi4+NvO9hrBQYG3vD89OnT0Wg01j1WrujSpQsTJkxgxowZN9wg0hF8fX2l5LEQotw4exZmz4b33+eWN0IUBSlVasJbdsWsdkPlKnPwRPHExysYMMADs1nBsWNKhg/Xcc1MfYYP1xf9ZCGKqSwuTSjxfzvvvvsuLVu2pGbNmsTGxpKdnU2/fv14+umnbdrt3LmTp4pTC/IW3WjvF5PJxOrVqwEKVPhq0qQJSqWStWvXYjQabdbkCCGEKNyJE3D//ZCQYNlr5KuvLJsfitsT0qg1Wq0WnU5388alJO/CRdJ3HcY10I9KHWKcFocoyGy2/L5du1Q2KspMp05GNm5UYzbDuXMKqleXfVVExVfipKVevXps376dDz74gNDQUDp37szwK1sH/+fdd98lNjaW+++//7YDvRWJiYlcvnwZDw8PKlWqZHNOrVbj6+tLRkYGp0+fpnbt2g6N7VoxMTEFkqahQ4cydOhQJ0UkhBCFO3fOsjAfYPduSE+3vZESJefsDZiNeRqyj53BNchfkpYyQq+HL75w4csvXahUycxvv9kuoB8/Xstzz+no0sWIvPcqHGX+/PksWLDA5pjRaHRY/7c1wN+wYUMWLVpU5PnXX3/9di5fYpcuWUo3FjX1ysfHh4yMDNLS0hwZVgG7du2S6WFCiHKhSxdYuhTefRd++00SFnsxGfTkJZ0lOzkJv9pNnRKDZ1QYKBToUjPQZ+Xi4uvllDjEVWq1JWk5dUrFiRNw/LiSunVN1vPNm5tu8GwhSsfgwYMZPHiwzbGsrCwiIyMd0n+FHNy/UqLNtYgVaAaD4YbnhRBCFNSnD/z9N1w3gC1ug8mg58xvX5O8awNGnXP2zlC5u+EeaqkGmncu0Skx3Mny8mDnTtvhEoUCnnnGsjalTRsDeXnOiEyIsqVCJi1XSjHnFfFbfmV/lJAQ2f1XCCFuxfVTUYxGmDcPnLgko1xTu3vi4u0POHe/Fs/ocADy4hKcFsOd6O23Xalb15vevT1IT7c99/jjev75J5e1a/Np2lRGVoSokPVfatSogbe3N2lpaWg0Gtyv2RktOzub3NxcgoODCQ8Pd2KUsqZFCFG+mUzwwguwcCGsXw/LlslGlCXhGRxKZk4G2vTLeFSuevMnlEYMUeGk7TxAblyi0/aMuRNlZirIyLC81j/84MKLL16t/OXnB35+kqyIsqNcr2kpq1QqFT179mTJkiUcOHCAFi1aWM8dOXIEgO7duzt98aOsaRFClGfHjsF331k+X7sWdu2Cdu2cG1N55BEURmbccbQZThxpqRIKSiWGrBz0Gdm4ymaTdmMyWaZ/rVyp5t13tbi4XD33zDN6vvnGhYcfNtC6teNu/oQoCVnTUkImk+XdB7O58DJ/48aNQ6VSsXLlSpvja9asQa1WM27cuNIOUQghKrQGDWDNGvD1tSQvkrCUjEdwGAC69EtOi0Hp6oJnlcq4h4dgzHfO2pqKauxYN7p18+Tzz135/Xfb2RX16pmIjc3h0081NG4soypC3Ei5TVouXLgAUOSmlU2aNGHq1Kl89tlnnD59GoBDhw4xd+5cZs6cSf369R0WqxBCVFT33QdnzsCjjzo7kvLLI8iStGgzUjCbnPdue5VB3Yn+X288wmW9Z0mlpiq4/r3ULl0M1s9/+smF6/n4lHZUQlQMdpsetmPHDgAUCgWtWrXCYDAwZswY/vjjDzp06MCMGTPssrNmQkICffv25cCBAwB8+eWX7N27lxkzZtC5c2ebtq+//jphYWH0798fLy8vzGYzX331Fb169brtOIQQQlgUVv542zaoXx8CAx0fT3nj6hOA0tUNk06LLjMVtwDnJA0K2S20xLZuVfHxx65s2qRi8+Y87r776qjJ/fcbefhhPd27G+je3XCDqwghbkRhLmp+1S1SKpXce++9TJkyhfbt2zNmzBg++ugj+vfvj06nIyIigtmzZ9ujq3IvKysLPz8/ateuLQvxhRAVztat0K0b1KoF69ZBWJizIyr7Ms8dx6R2Q+UVgMLJuwUatZZScCo32RaguBYtcmHkSEsVihdf1DF9utbJEQlhf0UtxI+NjSUzM7PU12nbLWmpVKkSFy5cwN3dnfj4eGrUqMEzzzzDp59+CsC9997Ln3/+aY+uyr0rSYsjfsBCCOFIBoNlhOXUKcvXH34Io0Y5NaRyQ6fTWfcZc5ZLG3aQvvsIIZ1aEtj8LqfGUhYdO6bkhx/UvPCCnvDwq7dP6elQq5Y3lSubGTxYx8iR+htcRYiK48pCfEfc09pteliDBg2spYWnT5+Oh4cHb7/9tvW8s3efF0IIUfrUasvi/Pvvh6pVYfhwZ0dUflw/8u4Mam9PMJvJO5coSct1FixwYcwYy31OYKDZJjEJCIC//sqjTh0TMstOiNJht18tDw8PNm7cyPfff8/nn3/OuHHjCPpvovPGjRs5duyYvboSQghRhtWqBXv2wPffF9yMMinJOTGVdUa9jrRju0jetaHIqpiO4FElFABNUorTYigLMjNBc10Rtfvuu/GC+nr1JGERojTZ7dfr448/ZvLkyQwePJi+ffsyfvx4ACZOnEj//v3tsghfCCFE+VCpkmWk5Vrx8VCvHjzzjOWmUFylUCpJ/GcDmSf3ok11XmbnXtnyZqMhOxdDTp7T4nCWAweUPPWUOzVrerNqle1klBo1zDz1lI6ZMzX8/HO+kyIU4s5ltzUtovhkTYsQ4k5jNkPXrrBhg+XrkSPho4+cGlKZc3bjD2ScPkRAg1YENWnvtDjOfLYMXWoGkf274l2z6s2fUIFs366iWzdPADp3NvDTT5KcCHEj5XJNi6NKHlckMTExUj1MCHHH6N8fdu607EsxaZKzoyl7/KLqknH6ELnxsU5NWtzDgtGlZqBJSqmwScuJE0qWLlXTtauB5s2vlidu1cpIZKQJjQZq1TJhNoNC4cRAhShDiqoe5ihS8tgJZKRFCHGnOncOEhOhVSvb4ykpEBzsnJjKCoMmj0NfvwdmE1EPDcHFx98pcaT9c5DLv/+Nd+0oIvs94JQYStO6dSoefdQymvLkkzrmzrWt2BYbqyAqyoxLwWUrQojrOHKkxW5rWoKCgli3bh3t27cnPj6euXPnMnjwYL7//nuWL1/Ovn377NWVEEKIcioqqmDCkp8PLVpAr14QF+eUsMoEtbsn3mFRAOQmnHJaHJ5Vw/BtWBPv2tFOi8FeUlMVXL5sO1Ry771GvL0t79f++qsaw3X7PdasKQmLEGWR3ZIWKXkshBCiJKZNgzNnYPVqeOklZ0fjXH5R9QDIjY91WgzuYZUI73Uf/o3rOC2G23X8uJIHH/SgRg0vPvnENgPx9ISXX9YxbZqGf/7JQ223ifJCiNIkJY+FEEI4VYMGEBpq2ePl/fedHY1z+UXXBRSYzSbMJtNN2wtLkQf9dXs5Bgeb2bFDhcmk4JdfCmYlr7yi46WX9FSuLLWIhCgv7LamJTY2lqeeeooDBw7Qs2dPFi9ejEqlYuLEiXzyySdoNBry8u688omFkTUtQghhKysLtm6Fnj1tj58/b9nrJSLCOXE5g0GTh87k2AWu1zObTOhSMzGbzbiHBDotjhs5d07BZ5+5smaNmsGDdQwbZpu5dO3qwaVLSnr21DNxog5XVycFKkQF5sg1LVLy2AmuJC21a9eW6mFCCFEEsxkefBC2bYPJky1lku+UqTw6nQ6tVnvzhqUk7d9DXN64E+/a0UT26+K0OG7kxAklMTFeALRpY2DtWtvyxJmZ4Osr1b+EsJeiqofFxsZK0lJRyUiLEELc3IoV8PDDls/r14dDh7hjdhw3mUxkpaWgdHFFoVTd/Al2lncukfOLf0Xt60XN4Y85vP8rMjNh7Vo1a9ao6dvXQO/eV1fNm81wzz1enD2roH17I8uX598xSa0QZUW5rB4GcPnyZQYPHkzlypVxc3OjQYMGvPvuu2g0Gnt2I4QQ4g7Qvj288ILlnfJx4womLNnZzonLEc5tWsbZn+aQf/mCU/p3C7XUnzZk5WLIdd4Gi4cOqRg82INVq1z4+WfbjEShgEWL8jlzJoeVKyVhEaKis1vSEh8fT0xMDF988QX5+fnUqVOHgIAA5s6dS7NmzcjMzLRXV0IIIe4AgYHw2Wewdy8MHGh7LjnZss7lySctIzAVjdLFFcxmcuOdU/pY5eaKa6AfAJqLKaXaV0qKgsWL1fzvf+78/rvtqFLLlkYCAy0FCfbtU3F9bYLGjU0EBJRqeEKIMsJuScurr76Kt7c3q1atIisri4MHD7Jt2zYSExMZOXIkEydOtFdXQggh7iBNmlBgEfXcuZaRlm+/hS+/dEpYpcovqi5gKX3srFnc7mGW0RZNUnKp9rNrl5KXXvLgp59c+O032+EStRpmztSydm0e+/bl3jHTA4UQBdnt13/btm1s2bKFnteXfgGef/55Tp48aa+uhBBC3OH8/CwjMWo1vPyy7bncXIiPd05c9uITWROFSo0hNxNdRukmDUVxD6sEQH78pdu+1qlTCqZPd6VrVw/+/df21qNtWyNqtSUx27274Pqdfv0MtGljlOlfQtzh7Ja01KhRg5CQkCLPX7jgnHm5QgghKp7Roy3lkH/9FapWtT3344+WY506wd9/Oye+26VyccUnsgYAuQnO2WjSs2o4APkXLmI23t6eMX//reKdd9zYsUPNpk222YePD3z0kZaNG3PZvFm2RhBCFM5uSYuXlxdxcXEFjut0OkaMGIG3t7e9uhJCCCHw8oIHHih4/JtvLJWlNm1yfEz25BdVD8Bp61rcQoMIanM3EX07QzHKBq9YoaZnTw/q1PEiNtb2CR07Xt1z5sSJgrceTz6pp0ULk4ymCCGKZLc/D6+99hotWrTgscceo1atWuTm5nLq1Cl++uknMjIyWLlypb26qjBiYmJknxYhhLAjsxnuu88yCqNUQosWtuf/+guWL7ckOx06gKenU8IsFr+oOlwAtKlJGPJzUHs49s0/hUJBpQ4xNsfmz3fh779VXLigZMOGPJs9UFJSFGzdarmt2LJFTc2aVzd7jIw0s3BhPs2bG4mKkp0WhCiPitqnxVHsuk/L5s2bGTJkCLGxV4eyq1atyqxZs3j4SrF9Ifu0CCFEKTOb4eJFCAuzPf7EE7B4seXzrVvh3nsdH9utiN/xG0pPXzyr1EHl6l7q/eXnw3ffuXDypJLQUDOjR+tszj/yiAcbNlgSk+PHcwgPv3oL8ccfKh56yJPAQBPPPWfZhV4IUbE5cp+WUtlccteuXcTFxREZGck999yD6/VlX+5wkrQIIYTj6XQQGgrp6eDtDamptlXJtmyBefOgc2d46KGCCY+z6HQ6tFqt3a6XnKxgxw4VJ08qadfOSMuWV98p1euhcmVvDAYFjRoZ+euvXHJPXyDvXCLB7Zsx4Q0v5s1zRaUys3Ztvs1zNRrIzVUQFCQjKULcKRyZtNhtetg333yDp6cnjzzyCDExMcTExNz8SUIIIYSDuLrCmTOW5CQpqWAZ5V9+gZ9+sjyCguCRR66ey8uDs2ehTh0cvu5CrVbfctKSmqrg+HElJ08q6dtXz7X3Ert3K3niCQ8Axo7V2iQeLi4QHW0mNlZBbKwSkwkurvkTQ04eXjWqMmRIBE8/radaNVOB18/dHdzdJWERQpQOuy3EHzZsGEuWLLHX5YQQQgi78/eHPn3gpZcKnvv3X8tHhcKyLuZaf/8NDRtaKl3NnFnwufHxYMfBEBu6rDSyTu1Dl2m7yaNeDydPKtm2rWCZ4LffdqVbN09GjnTn2DHb/+pr1bpaCSw2tuBtwPTpGn77LY+DB3NRKhV4RluqiOXFJRAVZaZOnYIJixBClDa7vV/0yCOPMPD6LYuvMXLkSGbPnm2v7oQQQgi72roV9uyBgwcte8Bca98+y0eNBoKDbc/l5kKVKpbPH3wQ1qyxPf/VV5CQYFn0P3So7QjPxYsQFwdubhAdjc3u7hoNrFoF8btP4amL5cH+eQTedbXzpk29OHdOib+/mXPncmwWxdesaZuYtGhx9evoaDMTJ2qpVctEw4YFF9F27mx7zDMqnKzDseTFJRZoK4QQjmK3pOWxxx5j4cKFpKamEh4ebj1uNps5fvw4n3/+uSQtQgghyiyVCpo3tzyuV6cOPPoo7N8Pd99te+7y5aufF1bd/8svLVXLwJK0XOuXX2DwYMvnS5bAgAFXz+XmXvm6FW0bBtKxwx9wVxvr+erVTZw7pyQjQ0FqqoLg4KtTs1q2NPLCCzpq1zbRooVtEqJWw7hxxV8k71UtAoD8xMsYtTpUbjLMIoRwPLslLc8//zznzp1j6dKlBc6ZzWYUimIUeRdCCCHKoB49LI/CKBSWKWeXLlmmkF0v77/9ElUqy5qRa+XnX/38+ullbm5XP9fpVWiSEzAZ9CjVlot07GgkIMBM7doFN3685x4T99xjn/lqLn4+uPj7oM/IJv/CRbxrVr35k4QQws7slrQMGjSIxMRE2rZtW2DvkYsXLzJlyhR7dSWEEEKUGdHR8PPPRZ+fOxeSky3Vy65//65xYxg50pKw1Kple87dHT78EFxczHB6P2aTEU1KAp6h0QCMGuW4ksKe0RFk7j9OXlyiJC1CCKew6/Qws9lM/fr1Cz1//vx5e3UlhBBClBstWxZ9rn17y6MwajWMGgWgIG6zmfSTkH/xnDVpcSSv6HAy9x9HcznV4X0LIQSU0j4tV6SkpODh4YGXl1dpdVEuyT4tQgghbkXq8b2c/+Mn3ILDqfLAkw7v36jVYcjOxTXIX6Z7CyGsysU+LV27duXff//Fz8+P4cOHM3jwYLyvW4EYEBDA7NmzefDBB6lbt+5tB1vRxMTEFJhKN3ToUIZev1JTCCHEHc0nohoAurRLNutaHEXl5ioL8IW4w82fP58FCxbYHDMaC1YgLC0lHmk5evQovXr1YtOmTVSteuP5rZMmTeLll1/G39+/JF1VODLSIoQQ4lZlXYhF7R+CEeeOdEhxHSHEFY4caSnx5pJLly7lk08+uWnCAjBkyBDmzZtX0q6EEEKIO55vlZq4eng6rX99di6JKzcRt/BnSnFmuRBCFKrEScuOHTvo3LlzsdqGhYVx5MiRknYlhBBCCECtVjttlEPl7kb2iTi0l1LRXk5zSgxCiDtXiZMWk6lgXfgbSUlJKWlXQgghxB3PbDaR+M8GLqz/BqMmz+H9K13U1o0mc06dc3j/Qog7W4mTFu31u2DdRFxcXEm7EkIIIe54CoWSzHPH0SQnkH/JOdsIeNeKBiDnpCQtQgjHKnHS4uPjw7///lustps3b8bDw6OkXQkhhBAC8ImoAUDeJeckDd61LOtYNUnJ6LNznRKDEOLOVOKk5emnn+app54iNfXGG02lp6fz0ksv8dBDD5W0KyGEEEIAPhHVAcsmk86g9vbEPTwEgNxTsmm0EMJxSpy09OvXj9DQUOrWrcusWbM4ffq0zfn09HQWLVpE48aNSUtLY/To0bcdrBBCCHEn8wqLBoUCfXYa+twsp8TgXTsKgGxZ1yKEcKASJy1KpZJly5ZRvXp1xo4dS+3atfHw8CAyMpLAwECCg4N57rnnyMnJ4ZdffiEgIMCecQshhBB3HLWbB56VLIvh8y/GOSUGn1pRuFUOwrNKqFP6F0LcmUqctABUqlSJ7du3M23aNKKjo9FqtSQmJpKRkYGrqyuDBg3iwIEDtGjRwl7xCiGEEHc036q1AciOO+qU/t1CAqn2XF+CWjdxSv9CiDuTwmzHHaISExO5cOEC7u7u1K1bFzc3N3tdukLJysrCz8/PIbuHCiGEqFi0WemcWD4Xn+j6BDXrLLvTCyGcJisri8jISIfc09o1aRHFI0mLEEKI22EyGjCazGg0GqfFYNTqyDubgHftKBTK25q4IYQopxyZtMhfGSGEEKKcUarUqNVqp/VvNps58+lSEn7aSH7iZafFIYS4czjvL54gJiYGlUplc2zo0KEMHTrUSREJIYQoT7RpF1Go1Lj6BTu0X4VCgVd0BFlHYsk5eQ7PSFmUL0RFN3/+fBYsWGBzzGg0Oqx/mR7mBDI9TAghxO1K2vU7F/dswSe6AZXb9HR4/1lHT5O4YhOuwf5Uf+FRh/cvhHC+Cjk9bPz48Y7qSgghhKjwfKvWASDnwgmMOsevbfGqXgWUCnQpGejSMh3evxDizmLX6WGnTp3iwIED5Ofnc+0ATlJSEh9//DHTp0+3Z3dCCCHEHcszJBL3gBA06ZfJOXccv1pNHNq/yt0Vz6rh5MUlkHPqHIEtGjm0fyHEncVuScuMGTN4/fXXMZvNFDbjTEoyCiGEEPajUCgIrNOUxL/XkXXmoMOTFgDv2lHkxSWQfVKSFiFE6bJb0jJ9+nQmTJhAy5Yt8fT0tDmXmprKs88+a6+uhBBCCAEE1r6bxH82oE1JRJeZ4vAF+d61qnJ5ww7yL1zEqNGicpf92YQQpcNuSUu7du2YMmVKkedjY2Pt1ZUQQgghABdPb/yiapMZd5ys04cIbtrRof27+vsS1rMDHlVCJWERQpQquy3E79ixI5mZRS/E0+v19upKCCGEEP8JrHMPAPmXzjmlf79GtXENkEqYQojSZbeSxxs3bmTp0qU8+eSTBc5dvHiRF154gfT0dHt0Ve5JyWMhhBD2YjYayU48g9I/FNnDQAjhSI4seWy3pCU0NJTLly8XuuDebDajUCgcugFNWSZJixBCCHvT6XRotVqn9J0Te570PUfxrVcdv0a1nRKDEMLxHJm02G1NS58+fahevTrBwcEolVdnnZnNZs6fP897771nr66EEEIIcR0XFxc0+fmYTUaUaheH9q25lEpu7HnMRqMkLUKIUmG3pOWJJ56gdevWRZ7PycmxV1dCCCGEuE7K0X9J2r0Z/3rNCajX3KF9+9avQcofu8iLS8SQk4fa2/PmTxJCiFtgt4X4N0pYwLKPixBCCCFKhwIw5ueQffpQofullSbXAF/cwyqB2Uz28bMO7VsIcWewW9ICcPnyZQYPHkzlypVxc3OjQYMGvPvuu2g0Gnt24xTJycn06NEDHx8fmjVrxoEDB2zO5+TkMHToUF577TVeeeUVRowY4bS5xUIIIe48/jUboVCp0WUmo0276PD+fRvUACDr6GmH9y2EqPjslrTEx8cTExPDF198QX5+PnXq1CEgIIC5c+fSrFmzG5ZDLg/effddRowYwerVq8nLy+Pxxx+3OT9+/Hhq1KjBe++9x8yZMzGZTLz99ttOilYIIcSdRu3mgX+1+gBknT7o8P596lmSlvwLF9FnypRwIYR92S1pefXVV/H29mbVqlVkZWVx8OBBtm3bRmJiIiNHjmTixIn26srhNBoNY8aMoUuXLnTs2JEpU6Zw4cIFmzZ//PEHUVFR1q/vuusuDh50/H8aQggh7lyBdZoCkHP+OGaTYyt2uvh64VE1DICsYzLaIoSwL7slLdu2bWPLli307NmzwLnnn3+ekydP2qsrh3N3dycyMtL6tV6v58UXX7Rp06hRIz755BNMJhMAu3fvZsiQIQ6NUwghxJ3NJ6I6ag9vTNp88pLiHN6/b/0aeFQJxcXfx+F9CyEqNrslLTVq1CAkJKTI89ePTJRX8fHxLF68mAkTJtgcnzVrFmfOnOHJJ59kx44d9O7dm27dujkpSiGEEHcihVJFQI27AMiOO+rw/v2b1iPqyYfwrVvd4X0LISo2u5U89vLyIi4ujujoaJvjOp2OsWPH4u3tba+u7GrMmDEFFtVfa9SoUfTo0QOA/fv3M3HiRNauXUvHjh3Ztm0bbm5uAISFhbF06VK+/vprHnzwQb799luHxC+EEEJcK7DO3ZgVCtwjHL9fSmEbTAshhD0ozHaqi7hz50569+7NY489Rq1atcjNzeXUqVP89NNPZGRksHLlSuvNf3m3YsUK+vbty+LFixk0aBAAx44dY8OGDYwcOZIvvviCF198kV9//ZUHHnigwPOzsrLw8/NzyO6hQggh7kx5eXkYjY5d13KFIU9D7pkL+DaoKYmMEBVYVlYWkZGRDrmntdtIS6tWrViyZAlDhgwhNjbWerxq1arMnz+/wiQsAH369KF9+/YkJiZajz333HN8/PHH1s+PHDnC2LFjC01ahBBCiNLm4uLilKTFpDdweu73mPUG3EKCcA8JdHgMQoiKx677tNx3332cPHmSf/75h6VLl7J9+3ZOnjzJww8/bM9uygR/f38aNWpk/frAgQO4urpav3722WcrzDoeIYQQ5YvZbCb/4jku//0bRq1j90pTuqjxqmYpXpN9JPYmrYUQonjslrR88803LF++HICYmBj69etHq1atbG7ky6usrCy+/vprsrKyADh48CA6nY4uXbpY23Tr1o21a9davz5x4kSFGl0SQghRfigUChJ2/kbW6YPkXjjh8P6v3WjSTrPQhRB3OLslLcOGDWPJkiX2utxNbd68mR49ejB16tQbtvvtt99o27Yt9957L61atWLx4sW33NelS5eYPHkyNWvW5NFHH2XJkiX88MMPNm2++OILTp48yaRJk5g5cyY7duxg3rx5t9yXEEIIYQ8BNRsDkH3O8VXEvGtFoXBRo8/IRpOY7PD+hRAVj93WtDzyyCMMHDiwyPMjR45k9uzZt91PXFwcixcv5ssvv+Ts2bM0a9asyLYLFixg5MiRbN++nbvvvptz584RExNDfHw8r776arH7rFWrFmfPnr1hGz8/PxYsWFDsawLWkZubcXNzs1YpE0IIIYojoOZdJP27gfyL5zDk56D2cFwVT6WLGu9aVck+eoack3F4RBS9JYIQouzRarVotdqbtsvOznZANBZ2S1oee+wxFi5cSGpqKuHh4dbjZrOZ48eP8/nnn9slaYmOjmbixInUrVuXfv36FdkuNjaWESNGMGTIEO6++24AoqKiGDVqFBMnTqRTp043THgcoUqVKsVqN2nSJCZPnly6wQghhKhQ3HwD8apcldxL58k5dwz/ujEO7d+7VpQlaYk9T6WOzR3atxDi9nzwwQdMmzbN2WHYsFvS8vzzz3Pu3DmWLl1a4JzZbLZ7ycPAwBtXI5k+fToajabAupIuXbowYcIEZsyYwbJly+wa0626cOFCscrDySiLEEKIkgio1YjcS+fJjjvq+KSlehVQKNAmp2PIyUPt7enQ/oUQJTdmzBiGDRt203bZ2dnUq1fPARHZMWkZNGgQiYmJtG3bFpVKZXPu4sWLTJkyxV5dAaBUFr0cx2QysXr1agCbCl8ATZo0QalUsnbtWoxGY4FYHcnX11f2aRFCCFFq/KvfRfz2NWhTk9Bnp+PiE+CwvlWe7kT2ewD38EqovTwc1q8Q4vaVxaUJdktaunbtSmBgIPXr1y9wbt++fZw8edJeXd1UYmIily9fxsPDg0qVKtmcU6vV+Pr6kpGRwenTp6ld2/E7BgshhBCO4OLpjU9EDTSZaRjysh2atAB416rq0P6EEBWX3ZKWN998k82bNxd6rl69ekRFRdmrq5u6dOkSQJGjGD4+PmRkZJCWluawmAoTExNTYKRn6NChDB061EkRCSGEqGiiOw/ArFSj0Th2vxYhRMUyf/78AkWnHLmB7W0lLX/++af184yMDP76669C67EnJiaycOFC3nzzzdvprtiuVDsoao8Yg8Fww/OOsmvXLpkeJoQQolSp3SxTs7RarVP2TMnYf5zMQ6cIatUY75oy8iJEeTV48GAGDx5scywrK4vIyEiH9H9bSYvRaGTy5Mls27YNgA4dOhTZtlevXrfT1S0JDg4GIC8vr9DzV0oNh4RICUYhhBB3BpVCQV5GMq5+wQ7tV3MxhfzzSeQE+0vSIoQosdtKWjp27Ej79u15+eWX2bBhQ6F7nygUCoKCgujcufPtdHVLatSogbe3N2lpaWg0Gtzd3a3nsrOzyc3NJTg42KY0sxBCCFFR5SUncOqXL1GqXYnq/aLdK3reiHfNqmTsOUrOqfOYu9q/mqgQ4s5w22talEols2fPZu7cuTz11FP2iOm2qVQqevbsyZIlSzhw4AAtWrSwnjty5AgA3bt3v2EFMkeQNS1CCCEcwT0gBDBjyMtCkxyPR0jx9gmzB8/ocBQuagzZuWgvp+FeOchhfQsh7Kdcr2m5VnFqOduTyWQCKHJ+7rhx41i2bBkrV660SVrWrFmDWq1m3LhxDonzRmRNixBCCEdQql3wj65P2sl95MQdc2jSolSr8YoOJ+fUeXJiz0vSIkQ55ew1LXYbajCZTEyaNMkmGYiPj2fGjBns37/fXt1YXbhwwdpHYZo0acLUqVP57LPPOH36NACHDh1i7ty5zJw5s9DSzEIIIURFFVCrMQDZ549hNjnu3VEA75qWCqK5p847tF8hRMVht5GWiRMnMm3aNCIjI5kxYwYAkZGRvPLKKwwaNIgxY8bQrFmz2+4nISGBvn37cuDAAQC+/PJL9u7dy4wZMwqsm3n99dcJCwujf//+eHl5YTab+eqrrxxaFEAIIYQoC3wiqqN298KgySXv4jm8wqs7rG+v/xbg5ydcwpCbL5tNCiFumcJsp/qHNWvW5K233qJLly4EBdkO/W7dupWpU6eyadMme3RV7mVlZeHn50dmZqZMDxNCCOEwF7b9Qsrhv/GMqEF4h34O7fvc16tQebgRcn9LXIP8Hdq3EKJ0XJke5oh7WruNtERGRjJw4MBCz7m4uLBr1y57dVVhyEJ8IYQQjlSpYStSjvxLXuJZ9DkZuHj7O6zvqk8+JJXDhCjHnL0Q324jLV27duXbb7+lUqVKNsdNJhN9+vTh8OHD1rUldzoZaRFCCOEsqcd24xkWjUHl5uxQhBDlXLkcaRk9ejTt2rVjwoQJ3H333ej1evbt28ecOXOsC+CFEEII4VxB9SzrS/Pz8zEYDA7vX5eRBShw9fdxeN9CiPLLbklLly5dmDRpEsOHDyc9PR2wlCP28PDgnXfe4cUXX7RXV0IIIYS4TW5ubuSlXcbFJ8Bh07aS/9xN6l978b+nPqFd2zqkTyFExWC3pAVg4MCB9O7dm02bNpGVlYW3tzcdOnSQKVBCCCFEGXPhz5WkHd9DWId+eEXUcEifHmEhAOScPIf5gTayxkUIUWx2TVo++eQT3n33Xdzd3YmNjeXUqVM8//zzPPvss3Tp0sWeXVUIshBfCCGEs6jdLGWH0w78iWd4dYckEJ7VwlG4qDFk56JJSsEjvNLNnySEKBMqzEL8d999l4kTJxIUFERAQAAnT54E4OzZs9SpU4fvvvuOfv0cW16xrJKF+EIIIZzNkJ/Lke/fx6TXEdquN95V6zqk34SfNpJ9/CxBbe6mUocYh/QphCgdjlyIr7TXhT755BO+/vprkpOTiYiIsB6vVq0a4eHhTJo0yV5dCSGEEOI2qT28CGnUBoC0g9swm0wO6de7djQAOSfjHNKfEKJisFvSEhUVxRNPPAFgM8RsMBi4dOkSZ8+etVdXQgghhLCDkEZtUbl5oMtMIefcUYf06V2zKigUaJPT0aVnOaRPIUT5Z7ekxd/fn5ycHMBSNeyKOXPmoNVqqV27tr26EkIIIYQdqNzcCWnSDrgy2lL689NVHm54RoUBkH0irtT7E0JUDHZLWkaMGEHXrl1Zv349Go2GnTt3MnbsWMaNG4dCoeDNN9+0V1dCCCGEsJNKDVuh9vDCpNehy0x1SJ9BrZoQ3rcTAU3rOaQ/IUT5Z7fqYQ888AD5+fm89NJLnD17lrZt22I2mwkNDWX69On07dvXXl0JIYQQwk5ULq5U7/oErv7BaHSO2WzSq3qkQ/oRQlQcdqsedq3Y2FguX76Mn58f9erVQ6m024BOhXCleljt2rWl5LEQQogyQ6PRoNfrnR2GEKIMKqrkcWxsrEOqh5VK0lIYg8GAWm3XbWHKLSl5LIQQoiwymUxcPr4Pj7BolGrXUu1Ln5VL5v7jmPR6Qu5vWap9CSFKR7kseXwzLVq0cFRXQgghhCiBc78vJenPn8k8safU+zLma0j5aw/pu49g0jtmWpoQovwq0dDHc889h+kW6rknJCSwf//+knQlhBBCCAfxi65LxpnDpB/9B99ad6NydS+1vtxCAlH7eWPIzCH3TDw+daJLrS8hRPlXoqQlISGB9evX39Jzrt27RQghhBBlT0DNxlzatxVNejIZx3cR1KhdqfWlUCjwqR1N+q7D5JyMk6RFCHFDJZoe9sILL/DJJ5+g1+sxmUw3fVy4cIGIiAh7xy6EEEIIO1IolYQ26wRAxpG/0aZfLtX+vGtHA5Bz6hzmW5jBIYS485QoaenZsyddunQpUPmqKBEREfz0008l6UoIIYQQDuRfvQG+VWtjNhm5uG0VJoOu1PryrBqK0t0NY76W/PhLpdaPEKL8K1HSolKpqF69erHabty4EaPRSExMTEm6EkIIIYQDKRQKqnZ8BBdPH/RZqaTs2Vx6fSmVeNeqCkD2ibhS60cIUf6VuAbxnDlzAKhcuTL9+/cvsl1AQAAxMTF07NiRDz74oKTdVUgxMTGyT4sQQogyx8XDi6j7HyV++68E1G1Wqn351I4m5+Q5FEpZ+ypEWVbUPi2OUuJ9WlxcXPj222/p168fKpWKrVu3Flhsf++99wKwd+9emjdvjsEgJQ1B9mkRQghRPphNJkxmM3l5eaXXh9EEmFEUc8q5EKLsKBf7tLRs2ZIBAwZYRwoCAgJYt24dHTt2ZMWKFQQFBVnbNm3alIYNG95+tEIIIYRwGIVSiUqlws3NDU3aRcxG+7/5qFApJWERQtxUiaeH+fv723zdqFEj7rrrLpYsWcKHH35YoH1AQEBJuxJCCCGEE2We3Ev89jX41WlKpXs6lUofZrMZfXoWroF+pXJ9IUT5VuKRlsL2XVEoFERHRxe7vRBCCCHKPlcffzCbyDy+m9z4U3a/vslg4MynSznz6VIMufl2v74QovwrcdJS1FIYSU6EEEKIisUvqi6VGrUB4NLONehzs+x6faVajdLFMvkj71yiXa8thKgYSpy0gCVxuX4jSbPZXOB4Tk4OSUlJ9opZCCGEEA4W3qILHpXCMek0XNq+2u6bQXpGhwOQFydJixCioBInLWvWrEGtVuPi4mLz+PPPPwsc9/Pz4+TJk/aMWwghhBAOpFSpqdZpAEoXNzTJ8aQd2mbX63tGRQCQKyMtQohClHghvkqlolGjRsUqb5aVlcX+/ftL2pUQQgghygA3vyCq3NuLc5uWkX54B95VauMWGGqXa3tWDQOFAn1aJvqsHFx8ve1yXSFExVDipGXy5MlMmDCh2O2HDBlS0q6EEEIIUUYE1mpM7sVzuPgE4hpQ2W7XVbm74h5WCU3iZfLiEvFrVNtu1xZClH8lTlq6du16S+0fe+yxknZVYcXExFj3ubli6NChDB061EkRCSGEEDdXpd1DAOh0OrRard2u6xUdjibxMrlxCZK0CFHGzJ8/nwULFtgcMxqNDutfYS6qDNhtev7555k6dSphYWGlcflyLSsrCz8/P4fsHiqEEEKUppzMDDLPHsG3RqPbvlZ+wiWyjp7Gu2YUXtUi7BCdEKI0ZWVlERkZ6ZB72tuqHnYjX3/9Nenp6aV1eSGEEEI4mUmv4/zar7j8929knz1y29fziKhM5c6tJWERQhRQaklLKQ3gCCGEEKKMULq44l+9AQCX/1mLNu2SkyMSQlRUpZa0CCGEEKLiC2t2Pz5VamE2Gkj662eM2tvb0d5kMJAbl0jWkVg7RSiEsAdnD0hI0iKEEEKIElMolUTf/yiuPgEYcjJve+NJzcUULnz3K5c27HD6TZIQ4hpm+24oe6tKLWnZtGkT1apVK63LCyGEEKKMULt7Uu2Bx1CoXchLOkvawZJvPOkRFoLCRY0xT4P2cpodoxRCFIfZbCb/0nku/rWSnPMnrjmjcOobCSVOWp544okbnr/33nvx8PCwfv3kk0+WtCshhBBClHGewWFUvbc3ANlnD2HSl6wUskKltGw0CeTFJdgrPCHETZj0WjJP7uXCmoUk/P49OeePk3Fit/W8QqlEoVA4Lb4S79OyZs0ajh07Rq1atVAqi859TCYTp0+f5rfffitpV0IIIYQoBwJrN8Gk1+JdtS7625jM4RkdQe7pC+TGJRLY4vZLKQshiqbNSCbr5D6yzh7GbNABoFC54FOtAX617nZydFeVOGnJyMigYcOG9oxFCCGEEOVccIMWAChvY+NJr+hwkoH880mYTSYUN3hzVAhxe5L/XY8mOR4AF99A/Go1xad6Q1Su7k6OzFaJkxZ/f38yMjIICwujdu2id601mUycP3+e8+fPl7QrIYQQQpQzrq6upJ/aT9aFWCq37nFL00rcKgehdHfDpNGiSUrGI6JyKUYqxJ3DkJdNVuwB/Orcg8rNsozDr/Y9qNw98avdFI/KUU6dAnYjJU5aLly4wOeff87s2bPx9vZm/PjxtG3bttC2BoOBWrVqlThIIYQQQpQvuuwMkrb/itlkxM0/mIAGrYr9XIVCgWdUGDkn4sg7lyRJixC34crC+sxTe8m9cBLMZhRqFwLqW0ZFfaLr4RNdz8lR3lyJx1u9vLwYPXo0p0+fpl+/frz00ku0bduWX3/9tUBbtVrNyy+/fFuBCiGEEKL8cPXxJ7JtDwBSD/xJXtLZW3p+cNumRD/7MIGtGpdGeEJUeCa9lowTezj/6xckblpC7vkTYDbjXikSV79gZ4d3yxRmO9YuW7NmDTNmzCA1NZVx48bx2GOPoVKp7HX5CiMrKws/Pz8yMzPx9fV1djhCCCFEqTn/xwpSj+9G6epOlW5P4+Lt7+yQhKjwTAY9cSvmYdJpAFCoXfCp1hC/WnfjFhBit36ysrKIjIx0yD2tXVe2de/ena1bt7Jw4UJWrVpFzZo1mT17Nnl5efbsRgghhBDlRGTbHnhWisCk05D058+YDPpbvsbtbFYpxJ3AqMkj++wR69dKtQueYdVx8Q0iuFlnqj08jJDmD9g1YXE0u460XCs3N5cxY8awYMECAgICGDp0KFOmTCmNrsqdKyMttWvXLjASNXToUIYOHeqkyIQQQgj70+VkcGL5Jxg0uXiERhHesX+xKoIZtToub9hB7pl4qr/YH6WriwOiFaJ8MJtM5CWeJuvMIXLjY8FsomqP56xTv0x6LQq1q90W1s+fP58FCxbYHDMajcTGxjpkpKXEC/GLkpaWxpw5c5g3bx5paWmYzWaaNm1K+/bt7d1Vubdr1y6ZHiaEEKLCc/X2p1rXxzi95it8wqsXu4Sx0tWFvPNJGHLyyD5+Fr9GRVcrFeJOoctMJevMQbLPHMaoybUedwsMxfjfdDAApYubXfsdPHgwgwcPtjl2ZXqYI9gtaYmPj+eDDz7giy++IC8vD4VCwSOPPML48eNp2rSpvboRQgghRDnkHRpFvf4jcfX2R6vVotPpbvochUKBX+M6pGzdTcb+45K0iDteXtJZEjcvtX6tcvPEp1oDfGo0ws2/khMjK323nbQcP36c6dOns2TJEnQ6HW5ubjz//PO88sor1KhRwx4xCiGEEKICcP1vEb6bmxtGvZaLuzYR1LjdDd8R9mtUh5Q/95B/4SLa1AzcgvwdE6wQTmY2m8m/fB6zXodXpGXrEI+QKqjcvXALCsO3RiO8wmuguEOKXpU4adm1axfvvfceq1evxmQy4efnx+jRoxk5ciSVKxesp/6///2PRYsW3VawQgghhKgYkv5aRda542hTkwi/79EiExcXXy+8alQhN/Y8mQdOEHJfCwdHKoRj6XMzyT5zmKwzhzDkZKD29sczoiYKhQKFSk1UryEo1Xfe+q4SJy0tWrRAoVAQFhbGqFGjGDJkCN7e3oW2PXPmTKH7twghhBDizhR6T0dyk+LQpCSQsHkp4R0fReXqXmhb/8Z1LEnLwZNUah+DQmXX4qdCOJ3JaCD3wkmyzhwi/5o9jRRqVzxDozAbdCj+S+zvxIQFbqN6mFKpxNXVlcqVK6NUKousTGAymbh8+TJarRaj0XhbwVYUsk+LEEIIAXnJCcT+ugijNh+3oHDC7ys8cTEbTcR+/B3G3HwiHumCT51oxwcrRCm6/M86smL3W7/2qFwVn+qN8K5ap0wnKY7cp6XEIy2+vr4cO3aMsLCwm7bNzs6mdm1ZPCeEEEKIqzwrRVCz5zPE/vIl2tREEjcvJfy+/gUSF4VKSWDLxpj1etzDKvZiY1HxmQx6cs4ds+xM7xsIgE90ffISz+BTvSG+1Rvh4uPv3CDLoBInLYMHDy5WwgLg4+PD008/XdKuhBBCCFFBeQaHU7Pns8T++iXa1CQubVtN+H2PFmgX1LKRE6ITwn70ORlkntpH1umDmLT5+NVqSqXmXQBwD6lCVK8hxS4Hfiey2+aSSUlJnDhxgkuXLhEQEECzZs0IDAy0x6UrHJkeJoQQQtjKT71I3O8/ENauDyofuX8QFYPZbCYv6SyZJ/eSlxBrPa729MW/XnP86zZzYnS3r1xMD7ti//79TJgwgfXr13Nt/qNQKOjVqxfvv/8+1apVu91uhBBCCFGBeQSFUrffCBRKJRqNBr1ej9lstlkzazaayD4VR+6p84T2aG+3nb6FKC0JG79Dkxxv/dojNBq/2k3xiqgpoyq36LaSlpUrV/LYY4+Rn5+Pi4sLtWvXto4gnDhxghUrVrBlyxZ+//132WBSCCGEEDd05SbO3d2dvEvnubTrd0LbP4za3QsAs9HIxV+2YtLp8b2rNl7R4c4MV4gCtBnJuPoFWxNqj5Aq6DKS8aneEL/aTXH1DXJyhOVXiVO8pKQk/ve//+Hh4cHnn39ORkYGhw4dYtu2bRw6dIj09HTmzp0LQK9evdBoNHYLWgghhBAVl9lk5OKOX9GkJBC/7mu0aRcBULq64NvAsnF1xr5jzgxRCCuz0Uh23DHiNyzmwpqF5CWesZ7zr9+C6D5DqdSssyQst6nEScu8efPw8PBgz549PP/883h4eNic9/Ly4qWXXuKff/5Bp9PxxRdf3HawzpScnEyPHj3w8fGhWbNmHDhwwOZ8VlYWzz33HC+++CJdu3aVjTSFEEKIElIoVVTv9iSuvoEYcrOI37CY7LNHAPC/uz4A2cfOoM/MdmaY4g5nyMsm9eBfxK38hEvbV1mmgSkU6DKSrW1Uru4oXVydGGXFUeKF+O3atWPMmDH07t37pm1/+OEHvvrqK9atW1eSrsqEl19+mW7duuHi4sLQoUNRqVQcOnTIev6RRx6hR48ePP300+Tm5tKwYUPmzJlDz549C1xLFuILIYQQN2fQ5nPu92VkXTgJgF/dZgTf3ZELS9aSF5dIQPOGVO7c2slRijuNSa/j8t+/kXPhJJhNAKjcvfCt1QS/mk1Qe/o4OULHceRC/BInLY0aNeLgwYPFams0GmnUqBFHjhwpSVdOp9FoSElJITIyEoAff/yR559/noyMDABOnDhB3bp1uXTpEiEhIQBMnjyZX3/9ld27dxe4niQtQgghRPGYTSaSdm/i0t4/AMume75VW5Hw40YULmpqDn8MlYebc4MUFZ7ZZLKuuTKbzVz47Ut0Gcm4V4rEr/Y9eFepjUKlcnKUjlcuqodVrly52G1VKhXBwcEl7crp3N3drQkLgF6v58UXX7R+fWXE5dr8r3HjxkydOpWcnBy8vb0dF6wQQghRgSiUSsKbd8azUjjnNi/H1dsfr5pRuIUEor2cRsbeowS1udvZYYoKyKjVkJd0htyEWDSXL1C1x3MoXdxQKBRUiumC0sUNt4AQZ4d5xyhx0mIwGG6pvVarLWlXZUp8fDyLFy9m2bJl1mMBAQEAHDhwgC5duliPm81mSVqEEEIIO/Cv1gD3vpVx9fZDqzcQ2LIRaf8ewrWS7Oki7EeXlUZuwily42Mta1SueUM65/wJfGtYNjn1CKnirBDvWCVOWk6ePInZbKY4s8vMZjOnT58uaVelasyYMQUW1V9r1KhR9OjRA7DsSTNx4kTWrl1Lx44d2bZtG25ubrRp04bo6GimTp1K27ZtUalUrFmzBldX11sakRJCCCFE0dz9LbM23FVq/BvVISflAJrME3gbI+/IqTnCvrLOHOLyzjU2x1z9gvGMqIlXRA3cK0UW8UzhCCVe06JUKm95Uyej0ViSrsqcFStW0LdvXxYvXsygQYMASxI3duxYUlJSuO+++9i8eTMeHh5s2rSpwPNlTYsQQghxe7ITzhD7y0IA3CtFEtquN2oPmdkgbs6ozScv0TLtyzO8Or7V7wJAn5vJudXz8QipgldETbwiauLi4+/cYMu4crEQX6VScdddd1mnRhXFbDaTmprK0aNHK0zSAtCxY0e6d+/O2LFjC5y7ePEi0dHRfP/99zz88MMFzkvSIoQQQty+9LNHObdxOcZ0BSpvFyIe7I17cISzwxJljNlsRp+VSm58rGV9SkqCddqXZ3gNwjv2s7Y1GXQo1VKiuLjKxUL8N954g8mTJxe7/eDBg0vaVZnk7+9Po0aNCj33+uuv07Nnz0ITFiGEEELYR0C1+mSEtCDlzGFMeXriN3yHb427CGjQGhdvP2eHJ8oAs8nE+TUL0Wel2hx39a9kGU2JrGVzXBKWsqvESUv37t1vqX3//v1L2pXTZWVlsWLFCvr06YOvry8HDx5Ep9PZLLq/Ys6cOaSkpLBkyRInRCqEEELcWSLua0Pqv0cxa10w5yvJij2APjeLiPvK732HKBmjJo/cxNPoMlMJvrsDYKk+p/bwRp+TgWflKDwjalimfUlSW+6UeHqYs23evJlZs2bRvHlz3nzzzSLb/fbbb7z77rsolUr0ej1Dhw7l8ccfv6W+Tp06RZcuXcjNzaVDhw7UqFGD119/HR8fy+ZBOTk5rFq1iuPHj1O1alWeffZZlP/V8i6MTA8TQggh7Ofsj+tI2XMEj+jKqEPzCGzYBo/KVQHL+gWTQYeLl9ykVjRmsxlt+iXyEs+Ql3DaMu3rP9EPD0ft4QWAPjsDlbun7ExfCsrF9DBniYuLY/HixXz55ZecPXuWZs2aFdl2wYIFjBw5ku3bt3P33Xdz7tw5YmJiiI+P59VXXy12n7Vq1eLs2bNFnvf29uaxxx67pe8DLD/o4nBzc8PN7c7YOOuPP/7gvffeY9CgQTz11FMFziclJTF69GiCg4NJSkrihRdeoHPnzrfc5kbS09P59ttvWbhwIS+//DJPP/307X5bdpWUlET16tXRaDTWY7t27aJZs2YYjUZGjRqFWq0mLi6OV155hdatC+4WvWnTJrZs2cLbb79dZD+nTp3il19+Yfz48RgMBp599ln69Olz01HWS5cuMW/ePHbt2kXlypVxcXEB4P7772fp0qUsWrSIlJQUfvvtN1555RXUajX9+l2dT2wwGNi1axceHh7s37//Fl8dIcSdKPTeGFL2HCE/7hLVuz6Ka5C/9Vz6kb/JOLEL3xqNCWzYCrWnvFlYEWSe3Efaob8wavJsjrsFVMYzoobNMVlMf+u0Wm2xtivJzs52QDQW5S5piY6OZuLEidStW9fmRud6sbGxjBgxgiFDhnD33ZZNp6Kiohg1ahQTJ06kU6dON0x4HKFKleLV+J40adItrR8qr9auXcsPP/zAhg0bGDhwYIHz2dnZ3Hvvvbz11lsMGDCA1NRU6tWrx4oVK2jTpk2x29yMyWQiODiYgwcP2vX7s5eZM2fy+uuv4+HhAVjWV135t/zJJ5+wb98+tm3bxsqVK+nfvz+nT5/G1fXqu0vJycnMmzfPZq+hwtSqVYvRo0fz/fffExsbyxdffHHT2P78808effRRRo4cya+//orqvxKk+fn5jB8/npUrV7Jo0SJq1qzJiBEjWLx4MYmJiXz11Vc218nLy+N///vfrbwsQog7mEflIPzqVifz+BnS/jlI6IP3ApZ34nWZKWAykXVqH1mnD+JXszEBDVqh9vRxctSiOMwmE5rUJPKSzuAT3QBXX8u+PAq1GqMmD4XaFc/QKDzDquMVWUOSUjv54IMPmDZtmrPDsFHukpYrAgNvvJnU9OnT0Wg01j1WrujSpQsTJkxgxowZN71pK20XLlwo1lDanTLK0q1bN6Kiovjmm28KPT9t2jQyMjKs66OCgoLo0aMHw4YNY9++fcVuczNBQUG0bNnSDt+R/V28eJGUlBRmzZpV6Pn169dTrVo1AOrUqUN8fDxHjx6lSZMmgOU/8FGjRjFr1izU6uL9+nt7exdrg9S4uDh69epFnz59eO2112zOeXh4MHv2bE6dOmVz3NPTs9BreXp6MmDAgGLFJ4QQAGH3xpB5/AyZB08RfG8z1N6eKBQKwjv2I+/SOdIObkNz+QKZJ/eSGXtAkpcyzJCfY5nylXiGvItxmHSWmQVKtSuu9VsA4BVRk/D7B+JRSfboKQ1jxoxh2LBhN22XnZ1NvXr1HBBROU5abrRmxGQysXr1aoACFb6aNGmCUqlk7dq1GI1G6zvBzuDr6ytrWq5zZfSgMIsXLyYmJsZmf6DmzZuzaNEiDh48SKNGjYrVpjhu9O/LmaZPn87atWt56qmnGDBgAN26dbM57+HhgV6vB7B+vPY1/eijj+jVqxfR0dF2j+2NN94gIyOj0DLgAAqFgtdff73Yr22fPn3sGZ4QooLzrhaBV5Uw3AL9ULrY3t54Vo7Co1NV8i+dJ+3gX2iS48k8uReASjEFi+oI59Blp3PxrxXo0i/bHFe6uuEZWg1X/0rWYyo3DzxDoxwd4h2jLC5NKLdJy40kJiZy+fJlPDw8qFSpks05tVqNr68vGRkZnD59mtq1azspSoiJiSmQNA0dOpShQ4fe8HnNmsHFi6UZWcmEhsLu3bd3jaI2LI2Pj+f8+fN06NDB5nhYWBhgWdMRGBh40zZFJS05OTm8+uqr6PV6zGYzmZmZBdpMmzaNU6dOERgYyIYNG+jXrx8TJ04kMTGRxx9/nC1bttCqVSu++OIL6tevj0ajYfTo0axdu5bly5fTtGlTpkyZAlhG2b7//ntmzpxZrHcywLI5a1ZWFvXq1WPp0qV88803PPHEE3z22WfWEYunnnqKl19+Gb1ez86dO7nnnnuoVctSznHv3r2cOXOGl19+uVj93Yq8vDyWL19OaGgo9evXL7Jdu3btinW9SZMmWV+r6+n1en755RcWLVpE5cqVefjhhxk1ahTJycn06NGDjz/+GH9/fwByc3OZNm0amZmZ7N69Gw8PDz744AOaNGnCjh07WLhwIf/++y/z589nwIABVK1alb/++qtAn6mpqUydOpXAwED27NnDL7/8wqFDh2jYsCEAv//+O7/88guXL1/mwIED9O/f35rEffvttyxatIhRo0ZhNBqZOnUqubm5zJ4927oW7mbXP3jwILNnzyYwMJC9e/dSqVIlZs6cSZUqVUhPT+e7777jyy+/ZOjQoezcuZNly5axZMmSW67yKER5plAoqP2/Pqg9PdDpdAXm4ysUCjxDo/CoXJX8S+dIP7wD//pXR9U1KQkY8nPxiqiBQinv3Jc2fU4GeYlnUajV1s0d1R7e6LPSAHALCsMzvDqeYdVxDwpDUUbfTLyTzJ8/nwULFtgcc+QejBUyabl06RJAkaMYPj4+ZGRkkJaW5siwCti1a1eJRlouXoSEhJu3q0gu/pelBQUF2Ry/UsHt8uXLxWpTGJPJRK9evbj33nuZNGkSQIEb5sWLF/Paa6+h0Whwc3OjRo0avPjiizzyyCPUrVuXFStWULduXaKioqw37e7u7kRERDBt2jTuuecefvzxRzIzM/nwww8By8L0W/k3qFKpWLjQsvtzamoqr732GgsWLEChUPD1118D0KNHD7RaLe+88w4Gg4G1a9eiVCrJyclh6tSpfP/998Xu71acPXsWjUZDeHj4LT83IyODIUOGAJZF+Dt27CAkJKTI9gaDgerVq7N582bq169vff0XL15snX/77bffAjBw4EDmzJlDdHQ0RqORBx54gK5du3Ly5Em8vb05cOAAFy9eZPv27bzxxhucPHmy0D7ffPNNunTpQq9evQDbfaf++OMPVq9ezZw5cwDYunUrHTp0wMPDg2eeeYYaNWpw4MABVqxYwZAhQ9izZw8DBgxg2LBhDBgwAJVKdcPrx8bG0rlzZ3bv3k2VKlUwGAx069aNdu3acfjwYbRaLZ6enuzbt48ffviB8ePHAxAaGnrLPwshyju1p2Vk2dXVFZ1Oh1GnLzDqYkleovEMjbY5nn7kb3LjT6F088Anuj4+1e/CLaBykW+mieIz6bVo05PRZVxGm36Z/MvnrcmJq1+wNWlRql0I6/AIbv4hqNwLnz4snGfw4MEF9l28Uj3MESpk0nLl3ZVrFx9fy2Aw3PB8WVdW70UcEdf1ayCuZPjX/iyL0+ZaS5cuZfPmzaxYscJ6rH///jbFD+rUqcPYsWOtQ6VXbqqTk5OpW7cufn5+jB49mokTJ5KQkEBERARms5m1a9eyZcsWABISEvjhhx/o27cvbdu2pX///mzfvr0kLwNBQUHMnz8fhULBggULmDFjBpUrVwagb9++9O3b16b92LFjeeutt/D09MRoNPLRRx+h0+nw9PRk+PDhtz0d7kolvJL8Tvn7+/PZZ59Zv05JSWHEiBFFtvfw8KBJkyYEBwdTvXp163S09957j82bN7N48WJmz57N4cOH2bt3r80i//DwcAwGA+fOnaNRo0bUr1+fc+fOWSuuFSUhIYGZM2fSpEkToqKieOWVV6zT7qZOnUp4eLj134vJZKJ9+/ZcunSJ4OBgaxLbq1cv63S+3r17s2nTJi5dukR4ePgNr//GG2/QuHFja+EOtVrN1KlTad26NbNnz2bChAnW0cUePXrQqVMnOnXqdAs/ASEqHn1OHok/bcRoMBDR74FiJR6ufsFoUhIxanLJPLGHzBN7cPWrhE/1hvhUa4Da4+Zr++50ZrMZQ24WhtxMa8lpgPj131oKIlxLocA9OALP8OqYzWbrz+j6ZFKIKypk0hIcHAxYpqwU5soN1o3ezS3LbncKVnl05YYtPT3d5vi1P8vitCnMqlWrCAwMtBn1cnd3t2kTExNjHS3Zu3cvubm5gO2w6JAhQ3j33Xd5//33+fDDD9myZQvt2rWzlvx94oknWLx4Me3ataNDhw5MmDDhtm8u33nnHRYsWMCZM2esScv1Fi9eTOPGjbnrLss7Wa+99hpJSUl8++23DBw4kEuXLvHuu+/eVhxRUZZ5xYmJibd1HbD8/l4/xa8wCoWiwBqoBx98kH///ZeTJ0+ya9cuvLy8blh5T6lU4uXlddOiBBMmTKB79+7UqlXLOvXryjtLu3btYv78+YVWvLvSx7UfAWthA51Od9Prb9y4kfvuu8/mmlemlv7999821/bzk30ohADQZ+eSdfIcZqORzAMn8G9S96bPCWrSnsBG7chLOkv2mUPkxp9Cl5lM6r4t5Jw/TpWuBcvw38lMRgO6jBR0GZfQpltGUHQZlzHptChUaqo/Oto6pcs1IASjTotbQIjlERiKR2gUKlf3m/QixFUVcoJgjRo18Pb2Ji0tzWYvC7BUOcjNzSU4OLhEU1mEc4SEhBAREUFSUpLN8fj4eADatm1brDaFyc7OJisr64bzMi9evEibNm3Q6/W89957PPzwwwXa+Pj4MHToUBYsWEBqaipffPGFzTBqUFAQ//zzDwsXLiQuLo7OnTvfdrIQHBxMUFBQkeWzY2Nj2bhxIy+++KL12Ndff03z5s0BaNmyJV9++eVtxXBltODuu+/m/PnzBSqEXe9KgYAbuX74ubiuJG7u7u7o9XrOnj1bYFqg0Wi85amhMTExnDp1ivHjx7Nq1SoaN27M5s2bAcv3888//xR4TnJysl2ubzabCySDarWaSpUqWRNiIYQtz7BKRHSx7FF1eeNOdBnF2xdNoVTiFVGD0Ha9iX54OJWaP4B7cAQ+1Rpa2xh1Gi7/u5785ATK6f7ct8RsNluqeSWdtfl+L21bRfy6r7j891oyT+xBc/kCJp0WlEpcfAMxavOtbSu36k61h4cS3rEfQU3a4121jiQs4pZVyKRFpVLRs2dPzGYzBw4csDl35MgRALp37+70ClExMTHUr1/f5jFv3jynxlRWKRQKnnnmGbZt22bzR3Pv3r20bNmSatWqFatNYerXr4/BYOD3338vcM5kMgEwbtw4DAYDgwYNumGcI0eOxGQyMXHiRHJzc236vDL97JlnnuH48eM8+uijTJ8+vfgvQiHOnDlDmzZtCp1PqtfrGT9+PB999JHN8ZycHOs0Ljc3N/Lz8ws8t7jy8vKs39c777wDWKYzFWX16tWcOHGiWNc2mUx8/vnnN2xzfaKZkJBAcHAwDRs2pFGjRuj1el5//XWbNosWLSpyFLYoy5cvx8/Pj7feeovjx49TrVo1Zs+eDVgqFM6fP98mWcvLy2PRokV2uX7r1q3ZvXs3qamp1vYmk4m0tDQefPDBW/o+hLiThLZrhnd0BCadnqTVf2D+7+95canc3PGrdTeRDzyBX+2m1uM554+TdWofCRu+5fwvC0g9+BfZcUfJT07AkJddbhIZk9GAPifD5nXJjT/F5X/Xk7T1Jy6s+4a4lZ9w5of3ift5Lombl2LMz7G2dfWvhNLNA4/QKPzrxhDSqjtVHvwfNR4dQ9UHn7HuRg9IYYMKYv78+cTExNg8ijMzwl7KbdJy5WayqD8O48aNQ6VSsXLlSpvja9asQa1WM27cuNIO8aZ27drF0aNHbR43qxxW0V15F/7KuqNrjRo1CldXVzZu3AhY3uFft26dzU15cdpcb8SIEXh5efHiiy+yZ88eDAYDq1atAmDfvn2cO3eO/Px8jhw5wpYtW9i9e7d1o8VTp06xdetW67UqVarEM888w2effVZgc8QTJ05Y1264ubnRv39/a23zI0eOULNmTZ5//vki49yyZQv9+vWzJuJnz55l4sSJzJ8/v9D2b7zxBi+//DIBAQE2xzt16mS9wT527NhNK3rl5uaSnZ1t/Z27QqPR8Nxzz1n3tOnWrRsffvghP/74I0899ZTNiJder2fBggUYjUZrRawr1yhq5GXcuHHWaWdFOXjwoDVx0Wg0fPvtt7zzzjuo1Wq6du1KTEwMCxcupHv37nz22WeMGTOGgwcPWpM8k8lUYDS2MIsXL2bvXkt51PDwcDp16mT92U2cOJH8/HxatWrFlClT+Pjjj3nwwQeto3FXvr/rXz+4+u/8Rtd/++23gatJIcCyZcuoX78+Tz75pM21i/O9CHGnUCiVVOvXFaWrC/kXLpL276GSX+uaNTFuAZXxqdYAhcoFfXYa6Ye2c2n7ahI2fEvcinloU6/+7ctLiiP14F9knT5I3sU49NnpmEuh2pLZbMZsNGLSazHk59gmIgmnSd7zOxe3rSLh9+8598sCziz7kDM/vM+5VZ9hyL06CqVJSSTr1D5y40+hTU3EkJuF2WSJ18U3EIMm19o2sGEbqvUdQcT9Awm+5358rxQukD1TKqzBgweza9cum8cff/zhsP7L7ZqWCxcuAFen/lyvSZMmTJ06lZkzZ/Lcc89Ro0YNDh06xNy5c5k5c+YNy7IK59i5c6d1qtI333xDSEgIDz30kPV8YGAgmzdv5vXXX+f3338nPj6e5cuX06JFi1tqc70qVaqwbt06Ro4cSatWrWjSpAkjR44kLCwMX19fzGYzEyZM4NChQ/Tt29e6SH/9+vUsW7aMH374weZ6Y8eOZfXq1fTs2bNAX8OHD2f9+vU0bdqUxMRE63NTU1PJyMhg9erVDBs2jMaNGxd4bmBgIEeOHKFFixY0atSI++67jwULFuDl5VWg7bp16/D29i50Stynn37KsGHDmDRpEnFxcXz66aeFvi6nTp1i5cqV7N+/H4PBQMOGDQkLC0OhUJCbm8vx48fx9/e3blwJlqSxVatWzJo1ixYtWhAUFET16tUJDQ3lueee4+677wYs09aWLVvG3r170ev19OrVy5pc6XQ6Dh48SHJy8k1341Wr1YwYMYKgoCAOHz7MhAkTrImfUqlk3bp1jBo1ilWrVrF3714GDRrEzJkzAcvoxpXF8JMmTWL48OHW9XDX02g0tGvXjgEDBhAREYFarbZWmHvooYf48ccfmTRpEtOmTaNRo0bMnj2bmjVrEh8fzwcffADAd999R4MGDVCpVHz33XcAzJo1y1qVrqjrN27cmI0bN/Lqq6/StWtXatasCcDmzZtxdXUlLi7OOmL3ySefUKtWLe6///4bvm5C3Cncg/yp2qMDcT9vJOWPXXhVr4J7yI03p775NcNwb92TSjFacs6fIP/iOfS5mZYF6PnZqL2uro/MSzxNxvFdBa6h8vBG7eVL5dY9cfWx/O3LPneM/IvnMJsMmI1GzEYDZpPR8rnJSGi73tZiAGmHtpN5co/1nNlo+0Zf1EMv4PLfdTXJCWQeL3wxrEKpwqjLxwV/ADxCo0GpRO3ujcrDC7WHFyp3L1Qe3ihV11Vhk+REOJjCXF7GMf+TkJBA3759OXDggPVdxSZNmjBjxgw6d+5coP2iRYuYN28eXl5emM1mxowZYy0r6ixZWVn4+fmRmZkpm0tWQDt27GDDhg03XABelGHDhjF+/Pgi16iIq6Kjo+nQoYNNdTAhhLie2Wwm9puV5CWlENb7PjwiSq8Ij9lkAoXCOjKTc/4EeUln0P9XUcuQm2WTYEQ/PMyaiCTv3kjmiT1FXrtqz8G4+loSrtT9W0k/srPItpEPPIF7cARgGe3Ju3gWtYf3fwmIlzUpUbq4SUlncVuulDx2xD1tuUtaKoIrSUvt2rVLtLmkKNueeuop3nnnnVuuW/7vv/+yadMmXnvttVKKrGKRpEUIUVz63DyUajVao8Ghm+Fdz2w2Y9TmWcsCe1WpY00achPPoE1NQqFUoVCpUCjVlo8qNQqlCs+waJQulrL7hrxsjNp86zmF6r+2Vz6XRESUgqI2l4yNjZWkpaKSkZaK54MPPuDw4cMEBwdjNpt5//33b+n5J0+eJD4+vkBpW1G0yMhI2rdvb51qJYQQN2MymcjLy8NkNMoO60LYgSNHWuQ3Vgg7OHToED/99BNarfam6zAKU7t2bUlYiuny5cvMnDmTpKQkfv/9dxYvXuzskIQQ5YRCoSB7/wniFv6MSXfz8utCiLJDRlqcQEZahBBCCMczanUc/vArdBnZ+DetR2i3G1dPFELcmIy0CCGEEELYmcrNlWr9ugKQsfcYeRcuOjkiIURxSdIihBBCiDuGb42qBDez7Bd18bc/MRmctzBfCFF85XafloogJiZGqocJIYQQDlblwXvJOH4GXUoGaTsPENyu6c2fJMQdrqjqYY4ia1qcQNa0CCGEEM6VeuA4Z5asQaFSEv38I7gF+Ts7JCHKHVnTIoQQQghRigIb1cGvTjXMZjP552VtixBlnUwPE0IIIcQdR6FQENW7E8Z8DWY/L0wmk7NDEkLcgCQtQgghhLgjuQX4QoAvRqORvLw8Z4cjhLgBmR4mhBBCiDuaSqXCmJZFyra9zg5FCFEEGWkRQgghxB1Nn51L7IIfMRuMuIcG412zqrNDEkJcR5IWJ5KSxwX98ccfvPfeewwaNIinnnqqwPmkpCRGjx5NcHAwSUlJvPDCC3Tu3PmW29xIeno63377LQsXLuTll1/m6aefvt1vy67y8vJYuHAh77//PufOnSu0zS+//MJXX31FaGgoOTk5vP/++1SqVMl6/q233iIhIYH09HQGDhxI7969C1zj6NGjzJo1iy+++KLIWBISElizZg3jxo0jMzOT3r17079/fwYMGHDD7yErK4vPP/+cTZs2UblyZVxdXTGZTLRp04Zdu3bxwgsvEBISwtq1axk3bhxpaWk8+eSTKBQKwFJi8fDhw5w9e5aMjIybv2hCCHEDLj5ehLRswqVte7i4bhvVB/dD6eri7LCEKFOk5PEdSEoeF27t2rX88MMPfPPNNyxatKhAspCdnU3Tpk156623GDBgAKmpqdSrV48VK1bQpk2bYre5mdTUVNavX89jjz1WaBzOlJWVxY8//siMGTM4efIkhf36/vrrr7zwwgscPXoUPz8/Fi5cyLx58/j7779xdXXll19+4aWXXuL8+fMcOHCAtm3bcvbsWZukRqPRMGDAAL755pti/Rt95JFH+Omnn0hPT8ff3/+GbY8cOULv3r3p2rUrM2fOxN3dHbD84Xv//fd59dVX2bdvH02aNAHg0Ucf5ccff0Sv16NWX32fxWg00rdvX1auXHnzF04IIW7CqNVx+MOv0GVkE9iiESGdWjo7JCHKPCl5LO5I3bp1Y/z48UWenzZtGhkZGfTv3x+AoKAgevTowbBhw26pzc0EBQXRsmXZ/M/K19eXZ599ll69ehV6Xq/XM3z4cAYOHIifnx8ATzzxBMeOHeOTTz4BYP369URHR6NQKKhTpw65ubls377d5jrjx4/n9ddfL/YfIG9vb5uPRcnIyKBnz55UqVKFjz/+2JqwgGVO+fjx43nhhRdsnuPp6VnotVQqVaGjcUIIURIqN1eiencCIO3fQ2iSUpwckRDiWpK0iDLFw8OjyHOLFy8mJibGOkUIoHnz5uzfv5+DBw8Wu01xKJVl+1ejqNdp27ZtxMXF0bx5c+sxV1dXGjduzDfffGN9rl6vB7B+vPZ6K1asIDIy0uYa9vLBBx9w9uxZxo4dW2Sb8ePH4+rqWqzr9enTx16hCSEE/nWrE9ioDpjNXPztT8xSBlmIMqNs35mJIs2aBZGRN3889FDB5z70UPGeO2uW7fOys4vftqSuTTauFR8fz/nz522mMAGEhYUBsGvXrmK1KUpOTg7Dhg3jhRdeYPDgwYWO+EybNo1nn32WV155hcaNG/P2228DkJiYyH333YdCoaB169YcPXoUsEyxeumll6hWrRp79uzBbDYzefJkJk+ezLPPPouHhwdz584t5itjq6jX6cqISWGvwcGDB9FqtTz22GPExsaSkZHBzp07iYyMpG3btgCcP3+eZcuW3TCpuB1ff/01CoWCjh07FtmmWrVq1K9f/6bXevPNN4s8Zzab2bBhAwMHDqRbt278+++/NGnSBF9fX3r27ElCQoK1rcFgYNq0aYwaNYr77ruPFi1asGXLFgAOHjzIyJEjCQ4O5uTJkzRo0IC6deuSn59foE+tVsvYsWOZOnUqAwcORK1W8+uvv1rP7927l1GjRvHkk0/SsGFDRo4cSV5eHrm5uSxcuJC2bdsyZcoUVq9eTd26dfH19WXmzJnFvv758+cZPHgw48aNo3v37nTv3p0jR44AlnVQX331Fffeey9TpkzhzTffxMfHh88///ymr7MQd5qqPTuicndDczGFrMOxzg5HCPEfWYhfTmVlwTX3XUWqUqXgseTk4j03K8v2a7O56Odd39beLl607FYcFBRkc9zHxweAy5cvF6tNYUwmE7169eLee+9l0qRJAEyZMsWmzeLFi3nttdfQaDS4ublRo0YNXnzxRR555BHq1q3LihUrqFu3LlFRUdYbbnd3dyIiIpg2bRr33HMPP/74I5mZmXz44YcA3H///aSlpZX4NSnMjV4Do9FIWloaTZo04ccff+SDDz7AaDTy559/4uXlhdFoZMyYMcydO7fIpOh25ObmcuHCBQIDA284olaUoUOHolAoMJlM7Nu3j5SUFKZOnVpoW7PZTEREBNu3b8fT05O1a9eyePFiNm3axMsvv8zAgQP5888/ARgyZAiDBw+2jiz973//o0ePHhw5cgQ3NzfOnDlDamoqy5cvZ/LkyWzYsAE3N7cCfc6ZM4cqVaowcuRIAO666y7ruePHjzNjxgyWLFmCQqHg9OnT1K1bF71ez3vvvUfTpk157rnncHFxoU6dOmzfvp3Ro0fz2muv8dRTTxESEnLD66emptK6dWtWrlxJs2bNrN9H27ZtOXToEJ6enlSuXJm//voLnU7HxIkTefrpp4mOjr7ln4MQFZ2LjxdVe3bEkK/Bp3EdTLL0V4gyQZKWcsrXFyIibt7uujfcrceK89zrlzMoFEU/z1H1BK5f33ClasW104mK0+ZaS5cuZfPmzaxYscJ6rH///kyePNn6dZ06dRg7dqz1ZjUkJASA5ORk6tati5+fH6NHj2bixIkkJCQQERGB2Wxm7dq11nftExIS+OGHH+jbty9t27alf//+BdaS2MvNXoOOHTsWGO14++23ef7556lcuTIAX375JYmJiSiVSkaPHm2z/qQksv7LbIs79et68+bNsy7Ez8/Pt65bKoxSqaRBgwZUq1YNjUZjTUYbNmzI9u3b+fHHH9m3bx8BAQEsW7aMyMhIfvvtNwBcXFyIiYnh1KlTdO7cmbvvvptff/2VoUOH4ufnR79+/QrtMyEhgXXr1nH//ffTsGFDXnzxRWt1txkzZpCTk2OTDHfs2JHMzEz8/Pxo3LgxAO3atbNWXuvXrx/ffPMNsbGxhISE3PD677//Pp6entaEBSwV4r799lumTJnCggULeOCBBwBo1aoVPXr0oEePHrf+QxDiDhF8TwPA8qZWXl5eoUVPhBCOJUlLOTV6tOVREqtXl+x5Pj4QH1+y596uKv8NGaWnp9scv3IjHBISUqw2hVm1ahWBgYE2i86vv0GPiYmxjpbs3buX3NxcwLbU35AhQ3j33Xd5//33+fDDD9myZQvt2rXDxcVSNvOJJ55g8eLFtGvXjg4dOjBhwgQ6dep0ay/ETdzoNVCr1QQGBhb6vK1bt6LVaunSpQsAn3zyCUuWLOGvv/7itdde45lnnuH777+/rdgqVaqEp6cnKSkp6PV66+tSEh4eHsW66VYoFAVGdbp3786PP/7IiRMncHFxQa/XM2nSpCJHl66sb7pS2KAoI0aM4JdffqFx48b06NGDN954w5pE7Nq1i0GDBvHaa6/dsI9r11JdKWqg0+luev2NGzcWqNoWGRlJZGQkf//99y19H0KIq5RKJWqzguQDx/FvXMfZ4QhxR5M1LU4UExND/fr1bR7z5s1zdlhlUkhICBERESQlJdkcj/8vi2rbtm2x2hQmOzubrKysG9Yav3jxIm3atLFO53n44YcLtPHx8WHo0KEsWLCA1NRUvvjiCwYPHmw9HxQUxD///MPChQuJi4ujc+fOvPvuu8V7AYqpadOmAIW+Bq1bty70xjwtLY05c+bYjAJ8/fXX1ulSLVu2ZNmyZdZErSQuXbqEWq3mgQcewGAw8Mcff9yw/ZUCATdy7Wt7K66MJLm7u6PX69FoNBw4cKBAu+Tk5Fu6bvXq1Tly5AgzZsxglrBrqAAAOupJREFU586dtGjRgsWLFwOW7+eff/65rT5udH2z2UxiYmKB54SFhd1WcijEnc6kN3D6y5+4+OtW0v495OxwhHCq+fPnExMTY/Po0KGDw/qXpMWJdu3axdGjR20ed/LGkjeiUCh45pln2LZtm80w/d69e2nZsiXVqlUrVpvC1K9fH4PBwO+//17gnOm/yjHjxo3DYDAwaNCgG8Y5cuRITCYTEydOJDc316bPK9PPnnnmGY4fP86jjz7K9OnTi/8iFMP9999PdHS0db0GWAoCHDt2rMjYR40axcyZM21ubnNycqzTuNzc3DAajdZ3/G+VyWRiyZIlAEyePBkXFxcmTZpkfW2vt337dnbs2FHs618p5VyU65PRhIQEXF1dad26NY0aNQIsFcuubbdx40ZOnjxZ7BgAli9fjru7O2PGjOHUqVO0bdvWupC+UaNGrF69mr/++ssmrs8++8wu12/dujUJCQnWhfdXpKSk8OCDD97S9yGEuErpoiagYS0ALm/cSebBW/u7IERFMnjwYHbt2mXzuNmbkPYkSYsoU668w24wGAqcGzVqFK6urmzcuBGwvHu/bt06Pvroo1tqc70RI0bg5eXFiy++yJ49ezAYDKxatQqAffv2ce7cOfLz8zly5Ahbtmxh9+7d1l3iT506xdatW63XqlSpEs888wyfffYZ//vf/2z6OXHihPUm1c3Njf79+1OvXj3AsuFizZo1ef7552/rdVIqlUybNo2ff/4ZrVYLwKJFi7jrrrt49tlnC1xn7ty5PPjgg1SvXt3meKdOnTh16hQAx44do0GDBgQEBBQZz5VRmOt3pzcajYwfP9664LtRo0Z8//337Nmzh+7duxMbe7Uyj8lk4scff+TYsWO0b9/eelyj0dh8z9f6+OOPb1qe+uTJk9b4TCYTCxcuZMyYMYSEhFC/fn0efvhhNmzYwL333su8efN44403+OKLL6ybkV5Jrq7EUZRNmzaxZs0awDIFq1evXtaf77hx43BxcaFr166MHz+eTz/9lAcffNC6rujK91ZYInflZ3yj67/++usEBATYJIN///03Go2GMWPG3NL3IYSwFXZfSyq3+W8U+9etZJ+Ic25AQtyhZE2LKDN27tzJl19+CcA333xDSEgID11TszkwMJDNmzfz+uuv8/vvvxMfH8/y5ctp0aLFLbW5XpUqVVi3bh0jR46kVatWNGnShJEjRxIWFoavry9ms5kJEyZw6NAh+vbta12kv379epYtW8YPP/xgc72xY8eyevVqevbsWaCv4cOHs379epo2bUpiYqL1uampqWRkZLB69WqGDRtmXZh9PZPJxPfff2/dBX7SpEkMGjSIBg0aWNv079+f/Px8nn76aUJDQ8nLy2PDhg02u8mDpZzvsWPHCp2S+NZbb/Hiiy8yceJEDhw4YB0puV5CQgK//PILGzZsACx74kRHR6NUKtFoNJw6dYrs7GybqWePPPIIDRs2ZMaMGXTq1AkfHx9q1qxJcHAwjz32mHWhe2JiIsuXL2ft2rUA9OjRw7pmx2AwcPLkSfbv319gKtz1fH19GTt2LJUrV+bYsWP06NHDZm3J4sWLefXVV/nuu+9488036dmzJ/PnzwcsIy4///wzAC+//DKvvvoqUVFRhfZjNpvp3bs3jzzyCHXq1OHy5cvWUaBmzZqxfv16xo0bx+zZs6lVqxbvvfcebdu2JT093VpRbtWqVbRv357w8HBrDJ9//jmRkZE3vH5YWBh//vknY8aMoUOHDtx1113odDp27NhBQEAAycnJzJ492/r9Nm/evNApjkKIghQKBVW6d8CQryV17xESV2wickA3vKLDnR2aEHcUhVlKYjhcVlYWfn5+ZGZmFnvHcVF+7Nixgw0bNthUHyuuYcOGMX78eOvNubg9V+baOnL4WghRMZmNJmK/W03G0dMoXV2o8lh3PMILL/AixJ0iKyuLyMhIh9zTyvQwIezs888/57nnnrvl5/37779ERERIwiKEEGWQQqWkxsAe+FSvgsrdDaWrFLkQwpFkepgQdvDBBx9w+PBhgoODqVSpEpGRkbf0/JMnT5KTk1NkSVxRMgaDQfZXEELYjdJFTa2nemPI16D08pA1YkI4kIy0CGEHhw4d4qeffkKr1TJt2rRbfn7t2rW57777SiGyO1NOTg6ffvopBw4cYP/+/Xz++edycyGEsAuVmytu/r64uLjg5uZG3rlEDDl5zg5LiApP1rQ4gaxpEUIIIcq/jONniP12Fa7BAVR9vAcqdzdnhySEQ8maFiGEEEKIMs49OACVhzvaS6nEL1uPSV+wXL8Qwj5kpMUJroy01K5dG5VKZXNu6NChssGkEEIIUU7kJV7m+PxlGDVavGpUIbJfFxTX/d8uREUwf/58FixYYHPMaDQSGxvrkJEWSVqcQKaHCSGEEBVHdlwCJxcux6Q34FO/BuG9OqK4yca3QlQEMj1MCCGEEKKc8ImOoObjD6FQKck+epqLa7dJ5UIh7EySFiGEEEKI2+RXpxrVHu0GClCpZXqYEPYm+7QIIYQQQthBUOO6uHh54F0tEr3BgE6nc3ZIQlQYMtIihBBCCGEnvjWjUKpUuLm54ap2IXHVZvLOJzk7LCHKPUlahBBCCCFKQdquQ2QdjuX8d2vIOHDC2eEIYRcmg9H6uYuLi8P6lelhQgghhBClIKRlE3IvXCT90Eku/roVXUo6lTo2l8piotwxanVkHz9L1pFYDFm51B42CDc3N4cWnJDfGlGm/PHHHzzwwAN8/fXXhZ5PSkpi4MCBDB8+nEceeYSNGzeWqM2NpKenM2fOHBo3bsxXX31Vkm+j1Bw5coTOnTvj6+tLjRo1mDp1Knq9vkC7pKQkPDw8UCgU1sfu3bsBS0314cOH8/LLL9OnTx927NhRaF+bNm1i4sSJN4zn1KlTzJo1CxcXFxQKBc899xxr1qy56fdx6dIl3nzzTbp168bTTz/N888/z/PPP88PP/xAnz59yMjIIDY2ljlz5uDm5oaXlxdPP/209fH4449Tp04dmjRpcvMXTQghnETl6kKNgT0Iv78lAGl/HyT+xw0YtbLWRZR9ZpOJ7BNxJPz8O7EffcvFX7eSdzYBXWoGZOWidHDyLfu0OIHs01K4tWvX8sMPP/DNN9+waNEinn76aZvz2dnZNG3alLfeeosBAwaQmppKvXr1WLFiBW3atCl2m5tJTU1l/fr1PPbYY4XG4SwpKSk88MADDBo0iNDQUBYvXsy6desYPnw4c+bMsWk7evRoAgIC8PDwAMDf35/nnnsOgI8//pilS5eybds2Vq5cyfDhwzl9+jSurq7W5ycnJ/PCCy+wbNky1OqbD8g2a9aM2NhYMjIybtr2zz//5NFHH2XkyJGMGzfOusFqfn4+48eP5+OPPyY9PR1/f38AmjdvTmJiIvHx8TbXycvL43//+x9Lly69aZ9CCOFsqQeOc/bHdZgNRtwqBRDxaFdc/X2cHZYQRTKbzZyZuwR9Vg4A7pUCCWpSj8AmdXEP8gcce08r08NEmdGtWzeioqL45ptvCj0/bdo0MjIy6N+/PwBBQUH06NGDYcOGsW/fvmK3uZmgoCBatmxph+/Ivn7++WeWLVtGjRo1ABg0aBDt2rXj888/5/3337cmHRcvXiQlJYVZs2YVep3169dTrVo1AOrUqUN8fDxHjx61jlqYzWZGjRrFrFmzipWwAHh7e+Pt7X3TdnFxcfTq1Ys+ffrw2muv2Zzz8PBg9uzZnDp1yua4p6dnodfy9PRkwIABxYpPCCGcLahxXdwC/Yj9ZhX6zBxMGi0gSYsoGzSX08g6fIq8swlUe6YPLm5uuLi4ULltU/RZuQTdXQ/P8BAUCoXTYpTpYaJMuTIyUJjFixcTExNj8wvTvHlz9u/fz8GDB4vdpjgcPeRZHN27d7cmLAAKhYJHHnkEnU5Hdna29fj06dNZu3YtTz31FGvXri1wHQ8PD+uUsisfr33dP/roI3r16kV0dLTdv4c33niDjIwMxo4dW+h5hULB66+/XuzXv0+fPvYMTwghSpV3lTDqD3uMmk/0IqBaFafeAAqhz8ohded+zi5YTtyC5aTtPIDmYgqGxBTc3NxQKpWE3RtD1R4d8Iqo7PR/r2XvzkwUy6xZEBlpefzxh+25s2evnhs+vOBzH3ro6vnrffXV1XM//2x7Ljv76rnHHrPXd2KrqF+I+Ph4zp8/T6VKlWyOh4WFAbBr165itSlKTk4Ow4YN44UXXmDw4MGMHz++QJtp06bx7LPP8sorr9C4cWPefvttABITE7nvvvtQKBS0bt2ao0ePAqDRaHjppZeoVq0ae/bswWw2M3nyZCZPnsyzzz6Lh4cHc+fOLeYrAxEREQWO6fV66tevT1BQEGBZr5KVlUW9evVYunQpDz74IE8++SR5eXnW5zz11FPs2bMHvV7Pzp07ueeee6hVqxYAe/fu5cyZMzz66KPFjqu48vLyWL58OaGhodSvX7/Idu3atSvWEPOkSZOKPKfX6/n555/p2bMnzz33HL/99hu1a9cmICCAJ554wmYaW25uLm+88QYjRoygdevW3H///ezfvx+AHTt28Oyzz3LXXXexc+dOoqKiaNeuXaF9pqamMnLkSKZMmcJDDz2EQqHg8OHD1vO///47I0eOZODAgdSvX58pU6ZgMplIS0tj9uzZNGnShK+++oqFCxcSFRVFcHAw3/2/vTuPj+nq/wD+mSX7viKhQlBSIqqqqSVpG0oFfXhaWx9F6UOppBQl/KK8FNVYamkqKJWi1JbWrpYWz2Ov9UFEkEREIkgy2WYy5/dHmsvIYiaSzAyf9+s1r3TuOffc771zTO937j3n/vST3u2fPXtW6p9vvfUW+vbti6SkJADF47QWLVqEl19+GcuXL8fQoUPh6Oio1/gjIqpalk4OcGpcH0qlEra2tshLTkParsMQRUVPXpmoCuSl3MGN1b8iYeEapO87hoI7mZAp5HD284Vv/1A4+tYzdohl4u1hZiorC0hJKf7vggLdsqKih2X37pVeNz39YfnjVKqHZY+c5wIAhHhYlpFRubgr6/bt2wAgnZyXcHAovrR+584dveqURavVomfPnujYsaN0Ivzll1/q1ImNjcXEiRORn58PKysr+Pr6YsSIEfjnP/+Jpk2bYvPmzWjatCnq168vnZBbW1vD29sbs2bNQuvWrbFhwwY8ePAA8+bNAwC89dZbyMzMrPQxAYBt27bpnLwrFAosX74cQPFJ7sSJExETEwOZTCZNbhAaGoqCggLMmDEDGo0GO3bsgFwuR05ODqZNm4Y1a9Y8VUzlSUxMRH5+Pry8vAxe9/79+xg+fDgAQKPR4MiRI/D09Cy3vkajQcOGDbFv3z74+flJn1FsbCxmzZoFAFi9ejUAoF+/fvj222/h4+ODoqIivP322+jSpQuuXLkCe3t7nDlzBrdv38bhw4cxZcoUXLlypcxt/t///R86d+6Mnj17AgA+/vhjqezAgQOIi4uTxh4dPHgQwcHBsLGxwZAhQ+Dr64szZ85g8+bNGD58OE6ePIm+ffti1KhR6Nu3LxQKRYXtX716FZ06dcKJEydQr149aDQadO3aFR06dMD58+dRUFAAW1tbnD59GuvWrZOS8tq1axv8WRBR1RGaItzatBfqbBXyb2fAu1cnKB3KviWWqLK0Gg20+YVQ2hf3LaWVJfL+fnaQQ4O6cA1oBtcWjaG0Lf9uF1PApMWI2rRpIw1CLjFy5EiMHDnyies6OgIlP7xbWemWKRQPy1xcSq/r4fGw/HF2dg/LHh9KIJM9LHN3f2KI1eLx8Q1Ff/8y9eggcn3qPOrnn3/Gvn37sHnzZmlZnz59MHXqVOn9iy++iM8//xxWfx/skhPm9PR0NG3aFE5OThgzZgwmT56MlJQUeHt7QwiBHTt2YP/+/QCAlJQUrFu3Dr1790b79u3Rp08fHD58uDKHAQCwb98+ODg4lHtVxM3NDUuXLoVMJkNMTAy+/vpr1KpVCwDQu3dv9O7dW6f+559/junTp8PW1hZFRUWYP38+CgsLYWtri08//fSpb5nLysoCUP7nUBFnZ2dER0dL7zMyMjB69Ohy69vY2CAgIADu7u5o2LChdDvazJkzsW/fPsTGxmLBggU4f/48Tp06pTNLnJeXFzQaDW7cuAF/f3/4+fnhxo0bCA8Pr3CMT0pKCubMmYOAgADUr18f48aNk267mzZtGry8vKQ+pdVqERQUhLS0NLi7u0uJbs+ePdG1a1cAwLvvvovff/8daWlp8PLyqrD9KVOmoGXLlqhXr/jXMaVSiWnTpuH111/HggULEBERgeDgYADFSWtISAhCQkIM+ASIqDooLC3g06sTrv28A3nJabi+YhO8eofAti5/UKCnI4RA7o1UZJ2PR/alRDg08YHPe28XP1fF3h71/xECpxcbwMpZ/8HzixcvxuLFi3WWFdXgFUImLUZ0/PjxSs+0MGZM8assDRoAj020pCMurvyyQYOKX2VxcKi43epUcjJ277FLRyUnwp6ennrVKcvWrVvh6uqq81lYW1vr1GnTpo10teTUqVNQqVQAdP+xDh8+HF999RW++eYbzJs3D/v370eHDh2kBy/961//QmxsLDp06IDg4GBERERU+sQxMzMT33zzDdatW/fEujNmzEBMTAyuXbsmJS2Pi42NRcuWLdGiRQsAwMSJE5GamorVq1ejX79+SEtLw1dffVWpWEvUr18fQPHtdE/L3d1dOgmviEwmKzVO6p133sGxY8dw5coVHD9+HHZ2djoJ6uPkcjns7OyeOClBREQEunXrhsaNG6NPnz6YMmUK6v59D+bx48exdOlS9OvXr9xtPPoXgDSxQWFh4RPb37NnD958802dNkt+FPnvf/+r07aTk1OF+0FENcu5mS/8Rg3A1dVbkZd2FzdX/4ZanQLh3NrP6GMIyPyoH2TjwdkreHDmCtQPHo53LUjLgIXSQupTnm1bGtx2WT+sl8weVhM4poXMgqenJ7y9vZGamqqzvGQa3Pbt2+tVpyzZ2dnIysqq8NeC27dvo127dlCr1Zg5cyZ69epVqo6DgwNGjhyJmJgY3L17F8uWLdO5hcfNzQ1Hjx7F8uXLcf36dXTq1KlSiUBBQQFGjx6N7777Tq+k193dHW5ublJS97irV69iz549GDFihLRs1apVePXVVwEAr732GlasWGFwnI8quVrQqlUr3Lx5s9QMYY8r69kzj3v02BqiJHGztraGWq1GYmJiqVsHi4qKDL51r02bNoiPj8eECROwdetWtGzZEvv27QNQvD9Hjx4ttU56enqVtC+EKJUMKpVKeHh41OjTiomocqzdXdDsk/5wadEE0GqRtuswbv92EKJIa+zQyIzc3v4HEhatRcYfJ6F+kA2FlSU8Xm2Bph/3QfPwQZDJzTsJZtJCZkEmk2HIkCE4dOiQztNXT506hddeew0NGjTQq05Z/Pz8oNFosHfv3lJlWm3x/zDGjx8PjUaD/v37VxhnWFgYtFotJk+eDJVKpbPNktvPhgwZgkuXLuH999/H7Nmz9T8IKP7VfeTIkYiIiJCuXAAolag96tq1a2jXrp30q/yj1Go1JkyYgPnz5+ssz8nJkW7jsrKyQl5enkFxPio3N1fa9xkzZgAovp2pPHFxcbh8+bJebWu1Wnz//fcV1nk8GU1JSYG7uzuaN28Of39/qNVqTJo0SafODz/8oDN5gT5++eUXODk5Yfr06bh06RIaNGiABQsWAAD8/f2xdOlSnWQtNzcXP/zwQ5W0//rrr+PEiRO4e/euVL9kkP8777xj0H4QkXEorCzh2z8Udbt2BGSy4oH5Zn6SSdVHCIH81HRoNRoAxWNabdyKxwQ4+L6Ahn3eQcuI4fDp1RkODeuafcICMGkhE1PyC7vm73+EjwoPD4elpaX0hPu0tDTs3LlT54RbnzqPGz16NOzs7DBixAicPHkSGo0GW7duBQCcPn0aN27cQF5eHi5cuID9+/fjxIkTWLZsGYDiJ8IfPHhQasvDwwNDhgxBdHQ0Bg8erLOdy5cvS+MyrKys0KdPHzRr1gxA8ZPuGzVqhGHDhpUbZ35+Pt577z00adIEN27cwM6dO7F9+3bMnz9fmoVs//79eO+993DmzBkAxYPfJ0+ejKVLl5bZ5pQpU/DZZ5/B5bHBTyEhIdIJ9v/+979yZ8wqoVKpkJ2dLSV5j8Y8dOhQ6bk3Xbt2xbx587BhwwZ8+OGHOsmWWq1GTEwMioqK0Lx5c502yrvyMn78eJ3krSxnz56VEpf8/HysXr0aM2bMgFKpRJcuXdCmTRssX74c3bp1Q3R0NMaOHYuzZ89KSZ5Wq0V+fn6F2wCKb7E7deoUgOJxMSEhIdLnO3nyZOTl5SEwMBBffvklFi5ciHfeeUe6Yleyf48fP+Dhv4WK2i+Zya4kKQSA9evXw8/PDwMHDtRpW599ISLjkMlkqBPUBk2HvY9G778jjaPkc8CphCY3H5nHzuH6so24vmIz8hOSYWdnB1tbW9QObAX/8UPRdNh7cGvVDArLZ+tKO8e0kMn4z3/+I92G9OOPP8LT0xM9evSQyl1dXbFv3z5MmjQJe/fuRXJyMn755Re0bdvWoDqPq1evHnbu3ImwsDAEBgYiICAAYWFhqFOnDhwdHSGEQEREBM6dO4fevXtLg/R37dqF9evXlxpX8vnnnyMuLg7du3cvta1PP/0Uu3btwssvv4xbt25J6969exf3799HXFwcRo0ahZYtS99r2rVrV2kWqseVTOfs6uqKCxcuoG3btvD398ebb76JmJgY2NnZlVpn586dsLe3L/O2ue+++w6jRo1CZGQkrl+/ju+++67MYxcfH48tW7bgr7/+gkajQfPmzVGnTh3IZDKoVCpcunQJzs7O0oMrgeLEMjAwEHPnzkXbtm3h5uaGhg0bonbt2hg6dChatWoFoPi2tfXr1+PUqVNQq9Xo2bOnlFwVFhbi7NmzSE9Pl2YDK49SqcTo0aPh5uaG8+fPIyIiQkoO5XI5du7cifDwcGzduhWnTp1C//79MWfOHADFVzdKBsNHRkbi008/hXs5s1Dk5+ejQ4cO6Nu3L7y9vaFUKqVZ6Hr06IENGzYgMjISs2bNgr+/PxYsWIBGjRohOTkZUVFRAICffvoJL730EhQKhTTd8dy5c6WZ68prv2XLltizZw+++OILdOnSBY0aNQJQPFmDpaUlrl+/Ll3VW7JkCRo3boy33nqrwuNGRMbj0LD4RxMFir+nrq3dBqs6HnBt24LjXJ5DQquF6loyHpy5jOwrN4C/f4SSKRXQqvKkMYtKOxso7Ux7BrCnIRNM32tcyaClBw8eVHogPpmuI0eOYPfu3RUO7i7PqFGjMGHChHLHn5BhfHx8EBwcrDM7GBGRObl/6RriVxbfYuvQtAE8Q16DhZODkaOimqJVa3BtyTpoch7esmxbtxY8XmkOV/+mUNpaV7B29avJc1peaSGqYt9//73ObTr6OnbsGLy9vZmwEBGRxOnFBnih55tI+vUAsi8lIif+Bpxf9oNbu1bP9K/qz6PC+9lQxd+AOlsFzzfbQqFQwMrKClYujoBWC7dWfnB/pTls63g8ubFnEJMWoioQFRWF8+fPw93dHR4eHmUOeq/IlStXkJOTg4kTJ1ZThM8njUaj10xkRESmSiaToVZgK9jX80LSjj+QnXAT946fx/2/LsH11RZwa/8y5ErFkxsikyO0WuSl3EHO1ZtQxd9AQfrfj2yQy/FCp3aw+Pthj779u8PCwe65/5yZtBBVgXPnzmHTpk0YNGgQ5s6da/D6TZo0QZMmTaohsufTnTt3sGrVKqSmpmLv3r2IjY3FBx98YOywiIgqza5uLTQd9h4exN9Ayq5DUCXfhiohCe5Brxg7NKqEzKNncffwaRTlFTxcKJfBob43nJo11Bm7ZOXCoQQAx7QYBce0EBERUWUJIXDvwlUoba1hV98LhYWFKMzNQ9aFq3DyfxEyBSeHNRVCCBTefQDVtSQ4NG0IC0c7KBQK3D/1P6T8dgAKays4vdgAzs0awqmJD5S25nXLH8e0EBEREVGZZDIZXJs3lt7b2Ngg88hfuL3nCO4e+QvuQa/A8aVGnGnMCLSFauSlpiMvOQ15yWnIT0mTrqZYWlvB5fWXIZPJYBHQDPZennCo780kU09MWoiIiIjMnKWjPZT2tlDfz0bq1v3I/M8ZuAe1gX3jF5i8VBMhBESRVhprkpechhs/xgGP3cQkUypg/4IXrJ0dpc/Cwt4WFva2NR6zOWPSUgnbt2/HyJEjkZiYqLN85syZuHbtGnJzc/HRRx/hzTffNFKERERE9DzxaNMCri2bIu3wKdw+eBwFdzKRsmEXrDzd4Ny6GVxe9jN2iGZPq9Eg/3YG8pLvIC/5NvKS0+Dk3wRendsVz/T1ghduQMDC0R729b2KXy94wdbL87kfRF8VmLQY6N69exg9enSpp9MuWrQI8fHxWLFiBfLz8xEQEIBt27bB19e3VBsFBQU6f4lMXUFBAWbOnImJEydKT2gmMnXst2RunrbPKiwt4PVGW3i29UfqweO4c+Q0Cu7cReHtu5DJZMVXBoSAUGsgf8aell5dtBoN0g8cR15yGgpuZ0AUaXXKC1MzYGPz9zgUS0sETBoOC4fSD3R+VtXkOS0H4hto5MiRqFWrFlasWIHr168DKH46t5eXFzZu3IigoCAAQFhYGLKzs6UnvD8qOTkZ9erVQ1JSksFT4xIZAyePIHPEfkvmpqr7rEaVh8yzl2FXrzbs6taGWq1G1vUUJP64FQ5NG8CpeWPY+nhBJn++x1QIIaDJUqHw7n0UZBRPO+z6agvIZDLIZDJcmbsKGlUeAEBpbwv7F7xgX78O7Ot7wc67FuQWz28CWJPntLzSYoAtW7agTZs2pZYfPnwYd+/eRfPmzaVlLVu2xBdffFGT4RERERFJlHY28AwMkN5bWFgg/3oKhFqDrHPxyDoXD6W9LRyaNYRN3Vqw9vKEhZP9czEGJvPoWeTduoPCuw9QePc+hKZIKlM62KFucPHDHQHAu1M7yK0sYP+CF6xcnZ6L42OKmLToKSMjA9u2bUNMTAxWrlypU3bx4kXI5XK4uLhIy1xcXJCeno579+7pLCciIiIyFq+Q1+HUpAHunr6Iu2cvQ5OTi3vHz+Pe8fMAgAb/fh9W7s4AAHVWDuSWllBYWxoxYv2ps1XQZOeiKDcPRbn50OTmQ30/S0pK6n/YE3K5HHK5HKr4m1DduCWtK1PIYeXmDGt3V9h5e+LRtMTztZY1vzNUynOftIwdOxZnzpwptzw8PByhoaGYMmUKpk+fXmadBw8ewNnZGfJHLq+W3Iuam5vLpIWIiIhMgkwmkwaJ1wt9Aw8uJyLr6g3kJKWi8F4WXOp5QSu0KCoqwq19x5B14Sos3Z1h4+UJay9PKO1tIbe0gNzKEjZeHlK7QoinugIhhACEgNBqIVc+PD3NT82A+kE2NH8nIkW5+SjKK/4riorwwgfdpbq3fz0AVWJKOTsO2FlbS7dyeb7qD/VLjWDj7gprT1dYuThx6mET99x/OlFRUdi7d2+5r9DQUPz8888IDg5G7dq1y2zD1dW11ACkvLziex+fl4Rl8eLFz9S2q6LNyrZhyHpVWdeYn6GxGGufq2u7xuq3hq6jb3196j1v/ZZ9turaqI7vWnPrs3KlAi4vNUL9nm/hpVEfIGDScCiUClhYWMDa2hri7+eLFGbcx4OzV5C28xBSftmNpDXbkPzzDsjlcilRSfrpN1yetQzxc1fh6qI1uLZ0A66v2IwTs5cWTwP8iORfdiN+7ipc+eYHXJ69HJdmLsPlr2JweeYyxEetAlCcXMnlcqQfOIaUjXuQtuMQMg6ewL3j55F1/ipU15KReyMVttY2sLe3h4ODA2xcnWHhZA9bL084Nq6PDFs5age/igb/fBvNRvSDTP5wBi/31i+hTsc2cPbzhbW7i94Ji7n0WUPqm0u/5UB8Pbzxxhs4cOBAmWX79++HWq1G586dkZeXB2trawBAdHQ0ZsyYgaSkpFLrPIsD8f38/HDx4sVnZttV0WZl2zBkvaqsW1H5szqg2Vj9trq2a6x+a+g6+tbXp97z1m/ZZ6uujer4rn0W+6w6JxeqpFTkJN1GbkoaNHn50BaoobCxQrPhfaV6FxbFIjc5rcw2FNZWCPi/T6T3V1ZsRPbVm2XWlcnleOWrz6T3N387gJybt2BhZwulnY30Knnv1Nin3ITjeT4/MKT+0/RbDsQ3MTExMcjJyZHex8XFITo6Gtu3b0ejRo1gZWUFd3d3HD9+HB06dABQPM6lW7duxgqZiIiI6KlZ2NvCuZkvnJuVfoTDo1786J8oKiiEtkBd/LewEFpNET7+97+x7IcVOrfQN+jVGVq1BjKFHDK5XPevQvd5Ji+EBlfHbpEZYtKih0aNGum8/+uvv2BpaYmAgABp2WeffYZNmzahQ4cOUKlU2LNnD3bs2FHDkRIRERHVPKWNNZQ21qWWn09PgWPDejrLrFydaioseoaY7ZiWffv2ITQ0FNOmTauw3vbt29G+fXt07NgRgYGBiI2NrZZ4JkyYAKVSibCwMIwcORLLli2Dj49PtWyLiIiIiOh5YnZXWq5fv47Y2FisWLECiYmJeOWVV8qtGxMTg7CwMBw+fBitWrXCjRs30KZNGyQnJz/VM1QGDRqEQYMG6SxTKBSYM2eOXuuXDCNKTU3Vq76VlZXJP825qKgIWVlZz8y2q6LNyrZhyHpVWbei8pLlxvqMq4ux+m11bddY/dbQdfStr0+9563fss9WXRvV8V3LPlsazw+qro2a/K4tKCjQmWSqsLAQycnJpdYtOZetkSHywkxt2LBBABCRkZFllsfHxwtra2sRHh6us3zGjBlCoVCI48eP10CUZUtISBAA+OKLL7744osvvvjiy+xfCQkJ1X7+bHZXWkq4urpWWD579mzk5+cjNDRUZ3nnzp0RERGBr7/+GuvXr6/OEMvl4+ODhIQEWFhY6DWnuTlcaSEiIiKiZ8PjV1rKI4SAWq2ukSERZpu0PDoLxeO0Wi3i4ornBPf399cpCwgIgFwux44dO1BUVATFY7NU1AS5XI6GDRvW+HaJiIiIiMyR2Q7Er8itW7dw584d2NjYwMPDQ6dMqVTC0dEROTk5SEhIMFKERERERESkr2cyaUlLK364UXkPZnJwcAAAZGZm1lhMRERERERUOc9k0lJyD56lpWWZ5RqNpsJyIiIiIiIyHc9k0uLu7g4AyM3NLbO8ZFo3T0/PGouJiIiIiIgq55lMWnx9fWFvb4/MzEzk5+frlGVnZ0OlUsHd3R1eXl5GipCIiIiIiPT1TCYtCoUC3bt3hxACZ86c0Sm7cOECAKBbt24VzkBGRERERESmwWzP2rVaLQCU+wTO8ePHQ6FQYMuWLTrLt23bBqVSifHjx1d3iNUiIyMD48aNwyeffGLsUIhKmTlzJoYNG4YBAwZg3759xg6HSC/8XiVzcuPGDbz99ttwcHBAq1atsH//fmOHRPRE6enpCA0NhYODA1555ZVSFxX0YbZJS1JSEgAgOTm5zPKAgABMmzYN0dHR0tTG586dw6JFizBnzhz4+fnVWKxVRa1W49ChQ9i6dWu543WIjGXRokWIj49HTEwMli9fjk8++YTTipPJ4/cqmRMhBIYNG4aQkBAsWbIEQgiEhobyu5ZM3ldffYXRo0cjLi4Oubm5+OCDDwxuQybKu1RholJSUtC7d2+cOXNGGq8SEBCAr7/+Gp06dSpV/4cffsDixYthZ2cHIQTGjh2Lnj171nTYVWrAgAGwsLDAypUrjR0KEQCgsLAQXl5e2LhxI4KCggAAYWFhyM7OxooVK4wcHdGT8XuVzMG5c+dw584dvPXWWwCAO3fuoGHDhpgxYwbCwsKMHB1R2fLz85GRkYG6desCADZs2IBhw4bh/v37BrWjrIbYqpW3tzf++9//6l1/8ODBGDx4cDVGVPMsLCyMHQKRjsOHD+Pu3bto3ry5tKxly5b44osvjBgVkf74vUrm4MUXX0SLFi2k956envDz84OVlZURoyKqmLW1tZSwAMVXuEeMGGFwO2aXtBCR6bl48SLkcjlcXFykZS4uLkhPT8e9e/d0lhMRUeWU9Xy5e/fuITQ01AjREBkuOTkZsbGxWL9+vcHrMmkxAWPHjq1wQFJ4eDi/kMikPXjwAM7Ozjoz8pX88pebm8ukhYioGvzxxx/o1auXzq/YRKbqr7/+wuTJk7Fjxw688cYbOHTokEFXCZm0mICoqChjh0D0VFxdXVFQUKCzLC8vDwCYsBARVQO1Wo21a9di/vz5xg6FSC8BAQH47bffsHnzZvTu3RsbN25E//799V7fbGcPIyLT4evrC5VKpfMw1/T0dNStWxe2trZGjIyI6NkUFRWFSZMmcTwLmZ1//OMfCAoKwq1btwxaj0kLET214OBguLu74/jx49Kyixcvolu3bkaMiojo2bRs2TJ06dIF9erVAwAUFBSgqKjIyFER6c/Z2Rn+/v4GrcOkxQwVFRVJD9ckMgUWFhb47LPPsGnTJgCASqXCnj17OHsYmQ1+r5K5WLJkCa5du4bbt29j586d2LRpE4YOHQqZTGbs0IjKlJWVhVWrViErKwsAcPbsWRQWFqJz584GtcOk5Sns27cPoaGhmDZtWoX1tm/fjvbt26Njx44IDAxEbGxspbf5888/448//sCff/6JDRs2VLodIqBq+/CECROgVCoRFhaGkSNHYtmyZfDx8ammyOl5VtXfvfxepepWVX12xYoVGDlyJGbOnImuXbuia9eu6N27N1xcXHQmQiGqClXVb9PS0jB16lQ0atQI77//PtauXYt169YZHpAggyUmJorp06eLBg0aCAAiMjKy3LpLly4VNjY24tSpU0IIIa5fvy48PDzEzJkzayhaotLYh8kcsd+SuWGfJXNkqv2WSctT2LBhQ4UfZnx8vLC2thbh4eE6y2fMmCEUCoU4fvx4DURJVD72YTJH7LdkbthnyRyZWr/ltcSn4OrqWmH57NmzkZ+fX+oZK507d0ZRURG+/vrr6gyP6InYh8kcsd+SuWGfJXNkav2WSctTqOj+Ua1Wi7i4OAAoNTtCQEAA5HI5duzYwdk+yKjYh8kcsd+SuWGfJXNkav2WSUs1uXXrFu7cuQMbGxt4eHjolCmVSjg6OiInJwcJCQlGipCoYuzDZI7Yb8ncsM+SOTJGv2XSUk3S0tIAAI6OjmWWOzg4AAAyMzNrLCYiQ7APkzlivyVzwz5L5sgY/ZZJSzUpKCgAAFhaWpZZrtFoKiwnMjb2YTJH7LdkbthnyRwZo98yaakm7u7uAIDc3Nwyy0sesOPp6VljMREZgn2YzBH7LZkb9lkyR8bot0xaqomvry/s7e2RmZmJ/Px8nbLs7GyoVCq4u7vDy8vLSBESVYx9mMwR+y2ZG/ZZMkfG6LdMWqqJQqFA9+7dIYTAmTNndMouXLgAAOjWrRufYEsmi32YzBH7LZkb9lkyR8bot/wX8BS0Wi0AQAhRZvn48eOhUCiwZcsWneXbtm2DUqnE+PHjqztEogqxD5M5Yr8lc8M+S+bI1Potk5ankJSUBABITk4uszwgIADTpk1DdHS0NOXbuXPnsGjRIsyZMwd+fn41FitRWdiHyRyx35K5YZ8lc2Ry/VaQwZKTk0Xbtm2FtbW1ACAAiICAALF79+4y669YsUK0bt1adOzYUXTo0EFs2bKlhiMm0sU+TOaI/ZbMDfssmSNT7bcyIcq55kNERERERGQCeHsYERERERGZNCYtRERERERk0pi0EBERERGRSWPSQkREREREJo1JCxERERERmTQmLUREREREZNKYtBARERERkUlj0kJERERERCaNSQsREREREZk0Ji1ERERERGTSmLQQEREREZFJY9JCREREREQmTWnsAIiIiOjZFhcXh/j4eNy6dQupqalYsGABPDw8jB0WEZkRXmkhIiKianP9+nVcvXoVY8eORVRUFDw8PDBo0CBjh0VEZoZJCxEREVWbs2fPYtKkScjLywMAhISEYP/+/UaOiojMDZMWIiITdO/ePURFRcHX1xcHDhwwdjhEOmbPno1ff/1Vr7pdu3bF4cOHYWNjAwC4efMmGjduXJ3hEdEziEkLEdETHDt2DJGRkZDL5ZDJZKhTpw6CgoIQEhKCFi1aoH379oiIiMCtW7eqbJsnTpzAwYMHce3aNYPWGzduHJycnCCTySCTyXD06NEK6+fk5MDV1RUymQzOzs4YMGDA04RdI8x5H1euXAmZTIbg4GAEBwfj008/BVBz+xQbGws/Pz9pO8OHD8fFixd16qhUKkRERMDS0hJubm747rvvSrWze/duvP3223pt08LCAq1btwYAFBYWYunSpVi4cKFUfvLkSel4+Pj4wMfHR692ieg5I4iISC/+/v4CgFCr1dKyoqIisXHjRlG7dm3h5OQkNm3aVGXbW7p0qQAg9u/fb9B6ycnJwtLSUgAQPXv2rLBuVFSUACAAiHPnzhkc4++//15mfOUtryo1uY9V6YcffhDl/a+3pvYpNTVVWFtbCwDi3r175dZr2bKl2L59e6nlCQkJYuDAgQZts8Rnn30m4uLiyi2PjIwU9evXr1TbRPRs45UWIiI9ubi4lFoml8vRq1cvHDlyBAqFAv3798epU6eqZHsWFhaVWs/b2xu1atVCnTp1EBcXV+qX9BJqtRrz589H7dq1AQBNmjQxaDtFRUWYOnWq3surUk3tY02qqX2qXbs2Bg4cCADYvHlzmXVu3rwJa2trdO3atVTZhg0b8N577xm0TQBYtGgR3n33XXTv3h3x8fEGr09EzzcmLUREVaBBgwaYOXMm8vPzERERYexwIJfLER4eDiEEZs+eXWadNWvWoEWLFnjxxReldQwxZswY/Pnnn3ovr2o1sY81rab2KTw8HDKZTOc2rUctXboUI0aMKLNs9+7d6Ny5s0HbW7t2LV544QU0adIEt2/fxqZNmwyOmYieb6b97U1EZEY++OADWFhYYNeuXUhJSQEA5OXlYezYsejYsSP8/f3h5+eH1atXAwAuXryIiIgINGjQAHv37sXgwYPh4OCAlStXltn+wYMHYW1tDZlMBh8fH6xatarCeIYPHw4nJyesXbsWN2/e1CkTQuCbb77BhAkTyl2/otgXLlyIXbt2ASg+AQ4ODsaxY8fKXQ4Au3btwjvvvIPXX38d3t7emDFjBoQQlToWNbGPABAfH48uXbogKCgI7u7ukMlkOHTokN7llfG0+1TRcS7RrFkzdOnSBadPny410UNhYSE2b96MPn36lGr72rVrqFu3LiwtLaVlJeNbgoKC4Ovri4kTJ0Kj0UjlR44cwb/+9S/07NkTderUQZ06dWokqSWiZ4zx7kwjIjIvQUFBpca0PO6ll14SAMTvv/8uhBBi+PDhwtfXVxQWFgqtViu6d+8ulEqlSE1NFefPnxeDBg0SAESfPn3Ezp07xbvvvit+/vlnIcTD8Q8lY0OysrJE27ZtxZo1a54Ya8m4gC+++EIAEKNHj9Yp/+2330RgYGCF+1VR7GXFV6Ks5Zs3bxaBgYHi/v37QgghVq5cKQCIhQsXCiHEE4+FsfYxMDBQ7Ny5UwghhEqlEh06dBB//vmntP6Tyh9X0ZiWqtinJx3nR+3evVsAED169NBZvmbNGjF+/Pgy45s1a5b49ddfdeo2btxYOl4//fSTACAmT55c7j5WhGNaiKg8vNJCRFSFnJycAABpaWkAimcBa968OSwsLCCTyRASEgKNRoPExES89NJLaN++PQCgR48eePvtt7F582a8//77pdpNSkpCnz59EB0djX79+ukdT1hYGKysrLBs2TJkZGRIy2fPnl3hr/VPit1QY8aMwZQpU6Tj8+GHH8LNzQ1fffUVABh0LGpyH8+cOYP09HQAgK2tLb788kvIZDJp/SeVV1Zl9+lJx/lRnTp1QvPmzfHbb7/h6tWr0vLo6Gi9bg3Lzc3FqFGjMGnSJGl8TefOneHt7Y3c3FzDd5qIqAJMWoiIqtCDBw8APBy0/+OPP2LJkiUAgPPnz0u3DhUWFgIAFAoFAMDLy6vcNk+cOIE33ngD0dHRCAgIMCie2rVr48MPP0Rubi6+/fZbAMDRo0eRkZGBHj16VLjuk2LXV3x8PBITEzF16lRpatvg4GA4OzvDysoK2dnZAPQ7FjW9j927d8fgwYMxatQoJCUl4Y033kC7du2k9Z9UXlmV2Sd9j/OjwsPDodVqMX/+fADFD4J0dHQsc9rhx28NO3z4MDIzM/Hyyy9Lddzd3ZGcnIyoqKinPAJERLqYtBARVZGcnBxcvnwZMpkMr7zyCoDisQNHjx5Fjx49sGPHDrz66qsAUGqMQUUuXLiAhIQEzJs3r1JxjRs3DnK5HIsXL0ZOTg5mz56N8ePHP/GKQFXEDgB37twBAMydOxcHDhyQXlevXkViYiIcHBwqtV+Pqq59XL16NSIjI/Hjjz/C19cXY8aMgVqtltZ/UnlN7lNljvOAAQPg6emJlStX4v79+1i8eDFGjhxZZvvr16/XmTWsZHtVtb9ERBVh0kJEVEV+/PFHaDQa9OjRA+7u7gCAYcOGITIyEitXrsS4ceOk5Yb48MMPMW7cOMyfP1+6KmCIRo0aoXfv3sjMzMTYsWNx8uRJvR5GWBWxAw9vmdu4cWOpsitXrhh85aYs1bWPFhYWmDx5MhISEjB48GDMmzcPY8aM0bu8JvepMsfZ2toaI0aMgEqlwpw5c3D06NFyHxq5Z88enVnDXF1dAQCnT58uVTcpKaninSMiMhCTFiIiPVV0hSE+Ph4RERFwdHSUbo05d+4cli1bhn//+9/SCV5l2gaAWbNmoXv37hg9ejS2b9/+xFi1Wi20Wq30vmQcxNKlSxEWFqbzDJiSeo/W1yf28n7xf3x5s2bNULt2bSxYsABRUVHSL/OJiYnSk9cfpe+VnJrYx5Lpqz08PPD999+jb9++OrNtPancUE+zT4Ye5xIjRoyAlZUVZs6ciYEDB5b5uSYkJKBevXo6bQQGBsLGxgbffvutzmxhWq0Wa9asqewhICIqE5MWIiI9ZWZmllqm0Wiwbt06tGvXDjY2Nti7dy98fX0BAHZ2dgCKxyIAgEqlwu+//w6geBDz1atXpQH7ly5dKtV2ybTJd+/ehVwux5o1a1CvXj306dMHBw8eLDfO7OxspKen6wyYb926NUJCQuDi4oKPP/5YJ/6SX8WvXbsmLdcndjc3NwDArVu3kJeXJz1U8/HlZ86cwezZs6HVavH555/DwcEB9evXR+PGjTFkyBBpmxUdC2Pt45IlS7B7925pncLCQgQFBUnvn1RuiKfdJ4VCoddxflytWrXQv39/WFtbY/DgwWXWKeuBks7OzpgyZQrOnTuHXr164fDhwzh48CD69++PTp06VeoYEBGVy5hTlxERmYM9e/aIoUOHCgACgHB1dRVt2rQRHTp0EE2bNhWdOnUS8+fPFyqVqtS606dPF05OTqJTp04iIiJCrF+/Xri5uYk+ffqIrl27CisrKwFAWFhYiFGjRknr9evXT1haWgoAwsXFRURGRoo//vhD2NraCgBCLpeLTp06ldrehAkTRK1ataQ4Bw4cqLMfj05FO3/+fFG/fn1pv1xcXMSAAQP0iv306dMiPz9f9OzZU9StW1dERkaKvLw8IYQod/n69etF8+bNhaWlpWjSpIlYu3attK0RI0aUeyyMuY8lMTVr1ky0a9dOjBo1SuTm5krrP6n8ceVNeVyV+1TRcS7P2bNnxUcffVRu+ZtvvikKCgrKLFu0aJHw8fERdnZ2Ijg4WBw9evSJ2ysPpzwmovLIhDBwRCURERFVysqVKzF48GCDJzN4XkydOhUrV67E9evXjR0KEZkY3h5GREREREQmjUkLERERERGZNKWxAyAiInreBAcHAwBatGiBhQsXGjcYIzt58iTGjh0LALwtjIjKxTEtRERERERk0nh7GBERERERmTQmLUREREREZNKYtBARERERkUlj0kJERERERCaNSQsREREREZk0Ji1ERERERGTSmLQQEREREZFJY9JCREREREQmjUkLERERERGZNCYtRERERERk0pi0EBERERGRSft/w2cH/SveXwwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = ['blue','green','magenta']\n", + "\n", + "lss = ['-','-','-']\n", + " \n", + "fig, ax = plt.subplots(1, 1, figsize=(9.5,6))\n", + "\n", + "plotexcludedmassive(ax, details=False)\n", + "#leg_params = {'loc':'upper right',\n", + "# 'frameon':True,\n", + "# 'handlelength':1.5,\n", + "# 'fontsize':9,\n", + "# 'ncol':3 }\n", + "leg_params = {'loc':'upper left',\n", + " 'frameon':False,\n", + " 'handlelength':1.5,\n", + " 'fontsize':11,\n", + " 'ncol':1 }\n", + "load_and_plot_existing(ax,leg=True,lw=1.5,leg_params=leg_params, ER_model='massive')\n", + "\n", + "#ax.text(1.3e-2,1.5e-40,'HeRALD - {:0.1f} g \\n2x2 device array \\n {:0.0f} livedays'.format(mass_det*1e3,times[i]),fontsize=15)\n", + "#ax.text(1.25e0,2e-37,'SPICE\\n5.3 g GaAs', fontsize=15)\n", + "\n", + "leg_hands = []\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_100days_3fold_lce05/HeRALD_FC_100d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls='-', lw=2, label='100 days, 5% LCE per sensor')\n", + "leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls='--', lw=2, label='100 days, 10% LCE per sensor')\n", + "leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massive_100days_3fold_lce25/HeRALD_FC_100d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls=':', lw=2, label='100 days, 25% LCE per sensor')\n", + "leg_hands.append(lh)\n", + "\n", + "ax.text(1e2, 5e-36, r'F$_{DM}$ = 1', fontsize=18)\n", + "\n", + "#x = 6.5e-2\n", + "# if n<4:\n", + "# y = sigs[0]\n", + "# else: \n", + "# y = sigs[0]*0.75\n", + "# ax.text(x,y,lab,fontsize=14,color=colors[j],alpha=0.95)\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_xscale('log')\n", + "\n", + "ax.set_ylim(1e-40, 2e-35)\n", + "ax.set_xlim(1e-1, 1e3)\n", + "ax.set_xlabel(\"Dark Matter Mass [MeV/c$^2$]\", fontsize=14)\n", + "ax.set_ylabel(\"DM-Electron Cross Section [cm$^2$]\", fontsize=14)\n", + "\n", + "#ax.grid(lw=0.3,ls='--',color='grey')\n", + "#ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "ax.legend(handles=leg_hands, frameon=False, ncol=1, loc='lower left', fontsize=13)\n", + "\n", + "#plt.savefig('./pretty_plots/herald_limits_{:0.1f}g_{:0.0f}d.png'.format(mass_det*1e3,times[i]),facecolor='white',bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1167612/1832352967.py:14: RuntimeWarning: All-NaN axis encountered\n", + " result = np.nanmin([result, yinterp], axis=0)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = ['blue','green','magenta']\n", + "\n", + "lss = ['-','-','-']\n", + " \n", + "fig, ax = plt.subplots(1, 1, figsize=(9.5,6))\n", + "\n", + "plotexcludedmassless(ax, details=False)\n", + "#leg_params = {'loc':'upper right',\n", + "# 'frameon':True,\n", + "# 'handlelength':1.5,\n", + "# 'fontsize':9,\n", + "# 'ncol':3 }\n", + "leg_params = {'loc':'upper left',\n", + " 'frameon':False,\n", + " 'handlelength':1.5,\n", + " 'fontsize':11,\n", + " 'ncol':1 }\n", + "load_and_plot_existing(ax,leg=True,lw=1.5,leg_params=leg_params, ER_model='massless')\n", + "\n", + "#ax.text(1.3e-2,1.5e-40,'HeRALD - {:0.1f} g \\n2x2 device array \\n {:0.0f} livedays'.format(mass_det*1e3,times[i]),fontsize=15)\n", + "#ax.text(1.25e0,2e-37,'SPICE\\n5.3 g GaAs', fontsize=15)\n", + "\n", + "leg_hands = []\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_100days_3fold_lce05/HeRALD_FC_100d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls='-', lw=2, label='100 days, 5% LCE per sensor')\n", + "leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_100days_3fold_lce10/HeRALD_FC_100d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls='--', lw=2, label='100 days, 10% LCE per sensor')\n", + "leg_hands.append(lh)\n", + "\n", + "m_limit, x_limit = np.loadtxt('gaas_for_review/results_gaas_oi_electron_massless_100days_3fold_lce25/HeRALD_FC_100d_3device_3fold_100mus.txt', unpack=True)\n", + "lh, = plt.plot(m_limit*1e3, x_limit, color='blue', ls=':', lw=2, label='100 days, 25% LCE per sensor')\n", + "leg_hands.append(lh)\n", + "\n", + "ax.text(1e1, 4e-35, r'F$_{DM}$ = ($\\alpha$ m$_e$ / q)$^2$', fontsize=18)\n", + "\n", + "#x = 6.5e-2\n", + "# if n<4:\n", + "# y = sigs[0]\n", + "# else: \n", + "# y = sigs[0]*0.75\n", + "# ax.text(x,y,lab,fontsize=14,color=colors[j],alpha=0.95)\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_xscale('log')\n", + "\n", + "ax.set_ylim(2e-38, 1e-34)\n", + "ax.set_xlim(1e-1, 1e3)\n", + "ax.set_xlabel(\"Dark Matter Mass [MeV/c$^2$]\", fontsize=14)\n", + "ax.set_ylabel(\"DM-Electron Cross Section [cm$^2$]\", fontsize=14)\n", + "\n", + "#ax.grid(lw=0.3,ls='--',color='grey')\n", + "#ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "ax.legend(handles=leg_hands, frameon=False, ncol=1, loc='lower right', fontsize=13)\n", + "\n", + "#plt.savefig('./pretty_plots/herald_limits_{:0.1f}g_{:0.0f}d.png'.format(mass_det*1e3,times[i]),facecolor='white',bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.41708574 0.68063053 0.83823145 1. ]\n", + " [0.12710496 0.44018454 0.70749712 1. ]\n", + " [0.03137255 0.18823529 0.41960784 1. ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import matplotlib as mpl\n", + "\n", + "n_lines = 3\n", + "cmap = mpl.colormaps['Blues']\n", + "\n", + "# Take colors at regular intervals spanning the colormap.\n", + "colors = cmap(np.linspace(0.5, 1, n_lines))\n", + "\n", + "print(colors)\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for i, color in enumerate(colors):\n", + " ax.plot([0, i], color=color)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "colors = ['blue','green','orange']\n", + "\n", + "for i,t in enumerate(times):\n", + " \n", + " fig, ax = plt.subplots(figsize=(9, 6))\n", + " #plt.plot(*(x1tmig.T),color=(0.,0.6,0.,0.7),ls='--',label='XENON1T Migdal')\n", + " load_and_plot_existing(ax,leg=True,leg_params={'loc':'center left' } ) #,'bbox_to_anchor':(0.1,0.5)\n", + " #fig.subplots_adjust(right=0.6)\n", + " # Shrink current axis by 20%\n", + " #box = ax.get_position()\n", + " #ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n", + "\n", + " \n", + " leg_hands = []\n", + " for j,n in enumerate(coinc):\n", + " lab = '{:d}-fold coinc in {:0.0f}$\\mu$s'.format(n,window/1e-6)\n", + " #lh, = plt.plot(*(data['{:d}_{:d}'.format(i,j)].T), marker='.',ms=5,label=lab,color=colors[j])\n", + " acc_mask = data['{:d}_{:d}'.format(i,j)][:,2]>acc_cut\n", + " masses = data['{:d}_{:d}'.format(i,j)][:,0][acc_mask]\n", + " sigs = data['{:d}_{:d}'.format(i,j)][:,1][acc_mask]\n", + " lh, = plt.plot(masses,sigs, marker='.',ms=5,label=lab,color=colors[j])\n", + " leg_hands.append(lh)\n", + "\n", + " ax.set_title('{:0.1f} g LHe; {:0.0f} days; {:0.1f} g-day'.format(mass_det*1e3,times[i],mass_det*1e3*times[i]))\n", + " ax.set_yscale('log')\n", + " ax.set_xscale('log')\n", + "\n", + " ax.set_ylim(1e-40, 1e-34)\n", + " ax.set_xlim(0.01, 5)\n", + " ax.set_xlabel(\"Dark Matter Mass [GeV/c$^2$]\", fontsize=14)\n", + " ax.set_ylabel(\"Spin Indepedent Cross Section [cm$^2$]\", fontsize=14)\n", + " #ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + " #ax.grid(lw=0.3,ls='--',color='grey')\n", + " #ax.grid(which=\"minor\", linestyle='dotted')\n", + " ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + " ax.legend(handles=leg_hands,loc='upper left',frameon=True,ncol=1,title='{:d} Devices'.format(n_devices)) #bbox_to_anchor=(0.5, 1.05)\n", + " \n", + " #bbox_extra_artists=(lgd,text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "colors = ['blue','green','orange','red','pink','brown']\n", + "\n", + "for j,n in enumerate(coinc):\n", + " \n", + " fig, ax = plt.subplots(figsize=(9, 6))\n", + " #plt.plot(*(x1tmig.T),color=(0.,0.6,0.,0.7),ls='--',label='XENON1T Migdal')\n", + " load_and_plot_existing(ax,leg=False,leg_params={'loc':'center left','bbox_to_anchor':(0.8,0.5) } )\n", + " \n", + " for i,t in enumerate(times):\n", + " #print(coinc[j])\n", + " #lab = '{:0.1f} g-day;\\n{:d} devices, {:d}-fold coinc in {:0.0f}$\\mu$s'.format(mass_det*1e3*t,n_devices,n,window/1e-6)\n", + " lab = '{:0.1f} g-day'.format(mass_det*1e3*t)\n", + " acc_mask = data['{:d}_{:d}'.format(i,j)][:,2]>acc_cut\n", + " masses = data['{:d}_{:d}'.format(i,j)][:,0][acc_mask]\n", + " sigs = data['{:d}_{:d}'.format(i,j)][:,1][acc_mask]\n", + " plt.plot(masses,sigs, marker='.',ms=5,label=lab,color=colors[i])\n", + " \n", + " title = '{:d} devices, {:d}-fold coinc in {:0.0f}$\\mu$s'.format(n_devices,n,window/1e-6)\n", + " ax.set_title(title)\n", + " ax.set_yscale('log')\n", + " ax.set_xscale('log')\n", + "\n", + " ax.set_ylim(1e-41, 1e-35)\n", + " ax.set_xlim(0.01, 5)\n", + " ax.set_xlabel(\"Dark Matter Mass [GeV/c$^2$]\", fontsize=14)\n", + " ax.set_ylabel(\"Spin Indepedent Cross Section [cm$^2$]\", fontsize=14)\n", + " #ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + " ax.grid(lw=0.3,ls='--')\n", + " ax.grid(which=\"minor\", linestyle='dotted')\n", + " ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + " ax.legend(loc='lower left',frameon=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# for fixed DM mass, plot limit vs runtime for each coinc level\n", + "m = 0.3 # GeV\n", + "m_idx, m = find_nearest(data['0_0'][:,0], m)\n", + "print(m)\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "leg_hands = []\n", + "for j,n in enumerate(coinc):\n", + "#for j,n in enumerate([4]):\n", + " \n", + " sigs = np.zeros(len(times))\n", + " for i,t in enumerate(times):\n", + " sigs[i] = data['{:d}_{:d}'.format(i,j)][m_idx,1]\n", + " \n", + " # find times to avoid FC jumps\n", + " #slope_mask = np.diff(sigs)<0\n", + " #print(slope_mask)\n", + " #slope_mask = np.insert(slope_mask,0,1) #add True to make len equal\n", + " #print(len(slope_mask))\n", + " \n", + " tmask = np.ones(len(times), dtype=bool)\n", + " tmask[[5,14,24]] = False\n", + " thetimes = times[tmask]\n", + " thesigs = sigs[tmask]\n", + " \n", + " lab = '{:d}-fold'.format(n)\n", + " lh, = plt.plot(thetimes,thesigs, label=lab,lw=2, color=colors[j])\n", + " \n", + " #plt.plot(times[slope_mask],sigs[slope_mask], marker='.',ms=7,label=lab,ls='')#,color=colors[j])\n", + " \n", + " leg_hands.append(lh)\n", + "\n", + "ts = np.linspace(50,500,500)\n", + "#plt.plot(ts,3.5e-37*ts**(-0.5),ls='--',lw=0.5,color='black')\n", + "ax.text(345,2.1e-38,r'$\\propto t^{-1/2}$',fontsize=15)\n", + "\n", + "ts2 = np.linspace(1,500,500)\n", + "#plt.plot(ts2,4e-37*ts2**(-1),ls='--',lw=0.5,color='black')\n", + "ax.text(345,1.1e-39,r'$\\propto t^{-1}$',fontsize=15)\n", + "\n", + "#ax.set_title('{:0.1f} grams LHe'.format(mass_det*1e3))\n", + "ax.set_yscale('log')\n", + "#ax.set_xscale('log')\n", + "\n", + "ax.set_ylim(1e-40, 1e-36)\n", + "ax.set_xlim(times[0],400)\n", + "ax.set_xlabel(\"Livetime [days]\", fontsize=14)\n", + "ax.set_ylabel(r'{:0.3f} GeV 90% CL Cross Section UL [cm$^2$]'.format(m), fontsize=14)\n", + "\n", + "#ax.grid(lw=0.3,ls='--',color='grey')\n", + "#ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "ax.legend(handles=leg_hands,loc='upper right',frameon=False,ncol=1,\n", + " title='Coincidence Level in {:0.0f} $\\mu$s:'.format(window/1e-6),\n", + " fontsize=14,\n", + " title_fontsize=14\n", + " ) #bbox_to_anchor=(0.5, 1.05)\n", + "\n", + "#bbox_extra_artists=(lgd,text)\n", + "\n", + "ax.text(13,2e-40,'HeRALD - {:0.1f} g \\n'.format(mass_det*1e3)+r'$2\\times2$'+' device array',fontsize=16)\n", + "\n", + "plt.savefig('herald_{:0.3f}GeV_limit_vs_time_{:0.1f}g_{:0.0f}d.png'.format(m,mass_det*1e3,times[i]),facecolor='white',bbox_inches='tight')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tmask = np.ones(len(times), dtype=bool)\n", + "tmask[[5,14,24]] = False\n", + "times[tmask]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "times" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for k, s in sigs:\n", + " if k!=(len(sigs)-1):\n", + " \n", + " \n", + "\n", + "print(np.diff( sigs ))\n", + "print( np.diff(sigs)<0 )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "x1tmig = np.loadtxt('/global/cfs/cdirs/lz/users/haselsco/TESSERACT_Limits/snowmass2021-wp-cf1-neutrinofloor/data/published/SI/XENON1T-Migdal.txt')\n", + "#cresst = np.loadtxt('/global/cfs/cdirs/lz/users/haselsco/TESSERACT_Limits/snowmass2021-wp-cf1-neutrinofloor/data/published/SI/CRESST.txt')\n", + "\n", + "plt.plot(*(x1tmig.T),color=(0.,0.6,0.,0.7),ls='--',label='XENON1T Migdal')\n", + "#plt.plot(*(cresst.T),color=(0.8,0.1,0.1,0.7),label='CRESST')\n", + "\n", + "for i,t in enumerate(exposures):\n", + " for j,n in enumerate(coinc):\n", + " \n", + " #lab = 'HeRALD {:0.3f} kg-day;\\n{:d} devices, {:d}-fold coinc in {:0.0f}$\\mu$s'.format(SE.exposure,n_devices,coinc,window/1e-6)\n", + " plt.plot(*(data['{:d}_{:d}'.format(i,j)].T), marker='.',ms=5)#,label=lab)\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_xscale('log')\n", + "\n", + "ax.set_ylim(1e-41, 1e-35)\n", + "ax.set_xlim(0.01, 5)\n", + "ax.set_xlabel(\"Dark Matter Mass [GeV/c$^2$]\", fontsize=14)\n", + "ax.set_ylabel(\"Spin Indepedent Cross Section [cm$^2$]\", fontsize=14)\n", + "#ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid(lw=0.3,ls='--')\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "ax.legend(loc='upper right',frameon=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "flamedisx", + "language": "python", + "name": "flamedisx" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From ec8eb971564dcc978a13e8eb0dba6c5c36910086 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Sun, 7 Jul 2024 19:26:29 -0700 Subject: [PATCH 37/39] Compare rates of 2-fold to 3-fold coincidence to show why it doesnt affect limit very much --- examples/coincidence_comparison.py | 73 ++++++++++++++++++++++++ examples/fig_coincidence_comparison.png | Bin 0 -> 151932 bytes 2 files changed, 73 insertions(+) create mode 100644 examples/coincidence_comparison.py create mode 100644 examples/fig_coincidence_comparison.png diff --git a/examples/coincidence_comparison.py b/examples/coincidence_comparison.py new file mode 100644 index 0000000..f4daa37 --- /dev/null +++ b/examples/coincidence_comparison.py @@ -0,0 +1,73 @@ +import numpy as np +import math +from darklim import constants +import time +from scipy import integrate, interpolate +from darklim import elf +import darklim.sensitivity._sens_est as sens_est +import matplotlib.pyplot as plt +import matplotlib as mpl +import darklim.detector._detector as detector +mpl.rcParams.update({'font.size': 17}) +t_start = time.time() + +#fun = lambda x: sens_est.drde_wimp_obs(x, 1, 1e-41, 'GaAs', 1) +#E_interp = np.geomspace(0.1, 1000, int(1e4)) +#dRdE_interp = fun(E_interp) + +fig, ax = plt.subplots(2, 1, figsize=(12,14)) + + + +SE = sens_est.SensEst(5.3e-3, 1., 1., 'GaAs', 1.) +fun_2fold_lee = lambda x: sens_est.n_fold_lee(x,m=2,n=2,e0=0.020,R=0.12,w=100e-6) / 5.3e-3 +fun_3fold_lee = lambda x: sens_est.n_fold_lee(x,m=3,n=3,e0=0.020,R=0.12,w=100e-6) / 5.3e-3 + +keV_arr = np.geomspace(0.1e-3, 1, 1000) +dRdE_2fold_arr = fun_2fold_lee(keV_arr) +dRdE_3fold_arr = fun_3fold_lee(keV_arr) +integral_2fold = sum(dRdE_2fold_arr[1:] * np.diff(keV_arr)) +integral_3fold = sum(dRdE_3fold_arr[1:] * np.diff(keV_arr)) + +ax[1].plot(keV_arr, dRdE_2fold_arr, 'c-', label='2-fold coinc. LEE') +ax[1].plot(keV_arr, dRdE_3fold_arr, 'r-', label='3-fold coinc. LEE') +ax[1].text(0.5, 0.18, f'Total rate = {integral_2fold:.3e} counts/kg/day', ha='center', color='c', transform=ax[1].transAxes, fontsize=18) +ax[1].text(0.5, 0.10, f'Total rate = {integral_3fold:.3e} counts/kg/day', ha='center', color='r', transform=ax[1].transAxes, fontsize=18) + + + +fun = elf.get_dRdE_lambda_GaAs_electron(mX_eV=1e8, sigmae=1e-41, mediator='massive', kcut=0, method='grid', withscreening=True, gain=1) +E_interp = np.geomspace(0.1e-3, 1, int(1e5)) +dRdE_interp = fun(E_interp) + + +ax[0].plot(E_interp, dRdE_interp, 'k-', label='Deposited energy, DM-e scattering') + +for i, coin in enumerate([1, 2]): + + E_obs_keV, dRdE_obs_DRU, energies_obs_keV = \ + detector.convert_dRdE_dep_to_obs_gaas(E_interp, dRdE_interp, pce=0.40, lce_per_channel=0.10, res=0.17, n_coincidence_light=coin, calorimeter_threshold_eV=0.25*3.7) + print(f'Out of {len(energies_obs_keV)}, {sum(energies_obs_keV > 0)} are detected') + print(f'Energies range between {min(energies_obs_keV[energies_obs_keV > 0])} and {max(energies_obs_keV[energies_obs_keV > 0])} keV are detected') + integral = sum(dRdE_obs_DRU[1:] * np.diff(E_obs_keV)) + print(f'Integral is {integral} counts/kg/day') + + color = ['m', 'g'][i] + ax[0].plot(E_obs_keV, dRdE_obs_DRU, '-', color=color, alpha=0.5, label=f'Observed energy (Coincidence in {coin} light detectors)') + ax[0].text(0.5, 0.18 - 0.08*i, f'Total rate = {integral:.3e} counts/kg/day', ha='center', color=color, transform=ax[0].transAxes, fontsize=18) + +for i in range(2): + ax[i].set_xscale('log') + ax[i].set_yscale('log') + ax[i].set_xlim([1e-3, 1e-1]) + ax[i].set_xlabel('Energy [keV]') + ax[i].set_ylabel('Rate [DRU]') + ax[i].legend(loc='upper left') +ax[0].set_ylim([1e-8, 1e2]) +ax[1].set_ylim([1e-8, 1e5]) +fig.tight_layout() +fig.savefig('fig_coincidence_comparison.png') + +t_end = time.time() +print(f'Took {(t_end-t_start)/60} minutes') + diff --git a/examples/fig_coincidence_comparison.png b/examples/fig_coincidence_comparison.png new file mode 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za|Yao+QAy|=z`M%_lY0pF=6rX@%DJa^OMvF!Fd`CkPcWp1dj@~5=_i&pOMQ!>;i08 z9eX^412}LKOuLhOff^)2y9c3-7O`_!>Frz)EX{!mJc?6*x|K2HCe-hNu$r%Jl--~z zf=_!vMFSiYMTi4>axgW9lJnpyaFBSeEj64&7`{=Kg}-3HG@ahLOb#g~blfJT!KQ@? zHr@c$17|Ao%EQw`6^z$r0wTEft3VF&wM2P9o1go;yYgq^PFAau4ah@co?o%VMvLh4vWmbnfWch085D+l7x|v;ud0F zSZ4rnn)9g-;<+BlgGCzv!nW-`bDx`5;lN>Lb*3Ag2IUMM<1%$1IOlM;YJzQ#`0!!E za|<3t2(5rTlwvCuDu@W3Q(L(bu9o>#1N@^gRTMF|TLvMYKT=Clmy@eGfWrV>+fg-9 z(g3Q~x-eb{VQmBu?V)at(uBhL+WMeNl%X?B-TM6g_c~Gxm?RL8#|lp@+tn8dK*6Gv zSJ7O)tUtL%?&{yBW@1`Qjf8uBec+@Q&@^Q1|ZN&A)9P^xpvf#5%tK literal 0 HcmV?d00001 From 3bedee261e8460df96a02d1a9fa3fa6f21ab5e12 Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Wed, 10 Jul 2024 17:27:03 -0700 Subject: [PATCH 38/39] Added some old working notebooks --- examples/all_elf_models.ipynb | 1220 +++++++++++++++++++++++++++ examples/sapphire_sensitivity.ipynb | 997 ++++++++++++++++++++++ 2 files changed, 2217 insertions(+) create mode 100644 examples/all_elf_models.ipynb create mode 100644 examples/sapphire_sensitivity.ipynb diff --git a/examples/all_elf_models.ipynb b/examples/all_elf_models.ipynb new file mode 100644 index 0000000..0feb3b0 --- /dev/null +++ b/examples/all_elf_models.ipynb @@ -0,0 +1,1220 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "import darklim\n", + "\n", + "sys.path.append('/Users/vetri/GitRepos/DarkELF/')\n", + "from darkelf import darkelf\n", + "\n", + "from IPython.utils import io\n", + "import datetime as dt\n", + "import time\n", + "\n", + "%matplotlib inline\n", + "import matplotlib as mpl\n", + "mpl.rcParams['font.size'] = 14" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Signal Models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Nuclear Recoils" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_11414/3014182000.py:30: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "keV_arr = np.geomspace(1e-6, 100, 250)\n", + "\n", + "for mX, color in zip([1e7, 3e7, 1e8, 3e8, 1e9, 3e9, 1e10], \n", + " ['#d62728', '#ff7f0e', '#bcbd22', '#2ca02c', '#17becf', '#1f77b4','#e377c2', '#9467bd', '#8c564b']):\n", + "\n", + " dRdE_arr = darklim.limit._limit.drde(keV_arr, mX / 1e9, 1e-36, 'Al2O3')\n", + " axes[0].plot(keV_arr*1000, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " dRdE_arr = darklim.limit._limit.drde(keV_arr, mX / 1e9, 1e-36, 'GaAs')\n", + " axes[1].plot(keV_arr*1000, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + "for ax in axes:\n", + "\n", + " ax.set_yscale(\"log\")\n", + " ax.set_xscale('log')\n", + " ax.set_ylim([1e1, 1e13])\n", + " ax.set_xlim([1e-3, 1e5])\n", + "\n", + " ax.set_xlabel(\"E [eV]\")\n", + " ax.set_ylabel(\"dR/dE [DRU]\")\n", + "\n", + "axes[0].set_title(r'Al2O3 Nuclear Recoil, $\\sigma_n = 10^{-36} cm^2$')\n", + "axes[1].set_title(r'GaAs Nuclear Recoil, $\\sigma_n = 10^{-36} cm^2$')\n", + "\n", + "axes[0].legend(ncol=2, loc=\"upper right\")\n", + "\n", + "fig.tight_layout()\n", + "fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DM-Electron Scattering, Massless Mediator" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_12377/2857544601.py:60: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Note the dimensional analysis in this cell\n", + "# DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV\n", + "# But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU)\n", + "\n", + "mX_arr_eV = np.array([1e7, 3e7, 1e8, 3e8, 1e9, 3e9, 1e10])\n", + "color_arr = np.array(['#d62728', '#ff7f0e', '#bcbd22', '#2ca02c', '#17becf', '#1f77b4','#e377c2', '#9467bd', '#8c564b'])\n", + "keV_arr = np.geomspace(1e-4, 100e-3, 1000)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "for i, (mX, color) in enumerate(zip(mX_arr_eV, color_arr)):\n", + "\n", + " # Initialize each DarkELF object\n", + " if i == 0:\n", + " print('WARNING: You are suppressing DarkELF output')\n", + "\n", + " with io.capture_output() as captured:\n", + " darkelf_sapphire = darkelf(target='Al2O3', filename=\"Al2O3_mermin.dat\")\n", + "\n", + " darkelf_sapphire.update_params(mX=mX, mediator='massless')\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * darklim.constants.bandgap_Al203_eV, 1) * \\\n", + " darkelf_sapphire.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + "\n", + " axes[0].plot(keV_arr * 1000, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " with io.capture_output() as captured:\n", + " darkelf_gaas = darkelf(target='GaAs', filename=\"GaAs_mermin.dat\")\n", + "\n", + " darkelf_gaas.update_params(mX=mX, mediator='massless')\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * darklim.constants.bandgap_GaAs_eV, 1) * \\\n", + " darkelf_gaas.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + "\n", + " axes[1].plot(keV_arr * 1000, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + "for ax in axes:\n", + " ax.set_yscale(\"log\")\n", + " ax.set_xlim([0, 100])\n", + " ax.set_ylim([1e-6, 1e10])\n", + "\n", + " ax.set_xlabel(\"E [eV]\")\n", + " ax.set_ylabel(\"dR/dE [DRU]\")\n", + "\n", + "axes[0].set_title(r'Al2O3 Electron Recoil, Massless Mediator, $\\sigma_e = 10^{-31} cm^2$')\n", + "axes[1].set_title(r'GaAs Electron Recoil, Massless Mediator, $\\sigma_e = 10^{-31} cm^2$')\n", + "\n", + "axes[0].legend(ncol=2, loc=\"upper right\")\n", + "\n", + "fig.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DM-Electron Scattering, Massive Mediator" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_12377/1801436035.py:74: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Note the dimensional analysis in this cell\n", + "# DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV\n", + "# But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU)\n", + "\n", + "mX_arr_eV = np.array([1e7, 3e7, 1e8, 3e8, 1e9, 3e9, 1e10])\n", + "color_arr = np.array(['#d62728', '#ff7f0e', '#bcbd22', '#2ca02c', '#17becf', '#1f77b4','#e377c2', '#9467bd', '#8c564b'])\n", + "keV_arr = np.geomspace(1e-4, 100e-3, 1000)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "for i, (mX, color) in enumerate(zip(mX_arr_eV, color_arr)):\n", + "\n", + " # Initialize each DarkELF object\n", + " if i == 0:\n", + " print('WARNING: You are suppressing DarkELF output')\n", + "\n", + " # Grid search Al2O3\n", + " with io.capture_output() as captured:\n", + " darkelf_sapphire = darkelf(target='Al2O3', filename=\"Al2O3_mermin.dat\")\n", + "\n", + " darkelf_sapphire.update_params(mX=mX, mediator='massive')\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * darklim.constants.bandgap_Al203_eV, 1) * \\\n", + " darkelf_sapphire.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + "\n", + " axes[0].plot(keV_arr * 1000, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " # Lindhard Al2O3\n", + " with io.capture_output() as captured:\n", + " darkelf_sapphire = darkelf(target='Al2O3')\n", + "\n", + " darkelf_sapphire.update_params(mX=mX, mediator='massive')\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * darklim.constants.bandgap_Al203_eV, 1) * \\\n", + " darkelf_sapphire.dRdomega_electron(keV * 1000, method=\"Lindhard\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + "\n", + " axes[0].plot(keV_arr * 1000, dRdE_arr, '--', label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " # GaAs grid search\n", + " with io.capture_output() as captured:\n", + " darkelf_gaas = darkelf(target='GaAs', filename=\"GaAs_mermin.dat\")\n", + "\n", + " darkelf_gaas.update_params(mX=mX, mediator='massive')\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * darklim.constants.bandgap_GaAs_eV, 1) * \\\n", + " darkelf_gaas.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + "\n", + " axes[1].plot(keV_arr * 1000, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " # Lindhard GaAs \n", + " with io.capture_output() as captured:\n", + " darkelf_gaas = darkelf(target='GaAs')\n", + "\n", + " darkelf_gaas.update_params(mX=mX, mediator='massive')\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * darklim.constants.bandgap_GaAs_eV, 1) * \\\n", + " darkelf_gaas.dRdomega_electron(keV * 1000, method=\"Lindhard\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + "\n", + " axes[1].plot(keV_arr * 1000, dRdE_arr, '--', label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + "for ax in axes:\n", + " ax.set_yscale(\"log\")\n", + " ax.set_xlim([0, 100])\n", + "# ax.set_ylim([1e-2, 1e12])\n", + "\n", + " ax.set_xlabel(\"E [eV]\")\n", + " ax.set_ylabel(\"dR/dE [DRU]\")\n", + "\n", + "axes[0].set_title(r'Al2O3 Electron Recoil, Massive Mediator, $\\sigma_e = 10^{-31} cm^2$')\n", + "axes[1].set_title(r'GaAs Electron Recoil, Massive Mediator, $\\sigma_e = 10^{-31} cm^2$')\n", + "\n", + "axes[0].legend(ncol=2, loc=\"upper right\")\n", + "\n", + "fig.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DM Dark Photon Absorption" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def kappa_limit(target,mX):\n", + " scalar_input = np.isscalar(mX)\n", + " mXlist = np.atleast_1d(mX)\n", + " lim=[]\n", + " \n", + " for mX in mXlist:\n", + " target.update_params(mX=mX)\n", + " rate=target.R_absorption(kappa=1.0)\n", + " if rate==0.0:\n", + " lim.append(np.nan)\n", + " else:\n", + " lim.append(np.sqrt(3.0/rate))\n", + " \n", + " if(scalar_input):\n", + " return lim[0]\n", + " else:\n", + " return np.array(lim) " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " .... Loading files for Ge\n", + "Loaded Ge_gpaw_noLFE.dat for epsilon in electron regime\n", + "electronic ELF taken or calculated from J. Enkovaara et al.,Electronic structure calculations with GPAW: a real-space implementation of the projector augmented-wave method,Journal of Physics:Condensed Matter22(2010) 253202.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vetri/GitRepos/DarkELF/darkelf/epsilon.py:44: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " data.fillna(inplace=True,method='bfill')# fill in some NaN values\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "phonon ELF taken or calculated from M. Ikezawa and T. Nanba, Two-Phonon Difference Absorption Spectra in Ge Crystals, Journal of the Physical Society of Japan 45 (1978) 148.\n", + "Loaded Ge_epsphonon_data2K.dat for epsilon in phonon regime\n", + "Zion(k) for Migdal calculation taken or calculated from: P. J. Brown, A. G. Fox, E. N. Maslen, M. A. OKeefe,and B. T. M. Willis, “Intensity of diffracted intensities,” in International Tables for Crystallography (American Cancer Society, 2006) Chap. 6.1, pp. 554–595, https://onlinelibrary.wiley.com/doi/pdf/10.1107/97809553602060000\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Ge/Ge_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Ge/Ge_Fn.dat for Fn(omega)\n", + " .... Loading files for Si\n", + "Loaded Si_mermin.dat for epsilon in electron regime\n", + "electronic ELF taken or calculated from Y. Sun, H. Xu, B. Da, S.-f. Mao and Z.-j. Ding, Calculations of Energy-Loss Function for 26 Materials, Chinese Journal of Chemical Physics9(2016) 663.\n", + "phonon ELF taken or calculated from M. Ikezawa and M. Ishigame, Far-Infrared AbsorptionDue to the Two-Phonon Difference Process in Si, Journal of the Physical Society of Japan 50(1981) 3734.\n", + "Loaded Si_epsphonon_data6K.dat for epsilon in phonon regime\n", + "Zion(k) for Migdal calculation taken or calculated from: P. J. Brown, A. G. Fox, E. N. Maslen, M. A. OKeefe,and B. T. M. Willis, “Intensity of diffracted intensities,” in International Tables for Crystallography (American Cancer Society, 2006) Chap. 6.1, pp. 554–595, https://onlinelibrary.wiley.com/doi/pdf/10.1107/97809553602060000\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Si/Si_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Si/Si_Fn.dat for Fn(omega)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vetri/GitRepos/DarkELF/darkelf/epsilon.py:44: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " data.fillna(inplace=True,method='bfill')# fill in some NaN values\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " .... Loading files for Ge\n", + "Loaded Ge_mermin.dat for epsilon in electron regime\n", + "electronic ELF taken or calculated from M. Nov ́ak, L. K ̈ov ́er, S. Egri, I. Cserny, J. T ́oth, D. Varga et al., A simple statistical model for quantitative analysis of plasmon structures in xps and auger spectra of free-electron-like materials,Journal ofElectron Spectroscopy and Related Phenomena163(2008) 7\n", + "phonon ELF taken or calculated from M. Ikezawa and T. Nanba, Two-Phonon Difference Absorption Spectra in Ge Crystals, Journal of the Physical Society of Japan 45 (1978) 148.\n", + "Loaded Ge_epsphonon_data2K.dat for epsilon in phonon regime\n", + "Zion(k) for Migdal calculation taken or calculated from: P. J. Brown, A. G. Fox, E. N. Maslen, M. A. OKeefe,and B. T. M. Willis, “Intensity of diffracted intensities,” in International Tables for Crystallography (American Cancer Society, 2006) Chap. 6.1, pp. 554–595, https://onlinelibrary.wiley.com/doi/pdf/10.1107/97809553602060000\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Ge/Ge_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Ge/Ge_Fn.dat for Fn(omega)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vetri/GitRepos/DarkELF/darkelf/epsilon.py:44: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " data.fillna(inplace=True,method='bfill')# fill in some NaN values\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " .... Loading files for GaAs\n", + "Loaded GaAs_mermin.dat for epsilon in electron regime\n", + "electronic ELF taken or calculated from Y. Tu, C. Kwei and C. Tung,Angular and energy dependences of the surface excitation parameter for semiconducting iii–v compounds, Surface Science601(2007) 865.\n", + "phonon ELF taken or calculated from H.M. Lawler and E.L. Shirley, Anharmonic effects on infrared spectra of GaAs and gap: First-principles calculations, Phys. Rev. B70(2004) 245209 and E.D. Palik, Gallium arsenide (gaas), in Handbook of Optical Constants of Solids, E.D. Palik, ed.,pp. 429–443, Elsevier (1985).\n", + "Loaded GaAs_epsphonon_data10K.dat for epsilon in phonon regime\n", + "Warning! Atomic Migdal calculation not present\n", + "Warning! Momentum dependent Zion for Migdal calculation not loaded. Using Z - number of valence electrons.\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/GaAs/Ga_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/GaAs/As_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/GaAs/Ga_Fn.dat for Fn(omega)\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/GaAs/As_Fn.dat for Fn(omega)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vetri/GitRepos/DarkELF/darkelf/epsilon.py:44: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " data.fillna(inplace=True,method='bfill')# fill in some NaN values\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " .... Loading files for Al2O3\n", + "Loaded Al2O3_mermin.dat for epsilon in electron regime\n", + "electronic ELF taken or calculated from Y. Sun, H. Xu, B. Da, S.-f. Mao and Z.-j. Ding, Calculations of Energy-Loss Function for 26 Materials, Chinese Journal of Chemical Physics9(2016) 663.\n", + "phonon ELF taken or calculated from F. Gervais and B. Piriou, Anharmonicity in several-polar-mode crystals: adjusting phonon self-energy of LO and TO modes in Al2O3 and TiO2 to fit infrared reflectivity,Journal of Physics C Solid State Physics 7 (1974) 2374 and M. Schubert, T.E. Tiwald and C.M. Herzinger, Infrared dielectric anisotropy and phonon modes of sapphire, Phys. Rev. B61(2000) 8187\n", + "Loaded Al2O3_epsphonon_o.dat for epsilon in phonon regime\n", + "Warning! Atomic Migdal calculation not present\n", + "Warning! Momentum dependent Zion for Migdal calculation not loaded. Using Z - number of valence electrons.\n", + "Warning, /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/Al_atomic_Zion.dat does not exist! Must load in effective charges for each atom if dark photon calculations are desired.\n", + "Warning, /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/O_atomic_Zion.dat does not exist! Must load in effective charges for each atom if dark photon calculations are desired.\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/Al_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/O_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/Al_Fn.dat for Fn(omega)\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/O_Fn.dat for Fn(omega)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vetri/GitRepos/DarkELF/darkelf/epsilon.py:44: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " data.fillna(inplace=True,method='bfill')# fill in some NaN values\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " .... Loading files for Al2O3\n", + "Loaded Al2O3_mermin.dat for epsilon in electron regime\n", + "electronic ELF taken or calculated from Y. Sun, H. Xu, B. Da, S.-f. Mao and Z.-j. Ding, Calculations of Energy-Loss Function for 26 Materials, Chinese Journal of Chemical Physics9(2016) 663.\n", + "phonon ELF taken or calculated from F. Gervais and B. Piriou, Anharmonicity in several-polar-mode crystals: adjusting phonon self-energy of LO and TO modes in Al2O3 and TiO2 to fit infrared reflectivity,Journal of Physics C Solid State Physics 7 (1974) 2374 and M. Schubert, T.E. Tiwald and C.M. Herzinger, Infrared dielectric anisotropy and phonon modes of sapphire, Phys. Rev. B61(2000) 8187\n", + "Loaded Al2O3_epsphonon_e.dat for epsilon in phonon regime\n", + "Warning! Atomic Migdal calculation not present\n", + "Warning! Momentum dependent Zion for Migdal calculation not loaded. Using Z - number of valence electrons.\n", + "Warning, /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/Al_atomic_Zion.dat does not exist! Must load in effective charges for each atom if dark photon calculations are desired.\n", + "Warning, /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/O_atomic_Zion.dat does not exist! Must load in effective charges for each atom if dark photon calculations are desired.\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/Al_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/O_pDoS.dat for partial densities of states\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/Al_Fn.dat for Fn(omega)\n", + "Loaded /Users/vetri/GitRepos/DarkELF/darkelf/../data/Al2O3/O_Fn.dat for Fn(omega)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vetri/GitRepos/DarkELF/darkelf/epsilon.py:44: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " data.fillna(inplace=True,method='bfill')# fill in some NaN values\n" + ] + } + ], + "source": [ + "ge = darkelf(target='Ge',filename='Ge_gpaw_noLFE.dat',phonon_filename=\"Ge_epsphonon_data2K.dat\")\n", + "simermin = darkelf(target='Si',filename='Si_mermin.dat',phonon_filename=\"Si_epsphonon_data6K.dat\")\n", + "gemermin = darkelf(target='Ge',filename='Ge_mermin.dat',phonon_filename=\"Ge_epsphonon_data2K.dat\")\n", + "\n", + "GaAs = darkelf(target='GaAs',filename=\"GaAs_mermin.dat\",phonon_filename=\"GaAs_epsphonon_data10K.dat\")\n", + "\n", + "Al2O3 = darkelf(target='Al2O3',filename=\"Al2O3_mermin.dat\",phonon_filename=\"Al2O3_epsphonon_o.dat\")\n", + "Al2O3_e = darkelf(target='Al2O3',targetyaml='Al2O3_extraordinary',filename=\"Al2O3_mermin.dat\",phonon_filename=\"Al2O3_epsphonon_e.dat\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_14442/4228469574.py:12: RuntimeWarning: invalid value encountered in sqrt\n", + " lim.append(np.sqrt(3.0/rate))\n", + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_14442/1927280363.py:33: RankWarning: Polyfit may be poorly conditioned\n", + " fitparam=np.polyfit(mVelectron[:-3],np.log10(a)[:-3],30) # need to smooth out Ge curve, due to numerical artifact\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "kappa_limit_xenon.txt not found.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 59\u001b[0m\n\u001b[1;32m 56\u001b[0m axs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAbsorption into phonons\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 58\u001b[0m \u001b[38;5;66;03m### Plot settings for right panel\u001b[39;00m\n\u001b[0;32m---> 59\u001b[0m dat \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloadtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mkappa_limit_xenon.txt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m 60\u001b[0m axs[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mfill_between(dat[\u001b[38;5;241m0\u001b[39m],dat[\u001b[38;5;241m1\u001b[39m],dat[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m0.0\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1e-13\u001b[39m,color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSilver\u001b[39m\u001b[38;5;124m'\u001b[39m,alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.6\u001b[39m)\n\u001b[1;32m 61\u001b[0m dat \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mloadtxt(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkappa_limit_SENSEI2020.txt\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mT\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/numpy/lib/npyio.py:1373\u001b[0m, in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows, quotechar, like)\u001b[0m\n\u001b[1;32m 1370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delimiter, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[1;32m 1371\u001b[0m delimiter \u001b[38;5;241m=\u001b[39m delimiter\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1373\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdelimiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1374\u001b[0m \u001b[43m \u001b[49m\u001b[43mconverters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconverters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiplines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskiprows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1375\u001b[0m \u001b[43m \u001b[49m\u001b[43munpack\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munpack\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mndmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1376\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_rows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/numpy/lib/npyio.py:992\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(fname, delimiter, comment, quote, imaginary_unit, usecols, skiplines, max_rows, converters, ndmin, unpack, dtype, encoding)\u001b[0m\n\u001b[1;32m 990\u001b[0m fname \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(fname)\n\u001b[1;32m 991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m--> 992\u001b[0m fh \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_datasource\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 993\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 994\u001b[0m encoding \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fh, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/numpy/lib/_datasource.py:193\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;124;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 189\u001b[0m \n\u001b[1;32m 190\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 192\u001b[0m ds \u001b[38;5;241m=\u001b[39m DataSource(destpath)\n\u001b[0;32m--> 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/numpy/lib/_datasource.py:533\u001b[0m, in \u001b[0;36mDataSource.open\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m 531\u001b[0m encoding\u001b[38;5;241m=\u001b[39mencoding, newline\u001b[38;5;241m=\u001b[39mnewline)\n\u001b[1;32m 532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 533\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: kappa_limit_xenon.txt not found." + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "cmap = plt.get_cmap('tab10')\n", + "colors = cmap(np.linspace(0.0,1.0,10))\n", + "\n", + "fig, axs = plt.subplots(1,2,figsize=(15,5),sharey=True)\n", + "fig.subplots_adjust(wspace=0.1)\n", + "\n", + "props = dict(facecolor='white', alpha=0.9, edgecolor='white',boxstyle=\"square,pad=0.025\")\n", + "\n", + "# Al2O3\n", + "mVelectron = np.linspace(Al2O3.E_gap,100,200)\n", + "mVphonon = np.linspace(.03,.2,100)\n", + "a = kappa_limit(Al2O3,mVelectron)\n", + "axs[1].plot(mVelectron,a,color=colors[0],label=r'Al$_2$O$_3$')\n", + "b1 = kappa_limit(Al2O3,mVphonon)\n", + "b2 = kappa_limit(Al2O3_e,mVphonon)\n", + "b = pow(1./3*1./b1**2 + 2./3*1./b2**2,-0.5)\n", + "axs[0].plot(mVphonon,b,color=colors[0],label=r'Al$_2$O$_3$')\n", + "\n", + "\n", + "# GaAs\n", + "mVelectron = np.linspace(GaAs.E_gap,100,200)\n", + "mVphonon = np.linspace(.01,.07,100)\n", + "a = kappa_limit(GaAs,mVelectron)\n", + "axs[1].plot(mVelectron,a,color=colors[2],label='GaAs')\n", + "b = kappa_limit(GaAs,mVphonon)\n", + "axs[0].plot(mVphonon,b,color=colors[2],label='GaAs')\n", + "\n", + "\n", + "# Ge\n", + "mVelectron = np.linspace(gemermin.E_gap,100,200)\n", + "mVphonon = np.linspace(.03,.08,100)\n", + "a = kappa_limit(gemermin,mVelectron)\n", + "fitparam=np.polyfit(mVelectron[:-3],np.log10(a)[:-3],30) # need to smooth out Ge curve, due to numerical artifact\n", + "aa = (lambda mV: 10**np.poly1d(fitparam)(mV))\n", + "axs[1].plot(mVelectron[:-3],list(map(aa,mVelectron[:-3])),color=colors[3],label='Ge')\n", + "b = kappa_limit(gemermin,mVphonon)\n", + "axs[0].plot(mVphonon,b,color=colors[3],label='Ge')\n", + "\n", + "# SI\n", + "mVelectron = np.linspace(simermin.E_gap,100,200)\n", + "mVphonon = np.linspace(.03,.15,100)\n", + "a = kappa_limit(simermin,mVelectron)\n", + "axs[1].plot(mVelectron,a,color=colors[4],label='Si')\n", + "b = kappa_limit(simermin,mVphonon)\n", + "axs[0].plot(mVphonon,b,color=colors[4],label='Si')\n", + "\n", + "\n", + "\n", + "### Plot settings for left panel\n", + "axs[0].set_yscale('log')\n", + "axs[0].set_ylim([3e-18,3e-14])\n", + "axs[0].tick_params(direction='in',which='both',pad=8)\n", + "axs[0].set_xlabel(r'$m_V$ [eV]',fontsize=16)\n", + "axs[0].set_ylabel(r'Kinetic mixing $\\kappa$',fontsize=18)\n", + "\n", + "axs[0].set_title('Absorption into phonons')\n", + "\n", + "### Plot settings for right panel\n", + "dat = np.loadtxt('kappa_limit_xenon.txt').T\n", + "axs[1].fill_between(dat[0],dat[1],dat[1]*0.0+1e-13,color='Silver',alpha=0.6)\n", + "dat = np.loadtxt('kappa_limit_SENSEI2020.txt').T\n", + "axs[1].fill_between(dat[0],dat[1],dat[1]*0.0+1e-13,color='Silver',alpha=0.6)\n", + "axs[1].text(30,1e-14,'XENON10/100',fontsize=10)\n", + "axs[1].text(10,1.2e-14,'SENSEI',fontsize=10)\n", + "\n", + "axs[1].set_xlim([1.0,100])\n", + "axs[1].tick_params(direction='in',which='both',pad=8)\n", + "axs[1].set_xlabel(r'$m_V$ [eV]',fontsize=16)\n", + "\n", + "lines = axs[1].get_lines()\n", + "#labelLines(lines,xvals=[65,70,52,55,17,43,45],fontsize=9,zorder=2.5,\n", + "# ha='center',va='center',align=False,bbox=props)\n", + "axs[1].set_title('Absorption into $e^-$')\n", + "\n", + "axs[0].legend(loc='best')\n", + "\n", + "fig.savefig('plots/darkelf_absorption_1.pdf',bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We try two methods of passing the DarkELF dRdomega_electron() function to DarkLim: defining an anonymous function in this Jupyter notebook, and getting an anonymous function from _sens_est.py. In theory, they are equivalent, but for some reason, only the latter method works. So the former method is grayed out, but we keep it for posterity." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "if False:\n", + " SE.reset_sim()\n", + "\n", + " # flat background of 1 DRU\n", + " SE.add_flat_bkgd(1)\n", + " # noise background assuming 10,000 independent samples (1 ms window), using the data sample rate of 1 MHz\n", + " SE.add_noise_bkgd(energy_res, 1e4, 1e6)\n", + " # LEE background assuming mean rate of 0.12 events/sec\n", + " SE.add_exponential_bkgd(0.020, 0.12 * 86400, normalize_mass=True)\n", + "\n", + "mX_arr_eV = np.geomspace(1e7, 1e10, 10)\n", + "mX_arr_GeV = mX_arr_eV / 1e9\n", + "\n", + "# Set up DarkELF Al2O3 object\n", + "print('WARNING: You are suppressing DarkELF output')\n", + "with io.capture_output() as captured:\n", + " darkelf_sapphire_arr = [darkelf(target='Al2O3', filename=\"Al2O3_mermin.dat\") for m in mX_arr_eV]\n", + "\n", + "# Set up data structure for anonymous functions\n", + "drdefunction_list = [None for m in mX_arr_eV]\n", + "\n", + "for i, mX in enumerate(mX_arr_eV):\n", + " darkelf_sapphire_arr[i].update_params(mX=mX, mediator='massless')\n", + " drdefunction_list[i] = lambda keV : np.heaviside(keV * 1000 - 2 * band_gap_sapphire_eV, 1) * \\\n", + " darkelf_sapphire_arr[i].dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + "\n", + "# run the simulation for 1 experiment\n", + "m_dm, sig = SE.run_sim(\n", + " threshold=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_low=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_high=0.1,\n", + " m_dms=mX_arr_GeV,\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-31,\n", + " drdefunction=drdefunction_list,\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "#ax.set_ylim(1e-37, 1e-32)\n", + "#ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "Starting Experiment 0\n", + " Finished mass 0, 0.01000 GeV. Found sigma = 3.110e-31 cm2.\n", + " Finished mass 1, 0.01638 GeV. Found sigma = 1.150e-31 cm2.\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Run the simulation\u001b[39;00m\n\u001b[1;32m 5\u001b[0m t_start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 6\u001b[0m m_dm, sigs \u001b[38;5;241m=\u001b[39m \u001b[43mSE\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_sim\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43menergy_threshold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mband_gap_sapphire_eV\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43me_low\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43menergy_threshold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mband_gap_sapphire_eV\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43me_high\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m 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np\u001b[38;5;241m.\u001b[39mmedian(np\u001b[38;5;241m.\u001b[39mstack(sigs, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 18\u001b[0m t_end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/darklim/sensitivity/_sens_est.py:316\u001b[0m, in \u001b[0;36mSensEst.run_sim\u001b[0;34m(self, threshold, e_high, e_low, m_dms, nexp, npts, plot_bkgd, drdefunction, sigma0, elf_model, elf_params)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStarting Experiment \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mii\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 312\u001b[0m evts_sim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate_background(\n\u001b[1;32m 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\u001b[49m\u001b[43mthreshold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 320\u001b[0m \u001b[43m \u001b[49m\u001b[43mm_dms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 321\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexposure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 322\u001b[0m \u001b[43m \u001b[49m\u001b[43mtm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 323\u001b[0m \u001b[43m \u001b[49m\u001b[43mhard_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthreshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 324\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrdefunction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrdefunction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 325\u001b[0m \u001b[43m \u001b[49m\u001b[43msigma0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msigma0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 326\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 328\u001b[0m sigs\u001b[38;5;241m.\u001b[39mappend(sig_temp)\n\u001b[1;32m 330\u001b[0m sig \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmedian(np\u001b[38;5;241m.\u001b[39mstack(sigs, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/darklim/limit/_limit.py:491\u001b[0m, in \u001b[0;36moptimuminterval\u001b[0;34m(eventenergies, effenergies, effs, masslist, exposure, tm, cl, res, gauss_width, verbose, drdefunction, hard_threshold, sigma0)\u001b[0m\n\u001b[1;32m 489\u001b[0m rate \u001b[38;5;241m=\u001b[39m init_rate \u001b[38;5;241m*\u001b[39m curr_exp(en_interp)\n\u001b[1;32m 490\u001b[0m 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\u001b[38;5;66;03m# Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV\u001b[39;00m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU)\u001b[39;00m\n\u001b[1;32m 57\u001b[0m sapphire\u001b[38;5;241m.\u001b[39mupdate_params(mX\u001b[38;5;241m=\u001b[39mmX_eV, mediator\u001b[38;5;241m=\u001b[39mmediator)\n\u001b[1;32m 58\u001b[0m fun \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m keV : np\u001b[38;5;241m.\u001b[39mheaviside(keV \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m constants\u001b[38;5;241m.\u001b[39mbandgap_Al203, \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \\\n\u001b[1;32m 59\u001b[0m (\u001b[38;5;241m1000\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m365.25\u001b[39m) \u001b[38;5;241m*\u001b[39m \\\n\u001b[0;32m---> 60\u001b[0m \u001b[43msapphire\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdRdomega_electron\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeV\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msigmae\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msigmae\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkcut\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkcut\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwithscreening\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwithscreening\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fun\n", + "File \u001b[0;32m~/GitRepos/DarkELF/darkelf/electron.py:87\u001b[0m, in \u001b[0;36mdRdomega_electron\u001b[0;34m(self, omega, sigmae, kcut, withscreening, method)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# Note: division and multiplication by self.eVtoInvYr in the integrand improves performance of quad\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m dRdomega[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meVtoInvYr \u001b[38;5;241m*\u001b[39m \u001b[43mintegrate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquad\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdRdomegadk_electron\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43msigmae\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mwithscreening\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwithscreening\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meVtoInvYr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m] \n\u001b[1;32m 89\u001b[0m \u001b[38;5;66;03m# units of 1/kg/yr/eV\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(scalar_input):\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/scipy/integrate/_quadpack_py.py:465\u001b[0m, in \u001b[0;36mquad\u001b[0;34m(func, a, b, args, full_output, epsabs, epsrel, limit, points, weight, wvar, wopts, maxp1, limlst, complex_func)\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n\u001b[1;32m 464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 465\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[43m_quad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfull_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepsabs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepsrel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 466\u001b[0m \u001b[43m \u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 467\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m points \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/scipy/integrate/_quadpack_py.py:577\u001b[0m, in \u001b[0;36m_quad\u001b[0;34m(func, a, b, args, full_output, epsabs, epsrel, limit, points)\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m points \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m infbounds \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 577\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_quadpack\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_qagse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mfull_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepsabs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepsrel\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlimit\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 578\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 579\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _quadpack\u001b[38;5;241m.\u001b[39m_qagie(func,bound,infbounds,args,full_output,epsabs,epsrel,limit)\n", + "File \u001b[0;32m~/GitRepos/DarkELF/darkelf/electron.py:87\u001b[0m, in \u001b[0;36mdRdomega_electron..\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# Note: division and multiplication by self.eVtoInvYr in the integrand improves performance of quad\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m dRdomega[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meVtoInvYr \u001b[38;5;241m*\u001b[39m integrate\u001b[38;5;241m.\u001b[39mquad(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdRdomegadk_electron\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43msigmae\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mwithscreening\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwithscreening\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meVtoInvYr, kmin, kmax, limit\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m)[\u001b[38;5;241m0\u001b[39m] \n\u001b[1;32m 89\u001b[0m \u001b[38;5;66;03m# units of 1/kg/yr/eV\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(scalar_input):\n", + "File \u001b[0;32m~/GitRepos/DarkELF/darkelf/electron.py:26\u001b[0m, in \u001b[0;36mdRdomegadk_electron\u001b[0;34m(self, omega, k, sigmae, withscreening, method)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;124;03mReturns double differential rate for DM-electron scattering in 1/kg/year/eV^2\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m use interpolated grid of epsilon, or Lindhard analytic epsilon\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 25\u001b[0m etav_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39metav(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvmin(omega,k))\n\u001b[0;32m---> 26\u001b[0m temp_eps1\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps1\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m,\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 27\u001b[0m temp_eps2\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meps2(omega,k,method\u001b[38;5;241m=\u001b[39mmethod)\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(method\u001b[38;5;241m!=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m method\u001b[38;5;241m!=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLindhard\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", + "File \u001b[0;32m~/GitRepos/DarkELF/darkelf/epsilon.py:160\u001b[0m, in \u001b[0;36meps1\u001b[0;34m(self, omega, k, method)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 157\u001b[0m \u001b[38;5;66;03m# If k is smaller than grid kmin, we will extrapolate from lowest k point\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;66;03m# This is implemented by changing all small k values to kmin\u001b[39;00m\n\u001b[1;32m 159\u001b[0m k \u001b[38;5;241m=\u001b[39m k\u001b[38;5;241m*\u001b[39m(k \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkmin) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkmin\u001b[38;5;241m*\u001b[39m(k \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkmin)\n\u001b[0;32m--> 160\u001b[0m eps1\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps1_grid\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m,\u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m(method\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLindhard\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 162\u001b[0m eps1\u001b[38;5;241m=\u001b[39m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meps1_electrongas(om,k) \u001b[38;5;28;01mfor\u001b[39;00m om \u001b[38;5;129;01min\u001b[39;00m omega]\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/numpy/lib/utils.py:144\u001b[0m, in \u001b[0;36m_Deprecate.__call__..newfunc\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnewfunc\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds):\n\u001b[1;32m 143\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(depdoc, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/scipy/interpolate/_interpolate.py:346\u001b[0m, in \u001b[0;36minterp2d.__call__\u001b[0;34m(self, x, y, dx, dy, assume_sorted)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbounds_error \u001b[38;5;129;01mand\u001b[39;00m (any_out_of_bounds_x \u001b[38;5;129;01mor\u001b[39;00m any_out_of_bounds_y):\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValues out of range; x must be in \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m, y in \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;241m%\u001b[39m ((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx_min, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx_max),\n\u001b[1;32m 344\u001b[0m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my_min, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my_max)))\n\u001b[0;32m--> 346\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[43m_fitpack_py\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbisplev\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtck\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 347\u001b[0m z \u001b[38;5;241m=\u001b[39m atleast_2d(z)\n\u001b[1;32m 348\u001b[0m z \u001b[38;5;241m=\u001b[39m transpose(z)\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/scipy/interpolate/_fitpack_impl.py:665\u001b[0m, in \u001b[0;36mbisplev\u001b[0;34m(x, y, tck, dx, dy)\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m dy \u001b[38;5;241m<\u001b[39m ky):\n\u001b[1;32m 664\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0 <= dy = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m < ky = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m must hold\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (dy, ky))\n\u001b[0;32m--> 665\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(atleast_1d, [x, y])\n\u001b[1;32m 666\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mlen\u001b[39m(x\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mlen\u001b[39m(y\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 667\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst two entries should be rank-1 arrays.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/numpy/core/shape_base.py:66\u001b[0m, in \u001b[0;36matleast_1d\u001b[0;34m(*arys)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ary \u001b[38;5;129;01min\u001b[39;00m arys:\n\u001b[1;32m 65\u001b[0m ary \u001b[38;5;241m=\u001b[39m asanyarray(ary)\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ary\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 67\u001b[0m result \u001b[38;5;241m=\u001b[39m ary\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mX_arr_eV = np.geomspace(1e7, 1e10, 15)\n", + "mX_arr_GeV = mX_arr_eV / 1e9\n", + "\n", + "# Run the simulation\n", + "t_start = time.time()\n", + "m_dm, sigs = SE.run_sim(\n", + " threshold=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_low=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_high=0.1,\n", + " m_dms=mX_arr_GeV,\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-31,\n", + " elf_model='electron',\n", + " elf_params={'mediator': 'massless', 'kcut': 0, 'suppress_darkelf_output': True},\n", + ")\n", + "sig = np.median(np.stack(sigs, axis=1), axis=1)\n", + "t_end = time.time()\n", + "print(f'Simulation took {(t_end - t_start):.2f} seconds')\n", + "\n", + "fn = 'sapphire_results/Massless_ER_Limit_' + dt.datetime.now().strftime('%Y%m%d_%H%M%S') + '.txt'\n", + "np.savetxt(fn, np.vstack([m_dm, sig]).transpose(), fmt='%.3e')\n", + "\n", + "f_out = open(fn + '_detailed', 'w')\n", + "f_out.write(str(m_dm))\n", + "f_out.write('\\n')\n", + "f_out.write(str(sigs))\n", + "f_out.write('\\n')\n", + "f_out.close()\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "#ax.set_ylim(1e-37, 1e-32)\n", + "#ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DM-Electron Scattering, Massive Mediator" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "10.0 MeV, 49.7 events above threshold\n", + " After selecting k < 25 keV, 49.7 events above threshold\n", + "30.0 MeV, 387.8 events above threshold\n", + " After selecting k < 25 keV, 380.7 events above threshold\n", + "100.0 MeV, 250.0 events above threshold\n", + " After selecting k < 25 keV, 243.3 events above threshold\n", + "300.0 MeV, 101.1 events above threshold\n", + " After selecting k < 25 keV, 98.2 events above threshold\n", + "1000.0 MeV, 32.4 events above threshold\n", + " After selecting k < 25 keV, 31.4 events above threshold\n", + "3000.0 MeV, 11.0 events above threshold\n", + " After selecting k < 25 keV, 10.7 events above threshold\n", + "10000.0 MeV, 3.3 events above threshold\n", + " After selecting k < 25 keV, 3.2 events above threshold\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_9657/3596589769.py:51: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Note the dimensional analysis in this cell\n", + "# DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV\n", + "# But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU)\n", + "\n", + "mX_arr_eV = np.array([1e7, 3e7, 1e8, 3e8, 1e9, 3e9, 1e10])\n", + "color_arr = np.array(['#d62728', '#ff7f0e', '#bcbd22', '#2ca02c', '#17becf', '#1f77b4','#e377c2', '#9467bd', '#8c564b'])\n", + "keV_arr = np.geomspace(10e-3, 100e-3, 250)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", + "for i, (mX, color) in enumerate(zip(mX_arr_eV, color_arr)):\n", + "\n", + " # Initialize each DarkELF Al2O3 object\n", + " if i == 0:\n", + " print('WARNING: You are suppressing DarkELF output')\n", + " with io.capture_output() as captured:\n", + " darkelf_sapphire = darkelf(target='Al2O3', filename=\"Al2O3_mermin.dat\")\n", + "\n", + " darkelf_sapphire.update_params(mX=mX, mediator='massive')\n", + "\n", + " # No cut on k\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * band_gap_sapphire_eV, 1) * \\\n", + " darkelf_sapphire.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-34, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + " \n", + " ax.plot(keV_arr, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " n_above_threshold = np.trapz(dRdE_arr[keV_arr > energy_threshold], keV_arr[keV_arr > energy_threshold]) * time_elapsed * mass_det\n", + " print(f'{mX / 1e6} MeV, {n_above_threshold:.1f} events above threshold')\n", + "\n", + " # With a 25 keV cut on k (without cut, max k is 37 keV)\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * band_gap_sapphire_eV, 1) * \\\n", + " darkelf_sapphire.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-34, kcut=25e3, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + " \n", + " ax.plot(keV_arr, dRdE_arr, '--', color=color)\n", + "\n", + " n_above_threshold = np.trapz(dRdE_arr[keV_arr > energy_threshold], keV_arr[keV_arr > energy_threshold]) * time_elapsed * mass_det\n", + " print(f' After selecting k < 25 keV, {n_above_threshold:.1f} events above threshold')\n", + "\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xscale('log')\n", + "ax.set_ylim([1e-6, 1e10])\n", + "ax.set_xlim([keV_arr[0], keV_arr[-1]])\n", + "\n", + "ax.set_xlabel(\"E [keV]\")\n", + "ax.set_ylabel(\"dR/dE [DRU]\")\n", + "ax.set_title(r'Electron Recoil, Massive Mediator, $\\sigma = 10^{-34} cm^2$')\n", + "\n", + "ax.legend(ncol=2, fontsize=10, loc=\"upper right\")\n", + "fig.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "Starting Experiment 0\n", + "Simulated 3336 events\n", + " Finished mass 0, 0.01000 GeV. Found sigma = 1.556e-33 cm2.\n", + " Finished mass 1, 0.01638 GeV. Found sigma = 5.019e-34 cm2.\n", + " Finished mass 2, 0.02683 GeV. Found sigma = 3.916e-34 cm2.\n", + " Finished mass 3, 0.04394 GeV. Found sigma = 4.382e-34 cm2.\n", + " Finished mass 4, 0.07197 GeV. Found sigma = 5.839e-34 cm2.\n", + " Finished mass 5, 0.11788 GeV. Found sigma = 8.506e-34 cm2.\n", + " Finished mass 6, 0.19307 GeV. Found sigma = 1.301e-33 cm2.\n", + " Finished mass 7, 0.31623 GeV. Found sigma = 2.046e-33 cm2.\n", + " Finished mass 8, 0.51795 GeV. Found sigma = 3.271e-33 cm2.\n", + " Finished mass 9, 0.84834 GeV. Found sigma = 5.279e-33 cm2.\n", + " Finished mass 10, 1.38950 GeV. Found sigma = 8.570e-33 cm2.\n", + " Finished mass 11, 2.27585 GeV. Found sigma = 1.396e-32 cm2.\n", + " Finished mass 12, 3.72759 GeV. Found sigma = 2.279e-32 cm2.\n", + " Finished mass 13, 6.10540 GeV. Found sigma = 3.725e-32 cm2.\n", + " Finished mass 14, 10.00000 GeV. Found sigma = 6.094e-32 cm2.\n", + "\n", + "Simulation took 2213.48 seconds\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mX_arr_eV = np.geomspace(1e7, 1e10, 15)\n", + "mX_arr_GeV = mX_arr_eV / 1e9\n", + "\n", + "# Run the simulation\n", + "t_start = time.time()\n", + "m_dm, sigs = SE.run_sim(\n", + " threshold=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_low=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_high=0.1,\n", + " m_dms=mX_arr_GeV,\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-31,\n", + " elf_model='electron',\n", + " elf_params={'mediator': 'massive', 'kcut': 0, 'suppress_darkelf_output': True},\n", + ")\n", + "sig = np.median(np.stack(sigs, axis=1), axis=1)\n", + "t_end = time.time()\n", + "print(f'Simulation took {(t_end - t_start):.2f} seconds')\n", + "\n", + "fn = 'sapphire_results/Massive_ER_kcut_None_Limit_' + dt.datetime.now().strftime('%Y%m%d_%H%M%S') + '.txt'\n", + "np.savetxt(fn, np.vstack([m_dm, sig]).transpose(), fmt='%.3e')\n", + "\n", + "f_out = open(fn + '_detailed', 'w')\n", + "f_out.write(str(m_dm))\n", + "f_out.write('\\n')\n", + "f_out.write(str(sigs))\n", + "f_out.write('\\n')\n", + "f_out.close()\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "#ax.set_ylim(1e-37, 1e-32)\n", + "#ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "Starting Experiment 0\n", + "Simulated 3287 events\n", + " Finished mass 0, 0.01000 GeV. Found sigma = 1.404e-33 cm2.\n", + " Finished mass 1, 0.01638 GeV. Found sigma = 4.801e-34 cm2.\n", + " Finished mass 2, 0.02683 GeV. Found sigma = 3.789e-34 cm2.\n", + " Finished mass 3, 0.04394 GeV. Found sigma = 4.219e-34 cm2.\n", + " Finished mass 4, 0.07197 GeV. Found sigma = 5.604e-34 cm2.\n", + " Finished mass 5, 0.11788 GeV. Found sigma = 8.151e-34 cm2.\n", + " Finished mass 6, 0.19307 GeV. Found sigma = 1.246e-33 cm2.\n", + " Finished mass 7, 0.31623 GeV. Found sigma = 1.958e-33 cm2.\n", + " Finished mass 8, 0.51795 GeV. Found sigma = 3.130e-33 cm2.\n", + " Finished mass 9, 0.84834 GeV. Found sigma = 5.050e-33 cm2.\n", + " Finished mass 10, 1.38950 GeV. Found sigma = 8.198e-33 cm2.\n", + " Finished mass 11, 2.27585 GeV. Found sigma = 1.335e-32 cm2.\n", + " Finished mass 12, 3.72759 GeV. Found sigma = 2.180e-32 cm2.\n", + " Finished mass 13, 6.10540 GeV. Found sigma = 3.563e-32 cm2.\n", + " Finished mass 14, 10.00000 GeV. Found sigma = 5.829e-32 cm2.\n", + "\n", + "Simulation took 1126.52 seconds\n" + ] + }, + { + "data": { + "image/png": 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QRxAEoQeRSARra2u921QqFZ+GX5uTJ08CALy8vABU3CHs0qULkpOTq/XFzcbGBh4eHkhPT8f169cfWV8ikQBAje4icHDrwOkLKLnppk+azp07w8LCAufOnUNpaanOdi5VfX3e6ZDJZJBKpfXWviEcHBwA6PpDo9FU+45iamoqgIoAojLcuVkZsVhs8Pzx9vZGTk5Otc5FoGJqcElJCRISEqp9/LokICAAzZo1wx9//AGNRoOdO3fC1tZWZzy4abj63sN1RXXfm7a2thg5ciQ2bdqEWbNm4f79+/U2lZcgiKcLCvIIgnhq+fnnn3Hu3Dm920JCQpCcnIwmTZrwqdG1WbZsmWAZgtu3b2PDhg2QyWSYOnUqX75w4UKUlpZi7ty5OtO/gIq1urTXP1uwYAHUajXeeOMNlJWVCerK5XJ+qhkANG3aFACQmZlZPcFa2NnZoVOnTjh16hRu3LjBlxcVFeHDDz+scXt1gbm5OaZNm4bs7GysXr1asC0sLAzh4eHo2LEjBgwYUKvjrFu3TvAsmTbff/89iouL0blzZ36phSdBnz59AEBnjbr169cL7pZVhYuLCwDg1KlTgvLo6GiDa9U1bdoUt2/f1rtt4cKFAIBXXnlF713Ne/fuITk5mX/N3TH76KOPBMHN5cuX+btr9YlUKsWUKVNw69YtrF27FtevX8fEiRNhaWkpqDdu3Dg4Oztj/fr1iImJ0WlHqVTqjGFNqeq9GRMTozf4e/DgAQDAwsKiVscmCIIA6Jk8giCeYo4cOYL58+fzgUPr1q1RUlKCxMREnDx5EmKxGD/88ANkMplgv1atWqGkpAQ9evTAmDFj+HXycnJy8O233wqeqXvttdcQFxeH7du34/Tp0xgxYgRat26N+/fv49q1azh79iz++OMPfqHt119/HdHR0di7dy/c3d0xduxY2NnZ4datWwgPD8eWLVswfvx4AMCwYcOwb98+TJw4EaNGjYKFhQU8PT0xZsyYaul/9913MW/ePPTv3x+TJk2CRqPBkSNH+ICjIVizZg2io6OxcuVKxMbGwtvbGxkZGfjzzz9hZWWFrVu36n0esibs2LED7733Hrp37w5vb2+0aNEC+fn5iIuLQ0JCAiwtLfHjjz/WkaLqMXv2bKxduxbLly/HxYsX4ebmhvPnz+PKlSsYMmQIv7h2VYwZMwbt27fH2rVrceXKFXTr1g0pKSk4dOgQJkyYgH379unsM2zYMOzduxfjx4+Hl5cXJBIJxo4dix49emDkyJH45JNP8Nlnn6Fjx44YOXIkXFxckJOTgxs3buDkyZNYuXIlunTpAgCYOXMm/vjjD4SFhcHLywujRo1Cbm4udu3aBT8/Pxw6dKjG4/LLL78gLCxM77Z+/frpTEOdPn06fvjhBwQFBfGvKyOTybBv3z6MGjUKQ4YMwbBhw9C9e3eIRCLcvHkTJ0+eRLNmzQz+EFAdqnpvLly4EHfv3sXAgQPRvn17iEQinDp1CvHx8ejXrx+/DiRBEESteJKpPAmCIBoT165dY2vXrmW+vr7M1dWVWVhYMAsLC+bm5sZmzpwpWL+Og0uln5uby+bNm8ecnJyYTCZjnp6e7I8//jB4rD179rARI0YwBwcHJpVKWZs2bZiPjw9bt24de/jwoaCuRqNhv/zyC+vXrx+ztrZmVlZWzN3dnc2fP5/dunWLr6dUKtn777/PnJ2dmZmZmWBJhEctkcCxceNG5u7uzqRSKXN2dmZBQUFMoVBUuYSCPvQtMcChr62qePjwIVu4cCFzcXFhUqmUOTo6shdeeIFdvnxZp+7jLKGQkJDAVqxYwYYMGcLatWvHzM3NmaWlJevcuTN7/fXXDS6dYWgJhcjISJ36+pY94DA0jhcvXmTDhw9nVlZWzM7Ojo0bN45dv369xuvkTZw4kTVv3pxZWVmxPn36sN27dxusn5WVxSZPnswcHR2ZWCxmANjWrVsFdY4ePcrGjBnDmjdvzqRSKWvZsiXr378/++yzzwTnI2OMlZSUsPfff5+1adOGyWQy5uHhwTZt2lRjP1VnCYVFixbp3dfd3Z0BYG3bthWsaVeZ27dvs0WLFjF3d3cmk8mYnZ0d69KlC3v11VfZ8ePHBXWrOof1nRtVvTd3797NJk+ezNzc3JiVlRWzt7dnnp6ebM2aNTpLNBAEQTwuIsYYe2IRJUEQhJHD3XHTnmJJEARBEATRmKBn8giCIAiCIAiCIEwICvIIgiAIgiAIgiBMCAryCIIgCIIgCIIgTAh6Jo8gCIIgCIIgCMKEoDt5BEEQBEEQBEEQJgQFeQRBEARBEARBECYELYZeCY1Gg7t378LW1hYikaihu0MQBEEQBEEQhAnBGENRURFat24Nsbh+7rlRkFeJu3fvol27dg3dDYIgCIIgCIIgTJjU1FR06NChXtqmIK8Stra2AIDMzEzY2dnVadu5ublwdXVFeno6mjZtWqdtE4+Gxr/hIR80POSDhoXGv+EhHzQsNP4ND/mg4bl58yZ69OgBqVRab8egIK8S3BRNOzu7Og/ylEolgIpAsq7bJh4NjX/DQz5oeMgHDQuNf8NDPmhYaPwbHvJBw8PdVKrPR8Mo8QpBEARBEARBEIQJQUGeAdRqNf9fn61SqQS2RqOp0lYqleCWJFSpVLzNlTPGdGwAAluj0QhslUpVpa1WqwV2fWjSp6+xalKr1TAzMzMpTcbmJ41Gw09NMBVNxugnmUxmcpqMyU9isfiROoxNk7H5iatvSpqMxU9qtZpPNGEqmozNT9X5PmRsmozNT9yx6xMK8gzg5eUFDw8PBAUFITk5GQCQlJSE69evAwASExORnp4OAIiPj0dmZiYAIDY2FllZWQCAmJgYZGdnAwBOnDiB8vJyTJkyBefOnUNRUREAIDQ0FHK5HCqVCqGhoVCpVJDL5QgNDQUAFBUVISIiAgCQn5+PEydOAACys7MRExMDAMjKykJsbCyAimcJ4+PjAQDp6elITEwEAFy/fh1JSUkAgOTk5DrTlJ+fDwCIiIho9JpSUlKwZMkSyGQyk9FkbH7KzMzEZ599BplMZjKajM1Pubm5+OabbyCTyUxGkzH5SSaTYf78+XwfTUGTsfkJAF566SXExcWZjCZj8lNSUhLeeustyGQyk9FkbH5KTU3FsmXLIJPJTEaTsfhp2bJl8PDwwPDhwwGA/9G1PhAxLnQlAACFhYWwt7dHbm4uHBwc+F8FJBKJwFapVBCJRLwtFov5X2f12dwvBmFhYfD19YWlpSVEIhGUSqXg1xRtWyqVgjHG2xqNBmq1mrc1Gg3MzMwM2mq1Gowx3tano7aaJBIJb5uZmTVqTWVlZYiIiEBAQABEIpFJaDI2P8nlcoSHhyMgIID/JdfYNRmbn8rLyxEWFsb7wBQ0GZOfgIovEX5+frC0tDQJTcbmJ8YYjhw5wn8Wm4ImY/KT9mcxt5+xazI2P2l/Fhv6PmRsmozNTw8ePICTkxMKCgrq7blISrxiAIlEIvhf2eacXl1bKpXyt3SlUin/oKV2Vh19tkgk4m3uxKyubajvdanpcWzSRJpIU8NqqokOY9FkLH7iPge4Nk1BU3V1NBZNVX0WG6umqmzSRJpqq88YNBmzn+oLmq5JEARBEARBEARhQtCdPIIgCIIgAFQkHeCmQJkq3HQsuVxu8lobIzT+DQ/5oO6RSCSCO3+NAQryCIIgCOIpp7CwENnZ2SgvL2/ortQ7jDG0bNkSmZmZ9bpGFaEfGv+Gh3xQP8hkMjg6OjaatQcpyCMIgiCIp5jCwkLcuXMHNjY2cHR0FDyrZopoNBoUFxfDxsZG8Iwq8WSg8W94yAd1C7esQkFBAe7cuQMAjSLQoyCPIAiCIJ5isrOzYWNjg7Zt25p0cMeh0WigUChgYWFBX3AbABr/hod8UPdYWlrC1tYWt2/fRnZ2dqMI8sizBEEQBPGUolQqUV5eDnt7+6ciwCMIgqgvRCIR7O3tUV5ezmfxbUgoyCMIgiCIpxQu6UJjSxhAEARhjHDX0saQ0IaCPIIgCIJ4yqG7eARBELWnMV1LKcgjCIIgCIIgCIIwISjIMwB3m1WtVuu1VSqVwNZoNFXaSqVSYDPGBDaXmUfbBiCwNRqNwFapVFXaarVaYJMmFe9fU9JkbH7iMCVNxuYnbR+YiiZj8hPXZmPRxPWVO25lmzGmY3P7GbK5NhrK1qeDszm4+qagyZj8pI2paDI2P2ljKpoam5+qcy2vbyjIM4CXlxc8PDwQFBSE5ORkAEBSUhKuX78OAEhMTER6ejoAID4+HpmZmQCA2NhYZGVlAQBiYmKQnZ0NADhx4gQKCgoAAJGRkSgqKgIAhIaGQi6XQ6VSITQ0FCqVCnK5HKGhoQCAoqIiREREAADy8/Nx4sQJABXZ0GJiYgAAWVlZiI2NBQBkZmYiPj4eAJCeno7ExEQAwPXr15GUlAQASE5OrjNN+fn5AICIiIhGrykhIYH3r6loMjY/paSk8D4wFU3G5icuvbMpaTI2P3FtNhZN2gsiFxYW8l9aCgsL+S8ulW2g4ssLZ6vVan4sVCoVbyuVShQXFwMAFAoFSkpKAADl5eUoLS3lj19WVsbbcrkcAFBWVsbbpaWl/Bp+JSUlUCgUAIDi4mI+eC4qKuK/QBUVFVWpiavzOJp8fHz4jISNQdOVK1cgFosxa9Yso/DT1atX4eDggNmzZz/ST6Z47jUWTRympKmx+EmtVvPX7MrX8mXLlsHDwwPe3t6odxghoKCggAFgubm5jDHGVCoVU6lUOrZSqRTYarW6SluhUDC5XM5CQkJYSUkJ02g0fLlGo2EajUbHZowJbLVaLbCVSmWVtkqlEtj6dNRWk7bd2DWVlpaykJAQplAoTEaTsfmprKyM94GpaDI2P3HXIc4HpqDJmPykUChYSEgIKy0tbRSaysrK2NWrVwX94TRxtkaj0bE5TYZsro2GsvXp4Gy1Ws3y8vKYSqViGo2GFRUVsZUrVzIvLy9mbW3NzM3NWZs2bdjAgQPZBx98wG7cuCFoe8iQIYz7+tQYNKWmpjIAbObMmbXyk4uLC3Nxcal3HVx/Z8yYUaWfuH2CgoIYgCr/goKCGu25N3PmTAaApaam1tv7KSkpic2YMYO5uLgwc3NzZmdnx9zc3NiECRPY119/zdfV7ldeXp6gH49z7tX2GpGens6fuw3tp7rSxF1Ti4qK+Pb0Xcvv37/PALCCggJWX9A6eQaQSCSC/5VtMzOzGtlSqZT/JUF7oVntjGb6bJFIxNtisZj/9bA6tqG+16Wmx7FJE2kiTQ2rqSY6jEWTsfiJ+xzg2mwMmkQiEf+ZpH1+aNvayQQ4W3u/ynZV+z0J25AOsVjM/2IvEolQXFyMgQMHIikpCR07dsTLL7+MZs2aITs7G/Hx8VizZg06duwINzc3vo3ffvuNv8PQWDTpsxurn7SpiY6JEyeiW7duetvx8fF54joex09Vjf3j+uno0aMYPXo0VCoVRowYgQkTJsDCwgKpqamIjo7G33//jTfffFPwntemrs69x9VU0748yfdTbfyk7xpv6JpdX1CQRxAEQRDEU8k333yDpKQkvPrqq9i0aZPOl8/09HR+ChiHs7Pzk+wi8f+88MILmDp1akN3o9Hx+uuvQ61W49ixYxg6dKhgG2MMERERTySgIBof9EweQRAEQRBPJWfOnAEALFiwQO/dBVdXV3Tu3FlQ5uPjo1N327ZtEIlE2LZtGw4ePAhvb29YWVmhTZs2+OSTT/i7h9u3b4enpycsLS3h7OyML7/8UueYs2bNgkgkQkZGhs625cuXQyQSISoq6pHaLly4gDfffBPdunWDvb09LC0t0b17d3zxxReCJEAZGRkQiUS4efMmbt68yd+dEIlEWL58uaDNmJgYjBkzBo6OjpDJZHB3d8fHH38seMaLQ61W83dCLSws0LFjR6xevVqQ/Kau2bFjB0QiET799FO92xMSEiASifDSSy8Jyh88eIB33nkHHTt2hEwmg6OjIyZOnIgrV67otNG+fXu0b98excXFWLRoEVq3bg2ZTIYePXpg3759OnW3b98OoOJc4saVu/PI9emFF16As7MzZDIZmjdvjj59+uDzzz9/pN4HDx4gNTUV3bp10wnwgIo7Sv7+/oLzNSoqChKJBF988QVOnToFHx8f2NraokmTJpg4cSJu3Lhh8FjVHSOu/rvvvotOnTrB0tISTZs2hbe3N7766isAFe8ZV1dXABXvC+3zjju/tc/3bdu24dlnn4WVlRU/flW9H7TfkxzcuT5r1iwkJydj9OjRaNKkCRwcHDBt2jT+OeUzZ85g+PDhsLOzg4ODA1599VX+WUFjgu7kEQRBEATxVNKsWTMAwH///YeePXvWur2///4bERERGD9+PAYMGIDDhw9j5cqVYIzB3t4eK1euxLhx4+Dj44O//voL77//PpycnDBjxoxaH7symzdvxsGDBzF48GAEBASgtLQUUVFR+PDDD3Hu3Dn89ddfAIAmTZogODgY33zzDQDg7bff5tvQDkZ+/PFHLFiwAE2aNMGYMWPQokULnD9/Hp9//jkiIyMRGRkJc3Nzvv68efPw66+/wtXVFQsWLIBcLsf69etx+vTpOtfK8fzzz+P111/Hzp07ERQUpLN9x44dAIDp06fzZampqfDx8cHt27fh5+eH8ePH48GDB/jrr78QHh6O48eP6yTJUCqV8PPzQ15eHiZOnIjS0lLs3r0bkydPRlhYGPz8/ABUjOW2bdtw6dIlLFq0CE2aNAFQEfwBwMWLF/Hcc89BIpFg3LhxcHFxQX5+Pv79919s2rQJH330UZV67e3tYWZmhqysLJSUlMDa2rraY3X+/Hl8/fXXGDlyJN566y1cvXoVf//9N06ePIm4uDh06NDhsccoJSUFQ4cORVZWFgYOHIjx48ejpKQEV69exapVq/Dee++hZ8+eWLRoETZs2ABPT0+MHz+e358bH44vv/wSkZGRGDduHPz8/Gp9ZzI9PR3PPfccevfujVdffRXnz5/H7t27kZmZiS+++AJ+fn7w9fXFvHnzEBUVhS1btkCj0eDXX3+t1XGfOPX2tJ+RwiVeqY8HIbkH7rkH8YknC41/w0M+aHjIBw1LYxv/srIy9u+//7KysjKdbRqNhhUXlzb6Py5hQnXRTjpx4MABBoDZ2tqyd999l4WHh7Ps7Owq99dOvMKxdetWBoBJpVIWHx/PlxcWFrIWLVowKysr1rJlS5aamspvu3XrFjM3N2fdu3cXtMUl6khPT9c5dnBwMAPAIiMj+bLKySs4bt68ySfg4dBoNOyVV15hANipU6cE27jEK/q4evUqMzMzY56enjrjs3r1agaAffXVV3xZZGQkA8A8PT1ZcXExX3779m3m6OjIJ16pDpzmiRMnsuDgYL1/WVlZfP2XX36ZAWBnz54VtKNSqZiTkxNr2bKlYFyee+45JpFIWFhYmKB+SkoKs7W11fGPi4sLA8DGjRvHysvL+fJjx44xAMzf319Qvyp/Ll68mAFgISEhOtsedR5yPP/88wwA6969O/v222/Z+fPnBf2qDOcbAOyHH34QbPvpp58YADZ69GhBeU3HqHfv3gwA27Rpk87xMzMzedvQucvB+d7a2polJSUZ3K79fuDg3pNbt27VOR4A9s033/DlGo2GBQQEMACsSZMmAn8oFArWo0cPZmZmxu7du6e3n9pUdU3VJjs7mxKvEARBEATx5CktlaOD0/CG7sYjSbt/HNbWlo+179ixY7Fu3ToEBwdj3bp1WLduHQDAzc0NI0eOxKJFi+Du7l7t9l5++WX06dOHf21ra4vRo0fj119/xQcffCC4O9KuXTsMHDgQ0dHRUKlUBhNjPC76nh0UiURYsGABfv31Vxw7dgwDBgyoVls///wzVCoVvvvuO/7uJ8f777+P9evXY9euXXj33XcBVCSnAYCgoCDB3aU2bdpg4cKFeu+yPYq//vqLv/tYmfHjx6Nly5YAKu7S/f777/j999/Rt29fvk5ERATu37+Pt99+m78TlJiYiNjYWLzyyivw9/cXtPnMM89g7ty5WL9+Pa5cuaKT9OXrr78W3LkcPnw4XFxccO7cuRprs7TUPX8rj7MhNm3aBKVSiYMHD2LhwoUAAHNzc/Tu3RtTpkzB3Llz9bbfsWNHzJ07V1A2d+5crFu3DocPH8bDhw/RvHnzGo9RfHw8zp8/j8GDB+u0DwBt27atli5t5s2bh+7du9d4P0O4ubnxYwVUvC+mTp2K0NBQeHl5Ydy4cfw2qVSKF154AUFBQfj333/h5ORUZ/2obyjIIwiCIAjiqWXx4sWYO3cuwsLCEBsbi/Pnz+Ps2bPYuHEjtmzZgj179mDs2LHVakvflM9WrVpVuU2tVuP+/fto06ZNbWTooFAo8P3332P37t24du0aiouLBYth3717t9ptxcXFAQA/Na8yUqkU165d419funQJADBo0CCdugMHDqz2cbXZtWtXtRKvDB8+HK1atcLu3buxfv16Pnj+/fffAQinanK67t+/r/P8IQBe07Vr1wRBXpMmTfjnybRp27Yt/5xndZg8eTK++eYbTJgwAVOmTIGvry8GDx5co3OhWbNm+Oeff3D9+nWEhYUhPj4ecXFxiI2NRWxsLDZv3ozo6Gg0bdpUsJ+3t7cgkyRQkQlywIABuH79Oi5duoQRI0bUeIy4NeG4Kat1gXawXhf06NFD57naR71PgZq9ZxoDFOQRBEEQBKGDlZUF0u7rfqFvbFhZWdS6DVtbW0yaNAmTJk0CABQUFGDZsmX44YcfMGfOHNy5c0dw18YQdnZ2OmVckFHVNu1EKHXFCy+8gIMHD+KZZ57BlClT0KJFC0ilUuTn52PDhg06WUOrIjc3FwCqlQwEqBg/sVgMR0dHnW31fSdEIpHgxRdfxLp16xAeHo7AwEAUFxcjJCQEHh4eePbZZ/m6nK7Dhw/j8OHDBtusnHTD3t5ebz0zM7MaJZbx9vZGVFQUVq1ahT/++ANbt24FAPTp0wdr1qzRm0zFEO7u7oK7zhcvXsTLL7+MK1euYMWKFdiwYYOgfvPmzfW2w/mnoKAAQM3HiNuvLn+0qOtzpjG9T+sTCvIIgiAIgtBBJBI99jRIY8fe3h7ff/89Dh8+jJs3b+Ly5cvo1avXEzk2d3dFpVLpbOO+QD+Kc+fO4eDBg/D398fhw4cFiSri4uJ0vvA/Cu6Lb2FhIWxtbR9Z397eHhqNBtnZ2TrBxP3792t07Mdh+vTpWLduHX7//XcEBgbir7/+QmlpqeAuHvA/Xd999x3efPPNeu+XPgYNGoQjR46grKwMZ8+excGDB/HDDz8gMDAQV65cEUzxrQk9e/bEd999h2HDhuHEiRM62x8+fKh3P84/XCBb0zHiksvcuXPncbqtl0ets1ib94opQ0soEARBEARBVKIiyK1+tsK6wsHBAYD+L8mJiYnVaiM1NRUAEBgYqJOJ8OTJk3r3kUgkUKvVerdxmRO5qXuPwtPT0+CxTp06Va02aoOnpye6d++OAwcOoKioCL///rvepRM4XTWZYllTuPE3NLYclpaW8PHxwbp167Bs2TKUlZXh6NGjtTq2jY2NwW1nz57Vueuo0WgQGxsLkUjE+7CmY8RNrYyIiHhk3eqOjSHq4r1iylCQRxAEQRDEU8nPP/9sMFFGSEgIkpOT0aRJE52kG/UJl7hFe30vANi3bx+io6Or1YaLiwsA3YDq6tWrWL16td59mjZtiuzsbMjlcp1tb7zxBszMzPDWW2/h1q1bOtvz8/MFX6q5O2affvqpYKrjnTt38O2331ZLQ22ZPn06ysrK8O233+LEiRMYMmQI2rVrJ6jTt29feHt7Y9euXdizZ49OGxqNptpjbgjuWbjMzEydbWfOnNE73tzdNAuLqqcil5SU4PPPP+fXd9NGpVLx6zDqew7yxo0b2Lx5s6Bs8+bN+O+//xAYGMjfga3pGPXp0wd9+vRBTEyMTvuAMCBzcHCASCTSOzbVgXuv/Pbbb4KA9cyZM9i5c+djtWlK0HRNgiAIgiCeSo4cOYL58+ejY8eOGDBgAFq3bo2SkhIkJibi5MmTEIvF+OGHHyCTyZ5Yn8aNGwc3Nzds27YNmZmZ8PLyQnJyMk6cOIGAgACEhoY+so2+ffuib9++2Lt3L7KystCvXz/cunUL//zzDwIDA3UW7QaAYcOG4fz58xg1ahQGDRoEc3NzDB48GIMHD0a3bt3www8/4PXXX0enTp0QEBAANzc3FBUVIS0tDdHR0Zg1axZ++uknAMDQoUMxe/ZsbN26Fd27d8eECRNQXl6OPXv2wNvbu8pnuwyxb98+QXIXbTp37qyTlOXFF1/E0qVLsWLFCmg0Gp2pmhy7du3C0KFDMXXqVHzzzTd49tlnYWlpiVu3buHMmTN4+PCh3kCsugwbNgxfffUV5s2bh4kTJ8La2houLi6YPn061qxZg8jISAwePBiurq6wsLBAQkICjh8/jg4dOmDChAlVtq1UKvHxxx9j+fLl6N+/Pzw9PWFnZ4f79+8jPDwct2/fhqurK4KDg3X2HT58ON5++22EhYWha9euuHr1Kg4ePAhHR0ed6bw1HaOdO3fCx8cH8+bNw44dO9C/f3/I5XJcvXoViYmJyMnJAVBxp5ELCKdPnw53d3eIxWJMnz6d/6GiKvr164cBAwbgxIkT6N+/PwYPHoybN2/iwIEDGDNmDP7+++/quMhkoSCPIAiCIIinkjVr1mDAgAE4evQoYmJikJWVBaAiacTMmTPx1ltvPbFn8TgsLS1x7NgxvPPOOzh+/Dji4uLQr18/xMTE4NChQ9UK8iQSCQ4dOoSlS5ciLCwM586dg7u7O7766iuMGjVKb5D3ySefIC8vD4cOHcLJkyehVqsRHByMwYMHA6hIr9+zZ0+sX78eMTExOHjwIOzt7eHs7Ix33nkHM2fOFLS3efNmPPPMM9i8eTO+//57tG3bFosXL8YLL7zwWEFeVUsojBs3TifIa9OmDYYNG4Zjx47BwsICL7zwgt59XV1dkZiYiPXr1yMkJARbt26FRCJBq1atMHjwYIP7VZdRo0Zh7dq12Lx5M9atWwelUokhQ4Zg+vTpeP3112Fvb4+zZ88iOjoajDE4Oztj2bJleOedd/QmAdHGzs4OoaGhCA8Px6lTp/Dnn38iJycHVlZW/PIGixYt0psopnfv3ggKCkJQUBC+/fZbSCQSjB8/HmvXrtV5DrCmY+Tu7o6EhASsXr0aBw8exDfffAMbGxu4u7vj448/FtTdsWMH3nnnHRw6dAgFBQVgjGHgwIHVCvIA4MCBA1i8eDEOHTqEy5cvw9PTEwcPHsTdu3ef+iBPxLTz6RIoLCyEvb09cnNz4eDgwM8T1p6rLpFIoFKpIBKJeFssFkMsFhu0lUolNBoNwsLC4OvrC0tLS4hEIiiVSj5rj/Y6OSqVClKpFIwx3tZoNFCr1byt0Wj4TE76bLVaDcYYb+vTUVtNEomEt83MzBq1prKyMkRERCAgIAAikcgkNBmbn+RyOcLDwxEQEMA/MG3smozNT+Xl5QgLC+N9YAqajMlPABAaGgo/Pz9YWlo2uCaFQoG0tDS4urry/RGJRBCJRLwNAIwxgc2dO4ZsoOKZtoay9engbOB/CUS465CxazImP2k0GhQWFvIBjCloMiY/RUdHY+jQofjggw+watUqvh/GrKkx+am8vBxpaWlwdnaGjY2NwWv5gwcP4OTkhIKCgkcG848LPZNnAC8vL3h4eCAoKAjJyckAgKSkJFy/fh1AxQOd6enpAID4+Hh+PnFsbCz/S2BMTAw/T/rEiRN8pp/IyEgUFRUBqPiwl8vlUKlUCA0NhUqlglwu53+pKyoq4h9ezc/P5zMkZWdnIyYmBgCQlZWF2NhYABVzvrk1StLT0/k58tevX0dSUhIAIDk5uc405efnA6h4wLaxa0pISOD9ayqajM1PKSkpvA9MRZOx+Un7eQhT0WRsfuLabCya5HI5HyQWFhYKAiHuS0xlGwD/ZR2oSJzAjYVKpeJtpVKJ4uJiABXrtnHPZ5WXl6O0tJQ/fllZGW9z077Kysp4u7S0lE/5X1JSAoVCAQAoLi7m05oXFRXxgXRRUVGVmrg6pqTJWPzEHceUNBmbn7QxFU2NyU9qtZq/Zle+li9btgweHh58Qpv6hO7kVYLu5Jnur/R0J6/hNdGdvIbXRHfy6E4e3cmjO3l0J+/p9RPdyXt67uTRM3kG4NK6aqce1ra5D/Dq2lKplP8lQSqV8ieNVCoV1Klsi0Qi3uY+nKtrG+p7XWp6HJs0kSbS1LCaaqLDWDQZi5+4zwGuzcagiftywx2XQ9vmtmvb2vtVtqva70nYhnSIxWI+0NPXZ2PVVNW+jU2TNqaiydj8pI2paGpsftJ3jTd0za4vKMgjCIIgCIIgiKcAHx8fqNVqfsohYbrQM3kEQRAEQRAEQRAmBAV5BEEQBEEQBEEQJgQFeQRBEARBEARBECYEBXkEQRAEQRAEQRAmBAV5BEEQBEEQBEEQJgQFeQRBEARBEARBECYEBXkEQRAEQRAEQRAmBAV5BEEQBEEQBEEQJgQFeQRBEARBEARBECYEBXkEQRAEQRCNgKioKIhEIixfvryhu9LoobEiiKqhII8gCIIgiKeSjIwMiEQinT9ra2v06NEDK1asQHFxcUN3k6gDUlNTsXz5cowdOxZt2rSBSCRC+/btG7pbBFFvmDV0BwiCIAiCIBoSNzc3vPzyywAAxhgePnyII0eOYPny5QgLC8OpU6cgkUgauJdEbTh58iRWrFgBiUSCLl264N69ew3dJYKoVyjIIwiCIAjiqaZjx4460/7Ky8vRv39/xMXFITo6GsOGDWuYzhF1wuDBg3HmzBl4enrC0tISFhYWDd0lgqhXaLomQRAEQRBEJWQyGYYOHQoAyM7OFmyLjIzEK6+8gk6dOsHGxgY2Njbo3bs3Nm3aZLC9tLQ0zJs3D66urpDJZGjRogV8fHywbdu2R/aloKAAQ4YMgVgsxnfffSfYtn//fvTu3RuWlpZwcnLC3LlzkZeXh/bt2+tMR5w1axZEIhHS0tKwbt06eHh4QCaTYdasWXydK1euYPLkyWjRogVkMhlcXV3x9ttvIycnR6dfIpEIPj4+evts6PgSiQQ3b97Ed999h86dO0Mmk8HFxQUrVqyARqPRaaesrAxLly5Fu3btYGFhgW7dumHz5s2PHLPKdOjQAf369YOlpWWN9yUIY4Tu5BEEQRAEQVRCoVDwyT169uwp2LZmzRrcuHED/fr1w4QJE5Cfn4+wsDC89tprSElJwbp16wT1T506hcDAQBQVFcHf3x9Tp05FXl4eEhMTsWHDBkGQVZmsrCyMHDkS165dw65duzBlyhR+26+//oo5c+bAzs4OM2bMgL29PUJDQ+Hr6wulUgmpVKq3zbfeegtxcXEIDAzEmDFj0KJFC76f/v7+UCgUeOGFF9C+fXucOXMGGzZswKFDhxAXFwdHR8fHG1AtgoKCEBsbi9GjR8Pf3x8hISFYvnw5FAoFPv/8c76eRqPB2LFjcezYMXTv3h0vvvgicnJy8M477/ABOEEQ+qEgjyAIgiAIg8hLFQa3iSUimMuk1aorEosgs3jMumUKgOnWs7AyN9hGTbhx4wY/XZMxhuzsbISHh+POnTtYu3YtnnnmGUH9H3/8Ea6uroIylUqFgIAAbNiwAYsWLYKzszOAimmfU6dORXFxMUJDQzFy5EjBfrdv3zbYr//++w/+/v7IycnB4cOHMWLECH5bfn4+Fi1aBGtra5w/fx7u7u4AgFWrVsHf3x8XLlyAi4uL3naTkpKQmJjI9xGoCKhmzZqF0tJShIWFwd/fn9/2/vvv48svv8QHH3yALVu2GOxvdbl06RIuXryINm3aAAA++eQTuLu747vvvkNwcDDMzSv8+ttvv+HYsWMYOXIkDh06xD8XuWjRIvTu3bvW/SAIU4aCPIIgCIIgDDLB82OD2/oM6YxPf3mFfz213wqUlyn11u3etwPW7pzPv57psxqFeSV667p3b4tv9y/kX782ah0e3MnTqXfk+tpH9r86pKamYsWKFTrlo0ePFgRWHJUDPAAwMzPD/PnzcfToUURGRmLmzJkAgAMHDuDOnTuYMWOGToAHAG3bttXbp3PnziEwMBBAxfTQXr16CbYfOHAAxcXFWLhwIR/gcf1YuXIlnnvuOYN6lyxZIgjwAOD06dNITU3FqFGjBAEeUHHnbcuWLfjjjz/w448/8kHY47JkyRK0atWKf+3o6Ihx48Zh+/btSElJQffu3QFUBHkA8PnnnwsS33Tv3h3Tp0+vk4CTIEwVeiaPIAiCIIinGn9/fzDG+L/s7GwcOHAAV65cwYABA3D27FlB/aKiIgQHB8PT0xM2Njb80gsTJ04EANy9e5evGx8fDwDw8/Ordn9OnjyJYcOGwcbGBqdPn9YJ8ICKu2EAMHDgQJ1t3t7eMDMz/Dt+3759dcoSExMBQO8zdtwzh3K5HCkpKdWVYRBPT0+dMi7Yzc/P58suXboEa2trPPvsszr1Bw0aVOt+EIQpQ3fyCIIgCIIwyN+XVhrcJpaIBK93xwUbrCsSC+tuj/qw2nV/PvKu3uma9UWzZs0wduxYWFlZwdfXFx9//DGOHj0KoOJZPR8fHyQkJMDLywvTp09Hs2bNYGZmhoyMDGzfvh3l5eV8WwUFBQDAT02sDomJiSguLoafnx86dOigt05hYSEA8M/TaSMWi6t8ds7Jyclge/q2AeDvvHH1aoOdnZ1OGReUqtVqvqygoADt2rXT24ahfhIEUQHdyTMAd5FRq9V6bZVKJbC5jFCGbKVSKbAZYwKbMaZjAxDYGo1GYKtUqipttVotsEmTivevKWkyNj9xmJImY/OTtg9MRZMx+Ylrs7Fo4vrKHbeyLbOUwtzCDDJLKW9bWJlDZimFmVTCt6HRaPhymaVUxzaXmfFtc+0aqi+zkAr6JbPQ36Z2nUfp4GyOyvtq6+DsPn36AKiYOsnVCQkJQUJCAubMmYMLFy7ghx9+wMqVKxEcHMxPc9Ru297eHgBw584dg/3VtgFgwYIFmDNnDvbv348XX3yR95e2DltbWwDAvXv3+P25Omq1ms8Iqq2JQyQS6Ry/cnuV+8WtK2dnZ8eXi0QiqFQqvfW54FafPg5tTdrbuXJ7e3s8fPhQoI+rm5WVpddnjxrfyn2pSf3q2FWde5X9VLnvhuy67qM2pqKpsfmpOtfy+oaCPAN4eXnBw8MDQUFBSE5OBlDxoPL169cBVPzKlp6eDqBiKkZmZiYAIDY2lr/wxMTE8BfZEydO8Be8yMhIFBUVAQBCQ0Mhl8uhUqkQGhoKlUoFuVyO0NBQABVTQiIiIgBUTGE4ceIEgIp0zjExMQAqMm/FxsYCADIzM/mpIenp6fz0i+vXryMpKQkAkJycXGeauGkVERERjV5TQkIC719T0WRsftKe5mMqmozNT3fu3OF9YCqajM1PXJuNRZNcLue/cBQWFvJfWgoLC/kvLpVtoOLLC2er1Wp+LFQqFW8rlUoUFxcDqLgDVlJS8QxeeXk5SktL+eOXlZXxtlwuB1CROp+zS0tL+btjJSUlUCgqkrYUFxfzwXNRURH/BaqoqKhKTVwdzubqVtbEjZ1Go+E1ceM8btw4HU2RkZH8GHCavLy8AFScL9XRxLF582bMnDkTe/fuxUsvvYS8vDyBph49egAAoqKidPx09uxZwZdMbZ9xVPZT586dAVR8R6nsp5KSEpw/fx6Wlpbo1KkT7ycHBwfcvn1bR1NGRgby8/P58dX2kzbaftK++8np8PT0RElJCS5cuKBz7nFjzRh77HOPMfbEz73G8n7iMCVNjcVParWav2ZXvpYvW7YMHh4e8Pb2Rr3DCAEFBQUMAMvNzWWMMaZSqZhKpdKxlUqlwFar1VXaCoWCyeVyFhISwkpKSphGo+HLNRoN02g0OjZjTGCr1WqBrVQqq7RVKpXA1qejtpq07cauqbS0lIWEhDCFQmEymozNT2VlZbwPTEWTsfmJuw5xPjAFTcbkJ4VCwUJCQlhpaWmj0FRWVsauXr0q6A+nibM1Go2OzWkyZHNtNJStTwdnq9VqlpeXx1QqFUtLS2MAmJ+fn14dCxcuZABYYGAg38bOnTsZAPb+++8LjhkZGcmkUikDwIKCgvjysrIy1rZtWyYWi9mRI0d0+nv79m3ejoyM5Pfn+jtv3jwGgE2cOFHwXsjNzWU2NjbMxsaGXb9+ne+7QqFgI0aMYACYi4uLQNPMmTMZAJaenq4zdiqVirm5uTEALCIiQtDHpUuXMgBs9uzZgnJ/f38GgEVGRvLlcrmcTZgwgT++dn3u+JcuXeJ9wfUhKCiIb4sr37JlCwPARo4cKTh/L126xMzNzfmxepxzTyaT8ePzpM69xvJ+4t4D2v0wdk2NxU/cNbWoqIhvT9+1/P79+wwAKygoYPUFPZNnAC6Lk3Y2J21b+4Hm6thSqZT/JUEqlUIkEvG2dp3Ktkgk4m2xWAyxWFxt21Df61LT49ikiTSRpobVVBMdxqLJWPzEfQ5wbTYGTVzSEO64HNo2t13b1t6vsl3Vfk/CNqRDLBbzv9hr9zk1NZVfQgEAcnNzcfr0aSQkJMDBwQFr1qzh644dOxbt27fH2rVrceXKFXTr1g0pKSk4dOgQJkyYgH379gnatrCwwN69ezFy5EgEBARg5MiR8PT0RGFhIS5evIjS0lL+V//KWsRiMX766SdIJBL8+OOPmDZtGnbv3g0zMzM4ODhg/fr1mDdvHnr37o2pU6fy6+TJZDK0bt0aYrFY0Bd9x+BsiUSCbdu2wd/fH4GBgZg0aRJcXFxw5swZREVFwc3NDWvWrBHsu3jxYkRERCAwMBDTpk2DlZUVjh49iiZNmgiyZ+o7fmXf6PPfrFmzsGvXLoSFheHZZ5/FqFGjkJubi127dsHPzw+HDh2CSCTi61d1bmRnZ+O9997jy5RKJbKzszF79my+7KuvvoKjo2O9nXtV7fsk30/amIqmxuYnfdd4Q9fs+oKCPIIgCIIgnmoqL6Egk8nQtm1bvP7661i6dKlguQEbGxucOHECS5YsQUxMDKKiotC1a1fs3LkTTk5O2Ldvn077/fv3R0JCAlavXo3w8HAcO3YMDg4O8PDwwPz583XqayMSibBx40aIxWJs3LgRU6ZMwe7duyGVSjF37lw4ODhg1apV2LZtG+zt7TF27FisWbMGLi4ucHNzq9E4DBw4EHFxcfj0008RERGBgoICtG7dGosWLcLHH3+sk8zFz88Pe/fuxaeffoodO3agadOmmDRpElatWoVu3brV6Nj6EIvFOHDgAFasWIGdO3diw4YNcHNzw9dffw13d3ccOnSo2m0VFxdj+/btgrKSkhJB2fLly+tksXeCaAyIGNPzFOZTTGFhIezt7VFQUKA3+1NtUCqVCA0NRUBAgOCXXuLJQOPf8JAPGh7yQcPS2MZfLpcjPT0drq6usLCwaOjuPBE0//9MjZ2dneBXe1Pixo0bcHd3x+TJk7Fnz56G7o6Ap2H8Gzvkg/qjutfUnJwcODo61ku8wUGeJQiCIAiCMELy8vIECUuAimQU77zzDgBg/PjxDdArgiAaAzRdkyAIgiAIwgiJjo7GnDlz4OfnB2dnZ2RnZ+PEiRPIyMjAsGHDMGXKlIbuIkEQDQQFeQRBEARBEEZI165d4evri9OnTyMkJAQA0LFjR3z22Wd47733aCoeQTzFUJBHEARBEARhhLi7u2P37t0N3Q2CIBoh9BMPQRAEQRAEQRCECUFBHkEQBEEQBEEQhAlBQR5BEARBEARBEIQJQUEeQRAEQRAEQRCECUFBHkEQBEEQBEEQhAlBQR5BEARBEARBEIQJQUEeQRAEQRAEQRCECUFBHkEQBEEQBEEQhAlBQR5BEARBEEQjICoqCiKRCMuXL2/orjR6aKwIomooyCMIgiAI4qkkIyMDIpFI58/a2ho9evTAihUrUFxc3NDdJGoJYwxHjhzB66+/jh49esDe3h5WVlbw9PTEqlWrIJfLG7qLBFHnmDV0BwiCIAiCIBoSNzc3vPzyywAqAoKHDx/iyJEjWL58OcLCwnDq1ClIJJIG7iXxuJSXlyMgIAAymQw+Pj7w9/eHXC5HeHg4PvroI4SEhCAqKgpWVlYN3VWCqDMoyCMIgiAI4qmmY8eOOtP+ysvL0b9/f8TFxSE6OhrDhg1rmM4RtUYikWDlypV444034ODgwJcrlUpMnDgRBw8exMaNG7FkyZIG7CVB1C00XZMgCIIgCKISMpkMQ4cOBQBkZ2cLtkVGRuKVV15Bp06dYGNjAxsbG/Tu3RubNm0y2F5aWhrmzZsHV1dXyGQytGjRAj4+Pti2bdsj+1JQUIAhQ4ZALBbju+++E2zbv38/evfuDUtLSzg5OWHu3LnIy8tD+/bt0b59e0HdWbNmQSQSIS0tDevWrYOHhwdkMhlmzZrF17ly5QomT56MFi1aQCaTwdXVFW+//TZycnJ0+iUSieDj46O3z4aOL5FIcPPmTXz33Xfo3LkzZDIZXFxcsGLFCmg0Gp12ysrKsHTpUrRr1w4WFhbo1q0bNm/e/Mgx00YqleKjjz4SBHhc+YcffggAiI6OrlGbBNHYoTt5BEEQBEEQlVAoFHxyj549ewq2rVmzBjdu3EC/fv0wYcIE5OfnIywsDK+99hpSUlKwbt06Qf1Tp04hMDAQRUVF8Pf3x9SpU5GXl4fExERs2LBBEGRVJisrCyNHjsS1a9ewa9cuTJkyhd/266+/Ys6cObCzs8OMGTNgb2+P0NBQ+Pr6QqlUQiqV6m3zrbfeQlxcHAIDAzFmzBi0aNGC76e/vz8UCgVeeOEFtG/fHmfOnMGGDRtw6NAhxMXFwdHR8fEGVIugoCDExsZi9OjR8Pf3R0hICJYvXw6FQoHPP/+cr6fRaDB27FgcO3YM3bt3x4svvoicnBy88847fABeW7gxMjOjr8SEaUFnNEEQBEEQBiktVxrcJhGLIJOaVauuWCSChfnj1S1TKMGYbj0rmf4gpqbcuHGDn67JGEN2djbCw8Nx584drF27Fs8884yg/o8//ghXV1dBmUqlQkBAADZs2IBFixbB2dkZQMW0z6lTp6K4uBihoaEYOXKkYL/bt28b7Nd///0Hf39/5OTk4PDhwxgxYgS/LT8/H4sWLYK1tTXOnz8Pd3d3AMCqVavg7++PCxcuwMXFRW+7SUlJSExM5PsIVARUs2bNQmlpKcLCwuDv789ve//99/Hll1/igw8+wJYtWwz2t7pcunQJFy9eRJs2bQAAn3zyCdzd3fHdd98hODgY5ubmAIDffvsNx44dw8iRI3Ho0CH+uchFixahd+/ete4HUBEoA4Cfn1+dtEcQjQUK8giCIAiCMEi/pd8b3Daoiys2zhvPv/YJ+glyhUpv3d5ubfHrm5P416M+24K8kjK9dbu2c8KuxS/yryd88Rvu5hXq1Ev6+p1Hdb9apKamYsWKFTrlo0ePFgRWHJUDPKDiTtD8+fNx9OhRREZGYubMmQCAAwcO4M6dO5gxY4ZOgAcAbdu21dunc+fOITAwEEDF9NBevXoJth84cADFxcVYuHAhH+Bx/Vi5ciWee+45g3qXLFkiCPAA4PTp00hNTcWoUaMEAR5Qcedty5Yt+OOPP/Djjz/yQdjjsmTJErRq1Yp/7ejoiHHjxmH79u1ISUlB9+7dAVQEeQDw+eefCxLfdO/eHdOnT691wHnkyBH8/PPP6NKlC+bMmVOrtgiisUHP5BEEQRAE8VTj7+8Pxhj/l52djQMHDuDKlSsYMGAAzp49K6hfVFSE4OBgeHp6wsbGhl96YeLEiQCAu3fv8nXj4+MB1OxO0cmTJzFs2DDY2Njg9OnTOgEeUHE3DAAGDhyos83b27vK6Yd9+/bVKUtMTAQAvc/Ycc8cyuVypKSkVFeGQTw9PXXKuGA3Pz+fL7t06RKsra3x7LPP6tQfNGhQrfpw7tw5TJkyBfb29vjzzz8hk8lq1R5BNDboTh5BEARBEAaJ++JNg9skYpHgddSn8w3WFYuEdY98YvjOSeW6fy+doXe6Zn3RrFkzjB07FlZWVvD19cXHH3+Mo0ePAqh4Vs/HxwcJCQnw8vLC9OnT0axZM5iZmSEjIwPbt29HeXk531ZBQQEA8FMTq0NiYiKKi4vh5+eHDh066K1TWFhxZ5N7nk4bsVhc5bNzTk5OBtvTtw0Af+eNq1cb7OzsdMq4oFStVvNlBQUFaNeund42DPWzOpw/fx5+fn4Qi8UIDw9H165dH7stgmisUJBHEARBEIRBavLcW33VtTSvm2fvaoq3tzeAirs+HAcOHEBCQgLmzJmDX375RVB/9+7d2L59u6CsSZMmAIA7d+5U+7hvvvkm7t69iy1btuDFF1/Ezp07de7McYHSgwcPdPbXaDTIzs42GFiKKgXR2u3dv39f7z737t0T1OPaUan0T88tKCiAvb293m3Vxd7eHg8fPtS7zVA/H8X58+fh6+sLjUaDiIgI9OnTpzZdJIhGC03XJAiCIAiC0ENeXh4ACFL7p6amAgDGjRunU//kyZM6ZdzUyIiIiGofVywWY/PmzZg7dy727t2Ll156SSeY4qY8nj59Wmf/+Ph4g8GXIby8vAAAUVFROttKSkpw/vx5WFpaolOnTny5g4OD3uA1IyNDMO3ycfH09ERJSQkSEhJ0tukb60fBBXhqtRphYWF8EE8QpggFeQRBEARBEHpYv349AGDw4MF8GZex8tSpU4K60dHRetdvGzt2LNq2bYvff/8d4eHhOtsN3eETiUT4+eef8dprr2Hv3r2YNm2aIHAbN24cbGxssGXLFj7wBCqyfH7yySc1UFnBgAED4ObmhiNHjuDYsWOCbStXrkROTg6mTZsmSLrSp08fZGRkCNaYUygUWLx4cY2Pr4/p06cDAD766CPBNM7Lly9jx44dNWrrwoUL8PX1hUqlwpEjR9C/f/866SNBNFZouiZBEARBEE812ksoAEBubi5Onz6NhIQEODg4YM2aNfy2MWPGoH379li7di2uXLmCbt26ISUlBYcOHcKECROwb98+QdsymQx79+7FyJEjMWrUKIwcORKenp4oLCzExYsXUVpayic9qYxIJMKPP/4IsViMH3/8EYwx7N69G2ZmZmjSpAnWr1+PefPmoVevXpg6dSq/Tp5MJkPr1q0hFlf/t3yxWIxt27bB398fAQEBmDRpElxcXHDmzBlERUXBzc0NX3zxhWCfxYsXIyIiAgEBAZg2bRqsrKxw9OhRNGnSRJA983GZOXMm/vjjD4SFhcHLywujRo1Cbm4udu3aBT8/Pxw6dKha7eTm5sLX1xf5+fkYOXIkjh49yj9jydGkSRO8/fbbte4zQTQWKMgjCIIgCOKppvISCjKZDG3btsXrr7+OpUuXCpYbsLGxwYkTJ7BkyRLExMQgKioKXbt2xc6dO+Hk5KQT5AFA//79kZCQgNWrVyM8PBzHjh2Dg4MDPDw8MH++4WQ1QEWgt3HjRojFYmzcuBFTpkzB7t27IZVKMXfuXDg4OGDVqlXYtm0b7O3tMXbsWKxZswYuLi5wc3Or0TgMHDgQcXFx+PTTTxEREYGCggK0bt0aixYtwscff6yTzMXPzw979+7Fp59+ih07dqBp06aYNGkSVq1ahW7dutXo2PoQi8U4cOAAVqxYgZ07d2LDhg1wc3PD119/DXd392oHeYWFhfzU27CwMISFhenUcXFxoSCPMClEjD3JfFWNn8LCQtjb26OgoEBv9qfaoFQqERoaioCAAEilDfMQ+dMMjX/DQz5oeMgHDUtjG3+5XI709HS4urrCwsKiobvzRNBoNCgsLISdnV2N7nQZEzdu3IC7uzsmT56MPXv2NHR3BDwN49/YIR/UH9W9pubk5MDR0bFe4g2Op8azX3/9Nbp27QoPDw8sXLgQFNsSBEEQBGHM5OXlCZZrAICysjK8807FIvHjx49vgF4RBNEYeCqmaz58+BDff/89rl69CqlUisGDByMuLo4euiUIgiAIwmiJjo7GnDlz4OfnB2dnZ2RnZ+PEiRPIyMjAsGHDMGXKlIbuIkEQDcRTEeQBFdmm5HI5gIrpMvoWDyUIgiAIgjAWunbtCl9fX5w+fRohISEAgI4dO+Kzzz7De++9R1PxCOIpxije/TExMRgzZgxat24NkUjEX8i02bhxI9q3bw8LCwt4e3sjPj6e39a8eXO89957cHZ2RuvWrTFixIgaP4xMEARBEATRmHB3d8fu3btx69YtlJWVoaysDJcvX8bHH3/81DxjSRCEfowiyCspKYGnpyc2btyod/uePXuwePFiBAcHIyEhAZ6envD398eDBw8AVMxZP3ToEDIyMnDnzh3ExsYiJibmSUogCIIgCIIgCIJ4IhhFkDdq1CisXLkSEyZM0Lt9/fr1mDt3LmbPng0PDw/89NNPsLKywq+//goAOHbsGDp27IimTZvC0tISgYGBiIuLe5ISCIIgCIIgCIIgnghG/0yeQqHAhQsX8OGHH/JlYrEYI0aMwJkzZwAA7dq1Q2xsLORyOaRSKaKiojBv3rwq283NzYVSqeRfy2QyyGSyWvWVa0+7XeLJQePf8JAPGh7yQcPS2MZfqVSCMQaNRgONRtPQ3XkicNm1Od3Ek4XGv+EhH9QfGo0GjDEolUpIJBK+vLy8XJAJNzc3t977YvRBXnZ2NtRqNZycnATlTk5OuHbtGgCgX79+CAgIgJeXF8RiMYYPH46xY8dW2a6rq6vg9ZQpUzBt2rQ66fPRo0frpB3i8aDxb3jIBw0P+aBhaSzjb2ZmhpYtW6K4uBgKhaKhu/NEKSoqauguPNXQ+Dc85IO6R6FQoKysDDExMVCpVHz5rl27nvialUYf5FWXzz//HJ9//nm166enp8PW1pZ/XVd38o4ePQpfX99GsQju0waNf8NDPmh4yAcNS2Mbf7lcjszMTNjY2Dw1iToYYygqKoKtrS1EIlFDd+epg8a/4SEf1B9yuRyWlpYYPHiw4Jo6fPhwQW6R3NxcPPPMM/XaF6MP8hwdHSGRSHD//n1B+f3799GyZcvHbrdp06b1tgK9VCptFB/uTys0/g0P+aDhIR80LI1l/NVqNUQiEcRi8VOTbp+bnsbpJp4sNP4ND/mg/hCLxRCJRDrXeKlUChsbmyfblyd6tHrA3NwcvXr1wvHjx/kyjUaD48eP02LnBEEQBEEQBEE8dRjFnbzi4mLcuHGDf52eno6LFy+iadOmcHZ2xuLFizFz5kz07t0bffv2xTfffIOSkhLMnj27AXtNEARBEARBEATx5DGKO3nnz5+Hl5cXvLy8AACLFy+Gl5cXgoKCAFQkRfnqq68QFBSEnj174uLFiwgLC9NJxlIT1Go1/1+frVKpBDZ369uQrVQqBTaX2YizuUw82jYAga3RaAQ290CnIVutVgts0vS/B2BNSZOx+YnDlDQZm5+0fWAqmozJT1ybjUUT11fuuJVtLgOfts3tZ8jWzt7XELY+HZzNwdU3BU3G5CdtTEWTsflJG1PR1Nj8VJ1reX1jFEGej48PP3jaf9u2bePrvPnmm7h58ybKy8tx9uxZeHt71+qYXl5e8PDwQFBQEJKTkwEASUlJuH79OgAgMTER6enpAID4+HhkZmYCAGJjY5GVlQUAiImJQXZ2NgDgxIkTKCgoAABERkbyGY1CQ0Mhl8uhUqkQGhoKlUoFuVyO0NBQABWZjyIiIgAA+fn5OHHiBICKrKLcgu5ZWVmIjY0FAGRmZiI+Ph5AxR3PxMREAMD169eRlJQEAEhOTq4zTfn5+QCAiIiIRq8pISGB96+paDI2P6WkpPA+MBVNxuanO3fu8D4wFU3G5ieuzcaiSS6X8184CgsL+S8thYWF/OdtZRuo+PLC2Wq1mh8LlUrF20qlEsXFxQAqss6VlJQAqEgnXlpayh+/rKyMt+VyOQCgrKyMt0tLS/n04yUlJXwm0OLiYj54Lioq4r9AFRUVVamJq9PYNEVFRUEsFvM/YtdEk7H4iTtObTWFhoZCJBIhODi4wTUZo584TElTY/GTWq3mr9mVr+XLli2Dh4dHreOUasEIAQUFBQwAy83NZYwxplKpmEql0rGVSqXAVqvVVdoKhYLJ5XIWEhLCSkpKmEaj4cs1Gg3TaDQ6NmNMYKvVaoGtVCqrtFUqlcDWp6O2mrTtxq6ptLSUhYSEMIVCYTKajM1PZWVlvA9MRZOx+Ym7DnE+MAVNxuQnhULBQkJCWGlpaaPQVFZWxq5evSroD6eJszUajY7NaTJkc200lK1PB2er1WqWl5fHVCoVS0tLYwB0/qysrFj37t1ZcHAwKyoqemJ9j4yMZABYUFBQjTQZk5+48ed88biajh8/zo9VdTQdPnyYTZkyhXXq1InZ29szS0tL1qlTJzZ79myWkpLyRM69xuInbR+YiqbG4ifumlpUVMS3p+9afv/+fQaAFRQUsPrCKJ7Jawi4BQy1FzLUts3MzGpkS6VS/pcEqVTKp6ytnHmnss1l6AEgyH5WHdtQ3+tS0+PYpIk0kaaG1VQTHcaiyVj8xH0OcG02Bk0ikYj/TNI+P7Rt7TTrnK29X2W7qv2ehG1Ih1gs5n+x1+6zm5sbXn75ZQAVU60ePnyII0eOYMWKFQgPD8epU6f4Ma7vvmu/rq6mqtpsbH7SpjaauDqVbUP7HTlyBHFxcfD29saoUaMglUqRnJyM3377DX/88QdCQ0MxbNiwWuszBj9pYyqaGpuf9F3jDV2z6wsK8giCIAiCeKrp2LEjli9fLigrLy9H//79ERcXh+joaD4AIIyTL7/8Et99951O+fHjxzFixAh88MEHOHfuXAP0jCDqB6N4Jo8gCIIgCOJJIpPJMHToUADgn1/kiIyMxCuvvIJOnTrBxsYGNjY26N27NzZt2mSwvbS0NMybNw+urq6QyWRo0aIFfHx8BPkFDFFQUIAhQ4ZALBbrBCr79+9H7969YWlpCScnJ8ydOxd5eXlo37492rdvL6g7a9YsiEQipKWlYd26dfDw8IBMJsOsWbP4OleuXMHkyZPRokULyGQyuLq64u2330ZOTo5Ov0QiEXx8fPT22dDxJRIJbt68ie+++w6dO3eGTCaDi4sLVqxYIUiMw1FWVoalS5eiXbt2sLCwQLdu3bB58+ZHjllltBem1mb48OFwcHAQZHEnCFOA7uQZQK0WZgOUSCQCW6VSQSQS8TZ3O9aQXTkjm5mZGUQiEW8DFQ9tattSqZTP0COVSqHRaKBWq3lbo9HAzMzMoK1Wq8EY4219OmqrSSKR8LYxaOIwJU3G6KfK7y1T0GRMftL2galoMhY/aV+DGoMmQDfjHDcFibO5Otq2WCzm99NnAxUBQEPZ+nRwNoe2bu3/nI7y8nJERUVBJBLB09OTHwPGGNasWYMbN26gX79+GD9+PAoKChAWFobXXnsNKSkp+OqrrwT9OnXqFEaPHo2ioiL4+/tj6tSpyM3NxcWLF7FhwwbMnDkT2mj3JysrC6NGjcK1a9fwxx9/YOrUqbymrVu3Ys6cObCzs8P06dNhb2+PI0eOwNfXF0qlkj/3OE0cb731FuLi4hAYGIgxY8agefPmYIzh9OnT8Pf3h0KhwAsvvAAXFxfExcVhw4YNOHToEM6cOcPXrdzfyj7Qp4cjKCgIsbGxCAwMhJ+fHw4cOIDly5ejvLwcq1at4vVpNBqMGTMGx48fR/fu3TFt2jTk5ubinXfe4YNL9v8ZDh/33Dtz5gzy8vIwcOBAvTrq8txrLO8nfb4xdk2NxU9cXe56/6hreX1Cd/IMQNk1TS9zHmXXbHhNlF2z4TVRds2G18S12Vg0PSq7ZomyHPfzclCiLOftUpUCxQo5HubnAvhflrlSlQJF8lI8yM9FqUqBwrISPCzI423tzHk5RfkoVSmQV1yI3OIC3s4rLoRcrRRkzsstKkB+SUX72YV5KCgtruhDHWTX5MZ1+fLlCA4OxocffogFCxaga9eu+Pfff7F27Vp06NBBkA3wyy+/xI0bN7BlyxZ88skn+Omnn3DlyhUMGzYMGzZswPXr1/lsgAUFBZg6dSqKi4uxf/9+7N+/H6tXr8b69esRHR2NgwcPCrIBAv9b6iExMREDBgxAeno69u7dixdeeIHXkZubi0WLFsHa2hrx8fH46aef8NFHHyEhIQH29va4e/cu35Z2ZkDufImPj8f333+PNWvWYOHChSgsLMSsWbNQWlqKP//8E7t27cLy5ctx+PBhLFmyBKmpqXjvvff4c4bzjUaj0ZvhUDuI1vYTAFy6dIkPcNevX48LFy7A3t4e33//PRQKBe+n7du34/jx4/D398eFCxewbNky/PLLLzh16hSOHj3KH6cmWRsPHz6Mjz76CB9++CGef/55DBs2DI6Ojli9enWjz9pI2TUpu2aNYIQAyq5pupnzKLtmw2ui7JoNr4mya1J2TW27Otk1PUKCDf69FruD16dWq1mvgysN1p1x8ldBlrsBoWsM1p0U9bMgE96I8PV662nX0barypynrkZ2Te4vMDCQJSYmVjtz3759+xgAtnXrVr589+7dDACbMWPGI9uJ1MquGR8fz5o3b86aN2/Ozp8/r6Nj69atDAB76623dLIBnj59mgFgLi4uggyAM2fOZADYhg0bdI4fHR3NALBRo0bp9KuoqIg1bdqUWVhYsPLycr4cABsyZIheTS4uLszFxUVQzh3/+++/18muOWPGDAaAJSUl8eVDhw5lANj58+d1Mhm+8sorNcquydmLFy8W+Lhjx458+9Xx8aPsqs69yn6qrMmQXRf9qtxHyq5p+tk16U6eAbSza+qzzczMBDY3DcKQLZVKBTZ3+5ezRSKRjg1AYIvFYoGtnblHny2RSAQ2aRJmljMVTcbmJw5T0mRsftL2galoMiY/cW02Fk1cX7nj6rMNwW0XiUSCc0tv3Ur1H4V2v6pTpzo6tPuoXd/f35//xZ4xhuzsbBw4cABXr17FgAEDEB8fLzhOcXExgoOD0bNnT9ja2vL6uTttWVlZfH0umYefn5/B/lbWeurUKQwbNgw2NjY4ffo0evXqpaODu6M7aNAgfn+uTr9+/fhzRp9v+vbtq3P8ixcvAqhYm7hyv7hnDuVyOVJSUnT6a0iHvrEGAE9PTx3ftGvXDkDF3Xuu/NKlS7C2tkavXr0EOkQiEQYPHqyjrzrju27dOv7u39mzZ9GpUycMGDAAu3bteuS+1bGrOvcq+6ly3w3ZddGv6vjGmDU1Nj9V51pe39AzeQRBEARBGORc4DKD2ySVvjTGjFxisK64Ut0I37erXfefYQvADNStD5o1a4axY8fCysoKvr6++Pjjj/npgQqFAj4+PkhISICXlxemT5+OZs2awczMDBkZGdi+fTs/bQwA/6hGmzZtqn38xMREFBcXw8/PDx06dNBbh5sy1qJFC51tYrEYjo6OBtt3cnIy2J6+bQDQqlUrQb3aYGdnp1PGffnVflapoKCAD/4qY6if1cXGxgZ9+/ZFSEgIevfujXnz5sHX1xfNmzevVbsE0VigII8gCIIgCINYmZk3eF3LGtStS7jnZrRT6x84cAAJCQmYM2cOfvnlF0H93bt3Y/v27YKyJk2aABA+D/so3nzzTdy9exdbtmzBiy++iJ07dwpmowD/C5QePHigs79Go0F2drbBwFLfHR2uvfv37+vd5969e4J6XDvaCYW0KSgogL29vd5t1cXe3h4PHz7Uu81QP2uKmZkZhg4dikuXLuH8+fMYNWpUnbRLEA0NTdckCIIgCILQQ15eHgAIsnKmpqYCAMaNG6dT/+TJkzplffv2BQBB4p1HIRaLsXnzZsydOxd79+7FSy+9pBNMcVMeT58+rbN/fHy8weDLEF5eXgCAqKgonW0lJSU4f/48LC0t0alTJ77cwcFBb/CakZHBJyiqDZ6enigpKREkTuPQN9aPC5ekRntKNUEYOxTkEQRBEARB6GH9+vUAwD//BQAuLi4AKp6b0yY6Olrv+m1jx45F27Zt8fvvvyM8PFxnu6E7fCKRCD///DNee+017N27F9OmTRMEbuPGjYONjQ22bNnCB55ARZa/Tz75pAYqKxgwYADc3Nxw5MgRHDt2TLBt5cqVyMnJwbRp02Bu/r+7qn369EFGRgaio6P5MoVCgcWLF9f4+PqYPn06AOCjjz4STOO8fPkyduzYUaO2zp8/r7c8PDwcf//9N5o0aYL+/fs/fmcJopFB0zUNwF1M6nJtJe6XQFovitbJe9r9VPm9ZQqajMlP2j4wFU3G4ifta1Bj0ATQOnkAcOPGDSxfvpx/nZeXh9OnTyMhIQEODg744osv+DEYPXo02rdvj7Vr1+LKlSvo2rUr/vvvPxw6dAgTJkzAvn37+LZFIhHMzc2xZ88ejBo1CqNGjcLIkSPh6emJgoICXLp0CaWlpTp3qrh+AMAPP/wAsViMH3/8EYwx7N69G2KxGPb29li/fj3mzZuHXr16YcqUKfw6eTKZDK1bt9bxjb5jcGMnEomwbds2+Pv7IyAgAJMmTYKzszPi4uIQFRUFNzc3rF69WrDv4sWLERERgYCAAEybNg2WlpY4duwYmjRpwj/DV1mPNtq+0a7Dlc+YMQM7d+5EWFgYvLy8MHLkSOTl5WHXrl3w9fXF4cOHq71OXp8+fdCtWzf06NEDbdq0QWlpKZKSknDy5ElIpVJs2bIFVlZWesemrs69xvJ+quo8MFZNjcVPXF3uev+oa3l9QnfyDEDr5JneGli0Tl7Da6J18hpeE62T1/CauDYbi6ZHrZPHTHANLK4OZ6empmLFihX49NNP8emnn2LTpk0oKCjAnDlzcPHiRXTq1InXJJPJ8M8//2DixIk4d+4cNm7ciLt372Lr1q145ZVX+DHQ1uTl5YWEhATMmDEDSUlJWLduHfbt2wczMzMsXrzY4Dp5nI6NGzdi7ty5+OuvvzBlyhTk5ORAo9Fg7ty52LZtGzp06IDt27dj27Zt8Pb2RlhYGAoLC2FnZ6d3nTxDfho4cCBiYmIQGBiIiIgIrFu3DmlpaVi0aBEiIyNhY2Mj8JOfnx927NiBDh06YMeOHfjzzz8xdOhQHD16lP9xo7KftNH2k3ayGs5PIpEIv/32G5YsWYLc3Fx8++23iI2Nxbp16zB//nwA1V8nLzg4GK1atUJUVBQ2bNiALVu24N69e3jllVdw6dIljBo1qtGvv0br5NE6eTVBxAz9vPKUUlhYCHt7e+Tm5sLBwaHO7+SFhYXB19cXlpaWT/0v2k9aU1lZGf+LI5f21tg1GZuf5HI5wsPDERAQwP+qbOyajM1P5eXlCAsL431gCpqMyU9ARUDo5+cHS0vLBtekUCiQlpYGV1dXvj+m/is9UPFZb2trK7ibacyaKvvpxo0beOaZZzB58mTs3r27UWniviRzCVzoDtGT16TtA64fxq6psfipvLwcaWlpcHZ2ho2NjcFr+YMHD+Dk5ISCggK92WbrApquaQBD63pxaGe5qo4tlUr5XxIqr62kXaeyLRIJ14viPpCqYxvqe11qehybNJEm0tSwmmqiw1g0GYufuM8Brs3GoIn7csMdl0Pb5rZr29r7Vbar2u9J2IZ0iMViPtDT12dj05SXlwcrKyvIZDK+fllZGf9M3Pjx4xudJm2q8lNV7TQ2TdU99xqLJm1MRVNj85O+a7yha3Z9QUEeQRAEQRCEERIdHY05c+bAz88Pzs7OyM7OxokTJ5CRkYFhw4ZhypQpDd1FgiAaCAryCIIgCIIgjJCuXbvC19cXp0+fRkhICACgY8eO+Oyzz/Dee+8J7koQBPF0QUEeQRAEQRCEEeLu7o7du3c3dDcIgmiE0E88BEEQBEEQBEEQJgQFeQRBEARBEARBECYEBXkG0F4MXZ+tUqkENpety5BdeTF0LtUqZzPGdGwAAluj0Qhs7YUW9dlqtVpgkybhQsSmosnY/MRhSpqMzU/aPjAVTcbkJ67NxqKJ6yt33Mo2Y0zH5vYzZHNtNJStTwdnc3D1TUGTMflJG1PRZGx+0sZUNDU2P1XnWl7fUJBnAFoM3fQWOqbF0BteEy2G3vCaaDH0htfEtdlYND3ti6GbiiZj8RN3HFPSZIx+4jAlTY3FT7QYeiOFFkM33YWOaTH0htdEi6E3vCZaDJ0WQ9e2aTF001wMvTFr4r4k02LotBi6KfqJFkM3AmgxdFromDSRJlPVVBMdxqLJWPxEi6E/GduQDrHYtBZDr2wbg5+0MRVNxuYnbUxFU2PzU2NYDJ2maxIEQRAEQRAEQZgQdCevhjDGUFoqr7KOlZWFwV9MCIIgCIIgCIIg6hMK8gxwKOMrDHB9ES62nfkyxhjG+M7HubjLVe7bt38P/BPxIwV6BEEQBEEQBEE8cWi6pgFsRX8gNWcc9qYMwO7rC3H+QTgKi4seGeABQPyZpEfe7SMIgiAIgngUIpEIPj4+Dd2Nx2Lbtm0QiUTYtm1bQ3el0UNjRdQ1FOQZ4L6iDTRMBEdZNlpIw1FYuhDR9wehU4AMAHAl7RDS7h8X/F1JO9TAvSYIgiAIojpIJBI+cUJ1/qrLrFmzIBKJkJGRUX+dN0BUVJTevtva2qJv3774+uuvBes1EgRhutB0TQNMdj+Icmkhzj34G7nyaDiYXYOlpBx3EisujlbWljid+xtKlPfh6TgBHey6N3CPCYIgCIKoLkFBQTrB2zfffIOCggIEBwc3UK/qhl69emH06NEAKtbsunfvHg4ePIjFixcjNjYWf/75ZwP3kCCI+qZGQV6HDh1qfcC3334bCxcurHU7TwJHizYY4vgqgFehVMuRlH0axVmr+O05pXvgJLuDjNyduHCvKco1PeExVobkw48/VbM2iV0oKQxBEARBVI/g4GBBunSgYspcQUEBli9f3jCdqiN69+6toyEvLw/du3fHvn37kJaWViff6QiCaLzUaLpmRkYG8vLy+MX/avp38+ZN5Ofn15OUukWtVqO0VI4OTsPRwWk4OrUOxKQe/wvwlAoFzM0G4165M9RMhGbmuWhtcQKfrLqLTeeLsf/WfKhUKn49Hm4xdM7mFk3kbMYYFAoFxoyYzx/T0N9Yv9ehVqv5hXU1Gg2USiXG+FZvX5VKxS+cq1areVu7XLvvhuzqaKpsAxDYXN85W1uTPltbt3bfDdmVNXGYkiZj8xOHKWkyNj9p+8BUNBmTn7g2G4smrq/ccSvbjDEdm9vPkM210VC2Ph2czaH9/US7XHs7V/7w4UMsWrQIrq6ukMlkaNGiBSZPnozLly8Ljt++fXts374dAODq6spPl+SeqWOMYf/+/Zg2bRo6duwIKysr2NvbY9CgQdi3b59eTRxVaaqObxwcHODt7Q0AyM7OFhynqj4ZGuuLFy/ipZdeQtu2bSGTydCqVSuMHDkS//zzT5W+AYA7d+6gR48esLCw4HVzOn7++Wd07doVFhYWaNeuHZYsWYKysjKdcdRoNPDx8YFIJEJpaSk+/vhjuLm5QSqVYvny5fwxT58+jcDAQDRt2hQWFhbo3LkzgoKCUFpaKuhXeno6RCIRZs2apbfv3PG1y7njKxQKBAcHo3379pDJZHjmmWfwww8/6PVTTk4OXnvtNTg5OcHKygp9+vTB33//LRif+n4/VT7Pa/N+MsVrRF1oqs61vL6p8TN577zzDtLT0x/rT9/J1Vjx8vJC71699G7r278H/rueAk/2Ml7sdBSiu18jp2QO7pT1QIlSChupAkA+YmJikJ2dDQDYEv8+krLOAAAiIyNRVFQEAAgNDYVcLodKpULI3wdw7mz1ErtkZt5BTEwMACArKwuRkdHVTgqTmHgJycnJAICkpCRcv34dAJCYmIj09PSKevHxyMzMBADExsYiKysLAASaTpw4wQftERERejWFhoZCpVJBLpcjNDQUAFBUVISIiAgAQH5+Pk6cOAGg4kNHW1NsbCwAIDMzE/Hx8QAqLsKJiYkAgOvXryMpKQkAkJyc/EhNCQkJ/DiYiiZj81NKSgrvA1PRZGx+unPnDu8DU9FkbH7i2mwsmuRyOf+Fo7CwkP/SUlhYyH9xqWwDFV9eOFutVvNjoVKpeFupVKK4uBgAoFAoUFJSAgAoLy9HaWkpf/yysjLelssrZqSUlZXxdmlpKcrLywEAJSUlUCgUAIDi4mI+eC4qKuK/QBUVFVWpiatTWZM2nKaHDx+if//++Pbbb9G+fXssWrQIgwYNwv79+9GvXz8cPXqU1/TGG2/A09MTAPDGG28gODgYy5Ytw4svvshr+vDDD3H16lX069cPCxYswKRJk3Dt2jVMmjQJ3333nY4mjkdpYozxY8qVa/spPz8f8fHxsLa2RqdOnQR++vDDD3H58mUMHDgQCxYswPjx45GSkoJJkyZh3bp1On7avXs3vL298eeff6J3795YuHAhAgMDkZmZic2bN/N+4vxRXl7Oa7pw4QL8/f2RmZmJffv2Yfz48Xx/P/nkE8yfPx/Z2dl49dVX8cILL2DPnj2YPHkyf25VPvcAYMKECdi2bRuGDBmC+fPnw9XVFUqlEjt27MCQIUMQFRWFwMBAvP3227C0tMRnn32GYcOGIT8/n9fEnV9VnXtqtVpw7nHn0qRJk7B161b4+/tj+vTpyM3NxYIFC/Ddd98J/FRSUoKhQ4di06ZNcHNzw8KFC+Hq6oopU6bwwa72ucdpro/3E0ddvJ9M8RpRG01qtZq/Zle+li9btgweHh78Dy71CqsBIpGIrVixoia71On+T4KCggIGgOXm5jKNRsMKC4pYYUERKy4u5W2NRsOUSiVTqVSMMcaUSiVTq9WsuLiUtWzSnw19eSg7kxnKl/+Xn8iOpz/Djqc/w/Yk92Y7kuex2KwQplDJmUKhYBqNhmk0GpafV8BaWPdnLaz7s7t37rPi4lJWVFTC8vMKWHFxKbt/L5vfXlRYzJRKJWOMMbVazQryC/ltWXcf8P0tyC9kxcWl7F7WQ357YUER33eVSiXQUVlTVbZCoRDYGo1GYGs0Gh2bMSaw1Wq1wNbWpM9WqVQCW58OQ5pKS0tZSEgIUygUJqPJ2PxUVlbG+8BUNBmbn+RyucAHpqDJmPykUChYSEgIKy0tbRSaysrK2NWrVwX94TRxtlJVzBTKIqZUFfO2Sl1SYatKeX1qtZovV6qK9dilfNtcu4brl/FjyBhjSlWJ3vradbRtfTo4W61Ws7y8PKZSqfh9uHFxcXFh3Fcjrnz27NkMAFu6dKngOIcPH2YAWMeOHfljaDQaNnPmTAaApaWl6fRLo9GwGzdu6JQXFhay7t27M3t7e1ZcXCyoD4ANGTKkSk1cW8ePH2cAWK9evVhQUBALDg5mH3/8MXv11VdZq1atmJ2dHduxY4fO8W/cuKEzjkVFRXyfSkr+N9b37t1j1tbWzNramiUkJOjou3XrFm//+uuvDAD79ddfmUajYWfOnGFNmzZlTk5OLCEhQaAjOTmZSSQS1qZNG5aVlfW/70b5+czDw4MfB23fDBkyhAFgPXv2ZDk5OYK+5OfnM3t7eyaTydjFixf5cpVKxaZMmcIAsBUrVvDlaWlpDACbOXOm3vOKO752OXd8b29vlp+fz/sjOTmZmZmZsU6dOgn8FBQUxACwV199VXDuhYWFMQAMANu6davgnDR0jtfG5t4D2ufOo+o/6tyrbGv7SZ9d15oeZwzqQxN3TS0qKuLb03ctv3//PgPACgoKWH1Ro2fykpOT4ejo+NgBZW33f5JwWbds7Wz0bjczM9Nra5TA1b/l6P6TD19epixElrw9WshuwtGiEEAUyuRRiMj4GHnKzujSdB56tfCFmVTKt2Nnbwtra0uD/ROJxXz7YrEYEq0+2NrZVLmvWCKBRCLhdT5KkyFbqtXfmtgikYi3xWIx/0xEdWzt/lbHJk2kiTTp2jXRYSyajMVP3C/K2tfvhtaknT1S+/zg7JjMZ2GIZpZD4Om0iW8j6uZz0LAyvXWbyPri2VY7+GOeuTMcSk2e3rq25t3Qp/Vf/Ov4u6MhV9/RqTes/f9mB2g/b65PB2dr/v8Xe23d+p5VF4lEUCqV2LVrF5o1a4ZPPvlEUDcgIAC+vr44evQoTp8+jUGDBunsX7ltkUgENzc3nXJbW1vMmjUL7777Ls6fP48hQ4bUSFNl+8KFC7hw4YJOf15++WU899xzOm1zfdIut7Gx4ft07tw5vk/bt29HSUkJgoKC4OXlpXOMdu3a8bb2GBw5cgSTJk1C69atsW/fPnTv3l3Q9z179kCtVuPdd99Fy5Yt+XJ7e3t8/PHH/N3Qym0DwIoVK9C0aVNBX/755x8UFBTg9ddf5++uAhXvlbVr1+Kvv/7C9u3bERQUpDMehmzt19rlq1evhr29PYAKH3Tu3BkDBgxAdHQ0iouLYWtrC7FYjB07dsDc3ByfffaZoB1/f38MHz4cx48f1zt2j+pXTW19eqqyq3Pu6du3so761FRTu7416bvGG7pm1xc1mq7ZqVMnNGvW7LEPVtv9jZUejoPxUudw9GweiZSM8bgj90KJygKWEgVaWyShQHGXr+vYyQxt+0r1Ph9AEARBEMST49q1a5DL5ejbty+srKx0tg8dOhQAcPHixWq3+eDBAyxevBhdunSBlZUV/0Xx3XffBQDcvXv3ES08mtdee03wLNG9e/fw22+/ITQ0FN7e3khLS3vsPnFTz/z8/Krdnz///BPjxo1D586dcfLkSbi4uOjUuXTpEgBg4MCBOtsGDBhQZft9+/bVKeOmy+lbY9DZ2RkdOnRAWlqaYErs49JLz+M9bdu2BQB+endhYSHS09PRsWNHQRDLUflHAoKoLbVaQmHUqFGYN28exo4d+0QiUmOnicwRrfL7Y2jPjwAJcDk/CjdLwuHbyh8lJWUoLSmD/yIJRg++i4gsH8izeqK93Uj0aj4KFhLdDxeCIAiCqG+GOCdWsVX42T+oXWwVdYW/Kz/X9kS163q3OYyK2WxPFu7ZGycnJ73bW7VqJaj3KHJzc9GnTx/cunULAwYMwIgRI9CkSRNIJBJcvHgRBw4cEDwbVheIRCI4OTnh5Zdfhlwux9y5c7F69Wr+2bma9qmgoAAA0KZNm2r34cyZM1CpVBg0aBBatGihd7y4shYtWuhsMzT+VW2vju/+++8/FBYWwtbW9pEaqsLOzk6njLuTo/3cF6BfX1X9JIjHpVZBXnh4OCIiItCiRQvMmjULc+bMQceOHeuqbyYHYwxrV+zEay+vrbTled56dbs5lBoxHMwLAcSgXB6DExnByFU9A1vJQEhkgLocKC0RToep/JogCIIg6gKJuPo/MtZfXcOPINQn3Jf3+/fv691+7949Qb1HsWXLFty6dQufffYZPv74Y8G2L774AgcOHKhFbx8Nl+zh3Llzj92nJk2aAKhI4tS+fftqHXfVqlX4559/sGHDBkgkEn7qqzbcGD548EDnTp+h8efQNxWxpr7jptRxiTm04QLb2qCtTx+P0kgQNaXG2TW1uXHjBt5//32IxWKsWbMGnTp1wvDhw7F7924+qw3xP8pK5Ui9rvtMgTa/zFTgx3cHQm32Ae6W90GxyhIWEiVay65CjG3QqCp+yezWYTSe7eeHDk7D0MFpOLp1GP0kJBAEQRDEU0Pnzp1hYWGBc+fOCTISckRFRQEAevbsyZdxM5v0pUhPTU0FAIwbN05n28mTJ+ugx1WTl1fxDKT2IyE17RM3NVI7W+yjsLCwwN9//43AwECsX79eJ5gEwD83d/r0aZ1tXNbZmsA9L8j5SJvMzEykpqaiQ4cO/F087eC1MtzUz9pgZ2cHV1dX3Lhxgw8wtXkS/ieeLmoV5HXo0AGrV6/GrVu38PfffyMgIAAxMTF46aWX0Lp1ayxevBj//vtvXfXVqCgtKUNJpT/thcqvpB1C2v3jev/+3r8Jvm1fwcudfkdgh/Owt96IB8oAlLIR6NOXe3iYYdWfRfj+XBnm/ibFsy9ZwMyyYnkHKyuLhhFNEARBECaEubk5pk2bhuzsbKxevVqwLSwsDOHh4ejYsaPgmTEuAQi3dIU23B2qU6dOCcr/+OMPfrmN+kKtVmPDhg0AgMGDBz92n2bOnAkbGxusW7dO77OI+oIkAJDJZNi/fz8CAwOxceNGLF68WLB96tSpEIvFWLduHb+8B1CRDv/zzz+vnkgtxo0bB3t7e2zduhVXr17lyxlj+OCDD6BSqTBr1iy+3M7ODp06dcKpU6dw48YNvryoqAgffvhhjY+vj+nTp0OhUPDJXjgiIiL4pCsEUVfUaromh0Qiwbhx4zBu3DhkZWXh119/xdatW7FhwwZs2LAB/fv3x9y5czFlyhRYWDwdAcij7qxZWVtWmQGTQyI2Q6/mI9Cr+QgAwOQIhtJSOW6WJCOz+CVIxRoMe7YMw54Fyt83Q47yHg7fXI9ezV9AK2vdB5sJgiAIgqg+a9asQXR0NFauXInY2Fh4e3sjIyMDf/75J6ysrLB161ZBRr5hw4bhq6++wrx58zBx4kRYW1vDxcUF06dPx/Tp07FmzRq89dZbiIyMhIuLCy5duoTjx4/j+eefx/79++ukz+fPn8fy5cv51w8ePMCJEyeQkpICZ2dnwZ20mvapRYsW+O233zB16lT07dsXY8eORadOnZCdnY2zZ8+iffv2CAkJ0dsvc3Nz7Nu3D88//zwfcH7zzTcAKpLzLV26FKtWrUL37t0xefJkmJmZYf/+/ejevTuuXLkiGOdHYWdnh82bN2PatGnw9vbGlClT0Lx5cxw7dgwXLlxA3759sWTJEsE+7777LubNm4f+/ftj0qRJ0Gg0OHLkCPr06VPt41bF+++/j/3792Pz5s24evUqBg8ejMzMTOzduxeBgYE4fPhwnRyHIADUbJ286qJUKtnevXtZmzZtmEgkYiKRiInFYtasWTO2du1afk2Jxgi3Tt7jrFuh0WjY6BGv8evRGfoLHDaPX4+jNhSW57HjmdvYjmvTWch/Pfm1+I6nP8N+T3mVr6dSK1lhYTF//OLi0lof2xjh1qfi1q0injzkg4aHfNCwNLbxLysrY//++y8rKytr6K48MbTXCKuM9jp52jx8+JAtXLiQubi4MKlUyhwdHdkLL7zALl++rPcYa9euZe7u7kwqlQrWd2OMsYsXLzI/Pz/m4ODAbG1t2ZAhQ9ixY8fY1q1b+XXStKm8f1VERkby661p/1lYWLAuXbqwJUuWsOzsbJ39atonxhhLTExkkydPZk5OTkwqlbJWrVqxUaNGsUOHDvF19O2vVqvZgwcP2Lhx4xgAtnDhQkG7P/zwA+vSpQszNzdnbdu2Ze+99x7LzMxkANi4ceMEdbl16qoiJiaGjRo1ijVp0oSZm5uzZ555hn3yySesuLhYb/2NGzfyvnN2dmZBQUFMoVDo9UNVx+fWS0xPTxeU5+TksHnz5rHmzZszCwsL1qtXL7Z///4qx7quqeo9QNSO6l5Ts7Oz632dPBFjrM7SVf3333/45Zdf8Ntvv+Hhw4eQyWSYNGkSZs6ciYSEBHz//ffIzMzE4sWL8eWXX9bVYeuUwsJC2Nvbo6CgoNoPUmvDGBNMy9RGpVQiPDwc4yeMg7m5eW27KkCtUeFy7kmk5B0C05xDO/tFGNByIgDg9L39eFC0EolXbXHhmATbPt+N5vb6szuZMkqlEqGhoQgICBCsY0U8OcgHDQ/5oGFpbOMvl8uRnp4OV1fXp2amjUajQWFhIezs7Gp0Z4ioGx5n/I8dOwZfX1+8//77WLNmTT330PSh90D9Ud1rak5ODhwdHR873qgOtfasXC7Hjh07MGTIEHTp0gVfffUVmjZtinXr1uHOnTvYvn07hg0bhvfeew8pKSkYMGAAfvvtt7roe73CPTCtVqv12iqVSmBzDzGr1WpYWspgbW0JmUzK2+bmZrCwlEFmYQ6VSgUutlYqlfxaNpVtAAJbo9EIbC4DlEajAdMAPR2HYpLbl3jB7QQGtJwItVoNlUqFm4VHYS8tgU/Pe3j3vTu4kOOD36+NwT8ZX+JW4fVHajJkK5VKgV3XmvTZnKaqfFOVnzhMSZOx+YnDlDQZm5+0fWAqmozJT1ybjUUT11fuuJVt9v9rrWnb3H6GbK6NhrL16eBsDq6+KWgyJj9pU1nHgwcPoFarBTpyc3P5Z+LGjh3bKDUZm5+0MRVNjc1P1bmW1ze1CvLefPNNtG7dGrNmzcLZs2cxZcoUREZG4t9//8Xbb78NBwcHQX2ZTAZ/f3/BA7WNFS8vL3h4eCAoKAjJyckAgKSkJFy/fh1ARaal9PR0ABULg3IPWMfGxiIrKwsAEBMTw2s9ceIEn4I3MjKSX3wzNDQUcrkcKpUKoaGhUKlUkMvl/MPORUVFfAar/Px8nDhRsa5QdnY2YmJiAABZWVl85qnMzEx+odL09HQkJiZinOta3M95DZEXWyJXbglzsRqtLf6DDX7BjdzRiPz3wGNr4hb5jIiIeKKaAOD69etISkoCACQnJz/STwkJCbx/TUWTsfkpJSWF94GpaDI2P2knRTAVTcbmJ67NxqJJLpcL1vLivrQUFhbyX1wq28D/7gYAFV9YuLFQqVS8rVQqUVxcDABQKBQoKSkBAJSXl/PZKuVyOcrKynhbLq+YDVNWVsbbpaWl/FptJSUlfAbv4uJiPnguKiriv0AVFRVVqYmrY0qajMVP3HH0afr999/h4uKCF198EUuXLsXMmTPRuXNnnD9/HjNnzkTXrl0bpSZj9BOHKWlqLH5Sq9X8NbvytXzZsmXw8PDglzOpT2o1XVMsFsPNzQ3z5s3D7Nmz4ejo+Mh9Tp8+jWPHjiE4OPhxD1uvcNM1c3Nz4eDgwJ8AEolEYKtUKohEIt4Wi8UQi8UGbe7X37CwMPj6+sLS0hIikQhKpZJfMFOlUglsqVTK/xoglUqh0WigVqt5W6PRwMzMzKCtVqvBGIOZmRmKCovRsbUfAIZD1z5DWlk4NJpzsBAXYXT7OEilUqhUKuxNexMaVoKWVsPRp/lY2Fs0rVKTRCLhbTMzsyeqyZBvDPmprKwMERERCAgIgEgkMglN1T33GosmuVyO8PBwBAQE8FNEjF2TsfmpvLwcYWFhvA9MQZMx+QmoCAj9/PxgaWnZ4JoUCgXS0tLg6urK90ckEkEkEvE2UPHLtLbNnTuGbKBi7bKGsvXp4GwA/ALY3HXI2DUZk5+4L8ncFDVtHefOncOqVatw7tw55ObmQiKRoEuXLpg5cybeeOMN/rO7sWkyNj9p+4Drh7Fraix+Ki8vR1paGpydnWFjY2PwWv7gwQM4OTnV63TNWmXXPHr0KIYPH16jfQYMGCBINdxY4da54f5XtrkP8OraUqmU/yVBKpXyJ432Mxn6bJFIxNvch3N1be3+inlbBA+HAejTtiJbZ7Gi4H/HFTPYimNhbVYOqOMRd3cNshUdYCMbiJ6OE+Bi21lH0+PYdaWpOvbj+Ik0kSZT11QTHcaiyVj8xH0OcG02Bk3clxvuuBzaNrdd29ber7Jd1X5PwjakQywW84Gevj4bq6aq9m1smrSprMPb27taC8I3Nk3G5idtTEVTY/OTvmu8oWt2fVGr6Zo1DfCIxoeNub3gdRPrj5FVPgj5SltIxRq0srgBW9E2pOaMw+8pLzRQLwmCIAiCIAiCqC51sk4eUDH/NDs7m58PWxlnZ+e6OhRRT0hEZnjWbgyetRsDjUaD68UX8F9RKDSaeLSQ3YWIteJvXRcrCnAgfQFaWA+Fd/MJsJM1rfXxGTOcmZTDysrC4K9RBEEQBEEQBEHUQZB34cIFLFu2DDExMfxDjpURiUT8swhE44QxhjG+83Eu7rLe7XZtmkNkdhG7W7+OfyJ+RPzDf9BKdg5QnUPcna/wUNEeVubPwav582hv27XOj8/Rt38P/BPxIwV6BEEQBEEQBGGAWgV5Fy9exKBBg2BmZgY/Pz8cPHgQnp6eaNmyJRISEvDw4UP4+PjAxcWlrvpL1AGlJWV6y6oKsArvVDzUH38zCaWlcrSyegaJD4dAhkQ4mBeilUUagDSk5fyO+KymcLH/GN5OgdXvU6n8kQEeAMSfqTi+tbVltdsmCIIgqqYWOdgIgiCI/6cxXUtrFeR99tlnAICzZ8+iS5cuEIvFmDBhAoKCglBWVoZ3330X+/btw6+//lonnSXqhm4dRle5/UraIVhVCqJKS8oE+3Vx8EYXB29oNBr8V5iAqzkHUK46g+bmt+FonoumFm34umfvH0KB4g76tHgeDrLmj+xfdY5PEARB1B7u4X+lUglLS/rxjCAIojZwybWeRGKVR1GrIO/UqVMYO3YsunTpwpdxEaylpSW+//57xMbGYtmyZfjjjz9q11OiVlhZWaBv/x6IP5NUZb2+/XvAsblDtadDisVidG7SG52b9AYAZMuzcDE7DO72Pfk61/O2oLXFvzh/92s8KHeGpflz6NFsAjrae+rvq7Ul3akjCIJ4AkilUshkMhQUFMDW1pamwhMEQTwmjDEUFBRAJpMJsiw3FLUK8goKCtChQwf+tVQqFSxyKRaL4ePjg127dtXmMEQdIBKJ8E/Ej3WS2ETfdE+OZlYtMaLtbEGZVNIBOYosNDPPQyuLmwBu4lbeLiTed4AKvTGl47fV1kEQBEHULY6Ojrhz5w5u374Ne3t7wTI/pohGo4FCoYBcLhekSyeeDDT+DQ/5oG5hjEGpVKKgoADFxcVo06bNo3d6AtQqyGvRogXy8vL41y1btsT169cFdeRyOb9iPdGwiESiOrlDVtW0SX2JUaa4rwMA3Mi/hIQHf0GpiUML2S00M8/DPfkVlJWV84Fjz2ky5JU/gLU1PcdJEATxJOAW4s3OzsadO3cauDf1D2MMZWVlsLS0NOlgtrFC49/wkA/qB5lMhjZt2tTb4uY1pVZBnoeHB1JSUvjXAwYMQEhICM6cOYP+/fsjOTkZe/fuRefOnWvdUaJhqe50T0OJURhjePuFH/jkKlaOjnh2ijkUZcA7v1ast2jjJMZ7y7KQXOCPmIdtITPrj27NxsPdzqt+RD1haIkIgiAaK3Z2drCzs4NSqYRarW7o7tQrSqUSMTExGDx4cKOYUvW0QePf8JAP6h6JRNLoxrJWQV5gYCDeeecdZGVloVWrVvjggw/w999/Y+DAgWjatCny8vKg0WiwbNmyuuov0UA8arrnoxKjVM6eWZqtwamNwrYcnzHDgxJbtLIpREtZJoBM3MnfiysP7FGm8USHoeZIi9S/TEdjh5aIIAjCGJBKpY3ui0pdI5FIoFKpYGFhYfJaGyM0/g0P+eDpoFZB3vz58zF58mQ4ODgAADw9PXH8+HF8/vnnSEtLQ69evfDWW28hMLD6qfSJxktdTffUlz2Tw8rKAhlF/+Ji9t8oVZxGc/MMOJgXwAExeGZAW6RFVtTLK38INVPC0aJ1rfvzJKAlIgiCIAiCIIgnRa2CPKlUCicnJ0HZc889h8OHD9eqU4Rp86jsma52XeFqV7GgemF5LuIfHkBW0VGc//M2Xyfqzs+wFf2Oh4rWMDPrh64O49C5SZ96fYC4rqZb0hIRBEEQBEEQRH1SqyCPIOobO1lTjGg7GyUlU/FeynC+vFR5A/YyBifZHQB/4V7hX7iWY4syTU842/mhb4uxkEks6qwfdTndkpaIIAiCIAiCIOoTyptqAO7Bc7VarddWqVQCW6PRVGkrlUqBza0nyNlc+lVtG4DA1mg0AlulUlVpq9VqgV2fmlT/3y/O1qeJ43E0qf/fBoCiwmKMb/sjWlnsRq7iZdwtc4NSI0YTaRFayU6irCwYJeWFfH9LFSW8zfexhn4qLCiq9nTLkuJSvZq0qewnbX0aEz/3OExJk7H5SdsHpqLJmPzEtWlKmozNT/r0GbsmY/IThylpMiY/aX8Wm4omY/RTfVOjIO+VV155rL85c+bUV//rDS8vL3h4eCAoKAjJyckAgKSkJH6JiMTERKSnpwMA4uPjkZmZCQCIjY1FVlYWACAmJgbZ2dkAgBMnTqCgoAAAEBkZiaKiIgBAaGgo5HI5VCoVQkNDoVKpIJfLERoaCgAoKipCREQEACA/Px8nTpwAUJHqOiYmBgCQlZWF2NhYAEBmZibi4+MBAOnp6UhMTAQAXL9+HUlJFZkxk5OT60xTfn4+AOD48eP82IWHh+toKpf/b5pjcXFxjTWdP3+e39/TfRw6OA2HT8cFWPDsEbzbJx9zBzXDN1+3wbkbLrhd3AkPbuXymkJSR2FXig92XH0bORZp0Gg01dIUERHB+yk8PJw//lc/vIn/bofh34xD+G7LO0i7fxxxSbv57fkFBXo1aVPZT5cuXeK3Xblypd78pK2pIc497Wy8pqLJ2PyknSLfVDQZm5+4Nk1Jk7H5CQCOHj1qUpqMxU/ccUxJk7H5Sfs7h6loMhY/LVu2DB4eHvD29kZ9I2Lat1gegaHnnUQiEfQ1w5WLRCKjSclcWFgIe3t75ObmwsHBge+3RCIR2CqVCiKRiLfFYjHEYrFBm/vFICwsDL6+vvzaJEqlEmZmFbNmVSqVwJZKpWCM8bZGo4FareZtjUYDMzMzg7ZarQZjjLf16aitJolEArFYjIL8QjzTdiQA4OJ/IbC1swEAqFUqSMzMUFoqR/f/f+Ys9d4xyGTSGmlSq9V4PuCtRy7hAABJN/6BlZUFxBIJHpTcQmrJWIi1Zk8WKG1Qou6OtjZ+8G45FlJY6NXE+UYkEgn0/Xc7DHb2tgI/FReXwq3liAp9WUchszAXaCovV6KDU8V007T7x2FpKRP4qaS4FO5t/AEAN+5GwNbOpl78pK2pIc49uVyO8PBwBAQE8NcTY9dkbH4qLy9HWFgY7wNT0GRMfgIqvkT4+fnB0tLSJDQZm58YYzhy5Aj/WWwKmozJT2VlZYiIiEBAQAC/n7FrMjY/aX8Wi0Qik9BkbH568OABnJycUFBQUG/r6tXomTwuQubQaDRYtGgR4uLisGjRIgwaNAhOTk64f/8+YmJi8O2336J///74+uuv67TTTwKJRCL4X9nmnF5dWyqV8rd0pVIp/8yWdupafbZIJOJt7sSsrm2o73WpiS/Xsns+Mx5V8biaqruEQ4+OYwXbmro5ofdUMzw7oBxd2uTBXloMe+kZaFRn8FfqAbzcaY9eTYb0menxn/YzeCKxWEdfefn/pmlV1ieRSCDRGldxPZx7j2Mby7lHmmquqSY6jEWTsfiJ+xzg2jQFTdXV0Vg0VfVZbKyaqrJJE2mqrT5j0GTMfqovahTkubi4CF5/8cUXOHv2LC5duoRWrVrx5Z06dcLgwYMxe/ZseHl5Yd++fXj//ffrpsdEo6S6i6X37d8DVlaPlxClqiUcqjp+bqoKEZ+rEAFAauOAH6NmIp+dho3ZZdiZ9UdJSRkA4L/CC0gpXAaRuA86OYxGj6ZDBF+ICYIgCIIgCMIYqFV2zS1btmDy5MmCAE+bNm3aYPLkydi8eTMFeSbOoxZL56jOEgP1cXzuTp+ymOHV3tv+v1QGsXQvNMq9AIDnv7TApFH3ARxCbvEh/JNnhWJNd7S2Ho7utqPqvM8EQRAEQRAEUR/UKsi7ffs2LCyqvitjYWGB27dvV1mHMA3qarH0+ji+lZUF+nh3x7mz2hkyRdBozaI8/r0ShTltMHKiLRzNb8BOWgo7nAVUZ5GQswbufk64HlFevyIIgiAIgiAIopbUKshr27Yt/v77b3z22Wd6g73S0v9j787jo6rv/Y+/zizZF/awqYCKigoCAqLihoAiLqitW7W1alutttUu19qWWm29t/31dnVrvdXa2ip1Q8QIQYKmEjDsARswQtQAISGE7JnJLOf3R5zDTEhCkpnJZIb38/HgwTczZ875vuczyeSbc+b7beb1119n9OjR4RxGJGyGYfDa239kyetvMG/evJDP2MHhM33v/I+PP9//FvZkk6IDb1LekEeqsYU0u4tP1rRa27++++e4fQc5ecAVnDX4oj5OIyIiIiLSubA+cHTnnXeye/duzjvvPN544w0OHjwIwMGDB1myZAnnn38+n3zyCXfddVdEOisSDsMwSE5JshYjD/6XFnQGsLmpBZ/bYGrWVVwz6nHmDC9gpOOveJoOzyDr9S5jmDOXuqZv8tbus3n1szuY+bUUkgdE/lJUEREREZGeCOtM3ve//30++ugjnnvuOa699lqgbRaZwOKCpmly++238/3vfz/8nor0kcAsnZ3x+/2kJd/EPtd7DHJ8RIajhQzHRr71LfDca+PVz77Cbact7nIfIiIiIiLREtYgz2az8Ze//IXbbruN559/nuLiYurq6sjOzmbSpEnceuutXHTRRRHqqkj09GR20IyMNK7I/DbwbVy+ZjYeeJuy2rdx+jcyNK0ZODwtrs/v5ZXdDzIq+QIMu4np05k+EREREYmuHg/ynnzySa6++mpGjRpl3XbhhRdy4YUXRrRjIn2pt7ODptjTOG/4dZyVOZ9xOZcwenoWz/3zW9b9Ww6+y1DHm7T63uRPG5LY9vFA3q38K+eP+gLZyUOilkdEREREjl09/kzevffey/HHH8+0adP4xS9+wbZt247+IJE4EJids6t/XS//YLCnyMOJmZOsW5JsKexzT6TF5yTT2cq5p1WSavsdH+ydxT92zOODyreiH0xEREREjik9HuStX7+ehx56iNbWVn7yk59w1llnceKJJ/Ld736X9957z/o8nojAmYPP50unvMx5Q//NYz8dyYp1I6hpHYDD5mdEyieYHP5+OW5GEhOuScbja+1ij9FjmiZNTS1d/jNN8+g7EhEREZGY6vHlmlOnTmXq1Kk8+uijfPLJJ7z++ussXbqUP/zhD/zud79j0KBBLFiwgKuvvpq5c+eSlpYWjX6LxBWnPYVtr7rZ9iosqnyXvd4Sth9cxqwhc61tLrvX5JLJFbxfdT61FacxJPVipg27lsEpOVHvn2maXDnnG6xf1/WZ+ekzJ7I076moLGgvIiIiIpER1sQrY8aM4f777+f++++npqaGZcuWsWTJEl555RWef/55UlJSmD17NgsXLmTBggUMGzYsUv0WiWvjs6cwPntKyG1ul0GjJ4kMZytpjq3g38qmit9zoHU0yY5zWTDmIZLtR65HGWCaZo8/UxjQ3Ow66gAPoGhtMc3Nrpguei8iIiIiXQtrkBds0KBB3Hbbbdx222243W5WrlzJG2+8wbJly3jrrbew2Wycc845vP/++5E6pEhC+dtdrfzdnsHL2/+LPS3vYGcjQ5JqGJ5cTq0nF6fxsLVtyaEPGJc1yRr0RfJM3Pbdy0LWDYTDi8WLiIiISP8XsUFesOTkZBYsWMCCBQswTZN169axZMkSli5dGo3DiSQM02cwZdA8ZqVfA8Du+m1srV6C3eHEZmv7CK3b52J3zR3sroFD3lMZlHIhZ2QsiNiZuMBi8SIiIiISn6IyyAtmGAYzZ85k5syZ/PKXv4z24UQSyrisMxmXdWbIbWUN2/GbdtIdLlLt28Dcxkf1T/DI6gFs2ZDGHZf/jNOGnh3yGJ2JExERETl2RH2QJ3KsaW5q6dZtvXXqgLM5KWsDm6vz2VX3Fjb/RoYkV3Py0EOcfPkhPnK/xtnpswBw+ZrBhDR0Zk5ERETkWBHWIO+rX/3qUbex2WxkZWVxyimnsGDBgpBF1EUSUV+cMXPYnEwbNo9pw+YBsKNqC//z/HeZPLOFmWOvsLYrqPgHXvfvOdh6Epd8P5WN/4rN8gwiIiIi0nfCGuT99a9/tSZw6Gj9LMMwQm6/7777WLRoET/+8Y/DOaxIv5OWlsL0mRMpWlvc5XbTZ04kLa3zGTJ767j0U3j7Zy28DeyuPM+6/UDzWkYkeRiVWsJdX4Y7boM3yi/H6ZjBaQOvYsLAmdZn/UREREQkMYQ1yNu1axff+c53KCoq4tvf/jbnnXceOTk5VFZWsmbNGv7whz8wffp0fvSjH7F161Z+/vOf89Of/pSTTz6ZG264IVIZRGLOMAyW5j3V6yUMouXGk/5M8cECSmreoLWlkOMH1DM8pQJYQlXDEjKTlnBC5ml91h8RERERib6w/oS/ePFiPvjgA7Zs2cKDDz7IrFmzGD9+PLNmzeLBBx9k06ZNrFu3jtWrV3PnnXeyZs0aMjIyePLJJyPV/6jx+XzW/x21vV5vSNvv93fZ9ng8Ie3AGc5A2zTNI9pASNvv94e0vV5vl22fzxfSViavVd9oZPJ6vaSnp5KamkxSksNqJyc7rXagL+0z+YL65u9lnYIFbrfbHJwxYBZXjXyM/zrfyb0Lczjk+TL7XOOpcudwfMapbX33eLj/dTv/9ZbBir2/Y19TWWi+oGOYYdYp4Fh77fWnTME1SJRM8VSnwD4TKVO81amjfPGeKZ7qFJBImeKpTsHvxYmSKR7rFG1hDfL+8pe/8MUvfpGcnJwO7x8+fDhf+MIXeOaZZwAYNWoUCxYsYOvWreEctk9MnjyZCRMmsGjRIkpKSgAoLi6mtLQUgM2bN1NWVgZAUVER5eXlABQWFlJRUQFAQUEB1dXVAOTn51NXVwfA6tWraWhoACA3NxeXy4XX6yU3Nxev14vL5SI3NxeAhoYG8vLyAKitrSU/Px+A6upqCgoKAKioqKCwsBCA8vJyioqKACgrK2Pz5s0AlJaWUlzcdilhSUlJxDLV1tYCkJeX1+8zbdq0yapvf8sU/D2xffv2XtUp2BGZPn/tHSz1cl7mnXzp1DdJ3vEdK9OylW9w1okHOeuEagYk/ZUdBy7jpZ0X8symr7G1+l1qDh2y9l198GCv67Rz505rP8fSa68/Zdq7d69Vg0TJFG91CuwzkTLFW50AVq5cmVCZ4qVOgeMkUqZ4q1Pw7xyJkile6vTQQw8xYcIEZsyYQdSZYUhJSTG/973vdbnN9773PTMlJcX6+sEHHzSTk5PDOWxU1dXVmYBZU1NjmqZper1e0+v1HtH2eDwhbZ/P12W7tbXVdLlc5pIlS8ympibT7/dbt/v9ftPv9x/RNk0zpO3z+ULaHo+ny7bX6w1pd5Qj3EzB7f6eqbm52VyyZInZ2tra7zLV1dabw9JnmsPSZ5r1dQ09rlNjY7P1+MbG5iMyNdQ3Wvc3NDQdkanm4CHznAUXmvf+38XmP0tmmavKxof8+9t/rjr8+PrGXteppaXFqsGx9NrrT5kCP4cCNUiETPFUp9bWVnPJkiVmc3NzwmSKtzq53e6Q9+JEyBRPdQp+L06UTPFWp+D34kTJFG91qqysNAGzrq7OjJawPpM3atQolixZwqOPPkpKypGTSbhcLpYsWRIyo2ZVVRUDBw4M57B9wm63h/zfvu1wOHrUdjqd1ildp9NpfS7L6XSGbNO+bRiG1bbZbNYkGd1pd9b3SGbqTVuZjsxkD+qDrZevvWDtM7W2Hr48pqXZZb3+Ard7vX52r25l92r4n8pV1LGfjQdeo85VwGDnxyTZTwfeBaC6tYL3991PZvIspg65ltEZJ3WYqaO+x3udEiVTT3LES6Z4qVPgfSCwz0TI1N0c/SVTV+/F8Zqpq7YyKVO4+eIhUzzXKVrCGuTdcccd/OhHP+L8889n0aJFnHfeeQwePJiDBw+yZs0aHnnkEXbv3s2jjz5qPebf//43kyZNCrvjItI73VniYWT6WEamfxf4Li3eRmob6wkM8rYeWsrI5I+Aj/io+i+s2TsM7NM4OfsKJg2+ELtNy2+KiIiIxFJYv4394Ac/oKSkhBdeeIGFCxcCbSPWwAcZTdPk5ptv5sEHHwSgsrKSK664gssuuyzMboskrq4WTu/t7JzhLPGQ6sjA7zz8F6eTMmfxn4ZKvL4ihibtY2hyFfAWtU1vsawulRGZjzE9Z36P+ygiIiIikRHWIM9ut/O3v/2Nr3zlK/z973+nuLiY+vp6srKymDRpErfccguzZ8+2ts/JyeG3v/1t2J0WSWRdnWmbPnMiS/Oe6vFAL5JLPIzJOIPTc6YBsL/5MzYeeJ1DrvcY5PiIdHsLx2Wcbm27as9zNLTuY/LQa7VUg4iIiEgfich1VZdccgmXXHJJp/f7fL4+ufZUJF5190xb0dpimptdpKen9vgYhmH06nFdGZ52PFec8G3g27h8zWw/+G9GpJ9g3V/Z9CLDkz9l18G/8UHFYLxMpTltNF7/HJw4O99xB0zT7HKQ2tdrEIqIiIj0V2EN8p544gm++c1vdrmNz+fjxhtv5OWXXw7nUCIJ7Whn2pqbWqwzfB1dztnVJZ6R1vmxDKYOnRtyS7LzXPa7fQxL2suQpINAHpwMK8v/SYNvMrec8tduHdM0Ta6c8w3Wr9vW6Ta9PcspIiIikmjCGuR961vfIicnh+uvv77D+/1+PzfeeCOvvfZaOIcROSZ090xbdyZOiaaeXE563biHAah27WN95etUt+QzyLmTDIeLBu+BkMfmfvpHTh14EeOyzjxiv83Nri4HeBDeWU4RERGRRBLWIO/888/n1ltvZfDgwVx88cUh9/n9fm666SZeffVV7r333rA6KXKsC2filL48ftHaYqoPHCKt3UArlYFcMOSrOJ13seztN8k6G05OPbzNpw07SDEf55Oax9m4fxBepjAu+3KmDJmD054csq/tu5eF7D/4LKeIiIiIhDnIe/PNN5k1axYLFy7k3Xff5ayzzgLaBni33HILL7/8Mvfccw9/+MMfItFXkWNWJCdOicbxgwdaXQ24Tj/zJL7+nSuZmD4Ph9NJ0+eXflY17KPCdTzDkssZnFQDvENTyzusKEum1ncaY9NvtfaRlp6qs3UiIiIiXQhrkJeVlcWKFSuYOXMml19+OWvWrGHs2LHcfPPN/Otf/+Ib3/gGjz/+eKT6KnJMi8bEKZE6fnfP9H247WO+dcdvgY5n2T33svP5+m+mUt3yLgMc/yHN4SbNsYXa1ousbfa3fIrf6+Lk7LN6mUREREQksYU9u+bw4cNZsWIF559/PnPnzmXy5Mm8+uqr3HXXXTz55JOR6KOI9HNHPdNomlw19262F5d2uZ/C5f/hhef/SHr63Xh8bjZVr2R3XS4XDrsS+DsARQefISd5GVsrB9DKFEamzMaeDD53hEOJiIiIxKmILKEwfvx4cnNzueSSS3jttde48847+dOf/hSJXYtInDjamcZ31vyV+roGVqxYwbx5bZdrBnT0uTqnPZkZOQuYkbPAuqwTwGvW4jMNBiXVAvn4/fn8+QMn28sGkr//z8w54S5SHGmRjiciIiISN3o0yHvkkUe6vH/69Ols2bKFkSNHhmxrGAY/+clPetdDEUkIhmGQlp5KckoSaempOJ09Wycv4NrjHqfV0cj6A0uoason2/4f0h0upp9chcv3DBh3WdseaNnD4OSR2Gy2sPp+tDX6oOvPQ4b7eBEREZGe6NEg7+GHH+7Wdu0HgxrkiUgkDUweytzRdwF3UddQz1V3Xs3ZV8KUs08jxd52Fs/v91NQvhAAF2dxQuY8zh4237q/u7qzRh90vk5fuI8XERER6akeDfJWr14drX6IiPSKw+Zkx1tudrwFj1T+r3X73uaPyXQ04rD5gQJa3QXkf/IwNd7xDEy5kGlDr2NY2uij7r87a/RB5+v0hft4ERERkZ7q0SDvwgsv5Mknn+Tqq69m1KhR0eqTiEjYjssYT3ZSIeurllDRtIos+3YyHC2MtH8I5oesLN/ELac8D7Sd9QOOelln+zX6oGfr9IX7eBEREZHu6PHEK/feey/33XcfU6ZM4ZprruGqq67izDPPjEbfRETCkpU0kNmjbwdux+f3suXgu5TWvoXh38CojLnWdhsOLGdP/U9oMSdxXOY8pg+9ssPJW8Jdo09r/ImIiEhf6PEgb/369SxZsoSlS5fyk5/8hEWLFjFmzBhrwDdr1qywJzkQEYk0u83B1KGXMnXopUfct7v+bYY5GxnAGrzuNaz+9BEOek5mQMosJmRcGYPeioiIiPRej0djU6dO5dFHH2Xr1q3s3r2bX//61xx//PH84Q9/4JJLLiEnJ4fbb7+dJUuW0NzcHI0+i0gCa25qoandv+agJRSiYcEJPwfnj6lwn0O9J41ku5eRKSWk8Wc+abySMbOSonp8ERERkUgKa528MWPGcP/993P//fdTU1PDsmXLWLJkCa+88grPP/88KSkpzJ49m4ULF7JgwQKGDRsWqX6LSIKKxefTMpKyuWTUrcCt+P1+ttUUsOPQm5j+9aTa6/l0jRtom/XypdLv4PPXMipjDtOGXUWElhsVERERiZiI/XYyaNAgbrvtNm677TbcbjcrV67kjTfeYNmyZbz11lvYbDbOOecc3n///UgdUkQSRFpaCtNnTqRobXGX202fOZG0tJSo9sVmszFpyEVMGnIRABW1ezH9XwDA5/eRyntkJjfj96zl3+W/4IB7HHN/lMKGl7xhH7urM5ZaR09ERES6Kyp/gk5OTmbBggUsWLAA0zRZt26d9Tk+EZH2DMNgad5T/XLB8CznIKtt4mdA+g/Y07CSNNtWsp2NjEot5fab4Pab4LXPbubW017v9bG6OovZ1Tp6WmxdREREgvV4kOd2u0lOTu729oZhMHPmTGbOnMkvf/nLnh5ORI4RhmH0+5knHTYnF468CbgJv9/Pfw6tZVv16zQ1/psxg2oxjMOXpLu8zby6624GJ11AcrYNd52/w3129yxmZ+vo9XSxdREREUl8PR7kjRgxgptvvpmvfvWrTJkyJRp9EhHpU12dCevsEkqbzcYZg89jbMoUxuXMJvuEYbz93nes+9cfWMaI5HXAOp75t40d+wayfN+vOXv4Fzgh81Rru6OdxTzaOno9XWw9KUmfIRQREUl0PX63d7lcPPnkkzz11FNMmjSJO+64g1tuuYUBAwZEoXsiItHV3TNhR1P3qY/hqWOsr4eknMDmhgtIZjMDkxo4c/RB4AV2HXyBD/YN4bgB/8XMnKuAyJ3F1GLrIiIiAr1YQqGyspKnnnqKs88+my1btvCtb32LkSNHcvPNN7Nq1apo9FFEJGq6eyasp5O+nDZwBjePf4a5I97jv742nJffHsV+1yj8psGQ5GqynEOsbTdUrWDlnr9Q767pVYaAwGLrwf/aD/pEREQk8fX4TF5mZiZf//rX+frXv85//vMfnn32Wf7xj3/w0ksvsXjxYo4//ni++tWv8pWvfIXjjjsuGn0WEYmKjs6EBfR24hKbzcZnhR4+K/Tw68pVNNsOsqn6LU4bcI61zY6a/2NkSjHr9v6aA60nkJZ0HmcNuZaxWaf3OouIiIgcu3p8Ji/YhAkT+PWvf82ePXt47bXXuOKKK9i7dy8//elPGTt2LJdffjmvvPIKHo8nUv0VEelURwupB/6ZpnnUx3d0JizwL1IzUw5NHc28476OzXb4x6/TfjyHWrNx2PyMSCkj2/YCZTXX8q+PZrKk/HsYtqP3PZpM0+z0ee3J8ysiIiJ9IyKfwLfb7VxzzTVcc801VFZW8re//Y3nnnuOFStWkJeXx+DBg6mqqorEoUREOtXbJQhi7YaT/xe/309p/Wa2H1yC27uWoUl7GJJUQ5V7K6b/cJ/XVi7l1AEzGZg8tE/61tPZO/vj8ysiInKsifg0azk5OXz/+9/nsssu45577mHNmjUcPHgw0ocREQHCX4Kgv7DZbJwyYCqnDJgKQLWrgg1VS8hy2oDnAaj31NDQ9F9saDapch9PStK5nJJ+RdjHPtrsoj2ZvbO/Pr8iIiLHkogO8hoaGvjnP//JX/7yFzZu3IhpmqSnp/PFL34xkocREbGEuwRBfzUkZQSXHX83TU0tBAZ5FS27qPNmMzjpECNSPgU+parlRX67LoOt2zP5T10h09Jn9+g4PZldVLN3ioiIxIeIDPJWr17Ns88+y+uvv05LS9tnM8455xzuuOMObrjhBjIyMiJxGBGRDnV3CYKO1rzrbB28/uiUrGlMGbGOj+u2UnzwdVpa1zIs+VOGZzQy/JxGPmksYBptg7x6dw2tpptUBnS5z57MLjpk6EBdjikiIhIHej3I27NnD8899xx//etf+eSTTzBNk6FDh/KNb3yDO+64g9NOOy2S/QzLzp07ueGGG0K+fvHFF7nmmmti1ykR6XOJcsbppOxJnJQ9CYB9tXu497EvM+ViD9dOvdra5r2KZ0kx/48q9ygW/jKF9a/68Pv9Xe43GrOLioiISN/r8SBv8eLFPPvss+Tn5+Pz+bDZbMybN4877riDq6++Gocj4h/zC9spp5zCli1bAGhsbGTMmDHMmTMntp0SkT7R3c/s9XQdvI7E4kxhtnMwa55sYc2TcH/l4T+u1beWkJ5kMiJlD1+8Ar54BeRVXITLnERjxgm4fZfgdDpD9hWYXVRERETiW49HZDfddBMAY8eO5fbbb+crX/kKo0ePjnjHomXp0qXMnj2b9PT0WHdFRPrA0T6zFxCJM1X96UzhLeP/wicNH7Jh/ytUVK1g/IhDDEyqB/6Nf9z7NHhuIyMlEwCbE/xa6UZERCRh9HidvJtuuol33nmHXbt28eMf/7hPBngFBQVceeWVjBw5EsMwWLJkyRHbPPHEE4wZM4aUlBRmzJhBUVFRh/v617/+FXLppogkvsBn9rr619sBXuBM4dFE4kxhT43JPJ3LR/6An8+Dr180iCb3t9nTMoWy2hNI8w2kqamF5qYWfrrcz6Orfby592H+U/PBUS/rjDStwyciIhJZPT6T949//CMa/ehSU1MTkyZN4qtf/SrXXnvtEfcvXryYBx54gKeffpoZM2bwu9/9jnnz5rFz506GDRtmbVdfX09hYSEvvfRSX3ZfRBJYX54pDEdLjcmdU1+wvv4xlwGQPszGn1bVYjdMYAn765ew42AmLeYkjs+cx7ShC0hxpEWtX1qHT0REJPLC/gCd1+vlj3/8Iy+++CI7duygubkZr9cLwJYtW/jzn//Md77zHcaPH9/rY1x++eVcfvnlnd7/m9/8hrvuuovbb78dgKeffpq33nqLZ599lgcffNDa7o033mDu3LmkpBz9r+k1NTV4PIevX0pOTiY5ObnXGQBrf8H7lb6j5z/2ErkGSUld/zgN/FzsKW/Qc+X1ePB4HD263+m0M23Gmaz/oONBVFOVn+98YRjz7h7ClPPcDE3azQBnAwN4H4/7fV7++CVuPHFxr/t3ND1Zh6++rqHTiWHiRSJ/D8QL1SC29PzHnmrQ99xuN2632/q6pqYm6scMa5DX0tLC3LlzKSwsZMiQIWRlZdHU1GTdP3bsWJ577jkGDRrEz3/+87A725HW1lY2btzID3/4Q+s2m83GpZdeytq1a0O2/de//sXXvva1bu137NixIV/fcMMN1ucRw7Vy5cqI7Ed6R89/7KkG3ed2tVrtFStWkJyS1KP7Ae6873Ju+9qlXR4nKdmJsdOg3tbCwYztkPUhw7PKaDhwHLk7cwGoS6rAdvwL1NWfTGr9RAa5xtDqOjx47ez43c336yfvJTk5dDIYt9vD9+55vNf776/0PRB7qkFs6fmPPdWg77z44ossXtz5H0yjIaxB3mOPPcaaNWv4n//5H77//e/zs5/9jEcffdS6Pzs7mwsvvJAVK1ZEbZBXXV2Nz+cjJycn5PacnBx27NhhfV1XV0dRURGvvvpqt/ZbVlZGZmam9XWkzuStXLmSOXPmHDGrnUSfnv/YUw16rm12zt8CMG/evA4XI+/q/va6V4PrAPD5fXiP85Bsb7v6Yemnv2Cg4wAj0w/AiELqPem4PGcw9bYUil9zd+v4XeW76qoFHeYLDPI6279pmrQc5XLZ1H6yBIS+B2JPNYgtPf+xpxr0vdmzZ/PEE09YX9fU1IR1lWN3hDXIW7x4MRdffDE/+MEPADp8Ax03bhybN28O5zARkZ2dTWVlZbe3HzRoEFlZWVHpi9Pp1DdVDOn5jz3VoPscTm9Q+8jn7Wj3d6Y7NXDiBA5f3j5zxJfZcCCdete/GZL0MVnOJrKcH/C9H0Drd+1sa3iP8wdc2a3jd7f/R7s/Xj/Tp++B2FMNYkvPf+ypBn3H6XSSkZHRp8cMa5D32WefsXDhwi63yczMpK6uLpzDdGnIkCHY7fYjBnCVlZUMHz48ascVETnWjEwfy1Xp3we+T5OngfUH3qS8Po80tpCR5OakzLOtbZd+8v9o8VZwyoArOXPQLOy26Kyh2tzs6vZn+pqbXVoHUEREjglhvetmZmZSVVXV5Ta7du1i6NCh4RymS0lJSUydOpVVq1ZxzTXXAOD3+1m1ahX33ntv1I4rInIsS3dmctHIm2nKXsi4nEvImZjN2pUDrPtb3G8wNPkANY1vsexQGg3+MxmZPpvpw64hIyk7Kn3avntZh5d79qf1C0VERPpCWIO8c845hzfffJPa2loGDBhwxP3l5eXk5uYe9Wzf0TQ2NvLxxx9bX5eVlbFlyxYGDRrE8ccfzwMPPMCXv/xlzj77bKZPn87vfvc7mpqarNk2RUQkmgwqiw/P0ub3+0lPuY59LQUMdn5EprOZTD4A7wes2fNLDnjO5EunRP4D6Gmfr3koIiJyrOvxYujBvv/973Po0CFmz57NmjVrrCnCm5ubWbVqFfPmzcPr9fLAAw+E1ckNGzYwefJkJk+eDMADDzzA5MmTWbRoEdA28+Wvf/1rFi1axFlnncWWLVtYvnz5EZOx9ITP57P+76jt9XpD2oHFgztrezyekHZgYd9A2zTNI9pASNvv94e0A893Z22fzxfSVqbDn+1JpEzxVqeARMoUzTq1X6Kgo0wBZjczBdcgEpkCmptaqK9roLmphYuHfIMrhz/PtMHvYho/ZZ/7PGo9GThtPiuDaZq0ut1c9+sUTrk8GbfbdUSdzKBjmJ1kCtY+kz/oNefvJ99PgX0erU6xfu0l4vdTV/niPVM81SkgkTLFU52C34sTJVM81inawhrkXXDBBTz++ONs27aNCy64gMceewxou4xz7ty5fPzxxzz55JNMnTo1rE5edNFFIb8UBP799a9/tba59957+fTTT3G73XzwwQfMmDEjrGNOnjyZCRMmsGjRIkpKSgAoLi6mtLQUgM2bN1NWVgZAUVER5eXlABQWFlJRUQFAQUEB1dXVAOTn51ufTVy9ejUNDQ0A5Obm4nK58Hq95Obm4vV6cblc5Oa2TVne0NBAXl4eALW1teTn5wNts4oWFBQAUFFRQWFhIdB29rSoqAhoO+MZmPSmtLSU4uJiAEpKSiKWqba2FoC8vLx+n2nTpk1WfRMlU7zVaefOnVYNEiVTtOu0YsUK6zlbsWLFEZkaGxut+2vr6o6aae/evdb2kcoUcMa4BZw8ah4njpjDuJzZjB99GaePvpqbT3+cp25u5OIh7zA86294PznfqtPLa/6P6y/by8P/bx//PngRL+y8hdzdf+LNlUusTAHVBw92mClY+0zbt2+37tvx+esv1q+9wD67qlN/eO0l4vdTIBO0TR+fSJnipU7B37eJkine6rR161arBomSKV7q9NBDDzFhwoSwxyndYZjBfwbupZKSEp5++mk++OADampqyMrKYsaMGdxzzz2cfvrpkehnn6mvryc7O5uamhoGDhxojbTtdntI2+v1YhiG1bbZbNhstk7bgb8YLF++nDlz5pCamophGHg8HhyOtqtmvV5vSNvpdGKaptX2+/34fD6r7ff7cTgcnbZ9vra/mAfaHeUIN5PdbrfaDoejX2dqaWkhLy+P+fPnYxhGQmSKtzq5XC5WrFjB/PnzsdlsCZEp2nWqq61n/OjLAPhoz3KysjNDMjU2NnPi8LY18HZVrCQ5JanLTG63m+XLl1s1CDeTYRjdmt0SYNf+d8jISAup04Y9q1j90cOcfsIhUh1BZ9v9Ng60jmF06p188fT/tfKlpCaHZHK7PYzLmQ3A7spVpKQkhWRqbmrhpJFzAfh4Xx6ZWRkx/X6Ctl8i5s6dS2pqar9+7SXi91PgL/Nvv/229V6cCJniqU7B78WBx8V7pnirU/B7cWe/D8VbpnirU1VVFTk5OdTV1UVtNv+ITHd22mmn8fvf/77T+30+H3a7PRKH6jOB/gb3O7gdKHp3206n0zql63Q6rWm8g6eu7ahtGIbVDrwwu9vurO+RzNSbtjIpkzJ1P5MjqN3a6qU5aD241lZvyPpwhs3WrXw9ydGdTG+ufDqkX8GCJz7p6OfehEHnseBqP/bkbF7Y/C32u/JJZjMDk+oZkbIbH25r27Lm7bhaapg85FIcNic2mw232xNyvPZ9twV9bevg53p3amO329vlOzwYbW1ta6cFrcPX1Wsv8D4Q2H9/fu0l4vfT0d6L4zVTV21lUqZw88VDpniuU7SENch74okn+OY3v9nlNj6fjxtvvJGXX345nEOJiBzz+usskYZhhD3hic8NMwZfTXr6jfj9fj6q38SHB9/goqHzgLbF0Lce+gsjU97j7d0p1PkmMCztEs7IvDwCCToXr+vwiYjIsS2sz+R961vf4pVXXun0fr/fz4033shrr70WzmFERI5ZaWkpTJ858ajbTZ85kbS0lKNuFw9sNhunDjib6058lBR7WtA9dlp8SaQ7XIxM3oTD92s+PHQpi1aaXPmLFNy+js8mhqOn6/CJiIj0B2GdyTv//PO59dZbGTx4MBdffHHIfX6/n5tuuolXX301Lteraz8DUSSu/Q2exUfXM8cmU0AiZYrHOrX/3kqETNGqk9fr5Y0VT9Lc7MLn9WL/PJPP68XxeSaf10tWduYR+TrLFFyDaGcKmR0zaPYyqzbtZg+F1JAcwY+/ZtSvMZJMimvzKat7myQ2MSipltNG1DDishScRpKVaXdTMcenntbp7JrdzRRs++5lJCU5sNvtGDYbXo8Hd6uXMz8/wxqYFOxon8kLvr0/v/YS8fup/Sx6iZIp3uoUkEiZ4qlOwe/FiZIpXusUTWGdyXvzzTcZP348CxcuZMuWLdbtfr+fW265hZdffpl77rmHP/zhD+H2s89pds3+OzORZteM3zppds3eZXK73SQnO3n3vdUkJzux2w3efW816emp+P1e3l/zbwzD6FamaMyu2WWmoNkxAzOBBtep/eyhR9Sp3eyaH6wpYmbOVVyY/mOG7f9/DE39G/98YxS5bw/GZrNRVlbGhk0b2HngdlaVT+dfZTdzwbdTycix9Xp2zYC09FTWfbCWVo+b9PRU3l/zb/z+w7+wujW7ZtxkAs2uqdk1j906aXZNza7ZLfv372fmzJm4XC7WrFnD2LFjuemmm/jXv/7FN77xDZ588slI9bVPaHbN+PsrSHczaXbN2GfS7JqxzxTp2TWPlsntauXEEXOAjmfXrK9rCJk9NHtAVkimxoamw4/v5uyau+qK2VnzZdIdhydt8ZkGVe7RpDhnMnHQdYzLOrNbmVpa3CH7D5zJs/K5PYdnN93/DunpqUc9k6fZNWN/Jk+za2p2zWO5TppdM/aZ4mJ2zeHDh7NixQrOP/985s6dy+TJk3n11Ve566674m6AF8xu1+ya3cnUm7YyKZMyxTZTT3KEmykw+2Sgr+1zODpoB2cygvpr2GzW/gOZOppdc/ygyYwbsJFNB1ZSemgZZmsRI7MaGJFSDpSzobqWkwf+EQCf0dY/O/ZOcwRrX4P2+YL7Hry9ZtfsP5k0u6YyHeuZepovHjLFc52iJexBHsD48ePJzc3lkksu4bXXXuPOO+/kT3/6UyR2LSIi0mMOm5PpOfM5PeNixuXMJmficH7y10to9a/jlEGHZykt3P8KLa7/ocZzCoNSL2J6znUMSRkZw56LiIiEr0eDvEceeaTL+6dPn86WLVsYOXJkyLaGYfCTn/ykdz0UEREJU2Wxh4sG3Eda+g8AaGpqAWBvw3uMTPEw0r4dzO1sqXiCqtZROB0zOGPQ1Yx2nNHtYzR/vs+ueD+/hLW5qQWH8/BZwOB19kRERMLVo0Heww8/3K3t2g8GNcgTEZFY62idQcNmMv6ykVx0UzpnTDjEkORqhifvAfZQUfcq3tS/hbX/zv025CutsyciIpHUo0He6tWro9UPERGRiAusM1i0trjD+02/wc5cNztz3eyuXEW1/1O2VL9Oo/t9nLZDTBt8+Ezea5/dgWG0MCD1As4eeh3D044/6v67K7DOXriLyouIiEAPB3kXXnhhtPrR7wRm7In07JqgdfK0Tp7q1P57KxEyxVOdgmsQT+vkNTU2W2sFmkGzawbrKNPSvKdobGgCwGa3W+vl2ex2GuobmXTy1W2P9Xo5LnM8J2T+EK/Xix8fHnfb8e3JMDipmBS7B8z/sL3yaVa3jsBun84vXrqF44z/wZmUZO2ns/UMoW2piNmzZ5OSmkpTYzNnnnhl2/Pj91s1jtRrLzDDqWGz4fu8Nu3bTmfbpDPHyveT1smLfaaARMoUT3UKfi9OlEzxWqdo6vE6eU8++ST79u2LRl/6Fa2T13/XGNE6efFbJ62TF/tM8bxO3pknXsnJo+YxLmc2J46Yw8mj5h1xiWRHmQzD4NPPPuHTzz4hPT2Vj3eVsnffHtLTUyktPfya3LBhQ0imA5XV1n1+j8mglF+zs2omVe6h2AzISa5giOMNqhvu5NXyG7DbjaOuZ5iWnkpyShIbNq4nPT2V5pYm6xj79++P6GvPNE0uPe/LnDhiDuNyZnPyqHkdti8591YOHToEHDvfT6B18rRO3rFbJ62Tp3XyOmSz2TAMgylTpnDNNddw1VVXceaZZ0arf31O6+TF319BuptJ6+TFPpPWyYt9pnhbJ8/n83H13HtY/8G2rn50M+2cM3lz5dP4/f4eZaqva+DkUfMAKN27gozM9JAcna2Tt695NxuqXqGxdQ1DknZR57uCL570KwAONFWQt+duslJmcdbAqzg+e3yn6+QdbR3AcF57Ller1fej2VWxkozM9GPi+0nr5GmdvGO9TlonL/aZ+uU6eevXr2fJkiUsXbqUn/zkJyxatIgxY8ZYA75Zs2ZZv7zFM7td6+R1J1Nv2sqkTMoU20w9yRFupuB15FqaXdbPvsDtwfd3tE6e3W7nzXeeprnZRVcCs1P2NJM9qL92h8N6foJzBAv0a3TGSYzOeBCAJk8Drf7D2TYfepORKTuAHXx86BkK9+dg2Kdz6oAFnJo5M2T/R1sHsNs5OqlTwPbdy0hr93m/5qYW60xooB89fe05HA5rplIIrWfgUtq0tJSIZgr3+0nr5CnTsZ6pp/niIVM81ylaejzImzp1KlOnTuXRRx/lk08+4fXXX2fp0qX84Q9/4He/+x2DBg1iwYIFXH311cydO5e0tLRo9FtEROJMz2afPMwwjH49IUm6M5N0Mq2vT8qeycaqq/H7PmBY8n6GJVcCb3Kw8U3ePpRGVfr1wPw+7WNaemrEn0PTNLlyzjdYv67rs6yaOVREpO+FdcptzJgx3H///axevZrKykqee+45Zs2axSuvvMK1117LkCFDuPLKK3n22WepqqqKVJ9FRCROBGafPJrpMydaZ3zi3YlZE/niSb/ixlPe49Shy2nma+xznYbb5yDL2Uxa6zBr28ID/+KyRSkMOaXHf3ONueZm11EHeHB45lAREek7EXtXGTRoELfddhu33XYbbreblStX8sYbb7Bs2TLeeustbDYb55xzDu+//36kDikiIv2cYRgszXuq25dbJpqR6WMZmf5d4Lu0eBvZVPUOmZ7DOStdL/HlL+7ly1+EZXvmYNqmcfKAKzhr8EXYbfEz8Dva5aAiItK3ovIOkpyczIIFC1iwYAGmabJu3Trrc3wiInJs6e+XW/aVVEcG04deQS651m1Jtqnsqq5h7OBahiYfAHKpa8rlrbpUGnyTuOnk5+Lic+7RuBxURER6LyrvHKZpUlpaSnl5OYZhMHPmTH75y19aU56KiIgIXDHqR/z4Ijt3zx9Gvfdr7HOfjsvnJMPRAmZFyABv+WdP8WmD3kdFROTowhrkvfbaa9x2223W+joAn3zyCRMnTuTUU09lzJgx3HjjjX2y4F+kBfrs8/k6bHu93pC23+/vst1+MfTgxVhN08Q0zSPaQEjb7/eHtAPTcXfW9vl8IW1lCl0MPVEyxVudAhIpU7zVKbgGiZKpt3UKLFAeaHeUI1gkMgX2D1iLvdeX+7g05xvceOK/uGRMEc6kn3NC1jes/u6p30WS/3fsOngN/9p5Li+V3kdR5du4Pa4u6xSsfZ1CFqrvZabgx3dUp2D96fupo3zt63Qs/4yIdqaARMoUT3UK/vmQKJnisU7RFtYg76mnnmLLli0MHDjQuu3+++/nww8/5OKLL2bixIm8/PLLPPvss2F3tK9pMfT+u5CkFkOP3zppMfTYZ+rzxdD7eZ2CFwVuvxh6IFOwSGQKPE8A1QcPWrcFFkNPsadxvGc6tk+GW5k27FjDfvdo/KbBkOSDDHPm0djyHfI+nc4LO2/ijS3PdlinYEfUKejYgUXne5IpsLh94PEd1SlYf/p+Ai2GrsXQj906aTF0LYZ+VKNGjeLyyy/n//7v/4C24IMHD+a6667jxRdfxOPxMHnyZDIzM1m7dm3EOh1NWgw9fhaS7GkmLYYe+0xaDD32mfp6MfT+XqfgxdC3lr5BRkYahs2Gz3t4MfTA5CHBi6H3NhOEtxh6VdNeNhx4jRp3AQMdJaTa2/6K3MI9XHbcvdjtdvY1fEKTt5aRSaeELOSenOwMqZOrxR1y7J4uht7Y2MyJwy+1Hp+ckhRSJ7fbE3L81NTkfvH9pMXQtRj6sV4nLYYe+0z9cjH0YDU1NQwfPtz6+v3338fr9XLTTTcBbQsHzpkzh3/84x/h9TIGAosURnLRxcApXS2GrkzKpEyxzNSTHPGSqbd1Cl4MfdLJV3M04WYKvA8EnqeeLoY+LH0U89PvA+7D7XOx8cByPqlfwZzjrrO2K6z6PwbZX2ZL60BueyaFDW8ZeHytpDsOT4zicDgwbJ6QY/cmU/Dj29cssBh6QH/5furqvVg/I5TpWMikxdD7V6ZoCWuQl5WVxcGgyz1Wr16NzWZj1qxZ1m1Op5OmpqZwDiMiIhIVgXX8itYWd7ldf1zHL9mewrnDr+Hc4deE3N7qO4DPZjA46RCXz4TLZ8L7VbOorTiNIakXceHIW0l1ZMSm0yIi0ifCGuSdeuqpvPnmm/z85z/Hbrfzz3/+k6lTp4Z8Ru/TTz8lJycn7I6KiIhEWiKu43fz+D9R466kcO8rbC19iTPH1ZLhdJPm2EKrdxt+80vWtunDbDRVHTnBjIiIxLewBnnf+ta3+MIXvsDo0aOtM3Y///nPQ7ZZt24dU6ZMCauTIiIi0ZKI6/gNSs7h4pyvcsfEf2BzZvKv4h+wp2UlflpJd2Za2z3yRgt2m8nr5d9m3ID5TB16Gcn2/nPG0jTNhBqAi4j0lbAGeddddx1PPPEEf/nLXwC48cYb+cpXvmLd/95771FfX89ll10WVidFRESkd/wemDJoHrPSrwm5vbKlnGHpzThsfuA9XK73eOeTH3PIeyqDUy5mWs5ChqSMjEmfoW2Ad+Wcb7B+3bYut5s+cyJL857SQE9EJEhYg7zPPvuMW265hbvvvrvD+y+88EI+/fRTa4pSERER6R9yUo9j9tmDOOv6ZK69YxgDnCWkO1yk2reBuY3ln67hS6f8Ezi8vmLwxD3R1tzsOuoAD6BobTHNza6EOxsrIhKOsH5ajx07lt///vddbvPHP/6RsWPHhnMYERERiYLmapPCp11ce/xzXD5uA5lpf6DKM4/q1sHkpM+2ttt68F1e/3gG//zoLt6veAWXr7lP+7l99zJ2V64K+bd997I+7YOISDwJa5AXWAX+aNvEo8DaGz6fr8O21+sNaQf+ytlZO7BOXqAdeF4CbdM0j2gDIW2/3x/SDqy51Fnb5/OFtJXJa9U3kTLFW50CEilTvNUpuAaJkime6hTYJ4AZVA8zCpmCtc/U/tgOm5OzBl7CDSf9ni+OL+SCoV+ycuw89CYDk+oZnlRAq/tH5H8yndc+u4lLvp9K9gl2zE7qFKyjTKZp0lDfSEN9I01NLSHtxobDM3MnJztJSUkiPT2V5GQnqanJpLU7c9eTOnVUs/Z1SsTXXn/JFJBImeKpTsE/HxIlUzzWKdqift3Fnj17yMzMPPqG/czkyZOZMGECixYtoqSkBIDi4mJKS0sB2Lx5M2VlZQAUFRVRXl4OQGFhIRUVFQAUFBRQXV0NQH5+PnV1dUDbUhMNDQ1A26K4LpcLr9dLbm4uXq8Xl8tFbm4u0LbAfF5eHgC1tbXk5+cDUF1dTUFBAQAVFRUUFhYCUF5eTlFREQBlZWVs3rwZgNLSUoqL26YILykpiVimwKW4eXl5/T7Tpk2brPomSqZ4q9POnTutGiRKpnir0969e60aJEqmeKtTYJ8A1UHLEO3fvz/imYIdkSno2LWfvz8FZ1q+fLmVybZzOl7bD9jrOpsGTwopdg+jUku468t7eHJZFUUVeR3WKVj7TJs2beLKOd/gpJFzOWnkXMblzA5pTzzpKuuxW7du7bBOwXpSJ4CVK1cec6+9/pApcJxEyhRvddq6datVg0TJFC91euihh5gwYQIzZswg2gyzh6faHnnkEav98MMPc9FFF3HRRRcdsZ3P56O8vJyXXnqJc845x3oS+rv6+nqys7Opqalh4MCB1kjbbreHtL1eL4ZhWO3AYoedtQN/MVi+fDlz5swhNTUVwzDweDzW4oxerzek7XQ6MU3Tavv9fnw+n9X2+/04HI5O2z6fD9M0rXZHOcLNZLfbrbbD4ejXmVpaWsjLy2P+/PkYhpEQmeKtTi6XixUrVjB//vyQxUHjOVO81cntdrN8+XKrBomQKZ7qBG2/RMydO5fU1FQaG5o4ccQcAHZVrCQlNTlimVyuVsbltF1yubtyFcnJzpBMrhZ3yLEzMtO7lcnd6uY/9WvYUbOU1pZCBqe3cP7QAjLSM3E6nSwu/T4efzU5yRdz+4w/4Wky2V25itTU5JBMTY3NnDxqHkczfeZEXsv9IzabLaROLS3uTvN1VSfTNHn77bet9+Jj5bXXXzIFvxcHHhfvmeKtTsHvxZ39PhRvmeKtTlVVVeTk5FBXV0dWVtZRfw72Ro8nXnn44YettmEYvPvuu7z77rudbj9y5Eh++ctf9qZvMRVYib6z1ekDRe9u2+l0Wqd0nU6nNQuY0+kM2aZ92zAMqx14YXa33VnfI5mpN21lUiZlim2mnuSIl0zxUqfA+0Bgn0ZQPQybzbo9UpmCtc9k2A5fPhroR3cypSSnMGXobE5JO5dxObNJG5LM9g9TrV90kljN0OQGoJBnCu38p3wgeRV/YMaILzIyfazVX3tQf7bvXnbE5ZcB7ZdI6E6+rnJ09V6cyK89ZVKm3uaLh0zxXKdo6fEgb/Xq1UDbNamXXHIJX/nKV/jyl798xHZ2u51BgwZx6qmnhvxSISIiIomjuTros334GZD2HfY2riTDto0sZxOTx1QDz7LjwLPkl0/gS6e+fsQ+0tJTNTumiEgE9XiQd+GFF1rtn/70p1x88cVccMEFEe2UiIiIxB+7zcHFo74EfImGhibm3nIFZ19t49zzveQkV2LYBlnbenwu7nzeydZ37TR7G0hHgzwRkUgJa528n/70p5Hqh4iIiCQQm83G7vxWdufD/1Suoo79nGQevkR0a10+s6fuZ/ZU2HjwIqorTiQzZRZTh1zL6IyTYthzEZH4F9Z1lLNmzeKZZ57RYuciIiLSpZHpYzk+Y7z1dZYjh/zNw6lxpZJk8zEy5SMy+QsfVV/B4p2zKNy/JHadFRGJc2EN8tatW8c3vvENRowYwfXXX88bb7wRsg6QiIiISEfGZ03lmVs9fPPsVLKSnuag9xoq3SPwmzA0uYpU++EZ57ZUr2bVnudoaK2NXYdFROJIWJdr7tu3j3/+85/8/e9/57XXXuP1119n4MCB3HDDDdxyyy2ce+65keqniIiIJCSD07LP4eyRFwOwv/kzNh14g4mDD3/ef/vB/2Nk8gYK9/yK6taxpCefz6np82PVYRGRfi+sM3lDhw7l29/+Nhs2bOA///kPDz74IJmZmTz11FPMmjWLk046iZ/97Gd8/PHHkeqviIiIxJnmphaa2v1rbmrpcNvhaccz/4T7sNuCpju3DafWk4nT5mdEyi6yjOfZ13wD/6+wlVueSqauoe6I/Xf2r7mpBber1epTD5cLFhGJC2GdyQt26qmn8otf/IJf/OIXvPfee7zwwgu88sorPPLIIzz66KPWIrAiIiJybDlj3IKwHn/Dyf+L3+9nZ916PqxZSqt3HcOS9jI6qwHbJJh80rXWtqddlcynha00Vx9t8PZboG2x9aV5T4WsxSciEu8iNsgLduGFF3LccccxZMgQfvOb32iAJyIicoxJS0th+syJFK0t7nK76TMnkpaWctT92Ww2Ths4g9MGzgDgQMtefvaX71BZUQO0nRV0Zhj816OV2A2TjyoHsHldKutf9lFZ3Pl8AUVri2ludmmdPhFJKBEd5NXU1LB48WJeeOEF1q1bB0BWVhZf+MIXInmYPuHz+UL+t9vtIW2v14thGFY7sKJ9Z22Px4Pf37ZgrMfjweFwYBiG1Qbwer0hbafTiWmaVtvv9+Pz+ay23+/H4XB02vb5fJimabU7yhFuJrvdbrXjIVNAImWKxzq1/95KhEzxVKfgGiRKpnipU/DPIKfTiRlUD9Pvtx4biUzttc/U/tjQ8/en4Md7PJ4j6rRk+RM0NTZjdzgwP7/d7nDg/zyT3eEgObntMT2t09DUUfz26//E7WrFeMiGz+tld9NWdjU+wMCkOiaMqGHCQrhlIVS7B9FqnsXJ6ddySsZ08vLyOOecc5l2etvvJ16PB0hN6Ndef8oUkEiZ4qlOwe/FiZIpXusUTWF9Jg+gtbWVV155hWuuuYaRI0fyzW9+kw0bNrBgwQIWL17M/v37+fOf/xyJvvapyZMnM2HCBBYtWkRJSQkAxcXFlJaWArB582bKysoAKCoqory8HIDCwkIqKioAKCgooLq6GoD8/Hzq6uoAWL16NQ0NDQDk5ubicrnwer3k5ubi9XpxuVzk5uYC0NDQQF5eHgC1tbXk5+cDUF1dTUFBAQAVFRUUFhYCUF5eTlFREQBlZWVs3rwZgNLSUoqL2/6aWlJSErFMgeUz8vLy+n2mTZs2WfVNlEzxVqedO3daNUiUTPFWp71791o1SJRM8VanwD4Bqg8etG7bv39/xDMFOyJT0LFrP39/6kmmxsbGkMd3VKf9+/ezZetm0tNTOVhTzfYPt5Genkpl1X52frSD9PRUPv74417Xae3atdTV15KensqGjesZ6RjPdeOLqNz1PapcC9nvPg6faTAkuYaRKflsKP8XNrtBckoS/y7KJ31Y269BK1asOKJOifja6w+ZAsdJpEzxVqetW7daNUiUTPFSp4ceeogJEyYwY8YMos0ww/jE8Z133smrr75KfX09pmkyffp0br31Vm688UYGDx4cyX72mfr6erKzs6mpqWHgwIERP5O3fPly5syZQ2pqqv4K0seZWlpayMvLY/78+RiGkRCZ4q1OLpeLFStWMH/+fGw2W0Jkirc6ud1uli9fbtUgETLFU52g7ZeIuXPnkpqaSmNDEyeOmAPAroqVpKQmRyyTy9XKuJzZAOyuXEVysjMkk6vFbR172643Sc9Iw+vxYP88U6AN4PN6Q9oOp5OmphbO/PyzdrsqVpKcktQv61TZuIfNNW9S3fIuUwZ/l5OyzuLtt9/Gd+pHZCb9hdLKgQzIupAzBy9kbPqZVj7z89deVnYmpmn2q0zdfe05HA6amlqOyBRo+/1+MrMyOs0XjUzB78WBx+lnRN9mCn4v7uz3oXjLFG91qqqqIicnh7q6OrKyDi8XE0lhXa757LPPMmbMGO677z5uvfVWTj755Ej1K+bsdnvI/+3bgaJ3t+10Oq01BJ1Op3UpjdPpDNmmfdswDKsdeGF2t91Z3yOZqTdtZVImZYptpp7kiJdM8VKnwPtAYJ9GUD0Mm826PVKZgrXPZNgOf07tzBOv7PAx3WXYbD2qWV/WKSdjNJdl3A3cDWDVoMm/gwGGyanDa4DXOdDyOtsOZLB1eyYbcm38500Xfs/hiVk6qk1/fu05HA6unPMN1q/bRle6ytffMh0LPyP6IlNP88VDpniuU7SENcgrKCjg/PPPj1RfREREpI90d2KUo+nuxCn9zQ0nPsVt936djAmfMnlGC+OHH2J4RiPDz2lk3jlw9+Zh1Jb5KFpbTH1jA9mZ0flre7Q0N7uOOsADTTwjkqjCGuRpgCciIhKfDMNgad5TNDe7wtpPWlpKXC4/YBgGL/7pWSt/necgm2ve5KD7PWzU8/6qF6ylH97cewM2w0OS4xzOGHQ147OnhpwV7++2715GWrtBXHNTS9hLW4hI/xX27Jper5c//vGPvPjii+zYsYPm5mbrcwdbtmzhz3/+M9/5zncYP3582J0VERGRyDEM45g+gxOcP53RjBxw+LLOps8Xa08eYDAs6RMcNj9Qzr66l/nwQBYuzuKEzHmcPWw+Kfa0GCXonrT01GO6ziLHorD+DNXS0sLFF1/M9773PT799FOysrJCplQeO3Yszz33HH/729/C7qiIiIhIX3PXmhyX8U/q/bdR4RqH129jYFI9I5IKaHX/iFdKb4p1F0VEjhDWIO+xxx5jzZo1/Pd//zf79+/nzjvvDLk/OzubCy+80JqaWERERCTeHJ9+KteM+xG3nPo254wuxO94kH3uaTR6U8lIPjwVennjR7y482Je/viHfFhTiN/v72Kvic80TZqaWkL+NTe14Ha10vz512FM8i4iXQjrcs3Fixdz8cUX84Mf/ACgw2vyx40bZ61zISIiIhLPspIGcuno24Hb8fm9ePyt1n2bDrxGTvI+4DUq61/jo4OZtJiTGJ05l+lDryTF0b8v64wk0zSPMrvnb4HDs3vG4+c6RfqzsM7kffbZZ5x99tldbpOZmWktAi4iIiKSKOw2R8jA7eyhX6DBvJ0K18l4/HaynQ0MT3ofr3sRqz+dxgeVy2LY277V09k9RSSywjqTl5mZSVVVVZfb7Nq1i6FDh4ZzGBEREZF+b1TGiYzKeBCAhtZa1h94k32NK8m0bSPN0cLJA6ZZ27716e9p8uzh1IFXcsbA8+Nqts6eCp7d0+vxsGLFCmbNupCzxl8T246JJLCwBnnnnHMOb775JrW1tQwYMOCI+8vLy8nNzWXhwoXhHEZEREQkrmQmDeCSUbcCt+L3+/m4YQuDknOs++tdr5OTXEF1w1KW1qTT5J/IqIw5TBt2FenOzNh1PAqCZ/f0eBwkpyTF5dqKIvEkrD8bff/73+fQoUPMnj2bNWvWWEsnNDc3s2rVKubNm4fX6+WBBx6ISGdFRERE4o3NZmN89pSQ2zKTr2Sf6xRa/Q6ynE2MSF6L3/MI/y6fwQs7b4hRT0UkUYR1Ju+CCy7g8ccf59vf/jYXXHCBdXtmZttfoOx2O08++SRTp04Nr5ciIiIiCWTBmO8C36XJ08D6A2+yt2ElabZisp2NYLaGbPvq7ocZl3kBEwdfgN0W9hLHInIMCPsnxd13381FF13E008/zQcffEBNTQ1ZWVnMmDGDe+65h9NPPz0S/RQREUlozZ8vvt2RtLSUTmcfNE2zw4krutqfREZnz32wrmoHkO7M5KKRNwM34/f7+fBQIUODrtbcWbuRgbYXOdT0Istq02jwn8HI9EuZNuxqMpMGRCaIiCSciPw56LTTTuP3v/99JHYlIiJyTDpj3IJO7+tsmvmjT1Mv0dLd574nSwTYbDbOHHx+yG1efyv7XBMY7PyITGczmRSBt4jCPf9Ddes4xg64i3OHXxNOFBFJQIk7lVOYfD6f9X9Hba/XG9IOLHjaWdvj8YS0A4t/BtqmaR7RBkLafr8/pB34DGRnbZ/PF9JWJq9V30TKFG91CkikTPFWp+AaJEqmeKpTYJ8AKSlJTDvnTI4mMM18+xyNDU1HHWRMO+dM0tJSVKej5GufyQz6XjE7yNGTJQIaG5p6nem0ATP40qmvc/7IQhzOR6hwz6LWk4nT5mdEysc0t9ZaOUprtrKhKg+vz4s3+PXWQZ2Oli/cOvmC3nN97TJ15Fh57fWHTMHvxYmSKR7rFG0a5HVi8uTJTJgwgUWLFlFSUgJAcXExpaWlAGzevJmysjIAioqKKC8vB6CwsJCKigoACgoKqK6uBiA/P99aL3D16tU0NDQAkJubi8vlwuv1kpubi9frxeVykZubC0BDQwN5eXkA1NbWkp+fD0B1dTUFBQUAVFRUUFhYCLTNaFpUVARAWVmZtRB9aWkpxcXFAJSUlEQsU21tLQB5eXn9PtOmTZus+iZKpnir086dO60aJEqmeKvT3r17rRokSqZ4q1NgnwAHDx7k/h9+kd2Vq1iz+e/8/bVH2F25ivfWP8eLb/yC7btD11Vrn2lH0PfUkrzfsbtyFa/k/op31j7D7spVvPjGL3j8/36IYRiqU1AmgJUrV3adKWiN3+qDBzvMFLB99zLeWfsMr+T+it2Vq1j+76d4aen/WPcH6hROpsKCdZyROodbTvk/knb+lCzn0xz0XcehLalWnVaW/j/qm+8jt2wqL358KzO/nkLqIINVq1YdWaeDB63+7d+/P+J12rBhg7X/dR98YGVq/9wFHCuvvf6QaevWrdbzniiZ4qVODz30EBMmTGDGjBlEm2EGhq7dcPPNN3P99ddz7bXX9upg4T6+L9TX15OdnU1NTQ0DBw60Rtp2uz2k7fV6MQzDattsNmw2W6ftwF8Mli9fzpw5c0hNTcUwDDweDw5H21WzXq83pO10OjFN02r7/X58Pp/V9vv9OByOTts+nw/TNK12RznCzWS32622w+Ho15laWlrIy8tj/vz5GIaREJnirU4ul4sVK1Ywf/58a02oeM8Ub3Vyu90sX77cqkEiZIqnOkHbLxFz584lNTX1qJncbg/jcmYDsLtyFSkpSSE5mptaOGnkXAA+3pdHZlaG6nSUTKZp8vbbb1vvxZ1lamxo4sQRcwDYVbGSlNTko9YmOFNHtYn2a+/Fj+5jgONdUuyHz+J5/DYOuE8gPfk85o7+DklGSrfyhVun+roGTh41D4DSvSvIyEzHZrNZ78UXXXgx40dfZj1/SUmOhH/t9ZdMwe/Fnf0+FG+Z4q1OVVVV5OTkUFdXR1ZWFtHQo8/kvfTSS5x66qm9HqSF+/i+ZLfbQ/5v3w4Uvbttp9NpndJ1Op3WtflOpzNkm/ZtwzCsduCF2d12Z32PZKbetJVJmZQptpl6kiNeMsVLnQLvA4F9Hi2T2334l/WOctiCvg60Vaeu+97Ve3FwJiPoe8Ww2Y6oWW9qE+3X3s2nPI7L18zGA8spq30bh38jw9KaGJlaRpO3gmTHgzhsbdt/1rwDw25i+owO83WU6Wjt4P7a27UD+wzeJtix8NrrL5l6mi8eMsVznaKlxxOvbNmyhb/97W/R6IuIiIiIhCHFnsZ5w6/lrMzLGZdzCaOnZfKDP52HzY41wPP7/XxUfyd/3uBl266BrK58lvNGfZGByUNj3HsRiZQeD/KWLFnCG2+80eMD9eCqUBEREREJm8Ge9R4WjPoJ6emp1q3lTTtxGF7SHK3MPLUS+AMb9v2RKvfxpCady1lDrmNc1tEnBBKR/qtHg7znnnsu7AOeddZZYe9DREREpK90tOZgJNYhjMQ6e71xQuZpZJvvc+03F3L2fD/TproYnHSIESmfAp+ybn8F47L+hGma1Dc2YJomTntS1PoX6TUiI9k3kXjVo0Hel7/85Wj1Q0RERKRf6moNw96Kxjp7PeG0J/Hh6y4+fB1+WrmaCu9HFB98nZbWQsYNvMLqn2fsR9z3XwfYVjaQTe8mseklN42Vh5dfiET/orVGZLSeO5F4EJHF0EVEREQSSVpaCtNnTqRobXGX202fOZG0tJQe778n6+w1N7tCLreMhpOyJ3FS9iTr66amFtav28ZtdySR5vAw4+QqZpwMvjsNdh0YwOaiNDa85qNo7dZe9S+1m89vZ/m78/z11XMn0h9pkCciIiLSjmEYLM17qk8up9y+exlp7QcxTS1ROYPYU3//hpvLN/6CCu9qnMZmhiQfZPywQ4xfcIgbFsB/fW14r/Z7tOe3J/nbP3/95bkTiSUN8kREREQ6YBhGn5wFSktP7bdnm0yfwYwRV5Cefj0AZfUfsqX6dZpa1+DwVfJZYau17T92fhXTrGNQ6kWcnjH/qPuO1PPbn58/kVjRIE9EREREumVs1umMzTqdpqYWTho927rd43OT7VhPmr0VzO2U1j/Bo6uz2bIxnZ316zkr9fyQdTpFJLr03SYiIiIiPeYPWg/eZtgZkfkbqjyXccA9BJthctLQWq6/bC+17rt48aOrYtdRkWNQxAd5Pp+PDRs20NraevSNRURERCTu2W0Opg6bw40n/54bTlnDqPSX+du/RrGtfDAevw27/SRrW7etmZd2fZFln/yW/c2fxbDXIokr4pdr2u12zjvvPLZt28b48eMjvXsRERER6edGp53M24+4eBvYtnc19uTDk9PUZGxjfMp/gP+wvfJpVreOwG6fwWkDr+T0gefqsk6RCIjKZ/ImTZpEVVWVBnkiIiIREq0FuSX6YrGYen96baQ7sklPOTwxSprreKpa54O5kWHJleQkVwBLONCwhDdqMhiW+SPOyrw8qn2K1UL0x4pEf37jIV9UBnk/+tGPePDBB1myZAlDhgyJxiFERESOKZoSPn7FcjH1/ii7dQTzx96B0+lkb+MuNla/Rr3r3wxJ+phsZyPD0w5f2nnWjSmsqPgd54y4kZHpYyNy/FgvRJ/oEv35jZd8URnkLVy4ELvdzimnnMIXvvAFLrzwQs466yxOPfXUuCukiIhIrER7QW6Jnv6ymHp/f22MyjiRURnfB75Pk6eBLQfzODFrIk2fn4mc+yU3Axx/ZceBv1KwJwfDNo1TBi5gXPK0Xh+zvy1En2gS/fmNl3xRGeTt2LGDrVu3UlxczNatW/nhD3/IZ599RmpqKmeccQYffPBBNA4rIiKSUPpyQW6JrFgvph7J/feVdGcm5w2/LuS2HduSGTl8OEOT9zMsuRJYRk3jMj49lMbXX8jiz7eFN9Fff16IPhEk+vPbn/P1eJD34YcfMmHChC5/YIwfP57x48fzhS98wbqtrq7OGviJiIhI9/TVgtwSeVpMPXxLf+jid19dRT1VbDzwGrWu9xjsLCXT2czoUUmYfru17Tt7nuOUAecxyDiu2/tP5OeuP0j057c/5+vxIG/ixIn87Gc/48c//nGPHpednc0FF1zABRdc0NNDioiIiMgxbET6CSxIvx+4H5e3mTV7l/Dqn58B3ABUuyrA80tKq00OuIdy0+PJbHwTfH5fTPstEis9nqPWNE38fn/IbX/+85/50pe+FLFOiYiIiIh0JMWRxjlDFrLlRbd124GWzzjQOhK/CUOTD3DVRfv42f/uY3Xl+byw80usr1oRwx6L9L2ILERSUVHBiy++2OF9//3f/82MGTMicRgRERERkSOcNnAGN52Sz8ScVTR4v86GXUNp8TrIdLQwMnk9+5sPT5RR497PsDOiMi2FSL8R9dUmW1tb2bBhQ7QPE3E+n8/6v6O21+sNaQfObnbW9ng8IW3TNEPapmke0QZC2n6/P6Tt9Xq7bPt8vpC2Mnmt+iZSpnirU0AiZYq3OgXXIFEyxVOdAvtMpEzxVqeO8vXHTME6yxTod0/rZAYdw+wkU7D2OfxBP8/9HWTyBe3D1y7f0TJ1lS/wtTf4+yko36Ck4Vw64m7+92o/d83IBtvPqHBfyMSBV1l9X3vgOX7/UiW/LmxlyWffZX3V27g9Lqvvwdl8Cfj9FPxeHI1MndWmJ6+9WP+M6KpOneUL9LezfO0zRVvUB3nxavLkyUyYMIFFixZRUlICQHFxMaWlpQBs3ryZsrIyAIqKiigvLwegsLCQiooKAAoKCqiurgYgPz+furo6AFavXk1DQwMAubm5uFwuvF4vubm5eL1eXC4Xubm5ADQ0NJCXlwdAbW0t+fn5AFRXV1NQUAC0nUktLCwEoLy8nKKiIgDKysrYvHkzAKWlpdakNyUlJRHLVFtbC0BeXl6/z7Rp0yarvomSKd7qtHPnTqsGiZIp3uq0d+9eqwaJkine6hTYZyJlirc6AaxcubLfZwrWPlNjY6N1n7s3dTp40Hr8/v37O8wUrH2mHUE/zz/eteuITMF/4F/3wQdWpsBxOsoUXCe36/CspL4OMq1atcq6/7333mvLFFQnAJ8bZgy+mvNTfsTeD9teG2VlZdQ27sZnGozKamBE6ioamr9D3qfTef4/XySv/Bk2bTvc961btybc99PWrVutfNHItGLF4UtjA3Xq6Wsv1j8juqrT0V57+/fvt+7fs2dPSKaHHnqICRMm9M1VjmYPGYZh/uxnPwu57eGHHzZtNluH23d1X39UV1dnAmZNTY1pmqbp9XpNr9d7RNvj8YS0fT5fl+3W1lbT5XKZS5YsMZuamky/32/d7vf7Tb/ff0TbNM2Qts/nC2l7PJ4u216vN6TdUY5wMwW3+3um5uZmc8mSJWZra2vCZIq3OrW0tFg1SJRM8VanwM+hQA0SIVM81am1tdVcsmSJ2dzcnDCZ4q1Obrc75L24v2ZqbGw2h6XPNIelzzSrKg+atYfqzIb6RrOxsdmsPVRnVlYetO5vaGjqcZ0a6hutx1furzbrauvNxsZms6G+0ayrrTergvbf2Nh8RI76ugbr/vq6hiMy1dXWW/fX1dZb+YLfi7uqU0NDU4f5AplqD9VZ99ceqgupR/vnLpCpsbHZrK9rMCv2VZnjTjrP/MKPLjaf/8+15rKPTzdXlY03V5WNN1fsOtXcU/2Z9fjSzz4y6+sazMbGZrOutt6qQV1tfYf166vvJ6/XG5IpuB3ob3A9OnsvjsbPiM5q09Frr6G+MSY/I9xud8j3U0NDU0i7oaHJdLvdHdapq9deV/mCM1VWVpqAWVfX9vho0AXJnbDb7SH/t287HI4etZ1Op3Wq1+l0WktQOJ3OkG3atw3DsNo2mw2bzdbtdmd9j2Sm3rSVSZmUKbaZepIjXjLFS50C7wOBfSZCpu7m6C+Zunov7m+ZAo625pZhGCF9704mI+hnwZknXtnl/jvKYQv6OtAO7ru9XTtw3O7WqbX18GWd7fMZhoEjaHtHu3xu9+HL6bp67t77nYvnfvgCjhSDjQeW80n9CkyzmQEpQ6xt3j94M85aP1u2Z7Ix10bJMhf+z3c/feZEluY91effT6ZpcvW8e466IHegfxDe67CnmbqqDYS+9gybrcOfh9H8GWGaJtfOv6/bz19PXntd5eus79HSq0HeE088wZYtWzj77LM5++yzrdOdIiIiIhK+tLQUps+cSNHartcXnj5zImlpKf1u/7HU02yGYXDu8Gs4d/g1QNsgYPrMiZR8+iEjsw7hsPkZMbORy2dC0yIn28sGsum9JDa9tJ3mZlefr5PW3Ow66gAFoGhtcUz6198dK89fjwd5kydP5sMPP2TJkiUsWbIkZFH0G264gYkTJzJx4kQmTZrE8ccfH9HOioiIiBwLDMNgad5TNDe7utwuMEjpb/uPpXCzBT++1n2AzbVvcsj9HgOdO0h3upkxvooZ4+GDC4dZj/H7/SFXSvSV7buXkdZuENLc1HLUs7/SJpGfvx4P8jZu3IjH42Hbtm1s2rSJjRs3smnTJoqLi3n55Zd5+eWXrW+YrKws0tLSIt5pERERkURnGEZUzyJEe/+xFG62wOPT049n1KBvAt/E43OzqfodSg8tA08Rm95LgvPati+p/YCS6m/TymTGZF3O1KGXkWyP/hnQtPTUhK1hX0jk569Xl2s6nU6mTJnClClTuPPOO4G2qUA//PBDNm7caA38tm7dSl1dXdz9BUhEREREJJjTnsyMnCs4I+MSxuXMBprhobb7/lOzlMFJdcC7uFzv8s4nP+aQ91QGpVzI9JzrGJIyMpZdl2NQxCZesdvt1qWat99+O9B26rqkpISNGzdG6jAiIiIiIv3A4ZMYc4/7LkUHTqKyaRVZ9g/JcLhItW8DcxtbKp5gQPrvOXvYvBj2VY41ERnktbS0UF9fz9ChQ0OuR7bZbJx++umcfvrpkTiMiIiIiEi/k508hDmj7wDuwOv3sKU6n4/rcrH5N5DpqOWMQbOsbV/Z9WPc3v0cnzWPaUOvIMWhjzZJ5IU1yHvyySd5/PHHrQWObTYbw4cP59xzz+X6669n4cKFHU4FLCIiIiKSiBw2J2cPm2eduat2VYQO5HwrGZFci8f9b1Z/+jAHPeMZkHoBZw+9juFpmrRQIqPX0wDdfffd3HfffRw6dIirr76aG2+8kbFjx7J3715efvllbrzxRk477TTef//9SPZXRERERCRuDEkZYbX9fj+D0u5hn3s6Dd5Uku1eRqb8hzTzabZXzuGFHdfErqOSUHo1yNu5cyd/+tOfWLhwIWVlZbz22mvcfffdVFVV8Zvf/IYPP/yQH//4xxw6dIg5c+ZQUFAQ6X6LiIiIiMQVm83GJaO/zJdO+TsLxm1gYPpTHPBewQH3MGwGYGRa2/r8Xv750d28t+9FWryNseu0xKVeXUu5bt06DMPgV7/6FSkpbdPDPvjgg/z3f/83d999NwA/+9nPuPfee7nqqqu455572L59e+R6LSIiIiISx+w2B5OHXsLkoZcAsKfxY8b63db9m6tXMTwpH19rPu999nOqW08kO+UCJmRcEasuSxzp1Zk8t7vtBTh69Gjrts2bNzN9+vSQ7YYOHcpLL71ESUkJe/fuDaObIiIiIiKJa3TGSYzNOjxZYYZzEBXuc6j3pJNk8zIyZSfpPMOnjdfw3//2MumG6K/DJ/GrV2fyxo4di2malJSUMGnSJACOP/54fvGLX/D3v/+d9PR0a9uGhgYAsrOzI9BdEREREelPmptaunVbf91/f3XqwGmcOvB5/H4/22veZ0ftm/h9RQxL3s+YgXW0thz+fXv7wTUccJUxbeiVGCTFsNc901Ud09JStNZ2GHo1yJs9ezYVFRUMGDDAuu3RRx/lpptuYtSoUcyePZvx48fT3NzM4sWLueyyy8jIyIhUn0VERESknzhj3IK43n9/Z7PZmDjkAiYOuQCAXdU7+MmT97DjrcMDpK3VzzAieS3v73mMA+5xXPbjVNYv9sSqy93WVW2nz5zI0rynNNDrpV5drmmz2cjJySE5Odm67frrr6egoIALLriAt99+m1/+8pc88cQTTJs2jeeeey5iHRYRERGR2EpLS2H6zIlH3W76zImkpfX8ssJo7z+eDU89gVW/cmH6Dg9+HPYh1HkySLL5GJVaypdv3MPjr1fy5p45vFR6Py5fcwx7HKq7tS1aW0xzs6sPepSYIrqI3cyZM1m6dCkAVVVVZGRkkJamBR5FREREEolhGCzNe+qov4T39pK7aO8/0dxw0q/x+/2U1K5j24HXaWgoYOzgWoYlH+BQ6/skGYcHwpsPrsJti92g72i1bW5qOebP3kZCjwd5Tz75JFdffTWjRo3qcrthw4b1ulMiIiIi0r8ZhkF6emrc7j/R2Gw2Th90LmOSJzMuZzZZxw3j128uJCXZjs3WdvGey9dMddP3yD7dx+JdfyMj6XwmD72WEzJP69O+qrbR1+NB3r333st9993HlClTuOaaa7jqqqs488wzo9E3ERERERHphfpyH5eOuDtkMFXe+BEtvlQGJtUzMmU3sJtdB//GBxWD8Rtnc/qgL3Lm4PNj12mJmB5/Jm/9+vU89NBDtLa28pOf/ISzzjqLE088ke9+97u89957+P3+aPRTRERERETCcHL2WSw44X0+23Ef1Z7r2O8ejd80GJJ0kGHOFZQcesPatsXbSJ27Ooa9lXD0+Eze1KlTmTp1Ko8++iiffPIJr7/+OkuXLuUPf/gDv/vd7xg0aBALFizg6quvZu7cufpMnoiIiIhIP2Gz2RjoPo75Y76O0+mk2rWP9ZWvc9C1mrOGXGNt9++KF8HzGw54TiDNeS6ThixkXJau3osXYU28MmbMGO6//37uv/9+ampqWLZsGUuWLOGVV17h+eefJyUlhdmzZ7Nw4UIWLFigz+mJiIiIiPQjQ1JGcvkJ3wS+GXJ7dcsmhif5GZFcBpTxSc0/2Lh/EF6mMC57PlOGXIrTntzhPiX2erWEQkcGDRrEbbfdxmuvvUZ1dTVLly7llltuYcOGDdx5552MHDmS88/XNb4iIiIiIv3dzeOfYvTAFznku5H97uPxmQaDk2rISXqHppYH2NdcZm3r83tj2FPpSESXUAhITk5mwYIFLFiwANM0Wbt2LW+88Ya1vIKIiIiIiPRv47OnMD57CgA17kqKKl+juuVdbNRwSeap1nYvfnQtNsNFsmMm4zPmx6q7EiQqg7xghmFw7rnncu655/LLX/4y2ocTEREREZEIG5Scw2XH3w3cHXJ7k6eBoUkf47T5gE850PISv1uXztYPs1h/8C1mpiwg2X5sLVjfH/Tqcs3i4mLuu+8+brzxRh555BEqKys73basrIzf/va3ve6giIiIiIj0T+nOTE4a8hK1/luocI/F67eRk9HE3BkV+P0/4pXS62PdxWNSj8/kbdmyhXPPPRe3241pmgD89re/JS8vj2nTpgGwY8cOFi9ezOuvv862bdsAuP/++yPYbRERERER6Q9OzJrIiVkTAdhXu4dv/uLLTL3Ew6ST60hxTrW229/8Gfnlt5PkOIfTB13FKdnTrIXaJbJ6PMh75JFHcLlc3HDDDVx22WV8/PHH/PGPf+TGG29k9erV3HHHHeTn5wNgmibHH388V111VcQ7LiIiIiIi/Uu2czCFT7VQ+BR8VPFvklIPD+I2VL3K8OQ9wCtU1L3Cfw5k4eYsjs+cx9nD5pNi19JrkdLjQd6GDRuYNWsWL774onXbF77wBc4++2wuvPBCPv30U8aOHctXvvIVrrrqKiZNmhTRDouIiIiISP/nsDlJdaRaX08echXrq5ppal3D0KQyBibVAwW0ugvI/+RhBqT/nEmZ82LX4QTS40Hevn37+NKXvhRy28SJE7nqqqt47bXX+NrXvsaTTz6pU68iIiIiImIZlXEiozJ+BEB96yHWVy2homkVWfbtpNlbOCnrbGj7NBjn35fCm3sWcebQhZw24ByNLXqox4M8v99PSsqRM+SccsopGIbBww8/rCKIiIiIiPSSaZo0N7s6vK+5qaXb++loW6/Hg9vVas2t0dPjB6SlpWAYRrf70l5W0kBmj74duB2f38vO2o0MSxtN0+d9nnNNM8OSl1JZv5SdBzNoMScxOmMu04ctIMWeHvX+RUL7578v+9SrJRQ66lxSUhIAw4cPD69HIiIiIiLHKNM0uXLON1i/blvY+zpj3IJO7/vbn9/hzXeePuL3+u4ef/rMiSzNeyoigxa7zcGEQTNCbnt/ZTrp1w9mWFIZA5yNDGANvtY1vPvpo/xnz3B+dUUz0PmxI9m/3mr//O+uXEV6emonW0dWr065/e53v+Pyyy/nRz/6Ea+99hqffPJJhLslIiIiInLsaW52dWuAN33mRNLSjry6Li0thekzJx718es/2Nbh2bDuHr9obfFRz6aFY+X/tHDtca9w3ui14PwxFe5zqPekkWz3YjdcBA/w5j+cwriLk7Cu9eyD/nWmu89/tPX4TN7kyZP58MMPWbFiBStWrLBGx4FLNH/84x8zefJkpkyZwtixYyPb2zCUlZXx1a9+lcrKSux2O+vWrSM9PT3W3RIRERER6dD23ctI6+TMT2eX/hmGwdK8pzod4NTXNXDW+Gt6ffzmppYuzxBGWkZSNpeMuhW4Fb/fz4aKd/jX/z4GtLJ99zKq+ZSK5hvheqj3pFPnmcC/ntzH1lfcfdbHYF09/x0NyqOlx4O8jRs34vF42LZtG5s2bWLjxo1s2rSJ4uJi3G43jz32mPWCy87OZvLkyUydOpVf/epXEe98T3zlK1/h5z//ObNmzaKmpobk5OSY9kdEREREpCtp6am9urzPMIxOH+f1eKJ+/Gix2WycPmAWu1a1Am39o7WVfa5TGJK0iyxnE1nO9Xz3e9B6v53XPvsCJw68k3OHX9On/ezq+e8rvfpMntPpZMqUKUyZMoU777wTAJ/Px4cffsjGjRutgd/WrVtZvXo17777bkwHeR9++CFOp5NZs2YBMGjQoJj1RUREREREIuO0gTM4beBSWryNFFW9yWf1K0hlC4NSWhiVWkqT96C17WeNH7GvdheGzcT0x3ZSlmiL2DSYdrudiRMncvvtt/P4449TWFhIQ0MD27Zt47nnngtr3wUFBVx55ZWMHDkSwzBYsmTJEds88cQTjBkzhpSUFGbMmEFRUZF1X2lpKRkZGVx55ZVMmTKFxx57LKz+iIiIiIhI/5HqyODCkTdx7XFP8c2zU/nh3SOodF/J9KFXW9us3f9nmr3f4an1TdzzkoN/V/2Thtba2HU6iiIyyGtpaaGyshK/3x+6c5uN008/ndtuuy2s/Tc1NTFp0iSeeOKJDu9fvHgxDzzwAD/96U/ZtGkTkyZNYt68eVRVVQHg9Xr597//zZNPPsnatWtZuXIlK1euDKtPIiIiIiLSHxl88u9Wrhr9KNnJQ6xbTbMVt89BdrKbWWdUkmT8isI9M/nHjitYUvYYTZ6GGPY5snp1uWbAk08+yeOPP87OnTuBtkHd8OHDOffcc7n++utZuHAhDkdYhwDg8ssv5/LLL+/0/t/85jfcdddd3H777QA8/fTTvPXWWzz77LM8+OCDjBo1irPPPpvjjjsOgPnz57NlyxbmzJnT6T5ramrwBF2znJycHPbn+AL78/TgWmiJHD3/sacaxJ5qEFt6/mNPNYitRHj+gz/T5vV48HjC/123L/fvOcr+j3b8cO8/mmge/7ox/0tN/UFue/Amps4zmTqxkYFJDYxI+ZgW36eYvvvwff74oac5cLmbSUoKP5/b7cbtPjwRTE1NzVEfE65ev2ruvvtu/vznPzNs2DCuvvpqUlNTWb9+PR9//DEvv/wyr7zyCuPGjeO5557j/PPPj2SfQ7S2trJx40Z++MMfWrfZbDYuvfRS1q5dC8C0adOoqqri0KFDZGdnU1BQwNe//vUu99t+ZtAbbriBm266KSJ91lnE2NLzH3uqQeypBrGl5z/2VIPYiufn3+1qtdorVqwgOSUpofYf7fv7Q/+2vOhmy4sw6S/f59PsSpqzisHmxbs9//PHmyx6voE1By+m4pOx+OsnMKRhIkn+tF7le/HFF1m8eHG38kdKrwZ5O3fu5E9/+hPXXnstL7zwAikpKbz//vu89dZb/OY3v2HevHm89NJLPPHEE8yZM4cVK1ZwwQUXRLrvAFRXV+Pz+cjJyQm5PScnhx07dgDgcDh47LHHuOCCCzBNk7lz57JgQddTv5aVlZGZmWl9HakzeStXrmTOnDk4nc6w9iU9p+c/9lSD2FMNYkvPf+ypBrGVCM9/c1ML8FsA5s2b1+kSB/11/3W19V3u/2jHD/f+o4n28YPvv2ze5R3eP+jEP5KW1ECaw8PJg3bAoB14/Us40HoCTmMGw85wULXd2+18s2fPDvnYWU1NDePHjz/q48LRq0HeunXrMAyDX/3qV6SktK338OCDD/Lf//3f3H333QD87Gc/49577+Wqq67innvuYfv27ZHrdS8c7ZLP9gYNGkRWVlZU+uJ0OuP2B1si0PMfe6pB7KkGsaXnP/ZUg9iK5+ff4fQGtSOfI9r7D95fR/s/2vHDvf9oon387txfs8vL12Zk8c/N32GfaxXJbGZgUh0jUsqAMq55cDh//lLb4212AxMTh63znE6nk4yMjKOHj6BeDfIC15SOHj3aum3z5s38/ve/D9lu6NChvPTSS4wbN469e/cyatSoMLrasSFDhmC326msrAy5vbKykuHDh0f8eCIiIiIikth8bpg2+ErS078IwM7ajWw/+AYuTyHrc/1A22fzNhxYTlXDQ9T5JjAs7WKmDbuWgclDY9jzNr2aXXPs2LGYpklJSYl12/HHH88vfvELmpqaQrZtaGibpSY7OzuMbnYuKSmJqVOnsmrVKus2v9/PqlWrmDlzZlSOKSIiIiIix45TBkzluhMf4Zrj3mTzP13W7WX1eaQ73IxM3ozD9xs27JvFP3fO5dXdD1NatyVm/e3VIG/27NlUVFRw6qmnWrc9+uijvPnmm4waNYrrrruOH/7wh3z729/m0ksv5bLLLgvrFGVjYyNbtmxhy5YtQNvn5bZs2cJnn30GwAMPPMAzzzzD888/T0lJCXfffTdNTU3WbJu94fP5rP87anu93pB2YPmIztoejyekbZpmSNs0zSPaQEjb7/eHtL1eb5dtn88X0lamw6fnEylTvNUpIJEyxVudgmuQKJniqU6BfSZSpnirU0f54j1TPNUpIJ4zBYt0ncygY5hRyBTyXtxJnYJ1linQ7/aZ2s8+GehvT+oUrH3f/UH993dQJ1/QPrqbr6OfEe3zBb5un++6cf+P1JT/x/7W2RxsHYjdMBme/CkDbS9SfugGNlWttvoeXKdo69Ugz2azkZOTEzIRyfXXX09BQQEXXHABb7/9Nr/85S954oknmDZtWtiLoW/YsIHJkyczefJkoG1QN3nyZBYtWgS0zXz561//mkWLFnHWWWexZcsWli9ffsRkLD0xefJkJkyYwKJFi6wzlsXFxZSWlgJtl6eWlZUBUFRURHl5OQCFhYVUVFQAbYu4V1dXA5Cfn09dXR0Aq1evts5w5ubm4nK58Hq95Obm4vV6cblc5ObmAm1nQvPy8gCora0lPz8faJtwpqCgAICKigoKCwsBKC8vtxaCLysrY/PmzUDbgvDFxcUAlJSURCxTbW0tAHl5ef0+06ZNm6z6JkqmeKtTYLmVRMoUb3Xau3evVYNEyRRvdQrsM5EyxVudoG12x0TKFC91Chwn3jMFi3idPv99EaD64MGIZ9q2bZu1/w0bNnRYp2DtMzU2Nlr3uTvItGLFCuv+wJV2Pa1TsPaZdgT9LvHxrl1AaJ02bNhg3b/ugw86/H4K1r5Obtfhs3S+DuoUfPXge++9h9OezIm2cxi+53q+MH4dab4n+bhqHhWuMdR50mkqbft0XFlZGb8puIZfrT6Pb/x2/hH9iDTDDB6uRlBVVRUZGRmkpaVFY/dRU19fT3Z2NjU1NQwcONAaadvt9pC21+vFMAyrbbPZsNlsnbYDfwVZvnw5c+bMITU1FcMw8Hg81lqCXq83pO10Otv+YvB52+/34/P5rLbf78fhcHTa9vl8mKZptTvKEW4mu91utR0OR7/O1NLSQl5eHvPnz8cwjITIFG91Cvzwnz9/PjabLSEyxVud3G43y5cvt2qQCJniqU7Q9kvE3LlzSU1NTYhM8VYn0zR5++23rffiRMgUT3UKfi8OPC7eMrW0uBmXMxuA3ZWrSE52RrROjQ1NnDiibS3nXRUrSUlNjmimmoOHOO2EKwAo3buCjMz0kDq1z5eU5AjJ5HZ7OHH4pW392/8O6empIZnq6xoYP/oyAD7as5zsAVk9qpPb7Qk5fsrnSxQEcjQ3tXDSyLkAfLwvj8ysjJA61dc1cPKoeT3KF1ynzvIF6tRZvo4ytXiacRpJOBwOWj2tLP90OhmOFnZVn8DXpq2krq4uahM9RnZ1xSDDhg2L1q77hN1uD/m/fTt4kffutJ1Op3Wq1+l0YhiG1Q7epn3bMAyrHfgB0t12Z32PZKbetJVJmZQptpl6kiNeMsVLnQLvA4F9JkKm7uboL5m6ei+O10xdtZUpOpmCRTqTEfRz2rDZjvrzoqeZQtoOh7WfzvK1729r6+HLIQ3DCOk7tM042b7dkzq53Ycvh+yo77agrwPt4L7b27Ujmc8wjB7lS3UePtllt9sYkfXf7KrLZVDKiUB014qM2iBPREREREREwG5zMG3Y5UwbdjkHDx4E7o/q8Xr1mTwRERERERHpnzTIExERERERSSAa5HVCUx8nZqaARMoUb3UKSKRM8Van4BokSqZ4qlNgn4mUKd7q1FG+eM8UT3UKiOdMwSJdJy2hEKp93/vbEgrdzdS+TtGmQV4ntIRC4k3nrCUUYp9JSyjEPpOWUIh9psA+EylTvNUJtISCllDQEgqgJRQCmcJZQqG7mcrKynjooYeYMGECM2bMOKIfkRa1JRTilZZQSNzpnLWEQuwzaQmF2GfSEgpaQuFYr5OWUNASCpFcQmH77mXWEgqGzYb38zoZNhtOp92axbUnmfpyCYWtpW+QkZGGYbPhC8p3xrgFgJZQ2LZ7GWlpKfi8Xhyf16mhvpGzxl9zRL6evPaqqqrIycmJzyUU4p2WUEjsqY+VSZmO5Uw9yREvmeKlToH3gcA+EyFTd3P0l0xdvRfHa6au2soU3SUUAoOhjkyfOZGleU/1OFNfLqEw6eSrO+1/Z/09lpZQOLOL+vY0X2c1iBZdrikiIiIi0k1paSlMnznxqNsVrS2mudl11O36WmpaCieOH3XU7abPnEhaWkof9Kh/6W59+/vzozN5IiIiIiLdZBgGS/Oe6nQA19zU0uUZvlgzDIMfLLqFiy+6JOSsW3tpaSnW2e5jydHqG9Dfnx8N8kREREREesAwDNLTU2PdjV4zDIO09NSQSxXlsHivL+hyTRERERERkYSiQV4ntL5NYmYKSKRM8VangETKFG91Cq5BomSKpzoF9plImeKtTh3li/dM8VSngETKFJIj6Odsf18nL5zXXqDfibhOXl+89qJNg7xOaJ28xFuzR+vkxT6T1smLfSatreWBygAAMhVJREFUkxf7TIF9JlKmeKsTaJ08rZMXxToFrXMXWFOuP62Tt3XrVmv/vXntJfo6edF87WmdvBjSOnmJu2aP1smLfSatkxf7TFonT+vkHet10jp5Wicv2nVyu1oPr3O3/x0yMtJ6lCna6+QFvxd39vtQV6+9RF8nry9ee1onL4a0Tl5ir2+jTMp0LGfqSY54yRQvdQq8DwT2mQiZupujv2Tq6r04XjN11Vamvs/Ufh25nmbqy3XyelOnRF8nr6t2NF570aLLNUVERERERBKIBnkiIiIiIiIJRIM8ERERERGRBKJBnoiIiIiISALRIE9ERERERCSBaJDXCS1impiZAhIpU7zVKSCRMsVbnYJrkCiZ4qlOgX0mUqZ4q1NH+eI9UzzVKSCRMoXkCPo5q8XQtRh6Z3WKNg3yOqHF0BNvYVYthh77TFoMPfaZtBh67DMF9plImeKtTqDF0LUYuhZD71EmLYYOaDH0uKbF0BN3YVYthh77TFoMPfaZtBi6FkM/1uukxdC1GLoWQ9di6FoM/RimxdCP3UVMlUmZEj1TT3LES6Z4qVPgfSCwz0TI1N0c/SVTV+/F8Zqpq7YyaTH0cPNpMXQthi4iIiIiIiIxpkGeiIiIiIhIAtEgT0REREREJIFokCciIiIiIpJANMgTERERERFJIBrkiYiIiIiIJBAN8joRWE/E5/N12PZ6vSFtv9/fZTuwTl6gHVieMNA2TfOINhDS9vv9Ie3AmkudtX0+X0hbmQ5P+ZtImeKtTgGJlCne6hRcg0TJFE91CuwzkTLFW506yhfvmeKpTgGJlCkkR9DP2d5kCnl8FDIFvxeH89oL9Lt9Jm/wz7pe1ilY+777g/rv76BOvqB9+DrJFCxWr71o0yCvE5MnT2bChAksWrSIkpISAIqLiyktLQVg8+bNlJWVAVBUVER5eTkAhYWFVFRUAFBQUEB1dTUA+fn51NXVAbB69WoaGhqAtkVxXS4XXq+X3NxcvF4vLpeL3NxcABoaGsjLywOgtraW/Px8AKqrqykoKACgoqKCwsJCAMrLyykqKgKgrKyMzZs3A1BaWkpxcTEAJSUlEctUW1sLQF5eXr/PtGnTJqu+iZIp3uq0c+dOqwaJkine6rR3716rBomSKd7qFNhnImWKtzoBrFy5MqEyxUudAsdJpExH1Onz3/cAGhsbe54p6PHVBw9GPNPWrVut/ffmtRfIBODuINOKFSus+1etWtWrOgVrn2lH0O8SH+/aBYTWacOGDdb96z74oMNMwfrytffQQw8xYcIEZsyYcUQ/Is0wg4fjQn19PdnZ2dTU1DBw4EBrpG2320PaXq8XwzCsdmCxw87agb+CLF++nDlz5pCamophGHg8HmtxRq/XG9J2Op1tfxH5vO33+/H5fFbb7/fjcDg6bft8PkzTtNod5Qg3k91ut9oOh6NfZ2ppaSEvL4/58+djGEZCZIq3OgV++M+fPz9kcdB4zhRvdXK73SxfvtyqQSJkiqc6QdsvEXPnziU1NTUhMsVbnUzT5O2337beixMhUzzVKfi9OPC4eM/Uvk5uVysnjpgDwK7975CRkdajTI0NTYcfX7GSlNTkiGYKfi/u7Pehrl57breHE4dfauVLT08NyVRf18D40ZcB8NGe5WQPyOpRndxuD+NyZgOwu3IVKSlJITmam1o4aeRcAD7el0dmVkZInerrGjh51DwASveuICMzPSRTS4s7ZP9JSY4+f+1VVVWRk5NDXV0dWVlZREPHS7+LtRJ9Z6vTB4re3bbT6bRO6TqdTgzDsNrB27RvG4ZhtQPfbN1td9b3SGbqTVuZlEmZYpupJzniJVO81CnwPhDYZyJk6m6O/pKpq/fieM3UVVuZ+j5Ta+vhywV78/ueEfRz2rDZjvrzoqeZepqvfX/b5wvuO4Cjg3ZP6uR2H77cs6O+24K+DrSD+2tv1w7sP3ibYLF+7UWLLtcUERERERFJIBrkiYiIiIiIJBAN8kRERERERBKIBnkiIiIiIiIJRIM8ERERERGRBKJBnoiIiIiISALRIE9ERERERCSBaJAnIiIiIiKSQDTIExERERERSSAa5HXC5/NZ/3fU9nq9IW2/399l2+PxhLRN0wxpm6Z5RBsIafv9/pC21+vtsu3z+ULayuS16ptImeKtTgGJlCne6hRcg0TJFE91CuwzkTLFW506yhfvmeKpTgGJlCkkR9DP2d5kCnl8FDIFvxeH89oL9Lt9Jm/wz7pe1ilY+777g/rv76BOvqB9+DrJFCxWr71o0yCvE5MnT2bChAksWrSIkpISAIqLiyktLQVg8+bNlJWVAVBUVER5eTkAhYWFVFRUAFBQUEB1dTUA+fn51NXVAbB69WoaGhoAyM3NxeVy4fV6yc3Nxev14nK5yM3NBaChoYG8vDwAamtryc/PB6C6upqCggIAKioqKCwsBKC8vJyioiIAysrK2Lx5MwClpaUUFxcDUFJSErFMtbW1AOTl5fX7TJs2bbLqmyiZ4q1OO3futGqQKJnirU579+61apAomeKtToF9JlKmeKsTwMqVKxMqU7zUKXCcRMp0RJ0+/30PoLGxseeZgh5fffBgxDNt3brV2n9vXnuBTADuDjKtWLHCun/VqlW9qlOw9pl2BP0u8fGuXUBonTZs2GDdv+6DDzrMFKwvX3sPPfQQEyZMYMaMGUf0I9IMM3g4LtTX15OdnU1NTQ0DBw60Rtp2uz2k7fV6MQzDattsNmw2W6ftwF9Bli9fzpw5c0hNTcUwDDweDw6HA2j7C0Nw2+l0tv1F5PO23+/H5/NZbb/fj8Ph6LTt8/kwTdNqd5Qj3Ex2u91qOxyOfp2ppaWFvLw85s+fj2EYCZEp3uoU+OE/f/58bDZbQmSKtzq53W6WL19u1SARMsVTnaDtl4i5c+eSmpqaEJnirU6mafL2229b78WJkCme6hT8Xhx4XLxnal8nt6uVE0fMAWDb7mWkp6fi9Xiwf57D5/WGtB2fZwq0mxqbOfPEKwHYVbGSlNTkiGYKfi/u7Pehrl57breHE4df2ta//e+05QuqU31dA+NHXwbAR3uWkz0gq0d1crs9jMuZDcD23ctISUkCwGa34/f5aG52MfGkqwD4eF8emVkZIXWqr2vg5FHzACjdu4KMzPSQTC0tbmv/uytXkZTk6PPXXlVVFTk5OdTV1ZGVlUU0OKKy1wRgt9tD/m/fDhS9u22n02md0nU6nRiGYbWDt2nfNgzDage+2brb7qzvkczUm7YyKZMyxTZTT3LES6Z4qVPgfSCwz0TI1N0c/SVTV+/F8Zqpq7Yy9X2m1tbDlwueOW4B4TBstqP+vOhppp7ma1+D4HyGYYTUA8DRQbsndXK7D1/uecZRnj/b51mC+2tv1w7sP3ibYLF+7UWLLtcUEREREYmQtLQUps+cGPZ+ps+cSFpaSgR6FF+6+/wdq89Pd+lMnoiIiIhIhBiGwdK8p2hudoW1n7S0FOts87Gku8/fsfr8dJcGeSIiIiIiEWQYBunpqbHuRtzS8xc+Xa4pIiIiIiKSQDTIExERERERSSAa5ImIiIiIiCQQDfJEREREREQSiAZ5IiIiIiIiCUSDPBERERERkQSiQZ6IiIiIiEgC0SBPREREREQkgWiQJyIiIiIikkA0yBMREREREUkgGuR1wufzWf931PZ6vSFtv9/fZdvj8YS0TdMMaZumeUQbCGn7/f6Qttfr7bLt8/lC2srkteqbSJnirU4BiZQp3uoUXINEyRRPdQrsM5EyxVudOsoX75niqU4BiZQpnuoU/F4cTqZAv9tn8gb/rItBnXxBrzFfJ5mCxapO0aZBXicmT57MhAkTWLRoESUlJQAUFxdTWloKwObNmykrKwOgqKiI8vJyAAoLC6moqACgoKCA6upqAPLz86mrqwNg9erVNDQ0AJCbm4vL5cLr9ZKbm4vX68XlcpGbmwtAQ0MDeXl5ANTW1pKfnw9AdXU1BQUFAFRUVFBYWAhAeXk5RUVFAJSVlbF582YASktLKS4uBqCkpCRimWprawHIy8vr95k2bdpk1TdRMsVbnXbu3GnVIFEyxVud9u7da9UgUTLFW50C+0ykTPFWJ4CVK1cmVKZ4qVPgOImUKd7qtHXrVqsGvcnU2NhoPd7dQaYVK1ZY969atarP67Rhwwbr+Os++KDDTMH6sk4PPfQQEyZMYMaMGUf0I9IMM3g4LtTX15OdnU1NTQ0DBw60Rtp2uz2k7fV6MQzDattsNmw2W6ftwF9Bli9fzpw5c0hNTcUwDDweDw6HA2j7C0Nw2+l0tv1F5PO23+/H5/NZbb/fj8Ph6LTt8/kwTdNqd5Qj3Ex2u91qOxyOfp2ppaWFvLw85s+fj2EYCZEp3uoU+OE/f/58bDZbQmSKtzq53W6WL19u1SARMsVTnaDtl4i5c+eSmpqaEJnirU6mafL2229b78WJkCme6hT8Xhx4XLxnirc6Bb8Xd/b7UFeZ3G4PJw6/FIBd+98hPT01JFN9XQPjR18GwEd7lpM9IKtP61Rf18DJo+YBULp3BRmZ6SGZWlrcjMuZDcDuylUkJTn6vE5VVVXk5ORQV1dHVlYW0eCIyl4TgN1uD/m/fTtQ9O62nU6ndUrX6XRiGIbVDt6mfdswDKsd+GbrbruzvkcyU2/ayqRMyhTbTD3JES+Z4qVOgfeBwD4TIVN3c/SXTF29F8drpq7ayqRM4eZr39/W1sOXQxqGEdJ3AEcH7b6sk71dO7DP4G2CxbpO0aLLNUVERERERBKIBnkiIiIiIiIJRIM8ERERERGRBKJBnoiIiIiISALRIE9ERERERCSBaJAnIiIiIiKSQDTIExERERERSSAa5ImIiIiIiCQQDfJEREREREQSiAZ5IiIiIiIiCUSDPBERERERkQSiQZ6IiIiIiEgC0SBPREREREQkgWiQJyIiIiIikkA0yBMREREREUkgGuSJiIiIiIgkEA3yREREREREEogGeSIiIiIiIglEgzwREREREZEEokFeJ3w+n/V/R22v1xvS9vv9XbY9Hk9I2zTNkLZpmke0gZC23+8PaXu93i7bPp8vpK1MXqu+iZQp3uoUkEiZ4q1OwTVIlEzxVKfAPhMpU7zVqaN88Z4pnuoUkEiZ4qlOwe/F4WQK9Lt9Jm/wz7oY1MkX9BrzdZIpWKzqFG0a5HVi8uTJTJgwgUWLFlFSUgJAcXExpaWlAGzevJmysjIAioqKKC8vB6CwsJCKigoACgoKqK6uBiA/P5+6ujoAVq9eTUNDAwC5ubm4XC68Xi+5ubl4vV5cLhe5ubkANDQ0kJeXB0BtbS35+fkAVFdXU1BQAEBFRQWFhYUAlJeXU1RUBEBZWRmbN28GoLS0lOLiYgBKSkoilqm2thaAvLy8fp9p06ZNVn0TJVO81Wnnzp1WDRIlU7zVae/evVYNEiVTvNUpsM9EyhRvdQJYuXJlQmWKlzoFjpNImeKtTlu3brVq0JtMjY2N1uPdHWRasWKFdf+qVav6vE4bNmywjr/ugw86zBSsL+v00EMPMWHCBGbMmHFEPyLNMIOH40J9fT3Z2dnU1NQwcOBAa6Rtt9tD2l6vF8MwrLbNZsNms3XaDvwVZPny5cyZM4fU1FQMw8Dj8eBwOIC2vzAEt51OZ9tfRD5v+/1+fD6f1fb7/Tgcjk7bPp8P0zStdkc5ws1kt9uttsPh6NeZWlpayMvLY/78+RiGkRCZ4q1OgR/+8+fPx2azJUSmeKuT2+1m+fLlVg0SIVM81QnafomYO3cuqampCZEp3upkmiZvv/229V6cCJniqU7B78WBx8V7pnirU/B7cWe/D3WVye32cOLwSwHYtf8d0tNTQzLV1zUwfvRlAHy0ZznZA7L6tE71dQ2cPGoeAKV7V5CRmR6SqaXFzbic2QDsrlxFUpKjz+tUVVVFTk4OdXV1ZGVlEQ2OqOw1Adjt9pD/27cDRe9u2+l0Wqd0nU4nhmFY7eBt2rcNw7DagW+27rY763skM/WmrUzKpEyxzdSTHPGSKV7qFHgfCOwzETJ1N0d/ydTVe3G8ZuqqrUzKFG6+9v1tbT18OaRhGCF9B3B00O7LOtnbtQP7DN4mWKzrFC26XFNERERERCSBaJAnIiIiIiKSQDTIExERERERSSAa5ImIiIiIiCQQDfJEREREREQSiAZ5IiIiIiIiCUSDPBERERERkQSiQZ6IiIiIiEgC0SBPREREREQkgWiQJyIiIiIikkA0yBMREREREUkgGuSJiIiIiIgkEA3yREREREREEogGeSIiIiIiIglEgzwREREREZEEokGeiIiIiIhIAtEgT0REREREJIFokCciIiIiIpJANMgTERERERFJIBrkiYiIiIiIJBAN8kRERERERBKIBnkiIiIiIiIJRIM8ERERERGRBKJBnoiIiIiISALRIE9ERERERCSBaJAnIiIiIiKSQDTIExERERERSSAa5ImIiIiIiCQQDfJEREREREQSiAZ5IiIiIiIiCUSDPBERERERkQTiiHUH+sqYMWPIysrCZrMxcOBAVq9eHesuiYiIiIiIRNwxM8gDKCwsJCMjI9bdEBERERERiRpdrikiIiIiIpJA4mKQV1BQwJVXXsnIkSMxDIMlS5Ycsc0TTzzBmDFjSElJYcaMGRQVFYXcbxgGF154IdOmTeMf//hHH/VcRERERESkb8XFIK+pqYlJkybxxBNPdHj/4sWLeeCBB/jpT3/Kpk2bmDRpEvPmzaOqqsra5v3332fjxo0sXbqUxx57jOLi4r7qvoiIiIiISJ+Ji8/kXX755Vx++eWd3v+b3/yGu+66i9tvvx2Ap59+mrfeeotnn32WBx98EIBRo0YBMGLECObPn8+mTZuYOHFip/usqanB4/FYXycnJ5OcnBxWjsD+gvcrfUfPf+ypBrGnGsSWnv/YUw1iS89/7IVbA2/Q47weDx6Po0f3R1t/7J/b7cbtdltf19TURP2YcTHI60praysbN27khz/8oXWbzWbj0ksvZe3atUDbmUC/309mZiaNjY3k5+fzxS9+scv9jh07NuTrG264gZtuuikifV65cmVE9iO9o+c/9lSD2FMNYkvPf+ypBrGl5z/2elsDt6vVaq9YsYLklKQe3R9t/bF/L774IosXL476cYLF/SCvuroan89HTk5OyO05OTns2LEDgMrKShYuXAiAz+fjrrvuYtq0aV3ut6ysjMzMTOvrSJ3JW7lyJXPmzMHpdIa1L+k5Pf+xpxrEnmoQW3r+Y081iC09/7EXbg2am1qA3wIwb9480tJTe3R/tPXH/s2ePTvkY2c1NTWMHz8+qseM+0Fed4wbN46tW7f26DGDBg0iKysrKv1xOp36wRZDev5jTzWIPdUgtvT8x55qEFt6/mOvtzVwOL1B7SP3cbT7o60/9s/pdPb5Mm5xMfFKV4YMGYLdbqeysjLk9srKSoYPHx6jXnXM7Xbz4osvhlyTK31Hz3/sqQaxpxrElp7/2FMNYkvPf+ypBrEXeO6jWYO4H+QlJSUxdepUVq1aZd3m9/tZtWoVM2fOjGHPjuR2u1m8eLG+qWJEz3/sqQaxpxrElp7/2FMNYkvPf+ypBrHX2tr2ucBjfpDX2NjIli1b2LJlC9D2ebktW7bw2WefAfDAAw/wzDPP8Pzzz1NSUsLdd99NU1OTNdtmb/h8Puv/jtperzek7ff7u2x7PB5M07RuD7QDt5umeUQbCGn7/f6Qttfr7bLt8/lC2tHI1FG+/prJ5/PhcDgSKlO81cnv91uXRSRKpnisU+DzxYmUKZ7qZLPZjpoj3jLFW50C2ydSpnipk8/nw2azJVSmeKtTd34f6k6mQL/bZ2o/e2Vf18nnPXw5pq+TTMFiUaeO+hFpcTHI27BhA5MnT2by5MlA26Bu8uTJLFq0CGib+fLXv/41ixYt4qyzzmLLli0sX778iMlYemLy5MlMmDCBRYsWUVJSAkBxcTGlpaUAbN68mbKyMgCKioooLy8HoLCwkIqKCqBtEffq6moA8vPzaWhoAGD9+vVWOzc3F5fLhdfrJTc3F6/Xi8vlIjc3F4CGhgby8vIAqK2tJT8/H2ibcKagoACAiooKCgsLASgvL7cWgi8rK2Pz5s0AlJaWWmsDlpSURCxTbW0tAHl5ef0+U0lJCRdddFFCZYq3On3yySd86UtfSqhM8Vanqqoqa2mZRMkUb3U6+eST2bRpU0Jliqc6tba2kpqayrp16xImUzzVaevWrdYSVomSKd7q9NFHHzF//vxeZ2psbCTA3UGmFStWWPcHrrTryzpt2LDBOv66Dz7oMFOwvqzTQw89xIQJE7jkkkuO6EekGWbwcFyoq6tjwIABfPLJJwwcOND6q4Ddbg9pe71eDMOw2jabzfrrbEdtj8dDfX0948aNo7S0lKFDh2IYBh6PJ+SvKcFtp9PZ9heRz9t+vx+fz2e1/X4/Doej07bP58M0TavdUY5wM9ntdqvtcDj6daYDBw5wyimn8PHHH5OVlZUQmeKtTtXV1ZxyyimUlpaSnZ2dEJnirU7V1dWcdtpp7Ny5k+zs7ITIFE91amhoYNy4cXz00UcMGzYsITLFW53avxcnQqZ4qtOBAwc4+eST2b17N5mZmQmRKd7q1J3fh7rK9P/bu/eoqKstDuDfYQYYRB6+EEYEFFCUEBLfmojNFSwVxXvVfEEiYmUZPhK9rrDu8qZGPqJuPlEyH5mRmCYKLjBE1FQ0SUFBoKsiKAaJyOMy+/7hmolxBkOc39AM+7PWrBXnd35n9j6Hkdn9XjU1dfB2HwcAuHT9ICwtLdRy+r3iAfr2CgYAXLiaABtba72u0+8VD/Cix+O76mflfIe2VpZqOT16VIM+bmMBAD/nfQ8zM4ne16mgoAA+Pj749ddf0bVrVwiBi7wn3Lx5U7DJZowxxhhjjDEAyM/PR/fu3QUZm4u8JygUCty+fRtWVlYQiUQtHQ5jjDHGGGPMiBARHjx4AJlMprpGVde4yGOMMcYYY4wxI2IQN15hjDHGGGOMMdY0XOQxxhhjjDHGmBHhIu85fP7553BxcYFUKsXAgQNVt0htzDfffAMPDw9IpVJ4eXmpbscKPH4ux5IlS+Dl5QVLS0vIZDLMnDkTt2/fFjoNg6bLNQCAFStWwMPDA5aWlmjXrh3kcjnOnDkjZAoGT9dr0NDcuXMhEomwfv16HUdtPHQ9/6GhoRCJRGqvwMBAIVMweEJ8Bq5evYpx48bBxsYGlpaW6N+/v+rZsEydruf/yd9/5evjjz8WMg2Dpus1qKysxLx58+Do6AgLCwv07t0bGzduFDIFg6frNSgpKUFoaChkMhnatGmDwMBA1eMKmKZnmf9ffvkFEydOhIuLy1O/4zzrmmog1ix79+4lMzMziouLo19++YXCw8PJ1taWSkpKtPbPyMggsVhMa9asoStXrtDy5cvJ1NSULl++TERE5eXlJJfL6euvv6acnBzKzMykAQMGkK+vrz7TMii6XgMiol27dlFycjLl5+dTdnY2hYWFkbW1NZWWluorLYMixBooJSQkkLe3N8lkMlq3bp3AmRgmIeY/JCSEAgMDqbi4WPW6f/++vlIyOEKsQV5eHrVv354WL15MFy5coLy8PEpMTGx0zNZMiPlv+LtfXFxMcXFxJBKJKD8/X19pGRQh1iA8PJxcXV0pNTWVCgoKaNOmTSQWiykxMVFfaRkUXa+BQqGgQYMG0UsvvURnz56lnJwcmjNnDjk5OVFlZaU+UzMIzzr/Z8+epUWLFtGePXvI3t5e63ecZx1TGy7ymmnAgAH01ltvqX6ur68nmUxGH330kdb+kyZNoldffVWtbeDAgRQREdHoe5w9e5YAUFFRkW6CNjL6WIOKigoCQCkpKboJ2sgItQY3b96kLl26UHZ2Njk7O3OR1wgh5j8kJISCgoIEidcYCbEGkydPpunTpwsTsJHRx9+BoKAgGjlypG4CNkJCrIGnpyd9+OGHan369u1L//znP3UYufHQ9Rrk5uYSAMrOzlYbs1OnTrRlyxYBMjBszzr/DTX2Hed5xlTi0zWboba2FufPn4dcLle1mZiYQC6XIzMzU+s+mZmZav0BICAgoNH+wOMHs4tEItja2uokbmOijzWora3F5s2bYWNjA29vb90FbySEWgOFQoEZM2Zg8eLF8PT0FCZ4IyDkZyAtLQ12dnbo2bMn3njjDZSVlek+ASMgxBooFAocPnwYPXr0QEBAAOzs7DBw4EAcOHBAsDwMlT7+DpSUlODw4cMICwvTXeBGRKg1GDJkCA4ePIhbt26BiJCamopr165h1KhRwiRiwIRYg5qaGgCAVCpVG9Pc3BwnT57UdQoGrTnzr68xuchrhnv37qG+vh6dO3dWa+/cuTPu3LmjdZ87d+48U//q6mosWbIEr732GqytrXUTuBERcg0OHTqEtm3bQiqVYt26dUhOTkbHjh11m4AREGoNVq9eDYlEgnfeeUf3QRsRoeY/MDAQX375JY4fP47Vq1fjxIkTGD16NOrr63WfhIETYg1KS0tRWVmJVatWITAwEMeOHcOECRMQHByMEydOCJOIgdLH3+L4+HhYWVkhODhYN0EbGaHWIDY2Fr1794ajoyPMzMwQGBiIzz//HMOHD9d9EgZOiDXw8PCAk5MTli5dit9++w21tbVYvXo1bt68ieLiYmESMVDNmX99jSlp1rszQdXV1WHSpEkgInzxxRctHU6r4+/vj4sXL+LevXvYsmULJk2ahDNnzsDOzq6lQzN658+fx4YNG3DhwgWIRKKWDqdVmjJliuq/vby80KdPH7i6uiItLQ0vv/xyC0bWOigUCgBAUFAQIiMjAQA+Pj44deoUNm7cCD8/v5YMr9WJi4vDtGnT1I5oMOHFxsbi9OnTOHjwIJydnfHjjz/irbfegkwm0zgCxXTP1NQUCQkJCAsLQ/v27SEWiyGXyzF69GgQP17bYPCRvGbo2LEjxGIxSkpK1NpLSkpgb2+vdR97e/sm9VcWeEVFRUhOTuajeI0Qcg0sLS3h5uaGQYMGYdu2bZBIJNi2bZtuEzACQqxBeno6SktL4eTkBIlEAolEgqKiIixcuBAuLi6C5GGohPwMNNS9e3d07NgReXl5zx+0kRFiDTp27AiJRILevXur9enVqxffXfMJQn8G0tPTkZubi9mzZ+suaCMjxBo8evQIy5Ytw9q1azF27Fj06dMH8+bNw+TJkxETEyNMIgZMqM+Br68vLl68iPLychQXFyMpKQllZWXo3r277pMwYM2Zf32NyUVeM5iZmcHX1xfHjx9XtSkUChw/fhyDBw/Wus/gwYPV+gNAcnKyWn9lgXf9+nWkpKSgQ4cOwiRgBIRaA20UCoXq/HT2ByHWYMaMGfj5559x8eJF1Usmk2Hx4sU4evSocMkYIH19Bm7evImysjI4ODjoJnAjIsQamJmZoX///sjNzVXrc+3aNTg7O+s4A8Mm9Gdg27Zt8PX15Wuyn0KINairq0NdXR1MTNS/oorFYtWRbvYHoT8HNjY26NSpE65fv45z584hKChItwkYuObMv97GbPItWpiavXv3krm5Oe3YsYOuXLlCc+bMIVtbW7pz5w4REc2YMYOioqJU/TMyMkgikVBMTAxdvXqVoqOj1W5XW1tbS+PGjSNHR0e6ePGi2u2ba2pqWiTHvzpdr0FlZSUtXbqUMjMzqbCwkM6dO0evv/46mZubq91hiv1B12ugDd9ds3G6nv8HDx7QokWLKDMzkwoKCiglJYX69u1L7u7uVF1d3SI5/tUJ8RlISEggU1NT2rx5M12/fp1iY2NJLBZTenq63vP7qxPq36CKigpq06YNffHFF3rNxxAJsQZ+fn7k6elJqampdOPGDdq+fTtJpVL6z3/+o/f8DIEQa7Bv3z5KTU2l/Px8OnDgADk7O1NwcLDeczMEzzr/NTU1lJWVRVlZWeTg4ECLFi2irKwsun79epPHbAou8p5DbGwsOTk5kZmZGQ0YMIBOnz6t2ubn50chISFq/fft20c9evQgMzMz8vT0pMOHD6u2FRQUEACtr9TUVD1lZHh0uQaPHj2iCRMmkEwmIzMzM3JwcKBx48bR2bNn9ZWOQdLlGmjDRd7T6XL+q6qqaNSoUdSpUycyNTUlZ2dnCg8Pf6Y/Kq2REJ+Bbdu2kZubG0mlUvL29qYDBw4InYbBEmL+N23aRBYWFlReXi50+EZB12tQXFxMoaGhJJPJSCqVUs+ePemTTz4hhUKhj3QMkq7XYMOGDeTo6Eimpqbk5OREy5cv54MOT/Es89/Yd34/P78mj9kUIiK+gpIxxhhjjDHGjAVfk8cYY4wxxhhjRoSLPMYYY4wxxhgzIlzkMcYYY4wxxpgR4SKPMcYYY4wxxowIF3mMMcYYY4wxZkS4yGOMMcYYY4wxI8JFHmOMMcYYY4wZES7yGGOMMcYYY8yIcJHHGGOMtSKFhYUQiUSql729vdr2FStWQCQSIS0trWUCfEJUVJRavCtWrGjpkBhj7C+PizzGGDMyT36J1/ZycXFp6TAblZaW1qwv88r9nvYaMWKEIDEbIm9vb0RHR2PRokWCv9fmzZshEokQERHxp32HDh0KkUiEU6dOAQDkcjmio6MREhIidJiMMWY0JC0dAGOMMWG4urpi+vTpWrfZ2trqNxg98vX1xZgxY7Ru+ysXt/rm4+Ojt6NiU6ZMQWRkJPbu3Yv169fDwsJCa7/c3FycOnUKHh4eGDJkCIDHRZ5cLkdaWhri4+P1Ei9jjBk6LvIYY8xIubm5tcpT2/r169cq8/4rs7a2xj/+8Q/Ex8dj//79mDFjhtZ+cXFxAICwsDB9hscYY0aHT9dkjLFWrKqqClZWVnB1dW20T58+fWBhYYHff/9d1UZEiIuLw9ChQ2FtbY02bdqgX79+qi/pDTW8xmv37t3w8fGBhYUFHBwcMH/+fDx69Eitr7+/PwDggw8+UDvVsrCwUHeJ44/TWkNDQ5GXl4cJEyagXbt2sLS0hFwux6VLl7TuV1paisjISLi5ucHc3BwdO3bExIkTkZ2drdHXxcUFLi4uKC8vx7x589C1a1dIJBLs2LFD1WfTpk3w9PSEVCpF165d8d5776G6ulrj9NJhw4ZBIpGguLhYa1wzZ86ESCRCZmbmc81LY7Kzs+Ho6Ih27drh5MmTqvaCggLMnj0bTk5OMDc3h4ODA0JDQ1FUVKS2v7Jw0/Y7AgD19fXYuXMnTE1NMXPmTEFyYIyx1oKP5DHGWCvWpk0bTJw4EfHx8Th16pTqFDmlS5cu4fLly5g8eTKsra0BPC7wpk2bhj179sDd3R1Tp06FmZkZkpOTERYWhitXriAmJkbjvT777DMkJSUhKCgII0eORFJSEj799FPcu3cPu3btAgCMGDEChYWFiI+Ph5+fn1qRI9QppoWFhRg0aBA8PT0xa9Ys5OfnIzExEf7+/rh69So6d+6s6pufn48RI0bg5s2bGDVqFMaPH4/S0lJ8++23OHr0KI4fP46BAweqjV9TU4ORI0eisrIS48aNg0QiUY35/vvv41//+hc6d+6M8PBwmJqaYt++fcjJydGIMyIiAhkZGdi+fTuWLVumtq28vBz79++Hp6cnBg8erPM5OnnyJMaOHQtLS0ukp6fjhRdeAACcOXMGAQEBePjwIcaMGQN3d3cUFhZi165dOHLkCDIzM9G9e3cAwEsvvYQePXrgxIkTuHHjhqpd6ciRIyguLsaECRNgZ2en8xwYY6xVIcYYY0aloKCAAJCrqytFR0drfR05ckTVPyUlhQDQG2+8oTHWwoULCQAdOnRI1bZ582YCQK+//jrV1taq2mtqamjs2LEEgM6dO6dqj46OJgBkY2NDOTk5qvaqqirq0aMHmZiY0K1bt1TtqampBICio6OfKW/lfr6+vo3mnZmZqTFPAGjVqlVqYy1fvpwA0EcffaTWPmTIEBKLxZSUlKTWnpubS1ZWVuTl5aXW7uzsTAAoICCAqqqqNPYRi8XUpUsXKikpUbX//vvv1Lt3bwJAfn5+qvZHjx5R+/btqXv37qRQKNTG+uyzzwgArV+//k/nSZl3SEiI1u3K9UpNTSUiosTERLKwsKCePXtSUVGRql9tbS25uLiQlZUVXbhwQW2M9PR0EovFNGbMGLX2VatWEQBavny5xvtOmDBB43etoeb+XjDGWGvERR5jjBmZhsVLY6/58+er+tfX11OXLl2oQ4cOakVbfX09OTg4UKdOnaiurk7V3qdPH7K0tNQoWoiIfv75ZwJACxcuVLUpi4b3339fo79y28GDB1Vtz1vkPe21bt06jXnq1q0b1dfXq42l3BYcHKxqu3DhAgGgWbNmaX3/BQsWEAC6fPmyqk1Z5F26dEmj/4oVKwgArV27VmPb7t27NYo8IqLIyEgCQCkpKWrtL774Ipmbm1NZWVmj8/Nkbk0p8rZu3UpisZgGDBhAd+/eVeuXkJBAAOjDDz/UOk5wcDCZmJhQRUWFqq24uJgkEgl17dpVbc5LS0vJ1NSUZDIZ/e9//9M6Hhd5jDHWdHy6JmOMGamAgAAkJSX9aT8TExNMmzYNa9aswQ8//ICgoCAAwPHjx1FcXIy3334bEsnjPxdVVVW4fPkyZDIZVq9erTFWXV0dAGg93dDX11ejzdHREcDj0w11JSIiAhs3bmxyfx8fH5iYqF+iri2u06dPAwBKSkq03thFmXNOTo7qdEYAkEql8PLy0uivvOZv2LBhGtuGDh2qNdY5c+Zg3bp12LJlC15++WUAwPnz55GVlYWpU6eiffv2jaX5zNatW4eDBw8iICAA3377LSwtLdW2K+cjNzdX63zcuXMHCoUC165dQ79+/QAA9vb2ePXVV5GYmIjk5GQEBAQAAHbu3Im6ujqEhIRALBbrLAfGGGutuMhjjDGGGTNmYM2aNfjqq69URd7OnTtV25R+++03EBFu3bqFDz74oNHxHj58qNGmvKavIWXxWF9f/1zxP4+mxnX//n0AwOHDh3H48OFGx3sydzs7O4hEIo1+yhvZaLv+rOF1gA15eHjAz88PBw4cQFlZGTp06ICtW7cCAMLDwxuNqTnS09MBPP6fBU8WeMAf86G8nrIxT85HWFgYEhMTERcXpyrytm/fDgCYNWvWc8fNGGOM767JGGMMwAsvvAAfHx8cOnQIFRUVqKqqwnfffYeePXuif//+qn7KgsjX1xf0+JR/ra/U1NSWSkUwytxjY2OfmvuTD+3WVuA1HK+0tFRjW0lJSaNxzJ07FzU1Nfjyyy9RVVWlugGOrh/0vm3bNvj6+mLBggX49NNPNbYr4//++++fOh9+fn5q+73yyitwcHBAYmIi7t+/j59++gnZ2dnw8/ODm5ubTnNgjLHWios8xhhjAB4fsauursb+/fvx3XffobKyUuNh6lZWVujVqxeuXr2q01MsG1KerteSR/e0Ud41U1ePKPD29gYAZGRkaGw7depUo/sFBwejU6dO2Lp1K7755htUVFRg9uzZOompoXbt2iElJQX9+vXD/PnzsWHDBrXtzZ0PsViMkJAQ1NTU4KuvvuJn4zHGmAC4yGOMMQYAmDp1KsRiMXbu3ImdO3dCJBJpFHkA8M4776Cqqgrh4eFaT8ssKCh4rmfaKa8r++9//9vsMYQwYMAADBw4EHv27MHXX3+tsV2hUODEiRNNHm/KlCkwMTHBJ598gnv37qnaHz58iJUrVza6n5mZGUJDQ3HlyhUsW7YMpqamCA0NfaZcmsrW1hbJycno378/3n33Xaxfv161LSgoCE5OTli7di1+/PFHjX3r6urUnqfXkPK0zM2bN2Pv3r2wsbHB3//+d0FyYIyx1oivyWOMMSOVl5en9YYYSlFRUZBKpaqf7e3tIZfLcezYMZiYmGDYsGFwcXHR2C8iIgKnT59GfHw8MjIyIJfLIZPJUFJSgpycHJw5cwa7d+/Wum9TeHh4QCaTYe/evTA3N4ejoyNEIhHefvtt2NjY/On+586dazRvqVSKqKioZsUFAHv27IG/vz+mTJmC9evXo2/fvrCwsMCvv/6KzMxM3L17F9XV1U0aq2fPnoiKisK///1veHl5YdKkSZBIJEhISICXlxeys7M1bgijFBERgZiYGNy+fRsTJ04U9LlyykIvICAAkZGRICJERkbC3Nwc+/fvx+jRo+Hn54eRI0fCy8sLIpEIRUVFSE9PR4cOHbTehMfd3R3Dhw9XFYdz586FhYWFYDkwxlhrw0UeY4wZqfz8/KfeHOXdd99VK/KAx6dsHj16FPX19VqP4gGPrzHbsWMHXnnlFWzZsgWHDh1CZWUl7Ozs4O7ujpiYGMjl8mbHLRaLkZCQgCVLlmDPnj148OABAGD69OlNKvLOnz+P8+fPa91mY2PzXEVet27dkJWVhbVr1+LAgQPYvn07xGIxHBwcMHz48Gc+GrVy5Uo4OjoiNjYWGzduhJ2dHaZMmYL58+fj+++/13pTGABwdXXF0KFDcfLkSZ3fcEUbGxsbHDt2DIGBgViwYAEUCgUWLlyI/v3749KlS/j444/xww8/ICMjA+bm5ujSpQvGjx+P1157rdExw8LCVEUe33CFMcZ0S0RE1NJBMMYYY+wPKSkp+Nvf/ob33ntP66Mqqqur4ejoiLZt2+LGjRuNHvHTprCwEN26dUNISAh27Nihw6iFlZaWBn9/f0RHRz/1CDVjjDG+Jo8xxhhrMXfv3tW4wUx5eTmWLl0KABg/frzW/bZv346ysjJEREQ8U4HXUHx8PEQiEezt7Zu1v75ERUVBJBLB39+/pUNhjDGDwadrMsYYYy1k165diImJwciRIyGTyVBcXIykpCSUlpYiNDQUgwcPVuu/atUq3L17F5s2bYKdnR3efPPNZ35PW1tbREdHq35u27btc+chJLlcrnZasa4fFcEYY8aIT9dkjDHGWsjZs2excuVK/PTTT7h//z7EYjF69eqF0NBQvPnmmxpH6UQiEUxNTeHt7Y3Y2FgMGjSohSJnjDH2V8ZFHmOMMcYYY4wZEb4mjzHGGGOMMcaMCBd5jDHGGGOMMWZEuMhjjDHGGGOMMSPCRR5jjDHGGGOMGREu8hhjjDHGGGPMiHCRxxhjjDHGGGNGhIs8xhhjjDHGGDMiXOQxxhhjjDHGmBHhIo8xxhhjjDHGjMj/ATyWltfDE5exAAAAAElFTkSuQmCC", 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1tZXddtttbPjw4SwUCiW9G6679+R1Rbr37qGLd9xt3bqVXXHFFezwww9nffv2ZRMnTmT/+te/2KJFi7p8T11tbS276qqr2GGHHcZkWe7ynWoLFixg3/ve99jhhx/OIpEIO+KII9gZZ5zBnnrqKbZjx46ka7/66iv24IMPshEjRrBevXqx3Nxc9tprr6W03xXRaJR9+OGH7Pbbb2cnnXQSO+yww1goFGKDBg1iZ511FnvxxRdZW1tbl32t+pru/XFd+fqvf/2LXXXVVWzs2LGsb9++bMCAAeykk05izz33HGttbU3qn6pmb775JjvhhBNYr169GICka9LNAcYY+853vsMAsN69e7OWlpYur0k1xty5c9nEiRNZnz59zLndFWeddRYDwPLz81P6QRAEIRoSY4xlbEdJEARBEAQREKLRKI466igceuih2Lp1K2SZfouFIIjsgO5mBEEQBEH0SN544w00NDTgjjvuoA0eQRBZBX2SRxAEQRBEj+LZZ5/Fvn378Je//AWHHHIINm/ejAEDBvB2iyAIwjNok0cQBEEQRI9CkiREIhGcdNJJ+H//7//h9NNP5+0SQRCEp9DTNQmCIAiC6FHQz7cJgsh26AA6QRAEQRAEQRBEFkGbPIIgCIIgCIIgiCyCjmt6iK7r2L17N/r16wdJkni7QxAEQRAEQRBEFsEYQ2trK4488si0TwWmTZ6H7N69GyNHjuTtBkEQBEEQBEEQWUxNTQ2OOuqolP9OmzwP6devH4BE0vv372+pj6IoOPnkk7F27VpEIhFPr29sbMQxxxyD6upqDB482JI/PQ27+ecFLz/9tuvl+G7HctqfNMwX0jBf26Th7q8nHadHFA0D2bkW90QN2+0TNA3v378fI0eONPcdqaBNnocYRzT79+9veZMHJDaHQ4YM8fx6RVHM6+3409Owm39e8PLTb7teju92LKf9ScN8IQ3ztU0aTg/puHtE0TCQnWtxT9SwnT5B1XB3vxpGD17hjK7ruPHGG6Hrui/XE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPrNxLe6JGnZjSyRok8cZXddx6qmn2lpc7FxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NM2aZhwi0j5zMa1uCdq2I0tkZAYvRHUM/bv348BAwagpaUlEB/nHjhwAD/60Y/w17/+FYceeihvdwiCsAlpmCDEh3RMEGITNA1b3W/QJ3k+oGma+XdXbVVVzXY8HkdNTQ10XYeqquZPFNq3FUUx27FYDDt37oSu61AUBYwxMMY6tQEgJycH1157LXr16mVeD8C0la6taVpS205MqeJIFZPhe/t2qpjat72ISdd11NTUmOMENSZVVbFjxw7oup7ROsXj8U7zzcs6xeNx7Nq1C6qquo5J13Xs2LEjKWd26mRoS9M0WzGlqk2qOimKYmq+u5h69eqFa6+91vyl8KDrycu5ZzUmTdOwc+dOxGKxQMfUcX5msk6Gzox8eVknL+8Ruq5j586diMfjjuqUSlvdxdRVbdLNPU3TsGPHDmiaZqlOkUgkaS0Osp543CMURcGuXbsQj8cDH5OiKOb9PpN1cro+ZTomVVXNWjqpk6EtXddtxWT8X864d1iZe+3vF93VqeNaHAQ9WYE2eT4wfvx45ObmYtasWaioqAAAlJeXo7KyEgBQVlaG6upqAEBpaSk2btwIXddRXFyM2tpaAEBRURHq6+sBAIWFhWhubgYAfP7556isrISu68jLy0M0GoWqqsjLy4OqqohGo8jLywOQ+MmDQXNzMwoLCwEA9fX1KCoqAgDU1taiuLgYQOKpoCUlJQCA6upqlJWVAQAqKytRXl4OAKioqOg2ppKSEtTU1ACApZgKCgrQ2toKAN3G1NraioKCAs9i0nUdX375pRAxGf5msk6LFi3C5s2boeu6LzEtX74cW7ZswY4dO1zHpOs61q9fjx07djiqU35+PqqqqhCPx23FtG/fPqxbtw66rlueexs2bICu65bqBAC7du1yFFOm9WSlToC3eorH46iqqkJ+fn6gY9J1HWvXrkVTUxOXOm3ZsiXw9whd17Fp0yYsWbLEcZ2+/PJL6LpuKyZd17Fu3Trs27fP0tyLx+MoKytDPB63VKeGhgYYBF1PvO4RW7ZsESYm4/6dyTo5XZ94xLRlyxYsWbLEUZ0OHjyIsrIy6LpuKyZd17Fx40aUlpaaMXU399avX4///ve/0HXdUp2Ar7XMa+7NnDkTubm5mDBhAqxAxzU9xPj4tLGxEYMGDTJ32qFQKKmtqiokSTLbsixDluWUbUVREAqFzHY4HIYkSWYbSPy0oX07EokgHo9j3rx5uOCCC0wfIpGI+ZPLcDicsm38hNJodxUHj5gYY2bb+KkTxUQxZWtMxg9zZsyYgV69emVFTNlYJ4qJYkoXUywWQ35+vrkWZ0NM2VgnioliShWTpmlJazHvmJqamjB48GA6rsmDUChk/t1VOxwOm21Zls2Pi8PhsPnm+vbtSCRitkOhkPlRdiQSgSRJkCSpUxtIfrSqLMvm92VZNideqnYoFEpq24kpVRypYjJ8b99OF1P7ONzGpOuJ40GG/aDGBCQ+zdF1PaN1CoVC5vz0o06yLGP79u2mXTcx6bpufuLlpE6GthhjtmJKVZtUdZIkyTzeZqVOxlhOYsq0nqzUKV0cTmJijGHHjh3m2EGNSdd17N6926xpJutk6Mz4T4yXdfLyHmGcVDDGtlunVNrqLqaO947u5h5jDLt37wZjzHKdDIKuJx73CEmSsH37dsiyHPiYJEky7/eZrJPT9SnTMQEwa+mkToa2dF23FVPHe4eVudf+fmGlToY9uzH5pScr0CaPM8biouvWn+pl53oiPaLkk5efftv1cny3YzntTxrmiyj55Omnn7ZJw4RbRMpnNq7FPVHDbmyJBB3X9JCgPV1TURTk5eXhggsuSPrUgSAIMSANE4T4kI4JQmyCpmF6uqYgaJqGqqoqy0/KsXs9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCLSLlMxvX4p6oYTe2RII2eZxhjKGpqQlWP1C1ez2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9ukYcItIuUzG9finqhhN7ZEgo5reggd1yQIwktIwwQhPqRjghCboGmYjmsKgqZp2Lhxo61jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfGbjWtwTNezGlkjQJi8AtLW1+Xo9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCLSLlMxvX4p6oYTe2RIGOa3oIHdckCMJLSMMEIT6kY4IQm6BpmI5rCoKmadiwYYOtYyJ2rifSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHxm41rcEzXsxpZI0CbPB4wJo2lal21VVZPaxoepqqqaL2Vs31YUJaltXG+0GWOd2gCSnhik67r5fV3Xoapq2ramaUltuzF1FYdXMbWPw4uYdF0XIiZj7EzWqf385F0nKzG1z1Mm69RVbdLFZFxvJSZjrEzHRPcI72NqP1cyGVN3cQTpHsEYc1WnrrRlJaaOtemuTrqu26qTAenJenxBjam7+7eIdQpSTMa1dmNqf++wOvfa//+7u5gMezzr1L5tBdrk+cD48eORm5uLWbNmoaKiAgBQXl6OyspKAEBZWRmqq6sBAKtXr0b//v0RCoVQXFyM2tpaAEBRURHq6+sBAIWFhWhubgYALFy4EKNGjUIoFEJeXh6i0ShUVUVeXh5UVUU0GkVeXh4A4MCBA6ZPzc3NKCwsBADU19ejqKgIAFBbW4vi4mIAQE1NDUpKSgAA1dXVKCsrAwBUVlaivLwcAFBRUdFtTCUlJaipqQEASzEVFBSgtbUVALqNqbW1FQUFBZ7FFAqFIMsyNm/eHOiY9u7di+bmZoRCoYzW6YsvvsCIESMQCoV8qdPKlSsxbtw47N6923VMoVAIBw4cwO7dux3Vaf78+Tj22GPBGLMVU1NTE+rr6xEKhSzNva1bt0LTNIRCIUt1AoBdu3Y5iinTerJSJ8BbPTHGcOyxx2L+/PmBjsmYH0YcmazTunXrMG7cOGzdujXQ94hQKIRhw4Zh2bJljuq0efNmyLKMUChkK6ZQKIT6+no0NTVZmnuMMVRXV4MxZqlODQ0NMAi6nnjcI7Zu3Ypx48Zh3bp1gY9px44diMViCIVCGa2T0/Up0zHt3bsX48aNw7JlyxzVSVEUcz23E1MoFEL//v2xevVqANbm3pdffolIJIJQKGSpTsDXWualp5kzZyI3NxcTJkyAJRjhGS0tLQwAa2xsZIwxpqoqU1W1U1tRFLMdjUbZ6tWrmaqqTFEUpmmaeY3RjsfjZrutrc28Ph6PM13Xma7rndqMMRaLxdjs2bPN/sb3NU1jiqKkbRv+pIsjVUyp4kgVk+F7+3aqmNq3vYhJVVW2evVqFovFAh1TPB5nq1atYqqqZrRO7eenH3WKRqNszZo1LB6Pu45JVVW2atUq05bdOhnaUhTFVkypapOqTrFYzLy+u5ji8TibPXs2i0aj3OZe0O8RiqKw1atXs7a2tkDH1HF+ZrJOsViMrVmzhsVisUDfI4z7sTHf7dYpFouZvtiJqavapJt7iqKwVatWMUVRLNUpGo0mrcVB1hOPe0RX8zOoMVm5f/tRJ6frU6ZjisfjbM2aNSwajTqqk6EtVVVtxdTx3mFl7rW/X3RXp7Vr4+wf//jMHJ+3nhobGxkA1tLSwtIRtrYVJOwQCoWS/u7YDofDSe2+fft2+X2D9r/kGYlEzOs7fr9jW5Ik83uyLEOWZcvtVL5bjam7dne+p4vJaHsRk6Zp6Nu3b5c1C1pMhxxyiKWY0rXtxtR+fvoRUzgcRp8+fcyfvruJSdM0HHLIIeb4TurUt29fSJLU5fXpYuqqNunqZFzfXUzGcRA3MaVre60nKzFZbVuNw9Bw0GPqOD8zXac+ffogFArZXp+stL26Rxi1NMZ3UifDFzsx2a2NJEk45JBDIElSUhzp6mQQdD11FWsm9NRxfgY5pu7u337Vyen6lMmYNE1Dnz59EA6HHd/rDF/sxNTx3mF17nX3/+9wOIyCAuDKKyWMHn0qLrvMfkwd4/NaT+mgp2t6CD1dkyAILyENE4T4kI4JQkxeew348Y8BTQO+9a16fPHFABx2GH8N09M1BUFVVZSWlpq/TOn19UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGCbeIlM9sXIt7ooa766PrwC9+AdxxR2KDd911Oh5/fDkGDLDlFndok8cZSZIwaNCgpKOVXl5PpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NM2aZhwi0j5zMa1uCdqOF2fgweB738feP75xNdPPAG88YaGSETvYpRgQ8c1PYSOaxIE4SWkYYIQH9IxQYjBnj3AxRcDpaVATg7wxhvAddcFT8N0XFMQVFVFcXGxrWMidq4n0iNKPnn56bddL8d3O5bT/qRhvoiST55++mmbNEy4RaR8ZuNa3BM13FWfDRuA005LbPCGDAEWLkxs8ESGNnmckWUZI0aMSHr6lpfXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPrNxLe6JGu7Yp6AAOPNMYMcO4BvfAFasAM46y673wYOOa3oIHdckCMJLSMMEIT6kY4IILn/5C3D33YkHrEyZAnz0UeKTvPYETcN0XFMQVFVFUVGRrWMidq4n0iNKPnn56bddL8d3O5bT/qRhvoiST55++mmbNEy4RaR8ZuNa3BM1DADxuIprr63BnXcmNng33AAUFHTe4IkMbfI4I8syxo4da+uYiJ3rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn9m4FvdEDR88CFx9dQj/+tdIAMCTTwJvvQX06mXb5UAT7v4Swk+MM8F+XU+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrW4p2n46ydoSklP0MxGgv9jkyxHVVUUFhbaOiZi53oiPaLkk5efftv1cny3YzntTxrmiyj55Omnn7ZJw4RbRMpnNq7FPUnDyU/QZPjtb1fjqquCP++cQp/kcUaWZYwbN87WMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtwiUj6zcS3uKRqePx+48kqgtRX45jeBTz9lGDhwpBDzzim0yeOMLMsYOnSob9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGibcIlI+s3Et7gka7vgEzY8/BgYPlgGIMe+ckr3bV45ommb+3VVbVVWz3dbWhvz8fCiKAlVVoeu6eY3RVhTFbB88eNC8XlEUMMbAGOvUBoD2b8fQdd38vq7r5kfaqdqapiW17cSUKo5UMRm+t2+ni6l9HG5jUhQF+fn5iEajgY4pFouZdc9kndrPTz/q1NbWhvnz5yMWi7mOyahlLBZzVCdDW/F43FZMqWqTqk7RaNS83kqdjLGcxJRpPVmpU7o4nMQUj8eRn5+PgwcPBjqmjvMzk3WKRqOYP38+otFooO8RRo7a2toc1SmVtrqLqavapJt7xpyLx+OW62QQdD3xuEd0NT+DGpOV+7cfdXK6PmU6plgshvnz56Otrc1RnQxtGV9bjanjvcOYe7oO3H+/bj5B8/rrdcybp2Hw4OS4rdTJsMdr7nXUkxVok+cD48ePR25uLmbNmoWKigoAQHl5OSorKwEAZWVlqK6uNttHH300QqEQiouLUVtbCwAoKipCfX09AKCwsBDNzc0AgEWLFuFb3/oWQqEQ8vLyEI1Goaoq8vLyoKoqotEo8vLyAAAHDhwwfWpubkZhYSEAoL6+HkVFRQCA2tpaFBcXAwBqampQUlICAKiurkZZWRkAoLKyEuXl5QCAioqKbmMqKSlBTU0NAFiKqaCgAK2trQDQbUytra0oKCjwLKZQKITDDjvMjCOoMe3btw85OTkIhUIZrVNRURGOP/54hEIhX+pUWlqKiRMnYvfu3a5jCoVC6NOnD3bv3u2oTgsWLMBJJ50ExpitmJqamhAKhRAKhSzNverqagwcOBChUMhSnQBg165djmLKtJ6s1AnwVk+MMZx00klYsGBBoGMKhUJgjJlxZLJO5eXlmDhxotn2sk5e3iNCoRC+8Y1vmHHYrVNlZSUOO+wwhEIhWzEZ+m1qarI09xhjiMViYIxZqlNDQwMMgq4nHveI6upqTJw40WwHOaaamhr069cPoVAoo3Vyuj5lOqZ9+/Zh4sSJKC4udlQnRVEQi8UQCoVsxRQKhXD00UcnrbPLl6/D978PvPRSYqvz5JPAgw/+F1u3VpjxDRs2DKFQyFKdgK+1zEtPM2fORG5uLiZMmAAr0MvQPcR4OWFjYyMGDRpk7rRDoVBSW1VVSJJktmVZhizLKduKoiAUCpntcDgMSZLMNpD4aUP7diQSQTwex7x583DBBReYPkQiEei6Dl3XEQ6HU7Y1TQNjzGx3FQePmBhjZlvXdYqJYsrqmHRdR15eHmbMmIFevXplRUzZWCeKiWJKF5Pxab+xFmdDTNlYJ4ope2LatUvDJZfIWL1aQk4Ow9//zvCDH8iOY9I0LWkt5l2npqYmDB48mF6GzoNQKGT+3VU7HA6bbcYY5s2bZ04M4xdA27cjkYjZBhI/KVAUBZFIBJIkQZKkTm0AkCTJ7CPLsvl9WZbNiZeqHQqFktp2YkoVR6qYDN/bt9PF1D4OtzEZH/EbH7sHNSZN0zB//nwoipLROgEw56cfdWKMYe7cudA0zXVMiqJg/vz55o3bbp0AmD95sxNTqtqkqpOu6+YRESt1MsZyElOm9WSlTunicBKT8RNTg6DGpCgKCgoKzPmZyToZOtN1PdD3CEVRMG/ePPPok906pdJWdzF1vHd0N/dUVUVBQYH5H0MrdTIIup543CN0XcfcuXPBGAt8TLqum/f7TNYJcLY+ZTomTdPMWjqpk6EtRVFsxdT+3rFhAzBpUgirV0sYMgRYuFDCD34gd4qj/f3CSp0Me7zmXkc9WYE+yfMQ45O87nbW7TGO7/Tr1y9JzF5crygK8vLycMEFF5gTiUjGbv55wctPv+16Ob7bsZz2Jw3zhTTM1zZpuPvrScfpEUXDQHauxdmk4eLifrjqKsl8gubcucCxx7q3FTQNW91v0NM1OSNJkuUNoZPrifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn9m4FmeLht99tz9+8pPEA1amTgU++ggYPNg/X0WAjmtyRlEUzJkzx3zCjtfXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPrNxLRZdw4knaGq4667EBu/GG4GCgvQbPC98FQE6rukhTo9rRqNR9O7d2/IxEavXB+3j5SBiN/+84OWn33a9HN/tWE77k4b5Qhrma5s0TMc13SKKhoHsXItF1vDBg8D11yfeewcATz7J8OijEqyYFnktpuOaAmH8IqVf1xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtwiUj6zcS0WUcO1tcDFFwOrVgE5OQx//auG668PWdrg2bUlKnRckzPt36Xhx/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPbFyLRdTw+vXAaaclNnhDhgAFBRr6959ry65I884pdFzTQ5we1zTepWH1mIjV64P28XIQsZt/XvDy02+7Xo7vdiyn/UnDfCEN87VNGqbjmm4RRcNAdq7Foml4/nzgyiuR9ATNsWPt2xV5Lba636BP8gKAk586Et4hSj55+em3XS/HdzuWm58+ZsIO0TWi5JOnn37aJg0TbhEpn9m4Foui4VdfBS68MLHBmzoVWL7861ckOLEr0rxzAm3yONP+pap+XE+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrVYBA1rGvDAA0j5BE0ndkWad06h45oe4uS4pp8E7eNlgiDsQRomCPEhHROEc776KvEEzdmzE18/9RTwyCOw9YAVtwRNw3RcUxAYY9i/fz+s7rXtXk+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrU4yBqurU0cy5w9G+jVC3j3XeDRRztv8JzYFWneOYU2eZxRVRVLliyxdUzEzvVEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPbFyLg6ph4wmaq1cnnqC5cCFw7bXe2RVp3jmFjmt6CB3XJAjCS0jDBCE+pGOCsEd+PnDVVclP0DQesMKDoGmYjmsKgq7raGxshK7rvlxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NM2aZhwi0j5zMa1OGgafv75r3DRRQytrcC0aclP0PTSrkjzzim0yeOMpmkoLS2Fpmm+XE+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrU4KBrWNODnP2f4xS8OgaZJuOmmxDvxjCdoem1XpHnnFDqu6SF0XJMgCC8hDROE+JCOCSI9HZ+g+fTTwMyZmX2CZjqCpmE6rskR46cCmqZ12VZV1WzH43Hs2bMHuq5DVVXzY+P2bUVRzHYsFkNdXR10XYeiKGCMgTHWqQ0g6YlBxvVG2/hF01RtTdOS2nZiShVHqpgM39u308XUPg63Mem6jj179pjjBDUmVVVRW1sLXdczWqd4PN5pvnlZp3g8jr1790JVVdcx6bqO2trapJzZqZOhLU3TbMWUqjap6qQoinm9lToZYzmJKdN6slKndHE4iUnTNNTV1SEWiwU6po7zM5N1MnRm5MvLOnl5j9B1HXV1dYjH447qpCiKuZ7aiamr2qSbe5qmYffu3dA0zXKdDIKuJx73CEVRsHfvXsTj8cDHZOX+7UednK5PmY5JVVWzllbrtHs3w5Qp+v+eoMnwxz824eGHdQDWYzL+L2fcO6zMvfb3Cyt1MuxZiSkTerICbfJ8YPz48cjNzcWsWbNQUVEBACgvL0dlZSUAoKysDNXV1QCA0tJSrF27Frquo7i4GLW1tQCAoqIi1NfXAwAKCwvR3NwMAPj8889RXl4OXdeRl5eHaDQKVVWRl5cHVVURjUaRl5cHADhw4IDpU3NzMwoLCwEA9fX1KCoqAgDU1taiuLgYAFBTU4OSkhIAQHV1NcrKygAAlZWVKC8vBwBUVFR0G1NJSQlqamoAwFJMBQUFaG1tBYBuY2ptbUVBQYFnMem6jjVr1ggR06pVq6DrekbrtGjRIjNPfsS0fPlybNiwATt27HAdk67rWL16NXbs2OGoTvn5+Vi/fj3i8bitmPbt22fWxurcKysrg67rluoEALt27XIUU6b1ZKVOgLd6isfjWL9+PfLz8wMdk67rKC0tRVNTE5c6bdiwIfD3CF3XsW7dOixZssRxndasWQNd123FpOs6Vq1ahX379lmae/F4HKWlpYjH45bq1NDQAIOg64nXPWLDhg3CxGTMsUzWyen6xCOmDRs2YMmSJZbqVFoaw+mnA2vWyDjsMIZPP23DiBFF0HXdVky6rmPt2rUoLS01Y+pu7q1fv95ci63UCfhay7zm3syZM5Gbm4sJEybACnRc00OMj08bGxsxaNAgc6cdCoWS2qqqQpIksy3LMmRZTtlWFAWhUMhsh8NhSJJktoHETxvatyORCOLxOObNm4cLLrjA9CESiZg/uQyHwynbxk8ojXZXcfCIiTFmto2fOlFMFFO2xmT8MGfGjBno1atXVsSUjXWimCimdDHFYjHk5+eba3E2xJSNdaKYMhvTZ5+puO66EFpbJXzzmwxz5wJjxwYzJk3TktZi3nVqamrC4MGDuz2uSZs8D3HyO3nGT/+HDx8OWe7+g1U71ytKsM4QBxG7+ecFLz/9tuvl+G7HctqfNMwX0jBf26Th7q8nHadHFA0D2bkW89Dwn/8M/PSniYetTJsGfPQRMGhQ5jRst0/QNEy/kycIuq5jy5Yt0HXrj262cz2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9ukYcItIuUzG9fiTGpY04Cf/xz48Y8TbeMJmoMGufPFST+R5p1T6JM8D6GnaxIE4SWkYYIQH9IxQSSeoPmDHwBz5iS+DtoTNNMRNA3TJ3mCoOs6tm/fbusniHauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfGbjWpwJDdfWAlOnJjZ4vXoB770HPPJI5w1epjTsxpZI0CaPM7quY9euXbYWFzvXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPrNxLfZbw+XlwGmnAatXA4cdBhQWAtdc460vTvqJNO+cQsc1PYSOaxIE4SWkYYIQH9Ix0VPJzweuugpobQWOOw7mEzRFI2gapuOagqBpGqqqqiy/2NDu9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGCbeIlM9sXIv90vCf/wxceGFigzdtGrB8efcbvExp2I0tkaBNHmcYY2hqaoLVD1TtXk+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrXYaw3X1zfhgQck/PjHgK4DN9+c/ARNP3xx0k+keecUOq7pIXRckyAILyENE4T4kI6JnsJXXwHXXQd88kni61//Gnj4YTGeoJmOoGmYjmsKgqZp2Lhxo61jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfGbjWuzV2Lt3A1OmMHzyCdCrF8N779l/RUKmNOzGlkiEeTtAAG1tbb5eT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmmYcItI+czGtdjt2OvWARddBOzcKWHgQAWffCJj8uRQRn1x0k+keecEOq7pIXRckyAILyENE4T4kI6JbCYvD7j6auDAAeD44xNP0BwzhrdX3hI0DdNxTUHQNA0bNmywdUzEzvVEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPbFyL3Yz9yivA976X2OCdfTawZImGgweDr2E3tkSCNnkEQRAEQRAEQVhC04B77wV++tPEEzRvvRWYN8/aEzSJzEHHNT2EjmsSBOElpGGCEB/SMZFNHDgAXHst8Nlnia9/8xvgl78U/wma6Qiahum4piBomoaysjJbx0TsXE+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrXYzti7dgFTpiQ2eL17A//5D/DQQ19v8ETRsBtbIkFP1wwAffr08fV6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScOEW0TKZzauxVbGLitLPEFz925g6FBgzhzg9NOdjeXWF6/6iTTvnEDHNT2EjmsSBOElpGGCEB/SMSE6n36aOKL51VdAbm7ik7xjjuHtVeYImobpuKYgqKqK0tJSqKrqy/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPbFyLuxv7D38ALr00scH7zneAZctSb/BE0bAbWyJBmzzOSJKEQYMGQbL4G6t2ryfSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHxm41qcamxVTTw98957E0/QvO22xDvxBg70z89MadiNLZGg45oeQsc1CYLwEtIwQYgP6ZgQjdZW4JprEps6APjtb4EHHsjuJ2imI2gapuOagqCqKoqLi20dE7FzPZEeUfLJy0+/7Xo5vtuxnPYnDfNFlHzy9NNP26Rhwi0i5TMb1+KOY9fUAGedldjg9ekDfPgh8ItfWNvgiaJhN7ZEgp6uyRlZljFixAjIsrX9tt3rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn9m4Frcfe/Vq4HvfA2prgWHDEg9cmTgxc35mSsNubIlE9kbGEeOdG5qmddlWVdVs67qOkSNHQpZlqKoKXdfNa4y2oihmW9M0HH300ZBlGYqigDEGxlinNgC0P4mr67r5fV3XzZ9cpGprmpbUthNTqjhSxWT43r6dLqb2cbiNSZZlHHXUUab9oMYEwLwZZbJOmqaZ89OPOum6jlGjRpl23cRk3LAN7NbJ0JYkSbZiSlWbVHVijOGoo46CLMuW6mSM5SSmTOvJSp3SxeEkJkmScPTRR5tjBzUmWZZx5JFHmjXNZJ0MnTHGAn2PkGUZI0eONMe2W6dU2uoupo73ju7mniRJOPLIIyFJkuU6GQRdTzzuEYwxjBo1CrquBz4mxph5v89knZyuT3Zi+vRTGVOmMNTWAuPGAcXFGr79bXsxATBr6aROhrZkWbYVU8d7h5W51/5+YaVOhj27MfmlJyvQJs8Hxo8fj9zcXMyaNQsVFRUAgPLyclRWVgIAysrKUF1dDQBYuXIlFi5caH5sXFtbCwAoKipCfX09AKCwsBDNzc0AgIKCAnzxxRdQVRV5eXmIRqNmW1VVRKNR5P3vEPWBAwdMn5qbm1FYWAgAqK+vR1FREQCgtrYWxcXFAICamhqUlJQAAKqrq1FWVgYAqKysRHl5OQCgoqKi25hKSkpQU1MDAJZjam1tBYBuY2ptbUVBQYFnMamqigULFuDLL78MdEy7du1Cfn4+VFXNeJ2M+eZHnZYtW4aioiJs27bNdUyqqmL+/PnYtm2b4zotXrzYjMNqTHV1dZg3bx5UVbU09zZt2oSCggKoqmqpTkb9ncaUST1ZqRPgrZ6i0SgWL14c+JhUVcW8efPQ0NCQ8TqtXr0aRUVF2LRpU6DvEaqqYtGiRVi8eLGjOn355ZdYsGABVFW1FZNRm7q6Oktzz4ijfTtdnYyaO4kp29bcrmLatGkTioqKsHr16sDHtGXLFvP+nek6OVmfrMRUVbUFd921GZddxnDwoITTT9+PpUuBgwftx7Rr1y4UFRVh8eLFjup04MABs20nJlVVsXDhQqxcudLy3Fu3bh0+//xzqKpqqU7A11rmpaeZM2ciNzcXEyZMgCUY4RktLS0MAGtsbGSMMaaqKlNVtVNbURSzHYvF2I4dO5imaUxRFKZpmnmN0Y7H42Y7Go2ympoapmkai8fjTNd1put6p7Yx9uzZs83+xvcNW+naqqomtbuKI1VMqeJIFZPhe/t2qpjat72ISdM0tmPHDnOcoMakKArbvn070zQto3WKxWKd5puXdYrFYmznzp1MURTXMWmaxrZv356UMzt1MrSlqqqtmFLVJlWd4vG4qfnuYorH42z27NksGo1ym3tBv0eoqspqamrMHAU1po7zM5N1MnRm5MvLOnl5j9A0jdXU1LBYLOaoTqm01V1MXdUm3dxTVZVt376dqapqqU7RaDRpLQ6ynnjcI+LxONu5cyeLxWKBjykej5v3+0zWyen61F1MisLYHXeoDGAMYOyOOzQWjTqPSVEUs5ZO6mRoS9M0WzEZ/5cz7h1W5l77+0V3deq4FvPWU2NjIwPAWlpaWDro6ZoeQk/XJAjCS0jDBCE+pGMiiOzfD1x1FTB/fuKhKs8/D9x3X899gmY6gqZherqmIKiqah5T8eN6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScOEW0TKZ7asxTt2JJ6gOX8+0Lcvw+OPl+Oee1TXGzxRNOzGlkjQ0zU5I8syxo0bZ+upXnauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfGbDWrxqVeIJmnv2AEccAXzyCcOoUUf0KA27sSUStMnjjCzLGDp0qG/XE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPkVfiz/+GPjBD4C2NuDEE4HPPgNGjpQB9CwNu7ElEtm7fRUERVEwf/78pMexe3k9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCLSLlU9S1mLHE79xdcUVig/fd7wJLlwIjR/ZMDbuxJRK0yeNMKBTCxIkTEQqFfLmeSI8o+eTlp992vRzf7VhO+5OG+SJKPnn66adt0jDhFpHyKeJarCjAnXcCv/hFYrN3993AJ58A/fq5H9tLP930d9JPpHnnFDquyRlZljF48GDfrifSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHyKtha3tABXXgksWJB4auZLLwH33JP8BM2eqGE3tkSCPsnjjKIomDt3rq1jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfIq0Fm/bBpx5ZmKDd8ghwJw5wL33dn5FQk/UsBtbIkHvyfMQJ+/JY4yhtbUV/fr1g2Th2bV2rg/aez2CiN3884KXn37b9XJ8t2M57U8a5gtpmK9t0nD315OO0yOKhgFx1uKVK4GLLwb27gWOPDLxgJXx470Z20s/vervpJ/Ia7HV/QYd1+SMJEm2Xpxu93oiPaLkk5efftv1cny3YzntTxrmiyj55Omnn7ZJw4RbRMqnCGvxBx8AN9wARKPAyScDn34KHHWUN2N76aeX/Z30E2neOYWOa3JGURTMmTPH1jERO9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGibcIlI+g7wWMwY891zid/CiUeDCC4ElS9Jv8KyO7aWffvR30k+keecUOq7pIU6Pa0ajUfTu3dvyMRGr1wft4+UgYjf/vODlp992vRzf7VhO+5OG+UIa5mubNEzHNd0iioaB4K7FigLcdRfwt78lvr7nHuDFFwErD47siRq22ydoGqbjmgIRDtsrg93rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn0Fbi5ubge9/H1i4EJBl4OWXgZ/8xJuxnSCKht3YEgU6rskZVVWRl5cHVVV9uZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23SMOEWkfIZtLW4uhqYNCmxwTv00MT77+xu8Hqiht3YEgk6rukhTo9rqqqKcDhs+ZiI1euD9vFyELGbf17w8tNvu16O73Ysp/1Jw3whDfO1TRqm45puEUXDQLDW4uXLgUsuAfbtA0aMAObOBU46yZuxvfQzE/2d9BN5Lba636BP8gKAk59YEN4hSj55+em3XS/HdzuWm58+ZsIO0TWi5JOnn37aJg0TbhEpn0FYi//zH2D69MQG79vfBkpKnG3wuhrbLaJo2I0tUaBNHmdUVUVBQYGtYyJ2rifSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHzyXosVRcUzzwBXXw3EYol34RUVJd6F53bsnqRhN7ZEgo5reoiT45p+ErSPlwmCsAdpmCDEh3RMeEE8DtxxB/Dmm4mv77sP+N3vrD1Bk3BH0DRMxzUFgTGG/fv3w+pe2+71RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4YJt4iUT16+NjYyfOc7Kt58M/EEzT/9yforErqjJ2rYjS2RoE0eZ1RVxZIlS2wdE7FzPZEeUfLJy0+/7Xo5vtuxnPYnDfNFlHzy9NNP26Rhwi0i5ZOHr1u2AGecASxZEka/fgxz5ybeiecVPVHDbmyJBB3X9BA6rkkQhJeQhglCfEjHhFOWLQMuvRSorwdGjkw8QfOEE3h71fMImobpuKYg6LqOxsZG6Lruy/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPTPr63nvA2WcnNninnMKQn9+Eb32LNOxVP5HmnVNok8cZTdNQWloKTdN8uZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23SMOEWkfKZCV8ZA556CrjuusTDVi67DFiwQEVNTQlp2MN+Is07p9BxTQ+h45oEQXgJaZggxId0TFglFgNuvx14++3E1w88ADz3XOJhKwQ/gqZhOq4pCLquY+/evbaOidi5nkiPKPnk5affdr0c3+1YTvuThvkiSj55+umnbdIw4RaR8umnr42NwHnnJTZ4oRDwl78kXpEgy6RhP/qJNO+cQps8zui6jg0bNthaXOxcT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmmYcItI+fTL16oq4PTTEy82798fyMtLfKLnt12vxxZFw25siQQd1/QQOq5JEISXkIYJQnxIx0Q6lixJPEGzsREYNQr47DNg3DjeXhHtCZqG6bimIOi6jl27dtn6CaKd64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2iYNE24RKZ9e+/rOO8A55yQ2eKeeCqxY0fUGjzTsfT+R5p1TaJPHGV3XsWXLFluLi53rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn175yhgwaxZw442AogDf/z6waBFwxBH+2vV7bFE07MaWSNBxTQ+h45oEQXgJaZggxId0TLQnGgVuvTXxHjwAeOgh4Ne/pidoBpmgaZiOawqCruvYvn27rZ8g2rmeSI8o+eTlp992vRzf7VhO+5OG+SJKPnn66adt0jDhFpHy6dbXffsSxzPfew8Ih4G//Q34zW+63+CRhr3vJ9K8cwpt8jhDvwvAF1HyyctPv+32xN8FEGXOiYIo+eTpp5+2ScOEW0TKpxtfKyqA004DiouBgQOBgoLEJ3p+283k2KJo2I0tkaDjmh5CxzUJgvAS0jBBiA/pmFi4ELjiCqClBRgzBpg7Fzj+eN5eEVYJmobpuCZHNE0z/+6qraqq2Y7FYti8eTM0TYOqquZPFNq3FUUx29FoFJWVldA0DYqigDEGxlinNgC037/rum5+X9d1qKqatm34ky6OVDGliiNVTIbv7dvpYmofh9uYNE3D5s2bEY/HAx2ToijmPMlknWKxWKf55mWdYrEYqqqqoCiK65iMWhq27NbJ0JaqqrZiUhQFmzZt6lSbVHWKx+NmLa3UyRjLSUyZ1pOVOqWLw0lMqqqisrIS0Wg00DFpmoZNmzYlzZtM1Skej6OqqgrxeDzQ9whN01BZWYlYLOaoTqm01V1MXdUm3dxTVRWbNm2CqqqW62QQdD3xuEd0NT+DGpOV+3fHOr3+uo4ZMxIbvDPPZFi5Ejj22MysT37FlKpOiqKgqqoKsVjMUZ0MbWmaZismY/037h1W5l77uK3oybDHa+51jMkKtMnzgfHjxyM3NxezZs1CRUUFAKC8vByVlZUAgLKyMlRXVwMAVq1ahd27d4MxhuLiYtTW1gIAioqKUF9fDwAoLCxEc3MzAGDhwoXYt28fGGPIy8tDNBqFqqrIy8uDqqqIRqPIy8sDABw4cMD0qbm5GYWFhQCA+vp6FBUVAQBqa2tRXFwMAKipqUFJSQkAoLq6GmVlZQCAyspKlJeXAwAqKiq6jamkpAQ1NTUAYCmmgoICtLa2AkC3MbW2tqKgoMCzmBhj2LFjBzZu3BjomPbs2YOqqiowxjJapy+++AJ79+4FY8yXOq1YsQJNTU2exMQYw9atWx3Xaf78+WhoaDB/YmcnJqM2VuZeVVUVtm/fDsaYpToBwK5duxzFlGk9WakT4K2eFEVBQ0MD5s+fH+iYGGPYvHkztzo1NTWhqqoq0PcIxhj27NmDpUuXOqrTxo0bsWPHDjDGbMXEGENVVZXluacoCjZu3AhFUSzVqaGhAQZB1xOPe0RVVRWampqEiGnbtm3metNdnXQd+OEP9+H222WoKnDuufvwt7/twGGHZXZ98jImIP3c27NnD5qamrB06VJHdWpra8PGjRvBGLMVE2MMu3fvxqpVqyzPvQ0bNqCmpgaMMUtzD/hay7z0NHPmTOTm5mLChAmwAh3X9BDj49PGxkYMGjTI3GmHQqGktqqqkCTJbMuyDFmWU7YVRUEoFDLb4XAYkiSZbSDx04b27Ugkgng8jnnz5uGCCy4wfYhEItB1HbquIxwOp2wbP0Ux2l3FwSMm4yc9RhwUE8WUzTHpuo68vDzMmDEDvXr1yoqYsrFOFBPFlC6mWCyG/Px8cy3OhpiysU5exnTwIHDLLSF8+CEAAI89BjzyiIpQSNyYsrFOVmPSNC1pLeYdU1NTEwYPHkzHNXkQCoXMv7tqh8Nhsy1JkvlxcTgchvy/Ryy1b0ciEbMty7L5UXYkEoEkSZAkqVPbGNtAlmXz+7IsmxMvVTsUCiW17cSUKo5UMRm+t2+ni6l9HG5jMo4Hta9dEGMyftKsaVpG6yTLsjk//aiTJEnmT+3cxqRpmvmJmpM6GdrSdd1WTKlqk6pOAMzjbVbqZIzlJKZM68lKndLF4SQmXdexadMm89qgxqRpGrZs2WLOz0zWydCZ8bWXdfLyHmEcuWqvSTt1ArrWVncxdbx3dDf3dP3r92tZrZNB0PXE4x4BJD6FNf6THeSYAJj3+1Qx7dsXwjnnJDZ4OTnAP/4BPP44EIk4j8np+uRVTFbnHmPMrKWTOhna0jTNVkwd7x1W5h7w9f3Cip4Me3Zj8ktPVgh3fwnhN21tbb5eT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmmYcItI+Uzn6/r1wEUXATt2AEOGAB9/DEye7L/dII0tiobd2BIFOq7pIfR0TYIgvIQ0TBDiQzruGeTnA1ddBbS2At/8ZuIJmscey9srwguCpmF6uqYgaJqGDRs2WH5Sjt3rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn6l8/dOfgAsvTGzwpk0Dli/3doNHGva+n0jzzim0ySMIgiAIgiAIm2gacN99wN13A7oO3HwzMH8+MHgwb88Igo5regod1yQIwktIwwQhPqTj7OTAAeC664BPP018/cwzwEMPAe2eeUdkCUHTMB3XFARN01BWVmbrmIid64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2iYNE24RKZ+Gr9u3a5g8ObHB690b+M9/gIcf9m+DRxr2vp9I884p9HTNANCnTx9fryfSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHxWVw/ERRfJ2L0bGDoUmDMHOP10/+2Shr3vJ9K8cwId1/QQOq5JEISXkIYJQnxIx9nDJ58A114LHDwI5OYmnqA5ejRvrwi/CZqG6bimIKiqitLSUqiq6sv1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4YJt4iQT8aAl14CLr2U4eBB4Dvf0VFcnLkNHmnY+34izDu30CaPM5IkYdCgQZAsHuS2ez2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9ukYcItQc+nqiaennn//QBjEq65pgWffsowYEDmfCANe98v6PPOC+i4pofQcU2CILyENEwQ4kM6Fpf9+xMvOJ8/P/FQleefT7wyIYv3BUQXBE3DdFxTEFRVRXFxsa1jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuCWo+dy+HTjzzMQGr29f4KOPgHvuUbF8eXatxT1Rw25siQQ9XZMzsixjxIgRkGVr+2271xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtwSxHyWlAAXXwzU1QHDhydelTBhAqDr2bcW90QNu7ElEnRc00PouCZBEF5CGiYI8SEdi8UHHwA33ABEo8BJJwGffQYcdRRvrwieBE3DdFxTEFRVRVFRka1jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuCUo+WQMeO454MorExu8Cy8ElixJ3uBl41rcEzXsxpZI0CaPM7IsY+zYsbaOidi5nkiPKPnk5affdr0c3+1YTvuThvkiSj55+umnbdIw4ZYg5DMeB267DXjoocTX99yTeMl5v37J12XjWtwTNezGlkjQcU0PoeOaBEF4CWmYIMSHdBxsmpqA738fKCwEZBl4+WXgJz/h7RURJIKmYTquKQiqqqKwsNDWMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtzCM59btgBnnJHY4B16aOIBK+k2eNm4FvdEDbuxJRL0dE3OyLKMcePG2TomYud6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScOEW3jlc+lS4NJLgYYGYOTIxANWTjwxfZ9sXIt7oobd2BIJ2uRxRpZlDB061LfrifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbuGRz3ffBW65JfG7eBMmJD7BGz68+37ZuBb3RA27sSUS2bt9FQRFUTB//nwoiuLL9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGCbdkMp+MAU88AfzgB4kN3mWXAYsXW9vgAdm5FvdEDbuxJRL0SR5nQqEQJk6ciFAo5Mv1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4YJt2Qqn7EY8MMfAv/8Z+LrX/wCePbZxMNWrJKNa3FP1LAbWyJBmzzOyLKMwYMH+3Y9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCLZnIZ3194lO7pUuBUAj4858Tr0ywSzauxT1Rw25siQQd1+SMoiiYO3eurWMidq4n0iNKPnn56bddL8d3O5bT/qRhvoiST55++mmbNEy4xe98btoEnH56YoM3YACQn+9sgwdk51rcEzXsxpZI0HvyPMTJe/IYY2htbUW/fv0gSZKn1wftvR5BxG7+ecHLT7/tejm+27Gc9icN84U0zNc2abj760nH6fFzfi5aBFx+OdDcDIweDcydC+TmOh8vG9finqhhu32CpmGr+w06rskZSZJsvTjd7vVEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3+JXPN94Abr8dUNXEJ3lz5gBuH6aYjWtxT9SwG1siQcc1OaMoCubMmWPrmIid64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2iYNE27xOp+6DsycCdx6a2KDd/XViZede/G0/Gxci3uiht3YEgk6rukhTo9rRqNR9O7d2/IxEavXB+3j5SBiN/+84OWn33a9HN/tWE77k4b5Qhrma5s0TMc13eLlHGprA266CXj//cTXjz6aeGWCV++7zsa1uCdq2G6foGmYjmsKRDhsrwx2ryfSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLjFi3zW1QGXXAKsXAlEIsDrryc2fF6TjWtxT9SwG1uiQMc1OaOqKvLy8qCqqi/XE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3OJFPr/8EjjttMQGb9AgYMECfzZ42bgW90QNu7ElEnRc00OcHtdUVRXhcNjyMRGr1wft4+UgYjf/vODlp992vRzf7VhO+5OG+UIa5mubNEzHNd3itu4FBcCVVwL79wPHHpt4guY3v+mDo8jOtbgnathun6Bp2Op+gz7JCwBOfmJBeIco+eTlp992vRzf7VhufvqYCTtE14iST55++mmbNEy4xWk+//IX4IILEhu8yZOBFSv82+AZZONa3BM17MaWKNAmjzOqqqKgoMDWMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtziJJ+aBvz858CddybaN9yQOKI5ZIiPjiI71+KeqGE3tkTC0XHNMWPGuDb8s5/9DPfcc4/rcYKEk+OafhK0j5cJgrAHaZggxId07C1ffQX84AeJ994BwFNPAY88AgT4tDYhOEHTsK/HNbdt24ampiYwxhz92b59O5qbm53GllUwxrB//35Y3WvbvZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23SMOEWO/nctStxLHPOHKBXL+DddxOvScjUBi8b1+KeqGE3tkTC8XHN++67D9XV1Y7+ZHNC7aKqKpYsWWLrmIid64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2iYNE26xms81a4BTTwXKyoDDDwcWLQKuvTZDTv6PbFyLe6KG3dgSCUfHNWVZxuOPP45Zs2Y5Muq2f1Ch45oEQXgJaZggxId07J45c4DrrgMOHgRyc4HPPgOOOYa3V0RPIWga9vW4ZkVFBe6++27Hzrntn03ouo7Gxkbouu7L9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGCbekyydjwAsvAJddltjgnXceUFzMb4OXjWtxT9SwG1si4WiTd9xxx2GIi0cYue2fTWiahtLSUmia5sv1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4YJt6TKp6IAd9wBPPBAYrN3552Jd+ANGMDJUWTnWtwTNezGlkjQy9A9hI5rEgThJaRhghAf0rF9mpoSLzhfuDDxUJUXXwTuvZeeoEnwIWga9u24pq7r2LBhA3bv3t3p3xRFQVFRkd0hezS6rmPv3r22jonYuZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23SMOGWjvncsgWYNCmxwTvkkMTv4/3sZ8HY4GXjWtwTNezGlkjY2uRt374dJ5xwAk488USMHDkSF198MRoaGsx/b2xsxPTp0z13MpsxNs12Fhc71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtzSPp/LlgGnnw5s3AgcdRSwdCnwve/x9vBrsnEt7okadmNLJGwd17z66qvR2NiI119/Hc3NzXjggQewc+dOFBYW4sgjj0RdXR2GDx+e1QlLBx3XJAjCS0jDBCE+pGNr/POfwK23AvE4MGEC8MknwJFH8vaKIIKnYV+Oay5evBjPP/88Ro8ejZNPPhkFBQWYPHkyJk+ejB07dgAApCB8ni4Quq5j165dtn6CaOd6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScOEWzRNx3337cf11yc2eJddBixeHMwNXjauxT1Rw25siYStTd7BgwfRq1evrzvLMl5//XWcd955mDJlCrZs2eK5g9mOruvYsmWLrcXFzvVEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgk3RKPA9dcDv/994tOIBx8EPvgg8bt4QSQb1+KeqGE3tkTC1nHNiRMn4t5778X111/f6d9+/OMf41//+hdaWlqy+nGk6aDjmgRBeAlpmCDEh3TcNXv3ApdeCixfDoTDwKuvAj/8IW+vCKIzQdOwL8c1L7/8crz77rtd/tuf/vQnXHPNNaA3MthD13Vs377d1k8Q7VxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NM2aZhwwpdfAqedltjgDRzI8Pbbe3DLLcHPZzauxT1Rw25siYStTd7DDz+MvLy8lP/+pz/9KauT5Qf0uwB8ESWf2fh7AF6PL8rvAogy50RBlHzy9NNP26Rhwi4FBYlXJGzbBowdCyxdqmHUqK1C5DMb1+KeqGE3tkSCXobuIXRckyAILyENE4T4kI6/5tVXgZ/8BNA0YPJk4KOPgMMO4+0VQaQnaBr27WXo6dA0Ddu3b8fy5ctRVFTU5R8iGU3TUFVVZfn3GO1eT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmmYsIKmAffdB9x1V6J9443AggWJDZ5I+czGtbgnatiNLZHwZJOn6zqefvppHHHEERgzZgzOOussTJ8+vcs/RDKMMTQ1NVn+XUa71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJrrjwIHEA1Z+//vE108/Dbz5JmA8rF2kfGbjWtwTNezGlkh4clzzl7/8JX73u99h6NChuOiiizB8+HCEw+Eur33sscfcmgssdFyTIAgvIQ0ThPj0ZB3X1ADf+x6wbh3Quzfw1lvAVVfx9oog7BE0DVvdb3S9E7PJW2+9heOOOw6lpaU49NBDvRiyx6BpGiorK/GNb3wDoVDI8+uJ9IiST15++m3Xy/HdjuW0P2mYL6Lkk6efftomDQd3zvFm9erEBq+2Fhg6FPjkk8QTNTsiUj6zcS3uiRr2wlcR8OS45oEDB3DhhRcKvcFrbm7GKaecgpNPPhnjxo3D66+/bv5bTU0Npk2bhtzcXJx44ol4//33PbXd1tbm6/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22ThomOfPRR4sEqtbXAuHHAypVdb/AMRMpnNq7FPVHDbmyJgifHNSdNmoTRo0enfIeeCGiahlgshr59++Krr77CuHHjsGrVKgwZMgS1tbWoq6vDySefjD179mDChAnYvHkzDjnkkKQx6LgmQRBeQhomCPHpSTpmDPjd74Bf/jLx9YwZwL//DQTgv0QE4ZigaTijT9d85JFHMHv2bKxZs8aL4bgQCoXQt29fAEAsFgNjzPxlzOHDh+Pkk08GABxxxBE47LDD0NjY6IldTdOwYcMGW0/1snM9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCIB4Hbrvt6w3e3XcDn37a/QZPpHxm41rcEzXsxpZIePI7eRdeeCHefPNNfPe738XFF1+Mk046KeXO8sYbb3Rko6ioCL/73e+wevVq1NbW4uOPP8all16adM0f//hH/O53v8OePXtw0kkn4f/9v/+HU0891bKN5uZmTJ06FZWVlfjd736Hw7p4ecvq1auhaRpGjhzpKA6CIAiCIIhsoqkJuOIKYNEiQJYTT9L86U95e0UQPRtPjmvGYjH86Ec/wrvvvmt++iVJUtI1jDFIkuR4xzxv3jwsW7YMEyZMwOWXX95pk/fvf/8bN954I1599VWcdtpp+P3vf4/3338fmzZtwtChQwEAJ598MlRV7TR2QUEBjjzySPPruro6XH755fjoo48wbNgw8/uNjY2YPHkyXn/9dUyaNKnTOHRckyAILyENE4T4ZLuOq6qACy8ENm8GDj00cTzzggt4e0UQ3hE0DWf0uOb999+Pf/7znzjhhBPw5JNP4vXXX8ff//73pD9vvPEG/v73vzu28d3vfhdPP/00Lrvssi7//cUXX8Rtt92GW265Bbm5uXj11VfRt2/fJJtr167Fhg0bOv1pv8EDgGHDhuGkk07CkiVLzO/FYjFceumleOihh7rc4DlF0zSUlZXZOiZi53oiPaLkk5efftv1cny3YzntTxrmiyj55Omnn7ZJwz2bJUsSD1TZvBkYORJYtsz+Bk+kfGbjWtwTNezGlkh4clzz/fffx4QJE7B8+fKU78fzk3g8jtWrV+Phhx82vyfLMr7zne9g+fLllsaoq6tD37590a9fP7S0tKCoqAh33XUXgMSnkDfffDPOPvts3HDDDd2O1djYCEVRzK979eqFXsZbPzugaRpycnKgKAp0Xe92bDvXGz6094VIxm7+ecHLT7/tejm+27Gc9icN84U0zNc2abj767NVx++8I+HOO0NQFAmnnKLjww81DB8O2A1TFA0D2bkW90QN2+3DW8OxWAyxWMz8urW11VI/T45r9u/fH3feeSd++9vfuh3KEpIkJR3X3L17N0aMGIHi4mKcccYZ5nUPPvggFi9ejJUrV3Y7ZklJCW6//XbzgSt333037rjjDgDA0qVLMWXKFJx44onm9e+88w5OOOGEpDGMj087cvXVV+Paa691EipBEARBEERg0HXgvfeOx/vvHwcAmDRpF+69twy9emXvJyIEwZP33nsP//73vzt9PyMvQ58wYQKqqqq8GIobp556KtauXdvlv5111lm2fqJQXV2Nfv36mV+n+yRPVVWUl5fjxBNPtPQpqJ3rFUXBggULcO655wbiDHEQsZt/XvDy02+7Xo7vdiyn/UnDfCEN87VNGu7++mzScVsb8MMfhvDBB4nf9nnwQQ1PPjkUsny+4zFF0TCQnWtxT9Sw3T68NXzOOefgj3/8o/l1a2srjjnmmG77eTJTnnnmGZxzzjn47LPPcNFFF3kxpC0OO+wwhEIh1NXVJX2/rq4ORxxxRMb9GTx4sOUHr8iyjCFDhiAnJwehUMjz6wEgEokIv7D4hZN88oCXn37b9XJ8t2M57U8a5gtpmK9t0rB1O6LruK4OuOSSxIvNIxHgL38BbrklBIBv3TNJNq7FPVHDTvvw0nAkEsGhhx6a9LUVPNnkLViwANOmTcMll1yCs88+O+UrFCRJwq9+9SsvTCaRk5ODCRMmYOHCheYRTl3XsXDhQvzkJz/x3J6XhEIhHHvssb5dT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmm4Z7BhA3DRRcD27cCgQcDHHwNTp3oztkj5zMa1uCdq2I0tkfDk6ZqPP/448vPzwRjDwoUL8eKLL+Lxxx/v8o9TDhw4gLVr15pHKqurq7F27Vrs2LEDQOIJn6+//jreeustVFRU4K677sJXX32FW265xYMI/UNVVRQXF3f5agcvrifSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0nP3k5wOTJiU2eN/4BrBihXcbPECsfGbjWtwTNezGlkh48kneokWLvBgmLatWrcL06dPNr++//34AwE033YQ333wTV199Nfbt24dZs2Zhz549OPnkk5Gfn5/0nrsgIssyRowYAVm2tt+2ez2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9uk4ezmj38E7rkn8bCVqVOBDz8Ehgzx1oZI+czGtbgnatiNLZHw5OmaRAJ6GTpBEF5CGiYI8RFRx5oG3H8/8Ic/JL6++ebE7+Dl5HB1iyC4EDQNZ/Rl6IRzVFVFUVGRrWMidq4n0iNKPnn56bddL8d3O5bT/qRhvoiST55++mmbNJx9tLYCF1/89QbvN78B/v53/zZ4IuUzG9finqhhN7ZEwpNN3rJly3D//fdjz549Xf57bW0t7r//fqxYscILc4FH0zTz767aqqqabV3Xccwxx0CWZaiqar6qoX27/YsaNU3DmDFjIMsyFEUx3+vXsQ0kXuJuoOu6+X1d181JnaqtaVpS205MqeJIFZPhe/t2upjax+E2JlmWccwxx5j2gxoTAIwePRqyLGe0Trqud5pvXtZJ13WMHTvWtOsmJlmWMXr0aDNfdutkaEuSJFsxpapNqjoxxkzNW6mTMZaTmDKtJyt1SheHk5gkScKYMWPMsYMaU8f5mck6GTpjjAX6HiHLMsaMGZM0tp06pdJWdzF1VZt0c0+SJIwePRqSJFmuk0HQ9bR9O8OZZzLk5QG9ewP/+Q/Dz3+uQJL80xNjDGPHjoWu64FZc1O1GWPm/T6TdXK6PmU6JgBmLZ3UydCWLMu2YjL+L9f+/8rdzb329wsrdTLs2Y3JrzXXCp5s8l588UV8+umnKV9XMHz4cHz22Wd46aWXvDAXeMaPH4/c3FzMmjULFRUVAIDy8nJUVlYCAMrKylBdXQ0g8buGxiJTXFyM2tpaAEBRURHq6+sBAIWFhWhubgYAfP755+jfvz9kWUZeXh6i0ShUVUVeXh5UVUU0GkVeXh6AxMNqDJqbm1FYWAgAqK+vR1FREYDEBry4uBgAUFNTg5KSEgCJB9uUlZUBACorK1FeXg4AqKio6DamkpIS1NTUAIClmAoKCtDa2goA3cbU2tqKgoICz2KSZRktLS3YtGlToGOqq6vDtm3bIMtyRuu0aNEi9O3bF7Is+1KnFStWYMSIEdi1a5frmGRZxs6dO7Fr1y5HdcrPz8eQIUOg67qtmBobG1FVVQVZli3NvS1btqC+vh6yLFuqEwDHMWVaT1bqBHirJ13XMWTIEOTn5wc6JqPe+/fvz3id1q1bhxEjRmDLli2BvkfIsoycnBwsXbrUUZ02bdqElpYWyLJsKyZZllFVVYXGxkZLc0/XdZSVlUHXdUt1amhogEGQ9fTyy8U47TRg/XoJAwdGsXgxcP75/utpy5YtGDFiBNatWxeYNTdVTNu3b0ddXR1kWc5onZyuT5mOqa6uDiNGjMDSpUsd1Skej6OsrAyyLNuKyfgh66pVqwBYm3sbNmzAV199BVmWLdUJ+FrLvNbcmTNnIjc3FxMmTIAlmAeMHDmS3XzzzWmv+eEPf8iOPvpoL8wFlpaWFgaANTY2MsYYU1WVqaraqa0oitlua2tjn3/+OVMUhSmKwjRNM68x2vF43GwfPHjQvD4ejzNd15mu653ajDEWi8XY7Nmzzf7G9zVNY4qipG2rqprU7iqOVDGliiNVTIbv7dupYmrf9iImRVHY559/zqLRaKBjisViZt0zWaf289OPOrW1tbGFCxeyWCzmOiajlrFYzFGdDG3F43FbMaWqTao6RaNR8/ruYorH42z27Nnm/Ay6nryce1Zjisfj7PPPP2cHDx4MdEwd52cm6xSNRtnChQtZNBoN9D3CyFFbW5ujOqXSVncxdVWbdHPPmHPtv05Xp2g0mrQWB1FP//mPzvr00RnA2Akn6KyqKnP3iK7mJ+81N1Xbyv3bjzo5XZ8yHVMsFmMLFy5kbW1tjupkaEtRFFsxdbx3WJl77ePurk4d12Lea25jYyMDwFpaWlg6PNnk9erViz3yyCNpr3nkkUdY7969vTAXWIxNXndJb4+maayurs6cLF5eb0xKYxIRnbGbf17w8tNvu16O73Ysp/1Jw3whDfO1TRruniDrWNcZe+YZxoDEnwsuYGz//sz6IIqGGcvOtbgnathun6Bp2Op+w5NXKAwcONB8X10qtm/fnvS2diKBLMsYOnSob9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGhaXeBy44w7gzTcTX99zD/DCC0DYk/8ZWkekfGbjWtwTNezGlkh48jt5p59+Oj7++GPzDGtHduzYgdmzZ2PSpElemMsqFEXB/Pnzkx7i4OX1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4bFpKEBOO+8xAZPloFXXgFefjnzGzxArHxm41rcEzXsxpZIeLLJu//++3Hw4EGceeaZePvtt81fUqytrcVbb72FM888E21tbfj5z3/uhbmsIhQKYeLEiQiFQr5cT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmlYPCorgTPOABYvBvr1A+bOBe6+m58/IuUzG9finqhhN7ZEwpOf2UyZMgUvvvgifv7zn+OWW24BAPPRwkDiI9GXX34ZU6ZM8cJcViHLMgYPHuzb9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGxWLxYuDyy4HGRuDooxMbvHHj+PokUj6zcS3uiRp2Y0skPHsZ+r333os1a9bgjjvuwLe//W2MGTMGEyZMwF133YWysjLczfPHRAFGURTMnTvX1jERO9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGhaHN98Ezj03scE77TRg5Ur+GzxArHxm41rcEzXsxpZISMz4uI1wzf79+zFgwAC0tLSgf//+lvowxtDa2op+/fpBkiRPr1cUBXl5ebjgggsQiUQs+dPTsJt/XvDy02+7Xo7vdiyn/UnDfCEN87VNGu7+et461nXgkUeAZ59NfH3VVYkNX58+GXelS0TRMJCda3FP1LDdPrw13BGr+w0Ov2JLtEeSJMsbQifXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRoONl99BdxwA/Dxx4mvH3kEePLJxMNWgoJI+czGtbgnatiNLZEIkMyzB03TzL+7aquqarbb2towZ84cKIoCVVWh67p5jdFWFMVsHzx40LxeURSwxLsOO7UBoP2HtLqum9/XdR2qqqZta5qW1LYTU6o4UsVk+N6+nS6m9nG4jUlRFMyZMwfRaDTQMcViMbPumaxT+/npR52M8WOxmOuYjFrGYjFHdTK0FY/HbcWUqjap6hSNRs3rrdTJGMtJTJnWk5U6pYvDSUzxeBxz5szBwYMHAx1Tx/mZyToZcy4ajQb6HmHkqK2tzVGdUmmru5i6qk26uWfMuXg8brlOBpmce9u3q5gyheHjj4GcHIa33tLx9NOApgXrHtHV/OS95qZqW7l/+3Hfc7o+ZTomYz1sa2tzVCdDW8bXVmPqeO+wMvfax22lToY9uzH5pScrONrkXXfddfjoo4+cdPWkf9AZP348cnNzMWvWLFRUVAAAysvLUVlZCQAoKytDdXW12c7NzUU4HEZxcbH5ZNKioiLU19cDAAoLC9Hc3AwAWLRoESZNmoRwOIy8vDxEo1Goqoq8vDyoqopoNIq8vDwAwIEDB0yfmpubUVhYCACor69HUVERgMQTUIuLiwEANTU1KCkpAQBUV1ejrKwMAFBZWYny8nIAQEVFRbcxlZSUmK/TsBJTQUEBWltbAaDbmFpbW1FQUOBZTOFwGKNGjTLjCGpM9fX1GDBgAMLhcEbrVFRUhFNPPRXhcNiXOpWWluK8885DbW2t65jC4TCGDBlixmG3TgsWLDAfDmUnpubmZhxyyCEIh8OW5l51dTVGjBiBcDhsqU4AsGvXLkcxZVpPVuoEeKsnIPHwrwULFgQ6pnA4jF69epn35UzWqby8HOedd57Z9rJOXt4jwuEwvv3tb2P58uWO6lRZWYlRo0YhHA7biikcDuOQQw4x51t3c689VurU0NBgXp+pubd6NTBhgoY1ayQcdhjwm9+U4KKLUsfE8x5RXV2N8847z2ynigngf9/buXMnhg0bhnA4nNH7ntP1KdMx1dfX47zzzsPy5csd1cnYxITDYVsxhcNh5ObmJq2z3c29iooKjBkzBuFw2FKdgK+1zGvNnTlzJnJzczFhwgRYwdHv5MmyjMcffxyzZs2y29WT/kHFOCPb2NiIQYMGmTvtUCiU1FZVFZIkIRQKmT8NyMnJgaZpkGUZsixDVVWzrSgKQqEQZFlGPB4HAEQiEaiqivD/XmrTsR2JRBCPxzFv3jxccMEFpg+RSAS6rkPXdYTD4ZRtTdPAGDPbXcWRKqb2vqdqt49JURSEw2FIkmS2U8XEGDPbuq67jkmWZcRiMYTD4U6xBikmTdMQj8fRu3dvMMYyWidjfhq+e1mn9nVoH4eTmGRZRjQaRU5OTtr4UtXJ0JZh12pMqWqTau4ZC1mvXr3MnwSmiknXdeTl5WHGjBno1atX4PXE4x5hjAcAOTk5gY1JkqSk+ZnJOhnzTJIk098g3iPC4TDi8ThkWU4bX6o6pdJWdzF1VZt0c48xhmg0it69e0OSpG7rFIvFkJ+fb67Ffs+92bNl3HSTjIMHgdxchs8+k3DUUcG9RxifdhhrQRDW3FRtK/dvP+57TtenTMdkrFvGtXbrZGirz/9+YdRqTKFQKOneYWXutY9b07S0ddI0LWkt5r3mNjU1YfDgwd3+Tp7j45pr167F22+/7ehPtmO8cyMUCnXZDofDSe/lyM/PNyeD/L+D8u3bkUjEbEuShHnz5pmTxFi0O7aNaw2MiW+0jYmXqh0KhZLadmJKFUeqmAzf27fTxdQ+DrcxqaqK+fPnmwtMUGPSdR0FBQXmDSdTdQK+np9+1AlI/OTLuIm5iUlVVRQUFJgLjN06GdoybrpWY0pVm1R1Yoxh/vz55gbFyj1CFD1ZqVO6OJzEpGka5s2bl1THIMbUcX5msk5AQmeMsUDfI1RVRX5+vjmu3Tql0lZ3MXVVm3RzT9M0LFiwwPyPoZU6OY3JTp0YA55/Powrr0xs8M4/HygulnDMMcG+RzDGzE86grLmpmpbuX/7cd9zuj5lOibjB5OGDbt1MrRlbBitxtTx3mFl7rWP20qdnMbkl56s4PiTPMNZuxjmHs/iT/LsPl2z/U8/vbxeUYL1NKAgYjf/vODlp992vRzf7VhO+5OG+UIa5mubNByMp2vGYsAddwBvvZX4+ic/AV56CQgL8Hg9UTQMZOda3BM1bLdP0NZiX5+u+cYbbzh2zODkk092PUa20P4jXT+uJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzTMl/r6xAvOlywBQiHg5ZcB0V5NHKR8dkc2rsU9UcNubImCo+OaN910k+s/J510ktexCIlxTERVVV+uJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzTMl4qKxIvNlywB+vcH5s4Vc4MXlHx2RzauxT1Rw25siQS9DN1DnBzX9JOgfbxMEIQ9SMMEIT5+6XjBAuDKK4GWFuCYY4DPPgNycz0bniCI/xG0tdjqfoPek8cZxhj2798Pq3ttu9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGubDq68C3/1uYoN35pnAypXibvCCkE+rZONa3BM17MaWSNAmjzOqqmLJkiW2jonYuZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23ScGZRVeDee4G77gI0DbjhBmDhQuDwwzPuimeIomEgO9finqhhN7ZEgo5reggd1yQIwktIwwQhPl7peP9+4JprgHnzEl//+tfAww8DAX8gJUEIT9DWYjquKQi6rqOxsdF8P4/X1xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0azgzbtiWOZc6bB/TpA7z/PjBzZnZs8ETRMJCda3FP1LAbWyJBmzwf0DTN/LurtqqqZjsWi6GkpASapkFVVXOytW8rimK2o9Goeb2iKGCMgTHWqQ0g6Zyxruvm93VdNz+eTtU2/EkXR6qYUsWRKibD9/btdDG1j8NtTJqmoaSkBPF4PNAxKYpi1j2TdWo/P/2oUywWQ2lpKRRFcR2TUUvDlt06GdpSVdVWTKlqk6pO8XjcvN5KnYyxnMSUaT1ZqVO6OJzEpKoqSkpKEI1GAx1Tx/mZyTrF43GUlpYiHo8H+h5h5CgWizmqUyptdRdTV7VJN/eMOaeqquU6GTiZe0uXajj1VGDDBmD4cIaiIuDSS4OzPrnVU1fzM6gxWbl/+3Hfc7o+ZTomRVFQWlqKWCzmqE6GtjRNsxVTx3uHlbnXPm4rdTLs2Y3JLz1ZgTZ5PjB+/Hjk5uZi1qxZqKioAACUl5ejsrISAFBWVobq6mqz/X//93+IRCIoLi5GbW0tAKCoqAj19fUAgMLCQjQ3NwMAFi1ahEmTJiESiSAvLw/RaBSqqiIvLw+qqiIajSIvLw8AcODAAdOn5uZmFBYWAgDq6+tRVFQEAKitrUVxcTEAoKamBiUlJQCA6upqlJWVAQAqKytRXl4OAKioqOg2ppKSEtTU1ACApZgKCgrQ2toKAN3G1NraioKCAs9iikQiOOqoo1BVVRXomOrr69G3b19EIpGM1qmoqAinnnoqIpGIL3UqLS3F+eefjz179riOKRKJoH///tizZ4+jOi1YsABTp06FJEm2YmppaUFOTg4ikYilubdt2zYMGzYMkUjEUp0AYNeuXY5iyrSerNQJ8FZPkiRh6tSpWLBgQaBjikQikCQJX331VcbrtH79epx//vnYtm1boO8RkUgE3/72t7F8+XJHdaqqqsJRRx2FSCRiK6ZIJIKcnBy0tLRYmnuSJCEWi0GSJEt1amhogIHdmJ5/fjfOOUfCvn3AcccdxLvvbsEppwRrfXKrp23btuH888/H+vXrAx/Tzp07MWTIEEQikYze95yuT5mOqb6+Hueffz6WL1/uqE6apiEWiyESidiKKRKJ4P/+7//M+WZl7lVUVGD06NGIRCKW6gR8rWVeepo5cyZyc3MxYcIEWMGz38mLx+OYPXs2SktL0dzc3OUuU5Ik/O1vf/PCXCAxzsg2NjZi0KBBZg5CoVBSW1VVSJKEUCiEeDyOxsZGDB06FLquQ5ZlyLIMVVXNtqIoCIVCkGUZsVgMzc3NOPzww6FpmvkSR1VVk9qRSATxeBzz5s3DBRdcYPoQiUSg6zp0XUc4HE7ZNn6KYrS7iiNVTO19T9VuH5OiKAiHw5AkyWynisn4SY8Rh9uYJEnC3r17zRtcUGNSVRX79u3DsGHDACBjdYrH42hqakqab17WSVVVNDc3Y/DgwQDgKiZJklBXV4fDDz/czJmdOhnaOuyww0wfrcSUqjap5p6iKKivr8ewYcPMn/6liknXdeTl5WHGjBno1atX4PXE4x4hyzLq6+sxcOBA9OrVK7AxAUian5msk6ZpaG5uxqBBgyDLcmDvEaFQCPv27cOgQYOQk5Nju06KoqChoQFDhw5N0lZ3MXVVm3RzT9d11NXVYdiwYeY46eoUi8WQn59vrsVWYmIMeOqpEJ58EgCASy4B3npLw6GHBm99cqsnXdfR1NSEgQMHIhQKBTomK/dvP+57TtenTMcEAI2NjRg4cCDC4bDtOhnaOuKIIyBJkuWYZFnG3r17MXjwYOTk5Fiae+3vF7qup62TpmlJazFvPTU1NWHw4MHdPwOEecC2bdvYN77xDSbLMpMkKeUfWZa9MBdYWlpaGADW0tJiuY+iKGzhwoVMURTPr4/H42z27NksHo9b9qenYTf/vODlp992vRzf7VhO+5OG+UIa5mubNNw9dnV88CBjV1/NGJD48+CDjGmapa5CIoqGGcvOtbgnathun6CtxVb3G558knf55Zdj9uzZuOGGG3DrrbfiqKOOMnevHRk1apRbc4GFnq5JEISXkIYJQnzs6HjPnsSndiUlQDgM/OUvwK23ZshRgiC6JGhrcUafrllYWIhzzjkHb731FqZOnYqxY8di1KhRXf4hktF1Hbt27Ur6xU4vryfSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs07B3l5cCppyY2eIMHAwsW9IwNnigaBrJzLe6JGnZjSyQ82eTpuo7x48d7MVSPQ9d1bNmyxdbiYud6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScPe8NlniVck1NQA3/wmsGIFMG2aZ8MHGlE0DGTnWtwTNezGlkh4clzz3HPPRe/evfHpp5964ZOw0HFNgiC8hDRMEOKTTseMAS+9BDzwQKJ99tnABx8AgwZxcpYgiE4EbS3O6HHNZ599FoWFhfjggw+8GK5Hoes6tm/fbusniHauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzTsHEUB7rwT+PnPExu8228H8vN73gZPFA0D2bkW90QNu7ElEp5s8ubOnYvp06fj6quvxtlnn40HHngATz75ZKc/Tz31lBfmsoqg/C5AT0WUfGbj7wF4Pb4ovwsgypwTBVHyydNPP22Thp3R1ATMmAG89hogScCLLwKvvgoE4EOCjCOKhoHsXIt7oobd2BIJT45rGu+Z6daYJFl+S7uI0HFNgiC8hDRMEOLTUceVlcBFFwGbNwOHHgq8917ia4IggknQ1uKMHtdctGiRpT/Gm92Jr9E0DVVVVZY3v3avJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRsjy++AE47LbHBO/poYNky2uCJomEgO9finqhhN7ZEwpNN3tSpUy3/IZJhjKGpqQlWP1C1ez2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9ukYeu8+aaEc89NHNU87TRg5UrgxBNtDZGViKJhIDvX4p6oYTe2RMKT45pEAjquSRCEl5CGCUJ8olEF1167DbNnfwMAcPXVwBtvAH36cHaMIAhLBG0tzuhxTYNly5bhtttuw8SJE3Hcccdh4sSJuP3227F06VIvzQQe46NfTdO6bKuqarZjsRgqKiqgaRpUVTV/AbR9W1EUsx2NRs3rFUUBYwyMsU5tAEk/ndB13fy+rutQVTVt2/AnXRypYkoVR6qYDN/bt9PF1D4OtzFpmob//ve/iMfjgY5JURT897//haZpGa1T+/npR51isRg2btwIRVFcx2TU0rBlt06GtlRVtRVTqtqkqlM8Hjevt1InYywnMWVaT1bqlC4OJzGpqoqKigpEo9FAx9RxfmayTvF4HBs3bkQ8Hg/0PULTNFRUVCAWizmqUyptdRdTV7VJN/dUVcWXX34JVVW7jenAAeCqq2RzgzdrFsM776jo0yeYeuJxj+hqfgY1Jiv3bz/q5HR9ynRMiqJg48aNiMVijupkaEvTNFsxdbx3WJl77eO2UifDnt2Y/NKTFTzb5N13332YMmUK/va3v2H16tWoqqrC6tWr8de//hVTp07F/fff75WpwDN+/Hjk5uZi1qxZqKioAACUl5ejsrISAFBWVobq6moAwOrVq1FfXw8AKC4uRm1tLQCgqKjI/H5hYSGam5sBAAsXLsT+/fsBAHl5eYhGo1BVFXl5eVBVFdFoFHl5eQCAAwcOmD41NzebvxNZX1+PoqIiAEBtbS2Ki4sBADU1NSgpKQEAVFdXo6ysDABQWVmJ8vJyAEBFRUW3MZWUlKCmpsZyTAUFBWhtbbUUU2trKwoKCjyNac+ePdi4cWOgY9qzZw+2b9+e8Tp98cUXaGlp8a1OK1asQFtbG3bu3OlJTDU1Ndi5c6ejOs2fPx8HDhxwFNO2bdvMmLqbe1VVVdi9e7flmABg165djmLioSce94gDBw5g/vz5gY9p69atvuopVUxr165FW1sbqqqqAn+PaGpqMn8wbLdOGzduxJ49exzFtG3bNltzr6qqqtuY/v3vZZg8Gfj00xAiEQ1vvaXi9tt3Y/nyYOsp0/eIqqoqtLW1Ye3atYGPafv27eYak8k6uVmfMhnTnj170NbWhqVLlzquU1VVlaOY6uvrsXr1agDW5t6GDRtQV1dnuU4A0NDQ4Cgmr/Q0c+ZM5ObmYsKECbAE84A333yTSZLEjjvuOPbuu++y2tpapus627NnD3vvvffY8ccfz2RZZm+99ZYX5gJLS0sLA8AaGxsZY4ypqspUVe3UVhQlqa1pWtp2PB5Pauu6ntTWdb1TmzHGYrEYmz17ttnf+L6maUxRlLRtVVWT2l3FwSOm9m2KiWLK9pji8TibPXs2i0ajWRNTNtaJYqKYOsa0cqXGhg/XGcDY0KE6e/bZxea4osaUjXWimCgmKzF1XIt5x9TY2MgAsJaWFpYOTzZ5p512Ghs5ciRrbm7u8t+bm5vZyJEj2WmnneaFucBibPK6S3p7VFVl69evNyeXl9cbk9KYRERn7OafF7z89Nuul+O7Hctpf9IwX0jDfG2Thjvz/vuM9enDGMDYuHGMbd5MOk6HKBpmLDvX4p6oYbt9grYWW91veHJc88svv8QVV1yBAQMGdPnvAwYMwBVXXIEvv/zSC3MEQRAEQRCBgjHgmWeAK68E2tqA73438YqE0aN5e0YQRE/Ek6dr9uvXDz/60Y/w0ksvpbzm/vvvx+uvv26eW81G6OmaBEF4CWmYIMQgFgNuuw14553E1/feCzz/PBAOk44JQnSCpuGMPl3zW9/6Fj788MOkB320p7W1FR9++CG+9a1veWEuq9A0DWVlZZaflGP3eiI9ouSTl59+2/VyfLdjOe1PGuaLKPnk6aeftknDwL59wDnnJDZ4oRDw5z8Dv/99YoNHdI8oGgaycy3uiRp2Y0skPNnk3XHHHdi5cyfOOOMMfPjhh+YTderr6/HBBx9g0qRJ2LlzJ+666y4vzGUdfWy+LMfu9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bfdkDf/3v4kXmy9bBgwYAMybB9x5pyMXejSiaBjIzrW4J2rYjS1R8Oxl6Pfccw9eeeUVSJIEAJBl2XyHBGMMP/3pT/Hyyy97YSqw0HFNgiC8hDRMEMFl/nzgqquA/fuBsWOBzz4Djj++83WkY4IQm6BpOOMvQ//DH/6AxYsX4+abb8bJJ5+M0aNH4+STT8Ytt9yCxYsXZ/0GzymqqqK0tNR8waHX1xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tN1TNfzgg9txwQUM+/cDkycDK1Z0vcEjukcUDQPZuRb3RA27sSUSnp4Ynzx5MiZPnuzlkFmPJEkYNGiQ+Qmo19cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bTd0zSsqsDPfibjj38cBQC4+Wbg1VeBXr2ceEwA4mgYyM61uKdp2K0tkfDsuCZBxzUJgvAW0jBBBIeWFuDqqxPHNAHg2WeBBx8Euvs/IumYIMQmaBrO+HFNwhmqqqK4uNjWMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tN1TNFxdDUyalNjg9e3L8MwzG/Hzn6vdbvCI7hFFw0B2rsU9RcNe2RIJR8c1b731VkiShGeeeQbDhg3DrbfeaqmfJEn429/+5sRk1iLLMkaMGAFZtrbftns9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bPUHDy5YBl14K1NcDRx4JzJnDcPjhfQI/50RBFA0D2bkW9wQNe2lLJBwd15RlGZIkoaKiAt/85jctJ0iSpKx+HwUd1yQIwktIwwTBl3/8A/jhD4F4HPj2t4FPPgFGjLA3BumYIMQmaBr29bhmdXU1tm7dijFjxphfW/mzdetWZ9FkMaqqoqioyNZRLzvXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20na0a1nXg0UeBG25IbPAuvxwoKkps8ESZc6IgUj6zcS3OVg37ZUskHG3yRo0ahVGjRiEcTpz2NJ5QY3y/qz+DBw/O6ifYtMf4tFLTtC7bqqqabV3Xccwxx0CWZaiqar5bsH1bURSzrWkaxowZA1mWoSgKGGNgjHVqA4n3Exroum5+X9d1c1KnamualtS2E1OqOFLFZPjevp0upvZxuI1JlmUcc8wxpv2gxgQAo0ePNt8/mak66breab55WSdd1zF27FjTrpuYZFnG6NGjzXzZrZOhLUmSbMWUqjap6sQYMzVvpU7GWE5iyrSerNQpXRxOYpIkCWPGjDHHDmpMHednJutk6IwxFuh7hCzLGDNmTNLYduqUSlvdxdSxNs3NCq64guHXv058/ctfMrz/PpCTk4hDkiSMHj0akiRZrpNB0PXE4x7BGMPYsWOh63rgY2KMmff7TNbJ6fqU6ZgAmLV0UidDW7Is24rJ+L9c+/8rdzf32t8vrNTJsGc3Jr/0ZAVPDqIec8wx3b4H7w9/+AOOOeYYL8wFnvHjxyM3NxezZs1CRUUFAKC8vByVlZUAgLKyMlRXVwMAVq1aZS4yxcXFqK2tBQAUFRWhvr4eAFBYWIjm5mYAwOeff47+/ftDlmXk5eUhGo1CVVXk5eVBVVVEo1Hk5eUBAA4cOGD61NzcjMLCQgBAfX09ioqKAAC1tbUoLi4GANTU1KCkpARA4tPZsrIyAEBlZSXKy8sBABUVFd3GVFJSgpqaGgCwFFNBQQFaW1sBoNuYWltbUVBQ4FlMsiyjpaUFmzZtCnRMdXV12LZtG2RZzmidFi1ahL59+0KWZV/qtGLFCowYMQK7du1yHZMsy9i5cyd27drlqE75+fkYMmQIdF23FVNjYyOqqqogy7KlubdlyxbU19dDlmVLdQLgOKZM68lKnQBv9aTrOoYMGYL8/PxAx2TUe//+/Rmv07p16zBixAhs2bIl0PcIWZaRk5ODpUuXOqrTpk2b0NLSAlmWbcUkyzKqqqrQ2NiImhrglFPaMHu2hJwc4L771mLmzFbI8tcx6bqOsrIy6LpuqU4NDQ0wCLqeeNwjtmzZghEjRmDdunWBj2n79u2oq6uDLMsZrZPT9SnTMdXV1WHEiBFYunSpozrF43GUlZVBlmVbMRk/ZF21ahUAa3Nvw4YN+OqrryDLsqU6AV9rmZeeZs6cidzcXEyYMAGWYB4gSRJ74okn0l7z1FNPMVmWvTAXWFpaWhgA1tjYyBhjTFVVpqpqp7aiKGa7ra2Nff7550xRFKYoCtM0zbzGaMfjcbN98OBB8/p4PM50XWe6rndqM8ZYLBZjs2fPNvsb39c0jSmKkratqmpSu6s4UsWUKo5UMRm+t2+niql924uYFEVhn3/+OYtGo4GOKRaLmXXPZJ3az08/6tTW1sYWLlzIYrGY65iMWsZiMUd1MrQVj8dtxZSqNqnqFI1Gzeu7iykej7PZs2eb8zPoevJy7lmNKR6Ps88//5wdPHgw0DF1nJ+ZrFM0GmULFy5k0Wg00PcII0dtbW2O6pRKW93FZNhdsiTOhg1jDGDs8MN1tmxZ13Uy5lz7r9PVKRqNJq3FQdYTj3tEV/MzqDFZuX/7USen61OmY4rFYmzhwoWsra3NUZ0MbSmKYiumjvcOK3Ovfdzd1anjWsxbT42NjQwAa2lpYenw9GXo6di5cyf69euXKXNcCYVCSX93bBvHXAEgJycHJ5xwAmRZTnqATftr2v+SZ69evbq8vv01Rrv98dj211tpp/LdSkxW2l35a6UtSZLZ9iImXddxwgknmGMGNaZwOJxU90zVKdX89KpOOTk5GDduHMLhsOuYjFoaX9utk6GtUCjUZaypYkpVm1R1ikQiXeY0VUzGWE5iyrSe0rX90pNR917/ext1kGNqPz8zWSdJkjBu3DhEIpFuY+V5jzBqmZOT021MXfmeSltW4ti8+RTcd18YsRhw4onAJ59IGDUKADr7HgqFzHtF+zjS1ckg6Hrq2M6Enrqan0GNycr92486OV2fMh2TrusYN24ccnJyksbvLr6O2pJl2VZMHe8dVuaenfuFcTzSSUzt237oKR2ON3lPPvlk0tdffPFFl9dpmoaamhr861//wumnn+7UXNYiyzKGDh3q2/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22LrmFdB371KxnPPDMAAHDxxcA//wkceqh/fhLJiJTPbFyLRdew034izTunOP6dvMcff9z8I0kSvvjii6TvGX+eeuopvPnmmxg8eDCee+45L33PChRFwfz585Me4uDl9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bYus4a++Ar7/feCZZxJfP/CAho8/Tr/B88JPIhmR8pmNa7HIGnbTT6R55xTHn+QtWrQIQOJJMWeffTZuvvlm3HTTTZ2uC4VCGDx4MI4//vikj0OJBKFQCBMnTrT80avd64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2hZVwzU1iU/t1q4FcnIYXnrpK9x5Z19Y+W+KKHNOFETKZzauxaJq2G0/keadUxxv8qZOnWq2H3vsMUybNi3pe4Q1ZFnG4MGDfbueSI8o+eTlp992vRzf7VhO+5OG+SJKPnn66adtETW8ciVwySVAXR0wdCjw8ccSJk3q5uM7D/0kkhEpn9m4FouoYS/6iTTvnOLJR2uPPfYYbfAcoigK5s6da+uol53rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aFk3D774LTJ2a2OCdeCJQUgJMnEga5olI+czGtVg0DXvVT6R55xSJsXZvzHZIcXExPvjgAzz44IM44ogjOv17bW0tfve73+Gqq67K6oev7N+/HwMGDEBLSwv69+9vqQ9jDK2trejXr5+ll8XbuV5RFOTl5eGCCy5IesoP8TV2888LXn76bdfL8d2O5bQ/aZgvpGG+tkXRcOIBK1///l37B6z4qWGAdNwdomgYyM61WBQNe91P5LXY6n7Dk0/yXnjhBXz66addbvAAYPjw4fjss8/w0ksveWEuq5AkCf3797c8Ke1eT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTtgga7viAlV/+EkkPWCEN80WkfGbjWiyChv3oJ9K8c4onm7zS0lKcddZZaa+ZMmUKVqxY4YW5rEJRFMyZM8fWMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tB10DdfUAGedldjU5eQAb70FPPsskh6wQhrmi0j5zMa1OOga9qufSPPOKZ4c1+zduzceeOABPP300ymvefTRR/HCCy+gra3NrbnA4vS4ZjQaRe/evS0fE7F6fdA+Xg4idvPPC15++m3Xy/HdjuW0P2mYL6RhvraDrOEVK4BLL23/gBVg0iT3du1eTzpOjygaBrJzLQ6yhv3sJ/JanNHjmgMHDsSOHTvSXrN9+3Yc2t3LZ3oo4bC9h5zavZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp+0gavjdd4Fp05IfsNLVBs+pXVHmnCiIlM9sXIuDqOFM9BNp3jnBk03e6aefjo8//hg1NTVd/vuOHTswe/ZsTEp3h+2hqKqKvLw8qKrqy/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP20HTcOffZaHhx/W8YMfALFY4gEry5YBo0Z5Z1eUOScKIuUzG9fioGk4E+uwG1si4clxzaKiIkyfPh0jRozA008/jXPPPRfDhw9HbW0tCgoK8Oijj6K2thaFhYWYMmWKF34HEqfHNVVVRTgctnxMxOr1Qft4OYjYzT8vePnpt10vx3c7ltP+pGG+kIb52g6ShltbGW64gWHOnMTPrx96CPj1r9HtC8791DBAOu4OUTQMZOdaHCQNZ2odttsnaBq2ut/w5HPKKVOm4MUXX8TPf/5z3HLLLQAST60x9o+yLOPll1/O6g2eG4xJ5tf1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tB0HDNTWJT+3WrpWRk8Pw179KuOEG/+yKMudEQaR8ZuNaHAQNu+3vpJ9I884JnhzXBIB7770Xa9aswR133IFvf/vbGDNmDCZMmIC77roLZWVluPvuu70ylVWoqoqCggJbx0TsXE+kR5R88vLTb7teju92LKf9ScN8ESWfPP3003YQNLxiBTBxIrB2rYQBA6JYsECzvcEjDfNDpHxm41ocBA277e+kn0jzzimeHNckEjg5ruknQft4mSAIe5CGCSI9//wn8MMfJn7/7sQTgU8+Sf/7dzwgHROE2ARNwxl9uibhHMYY9u/fD6t7bbvXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20zUvDug488ghw/fWJDd4llwBLlzIMGkQaFg2R8pmNa3FPXIfd2BIJzzZ5qqripZdewqmnnor+/fsnnXFdu3YtfvzjH2Pz5s1emQs0mqaZf3fVVlXVbEejURQVFUFVVaiqCl3XzWuMtqIoZrutrc28XlEUMMbAGOvUBpA0cXVdN7+v67r58XSqtqZpSW07MaWKI1VMhu/t2+liah+H25hUVUVRURFisVigY4rH42bdM1mn9vPTjzpFo1EsWbIE8XjcdUxGLePxuKM6GdpSFMVWTKlqk6pOsVjMvN5KnYyxnMSUaT1ZqVO6OJzEpCgKioqKzHewBjWmjvMzk3WKxWJYsmQJYrFYoO8RRo6i0WjamFpaNFxxhY5nngEA4MEHdXz0ERCJdK2t7mLqqjbp5p4x59p/3V2dDIKuJx73iK7mZ1BjsnL/9qNOTtenTMcUj8exZMkSRKNRR3UytKWqqq2YOt47rMy99nFbqZNhz25MfunJCp5s8tra2jB9+nQ88MAD2L59O/r375+0wTjmmGPwxhtv4O233/bCXOAZP348cnNzMWvWLFRUVAAAysvLUVlZCQAoKytDdXW12T7hhBMQiURQXFyM2tpaAIknltbX1wMACgsL0dzcDABYtGgRpkyZgkgkgry8PESjUajq14+BjUajyMvLAwAcOHDA9Km5uRmFhYUAgPr6ehQVFQEAamtrUVxcDACoqalBSUkJAKC6uhplZWUAgMrKSpSXlwMAKioquo2ppKTEfJ2GlZgKCgrQ2toKAN3G1NraioKCAs9iikQiGD16NKqqqgIdU319Pfr3749IJJLROhUVFWHSpEmIRCK+1Km0tBQXXngh9uzZ4zqmSCSCwYMHY8+ePY7qtGDBApxzzjmQJMlWTC0tLejbty8ikYilubdt2zaMGDECkUjEUp0AYNeuXY5iyrSerNQJ8FZPkiThnHPOwYIFCwIdUyQSQSQSwVdffZXxOq1fvx4XXnghtm3bFuh7RCQSwamnnorly5enjKmmBjjjDBWzZ8vIyQGefXY3rrlmHWQZqKqqwujRoxGJRGzFFIlE0LdvX7S0tFiae5IkQdM0SJJkqU4NDQ0wCLqeeNwjtm3bhgsvvBDr168PfEw7d+7EsGHDEIlEMlonp+tTpmOqr6/HhRdeiOXLlzuqk7EJi0QitmKKRCI44YQTzPlmZe5VVFTg2GOPRSQSsVQn4Gst89LTzJkzkZubiwkTJsASzAMeffRRJkkSe+6555iu6+yxxx5jsiwnXXP++eezU045xQtzgaWlpYUBYI2NjYwxxlRVZaqqdmorimK2Y7EY27dvH9M0jSmKwjRNM68x2vF43GxHo1FWX1/PNE1j8Xic6brOdF3v1DbGnj17ttnf+L5hK11bVdWkdldxpIopVRypYjJ8b99OFVP7thcxaZrG9u3bZ44T1JgURWF79+5lmqZltE6xWKzTfPOyTrFYjDU0NDBFUVzHpGka27t3b1LO7NTJ0JaqqrZiSlWbVHWKx+Pm9d3FFI/H2ezZs1k0GuU294J+j1BVldXX15s5CmpMmqaxurq6pHmQqToZOjPy5WWdvLxHaJrG6uvrWSwW6zKmJUsUNmwYYwBjQ4fqbNmyZN/j8bi5ntqJqeO9o7u5p6oqq6urY6qqWqpTNBpNWouDrCce94h4PM4aGhpYLBYLfExW7t9+1Mnp+pTpmBRFMWvppE6GtjRNsxWT8X85495hZe61v190V6eOazFvPTU2NjIArKWlhaXDk0/y/v3vf2P69Ol48MEHIUlSl++bGDNmDHbs2OGFucATCoXMv7tqh8Nhsy1JElavXg1N0xAOhyH/74U+7duRSMRsy7KMVatWmT/pMPLdsW2MbSDLsvl9WZbN47Sp2qFQKKltJ6ZUcaSKyfC9fTtdTO3jcBuTpmlYvXp1Uu2CGBNjDGvWrIGmaRmtkyRJneabl3WSJAmlpaVgjLmOSdM0rFmzxjxFYLdOhrZ0XbcVU6rapKoTAPN6K3UyxnISU6b1ZKVO6eJwEpOu61i1alXSnA1iTJqmoayszJyfmayToTPjay/r5OU9QtM0rFq1KkmTRhzvvSfjO98Jo64u8YCVkhIJkyYl+w7AXE/txNTx3tHd3NN1HWVlZdB13XKdDIKuJx73CAAoLS2FJEmBjwno/v7tR52crk+ZjokxZtbSSZ0MbWn/+6TcakzG/+WMsa3MPQBJ///urk6GPbsx+aUnK3jydM3evXvj3nvvxXPPPQcAeOKJJ/Dkk08mnRn95S9/iZdfftk8L5uN0NM1CYLwEtIw0dPRdeBXv4L5+3eXXAL84x/AoYfy9csOpGOCEJugaTijT9fs168f9u7dm/aaLVu24PDDD/fCXFah6zr27t2b9IudXl5PpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NO2nxo+cAD4/ve/3uA99BDw0UepN3ikYTERKZ/ZuBb3xHXYjS2R8GSTd/rpp+PTTz81fwmxIzU1NcjLy8OUKVO8MJdV6LqODRs22Fpc7FxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NO2XxresQM46yzg44+BnBzg7beB3/wGkNP8r4U0LCYi5TMb1+KeuA67sSUSnhzXLCoqwvTp03HyySfjD3/4A/Lz8/HMM8+gtbUVy5cvx09/+lNUVVVh+fLl1p8IIyB0XJMgCC8hDRM9keXLgcsuA+rqgKFDExu9SZN4e+Uc0jFBiE3QNJzR45pTpkzBK6+8gvXr12PKlCl45n9nK/r164fzzjsPVVVV+NOf/pTVGzyn6LqOXbt22foJop3rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37a9lrD/+//NWL6dNbuASvWN3ikYTERKZ/ZuBb3xHXYjS2R8Oxl6HfddRfWrVuHn/zkJ5g4cSLGjh2L8ePH484770RZWRl+9KMfeWUqq9B1HVu2bLG1uNi5nkiPKPnk5affdr0c3+1YTvuThvkiSj55+umnba/G1nXgkUeAe+4ZjFhMwiWXAMuWAaNG+e8LaZgvIuUzG9finrgOu7ElEp4c1yQS0HFNgiC8hDRM9AQOHABuvDFxLBNIPGDl179O//t3IkE6JgixCZqGM3pcsytUVUVZWRnKysqgKIpfZoRH13Vs377d1k8Q7VxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NO227GTH7DC8OKL9fj1r3VHGzzSsJiIlM9sXIt74jrsxpZION7kVVdX4+9//zs2b97c6d8+++wzjBgxAqeccgpOOeUUDB8+HP/5z39cOZqt0O8C8EWUfGbj7wF4Pb4ovwsgypwTBVHyydNPP227GXv5cuDUU4F16xIPWPn8cw2nnbaZNNzDECmf2bgW98R12I0tkXB8XHPmzJl47rnnsHXrVoxqd2i+qqoKJ554IqLRKEaNGoVDDjkEFRUVkGUZJSUlGD9+vGfOBw06rkkQhJeQhols5R//AH70IyAWSzxg5ZNP7P3+nUiQjglCbIKmYd+Pay5duhQnn3xy0gYPAF5++WVEo1HcfffdqK6uxoYNG/Dhhx9C0zS88sorTs1lLZqmoaqqCpqm+XI9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bdsfWdWDmTOCGGxIbvPYPWCEN90xEymc2rsU9cR12Y0skXB3XPPXUUzt9Pz8/Hzk5OeZrFADg0ksvxeTJk7FkyRKn5rIWxhiamppg9QNVu9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRtZ+wDB4Arrki81BxIPGDlo4+AQw/1xk/SsJiIlM9sXIt74jrsxpZIOD6u2bt3b/ziF7/AU089ZX6vsbERhx12GCZPnozFixcnXf+zn/0Mr7/+Or766it3HgcYOq5JEISXkIaJbGHHDuDiixO/f5eTA/z1r4lP83oCpGOCEJugadj345qRSAQNDQ1J31u9ejUA4JRTTul0/SGHHOLUVFajaRo2btxo65iIneuJ9IiST15++m3Xy/HdjuW0P2mYL6Lkk6efftq2MnbHB6x88UXXGzzScM9EpHxm41rcE9dhN7ZEwvEm75vf/CYWLlyY9L2CggJIkoRJkyZ1un737t0YPny4U3NZTVtbm6/XE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20nW7sf/wDmDYNqKtLPGClpAQ44wxnY7n1xct+osw5URApn9m4FvfEddiNLVFwfFzzmWeewaOPPorbb78dP/7xj7F582b88Ic/BJDY0HX85O6b3/wmxowZg/z8fPdeBxQ6rkkQhJeQhglR0XXg0Ue//v27Sy8F3nnn69+/60mQjglCbIKmYd+Pa/7sZz/DCSecgNdeew3jx4/H1VdfjdbWVjzxxBOdNnirVq1CVVUVzj33XKfmshZN07BhwwZbx0TsXE+kR5R88vLTb7teju92LKf9ScN8ESWfPP3003ZXY3d8wMrDDwMfftj9Bo803DMRKZ/ZuBb3xHXYjS2RCDvt2LdvXyxbtgwvvfQSVqxYgSFDhuDKK6/E9773vU7XrlmzBpdccgkuvvhiV84SBEEQBBFcevIDVgiCIIKE4+OaRGfouCZBEF5CGiZEYvnyxLHMvXsTD1iZPTv979/1FEjHBCE2QdOw78c1CW/QNA1lZWW2jonYuZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp21j7L/+Vce0aYkN3kkndf+AFT/8JA2LiUj5zMa1uCeuw25siQRt8gJAnz59fL2eSI8o+eTlp992vRzf7VhO+5OG+SJKPnn66ZdtVQVeemkUbrtNRjwOXH45sHQpMGqUs/FIwz0TkfKZjWtxT1yH3dgSBTqu6SF0XJMgCC8hDRNBpqEBuPpqwHib0hNPJJ6oKdOPj5MgHROE2ARNw3RckyPGR7+apnXZVlXVbEejUZSUlEBVVaiqCl3XzWuMtqIoZrutrc28XlEUMMbAGOvUBoD2+3dd183v67oOVVXTtjVNS2rbiSlVHKliMnxv304XU/s43MakqipKSkoQi8UCHVM8HsfKlStNe5mqU/v56UedotEoSktLEY/HXcekqipWrlyJeDzuqE6GthRFsRVTqtqkqlMsFjNzaqVOxlhOYsq0nqzUKV0cTmJSFAUlJSXm+46CGlPH+ZnJOsViMZSWliIWi3kW0/r1Ok49NbHB69NHw/vva3j4YQWS5Dwm434cjUYd1SmVtrqLqavapJt7iqJg5cqVSV93VyeDoOuJxz2iq/kZ1JhisZh5v89knZyuT5mOKR6Po7S0FNFo1FGdDG2pqmorpo73Ditzr/39wkqdDHt2Y/JLT1agTZ4PjB8/Hrm5uZg1axYqKioAAOXl5aisrAQAlJWVobq6GkDiyaOSJEGSJBQXF6O2thYAUFRUhPr6egBAYWEhmpubzXbfvn0hSRLy8vIQjUahqiry8vKgqiqi0Sjy8vIAAAcOHDB9am5uRmFhIQCgvr4eRUVFAIDa2loUFxcDAGpqalBSUgIAqK6uRllZGQCgsrIS5eXlAICKiopuYyopKUFNTQ0AWIqpoKAAra2tANBtTK2trSgoKPAsJkmSEI/HsWnTpkDHVFdXh5aWFkiSlNE6LV68GL1794YkSb7UaeXKlRg0aBB27drlOiZJktDa2opdu3Y5qlNBQQH69esHTdNsxdTQ0ICmpiZIkmRp7m3ZsgVtbW2QJMlSnQA4jinTerJSJ8BbPWmahn79+plxBDUmSZJQX1+P/fv3Z7xO69atw6BBg7BlyxZPYvrNb/6LM84Atm4FjjjiIN55Zwsuu8x9TJIkIScnB8uWLXNUp02bNiEej0OSJFsxSZKEpqYmNDQ0WJp7mqZhz5490DTN8j3CIOh64nGP2LJlCwYNGoR169YFPqYdO3bg4MGDkCQpo3Vyuj5lOqa6ujoMGjQIy5Ytc1SnWCyGPXv2mOu51ZiM/0evWbMGgLW59+WXX0JVVUiSZKlOACzfI/zS08yZM5Gbm4sJEybACnRc00OMj08bGxsxaNAgc6cdCoWS2sakMtqyLEOW5ZRtRVEQCoXMdjgchiRJZhtI/LShfTsSiSAej2PevHm44IILTB8ikQh0XYeu6wiHwynbmqaBMWa2u4qDR0yMMbOt6zrFRDFldUy6riMvLw8zZsxAr169siKmbKxTT4lJkmT85jcyfvUrAACmTwf++U8Fw4aJG1Mm6hSLxZCfn2+uxdkQUzbWiWKimFLFZGyujbWYd0xNTU0YPHgwHdfkQSgUMv/uqh0Oh802AKxYscKcDPL/fpmhfTsSiZhtSZKwfPlyc5IYP73o2DauNZBl2fy+LMvmxEvVDoVCSW07MaWKI1VMhu/t2+liah+H25hUNXFMx/hZR1Bj0nXdPFaQyTpJkmTOTz/qBCR+UmbcxNzEpKqJ4xrGEQq7dTK0Zdx0rcaUqjap6sQYM4+jWL1HiKInK3VKF4eTmDRNw/Lly5PqGMSYVFVFaWmpOT8zWScgoTPGmOOYYrEwrr326w3eT34CzJ8PHH64d/cIVVWxYsUKGNitUyptdVenjveO7uaepmkoLS2FpmmW6+Q0pmxbc7tqM8bMTy6CHhNjzLzfZ7JOTtenTMek67pZSyd1MrRlbLqsxtTx3mFl7rW/X1ipk9OY/NKTFRy/DN0KjDFUVVWhd+/eGDlypJ+mhEWWZYwYMSJpEnl5PZEeUfLJy0+/7Xo5vtuxnPYnDfNFlHzy9NOt7e3bgUsuSbzgPBIB/vhH4LbbEv+m66Rhwh0i5TMb1+KeuA67sSUSnkT20Ucf4cYbb0RTU5P5vW3btuHEE0/E8ccfj9GjR+Oaa66x/IuCPQlZljFq1Chbi4ud64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59ubBcVAaecktjgDR0KFBZ+vcFzO7aXfrrpTxrmi0j5zMa1uCdq2I0tkfAksj//+c9Yu3YtBg0aZH7vvvvuw5dffonp06fjxBNPxPvvv4+///3vXpjLKlRVRVFREVRV9eV6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp1Pbr74KnHMOUF8PjB8PlJYCZ53lzdhe+um2P2mYLyLlMxvX4p6oYTe2RMKTTd5///tfnPr/2zvz8CiK9I9/ZyaBgNygIJdcKkQUwum93qirLoor7qrgtR4ruuoqIqsBvI/V9ViP9cBbxJ9KFIgQIECACAkhEI4AIYQQQjhCSEhIZqaP+v3RzpBAMpm+prpm3s/z+FiZVNV7VH27qHRN96hRwZ+rq6sxf/58jB8/HosXL0ZWVhYGDRpEm7xGcLvd6N+/v66/IOqpT4RGlHzy8tNuu1b2b7Yvo+1Jw3wRJZ88/dRr2+8HHnxQ+0+WtXfhrVwJ9O5tvm8r/bSqPWmYLyLlMxrX4ljUsBlbImFJZBUVFejWrVvw55UrV0KWZfzlL38BoH0R8corr0RhYaEV5qIK+i4AX0TJZzR+D8Dq/kX5LoAoc04URMknTz/12D54ELjySu0unssFvPQSMGsW0Lq1+b6t9NPK9qRhvoiUz2hci2NRw2ZsiYQlkbVr167Be2CWLl0Kt9uNiy66KPhZfHw8jh49aoW5qEKWZaSnp+s6JqKnPhEaUfLJy0+77VrZv9m+jLYnDfNFlHzy9DNc2xs2ACNHat/Da9sW+Pln4Omntc2e2b6t9NPq9qRhvoiUz2hci2NRw2ZsiYQlm7yBAwdi7ty5OHToECorK/Htt99i+PDhDb6jV1xcjK5du1phLqpwu90YPHiwrr8g6qlPhEaUfPLy0267VvZvti+j7UnDfBElnzz9DMf2Dz8A55+vPUlzwABg9Wrg+uut6dtKP+1oTxrmi0j5jMa1OBY1bMaWSFgS2SOPPIK9e/eiZ8+e6N27N8rKyvDggw82qLN69WoMGTLECnNRhdvtximnnKJrcdFTnwiNKPnk5afddq3s32xfRtuThvkiSj55+hnKtqoCycnAn/8M1NZqRzXXrAESE833baWfdrYnDfNFpHxG41ocixo2Y0skLIls3LhxeO+993DWWWfhjDPOwKuvvoo777wz+Pvly5fjyJEjuPrqq60wF1VIkoSFCxdCkiRb6hOhESWfvPy0266V/Zvty2h70jBfRMknTz+bsl1dDYwbBzz/vPbzY48BqalAp07m+7bST7vbk4b5IlI+o3EtjkUNm7ElEi7GGOPtRLRw5MgRtG/fHlVVVWjXrl1YbVRVRWVlJTp06BDWXxP01JckCampqbj22msRHx8flj+xht7884KXn3bbtbJ/s30ZbU8a5gtp2JjtnTuBG24ANm8GWrQAPvoImDjRmr6t9DMS7e3UMEA6bg5RNAxE51ocixrW28ZpGg53vxFnpxOMMezYsQMJCQno1auXnaaExe12o5OOP5vqrU+ERpR88vLTbrtW9m+2L6PtScN8ESWfPP083nZ6unY8s6IC6NYNmDMHOPdca/q20s9ItScN80WkfEbjWhyLGjZjSyQs+XPATz/9hAkTJuDw4cPBz3bt2oVzzjkHAwcORJ8+fXDrrbdCURQrzEUVkiRh/vz5uo6J6KlPhEaUfPLy0267VvZvti+j7UnDfBElnzz9DNj2+yW8+y5w1VXaBm/kSGDtWuMbvPp9k4YJo4iUz2hci2NRw2ZsiYQlxzWvvPJK7N+/H3l5ecHPbrzxRvz888+47LLLcOjQIeTl5eHDDz/E3/72N7PmHIuR45qMMVRXV6Nt27ZwhXpOtYH6Tru97ET05p8XvPy0266V/Zvty2h70jBfSMPh2S4vr8aUKW0xc6Zm+/bbtSOarVqZ75s0HBrScWhE0TAQnWtxLGpYbxunaTjc/YYld/K2bNmCUaNGBX+urq7G/PnzMX78eCxevBhZWVkYNGgQZs6caYW5qMLlcqFdu3ZhT0q99YnQiJJPXn7abdfK/s32ZbQ9aZgvouSTp58HDrgwdmw7zJzpgtsNvP468OWX5jd4AGmYMI9I+YzGtTgWNWzGlkhYssmrqKhAt27dgj+vXLkSsizjL3/5CwDtRehXXnklCgsLrTAXVUiShJ9//lnXMRE99YnQiJJPXn7abdfK/s32ZbQ9aZgvouSTl585OcCIEQyZmUD79gzz5wNPPBH6Bed6IA0TZhEpn9G4Fseihs3YEglLjmt27doVN910Ez744AMAwOTJk/Hmm2/i4MGDwReiT548Ge+99x6OHj1q1pxjMXpc0+v1IiEhIexjIuHWd9rtZSeiN/+84OWn3Xat7N9sX0bbk4b5QhpumlmzgLvvBrxe4IwzVPz8swsDB1p/1Is0HBrScWhE0TAQnWtxLGpYbxunaTiixzUHDhyIuXPn4tChQ6isrMS3336L4cOHBzd4AFBcXIyuXbtaYS7qiIvT95BTvfWJ0IiST15+2m3Xyv7N9mW0PWmYL6LkM1J+Kgrw9NPAX/+qbfCuvZZh1SoFZ55pjz3SMGEWkfIZjWtxLGrYjC1RsGST98gjj2Dv3r3o2bMnevfujbKyMjz44IMN6qxevRpDhgyxwlxUIcsyUlNTIcuyLfWJ0IiST15+2m3Xyv7N9mW0PWmYL6LkM1J+VlUBf/oT8Mor2s9PPQX8+KOMVavssU0aJswiUj6jcS2ORQ2bsSUSlr0M/YMPPsCnn34KALj11lvxxBNPBH+3fPlyjB07Fq+88gruv/9+K8w5EqPHNWVZRlxcXNjHRMKt77Tby05Eb/55wctPu+1a2b/Zvoy2Jw3zhTR8jO3btQ3e1q1AQgLwySfAbbfZa5s0TMc1zSKKhoHoXItjUcN62zhNwxF/GfqDDz54wt27AH/4wx8avEOPaEhgktlVnwiNKPnk5afddq3s32xfRtuThvkiSj7t9HPhQuDWW4HKSqBHDyAlBRgxIjK2ScOEWUTKZzSuxbGoYTO2RMGS45qEcWRZRlpamq5jInrqE6ERJZ+8/LTbrpX9m+3LaHvSMF9EyaddfjIGvPkmcO212gbvvPOA7OwTN3h25Yg0TJhFpHxG41ocixo2Y0skLDuuCQCrVq3C559/jvXr1+PIkSNo164dkpKSMGHCBFx44YVWmXEsRo5r2onTbi8TBKEP0jARCq8XuP9+7Z13gPYkzfffB1q25OsX0RDSMUGIjdM0HNGnawLAY489hosvvhiffvopcnJysGPHDuTk5OCTTz7BH/7wBzz++ONWmYoqGGM4cuQIwt1r661PhEaUfPLy0267VvZvti+j7UnDfBEln1b7uXcv8Ic/aBs8jwd4+23tO3iNbfDszBFpmDCLSPmMxrU4FjVsxpZIWLLJ++KLL/D222/j9NNPxzfffIO9e/dClmWUlZXh22+/xRlnnIG3334bXwb+3EgEkWUZK1as0HVMRE99IjSi5JOXn3bbtbJ/s30ZbU8a5oso+bTSzzVrtOOYWVlAx47AggXAI480/YJzO3NEGibMIlI+o3EtjkUNm7ElEpYc1zz33HOxd+9ebNy4Ee3btz/h91VVVTj77LPRvXt3rF692qw5x0LHNQmCsBLSMHE8X34J3Hcf4PMBiYnAL78A/fvz9ooIBemYIMTGaRqO6HHNzZs3Y9y4cY1u8ACgffv2GDduHDZv3myFuahCVVVUVFRAVVVb6hOhESWfvPy0266V/Zvty2h70jBfRMmnWT9lGfjnP4GJE7UN3g03AL/9Ft4Gz84ckYYJs4iUz2hci2NRw2ZsiUTEnq7p9Hef8EJRFGRnZ0NRFFvqE6ERJZ+8/LTbrpX9m+3LaHvSMF9EyacZPw8fBv74R+0pmgDwzDPAnDlAuAdG7MwRaZgwi0j5jMa1OBY1bMaWSFh6XHPLli1o06bNCb+vrq7G4MGDceqpp9JxzQjitNvLBEHogzRM5OdrLzgvKABatQK++AL48595e0XogXRMEGLjNA1H9Ljm/fffjz179uC8887Djz/+iPLycgBAeXk5fvjhB5x//vnYs2dPky9Lj2VUVcWBAwd0HRPRU58IjSj55OWn3Xat7N9sX0bbk4b5Iko+jfg5bx4werS2wevdG8jMNLbBszNHpGHCLCLlMxrX4ljUsBlbImHJJu+uu+7CpEmTsHnzZtxyyy3o2rUr4uPj0bVrV4wfPx6bN2/GpEmTMHHiRCvMRRWqqmLTpk26Fhc99YnQiJJPXn7abdfK/s32ZbQ9aZgvouRTj5+MAa+8on3vrroauOgi7QXnQ4fab5tn36Th2ESkfEbjWhyLGjZjSyQsfRn6ihUrGn0Z+sSJE3HRRRdZZcax0HFNgiCshDQce9TWAvfeC8yapf18//3AO+8ALVrw9YswDumYIMTGaRqO+MvQAeCiiy4Kvgy9oKAg+DL0WNjgGUVVVZSWlur6C6Ke+kRoRMknLz/ttmtl/2b7MtqeNMwXUfIZjp8lJdpdu1mzgLg44P33gQ8/NL/BszNHpGHCLCLlMxrX4ljUsBlbImHJJs/j8eC2226zoquYQ1VVFBYW6lpc9NQnQiNKPnn5abddK/s325fR9qRhvoiSz+b8XLVKe8H5unVA587A4sWAVV9jtzNHpGHCLCLlMxrX4ljUsBlbImHJcc2OHTvi/vvvxyuvvGKFT8ISuH1aUVGBjh07Bh/L6vF4GpRlWYbL5QqW3W433G53k2VJkuDxeILluLg4uFyuYBkAZFluUI6Pj4ff78evv/6Ka6+9NuhDfHw8VFWFqqqIi4trsqwoChhjwXJjcfCIiTEWLKuqSjFRTFEdk6qqSE1NxdVXX42WLVtGRUzROE5mY/rf/2Q8/LAHkuTC2Wcz/Pwz0KeP2DFF4zgZjcnn82HBggXBtTgaYorGcaKYKKamYlIUpcFazDumw4cPo1OnTpE5rjlq1Chs2LDBiq6igqSkJCQmJiI5ORn5+fkAgLy8PBQUFAAAcnNzUVRUBADIysrC+vXroaoqMjMzUVZWBgDIyMgIPqU0PT0dlZWVAIC0tDRs27Yt+I8/r9cLWZaRmpoKWZbh9XqRmpoKAKipqQn6VFlZifT0dADaU08zMjIAAGVlZcjMzAQAlJSUICsrCwBQVFSE3NxcAEBBQQHy8vIAAPn5+WHFVFJSAgBhx1RdXQ0AzcZUXV2NtLQ0y2JSVRVr1qzBli1bHB3T3r17sXTpUqiqGvFx2rp1K1RVtW2ciouLUVxcbDomVVWRkZGB4uJiw+O0Y8cO+P1+XTEdOHAAS5YsgaqqYc297du3IzMzE6qqhjVOAFBaWmo4pkjqKZxxAqzVk9/vx44dOxwfk6qqWLRoESoqKoIxVVRU45FHgAceiIMkuXDjjSqmTp2Pnj2tHad169ahuLgY27dvd/Q1QlVVbN68GcuXLzc0Tlu2bMGaNWugqqqumFRVxZIlS3DgwIGw5l7gGuH3+8Map0OHDiGA0/XE4xqxfft2FBcXY926dY6PaefOnVi5ciVUVY34OBlZnyId0969e1FcXIzly5cbGqfa2lqkpqZCVVVdMamqivXr1+uee9nZ2VBVNaxxAo5pmZeepk6disTERAwfPhxhwSwgMzOTtWjRgn3xxRdWdCcsVVVVDACrqKhgjDEmyzKTZfmEsiRJwXJdXR1buXIlkySJSZLEFEUJ1gmU/X5/sFxbWxus7/f7maqqTFXVE8qMMebz+VhKSkqwfeBzRVGYJEkhy7IsNyg3FkdTMTUVR1MxBXyvX24qpvplK2KSJImtXLmSeb1eR8fk8/mC4x7Jcao/P+0Yp7q6OrZq1Srm8/lMxxQYS5/PZ2icAtry+/26YmpqbJoaJ6/XG6zfXEx+v5+lpKQE56fT9WTl3As3Jr/fz1auXMlqa2sdHZMkSWzFihXB+bl7t59ddJHKtGdpMjZ9uspk2Z5x8nq9bNWqVczr9Tr6GhHQcF1dnaFxakpbzcV0/LWjubnn9/vZihUrGvwcapy8Xm+DtdjJeuJxjWhsfjo1pnCu33aMk9H1KdIx+Xw+tmrVKlZXV2donALakiRJV0zHXzvCmXv1425unI5fi3nrqaKiggFgVVVVLBSWHNd87rnnsGrVKixevBjDhg3DyJEj0bVrV7hcrgb1XC4Xnn32WbPmHAs9XZMgCCshDUcnmZnAzTcDZWVA27bAl18CY8fy9oqwC9IxQYiN0zQc7n4jzgpj06dPD5ZzcnKQk5PTaL1o3+QZQVEUFBUVoW/fvvB4PJbXJ0IjSj55+Wm3XSv7N9uX0fakYb6Ikk9FUbBzZxEWLuyHxx93Q5KAQYOAOXOAM8+037ZdOSINO3fOiYJI+YzGtTgWNWyFryJgyXfyli5dGtZ/gbOnxDEYYzh8+DDCvaGqtz4RGlHyyctPu+1a2b/Zvoy2Jw3zRZR81tYyPPpoBzz8sLbBu/lmYM0a+zd4gL05Ig0TZhEpn9G4Fseihs3YEglLX4Ye69BxTYIgrIQ0HB0UFQHjxgG5uYDbDbzyCvDEE8Bx32ggohTSMUGIjdM0HJGXob/44ouYOnUqJElqso7f78e//vWvmH+9QlMoioKtW7cGH+VqdX0iNKLkk5efdtu1sn+zfRltTxrmi9PzmZamvf8uNxfo2FHGr78qePLJyG7w7MwRaZgwi0j5jMa1OBY1bMaWSBje5C1evBjJycno3LlzyF1tixYt0LlzZ/zrX//C0qVLjZqLaurq6mytT4RGlHzy8tNuu1b2b7Yvo+1Jw3xxYj5VFXjpJeDqq4GKCmDECIavv96Kyy/n44+dOSINE2YRKZ/RuBbHoobN2BIFw8c1J0yYgNTUVJSWlqJly5Yh6/p8PvTs2RPXXHMNvvzyS0OOigAd1yQIwkpIw2Jy5AgwcSKQkqL9fO+9wLvvAgkJXN0iOEE6JgixcZqGbT+umZmZiSuuuKLZDR4AtGzZEldccQVWrVpl1FzUoigKNm3apOuYiJ76RGhEyScvP+22a2X/Zvsy2p40zBen5XPLFmDkSG2D16IF8NFHwMcfA/Hx/Py0M0ekYcIsIuUzGtfiWNSwGVsiYXiTt3fvXvTr1y/s+n379m3w1niCIAiCiCZ++AEYNQrYvh3o2RNYsQL42994e0UQBEHEIoaPa7Zp0wYPPvggXn/99bDqP/nkk/jggw9QU1NjxJwQ0HFNgiCshDQsBrIMTJ0KBJbDSy8FvvsOOOUUvn4RzoB0TBBi4zQN235cs3v37ti0aVPY9Tdt2oQePXoYNRe1KIqC3NxcXcdE9NQnQiNKPnn5abddK/s325fR9qRhvvDO58GDwJgxxzZ4TzyhPVHz+A0eTz/ttE0aJswiUj6jcS2ORQ2bsSUShjd5F110EdLT07Fr165m6+7atQvp6em4+OKLjZqLalq1amVrfSI0ouSTl59227Wyf7N9GW1PGuYLr3xmZwPDhwPp6cBJJwGzZ2ubvbi4xuvzHHc7bZOGCbOIlM9oXItjUcNmbImC4eOa69atw4gRIzBs2DAsWLAAXbp0abTeoUOHcPXVV2PdunXIzs7GsGHDTDnsZOi4JkEQVkIadi6ffgr8/e+A3w+cfjowZw5w1lm8vSKcCOmYIMTGaRq2/bjmsGHD8Oijj2LdunVITExEcnIyli5dioKCAhQUFGDZsmV49tlnkZiYiJycHDz22GNRvcEziizLyM7OhizLttQnQiNKPnn5abddK/s325fR9qRhvkQ6nz4fcP/92msR/H7ghhu0O3rNbfB4jrudtknDhFlEymc0rsWxqGEztkSiiUMl4fHGG28gISEBr7/+Ol588UW8+OKLDX7PGIPH48HTTz+NF154wZSj0YrL5ULHjh3hcrlsqU+ERpR88vLTbrtW9m+2L6PtScN8iWQ+S0qAm28GsrIAlwt4/nng6acBdxh/LuU57nbaJg0TZhEpn9G4Fseihs3YEgnDxzXrU1hYiM8++wyZmZnYt28fAKBbt2644IILcOedd6J///6mHRUBOq5JEISVkIadw9KlwPjx2oNWOnYEvv0WuPpq3l4RIkA6JgixcZqGbT+uWZ/+/fvjhRdeQHp6OrZs2YItW7YgPT0dzz//fMxs8IwiyzIyMzN1HRPRU58IjSj55OWn3Xat7N9sX0bbk4b5Ync+GQPeeAO48kptgzd0KLB2rf4NHs9xt9M2aZgwi0j5jMa1OBY1bMaWSJg6rkmYx+12o0ePHnCHc97HQH0iNKLkk5efdtu1sn+zfRltTxrmi535rKkB7rkH+P577ec77gA+/BBo3Vp/XzzH3U7bpGHCLCLlMxrX4ljUsBlbImHJcU1Cg45rEgRhJaRhfmzfDtx0E7B5s/ZKhP/8B3joIe27eAShB9IxQYiN0zQc0eOahHFkWUZGRoauYyJ66hOhESWfvPy0266V/Zvty2h70jBf7MjnL78AI0dqG7xu3YBly4BJk8xt8HiOu522ScOEWUTKZzSuxbGoYTO2RII2eZxxu93o37+/rmMieuoToREln7z8tNuulf2b7ctoe9IwX6zMp6IAzz4L/OlPwJEjwAUXAOvWaf93kp9Osk0aJswiUj6jcS2ORQ2bsSUSdFzTQui4JkEQVkIajhwVFcBttwELFmg/P/ww8O9/Ay1a8PWLEB/SMUGIjdM0TMc1BUGWZaSnp+s6JqKnPhEaUfLJy0+77VrZv9m+jLYnDfPFinyuXw+MGKFt8Fq1Ar76CnjnHWs3eDzH3U7bpGHCLCLlMxrX4ljUsBlbIkFP1+SM2+3G4MGDdR0T0VOfCI0o+eTlp912rezfbF9G25OG+WI2n19/Dfztb4DXC/TtC/z0k/aaBKvhOe522iYNE2YRKZ/RuBbHoobN2BIJ2uRxxu1245RTTrGtPhEaUfLJy0+77VrZv9m+jLYnDfPFaD79fuCJJ4B339V+vvpq4JtvgE6dLHbwd3iOu522ScOEWUTKZzSuxbGoYTO2RCJ6t6+CIEkSFi5cCEmSbKlPhEaUfPLy0267VvZvti+j7UnDfDGSz7Iy4LLLjm3wnn0WmDfPvg0ewHfc7bRNGibMIlI+o3EtjkUNm7ElEnQnjzMejwcjR46Ex+OxpT4RGlHyyctPu+1a2b/Zvoy2Jw3zRW8+V60Cbr4Z2LcPaNdOO655/fU2Owm+426nbdIwYRaR8hmNa3EsatiMLZGgTR5n3G43Oun487He+kRoRMknLz/ttmtl/2b7MtqeNMyXcPPJGPDee8BjjwGyDJx1FjBnDnD66RFwEnzH3U7bpGHCLCLlMxrX4ljUsBlbIkHHNTkjSRLmz5+v65hIOPVraoAXXnDj7beTrHAzatGbf17w8tNuu1b2b7Yvo+3t0jARHuHks7YWmDhRey2CLAPjxwOrV0dugxeunyLaJg0TZhEpn9G4Fseihs3YEgl6T56FGHlPHmMM1dXVaNu2LVwul2X18/OBwYMZVNWFFStkXHgh3bRtDL355wUvP+22a2X/Zvsy2t4uDQPOezePE2kunzt3AjfdBGzYAHg8wGuvaXfzIi13ntcaO22ThpuvTzoOjSjrMBCda3EsalhvG6dpmN6TJwgulwvt2rULe1KGW3/QIGDCBG3//q9/uUFb+cbRm39e8PLTbrtW9m+2L6Pt7dIwER6h8rlggfb+uw0bgJNPBhYvBh5/PPIbvOb8FNk2aZgwi0j5jMa1OBY1bMaWSNAmjzOSJOHnn3/WdUwk3PrPPqsgPl5BRoYbCxaY9TQ60Zt/XvDy0267VvZvti+j7e3UMNE8jeVTVYEXXgCuvRY4fBgYNQpYtw645BJn+RkNtknDhFlEymc0rsWxqGEztkSCjmtaiNHjml6vFwkJCWEfEwm3viRJuOWWXUhJOR3nnAPk5gJR/M5HQ+jNPy94+Wm3XSv7N9uX0fZ2a9hJR0ScyPH5rKoCJkwAfvlF+/399wNvvw20bOksP6PFNmmYjmuaRZR1GIjOtTgWNay3jdM0TMc1BSIuTt/35fTUHzeuAO3bM+TlAd9+q9ez2EBv/nnBy0+77VrZv9m+jLa3U8NE8wTyuXkzMHKktsFr2RL45BPgww/5b/AC8Bx3O22ThgmziJTPaFyLY1HDZmyJAm3yOCPLMlJTUyHLsi3127aV8MQTKgDthb8+n2FXoxK9+eQFLz/ttmtl/2b7Mtrebg0ToQnkc9YsBaNHAwUFQK9ewMqVwD338PbuGDzH3U7bpGHCLCLlMxrX4ljUsBlbIkHHNS3E6HFNWZYRFxcX9jGRcOsHbi9fcsm1GDQoHmVl2rGlRx4Jy7WYQG/+ecHLT7vtWtm/2b6Mto+Ehp1yRMSJSBLDlCkq3nxTe6Ht5ZcDs2ZpD1pxEjyvNXbaJg3TcU2ziLIOA9G5FseihvW2cZqG6bimQBj5i4UeWrcGpk3Tyi+8AFRX62oe9YjyVxxeftpt18r+zfZl5q+PkbBDNOTAAWDMGAQ3eJMna0/UdNoGLwDPcbfTNmmYMItI+YzGtTgWNWzGlijQJo8zsiwjLS1N1zERPfUD3H239uLfgweBN94w4ml0YjSfkYaXn3bbtbJ/s30ZbR8pDRMNycoChg8Hli51ISFBxqxZMl59FXDqVyx4jrudtknDhFlEymc0rsWxqGEztkSCjmtaiJHjmnZy/O3l//s/4JZbgDZtgMJC4JRTeHtIEEQonHZExCl8/DEwaRLg9wNnnAHMmQMkJvL2iiAah3RMEGLjNA3TcU1BYIzhyJEjCHevrbd+fW6+WXsxcE2NdmyTMJfPSMLLT7vtWtm/2b6Mto+khmOd2lrtYSr33adt8MaOBbKyGHr2dH4+eY67nbZJw4RZRMpnNK7FsahhM7ZEgjZ5nJFlGStWrNB1TERP/fq4XMArr2jlDz8Edu7U3UXUYSafkYSXn3bbtbJ/s30ZbR9JDccymzZpr0eYOVO7lr30EvDjj0Dr1mLkk+e422mbNEyYRaR8RuNaHIsaNmNLJOi4poU4/bhmgKuuAhYtAm67Dfj6a44OEgQREqcdEeEBY9r77h55BPB6gW7dgG++AS67jLdnBBEepGOCEBunaZiOawqCqqqoqKiAqqq21G+MwN28b78FNmww3E1UYEU+IwEvP+22a2X/Zvsy2p6HhmOFI0eAv/xFO57p9WpP0tywoeEGT5R88vTTTtukYcIsIuUzGtfiWNSwGVsiQZs8ziiKguzsbCiKYkv9xhg2DBg/XvsL+dNPG+4mKrAin5GAl59227Wyf7N9GW3PQ8OxwNq1QFISMHu29sTM114DUlNPfGCUKPnk6aedtknDhFlEymc0rsWxqGEztkSCjmtaiCjHNQFgxw5g0CBAloFly4A//IGPjwRBNI3TjohEAsaAt9/W3nknScBppwHffQecey5vzwjCGLGoY4KIJpymYTquKQiqquLAgQO6jonoqd8UAwYAf/ubVn7qKe0fVrGIVfm0G15+2m3Xyv7N9mW0PS8NRyOHDgE33AA89pi2wbvpJiA3N/QGT5R88vTTTtukYcIsIuUzGtfiWNSwGVsiQZs8zqiqik2bNulaXPTUD0VyMtC6NbBmDZCSYro7IbEyn3bCy0+77VrZv9m+jLbnqeFoYsUKYOhQYN48oGVL4L33gB9+ADp2DN1OlHzy9NNO26Rhwiwi5TMa1+JY1LAZWyJBxzUtRKTjmgGeeQZ48UVg4EBg40btuy8EQTgDpx0RsQNFAV5+GZg2DVBV7eXms2drGz6CiAZiQccEEc04TcN0XFMQVFVFaWmprr8g6qnfHE8+CXTuDGzdCnzxhSVdCoXV+bQLXn7abdfK/s32ZbQ9bw2LTFmZ9kqXZ5/VNngTJgA5Ofo2eKLkk6efdtomDRNmESmf0bgWx6KGzdgSCdrkcUZVVRQWFupaXPTUb4727YGpU7XytGlAXZ0l3QqD1fm0C15+2m3Xyv7N9mW0PW8Ni8rChcCQIUB6OnDSSdofmb74AmjTRl8/ouSTp5922iYNE2YRKZ/RuBbHoobN2BIJOq5pISIe1wS090+deSawe7f2mPInn4ygkwRBNInTjohYgSRpd+5efVX7+ZxztOOZAwfy9Ysg7CIadUwQsYTTNEzHNQVBVVUUFxfr+guinvrhkJAAPPecVn75ZaCy0rKuHY8d+bQDXn7abdfK/s32ZbS9EzQsCsXF2utaAhu8Bx8EVq82t8ETJZ88/bTTNmmYMItI+YzGtTgWNWzGlkjQJo8zTvkuwO23A4MHA4cPH/sHWCwgypnsaPwegNX9i/JdAFHmnNXMmaN91+6337Rj4j/8ALz/PtCqlbl+RcknTz/ttE0aJswiUj6jcS2ORQ2bsSUSdFzTQkQ9rhlg7lztHVUJCdrL0nv0iICTBEE0idOOiBjB6wWeeEJ7JQIAjB4NzJoF9O3L1y+CiBTRoGOCiGWcpmE6rqmDyspKjBgxAkOHDsXgwYPx8ccfh/U7K1AUBTt27ICiKLbU18N11wEXXKD9o2zGDMu7dyR25tNKePlpt10r+zfbl9H2TtKw09i+HTjvvGMbvCef1N6HZ+UGT5R88vTTTtukYcIsIuUzGtfiWNSwGVsiQZs8AG3btkVGRgbWr1+PNWvW4KWXXsKhQ4ea/Z0VMMZw+PBhhHtDVW99Pbhcx45qzpwJbNtmuQnHYWc+rYSXn3bbtbJ/s30Zbe8kDTuJr78Ghg0D1q8HunQBUlO1BztZ/UdQUfLJ0087bZOGCbOIlM9oXItjUcNmbIkEHdc8joqKCgwbNgxr165Fly5dwv4dIP5xzQA33KAd3Rw3TvveDEEQfHDaEZFwqKkBJk069t7NSy4BvvkG6N6dq1sEwQ0RdUwQxDGcpuGoOq6ZkZGB66+/Ht27d4fL5UJKSsoJdd577z306dMHCQkJGD16NLKysnTZqKysxJAhQ9CzZ088+eSTDTZxoX5nFkVRsHXrVl3HRPTUN8JLL2l39X78EdCZRuGIRD6tgJefdtu1sn+zfRlt70QN8yIvDxg5Utvgud3ase/Fi+3d4ImST55+2mmbNEyYRaR8RuNaHIsaNmNLJITY5B09ehRDhgzBe4EvdhzH7Nmz8fjjj2PatGlYt24dhgwZgjFjxuDAgQPBOoHv1B3/3969ewEAHTp0wIYNG1BUVIRvv/0W+/fvD7YN9TsrqNP5BnK99fUyeDAwYYJWnjIFiPZ7vXbn0yp4+Wm3XSv7N9uX0fZO03CkYQz48ENg1Chg61ZtU5eeDiQnAx6P/fZFySdPP+20TRomzCJSPqNxLY5FDZuxJQrCHdd0uVyYM2cOxo4dG/xs9OjRGDlyJP773/8C0B6L2qtXLzz88MOYMmWKbht///vfcdlll+Hmm2/W9bvA7dOioiK0bds2+HnLli3RsmVL3X6YRZIkLFq0CFdeeaXu28vFxcBZZ8XB73dh3jwZV10l1DQhiKjAjIYjRWUl8MADHvz0k/Y3w2uuUfHJJwpOPpmvXwThFETQMUEQTcNbwz6fDz6fL/hzdXU1+vbt2+xxTeE3eX6/H61bt8YPP/zQYOM3ceJEVFZW4ueff262z/3796N169Zo27YtqqqqcMEFF2DWrFk4++yzQ/7ueAKbvOMZP348/vKXvxiOmRczZ56FX34ZgL59K/HGG8vhFuK+L0EQkWL79g74979H4MCBk+DxqJgwYQuuv76QrhUEQRAEYRGzZs3C7NmzT/i8uU1enJ1ORYLy8nIoioKuXbs2+Lxr167YunVrWH0UFxfjvvvuA2MMjDE8/PDDwU1cqN81hZ47eYqiYPv27TjjjDPgCeNck576Zv/yMGoUsGwZQ1FRB9TU/BG33irU3wPCQm/+ecHLT7vtWtm/2b6Mtneyhu1CVYG33nLjmWfckGUX+vZl+PprFSNHngngzIj6Qhrma5s03Hx9p+rYKYiiYSA61+JY1LDeNrw1fPnllzf4ylrgTl5zCH8nb+/evejRowcyMzNx3nnnBetNnjwZy5cvx5o1ayLmW7Q8XbM+L7wAPPss0K8fkJ8PtGhhsZMEQTSJ057oBQAHDwITJwK//qr9/Oc/Ax9/DDRyiIEgCDhTxwRBhI/TNBxVT9cMRZcuXeDxeE54GMr+/fvRrVs3Tl6Fj6IoyM3N1fVULz31zfLYY0DXrsDOndo/5KKNSOfTKLz8tNuulf2b7ctoe6dr2EqWLQOGDtU2eAkJ2sNWZs/mu8ETJZ88/bTTNmmYMItI+YzGtTgWNWzGlkgIv8lr0aIFhg8fjiVLlgQ/U1UVS5YsaXBnz8m0atXK1vpmOOkk7Ql5APDcc9o7sKKNSObTDLz8tNuulf2b7ctoeydr2AoUBZg+Hbj8cmDvXmDgQO31Kvffr71uhTei5JOnn3baJg0TZhEpn9G4Fseihs3YEgUhjmvW1NRgx44dAICkpCS8+eabuPTSS9GpUyf07t0bs2fPxsSJE/G///0Po0aNwltvvYXvv/8eW7duPeG7enYSjcc1tX6AQYOAwkJto/fssxY6SRBEkzjhiEhpKXDbbcDy5drPd90FvPuu9gcggiCaxwk6JgjCOE7TcFQd11y7di2SkpKQlJQEAHj88ceRlJSE5N9vMY0fPx7//ve/kZycjKFDh2L9+vVYsGBBRDd4RpFlGdnZ2ZBl2Zb6VhAfr303DwBef137Tk60wCOfRuDlp912rezfbF9G24ugYaP8+qt2PHP5cqBNG+Crr4CZM521wRMlnzz9tNM2aZgwi0j5jMa1OBY1bMaWSAixybvkkkuCT7es/9/nn38erDNp0iQUFxfD5/NhzZo1GD16ND+HdeByudCxY0e4wjzzpLe+VdxyC5CUBFRXAy+9FFHTtsIrn3rh5afddq3s32xfRtuLomE9+P3AE08A114LlJdr2s/JAW6/nbdnJyJCPgG+ftppmzRMmEWkfEbjWhyLGjZjSySE2OSJRuBLnIqiNFqWZTlYZoyhX79+8Hg8kGUZqqoG6wTKkiQFy6qqon///vB4PJAkKbjhPb4c6DuAqqrBz1VVDf7loqmyoigNyowpeOUVra/332coLm46pqbiaCqmgO/1y6Fiqh+HmZgURYHH42nwGFqnxuRyudCnTx94PJ5mYwpV1huTqqrB+WnHODHGMGDAALhcLtMxeTwe9OnTJ3jB1jtOAW253W5dMTU1Nk2NEwD07dsXHo8nrHEK9GUkJiPjVFQEXHQRwxtvaLYnTWJYsULGGWc0r6dwxilUHEZicrvd6N+/f7Cu1dcIq2LyeDw47bTTgvMzkte9gM4CP1s5TlZeIzweD/r169dAk3rGCWhcW83FdPy1o7m553a7cdppp8Htdoc9TgF4zL1wYorUmttYGQAGDBgAxpjjYwIQvN5HcpyMrk+RjsnlcgXH0sg4BbTl8Xh0xXT8tSOcuQccu16EM04Be3pjsktP4UCbPBtISkpCYmIikpOTkZ+fDwDIy8tDQUEBACA3NxdFRUUAgDVr1mDp0qWQZRmZmZkoKysDAGRkZKC8vBwAkJ6ejsrKSgBAWloaVqxYAVmWkZqaCq/XGyzLsgyv14vU1FQA2ncZA1RWViI9PR2A9m7BjIwMAEBZWRkyMzMBACUlJcjKygKgvesvNzcXAFBQUIC8vDxceSUwenQN/H4XkpObjikrKwslJSUAEHZM1dXVANBsTNXV1UhLS7MsJlmWsWTJEmzevDnkOPGOqbS0FAsXLoQsy83GBAD5+fnNzr1wYwrMNzvGadWqVcjMzMSuXbtMxxTwcdeuXYbHaeXKlcE4wo1p//79WLBgAWRZDmvubdu2DYsXL4Ysy2GNU2D8jcakZ5xmzNiMpCQgK8uFNm0k/PQTMGXKXuTkhKencMYJsFZPXq8XK1eutO0aYVVMsixjwYIFOHTokOlx0htTTk4OMjMzsW3bNkdfI2RZRkZGBpb//gVQveO0efNmLFmyBLIs64opMDaBp3Q3N/cCcdQvhxqnwJgbiSnSerJ7zW0spm3btiEzMxM5OTmOj6mwsBCLFi2CLMsRHycj61OkYyotLUVmZiaWL19uaJxqamqCZT0xybKMpUuXBl+bFs7c27BhQ/C6E844Ace0zEtPU6dORWJiIoYPH46wYIRlVFVVMQCsoqKCMcaYLMtMluUTypIkBcs+n4/t3LmTKYrCJEliiqIE6wTKfr8/WPZ6vayoqIgpisL8fj9TVZWpqnpCOdB3SkpKsH3g84CtUGVZlhuUA/7+9pvMAMZcLsZycxuPqak4moop4Hv9clMx1S9bEZOiKGznzp3BfpoaJ94xSZLECgsLmaIoYY1TOHMvnJh8Pt8J883KcfL5fGzXrl1MkiTTMSmKwgoLCxvkTM84BbQly7KumJoam6bGye/3BzXfXEx+v5+lpKQwr9dr69yrrWXs/vtVBjAGMHbeeSrbscPYNcKquRduTLIss6KiomCOrL5GWBXT8fMzkte9gM4C+bJynKy8RiiKwoqKipjP5zM0Tk1pq7mYGhubUHNPlmVWWFjIZFkOa5y8Xm+DtdjJerJ7zW2s7Pf72a5du5jP53N8TOFcv+0YJ6PrU6RjkiQpOJZGximgLUVRdMUU+Ldc4NoRztyrH3dz43T8WsxbTxUVFQwAq6qqYqEQ4umaohCtT9c8nltuAf7v/4DrrgPmzrWsW4IgjsMuDddn61Zg/Hjg9z90YsoU7Sm6DniAGEFEBZHQMUEQ9uE0DUfV0zWjmcDxFLne2Wgr69vBCy8AHg8wbx6wciU3NyzBCfkMB15+2m3Xyv7N9mW0vYgaBrR7dp9/Dgwfrm3wTjkFWLgQePllsTZ4Tslnc/D0007bpGHCLCLlMxrX4ljUsBlbIkGbPM4EHhrgdoc3FHrr28EZZwD33quVn3pK+8eiqDghn+HAy0+77VrZv9m+jLYXUcPV1cCECdo772prtZecr18PXHUVN5cM44R8hgNPP+20TRomzCJSPqNxLY5FDZuxJRJ0XNNCYuW4JgDs3QsMGADU1QEpKcCf/mRp9wRBwB4Nr1+vHbkuKADcbu1o5pQp2t15giCsx2lHvQiC0IfTNEzHNQVBluXg033sqG8X3bsDjz6qladOBcJ8mqvjcEo+m4OXn3bbtbJ/s30ZbS+KhhkD/vtfYPRobYPXs6f2kvN//UvsDR5pmK9t0jBhFpHyGY1rcSxq2IwtkaBNHmfcbjcGDx6s65iInvp2Mnky0LEjsGUL8OWXvL0xhpPyGQpeftpt18r+zfZltL0IGj58GBg3Dnj4Ye1F59dfr93Ru/DCiLlgG6RhvrZJw4RZRMpnNK7FsahhM7ZEInojEwS3241TTjlF1+Kip76ddOig3cUDgGnTAK+XqzuGcFI+Q8HLT7vtWtm/2b6Mtne6hpcvB5KSgDlztAeq/Oc/wM8/A507R8S87ZCG+domDRNmESmf0bgWx6KGzdgSieiNTBAkScLChQuDb723ur7dTJqkHfsqKQHee4+3N/pxWj6bgpefdtu1sn+zfRlt71QNHzkC/P3vwCWXAMXFQP/+QGamdsza5bLVdEQhDfO1TRomzCJSPqNxLY5FDZuxJRK0ybMB5fcvqCmK0mhZluVgmTGG4cOHw+PxQJZlqKoarBMoS5IULKuqihEjRsDj8UCSJDDGwBg7oRzoO4CqqsHPVVUNnkFuqqwoSoNyUzHFxyuYMUOz8dJLDFVVaDKOpmIK+F6/HCqm+nGYjcnj8WD48OENxq6xceIdk8vlwrBhw+DxeAyNk9GYGGMnzDcrx4kxhpEjR8LlcpmOyePxYNiwYXD9voPRO04Bbbndbl0xNTU2TY0TgGD9cMYp0JeemFJTGQYPBj74QGt/770Ma9ZIGD7cXj2FM06h4jCiJ7fbjREjRjSYs06MyePxICkpKTg/I3ndC+gs8LOV42TlNcLj8WDEiBENNKlnnAAE11M9MR1/7Whu7rndbiQlJcHtdoc9TgGcrie719zGygAwcuRIMMYcHxPQ/PXbjnEyuj5FOiaXyxUcSyPjFNCWx+PRFVPg33KBvsOZewAa/Pu7uXEK2NMbk116Cgfa5NlAUlISEhMTkZycjPz8fABAXl4eCgoKAAC5ubkoKioCAKxduxZHjx6F2+1GZmYmysrKAAAZGRkoLy8HAKSnp6OyshIAsHjxYsTHx8PtdiM1NRVerxeyLCM1NRWyLMPr9SI1NRUAUFNTE/SpsrIS6enpAIDy8nJkZGQAAMrKypCZmQkAKCkpQVZWFgCgqKgIubm5AICCggLk/f6m5Pz8/BNimjAB6Nu3DhUVLrz2GpCVlYWSkhIACCumtLQ0VFdXA0CzMVVXVyMtLc2ymNxuN/bt24dt27aFHCfeMe3fvx/5+flwu92Gx8lITEuXLoXL5YLb7bZlnFavXo1OnTqhtLTUdExutxsFBQUoLS01NE4LFixA69atoaqqrpgqKiqwceNGuN3usOZeYWEh9uzZA7fbHdY4AQg7pooKYMyY/fjjH10oKQG6dj2K1FQf3n9fxsqV9uvJyrkXrp5UVUXr1q2xYMECR8fkdruRm5uLI0eONBuT1eO0YcMGdOrUCYWFhY6+RrjdbiiKgpW/v4BV7zht27YN+/btg9vt1hWT2+3Gxo0bUVFREdbcU1UVv/32G1RVDWucDh06hABO1xOPa0RhYSE6deqEDRs2OD6m4uJi7Nq1K7jeRGqcjK5PkY5p//796NSpE1auXGlonPx+P3777Te43W5dMbndbhw9ehRr164FEN7c27RpU4Nrc3PjBBzTMi89TZ06FYmJiQ1uToSEEZZRVVXFALCKigrGGGOyLDNZlk8oS5IULNfW1rK5c+cyv9/PJEliiqIE6wTKfr8/WD569Giwvt/vZ6qqMlVVTygzxpjP52MpKSnB9oHPFUVhkiSFLMuy3KDcWBz1yz/8IDOAsVatGNu9u/E4moop4Hv9clMx1S9bEZPf72dz585ldXV1IcepqTgiFZPX6w2Ou5lx0htT/flpxzjV1tayefPmMa/XazqmwFh6vV5D4xTQls/n0xVTU2PT1DjV1dUF64cTU0pKSlgxzZ6tsFNOYQxgzOVS2aOPMnb4cGT1ZOXcC1dPPp+PzZ07lx09etTRMR0/PyN53aurq2Pz5s1jdXV1jr5GBHJUW1traJya0lZzMTU2NqHmXmDO+Xy+sK8R9ddiJ+uJxzWisfnp1JjCuX7bMU5G16dIx+T1etm8efNYbW2toXEKaCvwc7gxHX/tCGfu1Y87nGtE/bWYt54qKioYAFZVVcVCQe/JsxAj78ljjKG6uhpt27YNHhWxqr4kRe69HowBF1wA/PYb8MADx46JOR29+ecFLz/ttmtl/2b7Mtqet4b37QMeegj46Sft50GDgE8/Bc47L+wQhIY0zNc2abj5+pFci0VEFA0D0bkWx6KG9bZxmobpPXmC4HK50K5du7Anpd76kcLlAl55RSt//LH2Hi4RcGo+j4eXn3bbtbJ/s30Zbc9Lw4xpry5JTNQ2eB6P9s673NzY2eABpGHetknDhFlEymc0rsWxqGEztkSCNnmckSQJP//8s66neumpH0kuvhi49lrtxejPPMPbm/Bwcj7rw8tPu+1a2b/Zvoy256Hh3bs1rU2cqL0DLykJWLsWeOEFoGVLw90KCWmYr23SMGEWkfIZjWtxLGrYjC2RoOOaFmL0uKbX60VCQkLYx0TCrc/j9nJeHjB0qHaXYe1aINzvhvJCb/55wctPu+1a2b/Zvoy2j6SGVRX43/+AyZOBmhptQzdtGvDEE9o78GIR0jBf26RhOq5pFlE0DETnWhyLGtbbxmkapuOaAhEXF2dr/UhyzjnAbbdp5SlT+PoSLk7OZ314+Wm3XSv7N9uX0faR0HBBAXDppdq772pqgPPPB9avB55+OnY3eAFIw3xtk4YJs4iUz2hci2NRw2ZsiQJt8jhT/zGrdtTnwXPPaf/oXLxY+8/JiJBPgJ+fdtu1sn+zfRltb7eGFcWFN99045xzgIwMoHVr4O23tfLAgbpcjUpIw3xtk4YJs4iUz2hci2NRw2ZsiQQd17QQo8c1ZVlGXFxc2MdEwq3P8/byP/4BvPOOdlwzKwtwO/TPCXrzzwteftpt18r+zfZltL2dGl63TsKtt9agoKAjAOCKK4CPPgL69g3bvaiHNMzXNmmYjmuaRRQNA9G5FseihvW2cZqG6bimQBj5i4XT+de/gDZtgJwc4IcfeHsTGhHyCfDz0267VvZvti8zf320sr7fD0yfDpx7bhwKCjqifXuGTz4B0tJog9cYpGG+tknDhFlEymc0rsWxqGEztkSBNnmckWUZaWlpuo6J6KnPi1NO0R4GAWgbPqc+vEiUfPLy0267VvZvti+j7a3WcHa2dgd8xgxAklwYNaoMGzbIuOce7VUlRENIw3xtk4YJs4iUz2hci2NRw2ZsiQQd17SQwO3TiooKdOzYEYqiAAA8Hk+DsizLcLlcwbLb7Ybb7W6yLEkSPB5PsBy4tRwoAwjecg6U4+Pj4ff78euvv+Laa68N+qA9oU+FqqqIi4trsqwoChhjwXJjcTQX09GjbvTvz3DwoAsffADce6/5mAK31wNxRDomO8aJYqKYPB4PamoUTJvmwltvuaGqQJcuDG+9peCkk+bjmmuuRsuWLYWLKRrHiWKimPTG5PP5sGDBguBaHA0xReM4UUwUU1MxKYqC1NRUXH21thbzjunw4cPo1KkTHdfkQVJSEhITE5GcnIz8/HwAQF5eHgp+f0N4bm4uioqKAABZWVnYvn07GGPIzMxEWVkZACAjIwPl5eUAgPT0dFRWVgIA0tLSsG/fPjDGkJqaCq/XC1k+9uVRr9eL1NRUAEBNTU3Qp8rKSqSnpwMAysvLkZGRAQAoKytDZmYmAKCkpARZWVkAgKKiIuTm5gIACgoKkJeXBwDIz88PK6aSkhK0bQv89a+FALS7EgsXrmwypurqagBoNqbq6mqkpaVZFhNjDOvXr8eWLVvCiglA2ONkdUwrVqwAY8zycWouptLSUjDGbBunI0eOYPfu3aZjCmho9+7dhsfp4MGDwbP34cZ08OBBLF26FIyxsObe9u3bkZOTA8ZYMKaMDCAxUcKbb2obvKuuOoR584pwyy0MLhdQWlrKde7ZdY2wIiZJknDw4EHHx8QYw5IlS3D48OGIj9O6detw5MgRbN++3dHXCMYYiouLDY/Tli1bsH79ejDGdMXEGMPSpUtx8ODBsOZe4BohSVJY43To0CEEcLqeeFwjtm/fjiNHjmDdunVCxJSVlQXGWMTHycj6xCOmI0eOGB6nuro6pKamgjGmKybGGLZv36577m3atAmMsbDGCTimZV5zb+rUqUhMTMTwcN9PxgjLqKqqYgBYRUUFY4wxWZaZLMsnlCVJCpZra2vZ3Llzmd/vZ5IkMUVRgnUCZb/fHywfPXo0WN/v9zNVVZmqqieUGWPM5/OxlJSUYPvA54qiMEmSQpZlWW5QbiyOpmKq7/vRoxLr109lAGPPPy83GlPA9/rlpmKqX7YiJr/fz+bOncvq6urCjimccbI6Jq/XGxx3O8apqXL9+WnHONXW1rJ58+Yxr9drOqbAWHq9XkPjFNCWz+fTFVNTY9PUONXV1QXrHz4sswcfVJj2ZknGundX2S+/NIwpJSXFcEyR1pOVcy/cmHw+H5s7dy47evSoo2M6fn5Gcpzq6urYvHnzWF1dnaOvEYEc1dbWGhqn+trSE1NjYxNq7gXmnM/nC/saUX8tdrKeeFwjGpufTo2pqTlm9zgZXZ8iHZPX62Xz5s1jtbW1hsYpoK3Az+HGdPy1I5y5Vz/ucK4R9ddi3nqqqKhgAFhVVRULBR3XtBAjT9e0k8BffHg/Dejbb7V357VrB+zcCXTuzM0VgnAMCxYA990H/P4HQ9x7L/D660CHDsfqOEXDBEEYh3RMEGLjNA3T0zUFQVVVVFRUQFVVW+o7gVtvBYYOBY4cAV5+mbc3DREln7z8tNuulf2b7ctoe73tystV/OUvPlxzjbbB69tXe5/kxx833OAR4UEa5ms7FjUsypwTBZHyGY1rcSxq2IwtkaBNHmcURUF2dnbwC6BW13cCbvexzd277wK/f2XKEYiST15+2m3Xyv7N9mW0vZ52P/4IDB7swnfftYTLxfCPfwAbNwKXX27IZQKkYd62Y03DZuwQjSNSPqNxLY5FDZuxJRJ0XNNC6Lhm0zAGXHYZsGwZcOedwGefcXWHICLKvn3ApEnaJg8ABg4EPv0UOP/80O2cpGGCIIxBOiYIsXGahum4piCoqooDBw7oOiaip75TcLmAV17Ryl9+CWzezNefAKLkk5efdtu1sn+zfRltH6odY9p8T0zUNngeDzB1KsPChQdw7rnOnnOiQBrmazvaNWylHaJxRMpnNK7FsahhM7ZEgjZ5nFFVFZs2bdK1uOip7yRGjwZuuglQVWDqVN7eaIiST15+2m3Xyv7N9mW0fVPtdu8G/vhHYOJE4PBh7Xup2dnAjBkKduxw/pwTBdIwX9vRrGGr7RCNI1I+o3EtjkUNm7ElEnRc00LouGbzbN0KnHWWttFbuRK44ALeHhGEtagq8L//AZMnAzU1QIsWwPTpwBNPAHpl6EQNEwShD9IxQYiN0zRMxzUFQVVVlJaW6voLop76TmPgQODuu7XylCnacTaeiJJPXn7abdfK/s32ZbR9/XY7dmjfPf3737UN3nnnAevXA08/fWyDJ8qcEwVR8snTTzttR5uG7bRDNI5I+YzGtTgWNWzGlkjQJo8zqqqisLBQ1+Kip74TmT4dSEjQ7uTNn8/XF1HyyctPu+1a2b/Zvoy2V1UV27cX4t//Zjj7bGD5cqB1a+Ctt4AVK4BBg6z1k2iIKPnk6aedtqNFw7G2DjsJkfIZjWtxLGrYjC2RoOOaFkLHNcPnqaeA114DBg/W7nR4PLw9IghjbNqk3Z3OztZ+vvxy4KOPgH79zPftZA0TBBEepGOCEBunaZiOawqCqqooLi7W9RdEPfWdypQp2oufN20CvvmGnx+i5JOXn3bbtbJ/s33pbe/3AzNmAMOGMWRnA+3aMXz8MbBoUegNnihzThREySdPP+20LbKGjbYTZc6Jgkj5jMa1OBY1bMaWSNAmzwYCL1ZUFKXRsizLwbLf78eePXugqipkWQ5OtvplSZKCZZ/PF6wvSRIYY2CMnVAGgPo3aQP1A2VZlkOWFUVpUNYTU1Nx1C+3aSPhqae0cnIyg9fLgrE2F1P9OMzGpKoq9uzZE+zHTEz1xyngu1UxybIcHPdIjlP9+Wl1TKqqwu/3o7S0FLIsm44pMJb1c6ZnnALaUhSl2ZiyslQMH84wfTogSS5ccEEF8vIU3H23CkUJPfckSQrmNJxxCvTFa+7xukaEG5OiKNizZw98Pp+jY1JVFSUlJQ3mQaTGSZIklJaWBvNl5ThZeY0IaNjv9xsap6a01VxMx187mpt7iqKgpKQEiqKEPU4BnK4nHteIxuanU2MK5/ptxzjpWZ94xiTLMkpLS+H3+w2NU0Bbqqrqiun4a0c4c69+3OGMU8Ce3pjs0lM40CbPBpKSkpCYmIjk5GTk5+cDAPLy8lBQUAAAyM3NRVFREQBg3bp16NmzJ+Li4pCZmYmysjIAQEZGBsrLywEA6enpqKysDJbPPvtsxMXFITU1FV6vF7IsIzU1FbIsw+v1IjU1FQBQU1MT9KmyshLp6ekAgPLycmRkZAAAysrKkJmZCQAoKSlBVlYWAKCoqAi5ubkAgIKCAuTl5QEA8vPzm40pKysLJSUlABAypttvr0T37kBxsQtvvaX9A625mKqrq5GWlmZZTHFxcWjfvn0wDrMxBcYpLS0N1dXVlsV08OBBKIqCuLi4iI7T8uXLMWjQIMTFxVkeU1lZGbKysnD++ecHy2ZiiouLg8vlCsahd5zS0tIwbNiwYHyNxTR37mJMngycd54Lmza50KUL8OGHVZgxYz1OOy0urLlXVFSE1q1bIy4uLqxxAoDS0lJDMUVaT+GME2CtngBg2LBhwTicGlNcXByOHj0avC5Hcpzy8vJw/vnnB8tWjpOV14i4uDicfvrpwTj0jlNBQQHat2+PuLg4XTHFxcXB5/M1WGdDzT0AOHToEACENU6BukZiirSeeFwjioqKcP755wfLTo6ppKQELVq0QFxcXETHKZz1yQkxHTx4EOeffz4yMzMNjZMsyzh06BDi4uJ0xRQXF4eePXti3bp1AMKbe1u2bEHnzp0RFxcX1jgBx7TMS09Tp05FYmIihg8fjrBghGVUVVUxAKyiooIxxpgsy0yW5RPKkiQFy16vl23bto3JsswkSWKKogTrBMp+vz9YrqurY9u3b2eyLDO/389UVWWqqp5QZowxn8/HUlJSgu0DnyuKwiRJClkO+BMqjqZiaiqOxmL66CPGAMa6dFFZVRVrNqb6ZStikmWZbdu2jfl8PstiCpRVVW1QNhOT3+8PzpNIjpPX6z1hvlkVk6IozOv1soKCAub3+03HFBjLgC294xTQliRJjca0fLnKBgxQmfZMWMbGj1fYgQNaH1u3bj1hbJoaJ5/PFxzL5mLy+/0sJSWFeb1ebnOP9zWiuZgkSWLbt29ndXV1jo5JlmW2devWoD+RHCefz8cKCgqYz+dz9DVClmW2ffv24HzXO05Naau5mBobm1BzT5IktnXrViZJUljj5PV6G6zFTtYTj2tEY/PTqTGFc/22Y5yaW5+cEpPf72cFBQXM6/UaGqeAtmRZ1hVTYP0PXDvCmXv1425unI5fi3nrqaKiggFgVVVVLBRx4W0FCT14fn+KiKfe00Tql+Pi4hp8XlVVBcZYg8/rl+t/yTMuLg6VlZVgjDX4vLGyy+UKfuZ2u+F2u8MuN+V7ODGFUw74eNddwBtvANu2ufDvfwPPPdd8TIGyFTHJsoyqqqpG6xiNSW85nJhcLldwnng8noiNk8fjCXu+6Y3J7XbD4/Hg8OHD6NOnT9Cu0ZgCYxmY93rHKaCt4z/3euPx6KPA++9r/XbvDnzwAXDDDe7f7bpw5MiRE8amqbnndrsb1XxjMQWOgwT64TH3eF8jmivLsozKykr07dvX0THJsowjR44E52ckx4kxFtSZnvUp0teI48dS7zg1pS07xubIkSMnxBFqnOr76GQ9HV+OhJ4am59OjSmc67cd49TU+uS0mGRZDo5loH+94xRYT/XEFFj/m7u+Gb1eBI5HGo3JTj2Fgp6uaSH0dE1j/PgjcPPNwEknAYWFQNeuvD0iCI2FC4H77gN279Z+vvde4PXXtYcGRQJRNEwQRNOQjglCbJymYXq6piAoioKtW7eG/SVKvfVF4KabgFGjgKNHgeefj6xtUfLJy0+77VrZv9m+6revqADuvBO4+mptg9enj/bUzI8/PnGDRxrmiyj55OmnnbadqmE724ky50RBpHxG41ocixo2Y0skaJPnAOrq6myt73RcLuCVV7Ty//6n3c2LJKLkk5efdtu1sn+zfVVUePHWWy4MGgR88YU2N//xD2DjRuCKK6yzK8qcEwVR8snTTzttO0nDRtuThvkiUj6jcS2ORQ2bsSUKdFzTQui4pjmuvlo7HveXvwDffsvbGyKWOHpU+57d668DBw5on515JjBzJnD++fz8Ek3DBEGcCOmYIMTGaRqm45qCoCgKNm3apOuYiJ76IvHyy9r/Z80Cfn/yre2Ikk9eftpt18r+jfRVXQ28+qp2HPPJJ7UNXp8+DB99BOTlhbfBIw3zRZR88vTTTtu8NWxFe9IwX0TKZzSuxbGoYTO2RII2eYRjSEoC/vpXrfz003x9IaKbI0eAl17SNndTpgDl5UC/fgzPPbcH+fkq/vY3oEUL3l4SBEEQBEEYg45rWggd1zTPzp3AwIGAJAHp6cCll/L2iIgmKiuBd98F/vMf4PBh7bMBA4BnngFuuw2o9+RkRyCihgmCaAjpmCDExmkapuOagqAoCnJzc3UdE9FTXzT69QPuv18rP/WU9tppOxEln7z8tNuulf2H6uvwYWD6dO3OXXKy9vOZZwJffQXk5wMTJwIulzFfSMN8ESWfPP2003akNGxne9IwX0TKZzSuxbGoYTO2RII2eQ6gVatWttYXjWee0d6Zl52tvUPPbkTJJy8/7bZrZf/H93XokDafTjsNmDEDqKoCEhO1731u3gzcfnvDu3dGfSEN80WUfPL0007bdmo4Uu1Jw3wRKZ/RuBbHoobN2BIFOq5pIXRc0zqmTQOeew444wztH+NOO0ZHOJvycuCNN4D//heoqdE+GzxYu4s3bhzgFuTPWyJrmCAIDdIxQYiN0zRMxzUFQZZlZGdnQ5ZlW+qLyj//CXTpAmzfrj3G3i5EyScvP+22a2X/sixj4cJcPPGEij59tHcv1tQAQ4Zod4Q3bAD+/OemN3hGfSEN80WUfPL0007bVmvYTF+kYTERKZ/RuBbHoobN2BIJ2uRxxuVyoWPHjnC5XLbUF5V27bRjdoD2XaraWnvsiJJPXn7abdeq/vftA5580o2xY4fgjTfcOHpUe1prSgqwbh1w003N370z6gtpmC+i5JOnn3batrJvs32RhsVEpHxG41ocixo2Y0skaJNnA4EvcSqK0mhZluVgmTGGfv36wePxQJZlqKoarBMoS5IULKuqiv79+8Pj8UCSJDDGwBg7oRzoO4CqqsHPVVUN/uWiqbKiKA3KemJqKo6mYgr4Xr/MGMM990jo04ehrAz4z3+O5at+HGZj8ng86Nu3b4OxszOmpsapuZhcLhf69OkDj8cT0XFSVTU4P62OSVVVMMYwYMAAuFwuQzHt3Qs88oiKvn0Z3nrLDa/XjREjGObOBdaskXH99Src7vDGKaAtt9utK6amxqapcQKAvn37wuPxhDVOgb54zT0nXyMkSYLb7Ub//v2DdZ0ak8fjwWmnnRb8B0Ukxymgs8DPVo6TldcIj8eDfv36NdCknnECGtdWczF5PB706dOnwdiEmntutxunnXYa3G532OMUwOl64nGNAIABAwaAMeb4mAAEr/eRHCej61OkY3K5XMGxNDJOAW15PB5dMR1/7Qhn7gHHrhfhjFPAnt6Y7NJTONAmzwaSkpKQmJiI5ORk5OfnAwDy8vJQUFAAAMjNzUVRUREAYM2aNVi6dClkWUZmZibKysoAABkZGSgvLwcApKeno7KyEgCQlpaGFStWQJZlpKamwuv1BsuyLMPr9SI1NRUAUBP4MhKAyspKpKenAwDKy8uRkZEBACgrK0NmZiYAoKSkBFlZWQCAoqIi5P7+RvKCggLk5eUBAPLz85uNKSsrCyUlJQAQdkzV1dUA0CCmJUtSkZysTeSXXlJRUQFUV1cjLS3Nspg0O0uwefPmiMTU2DiFE1NpaSkWLlwIWZYjPk6B+WZ1TGVlZVi1ahUyMzOxa9cuXTEtWpSPiROPoF8/4N133fB6XRg1imHGjGx8910RrrsO+O03/eO0cuXKYBzhxrR//34sWLAAsiyHNfe2bduGxYsXQ5blsMYpMP685p6TrxGB8sqVKx0fkyzLWLBgAQ4dOhTxccrJyUFmZia2bdvm6GuELMvIyMjA8uXLDY3T5s2bsWTJEsiyrCumwNjs378/rLkXiKN+OdQ4BcbcSEyR1hOPa8S2bduQmZmJnJwcx8dUWFiIRYsWQZbliI+TkfUp0jGVlpYiMzMTy5cvNzRONTU1wbKemGRZxtKlS7FmzRoA4c29DRs2BK874YwTcEzLvPQ0depUJCYmYvjw4QgLRlhGVVUVA8AqKioYY4zJssxkWT6hLElSsOzz+djOnTuZoihMkiSmKEqwTqDs9/uDZa/Xy4qKipiiKMzv9zNVVZmqqieUA32npKQE2wc+D9gKVZZluUG5sTiaiqmpOJqKKeB7/XIgDklS2dlnqwxg7MknWYP4rIhJURS2c+fOYD+RiOn4cQonJkmSWGFhIVMUJaLj5PP5TphvVsWkKArz+Xxs165dTJKksGLauVNmDz7IWIsW2pwAGDvvPJWlpspMlhVWWFjYIGd6ximgLVmWdcXU1Ng0NU5+vz+o+ebGye/3s5SUFOb1ernNPadfI2RZZkVFRcEcOTUmRWk4PyM5TgGdBfJl5ThZeY1QFIUVFRUxn89naJya0lZzMTU2NqHmnizLrLCwkMmyHNY4eb3eBmuxk/XE4xrh9/vZrl27mM/nc3xM4Vy/7Rgno+tTpGOSJCk4lkbGKaAtRVF0xRT4t1zg2hHO3Ksfd3PjdPxazFtPFRUVDACrqqpioaCna1oIPV3THubPB667DkhIAAoKgJ49eXtERJpdu4CXXwY++wz4/XQDLrpIewrrZZcB0XqkPlo0TBCxDOmYIMTGaRqmp2sKQuB4ilzvbLSV9aOBa6/V/kHv9QJXX609bdOqB7GIkk9eftptt7n+d+4E7r0XOP104KOPtA3epZcCS5cCGRnA5Zcf2+CZ9dVoe9IwX0TJJ08/7bRtZd+k4dhEpHxG41ocixo2Y0skaJPHmcBDA9xhvrhLb/1owOUC/vMfoE0b7Z1599wD9OgBPPYYsG2bub5FyScvP+2221T/BQXAXXdp70n89FNAloErrtA2dunpwCWXWO+r0fakYb6Ikk+eftpp28q+ScOxiUj5jMa1OBY1bMaWSNBxTQuh45r2cvCgdlzvww+Bes+kwOWXA3//O3DDDfTSdNHZtg148UXgm2+AwAOtxozRXmJ+/vl8feNBtGmYIGIR0jFBiI3TNEzHNQVBluXg033sqB9NnHwyMHkysGMHkJoKXH+9dpdvyRJg3DjgtNOAGTOAvXvD71OUfPLy0267gf43bpRx221AYiLw1VfaBu/aa4HVq4EFC8Lb4Jn11Wh70jBfRMknTz/ttG1l36Th2ESkfEbjWhyLGjZjSyRok8cZt9uNwYMH6zomoqd+NOJ2A9dcA/zyi3ZHb+pU4JRTtM3d9OlA797AzTdrm7/m7lOLkk9eftptd8sWN95553wMGeLBt99qm7vrrweys7UH7oweHTlfjbYnDfNFlHzy9NNO21b2TRqOTUTKZzSuxbGoYTO2RIKOa1oIHdfkh98P/PQT8P77wIoVxz4/80zggQeAiROBjh35+Uc0JC8PeP554Icfjn02dqx2LDMpiZtbjiOWNEwQ0QrpmCDExmkapuOagiBJEhYuXBh8673V9WOFFi2AW2/VHsyxcaP2Hb22bbXveD32mPaglnvuAXJyGrYTJZ+8/LTabm4ucOONwJAhxzZ4F164D9nZEubMMbfBM+ur0fakYb6Ikk+eftpp28q+ScOxiUj5jJa12K6+RdGwGVsiQZs8zng8HowcORIej8eW+rHI4MHAe+8BpaXABx8AZ58N1NVpr14YMUI7Avj559pnouSTl59W2V27VnswzrBhQEqK9l3K8eOBDRtU/PxzCwwbZj4us74abU8a5oso+eTpp522reybNBybiJRP0ddiu/sWRcNmbIkEbfI443a70alTJ13fBdBTP5Zp21Y7qrlhA7ByJfDXv2p3/LKytMfz9+gBPPmkG4cOOT+fvMbdrN2sLOCPfwRGjgTmztW+T/nXvwKbNgHffQecc451cZn11Wh70jBfRMknTz/ttG1l36Th2ESkfIq6Fkeqb1E0bMaWSERvZIIgSRLmz5+v65iInvqEdtfoggu0x/KXlAAvv6w9ifPwYeDNN7V3sV11lYqUFO19bE6E17gbtfvbb9qL60eP1p6E6nYDd9wBbNmijUNiorn+rfTVbHvSMF9EySdPP+20TRomzCJSPkVbiyPdtygaNmNLJOjBKxZi5MErjDFUV1ejbdu2cLlcltZ32hdFnYSiaI/mf/99hl9/BRjTctmzJ3DffcC99wKnnsrZyXronSe87K5cqb3GYvFi7WePR9vcTZ0KnH66+f6t9NWq9qRhvvDShl54+mmnbdJw8/VJx6ERRcOAOGsxr75F0bDeNk7TMD14RRBcLhfatWsX9qTUW59oHI9HO0Y4f74LhYUuPPUU0KULsGeP9oTH3r2BW24Bli1r/jUMkYDXuDdnV1W19xZ+/z1w2WXARRdpG7y4OO1BN9u2aS+wb2yDF07/VvpqV3vSMF9EySdPP+20TRomzCJSPp26Fjulb1E0bMaWSNAmjzOSJOHnn3/WdUxET30iNJIkIS/vZzz/vIQ9e4Cvv9aOdsoy8H//B1x6KXDWWcC77wJVVXz95DHu9e16vdoDVD75BJg0CbjwQqB9e20DN348sHQpEB+v3QktKNDq9e8ffv9W+hrJ9qRhvoiST55+2mmbNEyYRaR8OmEtdnLfomjYjC2RoOOaFmL0uKbX60VCQkLYx0TCre+028tOpKl85uVpT+b86ivg6FHts9atgdtuAx58MPLvctM7T8xSXg6sXw/k5jKsW6dg40YPtm51QVFOrNuypfYE0wsv1F5X0bt3+HasjMtsX0bbk4b5EmltGIWnn3baJg3TcU2ziKJhgJ+vpGHr24m8Foe734iLoE9EE8TF6RsGvfWJ0DSWz3PO0TZ5r76qbfQ++ADYvBn4+GPtv3PP1d7F9+c/AwkJ/Pw0i6oCO3dqG7r6/5WWBmq4UP8y0bmztsEdOvTYf2eeqR3PNIqVcZnty2h70jBfRMknTz/ttE0aJswiUj55+Uoatr6dSPPOCHRckzOyLCM1NRVymI911FufCE1z+WzXDnjoIe0F68uXay9cj48HVq8GJkzQHtQyeTJQWMjXz3AIddzyz38GXnwRmD//2AZvwADgpptU3HZbPlJSZOzZAxw8CCxaBLz+unZX86yzzG3wrJzPZvsy2p40zBdR8snTTzttk4YJs4iUT16+koatbyfSvDMKHde0kMDt04qKCnTs2BHK72fbPB5Pg7Isy3C5XPB4PJAkCaqqokWLFlAUBW63G263G7IsB8uSJMHj8cDtdsPv9wMA4uPjIcty8K8Qx5fj4+Ph9/vx66+/4tprrw36EB8fD1VVoaoq4uLimiwrigLGWLDcWBxNxVTf96bK9WOSJAlxcXFwuVzBclMxMcaCZVVVTcfkdrvh8/kQFxd3QqxNxVRaKuPzz9346CM3du/Wxt7l0l7D8OCDwHXXuaGq1sakKAr8fj8SEhLAGGt2nPbvV7BhA7Bxowe5uSo2bHAhP7/p45aDBzMMHQokJbkweLCMoUPdaN/e3WB+Bny3cpzqj0P9OIzMPbfbDa/XixYtWoSch03NvYC2AnbDjampsWlq7smyDFmW0bJlS6iqGjImVVWRmpqKq6++Gi1btnS8nnhcIwL9AUCLFi0cG5PL5WowPyM5ToF55nK5gv5aOU5WXSPi4uLg9/vhdrtDxtfUODWlreZiamxsQs294495NTdOPp8PCxYsCK7FTtYTj2sEYwyBf4q63W5HxxTO9duOcTK6PkU6psC6Faird5wC2mrVqlWD+JqLyePxNLh2hDP36setKErIcVIUpcFazFtPhw8fRqdOnejpmjxISkpCYmIikpOTkZ+fDwDIy8tDQUEBACA3NxdFRUUAgOzsbOz+faeQmZmJsrIyAEBGRgbKy8sBAOnp6aisrAQALFq0CFW/PwEkNTUVXq+3wV8jvF4vUlNTAQA1NTVBnyorK5Geng4AKC8vR0ZGBgCgrKwMmZmZAICSkhJkZWUBAIqKipCbmwsAKCgoQF5eHgAgPz+/2ZiysrJQUlISdkxpaWmorq4OK6bq6mqkpaVZGtPWrVt1xVRYmIk77yzDzp3A889vwGWX+cAYsHChG2PHutGvH3DvvYUoLKyxLKZ9+/ZhzZo1J8RUWFiEuXO34McfgUmTDuPSS2vQqxfQrZsHY8Z48MQTwDffuLFpk7bBa99ewvnn1+KJJ4Dk5O1IS9uLmhrgzTdXYMaMvXjoIUBRMuDzHRuniooK28bpt99+gyzLls29nJwcw3Pv119/xdGjRw3FtGrVKl1zb9OmTWHHBAClv99eFUFPPK4RR48exa+//ur4mJYvX85tnGRZtm2crLxGHDhwACtWrDA8Tlu3bjUU06pVq3TNvUWLFoUd06FDhxBABD3xuEbIsixETLt27cL69esjPk5m1qdIxrRv3z7IsowVK1YYHqdFixYZimn37t3Izs4GEN7c27hxo65xAo5pmZeepk6disTERAwfPhxhwQjLqKqqYgBYRUUFY4wxWZaZLMsnlCVJCpZra2tZ8fEv1gAAHL1JREFUSkoK8/v9TJIkpihKsE6g7Pf7g+WjR48G6/v9fqaqKlNV9YQyY4z5fL5gXUVRgp8risIkSQpZlmW5QbmxOJqKqak4moop4Hv9clMx1S9bEZPf72cpKSmsrq7OVEw7djD2+OMy69RJZdpLFxiLj1fZrbcytmSJxBTFXExer5d9//0vbPVqP/voI4X9/e8Ku/BCxtq2PWbv+P8GDFDZzTcz9txzCvv5Z5nt2cOY369vnOrPTzvGKdC/1+s1PfcCY+n1eg3NvYC2fD6frpi8Xm8DnTU39+rq6oL17Y4p0noKZ5zCnXvhxhS4zh09etTRMR0/lpEcp8Ccq6urs3ycrLxGBHJUW1traJya0lZzMTU2NqHmXmDO+Xw+W64R0bzmNlZubH46NaZwrt92jJPR9SnSMQXmem1traFxqv/vVj0xHX/tCGfu1Y/b6muE3XqqqKhgAFhVVRULBR3XtBAjT9e0E8lhTwOKBbxe7dUL77+vfW8vwFlnaU/lvOMO7Xt+zVFeDmzY0PBhKPn5CPl0y/oPQznnHKBtWysiInhCGiYI8SEdE4TYOE3D9HRNQWCMobq6Gm3btg370c166hOhsTqfCQnaRu6OO4DcXO2pnN98oz2Zc9Ik4KmngNtv1zZ8Q4ZoT7csKjrx6ZZ79jTef+fODElJLkufbhkKu+eblf2b7ctoe9IwX0TJJ08/7bRNGnbunBMFkfLJy1fSsPXtRJp3RqHv5HEmcHZZlsN/qpee+kRo7MxnUhLw0UfA3r3AO+8AAwdq79z73/+0zdmgQUCHDtpTLG++GXjhBWDevGMbvPqfp6TI+Pzzxdi7V7b06ZbNYfd8s7J/s30ZbU8a5oso+eTpp522ScOEWUTKJy9fScPWtxNp3hmFjmtaCB3XJELBmPYahvffB+bMAQLXFTpuSTQFaZggxId0TBBi4zQNh7vfoDt5nFFVFRUVFcFHzlpdnwhNJPPpcgGXXAJ8/z2wezeQkgJs2gTU1ADZ2dpL1h96CLjgghM3eLzG3W67VvZvti+j7UnDfBElnzz9tNM2aZgwi0j5jMa1OBY1bMaWSNAmjzOKoiA7Ozv47g6r6xOh4ZXPU08F/vSn8I9b8vLTbrtW9m+2L6PtScN8ESWfPP200zZpmDCLSPmMxrU4FjVsxpZI0HFNC6HjmgRBWAlpmCDEh3RMEGLjNA3TcU1BUFUVBw4c0HVMRE99IjSi5JOXn3bbtbJ/s30ZbU8a5oso+eTpp522ScOEWUTKZzSuxbGoYTO2RII2eZxRVRWbNm3StbjoqU+ERpR88vLTbrtW9m+2L6PtScN8ESWfPP200zZpmDCLSPmMxrU4FjVsxpZI0HFNC6HjmgRBWAlpmCDEh3RMEGLjNA3TcU1BUFUVpaWluv6CqKc+ERpR8snLT7vtWtm/2b6MticN80WUfPL0007bpGHCLCLlMxrX4ljUsBlbIkGbPM6oqorCwkJdi4ue+kRoRMknLz/ttmtl/2b7MtqeNMwXUfLJ0087bZOGCbOIlM9oXItjUcNmbIkEHde0EDquSRCElZCGCUJ8SMcEITZO0zAd1xQEVVVRXFys6y+IeuoToREln7z8tNuulf2b7ctoe9IwX0TJJ08/7bRNGibMIlI+o3EtjkUNm7ElErTJ4wx9F4AvouQzGr8HYHX/onwXQJQ5Jwqi5JOnn3baJg0TZhEpn9G4Fseihs3YEgk6rmkhdFyTIAgrIQ0ThPiQjglCbJymYTquKQiKomDHjh1QFMWW+kRoRMknLz/ttmtl/2b7MtqeNMwXUfLJ0087bZOGCbOIlM9oXItjUcNmbIkEbfI4wxjD4cOHEe4NVb31idCIkk9eftpt18r+zfZltD1pmC+i5JOnn3baJg0TZhEpn9G4Fseihs3YEgk6rmkhdFyTIAgrIQ0ThPiQjglCbJymYTquKQiKomDr1q26jonoqU+ERpR88vLTbrtW9m+2L6PtScN8ESWfPP200zZpmDCLSPmMxrU4FjVsxpZI0CbPAdTV1dlanwiNKPnk5afddq3s32xfRtuThvkiSj55+mmnbdIwYRaR8hmNa3EsatiMLVGg45oW4rTjmjU1Nbj33nvxySefoE2bNrzdIQhCJ6RhghAf0jFBiI3TNEzHNQVBURRs2rRJ1zGRcOv7fD7Mnj0bPp/PrJtRi97884KXn3bbtbJ/s30ZbU8a5gtpmK9t0nDzkI5DI4qGgehci2NRw3rbiKph2uTZQGDCKIrSaFmW5Qbl7777LlgOvJSxflmSpAblWbNmBcuMMTDGTigD2pODWrVqBUB76WPgc1VVIctyyLKiKA3KemNqLI5QMQVuKIcTU/04rIjpu+++EyKm2bNnR3ycZFk+Yb5ZPU6zZs2yLKbZs2ebGqdZs2YZiqmxsQkVU0Dz4cTUsmVL7nPP6deIWbNmCRFT/fkZyXFSFAWzZs0S4hoxa9YsU+PUmLbCien4sWlunGbPnh12TIwxJCQkGI5JhPXJbEzHz08nxxS43kd6nIyuT5GOKaBho+MU8EVvTN99953uuVf/39/NjVNCQkID33nrKRxok2cDSUlJSExMRHJyMvLz8wEAeXl5KCgoAADk5uaiqKgIAJCTk4Pdu3fD4/EgMzMTZWVlAICMjAyUl5cDANLT01FZWQkAWLJkCVatWgWPx4PU1FR4vV7IsozU1FTIsgyv14vU1FQAQG1tLT799FMAQGVlJdLT0wEA5eXlyMjIAACUlZUhMzMTAFBSUoKsrCwAQFFREXJzcwEABQUFyMvLAwDk5+c3G1NWVhZKSkoAIKyY0tLSUF1dDQDNxlRdXY20tDTLYvJ4PPD5fNi+fbujYzpw4ABOOeUUeDyeiI7TsmXLkJOTA4/HY8s4rVmzBnPmzMHevXtNx+TxeNCuXTvs3bvX0DgtXLgQS5YsAWNMV0yHDx9Gr1694PF4wpp7O3fuBAB4PJ6wxmnKlCk4cOCAoZgiradwxgmwVk+MMSxZsgQLFy50dEwejwenn356MI5IjtOGDRswZ84c7Ny509HXCI/Hg02bNmHVqlWGxmn79u3w+XzweDy6YvJ4POjVqxcOHz4c1txjjGHEiBFgjIU1TpWVlXjttde4zb1wYuJ5jdi5cyfmzJmDDRs2OD6m3bt3o2XLlvB4PBEdJ6PrU6RjOnDgAObMmYNVq1YZGidJkjBixAh4PB5dMXk8HuzevRs5OTkAwpt7mzdvRlVVFTweT1jj9NprrwXj4KWnqVOnIjExEcOHD0c40HfyLKSqqgodOnTArl270LFjx+BO2+PxNCjLsgyXyxXcYJx77rlYu3YtGGNwu91wu92QZTlYliQJHo8HbrcbXq8Xo0ePxrp166CqKuLi4gBof22oX46Pj8ehQ4eQmJiI/Px8dOjQAYqiID4+HqqqBts2VVYUBYyxYLmxOJqKqb7vTZXrxyRJEuLi4uByuYLlpmJijAXLqqqajgkARowYgTVr1qBFixaOjUmSJJx77rnIysqCy+WK2Dj5fD6MHj0aOTk5QZtWjpMkSRg5ciRycnLgcrlMxQQAo0aNwurVqxEfH697nALaysnJCdoPJ6amxqapuef3+3HuueciOzs7OAebiqmqqgpnnnkm8vPz0aVLF8fricc1wuVyYfjw4VizZk3wL61OjIkxhtGjR+O3334LzptIjZOiKBgxYgTWrl0Lj8fj2GuE2+0OjmXgDraecfL7/Rg9ejTWrl3bQFvNxcQYa3DtaG7uKYqCc889F6tXrw72GWqcDh06hEGDBmHr1q3o0KGDo/XE4xrR2Px0akzhXL/tGCej61OkY2KMYfjw4cjOzkZ8fLzucQpoa82aNcE+w4nJ5XJhxIgRWL16NVq2bBnW3Kt/vWCMhRynyspKDBw4EPn5+ejcuTN3PR0+fBh9+vRBZWUl2rdvj6agTZ6F7NmzB7169eLtBkEQBEEQBEEQUUxJSQl69uzZ5O9pk2chqqpi7969aNu2bfDOAkEQBEEQBEEQhBUwxlBdXY3u3bvD7W76m3e0ySMIgiAIgiAIgogi6MErBEEQBEEQBEEQUQRt8giCIAiCIAiCIKII2uQRBEEQBEEQBEFEEbTJI1BSUoJLLrkEiYmJOOecc/B///d/vF0iCMIAN954Izp27Iibb76ZtysEQYTBvHnzcOaZZ+L000/HJ598wtsdgiB04uR1lx68QqCsrAz79+/H0KFDsW/fPgwfPhzbt2/HSSedxNs1giB0sGzZMlRXV+OLL77ADz/8wNsdgiBCIMsyEhMTsXTpUrRv3x7Dhw9HZmYmOnfuzNs1giDCxMnrLt3JI3Dqqadi6NChAIBu3bqhS5cuqKio4OsUQRC6ueSSS9C2bVvebhAEEQZZWVk466yz0KNHD7Rp0wbXXHMN0tLSeLtFEIQOnLzu0iZPADIyMnD99deje/fucLlcSElJOaHOe++9hz59+iAhIQGjR49GVlaWIVs5OTlQFIVe6k4QFhNJHRMEYT9mNb1371706NEj+HOPHj1QWloaCdcJgkD0r8u0yROAo0ePYsiQIXjvvfca/f3s2bPx+OOPY9q0aVi3bh2GDBmCMWPG4MCBA8E6Q4cOxeDBg0/4b+/evcE6FRUVmDBhAj766CPbYyKIWCNSOiYIIjJYoWmCIPgR9RpmhFAAYHPmzGnw2ahRo9hDDz0U/FlRFNa9e3f28ssvh92v1+tlF110Efvyyy+tcpUgiCawS8eMMbZ06VI2btw4K9wkCCJMjGh61apVbOzYscHf/+Mf/2DffPNNRPwlCKIhZtZlp667dCdPcPx+P3JycnDFFVcEP3O73bjiiivw22+/hdUHYwx33nknLrvsMtxxxx12uUoQRBNYoWOCIJxDOJoeNWoUNm3ahNLSUtTU1ODXX3/FmDFjeLlMEEQ9omFdpk2e4JSXl0NRFHTt2rXB5127dsW+ffvC6mPVqlWYPXs2UlJSMHToUAwdOhQbN260w12CIBrBCh0DwBVXXIE///nPSE1NRc+ePYVZiAgi2ghH03FxcXjjjTdw6aWXYujQofjnP/9JT9YkCIcQ7rrs5HU3jrcDBH8uvPBCqKrK2w2CIEyyePFi3i4QBKGDG264ATfccANvNwiCMIiT1126kyc4Xbp0gcfjwf79+xt8vn//fnTr1o2TVwRB6IF0TBDRBWmaIMQmGjRMmzzBadGiBYYPH44lS5YEP1NVFUuWLMF5553H0TOCIMKFdEwQ0QVpmiDEJho0TMc1BaCmpgY7duwI/lxUVIT169ejU6dO6N27Nx5//HFMnDgRI0aMwKhRo/DWW2/h6NGjuOuuuzh6TRBEfUjHBBFdkKYJQmyiXsO8H+9JNM/SpUsZgBP+mzhxYrDOu+++y3r37s1atGjBRo0axVavXs3PYYIgToB0TBDRBWmaIMQm2jXsYoyxyG4rCYIgCIIgCIIgCLug7+QRBEEQBEEQBEFEEbTJIwiCIAiCIAiCiCJok0cQBEEQBEEQBBFF0CaPIAiCIAiCIAgiiqBNHkEQBEEQBEEQRBRBmzyCIAiCIAiCIIgogjZ5BEEQBEEQBEEQUQRt8giCIAiCIAiCIKII2uQRBEEQBEEQBEFEEbTJIwiCIIgo4vPPP4fL5Qr+d+utt/J2SRdbt25t4H+fPn14u0QQBCEctMkjCIIgbGPXrl0N/sHucrnQunVrdO/eHZdffjmSk5NRWFjYaNtly5YF24wYMaJJG7/++muw3iWXXBKWX3b27RT+9Kc/Ydq0abj55psb/X1dXR3ef/99jBkzBt26dUOLFi3Qtm1bDB48GH/729+wePFiw7arqqrQunVrdOzYEV6vN2Tdjz/+GC6XC/fddx8AoEuXLpg2bRqmTZuG9u3bG/aBIAgilonj7QBBEAQR/fTv3x+33347AMDn8+HAgQPIysrC888/j5deegmTJ0/Giy++CJfLdULbuLg45OTkIC8vD+ecc84Jv//0008RFxcHWZZ1+2Vn37wZO3Ys7rzzzkZ/t2HDBtx4440oKipCz549cdVVV6FHjx7w+XwoKCjA7Nmz8cknn+DRRx/Ff/7zH92227dvj3HjxuHrr7/Gjz/+iNtuu63JujNnzgQA3HPPPQC0Td706dMBaHclCYIgCP3QnTyCIAjCdgYMGIDp06dj+vTpePnll/Hpp59i48aNWLFiBXr16oWXX34ZycnJjbYdM2YM3G53cDNQn/LycsydOxfXXnutIb/s7Nup7NmzB1dddRV2796NN998E0VFRfjyyy/x8ssv480338TcuXNRVlaG//znP/D5fIbtBDZtjeU2QH5+PlavXo2zzjoLo0ePNmyLIAiCaAht8giCIAhuXHjhhViwYAFatmyJ1157DSUlJSfU6dmzJ6688kp888038Pv9DX739ddfw+/34+677zZk30zf27dvx+TJkzFs2DB07twZCQkJOOOMMzBlyhTU1NScUL+srAz/+Mc/cPrpp6NVq1bo0KEDBg0ahAceeABVVVXBelVVVUhOTkZiYiLatGmDdu3aYcCAAZg4cSKKi4sNxVmfp59+GgcOHMAzzzyDxx57DHFxJx7qOemkk/Doo4/inXfeOeF3fr8fb775JoYNG4aTTjoJbdu2xUUXXYRffvmlQb0//OEP6N+/P5YuXYpdu3Y16svxd/EIgiAIa6BNHkEQBMGVM888E7fccgv8fj9SUlIarXP33XcH76zVZ+bMmabvAhnt+6effsKnn36Kfv36YeLEiXjggQfQqVMnvPrqq7jyyishSVKwbm1tLS644AK8++676N+/Px5++GHceeedOOOMM/DVV1/h4MGDAADGGMaMGYPnn38enTp1wn333Yf77rsPSUlJ+OWXX1BQUGA4zoAfs2fPRqtWrfDPf/6z2frHbwB9Ph/GjBmDf/7zn2CM4Z577sHtt9+O4uJi/OlPf8J///vfYF2Xy4W7774bjDF89tlnJ/QtyzK++uortGjRAnfccYepuAiCIIiG0HfyCIIgCO5ccskl+Oqrr5Cdnd3o78eOHYvOnTtj5syZGDduHAAgOzsbGzduxBtvvGHKttG+77jjDjz++ONo0aJFg8+fe+45TJs2Dd9//33wu2hLlixBUVFRo99xq6mpQXx8PABg06ZNWLNmDcaOHYs5c+Y0qOfz+RpsHI2wdu1aSJKEc889F23bttXd/rnnnsOyZcvw7LPPYsaMGcHvUFZXV+Oyyy7DP//5T9x0003o3r07AODOO+9EcnIyvvjiC0ybNg1u97G/LaempmL//v24+eab0aVLF1NxEQRBEA2hO3kEQRAEdwKbgvLy8kZ/36JFC9x2221YuHAh9u7dC0C70xYfH2/6LpDRvnv06HHCBg8AJk2aBACNPp2yVatWJ3zWpk0btGzZstl6LVu2RJs2bUIH0wz79u0DcCzfxxP43mT9/wKoqooPPvgA/fv3b7DBA4C2bdsiOTkZfr8fP/30U/Dz7t274+qrr0ZxcTGWLFnSwFbgqKbRo7YEQRBE09CdPIIgCEII7r77brzzzjv44osv8Nhjj+G7777Dddddh5NPPjm4eYlk34FjiJ9//jk2bdqEqqoqqKoa/H1gwwgAF198MU499VS88sor2LBhA6677jr84Q9/wKBBgxpslgYNGoRzzjkHs2bNwp49ezB27FhccsklGDp0aIO7YHYxY8aMEz4LbPS2bduGw4cPo3v37o3WCxw53bp1a4PP7733XsyfPx8zZ87ElVdeCQA4cOAA5s+fj549e2LMmDEWR0EQBEHQJo8gCILgTmBDdPLJJzdZZ8iQIRg2bBg+++wz9O7dG5WVlZbdBTLS9yOPPIL//ve/6NWrF2644QaceuqpwTtyM2bMaPBkyvbt22P16tVITk7G3LlzkZqaCgDo1asXpkyZgr///e8AtO/ApaenY/r06fjxxx+D35s7+eSTMWnSJPzrX/+Cx+MxHGfXrl0BNNyA1ocxFiwPHDgQ27ZtC/5cUVEBANi8eTM2b97cpI2jR482+Pm6665D165dkZKSgsOHD6Njx4748ssvIcsy7rzzzohsXgmCIGINurISBEEQ3Fm2bBkAYOTIkSHr3XPPPSgoKMBTTz2F7t2745prrrHMBz19HzhwAO+99x7OOeccbN26FZ9//jlefvllTJ8+HQ888ECjbXr37o3PP/8cBw8eRG5uLl599VWoqoqHHnoIs2bNCtbr3Lkz3n33XZSWlmLLli3473//i06dOmHatGl47bXXTMU4YsQIxMfHIycnB9XV1bratmvXDgAwbtw4MMaa/O/4h6zExcVhwoQJ8Hq9+OabbwAAn332GVwuF+666y5T8RAEQRCNQ5s8giAIgivbt2/H999/j5YtW+LGG28MWfevf/0rEhISUFpaigkTJpi6q2Wm7507d4IxhiuuuAKtW7du8LsVK1aEtON2uzF06FBMnjw5uLk7/vUDgPZ0ykGDBuGhhx7CokWLmqynh5NOOgnjx49HbW2t7pecDxo0CO3atQs+vEUPgbuiM2fOxJo1a7BlyxZceuml6Nevn65+CIIgiPCgTR5BEATBjVWrVmHMmDHw+XyYMmUKevToEbJ+hw4dsHDhQsyZMwePPfaYpb7o6fu0004DAGRmZjb4Ht6ePXvw9NNPn1B/8+bN2L9//wmfBz5LSEgAAOzatavRd8odX88ML730Ek4++WQ899xzePvtt6Eoygl1vF7vCS9Cj4uLw4MPPoji4mI88cQTjW70Nm3ahAMHDpzw+cCBA3HBBRcgNzc3eASV3o1HEARhH/SdPIIgCMJ2duzYEXyAh9/vx4EDB5CVlYWNGzfC4/HgmWeewbRp08Lq6+KLL7bNz3D7PvXUUzFu3Dj8+OOPGDFiBC6//HLs378f8+bNw+WXX47CwsIG9RctWoQnn3wSF1xwAc444wx07twZO3fuxC+//IKEhAQ89NBDAID169fjpptuwqhRo5CYmIhu3bqhtLQUKSkpcLvdlmxse/XqhUWLFuHGG2/Eo48+in//+9+47LLL0KNHD9TV1aG0tBSLFi1CZWUlLrzwwgZtZ8yYgXXr1uGdd97B/PnzcfHFF+OUU05BaWkpNm7ciA0bNuC3337DKaeccoLde+65B6tWrcKqVavQoUMH3HTTTaZjIQiCIBqHNnkEQRCE7RQWFgafyNiqVSt06NABAwcOxLPPPouJEyeif//+nD3Uz+eff44+ffrgxx9/xLvvvovevXvj8ccfx1NPPYUffvihQd0xY8Zg165dyMjIwE8//YSamhr06NED48ePx+TJk5GYmAhA+87cU089hWXLlmH+/PmorKxEt27dcMUVV+DJJ5/Eueeea4nvQ4YMwebNmzFz5kykpKRgwYIFqKioQEJCAnr16oUbb7wRf/3rX3HFFVc0aNeyZUv8+uuv+PTTT/Hll1/ixx9/hM/nQ9euXZGYmIgHHngAZ599dqM2b7nlFvzjH/9AdXV18GgsQRAEYQ8uVv9RWgRBEARBCM3nn3+Ou+66C5999hnuvPNO3u6Yok+fPgDQ6BFWgiAIomnoO3kEQRAEEYXcddddcLlcuPXWW3m7ooutW7fC5XLB5XKhuLiYtzsEQRBCQsc1CYIgCCKKGDp0aIPvNw4ePJijN/rp0qVLA/87dOjAzxmCIAhBoeOaBEEQBEEQBEEQUQQd1yQIgiAIgiAIgogiaJNHEARBEARBEAQRRdAmjyAIgiAIgiAIIoqgTR5BEARBEARBEEQUQZs8giAIgiAIgiCIKII2eQRBEARBEARBEFEEbfIIgiAIgiAIgiCiCNrkEQRBEARBEARBRBG0ySMIgiAIgiAIgogiaJNHEARBEARBEAQRRfw/J9UyFkaBZvgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mX_arr_eV = np.geomspace(1e7, 1e10, 15)\n", + "mX_arr_GeV = mX_arr_eV / 1e9\n", + "\n", + "# Run the simulation\n", + "t_start = time.time()\n", + "m_dm, sigs = SE.run_sim(\n", + " threshold=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_low=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_high=0.1,\n", + " m_dms=mX_arr_GeV,\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-31,\n", + " elf_model='electron',\n", + " elf_params={'mediator': 'massive', 'kcut': 25e3, 'suppress_darkelf_output': True},\n", + ")\n", + "sig = np.median(np.stack(sigs, axis=1), axis=1)\n", + "t_end = time.time()\n", + "print(f'Simulation took {(t_end - t_start):.2f} seconds')\n", + "\n", + "fn = 'sapphire_results/Massive_ER_kcut_25keV_Limit_' + dt.datetime.now().strftime('%Y%m%d_%H%M%S') + '.txt'\n", + "np.savetxt(fn, np.vstack([m_dm, sig]).transpose(), fmt='%.3e')\n", + "\n", + "f_out = open(fn + '_detailed', 'w')\n", + "f_out.write(str(m_dm))\n", + "f_out.write('\\n')\n", + "f_out.write(str(sigs))\n", + "f_out.write('\\n')\n", + "f_out.close()\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "#ax.set_ylim(1e-37, 1e-32)\n", + "#ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/sapphire_sensitivity.ipynb b/examples/sapphire_sensitivity.ipynb new file mode 100644 index 0000000..f63d706 --- /dev/null +++ b/examples/sapphire_sensitivity.ipynb @@ -0,0 +1,997 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "import darklim\n", + "\n", + "sys.path.append('/Users/vetri/GitRepos/DarkELF/')\n", + "from darkelf import darkelf\n", + "\n", + "from IPython.utils import io\n", + "import datetime as dt\n", + "import time\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SPICE Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "calculate_substrate_mass() got an unexpected keyword argument 'rho'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m rho_sapphire \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3.98e3\u001b[39m \u001b[38;5;66;03m# kg/m^3\u001b[39;00m\n\u001b[1;32m 6\u001b[0m band_gap_sapphire_eV \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m8.8\u001b[39m\n\u001b[0;32m----> 8\u001b[0m mass_det \u001b[38;5;241m=\u001b[39m \u001b[43mdarklim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msensitivity\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate_substrate_mass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvol_detector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrho\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrho_sapphire\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# mass of the detector in kg\u001b[39;00m\n\u001b[1;32m 10\u001b[0m energy_res \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3.73e-4\u001b[39m \u001b[38;5;66;03m# energy resolution is 300 meV\u001b[39;00m\n\u001b[1;32m 11\u001b[0m energy_threshold \u001b[38;5;241m=\u001b[39m energy_res \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m5\u001b[39m\n", + "\u001b[0;31mTypeError\u001b[0m: calculate_substrate_mass() got an unexpected keyword argument 'rho'" + ] + } + ], + "source": [ + "vol_detector = 0.01 * 0.01 * 0.01 # Units of m^3\n", + "time_elapsed = 1 # Units of days\n", + "efficiency = 1. # Assume 80% efficiency of cuts/livetime\n", + "tm = 'Al2O3' # Assume a Al2O3 target mass\n", + "rho_sapphire = 3.98e3 # kg/m^3\n", + "band_gap_sapphire_eV = 8.8\n", + "\n", + "mass_det = darklim.sensitivity.calculate_substrate_mass(vol_detector, tm=tm, rho=rho_sapphire) # mass of the detector in kg\n", + "\n", + "energy_res = 3.73e-4 # energy resolution is 300 meV\n", + "energy_threshold = energy_res * 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Background Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are three types backgrounds we can add easily:\n", + "1. Flat background (energy independent)\n", + "2. Noise background (the expected background from noise events being triggered)\n", + "3. DM background (a WIMP background model)\n", + "4. Exponentially falling background\n", + "\n", + "If a different background function is to be inputted, one can use the `add_arb_bkgd` method." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulated 10496 events, with 9602 above threshold\n", + "Simulated 9445 events, with 9445 above threshold\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SE = darklim.sensitivity.SensEst(mass_det, time_elapsed, eff=efficiency, tm=tm)\n", + "\n", + "# flat background of 1 DRU\n", + "#SE.add_flat_bkgd(1)\n", + "\n", + "# noise background assuming 10,000 independent samples (1 ms window), using the data sample rate of 1 MHz\n", + "#SE.add_noise_bkgd(energy_res, 1e4, 1e6)\n", + "\n", + "# LEE background assuming mean rate of 0.12 events/sec\n", + "SE.add_exponential_bkgd(0.020, 0.12 * 86400, normalize_mass=True)\n", + "\n", + "gen_evts = SE.generate_background(e_high=1., e_low=0.0001, plot_bkgd=True, xlim=[0.0001,0.4], xscale='log')\n", + "print(f'Simulated {len(gen_evts)} events, with {sum(gen_evts > energy_threshold)} above threshold')\n", + "\n", + "gen_evts = SE.generate_background(e_high=1., e_low=energy_threshold, plot_bkgd=True, xlim=[0.0001,0.4], xscale='log')\n", + "print(f'Simulated {len(gen_evts)} events, with {sum(gen_evts > energy_threshold)} above threshold')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Signal Models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Nuclear Recoils" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0 MeV, 0.0 events above threshold\n", + "30.0 MeV, 0.0 events above threshold\n", + "100.0 MeV, 57.1 events above threshold\n", + "300.0 MeV, 198.0 events above threshold\n", + "1000.0 MeV, 170.5 events above threshold\n", + "3000.0 MeV, 201.1 events above threshold\n", + "10000.0 MeV, 278.5 events above threshold\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_11126/746462407.py:25: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", + "\n", + "keV_arr = np.geomspace(1e-3, 100, 250)\n", + "\n", + "for mX, color in zip([1e7, 3e7, 1e8, 3e8, 1e9, 3e9, 1e10], \n", + " ['#d62728', '#ff7f0e', '#bcbd22', '#2ca02c', '#17becf', '#1f77b4','#e377c2', '#9467bd', '#8c564b']):\n", + "\n", + " dRdE_arr = darklim.limit._limit.drde(keV_arr, mX / 1e9, 1e-36, 'Al2O3')\n", + " ax.plot(keV_arr, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " n_above_threshold = np.trapz(dRdE_arr[keV_arr > energy_threshold], keV_arr[keV_arr > energy_threshold]) * time_elapsed * mass_det\n", + " print(f'{mX / 1e6} MeV, {n_above_threshold:.1f} events above threshold')\n", + " \n", + "ax.set_yscale(\"log\")\n", + "ax.set_xscale('log')\n", + "ax.set_ylim([1e1, 1e8])\n", + "ax.set_xlim([keV_arr[0], keV_arr[-1]])\n", + "\n", + "ax.set_xlabel(\"E [keV]\")\n", + "ax.set_ylabel(\"dR/dE [DRU]\")\n", + "ax.set_title(r'Nuclear Recoil, $\\sigma = 10^{-36} cm^2$')\n", + "\n", + "ax.legend(ncol=2, fontsize=10, loc=\"upper right\")\n", + "fig.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting Experiment 0\n", + " Finished mass 0, 0.01000 GeV. Found sigma = inf cm2.\n", + " Finished mass 1, 0.01212 GeV. Found sigma = inf cm2.\n", + " Finished mass 2, 0.01468 GeV. Found sigma = inf cm2.\n", + " Finished mass 3, 0.01778 GeV. Found sigma = inf cm2.\n", + " Finished mass 4, 0.02154 GeV. Found sigma = inf cm2.\n", + " Finished mass 5, 0.02610 GeV. Found sigma = inf cm2.\n", + " Finished mass 6, 0.03162 GeV. Found sigma = inf cm2.\n", + " Finished mass 7, 0.03831 GeV. Found sigma = inf cm2.\n", + " Finished mass 8, 0.04642 GeV. Found sigma = 5.423e-32 cm2.\n", + " Finished mass 9, 0.05623 GeV. Found sigma = 1.726e-34 cm2.\n", + " Finished mass 10, 0.06813 GeV. Found sigma = 3.596e-35 cm2.\n", + " Finished mass 11, 0.08254 GeV. Found sigma = 1.371e-35 cm2.\n", + " Finished mass 12, 0.10000 GeV. Found sigma = 8.182e-36 cm2.\n", + " Finished mass 13, 0.12115 GeV. Found sigma = 6.590e-36 cm2.\n", + " Finished mass 14, 0.14678 GeV. Found sigma = 6.730e-36 cm2.\n", + " Finished mass 15, 0.17783 GeV. Found sigma = 8.108e-36 cm2.\n", + " Finished mass 16, 0.21544 GeV. Found sigma = 1.087e-35 cm2.\n", + " Finished mass 17, 0.26102 GeV. Found sigma = 1.555e-35 cm2.\n", + " Finished mass 18, 0.31623 GeV. Found sigma = 2.312e-35 cm2.\n", + " Finished mass 19, 0.38312 GeV. Found sigma = 3.366e-35 cm2.\n", + " Finished mass 20, 0.46416 GeV. Found sigma = 4.745e-35 cm2.\n", + " Finished mass 21, 0.56234 GeV. Found sigma = 2.899e-35 cm2.\n", + " Finished mass 22, 0.68129 GeV. Found sigma = 6.769e-36 cm2.\n", + " Finished mass 23, 0.82540 GeV. Found sigma = 1.751e-36 cm2.\n", + " Finished mass 24, 1.00000 GeV. Found sigma = 5.873e-37 cm2.\n", + "Simulation took 13.61 seconds\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if False:\n", + " SE.reset_sim()\n", + "\n", + " # flat background of 1 DRU\n", + " SE.add_flat_bkgd(1)\n", + " # noise background assuming 10,000 independent samples (1 ms window), using the data sample rate of 1 MHz\n", + " SE.add_noise_bkgd(energy_res, 1e4, 1e6)\n", + " # LEE background assuming mean rate of 0.12 events/sec\n", + " SE.add_exponential_bkgd(0.020, 0.12 * 86400, normalize_mass=True)\n", + "\n", + "\n", + "# run the simulation for 1 experiment\n", + "t_start = time.time()\n", + "m_dm, sigs = SE.run_sim(\n", + " threshold=energy_threshold,\n", + " e_low=energy_threshold,\n", + " e_high=1,\n", + " m_dms=np.geomspace(0.01, 1, num=25),\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-36,\n", + ")\n", + "sig = np.median(np.stack(sigs, axis=1), axis=1)\n", + "t_end = time.time()\n", + "print(f'Simulation took {(t_end - t_start):.2f} seconds')\n", + "\n", + "fn = 'sapphire_results/NR_Limit_' + dt.datetime.now().strftime('%Y%m%d_%H%M%S') + '.txt'\n", + "np.savetxt(fn, np.vstack([m_dm, sig]).transpose(), fmt='%.3e')\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "ax.set_ylim(1e-37, 1e-32)\n", + "ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DM-Electron Scattering, Massless Mediator" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "10.0 MeV, 163.3 events above threshold\n", + "30.0 MeV, 1001.4 events above threshold\n", + "100.0 MeV, 619.3 events above threshold\n", + "300.0 MeV, 248.9 events above threshold\n", + "1000.0 MeV, 79.5 events above threshold\n", + "3000.0 MeV, 27.0 events above threshold\n", + "10000.0 MeV, 8.1 events above threshold\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_11126/4086379164.py:39: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Note the dimensional analysis in this cell\n", + "# DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV\n", + "# But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU)\n", + "\n", + "mX_arr_eV = np.array([1e7, 3e7, 1e8, 3e8, 1e9, 3e9, 1e10])\n", + "color_arr = np.array(['#d62728', '#ff7f0e', '#bcbd22', '#2ca02c', '#17becf', '#1f77b4','#e377c2', '#9467bd', '#8c564b'])\n", + "keV_arr = np.geomspace(10e-3, 100e-3, 250)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", + "for i, (mX, color) in enumerate(zip(mX_arr_eV, color_arr)):\n", + "\n", + " # Initialize each DarkELF Al2O3 object\n", + " if i == 0:\n", + " print('WARNING: You are suppressing DarkELF output')\n", + " with io.capture_output() as captured:\n", + " darkelf_sapphire = darkelf(target='Al2O3', filename=\"Al2O3_mermin.dat\")\n", + "\n", + " darkelf_sapphire.update_params(mX=mX, mediator='massless')\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * band_gap_sapphire_eV, 1) * \\\n", + " darkelf_sapphire.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + " \n", + " ax.plot(keV_arr, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " n_above_threshold = np.trapz(dRdE_arr[keV_arr > energy_threshold], keV_arr[keV_arr > energy_threshold]) * time_elapsed * mass_det\n", + " print(f'{mX / 1e6} MeV, {n_above_threshold:.1f} events above threshold')\n", + " \n", + "ax.set_yscale(\"log\")\n", + "#ax.set_xscale('log')\n", + "ax.set_ylim([1e-6, 1e10])\n", + "ax.set_xlim([keV_arr[0], keV_arr[-1]])\n", + "\n", + "ax.set_xlabel(\"E [keV]\")\n", + "ax.set_ylabel(\"dR/dE [DRU]\")\n", + "ax.set_title(r'Electron Recoil, Massless Mediator, $\\sigma = 10^{-31} cm^2$')\n", + "\n", + "ax.legend(ncol=2, fontsize=10, loc=\"upper right\")\n", + "fig.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We try two methods of passing the DarkELF dRdomega_electron() function to DarkLim: defining an anonymous function in this Jupyter notebook, and getting an anonymous function from _sens_est.py. In theory, they are equivalent, but for some reason, only the latter method works. So the former method is grayed out, but we keep it for posterity." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "if False:\n", + " SE.reset_sim()\n", + "\n", + " # flat background of 1 DRU\n", + " SE.add_flat_bkgd(1)\n", + " # noise background assuming 10,000 independent samples (1 ms window), using the data sample rate of 1 MHz\n", + " SE.add_noise_bkgd(energy_res, 1e4, 1e6)\n", + " # LEE background assuming mean rate of 0.12 events/sec\n", + " SE.add_exponential_bkgd(0.020, 0.12 * 86400, normalize_mass=True)\n", + "\n", + "mX_arr_eV = np.geomspace(1e7, 1e10, 10)\n", + "mX_arr_GeV = mX_arr_eV / 1e9\n", + "\n", + "# Set up DarkELF Al2O3 object\n", + "print('WARNING: You are suppressing DarkELF output')\n", + "with io.capture_output() as captured:\n", + " darkelf_sapphire_arr = [darkelf(target='Al2O3', filename=\"Al2O3_mermin.dat\") for m in mX_arr_eV]\n", + "\n", + "# Set up data structure for anonymous functions\n", + "drdefunction_list = [None for m in mX_arr_eV]\n", + "\n", + "for i, mX in enumerate(mX_arr_eV):\n", + " darkelf_sapphire_arr[i].update_params(mX=mX, mediator='massless')\n", + " drdefunction_list[i] = lambda keV : np.heaviside(keV * 1000 - 2 * band_gap_sapphire_eV, 1) * \\\n", + " darkelf_sapphire_arr[i].dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-31, kcut=0, withscreening=True) / 365.25 * 1000\n", + "\n", + "# run the simulation for 1 experiment\n", + "m_dm, sig = SE.run_sim(\n", + " threshold=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_low=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_high=0.1,\n", + " m_dms=mX_arr_GeV,\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-31,\n", + " drdefunction=drdefunction_list,\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "#ax.set_ylim(1e-37, 1e-32)\n", + "#ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "Starting Experiment 0\n", + " Finished mass 0, 0.01000 GeV. Found sigma = 3.110e-31 cm2.\n", + " Finished mass 1, 0.01638 GeV. Found sigma = 1.150e-31 cm2.\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Run the simulation\u001b[39;00m\n\u001b[1;32m 5\u001b[0m t_start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 6\u001b[0m m_dm, sigs \u001b[38;5;241m=\u001b[39m \u001b[43mSE\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_sim\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43menergy_threshold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m 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\u001b[49m\u001b[43mthreshold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 320\u001b[0m \u001b[43m \u001b[49m\u001b[43mm_dms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 321\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexposure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 322\u001b[0m \u001b[43m \u001b[49m\u001b[43mtm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 323\u001b[0m \u001b[43m \u001b[49m\u001b[43mhard_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthreshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 324\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrdefunction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrdefunction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 325\u001b[0m \u001b[43m \u001b[49m\u001b[43msigma0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msigma0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 326\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 328\u001b[0m sigs\u001b[38;5;241m.\u001b[39mappend(sig_temp)\n\u001b[1;32m 330\u001b[0m sig \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmedian(np\u001b[38;5;241m.\u001b[39mstack(sigs, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/darklim/limit/_limit.py:491\u001b[0m, in \u001b[0;36moptimuminterval\u001b[0;34m(eventenergies, effenergies, effs, masslist, exposure, tm, cl, res, gauss_width, verbose, drdefunction, hard_threshold, sigma0)\u001b[0m\n\u001b[1;32m 489\u001b[0m rate \u001b[38;5;241m=\u001b[39m init_rate \u001b[38;5;241m*\u001b[39m curr_exp(en_interp)\n\u001b[1;32m 490\u001b[0m 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\u001b[38;5;66;03m# Note DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV\u001b[39;00m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU)\u001b[39;00m\n\u001b[1;32m 57\u001b[0m sapphire\u001b[38;5;241m.\u001b[39mupdate_params(mX\u001b[38;5;241m=\u001b[39mmX_eV, mediator\u001b[38;5;241m=\u001b[39mmediator)\n\u001b[1;32m 58\u001b[0m fun \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m keV : np\u001b[38;5;241m.\u001b[39mheaviside(keV \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m constants\u001b[38;5;241m.\u001b[39mbandgap_Al203, \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \\\n\u001b[1;32m 59\u001b[0m (\u001b[38;5;241m1000\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m365.25\u001b[39m) \u001b[38;5;241m*\u001b[39m \\\n\u001b[0;32m---> 60\u001b[0m \u001b[43msapphire\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdRdomega_electron\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeV\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msigmae\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msigmae\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkcut\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkcut\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwithscreening\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwithscreening\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fun\n", + "File \u001b[0;32m~/GitRepos/DarkELF/darkelf/electron.py:87\u001b[0m, in \u001b[0;36mdRdomega_electron\u001b[0;34m(self, omega, sigmae, kcut, withscreening, method)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# Note: division and multiplication by self.eVtoInvYr in the integrand improves performance of quad\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m dRdomega[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meVtoInvYr \u001b[38;5;241m*\u001b[39m \u001b[43mintegrate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquad\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdRdomegadk_electron\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43msigmae\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mwithscreening\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwithscreening\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meVtoInvYr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m] \n\u001b[1;32m 89\u001b[0m \u001b[38;5;66;03m# units of 1/kg/yr/eV\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(scalar_input):\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/scipy/integrate/_quadpack_py.py:465\u001b[0m, in \u001b[0;36mquad\u001b[0;34m(func, a, b, args, full_output, epsabs, epsrel, limit, points, weight, wvar, wopts, maxp1, limlst, complex_func)\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n\u001b[1;32m 464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 465\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[43m_quad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfull_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepsabs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepsrel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 466\u001b[0m \u001b[43m \u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 467\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m points \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/scipy/integrate/_quadpack_py.py:577\u001b[0m, in \u001b[0;36m_quad\u001b[0;34m(func, a, b, args, full_output, epsabs, epsrel, limit, points)\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m points \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m infbounds \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 577\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_quadpack\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_qagse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mfull_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepsabs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepsrel\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlimit\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 578\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 579\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _quadpack\u001b[38;5;241m.\u001b[39m_qagie(func,bound,infbounds,args,full_output,epsabs,epsrel,limit)\n", + "File \u001b[0;32m~/GitRepos/DarkELF/darkelf/electron.py:87\u001b[0m, in \u001b[0;36mdRdomega_electron..\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# Note: division and multiplication by self.eVtoInvYr in the integrand improves performance of quad\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m dRdomega[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meVtoInvYr \u001b[38;5;241m*\u001b[39m integrate\u001b[38;5;241m.\u001b[39mquad(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdRdomegadk_electron\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43msigmae\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mwithscreening\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwithscreening\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meVtoInvYr, kmin, kmax, limit\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m)[\u001b[38;5;241m0\u001b[39m] \n\u001b[1;32m 89\u001b[0m \u001b[38;5;66;03m# units of 1/kg/yr/eV\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(scalar_input):\n", + "File \u001b[0;32m~/GitRepos/DarkELF/darkelf/electron.py:26\u001b[0m, in \u001b[0;36mdRdomegadk_electron\u001b[0;34m(self, omega, k, sigmae, withscreening, method)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;124;03mReturns double differential rate for DM-electron scattering in 1/kg/year/eV^2\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m use interpolated grid of epsilon, or Lindhard analytic epsilon\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 25\u001b[0m etav_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39metav(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvmin(omega,k))\n\u001b[0;32m---> 26\u001b[0m temp_eps1\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps1\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m,\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 27\u001b[0m temp_eps2\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meps2(omega,k,method\u001b[38;5;241m=\u001b[39mmethod)\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(method\u001b[38;5;241m!=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m method\u001b[38;5;241m!=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLindhard\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", + "File \u001b[0;32m~/GitRepos/DarkELF/darkelf/epsilon.py:160\u001b[0m, in \u001b[0;36meps1\u001b[0;34m(self, omega, k, method)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 157\u001b[0m \u001b[38;5;66;03m# If k is smaller than grid kmin, we will extrapolate from lowest k point\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;66;03m# This is implemented by changing all small k values to kmin\u001b[39;00m\n\u001b[1;32m 159\u001b[0m k \u001b[38;5;241m=\u001b[39m k\u001b[38;5;241m*\u001b[39m(k \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkmin) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkmin\u001b[38;5;241m*\u001b[39m(k \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkmin)\n\u001b[0;32m--> 160\u001b[0m eps1\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps1_grid\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m,\u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m(method\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLindhard\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 162\u001b[0m eps1\u001b[38;5;241m=\u001b[39m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meps1_electrongas(om,k) \u001b[38;5;28;01mfor\u001b[39;00m om \u001b[38;5;129;01min\u001b[39;00m omega]\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/numpy/lib/utils.py:144\u001b[0m, in \u001b[0;36m_Deprecate.__call__..newfunc\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnewfunc\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds):\n\u001b[1;32m 143\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(depdoc, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/scipy/interpolate/_interpolate.py:346\u001b[0m, in \u001b[0;36minterp2d.__call__\u001b[0;34m(self, x, y, dx, dy, assume_sorted)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbounds_error \u001b[38;5;129;01mand\u001b[39;00m (any_out_of_bounds_x \u001b[38;5;129;01mor\u001b[39;00m any_out_of_bounds_y):\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValues out of range; x must be in \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m, y in \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;241m%\u001b[39m ((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx_min, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx_max),\n\u001b[1;32m 344\u001b[0m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my_min, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my_max)))\n\u001b[0;32m--> 346\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[43m_fitpack_py\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbisplev\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtck\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 347\u001b[0m z \u001b[38;5;241m=\u001b[39m atleast_2d(z)\n\u001b[1;32m 348\u001b[0m z \u001b[38;5;241m=\u001b[39m transpose(z)\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/scipy/interpolate/_fitpack_impl.py:665\u001b[0m, in \u001b[0;36mbisplev\u001b[0;34m(x, y, tck, dx, dy)\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m dy \u001b[38;5;241m<\u001b[39m ky):\n\u001b[1;32m 664\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0 <= dy = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m < ky = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m must hold\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (dy, ky))\n\u001b[0;32m--> 665\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(atleast_1d, [x, y])\n\u001b[1;32m 666\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mlen\u001b[39m(x\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mlen\u001b[39m(y\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 667\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst two entries should be rank-1 arrays.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Environments/env3p10/lib/python3.10/site-packages/numpy/core/shape_base.py:66\u001b[0m, in \u001b[0;36matleast_1d\u001b[0;34m(*arys)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ary \u001b[38;5;129;01min\u001b[39;00m arys:\n\u001b[1;32m 65\u001b[0m ary \u001b[38;5;241m=\u001b[39m asanyarray(ary)\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ary\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 67\u001b[0m result \u001b[38;5;241m=\u001b[39m ary\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mX_arr_eV = np.geomspace(1e7, 1e10, 15)\n", + "mX_arr_GeV = mX_arr_eV / 1e9\n", + "\n", + "# Run the simulation\n", + "t_start = time.time()\n", + "m_dm, sigs = SE.run_sim(\n", + " threshold=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_low=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_high=0.1,\n", + " m_dms=mX_arr_GeV,\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-31,\n", + " elf_model='electron',\n", + " elf_params={'mediator': 'massless', 'kcut': 0, 'suppress_darkelf_output': True},\n", + ")\n", + "sig = np.median(np.stack(sigs, axis=1), axis=1)\n", + "t_end = time.time()\n", + "print(f'Simulation took {(t_end - t_start):.2f} seconds')\n", + "\n", + "fn = 'sapphire_results/Massless_ER_Limit_' + dt.datetime.now().strftime('%Y%m%d_%H%M%S') + '.txt'\n", + "np.savetxt(fn, np.vstack([m_dm, sig]).transpose(), fmt='%.3e')\n", + "\n", + "f_out = open(fn + '_detailed', 'w')\n", + "f_out.write(str(m_dm))\n", + "f_out.write('\\n')\n", + "f_out.write(str(sigs))\n", + "f_out.write('\\n')\n", + "f_out.close()\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "#ax.set_ylim(1e-37, 1e-32)\n", + "#ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DM-Electron Scattering, Massive Mediator" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "10.0 MeV, 49.7 events above threshold\n", + " After selecting k < 25 keV, 49.7 events above threshold\n", + "30.0 MeV, 387.8 events above threshold\n", + " After selecting k < 25 keV, 380.7 events above threshold\n", + "100.0 MeV, 250.0 events above threshold\n", + " After selecting k < 25 keV, 243.3 events above threshold\n", + "300.0 MeV, 101.1 events above threshold\n", + " After selecting k < 25 keV, 98.2 events above threshold\n", + "1000.0 MeV, 32.4 events above threshold\n", + " After selecting k < 25 keV, 31.4 events above threshold\n", + "3000.0 MeV, 11.0 events above threshold\n", + " After selecting k < 25 keV, 10.7 events above threshold\n", + "10000.0 MeV, 3.3 events above threshold\n", + " After selecting k < 25 keV, 3.2 events above threshold\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/st/tlbg96ms2yn949bx7bk8hjc00000gn/T/ipykernel_9657/3596589769.py:51: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Note the dimensional analysis in this cell\n", + "# DarkELF expects recoil energies and WIMP masses in eV, and returns rates in counts/kg/yr/eV\n", + "# But DarkLim expects recoil energies in keV, WIMP masses in GeV, and rates in counts/kg/day/keV (DRU)\n", + "\n", + "mX_arr_eV = np.array([1e7, 3e7, 1e8, 3e8, 1e9, 3e9, 1e10])\n", + "color_arr = np.array(['#d62728', '#ff7f0e', '#bcbd22', '#2ca02c', '#17becf', '#1f77b4','#e377c2', '#9467bd', '#8c564b'])\n", + "keV_arr = np.geomspace(10e-3, 100e-3, 250)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", + "for i, (mX, color) in enumerate(zip(mX_arr_eV, color_arr)):\n", + "\n", + " # Initialize each DarkELF Al2O3 object\n", + " if i == 0:\n", + " print('WARNING: You are suppressing DarkELF output')\n", + " with io.capture_output() as captured:\n", + " darkelf_sapphire = darkelf(target='Al2O3', filename=\"Al2O3_mermin.dat\")\n", + "\n", + " darkelf_sapphire.update_params(mX=mX, mediator='massive')\n", + "\n", + " # No cut on k\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * band_gap_sapphire_eV, 1) * \\\n", + " darkelf_sapphire.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-34, kcut=0, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + " \n", + " ax.plot(keV_arr, dRdE_arr, label=f'{mX/1e6:.0f} MeV', color=color)\n", + "\n", + " n_above_threshold = np.trapz(dRdE_arr[keV_arr > energy_threshold], keV_arr[keV_arr > energy_threshold]) * time_elapsed * mass_det\n", + " print(f'{mX / 1e6} MeV, {n_above_threshold:.1f} events above threshold')\n", + "\n", + " # With a 25 keV cut on k (without cut, max k is 37 keV)\n", + " dRde_function = lambda keV : np.heaviside(keV * 1000 - 2 * band_gap_sapphire_eV, 1) * \\\n", + " darkelf_sapphire.dRdomega_electron(keV * 1000, method=\"grid\", sigmae=1e-34, kcut=25e3, withscreening=True) / 365.25 * 1000\n", + " dRdE_arr = dRde_function(keV_arr)\n", + " \n", + " ax.plot(keV_arr, dRdE_arr, '--', color=color)\n", + "\n", + " n_above_threshold = np.trapz(dRdE_arr[keV_arr > energy_threshold], keV_arr[keV_arr > energy_threshold]) * time_elapsed * mass_det\n", + " print(f' After selecting k < 25 keV, {n_above_threshold:.1f} events above threshold')\n", + "\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xscale('log')\n", + "ax.set_ylim([1e-6, 1e10])\n", + "ax.set_xlim([keV_arr[0], keV_arr[-1]])\n", + "\n", + "ax.set_xlabel(\"E [keV]\")\n", + "ax.set_ylabel(\"dR/dE [DRU]\")\n", + "ax.set_title(r'Electron Recoil, Massive Mediator, $\\sigma = 10^{-34} cm^2$')\n", + "\n", + "ax.legend(ncol=2, fontsize=10, loc=\"upper right\")\n", + "fig.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "Starting Experiment 0\n", + "Simulated 3336 events\n", + " Finished mass 0, 0.01000 GeV. Found sigma = 1.556e-33 cm2.\n", + " Finished mass 1, 0.01638 GeV. Found sigma = 5.019e-34 cm2.\n", + " Finished mass 2, 0.02683 GeV. Found sigma = 3.916e-34 cm2.\n", + " Finished mass 3, 0.04394 GeV. Found sigma = 4.382e-34 cm2.\n", + " Finished mass 4, 0.07197 GeV. Found sigma = 5.839e-34 cm2.\n", + " Finished mass 5, 0.11788 GeV. Found sigma = 8.506e-34 cm2.\n", + " Finished mass 6, 0.19307 GeV. Found sigma = 1.301e-33 cm2.\n", + " Finished mass 7, 0.31623 GeV. Found sigma = 2.046e-33 cm2.\n", + " Finished mass 8, 0.51795 GeV. Found sigma = 3.271e-33 cm2.\n", + " Finished mass 9, 0.84834 GeV. Found sigma = 5.279e-33 cm2.\n", + " Finished mass 10, 1.38950 GeV. Found sigma = 8.570e-33 cm2.\n", + " Finished mass 11, 2.27585 GeV. Found sigma = 1.396e-32 cm2.\n", + " Finished mass 12, 3.72759 GeV. Found sigma = 2.279e-32 cm2.\n", + " Finished mass 13, 6.10540 GeV. Found sigma = 3.725e-32 cm2.\n", + " Finished mass 14, 10.00000 GeV. Found sigma = 6.094e-32 cm2.\n", + "\n", + "Simulation took 2213.48 seconds\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mX_arr_eV = np.geomspace(1e7, 1e10, 15)\n", + "mX_arr_GeV = mX_arr_eV / 1e9\n", + "\n", + "# Run the simulation\n", + "t_start = time.time()\n", + "m_dm, sigs = SE.run_sim(\n", + " threshold=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_low=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_high=0.1,\n", + " m_dms=mX_arr_GeV,\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-31,\n", + " elf_model='electron',\n", + " elf_params={'mediator': 'massive', 'kcut': 0, 'suppress_darkelf_output': True},\n", + ")\n", + "sig = np.median(np.stack(sigs, axis=1), axis=1)\n", + "t_end = time.time()\n", + "print(f'Simulation took {(t_end - t_start):.2f} seconds')\n", + "\n", + "fn = 'sapphire_results/Massive_ER_kcut_None_Limit_' + dt.datetime.now().strftime('%Y%m%d_%H%M%S') + '.txt'\n", + "np.savetxt(fn, np.vstack([m_dm, sig]).transpose(), fmt='%.3e')\n", + "\n", + "f_out = open(fn + '_detailed', 'w')\n", + "f_out.write(str(m_dm))\n", + "f_out.write('\\n')\n", + "f_out.write(str(sigs))\n", + "f_out.write('\\n')\n", + "f_out.close()\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "#ax.set_ylim(1e-37, 1e-32)\n", + "#ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "WARNING: You are suppressing DarkELF output\n", + "Starting Experiment 0\n", + "Simulated 3287 events\n", + " Finished mass 0, 0.01000 GeV. Found sigma = 1.404e-33 cm2.\n", + " Finished mass 1, 0.01638 GeV. Found sigma = 4.801e-34 cm2.\n", + " Finished mass 2, 0.02683 GeV. Found sigma = 3.789e-34 cm2.\n", + " Finished mass 3, 0.04394 GeV. Found sigma = 4.219e-34 cm2.\n", + " Finished mass 4, 0.07197 GeV. Found sigma = 5.604e-34 cm2.\n", + " Finished mass 5, 0.11788 GeV. Found sigma = 8.151e-34 cm2.\n", + " Finished mass 6, 0.19307 GeV. Found sigma = 1.246e-33 cm2.\n", + " Finished mass 7, 0.31623 GeV. Found sigma = 1.958e-33 cm2.\n", + " Finished mass 8, 0.51795 GeV. Found sigma = 3.130e-33 cm2.\n", + " Finished mass 9, 0.84834 GeV. Found sigma = 5.050e-33 cm2.\n", + " Finished mass 10, 1.38950 GeV. Found sigma = 8.198e-33 cm2.\n", + " Finished mass 11, 2.27585 GeV. Found sigma = 1.335e-32 cm2.\n", + " Finished mass 12, 3.72759 GeV. Found sigma = 2.180e-32 cm2.\n", + " Finished mass 13, 6.10540 GeV. Found sigma = 3.563e-32 cm2.\n", + " Finished mass 14, 10.00000 GeV. Found sigma = 5.829e-32 cm2.\n", + "\n", + "Simulation took 1126.52 seconds\n" + ] + }, + { + "data": { + "image/png": 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QRxAEoQeRSARra2u921QqFZ+GX5uTJ08CALy8vABU3CHs0qULkpOTq/XFzcbGBh4eHkhPT8f169cfWV8ikQBAje4icHDrwOkLKLnppk+azp07w8LCAufOnUNpaanOdi5VfX3e6ZDJZJBKpfXWviEcHBwA6PpDo9FU+45iamoqgIoAojLcuVkZsVhs8Pzx9vZGTk5Otc5FoGJqcElJCRISEqp9/LokICAAzZo1wx9//AGNRoOdO3fC1tZWZzy4abj63sN1RXXfm7a2thg5ciQ2bdqEWbNm4f79+/U2lZcgiKcLCvIIgnhq+fnnn3Hu3Dm920JCQpCcnIwmTZrwqdG1WbZsmWAZgtu3b2PDhg2QyWSYOnUqX75w4UKUlpZi7ty5OtO/gIq1urTXP1uwYAHUajXeeOMNlJWVCerK5XJ+qhkANG3aFACQmZlZPcFa2NnZoVOnTjh16hRu3LjBlxcVFeHDDz+scXt1gbm5OaZNm4bs7GysXr1asC0sLAzh4eHo2LEjBgwYUKvjrFu3TvAsmTbff/89iouL0blzZ36phSdBnz59AEBnjbr169cL7pZVhYuLCwDg1KlTgvLo6GiDa9U1bdoUt2/f1rtt4cKFAIBXXnlF713Ne/fuITk5mX/N3TH76KOPBMHN5cuX+btr9YlUKsWUKVNw69YtrF27FtevX8fEiRNhaWkpqDdu3Dg4Oztj/fr1iImJ0WlHqVTqjGFNqeq9GRMTozf4e/DgAQDAwsKiVscmCIIA6Jk8giCeYo4cOYL58+fzgUPr1q1RUlKCxMREnDx5EmKxGD/88ANkMplgv1atWqGkpAQ9evTAmDFj+HXycnJy8O233wqeqXvttdcQFxeH7du34/Tp0xgxYgRat26N+/fv49q1azh79iz++OMPfqHt119/HdHR0di7dy/c3d0xduxY2NnZ4datWwgPD8eWLVswfvx4AMCwYcOwb98+TJw4EaNGjYKFhQU8PT0xZsyYaul/9913MW/ePPTv3x+TJk2CRqPBkSNH+ICjIVizZg2io6OxcuVKxMbGwtvbGxkZGfjzzz9hZWWFrVu36n0esibs2LED7733Hrp37w5vb2+0aNEC+fn5iIuLQ0JCAiwtLfHjjz/WkaLqMXv2bKxduxbLly/HxYsX4ebmhvPnz+PKlSsYMmQIv7h2VYwZMwbt27fH2rVrceXKFXTr1g0pKSk4dOgQJkyYgH379unsM2zYMOzduxfjx4+Hl5cXJBIJxo4dix49emDkyJH45JNP8Nlnn6Fjx44YOXIkXFxckJOTgxs3buDkyZNYuXIlunTpAgCYOXMm/vjjD4SFhcHLywujRo1Cbm4udu3aBT8/Pxw6dKjG4/LLL78gLCxM77Z+/frpTEOdPn06fvjhBwQFBfGvKyOTybBv3z6MGjUKQ4YMwbBhw9C9e3eIRCLcvHkTJ0+eRLNmzQz+EFAdqnpvLly4EHfv3sXAgQPRvn17iEQinDp1CvHx8ejXrx+/DiRBEESteJKpPAmCIBoT165dY2vXrmW+vr7M1dWVWVhYMAsLC+bm5sZmzpwpWL+Og0uln5uby+bNm8ecnJyYTCZjnp6e7I8//jB4rD179rARI0YwBwcHJpVKWZs2bZiPjw9bt24de/jwoaCuRqNhv/zyC+vXrx+ztrZmVlZWzN3dnc2fP5/dunWLr6dUKtn777/PnJ2dmZmZmWBJhEctkcCxceNG5u7uzqRSKXN2dmZBQUFMoVBUuYSCPvQtMcChr62qePjwIVu4cCFzcXFhUqmUOTo6shdeeIFdvnxZp+7jLKGQkJDAVqxYwYYMGcLatWvHzM3NmaWlJevcuTN7/fXXDS6dYWgJhcjISJ36+pY94DA0jhcvXmTDhw9nVlZWzM7Ojo0bN45dv369xuvkTZw4kTVv3pxZWVmxPn36sN27dxusn5WVxSZPnswcHR2ZWCxmANjWrVsFdY4ePcrGjBnDmjdvzqRSKWvZsiXr378/++yzzwTnI2OMlZSUsPfff5+1adOGyWQy5uHhwTZt2lRjP1VnCYVFixbp3dfd3Z0BYG3bthWsaVeZ27dvs0WLFjF3d3cmk8mYnZ0d69KlC3v11VfZ8ePHBXWrOof1nRtVvTd3797NJk+ezNzc3JiVlRWzt7dnnp6ebM2aNTpLNBAEQTwuIsYYe2IRJUEQhJHD3XHTnmJJEARBEATRmKBn8giCIAiCIAiCIEwICvIIgiAIgiAIgiBMCAryCIIgCIIgCIIgTAh6Jo8gCIIgCIIgCMKEoDt5BEEQBEEQBEEQJgQFeQRBEARBEARBECYELYZeCY1Gg7t378LW1hYikaihu0MQBEEQBEEQhAnBGENRURFat24Nsbh+7rlRkFeJu3fvol27dg3dDYIgCIIgCIIgTJjU1FR06NChXtqmIK8Stra2AIDMzEzY2dnVadu5ublwdXVFeno6mjZtWqdtE4+Gxr/hIR80POSDhoXGv+EhHzQsNP4ND/mg4bl58yZ69OgBqVRab8egIK8S3BRNOzu7Og/ylEolgIpAsq7bJh4NjX/DQz5oeMgHDQuNf8NDPmhYaPwbHvJBw8PdVKrPR8Mo8QpBEARBEARBEIQJQUGeAdRqNf9fn61SqQS2RqOp0lYqleCWJFSpVLzNlTPGdGwAAluj0QhslUpVpa1WqwV2fWjSp6+xalKr1TAzMzMpTcbmJ41Gw09NMBVNxugnmUxmcpqMyU9isfiROoxNk7H5iatvSpqMxU9qtZpPNGEqmozNT9X5PmRsmozNT9yx6xMK8gzg5eUFDw8PBAUFITk5GQCQlJSE69evAwASExORnp4OAIiPj0dmZiYAIDY2FllZWQCAmJgYZGdnAwBOnDiB8vJyTJkyBefOnUNRUREAIDQ0FHK5HCqVCqGhoVCpVJDL5QgNDQUAFBUVISIiAgCQn5+PEydOAACys7MRExMDAMjKykJsbCyAimcJ4+PjAQDp6elITEwEAFy/fh1JSUkAgOTk5DrTlJ+fDwCIiIho9JpSUlKwZMkSyGQyk9FkbH7KzMzEZ599BplMZjKajM1Pubm5+OabbyCTyUxGkzH5SSaTYf78+XwfTUGTsfkJAF566SXExcWZjCZj8lNSUhLeeustyGQyk9FkbH5KTU3FsmXLIJPJTEaTsfhp2bJl8PDwwPDhwwGA/9G1PhAxLnQlAACFhYWwt7dHbm4uHBwc+F8FJBKJwFapVBCJRLwtFov5X2f12dwvBmFhYfD19YWlpSVEIhGUSqXg1xRtWyqVgjHG2xqNBmq1mrc1Gg3MzMwM2mq1Gowx3tano7aaJBIJb5uZmTVqTWVlZYiIiEBAQABEIpFJaDI2P8nlcoSHhyMgIID/JdfYNRmbn8rLyxEWFsb7wBQ0GZOfgIovEX5+frC0tDQJTcbmJ8YYjhw5wn8Wm4ImY/KT9mcxt5+xazI2P2l/Fhv6PmRsmozNTw8ePICTkxMKCgrq7blISrxiAIlEIvhf2eacXl1bKpXyt3SlUin/oKV2Vh19tkgk4m3uxKyubajvdanpcWzSRJpIU8NqqokOY9FkLH7iPge4Nk1BU3V1NBZNVX0WG6umqmzSRJpqq88YNBmzn+oLmq5JEARBEARBEARhQtCdPIIgCIIgAFQkHeCmQJkq3HQsuVxu8lobIzT+DQ/5oO6RSCSCO3+NAQryCIIgCOIpp7CwENnZ2SgvL2/ortQ7jDG0bNkSmZmZ9bpGFaEfGv+Gh3xQP8hkMjg6OjaatQcpyCMIgiCIp5jCwkLcuXMHNjY2cHR0FDyrZopoNBoUFxfDxsZG8Iwq8WSg8W94yAd1C7esQkFBAe7cuQMAjSLQoyCPIAiCIJ5isrOzYWNjg7Zt25p0cMeh0WigUChgYWFBX3AbABr/hod8UPdYWlrC1tYWt2/fRnZ2dqMI8sizBEEQBPGUolQqUV5eDnt7+6ciwCMIgqgvRCIR7O3tUV5ezmfxbUgoyCMIgiCIpxQu6UJjSxhAEARhjHDX0saQ0IaCPIIgCIJ4yqG7eARBELWnMV1LKcgjCIIgCIIgCIIwISjIMwB3m1WtVuu1VSqVwNZoNFXaSqVSYDPGBDaXmUfbBiCwNRqNwFapVFXaarVaYJMmFe9fU9JkbH7iMCVNxuYnbR+YiiZj8hPXZmPRxPWVO25lmzGmY3P7GbK5NhrK1qeDszm4+qagyZj8pI2paDI2P2ljKpoam5+qcy2vbyjIM4CXlxc8PDwQFBSE5ORkAEBSUhKuX78OAEhMTER6ejoAID4+HpmZmQCA2NhYZGVlAQBiYmKQnZ0NADhx4gQKCgoAAJGRkSgqKgIAhIaGQi6XQ6VSITQ0FCqVCnK5HKGhoQCAoqIiREREAADy8/Nx4sQJABXZ0GJiYgAAWVlZiI2NBQBkZmYiPj4eAJCeno7ExEQAwPXr15GUlAQASE5OrjNN+fn5AICIiIhGrykhIYH3r6loMjY/paSk8D4wFU3G5icuvbMpaTI2P3FtNhZN2gsiFxYW8l9aCgsL+S8ulW2g4ssLZ6vVan4sVCoVbyuVShQXFwMAFAoFSkpKAADl5eUoLS3lj19WVsbbcrkcAFBWVsbbpaWl/Bp+JSUlUCgUAIDi4mI+eC4qKuK/QBUVFVWpiavzOJp8fHz4jISNQdOVK1cgFosxa9Yso/DT1atX4eDggNmzZz/ST6Z47jUWTRympKmx+EmtVvPX7MrX8mXLlsHDwwPe3t6odxghoKCggAFgubm5jDHGVCoVU6lUOrZSqRTYarW6SluhUDC5XM5CQkJYSUkJ02g0fLlGo2EajUbHZowJbLVaLbCVSmWVtkqlEtj6dNRWk7bd2DWVlpaykJAQplAoTEaTsfmprKyM94GpaDI2P3HXIc4HpqDJmPykUChYSEgIKy0tbRSaysrK2NWrVwX94TRxtkaj0bE5TYZsro2GsvXp4Gy1Ws3y8vKYSqViGo2GFRUVsZUrVzIvLy9mbW3NzM3NWZs2bdjAgQPZBx98wG7cuCFoe8iQIYz7+tQYNKWmpjIAbObMmbXyk4uLC3Nxcal3HVx/Z8yYUaWfuH2CgoIYgCr/goKCGu25N3PmTAaApaam1tv7KSkpic2YMYO5uLgwc3NzZmdnx9zc3NiECRPY119/zdfV7ldeXp6gH49z7tX2GpGens6fuw3tp7rSxF1Ti4qK+Pb0Xcvv37/PALCCggJWX9A6eQaQSCSC/5VtMzOzGtlSqZT/JUF7oVntjGb6bJFIxNtisZj/9bA6tqG+16Wmx7FJE2kiTQ2rqSY6jEWTsfiJ+xzg2mwMmkQiEf+ZpH1+aNvayQQ4W3u/ynZV+z0J25AOsVjM/2IvEolQXFyMgQMHIikpCR07dsTLL7+MZs2aITs7G/Hx8VizZg06duwINzc3vo3ffvuNv8PQWDTpsxurn7SpiY6JEyeiW7duetvx8fF54joex09Vjf3j+uno0aMYPXo0VCoVRowYgQkTJsDCwgKpqamIjo7G33//jTfffFPwntemrs69x9VU0748yfdTbfyk7xpv6JpdX1CQRxAEQRDEU8k333yDpKQkvPrqq9i0aZPOl8/09HR+ChiHs7Pzk+wi8f+88MILmDp1akN3o9Hx+uuvQ61W49ixYxg6dKhgG2MMERERTySgIBof9EweQRAEQRBPJWfOnAEALFiwQO/dBVdXV3Tu3FlQ5uPjo1N327ZtEIlE2LZtGw4ePAhvb29YWVmhTZs2+OSTT/i7h9u3b4enpycsLS3h7OyML7/8UueYs2bNgkgkQkZGhs625cuXQyQSISoq6pHaLly4gDfffBPdunWDvb09LC0t0b17d3zxxReCJEAZGRkQiUS4efMmbt68yd+dEIlEWL58uaDNmJgYjBkzBo6OjpDJZHB3d8fHH38seMaLQ61W83dCLSws0LFjR6xevVqQ/Kau2bFjB0QiET799FO92xMSEiASifDSSy8Jyh88eIB33nkHHTt2hEwmg6OjIyZOnIgrV67otNG+fXu0b98excXFWLRoEVq3bg2ZTIYePXpg3759OnW3b98OoOJc4saVu/PI9emFF16As7MzZDIZmjdvjj59+uDzzz9/pN4HDx4gNTUV3bp10wnwgIo7Sv7+/oLzNSoqChKJBF988QVOnToFHx8f2NraokmTJpg4cSJu3Lhh8FjVHSOu/rvvvotOnTrB0tISTZs2hbe3N7766isAFe8ZV1dXABXvC+3zjju/tc/3bdu24dlnn4WVlRU/flW9H7TfkxzcuT5r1iwkJydj9OjRaNKkCRwcHDBt2jT+OeUzZ85g+PDhsLOzg4ODA1599VX+WUFjgu7kEQRBEATxVNKsWTMAwH///YeePXvWur2///4bERERGD9+PAYMGIDDhw9j5cqVYIzB3t4eK1euxLhx4+Dj44O//voL77//PpycnDBjxoxaH7symzdvxsGDBzF48GAEBASgtLQUUVFR+PDDD3Hu3Dn89ddfAIAmTZogODgY33zzDQDg7bff5tvQDkZ+/PFHLFiwAE2aNMGYMWPQokULnD9/Hp9//jkiIyMRGRkJc3Nzvv68efPw66+/wtXVFQsWLIBcLsf69etx+vTpOtfK8fzzz+P111/Hzp07ERQUpLN9x44dAIDp06fzZampqfDx8cHt27fh5+eH8ePH48GDB/jrr78QHh6O48eP6yTJUCqV8PPzQ15eHiZOnIjS0lLs3r0bkydPRlhYGPz8/ABUjOW2bdtw6dIlLFq0CE2aNAFQEfwBwMWLF/Hcc89BIpFg3LhxcHFxQX5+Pv79919s2rQJH330UZV67e3tYWZmhqysLJSUlMDa2rraY3X+/Hl8/fXXGDlyJN566y1cvXoVf//9N06ePIm4uDh06NDhsccoJSUFQ4cORVZWFgYOHIjx48ejpKQEV69exapVq/Dee++hZ8+eWLRoETZs2ABPT0+MHz+e358bH44vv/wSkZGRGDduHPz8/Gp9ZzI9PR3PPfccevfujVdffRXnz5/H7t27kZmZiS+++AJ+fn7w9fXFvHnzEBUVhS1btkCj0eDXX3+t1XGfOPX2tJ+RwiVeqY8HIbkH7rkH8YknC41/w0M+aHjIBw1LYxv/srIy9u+//7KysjKdbRqNhhUXlzb6Py5hQnXRTjpx4MABBoDZ2tqyd999l4WHh7Ps7Owq99dOvMKxdetWBoBJpVIWHx/PlxcWFrIWLVowKysr1rJlS5aamspvu3XrFjM3N2fdu3cXtMUl6khPT9c5dnBwMAPAIiMj+bLKySs4bt68ySfg4dBoNOyVV15hANipU6cE27jEK/q4evUqMzMzY56enjrjs3r1agaAffXVV3xZZGQkA8A8PT1ZcXExX3779m3m6OjIJ16pDpzmiRMnsuDgYL1/WVlZfP2XX36ZAWBnz54VtKNSqZiTkxNr2bKlYFyee+45JpFIWFhYmKB+SkoKs7W11fGPi4sLA8DGjRvHysvL+fJjx44xAMzf319Qvyp/Ll68mAFgISEhOtsedR5yPP/88wwA6969O/v222/Z+fPnBf2qDOcbAOyHH34QbPvpp58YADZ69GhBeU3HqHfv3gwA27Rpk87xMzMzedvQucvB+d7a2polJSUZ3K79fuDg3pNbt27VOR4A9s033/DlGo2GBQQEMACsSZMmAn8oFArWo0cPZmZmxu7du6e3n9pUdU3VJjs7mxKvEARBEATx5CktlaOD0/CG7sYjSbt/HNbWlo+179ixY7Fu3ToEBwdj3bp1WLduHQDAzc0NI0eOxKJFi+Du7l7t9l5++WX06dOHf21ra4vRo0fj119/xQcffCC4O9KuXTsMHDgQ0dHRUKlUBhNjPC76nh0UiURYsGABfv31Vxw7dgwDBgyoVls///wzVCoVvvvuO/7uJ8f777+P9evXY9euXXj33XcBVCSnAYCgoCDB3aU2bdpg4cKFeu+yPYq//vqLv/tYmfHjx6Nly5YAKu7S/f777/j999/Rt29fvk5ERATu37+Pt99+m78TlJiYiNjYWLzyyivw9/cXtPnMM89g7ty5WL9+Pa5cuaKT9OXrr78W3LkcPnw4XFxccO7cuRprs7TUPX8rj7MhNm3aBKVSiYMHD2LhwoUAAHNzc/Tu3RtTpkzB3Llz9bbfsWNHzJ07V1A2d+5crFu3DocPH8bDhw/RvHnzGo9RfHw8zp8/j8GDB+u0DwBt27atli5t5s2bh+7du9d4P0O4ubnxYwVUvC+mTp2K0NBQeHl5Ydy4cfw2qVSKF154AUFBQfj333/h5ORUZ/2obyjIIwiCIAjiqWXx4sWYO3cuwsLCEBsbi/Pnz+Ps2bPYuHEjtmzZgj179mDs2LHVakvflM9WrVpVuU2tVuP+/fto06ZNbWTooFAo8P3332P37t24du0aiouLBYth3717t9ptxcXFAQA/Na8yUqkU165d419funQJADBo0CCdugMHDqz2cbXZtWtXtRKvDB8+HK1atcLu3buxfv16Pnj+/fffAQinanK67t+/r/P8IQBe07Vr1wRBXpMmTfjnybRp27Yt/5xndZg8eTK++eYbTJgwAVOmTIGvry8GDx5co3OhWbNm+Oeff3D9+nWEhYUhPj4ecXFxiI2NRWxsLDZv3ozo6Gg0bdpUsJ+3t7cgkyRQkQlywIABuH79Oi5duoQRI0bUeIy4NeG4Kat1gXawXhf06NFD57naR71PgZq9ZxoDFOQRBEEQBKGDlZUF0u7rfqFvbFhZWdS6DVtbW0yaNAmTJk0CABQUFGDZsmX44YcfMGfOHNy5c0dw18YQdnZ2OmVckFHVNu1EKHXFCy+8gIMHD+KZZ57BlClT0KJFC0ilUuTn52PDhg06WUOrIjc3FwCqlQwEqBg/sVgMR0dHnW31fSdEIpHgxRdfxLp16xAeHo7AwEAUFxcjJCQEHh4eePbZZ/m6nK7Dhw/j8OHDBtusnHTD3t5ebz0zM7MaJZbx9vZGVFQUVq1ahT/++ANbt24FAPTp0wdr1qzRm0zFEO7u7oK7zhcvXsTLL7+MK1euYMWKFdiwYYOgfvPmzfW2w/mnoKAAQM3HiNuvLn+0qOtzpjG9T+sTCvIIgiAIgtBBJBI99jRIY8fe3h7ff/89Dh8+jJs3b+Ly5cvo1avXEzk2d3dFpVLpbOO+QD+Kc+fO4eDBg/D398fhw4cFiSri4uJ0vvA/Cu6Lb2FhIWxtbR9Z397eHhqNBtnZ2TrBxP3792t07Mdh+vTpWLduHX7//XcEBgbir7/+QmlpqeAuHvA/Xd999x3efPPNeu+XPgYNGoQjR46grKwMZ8+excGDB/HDDz8gMDAQV65cEUzxrQk9e/bEd999h2HDhuHEiRM62x8+fKh3P84/XCBb0zHiksvcuXPncbqtl0ets1ib94opQ0soEARBEARBVKIiyK1+tsK6wsHBAYD+L8mJiYnVaiM1NRUAEBgYqJOJ8OTJk3r3kUgkUKvVerdxmRO5qXuPwtPT0+CxTp06Va02aoOnpye6d++OAwcOoKioCL///rvepRM4XTWZYllTuPE3NLYclpaW8PHxwbp167Bs2TKUlZXh6NGjtTq2jY2NwW1nz57Vueuo0WgQGxsLkUjE+7CmY8RNrYyIiHhk3eqOjSHq4r1iylCQRxAEQRDEU8nPP/9sMFFGSEgIkpOT0aRJE52kG/UJl7hFe30vANi3bx+io6Or1YaLiwsA3YDq6tWrWL16td59mjZtiuzsbMjlcp1tb7zxBszMzPDWW2/h1q1bOtvz8/MFX6q5O2affvqpYKrjnTt38O2331ZLQ22ZPn06ysrK8O233+LEiRMYMmQI2rVrJ6jTt29feHt7Y9euXdizZ49OGxqNptpjbgjuWbjMzEydbWfOnNE73tzdNAuLqqcil5SU4PPPP+fXd9NGpVLx6zDqew7yxo0b2Lx5s6Bs8+bN+O+//xAYGMjfga3pGPXp0wd9+vRBTEyMTvuAMCBzcHCASCTSOzbVgXuv/Pbbb4KA9cyZM9i5c+djtWlK0HRNgiAIgiCeSo4cOYL58+ejY8eOGDBgAFq3bo2SkhIkJibi5MmTEIvF+OGHHyCTyZ5Yn8aNGwc3Nzds27YNmZmZ8PLyQnJyMk6cOIGAgACEhoY+so2+ffuib9++2Lt3L7KystCvXz/cunUL//zzDwIDA3UW7QaAYcOG4fz58xg1ahQGDRoEc3NzDB48GIMHD0a3bt3www8/4PXXX0enTp0QEBAANzc3FBUVIS0tDdHR0Zg1axZ++uknAMDQoUMxe/ZsbN26Fd27d8eECRNQXl6OPXv2wNvbu8pnuwyxb98+QXIXbTp37qyTlOXFF1/E0qVLsWLFCmg0Gp2pmhy7du3C0KFDMXXqVHzzzTd49tlnYWlpiVu3buHMmTN4+PCh3kCsugwbNgxfffUV5s2bh4kTJ8La2houLi6YPn061qxZg8jISAwePBiurq6wsLBAQkICjh8/jg4dOmDChAlVtq1UKvHxxx9j+fLl6N+/Pzw9PWFnZ4f79+8jPDwct2/fhqurK4KDg3X2HT58ON5++22EhYWha9euuHr1Kg4ePAhHR0ed6bw1HaOdO3fCx8cH8+bNw44dO9C/f3/I5XJcvXoViYmJyMnJAVBxp5ELCKdPnw53d3eIxWJMnz6d/6GiKvr164cBAwbgxIkT6N+/PwYPHoybN2/iwIEDGDNmDP7+++/quMhkoSCPIAiCIIinkjVr1mDAgAE4evQoYmJikJWVBaAiacTMmTPx1ltvPbFn8TgsLS1x7NgxvPPOOzh+/Dji4uLQr18/xMTE4NChQ9UK8iQSCQ4dOoSlS5ciLCwM586dg7u7O7766iuMGjVKb5D3ySefIC8vD4cOHcLJkyehVqsRHByMwYMHA6hIr9+zZ0+sX78eMTExOHjwIOzt7eHs7Ix33nkHM2fOFLS3efNmPPPMM9i8eTO+//57tG3bFosXL8YLL7zwWEFeVUsojBs3TifIa9OmDYYNG4Zjx47BwsICL7zwgt59XV1dkZiYiPXr1yMkJARbt26FRCJBq1atMHjwYIP7VZdRo0Zh7dq12Lx5M9atWwelUokhQ4Zg+vTpeP3112Fvb4+zZ88iOjoajDE4Oztj2bJleOedd/QmAdHGzs4OoaGhCA8Px6lTp/Dnn38iJycHVlZW/PIGixYt0psopnfv3ggKCkJQUBC+/fZbSCQSjB8/HmvXrtV5DrCmY+Tu7o6EhASsXr0aBw8exDfffAMbGxu4u7vj448/FtTdsWMH3nnnHRw6dAgFBQVgjGHgwIHVCvIA4MCBA1i8eDEOHTqEy5cvw9PTEwcPHsTdu3ef+iBPxLTz6RIoLCyEvb09cnNz4eDgwM8T1p6rLpFIoFKpIBKJeFssFkMsFhu0lUolNBoNwsLC4OvrC0tLS4hEIiiVSj5rj/Y6OSqVClKpFIwx3tZoNFCr1byt0Wj4TE76bLVaDcYYb+vTUVtNEomEt83MzBq1prKyMkRERCAgIAAikcgkNBmbn+RyOcLDwxEQEMA/MG3smozNT+Xl5QgLC+N9YAqajMlPABAaGgo/Pz9YWlo2uCaFQoG0tDS4urry/RGJRBCJRLwNAIwxgc2dO4ZsoOKZtoay9engbOB/CUS465CxazImP2k0GhQWFvIBjCloMiY/RUdHY+jQofjggw+watUqvh/GrKkx+am8vBxpaWlwdnaGjY2NwWv5gwcP4OTkhIKCgkcG848LPZNnAC8vL3h4eCAoKAjJyckAgKSkJFy/fh1AxQOd6enpAID4+Hh+PnFsbCz/S2BMTAw/T/rEiRN8pp/IyEgUFRUBqPiwl8vlUKlUCA0NhUqlglwu53+pKyoq4h9ezc/P5zMkZWdnIyYmBgCQlZWF2NhYABVzvrk1StLT0/k58tevX0dSUhIAIDk5uc405efnA6h4wLaxa0pISOD9ayqajM1PKSkpvA9MRZOx+Un7eQhT0WRsfuLabCya5HI5HyQWFhYKAiHuS0xlGwD/ZR2oSJzAjYVKpeJtpVKJ4uJiABXrtnHPZ5WXl6O0tJQ/fllZGW9z077Kysp4u7S0lE/5X1JSAoVCAQAoLi7m05oXFRXxgXRRUVGVmrg6pqTJWPzEHceUNBmbn7QxFU2NyU9qtZq/Zle+li9btgweHh58Qpv6hO7kVYLu5Jnur/R0J6/hNdGdvIbXRHfy6E4e3cmjO3l0J+/p9RPdyXt67uTRM3kG4NK6aqce1ra5D/Dq2lKplP8lQSqV8ieNVCoV1Klsi0Qi3uY+nKtrG+p7XWp6HJs0kSbS1LCaaqLDWDQZi5+4zwGuzcagiftywx2XQ9vmtmvb2vtVtqva70nYhnSIxWI+0NPXZ2PVVNW+jU2TNqaiydj8pI2paGpsftJ3jTd0za4vKMgjCIIgCIIgiKcAHx8fqNVqfsohYbrQM3kEQRAEQRAEQRAmBAV5BEEQBEEQBEEQJgQFeQRBEARBEARBECYEBXkEQRAEQRAEQRAmBAV5BEEQBEEQBEEQJgQFeQRBEARBEARBECYEBXkEQRAEQRAEQRAmBAV5BEEQBEEQBEEQJgQFeQRBEARBEARBECYEBXkEQRAEQRCNgKioKIhEIixfvryhu9LoobEiiKqhII8gCIIgiKeSjIwMiEQinT9ra2v06NEDK1asQHFxcUN3k6gDUlNTsXz5cowdOxZt2rSBSCRC+/btG7pbBFFvmDV0BwiCIAiCIBoSNzc3vPzyywAAxhgePnyII0eOYPny5QgLC8OpU6cgkUgauJdEbTh58iRWrFgBiUSCLl264N69ew3dJYKoVyjIIwiCIAjiqaZjx4460/7Ky8vRv39/xMXFITo6GsOGDWuYzhF1wuDBg3HmzBl4enrC0tISFhYWDd0lgqhXaLomQRAEQRBEJWQyGYYOHQoAyM7OFmyLjIzEK6+8gk6dOsHGxgY2Njbo3bs3Nm3aZLC9tLQ0zJs3D66urpDJZGjRogV8fHywbdu2R/aloKAAQ4YMgVgsxnfffSfYtn//fvTu3RuWlpZwcnLC3LlzkZeXh/bt2+tMR5w1axZEIhHS0tKwbt06eHh4QCaTYdasWXydK1euYPLkyWjRogVkMhlcXV3x9ttvIycnR6dfIpEIPj4+evts6PgSiQQ3b97Ed999h86dO0Mmk8HFxQUrVqyARqPRaaesrAxLly5Fu3btYGFhgW7dumHz5s2PHLPKdOjQAf369YOlpWWN9yUIY4Tu5BEEQRAEQVRCoVDwyT169uwp2LZmzRrcuHED/fr1w4QJE5Cfn4+wsDC89tprSElJwbp16wT1T506hcDAQBQVFcHf3x9Tp05FXl4eEhMTsWHDBkGQVZmsrCyMHDkS165dw65duzBlyhR+26+//oo5c+bAzs4OM2bMgL29PUJDQ+Hr6wulUgmpVKq3zbfeegtxcXEIDAzEmDFj0KJFC76f/v7+UCgUeOGFF9C+fXucOXMGGzZswKFDhxAXFwdHR8fHG1AtgoKCEBsbi9GjR8Pf3x8hISFYvnw5FAoFPv/8c76eRqPB2LFjcezYMXTv3h0vvvgicnJy8M477/ABOEEQ+qEgjyAIgiAIg8hLFQa3iSUimMuk1aorEosgs3jMumUKgOnWs7AyN9hGTbhx4wY/XZMxhuzsbISHh+POnTtYu3YtnnnmGUH9H3/8Ea6uroIylUqFgIAAbNiwAYsWLYKzszOAimmfU6dORXFxMUJDQzFy5EjBfrdv3zbYr//++w/+/v7IycnB4cOHMWLECH5bfn4+Fi1aBGtra5w/fx7u7u4AgFWrVsHf3x8XLlyAi4uL3naTkpKQmJjI9xGoCKhmzZqF0tJShIWFwd/fn9/2/vvv48svv8QHH3yALVu2GOxvdbl06RIuXryINm3aAAA++eQTuLu747vvvkNwcDDMzSv8+ttvv+HYsWMYOXIkDh06xD8XuWjRIvTu3bvW/SAIU4aCPIIgCIIgDDLB82OD2/oM6YxPf3mFfz213wqUlyn11u3etwPW7pzPv57psxqFeSV667p3b4tv9y/kX782ah0e3MnTqXfk+tpH9r86pKamYsWKFTrlo0ePFgRWHJUDPAAwMzPD/PnzcfToUURGRmLmzJkAgAMHDuDOnTuYMWOGToAHAG3bttXbp3PnziEwMBBAxfTQXr16CbYfOHAAxcXFWLhwIR/gcf1YuXIlnnvuOYN6lyxZIgjwAOD06dNITU3FqFGjBAEeUHHnbcuWLfjjjz/w448/8kHY47JkyRK0atWKf+3o6Ihx48Zh+/btSElJQffu3QFUBHkA8PnnnwsS33Tv3h3Tp0+vk4CTIEwVeiaPIAiCIIinGn9/fzDG+L/s7GwcOHAAV65cwYABA3D27FlB/aKiIgQHB8PT0xM2Njb80gsTJ04EANy9e5evGx8fDwDw8/Ordn9OnjyJYcOGwcbGBqdPn9YJ8ICKu2EAMHDgQJ1t3t7eMDMz/Dt+3759dcoSExMBQO8zdtwzh3K5HCkpKdWVYRBPT0+dMi7Yzc/P58suXboEa2trPPvsszr1Bw0aVOt+EIQpQ3fyCIIgCIIwyN+XVhrcJpaIBK93xwUbrCsSC+tuj/qw2nV/PvKu3uma9UWzZs0wduxYWFlZwdfXFx9//DGOHj0KoOJZPR8fHyQkJMDLywvTp09Hs2bNYGZmhoyMDGzfvh3l5eV8WwUFBQDAT02sDomJiSguLoafnx86dOigt05hYSEA8M/TaSMWi6t8ds7Jyclge/q2AeDvvHH1aoOdnZ1OGReUqtVqvqygoADt2rXT24ahfhIEUQHdyTMAd5FRq9V6bZVKJbC5jFCGbKVSKbAZYwKbMaZjAxDYGo1GYKtUqipttVotsEmTivevKWkyNj9xmJImY/OTtg9MRZMx+Ylrs7Fo4vrKHbeyLbOUwtzCDDJLKW9bWJlDZimFmVTCt6HRaPhymaVUxzaXmfFtc+0aqi+zkAr6JbPQ36Z2nUfp4GyOyvtq6+DsPn36AKiYOsnVCQkJQUJCAubMmYMLFy7ghx9+wMqVKxEcHMxPc9Ru297eHgBw584dg/3VtgFgwYIFmDNnDvbv348XX3yR95e2DltbWwDAvXv3+P25Omq1ms8Iqq2JQyQS6Ry/cnuV+8WtK2dnZ8eXi0QiqFQqvfW54FafPg5tTdrbuXJ7e3s8fPhQoI+rm5WVpddnjxrfyn2pSf3q2FWde5X9VLnvhuy67qM2pqKpsfmpOtfy+oaCPAN4eXnBw8MDQUFBSE5OBlDxoPL169cBVPzKlp6eDqBiKkZmZiYAIDY2lr/wxMTE8BfZEydO8Be8yMhIFBUVAQBCQ0Mhl8uhUqkQGhoKlUoFuVyO0NBQABVTQiIiIgBUTGE4ceIEgIp0zjExMQAqMm/FxsYCADIzM/mpIenp6fz0i+vXryMpKQkAkJycXGeauGkVERERjV5TQkIC719T0WRsftKe5mMqmozNT3fu3OF9YCqajM1PXJuNRZNcLue/cBQWFvJfWgoLC/kvLpVtoOLLC2er1Wp+LFQqFW8rlUoUFxcDqLgDVlJS8QxeeXk5SktL+eOXlZXxtlwuB1CROp+zS0tL+btjJSUlUCgqkrYUFxfzwXNRURH/BaqoqKhKTVwdzubqVtbEjZ1Go+E1ceM8btw4HU2RkZH8GHCavLy8AFScL9XRxLF582bMnDkTe/fuxUsvvYS8vDyBph49egAAoqKidPx09uxZwZdMbZ9xVPZT586dAVR8R6nsp5KSEpw/fx6Wlpbo1KkT7ycHBwfcvn1bR1NGRgby8/P58dX2kzbaftK++8np8PT0RElJCS5cuKBz7nFjzRh77HOPMfbEz73G8n7iMCVNjcVParWav2ZXvpYvW7YMHh4e8Pb2Rr3DCAEFBQUMAMvNzWWMMaZSqZhKpdKxlUqlwFar1VXaCoWCyeVyFhISwkpKSphGo+HLNRoN02g0OjZjTGCr1WqBrVQqq7RVKpXA1qejtpq07cauqbS0lIWEhDCFQmEymozNT2VlZbwPTEWTsfmJuw5xPjAFTcbkJ4VCwUJCQlhpaWmj0FRWVsauXr0q6A+nibM1Go2OzWkyZHNtNJStTwdnq9VqlpeXx1QqFUtLS2MAmJ+fn14dCxcuZABYYGAg38bOnTsZAPb+++8LjhkZGcmkUikDwIKCgvjysrIy1rZtWyYWi9mRI0d0+nv79m3ejoyM5Pfn+jtv3jwGgE2cOFHwXsjNzWU2NjbMxsaGXb9+ne+7QqFgI0aMYACYi4uLQNPMmTMZAJaenq4zdiqVirm5uTEALCIiQtDHpUuXMgBs9uzZgnJ/f38GgEVGRvLlcrmcTZgwgT++dn3u+JcuXeJ9wfUhKCiIb4sr37JlCwPARo4cKTh/L126xMzNzfmxepxzTyaT8ePzpM69xvJ+4t4D2v0wdk2NxU/cNbWoqIhvT9+1/P79+wwAKygoYPUFPZNnAC6Lk3Y2J21b+4Hm6thSqZT/JUEqlUIkEvG2dp3Ktkgk4m2xWAyxWFxt21Df61LT49ikiTSRpobVVBMdxqLJWPzEfQ5wbTYGTVzSEO64HNo2t13b1t6vsl3Vfk/CNqRDLBbzv9hr9zk1NZVfQgEAcnNzcfr0aSQkJMDBwQFr1qzh644dOxbt27fH2rVrceXKFXTr1g0pKSk4dOgQJkyYgH379gnatrCwwN69ezFy5EgEBARg5MiR8PT0RGFhIS5evIjS0lL+V//KWsRiMX766SdIJBL8+OOPmDZtGnbv3g0zMzM4ODhg/fr1mDdvHnr37o2pU6fy6+TJZDK0bt0aYrFY0Bd9x+BsiUSCbdu2wd/fH4GBgZg0aRJcXFxw5swZREVFwc3NDWvWrBHsu3jxYkRERCAwMBDTpk2DlZUVjh49iiZNmgiyZ+o7fmXf6PPfrFmzsGvXLoSFheHZZ5/FqFGjkJubi127dsHPzw+HDh2CSCTi61d1bmRnZ+O9997jy5RKJbKzszF79my+7KuvvoKjo2O9nXtV7fsk30/amIqmxuYnfdd4Q9fs+oKCPIIgCIIgnmoqL6Egk8nQtm1bvP7661i6dKlguQEbGxucOHECS5YsQUxMDKKiotC1a1fs3LkTTk5O2Ldvn077/fv3R0JCAlavXo3w8HAcO3YMDg4O8PDwwPz583XqayMSibBx40aIxWJs3LgRU6ZMwe7duyGVSjF37lw4ODhg1apV2LZtG+zt7TF27FisWbMGLi4ucHNzq9E4DBw4EHFxcfj0008RERGBgoICtG7dGosWLcLHH3+sk8zFz88Pe/fuxaeffoodO3agadOmmDRpElatWoVu3brV6Nj6EIvFOHDgAFasWIGdO3diw4YNcHNzw9dffw13d3ccOnSo2m0VFxdj+/btgrKSkhJB2fLly+tksXeCaAyIGNPzFOZTTGFhIezt7VFQUKA3+1NtUCqVCA0NRUBAgOCXXuLJQOPf8JAPGh7yQcPS2MZfLpcjPT0drq6usLCwaOjuPBE0//9MjZ2dneBXe1Pixo0bcHd3x+TJk7Fnz56G7o6Ap2H8Gzvkg/qjutfUnJwcODo61ku8wUGeJQiCIAiCMELy8vIECUuAimQU77zzDgBg/PjxDdArgiAaAzRdkyAIgiAIwgiJjo7GnDlz4OfnB2dnZ2RnZ+PEiRPIyMjAsGHDMGXKlIbuIkEQDQQFeQRBEARBEEZI165d4evri9OnTyMkJAQA0LFjR3z22Wd47733aCoeQTzFUJBHEARBEARhhLi7u2P37t0N3Q2CIBoh9BMPQRAEQRAEQRCECUFBHkEQBEEQBEEQhAlBQR5BEARBEARBEIQJQUEeQRAEQRAEQRCECUFBHkEQBEEQBEEQhAlBQR5BEARBEARBEIQJQUEeQRAEQRAEQRCECUFBHkEQBEEQBEEQhAlBQR5BEARBEEQjICoqCiKRCMuXL2/orjR6aKwIomooyCMIgiAI4qkkIyMDIpFI58/a2ho9evTAihUrUFxc3NDdJGoJYwxHjhzB66+/jh49esDe3h5WVlbw9PTEqlWrIJfLG7qLBFHnmDV0BwiCIAiCIBoSNzc3vPzyywAqAoKHDx/iyJEjWL58OcLCwnDq1ClIJJIG7iXxuJSXlyMgIAAymQw+Pj7w9/eHXC5HeHg4PvroI4SEhCAqKgpWVlYN3VWCqDMoyCMIgiAI4qmmY8eOOtP+ysvL0b9/f8TFxSE6OhrDhg1rmM4RtUYikWDlypV444034ODgwJcrlUpMnDgRBw8exMaNG7FkyZIG7CVB1C00XZMgCIIgCKISMpkMQ4cOBQBkZ2cLtkVGRuKVV15Bp06dYGNjAxsbG/Tu3RubNm0y2F5aWhrmzZsHV1dXyGQytGjRAj4+Pti2bdsj+1JQUIAhQ4ZALBbju+++E2zbv38/evfuDUtLSzg5OWHu3LnIy8tD+/bt0b59e0HdWbNmQSQSIS0tDevWrYOHhwdkMhlmzZrF17ly5QomT56MFi1aQCaTwdXVFW+//TZycnJ0+iUSieDj46O3z4aOL5FIcPPmTXz33Xfo3LkzZDIZXFxcsGLFCmg0Gp12ysrKsHTpUrRr1w4WFhbo1q0bNm/e/Mgx00YqleKjjz4SBHhc+YcffggAiI6OrlGbBNHYoTt5BEEQBEEQlVAoFHxyj549ewq2rVmzBjdu3EC/fv0wYcIE5OfnIywsDK+99hpSUlKwbt06Qf1Tp04hMDAQRUVF8Pf3x9SpU5GXl4fExERs2LBBEGRVJisrCyNHjsS1a9ewa9cuTJkyhd/266+/Ys6cObCzs8OMGTNgb2+P0NBQ+Pr6QqlUQiqV6m3zrbfeQlxcHAIDAzFmzBi0aNGC76e/vz8UCgVeeOEFtG/fHmfOnMGGDRtw6NAhxMXFwdHR8fEGVIugoCDExsZi9OjR8Pf3R0hICJYvXw6FQoHPP/+cr6fRaDB27FgcO3YM3bt3x4svvoicnBy88847fABeW7gxMjOjr8SEaUFnNEEQBEEQBiktVxrcJhGLIJOaVauuWCSChfnj1S1TKMGYbj0rmf4gpqbcuHGDn67JGEN2djbCw8Nx584drF27Fs8884yg/o8//ghXV1dBmUqlQkBAADZs2IBFixbB2dkZQMW0z6lTp6K4uBihoaEYOXKkYL/bt28b7Nd///0Hf39/5OTk4PDhwxgxYgS/LT8/H4sWLYK1tTXOnz8Pd3d3AMCqVavg7++PCxcuwMXFRW+7SUlJSExM5PsIVARUs2bNQmlpKcLCwuDv789ve//99/Hll1/igw8+wJYtWwz2t7pcunQJFy9eRJs2bQAAn3zyCdzd3fHdd98hODgY5ubmAIDffvsNx44dw8iRI3Ho0CH+uchFixahd+/ete4HUBEoA4Cfn1+dtEcQjQUK8giCIAiCMEi/pd8b3Daoiys2zhvPv/YJ+glyhUpv3d5ubfHrm5P416M+24K8kjK9dbu2c8KuxS/yryd88Rvu5hXq1Ev6+p1Hdb9apKamYsWKFTrlo0ePFgRWHJUDPKDiTtD8+fNx9OhRREZGYubMmQCAAwcO4M6dO5gxY4ZOgAcAbdu21dunc+fOITAwEEDF9NBevXoJth84cADFxcVYuHAhH+Bx/Vi5ciWee+45g3qXLFkiCPAA4PTp00hNTcWoUaMEAR5Qcedty5Yt+OOPP/Djjz/yQdjjsmTJErRq1Yp/7ejoiHHjxmH79u1ISUlB9+7dAVQEeQDw+eefCxLfdO/eHdOnT691wHnkyBH8/PPP6NKlC+bMmVOrtgiisUHP5BEEQRAE8VTj7+8Pxhj/l52djQMHDuDKlSsYMGAAzp49K6hfVFSE4OBgeHp6wsbGhl96YeLEiQCAu3fv8nXj4+MB1OxO0cmTJzFs2DDY2Njg9OnTOgEeUHE3DAAGDhyos83b27vK6Yd9+/bVKUtMTAQAvc/Ycc8cyuVypKSkVFeGQTw9PXXKuGA3Pz+fL7t06RKsra3x7LPP6tQfNGhQrfpw7tw5TJkyBfb29vjzzz8hk8lq1R5BNDboTh5BEARBEAaJ++JNg9skYpHgddSn8w3WFYuEdY98YvjOSeW6fy+doXe6Zn3RrFkzjB07FlZWVvD19cXHH3+Mo0ePAqh4Vs/HxwcJCQnw8vLC9OnT0axZM5iZmSEjIwPbt29HeXk531ZBQQEA8FMTq0NiYiKKi4vh5+eHDh066K1TWFhxZ5N7nk4bsVhc5bNzTk5OBtvTtw0Af+eNq1cb7OzsdMq4oFStVvNlBQUFaNeund42DPWzOpw/fx5+fn4Qi8UIDw9H165dH7stgmisUJBHEARBEIRBavLcW33VtTSvm2fvaoq3tzeAirs+HAcOHEBCQgLmzJmDX375RVB/9+7d2L59u6CsSZMmAIA7d+5U+7hvvvkm7t69iy1btuDFF1/Ezp07de7McYHSgwcPdPbXaDTIzs42GFiKKgXR2u3dv39f7z737t0T1OPaUan0T88tKCiAvb293m3Vxd7eHg8fPtS7zVA/H8X58+fh6+sLjUaDiIgI9OnTpzZdJIhGC03XJAiCIAiC0ENeXh4ACFL7p6amAgDGjRunU//kyZM6ZdzUyIiIiGofVywWY/PmzZg7dy727t2Ll156SSeY4qY8nj59Wmf/+Ph4g8GXIby8vAAAUVFROttKSkpw/vx5WFpaolOnTny5g4OD3uA1IyNDMO3ycfH09ERJSQkSEhJ0tukb60fBBXhqtRphYWF8EE8QpggFeQRBEARBEHpYv349AGDw4MF8GZex8tSpU4K60dHRetdvGzt2LNq2bYvff/8d4eHhOtsN3eETiUT4+eef8dprr2Hv3r2YNm2aIHAbN24cbGxssGXLFj7wBCqyfH7yySc1UFnBgAED4ObmhiNHjuDYsWOCbStXrkROTg6mTZsmSLrSp08fZGRkCNaYUygUWLx4cY2Pr4/p06cDAD766CPBNM7Lly9jx44dNWrrwoUL8PX1hUqlwpEjR9C/f/866SNBNFZouiZBEARBEE812ksoAEBubi5Onz6NhIQEODg4YM2aNfy2MWPGoH379li7di2uXLmCbt26ISUlBYcOHcKECROwb98+QdsymQx79+7FyJEjMWrUKIwcORKenp4oLCzExYsXUVpayic9qYxIJMKPP/4IsViMH3/8EYwx7N69G2ZmZmjSpAnWr1+PefPmoVevXpg6dSq/Tp5MJkPr1q0hFlf/t3yxWIxt27bB398fAQEBmDRpElxcXHDmzBlERUXBzc0NX3zxhWCfxYsXIyIiAgEBAZg2bRqsrKxw9OhRNGnSRJA983GZOXMm/vjjD4SFhcHLywujRo1Cbm4udu3aBT8/Pxw6dKha7eTm5sLX1xf5+fkYOXIkjh49yj9jydGkSRO8/fbbte4zQTQWKMgjCIIgCOKppvISCjKZDG3btsXrr7+OpUuXCpYbsLGxwYkTJ7BkyRLExMQgKioKXbt2xc6dO+Hk5KQT5AFA//79kZCQgNWrVyM8PBzHjh2Dg4MDPDw8MH++4WQ1QEWgt3HjRojFYmzcuBFTpkzB7t27IZVKMXfuXDg4OGDVqlXYtm0b7O3tMXbsWKxZswYuLi5wc3Or0TgMHDgQcXFx+PTTTxEREYGCggK0bt0aixYtwscff6yTzMXPzw979+7Fp59+ih07dqBp06aYNGkSVq1ahW7dutXo2PoQi8U4cOAAVqxYgZ07d2LDhg1wc3PD119/DXd392oHeYWFhfzU27CwMISFhenUcXFxoSCPMClEjD3JfFWNn8LCQtjb26OgoEBv9qfaoFQqERoaioCAAEilDfMQ+dMMjX/DQz5oeMgHDUtjG3+5XI709HS4urrCwsKiobvzRNBoNCgsLISdnV2N7nQZEzdu3IC7uzsmT56MPXv2NHR3BDwN49/YIR/UH9W9pubk5MDR0bFe4g2Op8azX3/9Nbp27QoPDw8sXLgQFNsSBEEQBGHM5OXlCZZrAICysjK8807FIvHjx49vgF4RBNEYeCqmaz58+BDff/89rl69CqlUisGDByMuLo4euiUIgiAIwmiJjo7GnDlz4OfnB2dnZ2RnZ+PEiRPIyMjAsGHDMGXKlIbuIkEQDcRTEeQBFdmm5HI5gIrpMvoWDyUIgiAIgjAWunbtCl9fX5w+fRohISEAgI4dO+Kzzz7De++9R1PxCOIpxije/TExMRgzZgxat24NkUjEX8i02bhxI9q3bw8LCwt4e3sjPj6e39a8eXO89957cHZ2RuvWrTFixIgaP4xMEARBEATRmHB3d8fu3btx69YtlJWVoaysDJcvX8bHH3/81DxjSRCEfowiyCspKYGnpyc2btyod/uePXuwePFiBAcHIyEhAZ6envD398eDBw8AVMxZP3ToEDIyMnDnzh3ExsYiJibmSUogCIIgCIIgCIJ4IhhFkDdq1CisXLkSEyZM0Lt9/fr1mDt3LmbPng0PDw/89NNPsLKywq+//goAOHbsGDp27IimTZvC0tISgYGBiIuLe5ISCIIgCIIgCIIgnghG/0yeQqHAhQsX8OGHH/JlYrEYI0aMwJkzZwAA7dq1Q2xsLORyOaRSKaKiojBv3rwq283NzYVSqeRfy2QyyGSyWvWVa0+7XeLJQePf8JAPGh7yQcPS2MZfqVSCMQaNRgONRtPQ3XkicNm1Od3Ek4XGv+EhH9QfGo0GjDEolUpIJBK+vLy8XJAJNzc3t977YvRBXnZ2NtRqNZycnATlTk5OuHbtGgCgX79+CAgIgJeXF8RiMYYPH46xY8dW2a6rq6vg9ZQpUzBt2rQ66fPRo0frpB3i8aDxb3jIBw0P+aBhaSzjb2ZmhpYtW6K4uBgKhaKhu/NEKSoqauguPNXQ+Dc85IO6R6FQoKysDDExMVCpVHz5rl27nvialUYf5FWXzz//HJ9//nm166enp8PW1pZ/XVd38o4ePQpfX99GsQju0waNf8NDPmh4yAcNS2Mbf7lcjszMTNjY2Dw1iToYYygqKoKtrS1EIlFDd+epg8a/4SEf1B9yuRyWlpYYPHiw4Jo6fPhwQW6R3NxcPPPMM/XaF6MP8hwdHSGRSHD//n1B+f3799GyZcvHbrdp06b1tgK9VCptFB/uTys0/g0P+aDhIR80LI1l/NVqNUQiEcRi8VOTbp+bnsbpJp4sNP4ND/mg/hCLxRCJRDrXeKlUChsbmyfblyd6tHrA3NwcvXr1wvHjx/kyjUaD48eP02LnBEEQBEEQBEE8dRjFnbzi4mLcuHGDf52eno6LFy+iadOmcHZ2xuLFizFz5kz07t0bffv2xTfffIOSkhLMnj27AXtNEARBEARBEATx5DGKO3nnz5+Hl5cXvLy8AACLFy+Gl5cXgoKCAFQkRfnqq68QFBSEnj174uLFiwgLC9NJxlIT1Go1/1+frVKpBDZ369uQrVQqBTaX2YizuUw82jYAga3RaAQ290CnIVutVgts0vS/B2BNSZOx+YnDlDQZm5+0fWAqmozJT1ybjUUT11fuuJVtLgOfts3tZ8jWzt7XELY+HZzNwdU3BU3G5CdtTEWTsflJG1PR1Nj8VJ1reX1jFEGej48PP3jaf9u2bePrvPnmm7h58ybKy8tx9uxZeHt71+qYXl5e8PDwQFBQEJKTkwEASUlJuH79OgAgMTER6enpAID4+HhkZmYCAGJjY5GVlQUAiImJQXZ2NgDgxIkTKCgoAABERkbyGY1CQ0Mhl8uhUqkQGhoKlUoFuVyO0NBQABWZjyIiIgAA+fn5OHHiBICKrKLcgu5ZWVmIjY0FAGRmZiI+Ph5AxR3PxMREAMD169eRlJQEAEhOTq4zTfn5+QCAiIiIRq8pISGB96+paDI2P6WkpPA+MBVNxuanO3fu8D4wFU3G5ieuzcaiSS6X8184CgsL+S8thYWF/OdtZRuo+PLC2Wq1mh8LlUrF20qlEsXFxQAqss6VlJQAqEgnXlpayh+/rKyMt+VyOQCgrKyMt0tLS/n04yUlJXwm0OLiYj54Lioq4r9AFRUVVamJq9PYNEVFRUEsFvM/YtdEk7H4iTtObTWFhoZCJBIhODi4wTUZo584TElTY/GTWq3mr9mVr+XLli2Dh4dHreOUasEIAQUFBQwAy83NZYwxplKpmEql0rGVSqXAVqvVVdoKhYLJ5XIWEhLCSkpKmEaj4cs1Gg3TaDQ6NmNMYKvVaoGtVCqrtFUqlcDWp6O2mrTtxq6ptLSUhYSEMIVCYTKajM1PZWVlvA9MRZOx+Ym7DnE+MAVNxuQnhULBQkJCWGlpaaPQVFZWxq5evSroD6eJszUajY7NaTJkc200lK1PB2er1WqWl5fHVCoVS0tLYwB0/qysrFj37t1ZcHAwKyoqemJ9j4yMZABYUFBQjTQZk5+48ed88biajh8/zo9VdTQdPnyYTZkyhXXq1InZ29szS0tL1qlTJzZ79myWkpLyRM69xuInbR+YiqbG4ifumlpUVMS3p+9afv/+fQaAFRQUsPrCKJ7Jawi4BQy1FzLUts3MzGpkS6VS/pcEqVTKp6ytnHmnss1l6AEgyH5WHdtQ3+tS0+PYpIk0kaaG1VQTHcaiyVj8xH0OcG02Bk0ikYj/TNI+P7Rt7TTrnK29X2W7qv2ehG1Ih1gs5n+x1+6zm5sbXn75ZQAVU60ePnyII0eOYMWKFQgPD8epU6f4Ma7vvmu/rq6mqtpsbH7SpjaauDqVbUP7HTlyBHFxcfD29saoUaMglUqRnJyM3377DX/88QdCQ0MxbNiwWuszBj9pYyqaGpuf9F3jDV2z6wsK8giCIAiCeKrp2LEjli9fLigrLy9H//79ERcXh+joaD4AIIyTL7/8Et99951O+fHjxzFixAh88MEHOHfuXAP0jCDqB6N4Jo8gCIIgCOJJIpPJMHToUADgn1/kiIyMxCuvvIJOnTrBxsYGNjY26N27NzZt2mSwvbS0NMybNw+urq6QyWRo0aIFfHx8BPkFDFFQUIAhQ4ZALBbrBCr79+9H7969YWlpCScnJ8ydOxd5eXlo37492rdvL6g7a9YsiEQipKWlYd26dfDw8IBMJsOsWbP4OleuXMHkyZPRokULyGQyuLq64u2330ZOTo5Ov0QiEXx8fPT22dDxJRIJbt68ie+++w6dO3eGTCaDi4sLVqxYIUiMw1FWVoalS5eiXbt2sLCwQLdu3bB58+ZHjllltBem1mb48OFwcHAQZHEnCFOA7uQZQK0WZgOUSCQCW6VSQSQS8TZ3O9aQXTkjm5mZGUQiEW8DFQ9tattSqZTP0COVSqHRaKBWq3lbo9HAzMzMoK1Wq8EY4219OmqrSSKR8LYxaOIwJU3G6KfK7y1T0GRMftL2galoMhY/aV+DGoMmQDfjHDcFibO5Otq2WCzm99NnAxUBQEPZ+nRwNoe2bu3/nI7y8nJERUVBJBLB09OTHwPGGNasWYMbN26gX79+GD9+PAoKChAWFobXXnsNKSkp+OqrrwT9OnXqFEaPHo2ioiL4+/tj6tSpyM3NxcWLF7FhwwbMnDkT2mj3JysrC6NGjcK1a9fwxx9/YOrUqbymrVu3Ys6cObCzs8P06dNhb2+PI0eOwNfXF0qlkj/3OE0cb731FuLi4hAYGIgxY8agefPmYIzh9OnT8Pf3h0KhwAsvvAAXFxfExcVhw4YNOHToEM6cOcPXrdzfyj7Qp4cjKCgIsbGxCAwMhJ+fHw4cOIDly5ejvLwcq1at4vVpNBqMGTMGx48fR/fu3TFt2jTk5ubinXfe4YNL9v8ZDh/33Dtz5gzy8vIwcOBAvTrq8txrLO8nfb4xdk2NxU9cXe56/6hreX1Cd/IMQNk1TS9zHmXXbHhNlF2z4TVRds2G18S12Vg0PSq7ZomyHPfzclCiLOftUpUCxQo5HubnAvhflrlSlQJF8lI8yM9FqUqBwrISPCzI423tzHk5RfkoVSmQV1yI3OIC3s4rLoRcrRRkzsstKkB+SUX72YV5KCgtruhDHWTX5MZ1+fLlCA4OxocffogFCxaga9eu+Pfff7F27Vp06NBBkA3wyy+/xI0bN7BlyxZ88skn+Omnn3DlyhUMGzYMGzZswPXr1/lsgAUFBZg6dSqKi4uxf/9+7N+/H6tXr8b69esRHR2NgwcPCrIBAv9b6iExMREDBgxAeno69u7dixdeeIHXkZubi0WLFsHa2hrx8fH46aef8NFHHyEhIQH29va4e/cu35Z2ZkDufImPj8f333+PNWvWYOHChSgsLMSsWbNQWlqKP//8E7t27cLy5ctx+PBhLFmyBKmpqXjvvff4c4bzjUaj0ZvhUDuI1vYTAFy6dIkPcNevX48LFy7A3t4e33//PRQKBe+n7du34/jx4/D398eFCxewbNky/PLLLzh16hSOHj3KH6cmWRsPHz6Mjz76CB9++CGef/55DBs2DI6Ojli9enWjz9pI2TUpu2aNYIQAyq5pupnzKLtmw2ui7JoNr4mya1J2TW27Otk1PUKCDf69FruD16dWq1mvgysN1p1x8ldBlrsBoWsM1p0U9bMgE96I8PV662nX0barypynrkZ2Te4vMDCQJSYmVjtz3759+xgAtnXrVr589+7dDACbMWPGI9uJ1MquGR8fz5o3b86aN2/Ozp8/r6Nj69atDAB76623dLIBnj59mgFgLi4uggyAM2fOZADYhg0bdI4fHR3NALBRo0bp9KuoqIg1bdqUWVhYsPLycr4cABsyZIheTS4uLszFxUVQzh3/+++/18muOWPGDAaAJSUl8eVDhw5lANj58+d1Mhm+8sorNcquydmLFy8W+Lhjx458+9Xx8aPsqs69yn6qrMmQXRf9qtxHyq5p+tk16U6eAbSza+qzzczMBDY3DcKQLZVKBTZ3+5ezRSKRjg1AYIvFYoGtnblHny2RSAQ2aRJmljMVTcbmJw5T0mRsftL2galoMiY/cW02Fk1cX7nj6rMNwW0XiUSCc0tv3Ur1H4V2v6pTpzo6tPuoXd/f35//xZ4xhuzsbBw4cABXr17FgAEDEB8fLzhOcXExgoOD0bNnT9ja2vL6uTttWVlZfH0umYefn5/B/lbWeurUKQwbNgw2NjY4ffo0evXqpaODu6M7aNAgfn+uTr9+/fhzRp9v+vbtq3P8ixcvAqhYm7hyv7hnDuVyOVJSUnT6a0iHvrEGAE9PTx3ftGvXDkDF3Xuu/NKlS7C2tkavXr0EOkQiEQYPHqyjrzrju27dOv7u39mzZ9GpUycMGDAAu3bteuS+1bGrOvcq+6ly3w3ZddGv6vjGmDU1Nj9V51pe39AzeQRBEARBGORc4DKD2ySVvjTGjFxisK64Ut0I37erXfefYQvADNStD5o1a4axY8fCysoKvr6++Pjjj/npgQqFAj4+PkhISICXlxemT5+OZs2awczMDBkZGdi+fTs/bQwA/6hGmzZtqn38xMREFBcXw8/PDx06dNBbh5sy1qJFC51tYrEYjo6OBtt3cnIy2J6+bQDQqlUrQb3aYGdnp1PGffnVflapoKCAD/4qY6if1cXGxgZ9+/ZFSEgIevfujXnz5sHX1xfNmzevVbsE0VigII8gCIIgCINYmZk3eF3LGtStS7jnZrRT6x84cAAJCQmYM2cOfvnlF0H93bt3Y/v27YKyJk2aABA+D/so3nzzTdy9exdbtmzBiy++iJ07dwpmowD/C5QePHigs79Go0F2drbBwFLfHR2uvfv37+vd5969e4J6XDvaCYW0KSgogL29vd5t1cXe3h4PHz7Uu81QP2uKmZkZhg4dikuXLuH8+fMYNWpUnbRLEA0NTdckCIIgCILQQ15eHgAIsnKmpqYCAMaNG6dT/+TJkzplffv2BQBB4p1HIRaLsXnzZsydOxd79+7FSy+9pBNMcVMeT58+rbN/fHy8weDLEF5eXgCAqKgonW0lJSU4f/48LC0t0alTJ77cwcFBb/CakZHBJyiqDZ6enigpKREkTuPQN9aPC5ekRntKNUEYOxTkEQRBEARB6GH9+vUAwD//BQAuLi4AKp6b0yY6Olrv+m1jx45F27Zt8fvvvyM8PFxnu6E7fCKRCD///DNee+017N27F9OmTRMEbuPGjYONjQ22bNnCB55ARZa/Tz75pAYqKxgwYADc3Nxw5MgRHDt2TLBt5cqVyMnJwbRp02Bu/r+7qn369EFGRgaio6P5MoVCgcWLF9f4+PqYPn06AOCjjz4STOO8fPkyduzYUaO2zp8/r7c8PDwcf//9N5o0aYL+/fs/fmcJopFB0zUNwF1M6nJtJe6XQFovitbJe9r9VPm9ZQqajMlP2j4wFU3G4ifta1Bj0ATQOnkAcOPGDSxfvpx/nZeXh9OnTyMhIQEODg744osv+DEYPXo02rdvj7Vr1+LKlSvo2rUr/vvvPxw6dAgTJkzAvn37+LZFIhHMzc2xZ88ejBo1CqNGjcLIkSPh6emJgoICXLp0CaWlpTp3qrh+AMAPP/wAsViMH3/8EYwx7N69G2KxGPb29li/fj3mzZuHXr16YcqUKfw6eTKZDK1bt9bxjb5jcGMnEomwbds2+Pv7IyAgAJMmTYKzszPi4uIQFRUFNzc3rF69WrDv4sWLERERgYCAAEybNg2WlpY4duwYmjRpwj/DV1mPNtq+0a7Dlc+YMQM7d+5EWFgYvLy8MHLkSOTl5WHXrl3w9fXF4cOHq71OXp8+fdCtWzf06NEDbdq0QWlpKZKSknDy5ElIpVJs2bIFVlZWesemrs69xvJ+quo8MFZNjcVPXF3uev+oa3l9QnfyDEDr5JneGli0Tl7Da6J18hpeE62T1/CauDYbi6ZHrZPHTHANLK4OZ6empmLFihX49NNP8emnn2LTpk0oKCjAnDlzcPHiRXTq1InXJJPJ8M8//2DixIk4d+4cNm7ciLt372Lr1q145ZVX+DHQ1uTl5YWEhATMmDEDSUlJWLduHfbt2wczMzMsXrzY4Dp5nI6NGzdi7ty5+OuvvzBlyhTk5ORAo9Fg7ty52LZtGzp06IDt27dj27Zt8Pb2RlhYGAoLC2FnZ6d3nTxDfho4cCBiYmIQGBiIiIgIrFu3DmlpaVi0aBEiIyNhY2Mj8JOfnx927NiBDh06YMeOHfjzzz8xdOhQHD16lP9xo7KftNH2k3ayGs5PIpEIv/32G5YsWYLc3Fx8++23iI2Nxbp16zB//nwA1V8nLzg4GK1atUJUVBQ2bNiALVu24N69e3jllVdw6dIljBo1qtGvv0br5NE6eTVBxAz9vPKUUlhYCHt7e+Tm5sLBwaHO7+SFhYXB19cXlpaWT/0v2k9aU1lZGf+LI5f21tg1GZuf5HI5wsPDERAQwP+qbOyajM1P5eXlCAsL431gCpqMyU9ARUDo5+cHS0vLBtekUCiQlpYGV1dXvj+m/is9UPFZb2trK7ibacyaKvvpxo0beOaZZzB58mTs3r27UWniviRzCVzoDtGT16TtA64fxq6psfipvLwcaWlpcHZ2ho2NjcFr+YMHD+Dk5ISCggK92WbrApquaQBD63pxaGe5qo4tlUr5XxIqr62kXaeyLRIJ14viPpCqYxvqe11qehybNJEm0tSwmmqiw1g0GYufuM8Brs3GoIn7csMdl0Pb5rZr29r7Vbar2u9J2IZ0iMViPtDT12dj05SXlwcrKyvIZDK+fllZGf9M3Pjx4xudJm2q8lNV7TQ2TdU99xqLJm1MRVNj85O+a7yha3Z9QUEeQRAEQRCEERIdHY05c+bAz88Pzs7OyM7OxokTJ5CRkYFhw4ZhypQpDd1FgiAaCAryCIIgCIIgjJCuXbvC19cXp0+fRkhICACgY8eO+Oyzz/Dee+8J7koQBPF0QUEeQRAEQRCEEeLu7o7du3c3dDcIgmiE0E88BEEQBEEQBEEQJgQFeQRBEARBEARBECYEBXkG0F4MXZ+tUqkENpety5BdeTF0LtUqZzPGdGwAAluj0Qhs7YUW9dlqtVpgkybhQsSmosnY/MRhSpqMzU/aPjAVTcbkJ67NxqKJ6yt33Mo2Y0zH5vYzZHNtNJStTwdnc3D1TUGTMflJG1PRZGx+0sZUNDU2P1XnWl7fUJBnAFoM3fQWOqbF0BteEy2G3vCaaDH0htfEtdlYND3ti6GbiiZj8RN3HFPSZIx+4jAlTY3FT7QYeiOFFkM33YWOaTH0htdEi6E3vCZaDJ0WQ9e2aTF001wMvTFr4r4k02LotBi6KfqJFkM3AmgxdFromDSRJlPVVBMdxqLJWPxEi6E/GduQDrHYtBZDr2wbg5+0MRVNxuYnbUxFU2PzU2NYDJ2maxIEQRAEQRAEQZgQdCevhjDGUFoqr7KOlZWFwV9MCIIgCIIgCIIg6hMK8gxwKOMrDHB9ES62nfkyxhjG+M7HubjLVe7bt38P/BPxIwV6BEEQBEEQBEE8cWi6pgFsRX8gNWcc9qYMwO7rC3H+QTgKi4seGeABQPyZpEfe7SMIgiAIgngUIpEIPj4+Dd2Nx2Lbtm0QiUTYtm1bQ3el0UNjRdQ1FOQZ4L6iDTRMBEdZNlpIw1FYuhDR9wehU4AMAHAl7RDS7h8X/F1JO9TAvSYIgiAIojpIJBI+cUJ1/qrLrFmzIBKJkJGRUX+dN0BUVJTevtva2qJv3774+uuvBes1EgRhutB0TQNMdj+Icmkhzj34G7nyaDiYXYOlpBx3EisujlbWljid+xtKlPfh6TgBHey6N3CPCYIgCIKoLkFBQTrB2zfffIOCggIEBwc3UK/qhl69emH06NEAKtbsunfvHg4ePIjFixcjNjYWf/75ZwP3kCCI+qZGQV6HDh1qfcC3334bCxcurHU7TwJHizYY4vgqgFehVMuRlH0axVmr+O05pXvgJLuDjNyduHCvKco1PeExVobkw48/VbM2iV0oKQxBEARBVI/g4GBBunSgYspcQUEBli9f3jCdqiN69+6toyEvLw/du3fHvn37kJaWViff6QiCaLzUaLpmRkYG8vLy+MX/avp38+ZN5Ofn15OUukWtVqO0VI4OTsPRwWk4OrUOxKQe/wvwlAoFzM0G4165M9RMhGbmuWhtcQKfrLqLTeeLsf/WfKhUKn49Hm4xdM7mFk3kbMYYFAoFxoyYzx/T0N9Yv9ehVqv5hXU1Gg2USiXG+FZvX5VKxS+cq1areVu7XLvvhuzqaKpsAxDYXN85W1uTPltbt3bfDdmVNXGYkiZj8xOHKWkyNj9p+8BUNBmTn7g2G4smrq/ccSvbjDEdm9vPkM210VC2Ph2czaH9/US7XHs7V/7w4UMsWrQIrq6ukMlkaNGiBSZPnozLly8Ljt++fXts374dAODq6spPl+SeqWOMYf/+/Zg2bRo6duwIKysr2NvbY9CgQdi3b59eTRxVaaqObxwcHODt7Q0AyM7OFhynqj4ZGuuLFy/ipZdeQtu2bSGTydCqVSuMHDkS//zzT5W+AYA7d+6gR48esLCw4HVzOn7++Wd07doVFhYWaNeuHZYsWYKysjKdcdRoNPDx8YFIJEJpaSk+/vhjuLm5QSqVYvny5fwxT58+jcDAQDRt2hQWFhbo3LkzgoKCUFpaKuhXeno6RCIRZs2apbfv3PG1y7njKxQKBAcHo3379pDJZHjmmWfwww8/6PVTTk4OXnvtNTg5OcHKygp9+vTB33//LRif+n4/VT7Pa/N+MsVrRF1oqs61vL6p8TN577zzDtLT0x/rT9/J1Vjx8vJC71699G7r278H/rueAk/2Ml7sdBSiu18jp2QO7pT1QIlSChupAkA+YmJikJ2dDQDYEv8+krLOAAAiIyNRVFQEAAgNDYVcLodKpULI3wdw7mz1ErtkZt5BTEwMACArKwuRkdHVTgqTmHgJycnJAICkpCRcv34dAJCYmIj09PSKevHxyMzMBADExsYiKysLAASaTpw4wQftERERejWFhoZCpVJBLpcjNDQUAFBUVISIiAgAQH5+Pk6cOAGg4kNHW1NsbCwAIDMzE/Hx8QAqLsKJiYkAgOvXryMpKQkAkJyc/EhNCQkJ/DiYiiZj81NKSgrvA1PRZGx+unPnDu8DU9FkbH7i2mwsmuRyOf+Fo7CwkP/SUlhYyH9xqWwDFV9eOFutVvNjoVKpeFupVKK4uBgAoFAoUFJSAgAoLy9HaWkpf/yysjLelssrZqSUlZXxdmlpKcrLywEAJSUlUCgUAIDi4mI+eC4qKuK/QBUVFVWpiatTWZM2nKaHDx+if//++Pbbb9G+fXssWrQIgwYNwv79+9GvXz8cPXqU1/TGG2/A09MTAPDGG28gODgYy5Ytw4svvshr+vDDD3H16lX069cPCxYswKRJk3Dt2jVMmjQJ3333nY4mjkdpYozxY8qVa/spPz8f8fHxsLa2RqdOnQR++vDDD3H58mUMHDgQCxYswPjx45GSkoJJkyZh3bp1On7avXs3vL298eeff6J3795YuHAhAgMDkZmZic2bN/N+4vxRXl7Oa7pw4QL8/f2RmZmJffv2Yfz48Xx/P/nkE8yfPx/Z2dl49dVX8cILL2DPnj2YPHkyf25VPvcAYMKECdi2bRuGDBmC+fPnw9XVFUqlEjt27MCQIUMQFRWFwMBAvP3227C0tMRnn32GYcOGIT8/n9fEnV9VnXtqtVpw7nHn0qRJk7B161b4+/tj+vTpyM3NxYIFC/Ddd98J/FRSUoKhQ4di06ZNcHNzw8KFC+Hq6oopU6bwwa72ucdpro/3E0ddvJ9M8RpRG01qtZq/Zle+li9btgweHh78Dy71CqsBIpGIrVixoia71On+T4KCggIGgOXm5jKNRsMKC4pYYUERKy4u5W2NRsOUSiVTqVSMMcaUSiVTq9WsuLiUtWzSnw19eSg7kxnKl/+Xn8iOpz/Djqc/w/Yk92Y7kuex2KwQplDJmUKhYBqNhmk0GpafV8BaWPdnLaz7s7t37rPi4lJWVFTC8vMKWHFxKbt/L5vfXlRYzJRKJWOMMbVazQryC/ltWXcf8P0tyC9kxcWl7F7WQ357YUER33eVSiXQUVlTVbZCoRDYGo1GYGs0Gh2bMSaw1Wq1wNbWpM9WqVQCW58OQ5pKS0tZSEgIUygUJqPJ2PxUVlbG+8BUNBmbn+RyucAHpqDJmPykUChYSEgIKy0tbRSaysrK2NWrVwX94TRxtlJVzBTKIqZUFfO2Sl1SYatKeX1qtZovV6qK9dilfNtcu4brl/FjyBhjSlWJ3vradbRtfTo4W61Ws7y8PKZSqfh9uHFxcXFh3Fcjrnz27NkMAFu6dKngOIcPH2YAWMeOHfljaDQaNnPmTAaApaWl6fRLo9GwGzdu6JQXFhay7t27M3t7e1ZcXCyoD4ANGTKkSk1cW8ePH2cAWK9evVhQUBALDg5mH3/8MXv11VdZq1atmJ2dHduxY4fO8W/cuKEzjkVFRXyfSkr+N9b37t1j1tbWzNramiUkJOjou3XrFm//+uuvDAD79ddfmUajYWfOnGFNmzZlTk5OLCEhQaAjOTmZSSQS1qZNG5aVlfW/70b5+czDw4MfB23fDBkyhAFgPXv2ZDk5OYK+5OfnM3t7eyaTydjFixf5cpVKxaZMmcIAsBUrVvDlaWlpDACbOXOm3vOKO752OXd8b29vlp+fz/sjOTmZmZmZsU6dOgn8FBQUxACwV199VXDuhYWFMQAMANu6davgnDR0jtfG5t4D2ufOo+o/6tyrbGv7SZ9d15oeZwzqQxN3TS0qKuLb03ctv3//PgPACgoKWH1Ro2fykpOT4ejo+NgBZW33f5JwWbds7Wz0bjczM9Nra5TA1b/l6P6TD19epixElrw9WshuwtGiEEAUyuRRiMj4GHnKzujSdB56tfCFmVTKt2Nnbwtra0uD/ROJxXz7YrEYEq0+2NrZVLmvWCKBRCLhdT5KkyFbqtXfmtgikYi3xWIx/0xEdWzt/lbHJk2kiTTp2jXRYSyajMVP3C/K2tfvhtaknT1S+/zg7JjMZ2GIZpZD4Om0iW8j6uZz0LAyvXWbyPri2VY7+GOeuTMcSk2e3rq25t3Qp/Vf/Ov4u6MhV9/RqTes/f9mB2g/b65PB2dr/v8Xe23d+p5VF4lEUCqV2LVrF5o1a4ZPPvlEUDcgIAC+vr44evQoTp8+jUGDBunsX7ltkUgENzc3nXJbW1vMmjUL7777Ls6fP48hQ4bUSFNl+8KFC7hw4YJOf15++WU899xzOm1zfdIut7Gx4ft07tw5vk/bt29HSUkJgoKC4OXlpXOMdu3a8bb2GBw5cgSTJk1C69atsW/fPnTv3l3Q9z179kCtVuPdd99Fy5Yt+XJ7e3t8/PHH/N3Qym0DwIoVK9C0aVNBX/755x8UFBTg9ddf5++uAhXvlbVr1+Kvv/7C9u3bERQUpDMehmzt19rlq1evhr29PYAKH3Tu3BkDBgxAdHQ0iouLYWtrC7FYjB07dsDc3ByfffaZoB1/f38MHz4cx48f1zt2j+pXTW19eqqyq3Pu6du3so761FRTu7416bvGG7pm1xc1mq7ZqVMnNGvW7LEPVtv9jZUejoPxUudw9GweiZSM8bgj90KJygKWEgVaWyShQHGXr+vYyQxt+0r1Ph9AEARBEMST49q1a5DL5ejbty+srKx0tg8dOhQAcPHixWq3+eDBAyxevBhdunSBlZUV/0Xx3XffBQDcvXv3ES08mtdee03wLNG9e/fw22+/ITQ0FN7e3khLS3vsPnFTz/z8/Krdnz///BPjxo1D586dcfLkSbi4uOjUuXTpEgBg4MCBOtsGDBhQZft9+/bVKeOmy+lbY9DZ2RkdOnRAWlqaYErs49JLz+M9bdu2BQB+endhYSHS09PRsWNHQRDLUflHAoKoLbVaQmHUqFGYN28exo4d+0QiUmOnicwRrfL7Y2jPjwAJcDk/CjdLwuHbyh8lJWUoLSmD/yIJRg++i4gsH8izeqK93Uj0aj4KFhLdDxeCIAiCqG+GOCdWsVX42T+oXWwVdYW/Kz/X9kS163q3OYyK2WxPFu7ZGycnJ73bW7VqJaj3KHJzc9GnTx/cunULAwYMwIgRI9CkSRNIJBJcvHgRBw4cEDwbVheIRCI4OTnh5Zdfhlwux9y5c7F69Wr+2bma9qmgoAAA0KZNm2r34cyZM1CpVBg0aBBatGihd7y4shYtWuhsMzT+VW2vju/+++8/FBYWwtbW9pEaqsLOzk6njLuTo/3cF6BfX1X9JIjHpVZBXnh4OCIiItCiRQvMmjULc+bMQceOHeuqbyYHYwxrV+zEay+vrbTled56dbs5lBoxHMwLAcSgXB6DExnByFU9A1vJQEhkgLocKC0RToep/JogCIIg6gKJuPo/MtZfXcOPINQn3Jf3+/fv691+7949Qb1HsWXLFty6dQufffYZPv74Y8G2L774AgcOHKhFbx8Nl+zh3Llzj92nJk2aAKhI4tS+fftqHXfVqlX4559/sGHDBkgkEn7qqzbcGD548EDnTp+h8efQNxWxpr7jptRxiTm04QLb2qCtTx+P0kgQNaXG2TW1uXHjBt5//32IxWKsWbMGnTp1wvDhw7F7924+qw3xP8pK5Ui9rvtMgTa/zFTgx3cHQm32Ae6W90GxyhIWEiVay65CjG3QqCp+yezWYTSe7eeHDk7D0MFpOLp1GP0kJBAEQRDEU0Pnzp1hYWGBc+fOCTISckRFRQEAevbsyZdxM5v0pUhPTU0FAIwbN05n28mTJ+ugx1WTl1fxDKT2IyE17RM3NVI7W+yjsLCwwN9//43AwECsX79eJ5gEwD83d/r0aZ1tXNbZmsA9L8j5SJvMzEykpqaiQ4cO/F087eC1MtzUz9pgZ2cHV1dX3Lhxgw8wtXkS/ieeLmoV5HXo0AGrV6/GrVu38PfffyMgIAAxMTF46aWX0Lp1ayxevBj//vtvXfXVqCgtKUNJpT/thcqvpB1C2v3jev/+3r8Jvm1fwcudfkdgh/Owt96IB8oAlLIR6NOXe3iYYdWfRfj+XBnm/ibFsy9ZwMyyYnkHKyuLhhFNEARBECaEubk5pk2bhuzsbKxevVqwLSwsDOHh4ejYsaPgmTEuAQi3dIU23B2qU6dOCcr/+OMPfrmN+kKtVmPDhg0AgMGDBz92n2bOnAkbGxusW7dO77OI+oIkAJDJZNi/fz8CAwOxceNGLF68WLB96tSpEIvFWLduHb+8B1CRDv/zzz+vnkgtxo0bB3t7e2zduhVXr17lyxlj+OCDD6BSqTBr1iy+3M7ODp06dcKpU6dw48YNvryoqAgffvhhjY+vj+nTp0OhUPDJXjgiIiL4pCsEUVfUaromh0Qiwbhx4zBu3DhkZWXh119/xdatW7FhwwZs2LAB/fv3x9y5czFlyhRYWDwdAcij7qxZWVtWmQGTQyI2Q6/mI9Cr+QgAwOQIhtJSOW6WJCOz+CVIxRoMe7YMw54Fyt83Q47yHg7fXI9ezV9AK2vdB5sJgiAIgqg+a9asQXR0NFauXInY2Fh4e3sjIyMDf/75J6ysrLB161ZBRr5hw4bhq6++wrx58zBx4kRYW1vDxcUF06dPx/Tp07FmzRq89dZbiIyMhIuLCy5duoTjx4/j+eefx/79++ukz+fPn8fy5cv51w8ePMCJEyeQkpICZ2dnwZ20mvapRYsW+O233zB16lT07dsXY8eORadOnZCdnY2zZ8+iffv2CAkJ0dsvc3Nz7Nu3D88//zwfcH7zzTcAKpLzLV26FKtWrUL37t0xefJkmJmZYf/+/ejevTuuXLkiGOdHYWdnh82bN2PatGnw9vbGlClT0Lx5cxw7dgwXLlxA3759sWTJEsE+7777LubNm4f+/ftj0qRJ0Gg0OHLkCPr06VPt41bF+++/j/3792Pz5s24evUqBg8ejMzMTOzduxeBgYE4fPhwnRyHIADUbJ286qJUKtnevXtZmzZtmEgkYiKRiInFYtasWTO2du1afk2Jxgi3Tt7jrFuh0WjY6BGv8evRGfoLHDaPX4+jNhSW57HjmdvYjmvTWch/Pfm1+I6nP8N+T3mVr6dSK1lhYTF//OLi0lof2xjh1qfi1q0injzkg4aHfNCwNLbxLysrY//++y8rKytr6K48MbTXCKuM9jp52jx8+JAtXLiQubi4MKlUyhwdHdkLL7zALl++rPcYa9euZe7u7kwqlQrWd2OMsYsXLzI/Pz/m4ODAbG1t2ZAhQ9ixY8fY1q1b+XXStKm8f1VERkby661p/1lYWLAuXbqwJUuWsOzsbJ39atonxhhLTExkkydPZk5OTkwqlbJWrVqxUaNGsUOHDvF19O2vVqvZgwcP2Lhx4xgAtnDhQkG7P/zwA+vSpQszNzdnbdu2Ze+99x7LzMxkANi4ceMEdbl16qoiJiaGjRo1ijVp0oSZm5uzZ555hn3yySesuLhYb/2NGzfyvnN2dmZBQUFMoVDo9UNVx+fWS0xPTxeU5+TksHnz5rHmzZszCwsL1qtXL7Z///4qx7quqeo9QNSO6l5Ts7Oz632dPBFjrM7SVf3333/45Zdf8Ntvv+Hhw4eQyWSYNGkSZs6ciYSEBHz//ffIzMzE4sWL8eWXX9bVYeuUwsJC2Nvbo6CgoNoPUmvDGBNMy9RGpVQiPDwc4yeMg7m5eW27KkCtUeFy7kmk5B0C05xDO/tFGNByIgDg9L39eFC0EolXbXHhmATbPt+N5vb6szuZMkqlEqGhoQgICBCsY0U8OcgHDQ/5oGFpbOMvl8uRnp4OV1fXp2amjUajQWFhIezs7Gp0Z4ioGx5n/I8dOwZfX1+8//77WLNmTT330PSh90D9Ud1rak5ODhwdHR873qgOtfasXC7Hjh07MGTIEHTp0gVfffUVmjZtinXr1uHOnTvYvn07hg0bhvfeew8pKSkYMGAAfvvtt7roe73CPTCtVqv12iqVSmBzDzGr1WpYWspgbW0JmUzK2+bmZrCwlEFmYQ6VSgUutlYqlfxaNpVtAAJbo9EIbC4DlEajAdMAPR2HYpLbl3jB7QQGtJwItVoNlUqFm4VHYS8tgU/Pe3j3vTu4kOOD36+NwT8ZX+JW4fVHajJkK5VKgV3XmvTZnKaqfFOVnzhMSZOx+YnDlDQZm5+0fWAqmozJT1ybjUUT11fuuJVt9v9rrWnb3H6GbK6NhrL16eBsDq6+KWgyJj9pU1nHgwcPoFarBTpyc3P5Z+LGjh3bKDUZm5+0MRVNjc1P1bmW1ze1CvLefPNNtG7dGrNmzcLZs2cxZcoUREZG4t9//8Xbb78NBwcHQX2ZTAZ/f3/BA7WNFS8vL3h4eCAoKAjJyckAgKSkJFy/fh1ARaal9PR0ABULg3IPWMfGxiIrKwsAEBMTw2s9ceIEn4I3MjKSX3wzNDQUcrkcKpUKoaGhUKlUkMvl/MPORUVFfAar/Px8nDhRsa5QdnY2YmJiAABZWVl85qnMzEx+odL09HQkJiZinOta3M95DZEXWyJXbglzsRqtLf6DDX7BjdzRiPz3wGNr4hb5jIiIeKKaAOD69etISkoCACQnJz/STwkJCbx/TUWTsfkpJSWF94GpaDI2P2knRTAVTcbmJ67NxqJJLpcL1vLivrQUFhbyX1wq28D/7gYAFV9YuLFQqVS8rVQqUVxcDABQKBQoKSkBAJSXl/PZKuVyOcrKynhbLq+YDVNWVsbbpaWl/FptJSUlfAbv4uJiPnguKiriv0AVFRVVqYmrY0qajMVP3HH0afr999/h4uKCF198EUuXLsXMmTPRuXNnnD9/HjNnzkTXrl0bpSZj9BOHKWlqLH5Sq9X8NbvytXzZsmXw8PDglzOpT2o1XVMsFsPNzQ3z5s3D7Nmz4ejo+Mh9Tp8+jWPHjiE4OPhxD1uvcNM1c3Nz4eDgwJ8AEolEYKtUKohEIt4Wi8UQi8UGbe7X37CwMPj6+sLS0hIikQhKpZJfMFOlUglsqVTK/xoglUqh0WigVqt5W6PRwMzMzKCtVqvBGIOZmRmKCovRsbUfAIZD1z5DWlk4NJpzsBAXYXT7OEilUqhUKuxNexMaVoKWVsPRp/lY2Fs0rVKTRCLhbTMzsyeqyZBvDPmprKwMERERCAgIgEgkMglN1T33GosmuVyO8PBwBAQE8FNEjF2TsfmpvLwcYWFhvA9MQZMx+QmoCAj9/PxgaWnZ4JoUCgXS0tLg6urK90ckEkEkEvE2UPHLtLbNnTuGbKBi7bKGsvXp4GwA/ALY3HXI2DUZk5+4L8ncFDVtHefOncOqVatw7tw55ObmQiKRoEuXLpg5cybeeOMN/rO7sWkyNj9p+4Drh7Fraix+Ki8vR1paGpydnWFjY2PwWv7gwQM4OTnV63TNWmXXPHr0KIYPH16jfQYMGCBINdxY4da54f5XtrkP8OraUqmU/yVBKpXyJ432Mxn6bJFIxNvch3N1be3+inlbBA+HAejTtiJbZ7Gi4H/HFTPYimNhbVYOqOMRd3cNshUdYCMbiJ6OE+Bi21lH0+PYdaWpOvbj+Ik0kSZT11QTHcaiyVj8xH0OcG02Bk3clxvuuBzaNrdd29ber7Jd1X5PwjakQywW84Gevj4bq6aq9m1smrSprMPb27taC8I3Nk3G5idtTEVTY/OTvmu8oWt2fVGr6Zo1DfCIxoeNub3gdRPrj5FVPgj5SltIxRq0srgBW9E2pOaMw+8pLzRQLwmCIAiCIAiCqC51sk4eUDH/NDs7m58PWxlnZ+e6OhRRT0hEZnjWbgyetRsDjUaD68UX8F9RKDSaeLSQ3YWIteJvXRcrCnAgfQFaWA+Fd/MJsJM1rfXxGTOcmZTDysrC4K9RBEEQBEEQBEHUQZB34cIFLFu2DDExMfxDjpURiUT8swhE44QxhjG+83Eu7rLe7XZtmkNkdhG7W7+OfyJ+RPzDf9BKdg5QnUPcna/wUNEeVubPwav582hv27XOj8/Rt38P/BPxIwV6BEEQBEEQBGGAWgV5Fy9exKBBg2BmZgY/Pz8cPHgQnp6eaNmyJRISEvDw4UP4+PjAxcWlrvpL1AGlJWV6y6oKsArvVDzUH38zCaWlcrSyegaJD4dAhkQ4mBeilUUagDSk5fyO+KymcLH/GN5OgdXvU6n8kQEeAMSfqTi+tbVltdsmCIIgqqYWOdgIgiCI/6cxXUtrFeR99tlnAICzZ8+iS5cuEIvFmDBhAoKCglBWVoZ3330X+/btw6+//lonnSXqhm4dRle5/UraIVhVCqJKS8oE+3Vx8EYXB29oNBr8V5iAqzkHUK46g+bmt+FonoumFm34umfvH0KB4g76tHgeDrLmj+xfdY5PEARB1B7u4X+lUglLS/rxjCAIojZwybWeRGKVR1GrIO/UqVMYO3YsunTpwpdxEaylpSW+//57xMbGYtmyZfjjjz9q11OiVlhZWaBv/x6IP5NUZb2+/XvAsblDtadDisVidG7SG52b9AYAZMuzcDE7DO72Pfk61/O2oLXFvzh/92s8KHeGpflz6NFsAjrae+rvq7Ul3akjCIJ4AkilUshkMhQUFMDW1pamwhMEQTwmjDEUFBRAJpMJsiw3FLUK8goKCtChQwf+tVQqFSxyKRaL4ePjg127dtXmMEQdIBKJ8E/Ej3WS2ETfdE+OZlYtMaLtbEGZVNIBOYosNDPPQyuLmwBu4lbeLiTed4AKvTGl47fV1kEQBEHULY6Ojrhz5w5u374Ne3t7wTI/pohGo4FCoYBcLhekSyeeDDT+DQ/5oG5hjEGpVKKgoADFxcVo06bNo3d6AtQqyGvRogXy8vL41y1btsT169cFdeRyOb9iPdGwiESiOrlDVtW0SX2JUaa4rwMA3Mi/hIQHf0GpiUML2S00M8/DPfkVlJWV84Fjz2ky5JU/gLU1PcdJEATxJOAW4s3OzsadO3cauDf1D2MMZWVlsLS0NOlgtrFC49/wkA/qB5lMhjZt2tTb4uY1pVZBnoeHB1JSUvjXAwYMQEhICM6cOYP+/fsjOTkZe/fuRefOnWvdUaJhqe50T0OJURhjePuFH/jkKlaOjnh2ijkUZcA7v1ast2jjJMZ7y7KQXOCPmIdtITPrj27NxsPdzqt+RD1haIkIgiAaK3Z2drCzs4NSqYRarW7o7tQrSqUSMTExGDx4cKOYUvW0QePf8JAP6h6JRNLoxrJWQV5gYCDeeecdZGVloVWrVvjggw/w999/Y+DAgWjatCny8vKg0WiwbNmyuuov0UA8arrnoxKjVM6eWZqtwamNwrYcnzHDgxJbtLIpREtZJoBM3MnfiysP7FGm8USHoeZIi9S/TEdjh5aIIAjCGJBKpY3ui0pdI5FIoFKpYGFhYfJaGyM0/g0P+eDpoFZB3vz58zF58mQ4ODgAADw9PXH8+HF8/vnnSEtLQ69evfDWW28hMLD6qfSJxktdTffUlz2Tw8rKAhlF/+Ji9t8oVZxGc/MMOJgXwAExeGZAW6RFVtTLK38INVPC0aJ1rfvzJKAlIgiCIAiCIIgnRa2CPKlUCicnJ0HZc889h8OHD9eqU4Rp86jsma52XeFqV7GgemF5LuIfHkBW0VGc//M2Xyfqzs+wFf2Oh4rWMDPrh64O49C5SZ96fYC4rqZb0hIRBEEQBEEQRH1SqyCPIOobO1lTjGg7GyUlU/FeynC+vFR5A/YyBifZHQB/4V7hX7iWY4syTU842/mhb4uxkEks6qwfdTndkpaIIAiCIAiCIOoTyptqAO7Bc7VarddWqVQCW6PRVGkrlUqBza0nyNlc+lVtG4DA1mg0AlulUlVpq9VqgV2fmlT/3y/O1qeJ43E0qf/fBoCiwmKMb/sjWlnsRq7iZdwtc4NSI0YTaRFayU6irCwYJeWFfH9LFSW8zfexhn4qLCiq9nTLkuJSvZq0qewnbX0aEz/3OExJk7H5SdsHpqLJmPzEtWlKmozNT/r0GbsmY/IThylpMiY/aX8Wm4omY/RTfVOjIO+VV155rL85c+bUV//rDS8vL3h4eCAoKAjJyckAgKSkJH6JiMTERKSnpwMA4uPjkZmZCQCIjY1FVlYWACAmJgbZ2dkAgBMnTqCgoAAAEBkZiaKiIgBAaGgo5HI5VCoVQkNDoVKpIJfLERoaCgAoKipCREQEACA/Px8nTpwAUJHqOiYmBgCQlZWF2NhYAEBmZibi4+MBAOnp6UhMTAQAXL9+HUlJFZkxk5OT60xTfn4+AOD48eP82IWHh+toKpf/b5pjcXFxjTWdP3+e39/TfRw6OA2HT8cFWPDsEbzbJx9zBzXDN1+3wbkbLrhd3AkPbuXymkJSR2FXig92XH0bORZp0Gg01dIUERHB+yk8PJw//lc/vIn/bofh34xD+G7LO0i7fxxxSbv57fkFBXo1aVPZT5cuXeK3Xblypd78pK2pIc497Wy8pqLJ2PyknSLfVDQZm5+4Nk1Jk7H5CQCOHj1qUpqMxU/ccUxJk7H5Sfs7h6loMhY/LVu2DB4eHvD29kZ9I2Lat1gegaHnnUQiEfQ1w5WLRCKjSclcWFgIe3t75ObmwsHBge+3RCIR2CqVCiKRiLfFYjHEYrFBm/vFICwsDL6+vvzaJEqlEmZmFbNmVSqVwJZKpWCM8bZGo4FareZtjUYDMzMzg7ZarQZjjLf16aitJolEArFYjIL8QjzTdiQA4OJ/IbC1swEAqFUqSMzMUFoqR/f/f+Ys9d4xyGTSGmlSq9V4PuCtRy7hAABJN/6BlZUFxBIJHpTcQmrJWIi1Zk8WKG1Qou6OtjZ+8G45FlJY6NXE+UYkEgn0/Xc7DHb2tgI/FReXwq3liAp9WUchszAXaCovV6KDU8V007T7x2FpKRP4qaS4FO5t/AEAN+5GwNbOpl78pK2pIc49uVyO8PBwBAQE8NcTY9dkbH4qLy9HWFgY7wNT0GRMfgIqvkT4+fnB0tLSJDQZm58YYzhy5Aj/WWwKmozJT2VlZYiIiEBAQAC/n7FrMjY/aX8Wi0Qik9BkbH568OABnJycUFBQUG/r6tXomTwuQubQaDRYtGgR4uLisGjRIgwaNAhOTk64f/8+YmJi8O2336J///74+uuv67TTTwKJRCL4X9nmnF5dWyqV8rd0pVIp/8yWdupafbZIJOJt7sSsrm2o73WpiS/Xsns+Mx5V8biaqruEQ4+OYwXbmro5ofdUMzw7oBxd2uTBXloMe+kZaFRn8FfqAbzcaY9eTYb0menxn/YzeCKxWEdfefn/pmlV1ieRSCDRGldxPZx7j2Mby7lHmmquqSY6jEWTsfiJ+xzg2jQFTdXV0Vg0VfVZbKyaqrJJE2mqrT5j0GTMfqovahTkubi4CF5/8cUXOHv2LC5duoRWrVrx5Z06dcLgwYMxe/ZseHl5Yd++fXj//ffrpsdEo6S6i6X37d8DVlaPlxClqiUcqjp+bqoKEZ+rEAFAauOAH6NmIp+dho3ZZdiZ9UdJSRkA4L/CC0gpXAaRuA86OYxGj6ZDBF+ICYIgCIIgCMIYqFV2zS1btmDy5MmCAE+bNm3aYPLkydi8eTMFeSbOoxZL56jOEgP1cXzuTp+ymOHV3tv+v1QGsXQvNMq9AIDnv7TApFH3ARxCbvEh/JNnhWJNd7S2Ho7utqPqvM8EQRAEQRAEUR/UKsi7ffs2LCyqvitjYWGB27dvV1mHMA3qarH0+ji+lZUF+nh3x7mz2hkyRdBozaI8/r0ShTltMHKiLRzNb8BOWgo7nAVUZ5GQswbufk64HlFevyIIgiAIgiAIopbUKshr27Yt/v77b3z22Wd6g73S0v9j787jo6rv/Y+/zizZF/awqYCKigoCAqLihoAiLqitW7W1alutttUu19qWWm29t/31dnVrvdXa2ip1Q8QIQYKmEjDsARswQtQAISGE7JnJLOf3R5zDTEhCkpnJZIb38/HgwTczZ875vuczyeSbc+b7beb1119n9OjR4RxGJGyGYfDa239kyetvMG/evJDP2MHhM33v/I+PP9//FvZkk6IDb1LekEeqsYU0u4tP1rRa27++++e4fQc5ecAVnDX4oj5OIyIiIiLSubA+cHTnnXeye/duzjvvPN544w0OHjwIwMGDB1myZAnnn38+n3zyCXfddVdEOisSDsMwSE5JshYjD/6XFnQGsLmpBZ/bYGrWVVwz6nHmDC9gpOOveJoOzyDr9S5jmDOXuqZv8tbus3n1szuY+bUUkgdE/lJUEREREZGeCOtM3ve//30++ugjnnvuOa699lqgbRaZwOKCpmly++238/3vfz/8nor0kcAsnZ3x+/2kJd/EPtd7DHJ8RIajhQzHRr71LfDca+PVz77Cbact7nIfIiIiIiLREtYgz2az8Ze//IXbbruN559/nuLiYurq6sjOzmbSpEnceuutXHTRRRHqqkj09GR20IyMNK7I/DbwbVy+ZjYeeJuy2rdx+jcyNK0ZODwtrs/v5ZXdDzIq+QIMu4np05k+EREREYmuHg/ynnzySa6++mpGjRpl3XbhhRdy4YUXRrRjIn2pt7ODptjTOG/4dZyVOZ9xOZcwenoWz/3zW9b9Ww6+y1DHm7T63uRPG5LY9vFA3q38K+eP+gLZyUOilkdEREREjl09/kzevffey/HHH8+0adP4xS9+wbZt247+IJE4EJids6t/XS//YLCnyMOJmZOsW5JsKexzT6TF5yTT2cq5p1WSavsdH+ydxT92zOODyreiH0xEREREjik9HuStX7+ehx56iNbWVn7yk59w1llnceKJJ/Ld736X9957z/o8nojAmYPP50unvMx5Q//NYz8dyYp1I6hpHYDD5mdEyieYHP5+OW5GEhOuScbja+1ij9FjmiZNTS1d/jNN8+g7EhEREZGY6vHlmlOnTmXq1Kk8+uijfPLJJ7z++ussXbqUP/zhD/zud79j0KBBLFiwgKuvvpq5c+eSlpYWjX6LxBWnPYVtr7rZ9iosqnyXvd4Sth9cxqwhc61tLrvX5JLJFbxfdT61FacxJPVipg27lsEpOVHvn2maXDnnG6xf1/WZ+ekzJ7I076moLGgvIiIiIpER1sQrY8aM4f777+f++++npqaGZcuWsWTJEl555RWef/55UlJSmD17NgsXLmTBggUMGzYsUv0WiWvjs6cwPntKyG1ul0GjJ4kMZytpjq3g38qmit9zoHU0yY5zWTDmIZLtR65HGWCaZo8/UxjQ3Ow66gAPoGhtMc3Nrpguei8iIiIiXQtrkBds0KBB3Hbbbdx222243W5WrlzJG2+8wbJly3jrrbew2Wycc845vP/++5E6pEhC+dtdrfzdnsHL2/+LPS3vYGcjQ5JqGJ5cTq0nF6fxsLVtyaEPGJc1yRr0RfJM3Pbdy0LWDYTDi8WLiIiISP8XsUFesOTkZBYsWMCCBQswTZN169axZMkSli5dGo3DiSQM02cwZdA8ZqVfA8Du+m1srV6C3eHEZmv7CK3b52J3zR3sroFD3lMZlHIhZ2QsiNiZuMBi8SIiIiISn6IyyAtmGAYzZ85k5syZ/PKXv4z24UQSyrisMxmXdWbIbWUN2/GbdtIdLlLt28Dcxkf1T/DI6gFs2ZDGHZf/jNOGnh3yGJ2JExERETl2RH2QJ3KsaW5q6dZtvXXqgLM5KWsDm6vz2VX3Fjb/RoYkV3Py0EOcfPkhPnK/xtnpswBw+ZrBhDR0Zk5ERETkWBHWIO+rX/3qUbex2WxkZWVxyimnsGDBgpBF1EUSUV+cMXPYnEwbNo9pw+YBsKNqC//z/HeZPLOFmWOvsLYrqPgHXvfvOdh6Epd8P5WN/4rN8gwiIiIi0nfCGuT99a9/tSZw6Gj9LMMwQm6/7777WLRoET/+8Y/DOaxIv5OWlsL0mRMpWlvc5XbTZ04kLa3zGTJ767j0U3j7Zy28DeyuPM+6/UDzWkYkeRiVWsJdX4Y7boM3yi/H6ZjBaQOvYsLAmdZn/UREREQkMYQ1yNu1axff+c53KCoq4tvf/jbnnXceOTk5VFZWsmbNGv7whz8wffp0fvSjH7F161Z+/vOf89Of/pSTTz6ZG264IVIZRGLOMAyW5j3V6yUMouXGk/5M8cECSmreoLWlkOMH1DM8pQJYQlXDEjKTlnBC5ml91h8RERERib6w/oS/ePFiPvjgA7Zs2cKDDz7IrFmzGD9+PLNmzeLBBx9k06ZNrFu3jtWrV3PnnXeyZs0aMjIyePLJJyPV/6jx+XzW/x21vV5vSNvv93fZ9ng8Ie3AGc5A2zTNI9pASNvv94e0vV5vl22fzxfSViavVd9oZPJ6vaSnp5KamkxSksNqJyc7rXagL+0z+YL65u9lnYIFbrfbHJwxYBZXjXyM/zrfyb0Lczjk+TL7XOOpcudwfMapbX33eLj/dTv/9ZbBir2/Y19TWWi+oGOYYdYp4Fh77fWnTME1SJRM8VSnwD4TKVO81amjfPGeKZ7qFJBImeKpTsHvxYmSKR7rFG1hDfL+8pe/8MUvfpGcnJwO7x8+fDhf+MIXeOaZZwAYNWoUCxYsYOvWreEctk9MnjyZCRMmsGjRIkpKSgAoLi6mtLQUgM2bN1NWVgZAUVER5eXlABQWFlJRUQFAQUEB1dXVAOTn51NXVwfA6tWraWhoACA3NxeXy4XX6yU3Nxev14vL5SI3NxeAhoYG8vLyAKitrSU/Px+A6upqCgoKAKioqKCwsBCA8vJyioqKACgrK2Pz5s0AlJaWUlzcdilhSUlJxDLV1tYCkJeX1+8zbdq0yapvf8sU/D2xffv2XtUp2BGZPn/tHSz1cl7mnXzp1DdJ3vEdK9OylW9w1okHOeuEagYk/ZUdBy7jpZ0X8symr7G1+l1qDh2y9l198GCv67Rz505rP8fSa68/Zdq7d69Vg0TJFG91CuwzkTLFW50AVq5cmVCZ4qVOgeMkUqZ4q1Pw7xyJkile6vTQQw8xYcIEZsyYQdSZYUhJSTG/973vdbnN9773PTMlJcX6+sEHHzSTk5PDOWxU1dXVmYBZU1NjmqZper1e0+v1HtH2eDwhbZ/P12W7tbXVdLlc5pIlS8ympibT7/dbt/v9ftPv9x/RNk0zpO3z+ULaHo+ny7bX6w1pd5Qj3EzB7f6eqbm52VyyZInZ2tra7zLV1dabw9JnmsPSZ5r1dQ09rlNjY7P1+MbG5iMyNdQ3Wvc3NDQdkanm4CHznAUXmvf+38XmP0tmmavKxof8+9t/rjr8+PrGXteppaXFqsGx9NrrT5kCP4cCNUiETPFUp9bWVnPJkiVmc3NzwmSKtzq53e6Q9+JEyBRPdQp+L06UTPFWp+D34kTJFG91qqysNAGzrq7OjJawPpM3atQolixZwqOPPkpKypGTSbhcLpYsWRIyo2ZVVRUDBw4M57B9wm63h/zfvu1wOHrUdjqd1ildp9NpfS7L6XSGbNO+bRiG1bbZbNYkGd1pd9b3SGbqTVuZjsxkD+qDrZevvWDtM7W2Hr48pqXZZb3+Ard7vX52r25l92r4n8pV1LGfjQdeo85VwGDnxyTZTwfeBaC6tYL3991PZvIspg65ltEZJ3WYqaO+x3udEiVTT3LES6Z4qVPgfSCwz0TI1N0c/SVTV+/F8Zqpq7YyKVO4+eIhUzzXKVrCGuTdcccd/OhHP+L8889n0aJFnHfeeQwePJiDBw+yZs0aHnnkEXbv3s2jjz5qPebf//43kyZNCrvjItI73VniYWT6WEamfxf4Li3eRmob6wkM8rYeWsrI5I+Aj/io+i+s2TsM7NM4OfsKJg2+ELtNy2+KiIiIxFJYv4394Ac/oKSkhBdeeIGFCxcCbSPWwAcZTdPk5ptv5sEHHwSgsrKSK664gssuuyzMboskrq4WTu/t7JzhLPGQ6sjA7zz8F6eTMmfxn4ZKvL4ihibtY2hyFfAWtU1vsawulRGZjzE9Z36P+ygiIiIikRHWIM9ut/O3v/2Nr3zlK/z973+nuLiY+vp6srKymDRpErfccguzZ8+2ts/JyeG3v/1t2J0WSWRdnWmbPnMiS/Oe6vFAL5JLPIzJOIPTc6YBsL/5MzYeeJ1DrvcY5PiIdHsLx2Wcbm27as9zNLTuY/LQa7VUg4iIiEgfich1VZdccgmXXHJJp/f7fL4+ufZUJF5190xb0dpimptdpKen9vgYhmH06nFdGZ52PFec8G3g27h8zWw/+G9GpJ9g3V/Z9CLDkz9l18G/8UHFYLxMpTltNF7/HJw4O99xB0zT7HKQ2tdrEIqIiIj0V2EN8p544gm++c1vdrmNz+fjxhtv5OWXXw7nUCIJ7Whn2pqbWqwzfB1dztnVJZ6R1vmxDKYOnRtyS7LzXPa7fQxL2suQpINAHpwMK8v/SYNvMrec8tduHdM0Ta6c8w3Wr9vW6Ta9PcspIiIikmjCGuR961vfIicnh+uvv77D+/1+PzfeeCOvvfZaOIcROSZ090xbdyZOiaaeXE563biHAah27WN95etUt+QzyLmTDIeLBu+BkMfmfvpHTh14EeOyzjxiv83Nri4HeBDeWU4RERGRRBLWIO/888/n1ltvZfDgwVx88cUh9/n9fm666SZeffVV7r333rA6KXKsC2filL48ftHaYqoPHCKt3UArlYFcMOSrOJ13seztN8k6G05OPbzNpw07SDEf55Oax9m4fxBepjAu+3KmDJmD054csq/tu5eF7D/4LKeIiIiIhDnIe/PNN5k1axYLFy7k3Xff5ayzzgLaBni33HILL7/8Mvfccw9/+MMfItFXkWNWJCdOicbxgwdaXQ24Tj/zJL7+nSuZmD4Ph9NJ0+eXflY17KPCdTzDkssZnFQDvENTyzusKEum1ncaY9NvtfaRlp6qs3UiIiIiXQhrkJeVlcWKFSuYOXMml19+OWvWrGHs2LHcfPPN/Otf/+Ib3/gGjz/+eKT6KnJMi8bEKZE6fnfP9H247WO+dcdvgY5n2T33svP5+m+mUt3yLgMc/yHN4SbNsYXa1ousbfa3fIrf6+Lk7LN6mUREREQksYU9u+bw4cNZsWIF559/PnPnzmXy5Mm8+uqr3HXXXTz55JOR6KOI9HNHPdNomlw19262F5d2uZ/C5f/hhef/SHr63Xh8bjZVr2R3XS4XDrsS+DsARQefISd5GVsrB9DKFEamzMaeDD53hEOJiIiIxKmILKEwfvx4cnNzueSSS3jttde48847+dOf/hSJXYtInDjamcZ31vyV+roGVqxYwbx5bZdrBnT0uTqnPZkZOQuYkbPAuqwTwGvW4jMNBiXVAvn4/fn8+QMn28sGkr//z8w54S5SHGmRjiciIiISN3o0yHvkkUe6vH/69Ols2bKFkSNHhmxrGAY/+clPetdDEUkIhmGQlp5KckoSaempOJ09Wycv4NrjHqfV0cj6A0uoason2/4f0h0upp9chcv3DBh3WdseaNnD4OSR2Gy2sPp+tDX6oOvPQ4b7eBEREZGe6NEg7+GHH+7Wdu0HgxrkiUgkDUweytzRdwF3UddQz1V3Xs3ZV8KUs08jxd52Fs/v91NQvhAAF2dxQuY8zh4237q/u7qzRh90vk5fuI8XERER6akeDfJWr14drX6IiPSKw+Zkx1tudrwFj1T+r3X73uaPyXQ04rD5gQJa3QXkf/IwNd7xDEy5kGlDr2NY2uij7r87a/RB5+v0hft4ERERkZ7q0SDvwgsv5Mknn+Tqq69m1KhR0eqTiEjYjssYT3ZSIeurllDRtIos+3YyHC2MtH8I5oesLN/ELac8D7Sd9QOOelln+zX6oGfr9IX7eBEREZHu6PHEK/feey/33XcfU6ZM4ZprruGqq67izDPPjEbfRETCkpU0kNmjbwdux+f3suXgu5TWvoXh38CojLnWdhsOLGdP/U9oMSdxXOY8pg+9ssPJW8Jdo09r/ImIiEhf6PEgb/369SxZsoSlS5fyk5/8hEWLFjFmzBhrwDdr1qywJzkQEYk0u83B1KGXMnXopUfct7v+bYY5GxnAGrzuNaz+9BEOek5mQMosJmRcGYPeioiIiPRej0djU6dO5dFHH2Xr1q3s3r2bX//61xx//PH84Q9/4JJLLiEnJ4fbb7+dJUuW0NzcHI0+i0gCa25qoandv+agJRSiYcEJPwfnj6lwn0O9J41ku5eRKSWk8Wc+abySMbOSonp8ERERkUgKa528MWPGcP/993P//fdTU1PDsmXLWLJkCa+88grPP/88KSkpzJ49m4ULF7JgwQKGDRsWqX6LSIKKxefTMpKyuWTUrcCt+P1+ttUUsOPQm5j+9aTa6/l0jRtom/XypdLv4PPXMipjDtOGXUWElhsVERERiZiI/XYyaNAgbrvtNm677TbcbjcrV67kjTfeYNmyZbz11lvYbDbOOecc3n///UgdUkQSRFpaCtNnTqRobXGX202fOZG0tJSo9sVmszFpyEVMGnIRABW1ezH9XwDA5/eRyntkJjfj96zl3+W/4IB7HHN/lMKGl7xhH7urM5ZaR09ERES6Kyp/gk5OTmbBggUsWLAA0zRZt26d9Tk+EZH2DMNgad5T/XLB8CznIKtt4mdA+g/Y07CSNNtWsp2NjEot5fab4Pab4LXPbubW017v9bG6OovZ1Tp6WmxdREREgvV4kOd2u0lOTu729oZhMHPmTGbOnMkvf/nLnh5ORI4RhmH0+5knHTYnF468CbgJv9/Pfw6tZVv16zQ1/psxg2oxjMOXpLu8zby6624GJ11AcrYNd52/w3129yxmZ+vo9XSxdREREUl8PR7kjRgxgptvvpmvfvWrTJkyJRp9EhHpU12dCevsEkqbzcYZg89jbMoUxuXMJvuEYbz93nes+9cfWMaI5HXAOp75t40d+wayfN+vOXv4Fzgh81Rru6OdxTzaOno9XWw9KUmfIRQREUl0PX63d7lcPPnkkzz11FNMmjSJO+64g1tuuYUBAwZEoXsiItHV3TNhR1P3qY/hqWOsr4eknMDmhgtIZjMDkxo4c/RB4AV2HXyBD/YN4bgB/8XMnKuAyJ3F1GLrIiIiAr1YQqGyspKnnnqKs88+my1btvCtb32LkSNHcvPNN7Nq1apo9FFEJGq6eyasp5O+nDZwBjePf4a5I97jv742nJffHsV+1yj8psGQ5GqynEOsbTdUrWDlnr9Q767pVYaAwGLrwf/aD/pEREQk8fX4TF5mZiZf//rX+frXv85//vMfnn32Wf7xj3/w0ksvsXjxYo4//ni++tWv8pWvfIXjjjsuGn0WEYmKjs6EBfR24hKbzcZnhR4+K/Tw68pVNNsOsqn6LU4bcI61zY6a/2NkSjHr9v6aA60nkJZ0HmcNuZaxWaf3OouIiIgcu3p8Ji/YhAkT+PWvf82ePXt47bXXuOKKK9i7dy8//elPGTt2LJdffjmvvPIKHo8nUv0VEelURwupB/6ZpnnUx3d0JizwL1IzUw5NHc28476OzXb4x6/TfjyHWrNx2PyMSCkj2/YCZTXX8q+PZrKk/HsYtqP3PZpM0+z0ee3J8ysiIiJ9IyKfwLfb7VxzzTVcc801VFZW8re//Y3nnnuOFStWkJeXx+DBg6mqqorEoUREOtXbJQhi7YaT/xe/309p/Wa2H1yC27uWoUl7GJJUQ5V7K6b/cJ/XVi7l1AEzGZg8tE/61tPZO/vj8ysiInKsifg0azk5OXz/+9/nsssu45577mHNmjUcPHgw0ocREQHCX4Kgv7DZbJwyYCqnDJgKQLWrgg1VS8hy2oDnAaj31NDQ9F9saDapch9PStK5nJJ+RdjHPtrsoj2ZvbO/Pr8iIiLHkogO8hoaGvjnP//JX/7yFzZu3IhpmqSnp/PFL34xkocREbGEuwRBfzUkZQSXHX83TU0tBAZ5FS27qPNmMzjpECNSPgU+parlRX67LoOt2zP5T10h09Jn9+g4PZldVLN3ioiIxIeIDPJWr17Ns88+y+uvv05LS9tnM8455xzuuOMObrjhBjIyMiJxGBGRDnV3CYKO1rzrbB28/uiUrGlMGbGOj+u2UnzwdVpa1zIs+VOGZzQy/JxGPmksYBptg7x6dw2tpptUBnS5z57MLjpk6EBdjikiIhIHej3I27NnD8899xx//etf+eSTTzBNk6FDh/KNb3yDO+64g9NOOy2S/QzLzp07ueGGG0K+fvHFF7nmmmti1ykR6XOJcsbppOxJnJQ9CYB9tXu497EvM+ViD9dOvdra5r2KZ0kx/48q9ygW/jKF9a/68Pv9Xe43GrOLioiISN/r8SBv8eLFPPvss+Tn5+Pz+bDZbMybN4877riDq6++Gocj4h/zC9spp5zCli1bAGhsbGTMmDHMmTMntp0SkT7R3c/s9XQdvI7E4kxhtnMwa55sYc2TcH/l4T+u1beWkJ5kMiJlD1+8Ar54BeRVXITLnERjxgm4fZfgdDpD9hWYXVRERETiW49HZDfddBMAY8eO5fbbb+crX/kKo0ePjnjHomXp0qXMnj2b9PT0WHdFRPrA0T6zFxCJM1X96UzhLeP/wicNH7Jh/ytUVK1g/IhDDEyqB/6Nf9z7NHhuIyMlEwCbE/xa6UZERCRh9HidvJtuuol33nmHXbt28eMf/7hPBngFBQVceeWVjBw5EsMwWLJkyRHbPPHEE4wZM4aUlBRmzJhBUVFRh/v617/+FXLppogkvsBn9rr619sBXuBM4dFE4kxhT43JPJ3LR/6An8+Dr180iCb3t9nTMoWy2hNI8w2kqamF5qYWfrrcz6Orfby592H+U/PBUS/rjDStwyciIhJZPT6T949//CMa/ehSU1MTkyZN4qtf/SrXXnvtEfcvXryYBx54gKeffpoZM2bwu9/9jnnz5rFz506GDRtmbVdfX09hYSEvvfRSX3ZfRBJYX54pDEdLjcmdU1+wvv4xlwGQPszGn1bVYjdMYAn765ew42AmLeYkjs+cx7ShC0hxpEWtX1qHT0REJPLC/gCd1+vlj3/8Iy+++CI7duygubkZr9cLwJYtW/jzn//Md77zHcaPH9/rY1x++eVcfvnlnd7/m9/8hrvuuovbb78dgKeffpq33nqLZ599lgcffNDa7o033mDu3LmkpBz9r+k1NTV4PIevX0pOTiY5ObnXGQBrf8H7lb6j5z/2ErkGSUld/zgN/FzsKW/Qc+X1ePB4HD263+m0M23Gmaz/oONBVFOVn+98YRjz7h7ClPPcDE3azQBnAwN4H4/7fV7++CVuPHFxr/t3ND1Zh6++rqHTiWHiRSJ/D8QL1SC29PzHnmrQ99xuN2632/q6pqYm6scMa5DX0tLC3LlzKSwsZMiQIWRlZdHU1GTdP3bsWJ577jkGDRrEz3/+87A725HW1lY2btzID3/4Q+s2m83GpZdeytq1a0O2/de//sXXvva1bu137NixIV/fcMMN1ucRw7Vy5cqI7Ed6R89/7KkG3ed2tVrtFStWkJyS1KP7Ae6873Ju+9qlXR4nKdmJsdOg3tbCwYztkPUhw7PKaDhwHLk7cwGoS6rAdvwL1NWfTGr9RAa5xtDqOjx47ez43c336yfvJTk5dDIYt9vD9+55vNf776/0PRB7qkFs6fmPPdWg77z44ossXtz5H0yjIaxB3mOPPcaaNWv4n//5H77//e/zs5/9jEcffdS6Pzs7mwsvvJAVK1ZEbZBXXV2Nz+cjJycn5PacnBx27NhhfV1XV0dRURGvvvpqt/ZbVlZGZmam9XWkzuStXLmSOXPmHDGrnUSfnv/YUw16rm12zt8CMG/evA4XI+/q/va6V4PrAPD5fXiP85Bsb7v6Yemnv2Cg4wAj0w/AiELqPem4PGcw9bYUil9zd+v4XeW76qoFHeYLDPI6279pmrQc5XLZ1H6yBIS+B2JPNYgtPf+xpxr0vdmzZ/PEE09YX9fU1IR1lWN3hDXIW7x4MRdffDE/+MEPADp8Ax03bhybN28O5zARkZ2dTWVlZbe3HzRoEFlZWVHpi9Pp1DdVDOn5jz3VoPscTm9Q+8jn7Wj3d6Y7NXDiBA5f3j5zxJfZcCCdete/GZL0MVnOJrKcH/C9H0Drd+1sa3iP8wdc2a3jd7f/R7s/Xj/Tp++B2FMNYkvPf+ypBn3H6XSSkZHRp8cMa5D32WefsXDhwi63yczMpK6uLpzDdGnIkCHY7fYjBnCVlZUMHz48ascVETnWjEwfy1Xp3we+T5OngfUH3qS8Po80tpCR5OakzLOtbZd+8v9o8VZwyoArOXPQLOy26Kyh2tzs6vZn+pqbXVoHUEREjglhvetmZmZSVVXV5Ta7du1i6NCh4RymS0lJSUydOpVVq1ZxzTXXAOD3+1m1ahX33ntv1I4rInIsS3dmctHIm2nKXsi4nEvImZjN2pUDrPtb3G8wNPkANY1vsexQGg3+MxmZPpvpw64hIyk7Kn3avntZh5d79qf1C0VERPpCWIO8c845hzfffJPa2loGDBhwxP3l5eXk5uYe9Wzf0TQ2NvLxxx9bX5eVlbFlyxYGDRrE8ccfzwMPPMCXv/xlzj77bKZPn87vfvc7mpqarNk2RUQkmgwqiw/P0ub3+0lPuY59LQUMdn5EprOZTD4A7wes2fNLDnjO5EunRP4D6Gmfr3koIiJyrOvxYujBvv/973Po0CFmz57NmjVrrCnCm5ubWbVqFfPmzcPr9fLAAw+E1ckNGzYwefJkJk+eDMADDzzA5MmTWbRoEdA28+Wvf/1rFi1axFlnncWWLVtYvnz5EZOx9ITP57P+76jt9XpD2oHFgztrezyekHZgYd9A2zTNI9pASNvv94e0A893Z22fzxfSVqbDn+1JpEzxVqeARMoUzTq1X6Kgo0wBZjczBdcgEpkCmptaqK9roLmphYuHfIMrhz/PtMHvYho/ZZ/7PGo9GThtPiuDaZq0ut1c9+sUTrk8GbfbdUSdzKBjmJ1kCtY+kz/oNefvJ99PgX0erU6xfu0l4vdTV/niPVM81SkgkTLFU52C34sTJVM81inawhrkXXDBBTz++ONs27aNCy64gMceewxou4xz7ty5fPzxxzz55JNMnTo1rE5edNFFIb8UBP799a9/tba59957+fTTT3G73XzwwQfMmDEjrGNOnjyZCRMmsGjRIkpKSgAoLi6mtLQUgM2bN1NWVgZAUVER5eXlABQWFlJRUQFAQUEB1dXVAOTn51ufTVy9ejUNDQ0A5Obm4nK58Hq95Obm4vV6cblc5Oa2TVne0NBAXl4eALW1teTn5wNts4oWFBQAUFFRQWFhIdB29rSoqAhoO+MZmPSmtLSU4uJiAEpKSiKWqba2FoC8vLx+n2nTpk1WfRMlU7zVaefOnVYNEiVTtOu0YsUK6zlbsWLFEZkaGxut+2vr6o6aae/evdb2kcoUcMa4BZw8ah4njpjDuJzZjB99GaePvpqbT3+cp25u5OIh7zA86294PznfqtPLa/6P6y/by8P/bx//PngRL+y8hdzdf+LNlUusTAHVBw92mClY+0zbt2+37tvx+esv1q+9wD67qlN/eO0l4vdTIBO0TR+fSJnipU7B37eJkine6rR161arBomSKV7q9NBDDzFhwoSwxyndYZjBfwbupZKSEp5++mk++OADampqyMrKYsaMGdxzzz2cfvrpkehnn6mvryc7O5uamhoGDhxojbTtdntI2+v1YhiG1bbZbNhstk7bgb8YLF++nDlz5pCamophGHg8HhyOtqtmvV5vSNvpdGKaptX2+/34fD6r7ff7cTgcnbZ9vra/mAfaHeUIN5PdbrfaDoejX2dqaWkhLy+P+fPnYxhGQmSKtzq5XC5WrFjB/PnzsdlsCZEp2nWqq61n/OjLAPhoz3KysjNDMjU2NnPi8LY18HZVrCQ5JanLTG63m+XLl1s1CDeTYRjdmt0SYNf+d8jISAup04Y9q1j90cOcfsIhUh1BZ9v9Ng60jmF06p188fT/tfKlpCaHZHK7PYzLmQ3A7spVpKQkhWRqbmrhpJFzAfh4Xx6ZWRkx/X6Ctl8i5s6dS2pqar9+7SXi91PgL/Nvv/229V6cCJniqU7B78WBx8V7pnirU/B7cWe/D8VbpnirU1VVFTk5OdTV1UVtNv+ITHd22mmn8fvf/77T+30+H3a7PRKH6jOB/gb3O7gdKHp3206n0zql63Q6rWm8g6eu7ahtGIbVDrwwu9vurO+RzNSbtjIpkzJ1P5MjqN3a6qU5aD241lZvyPpwhs3WrXw9ydGdTG+ufDqkX8GCJz7p6OfehEHnseBqP/bkbF7Y/C32u/JJZjMDk+oZkbIbH25r27Lm7bhaapg85FIcNic2mw232xNyvPZ9twV9bevg53p3amO329vlOzwYbW1ta6cFrcPX1Wsv8D4Q2H9/fu0l4vfT0d6L4zVTV21lUqZw88VDpniuU7SENch74okn+OY3v9nlNj6fjxtvvJGXX345nEOJiBzz+usskYZhhD3hic8NMwZfTXr6jfj9fj6q38SHB9/goqHzgLbF0Lce+gsjU97j7d0p1PkmMCztEs7IvDwCCToXr+vwiYjIsS2sz+R961vf4pVXXun0fr/fz4033shrr70WzmFERI5ZaWkpTJ858ajbTZ85kbS0lKNuFw9sNhunDjib6058lBR7WtA9dlp8SaQ7XIxM3oTD92s+PHQpi1aaXPmLFNy+js8mhqOn6/CJiIj0B2GdyTv//PO59dZbGTx4MBdffHHIfX6/n5tuuolXX301Lteraz8DUSSu/Q2exUfXM8cmU0AiZYrHOrX/3kqETNGqk9fr5Y0VT9Lc7MLn9WL/PJPP68XxeSaf10tWduYR+TrLFFyDaGcKmR0zaPYyqzbtZg+F1JAcwY+/ZtSvMZJMimvzKat7myQ2MSipltNG1DDishScRpKVaXdTMcenntbp7JrdzRRs++5lJCU5sNvtGDYbXo8Hd6uXMz8/wxqYFOxon8kLvr0/v/YS8fup/Sx6iZIp3uoUkEiZ4qlOwe/FiZIpXusUTWGdyXvzzTcZP348CxcuZMuWLdbtfr+fW265hZdffpl77rmHP/zhD+H2s89pds3+OzORZteM3zppds3eZXK73SQnO3n3vdUkJzux2w3efW816emp+P1e3l/zbwzD6FamaMyu2WWmoNkxAzOBBtep/eyhR9Sp3eyaH6wpYmbOVVyY/mOG7f9/DE39G/98YxS5bw/GZrNRVlbGhk0b2HngdlaVT+dfZTdzwbdTycix9Xp2zYC09FTWfbCWVo+b9PRU3l/zb/z+w7+wujW7ZtxkAs2uqdk1j906aXZNza7ZLfv372fmzJm4XC7WrFnD2LFjuemmm/jXv/7FN77xDZ588slI9bVPaHbN+PsrSHczaXbN2GfS7JqxzxTp2TWPlsntauXEEXOAjmfXrK9rCJk9NHtAVkimxoamw4/v5uyau+qK2VnzZdIdhydt8ZkGVe7RpDhnMnHQdYzLOrNbmVpa3CH7D5zJs/K5PYdnN93/DunpqUc9k6fZNWN/Jk+za2p2zWO5TppdM/aZ4mJ2zeHDh7NixQrOP/985s6dy+TJk3n11Ve566674m6AF8xu1+ya3cnUm7YyKZMyxTZTT3KEmykw+2Sgr+1zODpoB2cygvpr2GzW/gOZOppdc/ygyYwbsJFNB1ZSemgZZmsRI7MaGJFSDpSzobqWkwf+EQCf0dY/O/ZOcwRrX4P2+YL7Hry9ZtfsP5k0u6YyHeuZepovHjLFc52iJexBHsD48ePJzc3lkksu4bXXXuPOO+/kT3/6UyR2LSIi0mMOm5PpOfM5PeNixuXMJmficH7y10to9a/jlEGHZykt3P8KLa7/ocZzCoNSL2J6znUMSRkZw56LiIiEr0eDvEceeaTL+6dPn86WLVsYOXJkyLaGYfCTn/ykdz0UEREJU2Wxh4sG3Eda+g8AaGpqAWBvw3uMTPEw0r4dzO1sqXiCqtZROB0zOGPQ1Yx2nNHtYzR/vs+ueD+/hLW5qQWH8/BZwOB19kRERMLVo0Heww8/3K3t2g8GNcgTEZFY62idQcNmMv6ykVx0UzpnTDjEkORqhifvAfZQUfcq3tS/hbX/zv025CutsyciIpHUo0He6tWro9UPERGRiAusM1i0trjD+02/wc5cNztz3eyuXEW1/1O2VL9Oo/t9nLZDTBt8+Ezea5/dgWG0MCD1As4eeh3D044/6v67K7DOXriLyouIiEAPB3kXXnhhtPrR7wRm7In07JqgdfK0Tp7q1P57KxEyxVOdgmsQT+vkNTU2W2sFmkGzawbrKNPSvKdobGgCwGa3W+vl2ex2GuobmXTy1W2P9Xo5LnM8J2T+EK/Xix8fHnfb8e3JMDipmBS7B8z/sL3yaVa3jsBun84vXrqF44z/wZmUZO2ns/UMoW2piNmzZ5OSmkpTYzNnnnhl2/Pj91s1jtRrLzDDqWGz4fu8Nu3bTmfbpDPHyveT1smLfaaARMoUT3UKfi9OlEzxWqdo6vE6eU8++ST79u2LRl/6Fa2T13/XGNE6efFbJ62TF/tM8bxO3pknXsnJo+YxLmc2J46Yw8mj5h1xiWRHmQzD4NPPPuHTzz4hPT2Vj3eVsnffHtLTUyktPfya3LBhQ0imA5XV1n1+j8mglF+zs2omVe6h2AzISa5giOMNqhvu5NXyG7DbjaOuZ5iWnkpyShIbNq4nPT2V5pYm6xj79++P6GvPNE0uPe/LnDhiDuNyZnPyqHkdti8591YOHToEHDvfT6B18rRO3rFbJ62Tp3XyOmSz2TAMgylTpnDNNddw1VVXceaZZ0arf31O6+TF319BuptJ6+TFPpPWyYt9pnhbJ8/n83H13HtY/8G2rn50M+2cM3lz5dP4/f4eZaqva+DkUfMAKN27gozM9JAcna2Tt695NxuqXqGxdQ1DknZR57uCL570KwAONFWQt+duslJmcdbAqzg+e3yn6+QdbR3AcF57Ller1fej2VWxkozM9GPi+0nr5GmdvGO9TlonL/aZ+uU6eevXr2fJkiUsXbqUn/zkJyxatIgxY8ZYA75Zs2ZZv7zFM7td6+R1J1Nv2sqkTMoU20w9yRFupuB15FqaXdbPvsDtwfd3tE6e3W7nzXeeprnZRVcCs1P2NJM9qL92h8N6foJzBAv0a3TGSYzOeBCAJk8Drf7D2TYfepORKTuAHXx86BkK9+dg2Kdz6oAFnJo5M2T/R1sHsNs5OqlTwPbdy0hr93m/5qYW60xooB89fe05HA5rplIIrWfgUtq0tJSIZgr3+0nr5CnTsZ6pp/niIVM81ylaejzImzp1KlOnTuXRRx/lk08+4fXXX2fp0qX84Q9/4He/+x2DBg1iwYIFXH311cydO5e0tLRo9FtEROJMz2afPMwwjH49IUm6M5N0Mq2vT8qeycaqq/H7PmBY8n6GJVcCb3Kw8U3ePpRGVfr1wPw+7WNaemrEn0PTNLlyzjdYv67rs6yaOVREpO+FdcptzJgx3H///axevZrKykqee+45Zs2axSuvvMK1117LkCFDuPLKK3n22WepqqqKVJ9FRCROBGafPJrpMydaZ3zi3YlZE/niSb/ixlPe49Shy2nma+xznYbb5yDL2Uxa6zBr28ID/+KyRSkMOaXHf3ONueZm11EHeHB45lAREek7EXtXGTRoELfddhu33XYbbreblStX8sYbb7Bs2TLeeustbDYb55xzDu+//36kDikiIv2cYRgszXuq25dbJpqR6WMZmf5d4Lu0eBvZVPUOmZ7DOStdL/HlL+7ly1+EZXvmYNqmcfKAKzhr8EXYbfEz8Dva5aAiItK3ovIOkpyczIIFC1iwYAGmabJu3Trrc3wiInJs6e+XW/aVVEcG04deQS651m1Jtqnsqq5h7OBahiYfAHKpa8rlrbpUGnyTuOnk5+Lic+7RuBxURER6LyrvHKZpUlpaSnl5OYZhMHPmTH75y19aU56KiIgIXDHqR/z4Ijt3zx9Gvfdr7HOfjsvnJMPRAmZFyABv+WdP8WmD3kdFROTowhrkvfbaa9x2223W+joAn3zyCRMnTuTUU09lzJgx3HjjjX2y4F+kBfrs8/k6bHu93pC23+/vst1+MfTgxVhN08Q0zSPaQEjb7/eHtAPTcXfW9vl8IW1lCl0MPVEyxVudAhIpU7zVKbgGiZKpt3UKLFAeaHeUI1gkMgX2D1iLvdeX+7g05xvceOK/uGRMEc6kn3NC1jes/u6p30WS/3fsOngN/9p5Li+V3kdR5du4Pa4u6xSsfZ1CFqrvZabgx3dUp2D96fupo3zt63Qs/4yIdqaARMoUT3UK/vmQKJnisU7RFtYg76mnnmLLli0MHDjQuu3+++/nww8/5OKLL2bixIm8/PLLPPvss2F3tK9pMfT+u5CkFkOP3zppMfTYZ+rzxdD7eZ2CFwVuvxh6IFOwSGQKPE8A1QcPWrcFFkNPsadxvGc6tk+GW5k27FjDfvdo/KbBkOSDDHPm0djyHfI+nc4LO2/ijS3PdlinYEfUKejYgUXne5IpsLh94PEd1SlYf/p+Ai2GrsXQj906aTF0LYZ+VKNGjeLyyy/n//7v/4C24IMHD+a6667jxRdfxOPxMHnyZDIzM1m7dm3EOh1NWgw9fhaS7GkmLYYe+0xaDD32mfp6MfT+XqfgxdC3lr5BRkYahs2Gz3t4MfTA5CHBi6H3NhOEtxh6VdNeNhx4jRp3AQMdJaTa2/6K3MI9XHbcvdjtdvY1fEKTt5aRSaeELOSenOwMqZOrxR1y7J4uht7Y2MyJwy+1Hp+ckhRSJ7fbE3L81NTkfvH9pMXQtRj6sV4nLYYe+0z9cjH0YDU1NQwfPtz6+v3338fr9XLTTTcBbQsHzpkzh3/84x/h9TIGAosURnLRxcApXS2GrkzKpEyxzNSTHPGSqbd1Cl4MfdLJV3M04WYKvA8EnqeeLoY+LH0U89PvA+7D7XOx8cByPqlfwZzjrrO2K6z6PwbZX2ZL60BueyaFDW8ZeHytpDsOT4zicDgwbJ6QY/cmU/Dj29cssBh6QH/5furqvVg/I5TpWMikxdD7V6ZoCWuQl5WVxcGgyz1Wr16NzWZj1qxZ1m1Op5OmpqZwDiMiIhIVgXX8itYWd7ldf1zHL9mewrnDr+Hc4deE3N7qO4DPZjA46RCXz4TLZ8L7VbOorTiNIakXceHIW0l1ZMSm0yIi0ifCGuSdeuqpvPnmm/z85z/Hbrfzz3/+k6lTp4Z8Ru/TTz8lJycn7I6KiIhEWiKu43fz+D9R466kcO8rbC19iTPH1ZLhdJPm2EKrdxt+80vWtunDbDRVHTnBjIiIxLewBnnf+ta3+MIXvsDo0aOtM3Y///nPQ7ZZt24dU6ZMCauTIiIi0ZKI6/gNSs7h4pyvcsfEf2BzZvKv4h+wp2UlflpJd2Za2z3yRgt2m8nr5d9m3ID5TB16Gcn2/nPG0jTNhBqAi4j0lbAGeddddx1PPPEEf/nLXwC48cYb+cpXvmLd/95771FfX89ll10WVidFRESkd/wemDJoHrPSrwm5vbKlnGHpzThsfuA9XK73eOeTH3PIeyqDUy5mWs5ChqSMjEmfoW2Ad+Wcb7B+3bYut5s+cyJL857SQE9EJEhYg7zPPvuMW265hbvvvrvD+y+88EI+/fRTa4pSERER6R9yUo9j9tmDOOv6ZK69YxgDnCWkO1yk2reBuY3ln67hS6f8Ezi8vmLwxD3R1tzsOuoAD6BobTHNza6EOxsrIhKOsH5ajx07lt///vddbvPHP/6RsWPHhnMYERERiYLmapPCp11ce/xzXD5uA5lpf6DKM4/q1sHkpM+2ttt68F1e/3gG//zoLt6veAWXr7lP+7l99zJ2V64K+bd997I+7YOISDwJa5AXWAX+aNvEo8DaGz6fr8O21+sNaQf+ytlZO7BOXqAdeF4CbdM0j2gDIW2/3x/SDqy51Fnb5/OFtJXJa9U3kTLFW50CEilTvNUpuAaJkime6hTYJ4AZVA8zCpmCtc/U/tgOm5OzBl7CDSf9ni+OL+SCoV+ycuw89CYDk+oZnlRAq/tH5H8yndc+u4lLvp9K9gl2zE7qFKyjTKZp0lDfSEN9I01NLSHtxobDM3MnJztJSUkiPT2V5GQnqanJpLU7c9eTOnVUs/Z1SsTXXn/JFJBImeKpTsE/HxIlUzzWKdqift3Fnj17yMzMPPqG/czkyZOZMGECixYtoqSkBIDi4mJKS0sB2Lx5M2VlZQAUFRVRXl4OQGFhIRUVFQAUFBRQXV0NQH5+PnV1dUDbUhMNDQ1A26K4LpcLr9dLbm4uXq8Xl8tFbm4u0LbAfF5eHgC1tbXk5+cDUF1dTUFBAQAVFRUUFhYCUF5eTlFREQBlZWVs3rwZgNLSUoqL26YILykpiVimwKW4eXl5/T7Tpk2brPomSqZ4q9POnTutGiRKpnir0969e60aJEqmeKtTYJ8A1UHLEO3fvz/imYIdkSno2LWfvz8FZ1q+fLmVybZzOl7bD9jrOpsGTwopdg+jUku468t7eHJZFUUVeR3WKVj7TJs2beLKOd/gpJFzOWnkXMblzA5pTzzpKuuxW7du7bBOwXpSJ4CVK1cec6+9/pApcJxEyhRvddq6datVg0TJFC91euihh5gwYQIzZswg2gyzh6faHnnkEav98MMPc9FFF3HRRRcdsZ3P56O8vJyXXnqJc845x3oS+rv6+nqys7Opqalh4MCB1kjbbreHtL1eL4ZhWO3AYoedtQN/MVi+fDlz5swhNTUVwzDweDzW4oxerzek7XQ6MU3Tavv9fnw+n9X2+/04HI5O2z6fD9M0rXZHOcLNZLfbrbbD4ejXmVpaWsjLy2P+/PkYhpEQmeKtTi6XixUrVjB//vyQxUHjOVO81cntdrN8+XKrBomQKZ7qBG2/RMydO5fU1FQaG5o4ccQcAHZVrCQlNTlimVyuVsbltF1yubtyFcnJzpBMrhZ3yLEzMtO7lcnd6uY/9WvYUbOU1pZCBqe3cP7QAjLSM3E6nSwu/T4efzU5yRdz+4w/4Wky2V25itTU5JBMTY3NnDxqHkczfeZEXsv9IzabLaROLS3uTvN1VSfTNHn77bet9+Jj5bXXXzIFvxcHHhfvmeKtTsHvxZ39PhRvmeKtTlVVVeTk5FBXV0dWVtZRfw72Ro8nXnn44YettmEYvPvuu7z77rudbj9y5Eh++ctf9qZvMRVYib6z1ekDRe9u2+l0Wqd0nU6nNQuY0+kM2aZ92zAMqx14YXa33VnfI5mpN21lUiZlim2mnuSIl0zxUqfA+0Bgn0ZQPQybzbo9UpmCtc9k2A5fPhroR3cypSSnMGXobE5JO5dxObNJG5LM9g9TrV90kljN0OQGoJBnCu38p3wgeRV/YMaILzIyfazVX3tQf7bvXnbE5ZcB7ZdI6E6+rnJ09V6cyK89ZVKm3uaLh0zxXKdo6fEgb/Xq1UDbNamXXHIJX/nKV/jyl798xHZ2u51BgwZx6qmnhvxSISIiIomjuTros334GZD2HfY2riTDto0sZxOTx1QDz7LjwLPkl0/gS6e+fsQ+0tJTNTumiEgE9XiQd+GFF1rtn/70p1x88cVccMEFEe2UiIiIxB+7zcHFo74EfImGhibm3nIFZ19t49zzveQkV2LYBlnbenwu7nzeydZ37TR7G0hHgzwRkUgJa528n/70p5Hqh4iIiCQQm83G7vxWdufD/1Suoo79nGQevkR0a10+s6fuZ/ZU2HjwIqorTiQzZRZTh1zL6IyTYthzEZH4F9Z1lLNmzeKZZ57RYuciIiLSpZHpYzk+Y7z1dZYjh/zNw6lxpZJk8zEy5SMy+QsfVV/B4p2zKNy/JHadFRGJc2EN8tatW8c3vvENRowYwfXXX88bb7wRsg6QiIiISEfGZ03lmVs9fPPsVLKSnuag9xoq3SPwmzA0uYpU++EZ57ZUr2bVnudoaK2NXYdFROJIWJdr7tu3j3/+85/8/e9/57XXXuP1119n4MCB3HDDDdxyyy2ce+65keqniIiIJCSD07LP4eyRFwOwv/kzNh14g4mDD3/ef/vB/2Nk8gYK9/yK6taxpCefz6np82PVYRGRfi+sM3lDhw7l29/+Nhs2bOA///kPDz74IJmZmTz11FPMmjWLk046iZ/97Gd8/PHHkeqviIiIxJnmphaa2v1rbmrpcNvhaccz/4T7sNuCpju3DafWk4nT5mdEyi6yjOfZ13wD/6+wlVueSqauoe6I/Xf2r7mpBber1epTD5cLFhGJC2GdyQt26qmn8otf/IJf/OIXvPfee7zwwgu88sorPPLIIzz66KPWIrAiIiJybDlj3IKwHn/Dyf+L3+9nZ916PqxZSqt3HcOS9jI6qwHbJJh80rXWtqddlcynha00Vx9t8PZboG2x9aV5T4WsxSciEu8iNsgLduGFF3LccccxZMgQfvOb32iAJyIicoxJS0th+syJFK0t7nK76TMnkpaWctT92Ww2Ths4g9MGzgDgQMtefvaX71BZUQO0nRV0Zhj816OV2A2TjyoHsHldKutf9lFZ3Pl8AUVri2ludmmdPhFJKBEd5NXU1LB48WJeeOEF1q1bB0BWVhZf+MIXInmYPuHz+UL+t9vtIW2v14thGFY7sKJ9Z22Px4Pf37ZgrMfjweFwYBiG1Qbwer0hbafTiWmaVtvv9+Pz+ay23+/H4XB02vb5fJimabU7yhFuJrvdbrXjIVNAImWKxzq1/95KhEzxVKfgGiRKpnipU/DPIKfTiRlUD9Pvtx4biUzttc/U/tjQ8/en4Md7PJ4j6rRk+RM0NTZjdzgwP7/d7nDg/zyT3eEgObntMT2t09DUUfz26//E7WrFeMiGz+tld9NWdjU+wMCkOiaMqGHCQrhlIVS7B9FqnsXJ6ddySsZ08vLyOOecc5l2etvvJ16PB0hN6Ndef8oUkEiZ4qlOwe/FiZIpXusUTWF9Jg+gtbWVV155hWuuuYaRI0fyzW9+kw0bNrBgwQIWL17M/v37+fOf/xyJvvapyZMnM2HCBBYtWkRJSQkAxcXFlJaWArB582bKysoAKCoqory8HIDCwkIqKioAKCgooLq6GoD8/Hzq6uoAWL16NQ0NDQDk5ubicrnwer3k5ubi9XpxuVzk5uYC0NDQQF5eHgC1tbXk5+cDUF1dTUFBAQAVFRUUFhYCUF5eTlFREQBlZWVs3rwZgNLSUoqL2/6aWlJSErFMgeUz8vLy+n2mTZs2WfVNlEzxVqedO3daNUiUTPFWp71791o1SJRM8VanwD4Bqg8etG7bv39/xDMFOyJT0LFrP39/6kmmxsbGkMd3VKf9+/ezZetm0tNTOVhTzfYPt5Genkpl1X52frSD9PRUPv74417Xae3atdTV15KensqGjesZ6RjPdeOLqNz1PapcC9nvPg6faTAkuYaRKflsKP8XNrtBckoS/y7KJ31Y269BK1asOKJOifja6w+ZAsdJpEzxVqetW7daNUiUTPFSp4ceeogJEyYwY8YMos0ww/jE8Z133smrr75KfX09pmkyffp0br31Vm688UYGDx4cyX72mfr6erKzs6mpqWHgwIERP5O3fPly5syZQ2pqqv4K0seZWlpayMvLY/78+RiGkRCZ4q1OLpeLFStWMH/+fGw2W0Jkirc6ud1uli9fbtUgETLFU52g7ZeIuXPnkpqaSmNDEyeOmAPAroqVpKQmRyyTy9XKuJzZAOyuXEVysjMkk6vFbR172643Sc9Iw+vxYP88U6AN4PN6Q9oOp5OmphbO/PyzdrsqVpKcktQv61TZuIfNNW9S3fIuUwZ/l5OyzuLtt9/Gd+pHZCb9hdLKgQzIupAzBy9kbPqZVj7z89deVnYmpmn2q0zdfe05HA6amlqOyBRo+/1+MrMyOs0XjUzB78WBx+lnRN9mCn4v7uz3oXjLFG91qqqqIicnh7q6OrKyDi8XE0lhXa757LPPMmbMGO677z5uvfVWTj755Ej1K+bsdnvI/+3bgaJ3t+10Oq01BJ1Op3UpjdPpDNmmfdswDKsdeGF2t91Z3yOZqTdtZVImZYptpp7kiJdM8VKnwPtAYJ9GUD0Mm826PVKZgrXPZNgOf07tzBOv7PAx3WXYbD2qWV/WKSdjNJdl3A3cDWDVoMm/gwGGyanDa4DXOdDyOtsOZLB1eyYbcm38500Xfs/hiVk6qk1/fu05HA6unPMN1q/bRle6ytffMh0LPyP6IlNP88VDpniuU7SENcgrKCjg/PPPj1RfREREpI90d2KUo+nuxCn9zQ0nPsVt936djAmfMnlGC+OHH2J4RiPDz2lk3jlw9+Zh1Jb5KFpbTH1jA9mZ0flre7Q0N7uOOsADTTwjkqjCGuRpgCciIhKfDMNgad5TNDe7wtpPWlpKXC4/YBgGL/7pWSt/necgm2ve5KD7PWzU8/6qF6ylH97cewM2w0OS4xzOGHQ147OnhpwV7++2715GWrtBXHNTS9hLW4hI/xX27Jper5c//vGPvPjii+zYsYPm5mbrcwdbtmzhz3/+M9/5zncYP3582J0VERGRyDEM45g+gxOcP53RjBxw+LLOps8Xa08eYDAs6RMcNj9Qzr66l/nwQBYuzuKEzHmcPWw+Kfa0GCXonrT01GO6ziLHorD+DNXS0sLFF1/M9773PT799FOysrJCplQeO3Yszz33HH/729/C7qiIiIhIX3PXmhyX8U/q/bdR4RqH129jYFI9I5IKaHX/iFdKb4p1F0VEjhDWIO+xxx5jzZo1/Pd//zf79+/nzjvvDLk/OzubCy+80JqaWERERCTeHJ9+KteM+xG3nPo254wuxO94kH3uaTR6U8lIPjwVennjR7y482Je/viHfFhTiN/v72Kvic80TZqaWkL+NTe14Ha10vz512FM8i4iXQjrcs3Fixdz8cUX84Mf/ACgw2vyx40bZ61zISIiIhLPspIGcuno24Hb8fm9ePyt1n2bDrxGTvI+4DUq61/jo4OZtJiTGJ05l+lDryTF0b8v64wk0zSPMrvnb4HDs3vG4+c6RfqzsM7kffbZZ5x99tldbpOZmWktAi4iIiKSKOw2R8jA7eyhX6DBvJ0K18l4/HaynQ0MT3ofr3sRqz+dxgeVy2LY277V09k9RSSywjqTl5mZSVVVVZfb7Nq1i6FDh4ZzGBEREZF+b1TGiYzKeBCAhtZa1h94k32NK8m0bSPN0cLJA6ZZ27716e9p8uzh1IFXcsbA8+Nqts6eCp7d0+vxsGLFCmbNupCzxl8T246JJLCwBnnnnHMOb775JrW1tQwYMOCI+8vLy8nNzWXhwoXhHEZEREQkrmQmDeCSUbcCt+L3+/m4YQuDknOs++tdr5OTXEF1w1KW1qTT5J/IqIw5TBt2FenOzNh1PAqCZ/f0eBwkpyTF5dqKIvEkrD8bff/73+fQoUPMnj2bNWvWWEsnNDc3s2rVKubNm4fX6+WBBx6ISGdFRERE4o3NZmN89pSQ2zKTr2Sf6xRa/Q6ynE2MSF6L3/MI/y6fwQs7b4hRT0UkUYR1Ju+CCy7g8ccf59vf/jYXXHCBdXtmZttfoOx2O08++SRTp04Nr5ciIiIiCWTBmO8C36XJ08D6A2+yt2ElabZisp2NYLaGbPvq7ocZl3kBEwdfgN0W9hLHInIMCPsnxd13381FF13E008/zQcffEBNTQ1ZWVnMmDGDe+65h9NPPz0S/RQREUlozZ8vvt2RtLSUTmcfNE2zw4krutqfREZnz32wrmoHkO7M5KKRNwM34/f7+fBQIUODrtbcWbuRgbYXOdT0Istq02jwn8HI9EuZNuxqMpMGRCaIiCSciPw56LTTTuP3v/99JHYlIiJyTDpj3IJO7+tsmvmjT1Mv0dLd574nSwTYbDbOHHx+yG1efyv7XBMY7PyITGczmRSBt4jCPf9Ddes4xg64i3OHXxNOFBFJQIk7lVOYfD6f9X9Hba/XG9IOLHjaWdvj8YS0A4t/BtqmaR7RBkLafr8/pB34DGRnbZ/PF9JWJq9V30TKFG91CkikTPFWp+AaJEqmeKpTYJ8AKSlJTDvnTI4mMM18+xyNDU1HHWRMO+dM0tJSVKej5GufyQz6XjE7yNGTJQIaG5p6nem0ATP40qmvc/7IQhzOR6hwz6LWk4nT5mdEysc0t9ZaOUprtrKhKg+vz4s3+PXWQZ2Oli/cOvmC3nN97TJ15Fh57fWHTMHvxYmSKR7rFG0a5HVi8uTJTJgwgUWLFlFSUgJAcXExpaWlAGzevJmysjIAioqKKC8vB6CwsJCKigoACgoKqK6uBiA/P99aL3D16tU0NDQAkJubi8vlwuv1kpubi9frxeVykZubC0BDQwN5eXkA1NbWkp+fD0B1dTUFBQUAVFRUUFhYCLTNaFpUVARAWVmZtRB9aWkpxcXFAJSUlEQsU21tLQB5eXn9PtOmTZus+iZKpnir086dO60aJEqmeKvT3r17rRokSqZ4q1NgnwAHDx7k/h9+kd2Vq1iz+e/8/bVH2F25ivfWP8eLb/yC7btD11Vrn2lH0PfUkrzfsbtyFa/k/op31j7D7spVvPjGL3j8/36IYRiqU1AmgJUrV3adKWiN3+qDBzvMFLB99zLeWfsMr+T+it2Vq1j+76d4aen/WPcH6hROpsKCdZyROodbTvk/knb+lCzn0xz0XcehLalWnVaW/j/qm+8jt2wqL358KzO/nkLqIINVq1YdWaeDB63+7d+/P+J12rBhg7X/dR98YGVq/9wFHCuvvf6QaevWrdbzniiZ4qVODz30EBMmTGDGjBlEm2EGhq7dcPPNN3P99ddz7bXX9upg4T6+L9TX15OdnU1NTQ0DBw60Rtp2uz2k7fV6MQzDattsNmw2W6ftwF8Mli9fzpw5c0hNTcUwDDweDw5H21WzXq83pO10OjFN02r7/X58Pp/V9vv9OByOTts+nw/TNK12RznCzWS32622w+Ho15laWlrIy8tj/vz5GIaREJnirU4ul4sVK1Ywf/58a02oeM8Ub3Vyu90sX77cqkEiZIqnOkHbLxFz584lNTX1qJncbg/jcmYDsLtyFSkpSSE5mptaOGnkXAA+3pdHZlaG6nSUTKZp8vbbb1vvxZ1lamxo4sQRcwDYVbGSlNTko9YmOFNHtYn2a+/Fj+5jgONdUuyHz+J5/DYOuE8gPfk85o7+DklGSrfyhVun+roGTh41D4DSvSvIyEzHZrNZ78UXXXgx40dfZj1/SUmOhH/t9ZdMwe/Fnf0+FG+Z4q1OVVVV5OTkUFdXR1ZWFtHQo8/kvfTSS5x66qm9HqSF+/i+ZLfbQ/5v3w4Uvbttp9NpndJ1Op3WtflOpzNkm/ZtwzCsduCF2d12Z32PZKbetJVJmZQptpl6kiNeMsVLnQLvA4F9Hi2T2334l/WOctiCvg60Vaeu+97Ve3FwJiPoe8Ww2Y6oWW9qE+3X3s2nPI7L18zGA8spq30bh38jw9KaGJlaRpO3gmTHgzhsbdt/1rwDw25i+owO83WU6Wjt4P7a27UD+wzeJtix8NrrL5l6mi8eMsVznaKlxxOvbNmyhb/97W/R6IuIiIiIhCHFnsZ5w6/lrMzLGZdzCaOnZfKDP52HzY41wPP7/XxUfyd/3uBl266BrK58lvNGfZGByUNj3HsRiZQeD/KWLFnCG2+80eMD9eCqUBEREREJm8Ge9R4WjPoJ6emp1q3lTTtxGF7SHK3MPLUS+AMb9v2RKvfxpCady1lDrmNc1tEnBBKR/qtHg7znnnsu7AOeddZZYe9DREREpK90tOZgJNYhjMQ6e71xQuZpZJvvc+03F3L2fD/TproYnHSIESmfAp+ybn8F47L+hGma1Dc2YJomTntS1PoX6TUiI9k3kXjVo0Hel7/85Wj1Q0RERKRf6moNw96Kxjp7PeG0J/Hh6y4+fB1+WrmaCu9HFB98nZbWQsYNvMLqn2fsR9z3XwfYVjaQTe8mseklN42Vh5dfiET/orVGZLSeO5F4EJHF0EVEREQSSVpaCtNnTqRobXGX202fOZG0tJQe778n6+w1N7tCLreMhpOyJ3FS9iTr66amFtav28ZtdySR5vAw4+QqZpwMvjsNdh0YwOaiNDa85qNo7dZe9S+1m89vZ/m78/z11XMn0h9pkCciIiLSjmEYLM17qk8up9y+exlp7QcxTS1ROYPYU3//hpvLN/6CCu9qnMZmhiQfZPywQ4xfcIgbFsB/fW14r/Z7tOe3J/nbP3/95bkTiSUN8kREREQ6YBhGn5wFSktP7bdnm0yfwYwRV5Cefj0AZfUfsqX6dZpa1+DwVfJZYau17T92fhXTrGNQ6kWcnjH/qPuO1PPbn58/kVjRIE9EREREumVs1umMzTqdpqYWTho927rd43OT7VhPmr0VzO2U1j/Bo6uz2bIxnZ316zkr9fyQdTpFJLr03SYiIiIiPeYPWg/eZtgZkfkbqjyXccA9BJthctLQWq6/bC+17rt48aOrYtdRkWNQxAd5Pp+PDRs20NraevSNRURERCTu2W0Opg6bw40n/54bTlnDqPSX+du/RrGtfDAevw27/SRrW7etmZd2fZFln/yW/c2fxbDXIokr4pdr2u12zjvvPLZt28b48eMjvXsRERER6edGp53M24+4eBvYtnc19uTDk9PUZGxjfMp/gP+wvfJpVreOwG6fwWkDr+T0gefqsk6RCIjKZ/ImTZpEVVWVBnkiIiIREq0FuSX6YrGYen96baQ7sklPOTwxSprreKpa54O5kWHJleQkVwBLONCwhDdqMhiW+SPOyrw8qn2K1UL0x4pEf37jIV9UBnk/+tGPePDBB1myZAlDhgyJxiFERESOKZoSPn7FcjH1/ii7dQTzx96B0+lkb+MuNla/Rr3r3wxJ+phsZyPD0w5f2nnWjSmsqPgd54y4kZHpYyNy/FgvRJ/oEv35jZd8URnkLVy4ELvdzimnnMIXvvAFLrzwQs466yxOPfXUuCukiIhIrER7QW6Jnv6ymHp/f22MyjiRURnfB75Pk6eBLQfzODFrIk2fn4mc+yU3Axx/ZceBv1KwJwfDNo1TBi5gXPK0Xh+zvy1En2gS/fmNl3xRGeTt2LGDrVu3UlxczNatW/nhD3/IZ599RmpqKmeccQYffPBBNA4rIiKSUPpyQW6JrFgvph7J/feVdGcm5w2/LuS2HduSGTl8OEOT9zMsuRJYRk3jMj49lMbXX8jiz7eFN9Fff16IPhEk+vPbn/P1eJD34YcfMmHChC5/YIwfP57x48fzhS98wbqtrq7OGviJiIhI9/TVgtwSeVpMPXxLf+jid19dRT1VbDzwGrWu9xjsLCXT2czoUUmYfru17Tt7nuOUAecxyDiu2/tP5OeuP0j057c/5+vxIG/ixIn87Gc/48c//nGPHpednc0FF1zABRdc0NNDioiIiMgxbET6CSxIvx+4H5e3mTV7l/Dqn58B3ABUuyrA80tKq00OuIdy0+PJbHwTfH5fTPstEis9nqPWNE38fn/IbX/+85/50pe+FLFOiYiIiIh0JMWRxjlDFrLlRbd124GWzzjQOhK/CUOTD3DVRfv42f/uY3Xl+byw80usr1oRwx6L9L2ILERSUVHBiy++2OF9//3f/82MGTMicRgRERERkSOcNnAGN52Sz8ScVTR4v86GXUNp8TrIdLQwMnk9+5sPT5RR497PsDOiMi2FSL8R9dUmW1tb2bBhQ7QPE3E+n8/6v6O21+sNaQfObnbW9ng8IW3TNEPapmke0QZC2n6/P6Tt9Xq7bPt8vpC2Mnmt+iZSpnirU0AiZYq3OgXXIFEyxVOdAvtMpEzxVqeO8vXHTME6yxTod0/rZAYdw+wkU7D2OfxBP8/9HWTyBe3D1y7f0TJ1lS/wtTf4+yko36Ck4Vw64m7+92o/d83IBtvPqHBfyMSBV1l9X3vgOX7/UiW/LmxlyWffZX3V27g9Lqvvwdl8Cfj9FPxeHI1MndWmJ6+9WP+M6KpOneUL9LezfO0zRVvUB3nxavLkyUyYMIFFixZRUlICQHFxMaWlpQBs3ryZsrIyAIqKiigvLwegsLCQiooKAAoKCqiurgYgPz+furo6AFavXk1DQwMAubm5uFwuvF4vubm5eL1eXC4Xubm5ADQ0NJCXlwdAbW0t+fn5AFRXV1NQUAC0nUktLCwEoLy8nKKiIgDKysrYvHkzAKWlpdakNyUlJRHLVFtbC0BeXl6/z7Rp0yarvomSKd7qtHPnTqsGiZIp3uq0d+9eqwaJkine6hTYZyJlirc6AaxcubLfZwrWPlNjY6N1n7s3dTp40Hr8/v37O8wUrH2mHUE/zz/eteuITMF/4F/3wQdWpsBxOsoUXCe36/CspL4OMq1atcq6/7333mvLFFQnAJ8bZgy+mvNTfsTeD9teG2VlZdQ27sZnGozKamBE6ioamr9D3qfTef4/XySv/Bk2bTvc961btybc99PWrVutfNHItGLF4UtjA3Xq6Wsv1j8juqrT0V57+/fvt+7fs2dPSKaHHnqICRMm9M1VjmYPGYZh/uxnPwu57eGHHzZtNluH23d1X39UV1dnAmZNTY1pmqbp9XpNr9d7RNvj8YS0fT5fl+3W1lbT5XKZS5YsMZuamky/32/d7vf7Tb/ff0TbNM2Qts/nC2l7PJ4u216vN6TdUY5wMwW3+3um5uZmc8mSJWZra2vCZIq3OrW0tFg1SJRM8VanwM+hQA0SIVM81am1tdVcsmSJ2dzcnDCZ4q1Obrc75L24v2ZqbGw2h6XPNIelzzSrKg+atYfqzIb6RrOxsdmsPVRnVlYetO5vaGjqcZ0a6hutx1furzbrauvNxsZms6G+0ayrrTergvbf2Nh8RI76ugbr/vq6hiMy1dXWW/fX1dZb+YLfi7uqU0NDU4f5AplqD9VZ99ceqgupR/vnLpCpsbHZrK9rMCv2VZnjTjrP/MKPLjaf/8+15rKPTzdXlY03V5WNN1fsOtXcU/2Z9fjSzz4y6+sazMbGZrOutt6qQV1tfYf166vvJ6/XG5IpuB3ob3A9OnsvjsbPiM5q09Frr6G+MSY/I9xud8j3U0NDU0i7oaHJdLvdHdapq9deV/mCM1VWVpqAWVfX9vho0AXJnbDb7SH/t287HI4etZ1Op3Wq1+l0WktQOJ3OkG3atw3DsNo2mw2bzdbtdmd9j2Sm3rSVSZmUKbaZepIjXjLFS50C7wOBfSZCpu7m6C+Zunov7m+ZAo625pZhGCF9704mI+hnwZknXtnl/jvKYQv6OtAO7ru9XTtw3O7WqbX18GWd7fMZhoEjaHtHu3xu9+HL6bp67t77nYvnfvgCjhSDjQeW80n9CkyzmQEpQ6xt3j94M85aP1u2Z7Ix10bJMhf+z3c/feZEluY91effT6ZpcvW8e466IHegfxDe67CnmbqqDYS+9gybrcOfh9H8GWGaJtfOv6/bz19PXntd5eus79HSq0HeE088wZYtWzj77LM5++yzrdOdIiIiIhK+tLQUps+cSNHartcXnj5zImlpKf1u/7HU02yGYXDu8Gs4d/g1QNsgYPrMiZR8+iEjsw7hsPkZMbORy2dC0yIn28sGsum9JDa9tJ3mZlefr5PW3Ow66gAFoGhtcUz6198dK89fjwd5kydP5sMPP2TJkiUsWbIkZFH0G264gYkTJzJx4kQmTZrE8ccfH9HOioiIiBwLDMNgad5TNDe7utwuMEjpb/uPpXCzBT++1n2AzbVvcsj9HgOdO0h3upkxvooZ4+GDC4dZj/H7/SFXSvSV7buXkdZuENLc1HLUs7/SJpGfvx4P8jZu3IjH42Hbtm1s2rSJjRs3smnTJoqLi3n55Zd5+eWXrW+YrKws0tLSIt5pERERkURnGEZUzyJEe/+xFG62wOPT049n1KBvAt/E43OzqfodSg8tA08Rm95LgvPati+p/YCS6m/TymTGZF3O1KGXkWyP/hnQtPTUhK1hX0jk569Xl2s6nU6mTJnClClTuPPOO4G2qUA//PBDNm7caA38tm7dSl1dXdz9BUhEREREJJjTnsyMnCs4I+MSxuXMBprhobb7/lOzlMFJdcC7uFzv8s4nP+aQ91QGpVzI9JzrGJIyMpZdl2NQxCZesdvt1qWat99+O9B26rqkpISNGzdG6jAiIiIiIv3A4ZMYc4/7LkUHTqKyaRVZ9g/JcLhItW8DcxtbKp5gQPrvOXvYvBj2VY41ERnktbS0UF9fz9ChQ0OuR7bZbJx++umcfvrpkTiMiIiIiEi/k508hDmj7wDuwOv3sKU6n4/rcrH5N5DpqOWMQbOsbV/Z9WPc3v0cnzWPaUOvIMWhjzZJ5IU1yHvyySd5/PHHrQWObTYbw4cP59xzz+X6669n4cKFHU4FLCIiIiKSiBw2J2cPm2eduat2VYQO5HwrGZFci8f9b1Z/+jAHPeMZkHoBZw+9juFpmrRQIqPX0wDdfffd3HfffRw6dIirr76aG2+8kbFjx7J3715efvllbrzxRk477TTef//9SPZXRERERCRuDEkZYbX9fj+D0u5hn3s6Dd5Uku1eRqb8hzTzabZXzuGFHdfErqOSUHo1yNu5cyd/+tOfWLhwIWVlZbz22mvcfffdVFVV8Zvf/IYPP/yQH//4xxw6dIg5c+ZQUFAQ6X6LiIiIiMQVm83GJaO/zJdO+TsLxm1gYPpTHPBewQH3MGwGYGRa2/r8Xv750d28t+9FWryNseu0xKVeXUu5bt06DMPgV7/6FSkpbdPDPvjgg/z3f/83d999NwA/+9nPuPfee7nqqqu455572L59e+R6LSIiIiISx+w2B5OHXsLkoZcAsKfxY8b63db9m6tXMTwpH19rPu999nOqW08kO+UCJmRcEasuSxzp1Zk8t7vtBTh69Gjrts2bNzN9+vSQ7YYOHcpLL71ESUkJe/fuDaObIiIiIiKJa3TGSYzNOjxZYYZzEBXuc6j3pJNk8zIyZSfpPMOnjdfw3//2MumG6K/DJ/GrV2fyxo4di2malJSUMGnSJACOP/54fvGLX/D3v/+d9PR0a9uGhgYAsrOzI9BdEREREelPmptaunVbf91/f3XqwGmcOvB5/H4/22veZ0ftm/h9RQxL3s+YgXW0thz+fXv7wTUccJUxbeiVGCTFsNc901Ud09JStNZ2GHo1yJs9ezYVFRUMGDDAuu3RRx/lpptuYtSoUcyePZvx48fT3NzM4sWLueyyy8jIyIhUn0VERESknzhj3IK43n9/Z7PZmDjkAiYOuQCAXdU7+MmT97DjrcMDpK3VzzAieS3v73mMA+5xXPbjVNYv9sSqy93WVW2nz5zI0rynNNDrpV5drmmz2cjJySE5Odm67frrr6egoIALLriAt99+m1/+8pc88cQTTJs2jeeeey5iHRYRERGR2EpLS2H6zIlH3W76zImkpfX8ssJo7z+eDU89gVW/cmH6Dg9+HPYh1HkySLL5GJVaypdv3MPjr1fy5p45vFR6Py5fcwx7HKq7tS1aW0xzs6sPepSYIrqI3cyZM1m6dCkAVVVVZGRkkJamBR5FREREEolhGCzNe+qov4T39pK7aO8/0dxw0q/x+/2U1K5j24HXaWgoYOzgWoYlH+BQ6/skGYcHwpsPrsJti92g72i1bW5qOebP3kZCjwd5Tz75JFdffTWjRo3qcrthw4b1ulMiIiIi0r8ZhkF6emrc7j/R2Gw2Th90LmOSJzMuZzZZxw3j128uJCXZjs3WdvGey9dMddP3yD7dx+JdfyMj6XwmD72WEzJP69O+qrbR1+NB3r333st9993HlClTuOaaa7jqqqs488wzo9E3ERERERHphfpyH5eOuDtkMFXe+BEtvlQGJtUzMmU3sJtdB//GBxWD8Rtnc/qgL3Lm4PNj12mJmB5/Jm/9+vU89NBDtLa28pOf/ISzzjqLE088ke9+97u89957+P3+aPRTRERERETCcHL2WSw44X0+23Ef1Z7r2O8ejd80GJJ0kGHOFZQcesPatsXbSJ27Ooa9lXD0+Eze1KlTmTp1Ko8++iiffPIJr7/+OkuXLuUPf/gDv/vd7xg0aBALFizg6quvZu7cufpMnoiIiIhIP2Gz2RjoPo75Y76O0+mk2rWP9ZWvc9C1mrOGXGNt9++KF8HzGw54TiDNeS6ThixkXJau3osXYU28MmbMGO6//37uv/9+ampqWLZsGUuWLOGVV17h+eefJyUlhdmzZ7Nw4UIWLFigz+mJiIiIiPQjQ1JGcvkJ3wS+GXJ7dcsmhif5GZFcBpTxSc0/2Lh/EF6mMC57PlOGXIrTntzhPiX2erWEQkcGDRrEbbfdxmuvvUZ1dTVLly7llltuYcOGDdx5552MHDmS88/XNb4iIiIiIv3dzeOfYvTAFznku5H97uPxmQaDk2rISXqHppYH2NdcZm3r83tj2FPpSESXUAhITk5mwYIFLFiwANM0Wbt2LW+88Ya1vIKIiIiIiPRv47OnMD57CgA17kqKKl+juuVdbNRwSeap1nYvfnQtNsNFsmMm4zPmx6q7EiQqg7xghmFw7rnncu655/LLX/4y2ocTEREREZEIG5Scw2XH3w3cHXJ7k6eBoUkf47T5gE850PISv1uXztYPs1h/8C1mpiwg2X5sLVjfH/Tqcs3i4mLuu+8+brzxRh555BEqKys73basrIzf/va3ve6giIiIiIj0T+nOTE4a8hK1/luocI/F67eRk9HE3BkV+P0/4pXS62PdxWNSj8/kbdmyhXPPPRe3241pmgD89re/JS8vj2nTpgGwY8cOFi9ezOuvv862bdsAuP/++yPYbRERERER6Q9OzJrIiVkTAdhXu4dv/uLLTL3Ew6ST60hxTrW229/8Gfnlt5PkOIfTB13FKdnTrIXaJbJ6PMh75JFHcLlc3HDDDVx22WV8/PHH/PGPf+TGG29k9erV3HHHHeTn5wNgmibHH388V111VcQ7LiIiIiIi/Uu2czCFT7VQ+BR8VPFvklIPD+I2VL3K8OQ9wCtU1L3Cfw5k4eYsjs+cx9nD5pNi19JrkdLjQd6GDRuYNWsWL774onXbF77wBc4++2wuvPBCPv30U8aOHctXvvIVrrrqKiZNmhTRDouIiIiISP/nsDlJdaRaX08echXrq5ppal3D0KQyBibVAwW0ugvI/+RhBqT/nEmZ82LX4QTS40Hevn37+NKXvhRy28SJE7nqqqt47bXX+NrXvsaTTz6pU68iIiIiImIZlXEiozJ+BEB96yHWVy2homkVWfbtpNlbOCnrbGj7NBjn35fCm3sWcebQhZw24ByNLXqox4M8v99PSsqRM+SccsopGIbBww8/rCKIiIiIiPSSaZo0N7s6vK+5qaXb++loW6/Hg9vVas2t0dPjB6SlpWAYRrf70l5W0kBmj74duB2f38vO2o0MSxtN0+d9nnNNM8OSl1JZv5SdBzNoMScxOmMu04ctIMWeHvX+RUL7578v+9SrJRQ66lxSUhIAw4cPD69HIiIiIiLHKNM0uXLON1i/blvY+zpj3IJO7/vbn9/hzXeePuL3+u4ef/rMiSzNeyoigxa7zcGEQTNCbnt/ZTrp1w9mWFIZA5yNDGANvtY1vPvpo/xnz3B+dUUz0PmxI9m/3mr//O+uXEV6emonW0dWr065/e53v+Pyyy/nRz/6Ea+99hqffPJJhLslIiIiInLsaW52dWuAN33mRNLSjry6Li0thekzJx718es/2Nbh2bDuHr9obfFRz6aFY+X/tHDtca9w3ui14PwxFe5zqPekkWz3YjdcBA/w5j+cwriLk7Cu9eyD/nWmu89/tPX4TN7kyZP58MMPWbFiBStWrLBGx4FLNH/84x8zefJkpkyZwtixYyPb2zCUlZXx1a9+lcrKSux2O+vWrSM9PT3W3RIRERER6dD23ctI6+TMT2eX/hmGwdK8pzod4NTXNXDW+Gt6ffzmppYuzxBGWkZSNpeMuhW4Fb/fz4aKd/jX/z4GtLJ99zKq+ZSK5hvheqj3pFPnmcC/ntzH1lfcfdbHYF09/x0NyqOlx4O8jRs34vF42LZtG5s2bWLjxo1s2rSJ4uJi3G43jz32mPWCy87OZvLkyUydOpVf/epXEe98T3zlK1/h5z//ObNmzaKmpobk5OSY9kdEREREpCtp6am9urzPMIxOH+f1eKJ+/Gix2WycPmAWu1a1Am39o7WVfa5TGJK0iyxnE1nO9Xz3e9B6v53XPvsCJw68k3OHX9On/ezq+e8rvfpMntPpZMqUKUyZMoU777wTAJ/Px4cffsjGjRutgd/WrVtZvXo17777bkwHeR9++CFOp5NZs2YBMGjQoJj1RUREREREIuO0gTM4beBSWryNFFW9yWf1K0hlC4NSWhiVWkqT96C17WeNH7GvdheGzcT0x3ZSlmiL2DSYdrudiRMncvvtt/P4449TWFhIQ0MD27Zt47nnngtr3wUFBVx55ZWMHDkSwzBYsmTJEds88cQTjBkzhpSUFGbMmEFRUZF1X2lpKRkZGVx55ZVMmTKFxx57LKz+iIiIiIhI/5HqyODCkTdx7XFP8c2zU/nh3SOodF/J9KFXW9us3f9nmr3f4an1TdzzkoN/V/2Thtba2HU6iiIyyGtpaaGyshK/3x+6c5uN008/ndtuuy2s/Tc1NTFp0iSeeOKJDu9fvHgxDzzwAD/96U/ZtGkTkyZNYt68eVRVVQHg9Xr597//zZNPPsnatWtZuXIlK1euDKtPIiIiIiLSHxl88u9Wrhr9KNnJQ6xbTbMVt89BdrKbWWdUkmT8isI9M/nHjitYUvYYTZ6GGPY5snp1uWbAk08+yeOPP87OnTuBtkHd8OHDOffcc7n++utZuHAhDkdYhwDg8ssv5/LLL+/0/t/85jfcdddd3H777QA8/fTTvPXWWzz77LM8+OCDjBo1irPPPpvjjjsOgPnz57NlyxbmzJnT6T5ramrwBF2znJycHPbn+AL78/TgWmiJHD3/sacaxJ5qEFt6/mNPNYitRHj+gz/T5vV48HjC/123L/fvOcr+j3b8cO8/mmge/7ox/0tN/UFue/Amps4zmTqxkYFJDYxI+ZgW36eYvvvwff74oac5cLmbSUoKP5/b7cbtPjwRTE1NzVEfE65ev2ruvvtu/vznPzNs2DCuvvpqUlNTWb9+PR9//DEvv/wyr7zyCuPGjeO5557j/PPPj2SfQ7S2trJx40Z++MMfWrfZbDYuvfRS1q5dC8C0adOoqqri0KFDZGdnU1BQwNe//vUu99t+ZtAbbriBm266KSJ91lnE2NLzH3uqQeypBrGl5z/2VIPYiufn3+1qtdorVqwgOSUpofYf7fv7Q/+2vOhmy4sw6S/f59PsSpqzisHmxbs9//PHmyx6voE1By+m4pOx+OsnMKRhIkn+tF7le/HFF1m8eHG38kdKrwZ5O3fu5E9/+hPXXnstL7zwAikpKbz//vu89dZb/OY3v2HevHm89NJLPPHEE8yZM4cVK1ZwwQUXRLrvAFRXV+Pz+cjJyQm5PScnhx07dgDgcDh47LHHuOCCCzBNk7lz57JgQddTv5aVlZGZmWl9HakzeStXrmTOnDk4nc6w9iU9p+c/9lSD2FMNYkvPf+ypBrGVCM9/c1ML8FsA5s2b1+kSB/11/3W19V3u/2jHD/f+o4n28YPvv2ze5R3eP+jEP5KW1ECaw8PJg3bAoB14/Us40HoCTmMGw85wULXd2+18s2fPDvnYWU1NDePHjz/q48LRq0HeunXrMAyDX/3qV6SktK338OCDD/Lf//3f3H333QD87Gc/49577+Wqq67innvuYfv27ZHrdS8c7ZLP9gYNGkRWVlZU+uJ0OuP2B1si0PMfe6pB7KkGsaXnP/ZUg9iK5+ff4fQGtSOfI9r7D95fR/s/2vHDvf9oon387txfs8vL12Zk8c/N32GfaxXJbGZgUh0jUsqAMq55cDh//lLb4212AxMTh63znE6nk4yMjKOHj6BeDfIC15SOHj3aum3z5s38/ve/D9lu6NChvPTSS4wbN469e/cyatSoMLrasSFDhmC326msrAy5vbKykuHDh0f8eCIiIiIikth8bpg2+ErS078IwM7ajWw/+AYuTyHrc/1A22fzNhxYTlXDQ9T5JjAs7WKmDbuWgclDY9jzNr2aXXPs2LGYpklJSYl12/HHH88vfvELmpqaQrZtaGibpSY7OzuMbnYuKSmJqVOnsmrVKus2v9/PqlWrmDlzZlSOKSIiIiIix45TBkzluhMf4Zrj3mTzP13W7WX1eaQ73IxM3ozD9xs27JvFP3fO5dXdD1NatyVm/e3VIG/27NlUVFRw6qmnWrc9+uijvPnmm4waNYrrrruOH/7wh3z729/m0ksv5bLLLgvrFGVjYyNbtmxhy5YtQNvn5bZs2cJnn30GwAMPPMAzzzzD888/T0lJCXfffTdNTU3WbJu94fP5rP87anu93pB2YPmIztoejyekbZpmSNs0zSPaQEjb7/eHtL1eb5dtn88X0lamw6fnEylTvNUpIJEyxVudgmuQKJniqU6BfSZSpnirU0f54j1TPNUpIJ4zBYt0ncygY5hRyBTyXtxJnYJ1linQ7/aZ2s8+GehvT+oUrH3f/UH993dQJ1/QPrqbr6OfEe3zBb5un++6cf+P1JT/x/7W2RxsHYjdMBme/CkDbS9SfugGNlWttvoeXKdo69Ugz2azkZOTEzIRyfXXX09BQQEXXHABb7/9Nr/85S954oknmDZtWtiLoW/YsIHJkyczefJkoG1QN3nyZBYtWgS0zXz561//mkWLFnHWWWexZcsWli9ffsRkLD0xefJkJkyYwKJFi6wzlsXFxZSWlgJtl6eWlZUBUFRURHl5OQCFhYVUVFQAbYu4V1dXA5Cfn09dXR0Aq1evts5w5ubm4nK58Hq95Obm4vV6cblc5ObmAm1nQvPy8gCora0lPz8faJtwpqCgAICKigoKCwsBKC8vtxaCLysrY/PmzUDbgvDFxcUAlJSURCxTbW0tAHl5ef0+06ZNm6z6JkqmeKtTYLmVRMoUb3Xau3evVYNEyRRvdQrsM5EyxVudoG12x0TKFC91Chwn3jMFi3idPv99EaD64MGIZ9q2bZu1/w0bNnRYp2DtMzU2Nlr3uTvItGLFCuv+wJV2Pa1TsPaZdgT9LvHxrl1AaJ02bNhg3b/ugw86/H4K1r5Obtfhs3S+DuoUfPXge++9h9OezIm2cxi+53q+MH4dab4n+bhqHhWuMdR50mkqbft0XFlZGb8puIZfrT6Pb/x2/hH9iDTDDB6uRlBVVRUZGRmkpaVFY/dRU19fT3Z2NjU1NQwcONAaadvt9pC21+vFMAyrbbPZsNlsnbYDfwVZvnw5c+bMITU1FcMw8Hg81lqCXq83pO10Otv+YvB52+/34/P5rLbf78fhcHTa9vl8mKZptTvKEW4mu91utR0OR7/O1NLSQl5eHvPnz8cwjITIFG91Cvzwnz9/PjabLSEyxVud3G43y5cvt2qQCJniqU7Q9kvE3LlzSU1NTYhM8VYn0zR5++23rffiRMgUT3UKfi8OPC7eMrW0uBmXMxuA3ZWrSE52RrROjQ1NnDiibS3nXRUrSUlNjmimmoOHOO2EKwAo3buCjMz0kDq1z5eU5AjJ5HZ7OHH4pW392/8O6empIZnq6xoYP/oyAD7as5zsAVk9qpPb7Qk5fsrnSxQEcjQ3tXDSyLkAfLwvj8ysjJA61dc1cPKoeT3KF1ynzvIF6tRZvo4ytXiacRpJOBwOWj2tLP90OhmOFnZVn8DXpq2krq4uahM9RnZ1xSDDhg2L1q77hN1uD/m/fTt4kffutJ1Op3Wq1+l0YhiG1Q7epn3bMAyrHfgB0t12Z32PZKbetJVJmZQptpl6kiNeMsVLnQLvA4F9JkKm7uboL5m6ei+O10xdtZUpOpmCRTqTEfRz2rDZjvrzoqeZQtoOh7WfzvK1729r6+HLIQ3DCOk7tM042b7dkzq53Ycvh+yo77agrwPt4L7b27Ujmc8wjB7lS3UePtllt9sYkfXf7KrLZVDKiUB014qM2iBPREREREREwG5zMG3Y5UwbdjkHDx4E7o/q8Xr1mTwRERERERHpnzTIExERERERSSAa5HVCUx8nZqaARMoUb3UKSKRM8Van4BokSqZ4qlNgn4mUKd7q1FG+eM8UT3UKiOdMwSJdJy2hEKp93/vbEgrdzdS+TtGmQV4ntIRC4k3nrCUUYp9JSyjEPpOWUIh9psA+EylTvNUJtISCllDQEgqgJRQCmcJZQqG7mcrKynjooYeYMGECM2bMOKIfkRa1JRTilZZQSNzpnLWEQuwzaQmF2GfSEgpaQuFYr5OWUNASCpFcQmH77mXWEgqGzYb38zoZNhtOp92axbUnmfpyCYWtpW+QkZGGYbPhC8p3xrgFgJZQ2LZ7GWlpKfi8Xhyf16mhvpGzxl9zRL6evPaqqqrIycmJzyUU4p2WUEjsqY+VSZmO5Uw9yREvmeKlToH3gcA+EyFTd3P0l0xdvRfHa6au2soU3SUUAoOhjkyfOZGleU/1OFNfLqEw6eSrO+1/Z/09lpZQOLOL+vY0X2c1iBZdrikiIiIi0k1paSlMnznxqNsVrS2mudl11O36WmpaCieOH3XU7abPnEhaWkof9Kh/6W59+/vzozN5IiIiIiLdZBgGS/Oe6nQA19zU0uUZvlgzDIMfLLqFiy+6JOSsW3tpaSnW2e5jydHqG9Dfnx8N8kREREREesAwDNLTU2PdjV4zDIO09NSQSxXlsHivL+hyTRERERERkYSiQV4ntL5NYmYKSKRM8VangETKFG91Cq5BomSKpzoF9plImeKtTh3li/dM8VSngETKFJIj6Odsf18nL5zXXqDfibhOXl+89qJNg7xOaJ28xFuzR+vkxT6T1smLfSatreWBygAAMhVJREFUkxf7TIF9JlKmeKsTaJ08rZMXxToFrXMXWFOuP62Tt3XrVmv/vXntJfo6edF87WmdvBjSOnmJu2aP1smLfSatkxf7TFonT+vkHet10jp5Wicv2nVyu1oPr3O3/x0yMtJ6lCna6+QFvxd39vtQV6+9RF8nry9ee1onL4a0Tl5ir2+jTMp0LGfqSY54yRQvdQq8DwT2mQiZupujv2Tq6r04XjN11Vamvs/Ufh25nmbqy3XyelOnRF8nr6t2NF570aLLNUVERERERBKIBnkiIiIiIiIJRIM8ERERERGRBKJBnoiIiIiISALRIE9ERERERCSBaJDXCS1impiZAhIpU7zVKSCRMsVbnYJrkCiZ4qlOgX0mUqZ4q1NH+eI9UzzVKSCRMoXkCPo5q8XQtRh6Z3WKNg3yOqHF0BNvYVYthh77TFoMPfaZtBh67DMF9plImeKtTqDF0LUYuhZD71EmLYYOaDH0uKbF0BN3YVYthh77TFoMPfaZtBi6FkM/1uukxdC1GLoWQ9di6FoM/RimxdCP3UVMlUmZEj1TT3LES6Z4qVPgfSCwz0TI1N0c/SVTV+/F8Zqpq7YyaTH0cPNpMXQthi4iIiIiIiIxpkGeiIiIiIhIAtEgT0REREREJIFokCciIiIiIpJANMgTERERERFJIBrkiYiIiIiIJBAN8joRWE/E5/N12PZ6vSFtv9/fZTuwTl6gHVieMNA2TfOINhDS9vv9Ie3AmkudtX0+X0hbmQ5P+ZtImeKtTgGJlCne6hRcg0TJFE91CuwzkTLFW506yhfvmeKpTgGJlCkkR9DP2d5kCnl8FDIFvxeH89oL9Lt9Jm/wz7pe1ilY+777g/rv76BOvqB9+DrJFCxWr71o0yCvE5MnT2bChAksWrSIkpISAIqLiyktLQVg8+bNlJWVAVBUVER5eTkAhYWFVFRUAFBQUEB1dTUA+fn51NXVAbB69WoaGhqAtkVxXS4XXq+X3NxcvF4vLpeL3NxcABoaGsjLywOgtraW/Px8AKqrqykoKACgoqKCwsJCAMrLyykqKgKgrKyMzZs3A1BaWkpxcTEAJSUlEctUW1sLQF5eXr/PtGnTJqu+iZIp3uq0c+dOqwaJkine6rR3716rBomSKd7qFNhnImWKtzoBrFy5MqEyxUudAsdJpExH1Onz3/cAGhsbe54p6PHVBw9GPNPWrVut/ffmtRfIBODuINOKFSus+1etWtWrOgVrn2lH0O8SH+/aBYTWacOGDdb96z74oMNMwfrytffQQw8xYcIEZsyYcUQ/Is0wg4fjQn19PdnZ2dTU1DBw4EBrpG2320PaXq8XwzCsdmCxw87agb+CLF++nDlz5pCamophGHg8HmtxRq/XG9J2Op1tfxH5vO33+/H5fFbb7/fjcDg6bft8PkzTtNod5Qg3k91ut9oOh6NfZ2ppaSEvL4/58+djGEZCZIq3OgV++M+fPz9kcdB4zhRvdXK73SxfvtyqQSJkiqc6QdsvEXPnziU1NTUhMsVbnUzT5O2337beixMhUzzVKfi9OPC4eM/Uvk5uVysnjpgDwK7975CRkdajTI0NTYcfX7GSlNTkiGYKfi/u7Pehrl57breHE4dfauVLT08NyVRf18D40ZcB8NGe5WQPyOpRndxuD+NyZgOwu3IVKSlJITmam1o4aeRcAD7el0dmVkZInerrGjh51DwASveuICMzPSRTS4s7ZP9JSY4+f+1VVVWRk5NDXV0dWVlZREPHS7+LtRJ9Z6vTB4re3bbT6bRO6TqdTgzDsNrB27RvG4ZhtQPfbN1td9b3SGbqTVuZlEmZYpupJzniJVO81CnwPhDYZyJk6m6O/pKpq/fieM3UVVuZ+j5Ta+vhywV78/ueEfRz2rDZjvrzoqeZepqvfX/b5wvuO4Cjg3ZP6uR2H77cs6O+24K+DrSD+2tv1w7sP3ibYLF+7UWLLtcUERERERFJIBrkiYiIiIiIJBAN8kRERERERBKIBnkiIiIiIiIJRIM8ERERERGRBKJBnoiIiIiISALRIE9ERERERCSBaJAnIiIiIiKSQDTIExERERERSSAa5HXC5/NZ/3fU9nq9IW2/399l2+PxhLRN0wxpm6Z5RBsIafv9/pC21+vtsu3z+ULayuS16ptImeKtTgGJlCne6hRcg0TJFE91CuwzkTLFW506yhfvmeKpTgGJlCkkR9DP2d5kCnl8FDIFvxeH89oL9Lt9Jm/wz7pe1ilY+777g/rv76BOvqB9+DrJFCxWr71o0yCvE5MnT2bChAksWrSIkpISAIqLiyktLQVg8+bNlJWVAVBUVER5eTkAhYWFVFRUAFBQUEB1dTUA+fn51NXVAbB69WoaGhoAyM3NxeVy4fV6yc3Nxev14nK5yM3NBaChoYG8vDwAamtryc/PB6C6upqCggIAKioqKCwsBKC8vJyioiIAysrK2Lx5MwClpaUUFxcDUFJSErFMtbW1AOTl5fX7TJs2bbLqmyiZ4q1OO3futGqQKJnirU579+61apAomeKtToF9JlKmeKsTwMqVKxMqU7zUKXCcRMp0RJ0+/30PoLGxseeZgh5fffBgxDNt3brV2n9vXnuBTADuDjKtWLHCun/VqlW9qlOw9pl2BP0u8fGuXUBonTZs2GDdv+6DDzrMFKwvX3sPPfQQEyZMYMaMGUf0I9IMM3g4LtTX15OdnU1NTQ0DBw60Rtp2uz2k7fV6MQzDattsNmw2W6ftwF9Bli9fzpw5c0hNTcUwDDweDw6HA2j7C0Nw2+l0tv1F5PO23+/H5/NZbb/fj8Ph6LTt8/kwTdNqd5Qj3Ex2u91qOxyOfp2ppaWFvLw85s+fj2EYCZEp3uoU+OE/f/58bDZbQmSKtzq53W6WL19u1SARMsVTnaDtl4i5c+eSmpqaEJnirU6mafL2229b78WJkCme6hT8Xhx4XLxnal8nt6uVE0fMAWDb7mWkp6fi9Xiwf57D5/WGtB2fZwq0mxqbOfPEKwHYVbGSlNTkiGYKfi/u7Pehrl57breHE4df2ta//e+05QuqU31dA+NHXwbAR3uWkz0gq0d1crs9jMuZDcD23ctISUkCwGa34/f5aG52MfGkqwD4eF8emVkZIXWqr2vg5FHzACjdu4KMzPSQTC0tbmv/uytXkZTk6PPXXlVVFTk5OdTV1ZGVlUU0OKKy1wRgt9tD/m/fDhS9u22n02md0nU6nRiGYbWDt2nfNgzDage+2brb7qzvkczUm7YyKZMyxTZTT3LES6Z4qVPgfSCwz0TI1N0c/SVTV+/F8Zqpq7Yy9X2m1tbDlwueOW4B4TBstqP+vOhppp7ma1+D4HyGYYTUA8DRQbsndXK7D1/uecZRnj/b51mC+2tv1w7sP3ibYLF+7UWLLtcUEREREYmQtLQUps+cGPZ+ps+cSFpaSgR6FF+6+/wdq89Pd+lMnoiIiIhIhBiGwdK8p2hudoW1n7S0FOts87Gku8/fsfr8dJcGeSIiIiIiEWQYBunpqbHuRtzS8xc+Xa4pIiIiIiKSQDTIExERERERSSAa5ImIiIiIiCQQDfJEREREREQSiAZ5IiIiIiIiCUSDPBERERERkQSiQZ6IiIiIiEgC0SBPREREREQkgWiQJyIiIiIikkA0yBMREREREUkgGuR1wufzWf931PZ6vSFtv9/fZdvj8YS0TdMMaZumeUQbCGn7/f6Qttfr7bLt8/lC2srkteqbSJnirU4BiZQp3uoUXINEyRRPdQrsM5EyxVudOsoX75niqU4BiZQpnuoU/F4cTqZAv9tn8gb/rItBnXxBrzFfJ5mCxapO0aZBXicmT57MhAkTWLRoESUlJQAUFxdTWloKwObNmykrKwOgqKiI8vJyAAoLC6moqACgoKCA6upqAPLz86mrqwNg9erVNDQ0AJCbm4vL5cLr9ZKbm4vX68XlcpGbmwtAQ0MDeXl5ANTW1pKfnw9AdXU1BQUFAFRUVFBYWAhAeXk5RUVFAJSVlbF582YASktLKS4uBqCkpCRimWprawHIy8vr95k2bdpk1TdRMsVbnXbu3GnVIFEyxVud9u7da9UgUTLFW50C+0ykTPFWJ4CVK1cmVKZ4qVPgOImUKd7qtHXrVqsGvcnU2NhoPd7dQaYVK1ZY969atarP67Rhwwbr+Os++KDDTMH6sk4PPfQQEyZMYMaMGUf0I9IMM3g4LtTX15OdnU1NTQ0DBw60Rtp2uz2k7fV6MQzDattsNmw2W6ftwF9Bli9fzpw5c0hNTcUwDDweDw6HA2j7C0Nw2+l0tv1F5PO23+/H5/NZbb/fj8Ph6LTt8/kwTdNqd5Qj3Ex2u91qOxyOfp2ppaWFvLw85s+fj2EYCZEp3uoU+OE/f/58bDZbQmSKtzq53W6WL19u1SARMsVTnaDtl4i5c+eSmpqaEJnirU6mafL2229b78WJkCme6hT8Xhx4XLxnirc6Bb8Xd/b7UFeZ3G4PJw6/FIBd+98hPT01JFN9XQPjR18GwEd7lpM9IKtP61Rf18DJo+YBULp3BRmZ6SGZWlrcjMuZDcDuylUkJTn6vE5VVVXk5ORQV1dHVlYW0eCIyl4TgN1uD/m/fTtQ9O62nU6ndUrX6XRiGIbVDt6mfdswDKsd+GbrbruzvkcyU2/ayqRMyhTbTD3JES+Z4qVOgfeBwD4TIVN3c/SXTF29F8drpq7ayqRM4eZr39/W1sOXQxqGEdJ3AEcH7b6sk71dO7DP4G2CxbpO0aLLNUVERERERBKIBnkiIiIiIiIJRIM8ERERERGRBKJBnoiIiIiISALRIE9ERERERCSBaJAnIiIiIiKSQDTIExERERERSSAa5ImIiIiIiCQQDfJEREREREQSiAZ5IiIiIiIiCUSDPBERERERkQSiQZ6IiIiIiEgC0SBPREREREQkgWiQJyIiIiIikkA0yBMREREREUkgGuSJiIiIiIgkEA3yREREREREEogGeSIiIiIiIglEgzwREREREZEEokFeJ3w+n/V/R22v1xvS9vv9XbY9Hk9I2zTNkLZpmke0gZC23+8PaXu93i7bPp8vpK1MXqu+iZQp3uoUkEiZ4q1OwTVIlEzxVKfAPhMpU7zVqaN88Z4pnuoUkEiZ4qlOwe/F4WQK9Lt9Jm/wz7oY1MkX9BrzdZIpWKzqFG0a5HVi8uTJTJgwgUWLFlFSUgJAcXExpaWlAGzevJmysjIAioqKKC8vB6CwsJCKigoACgoKqK6uBiA/P5+6ujoAVq9eTUNDAwC5ubm4XC68Xi+5ubl4vV5cLhe5ubkANDQ0kJeXB0BtbS35+fkAVFdXU1BQAEBFRQWFhYUAlJeXU1RUBEBZWRmbN28GoLS0lOLiYgBKSkoilqm2thaAvLy8fp9p06ZNVn0TJVO81Wnnzp1WDRIlU7zVae/evVYNEiVTvNUpsM9EyhRvdQJYuXJlQmWKlzoFjpNImeKtTlu3brVq0JtMjY2N1uPdHWRasWKFdf+qVav6vE4bNmywjr/ugw86zBSsL+v00EMPMWHCBGbMmHFEPyLNMIOH40J9fT3Z2dnU1NQwcOBAa6Rtt9tD2l6vF8MwrLbNZsNms3XaDvwVZPny5cyZM4fU1FQMw8Dj8eBwOIC2vzAEt51OZ9tfRD5v+/1+fD6f1fb7/Tgcjk7bPp8P0zStdkc5ws1kt9uttsPh6NeZWlpayMvLY/78+RiGkRCZ4q1OgR/+8+fPx2azJUSmeKuT2+1m+fLlVg0SIVM81QnafomYO3cuqampCZEp3upkmiZvv/229V6cCJniqU7B78WBx8V7pnirU/B7cWe/D3WVye32cOLwSwHYtf8d0tNTQzLV1zUwfvRlAHy0ZznZA7L6tE71dQ2cPGoeAKV7V5CRmR6SqaXFzbic2QDsrlxFUpKjz+tUVVVFTk4OdXV1ZGVlEQ2OqOw1Adjt9pD/27cDRe9u2+l0Wqd0nU4nhmFY7eBt2rcNw7DagW+27rY763skM/WmrUzKpEyxzdSTHPGSKV7qFHgfCOwzETJ1N0d/ydTVe3G8ZuqqrUzKFG6+9v1tbT18OaRhGCF9B3B00O7LOtnbtQP7DN4mWKzrFC26XFNERERERCSBaJAnIiIiIiKSQDTIExERERERSSAa5ImIiIiIiCQQDfJEREREREQSiAZ5IiIiIiIiCUSDPBERERERkQSiQZ6IiIiIiEgC0SBPREREREQkgWiQJyIiIiIikkA0yBMREREREUkgGuSJiIiIiIgkEA3yREREREREEogGeSIiIiIiIglEgzwREREREZEEokGeiIiIiIhIAtEgT0REREREJIFokCciIiIiIpJANMgTERERERFJIBrkiYiIiIiIJBAN8kRERERERBKIBnkiIiIiIiIJRIM8ERERERGRBKJBnoiIiIiISALRIE9ERERERCSBaJAnIiIiIiKSQDTIExERERERSSAa5ImIiIiIiCQQDfJEREREREQSiAZ5IiIiIiIiCUSDPBERERERkQTiiHUH+sqYMWPIysrCZrMxcOBAVq9eHesuiYiIiIiIRNwxM8gDKCwsJCMjI9bdEBERERERiRpdrikiIiIiIpJA4mKQV1BQwJVXXsnIkSMxDIMlS5Ycsc0TTzzBmDFjSElJYcaMGRQVFYXcbxgGF154IdOmTeMf//hHH/VcRERERESkb8XFIK+pqYlJkybxxBNPdHj/4sWLeeCBB/jpT3/Kpk2bmDRpEvPmzaOqqsra5v3332fjxo0sXbqUxx57jOLi4r7qvoiIiIiISJ+Ji8/kXX755Vx++eWd3v+b3/yGu+66i9tvvx2Ap59+mrfeeotnn32WBx98EIBRo0YBMGLECObPn8+mTZuYOHFip/usqanB4/FYXycnJ5OcnBxWjsD+gvcrfUfPf+ypBrGnGsSWnv/YUw1iS89/7IVbA2/Q47weDx6Po0f3R1t/7J/b7cbtdltf19TURP2YcTHI60praysbN27khz/8oXWbzWbj0ksvZe3atUDbmUC/309mZiaNjY3k5+fzxS9+scv9jh07NuTrG264gZtuuikifV65cmVE9iO9o+c/9lSD2FMNYkvPf+ypBrGl5z/2elsDt6vVaq9YsYLklKQe3R9t/bF/L774IosXL476cYLF/SCvuroan89HTk5OyO05OTns2LEDgMrKShYuXAiAz+fjrrvuYtq0aV3ut6ysjMzMTOvrSJ3JW7lyJXPmzMHpdIa1L+k5Pf+xpxrEnmoQW3r+Y081iC09/7EXbg2am1qA3wIwb9480tJTe3R/tPXH/s2ePTvkY2c1NTWMHz8+qseM+0Fed4wbN46tW7f26DGDBg0iKysrKv1xOp36wRZDev5jTzWIPdUgtvT8x55qEFt6/mOvtzVwOL1B7SP3cbT7o60/9s/pdPb5Mm5xMfFKV4YMGYLdbqeysjLk9srKSoYPHx6jXnXM7Xbz4osvhlyTK31Hz3/sqQaxpxrElp7/2FMNYkvPf+ypBrEXeO6jWYO4H+QlJSUxdepUVq1aZd3m9/tZtWoVM2fOjGHPjuR2u1m8eLG+qWJEz3/sqQaxpxrElp7/2FMNYkvPf+ypBrHX2tr2ucBjfpDX2NjIli1b2LJlC9D2ebktW7bw2WefAfDAAw/wzDPP8Pzzz1NSUsLdd99NU1OTNdtmb/h8Puv/jtperzek7ff7u2x7PB5M07RuD7QDt5umeUQbCGn7/f6Qttfr7bLt8/lC2tHI1FG+/prJ5/PhcDgSKlO81cnv91uXRSRKpnisU+DzxYmUKZ7qZLPZjpoj3jLFW50C2ydSpnipk8/nw2azJVSmeKtTd34f6k6mQL/bZ2o/e2Vf18nnPXw5pq+TTMFiUaeO+hFpcTHI27BhA5MnT2by5MlA26Bu8uTJLFq0CGib+fLXv/41ixYt4qyzzmLLli0sX778iMlYemLy5MlMmDCBRYsWUVJSAkBxcTGlpaUAbN68mbKyMgCKioooLy8HoLCwkIqKCqBtEffq6moA8vPzaWhoAGD9+vVWOzc3F5fLhdfrJTc3F6/Xi8vlIjc3F4CGhgby8vIAqK2tJT8/H2ibcKagoACAiooKCgsLASgvL7cWgi8rK2Pz5s0AlJaWWmsDlpSURCxTbW0tAHl5ef0+U0lJCRdddFFCZYq3On3yySd86UtfSqhM8Vanqqoqa2mZRMkUb3U6+eST2bRpU0Jliqc6tba2kpqayrp16xImUzzVaevWrdYSVomSKd7q9NFHHzF//vxeZ2psbCTA3UGmFStWWPcHrrTryzpt2LDBOv66Dz7oMFOwvqzTQw89xIQJE7jkkkuO6EekGWbwcFyoq6tjwIABfPLJJwwcOND6q4Ddbg9pe71eDMOw2jabzfrrbEdtj8dDfX0948aNo7S0lKFDh2IYBh6PJ+SvKcFtp9PZ9heRz9t+vx+fz2e1/X4/Doej07bP58M0TavdUY5wM9ntdqvtcDj6daYDBw5wyimn8PHHH5OVlZUQmeKtTtXV1ZxyyimUlpaSnZ2dEJnirU7V1dWcdtpp7Ny5k+zs7ITIFE91amhoYNy4cXz00UcMGzYsITLFW53avxcnQqZ4qtOBAwc4+eST2b17N5mZmQmRKd7q1J3fh7rK9P/bu/eoqKstDuDfYQYYRB6+EEYEFFCUEBLfmojNFSwVxXvVfEEiYmUZPhK9rrDu8qZGPqJuPlEyH5mRmCYKLjBE1FQ0SUFBoKsiKAaJyOMy+/7hmolxBkOc39AM+7PWrBXnd35n9j6Hkdn9XjU1dfB2HwcAuHT9ICwtLdRy+r3iAfr2CgYAXLiaABtba72u0+8VD/Cix+O76mflfIe2VpZqOT16VIM+bmMBAD/nfQ8zM4ne16mgoAA+Pj749ddf0bVrVwiBi7wn3Lx5U7DJZowxxhhjjDEAyM/PR/fu3QUZm4u8JygUCty+fRtWVlYQiUQtHQ5jjDHGGGPMiBARHjx4AJlMprpGVde4yGOMMcYYY4wxI2IQN15hjDHGGGOMMdY0XOQxxhhjjDHGmBHhIu85fP7553BxcYFUKsXAgQNVt0htzDfffAMPDw9IpVJ4eXmpbscKPH4ux5IlS+Dl5QVLS0vIZDLMnDkTt2/fFjoNg6bLNQCAFStWwMPDA5aWlmjXrh3kcjnOnDkjZAoGT9dr0NDcuXMhEomwfv16HUdtPHQ9/6GhoRCJRGqvwMBAIVMweEJ8Bq5evYpx48bBxsYGlpaW6N+/v+rZsEydruf/yd9/5evjjz8WMg2Dpus1qKysxLx58+Do6AgLCwv07t0bGzduFDIFg6frNSgpKUFoaChkMhnatGmDwMBA1eMKmKZnmf9ffvkFEydOhIuLy1O/4zzrmmog1ix79+4lMzMziouLo19++YXCw8PJ1taWSkpKtPbPyMggsVhMa9asoStXrtDy5cvJ1NSULl++TERE5eXlJJfL6euvv6acnBzKzMykAQMGkK+vrz7TMii6XgMiol27dlFycjLl5+dTdnY2hYWFkbW1NZWWluorLYMixBooJSQkkLe3N8lkMlq3bp3AmRgmIeY/JCSEAgMDqbi4WPW6f/++vlIyOEKsQV5eHrVv354WL15MFy5coLy8PEpMTGx0zNZMiPlv+LtfXFxMcXFxJBKJKD8/X19pGRQh1iA8PJxcXV0pNTWVCgoKaNOmTSQWiykxMVFfaRkUXa+BQqGgQYMG0UsvvURnz56lnJwcmjNnDjk5OVFlZaU+UzMIzzr/Z8+epUWLFtGePXvI3t5e63ecZx1TGy7ymmnAgAH01ltvqX6ur68nmUxGH330kdb+kyZNoldffVWtbeDAgRQREdHoe5w9e5YAUFFRkW6CNjL6WIOKigoCQCkpKboJ2sgItQY3b96kLl26UHZ2Njk7O3OR1wgh5j8kJISCgoIEidcYCbEGkydPpunTpwsTsJHRx9+BoKAgGjlypG4CNkJCrIGnpyd9+OGHan369u1L//znP3UYufHQ9Rrk5uYSAMrOzlYbs1OnTrRlyxYBMjBszzr/DTX2Hed5xlTi0zWboba2FufPn4dcLle1mZiYQC6XIzMzU+s+mZmZav0BICAgoNH+wOMHs4tEItja2uokbmOijzWora3F5s2bYWNjA29vb90FbySEWgOFQoEZM2Zg8eLF8PT0FCZ4IyDkZyAtLQ12dnbo2bMn3njjDZSVlek+ASMgxBooFAocPnwYPXr0QEBAAOzs7DBw4EAcOHBAsDwMlT7+DpSUlODw4cMICwvTXeBGRKg1GDJkCA4ePIhbt26BiJCamopr165h1KhRwiRiwIRYg5qaGgCAVCpVG9Pc3BwnT57UdQoGrTnzr68xuchrhnv37qG+vh6dO3dWa+/cuTPu3LmjdZ87d+48U//q6mosWbIEr732GqytrXUTuBERcg0OHTqEtm3bQiqVYt26dUhOTkbHjh11m4AREGoNVq9eDYlEgnfeeUf3QRsRoeY/MDAQX375JY4fP47Vq1fjxIkTGD16NOrr63WfhIETYg1KS0tRWVmJVatWITAwEMeOHcOECRMQHByMEydOCJOIgdLH3+L4+HhYWVkhODhYN0EbGaHWIDY2Fr1794ajoyPMzMwQGBiIzz//HMOHD9d9EgZOiDXw8PCAk5MTli5dit9++w21tbVYvXo1bt68ieLiYmESMVDNmX99jSlp1rszQdXV1WHSpEkgInzxxRctHU6r4+/vj4sXL+LevXvYsmULJk2ahDNnzsDOzq6lQzN658+fx4YNG3DhwgWIRKKWDqdVmjJliuq/vby80KdPH7i6uiItLQ0vv/xyC0bWOigUCgBAUFAQIiMjAQA+Pj44deoUNm7cCD8/v5YMr9WJi4vDtGnT1I5oMOHFxsbi9OnTOHjwIJydnfHjjz/irbfegkwm0zgCxXTP1NQUCQkJCAsLQ/v27SEWiyGXyzF69GgQP17bYPCRvGbo2LEjxGIxSkpK1NpLSkpgb2+vdR97e/sm9VcWeEVFRUhOTuajeI0Qcg0sLS3h5uaGQYMGYdu2bZBIJNi2bZtuEzACQqxBeno6SktL4eTkBIlEAolEgqKiIixcuBAuLi6C5GGohPwMNNS9e3d07NgReXl5zx+0kRFiDTp27AiJRILevXur9enVqxffXfMJQn8G0tPTkZubi9mzZ+suaCMjxBo8evQIy5Ytw9q1azF27Fj06dMH8+bNw+TJkxETEyNMIgZMqM+Br68vLl68iPLychQXFyMpKQllZWXo3r277pMwYM2Zf32NyUVeM5iZmcHX1xfHjx9XtSkUChw/fhyDBw/Wus/gwYPV+gNAcnKyWn9lgXf9+nWkpKSgQ4cOwiRgBIRaA20UCoXq/HT2ByHWYMaMGfj5559x8eJF1Usmk2Hx4sU4evSocMkYIH19Bm7evImysjI4ODjoJnAjIsQamJmZoX///sjNzVXrc+3aNTg7O+s4A8Mm9Gdg27Zt8PX15Wuyn0KINairq0NdXR1MTNS/oorFYtWRbvYHoT8HNjY26NSpE65fv45z584hKChItwkYuObMv97GbPItWpiavXv3krm5Oe3YsYOuXLlCc+bMIVtbW7pz5w4REc2YMYOioqJU/TMyMkgikVBMTAxdvXqVoqOj1W5XW1tbS+PGjSNHR0e6ePGi2u2ba2pqWiTHvzpdr0FlZSUtXbqUMjMzqbCwkM6dO0evv/46mZubq91hiv1B12ugDd9ds3G6nv8HDx7QokWLKDMzkwoKCiglJYX69u1L7u7uVF1d3SI5/tUJ8RlISEggU1NT2rx5M12/fp1iY2NJLBZTenq63vP7qxPq36CKigpq06YNffHFF3rNxxAJsQZ+fn7k6elJqampdOPGDdq+fTtJpVL6z3/+o/f8DIEQa7Bv3z5KTU2l/Px8OnDgADk7O1NwcLDeczMEzzr/NTU1lJWVRVlZWeTg4ECLFi2irKwsun79epPHbAou8p5DbGwsOTk5kZmZGQ0YMIBOnz6t2ubn50chISFq/fft20c9evQgMzMz8vT0pMOHD6u2FRQUEACtr9TUVD1lZHh0uQaPHj2iCRMmkEwmIzMzM3JwcKBx48bR2bNn9ZWOQdLlGmjDRd7T6XL+q6qqaNSoUdSpUycyNTUlZ2dnCg8Pf6Y/Kq2REJ+Bbdu2kZubG0mlUvL29qYDBw4InYbBEmL+N23aRBYWFlReXi50+EZB12tQXFxMoaGhJJPJSCqVUs+ePemTTz4hhUKhj3QMkq7XYMOGDeTo6Eimpqbk5OREy5cv54MOT/Es89/Yd34/P78mj9kUIiK+gpIxxhhjjDHGjAVfk8cYY4wxxhhjRoSLPMYYY4wxxhgzIlzkMcYYY4wxxpgR4SKPMcYYY4wxxowIF3mMMcYYY4wxZkS4yGOMMcYYY4wxI8JFHmOMMcYYY4wZES7yGGOMMcYYY8yIcJHHGGOMtSKFhYUQiUSql729vdr2FStWQCQSIS0trWUCfEJUVJRavCtWrGjpkBhj7C+PizzGGDMyT36J1/ZycXFp6TAblZaW1qwv88r9nvYaMWKEIDEbIm9vb0RHR2PRokWCv9fmzZshEokQERHxp32HDh0KkUiEU6dOAQDkcjmio6MREhIidJiMMWY0JC0dAGOMMWG4urpi+vTpWrfZ2trqNxg98vX1xZgxY7Ru+ysXt/rm4+Ojt6NiU6ZMQWRkJPbu3Yv169fDwsJCa7/c3FycOnUKHh4eGDJkCIDHRZ5cLkdaWhri4+P1Ei9jjBk6LvIYY8xIubm5tcpT2/r169cq8/4rs7a2xj/+8Q/Ex8dj//79mDFjhtZ+cXFxAICwsDB9hscYY0aHT9dkjLFWrKqqClZWVnB1dW20T58+fWBhYYHff/9d1UZEiIuLw9ChQ2FtbY02bdqgX79+qi/pDTW8xmv37t3w8fGBhYUFHBwcMH/+fDx69Eitr7+/PwDggw8+UDvVsrCwUHeJ44/TWkNDQ5GXl4cJEyagXbt2sLS0hFwux6VLl7TuV1paisjISLi5ucHc3BwdO3bExIkTkZ2drdHXxcUFLi4uKC8vx7x589C1a1dIJBLs2LFD1WfTpk3w9PSEVCpF165d8d5776G6ulrj9NJhw4ZBIpGguLhYa1wzZ86ESCRCZmbmc81LY7Kzs+Ho6Ih27drh5MmTqvaCggLMnj0bTk5OMDc3h4ODA0JDQ1FUVKS2v7Jw0/Y7AgD19fXYuXMnTE1NMXPmTEFyYIyx1oKP5DHGWCvWpk0bTJw4EfHx8Th16pTqFDmlS5cu4fLly5g8eTKsra0BPC7wpk2bhj179sDd3R1Tp06FmZkZkpOTERYWhitXriAmJkbjvT777DMkJSUhKCgII0eORFJSEj799FPcu3cPu3btAgCMGDEChYWFiI+Ph5+fn1qRI9QppoWFhRg0aBA8PT0xa9Ys5OfnIzExEf7+/rh69So6d+6s6pufn48RI0bg5s2bGDVqFMaPH4/S0lJ8++23OHr0KI4fP46BAweqjV9TU4ORI0eisrIS48aNg0QiUY35/vvv41//+hc6d+6M8PBwmJqaYt++fcjJydGIMyIiAhkZGdi+fTuWLVumtq28vBz79++Hp6cnBg8erPM5OnnyJMaOHQtLS0ukp6fjhRdeAACcOXMGAQEBePjwIcaMGQN3d3cUFhZi165dOHLkCDIzM9G9e3cAwEsvvYQePXrgxIkTuHHjhqpd6ciRIyguLsaECRNgZ2en8xwYY6xVIcYYY0aloKCAAJCrqytFR0drfR05ckTVPyUlhQDQG2+8oTHWwoULCQAdOnRI1bZ582YCQK+//jrV1taq2mtqamjs2LEEgM6dO6dqj46OJgBkY2NDOTk5qvaqqirq0aMHmZiY0K1bt1TtqampBICio6OfKW/lfr6+vo3mnZmZqTFPAGjVqlVqYy1fvpwA0EcffaTWPmTIEBKLxZSUlKTWnpubS1ZWVuTl5aXW7uzsTAAoICCAqqqqNPYRi8XUpUsXKikpUbX//vvv1Lt3bwJAfn5+qvZHjx5R+/btqXv37qRQKNTG+uyzzwgArV+//k/nSZl3SEiI1u3K9UpNTSUiosTERLKwsKCePXtSUVGRql9tbS25uLiQlZUVXbhwQW2M9PR0EovFNGbMGLX2VatWEQBavny5xvtOmDBB43etoeb+XjDGWGvERR5jjBmZhsVLY6/58+er+tfX11OXLl2oQ4cOakVbfX09OTg4UKdOnaiurk7V3qdPH7K0tNQoWoiIfv75ZwJACxcuVLUpi4b3339fo79y28GDB1Vtz1vkPe21bt06jXnq1q0b1dfXq42l3BYcHKxqu3DhAgGgWbNmaX3/BQsWEAC6fPmyqk1Z5F26dEmj/4oVKwgArV27VmPb7t27NYo8IqLIyEgCQCkpKWrtL774Ipmbm1NZWVmj8/Nkbk0p8rZu3UpisZgGDBhAd+/eVeuXkJBAAOjDDz/UOk5wcDCZmJhQRUWFqq24uJgkEgl17dpVbc5LS0vJ1NSUZDIZ/e9//9M6Hhd5jDHWdHy6JmOMGamAgAAkJSX9aT8TExNMmzYNa9aswQ8//ICgoCAAwPHjx1FcXIy3334bEsnjPxdVVVW4fPkyZDIZVq9erTFWXV0dAGg93dDX11ejzdHREcDj0w11JSIiAhs3bmxyfx8fH5iYqF+iri2u06dPAwBKSkq03thFmXNOTo7qdEYAkEql8PLy0uivvOZv2LBhGtuGDh2qNdY5c+Zg3bp12LJlC15++WUAwPnz55GVlYWpU6eiffv2jaX5zNatW4eDBw8iICAA3377LSwtLdW2K+cjNzdX63zcuXMHCoUC165dQ79+/QAA9vb2ePXVV5GYmIjk5GQEBAQAAHbu3Im6ujqEhIRALBbrLAfGGGutuMhjjDGGGTNmYM2aNfjqq69URd7OnTtV25R+++03EBFu3bqFDz74oNHxHj58qNGmvKavIWXxWF9f/1zxP4+mxnX//n0AwOHDh3H48OFGx3sydzs7O4hEIo1+yhvZaLv+rOF1gA15eHjAz88PBw4cQFlZGTp06ICtW7cCAMLDwxuNqTnS09MBPP6fBU8WeMAf86G8nrIxT85HWFgYEhMTERcXpyrytm/fDgCYNWvWc8fNGGOM767JGGMMwAsvvAAfHx8cOnQIFRUVqKqqwnfffYeePXuif//+qn7KgsjX1xf0+JR/ra/U1NSWSkUwytxjY2OfmvuTD+3WVuA1HK+0tFRjW0lJSaNxzJ07FzU1Nfjyyy9RVVWlugGOrh/0vm3bNvj6+mLBggX49NNPNbYr4//++++fOh9+fn5q+73yyitwcHBAYmIi7t+/j59++gnZ2dnw8/ODm5ubTnNgjLHWios8xhhjAB4fsauursb+/fvx3XffobKyUuNh6lZWVujVqxeuXr2q01MsG1KerteSR/e0Ud41U1ePKPD29gYAZGRkaGw7depUo/sFBwejU6dO2Lp1K7755htUVFRg9uzZOompoXbt2iElJQX9+vXD/PnzsWHDBrXtzZ0PsViMkJAQ1NTU4KuvvuJn4zHGmAC4yGOMMQYAmDp1KsRiMXbu3ImdO3dCJBJpFHkA8M4776Cqqgrh4eFaT8ssKCh4rmfaKa8r++9//9vsMYQwYMAADBw4EHv27MHXX3+tsV2hUODEiRNNHm/KlCkwMTHBJ598gnv37qnaHz58iJUrVza6n5mZGUJDQ3HlyhUsW7YMpqamCA0NfaZcmsrW1hbJycno378/3n33Xaxfv161LSgoCE5OTli7di1+/PFHjX3r6urUnqfXkPK0zM2bN2Pv3r2wsbHB3//+d0FyYIyx1oivyWOMMSOVl5en9YYYSlFRUZBKpaqf7e3tIZfLcezYMZiYmGDYsGFwcXHR2C8iIgKnT59GfHw8MjIyIJfLIZPJUFJSgpycHJw5cwa7d+/Wum9TeHh4QCaTYe/evTA3N4ejoyNEIhHefvtt2NjY/On+586dazRvqVSKqKioZsUFAHv27IG/vz+mTJmC9evXo2/fvrCwsMCvv/6KzMxM3L17F9XV1U0aq2fPnoiKisK///1veHl5YdKkSZBIJEhISICXlxeys7M1bgijFBERgZiYGNy+fRsTJ04U9LlyykIvICAAkZGRICJERkbC3Nwc+/fvx+jRo+Hn54eRI0fCy8sLIpEIRUVFSE9PR4cOHbTehMfd3R3Dhw9XFYdz586FhYWFYDkwxlhrw0UeY4wZqfz8/KfeHOXdd99VK/KAx6dsHj16FPX19VqP4gGPrzHbsWMHXnnlFWzZsgWHDh1CZWUl7Ozs4O7ujpiYGMjl8mbHLRaLkZCQgCVLlmDPnj148OABAGD69OlNKvLOnz+P8+fPa91mY2PzXEVet27dkJWVhbVr1+LAgQPYvn07xGIxHBwcMHz48Gc+GrVy5Uo4OjoiNjYWGzduhJ2dHaZMmYL58+fj+++/13pTGABwdXXF0KFDcfLkSZ3fcEUbGxsbHDt2DIGBgViwYAEUCgUWLlyI/v3749KlS/j444/xww8/ICMjA+bm5ujSpQvGjx+P1157rdExw8LCVEUe33CFMcZ0S0RE1NJBMMYYY+wPKSkp+Nvf/ob33ntP66Mqqqur4ejoiLZt2+LGjRuNHvHTprCwEN26dUNISAh27Nihw6iFlZaWBn9/f0RHRz/1CDVjjDG+Jo8xxhhrMXfv3tW4wUx5eTmWLl0KABg/frzW/bZv346ysjJEREQ8U4HXUHx8PEQiEezt7Zu1v75ERUVBJBLB39+/pUNhjDGDwadrMsYYYy1k165diImJwciRIyGTyVBcXIykpCSUlpYiNDQUgwcPVuu/atUq3L17F5s2bYKdnR3efPPNZ35PW1tbREdHq35u27btc+chJLlcrnZasa4fFcEYY8aIT9dkjDHGWsjZs2excuVK/PTTT7h//z7EYjF69eqF0NBQvPnmmxpH6UQiEUxNTeHt7Y3Y2FgMGjSohSJnjDH2V8ZFHmOMMcYYY4wZEb4mjzHGGGOMMcaMCBd5jDHGGGOMMWZEuMhjjDHGGGOMMSPCRR5jjDHGGGOMGREu8hhjjDHGGGPMiHCRxxhjjDHGGGNGhIs8xhhjjDHGGDMiXOQxxhhjjDHGmBHhIo8xxhhjjDHGjMj/ATyWltfDE5exAAAAAElFTkSuQmCC", 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1tZXddtttbPjw4SwUCiW9G6679+R1Rbr37qGLd9xt3bqVXXHFFezwww9nffv2ZRMnTmT/+te/2KJFi7p8T11tbS276qqr2GGHHcZkWe7ynWoLFixg3/ve99jhhx/OIpEIO+KII9gZZ5zBnnrqKbZjx46ka7/66iv24IMPshEjRrBevXqx3Nxc9tprr6W03xXRaJR9+OGH7Pbbb2cnnXQSO+yww1goFGKDBg1iZ511FnvxxRdZW1tbl32t+pru/XFd+fqvf/2LXXXVVWzs2LGsb9++bMCAAeykk05izz33HGttbU3qn6pmb775JjvhhBNYr169GICka9LNAcYY+853vsMAsN69e7OWlpYur0k1xty5c9nEiRNZnz59zLndFWeddRYDwPLz81P6QRAEIRoSY4xlbEdJEARBEAQREKLRKI466igceuih2Lp1K2SZfouFIIjsgO5mBEEQBEH0SN544w00NDTgjjvuoA0eQRBZBX2SRxAEQRBEj+LZZ5/Fvn378Je//AWHHHIINm/ejAEDBvB2iyAIwjNok0cQBEEQRI9CkiREIhGcdNJJ+H//7//h9NNP5+0SQRCEp9DTNQmCIAiC6FHQz7cJgsh26AA6QRAEQRAEQRBEFkGbPIIgCIIgCIIgiCyCjmt6iK7r2L17N/r16wdJkni7QxAEQRAEQRBEFsEYQ2trK4488si0TwWmTZ6H7N69GyNHjuTtBkEQBEEQBEEQWUxNTQ2OOuqolP9OmzwP6devH4BE0vv372+pj6IoOPnkk7F27VpEIhFPr29sbMQxxxyD6upqDB482JI/PQ27+ecFLz/9tuvl+G7HctqfNMwX0jBf26Th7q8nHadHFA0D2bkW90QN2+0TNA3v378fI0eONPcdqaBNnocYRzT79+9veZMHJDaHQ4YM8fx6RVHM6+3409Owm39e8PLTb7teju92LKf9ScN8IQ3ztU0aTg/puHtE0TCQnWtxT9SwnT5B1XB3vxpGD17hjK7ruPHGG6Hrui/XE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPrNxLe6JGnZjSyRok8cZXddx6qmn2lpc7FxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NM2aZhwi0j5zMa1uCdq2I0tkZAYvRHUM/bv348BAwagpaUlEB/nHjhwAD/60Y/w17/+FYceeihvdwiCsAlpmCDEh3RMEGITNA1b3W/QJ3k+oGma+XdXbVVVzXY8HkdNTQ10XYeqquZPFNq3FUUx27FYDDt37oSu61AUBYwxMMY6tQEgJycH1157LXr16mVeD8C0la6taVpS205MqeJIFZPhe/t2qpjat72ISdd11NTUmOMENSZVVbFjxw7oup7ROsXj8U7zzcs6xeNx7Nq1C6qquo5J13Xs2LEjKWd26mRoS9M0WzGlqk2qOimKYmq+u5h69eqFa6+91vyl8KDrycu5ZzUmTdOwc+dOxGKxQMfUcX5msk6Gzox8eVknL+8Ruq5j586diMfjjuqUSlvdxdRVbdLNPU3TsGPHDmiaZqlOkUgkaS0Osp543CMURcGuXbsQj8cDH5OiKOb9PpN1cro+ZTomVVXNWjqpk6EtXddtxWT8X864d1iZe+3vF93VqeNaHAQ9WYE2eT4wfvx45ObmYtasWaioqAAAlJeXo7KyEgBQVlaG6upqAEBpaSk2btwIXddRXFyM2tpaAEBRURHq6+sBAIWFhWhubgYAfP7556isrISu68jLy0M0GoWqqsjLy4OqqohGo8jLywOQ+MmDQXNzMwoLCwEA9fX1KCoqAgDU1taiuLgYQOKpoCUlJQCA6upqlJWVAQAqKytRXl4OAKioqOg2ppKSEtTU1ACApZgKCgrQ2toKAN3G1NraioKCAs9i0nUdX375pRAxGf5msk6LFi3C5s2boeu6LzEtX74cW7ZswY4dO1zHpOs61q9fjx07djiqU35+PqqqqhCPx23FtG/fPqxbtw66rlueexs2bICu65bqBAC7du1yFFOm9WSlToC3eorH46iqqkJ+fn6gY9J1HWvXrkVTUxOXOm3ZsiXw9whd17Fp0yYsWbLEcZ2+/PJL6LpuKyZd17Fu3Trs27fP0tyLx+MoKytDPB63VKeGhgYYBF1PvO4RW7ZsESYm4/6dyTo5XZ94xLRlyxYsWbLEUZ0OHjyIsrIy6LpuKyZd17Fx40aUlpaaMXU399avX4///ve/0HXdUp2Ar7XMa+7NnDkTubm5mDBhAqxAxzU9xPj4tLGxEYMGDTJ32qFQKKmtqiokSTLbsixDluWUbUVREAqFzHY4HIYkSWYbSPy0oX07EokgHo9j3rx5uOCCC0wfIpGI+ZPLcDicsm38hNJodxUHj5gYY2bb+KkTxUQxZWtMxg9zZsyYgV69emVFTNlYJ4qJYkoXUywWQ35+vrkWZ0NM2VgnioliShWTpmlJazHvmJqamjB48GA6rsmDUChk/t1VOxwOm21Zls2Pi8PhsPnm+vbtSCRitkOhkPlRdiQSgSRJkCSpUxtIfrSqLMvm92VZNideqnYoFEpq24kpVRypYjJ8b99OF1P7ONzGpOuJ40GG/aDGBCQ+zdF1PaN1CoVC5vz0o06yLGP79u2mXTcx6bpufuLlpE6GthhjtmJKVZtUdZIkyTzeZqVOxlhOYsq0nqzUKV0cTmJijGHHjh3m2EGNSdd17N6926xpJutk6Mz4T4yXdfLyHmGcVDDGtlunVNrqLqaO947u5h5jDLt37wZjzHKdDIKuJx73CEmSsH37dsiyHPiYJEky7/eZrJPT9SnTMQEwa+mkToa2dF23FVPHe4eVudf+fmGlToY9uzH5pScr0CaPM8biouvWn+pl53oiPaLkk5efftv1cny3YzntTxrmiyj55Omnn7ZJw4RbRMpnNq7FPVHDbmyJBB3X9JCgPV1TURTk5eXhggsuSPrUgSAIMSANE4T4kI4JQmyCpmF6uqYgaJqGqqoqy0/KsXs9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCLSLlMxvX4p6oYTe2RII2eZxhjKGpqQlWP1C1ez2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9ukYcItIuUzG9finqhhN7ZEgo5reggd1yQIwktIwwQhPqRjghCboGmYjmsKgqZp2Lhxo61jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfGbjWtwTNezGlkjQJi8AtLW1+Xo9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCLSLlMxvX4p6oYTe2RIGOa3oIHdckCMJLSMMEIT6kY4IQm6BpmI5rCoKmadiwYYOtYyJ2rifSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHxm41rcEzXsxpZI0CbPB4wJo2lal21VVZPaxoepqqqaL2Vs31YUJaltXG+0GWOd2gCSnhik67r5fV3Xoapq2ramaUltuzF1FYdXMbWPw4uYdF0XIiZj7EzWqf385F0nKzG1z1Mm69RVbdLFZFxvJSZjrEzHRPcI72NqP1cyGVN3cQTpHsEYc1WnrrRlJaaOtemuTrqu26qTAenJenxBjam7+7eIdQpSTMa1dmNqf++wOvfa//+7u5gMezzr1L5tBdrk+cD48eORm5uLWbNmoaKiAgBQXl6OyspKAEBZWRmqq6sBAKtXr0b//v0RCoVQXFyM2tpaAEBRURHq6+sBAIWFhWhubgYALFy4EKNGjUIoFEJeXh6i0ShUVUVeXh5UVUU0GkVeXh4A4MCBA6ZPzc3NKCwsBADU19ejqKgIAFBbW4vi4mIAQE1NDUpKSgAA1dXVKCsrAwBUVlaivLwcAFBRUdFtTCUlJaipqQEASzEVFBSgtbUVALqNqbW1FQUFBZ7FFAqFIMsyNm/eHOiY9u7di+bmZoRCoYzW6YsvvsCIESMQCoV8qdPKlSsxbtw47N6923VMoVAIBw4cwO7dux3Vaf78+Tj22GPBGLMVU1NTE+rr6xEKhSzNva1bt0LTNIRCIUt1AoBdu3Y5iinTerJSJ8BbPTHGcOyxx2L+/PmBjsmYH0YcmazTunXrMG7cOGzdujXQ94hQKIRhw4Zh2bJljuq0efNmyLKMUChkK6ZQKIT6+no0NTVZmnuMMVRXV4MxZqlODQ0NMAi6nnjcI7Zu3Ypx48Zh3bp1gY9px44diMViCIVCGa2T0/Up0zHt3bsX48aNw7JlyxzVSVEUcz23E1MoFEL//v2xevVqANbm3pdffolIJIJQKGSpTsDXWualp5kzZyI3NxcTJkyAJRjhGS0tLQwAa2xsZIwxpqoqU1W1U1tRFLMdjUbZ6tWrmaqqTFEUpmmaeY3RjsfjZrutrc28Ph6PM13Xma7rndqMMRaLxdjs2bPN/sb3NU1jiqKkbRv+pIsjVUyp4kgVk+F7+3aqmNq3vYhJVVW2evVqFovFAh1TPB5nq1atYqqqZrRO7eenH3WKRqNszZo1LB6Pu45JVVW2atUq05bdOhnaUhTFVkypapOqTrFYzLy+u5ji8TibPXs2i0aj3OZe0O8RiqKw1atXs7a2tkDH1HF+ZrJOsViMrVmzhsVisUDfI4z7sTHf7dYpFouZvtiJqavapJt7iqKwVatWMUVRLNUpGo0mrcVB1hOPe0RX8zOoMVm5f/tRJ6frU6ZjisfjbM2aNSwajTqqk6EtVVVtxdTx3mFl7rW/X3RXp7Vr4+wf//jMHJ+3nhobGxkA1tLSwtIRtrYVJOwQCoWS/u7YDofDSe2+fft2+X2D9r/kGYlEzOs7fr9jW5Ik83uyLEOWZcvtVL5bjam7dne+p4vJaHsRk6Zp6Nu3b5c1C1pMhxxyiKWY0rXtxtR+fvoRUzgcRp8+fcyfvruJSdM0HHLIIeb4TurUt29fSJLU5fXpYuqqNunqZFzfXUzGcRA3MaVre60nKzFZbVuNw9Bw0GPqOD8zXac+ffogFArZXp+stL26Rxi1NMZ3UifDFzsx2a2NJEk45JBDIElSUhzp6mQQdD11FWsm9NRxfgY5pu7u337Vyen6lMmYNE1Dnz59EA6HHd/rDF/sxNTx3mF17nX3/+9wOIyCAuDKKyWMHn0qLrvMfkwd4/NaT+mgp2t6CD1dkyAILyENE4T4kI4JQkxeew348Y8BTQO+9a16fPHFABx2GH8N09M1BUFVVZSWlpq/TOn19UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGCbeIlM9sXIt7ooa766PrwC9+AdxxR2KDd911Oh5/fDkGDLDlFndok8cZSZIwaNCgpKOVXl5PpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NM2aZhwi0j5zMa1uCdqOF2fgweB738feP75xNdPPAG88YaGSETvYpRgQ8c1PYSOaxIE4SWkYYIQH9IxQYjBnj3AxRcDpaVATg7wxhvAddcFT8N0XFMQVFVFcXGxrWMidq4n0iNKPnn56bddL8d3O5bT/qRhvoiST55++mmbNEy4RaR8ZuNa3BM13FWfDRuA005LbPCGDAEWLkxs8ESGNnmckWUZI0aMSHr6lpfXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPrNxLe6JGu7Yp6AAOPNMYMcO4BvfAFasAM46y673wYOOa3oIHdckCMJLSMMEIT6kY4IILn/5C3D33YkHrEyZAnz0UeKTvPYETcN0XFMQVFVFUVGRrWMidq4n0iNKPnn56bddL8d3O5bT/qRhvoiST55++mmbNEy4RaR8ZuNa3BM1DADxuIprr63BnXcmNng33AAUFHTe4IkMbfI4I8syxo4da+uYiJ3rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn9m4FvdEDR88CFx9dQj/+tdIAMCTTwJvvQX06mXb5UAT7v4Swk+MM8F+XU+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrW4p2n46ydoSklP0MxGgv9jkyxHVVUUFhbaOiZi53oiPaLkk5efftv1cny3YzntTxrmiyj55Omnn7ZJw4RbRMpnNq7FPUnDyU/QZPjtb1fjqquCP++cQp/kcUaWZYwbN87WMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtwiUj6zcS3uKRqePx+48kqgtRX45jeBTz9lGDhwpBDzzim0yeOMLMsYOnSob9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGibcIlI+s3Et7gka7vgEzY8/BgYPlgGIMe+ckr3bV45ommb+3VVbVVWz3dbWhvz8fCiKAlVVoeu6eY3RVhTFbB88eNC8XlEUMMbAGOvUBoD2b8fQdd38vq7r5kfaqdqapiW17cSUKo5UMRm+t2+ni6l9HG5jUhQF+fn5iEajgY4pFouZdc9kndrPTz/q1NbWhvnz5yMWi7mOyahlLBZzVCdDW/F43FZMqWqTqk7RaNS83kqdjLGcxJRpPVmpU7o4nMQUj8eRn5+PgwcPBjqmjvMzk3WKRqOYP38+otFooO8RRo7a2toc1SmVtrqLqavapJt7xpyLx+OW62QQdD3xuEd0NT+DGpOV+7cfdXK6PmU6plgshvnz56Otrc1RnQxtGV9bjanjvcOYe7oO3H+/bj5B8/rrdcybp2Hw4OS4rdTJsMdr7nXUkxVok+cD48ePR25uLmbNmoWKigoAQHl5OSorKwEAZWVlqK6uNttHH300QqEQiouLUVtbCwAoKipCfX09AKCwsBDNzc0AgEWLFuFb3/oWQqEQ8vLyEI1Goaoq8vLyoKoqotEo8vLyAAAHDhwwfWpubkZhYSEAoL6+HkVFRQCA2tpaFBcXAwBqampQUlICAKiurkZZWRkAoLKyEuXl5QCAioqKbmMqKSlBTU0NAFiKqaCgAK2trQDQbUytra0oKCjwLKZQKITDDjvMjCOoMe3btw85OTkIhUIZrVNRURGOP/54hEIhX+pUWlqKiRMnYvfu3a5jCoVC6NOnD3bv3u2oTgsWLMBJJ50ExpitmJqamhAKhRAKhSzNverqagwcOBChUMhSnQBg165djmLKtJ6s1AnwVk+MMZx00klYsGBBoGMKhUJgjJlxZLJO5eXlmDhxotn2sk5e3iNCoRC+8Y1vmHHYrVNlZSUOO+wwhEIhWzEZ+m1qarI09xhjiMViYIxZqlNDQwMMgq4nHveI6upqTJw40WwHOaaamhr069cPoVAoo3Vyuj5lOqZ9+/Zh4sSJKC4udlQnRVEQi8UQCoVsxRQKhXD00UcnrbPLl6/D978PvPRSYqvz5JPAgw/+F1u3VpjxDRs2DKFQyFKdgK+1zEtPM2fORG5uLiZMmAAr0MvQPcR4OWFjYyMGDRpk7rRDoVBSW1VVSJJktmVZhizLKduKoiAUCpntcDgMSZLMNpD4aUP7diQSQTwex7x583DBBReYPkQiEei6Dl3XEQ6HU7Y1TQNjzGx3FQePmBhjZlvXdYqJYsrqmHRdR15eHmbMmIFevXplRUzZWCeKiWJKF5Pxab+xFmdDTNlYJ4ope2LatUvDJZfIWL1aQk4Ow9//zvCDH8iOY9I0LWkt5l2npqYmDB48mF6GzoNQKGT+3VU7HA6bbcYY5s2bZ04M4xdA27cjkYjZBhI/KVAUBZFIBJIkQZKkTm0AkCTJ7CPLsvl9WZbNiZeqHQqFktp2YkoVR6qYDN/bt9PF1D4OtzEZH/EbH7sHNSZN0zB//nwoipLROgEw56cfdWKMYe7cudA0zXVMiqJg/vz55o3bbp0AmD95sxNTqtqkqpOu6+YRESt1MsZyElOm9WSlTunicBKT8RNTg6DGpCgKCgoKzPmZyToZOtN1PdD3CEVRMG/ePPPok906pdJWdzF1vHd0N/dUVUVBQYH5H0MrdTIIup543CN0XcfcuXPBGAt8TLqum/f7TNYJcLY+ZTomTdPMWjqpk6EtRVFsxdT+3rFhAzBpUgirV0sYMgRYuFDCD34gd4qj/f3CSp0Me7zmXkc9WYE+yfMQ45O87nbW7TGO7/Tr1y9JzF5crygK8vLycMEFF5gTiUjGbv55wctPv+16Ob7bsZz2Jw3zhTTM1zZpuPvrScfpEUXDQHauxdmk4eLifrjqKsl8gubcucCxx7q3FTQNW91v0NM1OSNJkuUNoZPrifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn9m4FmeLht99tz9+8pPEA1amTgU++ggYPNg/X0WAjmtyRlEUzJkzx3zCjtfXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPrNxLRZdw4knaGq4667EBu/GG4GCgvQbPC98FQE6rukhTo9rRqNR9O7d2/IxEavXB+3j5SBiN/+84OWn33a9HN/tWE77k4b5Qhrma5s0TMc13SKKhoHsXItF1vDBg8D11yfeewcATz7J8OijEqyYFnktpuOaAmH8IqVf1xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtwiUj6zcS0WUcO1tcDFFwOrVgE5OQx//auG668PWdrg2bUlKnRckzPt36Xhx/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPbFyLRdTw+vXAaaclNnhDhgAFBRr6959ry65I884pdFzTQ5we1zTepWH1mIjV64P28XIQsZt/XvDy02+7Xo7vdiyn/UnDfCEN87VNGqbjmm4RRcNAdq7Foml4/nzgyiuR9ATNsWPt2xV5Lba636BP8gKAk586Et4hSj55+em3XS/HdzuWm58+ZsIO0TWi5JOnn37aJg0TbhEpn9m4Foui4VdfBS68MLHBmzoVWL7861ckOLEr0rxzAm3yONP+pap+XE+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrVYBA1rGvDAA0j5BE0ndkWad06h45oe4uS4pp8E7eNlgiDsQRomCPEhHROEc776KvEEzdmzE18/9RTwyCOw9YAVtwRNw3RcUxAYY9i/fz+s7rXtXk+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrU4yBqurU0cy5w9G+jVC3j3XeDRRztv8JzYFWneOYU2eZxRVRVLliyxdUzEzvVEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPbFyLg6ph4wmaq1cnnqC5cCFw7bXe2RVp3jmFjmt6CB3XJAjCS0jDBCE+pGOCsEd+PnDVVclP0DQesMKDoGmYjmsKgq7raGxshK7rvlxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NM2aZhwi0j5zMa1OGgafv75r3DRRQytrcC0aclP0PTSrkjzzim0yeOMpmkoLS2Fpmm+XE+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrU4KBrWNODnP2f4xS8OgaZJuOmmxDvxjCdoem1XpHnnFDqu6SF0XJMgCC8hDROE+JCOCSI9HZ+g+fTTwMyZmX2CZjqCpmE6rskR46cCmqZ12VZV1WzH43Hs2bMHuq5DVVXzY+P2bUVRzHYsFkNdXR10XYeiKGCMgTHWqQ0g6YlBxvVG2/hF01RtTdOS2nZiShVHqpgM39u308XUPg63Mem6jj179pjjBDUmVVVRW1sLXdczWqd4PN5pvnlZp3g8jr1790JVVdcx6bqO2trapJzZqZOhLU3TbMWUqjap6qQoinm9lToZYzmJKdN6slKndHE4iUnTNNTV1SEWiwU6po7zM5N1MnRm5MvLOnl5j9B1HXV1dYjH447qpCiKuZ7aiamr2qSbe5qmYffu3dA0zXKdDIKuJx73CEVRsHfvXsTj8cDHZOX+7UednK5PmY5JVVWzllbrtHs3w5Qp+v+eoMnwxz824eGHdQDWYzL+L2fcO6zMvfb3Cyt1MuxZiSkTerICbfJ8YPz48cjNzcWsWbNQUVEBACgvL0dlZSUAoKysDNXV1QCA0tJSrF27Frquo7i4GLW1tQCAoqIi1NfXAwAKCwvR3NwMAPj8889RXl4OXdeRl5eHaDQKVVWRl5cHVVURjUaRl5cHADhw4IDpU3NzMwoLCwEA9fX1KCoqAgDU1taiuLgYAFBTU4OSkhIAQHV1NcrKygAAlZWVKC8vBwBUVFR0G1NJSQlqamoAwFJMBQUFaG1tBYBuY2ptbUVBQYFnMem6jjVr1ggR06pVq6DrekbrtGjRIjNPfsS0fPlybNiwATt27HAdk67rWL16NXbs2OGoTvn5+Vi/fj3i8bitmPbt22fWxurcKysrg67rluoEALt27XIUU6b1ZKVOgLd6isfjWL9+PfLz8wMdk67rKC0tRVNTE5c6bdiwIfD3CF3XsW7dOixZssRxndasWQNd123FpOs6Vq1ahX379lmae/F4HKWlpYjH45bq1NDQAIOg64nXPWLDhg3CxGTMsUzWyen6xCOmDRs2YMmSJZbqVFoaw+mnA2vWyDjsMIZPP23DiBFF0HXdVky6rmPt2rUoLS01Y+pu7q1fv95ci63UCfhay7zm3syZM5Gbm4sJEybACnRc00OMj08bGxsxaNAgc6cdCoWS2qqqQpIksy3LMmRZTtlWFAWhUMhsh8NhSJJktoHETxvatyORCOLxOObNm4cLLrjA9CESiZg/uQyHwynbxk8ojXZXcfCIiTFmto2fOlFMFFO2xmT8MGfGjBno1atXVsSUjXWimCimdDHFYjHk5+eba3E2xJSNdaKYMhvTZ5+puO66EFpbJXzzmwxz5wJjxwYzJk3TktZi3nVqamrC4MGDuz2uSZs8D3HyO3nGT/+HDx8OWe7+g1U71ytKsM4QBxG7+ecFLz/9tuvl+G7HctqfNMwX0jBf26Th7q8nHadHFA0D2bkW89Dwn/8M/PSniYetTJsGfPQRMGhQ5jRst0/QNEy/kycIuq5jy5Yt0HXrj262cz2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9ukYcItIuUzG9fiTGpY04Cf/xz48Y8TbeMJmoMGufPFST+R5p1T6JM8D6GnaxIE4SWkYYIQH9IxQSSeoPmDHwBz5iS+DtoTNNMRNA3TJ3mCoOs6tm/fbusniHauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfGbjWpwJDdfWAlOnJjZ4vXoB770HPPJI5w1epjTsxpZI0CaPM7quY9euXbYWFzvXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPrNxLfZbw+XlwGmnAatXA4cdBhQWAtdc460vTvqJNO+cQsc1PYSOaxIE4SWkYYIQH9Ix0VPJzweuugpobQWOOw7mEzRFI2gapuOagqBpGqqqqiy/2NDu9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGCbeIlM9sXIv90vCf/wxceGFigzdtGrB8efcbvExp2I0tkaBNHmcYY2hqaoLVD1TtXk+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrXYaw3X1zfhgQck/PjHgK4DN9+c/ARNP3xx0k+keecUOq7pIXRckyAILyENE4T4kI6JnsJXXwHXXQd88kni61//Gnj4YTGeoJmOoGmYjmsKgqZp2Lhxo61jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfGbjWuzV2Lt3A1OmMHzyCdCrF8N779l/RUKmNOzGlkiEeTtAAG1tbb5eT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmmYcItI+czGtdjt2OvWARddBOzcKWHgQAWffCJj8uRQRn1x0k+keecEOq7pIXRckyAILyENE4T4kI6JbCYvD7j6auDAAeD44xNP0BwzhrdX3hI0DdNxTUHQNA0bNmywdUzEzvVEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPbFyL3Yz9yivA976X2OCdfTawZImGgweDr2E3tkSCNnkEQRAEQRAEQVhC04B77wV++tPEEzRvvRWYN8/aEzSJzEHHNT2EjmsSBOElpGGCEB/SMZFNHDgAXHst8Nlnia9/8xvgl78U/wma6Qiahum4piBomoaysjJbx0TsXE+kR5R88vLTb7teju92LKf9ScN8ESWfPP300zZpmHCLSPnMxrXYzti7dgFTpiQ2eL17A//5D/DQQ19v8ETRsBtbIkFP1wwAffr08fV6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScOEW0TKZzauxVbGLitLPEFz925g6FBgzhzg9NOdjeXWF6/6iTTvnEDHNT2EjmsSBOElpGGCEB/SMSE6n36aOKL51VdAbm7ik7xjjuHtVeYImobpuKYgqKqK0tJSqKrqy/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPbFyLuxv7D38ALr00scH7zneAZctSb/BE0bAbWyJBmzzOSJKEQYMGQbL4G6t2ryfSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHxm41qcamxVTTw98957E0/QvO22xDvxBg70z89MadiNLZGg45oeQsc1CYLwEtIwQYgP6ZgQjdZW4JprEps6APjtb4EHHsjuJ2imI2gapuOagqCqKoqLi20dE7FzPZEeUfLJy0+/7Xo5vtuxnPYnDfNFlHzy9NNP26Rhwi0i5TMb1+KOY9fUAGedldjg9ekDfPgh8ItfWNvgiaJhN7ZEgp6uyRlZljFixAjIsrX9tt3rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn9m4Frcfe/Vq4HvfA2prgWHDEg9cmTgxc35mSsNubIlE9kbGEeOdG5qmddlWVdVs67qOkSNHQpZlqKoKXdfNa4y2oihmW9M0HH300ZBlGYqigDEGxlinNgC0P4mr67r5fV3XzZ9cpGprmpbUthNTqjhSxWT43r6dLqb2cbiNSZZlHHXUUab9oMYEwLwZZbJOmqaZ89OPOum6jlGjRpl23cRk3LAN7NbJ0JYkSbZiSlWbVHVijOGoo46CLMuW6mSM5SSmTOvJSp3SxeEkJkmScPTRR5tjBzUmWZZx5JFHmjXNZJ0MnTHGAn2PkGUZI0eONMe2W6dU2uoupo73ju7mniRJOPLIIyFJkuU6GQRdTzzuEYwxjBo1CrquBz4mxph5v89knZyuT3Zi+vRTGVOmMNTWAuPGAcXFGr79bXsxATBr6aROhrZkWbYVU8d7h5W51/5+YaVOhj27MfmlJyvQJs8Hxo8fj9zcXMyaNQsVFRUAgPLyclRWVgIAysrKUF1dDQBYuXIlFi5caH5sXFtbCwAoKipCfX09AKCwsBDNzc0AgIKCAnzxxRdQVRV5eXmIRqNmW1VVRKNR5P3vEPWBAwdMn5qbm1FYWAgAqK+vR1FREQCgtrYWxcXFAICamhqUlJQAAKqrq1FWVgYAqKysRHl5OQCgoqKi25hKSkpQU1MDAJZjam1tBYBuY2ptbUVBQYFnMamqigULFuDLL78MdEy7du1Cfn4+VFXNeJ2M+eZHnZYtW4aioiJs27bNdUyqqmL+/PnYtm2b4zotXrzYjMNqTHV1dZg3bx5UVbU09zZt2oSCggKoqmqpTkb9ncaUST1ZqRPgrZ6i0SgWL14c+JhUVcW8efPQ0NCQ8TqtXr0aRUVF2LRpU6DvEaqqYtGiRVi8eLGjOn355ZdYsGABVFW1FZNRm7q6Oktzz4ijfTtdnYyaO4kp29bcrmLatGkTioqKsHr16sDHtGXLFvP+nek6OVmfrMRUVbUFd921GZddxnDwoITTT9+PpUuBgwftx7Rr1y4UFRVh8eLFjup04MABs20nJlVVsXDhQqxcudLy3Fu3bh0+//xzqKpqqU7A11rmpaeZM2ciNzcXEyZMgCUY4RktLS0MAGtsbGSMMaaqKlNVtVNbURSzHYvF2I4dO5imaUxRFKZpmnmN0Y7H42Y7Go2ympoapmkai8fjTNd1put6p7Yx9uzZs83+xvcNW+naqqomtbuKI1VMqeJIFZPhe/t2qpjat72ISdM0tmPHDnOcoMakKArbvn070zQto3WKxWKd5puXdYrFYmznzp1MURTXMWmaxrZv356UMzt1MrSlqqqtmFLVJlWd4vG4qfnuYorH42z27NksGo1ym3tBv0eoqspqamrMHAU1po7zM5N1MnRm5MvLOnl5j9A0jdXU1LBYLOaoTqm01V1MXdUm3dxTVZVt376dqapqqU7RaDRpLQ6ynnjcI+LxONu5cyeLxWKBjykej5v3+0zWyen61F1MisLYHXeoDGAMYOyOOzQWjTqPSVEUs5ZO6mRoS9M0WzEZ/5cz7h1W5l77+0V3deq4FvPWU2NjIwPAWlpaWDro6ZoeQk/XJAjCS0jDBCE+pGMiiOzfD1x1FTB/fuKhKs8/D9x3X899gmY6gqZherqmIKiqah5T8eN6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScOEW0TKZ7asxTt2JJ6gOX8+0Lcvw+OPl+Oee1TXGzxRNOzGlkjQ0zU5I8syxo0bZ+upXnauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfGbDWrxqVeIJmnv2AEccAXzyCcOoUUf0KA27sSUStMnjjCzLGDp0qG/XE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3CJSPkVfiz/+GPjBD4C2NuDEE4HPPgNGjpQB9CwNu7ElEtm7fRUERVEwf/78pMexe3k9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCLSLlU9S1mLHE79xdcUVig/fd7wJLlwIjR/ZMDbuxJRK0yeNMKBTCxIkTEQqFfLmeSI8o+eTlp992vRzf7VhO+5OG+SJKPnn66adt0jDhFpHyKeJarCjAnXcCv/hFYrN3993AJ58A/fq5H9tLP930d9JPpHnnFDquyRlZljF48GDfrifSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHyKtha3tABXXgksWJB4auZLLwH33JP8BM2eqGE3tkSCPsnjjKIomDt3rq1jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuEWkfIq0Fm/bBpx5ZmKDd8ghwJw5wL33dn5FQk/UsBtbIkHvyfMQJ+/JY4yhtbUV/fr1g2Th2bV2rg/aez2CiN3884KXn37b9XJ8t2M57U8a5gtpmK9t0nD315OO0yOKhgFx1uKVK4GLLwb27gWOPDLxgJXx470Z20s/vervpJ/Ia7HV/QYd1+SMJEm2Xpxu93oiPaLkk5efftv1cny3YzntTxrmiyj55Omnn7ZJw4RbRMqnCGvxBx8AN9wARKPAyScDn34KHHWUN2N76aeX/Z30E2neOYWOa3JGURTMmTPH1jERO9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGibcIlI+g7wWMwY891zid/CiUeDCC4ElS9Jv8KyO7aWffvR30k+keecUOq7pIU6Pa0ajUfTu3dvyMRGr1wft4+UgYjf/vODlp992vRzf7VhO+5OG+UIa5mubNEzHNd0iioaB4K7FigLcdRfwt78lvr7nHuDFFwErD47siRq22ydoGqbjmgIRDtsrg93rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn0Fbi5ubge9/H1i4EJBl4OWXgZ/8xJuxnSCKht3YEgU6rskZVVWRl5cHVVV9uZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23SMOEWkfIZtLW4uhqYNCmxwTv00MT77+xu8Hqiht3YEgk6rukhTo9rqqqKcDhs+ZiI1euD9vFyELGbf17w8tNvu16O73Ysp/1Jw3whDfO1TRqm45puEUXDQLDW4uXLgUsuAfbtA0aMAObOBU46yZuxvfQzE/2d9BN5Lba636BP8gKAk59YEN4hSj55+em3XS/HdzuWm58+ZsIO0TWi5JOnn37aJg0TbhEpn0FYi//zH2D69MQG79vfBkpKnG3wuhrbLaJo2I0tUaBNHmdUVUVBQYGtYyJ2rifSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHzyXosVRcUzzwBXXw3EYol34RUVJd6F53bsnqRhN7ZEgo5reoiT45p+ErSPlwmCsAdpmCDEh3RMeEE8DtxxB/Dmm4mv77sP+N3vrD1Bk3BH0DRMxzUFgTGG/fv3w+pe2+71RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4YJt4iUT16+NjYyfOc7Kt58M/EEzT/9yforErqjJ2rYjS2RoE0eZ1RVxZIlS2wdE7FzPZEeUfLJy0+/7Xo5vtuxnPYnDfNFlHzy9NNP26Rhwi0i5ZOHr1u2AGecASxZEka/fgxz5ybeiecVPVHDbmyJBB3X9BA6rkkQhJeQhglCfEjHhFOWLQMuvRSorwdGjkw8QfOEE3h71fMImobpuKYg6LqOxsZG6Lruy/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3iJTPTPr63nvA2WcnNninnMKQn9+Eb32LNOxVP5HmnVNok8cZTdNQWloKTdN8uZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23SMOEWkfKZCV8ZA556CrjuusTDVi67DFiwQEVNTQlp2MN+Is07p9BxTQ+h45oEQXgJaZggxId0TFglFgNuvx14++3E1w88ADz3XOJhKwQ/gqZhOq4pCLquY+/evbaOidi5nkiPKPnk5affdr0c3+1YTvuThvkiSj55+umnbdIw4RaR8umnr42NwHnnJTZ4oRDwl78kXpEgy6RhP/qJNO+cQps8zui6jg0bNthaXOxcT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmmYcItI+fTL16oq4PTTEy82798fyMtLfKLnt12vxxZFw25siQQd1/QQOq5JEISXkIYJQnxIx0Q6lixJPEGzsREYNQr47DNg3DjeXhHtCZqG6bimIOi6jl27dtn6CaKd64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2iYNE24RKZ9e+/rOO8A55yQ2eKeeCqxY0fUGjzTsfT+R5p1TaJPHGV3XsWXLFluLi53rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn175yhgwaxZw442AogDf/z6waBFwxBH+2vV7bFE07MaWSNBxTQ+h45oEQXgJaZggxId0TLQnGgVuvTXxHjwAeOgh4Ne/pidoBpmgaZiOawqCruvYvn27rZ8g2rmeSI8o+eTlp992vRzf7VhO+5OG+SJKPnn66adt0jDhFpHy6dbXffsSxzPfew8Ih4G//Q34zW+63+CRhr3vJ9K8cwpt8jhDvwvAF1HyyctPv+32xN8FEGXOiYIo+eTpp5+2ScOEW0TKpxtfKyqA004DiouBgQOBgoLEJ3p+283k2KJo2I0tkaDjmh5CxzUJgvAS0jBBiA/pmFi4ELjiCqClBRgzBpg7Fzj+eN5eEVYJmobpuCZHNE0z/+6qraqq2Y7FYti8eTM0TYOqquZPFNq3FUUx29FoFJWVldA0DYqigDEGxlinNgC037/rum5+X9d1qKqatm34ky6OVDGliiNVTIbv7dvpYmofh9uYNE3D5s2bEY/HAx2ToijmPMlknWKxWKf55mWdYrEYqqqqoCiK65iMWhq27NbJ0JaqqrZiUhQFmzZt6lSbVHWKx+NmLa3UyRjLSUyZ1pOVOqWLw0lMqqqisrIS0Wg00DFpmoZNmzYlzZtM1Skej6OqqgrxeDzQ9whN01BZWYlYLOaoTqm01V1MXdUm3dxTVRWbNm2CqqqW62QQdD3xuEd0NT+DGpOV+3fHOr3+uo4ZMxIbvDPPZFi5Ejj22MysT37FlKpOiqKgqqoKsVjMUZ0MbWmaZismY/037h1W5l77uK3oybDHa+51jMkKtMnzgfHjxyM3NxezZs1CRUUFAKC8vByVlZUAgLKyMlRXVwMAVq1ahd27d4MxhuLiYtTW1gIAioqKUF9fDwAoLCxEc3MzAGDhwoXYt28fGGPIy8tDNBqFqqrIy8uDqqqIRqPIy8sDABw4cMD0qbm5GYWFhQCA+vp6FBUVAQBqa2tRXFwMAKipqUFJSQkAoLq6GmVlZQCAyspKlJeXAwAqKiq6jamkpAQ1NTUAYCmmgoICtLa2AkC3MbW2tqKgoMCzmBhj2LFjBzZu3BjomPbs2YOqqiowxjJapy+++AJ79+4FY8yXOq1YsQJNTU2exMQYw9atWx3Xaf78+WhoaDB/YmcnJqM2VuZeVVUVtm/fDsaYpToBwK5duxzFlGk9WakT4K2eFEVBQ0MD5s+fH+iYGGPYvHkztzo1NTWhqqoq0PcIxhj27NmDpUuXOqrTxo0bsWPHDjDGbMXEGENVVZXluacoCjZu3AhFUSzVqaGhAQZB1xOPe0RVVRWampqEiGnbtm3metNdnXQd+OEP9+H222WoKnDuufvwt7/twGGHZXZ98jImIP3c27NnD5qamrB06VJHdWpra8PGjRvBGLMVE2MMu3fvxqpVqyzPvQ0bNqCmpgaMMUtzD/hay7z0NHPmTOTm5mLChAmwAh3X9BDj49PGxkYMGjTI3GmHQqGktqqqkCTJbMuyDFmWU7YVRUEoFDLb4XAYkiSZbSDx04b27Ugkgng8jnnz5uGCCy4wfYhEItB1HbquIxwOp2wbP0Ux2l3FwSMm4yc9RhwUE8WUzTHpuo68vDzMmDEDvXr1yoqYsrFOFBPFlC6mWCyG/Px8cy3OhpiysU5exnTwIHDLLSF8+CEAAI89BjzyiIpQSNyYsrFOVmPSNC1pLeYdU1NTEwYPHkzHNXkQCoXMv7tqh8Nhsy1JkvlxcTgchvy/Ryy1b0ciEbMty7L5UXYkEoEkSZAkqVPbGNtAlmXz+7IsmxMvVTsUCiW17cSUKo5UMRm+t2+ni6l9HG5jMo4Hta9dEGMyftKsaVpG6yTLsjk//aiTJEnmT+3cxqRpmvmJmpM6GdrSdd1WTKlqk6pOAMzjbVbqZIzlJKZM68lKndLF4SQmXdexadMm89qgxqRpGrZs2WLOz0zWydCZ8bWXdfLyHmEcuWqvSTt1ArrWVncxdbx3dDf3dP3r92tZrZNB0PXE4x4BJD6FNf6THeSYAJj3+1Qx7dsXwjnnJDZ4OTnAP/4BPP44EIk4j8np+uRVTFbnHmPMrKWTOhna0jTNVkwd7x1W5h7w9f3Cip4Me3Zj8ktPVgh3fwnhN21tbb5eT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmmYcItI+Uzn6/r1wEUXATt2AEOGAB9/DEye7L/dII0tiobd2BIFOq7pIfR0TYIgvIQ0TBDiQzruGeTnA1ddBbS2At/8ZuIJmscey9srwguCpmF6uqYgaJqGDRs2WH5Sjt3rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbhEpn6l8/dOfgAsvTGzwpk0Dli/3doNHGva+n0jzzim0ySMIgiAIgiAIm2gacN99wN13A7oO3HwzMH8+MHgwb88Igo5regod1yQIwktIwwQhPqTj7OTAAeC664BPP018/cwzwEMPAe2eeUdkCUHTMB3XFARN01BWVmbrmIid64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2iYNE24RKZ+Gr9u3a5g8ObHB690b+M9/gIcf9m+DRxr2vp9I884p9HTNANCnTx9fryfSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLhFpHxWVw/ERRfJ2L0bGDoUmDMHOP10/+2Shr3vJ9K8cwId1/QQOq5JEISXkIYJQnxIx9nDJ58A114LHDwI5OYmnqA5ejRvrwi/CZqG6bimIKiqitLSUqiq6sv1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4YJt4iQT8aAl14CLr2U4eBB4Dvf0VFcnLkNHmnY+34izDu30CaPM5IkYdCgQZAsHuS2ez2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9ukYcItQc+nqiaennn//QBjEq65pgWffsowYEDmfCANe98v6PPOC+i4pofQcU2CILyENEwQ4kM6Fpf9+xMvOJ8/P/FQleefT7wyIYv3BUQXBE3DdFxTEFRVRXFxsa1jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuCWo+dy+HTjzzMQGr29f4KOPgHvuUbF8eXatxT1Rw25siQQ9XZMzsixjxIgRkGVr+2271xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtwSxHyWlAAXXwzU1QHDhydelTBhAqDr2bcW90QNu7ElEnRc00PouCZBEF5CGiYI8SEdi8UHHwA33ABEo8BJJwGffQYcdRRvrwieBE3DdFxTEFRVRVFRka1jInauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRMuCUo+WQMeO454MorExu8Cy8ElixJ3uBl41rcEzXsxpZI0CaPM7IsY+zYsbaOidi5nkiPKPnk5affdr0c3+1YTvuThvkiSj55+umnbdIw4ZYg5DMeB267DXjoocTX99yTeMl5v37J12XjWtwTNezGlkjQcU0PoeOaBEF4CWmYIMSHdBxsmpqA738fKCwEZBl4+WXgJz/h7RURJIKmYTquKQiqqqKwsNDWMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtzCM59btgBnnJHY4B16aOIBK+k2eNm4FvdEDbuxJRL0dE3OyLKMcePG2TomYud6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScOEW3jlc+lS4NJLgYYGYOTIxANWTjwxfZ9sXIt7oobd2BIJ2uRxRpZlDB061LfrifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aJg0TbuGRz3ffBW65JfG7eBMmJD7BGz68+37ZuBb3RA27sSUS2bt9FQRFUTB//nwoiuLL9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGCbdkMp+MAU88AfzgB4kN3mWXAYsXW9vgAdm5FvdEDbuxJRL0SR5nQqEQJk6ciFAo5Mv1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4YJt2Qqn7EY8MMfAv/8Z+LrX/wCePbZxMNWrJKNa3FP1LAbWyJBmzzOyLKMwYMH+3Y9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCLZnIZ3194lO7pUuBUAj4858Tr0ywSzauxT1Rw25siQQd1+SMoiiYO3eurWMidq4n0iNKPnn56bddL8d3O5bT/qRhvoiST55++mmbNEy4xe98btoEnH56YoM3YACQn+9sgwdk51rcEzXsxpZI0HvyPMTJe/IYY2htbUW/fv0gSZKn1wftvR5BxG7+ecHLT7/tejm+27Gc9icN84U0zNc2abj760nH6fFzfi5aBFx+OdDcDIweDcydC+TmOh8vG9finqhhu32CpmGr+w06rskZSZJsvTjd7vVEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgm3+JXPN94Abr8dUNXEJ3lz5gBuH6aYjWtxT9SwG1siQcc1OaMoCubMmWPrmIid64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2iYNE27xOp+6DsycCdx6a2KDd/XViZede/G0/Gxci3uiht3YEgk6rukhTo9rRqNR9O7d2/IxEavXB+3j5SBiN/+84OWn33a9HN/tWE77k4b5Qhrma5s0TMc13eLlHGprA266CXj//cTXjz6aeGWCV++7zsa1uCdq2G6foGmYjmsKRDhsrwx2ryfSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0TLjFi3zW1QGXXAKsXAlEIsDrryc2fF6TjWtxT9SwG1uiQMc1OaOqKvLy8qCqqi/XE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRom3OJFPr/8EjjttMQGb9AgYMECfzZ42bgW90QNu7ElEnRc00OcHtdUVRXhcNjyMRGr1wft4+UgYjf/vODlp992vRzf7VhO+5OG+UIa5mubNEzHNd3itu4FBcCVVwL79wPHHpt4guY3v+mDo8jOtbgnathun6Bp2Op+gz7JCwBOfmJBeIco+eTlp992vRzf7VhufvqYCTtE14iST55++mmbNEy4xWk+//IX4IILEhu8yZOBFSv82+AZZONa3BM17MaWKNAmjzOqqqKgoMDWMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtziJJ+aBvz858CddybaN9yQOKI5ZIiPjiI71+KeqGE3tkTC0XHNMWPGuDb8s5/9DPfcc4/rcYKEk+OafhK0j5cJgrAHaZggxId07C1ffQX84AeJ994BwFNPAY88AgT4tDYhOEHTsK/HNbdt24ampiYwxhz92b59O5qbm53GllUwxrB//35Y3WvbvZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23SMOEWO/nctStxLHPOHKBXL+DddxOvScjUBi8b1+KeqGE3tkTC8XHN++67D9XV1Y7+ZHNC7aKqKpYsWWLrmIid64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2iYNE26xms81a4BTTwXKyoDDDwcWLQKuvTZDTv6PbFyLe6KG3dgSCUfHNWVZxuOPP45Zs2Y5Muq2f1Ch45oEQXgJaZggxId07J45c4DrrgMOHgRyc4HPPgOOOYa3V0RPIWga9vW4ZkVFBe6++27Hzrntn03ouo7Gxkbouu7L9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGCbekyydjwAsvAJddltjgnXceUFzMb4OXjWtxT9SwG1si4WiTd9xxx2GIi0cYue2fTWiahtLSUmia5sv1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4YJt6TKp6IAd9wBPPBAYrN3552Jd+ANGMDJUWTnWtwTNezGlkjQy9A9hI5rEgThJaRhghAf0rF9mpoSLzhfuDDxUJUXXwTuvZeeoEnwIWga9u24pq7r2LBhA3bv3t3p3xRFQVFRkd0hezS6rmPv3r22jonYuZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23SMOGWjvncsgWYNCmxwTvkkMTv4/3sZ8HY4GXjWtwTNezGlkjY2uRt374dJ5xwAk488USMHDkSF198MRoaGsx/b2xsxPTp0z13MpsxNs12Fhc71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJtzSPp/LlgGnnw5s3AgcdRSwdCnwve/x9vBrsnEt7okadmNLJGwd17z66qvR2NiI119/Hc3NzXjggQewc+dOFBYW4sgjj0RdXR2GDx+e1QlLBx3XJAjCS0jDBCE+pGNr/POfwK23AvE4MGEC8MknwJFH8vaKIIKnYV+Oay5evBjPP/88Ro8ejZNPPhkFBQWYPHkyJk+ejB07dgAApCB8ni4Quq5j165dtn6CaOd6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScOEWzRNx3337cf11yc2eJddBixeHMwNXjauxT1Rw25siYStTd7BgwfRq1evrzvLMl5//XWcd955mDJlCrZs2eK5g9mOruvYsmWLrcXFzvVEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22Thgk3RKPA9dcDv/994tOIBx8EPvgg8bt4QSQb1+KeqGE3tkTC1nHNiRMn4t5778X111/f6d9+/OMf41//+hdaWlqy+nGk6aDjmgRBeAlpmCDEh3TcNXv3ApdeCixfDoTDwKuvAj/8IW+vCKIzQdOwL8c1L7/8crz77rtd/tuf/vQnXHPNNaA3MthD13Vs377d1k8Q7VxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NM2aZhwwpdfAqedltjgDRzI8Pbbe3DLLcHPZzauxT1Rw25siYStTd7DDz+MvLy8lP/+pz/9KauT5Qf0uwB8ESWf2fh7AF6PL8rvAogy50RBlHzy9NNP26Rhwi4FBYlXJGzbBowdCyxdqmHUqK1C5DMb1+KeqGE3tkSCXobuIXRckyAILyENE4T4kI6/5tVXgZ/8BNA0YPJk4KOPgMMO4+0VQaQnaBr27WXo6dA0Ddu3b8fy5ctRVFTU5R8iGU3TUFVVZfn3GO1eT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmmYsIKmAffdB9x1V6J9443AggWJDZ5I+czGtbgnatiNLZHwZJOn6zqefvppHHHEERgzZgzOOussTJ8+vcs/RDKMMTQ1NVn+XUa71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0aJrrjwIHEA1Z+//vE108/Dbz5JmA8rF2kfGbjWtwTNezGlkh4clzzl7/8JX73u99h6NChuOiiizB8+HCEw+Eur33sscfcmgssdFyTIAgvIQ0ThPj0ZB3X1ADf+x6wbh3Quzfw1lvAVVfx9oog7BE0DVvdb3S9E7PJW2+9heOOOw6lpaU49NBDvRiyx6BpGiorK/GNb3wDoVDI8+uJ9IiST15++m3Xy/HdjuW0P2mYL6Lkk6efftomDQd3zvFm9erEBq+2Fhg6FPjkk8QTNTsiUj6zcS3uiRr2wlcR8OS45oEDB3DhhRcKvcFrbm7GKaecgpNPPhnjxo3D66+/bv5bTU0Npk2bhtzcXJx44ol4//33PbXd1tbm6/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22ThomOfPRR4sEqtbXAuHHAypVdb/AMRMpnNq7FPVHDbmyJgifHNSdNmoTRo0enfIeeCGiahlgshr59++Krr77CuHHjsGrVKgwZMgS1tbWoq6vDySefjD179mDChAnYvHkzDjnkkKQx6LgmQRBeQhomCPHpSTpmDPjd74Bf/jLx9YwZwL//DQTgv0QE4ZigaTijT9d85JFHMHv2bKxZs8aL4bgQCoXQt29fAEAsFgNjzPxlzOHDh+Pkk08GABxxxBE47LDD0NjY6IldTdOwYcMGW0/1snM9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bpGHCIB4Hbrvt6w3e3XcDn37a/QZPpHxm41rcEzXsxpZIePI7eRdeeCHefPNNfPe738XFF1+Mk046KeXO8sYbb3Rko6ioCL/73e+wevVq1NbW4uOPP8all16adM0f//hH/O53v8OePXtw0kkn4f/9v/+HU0891bKN5uZmTJ06FZWVlfjd736Hw7p4ecvq1auhaRpGjhzpKA6CIAiCIIhsoqkJuOIKYNEiQJYTT9L86U95e0UQPRtPjmvGYjH86Ec/wrvvvmt++iVJUtI1jDFIkuR4xzxv3jwsW7YMEyZMwOWXX95pk/fvf/8bN954I1599VWcdtpp+P3vf4/3338fmzZtwtChQwEAJ598MlRV7TR2QUEBjjzySPPruro6XH755fjoo48wbNgw8/uNjY2YPHkyXn/9dUyaNKnTOHRckyAILyENE4T4ZLuOq6qACy8ENm8GDj00cTzzggt4e0UQ3hE0DWf0uOb999+Pf/7znzjhhBPw5JNP4vXXX8ff//73pD9vvPEG/v73vzu28d3vfhdPP/00Lrvssi7//cUXX8Rtt92GW265Bbm5uXj11VfRt2/fJJtr167Fhg0bOv1pv8EDgGHDhuGkk07CkiVLzO/FYjFceumleOihh7rc4DlF0zSUlZXZOiZi53oiPaLkk5efftv1cny3YzntTxrmiyj55Omnn7ZJwz2bJUsSD1TZvBkYORJYtsz+Bk+kfGbjWtwTNezGlkh4clzz/fffx4QJE7B8+fKU78fzk3g8jtWrV+Phhx82vyfLMr7zne9g+fLllsaoq6tD37590a9fP7S0tKCoqAh33XUXgMSnkDfffDPOPvts3HDDDd2O1djYCEVRzK979eqFXsZbPzugaRpycnKgKAp0Xe92bDvXGz6094VIxm7+ecHLT7/tejm+27Gc9icN84U0zNc2abj767NVx++8I+HOO0NQFAmnnKLjww81DB8O2A1TFA0D2bkW90QN2+3DW8OxWAyxWMz8urW11VI/T45r9u/fH3feeSd++9vfuh3KEpIkJR3X3L17N0aMGIHi4mKcccYZ5nUPPvggFi9ejJUrV3Y7ZklJCW6//XbzgSt333037rjjDgDA0qVLMWXKFJx44onm9e+88w5OOOGEpDGMj087cvXVV+Paa691EipBEARBEERg0HXgvfeOx/vvHwcAmDRpF+69twy9emXvJyIEwZP33nsP//73vzt9PyMvQ58wYQKqqqq8GIobp556KtauXdvlv5111lm2fqJQXV2Nfv36mV+n+yRPVVWUl5fjxBNPtPQpqJ3rFUXBggULcO655wbiDHEQsZt/XvDy02+7Xo7vdiyn/UnDfCEN87VNGu7++mzScVsb8MMfhvDBB4nf9nnwQQ1PPjkUsny+4zFF0TCQnWtxT9Sw3T68NXzOOefgj3/8o/l1a2srjjnmmG77eTJTnnnmGZxzzjn47LPPcNFFF3kxpC0OO+wwhEIh1NXVJX2/rq4ORxxxRMb9GTx4sOUHr8iyjCFDhiAnJwehUMjz6wEgEokIv7D4hZN88oCXn37b9XJ8t2M57U8a5gtpmK9t0rB1O6LruK4OuOSSxIvNIxHgL38BbrklBIBv3TNJNq7FPVHDTvvw0nAkEsGhhx6a9LUVPNnkLViwANOmTcMll1yCs88+O+UrFCRJwq9+9SsvTCaRk5ODCRMmYOHCheYRTl3XsXDhQvzkJz/x3J6XhEIhHHvssb5dT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmm4Z7BhA3DRRcD27cCgQcDHHwNTp3oztkj5zMa1uCdq2I0tkfDk6ZqPP/448vPzwRjDwoUL8eKLL+Lxxx/v8o9TDhw4gLVr15pHKqurq7F27Vrs2LEDQOIJn6+//jreeustVFRU4K677sJXX32FW265xYMI/UNVVRQXF3f5agcvrifSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs0nP3k5wOTJiU2eN/4BrBihXcbPECsfGbjWtwTNezGlkh48kneokWLvBgmLatWrcL06dPNr++//34AwE033YQ333wTV199Nfbt24dZs2Zhz549OPnkk5Gfn5/0nrsgIssyRowYAVm2tt+2ez2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9uk4ezmj38E7rkn8bCVqVOBDz8Ehgzx1oZI+czGtbgnatiNLZHw5OmaRAJ6GTpBEF5CGiYI8RFRx5oG3H8/8Ic/JL6++ebE7+Dl5HB1iyC4EDQNZ/Rl6IRzVFVFUVGRrWMidq4n0iNKPnn56bddL8d3O5bT/qRhvoiST55++mmbNJx9tLYCF1/89QbvN78B/v53/zZ4IuUzG9finqhhN7ZEwpNN3rJly3D//fdjz549Xf57bW0t7r//fqxYscILc4FH0zTz767aqqqabV3Xccwxx0CWZaiqar6qoX27/YsaNU3DmDFjIMsyFEUx3+vXsQ0kXuJuoOu6+X1d181JnaqtaVpS205MqeJIFZPhe/t2upjax+E2JlmWccwxx5j2gxoTAIwePRqyLGe0Trqud5pvXtZJ13WMHTvWtOsmJlmWMXr0aDNfdutkaEuSJFsxpapNqjoxxkzNW6mTMZaTmDKtJyt1SheHk5gkScKYMWPMsYMaU8f5mck6GTpjjAX6HiHLMsaMGZM0tp06pdJWdzF1VZt0c0+SJIwePRqSJFmuk0HQ9bR9O8OZZzLk5QG9ewP/+Q/Dz3+uQJL80xNjDGPHjoWu64FZc1O1GWPm/T6TdXK6PmU6JgBmLZ3UydCWLMu2YjL+L9f+/8rdzb329wsrdTLs2Y3JrzXXCp5s8l588UV8+umnKV9XMHz4cHz22Wd46aWXvDAXeMaPH4/c3FzMmjULFRUVAIDy8nJUVlYCAMrKylBdXQ0g8buGxiJTXFyM2tpaAEBRURHq6+sBAIWFhWhubgYAfP755+jfvz9kWUZeXh6i0ShUVUVeXh5UVUU0GkVeXh6AxMNqDJqbm1FYWAgAqK+vR1FREYDEBry4uBgAUFNTg5KSEgCJB9uUlZUBACorK1FeXg4AqKio6DamkpIS1NTUAIClmAoKCtDa2goA3cbU2tqKgoICz2KSZRktLS3YtGlToGOqq6vDtm3bIMtyRuu0aNEi9O3bF7Is+1KnFStWYMSIEdi1a5frmGRZxs6dO7Fr1y5HdcrPz8eQIUOg67qtmBobG1FVVQVZli3NvS1btqC+vh6yLFuqEwDHMWVaT1bqBHirJ13XMWTIEOTn5wc6JqPe+/fvz3id1q1bhxEjRmDLli2BvkfIsoycnBwsXbrUUZ02bdqElpYWyLJsKyZZllFVVYXGxkZLc0/XdZSVlUHXdUt1amhogEGQ9fTyy8U47TRg/XoJAwdGsXgxcP75/utpy5YtGDFiBNatWxeYNTdVTNu3b0ddXR1kWc5onZyuT5mOqa6uDiNGjMDSpUsd1Skej6OsrAyyLNuKyfgh66pVqwBYm3sbNmzAV199BVmWLdUJ+FrLvNbcmTNnIjc3FxMmTIAlmAeMHDmS3XzzzWmv+eEPf8iOPvpoL8wFlpaWFgaANTY2MsYYU1WVqaraqa0oitlua2tjn3/+OVMUhSmKwjRNM68x2vF43GwfPHjQvD4ejzNd15mu653ajDEWi8XY7Nmzzf7G9zVNY4qipG2rqprU7iqOVDGliiNVTIbv7dupYmrf9iImRVHY559/zqLRaKBjisViZt0zWaf289OPOrW1tbGFCxeyWCzmOiajlrFYzFGdDG3F43FbMaWqTao6RaNR8/ruYorH42z27Nnm/Ay6nryce1Zjisfj7PPPP2cHDx4MdEwd52cm6xSNRtnChQtZNBoN9D3CyFFbW5ujOqXSVncxdVWbdHPPmHPtv05Xp2g0mrQWB1FP//mPzvr00RnA2Akn6KyqKnP3iK7mJ+81N1Xbyv3bjzo5XZ8yHVMsFmMLFy5kbW1tjupkaEtRFFsxdbx3WJl77ePurk4d12Lea25jYyMDwFpaWlg6PNnk9erViz3yyCNpr3nkkUdY7969vTAXWIxNXndJb4+maayurs6cLF5eb0xKYxIRnbGbf17w8tNvu16O73Ysp/1Jw3whDfO1TRruniDrWNcZe+YZxoDEnwsuYGz//sz6IIqGGcvOtbgnathun6Bp2Op+w5NXKAwcONB8X10qtm/fnvS2diKBLMsYOnSob9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGhaXeBy44w7gzTcTX99zD/DCC0DYk/8ZWkekfGbjWtwTNezGlkh48jt5p59+Oj7++GPzDGtHduzYgdmzZ2PSpElemMsqFEXB/Pnzkx7i4OX1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tk4bFpKEBOO+8xAZPloFXXgFefjnzGzxArHxm41rcEzXsxpZIeLLJu//++3Hw4EGceeaZePvtt81fUqytrcVbb72FM888E21tbfj5z3/uhbmsIhQKYeLEiQiFQr5cT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTNmlYPCorgTPOABYvBvr1A+bOBe6+m58/IuUzG9finqhhN7ZEwpOf2UyZMgUvvvgifv7zn+OWW24BAPPRwkDiI9GXX34ZU6ZM8cJcViHLMgYPHuzb9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bZOGxWLxYuDyy4HGRuDooxMbvHHj+PokUj6zcS3uiRp2Y0skPHsZ+r333os1a9bgjjvuwLe//W2MGTMGEyZMwF133YWysjLczfPHRAFGURTMnTvX1jERO9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGhaHN98Ezj03scE77TRg5Ur+GzxArHxm41rcEzXsxpZISMz4uI1wzf79+zFgwAC0tLSgf//+lvowxtDa2op+/fpBkiRPr1cUBXl5ebjgggsQiUQs+dPTsJt/XvDy02+7Xo7vdiyn/UnDfCEN87VNGu7+et461nXgkUeAZ59NfH3VVYkNX58+GXelS0TRMJCda3FP1LDdPrw13BGr+w0Ov2JLtEeSJMsbQifXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20TRoONl99BdxwA/Dxx4mvH3kEePLJxMNWgoJI+czGtbgnatiNLZEIkMyzB03TzL+7aquqarbb2towZ84cKIoCVVWh67p5jdFWFMVsHzx40LxeURSwxLsOO7UBoP2HtLqum9/XdR2qqqZta5qW1LYTU6o4UsVk+N6+nS6m9nG4jUlRFMyZMwfRaDTQMcViMbPumaxT+/npR52M8WOxmOuYjFrGYjFHdTK0FY/HbcWUqjap6hSNRs3rrdTJGMtJTJnWk5U6pYvDSUzxeBxz5szBwYMHAx1Tx/mZyToZcy4ajQb6HmHkqK2tzVGdUmmru5i6qk26uWfMuXg8brlOBpmce9u3q5gyheHjj4GcHIa33tLx9NOApgXrHtHV/OS95qZqW7l/+3Hfc7o+ZTomYz1sa2tzVCdDW8bXVmPqeO+wMvfax22lToY9uzH5pScrONrkXXfddfjoo4+cdPWkf9AZP348cnNzMWvWLFRUVAAAysvLUVlZCQAoKytDdXW12c7NzUU4HEZxcbH5ZNKioiLU19cDAAoLC9Hc3AwAWLRoESZNmoRwOIy8vDxEo1Goqoq8vDyoqopoNIq8vDwAwIEDB0yfmpubUVhYCACor69HUVERgMQTUIuLiwEANTU1KCkpAQBUV1ejrKwMAFBZWYny8nIAQEVFRbcxlZSUmK/TsBJTQUEBWltbAaDbmFpbW1FQUOBZTOFwGKNGjTLjCGpM9fX1GDBgAMLhcEbrVFRUhFNPPRXhcNiXOpWWluK8885DbW2t65jC4TCGDBlixmG3TgsWLDAfDmUnpubmZhxyyCEIh8OW5l51dTVGjBiBcDhsqU4AsGvXLkcxZVpPVuoEeKsnIPHwrwULFgQ6pnA4jF69epn35UzWqby8HOedd57Z9rJOXt4jwuEwvv3tb2P58uWO6lRZWYlRo0YhHA7biikcDuOQQw4x51t3c689VurU0NBgXp+pubd6NTBhgoY1ayQcdhjwm9+U4KKLUsfE8x5RXV2N8847z2ynigngf9/buXMnhg0bhnA4nNH7ntP1KdMx1dfX47zzzsPy5csd1cnYxITDYVsxhcNh5ObmJq2z3c29iooKjBkzBuFw2FKdgK+1zGvNnTlzJnJzczFhwgRYwdHv5MmyjMcffxyzZs2y29WT/kHFOCPb2NiIQYMGmTvtUCiU1FZVFZIkIRQKmT8NyMnJgaZpkGUZsixDVVWzrSgKQqEQZFlGPB4HAEQiEaiqivD/XmrTsR2JRBCPxzFv3jxccMEFpg+RSAS6rkPXdYTD4ZRtTdPAGDPbXcWRKqb2vqdqt49JURSEw2FIkmS2U8XEGDPbuq67jkmWZcRiMYTD4U6xBikmTdMQj8fRu3dvMMYyWidjfhq+e1mn9nVoH4eTmGRZRjQaRU5OTtr4UtXJ0JZh12pMqWqTau4ZC1mvXr3MnwSmiknXdeTl5WHGjBno1atX4PXE4x5hjAcAOTk5gY1JkqSk+ZnJOhnzTJIk098g3iPC4TDi8ThkWU4bX6o6pdJWdzF1VZt0c48xhmg0it69e0OSpG7rFIvFkJ+fb67Ffs+92bNl3HSTjIMHgdxchs8+k3DUUcG9RxifdhhrQRDW3FRtK/dvP+57TtenTMdkrFvGtXbrZGirz/9+YdRqTKFQKOneYWXutY9b07S0ddI0LWkt5r3mNjU1YfDgwd3+Tp7j45pr167F22+/7ehPtmO8cyMUCnXZDofDSe/lyM/PNyeD/L+D8u3bkUjEbEuShHnz5pmTxFi0O7aNaw2MiW+0jYmXqh0KhZLadmJKFUeqmAzf27fTxdQ+DrcxqaqK+fPnmwtMUGPSdR0FBQXmDSdTdQK+np9+1AlI/OTLuIm5iUlVVRQUFJgLjN06GdoybrpWY0pVm1R1Yoxh/vz55gbFyj1CFD1ZqVO6OJzEpGka5s2bl1THIMbUcX5msk5AQmeMsUDfI1RVRX5+vjmu3Tql0lZ3MXVVm3RzT9M0LFiwwPyPoZU6OY3JTp0YA55/Powrr0xs8M4/HygulnDMMcG+RzDGzE86grLmpmpbuX/7cd9zuj5lOibjB5OGDbt1MrRlbBitxtTx3mFl7rWP20qdnMbkl56s4PiTPMNZuxjmHs/iT/LsPl2z/U8/vbxeUYL1NKAgYjf/vODlp992vRzf7VhO+5OG+UIa5mubNByMp2vGYsAddwBvvZX4+ic/AV56CQgL8Hg9UTQMZOda3BM1bLdP0NZiX5+u+cYbbzh2zODkk092PUa20P4jXT+uJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzTMl/r6xAvOlywBQiHg5ZcB0V5NHKR8dkc2rsU9UcNubImCo+OaN910k+s/J510ktexCIlxTERVVV+uJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzTMl4qKxIvNlywB+vcH5s4Vc4MXlHx2RzauxT1Rw25siQS9DN1DnBzX9JOgfbxMEIQ9SMMEIT5+6XjBAuDKK4GWFuCYY4DPPgNycz0bniCI/xG0tdjqfoPek8cZxhj2798Pq3ttu9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRNGubDq68C3/1uYoN35pnAypXibvCCkE+rZONa3BM17MaWSNAmjzOqqmLJkiW2jonYuZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp23ScGZRVeDee4G77gI0DbjhBmDhQuDwwzPuimeIomEgO9finqhhN7ZEgo5reggd1yQIwktIwwQhPl7peP9+4JprgHnzEl//+tfAww8DAX8gJUEIT9DWYjquKQi6rqOxsdF8P4/X1xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tE0azgzbtiWOZc6bB/TpA7z/PjBzZnZs8ETRMJCda3FP1LAbWyJBmzwf0DTN/LurtqqqZjsWi6GkpASapkFVVXOytW8rimK2o9Goeb2iKGCMgTHWqQ0g6Zyxruvm93VdNz+eTtU2/EkXR6qYUsWRKibD9/btdDG1j8NtTJqmoaSkBPF4PNAxKYpi1j2TdWo/P/2oUywWQ2lpKRRFcR2TUUvDlt06GdpSVdVWTKlqk6pO8XjcvN5KnYyxnMSUaT1ZqVO6OJzEpKoqSkpKEI1GAx1Tx/mZyTrF43GUlpYiHo8H+h5h5CgWizmqUyptdRdTV7VJN/eMOaeqquU6GTiZe0uXajj1VGDDBmD4cIaiIuDSS4OzPrnVU1fzM6gxWbl/+3Hfc7o+ZTomRVFQWlqKWCzmqE6GtjRNsxVTx3uHlbnXPm4rdTLs2Y3JLz1ZgTZ5PjB+/Hjk5uZi1qxZqKioAACUl5ejsrISAFBWVobq6mqz/X//93+IRCIoLi5GbW0tAKCoqAj19fUAgMLCQjQ3NwMAFi1ahEmTJiESiSAvLw/RaBSqqiIvLw+qqiIajSIvLw8AcODAAdOn5uZmFBYWAgDq6+tRVFQEAKitrUVxcTEAoKamBiUlJQCA6upqlJWVAQAqKytRXl4OAKioqOg2ppKSEtTU1ACApZgKCgrQ2toKAN3G1NraioKCAs9iikQiOOqoo1BVVRXomOrr69G3b19EIpGM1qmoqAinnnoqIpGIL3UqLS3F+eefjz179riOKRKJoH///tizZ4+jOi1YsABTp06FJEm2YmppaUFOTg4ikYilubdt2zYMGzYMkUjEUp0AYNeuXY5iyrSerNQJ8FZPkiRh6tSpWLBgQaBjikQikCQJX331VcbrtH79epx//vnYtm1boO8RkUgE3/72t7F8+XJHdaqqqsJRRx2FSCRiK6ZIJIKcnBy0tLRYmnuSJCEWi0GSJEt1amhogIHdmJ5/fjfOOUfCvn3AcccdxLvvbsEppwRrfXKrp23btuH888/H+vXrAx/Tzp07MWTIEEQikYze95yuT5mOqb6+Hueffz6WL1/uqE6apiEWiyESidiKKRKJ4P/+7//M+WZl7lVUVGD06NGIRCKW6gR8rWVeepo5cyZyc3MxYcIEWMGz38mLx+OYPXs2SktL0dzc3OUuU5Ik/O1vf/PCXCAxzsg2NjZi0KBBZg5CoVBSW1VVSJKEUCiEeDyOxsZGDB06FLquQ5ZlyLIMVVXNtqIoCIVCkGUZsVgMzc3NOPzww6FpmvkSR1VVk9qRSATxeBzz5s3DBRdcYPoQiUSg6zp0XUc4HE7ZNn6KYrS7iiNVTO19T9VuH5OiKAiHw5AkyWynisn4SY8Rh9uYJEnC3r17zRtcUGNSVRX79u3DsGHDACBjdYrH42hqakqab17WSVVVNDc3Y/DgwQDgKiZJklBXV4fDDz/czJmdOhnaOuyww0wfrcSUqjap5p6iKKivr8ewYcPMn/6liknXdeTl5WHGjBno1atX4PXE4x4hyzLq6+sxcOBA9OrVK7AxAUian5msk6ZpaG5uxqBBgyDLcmDvEaFQCPv27cOgQYOQk5Nju06KoqChoQFDhw5N0lZ3MXVVm3RzT9d11NXVYdiwYeY46eoUi8WQn59vrsVWYmIMeOqpEJ58EgCASy4B3npLw6GHBm99cqsnXdfR1NSEgQMHIhQKBTomK/dvP+57TtenTMcEAI2NjRg4cCDC4bDtOhnaOuKIIyBJkuWYZFnG3r17MXjwYOTk5Fiae+3vF7qup62TpmlJazFvPTU1NWHw4MHdPwOEecC2bdvYN77xDSbLMpMkKeUfWZa9MBdYWlpaGADW0tJiuY+iKGzhwoVMURTPr4/H42z27NksHo9b9qenYTf/vODlp992vRzf7VhO+5OG+UIa5mubNNw9dnV88CBjV1/NGJD48+CDjGmapa5CIoqGGcvOtbgnathun6CtxVb3G558knf55Zdj9uzZuOGGG3DrrbfiqKOOMnevHRk1apRbc4GFnq5JEISXkIYJQnzs6HjPnsSndiUlQDgM/OUvwK23ZshRgiC6JGhrcUafrllYWIhzzjkHb731FqZOnYqxY8di1KhRXf4hktF1Hbt27Ur6xU4vryfSI0o+efnpt10vx3c7ltP+pGG+iJJPnn76aZs07B3l5cCppyY2eIMHAwsW9IwNnigaBrJzLe6JGnZjSyQ82eTpuo7x48d7MVSPQ9d1bNmyxdbiYud6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp5+2ScPe8NlniVck1NQA3/wmsGIFMG2aZ8MHGlE0DGTnWtwTNezGlkh4clzz3HPPRe/evfHpp5964ZOw0HFNgiC8hDRMEOKTTseMAS+9BDzwQKJ99tnABx8AgwZxcpYgiE4EbS3O6HHNZ599FoWFhfjggw+8GK5Hoes6tm/fbusniHauJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzTsHEUB7rwT+PnPExu8228H8vN73gZPFA0D2bkW90QNu7ElEp5s8ubOnYvp06fj6quvxtlnn40HHngATz75ZKc/Tz31lBfmsoqg/C5AT0WUfGbj7wF4Pb4ovwsgypwTBVHyydNPP22Thp3R1ATMmAG89hogScCLLwKvvgoE4EOCjCOKhoHsXIt7oobd2BIJT45rGu+Z6daYJFl+S7uI0HFNgiC8hDRMEOLTUceVlcBFFwGbNwOHHgq8917ia4IggknQ1uKMHtdctGiRpT/Gm92Jr9E0DVVVVZY3v3avJ9IjSj55+em3XS/HdzuW0/6kYb6Ikk+efvppmzRsjy++AE47LbHBO/poYNky2uCJomEgO9finqhhN7ZEwpNN3tSpUy3/IZJhjKGpqQlWP1C1ez2RHlHyyctPv+16Ob7bsZz2Jw3zRZR88vTTT9ukYeu8+aaEc89NHNU87TRg5UrgxBNtDZGViKJhIDvX4p6oYTe2RMKT45pEAjquSRCEl5CGCUJ8olEF1167DbNnfwMAcPXVwBtvAH36cHaMIAhLBG0tzuhxTYNly5bhtttuw8SJE3Hcccdh4sSJuP3227F06VIvzQQe46NfTdO6bKuqarZjsRgqKiqgaRpUVTV/AbR9W1EUsx2NRs3rFUUBYwyMsU5tAEk/ndB13fy+rutQVTVt2/AnXRypYkoVR6qYDN/bt9PF1D4OtzFpmob//ve/iMfjgY5JURT897//haZpGa1T+/npR51isRg2btwIRVFcx2TU0rBlt06GtlRVtRVTqtqkqlM8Hjevt1InYywnMWVaT1bqlC4OJzGpqoqKigpEo9FAx9RxfmayTvF4HBs3bkQ8Hg/0PULTNFRUVCAWizmqUyptdRdTV7VJN/dUVcWXX34JVVW7jenAAeCqq2RzgzdrFsM776jo0yeYeuJxj+hqfgY1Jiv3bz/q5HR9ynRMiqJg48aNiMVijupkaEvTNFsxdbx3WJl77eO2UifDnt2Y/NKTFTzb5N13332YMmUK/va3v2H16tWoqqrC6tWr8de//hVTp07F/fff75WpwDN+/Hjk5uZi1qxZqKioAACUl5ejsrISAFBWVobq6moAwOrVq1FfXw8AKC4uRm1tLQCgqKjI/H5hYSGam5sBAAsXLsT+/fsBAHl5eYhGo1BVFXl5eVBVFdFoFHl5eQCAAwcOmD41NzebvxNZX1+PoqIiAEBtbS2Ki4sBADU1NSgpKQEAVFdXo6ysDABQWVmJ8vJyAEBFRUW3MZWUlKCmpsZyTAUFBWhtbbUUU2trKwoKCjyNac+ePdi4cWOgY9qzZw+2b9+e8Tp98cUXaGlp8a1OK1asQFtbG3bu3OlJTDU1Ndi5c6ejOs2fPx8HDhxwFNO2bdvMmLqbe1VVVdi9e7flmABg165djmLioSce94gDBw5g/vz5gY9p69atvuopVUxr165FW1sbqqqqAn+PaGpqMn8wbLdOGzduxJ49exzFtG3bNltzr6qqqtuY/v3vZZg8Gfj00xAiEQ1vvaXi9tt3Y/nyYOsp0/eIqqoqtLW1Ye3atYGPafv27eYak8k6uVmfMhnTnj170NbWhqVLlzquU1VVlaOY6uvrsXr1agDW5t6GDRtQV1dnuU4A0NDQ4Cgmr/Q0c+ZM5ObmYsKECbAE84A333yTSZLEjjvuOPbuu++y2tpapus627NnD3vvvffY8ccfz2RZZm+99ZYX5gJLS0sLA8AaGxsZY4ypqspUVe3UVhQlqa1pWtp2PB5Pauu6ntTWdb1TmzHGYrEYmz17ttnf+L6maUxRlLRtVVWT2l3FwSOm9m2KiWLK9pji8TibPXs2i0ajWRNTNtaJYqKYOsa0cqXGhg/XGcDY0KE6e/bZxea4osaUjXWimCgmKzF1XIt5x9TY2MgAsJaWFpYOTzZ5p512Ghs5ciRrbm7u8t+bm5vZyJEj2WmnneaFucBibPK6S3p7VFVl69evNyeXl9cbk9KYRERn7OafF7z89Nuul+O7Hctpf9IwX0jDfG2Thjvz/vuM9enDGMDYuHGMbd5MOk6HKBpmLDvX4p6oYbt9grYWW91veHJc88svv8QVV1yBAQMGdPnvAwYMwBVXXIEvv/zSC3MEQRAEQRCBgjHgmWeAK68E2tqA73438YqE0aN5e0YQRE/Ek6dr9uvXDz/60Y/w0ksvpbzm/vvvx+uvv26eW81G6OmaBEF4CWmYIMQgFgNuuw14553E1/feCzz/PBAOk44JQnSCpuGMPl3zW9/6Fj788MOkB320p7W1FR9++CG+9a1veWEuq9A0DWVlZZaflGP3eiI9ouSTl59+2/VyfLdjOe1PGuaLKPnk6aeftknDwL59wDnnJDZ4oRDw5z8Dv/99YoNHdI8oGgaycy3uiRp2Y0skPNnk3XHHHdi5cyfOOOMMfPjhh+YTderr6/HBBx9g0qRJ2LlzJ+666y4vzGUdfWy+LMfu9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bfdkDf/3v4kXmy9bBgwYAMybB9x5pyMXejSiaBjIzrW4J2rYjS1R8Oxl6Pfccw9eeeUVSJIEAJBl2XyHBGMMP/3pT/Hyyy97YSqw0HFNgiC8hDRMEMFl/nzgqquA/fuBsWOBzz4Djj++83WkY4IQm6BpOOMvQ//DH/6AxYsX4+abb8bJJ5+M0aNH4+STT8Ytt9yCxYsXZ/0GzymqqqK0tNR8waHX1xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tN1TNfzgg9txwQUM+/cDkycDK1Z0vcEjukcUDQPZuRb3RA27sSUSnp4Ynzx5MiZPnuzlkFmPJEkYNGiQ+Qmo19cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bTd0zSsqsDPfibjj38cBQC4+Wbg1VeBXr2ceEwA4mgYyM61uKdp2K0tkfDsuCZBxzUJgvAW0jBBBIeWFuDqqxPHNAHg2WeBBx8Euvs/IumYIMQmaBrO+HFNwhmqqqK4uNjWMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tN1TNFxdDUyalNjg9e3L8MwzG/Hzn6vdbvCI7hFFw0B2rsU9RcNe2RIJR8c1b731VkiShGeeeQbDhg3DrbfeaqmfJEn429/+5sRk1iLLMkaMGAFZtrbftns9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bPUHDy5YBl14K1NcDRx4JzJnDcPjhfQI/50RBFA0D2bkW9wQNe2lLJBwd15RlGZIkoaKiAt/85jctJ0iSpKx+HwUd1yQIwktIwwTBl3/8A/jhD4F4HPj2t4FPPgFGjLA3BumYIMQmaBr29bhmdXU1tm7dijFjxphfW/mzdetWZ9FkMaqqoqioyNZRLzvXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20na0a1nXg0UeBG25IbPAuvxwoKkps8ESZc6IgUj6zcS3OVg37ZUskHG3yRo0ahVGjRiEcTpz2NJ5QY3y/qz+DBw/O6ifYtMf4tFLTtC7bqqqabV3Xccwxx0CWZaiqar5bsH1bURSzrWkaxowZA1mWoSgKGGNgjHVqA4n3Exroum5+X9d1c1KnamualtS2E1OqOFLFZPjevp0upvZxuI1JlmUcc8wxpv2gxgQAo0ePNt8/mak66breab55WSdd1zF27FjTrpuYZFnG6NGjzXzZrZOhLUmSbMWUqjap6sQYMzVvpU7GWE5iyrSerNQpXRxOYpIkCWPGjDHHDmpMHednJutk6IwxFuh7hCzLGDNmTNLYduqUSlvdxdSxNs3NCq64guHXv058/ctfMrz/PpCTk4hDkiSMHj0akiRZrpNB0PXE4x7BGMPYsWOh63rgY2KMmff7TNbJ6fqU6ZgAmLV0UidDW7Is24rJ+L9c+/8rdzf32t8vrNTJsGc3Jr/0ZAVPDqIec8wx3b4H7w9/+AOOOeYYL8wFnvHjxyM3NxezZs1CRUUFAKC8vByVlZUAgLKyMlRXVwMAVq1aZS4yxcXFqK2tBQAUFRWhvr4eAFBYWIjm5mYAwOeff47+/ftDlmXk5eUhGo1CVVXk5eVBVVVEo1Hk5eUBAA4cOGD61NzcjMLCQgBAfX09ioqKAAC1tbUoLi4GANTU1KCkpARA4tPZsrIyAEBlZSXKy8sBABUVFd3GVFJSgpqaGgCwFFNBQQFaW1sBoNuYWltbUVBQ4FlMsiyjpaUFmzZtCnRMdXV12LZtG2RZzmidFi1ahL59+0KWZV/qtGLFCowYMQK7du1yHZMsy9i5cyd27drlqE75+fkYMmQIdF23FVNjYyOqqqogy7KlubdlyxbU19dDlmVLdQLgOKZM68lKnQBv9aTrOoYMGYL8/PxAx2TUe//+/Rmv07p16zBixAhs2bIl0PcIWZaRk5ODpUuXOqrTpk2b0NLSAlmWbcUkyzKqqqrQ2NiImhrglFPaMHu2hJwc4L771mLmzFbI8tcx6bqOsrIy6LpuqU4NDQ0wCLqeeNwjtmzZghEjRmDdunWBj2n79u2oq6uDLMsZrZPT9SnTMdXV1WHEiBFYunSpozrF43GUlZVBlmVbMRk/ZF21ahUAa3Nvw4YN+OqrryDLsqU6AV9rmZeeZs6cidzcXEyYMAGWYB4gSRJ74okn0l7z1FNPMVmWvTAXWFpaWhgA1tjYyBhjTFVVpqpqp7aiKGa7ra2Nff7550xRFKYoCtM0zbzGaMfjcbN98OBB8/p4PM50XWe6rndqM8ZYLBZjs2fPNvsb39c0jSmKkratqmpSu6s4UsWUKo5UMRm+t2+niql924uYFEVhn3/+OYtGo4GOKRaLmXXPZJ3az08/6tTW1sYWLlzIYrGY65iMWsZiMUd1MrQVj8dtxZSqNqnqFI1Gzeu7iykej7PZs2eb8zPoevJy7lmNKR6Ps88//5wdPHgw0DF1nJ+ZrFM0GmULFy5k0Wg00PcII0dtbW2O6pRKW93FZNhdsiTOhg1jDGDs8MN1tmxZ13Uy5lz7r9PVKRqNJq3FQdYTj3tEV/MzqDFZuX/7USen61OmY4rFYmzhwoWsra3NUZ0MbSmKYiumjvcOK3Ovfdzd1anjWsxbT42NjQwAa2lpYenw9GXo6di5cyf69euXKXNcCYVCSX93bBvHXAEgJycHJ5xwAmRZTnqATftr2v+SZ69evbq8vv01Rrv98dj211tpp/LdSkxW2l35a6UtSZLZ9iImXddxwgknmGMGNaZwOJxU90zVKdX89KpOOTk5GDduHMLhsOuYjFoaX9utk6GtUCjUZaypYkpVm1R1ikQiXeY0VUzGWE5iyrSe0rX90pNR917/ext1kGNqPz8zWSdJkjBu3DhEIpFuY+V5jzBqmZOT021MXfmeSltW4ti8+RTcd18YsRhw4onAJ59IGDUKADr7HgqFzHtF+zjS1ckg6Hrq2M6Enrqan0GNycr92486OV2fMh2TrusYN24ccnJyksbvLr6O2pJl2VZMHe8dVuaenfuFcTzSSUzt237oKR2ON3lPPvlk0tdffPFFl9dpmoaamhr861//wumnn+7UXNYiyzKGDh3q2/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP22LrmFdB371KxnPPDMAAHDxxcA//wkceqh/fhLJiJTPbFyLRdew034izTunOP6dvMcff9z8I0kSvvjii6TvGX+eeuopvPnmmxg8eDCee+45L33PChRFwfz585Me4uDl9UR6RMknLz/9tuvl+G7HctqfNMwXUfLJ008/bYus4a++Ar7/feCZZxJfP/CAho8/Tr/B88JPIhmR8pmNa7HIGnbTT6R55xTHn+QtWrQIQOJJMWeffTZuvvlm3HTTTZ2uC4VCGDx4MI4//vikj0OJBKFQCBMnTrT80avd64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59+2hZVwzU1iU/t1q4FcnIYXnrpK9x5Z19Y+W+KKHNOFETKZzauxaJq2G0/keadUxxv8qZOnWq2H3vsMUybNi3pe4Q1ZFnG4MGDfbueSI8o+eTlp992vRzf7VhO+5OG+SJKPnn66adtETW8ciVwySVAXR0wdCjw8ccSJk3q5uM7D/0kkhEpn9m4FouoYS/6iTTvnOLJR2uPPfYYbfAcoigK5s6da+uol53rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37aFk3D774LTJ2a2OCdeCJQUgJMnEga5olI+czGtVg0DXvVT6R55xSJsXZvzHZIcXExPvjgAzz44IM44ogjOv17bW0tfve73+Gqq67K6oev7N+/HwMGDEBLSwv69+9vqQ9jDK2trejXr5+ll8XbuV5RFOTl5eGCCy5IesoP8TV2888LXn76bdfL8d2O5bQ/aZgvpGG+tkXRcOIBK1///l37B6z4qWGAdNwdomgYyM61WBQNe91P5LXY6n7Dk0/yXnjhBXz66addbvAAYPjw4fjss8/w0ksveWEuq5AkCf3797c8Ke1eT6RHlHzy8tNvu16O73Ysp/1Jw3wRJZ88/fTTtgga7viAlV/+EkkPWCEN80WkfGbjWiyChv3oJ9K8c4onm7zS0lKcddZZaa+ZMmUKVqxY4YW5rEJRFMyZM8fWMRE71xPpESWfvPz0266X47sdy2l/0jBfRMknTz/9tB10DdfUAGedldjU5eQAb70FPPsskh6wQhrmi0j5zMa1OOga9qufSPPOKZ4c1+zduzceeOABPP300ymvefTRR/HCCy+gra3NrbnA4vS4ZjQaRe/evS0fE7F6fdA+Xg4idvPPC15++m3Xy/HdjuW0P2mYL6RhvraDrOEVK4BLL23/gBVg0iT3du1eTzpOjygaBrJzLQ6yhv3sJ/JanNHjmgMHDsSOHTvSXrN9+3Yc2t3LZ3oo4bC9h5zavZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp+0gavjdd4Fp05IfsNLVBs+pXVHmnCiIlM9sXIuDqOFM9BNp3jnBk03e6aefjo8//hg1NTVd/vuOHTswe/ZsTEp3h+2hqKqKvLw8qKrqy/VEekTJJy8//bbr5fhux3LanzTMF1HyydNPP20HTcOffZaHhx/W8YMfALFY4gEry5YBo0Z5Z1eUOScKIuUzG9fioGk4E+uwG1si4clxzaKiIkyfPh0jRozA008/jXPPPRfDhw9HbW0tCgoK8Oijj6K2thaFhYWYMmWKF34HEqfHNVVVRTgctnxMxOr1Qft4OYjYzT8vePnpt10vx3c7ltP+pGG+kIb52g6ShltbGW64gWHOnMTPrx96CPj1r9HtC8791DBAOu4OUTQMZOdaHCQNZ2odttsnaBq2ut/w5HPKKVOm4MUXX8TPf/5z3HLLLQAST60x9o+yLOPll1/O6g2eG4xJ5tf1RHpEyScvP/226+X4bsdy2p80zBdR8snTTz9tB0HDNTWJT+3WrpWRk8Pw179KuOEG/+yKMudEQaR8ZuNaHAQNu+3vpJ9I884JnhzXBIB7770Xa9aswR133IFvf/vbGDNmDCZMmIC77roLZWVluPvuu70ylVWoqoqCggJbx0TsXE+kR5R88vLTb7teju92LKf9ScN8ESWfPP3003YQNLxiBTBxIrB2rYQBA6JYsECzvcEjDfNDpHxm41ocBA277e+kn0jzzimeHNckEjg5ruknQft4mSAIe5CGCSI9//wn8MMfJn7/7sQTgU8+Sf/7dzwgHROE2ARNwxl9uibhHMYY9u/fD6t7bbvXE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20zUvDug488ghw/fWJDd4llwBLlzIMGkQaFg2R8pmNa3FPXIfd2BIJzzZ5qqripZdewqmnnor+/fsnnXFdu3YtfvzjH2Pz5s1emQs0mqaZf3fVVlXVbEejURQVFUFVVaiqCl3XzWuMtqIoZrutrc28XlEUMMbAGOvUBpA0cXVdN7+v67r58XSqtqZpSW07MaWKI1VMhu/t2+liah+H25hUVUVRURFisVigY4rH42bdM1mn9vPTjzpFo1EsWbIE8XjcdUxGLePxuKM6GdpSFMVWTKlqk6pOsVjMvN5KnYyxnMSUaT1ZqVO6OJzEpCgKioqKzHewBjWmjvMzk3WKxWJYsmQJYrFYoO8RRo6i0WjamFpaNFxxhY5nngEA4MEHdXz0ERCJdK2t7mLqqjbp5p4x59p/3V2dDIKuJx73iK7mZ1BjsnL/9qNOTtenTMcUj8exZMkSRKNRR3UytKWqqq2YOt47rMy99nFbqZNhz25MfunJCp5s8tra2jB9+nQ88MAD2L59O/r375+0wTjmmGPwxhtv4O233/bCXOAZP348cnNzMWvWLFRUVAAAysvLUVlZCQAoKytDdXW12T7hhBMQiURQXFyM2tpaAIknltbX1wMACgsL0dzcDABYtGgRpkyZgkgkgry8PESjUajq14+BjUajyMvLAwAcOHDA9Km5uRmFhYUAgPr6ehQVFQEAamtrUVxcDACoqalBSUkJAKC6uhplZWUAgMrKSpSXlwMAKioquo2ppKTEfJ2GlZgKCgrQ2toKAN3G1NraioKCAs9iikQiGD16NKqqqgIdU319Pfr3749IJJLROhUVFWHSpEmIRCK+1Km0tBQXXngh9uzZ4zqmSCSCwYMHY8+ePY7qtGDBApxzzjmQJMlWTC0tLejbty8ikYilubdt2zaMGDECkUjEUp0AYNeuXY5iyrSerNQJ8FZPkiThnHPOwYIFCwIdUyQSQSQSwVdffZXxOq1fvx4XXnghtm3bFuh7RCQSwamnnorly5enjKmmBjjjDBWzZ8vIyQGefXY3rrlmHWQZqKqqwujRoxGJRGzFFIlE0LdvX7S0tFiae5IkQdM0SJJkqU4NDQ0wCLqeeNwjtm3bhgsvvBDr168PfEw7d+7EsGHDEIlEMlonp+tTpmOqr6/HhRdeiOXLlzuqk7EJi0QitmKKRCI44YQTzPlmZe5VVFTg2GOPRSQSsVQn4Gst89LTzJkzkZubiwkTJsASzAMeffRRJkkSe+6555iu6+yxxx5jsiwnXXP++eezU045xQtzgaWlpYUBYI2NjYwxxlRVZaqqdmorimK2Y7EY27dvH9M0jSmKwjRNM68x2vF43GxHo1FWX1/PNE1j8Xic6brOdF3v1DbGnj17ttnf+L5hK11bVdWkdldxpIopVRypYjJ8b99OFVP7thcxaZrG9u3bZ44T1JgURWF79+5lmqZltE6xWKzTfPOyTrFYjDU0NDBFUVzHpGka27t3b1LO7NTJ0JaqqrZiSlWbVHWKx+Pm9d3FFI/H2ezZs1k0GuU294J+j1BVldXX15s5CmpMmqaxurq6pHmQqToZOjPy5WWdvLxHaJrG6uvrWSwW6zKmJUsUNmwYYwBjQ4fqbNmyZN/j8bi5ntqJqeO9o7u5p6oqq6urY6qqWqpTNBpNWouDrCce94h4PM4aGhpYLBYLfExW7t9+1Mnp+pTpmBRFMWvppE6GtjRNsxWT8X85495hZe61v190V6eOazFvPTU2NjIArKWlhaXDk0/y/v3vf2P69Ol48MEHIUlSl++bGDNmDHbs2OGFucATCoXMv7tqh8Nhsy1JElavXg1N0xAOhyH/74U+7duRSMRsy7KMVatWmT/pMPLdsW2MbSDLsvl9WZbN47Sp2qFQKKltJ6ZUcaSKyfC9fTtdTO3jcBuTpmlYvXp1Uu2CGBNjDGvWrIGmaRmtkyRJneabl3WSJAmlpaVgjLmOSdM0rFmzxjxFYLdOhrZ0XbcVU6rapKoTAPN6K3UyxnISU6b1ZKVO6eJwEpOu61i1alXSnA1iTJqmoayszJyfmayToTPjay/r5OU9QtM0rFq1KkmTRhzvvSfjO98Jo64u8YCVkhIJkyYl+w7AXE/txNTx3tHd3NN1HWVlZdB13XKdDIKuJx73CAAoLS2FJEmBjwno/v7tR52crk+ZjokxZtbSSZ0MbWn/+6TcakzG/+WMsa3MPQBJ///urk6GPbsx+aUnK3jydM3evXvj3nvvxXPPPQcAeOKJJ/Dkk08mnRn95S9/iZdfftk8L5uN0NM1CYLwEtIw0dPRdeBXv4L5+3eXXAL84x/AoYfy9csOpGOCEJugaTijT9fs168f9u7dm/aaLVu24PDDD/fCXFah6zr27t2b9IudXl5PpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NO2nxo+cAD4/ve/3uA99BDw0UepN3ikYTERKZ/ZuBb3xHXYjS2R8GSTd/rpp+PTTz81fwmxIzU1NcjLy8OUKVO8MJdV6LqODRs22Fpc7FxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NO2XxresQM46yzg44+BnBzg7beB3/wGkNP8r4U0LCYi5TMb1+KeuA67sSUSnhzXLCoqwvTp03HyySfjD3/4A/Lz8/HMM8+gtbUVy5cvx09/+lNUVVVh+fLl1p8IIyB0XJMgCC8hDRM9keXLgcsuA+rqgKFDExu9SZN4e+Uc0jFBiE3QNJzR45pTpkzBK6+8gvXr12PKlCl45n9nK/r164fzzjsPVVVV+NOf/pTVGzyn6LqOXbt22foJop3rifSIkk9efvpt18vx3Y7ltD9pmC+i5JOnn37a9lrD/+//NWL6dNbuASvWN3ikYTERKZ/ZuBb3xHXYjS2R8Oxl6HfddRfWrVuHn/zkJ5g4cSLGjh2L8ePH484770RZWRl+9KMfeWUqq9B1HVu2bLG1uNi5nkiPKPnk5affdr0c3+1YTvuThvkiSj55+umnba/G1nXgkUeAe+4ZjFhMwiWXAMuWAaNG+e8LaZgvIuUzG9finrgOu7ElEp4c1yQS0HFNgiC8hDRM9AQOHABuvDFxLBNIPGDl179O//t3IkE6JgixCZqGM3pcsytUVUVZWRnKysqgKIpfZoRH13Vs377d1k8Q7VxPpEeUfPLy02+7Xo7vdiyn/UnDfBElnzz99NO227GTH7DC8OKL9fj1r3VHGzzSsJiIlM9sXIt74jrsxpZION7kVVdX4+9//zs2b97c6d8+++wzjBgxAqeccgpOOeUUDB8+HP/5z39cOZqt0O8C8EWUfGbj7wF4Pb4ovwsgypwTBVHyydNPP227GXv5cuDUU4F16xIPWPn8cw2nnbaZNNzDECmf2bgW98R12I0tkXB8XHPmzJl47rnnsHXrVoxqd2i+qqoKJ554IqLRKEaNGoVDDjkEFRUVkGUZJSUlGD9+vGfOBw06rkkQhJeQhols5R//AH70IyAWSzxg5ZNP7P3+nUiQjglCbIKmYd+Pay5duhQnn3xy0gYPAF5++WVEo1HcfffdqK6uxoYNG/Dhhx9C0zS88sorTs1lLZqmoaqqCpqm+XI9kR5R8snLT7/tejm+27Gc9icN80WUfPL000/bdsfWdWDmTOCGGxIbvPYPWCEN90xEymc2rsU9cR12Y0skXB3XPPXUUzt9Pz8/Hzk5OeZrFADg0ksvxeTJk7FkyRKn5rIWxhiamppg9QNVu9cT6REln7z89Nuul+O7Hctpf9IwX0TJJ08//bRtZ+wDB4Arrki81BxIPGDlo4+AQw/1xk/SsJiIlM9sXIt74jrsxpZIOD6u2bt3b/ziF7/AU089ZX6vsbERhx12GCZPnozFixcnXf+zn/0Mr7/+Or766it3HgcYOq5JEISXkIaJbGHHDuDiixO/f5eTA/z1r4lP83oCpGOCEJugadj345qRSAQNDQ1J31u9ejUA4JRTTul0/SGHHOLUVFajaRo2btxo65iIneuJ9IiST15++m3Xy/HdjuW0P2mYL6Lkk6efftq2MnbHB6x88UXXGzzScM9EpHxm41rcE9dhN7ZEwvEm75vf/CYWLlyY9L2CggJIkoRJkyZ1un737t0YPny4U3NZTVtbm6/XE+kRJZ+8/PTbrpfjux3LaX/SMF9EySdPP/20nW7sf/wDmDYNqKtLPGClpAQ44wxnY7n1xct+osw5URApn9m4FvfEddiNLVFwfFzzmWeewaOPPorbb78dP/7xj7F582b88Ic/BJDY0HX85O6b3/wmxowZg/z8fPdeBxQ6rkkQhJeQhglR0XXg0Ue//v27Sy8F3nnn69+/60mQjglCbIKmYd+Pa/7sZz/DCSecgNdeew3jx4/H1VdfjdbWVjzxxBOdNnirVq1CVVUVzj33XKfmshZN07BhwwZbx0TsXE+kR5R88vLTb7teju92LKf9ScN8ESWfPP3003ZXY3d8wMrDDwMfftj9Bo803DMRKZ/ZuBb3xHXYjS2RCDvt2LdvXyxbtgwvvfQSVqxYgSFDhuDKK6/E9773vU7XrlmzBpdccgkuvvhiV84SBEEQBBFcevIDVgiCIIKE4+OaRGfouCZBEF5CGiZEYvnyxLHMvXsTD1iZPTv979/1FEjHBCE2QdOw78c1CW/QNA1lZWW2jonYuZ5Ijyj55OWn33a9HN/tWE77k4b5Iko+efrpp21j7L/+Vce0aYkN3kkndf+AFT/8JA2LiUj5zMa1uCeuw25siQRt8gJAnz59fL2eSI8o+eTlp992vRzf7VhO+5OG+SJKPnn66ZdtVQVeemkUbrtNRjwOXH45sHQpMGqUs/FIwz0TkfKZjWtxT1yH3dgSBTqu6SF0XJMgCC8hDRNBpqEBuPpqwHib0hNPJJ6oKdOPj5MgHROE2ARNw3RckyPGR7+apnXZVlXVbEejUZSUlEBVVaiqCl3XzWuMtqIoZrutrc28XlEUMMbAGOvUBoD2+3dd183v67oOVVXTtjVNS2rbiSlVHKliMnxv304XU/s43MakqipKSkoQi8UCHVM8HsfKlStNe5mqU/v56UedotEoSktLEY/HXcekqipWrlyJeDzuqE6GthRFsRVTqtqkqlMsFjNzaqVOxlhOYsq0nqzUKV0cTmJSFAUlJSXm+46CGlPH+ZnJOsViMZSWliIWi3kW0/r1Ok49NbHB69NHw/vva3j4YQWS5Dwm434cjUYd1SmVtrqLqavapJt7iqJg5cqVSV93VyeDoOuJxz2iq/kZ1JhisZh5v89knZyuT5mOKR6Po7S0FNFo1FGdDG2pqmorpo73Ditzr/39wkqdDHt2Y/JLT1agTZ4PjB8/Hrm5uZg1axYqKioAAOXl5aisrAQAlJWVobq6GkDiyaOSJEGSJBQXF6O2thYAUFRUhPr6egBAYWEhmpubzXbfvn0hSRLy8vIQjUahqiry8vKgqiqi0Sjy8vIAAAcOHDB9am5uRmFhIQCgvr4eRUVFAIDa2loUFxcDAGpqalBSUgIAqK6uRllZGQCgsrIS5eXlAICKiopuYyopKUFNTQ0AWIqpoKAAra2tANBtTK2trSgoKPAsJkmSEI/HsWnTpkDHVFdXh5aWFkiSlNE6LV68GL1794YkSb7UaeXKlRg0aBB27drlOiZJktDa2opdu3Y5qlNBQQH69esHTdNsxdTQ0ICmpiZIkmRp7m3ZsgVtbW2QJMlSnQA4jinTerJSJ8BbPWmahn79+plxBDUmSZJQX1+P/fv3Z7xO69atw6BBg7BlyxZPYvrNb/6LM84Atm4FjjjiIN55Zwsuu8x9TJIkIScnB8uWLXNUp02bNiEej0OSJFsxSZKEpqYmNDQ0WJp7mqZhz5490DTN8j3CIOh64nGP2LJlCwYNGoR169YFPqYdO3bg4MGDkCQpo3Vyuj5lOqa6ujoMGjQIy5Ytc1SnWCyGPXv2mOu51ZiM/0evWbMGgLW59+WXX0JVVUiSZKlOACzfI/zS08yZM5Gbm4sJEybACnRc00OMj08bGxsxaNAgc6cdCoWS2sakMtqyLEOW5ZRtRVEQCoXMdjgchiRJZhtI/LShfTsSiSAej2PevHm44IILTB8ikQh0XYeu6wiHwynbmqaBMWa2u4qDR0yMMbOt6zrFRDFldUy6riMvLw8zZsxAr169siKmbKxTT4lJkmT85jcyfvUrAACmTwf++U8Fw4aJG1Mm6hSLxZCfn2+uxdkQUzbWiWKimFLFZGyujbWYd0xNTU0YPHgwHdfkQSgUMv/uqh0Oh802AKxYscKcDPL/fpmhfTsSiZhtSZKwfPlyc5IYP73o2DauNZBl2fy+LMvmxEvVDoVCSW07MaWKI1VMhu/t2+liah+H25hUNXFMx/hZR1Bj0nXdPFaQyTpJkmTOTz/qBCR+UmbcxNzEpKqJ4xrGEQq7dTK0Zdx0rcaUqjap6sQYM4+jWL1HiKInK3VKF4eTmDRNw/Lly5PqGMSYVFVFaWmpOT8zWScgoTPGmOOYYrEwrr326w3eT34CzJ8PHH64d/cIVVWxYsUKGNitUyptdVenjveO7uaepmkoLS2FpmmW6+Q0pmxbc7tqM8bMTy6CHhNjzLzfZ7JOTtenTMek67pZSyd1MrRlbLqsxtTx3mFl7rW/X1ipk9OY/NKTFRy/DN0KjDFUVVWhd+/eGDlypJ+mhEWWZYwYMSJpEnl5PZEeUfLJy0+/7Xo5vtuxnPYnDfNFlHzy9NOt7e3bgUsuSbzgPBIB/vhH4LbbEv+m66Rhwh0i5TMb1+KeuA67sSUSnkT20Ucf4cYbb0RTU5P5vW3btuHEE0/E8ccfj9GjR+Oaa66x/IuCPQlZljFq1Chbi4ud64n0iJJPXn76bdfL8d2O5bQ/aZgvouSTp59ubBcVAaecktjgDR0KFBZ+vcFzO7aXfrrpTxrmi0j5zMa1uCdq2I0tkfAksj//+c9Yu3YtBg0aZH7vvvvuw5dffonp06fjxBNPxPvvv4+///3vXpjLKlRVRVFREVRV9eV6Ij2i5JOXn37b9XJ8t2M57U8a5oso+eTpp1Pbr74KnHMOUF8PjB8PlJYCZ53lzdhe+um2P2mYLyLlMxvX4p6oYTe2RMKTTd5///tfnPr/2zvz8CiK9I9/ZyaBgNygIJdcKkQUwum93qirLoor7qrgtR4ruuoqIqsBvI/V9ViP9cBbxJ9KFIgQIECACAkhEI4AIYQQQjhCSEhIZqaP+v3RzpBAMpm+prpm3s/z+FiZVNV7VH27qHRN96hRwZ+rq6sxf/58jB8/HosXL0ZWVhYGDRpEm7xGcLvd6N+/v66/IOqpT4RGlHzy8tNuu1b2b7Yvo+1Jw3wRJZ88/dRr2+8HHnxQ+0+WtXfhrVwJ9O5tvm8r/bSqPWmYLyLlMxrX4ljUsBlbImFJZBUVFejWrVvw55UrV0KWZfzlL38BoH0R8corr0RhYaEV5qIK+i4AX0TJZzR+D8Dq/kX5LoAoc04URMknTz/12D54ELjySu0unssFvPQSMGsW0Lq1+b6t9NPK9qRhvoiUz2hci2NRw2ZsiYQlkbVr167Be2CWLl0Kt9uNiy66KPhZfHw8jh49aoW5qEKWZaSnp+s6JqKnPhEaUfLJy0+77VrZv9m+jLYnDfNFlHzy9DNc2xs2ACNHat/Da9sW+Pln4Omntc2e2b6t9NPq9qRhvoiUz2hci2NRw2ZsiYQlm7yBAwdi7ty5OHToECorK/Htt99i+PDhDb6jV1xcjK5du1phLqpwu90YPHiwrr8g6qlPhEaUfPLy0267VvZvti+j7UnDfBElnzz9DMf2Dz8A55+vPUlzwABg9Wrg+uut6dtKP+1oTxrmi0j5jMa1OBY1bMaWSFgS2SOPPIK9e/eiZ8+e6N27N8rKyvDggw82qLN69WoMGTLECnNRhdvtximnnKJrcdFTnwiNKPnk5afddq3s32xfRtuThvkiSj55+hnKtqoCycnAn/8M1NZqRzXXrAESE833baWfdrYnDfNFpHxG41ocixo2Y0skLIls3LhxeO+993DWWWfhjDPOwKuvvoo777wz+Pvly5fjyJEjuPrqq60wF1VIkoSFCxdCkiRb6hOhESWfvPy0266V/Zvty2h70jBfRMknTz+bsl1dDYwbBzz/vPbzY48BqalAp07m+7bST7vbk4b5IlI+o3EtjkUNm7ElEi7GGOPtRLRw5MgRtG/fHlVVVWjXrl1YbVRVRWVlJTp06BDWXxP01JckCampqbj22msRHx8flj+xht7884KXn3bbtbJ/s30ZbU8a5gtp2JjtnTuBG24ANm8GWrQAPvoImDjRmr6t9DMS7e3UMEA6bg5RNAxE51ocixrW28ZpGg53vxFnpxOMMezYsQMJCQno1auXnaaExe12o5OOP5vqrU+ERpR88vLTbrtW9m+2L6PtScN8ESWfPP083nZ6unY8s6IC6NYNmDMHOPdca/q20s9ItScN80WkfEbjWhyLGjZjSyQs+XPATz/9hAkTJuDw4cPBz3bt2oVzzjkHAwcORJ8+fXDrrbdCURQrzEUVkiRh/vz5uo6J6KlPhEaUfPLy0267VvZvti+j7UnDfBElnzz9DNj2+yW8+y5w1VXaBm/kSGDtWuMbvPp9k4YJo4iUz2hci2NRw2ZsiYQlxzWvvPJK7N+/H3l5ecHPbrzxRvz888+47LLLcOjQIeTl5eHDDz/E3/72N7PmHIuR45qMMVRXV6Nt27ZwhXpOtYH6Tru97ET05p8XvPy0266V/Zvty2h70jBfSMPh2S4vr8aUKW0xc6Zm+/bbtSOarVqZ75s0HBrScWhE0TAQnWtxLGpYbxunaTjc/YYld/K2bNmCUaNGBX+urq7G/PnzMX78eCxevBhZWVkYNGgQZs6caYW5qMLlcqFdu3ZhT0q99YnQiJJPXn7abdfK/s32ZbQ9aZgvouSTp58HDrgwdmw7zJzpgtsNvP468OWX5jd4AGmYMI9I+YzGtTgWNWzGlkhYssmrqKhAt27dgj+vXLkSsizjL3/5CwDtRehXXnklCgsLrTAXVUiShJ9//lnXMRE99YnQiJJPXn7abdfK/s32ZbQ9aZgvouSTl585OcCIEQyZmUD79gzz5wNPPBH6Bed6IA0TZhEpn9G4Fseihs3YEglLjmt27doVN910Ez744AMAwOTJk/Hmm2/i4MGDwReiT548Ge+99x6OHj1q1pxjMXpc0+v1IiEhIexjIuHWd9rtZSeiN/+84OWn3Xat7N9sX0bbk4b5QhpumlmzgLvvBrxe4IwzVPz8swsDB1p/1Is0HBrScWhE0TAQnWtxLGpYbxunaTiixzUHDhyIuXPn4tChQ6isrMS3336L4cOHBzd4AFBcXIyuXbtaYS7qiIvT95BTvfWJ0IiST15+2m3Xyv7N9mW0PWmYL6LkM1J+Kgrw9NPAX/+qbfCuvZZh1SoFZ55pjz3SMGEWkfIZjWtxLGrYjC1RsGST98gjj2Dv3r3o2bMnevfujbKyMjz44IMN6qxevRpDhgyxwlxUIcsyUlNTIcuyLfWJ0IiST15+2m3Xyv7N9mW0PWmYL6LkM1J+VlUBf/oT8Mor2s9PPQX8+KOMVavssU0aJswiUj6jcS2ORQ2bsSUSlr0M/YMPPsCnn34KALj11lvxxBNPBH+3fPlyjB07Fq+88gruv/9+K8w5EqPHNWVZRlxcXNjHRMKt77Tby05Eb/55wctPu+1a2b/Zvoy2Jw3zhTR8jO3btQ3e1q1AQgLwySfAbbfZa5s0TMc1zSKKhoHoXItjUcN62zhNwxF/GfqDDz54wt27AH/4wx8avEOPaEhgktlVnwiNKPnk5afddq3s32xfRtuThvkiSj7t9HPhQuDWW4HKSqBHDyAlBRgxIjK2ScOEWUTKZzSuxbGoYTO2RMGS45qEcWRZRlpamq5jInrqE6ERJZ+8/LTbrpX9m+3LaHvSMF9EyaddfjIGvPkmcO212gbvvPOA7OwTN3h25Yg0TJhFpHxG41ocixo2Y0skLDuuCQCrVq3C559/jvXr1+PIkSNo164dkpKSMGHCBFx44YVWmXEsRo5r2onTbi8TBKEP0jARCq8XuP9+7Z13gPYkzfffB1q25OsX0RDSMUGIjdM0HNGnawLAY489hosvvhiffvopcnJysGPHDuTk5OCTTz7BH/7wBzz++ONWmYoqGGM4cuQIwt1r661PhEaUfPLy0267VvZvti+j7UnDfBEln1b7uXcv8Ic/aBs8jwd4+23tO3iNbfDszBFpmDCLSPmMxrU4FjVsxpZIWLLJ++KLL/D222/j9NNPxzfffIO9e/dClmWUlZXh22+/xRlnnIG3334bXwb+3EgEkWUZK1as0HVMRE99IjSi5JOXn3bbtbJ/s30ZbU8a5oso+bTSzzVrtOOYWVlAx47AggXAI480/YJzO3NEGibMIlI+o3EtjkUNm7ElEpYc1zz33HOxd+9ebNy4Ee3btz/h91VVVTj77LPRvXt3rF692qw5x0LHNQmCsBLSMHE8X34J3Hcf4PMBiYnAL78A/fvz9ooIBemYIMTGaRqO6HHNzZs3Y9y4cY1u8ACgffv2GDduHDZv3myFuahCVVVUVFRAVVVb6hOhESWfvPy0266V/Zvty2h70jBfRMmnWT9lGfjnP4GJE7UN3g03AL/9Ft4Gz84ckYYJs4iUz2hci2NRw2ZsiUTEnq7p9Hef8EJRFGRnZ0NRFFvqE6ERJZ+8/LTbrpX9m+3LaHvSMF9EyacZPw8fBv74R+0pmgDwzDPAnDlAuAdG7MwRaZgwi0j5jMa1OBY1bMaWSFh6XHPLli1o06bNCb+vrq7G4MGDceqpp9JxzQjitNvLBEHogzRM5OdrLzgvKABatQK++AL48595e0XogXRMEGLjNA1H9Ljm/fffjz179uC8887Djz/+iPLycgBAeXk5fvjhB5x//vnYs2dPky9Lj2VUVcWBAwd0HRPRU58IjSj55OWn3Xat7N9sX0bbk4b5Iko+jfg5bx4werS2wevdG8jMNLbBszNHpGHCLCLlMxrX4ljUsBlbImHJJu+uu+7CpEmTsHnzZtxyyy3o2rUr4uPj0bVrV4wfPx6bN2/GpEmTMHHiRCvMRRWqqmLTpk26Fhc99YnQiJJPXn7abdfK/s32ZbQ9aZgvouRTj5+MAa+8on3vrroauOgi7QXnQ4fab5tn36Th2ESkfEbjWhyLGjZjSyQsfRn6ihUrGn0Z+sSJE3HRRRdZZcax0HFNgiCshDQce9TWAvfeC8yapf18//3AO+8ALVrw9YswDumYIMTGaRqO+MvQAeCiiy4Kvgy9oKAg+DL0WNjgGUVVVZSWlur6C6Ke+kRoRMknLz/ttmtl/2b7MtqeNMwXUfIZjp8lJdpdu1mzgLg44P33gQ8/NL/BszNHpGHCLCLlMxrX4ljUsBlbImHJJs/j8eC2226zoquYQ1VVFBYW6lpc9NQnQiNKPnn5abddK/s325fR9qRhvoiSz+b8XLVKe8H5unVA587A4sWAVV9jtzNHpGHCLCLlMxrX4ljUsBlbImHJcc2OHTvi/vvvxyuvvGKFT8ISuH1aUVGBjh07Bh/L6vF4GpRlWYbL5QqW3W433G53k2VJkuDxeILluLg4uFyuYBkAZFluUI6Pj4ff78evv/6Ka6+9NuhDfHw8VFWFqqqIi4trsqwoChhjwXJjcfCIiTEWLKuqSjFRTFEdk6qqSE1NxdVXX42WLVtGRUzROE5mY/rf/2Q8/LAHkuTC2Wcz/Pwz0KeP2DFF4zgZjcnn82HBggXBtTgaYorGcaKYKKamYlIUpcFazDumw4cPo1OnTpE5rjlq1Chs2LDBiq6igqSkJCQmJiI5ORn5+fkAgLy8PBQUFAAAcnNzUVRUBADIysrC+vXroaoqMjMzUVZWBgDIyMgIPqU0PT0dlZWVAIC0tDRs27Yt+I8/r9cLWZaRmpoKWZbh9XqRmpoKAKipqQn6VFlZifT0dADaU08zMjIAAGVlZcjMzAQAlJSUICsrCwBQVFSE3NxcAEBBQQHy8vIAAPn5+WHFVFJSAgBhx1RdXQ0AzcZUXV2NtLQ0y2JSVRVr1qzBli1bHB3T3r17sXTpUqiqGvFx2rp1K1RVtW2ciouLUVxcbDomVVWRkZGB4uJiw+O0Y8cO+P1+XTEdOHAAS5YsgaqqYc297du3IzMzE6qqhjVOAFBaWmo4pkjqKZxxAqzVk9/vx44dOxwfk6qqWLRoESoqKoIxVVRU45FHgAceiIMkuXDjjSqmTp2Pnj2tHad169ahuLgY27dvd/Q1QlVVbN68GcuXLzc0Tlu2bMGaNWugqqqumFRVxZIlS3DgwIGw5l7gGuH3+8Map0OHDiGA0/XE4xqxfft2FBcXY926dY6PaefOnVi5ciVUVY34OBlZnyId0969e1FcXIzly5cbGqfa2lqkpqZCVVVdMamqivXr1+uee9nZ2VBVNaxxAo5pmZeepk6disTERAwfPhxhwSwgMzOTtWjRgn3xxRdWdCcsVVVVDACrqKhgjDEmyzKTZfmEsiRJwXJdXR1buXIlkySJSZLEFEUJ1gmU/X5/sFxbWxus7/f7maqqTFXVE8qMMebz+VhKSkqwfeBzRVGYJEkhy7IsNyg3FkdTMTUVR1MxBXyvX24qpvplK2KSJImtXLmSeb1eR8fk8/mC4x7Jcao/P+0Yp7q6OrZq1Srm8/lMxxQYS5/PZ2icAtry+/26YmpqbJoaJ6/XG6zfXEx+v5+lpKQE56fT9WTl3As3Jr/fz1auXMlqa2sdHZMkSWzFihXB+bl7t59ddJHKtGdpMjZ9uspk2Z5x8nq9bNWqVczr9Tr6GhHQcF1dnaFxakpbzcV0/LWjubnn9/vZihUrGvwcapy8Xm+DtdjJeuJxjWhsfjo1pnCu33aMk9H1KdIx+Xw+tmrVKlZXV2donALakiRJV0zHXzvCmXv1425unI5fi3nrqaKiggFgVVVVLBSWHNd87rnnsGrVKixevBjDhg3DyJEj0bVrV7hcrgb1XC4Xnn32WbPmHAs9XZMgCCshDUcnmZnAzTcDZWVA27bAl18CY8fy9oqwC9IxQYiN0zQc7n4jzgpj06dPD5ZzcnKQk5PTaL1o3+QZQVEUFBUVoW/fvvB4PJbXJ0IjSj55+Wm3XSv7N9uX0fakYb6Ikk9FUbBzZxEWLuyHxx93Q5KAQYOAOXOAM8+037ZdOSINO3fOiYJI+YzGtTgWNWyFryJgyXfyli5dGtZ/gbOnxDEYYzh8+DDCvaGqtz4RGlHyyctPu+1a2b/Zvoy2Jw3zRZR81tYyPPpoBzz8sLbBu/lmYM0a+zd4gL05Ig0TZhEpn9G4Fseihs3YEglLX4Ye69BxTYIgrIQ0HB0UFQHjxgG5uYDbDbzyCvDEE8Bx32ggohTSMUGIjdM0HJGXob/44ouYOnUqJElqso7f78e//vWvmH+9QlMoioKtW7cGH+VqdX0iNKLkk5efdtu1sn+zfRltTxrmi9PzmZamvf8uNxfo2FHGr78qePLJyG7w7MwRaZgwi0j5jMa1OBY1bMaWSBje5C1evBjJycno3LlzyF1tixYt0LlzZ/zrX//C0qVLjZqLaurq6mytT4RGlHzy8tNuu1b2b7Yvo+1Jw3xxYj5VFXjpJeDqq4GKCmDECIavv96Kyy/n44+dOSINE2YRKZ/RuBbHoobN2BIFw8c1J0yYgNTUVJSWlqJly5Yh6/p8PvTs2RPXXHMNvvzyS0OOigAd1yQIwkpIw2Jy5AgwcSKQkqL9fO+9wLvvAgkJXN0iOEE6JgixcZqGbT+umZmZiSuuuKLZDR4AtGzZEldccQVWrVpl1FzUoigKNm3apOuYiJ76RGhEyScvP+22a2X/Zvsy2p40zBen5XPLFmDkSG2D16IF8NFHwMcfA/Hx/Py0M0ekYcIsIuUzGtfiWNSwGVsiYXiTt3fvXvTr1y/s+n379m3w1niCIAiCiCZ++AEYNQrYvh3o2RNYsQL42994e0UQBEHEIoaPa7Zp0wYPPvggXn/99bDqP/nkk/jggw9QU1NjxJwQ0HFNgiCshDQsBrIMTJ0KBJbDSy8FvvsOOOUUvn4RzoB0TBBi4zQN235cs3v37ti0aVPY9Tdt2oQePXoYNRe1KIqC3NxcXcdE9NQnQiNKPnn5abddK/s325fR9qRhvvDO58GDwJgxxzZ4TzyhPVHz+A0eTz/ttE0aJswiUj6jcS2ORQ2bsSUShjd5F110EdLT07Fr165m6+7atQvp6em4+OKLjZqLalq1amVrfSI0ouSTl59227Wyf7N9GW1PGuYLr3xmZwPDhwPp6cBJJwGzZ2ubvbi4xuvzHHc7bZOGCbOIlM9oXItjUcNmbImC4eOa69atw4gRIzBs2DAsWLAAXbp0abTeoUOHcPXVV2PdunXIzs7GsGHDTDnsZOi4JkEQVkIadi6ffgr8/e+A3w+cfjowZw5w1lm8vSKcCOmYIMTGaRq2/bjmsGHD8Oijj2LdunVITExEcnIyli5dioKCAhQUFGDZsmV49tlnkZiYiJycHDz22GNRvcEziizLyM7OhizLttQnQiNKPnn5abddK/s325fR9qRhvkQ6nz4fcP/92msR/H7ghhu0O3rNbfB4jrudtknDhFlEymc0rsWxqGEztkSiiUMl4fHGG28gISEBr7/+Ol588UW8+OKLDX7PGIPH48HTTz+NF154wZSj0YrL5ULHjh3hcrlsqU+ERpR88vLTbrtW9m+2L6PtScN8iWQ+S0qAm28GsrIAlwt4/nng6acBdxh/LuU57nbaJg0TZhEpn9G4Fseihs3YEgnDxzXrU1hYiM8++wyZmZnYt28fAKBbt2644IILcOedd6J///6mHRUBOq5JEISVkIadw9KlwPjx2oNWOnYEvv0WuPpq3l4RIkA6JgixcZqGbT+uWZ/+/fvjhRdeQHp6OrZs2YItW7YgPT0dzz//fMxs8IwiyzIyMzN1HRPRU58IjSj55OWn3Xat7N9sX0bbk4b5Ync+GQPeeAO48kptgzd0KLB2rf4NHs9xt9M2aZgwi0j5jMa1OBY1bMaWSJg6rkmYx+12o0ePHnCHc97HQH0iNKLkk5efdtu1sn+zfRltTxrmi535rKkB7rkH+P577ec77gA+/BBo3Vp/XzzH3U7bpGHCLCLlMxrX4ljUsBlbImHJcU1Cg45rEgRhJaRhfmzfDtx0E7B5s/ZKhP/8B3joIe27eAShB9IxQYiN0zQc0eOahHFkWUZGRoauYyJ66hOhESWfvPy0266V/Zvty2h70jBf7MjnL78AI0dqG7xu3YBly4BJk8xt8HiOu522ScOEWUTKZzSuxbGoYTO2RII2eZxxu93o37+/rmMieuoToREln7z8tNuulf2b7ctoe9IwX6zMp6IAzz4L/OlPwJEjwAUXAOvWaf93kp9Osk0aJswiUj6jcS2ORQ2bsSUSdFzTQui4JkEQVkIajhwVFcBttwELFmg/P/ww8O9/Ay1a8PWLEB/SMUGIjdM0TMc1BUGWZaSnp+s6JqKnPhEaUfLJy0+77VrZv9m+jLYnDfPFinyuXw+MGKFt8Fq1Ar76CnjnHWs3eDzH3U7bpGHCLCLlMxrX4ljUsBlbIkFP1+SM2+3G4MGDdR0T0VOfCI0o+eTlp912rezfbF9G25OG+WI2n19/Dfztb4DXC/TtC/z0k/aaBKvhOe522iYNE2YRKZ/RuBbHoobN2BIJ2uRxxu1245RTTrGtPhEaUfLJy0+77VrZv9m+jLYnDfPFaD79fuCJJ4B339V+vvpq4JtvgE6dLHbwd3iOu522ScOEWUTKZzSuxbGoYTO2RCJ6t6+CIEkSFi5cCEmSbKlPhEaUfPLy0267VvZvti+j7UnDfDGSz7Iy4LLLjm3wnn0WmDfPvg0ewHfc7bRNGibMIlI+o3EtjkUNm7ElEnQnjzMejwcjR46Ex+OxpT4RGlHyyctPu+1a2b/Zvoy2Jw3zRW8+V60Cbr4Z2LcPaNdOO655/fU2Owm+426nbdIwYRaR8hmNa3EsatiMLZGgTR5n3G43Oun487He+kRoRMknLz/ttmtl/2b7MtqeNMyXcPPJGPDee8BjjwGyDJx1FjBnDnD66RFwEnzH3U7bpGHCLCLlMxrX4ljUsBlbIkHHNTkjSRLmz5+v65hIOPVraoAXXnDj7beTrHAzatGbf17w8tNuu1b2b7Yvo+3t0jARHuHks7YWmDhRey2CLAPjxwOrV0dugxeunyLaJg0TZhEpn9G4Fseihs3YEgl6T56FGHlPHmMM1dXVaNu2LVwul2X18/OBwYMZVNWFFStkXHgh3bRtDL355wUvP+22a2X/Zvsy2t4uDQPOezePE2kunzt3AjfdBGzYAHg8wGuvaXfzIi13ntcaO22ThpuvTzoOjSjrMBCda3EsalhvG6dpmN6TJwgulwvt2rULe1KGW3/QIGDCBG3//q9/uUFb+cbRm39e8PLTbrtW9m+2L6Pt7dIwER6h8rlggfb+uw0bgJNPBhYvBh5/PPIbvOb8FNk2aZgwi0j5jMa1OBY1bMaWSNAmjzOSJOHnn3/WdUwk3PrPPqsgPl5BRoYbCxaY9TQ60Zt/XvDy0267VvZvti+j7e3UMNE8jeVTVYEXXgCuvRY4fBgYNQpYtw645BJn+RkNtknDhFlEymc0rsWxqGEztkSCjmtaiNHjml6vFwkJCWEfEwm3viRJuOWWXUhJOR3nnAPk5gJR/M5HQ+jNPy94+Wm3XSv7N9uX0fZ2a9hJR0ScyPH5rKoCJkwAfvlF+/399wNvvw20bOksP6PFNmmYjmuaRZR1GIjOtTgWNay3jdM0TMc1BSIuTt/35fTUHzeuAO3bM+TlAd9+q9ez2EBv/nnBy0+77VrZv9m+jLa3U8NE8wTyuXkzMHKktsFr2RL45BPgww/5b/AC8Bx3O22ThgmziJTPaFyLY1HDZmyJAm3yOCPLMlJTUyHLsi3127aV8MQTKgDthb8+n2FXoxK9+eQFLz/ttmtl/2b7Mtrebg0ToQnkc9YsBaNHAwUFQK9ewMqVwD338PbuGDzH3U7bpGHCLCLlMxrX4ljUsBlbIkHHNS3E6HFNWZYRFxcX9jGRcOsHbi9fcsm1GDQoHmVl2rGlRx4Jy7WYQG/+ecHLT7vtWtm/2b6Mto+Ehp1yRMSJSBLDlCkq3nxTe6Ht5ZcDs2ZpD1pxEjyvNXbaJg3TcU2ziLIOA9G5FseihvW2cZqG6bimQBj5i4UeWrcGpk3Tyi+8AFRX62oe9YjyVxxeftpt18r+zfZl5q+PkbBDNOTAAWDMGAQ3eJMna0/UdNoGLwDPcbfTNmmYMItI+YzGtTgWNWzGlijQJo8zsiwjLS1N1zERPfUD3H239uLfgweBN94w4ml0YjSfkYaXn3bbtbJ/s30ZbR8pDRMNycoChg8Hli51ISFBxqxZMl59FXDqVyx4jrudtknDhFlEymc0rsWxqGEztkSCjmtaiJHjmnZy/O3l//s/4JZbgDZtgMJC4JRTeHtIEEQonHZExCl8/DEwaRLg9wNnnAHMmQMkJvL2iiAah3RMEGLjNA3TcU1BYIzhyJEjCHevrbd+fW6+WXsxcE2NdmyTMJfPSMLLT7vtWtm/2b6Mto+khmOd2lrtYSr33adt8MaOBbKyGHr2dH4+eY67nbZJw4RZRMpnNK7FsahhM7ZEgjZ5nJFlGStWrNB1TERP/fq4XMArr2jlDz8Edu7U3UXUYSafkYSXn3bbtbJ/s30ZbR9JDccymzZpr0eYOVO7lr30EvDjj0Dr1mLkk+e422mbNEyYRaR8RuNaHIsaNmNLJOi4poU4/bhmgKuuAhYtAm67Dfj6a44OEgQREqcdEeEBY9r77h55BPB6gW7dgG++AS67jLdnBBEepGOCEBunaZiOawqCqqqoqKiAqqq21G+MwN28b78FNmww3E1UYEU+IwEvP+22a2X/Zvsy2p6HhmOFI0eAv/xFO57p9WpP0tywoeEGT5R88vTTTtukYcIsIuUzGtfiWNSwGVsiQZs8ziiKguzsbCiKYkv9xhg2DBg/XvsL+dNPG+4mKrAin5GAl59227Wyf7N9GW3PQ8OxwNq1QFISMHu29sTM114DUlNPfGCUKPnk6aedtknDhFlEymc0rsWxqGEztkSCjmtaiCjHNQFgxw5g0CBAloFly4A//IGPjwRBNI3TjohEAsaAt9/W3nknScBppwHffQecey5vzwjCGLGoY4KIJpymYTquKQiqquLAgQO6jonoqd8UAwYAf/ubVn7qKe0fVrGIVfm0G15+2m3Xyv7N9mW0PS8NRyOHDgE33AA89pi2wbvpJiA3N/QGT5R88vTTTtukYcIsIuUzGtfiWNSwGVsiQZs8zqiqik2bNulaXPTUD0VyMtC6NbBmDZCSYro7IbEyn3bCy0+77VrZv9m+jLbnqeFoYsUKYOhQYN48oGVL4L33gB9+ADp2DN1OlHzy9NNO26Rhwiwi5TMa1+JY1LAZWyJBxzUtRKTjmgGeeQZ48UVg4EBg40btuy8EQTgDpx0RsQNFAV5+GZg2DVBV7eXms2drGz6CiAZiQccEEc04TcN0XFMQVFVFaWmprr8g6qnfHE8+CXTuDGzdCnzxhSVdCoXV+bQLXn7abdfK/s32ZbQ9bw2LTFmZ9kqXZ5/VNngTJgA5Ofo2eKLkk6efdtomDRNmESmf0bgWx6KGzdgSCdrkcUZVVRQWFupaXPTUb4727YGpU7XytGlAXZ0l3QqD1fm0C15+2m3Xyv7N9mW0PW8Ni8rChcCQIUB6OnDSSdofmb74AmjTRl8/ouSTp5922iYNE2YRKZ/RuBbHoobN2BIJOq5pISIe1wS090+deSawe7f2mPInn4ygkwRBNInTjohYgSRpd+5efVX7+ZxztOOZAwfy9Ysg7CIadUwQsYTTNEzHNQVBVVUUFxfr+guinvrhkJAAPPecVn75ZaCy0rKuHY8d+bQDXn7abdfK/s32ZbS9EzQsCsXF2utaAhu8Bx8EVq82t8ETJZ88/bTTNmmYMItI+YzGtTgWNWzGlkjQJo8zTvkuwO23A4MHA4cPH/sHWCwgypnsaPwegNX9i/JdAFHmnNXMmaN91+6337Rj4j/8ALz/PtCqlbl+RcknTz/ttE0aJswiUj6jcS2ORQ2bsSUSdFzTQkQ9rhlg7lztHVUJCdrL0nv0iICTBEE0idOOiBjB6wWeeEJ7JQIAjB4NzJoF9O3L1y+CiBTRoGOCiGWcpmE6rqmDyspKjBgxAkOHDsXgwYPx8ccfh/U7K1AUBTt27ICiKLbU18N11wEXXKD9o2zGDMu7dyR25tNKePlpt10r+zfbl9H2TtKw09i+HTjvvGMbvCef1N6HZ+UGT5R88vTTTtukYcIsIuUzGtfiWNSwGVsiQZs8AG3btkVGRgbWr1+PNWvW4KWXXsKhQ4ea/Z0VMMZw+PBhhHtDVW99Pbhcx45qzpwJbNtmuQnHYWc+rYSXn3bbtbJ/s30Zbe8kDTuJr78Ghg0D1q8HunQBUlO1BztZ/UdQUfLJ0087bZOGCbOIlM9oXItjUcNmbIkEHdc8joqKCgwbNgxr165Fly5dwv4dIP5xzQA33KAd3Rw3TvveDEEQfHDaEZFwqKkBJk069t7NSy4BvvkG6N6dq1sEwQ0RdUwQxDGcpuGoOq6ZkZGB66+/Ht27d4fL5UJKSsoJdd577z306dMHCQkJGD16NLKysnTZqKysxJAhQ9CzZ088+eSTDTZxoX5nFkVRsHXrVl3HRPTUN8JLL2l39X78EdCZRuGIRD6tgJefdtu1sn+zfRlt70QN8yIvDxg5Utvgud3ase/Fi+3d4ImST55+2mmbNEyYRaR8RuNaHIsaNmNLJITY5B09ehRDhgzBe4EvdhzH7Nmz8fjjj2PatGlYt24dhgwZgjFjxuDAgQPBOoHv1B3/3969ewEAHTp0wIYNG1BUVIRvv/0W+/fvD7YN9TsrqNP5BnK99fUyeDAwYYJWnjIFiPZ7vXbn0yp4+Wm3XSv7N9uX0fZO03CkYQz48ENg1Chg61ZtU5eeDiQnAx6P/fZFySdPP+20TRomzCJSPqNxLY5FDZuxJQrCHdd0uVyYM2cOxo4dG/xs9OjRGDlyJP773/8C0B6L2qtXLzz88MOYMmWKbht///vfcdlll+Hmm2/W9bvA7dOioiK0bds2+HnLli3RsmVL3X6YRZIkLFq0CFdeeaXu28vFxcBZZ8XB73dh3jwZV10l1DQhiKjAjIYjRWUl8MADHvz0k/Y3w2uuUfHJJwpOPpmvXwThFETQMUEQTcNbwz6fDz6fL/hzdXU1+vbt2+xxTeE3eX6/H61bt8YPP/zQYOM3ceJEVFZW4ueff262z/3796N169Zo27YtqqqqcMEFF2DWrFk4++yzQ/7ueAKbvOMZP348/vKXvxiOmRczZ56FX34ZgL59K/HGG8vhFuK+L0EQkWL79g74979H4MCBk+DxqJgwYQuuv76QrhUEQRAEYRGzZs3C7NmzT/i8uU1enJ1ORYLy8nIoioKuXbs2+Lxr167YunVrWH0UFxfjvvvuA2MMjDE8/PDDwU1cqN81hZ47eYqiYPv27TjjjDPgCeNck576Zv/yMGoUsGwZQ1FRB9TU/BG33irU3wPCQm/+ecHLT7vtWtm/2b6Mtneyhu1CVYG33nLjmWfckGUX+vZl+PprFSNHngngzIj6Qhrma5s03Hx9p+rYKYiiYSA61+JY1LDeNrw1fPnllzf4ylrgTl5zCH8nb+/evejRowcyMzNx3nnnBetNnjwZy5cvx5o1ayLmW7Q8XbM+L7wAPPss0K8fkJ8PtGhhsZMEQTSJ057oBQAHDwITJwK//qr9/Oc/Ax9/DDRyiIEgCDhTxwRBhI/TNBxVT9cMRZcuXeDxeE54GMr+/fvRrVs3Tl6Fj6IoyM3N1fVULz31zfLYY0DXrsDOndo/5KKNSOfTKLz8tNuulf2b7ctoe6dr2EqWLQOGDtU2eAkJ2sNWZs/mu8ETJZ88/bTTNmmYMItI+YzGtTgWNWzGlkgIv8lr0aIFhg8fjiVLlgQ/U1UVS5YsaXBnz8m0atXK1vpmOOkk7Ql5APDcc9o7sKKNSObTDLz8tNuulf2b7ctoeydr2AoUBZg+Hbj8cmDvXmDgQO31Kvffr71uhTei5JOnn3baJg0TZhEpn9G4Fseihs3YEgUhjmvW1NRgx44dAICkpCS8+eabuPTSS9GpUyf07t0bs2fPxsSJE/G///0Po0aNwltvvYXvv/8eW7duPeG7enYSjcc1tX6AQYOAwkJto/fssxY6SRBEkzjhiEhpKXDbbcDy5drPd90FvPuu9gcggiCaxwk6JgjCOE7TcFQd11y7di2SkpKQlJQEAHj88ceRlJSE5N9vMY0fPx7//ve/kZycjKFDh2L9+vVYsGBBRDd4RpFlGdnZ2ZBl2Zb6VhAfr303DwBef137Tk60wCOfRuDlp912rezfbF9G24ugYaP8+qt2PHP5cqBNG+Crr4CZM521wRMlnzz9tNM2aZgwi0j5jMa1OBY1bMaWSAixybvkkkuCT7es/9/nn38erDNp0iQUFxfD5/NhzZo1GD16ND+HdeByudCxY0e4wjzzpLe+VdxyC5CUBFRXAy+9FFHTtsIrn3rh5afddq3s32xfRtuLomE9+P3AE08A114LlJdr2s/JAW6/nbdnJyJCPgG+ftppmzRMmEWkfEbjWhyLGjZjSySE2OSJRuBLnIqiNFqWZTlYZoyhX79+8Hg8kGUZqqoG6wTKkiQFy6qqon///vB4PJAkKbjhPb4c6DuAqqrBz1VVDf7loqmyoigNyowpeOUVra/332coLm46pqbiaCqmgO/1y6Fiqh+HmZgURYHH42nwGFqnxuRyudCnTx94PJ5mYwpV1huTqqrB+WnHODHGMGDAALhcLtMxeTwe9OnTJ3jB1jtOAW253W5dMTU1Nk2NEwD07dsXHo8nrHEK9GUkJiPjVFQEXHQRwxtvaLYnTWJYsULGGWc0r6dwxilUHEZicrvd6N+/f7Cu1dcIq2LyeDw47bTTgvMzkte9gM4CP1s5TlZeIzweD/r169dAk3rGCWhcW83FdPy1o7m553a7cdppp8Htdoc9TgF4zL1wYorUmttYGQAGDBgAxpjjYwIQvN5HcpyMrk+RjsnlcgXH0sg4BbTl8Xh0xXT8tSOcuQccu16EM04Be3pjsktP4UCbPBtISkpCYmIikpOTkZ+fDwDIy8tDQUEBACA3NxdFRUUAgDVr1mDp0qWQZRmZmZkoKysDAGRkZKC8vBwAkJ6ejsrKSgBAWloaVqxYAVmWkZqaCq/XGyzLsgyv14vU1FQA2ncZA1RWViI9PR2A9m7BjIwMAEBZWRkyMzMBACUlJcjKygKgvesvNzcXAFBQUIC8vDxceSUwenQN/H4XkpObjikrKwslJSUAEHZM1dXVANBsTNXV1UhLS7MsJlmWsWTJEmzevDnkOPGOqbS0FAsXLoQsy83GBAD5+fnNzr1wYwrMNzvGadWqVcjMzMSuXbtMxxTwcdeuXYbHaeXKlcE4wo1p//79WLBgAWRZDmvubdu2DYsXL4Ysy2GNU2D8jcakZ5xmzNiMpCQgK8uFNm0k/PQTMGXKXuTkhKencMYJsFZPXq8XK1eutO0aYVVMsixjwYIFOHTokOlx0htTTk4OMjMzsW3bNkdfI2RZRkZGBpb//gVQveO0efNmLFmyBLIs64opMDaBp3Q3N/cCcdQvhxqnwJgbiSnSerJ7zW0spm3btiEzMxM5OTmOj6mwsBCLFi2CLMsRHycj61OkYyotLUVmZiaWL19uaJxqamqCZT0xybKMpUuXBl+bFs7c27BhQ/C6E844Ace0zEtPU6dORWJiIoYPH46wYIRlVFVVMQCsoqKCMcaYLMtMluUTypIkBcs+n4/t3LmTKYrCJEliiqIE6wTKfr8/WPZ6vayoqIgpisL8fj9TVZWpqnpCOdB3SkpKsH3g84CtUGVZlhuUA/7+9pvMAMZcLsZycxuPqak4moop4Hv9clMx1S9bEZOiKGznzp3BfpoaJ94xSZLECgsLmaIoYY1TOHMvnJh8Pt8J883KcfL5fGzXrl1MkiTTMSmKwgoLCxvkTM84BbQly7KumJoam6bGye/3BzXfXEx+v5+lpKQwr9dr69yrrWXs/vtVBjAGMHbeeSrbscPYNcKquRduTLIss6KiomCOrL5GWBXT8fMzkte9gM4C+bJynKy8RiiKwoqKipjP5zM0Tk1pq7mYGhubUHNPlmVWWFjIZFkOa5y8Xm+DtdjJerJ7zW2s7Pf72a5du5jP53N8TOFcv+0YJ6PrU6RjkiQpOJZGximgLUVRdMUU+Ldc4NoRztyrH3dz43T8WsxbTxUVFQwAq6qqYqEQ4umaohCtT9c8nltuAf7v/4DrrgPmzrWsW4IgjsMuDddn61Zg/Hjg9z90YsoU7Sm6DniAGEFEBZHQMUEQ9uE0DUfV0zWjmcDxFLne2Wgr69vBCy8AHg8wbx6wciU3NyzBCfkMB15+2m3Xyv7N9mW0vYgaBrR7dp9/Dgwfrm3wTjkFWLgQePllsTZ4Tslnc/D0007bpGHCLCLlMxrX4ljUsBlbIkGbPM4EHhrgdoc3FHrr28EZZwD33quVn3pK+8eiqDghn+HAy0+77VrZv9m+jLYXUcPV1cCECdo772prtZecr18PXHUVN5cM44R8hgNPP+20TRomzCJSPqNxLY5FDZuxJRJ0XNNCYuW4JgDs3QsMGADU1QEpKcCf/mRp9wRBwB4Nr1+vHbkuKADcbu1o5pQp2t15giCsx2lHvQiC0IfTNEzHNQVBluXg033sqG8X3bsDjz6qladOBcJ8mqvjcEo+m4OXn3bbtbJ/s30ZbS+KhhkD/vtfYPRobYPXs6f2kvN//UvsDR5pmK9t0jBhFpHyGY1rcSxq2IwtkaBNHmfcbjcGDx6s65iInvp2Mnky0LEjsGUL8OWXvL0xhpPyGQpeftpt18r+zfZltL0IGj58GBg3Dnj4Ye1F59dfr93Ru/DCiLlgG6RhvrZJw4RZRMpnNK7FsahhM7ZEInojEwS3241TTjlF1+Kip76ddOig3cUDgGnTAK+XqzuGcFI+Q8HLT7vtWtm/2b6Mtne6hpcvB5KSgDlztAeq/Oc/wM8/A507R8S87ZCG+domDRNmESmf0bgWx6KGzdgSieiNTBAkScLChQuDb723ur7dTJqkHfsqKQHee4+3N/pxWj6bgpefdtu1sn+zfRlt71QNHzkC/P3vwCWXAMXFQP/+QGamdsza5bLVdEQhDfO1TRomzCJSPqNxLY5FDZuxJRK0ybMB5fcvqCmK0mhZluVgmTGG4cOHw+PxQJZlqKoarBMoS5IULKuqihEjRsDj8UCSJDDGwBg7oRzoO4CqqsHPVVUNnkFuqqwoSoNyUzHFxyuYMUOz8dJLDFVVaDKOpmIK+F6/HCqm+nGYjcnj8WD48OENxq6xceIdk8vlwrBhw+DxeAyNk9GYGGMnzDcrx4kxhpEjR8LlcpmOyePxYNiwYXD9voPRO04Bbbndbl0xNTU2TY0TgGD9cMYp0JeemFJTGQYPBj74QGt/770Ma9ZIGD7cXj2FM06h4jCiJ7fbjREjRjSYs06MyePxICkpKTg/I3ndC+gs8LOV42TlNcLj8WDEiBENNKlnnAAE11M9MR1/7Whu7rndbiQlJcHtdoc9TgGcrie719zGygAwcuRIMMYcHxPQ/PXbjnEyuj5FOiaXyxUcSyPjFNCWx+PRFVPg33KBvsOZewAa/Pu7uXEK2NMbk116Cgfa5NlAUlISEhMTkZycjPz8fABAXl4eCgoKAAC5ubkoKioCAKxduxZHjx6F2+1GZmYmysrKAAAZGRkoLy8HAKSnp6OyshIAsHjxYsTHx8PtdiM1NRVerxeyLCM1NRWyLMPr9SI1NRUAUFNTE/SpsrIS6enpAIDy8nJkZGQAAMrKypCZmQkAKCkpQVZWFgCgqKgIubm5AICCggLk/f6m5Pz8/BNimjAB6Nu3DhUVLrz2GpCVlYWSkhIACCumtLQ0VFdXA0CzMVVXVyMtLc2ymNxuN/bt24dt27aFHCfeMe3fvx/5+flwu92Gx8lITEuXLoXL5YLb7bZlnFavXo1OnTqhtLTUdExutxsFBQUoLS01NE4LFixA69atoaqqrpgqKiqwceNGuN3usOZeYWEh9uzZA7fbHdY4AQg7pooKYMyY/fjjH10oKQG6dj2K1FQf3n9fxsqV9uvJyrkXrp5UVUXr1q2xYMECR8fkdruRm5uLI0eONBuT1eO0YcMGdOrUCYWFhY6+RrjdbiiKgpW/v4BV7zht27YN+/btg9vt1hWT2+3Gxo0bUVFREdbcU1UVv/32G1RVDWucDh06hABO1xOPa0RhYSE6deqEDRs2OD6m4uJi7Nq1K7jeRGqcjK5PkY5p//796NSpE1auXGlonPx+P3777Te43W5dMbndbhw9ehRr164FEN7c27RpU4Nrc3PjBBzTMi89TZ06FYmJiQ1uToSEEZZRVVXFALCKigrGGGOyLDNZlk8oS5IULNfW1rK5c+cyv9/PJEliiqIE6wTKfr8/WD569Giwvt/vZ6qqMlVVTygzxpjP52MpKSnB9oHPFUVhkiSFLMuy3KDcWBz1yz/8IDOAsVatGNu9u/E4moop4Hv9clMx1S9bEZPf72dz585ldXV1IcepqTgiFZPX6w2Ou5lx0htT/flpxzjV1tayefPmMa/XazqmwFh6vV5D4xTQls/n0xVTU2PT1DjV1dUF64cTU0pKSlgxzZ6tsFNOYQxgzOVS2aOPMnb4cGT1ZOXcC1dPPp+PzZ07lx09etTRMR0/PyN53aurq2Pz5s1jdXV1jr5GBHJUW1traJya0lZzMTU2NqHmXmDO+Xy+sK8R9ddiJ+uJxzWisfnp1JjCuX7bMU5G16dIx+T1etm8efNYbW2toXEKaCvwc7gxHX/tCGfu1Y87nGtE/bWYt54qKioYAFZVVcVCQe/JsxAj78ljjKG6uhpt27YNHhWxqr4kRe69HowBF1wA/PYb8MADx46JOR29+ecFLz/ttmtl/2b7Mtqet4b37QMeegj46Sft50GDgE8/Bc47L+wQhIY0zNc2abj5+pFci0VEFA0D0bkWx6KG9bZxmobpPXmC4HK50K5du7Anpd76kcLlAl55RSt//LH2Hi4RcGo+j4eXn3bbtbJ/s30Zbc9Lw4xpry5JTNQ2eB6P9s673NzY2eABpGHetknDhFlEymc0rsWxqGEztkSCNnmckSQJP//8s66neumpH0kuvhi49lrtxejPPMPbm/Bwcj7rw8tPu+1a2b/Zvoy256Hh3bs1rU2cqL0DLykJWLsWeOEFoGVLw90KCWmYr23SMGEWkfIZjWtxLGrYjC2RoOOaFmL0uKbX60VCQkLYx0TCrc/j9nJeHjB0qHaXYe1aINzvhvJCb/55wctPu+1a2b/Zvoy2j6SGVRX43/+AyZOBmhptQzdtGvDEE9o78GIR0jBf26RhOq5pFlE0DETnWhyLGtbbxmkapuOaAhEXF2dr/UhyzjnAbbdp5SlT+PoSLk7OZ314+Wm3XSv7N9uX0faR0HBBAXDppdq772pqgPPPB9avB55+OnY3eAFIw3xtk4YJs4iUz2hci2NRw2ZsiQJt8jhT/zGrdtTnwXPPaf/oXLxY+8/JiJBPgJ+fdtu1sn+zfRltb7eGFcWFN99045xzgIwMoHVr4O23tfLAgbpcjUpIw3xtk4YJs4iUz2hci2NRw2ZsiQQd17QQo8c1ZVlGXFxc2MdEwq3P8/byP/4BvPOOdlwzKwtwO/TPCXrzzwteftpt18r+zfZltL2dGl63TsKtt9agoKAjAOCKK4CPPgL69g3bvaiHNMzXNmmYjmuaRRQNA9G5FseihvW2cZqG6bimQBj5i4XT+de/gDZtgJwc4IcfeHsTGhHyCfDz0267VvZvti8zf320sr7fD0yfDpx7bhwKCjqifXuGTz4B0tJog9cYpGG+tknDhFlEymc0rsWxqGEztkSBNnmckWUZaWlpuo6J6KnPi1NO0R4GAWgbPqc+vEiUfPLy0267VvZvti+j7a3WcHa2dgd8xgxAklwYNaoMGzbIuOce7VUlRENIw3xtk4YJs4iUz2hci2NRw2ZsiQQd17SQwO3TiooKdOzYEYqiAAA8Hk+DsizLcLlcwbLb7Ybb7W6yLEkSPB5PsBy4tRwoAwjecg6U4+Pj4ff78euvv+Laa68N+qA9oU+FqqqIi4trsqwoChhjwXJjcTQX09GjbvTvz3DwoAsffADce6/5mAK31wNxRDomO8aJYqKYPB4PamoUTJvmwltvuaGqQJcuDG+9peCkk+bjmmuuRsuWLYWLKRrHiWKimPTG5PP5sGDBguBaHA0xReM4UUwUU1MxKYqC1NRUXH21thbzjunw4cPo1KkTHdfkQVJSEhITE5GcnIz8/HwAQF5eHgp+f0N4bm4uioqKAABZWVnYvn07GGPIzMxEWVkZACAjIwPl5eUAgPT0dFRWVgIA0tLSsG/fPjDGkJqaCq/XC1k+9uVRr9eL1NRUAEBNTU3Qp8rKSqSnpwMAysvLkZGRAQAoKytDZmYmAKCkpARZWVkAgKKiIuTm5gIACgoKkJeXBwDIz88PK6aSkhK0bQv89a+FALS7EgsXrmwypurqagBoNqbq6mqkpaVZFhNjDOvXr8eWLVvCiglA2ONkdUwrVqwAY8zycWouptLSUjDGbBunI0eOYPfu3aZjCmho9+7dhsfp4MGDwbP34cZ08OBBLF26FIyxsObe9u3bkZOTA8ZYMKaMDCAxUcKbb2obvKuuOoR584pwyy0MLhdQWlrKde7ZdY2wIiZJknDw4EHHx8QYw5IlS3D48OGIj9O6detw5MgRbN++3dHXCMYYiouLDY/Tli1bsH79ejDGdMXEGMPSpUtx8ODBsOZe4BohSVJY43To0CEEcLqeeFwjtm/fjiNHjmDdunVCxJSVlQXGWMTHycj6xCOmI0eOGB6nuro6pKamgjGmKybGGLZv36577m3atAmMsbDGCTimZV5zb+rUqUhMTMTwcN9PxgjLqKqqYgBYRUUFY4wxWZaZLMsnlCVJCpZra2vZ3Llzmd/vZ5IkMUVRgnUCZb/fHywfPXo0WN/v9zNVVZmqqieUGWPM5/OxlJSUYPvA54qiMEmSQpZlWW5QbiyOpmKq7/vRoxLr109lAGPPPy83GlPA9/rlpmKqX7YiJr/fz+bOncvq6urCjimccbI6Jq/XGxx3O8apqXL9+WnHONXW1rJ58+Yxr9drOqbAWHq9XkPjFNCWz+fTFVNTY9PUONXV1QXrHz4sswcfVJj2ZknGundX2S+/NIwpJSXFcEyR1pOVcy/cmHw+H5s7dy47evSoo2M6fn5Gcpzq6urYvHnzWF1dnaOvEYEc1dbWGhqn+trSE1NjYxNq7gXmnM/nC/saUX8tdrKeeFwjGpufTo2pqTlm9zgZXZ8iHZPX62Xz5s1jtbW1hsYpoK3Az+HGdPy1I5y5Vz/ucK4R9ddi3nqqqKhgAFhVVRULBR3XtBAjT9e0k8BffHg/Dejbb7V357VrB+zcCXTuzM0VgnAMCxYA990H/P4HQ9x7L/D660CHDsfqOEXDBEEYh3RMEGLjNA3T0zUFQVVVVFRUQFVVW+o7gVtvBYYOBY4cAV5+mbc3DREln7z8tNuulf2b7ctoe73tystV/OUvPlxzjbbB69tXe5/kxx833OAR4UEa5ms7FjUsypwTBZHyGY1rcSxq2IwtkaBNHmcURUF2dnbwC6BW13cCbvexzd277wK/f2XKEYiST15+2m3Xyv7N9mW0vZ52P/4IDB7swnfftYTLxfCPfwAbNwKXX27IZQKkYd62Y03DZuwQjSNSPqNxLY5FDZuxJRJ0XNNC6Lhm0zAGXHYZsGwZcOedwGefcXWHICLKvn3ApEnaJg8ABg4EPv0UOP/80O2cpGGCIIxBOiYIsXGahum4piCoqooDBw7oOiaip75TcLmAV17Ryl9+CWzezNefAKLkk5efdtu1sn+zfRltH6odY9p8T0zUNngeDzB1KsPChQdw7rnOnnOiQBrmazvaNWylHaJxRMpnNK7FsahhM7ZEgjZ5nFFVFZs2bdK1uOip7yRGjwZuuglQVWDqVN7eaIiST15+2m3Xyv7N9mW0fVPtdu8G/vhHYOJE4PBh7Xup2dnAjBkKduxw/pwTBdIwX9vRrGGr7RCNI1I+o3EtjkUNm7ElEnRc00LouGbzbN0KnHWWttFbuRK44ALeHhGEtagq8L//AZMnAzU1QIsWwPTpwBNPAHpl6EQNEwShD9IxQYiN0zRMxzUFQVVVlJaW6voLop76TmPgQODuu7XylCnacTaeiJJPXn7abdfK/s32ZbR9/XY7dmjfPf3737UN3nnnAevXA08/fWyDJ8qcEwVR8snTTzttR5uG7bRDNI5I+YzGtTgWNWzGlkjQJo8zqqqisLBQ1+Kip74TmT4dSEjQ7uTNn8/XF1HyyctPu+1a2b/Zvoy2V1UV27cX4t//Zjj7bGD5cqB1a+Ctt4AVK4BBg6z1k2iIKPnk6aedtqNFw7G2DjsJkfIZjWtxLGrYjC2RoOOaFkLHNcPnqaeA114DBg/W7nR4PLw9IghjbNqk3Z3OztZ+vvxy4KOPgH79zPftZA0TBBEepGOCEBunaZiOawqCqqooLi7W9RdEPfWdypQp2oufN20CvvmGnx+i5JOXn3bbtbJ/s33pbe/3AzNmAMOGMWRnA+3aMXz8MbBoUegNnihzThREySdPP+20LbKGjbYTZc6Jgkj5jMa1OBY1bMaWSNAmzwYCL1ZUFKXRsizLwbLf78eePXugqipkWQ5OtvplSZKCZZ/PF6wvSRIYY2CMnVAGgPo3aQP1A2VZlkOWFUVpUNYTU1Nx1C+3aSPhqae0cnIyg9fLgrE2F1P9OMzGpKoq9uzZE+zHTEz1xyngu1UxybIcHPdIjlP9+Wl1TKqqwu/3o7S0FLIsm44pMJb1c6ZnnALaUhSl2ZiyslQMH84wfTogSS5ccEEF8vIU3H23CkUJPfckSQrmNJxxCvTFa+7xukaEG5OiKNizZw98Pp+jY1JVFSUlJQ3mQaTGSZIklJaWBvNl5ThZeY0IaNjv9xsap6a01VxMx187mpt7iqKgpKQEiqKEPU4BnK4nHteIxuanU2MK5/ptxzjpWZ94xiTLMkpLS+H3+w2NU0Bbqqrqiun4a0c4c69+3OGMU8Ce3pjs0lM40CbPBpKSkpCYmIjk5GTk5+cDAPLy8lBQUAAAyM3NRVFREQBg3bp16NmzJ+Li4pCZmYmysjIAQEZGBsrLywEA6enpqKysDJbPPvtsxMXFITU1FV6vF7IsIzU1FbIsw+v1IjU1FQBQU1MT9KmyshLp6ekAgPLycmRkZAAAysrKkJmZCQAoKSlBVlYWAKCoqAi5ubkAgIKCAuTl5QEA8vPzm40pKysLJSUlABAypttvr0T37kBxsQtvvaX9A625mKqrq5GWlmZZTHFxcWjfvn0wDrMxBcYpLS0N1dXVlsV08OBBKIqCuLi4iI7T8uXLMWjQIMTFxVkeU1lZGbKysnD++ecHy2ZiiouLg8vlCsahd5zS0tIwbNiwYHyNxTR37mJMngycd54Lmza50KUL8OGHVZgxYz1OOy0urLlXVFSE1q1bIy4uLqxxAoDS0lJDMUVaT+GME2CtngBg2LBhwTicGlNcXByOHj0avC5Hcpzy8vJw/vnnB8tWjpOV14i4uDicfvrpwTj0jlNBQQHat2+PuLg4XTHFxcXB5/M1WGdDzT0AOHToEACENU6BukZiirSeeFwjioqKcP755wfLTo6ppKQELVq0QFxcXETHKZz1yQkxHTx4EOeffz4yMzMNjZMsyzh06BDi4uJ0xRQXF4eePXti3bp1AMKbe1u2bEHnzp0RFxcX1jgBx7TMS09Tp05FYmIihg8fjrBghGVUVVUxAKyiooIxxpgsy0yW5RPKkiQFy16vl23bto3JsswkSWKKogTrBMp+vz9YrqurY9u3b2eyLDO/389UVWWqqp5QZowxn8/HUlJSgu0DnyuKwiRJClkO+BMqjqZiaiqOxmL66CPGAMa6dFFZVRVrNqb6ZStikmWZbdu2jfl8PstiCpRVVW1QNhOT3+8PzpNIjpPX6z1hvlkVk6IozOv1soKCAub3+03HFBjLgC294xTQliRJjca0fLnKBgxQmfZMWMbGj1fYgQNaH1u3bj1hbJoaJ5/PFxzL5mLy+/0sJSWFeb1ebnOP9zWiuZgkSWLbt29ndXV1jo5JlmW2devWoD+RHCefz8cKCgqYz+dz9DVClmW2ffv24HzXO05Naau5mBobm1BzT5IktnXrViZJUljj5PV6G6zFTtYTj2tEY/PTqTGFc/22Y5yaW5+cEpPf72cFBQXM6/UaGqeAtmRZ1hVTYP0PXDvCmXv1425unI5fi3nrqaKiggFgVVVVLBRx4W0FCT14fn+KiKfe00Tql+Pi4hp8XlVVBcZYg8/rl+t/yTMuLg6VlZVgjDX4vLGyy+UKfuZ2u+F2u8MuN+V7ODGFUw74eNddwBtvANu2ufDvfwPPPdd8TIGyFTHJsoyqqqpG6xiNSW85nJhcLldwnng8noiNk8fjCXu+6Y3J7XbD4/Hg8OHD6NOnT9Cu0ZgCYxmY93rHKaCt4z/3euPx6KPA++9r/XbvDnzwAXDDDe7f7bpw5MiRE8amqbnndrsb1XxjMQWOgwT64TH3eF8jmivLsozKykr07dvX0THJsowjR44E52ckx4kxFtSZnvUp0teI48dS7zg1pS07xubIkSMnxBFqnOr76GQ9HV+OhJ4am59OjSmc67cd49TU+uS0mGRZDo5loH+94xRYT/XEFFj/m7u+Gb1eBI5HGo3JTj2Fgp6uaSH0dE1j/PgjcPPNwEknAYWFQNeuvD0iCI2FC4H77gN279Z+vvde4PXXtYcGRQJRNEwQRNOQjglCbJymYXq6piAoioKtW7eG/SVKvfVF4KabgFGjgKNHgeefj6xtUfLJy0+77VrZv9m+6revqADuvBO4+mptg9enj/bUzI8/PnGDRxrmiyj55OmnnbadqmE724ky50RBpHxG41ocixo2Y0skaJPnAOrq6myt73RcLuCVV7Ty//6n3c2LJKLkk5efdtu1sn+zfVVUePHWWy4MGgR88YU2N//xD2DjRuCKK6yzK8qcEwVR8snTTzttO0nDRtuThvkiUj6jcS2ORQ2bsSUKdFzTQui4pjmuvlo7HveXvwDffsvbGyKWOHpU+57d668DBw5on515JjBzJnD++fz8Ek3DBEGcCOmYIMTGaRqm45qCoCgKNm3apOuYiJ76IvHyy9r/Z80Cfn/yre2Ikk9eftpt18r+jfRVXQ28+qp2HPPJJ7UNXp8+DB99BOTlhbfBIw3zRZR88vTTTtu8NWxFe9IwX0TKZzSuxbGoYTO2RII2eYRjSEoC/vpXrfz003x9IaKbI0eAl17SNndTpgDl5UC/fgzPPbcH+fkq/vY3oEUL3l4SBEEQBEEYg45rWggd1zTPzp3AwIGAJAHp6cCll/L2iIgmKiuBd98F/vMf4PBh7bMBA4BnngFuuw2o9+RkRyCihgmCaAjpmCDExmkapuOagqAoCnJzc3UdE9FTXzT69QPuv18rP/WU9tppOxEln7z8tNuulf2H6uvwYWD6dO3OXXKy9vOZZwJffQXk5wMTJwIulzFfSMN8ESWfPP2003akNGxne9IwX0TKZzSuxbGoYTO2RII2eQ6gVatWttYXjWee0d6Zl52tvUPPbkTJJy8/7bZrZf/H93XokDafTjsNmDEDqKoCEhO1731u3gzcfnvDu3dGfSEN80WUfPL0007bdmo4Uu1Jw3wRKZ/RuBbHoobN2BIFOq5pIXRc0zqmTQOeew444wztH+NOO0ZHOJvycuCNN4D//heoqdE+GzxYu4s3bhzgFuTPWyJrmCAIDdIxQYiN0zRMxzUFQZZlZGdnQ5ZlW+qLyj//CXTpAmzfrj3G3i5EyScvP+22a2X/sixj4cJcPPGEij59tHcv1tQAQ4Zod4Q3bAD+/OemN3hGfSEN80WUfPL0007bVmvYTF+kYTERKZ/RuBbHoobN2BIJ2uRxxuVyoWPHjnC5XLbUF5V27bRjdoD2XaraWnvsiJJPXn7abdeq/vftA5580o2xY4fgjTfcOHpUe1prSgqwbh1w003N370z6gtpmC+i5JOnn3batrJvs32RhsVEpHxG41ocixo2Y0skaJNnA4EvcSqK0mhZluVgmTGGfv36wePxQJZlqKoarBMoS5IULKuqiv79+8Pj8UCSJDDGwBg7oRzoO4CqqsHPVVUN/uWiqbKiKA3KemJqKo6mYgr4Xr/MGMM990jo04ehrAz4z3+O5at+HGZj8ng86Nu3b4OxszOmpsapuZhcLhf69OkDj8cT0XFSVTU4P62OSVVVMMYwYMAAuFwuQzHt3Qs88oiKvn0Z3nrLDa/XjREjGObOBdaskXH99Src7vDGKaAtt9utK6amxqapcQKAvn37wuPxhDVOgb54zT0nXyMkSYLb7Ub//v2DdZ0ak8fjwWmnnRb8B0Ukxymgs8DPVo6TldcIj8eDfv36NdCknnECGtdWczF5PB706dOnwdiEmntutxunnXYa3G532OMUwOl64nGNAIABAwaAMeb4mAAEr/eRHCej61OkY3K5XMGxNDJOAW15PB5dMR1/7Qhn7gHHrhfhjFPAnt6Y7NJTONAmzwaSkpKQmJiI5ORk5OfnAwDy8vJQUFAAAMjNzUVRUREAYM2aNVi6dClkWUZmZibKysoAABkZGSgvLwcApKeno7KyEgCQlpaGFStWQJZlpKamwuv1BsuyLMPr9SI1NRUAUBP4MhKAyspKpKenAwDKy8uRkZEBACgrK0NmZiYAoKSkBFlZWQCAoqIi5P7+RvKCggLk5eUBAPLz85uNKSsrCyUlJQAQdkzV1dUA0CCmJUtSkZysTeSXXlJRUQFUV1cjLS3Nspg0O0uwefPmiMTU2DiFE1NpaSkWLlwIWZYjPk6B+WZ1TGVlZVi1ahUyMzOxa9cuXTEtWpSPiROPoF8/4N133fB6XRg1imHGjGx8910RrrsO+O03/eO0cuXKYBzhxrR//34sWLAAsiyHNfe2bduGxYsXQ5blsMYpMP685p6TrxGB8sqVKx0fkyzLWLBgAQ4dOhTxccrJyUFmZia2bdvm6GuELMvIyMjA8uXLDY3T5s2bsWTJEsiyrCumwNjs378/rLkXiKN+OdQ4BcbcSEyR1hOPa8S2bduQmZmJnJwcx8dUWFiIRYsWQZbliI+TkfUp0jGVlpYiMzMTy5cvNzRONTU1wbKemGRZxtKlS7FmzRoA4c29DRs2BK874YwTcEzLvPQ0depUJCYmYvjw4QgLRlhGVVUVA8AqKioYY4zJssxkWT6hLElSsOzz+djOnTuZoihMkiSmKEqwTqDs9/uDZa/Xy4qKipiiKMzv9zNVVZmqqieUA32npKQE2wc+D9gKVZZluUG5sTiaiqmpOJqKKeB7/XIgDklS2dlnqwxg7MknWYP4rIhJURS2c+fOYD+RiOn4cQonJkmSWGFhIVMUJaLj5PP5TphvVsWkKArz+Xxs165dTJKksGLauVNmDz7IWIsW2pwAGDvvPJWlpspMlhVWWFjYIGd6ximgLVmWdcXU1Ng0NU5+vz+o+ebGye/3s5SUFOb1ernNPadfI2RZZkVFRcEcOTUmRWk4PyM5TgGdBfJl5ThZeY1QFIUVFRUxn89naJya0lZzMTU2NqHmnizLrLCwkMmyHNY4eb3eBmuxk/XE4xrh9/vZrl27mM/nc3xM4Vy/7Rgno+tTpGOSJCk4lkbGKaAtRVF0xRT4t1zg2hHO3Ksfd3PjdPxazFtPFRUVDACrqqpioaCna1oIPV3THubPB667DkhIAAoKgJ49eXtERJpdu4CXXwY++wz4/XQDLrpIewrrZZcB0XqkPlo0TBCxDOmYIMTGaRqmp2sKQuB4ilzvbLSV9aOBa6/V/kHv9QJXX609bdOqB7GIkk9eftptt7n+d+4E7r0XOP104KOPtA3epZcCS5cCGRnA5Zcf2+CZ9dVoe9IwX0TJJ08/7bRtZd+k4dhEpHxG41ocixo2Y0skaJPHmcBDA9xhvrhLb/1owOUC/vMfoE0b7Z1599wD9OgBPPYYsG2bub5FyScvP+2221T/BQXAXXdp70n89FNAloErrtA2dunpwCWXWO+r0fakYb6Ikk+eftpp28q+ScOxiUj5jMa1OBY1bMaWSNBxTQuh45r2cvCgdlzvww+Bes+kwOWXA3//O3DDDfTSdNHZtg148UXgm2+AwAOtxozRXmJ+/vl8feNBtGmYIGIR0jFBiI3TNEzHNQVBluXg033sqB9NnHwyMHkysGMHkJoKXH+9dpdvyRJg3DjgtNOAGTOAvXvD71OUfPLy0267gf43bpRx221AYiLw1VfaBu/aa4HVq4EFC8Lb4Jn11Wh70jBfRMknTz/ttG1l36Th2ESkfEbjWhyLGjZjSyRok8cZt9uNwYMH6zomoqd+NOJ2A9dcA/zyi3ZHb+pU4JRTtM3d9OlA797AzTdrm7/m7lOLkk9eftptd8sWN95553wMGeLBt99qm7vrrweys7UH7oweHTlfjbYnDfNFlHzy9NNO21b2TRqOTUTKZzSuxbGoYTO2RIKOa1oIHdfkh98P/PQT8P77wIoVxz4/80zggQeAiROBjh35+Uc0JC8PeP554Icfjn02dqx2LDMpiZtbjiOWNEwQ0QrpmCDExmkapuOagiBJEhYuXBh8673V9WOFFi2AW2/VHsyxcaP2Hb22bbXveD32mPaglnvuAXJyGrYTJZ+8/LTabm4ucOONwJAhxzZ4F164D9nZEubMMbfBM+ur0fakYb6Ikk+eftpp28q+ScOxiUj5jJa12K6+RdGwGVsiQZs8zng8HowcORIej8eW+rHI4MHAe+8BpaXABx8AZ58N1NVpr14YMUI7Avj559pnouSTl59W2V27VnswzrBhQEqK9l3K8eOBDRtU/PxzCwwbZj4us74abU8a5oso+eTpp522reybNBybiJRP0ddiu/sWRcNmbIkEbfI443a70alTJ13fBdBTP5Zp21Y7qrlhA7ByJfDXv2p3/LKytMfz9+gBPPmkG4cOOT+fvMbdrN2sLOCPfwRGjgTmztW+T/nXvwKbNgHffQecc451cZn11Wh70jBfRMknTz/ttG1l36Th2ESkfIq6Fkeqb1E0bMaWSERvZIIgSRLmz5+v65iInvqEdtfoggu0x/KXlAAvv6w9ifPwYeDNN7V3sV11lYqUFO19bE6E17gbtfvbb9qL60eP1p6E6nYDd9wBbNmijUNiorn+rfTVbHvSMF9EySdPP+20TRomzCJSPkVbiyPdtygaNmNLJOjBKxZi5MErjDFUV1ejbdu2cLlcltZ32hdFnYSiaI/mf/99hl9/BRjTctmzJ3DffcC99wKnnsrZyXronSe87K5cqb3GYvFi7WePR9vcTZ0KnH66+f6t9NWq9qRhvvDShl54+mmnbdJw8/VJx6ERRcOAOGsxr75F0bDeNk7TMD14RRBcLhfatWsX9qTUW59oHI9HO0Y4f74LhYUuPPUU0KULsGeP9oTH3r2BW24Bli1r/jUMkYDXuDdnV1W19xZ+/z1w2WXARRdpG7y4OO1BN9u2aS+wb2yDF07/VvpqV3vSMF9EySdPP+20TRomzCJSPp26Fjulb1E0bMaWSNAmjzOSJOHnn3/WdUxET30iNJIkIS/vZzz/vIQ9e4Cvv9aOdsoy8H//B1x6KXDWWcC77wJVVXz95DHu9e16vdoDVD75BJg0CbjwQqB9e20DN348sHQpEB+v3QktKNDq9e8ffv9W+hrJ9qRhvoiST55+2mmbNEyYRaR8OmEtdnLfomjYjC2RoOOaFmL0uKbX60VCQkLYx0TCre+028tOpKl85uVpT+b86ivg6FHts9atgdtuAx58MPLvctM7T8xSXg6sXw/k5jKsW6dg40YPtm51QVFOrNuypfYE0wsv1F5X0bt3+HasjMtsX0bbk4b5EmltGIWnn3baJg3TcU2ziKJhgJ+vpGHr24m8Foe734iLoE9EE8TF6RsGvfWJ0DSWz3PO0TZ5r76qbfQ++ADYvBn4+GPtv3PP1d7F9+c/AwkJ/Pw0i6oCO3dqG7r6/5WWBmq4UP8y0bmztsEdOvTYf2eeqR3PNIqVcZnty2h70jBfRMknTz/ttE0aJswiUj55+Uoatr6dSPPOCHRckzOyLCM1NRVymI911FufCE1z+WzXDnjoIe0F68uXay9cj48HVq8GJkzQHtQyeTJQWMjXz3AIddzyz38GXnwRmD//2AZvwADgpptU3HZbPlJSZOzZAxw8CCxaBLz+unZX86yzzG3wrJzPZvsy2p40zBdR8snTTzttk4YJs4iUT16+koatbyfSvDMKHde0kMDt04qKCnTs2BHK72fbPB5Pg7Isy3C5XPB4PJAkCaqqokWLFlAUBW63G263G7IsB8uSJMHj8cDtdsPv9wMA4uPjIcty8K8Qx5fj4+Ph9/vx66+/4tprrw36EB8fD1VVoaoq4uLimiwrigLGWLDcWBxNxVTf96bK9WOSJAlxcXFwuVzBclMxMcaCZVVVTcfkdrvh8/kQFxd3QqxNxVRaKuPzz9346CM3du/Wxt7l0l7D8OCDwHXXuaGq1sakKAr8fj8SEhLAGGt2nPbvV7BhA7Bxowe5uSo2bHAhP7/p45aDBzMMHQokJbkweLCMoUPdaN/e3WB+Bny3cpzqj0P9OIzMPbfbDa/XixYtWoSch03NvYC2AnbDjampsWlq7smyDFmW0bJlS6iqGjImVVWRmpqKq6++Gi1btnS8nnhcIwL9AUCLFi0cG5PL5WowPyM5ToF55nK5gv5aOU5WXSPi4uLg9/vhdrtDxtfUODWlreZiamxsQs294495NTdOPp8PCxYsCK7FTtYTj2sEYwyBf4q63W5HxxTO9duOcTK6PkU6psC6Faird5wC2mrVqlWD+JqLyePxNLh2hDP36setKErIcVIUpcFazFtPhw8fRqdOnejpmjxISkpCYmIikpOTkZ+fDwDIy8tDQUEBACA3NxdFRUUAgOzsbOz+faeQmZmJsrIyAEBGRgbKy8sBAOnp6aisrAQALFq0CFW/PwEkNTUVXq+3wV8jvF4vUlNTAQA1NTVBnyorK5Geng4AKC8vR0ZGBgCgrKwMmZmZAICSkhJkZWUBAIqKipCbmwsAKCgoQF5eHgAgPz+/2ZiysrJQUlISdkxpaWmorq4OK6bq6mqkpaVZGtPWrVt1xVRYmIk77yzDzp3A889vwGWX+cAYsHChG2PHutGvH3DvvYUoLKyxLKZ9+/ZhzZo1J8RUWFiEuXO34McfgUmTDuPSS2vQqxfQrZsHY8Z48MQTwDffuLFpk7bBa99ewvnn1+KJJ4Dk5O1IS9uLmhrgzTdXYMaMvXjoIUBRMuDzHRuniooK28bpt99+gyzLls29nJwcw3Pv119/xdGjRw3FtGrVKl1zb9OmTWHHBAClv99eFUFPPK4RR48exa+//ur4mJYvX85tnGRZtm2crLxGHDhwACtWrDA8Tlu3bjUU06pVq3TNvUWLFoUd06FDhxBABD3xuEbIsixETLt27cL69esjPk5m1qdIxrRv3z7IsowVK1YYHqdFixYZimn37t3Izs4GEN7c27hxo65xAo5pmZeepk6disTERAwfPhxhwQjLqKqqYgBYRUUFY4wxWZaZLMsnlCVJCpZra2tZ8fEv1gAAHL1JREFUSkoK8/v9TJIkpihKsE6g7Pf7g+WjR48G6/v9fqaqKlNV9YQyY4z5fL5gXUVRgp8risIkSQpZlmW5QbmxOJqKqak4moop4Hv9clMx1S9bEZPf72cpKSmsrq7OVEw7djD2+OMy69RJZdpLFxiLj1fZrbcytmSJxBTFXExer5d9//0vbPVqP/voI4X9/e8Ku/BCxtq2PWbv+P8GDFDZzTcz9txzCvv5Z5nt2cOY369vnOrPTzvGKdC/1+s1PfcCY+n1eg3NvYC2fD6frpi8Xm8DnTU39+rq6oL17Y4p0noKZ5zCnXvhxhS4zh09etTRMR0/lpEcp8Ccq6urs3ycrLxGBHJUW1traJya0lZzMTU2NqHmXmDO+Xw+W64R0bzmNlZubH46NaZwrt92jJPR9SnSMQXmem1traFxqv/vVj0xHX/tCGfu1Y/b6muE3XqqqKhgAFhVVRULBR3XtBAjT9e0E8lhTwOKBbxe7dUL77+vfW8vwFlnaU/lvOMO7Xt+zVFeDmzY0PBhKPn5CPl0y/oPQznnHKBtWysiInhCGiYI8SEdE4TYOE3D9HRNQWCMobq6Gm3btg370c166hOhsTqfCQnaRu6OO4DcXO2pnN98oz2Zc9Ik4KmngNtv1zZ8Q4ZoT7csKjrx6ZZ79jTef+fODElJLkufbhkKu+eblf2b7ctoe9IwX0TJJ08/7bRNGnbunBMFkfLJy1fSsPXtRJp3RqHv5HEmcHZZlsN/qpee+kRo7MxnUhLw0UfA3r3AO+8AAwdq79z73/+0zdmgQUCHDtpTLG++GXjhBWDevGMbvPqfp6TI+Pzzxdi7V7b06ZbNYfd8s7J/s30ZbU8a5oso+eTpp522ScOEWUTKJy9fScPWtxNp3hmFjmtaCB3XJELBmPYahvffB+bMAQLXFTpuSTQFaZggxId0TBBi4zQNh7vfoDt5nFFVFRUVFcFHzlpdnwhNJPPpcgGXXAJ8/z2wezeQkgJs2gTU1ADZ2dpL1h96CLjgghM3eLzG3W67VvZvti+j7UnDfBElnzz9tNM2aZgwi0j5jMa1OBY1bMaWSNAmjzOKoiA7Ozv47g6r6xOh4ZXPU08F/vSn8I9b8vLTbrtW9m+2L6PtScN8ESWfPP200zZpmDCLSPmMxrU4FjVsxpZI0HFNC6HjmgRBWAlpmCDEh3RMEGLjNA3TcU1BUFUVBw4c0HVMRE99IjSi5JOXn3bbtbJ/s30ZbU8a5oso+eTpp522ScOEWUTKZzSuxbGoYTO2RII2eZxRVRWbNm3StbjoqU+ERpR88vLTbrtW9m+2L6PtScN8ESWfPP200zZpmDCLSPmMxrU4FjVsxpZI0HFNC6HjmgRBWAlpmCDEh3RMEGLjNA3TcU1BUFUVpaWluv6CqKc+ERpR8snLT7vtWtm/2b6MticN80WUfPL0007bpGHCLCLlMxrX4ljUsBlbIkGbPM6oqorCwkJdi4ue+kRoRMknLz/ttmtl/2b7MtqeNMwXUfLJ0087bZOGCbOIlM9oXItjUcNmbIkEHde0EDquSRCElZCGCUJ8SMcEITZO0zAd1xQEVVVRXFys6y+IeuoToREln7z8tNuulf2b7ctoe9IwX0TJJ08/7bRNGibMIlI+o3EtjkUNm7ElErTJ4wx9F4AvouQzGr8HYHX/onwXQJQ5Jwqi5JOnn3baJg0TZhEpn9G4Fseihs3YEgk6rmkhdFyTIAgrIQ0ThPiQjglCbJymYTquKQiKomDHjh1QFMWW+kRoRMknLz/ttmtl/2b7MtqeNMwXUfLJ0087bZOGCbOIlM9oXItjUcNmbIkEbfI4wxjD4cOHEe4NVb31idCIkk9eftpt18r+zfZltD1pmC+i5JOnn3baJg0TZhEpn9G4Fseihs3YEgk6rmkhdFyTIAgrIQ0ThPiQjglCbJymYTquKQiKomDr1q26jonoqU+ERpR88vLTbrtW9m+2L6PtScN8ESWfPP200zZpmDCLSPmMxrU4FjVsxpZI0CbPAdTV1dlanwiNKPnk5afddq3s32xfRtuThvkiSj55+mmnbdIwYRaR8hmNa3EsatiMLVGg45oW4rTjmjU1Nbj33nvxySefoE2bNrzdIQhCJ6RhghAf0jFBiI3TNEzHNQVBURRs2rRJ1zGRcOv7fD7Mnj0bPp/PrJtRi97884KXn3bbtbJ/s30ZbU8a5gtpmK9t0nDzkI5DI4qGgehci2NRw3rbiKph2uTZQGDCKIrSaFmW5Qbl7777LlgOvJSxflmSpAblWbNmBcuMMTDGTigD2pODWrVqBUB76WPgc1VVIctyyLKiKA3KemNqLI5QMQVuKIcTU/04rIjpu+++EyKm2bNnR3ycZFk+Yb5ZPU6zZs2yLKbZs2ebGqdZs2YZiqmxsQkVU0Dz4cTUsmVL7nPP6deIWbNmCRFT/fkZyXFSFAWzZs0S4hoxa9YsU+PUmLbCien4sWlunGbPnh12TIwxJCQkGI5JhPXJbEzHz08nxxS43kd6nIyuT5GOKaBho+MU8EVvTN99953uuVf/39/NjVNCQkID33nrKRxok2cDSUlJSExMRHJyMvLz8wEAeXl5KCgoAADk5uaiqKgIAJCTk4Pdu3fD4/EgMzMTZWVlAICMjAyUl5cDANLT01FZWQkAWLJkCVatWgWPx4PU1FR4vV7IsozU1FTIsgyv14vU1FQAQG1tLT799FMAQGVlJdLT0wEA5eXlyMjIAACUlZUhMzMTAFBSUoKsrCwAQFFREXJzcwEABQUFyMvLAwDk5+c3G1NWVhZKSkoAIKyY0tLSUF1dDQDNxlRdXY20tDTLYvJ4PPD5fNi+fbujYzpw4ABOOeUUeDyeiI7TsmXLkJOTA4/HY8s4rVmzBnPmzMHevXtNx+TxeNCuXTvs3bvX0DgtXLgQS5YsAWNMV0yHDx9Gr1694PF4wpp7O3fuBAB4PJ6wxmnKlCk4cOCAoZgiradwxgmwVk+MMSxZsgQLFy50dEwejwenn356MI5IjtOGDRswZ84c7Ny509HXCI/Hg02bNmHVqlWGxmn79u3w+XzweDy6YvJ4POjVqxcOHz4c1txjjGHEiBFgjIU1TpWVlXjttde4zb1wYuJ5jdi5cyfmzJmDDRs2OD6m3bt3o2XLlvB4PBEdJ6PrU6RjOnDgAObMmYNVq1YZGidJkjBixAh4PB5dMXk8HuzevRs5OTkAwpt7mzdvRlVVFTweT1jj9NprrwXj4KWnqVOnIjExEcOHD0c40HfyLKSqqgodOnTArl270LFjx+BO2+PxNCjLsgyXyxXcYJx77rlYu3YtGGNwu91wu92QZTlYliQJHo8HbrcbXq8Xo0ePxrp166CqKuLi4gBof22oX46Pj8ehQ4eQmJiI/Px8dOjQAYqiID4+HqqqBts2VVYUBYyxYLmxOJqKqb7vTZXrxyRJEuLi4uByuYLlpmJijAXLqqqajgkARowYgTVr1qBFixaOjUmSJJx77rnIysqCy+WK2Dj5fD6MHj0aOTk5QZtWjpMkSRg5ciRycnLgcrlMxQQAo0aNwurVqxEfH697nALaysnJCdoPJ6amxqapuef3+3HuueciOzs7OAebiqmqqgpnnnkm8vPz0aVLF8fricc1wuVyYfjw4VizZk3wL61OjIkxhtGjR+O3334LzptIjZOiKBgxYgTWrl0Lj8fj2GuE2+0OjmXgDraecfL7/Rg9ejTWrl3bQFvNxcQYa3DtaG7uKYqCc889F6tXrw72GWqcDh06hEGDBmHr1q3o0KGDo/XE4xrR2Px0akzhXL/tGCej61OkY2KMYfjw4cjOzkZ8fLzucQpoa82aNcE+w4nJ5XJhxIgRWL16NVq2bBnW3Kt/vWCMhRynyspKDBw4EPn5+ejcuTN3PR0+fBh9+vRBZWUl2rdvj6agTZ6F7NmzB7169eLtBkEQBEEQBEEQUUxJSQl69uzZ5O9pk2chqqpi7969aNu2bfDOAkEQBEEQBEEQhBUwxlBdXY3u3bvD7W76m3e0ySMIgiAIgiAIgogi6MErBEEQBEEQBEEQUQRt8giCIAiCIAiCIKII2uQRBEEQBEEQBEFEEbTJI1BSUoJLLrkEiYmJOOecc/B///d/vF0iCMIAN954Izp27Iibb76ZtysEQYTBvHnzcOaZZ+L000/HJ598wtsdgiB04uR1lx68QqCsrAz79+/H0KFDsW/fPgwfPhzbt2/HSSedxNs1giB0sGzZMlRXV+OLL77ADz/8wNsdgiBCIMsyEhMTsXTpUrRv3x7Dhw9HZmYmOnfuzNs1giDCxMnrLt3JI3Dqqadi6NChAIBu3bqhS5cuqKio4OsUQRC6ueSSS9C2bVvebhAEEQZZWVk466yz0KNHD7Rp0wbXXHMN0tLSeLtFEIQOnLzu0iZPADIyMnD99deje/fucLlcSElJOaHOe++9hz59+iAhIQGjR49GVlaWIVs5OTlQFIVe6k4QFhNJHRMEYT9mNb1371706NEj+HOPHj1QWloaCdcJgkD0r8u0yROAo0ePYsiQIXjvvfca/f3s2bPx+OOPY9q0aVi3bh2GDBmCMWPG4MCBA8E6Q4cOxeDBg0/4b+/evcE6FRUVmDBhAj766CPbYyKIWCNSOiYIIjJYoWmCIPgR9RpmhFAAYHPmzGnw2ahRo9hDDz0U/FlRFNa9e3f28ssvh92v1+tlF110Efvyyy+tcpUgiCawS8eMMbZ06VI2btw4K9wkCCJMjGh61apVbOzYscHf/+Mf/2DffPNNRPwlCKIhZtZlp667dCdPcPx+P3JycnDFFVcEP3O73bjiiivw22+/hdUHYwx33nknLrvsMtxxxx12uUoQRBNYoWOCIJxDOJoeNWoUNm3ahNLSUtTU1ODXX3/FmDFjeLlMEEQ9omFdpk2e4JSXl0NRFHTt2rXB5127dsW+ffvC6mPVqlWYPXs2UlJSMHToUAwdOhQbN260w12CIBrBCh0DwBVXXIE///nPSE1NRc+ePYVZiAgi2ghH03FxcXjjjTdw6aWXYujQofjnP/9JT9YkCIcQ7rrs5HU3jrcDBH8uvPBCqKrK2w2CIEyyePFi3i4QBKGDG264ATfccANvNwiCMIiT1126kyc4Xbp0gcfjwf79+xt8vn//fnTr1o2TVwRB6IF0TBDRBWmaIMQmGjRMmzzBadGiBYYPH44lS5YEP1NVFUuWLMF5553H0TOCIMKFdEwQ0QVpmiDEJho0TMc1BaCmpgY7duwI/lxUVIT169ejU6dO6N27Nx5//HFMnDgRI0aMwKhRo/DWW2/h6NGjuOuuuzh6TRBEfUjHBBFdkKYJQmyiXsO8H+9JNM/SpUsZgBP+mzhxYrDOu+++y3r37s1atGjBRo0axVavXs3PYYIgToB0TBDRBWmaIMQm2jXsYoyxyG4rCYIgCIIgCIIgCLug7+QRBEEQBEEQBEFEEbTJIwiCIAiCIAiCiCJok0cQBEEQBEEQBBFF0CaPIAiCIAiCIAgiiqBNHkEQBEEQBEEQRBRBmzyCIAiCIAiCIIgogjZ5BEEQBEEQBEEQUQRt8giCIAiCIAiCIKII2uQRBEEQBEEQBEFEEbTJIwiCIIgo4vPPP4fL5Qr+d+utt/J2SRdbt25t4H+fPn14u0QQBCEctMkjCIIgbGPXrl0N/sHucrnQunVrdO/eHZdffjmSk5NRWFjYaNtly5YF24wYMaJJG7/++muw3iWXXBKWX3b27RT+9Kc/Ydq0abj55psb/X1dXR3ef/99jBkzBt26dUOLFi3Qtm1bDB48GH/729+wePFiw7arqqrQunVrdOzYEV6vN2Tdjz/+GC6XC/fddx8AoEuXLpg2bRqmTZuG9u3bG/aBIAgilonj7QBBEAQR/fTv3x+33347AMDn8+HAgQPIysrC888/j5deegmTJ0/Giy++CJfLdULbuLg45OTkIC8vD+ecc84Jv//0008RFxcHWZZ1+2Vn37wZO3Ys7rzzzkZ/t2HDBtx4440oKipCz549cdVVV6FHjx7w+XwoKCjA7Nmz8cknn+DRRx/Ff/7zH92227dvj3HjxuHrr7/Gjz/+iNtuu63JujNnzgQA3HPPPQC0Td706dMBaHclCYIgCP3QnTyCIAjCdgYMGIDp06dj+vTpePnll/Hpp59i48aNWLFiBXr16oWXX34ZycnJjbYdM2YM3G53cDNQn/LycsydOxfXXnutIb/s7Nup7NmzB1dddRV2796NN998E0VFRfjyyy/x8ssv480338TcuXNRVlaG//znP/D5fIbtBDZtjeU2QH5+PlavXo2zzjoLo0ePNmyLIAiCaAht8giCIAhuXHjhhViwYAFatmyJ1157DSUlJSfU6dmzJ6688kp888038Pv9DX739ddfw+/34+677zZk30zf27dvx+TJkzFs2DB07twZCQkJOOOMMzBlyhTU1NScUL+srAz/+Mc/cPrpp6NVq1bo0KEDBg0ahAceeABVVVXBelVVVUhOTkZiYiLatGmDdu3aYcCAAZg4cSKKi4sNxVmfp59+GgcOHMAzzzyDxx57DHFxJx7qOemkk/Doo4/inXfeOeF3fr8fb775JoYNG4aTTjoJbdu2xUUXXYRffvmlQb0//OEP6N+/P5YuXYpdu3Y16svxd/EIgiAIa6BNHkEQBMGVM888E7fccgv8fj9SUlIarXP33XcH76zVZ+bMmabvAhnt+6effsKnn36Kfv36YeLEiXjggQfQqVMnvPrqq7jyyishSVKwbm1tLS644AK8++676N+/Px5++GHceeedOOOMM/DVV1/h4MGDAADGGMaMGYPnn38enTp1wn333Yf77rsPSUlJ+OWXX1BQUGA4zoAfs2fPRqtWrfDPf/6z2frHbwB9Ph/GjBmDf/7zn2CM4Z577sHtt9+O4uJi/OlPf8J///vfYF2Xy4W7774bjDF89tlnJ/QtyzK++uortGjRAnfccYepuAiCIIiG0HfyCIIgCO5ccskl+Oqrr5Cdnd3o78eOHYvOnTtj5syZGDduHAAgOzsbGzduxBtvvGHKttG+77jjDjz++ONo0aJFg8+fe+45TJs2Dd9//33wu2hLlixBUVFRo99xq6mpQXx8PABg06ZNWLNmDcaOHYs5c+Y0qOfz+RpsHI2wdu1aSJKEc889F23bttXd/rnnnsOyZcvw7LPPYsaMGcHvUFZXV+Oyyy7DP//5T9x0003o3r07AODOO+9EcnIyvvjiC0ybNg1u97G/LaempmL//v24+eab0aVLF1NxEQRBEA2hO3kEQRAEdwKbgvLy8kZ/36JFC9x2221YuHAh9u7dC0C70xYfH2/6LpDRvnv06HHCBg8AJk2aBACNPp2yVatWJ3zWpk0btGzZstl6LVu2RJs2bUIH0wz79u0DcCzfxxP43mT9/wKoqooPPvgA/fv3b7DBA4C2bdsiOTkZfr8fP/30U/Dz7t274+qrr0ZxcTGWLFnSwFbgqKbRo7YEQRBE09CdPIIgCEII7r77brzzzjv44osv8Nhjj+G7777Dddddh5NPPjm4eYlk34FjiJ9//jk2bdqEqqoqqKoa/H1gwwgAF198MU499VS88sor2LBhA6677jr84Q9/wKBBgxpslgYNGoRzzjkHs2bNwp49ezB27FhccsklGDp0aIO7YHYxY8aMEz4LbPS2bduGw4cPo3v37o3WCxw53bp1a4PP7733XsyfPx8zZ87ElVdeCQA4cOAA5s+fj549e2LMmDEWR0EQBEHQJo8gCILgTmBDdPLJJzdZZ8iQIRg2bBg+++wz9O7dG5WVlZbdBTLS9yOPPIL//ve/6NWrF2644QaceuqpwTtyM2bMaPBkyvbt22P16tVITk7G3LlzkZqaCgDo1asXpkyZgr///e8AtO/ApaenY/r06fjxxx+D35s7+eSTMWnSJPzrX/+Cx+MxHGfXrl0BNNyA1ocxFiwPHDgQ27ZtC/5cUVEBANi8eTM2b97cpI2jR482+Pm6665D165dkZKSgsOHD6Njx4748ssvIcsy7rzzzohsXgmCIGINurISBEEQ3Fm2bBkAYOTIkSHr3XPPPSgoKMBTTz2F7t2745prrrHMBz19HzhwAO+99x7OOeccbN26FZ9//jlefvllTJ8+HQ888ECjbXr37o3PP/8cBw8eRG5uLl599VWoqoqHHnoIs2bNCtbr3Lkz3n33XZSWlmLLli3473//i06dOmHatGl47bXXTMU4YsQIxMfHIycnB9XV1bratmvXDgAwbtw4MMaa/O/4h6zExcVhwoQJ8Hq9+OabbwAAn332GVwuF+666y5T8RAEQRCNQ5s8giAIgivbt2/H999/j5YtW+LGG28MWfevf/0rEhISUFpaigkTJpi6q2Wm7507d4IxhiuuuAKtW7du8LsVK1aEtON2uzF06FBMnjw5uLk7/vUDgPZ0ykGDBuGhhx7CokWLmqynh5NOOgnjx49HbW2t7pecDxo0CO3atQs+vEUPgbuiM2fOxJo1a7BlyxZceuml6Nevn65+CIIgiPCgTR5BEATBjVWrVmHMmDHw+XyYMmUKevToEbJ+hw4dsHDhQsyZMwePPfaYpb7o6fu0004DAGRmZjb4Ht6ePXvw9NNPn1B/8+bN2L9//wmfBz5LSEgAAOzatavRd8odX88ML730Ek4++WQ899xzePvtt6Eoygl1vF7vCS9Cj4uLw4MPPoji4mI88cQTjW70Nm3ahAMHDpzw+cCBA3HBBRcgNzc3eASV3o1HEARhH/SdPIIgCMJ2duzYEXyAh9/vx4EDB5CVlYWNGzfC4/HgmWeewbRp08Lq6+KLL7bNz3D7PvXUUzFu3Dj8+OOPGDFiBC6//HLs378f8+bNw+WXX47CwsIG9RctWoQnn3wSF1xwAc444wx07twZO3fuxC+//IKEhAQ89NBDAID169fjpptuwqhRo5CYmIhu3bqhtLQUKSkpcLvdlmxse/XqhUWLFuHGG2/Eo48+in//+9+47LLL0KNHD9TV1aG0tBSLFi1CZWUlLrzwwgZtZ8yYgXXr1uGdd97B/PnzcfHFF+OUU05BaWkpNm7ciA0bNuC3337DKaeccoLde+65B6tWrcKqVavQoUMH3HTTTaZjIQiCIBqHNnkEQRCE7RQWFgafyNiqVSt06NABAwcOxLPPPouJEyeif//+nD3Uz+eff44+ffrgxx9/xLvvvovevXvj8ccfx1NPPYUffvihQd0xY8Zg165dyMjIwE8//YSamhr06NED48ePx+TJk5GYmAhA+87cU089hWXLlmH+/PmorKxEt27dcMUVV+DJJ5/Eueeea4nvQ4YMwebNmzFz5kykpKRgwYIFqKioQEJCAnr16oUbb7wRf/3rX3HFFVc0aNeyZUv8+uuv+PTTT/Hll1/ixx9/hM/nQ9euXZGYmIgHHngAZ599dqM2b7nlFvzjH/9AdXV18GgsQRAEYQ8uVv9RWgRBEARBCM3nn3+Ou+66C5999hnuvPNO3u6Yok+fPgDQ6BFWgiAIomnoO3kEQRAEEYXcddddcLlcuPXWW3m7ooutW7fC5XLB5XKhuLiYtzsEQRBCQsc1CYIgCCKKGDp0aIPvNw4ePJijN/rp0qVLA/87dOjAzxmCIAhBoeOaBEEQBEEQBEEQUQQd1yQIgiAIgiAIgogiaJNHEARBEARBEAQRRdAmjyAIgiAIgiAIIoqgTR5BEARBEARBEEQUQZs8giAIgiAIgiCIKII2eQRBEARBEARBEFEEbfIIgiAIgiAIgiCiCNrkEQRBEARBEARBRBG0ySMIgiAIgiAIgogiaJNHEARBEARBEAQRRfw/J9UyFkaBZvgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mX_arr_eV = np.geomspace(1e7, 1e10, 15)\n", + "mX_arr_GeV = mX_arr_eV / 1e9\n", + "\n", + "# Run the simulation\n", + "t_start = time.time()\n", + "m_dm, sigs = SE.run_sim(\n", + " threshold=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_low=max(energy_threshold, 2 * band_gap_sapphire_eV / 1000),\n", + " e_high=0.1,\n", + " m_dms=mX_arr_GeV,\n", + " plot_bkgd=True,\n", + " nexp=1, # increase for a better estimate, 1 is generally used for diagnostics\n", + " sigma0=1e-31,\n", + " elf_model='electron',\n", + " elf_params={'mediator': 'massive', 'kcut': 25e3, 'suppress_darkelf_output': True},\n", + ")\n", + "sig = np.median(np.stack(sigs, axis=1), axis=1)\n", + "t_end = time.time()\n", + "print(f'Simulation took {(t_end - t_start):.2f} seconds')\n", + "\n", + "fn = 'sapphire_results/Massive_ER_kcut_25keV_Limit_' + dt.datetime.now().strftime('%Y%m%d_%H%M%S') + '.txt'\n", + "np.savetxt(fn, np.vstack([m_dm, sig]).transpose(), fmt='%.3e')\n", + "\n", + "f_out = open(fn + '_detailed', 'w')\n", + "f_out.write(str(m_dm))\n", + "f_out.write('\\n')\n", + "f_out.write(str(sigs))\n", + "f_out.write('\\n')\n", + "f_out.close()\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 6))\n", + "\n", + "ax.plot(m_dm, sig, color='b')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "\n", + "#ax.set_ylim(1e-37, 1e-32)\n", + "#ax.set_xlim(0.1, 10)\n", + "ax.set_xlabel(\"DM Mass [GeV]\", fontsize=14)\n", + "ax.set_ylabel(\"Cross Section [cm$^2$]\", fontsize=14)\n", + "ax.set_title(\"Estimated Sensitivity\", fontsize=14)\n", + "\n", + "ax.grid()\n", + "ax.grid(which=\"minor\", linestyle='dotted')\n", + "ax.tick_params(which=\"both\", direction=\"in\", right=True, top=True)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From ac0764407fc0e4cb10f6a538062868554dd0fd9e Mon Sep 17 00:00:00 2001 From: Vetri Velan Date: Thu, 11 Jul 2024 14:55:38 -0700 Subject: [PATCH 39/39] Updated some print statements to be cleaner, and added comments --- darklim/sensitivity/_sens_est.py | 39 +++++++++++++++++++------------- 1 file changed, 23 insertions(+), 16 deletions(-) diff --git a/darklim/sensitivity/_sens_est.py b/darklim/sensitivity/_sens_est.py index 07eb96f..26ec3eb 100644 --- a/darklim/sensitivity/_sens_est.py +++ b/darklim/sensitivity/_sens_est.py @@ -344,12 +344,15 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, en_interp = np.geomspace(e_low, e_high, num=npts) + ######################## + # Get the dRdE lambda function to convert E (keV) to dRdE (DRU) + ######################## + drdefunction = None if elf_model is None: drdefunction = [(lambda x: drde_wimp_obs( x, m, sigma0, self.tm, self.gain )) for m in m_dms ] - print('Using WIMPs') elif elf_model == 'electron' and elf_target == 'Al2O3': @@ -388,7 +391,7 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, drdefunction = \ [elf.get_dRdE_lambda_GaAs_electron(mX_eV=m*1e9, sigmae=sigma0, mediator=elf_mediator, kcut=elf_kcut, method=elf_method, withscreening=elf_screening, - suppress_darkelf_output=elf_suppress, gain=1.) + suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] elif elf_model == 'phonon' and elf_target == 'GaAs': @@ -403,7 +406,7 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, suppress_darkelf_output=elf_suppress, gain=self.gain) for m in m_dms] - + # If appropriate, convert dRdE from deposited energy to observed energy if self.tm == 'GaAs' and gaas_params is not None: for j, m in enumerate(m_dms): @@ -428,14 +431,17 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, coincidence_window_us=gaas_params['coincidence_window_us'], phonon_tau_us=gaas_params['phonon_tau_us']) - - print(f'In run_sim(). The integral is {sum(dRdE_observed_DRU_arr[1:] * np.diff(E_observed_keV_arr))} cts/kg/day') drdefunction[j] = lambda E: np.interp(E, E_observed_keV_arr, dRdE_observed_DRU_arr, left=0., right=0.) - + # Optionally, just return the anonymous lambda function without doing anything else if return_only_drde: return drdefunction + ######################## + # For each pseudoexperiment, calculate the + # limit using the optimum interval method. + ######################## + for ii in range(nexp): evts_sim = self._generate_background( en_interp, plot_bkgd=plot_bkgd and ii==0, @@ -449,16 +455,20 @@ def run_sim(self, threshold, e_high, e_low=1e-6, m_dms=None, nexp=1, npts=1000, self.exposure, #exposure tm=self.tm, # target material cl=0.9, # C.L. -# res=res, # include smearing of DM spectrum -# gauss_width=10, # if smearing, number of sigma to go out to + res=res, # include smearing of DM spectrum + gauss_width=10, # if smearing, number of sigma to go out to verbose=verbose, # print outs - drdefunction=drdefunction, # - hard_threshold=threshold, - sigma0=sigma0 + drdefunction=drdefunction, # lambda function for dRdE(E) + hard_threshold=threshold, # hard threshold for energies + sigma0=sigma0 # Starting guess for sigma ) sigs.append(sig_temp) + ######################## + # Get median limit and return + ######################## + sig = np.median(np.stack(sigs, axis=1), axis=1) return m_dms, sig @@ -574,7 +584,7 @@ def run_sim_fc(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms=None, ax.axvline(ul,ls='--',color='red') ax.set_xlabel('Upper Limit [Events]') ax.set_xlim(0,max(np.asarray(uls))) - outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' + outdir = '/global/scratch/users/vvelan/DarkLim/examples/' plt.savefig(outdir+pltname+'.png',dpi=300, facecolor='white',bbox_inches='tight') return m_dms, sig, ul @@ -597,7 +607,6 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= if use_drdefunction and elf_model is None: drdefunction = [(lambda x: drde_wimp_obs( x, m, sigma0, self.tm, self.gain )) for m in m_dms ] - print('Using WIMPs') elif elf_model == 'electron' and elf_target == 'GaAs': @@ -667,8 +676,6 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= coincidence_window_us=gaas_params['coincidence_window_us'], phonon_tau_us=gaas_params['phonon_tau_us']) - - print(f'In fast sim. The integral is {sum(dRdE_observed_DRU_arr[1:] * np.diff(E_observed_keV_arr))} cts/kg/day') drdefunction[j] = lambda E: np.interp(E, E_observed_keV_arr, dRdE_observed_DRU_arr, left=0., right=0.) if return_only_drde: @@ -738,7 +745,7 @@ def run_fast_fc_sim(self, known_bkgs_list, threshold, e_high, e_low=1e-6, m_dms= ax.axvline(median_ul,ls='--',color='red') ax.set_xlabel('Upper Limit [Events]') ax.set_xlim(0,max(uls)) - outdir = '/global/cfs/cdirs/lz/users/vvelan/Test/DarkLim/examples/' + outdir = '/global/scratch/users/vvelan/DarkLim/examples/' plt.savefig(outdir+pltname+'.png',dpi=300, facecolor='white',bbox_inches='tight') # expected bkg rate, made to match m_dm len just to make analysis easier