diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index a477a444..63aae357 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -28,7 +28,7 @@ jobs: - name: install reqs run: | conda install pip compilers pytest pytest-cov pyccl cython - pip install cobaya + pip install cobaya nemo-sz env: MATRIX_OS: ${{ matrix.os }} diff --git a/.gitignore b/.gitignore index 894a44cc..616acdd0 100644 --- a/.gitignore +++ b/.gitignore @@ -102,3 +102,22 @@ venv.bak/ # mypy .mypy_cache/ + + +# clusters +soliket/clusters/data/selFn* +soliket/clusters/data/*zip +soliket/clusters/data/*fits +soliket/clusters/chains +soliket/clusters/notebooks/figures +soliket/clusters/data/act/* +soliket/clusters/data/advact/* +.DS_Store +soliket/ymap +soliket/cosmopower +soliket/clusters/checks/* +soliket/clusters/notebooks/figs/* +soliket/clusters/notebooks/ongoing/* + +# cibxlensing +soliket/cibxlensing/chains diff --git a/chains/test_unbinned_classy_sz_evaluate.input.yaml b/chains/test_unbinned_classy_sz_evaluate.input.yaml new file mode 100644 index 00000000..ab468994 --- /dev/null +++ b/chains/test_unbinned_classy_sz_evaluate.input.yaml @@ -0,0 +1,117 @@ +theory: + classy_szfast.classy_sz.classy_sz: + use_class_sz_fast_mode: 1 + stop_at_error: true + extra_args: + output: sz_cluster_counts_fft,m500c_to_m200c,m200c_to_m500 + mass function: T08M200c + concentration parameter: B13 + B: 1.0 + N_ncdm: 1 + N_ur: 2.0328 + m_ncdm: 0.06 + T_ncdm: 0.71611 + M_min: 10000000000000.0 + M_max: 1.0e+16 + ndim_redshifts: 100 + szcounts_fft_nz: 100, + n_m_dndlnM: 100, + n_z_dndlnM: 100, + has_selection_function: 1 + experiment: 1 + y_m_relation: 1 + signal-to-noise_cut-off_for_survey_cluster_completeness: 5.0 + sz_selection_function_thetas_file: /Users/boris/Work/CLASS-SZ/SO-SZ/class_sz/class_sz_auxiliary_files/nemo_sim_thetas_120923_30bins.txt + sz_selection_function_skyfracs_file: /Users/boris/Work/CLASS-SZ/SO-SZ/class_sz/class_sz_auxiliary_files/nemo_sim_skyfracs_120923_30bins.txt + sz_selection_function_ylims_file: /Users/boris/Work/CLASS-SZ/SO-SZ/class_sz/class_sz_auxiliary_files/nemo_sim_ylims_120923_30bins.txt + SZ_cat_file: /Users/boris/Work/CLASS-SZ/SO-SZ/class_sz/class_sz_auxiliary_files/SZ_cat_nemosimkit_130923.txt + A_ym: 1.9e-05 + B_ym: 0.08 + C_ym: 0.0 + sigmaM_ym: 0.173 + m_pivot_ym_[Msun]: 425000000000000.0 + use_m500c_in_ym_relation: 0 + use_m200c_in_ym_relation: 1 + use_skyaveraged_noise: 0 + N_samp_fftw: 2048 + z_min: 0.0 + z_max: 2.0 + szcounts_fft_z_min: 0.0 + szcounts_fft_z_max: 2.0 + tol_dlnm_dlnq: 0.01 + ntab_dlnm_dlnq: 80 + szcounts_qmax_fft_padded: 200.0 + sigma_derivative: 0 + szcc_dof: 3.0 + szcc_qtrunc: 2.0 + HMF_prescription_NCDM: 1 + no_spline_in_tinker: 1 +likelihood: + soliket.UnbinnedClusterLikelihood: + stop_at_error: true + verbose: false + data: + data_path: data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/ + cat_file: NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits + Q_file: selFn/QFit.fits + tile_file: selFn/tileAreas.txt + rms_file: selFn/RMSTab.fits + theorypred: + choose_theory: classy_sz + massfunc_mode: ccl + md_hmf: 200c + md_ym: 200c + compl_mode: erf_diff + rel_correction: false + YM: + Mpivot: 425000000000000.0 + selfunc: + SNRcut: 5.0 + method: SNRbased + whichQ: fit + resolution: downsample + dwnsmpl_bins: 30 + save_dwsmpld: false + debiasDOF: 0.0 + debias_cutoff: 0.0 + binning: + z: + zmin: 0.0 + zmax: 2.0 + dz: 0.1 + q: + log10qmin: 0.6 + log10qmax: 2.0 + dlog10q: 0.25 + M: + Mmin: 50000000000000.0 + Mmax: 1.0e+16 + dlogM: 0.01 + exclude_zbin: 0 +params: + h: 0.68 + n_s: 0.965 + Omega_b: 0.049 + Omega_cdm: 0.261 + tau_reio: 0.06 + tenToA0: 1.9e-05 + B0: 0.08 + scatter_sz: 0.2 + bias_sz: 1 + C0: 0.0 + sigma8: + prior: + min: 0.0 + max: 4.0 + ref: + dist: norm + loc: 0.8 + scale: 0.001 + proposal: 0.001 + latex: \sigma_8 +sampler: + evaluate: + override: + sigma8: 0.81 +output: test_unbinned_classy_sz_evaluate +stop_at_error: true diff --git a/chains/test_unbinned_classy_sz_evaluate.updated.yaml b/chains/test_unbinned_classy_sz_evaluate.updated.yaml new file mode 100644 index 00000000..9208496b --- /dev/null +++ b/chains/test_unbinned_classy_sz_evaluate.updated.yaml @@ -0,0 +1,185 @@ +theory: + classy_szfast.classy_sz.classy_sz: + use_class_sz_fast_mode: 1 + lensing_lkl: SOLikeT + ell_factor: false + path: null + speed: 0.2 + stop_at_error: true + extra_args: + output: sz_cluster_counts_fft,m500c_to_m200c,m200c_to_m500 + mass function: T08M200c + concentration parameter: B13 + B: 1.0 + N_ncdm: 1 + N_ur: 2.0328 + m_ncdm: 0.06 + T_ncdm: 0.71611 + M_min: 10000000000000.0 + M_max: 1.0e+16 + ndim_redshifts: 100 + szcounts_fft_nz: 100, + n_m_dndlnM: 100, + n_z_dndlnM: 100, + has_selection_function: 1 + experiment: 1 + y_m_relation: 1 + signal-to-noise_cut-off_for_survey_cluster_completeness: 5.0 + sz_selection_function_thetas_file: /Users/boris/Work/CLASS-SZ/SO-SZ/class_sz/class_sz_auxiliary_files/nemo_sim_thetas_120923_30bins.txt + sz_selection_function_skyfracs_file: /Users/boris/Work/CLASS-SZ/SO-SZ/class_sz/class_sz_auxiliary_files/nemo_sim_skyfracs_120923_30bins.txt + sz_selection_function_ylims_file: /Users/boris/Work/CLASS-SZ/SO-SZ/class_sz/class_sz_auxiliary_files/nemo_sim_ylims_120923_30bins.txt + SZ_cat_file: /Users/boris/Work/CLASS-SZ/SO-SZ/class_sz/class_sz_auxiliary_files/SZ_cat_nemosimkit_130923.txt + A_ym: 1.9e-05 + B_ym: 0.08 + C_ym: 0.0 + sigmaM_ym: 0.173 + m_pivot_ym_[Msun]: 425000000000000.0 + use_m500c_in_ym_relation: 0 + use_m200c_in_ym_relation: 1 + use_skyaveraged_noise: 0 + N_samp_fftw: 2048 + z_min: 0.0 + z_max: 2.0 + szcounts_fft_z_min: 0.0 + szcounts_fft_z_max: 2.0 + tol_dlnm_dlnq: 0.01 + ntab_dlnm_dlnq: 80 + szcounts_qmax_fft_padded: 200.0 + sigma_derivative: 0 + szcc_dof: 3.0 + szcc_qtrunc: 2.0 + HMF_prescription_NCDM: 1 + no_spline_in_tinker: 1 + ignore_obsolete: false + use_renames: false + renames: + As: A_s + ns: n_s + nrun: alpha_s + nrunrun: beta_s + nt: n_t + ntrun: alpha_t + rdrag: rs_drag + omegak: Omega_k + omegal: Omega_Lambda + w: w0_fld + wa: wa_fld + omegabh2: omega_b + omegab: Omega_b + omegach2: omega_cdm + omegac: Omega_cdm + omegam: Omega_m + omegan: Omega_nu + tau: tau_reio + zrei: z_reio + deltazrei: reionization_width + helium_redshift: helium_fullreio_redshift + helium_delta_redshift: helium_fullreio_width + yhe: YHe + yheused: YHe + version: null +likelihood: + soliket.UnbinnedClusterLikelihood: + name: Unbinned Clusters + columns: + - z + - tsz_signal + - tsz_signal_err + - tile_name + verbose: false + data: + data_path: data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/ + cat_file: NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits + Q_file: selFn/QFit.fits + tile_file: selFn/tileAreas.txt + rms_file: selFn/RMSTab.fits + theorypred: + choose_theory: classy_sz + massfunc_mode: ccl + md_hmf: 200c + md_ym: 200c + compl_mode: erf_diff + rel_correction: false + selfunc: + SNRcut: 5.0 + method: SNRbased + whichQ: fit + resolution: downsample + dwnsmpl_bins: 30 + save_dwsmpld: false + debiasDOF: 0.0 + debias_cutoff: 0.0 + binning: + z: + zmin: 0.0 + zmax: 2.0 + dz: 0.1 + q: + log10qmin: 0.6 + log10qmax: 2.0 + dlog10q: 0.25 + M: + Mmin: 50000000000000.0 + Mmax: 1.0e+16 + dlogM: 0.01 + exclude_zbin: 0 + YM: + Mpivot: 425000000000000.0 + type: [] + speed: -1 + stop_at_error: true + version: null +params: + h: + value: 0.68 + n_s: + value: 0.965 + renames: + - ns + Omega_b: + value: 0.049 + renames: + - omegab + Omega_cdm: + value: 0.261 + renames: + - omegac + tau_reio: + value: 0.06 + renames: + - tau + tenToA0: + derived: false + value: 1.9e-05 + B0: + derived: false + value: 0.08 + scatter_sz: + derived: false + value: 0.2 + bias_sz: + derived: false + value: 1 + C0: + derived: false + value: 0.0 + sigma8: + prior: + min: 0.0 + max: 4.0 + ref: + dist: norm + loc: 0.8 + scale: 0.001 + proposal: 0.001 + latex: \sigma_8 +sampler: + evaluate: + N: 1 + override: + sigma8: 0.81 + seed: null + version: null +output: test_unbinned_classy_sz_evaluate +stop_at_error: true +version: '3.3' diff --git a/setup.py b/setup.py index 9075ccc5..0ff960b0 100644 --- a/setup.py +++ b/setup.py @@ -14,19 +14,19 @@ "*.yaml", "*.bibtex", # "data/simulated*/*.txt", - "clusters/data/*", - "clusters/data/selFn_equD56/*", - "lensing/data/*.txt", + # "clusters/data/*", + # "clusters/data/selFn_equD56/*", + # "lensing/data/*.txt", ] }, install_requires=[ "astropy", "scikit-learn", - "cobaya", + #"cobaya", "sacc", "pyccl", - "fgspectra @ git+https://github.com/simonsobs/fgspectra@act_sz_x_cib#egg=fgspectra", # noqa E501 - "mflike @ git+https://github.com/simonsobs/lat_mflike@master" + #"fgspectra @ git+https://github.com/simonsobs/fgspectra@act_sz_x_cib#egg=fgspectra", # noqa E501 + #"mflike @ git+https://github.com/simonsobs/lat_mflike@master" ], test_suite="soliket.tests", include_package_data=True, diff --git a/soliket/__init__.py b/soliket/__init__.py index 2de95714..922d6603 100644 --- a/soliket/__init__.py +++ b/soliket/__init__.py @@ -1,12 +1,17 @@ from .lensing import LensingLiteLikelihood, LensingLikelihood # noqa: F401 from .gaussian import GaussianLikelihood, MultiGaussianLikelihood # noqa: F401 from .ps import PSLikelihood, BinnedPSLikelihood # noqa: F401 -from .clusters import ClusterLikelihood # noqa: F401 +try: + from .clusters import BinnedClusterLikelihood, UnbinnedClusterLikelihood # noqa: F401 +except ImportError: + print('Skipping clusters module as not all requirements installed') + pass from .mflike import MFLike # noqa: F401 from .mflike import TheoryForge_MFLike from .xcorr import XcorrLikelihood # noqa: F401 from .foreground import Foreground from .bandpass import BandPass +from .cibxlensing import CIBxKAPPA_Likelihood try: import pyccl as ccl # noqa: F401 diff --git a/soliket/cash.py b/soliket/cash.py index f33b1d9e..f8950c18 100644 --- a/soliket/cash.py +++ b/soliket/cash.py @@ -1,28 +1,21 @@ -import numpy as np -from typing import Optional - from cobaya.likelihood import Likelihood from .cash_data import CashCData class CashCLikelihood(Likelihood): name: str = "Cash-C" - datapath = Optional[str] def initialize(self): - - x, N = self._get_data() + N = self._get_data() self.data = CashCData(self.name, N) def _get_data(self): - data = np.loadtxt(self.datapath, unpack=False) - N = data[:, -1] # assume data stored like column_stack([z, q, N]) - x = data[:, :-1] - return x, N + raise NotImplementedError def _get_theory(self, pk_intp, **kwargs): raise NotImplementedError - def logp(self, **params_values): - theory = self._get_theory(**params_values) + def logp(self, **kwargs): + pk_intp = self.theory.get_Pk_interpolator() + theory = self._get_theory(pk_intp, **kwargs) return self.data.loglike(theory) diff --git a/soliket/cash_data.py b/soliket/cash_data.py index c566dd2b..e5a47712 100644 --- a/soliket/cash_data.py +++ b/soliket/cash_data.py @@ -1,39 +1,57 @@ import numpy as np from scipy.special import factorial -import math as m -def cash_c_logpdf(theory, data, usestirling=True): +def cash_c_logpdf(theory, data, usestirling=True, name="binned"): - data = np.asarray(data, dtype=int) + __, __, delN2Dcat, zcut = data - ln_fac = np.zeros_like(data, dtype=float) + obs = np.asarray(delN2Dcat, dtype=int) + ln_fac = np.zeros_like(obs, dtype=float) + zcut_arr = np.arange(zcut) + + if zcut > 0: + theory = np.delete(theory, zcut_arr, 0) + obs = np.delete(obs, zcut_arr, 0) + ln_fac = np.delete(ln_fac, zcut_arr, 0) + # print("\r ::: Excluding first {} redshift bins in likelihood.".format(zcut)) + # + # for i in range(theory.shape[0]): + # print('\r Number of clusters in redshift bin {}: {}.'.format(i, theory[i,:].sum())) + # print('------------') + # for i in range(theory.shape[1]): + # print('\r Number of clusters in SNR bin {}: {}.'.format(i, theory[:,i].sum())) + # print('------------') + # print('\r Total predicted N = {}'.format(theory.sum())) + # print('\r Total observed N = {}'.format(obs.sum())) if usestirling: # use Stirling's approximation for N > 10 - ln_fac[data > 10] = 0.918939 + (data[data > 10] + 0.5) \ - * np.log(data[data > 10]) - data[data > 10] - ln_fac[data <= 10] = np.log(factorial(data[data <= 10])) - else: - ln_fac[data > 0] = np.log(factorial(data[data > 0])) - ln_fac[data == 0] = 0. + ln_fac[obs > 10] = 0.918939 + (obs[obs > 10] + 0.5) * np.log(obs[obs > 10]) - obs[obs > 10] + ln_fac[obs <= 10] = np.log(factorial(obs[obs <= 10])) + else: # direct compuation of factorial + ln_fac[obs > 0] = np.log(factorial(obs[obs > 0])) + ln_fac[obs == 0] = 0. + + log_theory = np.zeros_like(theory, dtype=float) + log_theory[theory > 0] = np.log(theory[theory > 0]) + log_theory[theory == 0] = 0. + + loglike = obs * log_theory - theory - ln_fac - loglike = data * np.log(theory) - theory - ln_fac + # print("\r ::: 2D ln likelihood = ", np.nansum(loglike[np.isfinite(loglike)])) return np.nansum(loglike[np.isfinite(loglike)]) + class CashCData: """Named multi-dimensional Cash-C distributed data """ def __init__(self, name, N, usestirling=True): - self.name = str(name) self.data = N self.usestirling = usestirling - def __len__(self): - return len(self.data) - def loglike(self, theory): - return cash_c_logpdf(theory, self.data) + return cash_c_logpdf(theory, self.data, name=self.name) diff --git a/soliket/cibxlensing/__init__.py b/soliket/cibxlensing/__init__.py new file mode 100644 index 00000000..f0fe4a56 --- /dev/null +++ b/soliket/cibxlensing/__init__.py @@ -0,0 +1 @@ +from .cibxlensing_likelihood import CIBxKAPPA_Likelihood diff --git a/soliket/cibxlensing/cibxlensing_likelihood.py b/soliket/cibxlensing/cibxlensing_likelihood.py new file mode 100644 index 00000000..8dd0d833 --- /dev/null +++ b/soliket/cibxlensing/cibxlensing_likelihood.py @@ -0,0 +1,121 @@ +""" +.. module:: cib x kappa cross spectrum likelihood (in progress) + +""" + +# Acronyms: +# ps: power spectrum +# fg: foregrounds +# cib: cosmic infrared background +# rs: radio sources +# ir: infrared sources +# f_sky: sky fraction + + +from cobaya.theory import Theory +# from cobaya.conventions import _packages_path +_packages_path = 'packages_path' +# from cobaya.likelihoods._base_classes import _InstallableLikelihood +from soliket.gaussian import GaussianLikelihood +import numpy as np +import os +from scipy.ndimage.interpolation import shift +from typing import Optional, Sequence +from pkg_resources import resource_filename +import logging +# from myfuncs import alm as yalm + + + +class CIBxKAPPA_Likelihood(GaussianLikelihood): + cib_spectra_directory: Optional[str] = '/Users/boris/CIBxKAPPA/ymehta3/output/param-fitting/' + cib_spectra_file: Optional[str] = 'toy_data.npy' + cib_cov_directory: Optional[str] = '/Users/boris/CIBxKAPPA/ymehta3/output/param-fitting/' + # cib_cov_file: Optional[str] = 'covCl_avg_v3.npy' + cib_cov_file: Optional[str] = 'toy_cov.npy' + cross_wsp_directory: Optional[str] = '/Users/boris/CIBxKAPPA/ymehta3/output/cross_errs/gaussian_covariances/' + cross_wsp_file: Optional[str] = 'cov_wsp_cross_545_3.0e+20_gp40.fits' + cov_ell_info_directory: Optional[str] = '/Users/boris/CIBxKAPPA/ymehta3/' + cov_ell_info_file: Optional[str] = 'input_data/bandpower_ell_info.txt' + + def initialize(self): + # self.log("Initializing CIBxKAPPA likelihood") + + self.data_directory = self.cib_spectra_directory + self.data_file = self.cib_spectra_file + self.cov_directory = self.cib_cov_directory + self.cov_file = self.cib_cov_file + + #Load the Data + Dpoints = np.load(self.data_directory + self.data_file) + self.cov = np.load(os.path.join(self.cov_directory, self.cov_file)) + self.wsp_name = os.path.join(self.cross_wsp_directory, self.cross_wsp_file) + + #Get Ell Info + self.lmax, self.binsize = np.loadtxt(os.path.join(self.cov_ell_info_directory, self.cov_ell_info_file)) + + #Extract the Data + self.datavector = Dpoints[:,1] + self.ellsvector = Dpoints[:,0] # for mock data, this is binned class_sz theory ells + + super().initialize() + + + def get_requirements(self): + return {"cl_cib_kappa": {}} + + def _get_data(self): + ell = self.ellsvector + Cl = self.datavector + return ell, Cl + + def _get_cov(self): + return self.cov + + + def _get_theory(self, **params_values): + theory = self.provider.get_cl_cib_kappa() + theoryvector_unbinned = [] + + #Extract All Cross Spectra + freq_list = np.sort(np.array(list(theory.keys())).astype(int)) + for nu in freq_list: + Dl_1h_theory = np.array(theory[str(nu)]['1h']) + Dl_2h_theory = np.array(theory[str(nu)]['2h']) + ells_theory = np.array(theory[str(nu)]['ell']) + + fac = ells_theory * (ells_theory+1) / 2/np.pi + Dl_tot_theory = Dl_1h_theory + Dl_2h_theory + Cl_kappa_cib = Dl_tot_theory / fac + theoryvector_unbinned.append(Cl_kappa_cib) + + # #Bin Theory + # theoryvector_binned = [] + # for Cls_unbinned in theoryvector_unbinned: + # #Interpolate the Theory Vector + # full_ells = np.arange(lmax) + # interp_Cls_unbinned = np.interp(full_ells, ells_theory, Cls_unbinned) + + # # for mock data, the data ells are the binned theory ells, so there's already perfect syncronization with the theory Cl's + # theoryvector_binned.append( yalm.binTheory(interp_Cls_unbinned, self.wsp_name) ) + + theoryvector_binned = theoryvector_unbinned + + #Create large, Multifreq Theory Vector + theoryvector = np.array(theoryvector_binned).flatten() + + # #Debugging + # debugfname = self.data_directory + 'mcmcresults/' + 'steps_Cls.txt' + # if not os.path.isfile(debugfname): + # np.savetxt(debugfname, [theoryvector]) + # else: + # # import pdb; pdb.set_trace() + # all_theoryCls = np.loadtxt(debugfname) + # if all_theoryCls.ndim == 1: + # np.savetxt(debugfname, np.stack( (all_theoryCls, theoryvector) )) + # elif all_theoryCls.ndim == 2: + # np.savetxt(debugfname, np.concatenate( (all_theoryCls, theoryvector[None, :]) )) + # else: + # raise ValueError('something has gone horribly wrong with saving the previous spectra!') + + return theoryvector diff --git a/soliket/cibxlensing/input_files/cibxkappa_classy_sz.yml b/soliket/cibxlensing/input_files/cibxkappa_classy_sz.yml new file mode 100644 index 00000000..e4c4a876 --- /dev/null +++ b/soliket/cibxlensing/input_files/cibxkappa_classy_sz.yml @@ -0,0 +1,207 @@ +output: /Users/boris/Work/CLASS-SZ/SO-SZ/SOLikeT/soliket/cibxlensing/chains/test + +likelihood: + soliket.CIBxKAPPA_Likelihood: + # cib_spectra_directory: + # cib_spectra_file: + # cib_cov_directory: "/scratch/r/rbond/ymehta3/output/param-fitting/" + cib_cov_file: "toy_cov_fudge.npy" + + # ymap_ps_file: "data_ps-ell-y2-erry2_total-planck-collab-15.txt" + # f_sky: 0.47 #sky_fraction of Planck y-map (communicated by Barbara Commis) + # trispectrum_directory: "/Users/boris/Work/CLASS-SZ/SO-SZ/Likelihoods_sz/solike/ymap/chains/sz_ps_completeness_analysis/" + # trispectrum_ref: "tSZ_trispectrum_ref_total-planck-collab-15_step_1.txt" + stop_at_error: True + + + +theory: + classy_szfast.classy_sz.classy_sz: + extra_args: + N_ncdm: 1 + cosmo_model: 0 + output : 'lens_cib_1h, lens_cib_2h' + skip_background_and_thermo: 0 + skip_chi: 1 + skip_hubble: 1 + skip_pkl: 0 + skip_pknl: 1 + skip_sigma8_and_der: 0 + skip_sigma8_at_z: 1 + A_s: 2.2e-09 + Dust_temperature_today_in_Kelvins: 24.4 + L_sat_epsabs: 1.0e-40 + L_sat_epsrel: 0.001 + M_max: 6711000000000000.0 + M_min: 67110000.0 + M_min_HOD: 10000000000.0 + Most_efficient_halo_mass_in_Msun: 3981071705534.969 + # N_ncdm: 1 + # N_ur: 0.00641 + Normalisation_of_L_-_M_relation_in_[JyMPc2/Msun]: 6.4e-08 + Omega_cdm: 0.2685008554866952 + Power_law_index_of_SED_at_high_frequency: 1.7 + Redshift_evolution_of_L_-_M_normalisation: 3.6 + # Redshift_evolution_of_dust_temperature : 0.66 + Size_of_halo_masses_sourcing_CIB_emission: 0.5 + T_ncdm: 0.71611 + # cib_frequency_list_num: 3 + cib_frequency_list_num: 1 + # cib_Snu_cutoff_list [mJy]: 710, 315, 350 + cib_Snu_cutoff_list [mJy]: 350 + # cib_frequency_list_in_GHz: 857, 353, 545 + cib_frequency_list_in_GHz: 545 + concentration parameter: D08 + damping_1h_term: 0 + # deg_ncdm: 3 + dell: 64 + delta for cib: 200m + ell_max: 3968.0 + ell_min: 2.0 + freq_max: 50000.0 + freq_min: 10.0 + h: 0.6711 + has_cib_flux_cut: 1 + hm_consistency: 1 + k_max_tau0_over_l_max: 5.0 + k_pivot: 0.05 + # m_ncdm: 0.02 + mass function: T10 + mass_epsabs: 1.0e-40 + mass_epsrel: 0.0001 + n_s: 0.9624 + ndim_masses: 150 + ndim_redshifts: 150 + non_linear: halofit + omega_b: 0.022068 + perturb_sampling_stepsize: 0.2 + pressure_profile_epsabs: 1.0e-08 + pressure_profile_epsrel: 0.001 + redshift_epsabs: 1.0e-40 + redshift_epsrel: 0.0001 + tau_reio: 0.0925 + z_max: 6.0 + z_max_pk: 6.0 + z_min: 0.07 + path: null + stop_at_error: true + use_class_sz_fast_mode: 1 + use_class_sz_no_cosmo_mode: 1 + + + + + # #M_min_HOD is the threshold above which nc = 1: + # M_min_HOD : 10.**10, + + # M_min : 1e10*common_settings['h'], + # M_max : 1e16*common_settings['h'], + # z_min : 0.06, + # z_max : 15, + + # ### Precision + # #redshift_epsabs : 1.0e-40 + # #redshift_epsrel : 0.0005 + # #mass_epsabs : 1.0e-40 + # #mass_epsrel : 0.0005 + # dell : 64, + # #multipoles_sz : 'ell_mock' + # ell_max : 3968.0, + # ell_min : 2.0, + # ndim_masses : 100, + # ndim_redshifts : 100, + + # # z_min : 0.005 + # # z_max : 3.0 + # # M_min : 1.0e10 + # # M_max : 3.5e15 + # # z_max_pk : 4. + + # cib_frequency_list_num : 3, + # cib_frequency_list_in_GHz : '353, 545, 857' + + # non_linear: 'halofit' + # k_per_decade_class_sz : 20. + # k_min_for_pk_class_sz : 1e-3 + # k_max_for_pk_class_sz : 1e1 + + # perturbations_verbose: 10 + # thermodynamics_verbose: 10 + # background_verbose: 10 + # class_sz_verbose: 10 + # spectra_verbose: 10 + + # mass function : 'T10' + # # harmonic_verbose: 0 + # # fourier_verbose : 0 + # input_verbose : 10 + # # lensing_verbose : 0 + # # The base model features two massless + # # and one massive neutrino with m=0.06eV. + # # The settings below ensures that Neff=3.046 + # # and m/omega = 93.14 eV + # N_ur : 2.0328 + # N_ncdm : 1 + # m_ncdm : 0.06 + # T_ncdm : 0.71611 + # YHe: 'BBN' + # # following parameters (50,50) give less than 1% difference w.r.t. ndimSZ = 100 and n_arraySZ=1000 + # # [relevant for tabulation of sigma(R,z)] + # # ndimSZ: 50 + # # n_arraySZ: 50 + # #write parameters : 'yeap' + + + # create reference trispectrum for likelihood code: 'NO' + # append_name_trispectrum_ref: 'total-planck-collab-15_step_1' + # path to reference trispectrum for likelihood code: '/Users/boris/Work/CLASS-SZ/SO-SZ/Likelihoods_sz/solike/ymap/chains/sz_ps_completeness_analysis/' + + + +params: + Redshift_evolution_of_dust_temperature: + prior: + min: 0 + max: 2 + ref: + dist: norm + loc: 0.36 + scale: 0.05 + latex: \alpha + # drop: True + + + # gamma: + # prior: + # min: 0 + # max: 2 + # ref: + # dist: norm + # loc: 0.36 + # scale: 0.05 + # latex: \gamma + # drop: True + + # Power law index of SED at high frequency: + # value: 'lambda gamma: gamma' + + +sampler: + #settings for covmat see https://cobaya.readthedocs.io/en/latest/sampler_mcmc.html + # mcmc: + # covmat: #auto + # Rminus1_stop: 0.01 + # # drag: true + # proposal_scale: 2.4 + # learn_proposal: True + # learn_proposal_Rminus1_max: 2. + + evaluate: + override: + # #parameter values: + Redshift_evolution_of_dust_temperature : 0.66 + + +debug : False +verbose: False +timing: True \ No newline at end of file diff --git a/soliket/clusters/__init__.py b/soliket/clusters/__init__.py index d790c894..6aed1358 100644 --- a/soliket/clusters/__init__.py +++ b/soliket/clusters/__init__.py @@ -1 +1,2 @@ -from .clusters import ClusterLikelihood # noqa: F401 +from .clusters import BinnedClusterLikelihood, UnbinnedClusterLikelihood # noqa: F401 +from .ccl_th import CCL diff --git a/soliket/clusters/ccl_th.py b/soliket/clusters/ccl_th.py new file mode 100644 index 00000000..e1ce3187 --- /dev/null +++ b/soliket/clusters/ccl_th.py @@ -0,0 +1,266 @@ +""" +Simple CCL theory wrapper that returns the cosmology object +and optionally a number of methods depending only on that +object. + +This is based on an earlier implementation by Antony Lewis: +https://github.com/cmbant/SZCl_like/blob/methods/szcl_like/ccl.py + +`get_CCL` results a dictionary of results, where `results['cosmo']` +is the CCL cosmology object. + +Classes that need other CCL-computed results (without additional +free parameters), should pass them in the requirements list. + +e.g. a `Likelihood` with `get_requirements()` returning +`{'CCL': {'methods:{'name': self.method}}}` +[where self is the Likelihood instance] will have +`results['name']` set to the result +of `self.method(cosmo)` being called with the CCL cosmo +object. + +The `Likelihood` class can therefore handle for itself which +results specifically it needs from CCL, and just give the +method to return them (to be called and cached by Cobaya with +the right parameters at the appropriate time). + +Alternatively the `Likelihood` can compute what it needs from +`results['cosmo']`, however in this case it will be up to the +`Likelihood` to cache the results appropriately itself. + +Note that this approach precludes sharing results other than +the cosmo object itself between different likelihoods. + +Also note lots of things still cannot be done consistently +in CCL, so this is far from general. +""" + +import numpy as np +import pyccl as ccl +from typing import NamedTuple, Sequence, Union, Optional, Callable +from copy import deepcopy + +from cobaya.theory import Theory +from cobaya.log import LoggedError +from cobaya.tools import Pool1D, Pool2D, PoolND, combine_1d + +# Result collector +# NB: cannot use kwargs for the args, because the CLASS Python interface +# is C-based, so args without default values are not named. +class Collector(NamedTuple): + method: str + args: Sequence = [] + args_names: Sequence = [] + kwargs: dict = {} + arg_array: Union[int, Sequence, None] = None + z_pool: Optional[PoolND] = None + post: Optional[Callable] = None + +class CCL(Theory): + """ + This implements CCL as a `Theory` object that takes in + cosmological parameters directly (i.e. cannot be used + downstream from camb/CLASS. + """ + # CCL options + transfer_function: str = 'boltzmann_camb' + matter_pk: str = 'halofit' + baryons_pk: None # CCL v3 + md_hmf: str = '200m' + # Params it can accept + params = {'Omega_c': None, + 'Omega_b': None, + 'h': None, + 'n_s': None, + 'sigma8': None, + 'm_nu': None} + + def initialize(self): + self.collectors = {} + self._required_results = {} + + def get_requirements(self): + return {} + + def must_provide(self, **requirements): + # requirements is dictionary of things requested by likelihoods + # Note this may be called more than once + + # CCL currently has no way to infer the required inputs from + # the required outputs + # So a lot of this is fixed + # if 'CCL' not in requirements: + # return {} + # options = requirements.get('CCL') or {} + # if 'methods' in options: + # self._required_results.update(options['methods']) + + self._required_results.update(requirements) + + for k, v in self._required_results.items(): + + if k == "Hubble": + self.set_collector_with_z_pool( + k, v["z"], "Hubble", args_names=["z"], arg_array=0) + + elif k == "angular_diameter_distance": + self.set_collector_with_z_pool( + k, v["z"], "angular_diameter_distance", args_names=["z"], arg_array=0) + + return {} + + def get_can_provide_params(self): + # return any derived quantities that CCL can compute + return ['H0'] + + def get_param(self, p: str) -> float: + """ + Interface function for likelihoods and other theory components to get derived + parameters. + """ + return self.current_state["derived"][p] + + def get_can_support_params(self): + # return any nuisance parameters that CCL can support + return [] + + def calculate(self, state, want_derived=True, **params_values_dict): + # Generate the CCL cosmology object which can then be used downstream + if self.baryons_pk == 'nobaryons': # For CCL v3, in case people don't want to update SOLikeT .yml configs + self.baryons_pk=None + cosmo = ccl.Cosmology(Omega_c=self.provider.get_param('Omega_c'), + Omega_b=self.provider.get_param('Omega_b'), + h=self.provider.get_param('h'), + n_s=self.provider.get_param('n_s'), + sigma8=self.provider.get_param('sigma8'), + #T_CMB=2.7255, # to agree with Nemo setting (default is 2.725) + m_nu=self.provider.get_param('m_nu'), + transfer_function=self.transfer_function, + matter_power_spectrum=self.matter_pk, + baryonic_effects=self.baryons_pk) # baryonic_effects is CCL v3 + + state['derived'] = {'H0': cosmo.cosmo.params.H0} + for req_res, attrs in self._required_results.items(): + if req_res == 'Hubble': + a = 1./(1. + attrs['z']) + state[req_res] = ccl.h_over_h0(cosmo, a)*cosmo.cosmo.params.H0 + elif req_res == 'angular_diameter_distance': + a = 1./(1. + attrs['z']) + state[req_res] = ccl.angular_diameter_distance(cosmo, a) + elif req_res == 'Pk_interpolator': + state[req_res] = None + elif req_res == 'nc_data': + if self.md_hmf == '200m': + md = ccl.halos.MassDef(200, 'matter') + elif self.md_hmf == '200c': + md = ccl.halos.MassDef(200, 'critical') + elif self.md_hmf == '500c': + md = ccl.halos.MassDef(500, 'critical') + else: + raise NotImplementedError('Only md_hmf = 200m, 200c and 500c currently supported.') + mf = ccl.halos.MassFuncTinker08(mass_def=md) + state[req_res] = {'HMF': mf, + 'md': md} + elif req_res == 'CCL': + state[req_res] = {'cosmo': cosmo} + elif attrs is None: + pass + # General derived parameters + # if req_res not in self.derived_extra: + # self.derived_extra += [req_res] + + def set_collector_with_z_pool(self, k, zs, method, args=(), args_names=(), + kwargs=None, arg_array=None, post=None, d=1): + """ + Creates a collector for a z-dependent quantity, keeping track of the pool of z's. + If ``z`` is an arg, i.e. it is in ``args_names``, then omit it in the ``args``, + e.g. ``args_names=["a", "z", "b"]`` should be passed together with + ``args=[a_value, b_value]``. + """ + if k in self.collectors: + z_pool = self.collectors[k].z_pool + z_pool.update(zs) + else: + Pool = {1: Pool1D, 2: Pool2D}[d] + z_pool = Pool(zs) + # Insert z as arg or kwarg + kwargs = kwargs or {} + if d == 1 and "z" in kwargs: + kwargs = deepcopy(kwargs) + kwargs["z"] = z_pool.values + elif d == 1 and "z" in args_names: + args = deepcopy(args) + i_z = args_names.index("z") + args = list(args[:i_z]) + [z_pool.values] + list(args[i_z:]) + elif d == 2 and "z1" in args_names and "z2" in args_names: + # z1 assumed appearing before z2! + args = deepcopy(args) + i_z1 = args_names.index("z1") + i_z2 = args_names.index("z2") + args = (list(args[:i_z1]) + [z_pool.values[:, 0]] + list(args[i_z1:i_z2]) + + [z_pool.values[:, 1]] + list(args[i_z2:])) + else: + raise LoggedError( + self.log, + f"I do not know how to insert the redshift for collector method {method} " + f"of requisite {k}") + self.collectors[k] = Collector( + method=method, z_pool=z_pool, args=args, args_names=args_names, kwargs=kwargs, + arg_array=arg_array, post=post) + + def get_CCL(self): + """ + Get dictionary of CCL computed quantities. + results['cosmo'] contains the initialized CCL Cosmology object. + Other entries are computed by methods passed in as the requirements + + :return: dict of results + """ + return self._current_state['CCL'] + + def get_nc_data(self): + """ + Get dictionary of CCL computed quantities. + results['cosmo'] contains the initialized CCL Cosmology object. + Other entries are computed by methods passed in as the requirements + + :return: dict of results + """ + return self._current_state['nc_data'] + + def _get_z_dependent(self, quantity, z, pool=None): + if pool is None: + pool = self.collectors[quantity].z_pool + try: + i_kwarg_z = pool.find_indices(z) + except ValueError: + raise LoggedError(self.log, f"{quantity} not computed for all z requested. " + f"Requested z are {z}, but computed ones are " + f"{pool.values}.") + return np.array(self.current_state[quantity], copy=True)[i_kwarg_z] + + def get_Hubble(self, z): + r""" + Returns the Hubble rate at the given redshift(s) ``z``. + The redshifts must be a subset of those requested when + :func:`~BoltzmannBase.must_provide` was called. + The available units are ``"km/s/Mpc"`` (i.e. :math:`cH(\mathrm(Mpc)^{-1})`) and + ``1/Mpc``. + """ + + return self._get_z_dependent("Hubble", z) + + def get_angular_diameter_distance(self, z): + r""" + Returns the physical angular diameter distance in :math:`\mathrm{Mpc}` to the + given redshift(s) ``z``. + The redshifts must be a subset of those requested when + :func:`~BoltzmannBase.must_provide` was called. + """ + return self._get_z_dependent("angular_diameter_distance", z) + + def get_Pk_interpolator(self, var_pair=("delta_tot", "delta_tot"), nonlinear=True, + #def get_Pk_interpolator(self, var_pair=("delta_nonu", "delta_nonu"), nonlinear=True, + extrap_kmin=None, extrap_kmax=None): + + return None diff --git a/soliket/clusters/cluster_utils.py b/soliket/clusters/cluster_utils.py new file mode 100644 index 00000000..86a4f61e --- /dev/null +++ b/soliket/clusters/cluster_utils.py @@ -0,0 +1,49 @@ +import numpy as np +import scipy.stats +import cashstatistic as cashstat + +def gof_cash(npred, nobs): + """ + Computes p-value for Poisson statistic based on Kaastra 2017 (https://arxiv.org/abs/1707.09202). + Parameters + ---------- + npred: predicted number of clusters in bins + nobs: observed number of clusters for same binning + Returns + ------- + pval: Gaussian p-value for C-stat + Ce: expectation value for C-stat + Cv: variance of C-stat + Cd: observed value of C-stat + + Note that cashstatistic doesn't handle the case of mu=0, + I had to edit the function cash_mod_expectations + in cashstatistic.py slightly: + + mu = np.asarray(mu_in) + lnmu = np.empty(mu.shape) + lnmu[mu > 0] = np.log(mu[mu > 0]) + lnmu[mu == 0] = 0. + mi = np.empty(mu.shape) + mi[mu > 0] = 1.0/mu[mu > 0] + mi[mu == 0] = 1. + C_e = np.empty(mu.shape) + C_v = np.empty(mu.shape) + C_e[mu == 0.0] = 0. + C_v[mu == 0.0] = 0. + + """ + + Ce_bin, Cv_bin = cashstat.cash_mod_expectations(npred) + Ce = np.sum(Ce_bin) + Cv = np.sum(Cv_bin) + + logterm = np.zeros_like(nobs, dtype=float) + logterm[nobs > 0] = np.log(nobs[nobs > 0]/npred[nobs > 0]) + logterm[nobs == 0] = 0. + + Cd = 2*np.sum(npred - nobs + nobs*logterm) + + pval = scipy.stats.norm.sf(Cd, Ce, np.sqrt(Cv)) + + return pval, Ce, Cv, Cd diff --git a/soliket/clusters/clusters.py b/soliket/clusters/clusters.py index 289db327..b6f81e1d 100644 --- a/soliket/clusters/clusters.py +++ b/soliket/clusters/clusters.py @@ -1,240 +1,1536 @@ """ -requires extra: astlib +requires extra: astlib,fits,os,sys,nemo +======= +.. module:: clusters + +:Synopsis: Poisson likelihood for SZ clusters for Simons Osbervatory +:Authors: Nick Battaglia, Eunseong Lee + +Likelihood for unbinned tSZ galaxy cluster number counts. Currently under development and +should be used only with caution and advice. Uses the SZ scaling relations from +Hasselfield et al (2013) [1]_ to compare observed number of :math:`y`-map detections +with the prediction from a Tinker [2]_ Halo Mass Function. + +References +---------- +.. [1] Hasselfield et al, JCAP 07, 008 (2013) `arXiv:1301.0816 + `_ +.. [2] Tinker et al, Astrophys. J. 688, 2, 709 (2008) `arXiv:0803.2706 + `_ + """ + import numpy as np import pandas as pd -from scipy.interpolate import interp1d -from pkg_resources import resource_filename - +import nemo as nm # needed for reading Q-functions +import logging +import os, sys +import time # for timing +from scipy import special, stats, interpolate, integrate +from scipy.interpolate import interp1d, interp2d +from astropy.io import fits +import astropy.table as atpy import pyccl as ccl +import soliket.clusters.nemo_mocks as selfunc from ..poisson import PoissonLikelihood -from . import massfunc as mf -from .survey import SurveyData -from .sz_utils import szutils +from ..cash import CashCLikelihood +from ..constants import MPC2CM, MSUN_CGS, G_CGS +#from classy_sz import Class # TBD: change this import as optional C_KM_S = 2.99792e5 +MPIVOT_THETA = 3e14 # [Msun] +rho_crit0H100 = (3. / (8. * np.pi) * (100. * 1.e5) ** 2.) / G_CGS * MPC2CM / MSUN_CGS -class SZModel: - pass +class BinnedClusterLikelihood(CashCLikelihood): + name = "Binned Clusters" + data: dict = {} + theorypred: dict = {} + YM: dict = {} + selfunc: dict = {} + binning: dict = {} + verbose: bool = False + debug: bool = False -class ClusterLikelihood(PoissonLikelihood): - name = "Clusters" - columns = ["tsz_signal", "z", "tsz_signal_err"] - data_path = resource_filename("soliket", "clusters/data/selFn_equD56") - # data_path = resource_filename("soliket", "clusters/data/selFn_SO") - data_name = resource_filename("soliket", "clusters/data/E-D56Clusters.fits") - # data_name = resource_filename("soliket", - # "clusters/data/MFMF_WebSkyHalos_A10tSZ_3freq_tiles_mass.fits") + params = {"tenToA0":None, "B0":None, "C0":None, "scatter_sz":None, "bias_sz":None, + "opt_bias_A":None, "opt_bias_B":None} def initialize(self): - self.zarr = np.arange(0, 2, 0.05) - self.k = np.logspace(-4, np.log10(5), 200) - # self.mdef = ccl.halos.MassDef(500, 'critical') + + # constant binning in log10 + # WARNING: This seems brittle + # qbins = np.arange(self.binning['q']['log10qmin'], self.binning['q']['log10qmax']+self.binning['q']['dlog10q'], self.binning['q']['dlog10q']) + # self.qbins = 10**qbins + # self.qarr = 10**(0.5*(qbins[:-1] + qbins[1:])) + # self.Nq = int((self.binning['q']['log10qmax'] - self.binning['q']['log10qmin'])/self.binning['q']['dlog10q']) + 1 + # Revised binning + self.Nq = int(np.ceil((self.binning['q']['log10qmax']-self.binning['q']['log10qmin'])/self.binning['q']['dlog10q'])) + qbins = np.linspace(self.binning['q']['log10qmin'], self.binning['q']['log10qmax'], self.Nq) + self.qbins = np.power(10, qbins) # bin edges + self.qarr = np.power(10, (qbins[1:]+qbins[:-1])/2) + self.Nq = self.Nq - 1 + assert(self.Nq > 0) + + # this is for liklihood computation + self.zcut = self.binning['exclude_zbin'] + + initialize_common(self) + + delNcat, _ = np.histogram(self.z_cat, bins=self.zbins) + self.delNcat = self.zarr, delNcat + + self.log.info("Number of redshift bins = {}.".format(len(self.zarr))) + self.log.info('Number of SNR bins = {}.'.format(self.Nq)) + + delN2Dcat, _, _ = np.histogram2d(self.z_cat, self.q_cat, bins=[self.zbins, self.qbins]) + self.delN2Dcat = self.zarr, self.qarr, delN2Dcat, self.zcut + + if self.theorypred['choose_theory'] == 'classy_sz': + self.params = {} + else: + # finner binning for low redshift + minz = self.zbins[0] + maxz = self.zbins[-1] + if minz < 0: minz = 0.0 + zi = minz + + # counting redshift bins + Nzz = 0 + while zi <= maxz : + zi = self._get_hres_z(zi) + Nzz += 1 + + zi = minz + zz = np.zeros(Nzz) + for i in range(Nzz): + zz[i] = zi + zi = self._get_hres_z(zi) + if zz[0] == 0. : zz[0] = 1e-5 + self.zz = zz + + self.log.info('Number of redshift points for theory calculation = {}.'.format(len(self.zz))) + self.log.info('Number of mass points for theory calculation = {}.'.format(len(self.lnmarr))) + self.log.info('Number of y0 points for theory calculation = {}.'.format(len(self.lny))) super().initialize() def get_requirements(self): - return { - "Pk_interpolator": { - "z": self.zarr, - "k_max": 5.0, - "nonlinear": False, - "hubble_units": False, # cobaya told me to - "k_hunit": False, # cobaya told me to - "vars_pairs": [["delta_nonu", "delta_nonu"]], - }, - "Hubble": {"z": self.zarr}, - "angular_diameter_distance": {"z": self.zarr}, - "comoving_radial_distance": {"z": self.zarr} - # "CCL": {"methods": {"sz_model": self._get_sz_model}, "kmax": 10}, - } - - def _get_sz_model(self, cosmo): - model = SZModel() - model.hmf = ccl.halos.MassFuncTinker08(cosmo, mass_def=self.mdef) - model.hmb = ccl.halos.HaloBiasTinker10( - cosmo, mass_def=self.mdef, mass_def_strict=False - ) - model.hmc = ccl.halos.HMCalculator(cosmo, model.hmf, model.hmb, self.mdef) - # model.szk = SZTracer(cosmo) - return model + if self.theorypred['choose_theory'] == 'classy_sz': + return {"sz_binned_cluster_counts": {}} + else: + return get_requirements(self) - def _get_catalog(self): - self.survey = SurveyData( - self.data_path, self.data_name - ) # , MattMock=False,tiles=False) + def _get_hres_z(self, zi): + # bins in redshifts are defined with higher resolution for low redshift + # for now using the same binning as in NEMO for comparison + hr = 0.2 + if zi < hr : + dzi = 1e-2 + elif zi >= hr and zi <=1.: + dzi = 1e-2 + else: + dzi = 1e-2 + hres_z = zi + dzi + return hres_z + + def _get_data(self): + return self.delN2Dcat + + def _get_theory(self, pk_intp, **kwargs): + if self.theorypred['choose_theory'] == 'classy_sz': + theory = self.provider.get_sz_binned_cluster_counts() + dNdzdy_theoretical = theory['dndzdy'] + z_edges = theory['z_edges'] + log10y_edges = theory['log10y_edges'] + return dNdzdy_theoretical,z_edges,log10y_edges + else: + start = time.time() + delN = self._get_integrated(pk_intp, **kwargs) + elapsed = time.time() - start + self.log.info("Theory N calculation took {:.3f} seconds.".format(elapsed)) + return delN + + def _get_integrated(self, pk_intp, **kwargs): + + zarr = self.zarr + zz = self.zz + marr = np.exp(self.lnmarr) + Nq = self.Nq + + h = self.provider.get_param("H0") / 100.0 + + dndlnm = get_dndlnm(self, zz, pk_intp) + dVdzdO = get_dVdz(self, zz, dVdz_interp=False) + surveydeg2 = self.skyfracs.sum() + intgr = dndlnm * dVdzdO * surveydeg2 + intgr = intgr.T + + if self.theorypred['md_hmf'] != self.theorypred['md_ym']: + marr_ymmd = convert_masses(self, marr, zz) + else: + marr_ymmd = marr + + if self.theorypred['md_ym'] != '500c': + marr_500c = get_m500c(self, marr, zz) + else: + marr_500c = marr_ymmd + + if self.selfunc['method'] == 'SNRbased': + y0 = get_y0(self, marr_ymmd, zz, marr_500c, use_Q=True, Ez_interp=False, **kwargs) + else: + y0 = None + + cc = np.array([self._get_completeness(marr, zz, y0, kk, marr_500c, **kwargs) for kk in range(Nq)]) + + nzarr = self.zbins + delN2D = np.zeros((len(zarr), Nq)) + + # integrate over mass + dndzz = integrate.simpson(intgr[None, ...]*cc, x=self.lnmarr, axis=2) + + # integrate over fine z bins and sum over in larger z bins + for i in range(len(zarr)): + + test = np.abs(zz - nzarr[i]) + i1 = np.argmin(test) + test = np.abs(zz - nzarr[i+1]) + i2 = np.argmin(test) + + delN2D[i,:] = integrate.simpson(dndzz[:, i1:i2+1], x=zz[i1:i2+1]).T + + self.log.info("\r Total predicted 2D N = {}".format(delN2D.sum())) + + for i in range(len(zarr)): + self.log.info('Number of clusters in redshift bin {}: {}.'.format(i, delN2D[i,:].sum())) + self.log.info('------------') + for kk in range(Nq): + self.log.info('Number of clusters in snr bin {}: {}.'.format(kk, delN2D[:,kk].sum())) + self.log.info("Total predicted 2D N = {}.".format(delN2D.sum())) + + # Debugging + # from astLib import astImages + # astImages.saveFITS("theory_%.2f_%.2f.fits" % (self.provider.get_param('sigma8'), self.provider.get_param('Omega_c')), delN2D.transpose()) + # astImages.saveFITS("obs_%.2f_%.2f.fits" % (self.provider.get_param('sigma8'), self.provider.get_param('Omega_c')), self.delN2Dcat[2].transpose()) + # astImages.saveFITS("diff_%.2f_%.2f.fits" % (self.provider.get_param('sigma8'), self.provider.get_param('Omega_c')), (self.delN2Dcat[2]-delN2D).transpose()) + # print("inspect grid") + # import IPython + # IPython.embed() + # sys.exit() + + return delN2D + + def _get_completeness_inj(self, mass, z, mass_500c, qbin, **params): + + scatter = params["scatter_sz"] + + y0 = get_y0(self, mass, z, mass_500c, use_Q=False, Ez_interp=False, **params) + theta = get_theta(self, mass_500c, z) - self.szutils = szutils(self.survey) + if scatter == 0: + comp = np.zeros_like(theta) + for i in range(theta.shape[0]): + comp[i, :] = self.compThetaInterpolator[qbin](theta[i, :], y0[i, :]/1e-4, grid=False) + comp[comp < 0] = 0 - df = pd.DataFrame( + else: + comp = np.zeros((theta.shape[0], theta.shape[1], self.lny.shape[0])) + for i in range(theta.shape[0]): + comp[i, :] = self.compThetaInterpolator[qbin](theta[i, :], np.exp(self.lny)/1e-4, grid=True) + comp[comp < 0] = 0 + + fac = 1. / np.sqrt(2. * np.pi * scatter ** 2) + arg = (self.lny[None, None, :] - np.log(y0[:, :, None])) / (np.sqrt(2.) * scatter) + PY = fac * np.exp(-arg ** 2.) + comp = integrate.simpson(comp*PY, self.lny, axis=-1) + + return comp + + + def _get_completeness(self, marr, zarr, y0, qbin, marr_500c=None, **params): + """Calculate completeness on (mass, z) grid for given signal-to-noise-bin. + + Args: + marr (array): Masses defining the (mass, z) grid + zarr (array): Redshifts defining the (mass, z) grid + y0 (array): Cube of predicted y0 values on the (mass, z) grid - each + plane corresponds to a different patch. + qbin (int): Index of the signal-to-noise (q) bin for which completeness + will be calculated. + + Returns: + Completeness on (mass, z) grid for given signal to noise bin, averaged + over all patches. + + """ + + if self.selfunc['method'] == 'SNRbased': + + scatter = params["scatter_sz"] + noise = self.noise + qcut = self.qcut + skyfracs = self.skyfracs/self.skyfracs.sum() + Npatches = len(skyfracs) + compl_mode = self.theorypred['compl_mode'] + + Nq = self.Nq + qbins = self.qbins + kk = qbin + qmin = qbins[kk] + qmax = qbins[kk+1] + + opt_bias_corr_factor = np.ones(y0.shape) + if self.selfunc['bias_handler'] == 'theory': + for i in range(Npatches): + trueSNR = y0[i] / noise[i] + opt_bias_corr_factor[i] = _opt_bias_func(trueSNR, params['opt_bias_A'], params['opt_bias_B']) + + + if scatter == 0.: + + arg = [] + for i in range(Npatches): + + if compl_mode == 'erf_prod': + arg.append(get_erf_prod(y0[i]*opt_bias_corr_factor[i], noise[i], qmin, qmax, qcut, kk, Nq, dof=self.selfunc['debiasDOF'])) + elif compl_mode == 'erf_diff': + arg.append(get_erf_diff(y0[i]*opt_bias_corr_factor[i], noise[i], qmin, qmax, qcut, dof=self.selfunc['debiasDOF'])) + + comp = np.einsum('ijk,i->jk', np.nan_to_num(arg), skyfracs) + + else: + + lnyy = np.float32(self.lny) + yy0 = np.exp(lnyy) + mu = np.float32(np.log(y0*opt_bias_corr_factor)) + fac = np.float32(1./np.sqrt(2.*np.pi*scatter**2)) + + comp = 0. + for i in range(Npatches): + if compl_mode == 'erf_prod': + arg = get_erf_prod(yy0, noise[i], qmin, qmax, qcut, kk, Nq, dof=self.selfunc['debiasDOF']) + elif compl_mode == 'erf_diff': + arg = get_erf_diff(yy0, noise[i], qmin, qmax, qcut, dof=self.selfunc['debiasDOF']) + + cc = np.float32(arg * skyfracs[i]) + arg0 = np.float32((lnyy[:, None, None] - mu[i])/(np.sqrt(2.)*scatter)) + args = fac * np.exp(np.float32(-arg0**2.)) * cc[:, None, None] + + # truncation for debiasing: + qtab_filter = yy0 / noise[i] + qtab_filter[qtab_filter < self.selfunc['debias_cutoff']] = 0. + qtab_filter[qtab_filter >= self.selfunc['debias_cutoff']] = 1. + # end truncation for debiasing. + + comp += integrate.simpson(np.float32(args)*qtab_filter[:, None, None], x=lnyy, axis=0) + + comp[comp < 0.] = 0. + comp[comp > 1.] = 1. + + else: + comp = self._get_completeness_inj(marr, zarr, marr_500c, qbin, **params) + return comp + + def logp(self, **params_values): + if self.theorypred['choose_theory'] == 'classy_sz': + pk_intp = None + theory = self._get_theory(pk_intp,**params_values) + dNdzdy_theoretical,z_edges,log10y_edges = theory + dNdzdy_catalog, zedges, yedges = np.histogram2d(self.z_cat,np.log10(self.q_cat), bins=[z_edges,log10y_edges]) + SZCC_Cash = 0. + N_z,N_y = np.shape(dNdzdy_theoretical) + # if self.debug == True: + # print('N_z,N_y',N_z,N_y) + for index_z in range(N_z): + for index_y in range(N_y): + if not dNdzdy_theoretical[index_z][index_y] == 0.: + ln_factorial = 0. + if not dNdzdy_catalog[index_z,index_y] == 0.: + if dNdzdy_catalog[index_z,index_y] > 10.: + # Stirling approximation only for more than 10 elements + ln_factorial = 0.918939 + (dNdzdy_catalog[index_z,index_y] + 0.5) * np.log(dNdzdy_catalog[index_z,index_y]) - dNdzdy_catalog[index_z,index_y] + else: + # Direct computation of factorial + ln_factorial = np.log(np.math.factorial(int(dNdzdy_catalog[index_z,index_y]))) + SZCC_Cash += (dNdzdy_catalog[index_z,index_y] * np.log(dNdzdy_theoretical[index_z][index_y]) - dNdzdy_theoretical[index_z][index_y] - ln_factorial) + # if self.debug == True: + # print("theory: %.5e, catalogue: %.5e"%(dNdzdy_theoretical[index_z][index_y],dNdzdy_catalog[index_z,index_y])) + if self.debug == True: + print("Ntot cat:",np.sum(dNdzdy_catalog[:,:])) + print("Ntot theory:",np.sum(dNdzdy_theoretical[:][:])) + # return ln(L) + loglkl = SZCC_Cash + return loglkl + else: + pk_intp = self.provider.get_Pk_interpolator() + theory = self._get_theory(pk_intp, **params_values) + return self.data.loglike(theory) + + +class UnbinnedClusterLikelihood(PoissonLikelihood): + name = "Unbinned Clusters" + columns = ["z", "tsz_signal", "tsz_signal_err", "tile_name"] + + verbose: bool = False + data: dict = {} + theorypred: dict = {} + selfunc: dict = {} + binning: dict = {} + YM: dict = {} + params = {"tenToA0":None, "B0":None, "C0":None, "scatter_sz":None, "bias_sz":None, + "opt_bias_A":None, "opt_bias_B":None} + + def initialize(self): + + initialize_common(self) + + zmax = self.binning['z']['zmax'] + + self.zz = np.arange(0, zmax, 1e-2) # redshift bounds should correspond to catalogue + if self.zz[0] == 0: self.zz[0] = 1e-5 + + self.log.info('Number of redshift points for theory calculation = {}.'.format(len(self.zz))) + self.log.info('Number of mass points for theory calculation = {}.'.format(len(self.lnmarr))) + self.log.info('Number of y0 points for theory calculation = {}.'.format(len(self.lny))) + + self.catalog = pd.DataFrame( { - "z": self.survey.clst_z.byteswap().newbyteorder(), - "tsz_signal": self.survey.clst_y0.byteswap().newbyteorder(), - "tsz_signal_err": self.survey.clst_y0err.byteswap().newbyteorder(), + "z": self.z_cat, + "tsz_signal": self.cat_tsz_signal, + "tsz_signal_err": self.cat_tsz_signal_err, + "tile_name": self.cat_tile_name } ) - return df - def _get_om(self): - return (self.theory.get_param("omch2") + self.theory.get_param("ombh2")) / ( - (self.theory.get_param("H0") / 100.0) ** 2 - ) + # this is for liklihood computation + self.zcut = self.binning['exclude_zbin'] + if self.theorypred['choose_theory'] == 'classy_sz': + self.params = {} + super().initialize() - def _get_ob(self): - return (self.theory.get_param("ombh2")) / ( - (self.theory.get_param("H0") / 100.0) ** 2 - ) + def get_requirements(self): + if self.theorypred['choose_theory'] == 'classy_sz': + return {"sz_unbinned_cluster_counts": {}} + else: + return get_requirements(self) + + def _get_catalog(self): + return self.catalog, self.columns + + def Pfunc_inj(self, marr, z, **params): + if self.theorypred['md_hmf'] != self.theorypred['md_ym']: + marr_ymmd = convert_masses(self, marr, z) + else: + marr_ymmd = marr + if self.theorypred['md_ym'] != '500c': + marr_500c = get_m500c(self, marr, z) + else: + marr_500c = marr_ymmd + + y0 = get_y0(self, marr_ymmd, z, marr_500c, use_Q=False, Ez_interp=False, **params) + theta = get_theta(self, marr_500c, z) + + comp = np.zeros_like(theta) + for i in range(theta.shape[0]): + comp[i] = self.compThetaInterpolator(theta[i], y0[i]/1e-4, grid=False) + comp[comp < 0] = 0 + + return comp.T + + + def _get_n_expected(self, pk_intp, **kwargs): + + start = time.time() + + zz = self.zz + marr = np.exp(self.lnmarr) + Ynoise = self.noise + + dVdz = get_dVdz(self, zz, dVdz_interp=False) + dndlnm = get_dndlnm(self, zz, pk_intp) + + zcut = self.zcut + + if self.selfunc['method'] == 'injection': + Pfunc = self.Pfunc_inj(marr, zz, **kwargs) + Pfunc = np.repeat(Pfunc[..., np.newaxis], Ynoise.shape[0], axis=2) + else: + Pfunc = self.PfuncY(Ynoise, marr, zz, kwargs) + + Ntot = 0 + for index, frac in enumerate(self.skyfracs): + if zcut > 0: + Nz = self._get_n_expected_zbinned(zz, dVdz, dndlnm, Pfunc) + zcut_arr = np.arange(zcut) + Ntot = np.sum(np.delete(Nz, zcut_arr, 0)) + else: + Nz = integrate.simpson(dndlnm * Pfunc[..., index], self.lnmarr[:, None], axis=0) + Ntot += integrate.simpson(Nz * dVdz, x=zz) * frac + + self.log.info("Total predicted N = {}".format(Ntot)) + + elapsed = time.time() - start + self.log.info("::: theory N calculation took {:.3f} seconds.".format(elapsed)) + + return Ntot + + def _get_n_expected_zbinned(self, zz, dVdz, dndlnm, Pfunc): + + zarr = self.zarr + nzarr = self.zbins + + Nz = 0 + for index, frac in enumerate(self.skyfracs): + Nz += integrate.simpson(dndlnm * dVdz * Pfunc[:,:,index], x=self.lnmarr[:, None], axis=0) * frac + + Nzz = np.zeros(len(zarr)) + for i in range(len(zarr)): + test = np.abs(zz - nzarr[i]) + i1 = np.argmin(test) + test = np.abs(zz - nzarr[i+1]) + i2 = np.argmin(test) + + Nzz[i] = integrate.simpson(Nz[i1:i2+1], x=zz[i1:i2+1]) + + # self.log.info("\r Total predicted N = {}".format(Nzz.sum())) + # for i in range(len(zarr)): + # self.log.info('Number of clusters in redshift bin {}: {}.'.format(i, Nzz[i])) + # self.log.info('------------') + + return Nzz + + + def _get_rate_fn(self, pk_intp, **kwargs): + + zarr = self.zz + marr = np.exp(self.lnmarr) + + dndlnm = get_dndlnm(self, zarr, pk_intp) + dndlnm_intp = interp2d(zarr, self.lnmarr, dndlnm, kind='cubic', fill_value=0) + + def Prob_per_cluster(z, tsz_signal, tsz_signal_err, tile_name): - def _get_Ez(self): - return self.theory.get_Hubble(self.zarr) / self.theory.get_param("H0") - - def _get_Ez_interpolator(self): - return interp1d(self.zarr, self._get_Ez()) - - def _get_DAz(self): - return self.theory.get_angular_diameter_distance(self.zarr) - - def _get_DAz_interpolator(self): - return interp1d(self.zarr, self._get_DAz()) - - def _get_HMF(self): - h = self.theory.get_param("H0") / 100.0 - - Pk_interpolator = self.theory.get_Pk_interpolator( - ("delta_nonu", "delta_nonu"), nonlinear=False - ).P - pks = Pk_interpolator(self.zarr, self.k) - # pkstest = Pk_interpolator(0.125, self.k ) - # print (pkstest * h**3 ) - - Ez = ( - self._get_Ez() - ) # self.theory.get_Hubble(self.zarr) / self.theory.get_param("H0") - om = self._get_om() - - hmf = mf.HMF(om, Ez, pk=pks * h**3, kh=self.k / h, zarr=self.zarr) - - return hmf - - def _get_param_vals(self, **kwargs): - # Read in scaling relation parameters - # scat = kwargs['scat'] - # massbias = kwargs['massbias'] - # B0 = kwargs['B'] - B0 = 0.08 - scat = 0.2 - massbias = 1.0 - - H0 = self.theory.get_param("H0") - ob = self._get_ob() - om = self._get_om() - param_vals = { - "om": om, - "ob": ob, - "H0": H0, - "B0": B0, - "scat": scat, - "massbias": massbias, - } - return param_vals - - def _get_rate_fn(self, **kwargs): - HMF = self._get_HMF() - param_vals = self._get_param_vals(**kwargs) - - Ez_fn = self._get_Ez_interpolator() - DA_fn = self._get_DAz_interpolator() - - dn_dzdm_interp = HMF.inter_dndmLogm(delta=500) - - h = self.theory.get_param("H0") / 100.0 - - def Prob_per_cluster(z, tsz_signal, tsz_signal_err): - c_y = tsz_signal - c_yerr = tsz_signal_err c_z = z + c_y = tsz_signal * 1e-4 + c_yerr = tsz_signal_err * 1e-4 + c_tile = tile_name - Pfunc_ind = self.szutils.Pfunc_per( - HMF.M, c_z, c_y * 1e-4, c_yerr * 1e-4, param_vals, Ez_fn, DA_fn - ) + tile_index = [self.tiles_dwnsmpld[c] for c in c_tile] - dn_dzdm = 10 ** np.squeeze(dn_dzdm_interp(c_z, np.log10(HMF.M))) * h**4.0 + c_z, c_y, c_yerr, tile_index = zip(*sorted(zip(c_z, c_y, c_yerr, tile_index))) + c_z, c_y, c_yerr, tile_index = np.array(c_z), np.array(c_y), np.array(c_yerr), np.array(tile_index) + + zcut = self.zcut + if zcut > 0: + ind = np.where(c_z > self.zbins[zcut])[0] + c_z, c_y, c_yerr, tile_index = c_z[ind], c_y[ind], c_yerr[ind], tile_index[ind] + print("::: Excluding clusters of z < {} in likelihood.".format(self.zbins[zcut])) + print("Total observed N = {}".format(len(c_z))) + + Pfunc_ind = self.Pfunc_per(tile_index, marr, c_z, c_y, c_yerr, kwargs).T + dn_dlnm = np.squeeze(dndlnm_intp(c_z, self.lnmarr)) + dVdz = get_dVdz(self, c_z, dVdz_interp=True) + + if self.selfunc['bias_handler'] == 'theory': + + skyfracs = self.skyfracs / self.skyfracs.sum() + ans = 0 + for index, frac in enumerate(skyfracs): + ans += integrate.simpson(dn_dlnm[None, ...] * Pfunc_ind[index,...] * dVdz[None, None, :] * frac, x=self.lnmarr[None, :, None], axis=1) + + else: + ans = integrate.simpson(dn_dlnm * Pfunc_ind * dVdz[None, :], x=self.lnmarr[:, None], axis=0) - ans = np.trapz(dn_dzdm * Pfunc_ind, dx=np.diff(HMF.M, axis=0), axis=0) return ans return Prob_per_cluster - # Implement a function that returns a rate function (function of (tsz_signal, z)) - def _get_dVdz(self): - """dV/dzdOmega""" - DA_z = self.theory.get_angular_diameter_distance(self.zarr) + def P_Yo(self, tile_index, LgY, Ynoise, marr, z, params): - dV_dz = ( - DA_z**2 - * (1.0 + self.zarr) ** 2 - / (self.theory.get_Hubble(self.zarr) / C_KM_S) - ) + if self.theorypred['md_hmf'] != self.theorypred['md_ym']: + marr_ymmd = convert_masses(self, marr, z) + else: + marr_ymmd = marr + if self.theorypred['md_ym'] != '500c': + marr_500c = get_m500c(self, marr, z) + else: + marr_500c = marr_ymmd - # dV_dz *= (self.theory.get_param("H0") / 100.0) ** 3.0 # was h0 - return dV_dz + Ytilde = get_y0(self, marr_ymmd, z, marr_500c, use_Q=True, Ez_interp=True, tile_index=tile_index, **params) - def _get_n_expected(self, **kwargs): - # def Ntot_survey(self,int_HMF,fsky,Ythresh,param_vals): + Ytilde = np.repeat(Ytilde[..., np.newaxis], LgY.shape[-1], axis=2) + LgY = np.repeat(LgY[np.newaxis, ...], Ytilde.shape[0], axis=0) - HMF = self._get_HMF() - param_vals = self._get_param_vals(**kwargs) - Ez_fn = self._get_Ez_interpolator() - DA_fn = self._get_DAz_interpolator() + if self.selfunc['bias_handler'] == 'theory': + Ytilde = np.repeat(Ytilde[..., np.newaxis], Ynoise.shape[0], axis=3) + LgY = np.repeat(LgY[..., np.newaxis], Ynoise.shape[0], axis=3) - z_arr = self.zarr + trueSNR = Ytilde / Ynoise[None, None, None, :] + opt_bias_corr_factor = _opt_bias_func(trueSNR, params['opt_bias_A'], params['opt_bias_B']) + Ytilde = Ytilde * opt_bias_corr_factor - h = self.theory.get_param("H0") / 100.0 + Y = np.exp(LgY) + numer = -1.0 * (np.log(Y / Ytilde)) ** 2 + ans = ( + 1.0 / (params["scatter_sz"] * np.sqrt(2 * np.pi)) * + np.exp(numer / (2.0 * params["scatter_sz"] ** 2)) + ) + return ans - Ntot = 0 - dVdz = self._get_dVdz() - dn_dzdm = HMF.dn_dM(HMF.M, 500.0) * h**4.0 # getting rid of hs - - for Yt, frac in zip(self.survey.Ythresh, self.survey.frac_of_survey): - Pfunc = self.szutils.PfuncY(Yt, HMF.M, z_arr, param_vals, Ez_fn, DA_fn) - N_z = np.trapz( - dn_dzdm * Pfunc, dx=np.diff(HMF.M[:, None] / h, axis=0), axis=0 - ) - Ntot += ( - np.trapz(N_z * dVdz, x=z_arr) - * 4.0 - * np.pi - * self.survey.fskytotal - * frac - ) + def P_Yo_vec(self, LgY, Ynoise, marr, z, params): - return Ntot + if self.theorypred['md_hmf'] != self.theorypred['md_ym']: + marr_ymmd = convert_masses(self, marr, z) + else: + marr_ymmd = marr + if self.theorypred['md_ym'] != '500c': + marr_500c = get_m500c(self, marr, z) + else: + marr_500c = marr_ymmd - def _test_n_tot(self, **kwargs): + Y = np.exp(LgY).T + Ytilde = get_y0(self, marr_ymmd, z, marr_500c, use_Q=True, Ez_interp=False, **params) - HMF = self._get_HMF() - # param_vals = self._get_param_vals(**kwargs) - # Ez_fn = self._get_Ez_interpolator() - # DA_fn = self._get_DAz_interpolator() + Y = np.repeat(Y[np.newaxis, ...], Ytilde.shape[0], axis=0) + Ytilde = np.repeat(Ytilde[:, np.newaxis, ...], Y.shape[1], axis=1) - z_arr = self.zarr + if self.selfunc['bias_handler'] == 'theory': + trueSNR = Ytilde / Ynoise[:, None, None, None] + opt_bias_corr_factor = _opt_bias_func(trueSNR, params['opt_bias_A'], params['opt_bias_B']) + Ytilde = Ytilde * opt_bias_corr_factor - h = self.theory.get_param("H0") / 100.0 + numer = -1.0 * (np.log(Y / Ytilde)) ** 2 - Ntot = 0 - dVdz = self._get_dVdz() - dn_dzdm = HMF.dn_dM(HMF.M, 500.0) * h**4.0 # getting rid of hs - # Test Mass function against Nemo. - Pfunc = 1.0 - N_z = np.trapz(dn_dzdm * Pfunc, dx=np.diff(HMF.M[:, None] / h, axis=0), axis=0) - Ntot = ( - np.trapz(N_z * dVdz, x=z_arr) - * 4.0 - * np.pi - * (600.0 / (4 * np.pi * (180 / np.pi) ** 2)) + ans = ( + 1.0 / (params["scatter_sz"] * np.sqrt(2 * np.pi)) * + np.exp(numer / (2.0 * params["scatter_sz"] ** 2)) ) + return ans - return Ntot + def P_of_gt_SN(self, LgY, marr, z, Ynoise, params): + + if params['scatter_sz'] == 0: + + if self.theorypred['md_hmf'] != self.theorypred['md_ym']: + marr_ymmd = convert_masses(self, marr, z) + else: + marr_ymmd = marr + if self.theorypred['md_ym'] != '500c': + marr_500c = get_m500c(self, marr, z) + else: + marr_500c = marr_ymmd + + Ytilde = get_y0(self, marr_ymmd, z, marr_500c, use_Q=True, Ez_interp=False, **params) + + qcut = np.outer(np.ones(np.shape(Ytilde)), self.qcut) + qcut_a = np.reshape(qcut, (Ytilde.shape[0], Ytilde.shape[1], Ytilde.shape[2])) + + Ynoise = np.outer(Ynoise, np.ones(np.shape(Ytilde[0, ...]))) + Ynoise_a = np.reshape(Ynoise, (Ytilde.shape[0], Ytilde.shape[1], Ytilde.shape[2])) + + if self.selfunc['bias_handler'] == 'theory': + trueSNR = Ytilde / Ynoise_a + opt_bias_corr_factor = _opt_bias_func(trueSNR, params['opt_bias_A'], params['opt_bias_B']) + Ytilde = Ytilde * opt_bias_corr_factor + + ans = np.nan_to_num(get_erf(Ytilde, Ynoise_a, qcut_a)).T + #ans = np.nan_to_num(get_stf(Ytilde, Ynoise_a, qcut_a)).T + + else: + + Y = np.exp(LgY) + + Y_a = np.repeat(Y[:, np.newaxis], np.shape(Ynoise), axis=1) + Ynoise_a = np.repeat(Ynoise[np.newaxis, :], np.shape(Y), axis=0) + + qcut = np.outer(np.ones(np.shape(Y_a)), self.qcut) + qcut_a = np.reshape(qcut, (Y_a.shape[0], Y_a.shape[1])) + + Yerf = get_erf(Y_a, Ynoise_a, qcut_a) + + sig_tr = np.outer(np.ones([marr.shape[0], z.shape[0]]), Yerf) + sig_thresh = np.reshape(sig_tr, (marr.shape[0], z.shape[0], Yerf.shape[0], Yerf.shape[1])) + + LgYa = np.outer(np.ones([marr.shape[0], z.shape[0]]), LgY) + LgYa2 = np.reshape(LgYa, (marr.shape[0], z.shape[0], len(LgY))) + + # replace nan with 0's: + P_Y = np.nan_to_num(self.P_Yo_vec(LgYa2, Ynoise, marr, z, params).T) + + # truncation for debiasing: + qtab_filter = Y_a / Ynoise_a + qtab_filter[qtab_filter < self.selfunc['debias_cutoff']] = 0. + qtab_filter[qtab_filter >= self.selfunc['debias_cutoff']] = 1. + # end truncation for debiasing. + + ans = integrate.simpson(P_Y * sig_thresh * qtab_filter[None, None, ...], x=LgY, axis=2) #* np.log(10) # why log10? + + return ans + + def PfuncY(self, Ynoise, marr, z, params): + LgY = self.lny + P_func = np.outer(marr, np.zeros([len(z)])) + P_func = self.P_of_gt_SN(LgY, marr, z, Ynoise, params) + return P_func + + def Y_prob(self, Y_c, LgY, Ynoise): + Y = np.exp(LgY) + ans = gaussian(Y, Y_c, Ynoise) + return ans + + def Pfunc_per(self, tile_index, marr, z, Y_c, Y_err, params): + + Ynoise = self.noise + + if params["scatter_sz"] == 0: + if self.theorypred['md_hmf'] != self.theorypred['md_ym']: + marr_ymmd = convert_masses(self, marr, z) + else: + marr_ymmd = marr + if self.theorypred['md_ym'] != '500c': + marr_500c = get_m500c(self, marr, z) + else: + marr_500c = marr_ymmd + + Ytilde = get_y0(self, marr_ymmd, z, marr_500c, use_Q=True, Ez_interp=True, tile_index=tile_index, **params) + # LgYtilde = np.log(Ytilde) + # P_Y_sig = np.nan_to_num(self.Y_prob(Y_c[:, None], LgYtilde, Y_err[:, None])) + # ans = P_Y_sig + + if self.selfunc['bias_handler'] == 'theory': + + Ytilde = np.repeat(Ytilde[..., np.newaxis], Ynoise.shape[-1], axis=2) + + Ynoise = np.outer(Ynoise, np.ones(np.shape(Ytilde[..., 0]))) + Ynoise_a = np.reshape(Ynoise, (Ytilde.shape[0], Ytilde.shape[1], Ytilde.shape[2])) + + trueSNR = Ytilde / Ynoise_a + opt_bias_corr_factor = _opt_bias_func(trueSNR, params['opt_bias_A'], params['opt_bias_B']) + Ytilde = Ytilde * opt_bias_corr_factor + + ans = np.nan_to_num(get_erf(Ytilde, Y_err[:, None, None], self.qcut)) + + else: + ans = np.nan_to_num(get_erf(Ytilde, Y_err[:, None], self.qcut)) + else: + LgY = self.lny + Y = np.exp(LgY) + LgYa = np.outer(np.ones(len(marr)), LgY) + + P_Y_sig = np.nan_to_num(get_erf(Y, Y_err[:, None], self.qcut)) + #P_Y_sig = self.Y_prob(Y_c[:, None], LgY, Y_err[:, None]) + + if self.selfunc['bias_handler'] == 'theory': + + P_Y = np.nan_to_num(self.P_Yo(tile_index, LgYa, Ynoise, marr, z, params)) + + P_Y_sig = np.repeat(P_Y_sig[:, np.newaxis, :], P_Y.shape[1], axis=1) + LgY = LgY[None, None, :] + + P_Y_sig = np.repeat(P_Y_sig[..., np.newaxis], Ynoise.shape[0], axis=3) + LgY = np.repeat(LgY[..., np.newaxis], Ynoise.shape[0], axis=3) + ans = integrate.simpson(P_Y * P_Y_sig, LgY, np.diff(LgY), axis=2) + + else: + P_Y = np.nan_to_num(self.P_Yo(tile_index, LgYa, Ynoise, marr, z, params)) + P_Y_sig = np.repeat(P_Y_sig[:, np.newaxis, :], P_Y.shape[1], axis=1) + LgY = LgY[None, None, :] + ans = integrate.simpson(P_Y * P_Y_sig, LgY, np.diff(LgY), axis=2) + + return ans + + + def logp(self, **kwargs): + if self.theorypred['choose_theory'] == 'classy_sz': + szcounts = self.provider.get_sz_unbinned_cluster_counts() + # print('Ntot,lnlik,rates:',szcounts[1],szcounts[0],np.shape(szcounts[2]),szcounts[2]) + return szcounts[0] + else: + pk_intp = self.provider.get_Pk_interpolator() + rate_densities = self._get_rate_fn(pk_intp, **kwargs) + n_expected = self._get_n_expected(pk_intp, **kwargs) + return self.data.loglike(rate_densities, n_expected) + +def initialize_common(self): + self.log = logging.getLogger(self.name) + handler = logging.StreamHandler() + self.log.addHandler(handler) + self.log.propagate = False + if self.verbose: + self.log.setLevel(logging.INFO) + else: + self.log.setLevel(logging.ERROR) + self.log.info('Initializing clusters.py ' + self.name) + + self.qcut = self.selfunc['SNRcut'] + self.datafile = os.path.abspath(self.data['cat_file']) + self.selfn_dir = os.path.abspath(self.data['selfn_path']) + if 'footprint' in self.data.keys(): + self.footprint = self.data['footprint'] + self.log.info('Footprint = {}.'.format(self.footprint)) + else: + self.footprint = None + + if self.selfunc['method'] == 'SNRbased': + self.log.info('Running SNR based selection function.') + elif self.selfunc['method'] == 'injection': + self.log.info('Running injection based selection function.') + else: + print('please choose the method : SNRbased or injection') + exit(0) + + if self.selfunc['whichQ'] == 'fit': + self.log.info('Using Qfit data.') + elif self.selfunc['whichQ'] == 'injection': + self.log.info('Using averaged Q from source injection.') + # Qsource only provides the average + else: + print('please choose the Q data : Qfit or injection') + exit(0) + + if self.selfunc['resolution'] == 'full': + self.log.info('Running completeness with full selection function inputs. No downsampling.') + elif self.selfunc['resolution'] == 'downsample': + assert self.selfunc['dwnsmpl_bins'] is not None, 'resolution = downsample but no bin number given. Aborting.' + self.log.info('Running completeness with down-sampled selection function inputs.') + + cat_tab = atpy.Table().read(self.datafile) + if self.footprint is not None: + cat_tab = cat_tab[cat_tab['footprint_{}'.format(self.footprint)] == True] + zcat = cat_tab['redshift'].data.astype(float) + qcat = cat_tab['fixed_SNR'].data.astype(float) + cat_tsz_signal = cat_tab['fixed_y_c'].data.astype(float) + cat_tsz_signal_err = cat_tab['fixed_err_y_c'].data.astype(float) + cat_tile_name = np.array(cat_tab['tileName'].data, dtype = str) + + # #----------------------- zcut + keep = zcat < self.binning['z']['zmax'] + qcat = qcat[keep] + cat_tsz_signal = cat_tsz_signal[keep] + cat_tsz_signal_err = cat_tsz_signal_err[keep] + cat_tile_name = cat_tile_name[keep] + zcat = zcat[keep] + # #----------------------- zcut + + + # #----------------------- split + # if self.selfunc['split'] == 'east': + # RAcat = cat_tab['RADeg'].data.astype(float) + # keep = RAcat/15. > 12. + # zcat = zcat[keep] + # qcat = qcat[keep] + # cat_tsz_signal = cat_tsz_signal[keep] + # cat_tsz_signal_err = cat_tsz_signal_err[keep] + # cat_tile_name = cat_tile_name[keep] + # #----------------------- split + + # Optimization bias handler + if self.selfunc['bias_handler'] not in ['theory', 'catalog']: + raise NotImplementedError('bias_handler should be either "theory" or "catalog"') + if self.selfunc['bias_handler'] == 'catalog': + debiasDOF = self.selfunc['debiasDOF'] + qcat = np.sqrt(np.power(qcat, 2) - debiasDOF) + + # only above given SNR cut + ind = np.where(qcat >= self.qcut)[0] + self.z_cat = zcat[ind] + self.q_cat = qcat[ind] + self.cat_tsz_signal = cat_tsz_signal[ind] + self.cat_tsz_signal_err = cat_tsz_signal_err[ind] + self.cat_tile_name = cat_tile_name[ind] + + self.N_cat = len(self.q_cat) + self.log.info('Total number of clusters in catalogue = {}.'.format(len(zcat))) + self.log.info('SNR cut = {}.'.format(self.qcut)) + self.log.info('Number of clusters above the SNR cut = {}.'.format(self.N_cat)) + self.log.info('The lowest redshift = {:.2f}'.format(self.z_cat.min())) + self.log.info('The highest redshift = {:.2f}'.format(self.z_cat.max())) + self.log.info('The lowest SNR = {:.2f}.'.format(self.q_cat.min())) + self.log.info('The highest SNR = {:.2f}.'.format(self.q_cat.max())) + + if self.z_cat.max() > self.binning['z']['zmax']: + print("Maximum redshift from catalogue is out of given redshift range. Please widen the redshift range for prediction.") + exit(0) + + # redshift bins for N(z) + self.zbins = np.arange(self.binning['z']['zmin'], self.binning['z']['zmax'] + self.binning['z']['dz'], self.binning['z']['dz']) + self.zarr = 0.5*(self.zbins[:-1] + self.zbins[1:]) + + self.lnmmin = np.log(self.binning['M']['Mmin']) + self.lnmmax = np.log(self.binning['M']['Mmax']) + self.dlnm = self.binning['M']['dlogM'] + self.lnmarr = np.arange(self.lnmmin+(self.dlnm/2.), self.lnmmax, self.dlnm) + + # Ytrue bins if scatter != 0: + lnymin = -14. # ln(1e-6) = -13.8 + lnymax = -5. # ln(1e-2.5) = -5.7 + dlny = 0.2 + lnybins = np.arange(lnymin, lnymax, dlny) + self.lny = 0.5*(lnybins[:-1] + lnybins[1:]) + + # this is to be consist with szcounts.f90 + self.k = np.logspace(-4, np.log10(4), 200, endpoint=False) + + if self.footprint is None: + self.datafile_rms = self.selfn_dir + os.path.sep + "RMSTab.fits" + else: + self.datafile_rms = self.selfn_dir + os.path.sep + "RMSTab_%s.fits" % (self.footprint) + self.datafile_Q = self.selfn_dir + os.path.sep + "QFit.fits" + + # We need to get rid of the below some how + self.datafile_tile = self.selfn_dir + os.path.sep + "tileAreas.txt" + + with fits.open(self.datafile_rms) as in_file: + file_rms = in_file[1].data + + if self.selfunc['resolution'] == 'downsample': + + filename_Q, ext = os.path.splitext(self.datafile_Q) + datafile_Q_dwsmpld = filename_Q + \ + 'dwsmpld_nbins={}'.format(self.selfunc['dwnsmpl_bins']) + '.npz' + + if os.path.exists(datafile_Q_dwsmpld): + Qfile = np.load(datafile_Q_dwsmpld) + self.Q = Qfile['Q_dwsmpld'] + self.tt500 = Qfile['tt500'] + self.log.info("Down-sampled Q funcs exists. Number of Q funcs = {}.".format(len(self.Q[0]))) + + else: + self.log.info("Reading in full Q function.") + + # Old - we want to ultimately remove + # tile_info = np.genfromtxt(self.datafile_tile, dtype=str) + # tile_area0 = tile_info[:, 1] + # zero_index = np.where(tile_area0 == '0.000000')[0] + # tile_area = np.delete(tile_info, zero_index, 0) + # tile_name = tile_area[:, 0] + + tile_name = np.unique(file_rms['tileName']) + QFit = nm.signals.QFit(QSource=self.selfunc['whichQ'], selFnDir=self.selfn_dir, + tileNames = tile_name) + Nt = len(tile_name) + self.log.info("Number of tiles = {}.".format(Nt)) + self.tname = np.array(file_rms['tileName'], dtype = str) # Avoids potential chararray weirdness + + with fits.open(self.datafile_Q) as hdulist: + data = hdulist[1].data + tt500 = data.field("theta500Arcmin") + + # reading in all Q functions + allQ = np.zeros((len(tt500), Nt)) + for i in range(Nt): + allQ[:, i] = QFit.getQ(tt500, tileName=tile_name[i]) + assert len(tt500) == len(allQ[:, 0]) + self.tt500 = tt500 + self.Q = allQ + + filename_rms, ext = os.path.splitext(self.datafile_rms) + filename_tile, ext = os.path.splitext(self.datafile_tile) + datafile_rms_dwsmpld = filename_rms + \ + 'dwsmpld_nbins={}'.format(self.selfunc['dwnsmpl_bins']) + '.npz' + datafile_tiles_dwsmpld = filename_tile + \ + 'dwsmpld_nbins={}'.format(self.selfunc['dwnsmpl_bins']) + '.npy' + + if os.path.exists(datafile_rms_dwsmpld): + rms = np.load(datafile_rms_dwsmpld) + self.noise = rms['noise'] + self.skyfracs = rms['skyfracs'] + self.log.info("Down-sampled RMS table exists. Number of RMS bins = {}.".format(self.skyfracs.size)) + + self.tiles_dwnsmpld = np.load(datafile_tiles_dwsmpld, allow_pickle='TRUE').item() + + else: + + self.log.info("Reading in full RMS table.") + + self.noise = file_rms['y0RMS'] + + # print(self.noise.min(), self.noise.max()) # 5.5760333e-06 4.0576047e-05 + + self.skyfracs = file_rms['areaDeg2'] * np.deg2rad(1.) ** 2 + self.log.info("Number of RMS values = {}.".format(self.skyfracs.size)) + self.log.info("Down-sampling RMS and Q function using {} bins.".format(self.selfunc['dwnsmpl_bins'])) + binned_stat = stats.binned_statistic(self.noise, self.skyfracs, statistic='sum', + bins=self.selfunc['dwnsmpl_bins']) + binned_area = binned_stat[0] + binned_rms_edges = binned_stat[1] + + bin_ind = np.digitize(self.noise, binned_rms_edges) + tiledict = dict(zip(tile_name, np.arange(tile_name.shape[0]))) + + Qdwnsmpld = np.zeros((self.Q.shape[0], self.selfunc['dwnsmpl_bins'])) + tiles_dwnsmpld = {} + + for i in range(self.selfunc['dwnsmpl_bins']): + tempind = np.where(bin_ind == i + 1)[0] + if len(tempind) == 0: + #self.log.info('Found empty bin.') + Qdwnsmpld[:, i] = np.zeros(self.Q.shape[0]) + else: + #print('dowsampled rms bin ',i) + temparea = self.skyfracs[tempind] + #print('areas of tiles in bin',temparea) + temptiles = self.tname[tempind] + #print('names of tiles in bin',temptiles) + for t in temptiles: + tiles_dwnsmpld[t] = i + + test = [tiledict[key] for key in temptiles] + Qdwnsmpld[:, i] = np.average(self.Q[:, test], axis=1, weights=temparea) + + self.noise = 0.5*(binned_rms_edges[:-1] + binned_rms_edges[1:]) + self.skyfracs = binned_area + self.Q = Qdwnsmpld + self.tiles_dwnsmpld = tiles_dwnsmpld + + if 'override_noise' in self.selfunc.keys(): + self.log.info("Overriden noise") + self.noise[:]=self.selfunc['override_noise'] + + if 'force_Q_equals_1' in self.selfunc.keys() and self.selfunc['force_Q_equals_1'] == True: + self.log.info("Forced Q == 1") + self.Q[:] = 1.0 + + + self.log.info("Number of down-sampled RMS = {}.".format(self.skyfracs.size)) + self.log.info("Number of down-sampled Q funcs = {}.".format(len(self.Q[0]))) + + assert self.noise.shape[0] == self.skyfracs.shape[0] and self.noise.shape[0] == self.Q.shape[1] + + if self.selfunc['save_dwsmpld']: + np.savez(datafile_Q_dwsmpld, Q_dwsmpld=Qdwnsmpld, tt500=self.tt500) + np.savez(datafile_rms_dwsmpld, noise=self.noise, skyfracs=self.skyfracs) + np.save(datafile_tiles_dwsmpld, self.tiles_dwnsmpld) + + + elif self.selfunc['resolution'] == 'full': + + self.log.info('Reading in full Q function.') + tile_info = np.genfromtxt(os.path.join(self.data_directory, self.data['tile_file']), dtype=str) + + # removing tiles with zero areas + tile_area0 = tile_info[:, 1] + zero_index = np.where(tile_area0 == '0.000000')[0] + tile_area = np.delete(tile_info, zero_index, 0) + + tile_name = tile_area[:, 0] + QFit = nm.signals.QFit(QFitFileName=os.path.join(self.data_directory, self.datafile_Q), + tileNames=tile_name, QSource=self.selfunc['whichQ'], selFnDir=self.data_directory+'/selFn') + Nt = len(tile_name) + self.log.info("Number of tiles = {}.".format(Nt)) + + hdulist = fits.open(os.path.join(self.data_directory, self.datafile_Q)) + data = hdulist[1].data + tt500 = data.field("theta500Arcmin") + + # reading in all Q functions + allQ = np.zeros((len(tt500), Nt)) + for i in range(Nt): + allQ[:, i] = QFit.getQ(tt500, tileName=tile_name[i]) + assert len(tt500) == len(allQ[:, 0]) + self.tt500 = tt500 + self.Q = allQ + + # when using full Q functions, noise values should be downsampled + # in a current setting, the number of noise values has to be same as the number of Q funcs + # hence the number of tiles - they are averaged by each tile + + self.log.info("Reading in full RMS table.") + self.log.info("Number of RMS values = {}.".format(len(file_rms['y0RMS']))) + self.log.info("Down-sampling RMS using {} bins.".format(Nt)) + + self.tname = file_rms['tileName'] + + noisebyTile = {} + areabyTile = {} + for t in tile_name: + tileTab = file_rms[self.tname == t] + areaWeights = tileTab['areaDeg2'] / tileTab['areaDeg2'].sum() + noisebyTile[t] = np.average(tileTab['y0RMS'], weights=areaWeights) + areabyTile[t] = tileTab['areaDeg2'].sum() + + self.noise = np.array([noisebyTile[t] for t in tile_name]) + self.skyfracs = np.array([areabyTile[t] for t in tile_name]) * np.deg2rad(1.)**2 + + self.log.info("Number of down-sampled RMS = {}.".format(self.skyfracs.size)) + self.log.info("Number of Q funcs = {}.".format(len(self.Q[0]))) + + assert self.noise.shape[0] == self.skyfracs.shape[0] and self.noise.shape[0] == self.Q.shape[1] + + + # choosing tile ---------------------------------------------------- + + if self.selfunc['tiletest'] == True: + + tile_index = 0 + # tile_index = slice(120, 123, None) + print('Name of tile : ', tile_name[tile_index]) + + self.Q = self.Q[:, tile_index] + self.Q = self.Q[:, None] + self.noise = np.array([self.noise[tile_index]]) + self.skyfracs = np.array([self.skyfracs[tile_index]]) + # self.noise = self.noise[tile_index] + # self.skyfracs = self.skyfracs[tile_index] + + #------------------------------------------------------------------- + + + if self.selfunc['method'] == 'injection': + + try: + self.compThetaInterpolator = selfunc.get_completess_inj_theta_y(self.data_directory, self.qcut, self.qbins) + except: + self.compThetaInterpolator = selfunc.get_completess_inj_theta_y_unb(self.data_directory, self.qcut) + + + self.log.info('Entire survey area = {} deg2.'.format(self.skyfracs.sum()/(np.deg2rad(1.)**2.))) + + +def get_requirements(self): + if self.theorypred['choose_theory'] == "camb": + req = {"Hubble": {"z": self.zz}, + "angular_diameter_distance": {"z": self.zz}, + "H0": None, #NB H0 is derived + "Pk_interpolator": {"z": np.linspace(0, 3., 140), # should be less than 150 + "k_max": 4.0, + "nonlinear": False, + "hubble_units": False, # CLASS doesn't like this + "k_hunit": False, # CLASS doesn't like this + "vars_pairs": [["delta_nonu", "delta_nonu"]]}} + elif self.theorypred['choose_theory'] == "class": + req = {"Hubble": {"z": self.zz}, + "angular_diameter_distance": {"z": self.zz}, + "Pk_interpolator": {"z": np.linspace(0, 3., 100), # should be less than 110 + "k_max": 4.0, + "nonlinear": False, + "vars_pairs": [["delta_nonu", "delta_nonu"]]}} + elif self.theorypred['choose_theory'] == 'CCL': + req = {'CCL': {}, + 'nc_data': {}, + 'Hubble': {'z': self.zz}, + 'angular_diameter_distance': {'z': self.zz}, + 'Pk_interpolator': {}, + 'H0': None #NB H0 is derived + } + elif self.theorypred['choose_theory'] == 'classy_sz': + req = {} + else: + raise NotImplementedError('Only theory modules camb, class and CCL implemented so far.') + return req + +def get_Ez(both, zarr, Ez_interp): + if Ez_interp: # interpolation is needed for Pfunc_per in unbinned + Ez = interp1d(both.zz, both.provider.get_Hubble(both.zz) / both.provider.get_param("H0")) + return Ez(zarr) + else: + return both.provider.get_Hubble(zarr) / both.provider.get_param("H0") + +def get_om(both): + if both.theorypred['choose_theory'] == "camb": + om = (both.provider.get_param("omch2") + both.provider.get_param("ombh2") + + both.provider.get_param("omnuh2"))/((both.provider.get_param("H0")/100.0)**2) + elif both.theorypred['choose_theory'] == "class": + om = (both.provider.get_param("omega_cdm") + + both.provider.get_param("omega_b"))/((both.provider.get_param("H0")/100.0)**2) # for CLASS + else: + print('please specify theory: camb/class') + exit(0) + return om + +def get_dVdz(both, zarr, dVdz_interp): + """dV/dzdOmega""" + + if dVdz_interp: + Da_intp = interp1d(both.zz, both.provider.get_angular_diameter_distance(both.zz)) + DA_z = Da_intp(zarr) + H_intp = interp1d(both.zz, both.provider.get_Hubble(both.zz)) + H_z = H_intp(zarr) + else: + DA_z = both.provider.get_angular_diameter_distance(zarr) + H_z = both.provider.get_Hubble(zarr) + + dV_dz = ( + DA_z**2 + * (1.0 + zarr) ** 2 + / (H_z / C_KM_S) + ) + h = both.provider.get_param("H0") / 100.0 + return dV_dz*h**3 + +def get_dndlnm(self, z, pk_intp): + + marr = self.lnmarr # Mass in units of Msun/h + + if self.theorypred['massfunc_mode'] == 'internal': + h = self.provider.get_param("H0")/100.0 + Ez = get_Ez(self,z) + + om = get_om(self) + rhocrit0 = rho_crit0H100 # [h2 msun Mpc-3] + + rhom0 = rhocrit0 * om + + # redshift bin for P(z,k) + zpk = np.linspace(0, 3., 200) + if zpk[0] == 0.: + zpk[0] = 1e-5 + + k = self.k #np.logspace(-4, np.log10(4), 200, endpoint=False) + pks0 = pk_intp.P(zpk, k) + + def pks_zbins(newz): + newp = np.zeros((len(newz),len(k))) + for i in range(k.size): + tck = interpolate.splrep(zpk, pks0[:,i]) + newp[:,i] = interpolate.splev(newz, tck) + return newp + + # rebin + pks = pks_zbins(z) + + pks *= h**3. + kh = k/h + + def radius(M): # R in units of Mpc/h + return (0.75*M/np.pi/rhom0)**(1./3.) + + def win(x): + return 3.*(np.sin(x) - x*np.cos(x))/(x**3.) + + def win_prime(x): + return 3.*np.sin(x)/(x**2.) - 9.*(np.sin(x) - x*np.cos(x))/(x**4.) + + def sigma_sq(R, k): + integral = np.zeros((len(k), len(marr), len(z))) + for i in range(k.size): + integral[i,:,:] = np.array((k[i]**2.)*pks[:,i]*(win(k[i]*R)**2.)) + return integrate.simps(integral, k, axis=0)/(2.*np.pi**2.) + + def sigma_sq_prime(R, k): + # this is derivative of sigmaR squared + # so 2 * sigmaR * dsigmaR/dR + integral = np.zeros((len(k), len(marr), len(z))) + for i in range(k.size): + integral[i,:,:] = np.array((k[i]**2.)*pks[:,i]*2.*k[i]*win(k[i]*R)*win_prime(k[i]*R)) + return integrate.simps(integral, k, axis=0)/(2.*np.pi**2.) + + def tinker(sgm, z): + + total = 9 + delta = np.zeros(total) + par_aa = np.zeros(total) + par_a = np.zeros(total) + par_b = np.zeros(total) + par_c = np.zeros(total) + + delta[0] = 200 + delta[1] = 300 + delta[2] = 400 + delta[3] = 600 + delta[4] = 800 + delta[5] = 1200 + delta[6] = 1600 + delta[7] = 2400 + delta[8] = 3200 + + par_aa[0] = 0.186 + par_aa[1] = 0.200 + par_aa[2] = 0.212 + par_aa[3] = 0.218 + par_aa[4] = 0.248 + par_aa[5] = 0.255 + par_aa[6] = 0.260 + par_aa[7] = 0.260 + par_aa[8] = 0.260 + + par_a[0] = 1.47 + par_a[1] = 1.52 + par_a[2] = 1.56 + par_a[3] = 1.61 + par_a[4] = 1.87 + par_a[5] = 2.13 + par_a[6] = 2.30 + par_a[7] = 2.53 + par_a[8] = 2.66 + + par_b[0] = 2.57 + par_b[1] = 2.25 + par_b[2] = 2.05 + par_b[3] = 1.87 + par_b[4] = 1.59 + par_b[5] = 1.51 + par_b[6] = 1.46 + par_b[7] = 1.44 + par_b[8] = 1.41 + + par_c[0] = 1.19 + par_c[1] = 1.27 + par_c[2] = 1.34 + par_c[3] = 1.45 + par_c[4] = 1.58 + par_c[5] = 1.80 + par_c[6] = 1.97 + par_c[7] = 2.24 + par_c[8] = 2.44 + + delta = np.log10(delta) + omz = om*((1. + z)**3.)/(Ez**2.) + + if self.theorypred['md_hmf'] == '500c': + dsoz = 500./omz # M500c + elif self.theorypred['md_hmf'] == '200m': + dsoz = 200 # M200m + else: + raise NotImplementedError() + + tck1 = interpolate.splrep(delta, par_aa) + tck2 = interpolate.splrep(delta, par_a) + tck3 = interpolate.splrep(delta, par_b) + tck4 = interpolate.splrep(delta, par_c) + + par1 = interpolate.splev(np.log10(dsoz), tck1) + par2 = interpolate.splev(np.log10(dsoz), tck2) + par3 = interpolate.splev(np.log10(dsoz), tck3) + par4 = interpolate.splev(np.log10(dsoz), tck4) + + alpha = 10.**(-((0.75/np.log10(dsoz/75.))**1.2)) + A = par1*((1. + z)**(-0.14)) + a = par2*((1. + z)**(-0.06)) + b = par3*((1. + z)**(-alpha)) + c = par4*np.ones(z.size) + + return A * (1. + (sgm/b)**(-a)) * np.exp(-c/(sgm**2.)) + + dRdM = radius(np.exp(marr)) / (3. * np.exp(marr)) + dRdM = dRdM[:, None] + R = radius(np.exp(marr))[:, None] + sigma = sigma_sq(R, kh) ** 0.5 + sigma_prime = sigma_sq_prime(R, kh) + hmf_internal = -rhom0 * tinker(sigma, z) * dRdM * (sigma_prime / (2. * sigma ** 2.)) + return hmf_internal + + elif self.theorypred['massfunc_mode'] == 'ccl': + # First, gather all the necessary ingredients for the number counts + mf = self.provider.get_nc_data()['HMF'] + cosmo = self.provider.get_CCL()['cosmo'] + + h = self.provider.get_param("H0") / 100.0 + a = 1./(1+z) + marr = np.exp(marr) + dn_dlog10M = np.array([mf(cosmo, marr/h, ai) for ai in a]) + # For consistency with internal mass function computation + dn_dlog10M /= h**3 * np.log(10.) + + return dn_dlog10M.T + + # elif self.theorypred['massfunc_mode'] == 'class_sz': + # return self.get_dndlnM_at_z_and_M(z,marr) + + +def get_erf(y, noise, cut, dof=0): + #arg = (y - cut*noise)/np.sqrt(2.)/noise + arg = (np.sqrt((y/noise)**2.+dof) - cut)/np.sqrt(2.) + erfc = (special.erf(arg) + 1.)/2. + return erfc + +# def get_stf(y, noise, qcut): +# ans = y * 0.0 +# ans[y - qcut*noise > 0] = 1.0 +# return ans + +def get_erf_diff(y, noise, qmin, qmax, qcut, dof=0): + arg1 = (np.sqrt((y/noise)**2.+dof) - qmax)/np.sqrt(2.) + if qmin > qcut: + qlim = qmin + else: + qlim = qcut + arg2 = (np.sqrt((y/noise)**2.+dof) - qlim)/np.sqrt(2.) + erf_compl = (special.erf(arg2) - special.erf(arg1)) / 2. + # HACK: 100% completeness above S/N limit, for noiseless data vector - doesn't actually help + # erf_compl[erf_compl > 0]=1.0 + return erf_compl + +# def get_stf_diff(y, noise, qmin, qmax, qcut): +# if qmin > qcut: +# qmin = qmin +# else: +# qmin = qcut +# ans = y * 0.0 +# ans[(y - qmin*noise > 0) & (y - qmax*noise < 0)] = 1.0 +# return ans + +def get_erf_prod(y, noise, qmin, qmax, qcut, k, Nq, dof = 0): + + arg0 = get_erf(y, noise, qcut, dof) + arg1 = get_erf(y, noise, qmin, dof) + arg2 = 1. - get_erf(y, noise, qmax, dof) + + if k == 0: arg1 = 1 + if k == Nq-1: arg2 = 1 + + return arg0 * arg1 * arg2 +# +# def get_stf_prod(y, noise, qmin, qmax, qcut, k, Nq): +# +# arg0 = get_stf(y, noise, qcut) +# arg1 = get_stf(y, noise, qmin) +# arg2 = 1. - get_stf(y, noise, qmax) +# +# if k == 0: arg1 = 1 +# if k == Nq-1: arg2 = 1 +# +# return arg0 * arg1 * arg2 + +def gaussian(xx, mu, sig, noNorm=False): + if noNorm: + return np.exp(-1.0 * (xx - mu) ** 2 / (2.0 * sig ** 2.0)) + else: + return 1.0 / (sig * np.sqrt(2 * np.pi)) \ + * np.exp(-1.0 * (xx - mu) ** 2 / (2.0 * sig ** 2.0)) + +def convert_masses(both, marr, zz): + + h = both.provider.get_param("H0") / 100.0 + if both.theorypred['choose_theory'] == 'CCL': + mf_data = both.provider.get_nc_data() + md_hmf = mf_data['md'] + + if both.theorypred['md_ym'] == '200m': + md_ym = ccl.halos.MassDef(200, 'matter') + elif both.theorypred['md_ym'] == '200c': + md_ym = ccl.halos.MassDef(200, 'critical') + elif both.theorypred['md_ym'] == '500c': + md_ym = ccl.halos.MassDef(500, 'critical') + else: + raise NotImplementedError('Only md_hmf = 200m, 200c and 500c currently supported.') + cosmo = both.provider.get_CCL()['cosmo'] + a = 1. / (1. + zz) + mass_trans=ccl.halos.mass_translator(mass_in = md_hmf, mass_out = md_ym, concentration = 'Bhattacharya13') + marr_ymmd = np.array([mass_trans(cosmo, marr / h, ai) for ai in a]) * h + else: + if both.theorypred['md_hmf'] == '200m' and both.theorypred['md_ym'] == '500c': + marr_ymmd = both._get_M500c_from_M200m(marr, zz).T + else: + raise NotImplementedError() + return marr_ymmd + +def get_m500c(both, marr, zz): + + h = both.provider.get_param("H0") / 100.0 + mf_data = both.provider.get_nc_data() + md_hmf = mf_data['md'] + md_500c = ccl.halos.MassDef(500, 'critical') + cosmo = both.provider.get_CCL()['cosmo'] + a = 1. / (1. + zz) + + mass_trans=ccl.halos.mass_translator(mass_in = md_hmf, mass_out = md_500c, concentration = 'Bhattacharya13') + if a.ndim == 1: + marr_500c = np.array([mass_trans(cosmo, marr/h, ai) for ai in a]) * h + else: + marr_500c = mass_trans(cosmo, marr/h, a) * h + + return marr_500c + +def get_splQ(self, theta, tile_index=None): + + if self.selfunc['whichQ'] == 'injection': + # this is because injection Q is survey-wide averaged for now + + tck = interpolate.splrep(self.tt500, self.Q[:,0]) + newQ0 = interpolate.splev(theta, tck) + newQ = np.repeat(newQ0[np.newaxis,...], self.Q.shape[1], axis=0) + + if tile_index is not None: # for faster rate_fn in unbinned + + chosenQ = np.zeros((newQ.shape[1], newQ.shape[2])) + for i in range(len(tile_index)): + chosenQ[i, :] = newQ[tile_index[i], i, :] + newQ = chosenQ + + else: + newQ = [] + for i in range(len(self.Q[0])): + tck = interpolate.splrep(self.tt500, self.Q[:, i]) + newQ.append(interpolate.splev(theta, tck)) + + if tile_index is not None: # for faster rate_fn in unbinned + + newQ = np.array(newQ) + + chosenQ = np.zeros((newQ.shape[1], newQ.shape[2])) + for i in range(len(tile_index)): + chosenQ[i, :] = newQ[tile_index[i], i, :] + newQ = chosenQ + + return np.asarray(np.abs(newQ)) + +def get_theta(self, mass_500c, z, Ez=None, Ez_interp=False): + + thetastar = 6.997 + alpha_theta = 1. / 3. + H0 = self.provider.get_param("H0") + h = H0/100.0 + + if Ez is None: + Ez = get_Ez(self, z, Ez_interp) + Ez = Ez[:, None] + + DAz_interp = interp1d(self.zz , self.provider.get_angular_diameter_distance(self.zz) * h) + DAz = DAz_interp(z) + try: + DAz = DAz[:, None] + except: + DAz = DAz + ttstar = thetastar * (H0 / 70.) ** (-2. / 3.) + + return ttstar * (mass_500c / MPIVOT_THETA / h) ** alpha_theta * Ez ** (-2. / 3.) * (100. * DAz / 500 / H0) ** (-1.) + +# y-m scaling relation for completeness +def get_y0(self, mass, z, mass_500c, use_Q=True, Ez_interp=False, tile_index=None, **params): + + A0 = params["tenToA0"] + B0 = params["B0"] + C0 = params["C0"] + bias = params["bias_sz"] + + Ez = get_Ez(self, z, Ez_interp) + try: + Ez = Ez[:, None] + except: + Ez = Ez + + h = self.provider.get_param("H0") / 100.0 + + mb = mass * bias + mb_500c = mass_500c * bias + + Mpivot = self.YM['Mpivot'] * h # convert to Msun/h. + + def rel(m): + if self.theorypred['rel_correction']: + t = -0.008488*(mm*Ez)**(-0.585) + res = 1.+ 3.79*t - 28.2*(t**2.) + else: + res = 1. + return res + + if use_Q is True: + theta = get_theta(self, mb_500c, z, Ez) + splQ = get_splQ(self, theta, tile_index) + else: + splQ = 1. + + y0 = A0 * (Ez ** C0) * (mb / Mpivot) ** (1. + B0) * splQ + y0[y0 <= 0] = 1e-9 + + return y0 + +def _opt_bias_func(snr, A, B): + """Return optimization bias correction factor - multiply true y0 by this to get what the cluster finder recovers """ + + corr = 1. + A/snr + B/(snr**2) + corr[snr < 2.0] = 1.0 + + return corr \ No newline at end of file diff --git a/soliket/clusters/data/ACTPol_Cond_scatv5.fits b/soliket/clusters/data/ACTPol_Cond_scatv5.fits deleted file mode 100644 index b48f2dc0..00000000 Binary files a/soliket/clusters/data/ACTPol_Cond_scatv5.fits and /dev/null differ diff --git a/soliket/clusters/data/E-D56Clusters.fits b/soliket/clusters/data/E-D56Clusters.fits deleted file mode 100644 index 3ffcba81..00000000 --- a/soliket/clusters/data/E-D56Clusters.fits +++ /dev/null @@ -1,165 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 348 / length of dimension 1 NAXIS2 = 182 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 40 / number of table fields TTYPE1 = 'name ' TFORM1 = '19A ' TTYPE2 = 'RADeg ' TFORM2 = 'D ' TTYPE3 = 'decDeg ' TFORM3 = 'D ' TTYPE4 = 'SNR ' TFORM4 = 'D ' TTYPE5 = 'SNR2p4 ' TFORM5 = 'D ' TTYPE6 = 'y0tilde ' TFORM6 = 'D ' TTYPE7 = 'y0tilde_err' TFORM7 = 'D ' TTYPE8 = 'H13Match' TFORM8 = 'K ' TTYPE9 = 'PSZ2Match' TFORM9 = 'K ' TTYPE10 = 'RMMatch ' TFORM10 = 'K ' TTYPE11 = 'AltName ' TFORM11 = '29A ' TTYPE12 = 'BCG_RADeg' TFORM12 = 'D ' TTYPE13 = 'BCG_decDeg' TFORM13 = 'D ' TTYPE14 = 'z ' TFORM14 = 'D ' TTYPE15 = 'zErr ' TFORM15 = 'D ' TTYPE16 = 'zType ' TFORM16 = '4A ' TTYPE17 = 'zSource ' TFORM17 = '8A ' TTYPE18 = 'deltaSDSS' TFORM18 = 'D ' TTYPE19 = 'deltaSDSS_err' TFORM19 = 'D ' TTYPE20 = 'deltaS82' TFORM20 = 'D ' TTYPE21 = 'deltaS82_err' TFORM21 = 'D ' TTYPE22 = 'deltaCFHT' TFORM22 = 'D ' TTYPE23 = 'deltaCFHT_err' TFORM23 = 'D ' TTYPE24 = 'deltaSOAR' TFORM24 = 'D ' TTYPE25 = 'deltaSOAR_err' TFORM25 = 'D ' TTYPE26 = 'M500cUPP' TFORM26 = 'D ' TTYPE27 = 'M500cUPP_errPlus' TFORM27 = 'D ' TTYPE28 = 'M500cUPP_errMinus' TFORM28 = 'D ' TTYPE29 = 'M500cUnc' TFORM29 = 'D ' TTYPE30 = 'M500cUnc_errPlus' TFORM30 = 'D ' TTYPE31 = 'M500cUnc_errMinus' TFORM31 = 'D ' TTYPE32 = 'M200mUPP' TFORM32 = 'D ' TTYPE33 = 'M200mUPP_errPlus' TFORM33 = 'D ' TTYPE34 = 'M200mUPP_errMinus' TFORM34 = 'D ' TTYPE35 = 'M200mUnc' TFORM35 = 'D ' TTYPE36 = 'M200mUnc_errPlus' TFORM36 = 'D ' TTYPE37 = 'M200mUnc_errMinus' TFORM37 = 'D ' TTYPE38 = 'M500cCal' TFORM38 = 'D ' TTYPE39 = 'M500cCal_errPlus' TFORM39 = 'D ' TTYPE40 = 'M500cCal_errMinus' TFORM40 = 'D ' END ACT-CL J0001.4-0306?×@í$Ì`ÀЫM§‡@\. c;ú@cæ¾ét?寣f o?Å+Óz!™?×[}8BÀ°ÝKÄj?º¬1&éspecSDSS@Ò½Ó:2?ÎBÿ "ë@$…RÛ ;?êa|+tÑÿ?ãß© ÷Ñ%@¸3r–z?ñs}^c(?éËé»Ûe@ëé­*nA?úX|ùN?ó§Îíòû‘@•éÉ­„@[H¥€"?ù¨!¤p0@ Ÿjɨ’?õ¢ÈÐo?ñy¥¤z<ÿACT-CL J0003.1-0605?é“ã‡àŽÀYÙ_?:Ã@ òKC´¨f@ FãÕ½ •@4B\%ú?ÏÛ_UKƒñABELL 2697?é‹bqƒýÀ]熾†Ó?ÍÒñ©ûçmspecSDSS@+ÀüIØ?ë\µ2‰Ë @‰ˆðüv?ôöÒëQÿ?ñ%nÄa¿úQÝ­Å ‰?ï\(õÂ\?©™™™™™šphotzC_SOAR@2 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delN2Dcat, _, _ = np.histogram2d(catalog['redshift'], catalog[SNR_tag], bins=[zbins, qbins]) + + return delN2Dcat, zarr, qarr + + +def get_completess_inj_theta_y(pathdata, SNRCut, qbins): + + selFnDir = os.path.join(pathdata, 'selFn') + + # Stuff from the source injection sims (now required for completeness calculation) + injDataPath = selFnDir + os.path.sep + "sourceInjectionData.fits" + inputDataPath = selFnDir + os.path.sep + "sourceInjectionInputCatalog.fits" + if os.path.exists(injDataPath) == False or os.path.exists(inputDataPath) == False: + raise Exception( + "%s not found - run a source injection test to generate (now required for completeness calculations)." % ( + injDataPath)) + + theta500s, binCentres, compThetaGrid, thetaQ = _parseSourceInjectionData(injDataPath, inputDataPath, SNRCut, qbins) + nq = qbins.shape[0]-1 + compThetaInterpolator = [0 for i in range(nq)] + for i in range(nq): + compThetaInterpolator[i] = scipy.interpolate.RectBivariateSpline(theta500s, binCentres, compThetaGrid[i, :], kx=3, ky=3) + + return compThetaInterpolator + +def _parseSourceInjectionData(injDataPath, inputDataPath, SNRCut, qbins): + """Produce arrays for constructing interpolator objects from source injection test data. + Args: + injDataPath (:obj:`str`): Path to the output catalog produced by the source injection test. + inputDataPath (:obj:`str`): Path to the input catalog produced by the source injectio test. + SNRCut (:obj:`float`): Selection threshold in S/N to apply. + Returns: + theta500s, ycBinCentres, compThetaGrid, thetaQ + """ + + injTab= table.Table().read(injDataPath) + inputTab= table.Table().read(inputDataPath) + + # Completeness given y0 (NOT y0~) and theta500 and the S/N cut as 2D spline + # We also derive survey-averaged Q here from the injection sim results [for y0 -> y0~ mapping] + # NOTE: This is a survey-wide average, doesn't respect footprints at the moment + # NOTE: This will need re-thinking for evolving, non-self-similar models? + + nq = qbins.shape[0] - 1 + theta500s=np.unique(inputTab['theta500Arcmin']) + binEdges=np.linspace(inputTab['inFlux'].min(), inputTab['inFlux'].max(), 101) + binCentres=(binEdges[1:]+binEdges[:-1])/2 + compThetaGrid=np.zeros((nq, theta500s.shape[0], binCentres.shape[0])) + thetaQ=np.zeros(len(theta500s)) + + for i in range(len(theta500s)): + t = theta500s[i] + for ii in range(nq): + qmin = max(qbins[ii], SNRCut) + qmax = qbins[ii + 1] + + injMask = (injTab['theta500Arcmin'] == t)*(injTab['SNR'] > qmin)*(injTab['SNR'] < qmax) + inputMask=inputTab['theta500Arcmin'] == t + injFlux=injTab['inFlux'][injMask] + outFlux=injTab['outFlux'][injMask] + inputFlux=inputTab['inFlux'][inputMask] + recN, binEdges=np.histogram(injFlux, bins = binEdges) + inpN, binEdges=np.histogram(inputFlux, bins = binEdges) + valid=inpN > 0 + + compThetaGrid[ii, i][valid]=recN[valid]/inpN[valid] + + thetaQ[i]=np.nan_to_num(np.median(outFlux/injFlux)) + + return theta500s, binCentres, compThetaGrid, thetaQ + + + + +# unbinned test + +def get_completess_inj_theta_y_unb(pathdata, SNRCut): + + selFnDir = os.path.join(pathdata, 'selFn') + + # Stuff from the source injection sims (now required for completeness calculation) + injDataPath = selFnDir + os.path.sep + "sourceInjectionData.fits" + inputDataPath = selFnDir + os.path.sep + "sourceInjectionInputCatalog.fits" + if os.path.exists(injDataPath) == False or os.path.exists(inputDataPath) == False: + raise Exception( + "%s not found - run a source injection test to generate (now required for completeness calculations)." % ( + injDataPath)) + + theta500s, binCentres, compThetaGrid, thetaQ = _parseSourceInjectionData_unb(injDataPath, inputDataPath, SNRCut) + + compThetaInterpolator = scipy.interpolate.RectBivariateSpline(theta500s, binCentres, compThetaGrid, kx=3, ky=3) + + return compThetaInterpolator + +def _parseSourceInjectionData_unb(injDataPath, inputDataPath, SNRCut): + """Produce arrays for constructing interpolator objects from source injection test data. + Args: + injDataPath (:obj:`str`): Path to the output catalog produced by the source injection test. + inputDataPath (:obj:`str`): Path to the input catalog produced by the source injectio test. + SNRCut (:obj:`float`): Selection threshold in S/N to apply. + Returns: + theta500s, ycBinCentres, compThetaGrid, thetaQ + """ + + injTab= table.Table().read(injDataPath) + inputTab= table.Table().read(inputDataPath) + + # Completeness given y0 (NOT y0~) and theta500 and the S/N cut as 2D spline + # We also derive survey-averaged Q here from the injection sim results [for y0 -> y0~ mapping] + # NOTE: This is a survey-wide average, doesn't respect footprints at the moment + # NOTE: This will need re-thinking for evolving, non-self-similar models? + + theta500s=np.unique(inputTab['theta500Arcmin']) + binEdges=np.linspace(inputTab['inFlux'].min(), inputTab['inFlux'].max(), 101) + binCentres=(binEdges[1:]+binEdges[:-1])/2 + compThetaGrid=np.zeros((theta500s.shape[0], binCentres.shape[0])) + thetaQ=np.zeros(len(theta500s)) + + for i in range(len(theta500s)): + t = theta500s[i] + injMask=np.logical_and(injTab['theta500Arcmin'] == t, injTab['SNR'] > SNRCut) + inputMask=inputTab['theta500Arcmin'] == t + injFlux=injTab['inFlux'][injMask] + outFlux=injTab['outFlux'][injMask] + inputFlux=inputTab['inFlux'][inputMask] + recN, binEdges=np.histogram(injFlux, bins = binEdges) + inpN, binEdges=np.histogram(inputFlux, bins = binEdges) + valid=inpN > 0 + compThetaGrid[i][valid]=recN[valid]/inpN[valid] + thetaQ[i]=np.nan_to_num(np.median(outFlux/injFlux)) + + return theta500s, binCentres, compThetaGrid, thetaQ diff --git a/soliket/clusters/notebooks/CCL_nemo_vs_SOLikeT_binned_SNRbased_inj.ipynb b/soliket/clusters/notebooks/CCL_nemo_vs_SOLikeT_binned_SNRbased_inj.ipynb new file mode 100644 index 00000000..f883e285 --- /dev/null +++ b/soliket/clusters/notebooks/CCL_nemo_vs_SOLikeT_binned_SNRbased_inj.ipynb @@ -0,0 +1,1234 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c42b0b2f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from soliket import BinnedClusterLikelihood\n", + "from cobaya.model import get_model\n", + "import camb\n", + "from astropy.io import fits\n", + "from astropy import table\n", + "from astLib import astWCS\n", + "import math\n", + "from nemo import completeness, MockSurvey\n", + "\n", + "import sys\n", + "sys.path.append('../')\n", + "import nemo_mocks\n", + "import imp\n", + "imp.reload(nemo_mocks)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "72955ee8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing clusters.py Binned Clusters\n", + "Running SNR based selection function.\n", + "Using averaged Q from source injection.\n", + "Running completeness with down-sampled selection function inputs.\n", + "Total number of clusters in catalogue = 5738.\n", + "SNR cut = 5.0.\n", + "Number of clusters above the SNR cut = 3169.\n", + "The lowest redshift = 0.01\n", + "The highest redshift = 1.96\n", + "The lowest SNR = 5.00.\n", + "The highest SNR = 51.99.\n", + "Reading in full Q function.\n", + "Initial number of tiles = 280.\n", + "Number of tiles after removing the tiles with zero area = 264. \n", + "Reading in full RMS table.\n", + "Number of RMS values = 40672.\n", + "Down-sampling RMS and Q function using 100 bins.\n", + "Number of down-sampled RMS = 100.\n", + "Number of down-sampled Q funcs = 100.\n", + "Entire survey area = 13631.324739141015 deg2.\n", + "Number of redshift bins = 20.\n", + "Number of SNR bins = 6.\n", + "Number of redshift points for theory calculation = 200.\n", + "Number of mass points for theory calculation = 530.\n", + " Total predicted 2D N = 2914.866332304512\n", + "Number of clusters in redshift bin 0: 19.221338664465883.\n", + "Number of clusters in redshift bin 1: 303.66031427024393.\n", + "Number of clusters in redshift bin 2: 437.3405080937954.\n", + "Number of clusters in redshift bin 3: 456.85717205043443.\n", + "Number of clusters in redshift bin 4: 412.1891190946041.\n", + "Number of clusters in redshift bin 5: 343.04635461352007.\n", + "Number of clusters in redshift bin 6: 270.4412194904809.\n", + "Number of clusters in redshift bin 7: 203.83736665016897.\n", + "Number of clusters in redshift bin 8: 148.50011255777852.\n", + "Number of clusters in redshift bin 9: 105.23886818800673.\n", + "Number of clusters in redshift bin 10: 72.88556197695006.\n", + "Number of clusters in redshift bin 11: 49.540326533697474.\n", + "Number of clusters in redshift bin 12: 33.155855467111195.\n", + "Number of clusters in redshift bin 13: 21.899709598785485.\n", + "Number of clusters in redshift bin 14: 14.303248384509745.\n", + "Number of clusters in redshift bin 15: 9.253132442556007.\n", + "Number of clusters in redshift bin 16: 5.936729450813923.\n", + "Number of clusters in redshift bin 17: 3.781126326775194.\n", + "Number of clusters in redshift bin 18: 2.3929943501828803.\n", + "Number of clusters in redshift bin 19: 1.3852740996313966.\n", + "------------\n", + "Number of clusters in snr bin 0: 1746.5541832080712.\n", + "Number of clusters in snr bin 1: 937.0369428631908.\n", + "Number of clusters in snr bin 2: 195.65374617004755.\n", + "Number of clusters in snr bin 3: 31.822357329977418.\n", + "Number of clusters in snr bin 4: 3.5644608030153018.\n", + "Number of clusters in snr bin 5: 0.23464193020985416.\n", + "Total predicted 2D N = 2914.866332304512.\n", + "Theory N calculation took 2.003 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " ::: 2D ln likelihood = 161.0196253903858\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-161.01962539])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.261, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-5,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='/Users/eunseonglee/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 5.,\n", + " 'method': 'SNRbased',\n", + " 'whichQ': 'injection',\n", + " 'resolution': 'downsample',\n", + " 'dwnsmpl_bins': 100,\n", + " 'save_dwsmpld': False,\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " 'zmin': 0.,\n", + " 'zmax': 2.0,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " 'Mmin': 5e13,\n", + " 'Mmax': 1e16,\n", + " 'dlogM': 0.01\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "acdf74cd", + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "172aa5f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 2914.866332304512\n", + "Number of clusters in redshift bin 0: 19.221338664465883.\n", + "Number of clusters in redshift bin 1: 303.66031427024393.\n", + "Number of clusters in redshift bin 2: 437.3405080937954.\n", + "Number of clusters in redshift bin 3: 456.85717205043443.\n", + "Number of clusters in redshift bin 4: 412.1891190946041.\n", + "Number of clusters in redshift bin 5: 343.04635461352007.\n", + "Number of clusters in redshift bin 6: 270.4412194904809.\n", + "Number of clusters in redshift bin 7: 203.83736665016897.\n", + "Number of clusters in redshift bin 8: 148.50011255777852.\n", + "Number of clusters in redshift bin 9: 105.23886818800673.\n", + "Number of clusters in redshift bin 10: 72.88556197695006.\n", + "Number of clusters in redshift bin 11: 49.540326533697474.\n", + "Number of clusters in redshift bin 12: 33.155855467111195.\n", + "Number of clusters in redshift bin 13: 21.899709598785485.\n", + "Number of clusters in redshift bin 14: 14.303248384509745.\n", + "Number of clusters in redshift bin 15: 9.253132442556007.\n", + "Number of clusters in redshift bin 16: 5.936729450813923.\n", + "Number of clusters in redshift bin 17: 3.781126326775194.\n", + "Number of clusters in redshift bin 18: 2.3929943501828803.\n", + "Number of clusters in redshift bin 19: 1.3852740996313966.\n", + "------------\n", + "Number of clusters in snr bin 0: 1746.5541832080712.\n", + "Number of clusters in snr bin 1: 937.0369428631908.\n", + "Number of clusters in snr bin 2: 195.65374617004755.\n", + "Number of clusters in snr bin 3: 31.822357329977418.\n", + "Number of clusters in snr bin 4: 3.5644608030153018.\n", + "Number of clusters in snr bin 5: 0.23464193020985416.\n", + "Total predicted 2D N = 2914.866332304512.\n", + "Theory N calculation took 1.935 seconds.\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cf486863", + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ebc847ac", + "metadata": {}, + "outputs": [], + "source": [ + "bin_params = info['likelihood']['soliket.BinnedClusterLikelihood']['binning']\n", + "\n", + "zbins = np.arange(bin_params['z']['zmin'], bin_params['z']['zmax'] + bin_params['z']['dz'], \\\n", + " bin_params['z']['dz'])\n", + "\n", + "logqmin = bin_params['q']['log10qmin']\n", + "logqmax = bin_params['q']['log10qmax']\n", + "dlogq = bin_params['q']['dlog10q']\n", + "\n", + "# TODO: I removed the bin where everything is larger than qmax - is this ok?\n", + "qbins = 10**np.arange(logqmin, logqmax+dlogq, dlogq)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ffcdcba6", + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig_pred = {\n", + " 'predSNRCut': 5,\n", + " 'path2truthcat': '/Users/eunseonglee/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits', \n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'fast',\n", + " 'QSource': 'injection'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4fadf643", + "metadata": {}, + "outputs": [], + "source": [ + "nemoNz = nemo_mocks.get_nemo_pred(mockconfig_pred, zbins)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8e67f27d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))\n", + "\n", + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], marker='o', alpha=0.4, label='SOLikeT pred (SNRbased-inj)')\n", + "plt.plot(z, nemoNz, color=color_list[3], marker='o', linestyle='--', alpha=1, label='Nemo pred (fast-inj)')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[9], fmt='o', ms=7, mfc='white', zorder=0, capsize=5, capthick=1, ls='none', alpha=0.8, label='obs catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.xlim(0, 2.0)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_SNRbased-inj.pdf')\n", + "plt.savefig('0Nz_SNRbased-inj.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9820caae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chi2 : 506.87303779469954\n", + "dof : 20\n" + ] + } + ], + "source": [ + "obs = catNz\n", + "exp = Nz\n", + "\n", + "chi2 = (np.power(obs - exp, 2) / exp).sum()\n", + "\n", + "print(\"chi2 : \", chi2)\n", + "print(\"dof : \", len(exp))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "71107467", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chi2 : 30.5171511823263\n", + "dof : 6\n" + ] + } + ], + "source": [ + "obs = catNq\n", + "exp = Nq\n", + "\n", + "chi2 = (np.power(obs - exp, 2) / exp).sum()\n", + "\n", + "print(\"chi2 : \", chi2)\n", + "print(\"dof : \", len(exp))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69492d69", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "96f8a29e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2913.550743490729" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nemoNz.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ac6260a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2914.866332304512" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Nz.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ec25a957", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3169.0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catNz.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7faf892d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z, np.abs(Nz/nemoNz-1), color=color_list[3], marker='o', ls='--', mfc='none', ms=7, label='fractional error')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('fractional error', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "#plt.xlim(0, 2.0)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_SNRbased-inj_frac.pdf')\n", + "plt.savefig('0Nz_SNRbased-inj_frac.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fbe53ad8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z, Nz-nemoNz, color=color_list[3], marker='o', ls='-', mfc='none', ms=7, label='$N_{SOLikeT}-N_{Nemo}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{Nemo}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_SNRbased-inj_diff.pdf')\n", + "plt.savefig('0Nz_SNRbased-inj_diff.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6d040b5f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-9.67137703e-01, 1.01091291e+00, 8.93292835e-03, 1.38042946e-01,\n", + " 2.26244953e-01, 2.30719489e-01, 2.12333614e-01, 1.78631269e-01,\n", + " 1.29726917e-01, 9.47906221e-02, 6.98733483e-02, 4.87632341e-02,\n", + " 3.17010623e-02, 1.89423688e-02, 9.61343604e-03, 3.08843288e-03,\n", + " -8.39769155e-04, -2.52454585e-03, -2.75387811e-03, -1.23472817e-01])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Nz-nemoNz" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "44da2bd2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z, np.abs(Nz/catNz-1), color=color_list[9], marker='o', ls='--', mfc='none', ms=7, label='fractional error')\n", + "plt.fill_between(z, 0, np.sqrt(catNz)/catNz, alpha=0.2, color='gray', label='$\\sqrt{N_{obs}}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('fractional error', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "#plt.xlim(0, 2.0)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_SNRbased-inj_obs_frac.pdf')\n", + "plt.savefig('0Nz_SNRbased-inj_obs_frac.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9126d0ba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z, Nz-catNz, color=color_list[9], marker='o', ls='-', mfc='none', ms=7, label='$N_{SOLikeT}-N_{obs}$')\n", + "plt.fill_between(z, -np.sqrt(catNz), np.sqrt(catNz), alpha=0.2, color='gray', label='$\\pm\\sqrt{N_{obs}}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{obs}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_SNRbased-inj_obs_diff.pdf')\n", + "plt.savefig('0Nz_SNRbased-inj_obs_diff.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e44c215e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([115., 341., 465., 441., 435., 360., 287., 210., 177., 105., 85.,\n", + " 56., 34., 16., 12., 8., 7., 7., 4., 4.])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catNz" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "75ab3bc2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 19.22133866, 303.66031427, 437.34050809, 456.85717205,\n", + " 412.18911909, 343.04635461, 270.44121949, 203.83736665,\n", + " 148.50011256, 105.23886819, 72.88556198, 49.54032653,\n", + " 33.15585547, 21.8997096 , 14.30324838, 9.25313244,\n", + " 5.93672945, 3.78112633, 2.39299435, 1.3852741 ])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Nz" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "177c224d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 20.18847637, 302.64940136, 437.33157517, 456.7191291 ,\n", + " 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], marker='o', alpha=0.8, label='SOLikeT pred (SNRbased-inj)')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[9], fmt='o', ms=7, mfc='white', zorder=0, capsize=5, capthick=1, ls='none', alpha=1, label='obs catalogue')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.xlim(0, 2.0)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_SNRbased-inj.pdf')\n", + "plt.savefig('0Nq_SNRbased-inj.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "1a1d2ef8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(q, Nq-catNq, color=color_list[9], marker='o', ls='-', mfc='none', ms=7, label='$N_{SOLikeT}-N_{obs}$')\n", + "plt.fill_between(q, -np.sqrt(catNq), np.sqrt(catNq), alpha=0.2, color='gray', label='$\\pm\\sqrt{N_{obs}}$')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{obs}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_SNRbased-inj_obs_diff.pdf')\n", + "plt.savefig('0Nq_SNRbased-inj_obs_diff.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7ba58634", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(q, np.abs(Nq/catNq-1), color=color_list[9], marker='o', ls='--', mfc='none', ms=7, label='fractional error')\n", + "plt.fill_between(q, 0, np.sqrt(catNq)/catNq, alpha=0.2, color='gray', label='$\\sqrt{N_{obs}}$')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('fractional error', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "#plt.xlim(0, 2.0)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_SNRbased-inj_obs_frac.pdf')\n", + "plt.savefig('0Nq_SNRbased-inj_obs_frac.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "09ea6124", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z[2:], Nz[2:], color=color_list[0], marker='o', alpha=0.5, label='SOLikeT pred (SNRbased-inj)')\n", + "plt.plot(z[2:], nemoNz[2:], color=color_list[3], marker='o', linestyle='--', alpha=0.5, label='Nemo pred (fast-inj)')\n", + "plt.errorbar(z[2:], catNz[2:], yerr=np.sqrt(catNz[2:]), color=color_list[9], fmt='o', ms=7, mfc='white', zorder=0, capsize=5, capthick=1, ls='none', alpha=0.8, label='obs catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.xlim(0, 2.0)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_SNRbased-inj_ex2zbins.pdf')\n", + "plt.savefig('0Nz_SNRbased-inj_ex2zbins.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b7e6904d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z[2:], Nz[2:]-catNz[2:], color=color_list[9], marker='o', ls='-', mfc='none', ms=7, label='$N_{SOLikeT}-N_{obs}$')\n", + "plt.fill_between(z[2:], -np.sqrt(catNz[2:]), np.sqrt(catNz[2:]), alpha=0.2, color='gray', label='$\\pm\\sqrt{N_{obs}}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{obs}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_SNRbased-inj_obs_diff_ex2zbins.pdf')\n", + "plt.savefig('0Nz_SNRbased-inj_obs_diff_ex2zbins.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "11897679", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z[2:], np.abs(Nz[2:]/catNz[2:]-1), color=color_list[9], marker='o', ls='--', mfc='none', ms=7, label='fractional error')\n", + "plt.fill_between(z[2:], 0, np.sqrt(catNz[2:])/catNz[2:], alpha=0.2, color='gray', label='$\\sqrt{N_{obs}}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('fractional error', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "#plt.xlim(0, 2.0)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_SNRbased-inj_obs_frac_ex2zbins.pdf')\n", + "plt.savefig('0Nz_SNRbased-inj_obs_frac_ex2zbins.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ba8b0973", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 2914.866332304512\n", + "Number of clusters in redshift bin 0: 19.221338664465883.\n", + "Number of clusters in redshift bin 1: 303.66031427024393.\n", + "Number of clusters in redshift bin 2: 437.3405080937954.\n", + "Number of clusters in redshift bin 3: 456.85717205043443.\n", + "Number of clusters in redshift bin 4: 412.1891190946041.\n", + "Number of clusters in redshift bin 5: 343.04635461352007.\n", + "Number of clusters in redshift bin 6: 270.4412194904809.\n", + "Number of clusters in redshift bin 7: 203.83736665016897.\n", + "Number of clusters in redshift bin 8: 148.50011255777852.\n", + "Number of clusters in redshift bin 9: 105.23886818800673.\n", + "Number of clusters in redshift bin 10: 72.88556197695006.\n", + "Number of clusters in redshift bin 11: 49.540326533697474.\n", + "Number of clusters in redshift bin 12: 33.155855467111195.\n", + "Number of clusters in redshift bin 13: 21.899709598785485.\n", + "Number of clusters in redshift bin 14: 14.303248384509745.\n", + "Number of clusters in redshift bin 15: 9.253132442556007.\n", + "Number of clusters in redshift bin 16: 5.936729450813923.\n", + "Number of clusters in redshift bin 17: 3.781126326775194.\n", + "Number of clusters in redshift bin 18: 2.3929943501828803.\n", + "Number of clusters in redshift bin 19: 1.3852740996313966.\n", + "------------\n", + "Number of clusters in snr bin 0: 1746.5541832080712.\n", + "Number of clusters in snr bin 1: 937.0369428631908.\n", + "Number of clusters in snr bin 2: 195.65374617004755.\n", + "Number of clusters in snr bin 3: 31.822357329977418.\n", + "Number of clusters in snr bin 4: 3.5644608030153018.\n", + "Number of clusters in snr bin 5: 0.23464193020985416.\n", + "Total predicted 2D N = 2914.866332304512.\n", + "Theory N calculation took 1.976 seconds.\n" + ] + } + ], + "source": [ + "# without first two redshift bins\n", + "\n", + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[2:,i].sum() \n", + " catNq[i] = catNzq[2:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "703b2643", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], marker='o', alpha=0.8, label='SOLikeT pred (SNRbased-inj)')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[9], fmt='o', ms=7, mfc='white', zorder=0, capsize=5, capthick=1, ls='none', alpha=1, label='obs catalogue')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.xlim(0, 2.0)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_SNRbased-inj_ex2zbins.pdf')\n", + "plt.savefig('0Nq_SNRbased-inj_ex2zbins.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4e95663b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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M9OnTh5kzZ7Jo0SKuvvpqoCqEpaSk8MYbb5CRkQHAfffdx9133w3A0qVLmTVrFpMmTQKgZ8+ehEIhSkpKmv21V6dQITFjGEatkBEIBPD7/RQUFGCaZiRkJCUl1VhhSiFDRERamnvyWGw9ulLxzvuULVwMPh84HLgnjSbpsotj3p9ix44deL3eOicC9+3bl+zsbJYtWwbUHtc/duxY9u7dy/Lly1myZAkvvPACTz31FKtWrWLEiBFs2bIFgGHDhtV57tzcXDIzM+nWrRtbtmxpcN7A6ZOO8/LyWLt2LZs3b2b58uU1AseuXbtYtWoVmzZtYv78+ZHtfr+fGTNm1Djuli1b6h1y1ZzO9vu+fft28vPzeeedd2o8x+l0UlFREfk6Ly+P66+/PhIoAJKTkyPL+l922WX89Kc/Ze3atVx11VXMnDmTpKSkqL7GxtDwJ4kb4TkXSUlJpKWl1eiVUVhYyMGDB9m3bx/79u3j2LFjlJSUqFeGiIi0KHuvHqT/4CY6/+03dPrbb+n88m9J/8FNMQ8UUPe4/upmz54d+byufex2O9OmTeOXv/xlZDWjhQsXAlUX8eE3A09XWVnJggULuO6664Cq0NDQCkd13akIDxMqKyursW9+fj7dunUjPz+frVu3Rj527NhRa8WprVu3xiRUnO33fceOHaSnp9O9e/ca+3/00UcMGTIk8nVdKz9t2bIlsu3BBx8kPz+fsWPH8vDDDzNw4ECKi4ub9qLOgu5USNwyDCPSKyMsfCfj5MmTNbp+q1eGiIi0JMNiafLk72ibO3cuc+fOrffxhx56iIceeqhRxwoEAgSDQTp27AhULRFrmiZr1qzhwgsvrLHvnXfeic1m48EHH+STTz6hoqKi3ov7oqIiDhw4ELkgLi4uZt++fdx7772cd955zJo1i02bNkUutO12O4WFhWRlZdUZaMI+//xziouLYxIqzvb7npKSgs/nIxgMRq5b1q9fz6ZNm/jzn/8MQGlpKXv27CEYDEaeV1lZyQsvvMAvfvGLyLaBAwcycOBAbrrpJrp168bBgwfp0aNlg65ChbQq9TXkKy4uprCwEIvFUiNkhFeYUsgQERGp2/XXX8/gwYOZOnUqnTp1YufOnfz3f/83vXr14sYbbwSqGrlNnTqVuXPn8r//+78MGTKEffv28T//8z+sWbOGJUuW0LFjR5YuXYrFYsE0TbZv3x45R0pKCr169SIvLw+3202/fv0Aanx97733kpeXx8yZM1m1ahVut5vx48fjcDiYO3cu8+bNw+Fw8PHHH5Obm8sjjzwSOf6WLVuwWq013uGPd+HX9vDDDzN37lx27NjB97//fR544IHIZO5t27bhdDp57rnnGDduHDabjdtvv53zzjuP66+/nieffJLu3bszatQogsEgTz/9NAMGDGDAgAGR+aotRcOfpFU7vSFfcnIyUJXsv/jiC/bv38/evXvVkE9ERKQew4cP54033uBrX/saAwcO5O677+biiy/mww8/jKzQBPDvf/+bK664gttuu43zzjuPG264gW7durF9+/bIXIEtW7YQCoUYOXIkQ4YMiXyE36nPz89n0KBBkUnceXl5Nb5+/vnnMQwj8s5/x44defvtt9m7dy/jx49nzJgxPPHEE7VWltqyZQsDBgyI9M5qDdq3b8/ChQt56623GDhwIPfddx8PPvggP/vZzyL7hL8/d999N5dccgkTJ07k/PPPZ/HixVitVjweD/Pnzyc7O5upU6dSUVHBu+++G5M3Uw1T63fGjZKSEtLT0ykuLq7RaKYlVFZWsn//fpKTk+tdraE1Cjfk8/v9BIPBOhvyORwO7HZ7rEuNa36/n0WLFnHppZfqeyUiccvj8bBnz55IR2iRtiq8+lNaWlq913WN/ffS2OtTDX+ShFZfQ76KiooaDfkcDkekIV+467dWmBIRERFpHIUKaVPq65Xh8XgoKyuLTA53Op243W5cLlfkToZChoiIiEjdFCqkTauvV0a4IZ9pmtjt9hoN+dQrQ0RERKQmhQqRasLDocLzBkzTJBgM4vP5OHHiBKFQqEZDvvCdDIfDkVBzUURERETOhEKFSAPOpFdGOGSE72YoZIiIiEhboVAhcobq65VRVFREKBSKzNtQQz4RERFpKxQqRJrIarVG+mVA/Q35Tl/GtvrdDxEREZHWTFc1IlF2esgI98ooKyujuLi4Vq+M8HAphQwRERFprXQVI9LM6uqV4ff7KS8vr9Erw+l01goZWmFKREREWgOFCpEWVl9DvsrKSsrKyjBNMzJEqnrIUK8MERERiVcKFSIxVl9DPq/XW6MhX/VeGU6nUyFDRERE4oZChUicqa8hn9/vp6CgANM0a/TKqL7ClEKGiIiIxIJChUicq68hn9/vp7CwkBMnTqghn4iIiMSUQoVIK3MmDfnUK0NERERagkKFSAKoryHf6b0ywiEjPPlbIUNERM6W3++PDNVtbUzTjHUJCUehQiQB1deQr7S0lKKiosjk8OTk5Mi8DAWMxGSGQpg+P4bDjqHhcCISRY8//jgbN25k1KhRsS5F4oBChUgbEA4ZYaFQCJ/Px8mTJzlx4gROp5Pk5GSSk5NxuVxqxJcA/HsPUPH2UirXbAKfDxwO3BNHkXT5NOy9esS6PBFJAJs3b+bhhx+OdRkSJ3TlINIGWSwWXC4XLpcrEjCKioooLCyMLF0bDhjhCeLSelSu2kDxsy9iad+OlFlfw9qlE8Ejx6j4z2oqV24g/Y7v4p48NtZlikgrdvDgQbp3765VByVC98KrOXToEL/+9a+ZPn06WVlZOBwOunTpwuzZs9mwYUOt/R955BEMw6jzIzzsRCTehQNGamoqqampWCwWiouLOXjwIPv37+fw4cOUlpbGukxpJP/eAxQ/+yKuyWPo+JvHSfnW13FPHkvKt75Ox988jmvyGIqffRH/3gOxLlVEWrG33nqLr3/967EuQ+KI7lRU8+yzz/Lkk0/St29fpk2bRqdOndi1axdvvPEGb7zxBn//+9+56qqraj3vxhtvpFevXjW2afiItEaGYUSa64Wb8JWWlnLy5EkAvvjiC9LT0yPL1uodqvhT8fZSLO3bkX7bjRg2G4EjxwiVlmHv2wvDZiP9thvxbf+EinfeJ/0HN8W6XBFppZYtW8Zf//rXWJchcURXvtWMGTOGlStXMnny5BrbV61axUUXXcRtt93GN77xDZxOZ43H58yZw5QpU1qwUpHmV70JXyAQAKC8vJzy8vLISlMpKSm4XC6cTqcCRhwwQyEq12wiZdbXCBWVUPaPN6hcvg5ME0tme9wXjMOdM46kiydTtnAxad+fo783aVOCwSChUCjWZdTJYrG0mgUzysrKsNvtta6HpG1TqKhm1qxZdW6fPHkyU6dOZcmSJWzbtk2rHEibE77wTElJwWq14vf7qayspLS0FKvVWiNguFwuXajGiOnzg8+Hf9ceyhYuAp8fAMPlJFRwkvKFiyhfuAhLp0zw+QgeP4GtU2aMqxZpGcFgkIMHD+L3+2NdSp3sdjvdu3dvFcHivffeY/r06fU+PnToULZt24bD4eDYsWOkp6fXeNw0TTIyMrBarZw4caK5y5UWolDRSOHJqnUNa1q1ahUffvghVquVAQMGcPHFFyu9S0Kr3uE7EAjg9XopKyvDarXidDpJSUkhKSkJp9Oprt4txPT7qViyHADv5nwA7AP7k3r9N7H37I5nUx6eFevwbt1B6FgBAAXf/wnOEUNw5YzHNSobw9k615sXaYxQKITf78discTdEOVwA9NQKNRsoWLKlCns3buXvXv3NvlYixYt4he/+EWdj3k8Hnbu3AmAz+fjrbfe4jvf+U6NfXbv3k1xcTEXX3xxk2uR+BFf/6ri1P79+3n//ffp0qULQ4YMqfX4f//3f9f4+pxzzmHBggVMmzatweN6vV68Xm/k65KSEqCqmUxLv5MSCAQIBoMEg0E1hJFagsFgjT+rqz4PIxgM4vF4KC0txWKxRJaqVcBoPmYohG/tJipe+Teh46fe8bNaSfnhXByjh2EYBgHANmYYKWOG4T5ZRPF9j4JhYJaV492cj3dzPiVuF45xI3BeMA7beX3V00JaJb/fj2mahEKhWsOcwtvsdnvc3Q0wTZNAIFBn3dHW1OOHQiFOnjxJhw4d6jzWli1bCAQCTJ8+nQ8++IB//vOfXHvttTX22bhxIwDDhw+P2+ForV34Wi7876EuoVAoMn+yoX8Tjb0mVaj4Cn6/n+uvvx6v18tTTz1V45s+bNgwFixYQE5ODp07d+bgwYP84x//4Oc//zlXXHEF69evJzs7u95jP/HEE8yfP7/W9iVLlpCUlNQsr0ekKfLy8mJdglST9kUBWZs/IvlkMQA+t5Nj/bLouu0z9ryxiD3HDmFWCwdGKETvtXl0qKhkx2WTCVktZH5+kMzdB3FWePB+sLbqI9lNQZ/uFPTpjic9JVYvT+SM2Ww2unTpQllZGT6fr8Zjfr+fiooK/H5/3N6pKCkpabZlvMOhJfwG5tlat24dw4YNq/c4a9euBWDSpEkEAgHee+89Dh8+TEpKSo1jAJx//vlNrkca1tDqjT6fj8rKSlauXBmZO1mXioqKRp0rvv5VxZlQKMR3v/tdVq5cyS233ML1119f4/GZM2fW+Prcc8/lpz/9KZ07d+a//uu/eOyxx3jttdfqPf5PfvIT7r777sjXJSUl9OjRg+nTp5OWlhbV1/JVPB4PBw4cIDk5We8mSy3BYJC8vDyys7PP6h2+cC+M8C/56t28nU5n3P2Cj3eBfQepePlf+PM+AsBwu3BdMZ32X7uQc1xOvGs2Yvm/P9G5pALn1IlYO2cSPFqA94N1hAqLSLn9JqZMHB05nhkKEdj5Gd5VG/BtyMVZXkm3bbvotm0Xtr69cEwei3PCKCxpChgS38K/y8JzvKrz+/0UFhbG5f854WGkaWlpUQkVdb1hefDgQUpKSvjVr35V67Ef/vCHtGvXrlHHXrZsGTfddFO91ykffVT1/9L48ePJzMxk2bJlrF69usbqmeF9Jk6c2OLXO22FaZqUlpaSmppa7zxHj8eD2+3mggsuaLAVQmODn2FqrEudTNPk5ptv5sUXX+Q73/kOCxYsaPTFts/nIzk5mY4dO3L48OFGn7OkpIT09HSKi4tb/B9ZZWUl+/fvV6iQOgWDQXJzcxkxYkSThw2EA0Z4mIKa7TVesOBkjRWdsFpJmpFDyje/jiU9tca+/r0HqHjnfSpXb/yyo/ak0SRddnGDHbVNrxfPxi/nXxC+bW614hw+GPeU8ThHZmM49Pck8cfj8bBnzx569+5dZ6jYt28fDocjLkOFz+ejZ8+eUfk/8EwXy9izZ0+NpfHXrFnDL3/5S954441a+15xxRX8+9//rvdYI0aMYMuWLRQWFuL1eunatSvf/OY3eeWVVyL7tG/fnlAoRGFhoRb2aCbhu1JpaWn1Xtc19O+lusZen8bXv6o4EQqFuPnmm3nppZe45ppr+NOf/nRGF9oOh4PU1NRG3y4SaUuqd/M2TbNGN2+Hw0FSUlIkYDgcmjgMECqvqFq5adF/Iis6uSaMIuXaK7Gd07nO59h79SD9BzeRdtuNmD4/hrNxfUUMpxP3pDG4J40hWFSMZ/WHVK5YR+Dz/Xg35eHdlIeR5MY1YTTuKeOxDzhXFwUicaau94sbO1F7/fr1/PrXv+bNN9+MXHCGffbZZ/Tv37/e5/p8PrZv306fPn0idz4mTZrEO++8Q2VlJW63m88//5zCwkKmTp16xv933HrrrZSVlak/RpxSqDhN9UBx9dVX85e//OWM35ndtWsXhYWFDc6nEJG6m+2VlJRQVFSEw+HA7XaTnJyM2+3Gbre3uYtX0++n4t0PKPvnO5hl5cCXKzo5+vdp1DEMiwXDdXar0VnbpZN8+TSSL59G4MBhKleso3LlekInCql8fyWV76/E2ikT1wXjcOeMx9a17oAjIq3HuHHjeO211+jfvz///ve/+eEPfxh57M0332ywi3Z+fj5+v58RI0ZEts2ePZuVK1fy3nvvMXPmTHJzcwFq7NNY+fn5XHnllWf8PGkZGudSTSgUYu7cubz00kt861vf4q9//Wu9gaK0tJT8/Pxa2wsLC5k7dy4A11xzTbPWK5JIws32UlNTSUtLw2q1UlZWxuHDh9m/fz+HDh2iqKgIr9eb8CuUmaEQlas2UHDnQ5T+6VXMsnJs3bvS7se30/5n9zU6UESTrUdXUr8zm46/e5KMR+7BPXUChstJ8FgB5f98m4I7HuTET35OxbsfECota/H6RCS6Zs6cyZtvvllj25o1a5g0aVK9zwkHhpEjR0a2zZo1C8MweP311wHYvHlzrX0awzRNtm3bxtChQ8/oedJydKeimp/97Gf86U9/IiUlhf79+/PYY4/V2mfmzJkMGzaMEydOkJ2dzahRoxgyZAidOnXi0KFDLF68mBMnTjBt2jTuuuuuGLwKkdavejdv+HLVltLSUmw2Gy6Xi9TU1ITs5u3dtpPSP79G4PP9AFgy0kn59jdwT52IEQfLYBoWC84h5+Mccj5pt1yH58OtVK5Yhy9vB/5PP8f/6eeUvPQPnCOG4s4Zh3PkUAzNkxFpda644gp+9atfUVhYSEZGBidPniQ9Pb3B0RvhwFD9LkT37t0ZPXo0b731Fj6fr859qj9/3rx5rF+/noyMDH7wgx/w4x//GIDPP/+csrIyysrKGDNmDDt27GDcuHEsWLCA7t27A1VzUx577DH+8pe/cOjQITp06MCNN97Iz3/+86h9X6R+ChXVhMcZlpWV8fjjj9e5T69evRg2bBjt27fnBz/4AevXr+ett96iqKiI5ORkhgwZwne+8x1uvvnmuFsHW6S1Or3ZnsfjqdFsLzU1Fbfb3ap7Yfj3HaT0L//Et2U7ULWiU/LMS0i6fBqWsxy+1NwMpxP35LG4J48lWFiEZ9WHVK5cR2DPAbwfbsH74RaMlKSq+Rc547Gf1zehAqBIa7J8+fIz2n/ChAlkZGSwaNEirrvuOhYtWsSll17a4HPqG9o0e/Zs5s2bx/vvv8+WLVtITU2tNTdjw4YNXHzxxTz44IP84Q9/4KOPPuLqq6+md+/eXH311eTl5WGxWHjmmWf47W9/i81m44YbbuDOO+9k4cKFADz66KMsXryYBQsW0KNHDz7//HOOHz9+Rq9bzp5Wf4ojWv1J4lU0V3+KpvCKKeEuudUDhsvlahU/y2eyolNr4d93EM+KdVSu2kDoZFFku7VLxy/nX3TpFLsCJWFp9acqjzzyyBnt/6Mf/ajOJWVvuukmysvLefXVV7n++uv5v//7P1JT6/5/ye/3k5qaSufOndm3b1+Nx3bv3s25557LhRdeyLJly5g8eTIrV66ssU92djaXXHIJTz75ZGTbddddh81mY8GCBTzyyCP86le/Yu/evWRkZADw5z//mbvvvpuCggKgKghdccUVkbsbbZlWfxIROQM2my1ycRAMBvF6vRw/frzG/IxwwIinMARnt6JTa2Hv2R37Dd8i5brZ+LZ/TOXKdXjX5xI8cpzyV9+i/NW3sJ/XF3fOeFwTRmFJVf8LkWiqq09FQ+bMmVNnqPjGN77BDTfcQFlZGV6vt95AAbBjxw68Xm+dw5r69u1LdnY2y5YtA2rPp9i+fTv5+fm88847NbY7nc7ISpp5eXlcf/31kUABkJycXKNb9GWXXcZPf/pT1q5dy1VXXcXMmTNrNN2T5qVQISIJwWq1RjrRh0IhvF5v5N0rp9MZabbncrli+i5lNFZ0ai0MqwVn9kCc2QMJ3fIdvBtyqVyxHt+2j/B/shv/J7spefEfOEcOxZ0zHueIIRh2/VqS5tVQ5+BYiXZN0RqEMn36dPx+Pw8//DA5OTkN7tvQXAmoGgKVl5dX5z47duwgPT09Mjci7KOPPoqsNpWfn19r+NWWLVtqrLT54IMPcuWVV/Kvf/2Lhx9+mAceeIBt27aRnp7eiFcrTaX/vUUk4VgsFtxuN263O9Jsr7CwkBMnTtQIGG63u8UChhkK4VmzkbKX/0XwWFXYsXY/h9TvzMY5Kjvh5xpYXE7cOeNx54wneLIIz6oNVf0v9h3EuyEX74ZcjJRk3JPG4MoZh71fn4T/nkjLslgs2O12/H4/Pp8v1uXUYrfb427IZlJSEhdffDHPPPMMu3fvbnDfuXPnRla/rMtDDz3EQw89VOdjKSkp+Hw+gsFg5K7y+vXr2bRpE3/+858pLS1lz549BIPByHMqKyt54YUX+MUvflHjWAMHDmTgwIHcdNNNdOvWjYMHDypUtBCFChFJaNWb7YVCIfx+f6TZXkt18473FZ1amrV9O5K/MYPkb8zAv/cAlSvW41m1nlBhMRXvfkDFux9gPacz7gvG4bpgHLYuHWNdsiQAq9VK9+7dawyXiScWiyXuhmlC1SpQBw8eJCsrq9nOMX78eBwOBw8//DBz585lx44dfP/73+eBBx6gf//+rF27FqfTyXPPPce4ceOw2WzcfvvtnHfeeVx//fUAPPnkk3Tv3p1Ro0YRDAZ5+umnGTBgAAMGDGi2uqUmhQoRaTPCk7nDzfZ8Ph/FxcWRbt5ut5uUlJSodfNujSs6tTR7rx7Ye/Ug9Tuz8W3bSeWKdXg35BL84ihlr7xJ2StvYj+/X1XAmDAKS0pyrEuWVsxqtcblhXs8u+KKK0hObt5/d+3bt2fhwoXcddddPP300/Tq1YsHH3yQ733ve0DVfIpBgwZxxx13cMkll1BZWcm1117L008/Hfn79Hg8zJ8/n/3795Oens6FF17Iu+++q7/vFqTVn+KIVn+SeBWvqz9FS7ibd/j2u91urxUwzmQoTiKu6NSSQpWeU/Mv1uHb9nHV9xDAbsM5Krtq/sWwwZp/IbU0djUbkUSn1Z9ERGKgerM90zQJBAKUl5dTUlJyRs326lzRafwoUq5r/Ss6tSSL24V7ygTcUyYQPHGSylUb8KxYT2D/IbzrNuNdtxkjNeXL+Rfn9tb8CxGRGFOoEBGpxjCMBpvtVb+D4XK5MAyjTa3o1NKsHdqTMvNrJH/jEgJ7D1C5fB2e1RsIFZVQsXgZFYuXYe3auWoS+AXjsXbqEOuSRUTaJIUKEZEGVO+FEQgE8Hq9lJeXYxgGLoeTpJ2fEXpzCWbBSaBtrejUkgzDwN47C3vvLFJv+Ca+/Kr5F54NWwgePkrZ39+g7O9vYB/YH3fOOFzjR2FJTop12SIibYZChYhII1UPGKGdn2G8+R7BQ0cBMNNSsH5jOkkXTcaRnKxA0YwMqxXn8ME4hw8mVFGJZ30unpXr8G3/BP9Hn+L/6FNK/vh3XKOyceWMxzlsEEacdVAWEUk0+l9WRORMHD6K8eYSbB/tAsB0OQldNBHP+OH4gMLDh3E4HDWWqk3Eye3xwpLkJunCiSRdOJFgwUkqV66vmn9x8DCetZvwrN2EJS0V16QxuHPGY+vbU4FPRKQZKFSIiDRGYTHGO8vgw60YpolpscDk0ZgzpmCkJuMG3BBptnfy5ElOnjyJw+EgJSUlLrp5JzprZntSZl1K8pVfI/D5vqr+F6s3ECoupWLRf6hY9B+s3bpUTQKfPBZrR82/SFRa2FLkq0X734l+u4mINKSiEmPpKlixHsMfAMAcPgjz6xdDHRelpzfbC3fzPnnyZIs122vrDMPA3rcX9r69quZf5H1UNf9i41aCh45Q9reFlP1tIY5B5+GaMh7XuJFYktyxLluioPr8JxFpmN9ftUphtO6mK1SIiNTFH4BVH2K8twKjohIA89yemN+YDr16NOoQ1QNGuNleuJu3w+EgKSkpEjCi0WxPajNsNpwjh+IcOZRQeUXV/IsV6/Dt+CTyUfL833CNHo47ZxyOYYPaZJfzRBFubldSUkJqqnrCiNTHNE2Ki4txOp1Re4NLoUJEpLpQCHK3Y7z1PsbJIgDMLh0xr5gGg8+DsxyPbxhGjW7efr+fkpISioqKsNvttQKGxv1HnyU5iaSLJpF00SSCx05QuWo9lSvWETx0BM+aD/Gs+RBLu7Qv51/0ztLfQytjGAadOnXiiy++wOl0kqxFE6SNCt8p93g8NZrfhX//FBcXU1ZWRrdu3aJ2TnXUjiPqqC3xKtE7akd88jnGm+9hHPgCADMtFfOyC2HsMGim111XN2+Xy0VKSgput1sBo5mZpklg9z4qV6ylcvWHmCVlkcdsPbriyhmP+4KxWDu0j2GVciZM0+TIkSMUFxdrboW0WaZpUllZidvtrvN3iNPpJDMzs1HXm429PlWoiCMKFRKvEj5UnFrRyai2opN58SSYMh6cLTssye/34/V6CQaDkW7e4YDRUDdvaTozEMC7dQee5evwbNpaNQQOwDBwDD4Pd854nONGYnG7YlqnNE4wGIyMGRdpa/x+PytXruSCCy6oNbzJarWe0ZCnxl6faviTiLRdDazoRGpyTEpqqJu30+kkNTU1EjD0BkB0GTZbVW+LUdlV8y/WbaJyxXr8H32Kb9vH+LZ9DH/4G66xw3HnjMcx9HzNv4hj4fkVIm2R1WolEAi06KIgChUi0vac4YpOsXJ6N2+fz8fRo0exWCw1AobL5VLAiDJLchJJF19A0sUXEDh6HM+qDVXzLw4fxbNqA55VG7C0S8c1+dT8i149dBdJRNo0hQoRaTuisKJTrFQPGMFgEJ/Px/HjxzEMA4fDUSNg6N3Z6LJ17kjKNy8nefZl+HftwbNiHZVrNhIqKqbiraVUvLUUW1Y33DnjcU0ei7VDRqxLFhFpcQoVIpL4mmlFp1ixWq243W7cbndkhY+CggIANdtrRoZh4OjfB0f/PqTOuRrvlm1UrliPd1Megf2HKP3LPyn96+s4hgzAnTMB59jhmn8hIm2GftuISGL75POqSdgHDgMts6JTS2qo2Z7D4SA5OZmkpCTsdjs2mw2r1aphOlFg2G24xgzHNWY4obJyPGs3UblyPf6du/Dl78SXvxPjDw6cY0fgnjIex+DzMawaoiYiiUuhQkQSUxyt6NRS6mu2d/LkycikVavVisPhwOFwYLfbsVqtkaFVChxnx5KSTNL0HJKm5xA4chzPylP9L44cw7NyPZ6V67G0b4dr0ljcOeOwx/lQOxGRs6FQISKJJQ5XdIqF6s32oKoRUiAQIBQKUV5eTklJCaZpYhgGFoulzsARDhoKHI1n69KRlKu+TvK3Lsf/6edUrliHZ81GQieLqPj3e1T8+z1svXrgzhlXNf8io12sSxYRiQqFChFJDK1kRadYsVgsOBx136EJBoMEg0FCoRBlZWWEQiEFjiYyDAPHeX1xnNeXtJu+jTd3G5Ur1uLdnE9g7wFK9x6g9C//xDF0YFX/izHDsbicsS5bROSsKVSISOvWild0ihcNredfX+Co/jwFjoYZdhuuscNxjR1OqLSsav7FinX4P9mNb+sOfFt3YLicOMeNwJ0zAceg8zT/QkRaHYUKEWmdEmxFp3jVmMARDAYpKyvDNM3IhwJH3SypKSTNmELSjCkEvjhK5cr1eFasJ3j0eFUn7+XrsLTPwH3BWFw547FndYt1ySIijaJQISKtT4Kv6NRa1Bc4TNMkFArVCByhUCjy2OmBw+l0RiaLh8NGWwgctnM6k3r1N0i56gr8n3xG5Yr1p+ZfFFL+xruUv/Eutt5ZVfMvJo3FmpH+lcc0QyFMnx/DYcdQQ0QRaUEKFSLSerTBFZ1aI8MwzihwlJSURB5ri4HDMAwcA/rhGNCPtO9+G++mfCpXrMO7ZRuBPfsp3bOf0j//E0d21fwL15hhGM6a8y/8ew9Q8fZSKtdsAp8PHA7cE0eRdPk0rTYlIi1CoUJE4p9WdEoY0QocTqcTh8ORcIHDsNtxjR+Ja/zIqvkXazZSuXwd/l2f49uyHd+W7ZS4XbjGjcCVMwHHoP541myk+NkXsbRvR8qsr2Ht0ongkWNU/Gc1lSs3kH7Hd3FPHhvrlyYiCU6hQkTil1Z0alPOJHCUlpZGhlQBkVWqbDZbZA5Haw8cltQUki6ZStIlUwkcPlI1PGrleoLHCqj8YC2VH6zFaJeGWVyKc1Q27e69FaNaB/XkK79G8XMLKH72RWw9uuqOhYg0K4UKEYk/WtFJTtPYwBEIBPB6vXUui1s9cJze+M9iscR14LB17ULqNTNJufoK/B+fmn+xdiNmUdWdHO/GrZx44AncF4zHMbAf1s4dsSQnkX7bjfi2f0LFO++T/oObYvwqRCSRKVSISPzQik5yFpo7cNS3+lUsGBYLjoH9cQzsT+pNV3Psxh9i7ZRJ8OhxArv3Ubp735f7pqVg69wJS3ISlSvWYR94HrZzOmE9pxOWtNS4DlEi0vooVIhIfNCKTtIMohk4wpPG4ypwBAKkXPV1nEMHUrlmI571mwge/IJQcSlmSRn+krLIriW/eTHyueFyYj2nE9bOnbB16Yi1SyesXao+t3TI0MpRInLGFCpEJLa0opPEyJkGjuLi4kjgsFqtWCwWbDZbjUnjLRk4DIcdHA6CR45hmTyW5EsvJPnSCwEIVXoIHjlG8MhxKpauwLftY+wD+xE8cpzQiUJMj5fAngME9hzAe/qBbTasnTOxnQoa1i4dT33eEWvHTAy7Lh1EpDb9zyAisaEVnSSOfVXgCHcZr+sOR32B4/TGf02u0WLBPXEUFf9ZTfKVX6sxSdvidmHpnYWtR1dK/vQK7injI3MqTL+f4NECAkePE/ziGMGjxwh8cYzg0eMEjxVAIEDw0BGCh47UPqnFwJrZoSpgdOn0ZdjoXPW1xeWs/RwRaRMUKkSkZVVUYry/Gpav04pO0ioZhoHNVvevz5YOHEmXT6Ny5QaKn1tA+m031ggWZiBA8f8tIFRYTNJlF39Zv92Orfs52LqfU7v+YIjgiZOnwsZxgkeOETh1xyN45Bim10fwWEFV+MjfWev5lnbpp4ZVdayav1HtT0tqSqNfl4i0PgoVItIy/AFY/SHGu1rRSRJXYwOH3++PSuCw9+pB+h3fpfjZF/Ft/4Skiydj7dyR4NHjVLy/ilBhMel3fLfRy8kaVgu2TpnYOmXWWX+oqOTUsKpjBE4FjfDnZlk5oaJiQkXF+Hfuqn3slKRTczg6VbvTceoOR0a6Jo6LtHIKFSLSvLSikwhQM3A4HDXnC9UXOMIaavxnHTOMjCd+gufdDyhbuPjLjtqTRpN02cVR609hGAbWjHSsGelwfr9aj4fKygkeOX7qzka14HH0OKGTRZhlFQTK9hLYvbf2sZ2OyBCqGnM4unTCmtkeI9YT4kXkKylUiEjz0YpOIo3SmMARDAYjgSMYDEbe2Y8EjssvxHnlDOyGBcPpAKuVSsPAU1oa6cMRXtWqrs+beqfAkpKM5dxk7Of2qvWY6fUSOFpQew7HF8cIFpzA9PoI7D9EYP+h2ge2WrF2yqwZNsLDqjp1rJqwLiIxp1AhItGnFZ1EoiYcOOoaVvVVgSM8tKr6saoHivC2cLgID8EKB5Xw9saEkoYCiuF0Ys/qhj2rW+3X4A8QPH6iqtfGqdARueNx9Dj4AwS/OErwi6P4an9zsHTIwNa542lzOarChyXJ3bRvvog0mkKFiESPVnQSaVENBY7TmaZZ70d4boff748Mu6o+3yP8/DMJKKeHE6vVWn8QyUjD0qEd9kH9cVTbjmliFhZXG1J1vObE8UoPoYKT+ApOwo5Par1mS1pqJGx8eaejal6HkZaieRwiUaRQISJNpxWdROJeNIY4hTUmoNS17awDSnoy1vbnYh1yXiRIWQCjvAKOn4TjJzCPncA8XkDo2AlCR49jlpQRKiklVFKK/5Pdtb8fbledczhsXTphad9ODQBFzpBChYicPa3oJNImxTqghJ9nGAYkOTCzumD0PKdGbYbXh62wGONEEZYThVhOFmEpKMQ4UQjFpVDpIbBnP4E9+2s3ALTbsHTsgKVzx8hdjvBqVbbOHbHY7brLIXIahYoo2LhxIw8//DDr1q3D5/MxaNAgfvSjH3HttdfGujSR5qEVnUQkSpotoLhcVYtDZHUjaJoEqj/m81f933X8JNaTRRgnC7GeKMJyshhLYTGGP0Do8FFCh48SOP0cFgOzXTpktsfMzMDo1AFLp0yMTlVdyC0u51nNQznj1xoKVb0Oh113VdqA1vD3rVDRRMuXL2fGjBk4HA6+/e1vk56ezsKFC7nuuuvYu3cvDzzwQKxLFIkuregkInGq0RfoSUC7dOjTs9ZDZjCIWVgCBVVDqoyCk1BwEo6fxDhRiOEPVAWSk0XwabXnAX4glJpMqEMGwfbphNq3I9Qho+rPzAyMJHeNoV7Vw0X4o/oclLomypsHvsD33nL8G3LB5weHHef4UbgvvQh77x5RW81L4oN/7wEq3l5K5ZpNXy4XPXEUSZdPi9py0dFimKZpxrqI1ioQCDBgwAAOHjzIunXrGD58OAClpaWMHz+eTz75hI8++oh+/Wqv512XkpIS0tPTKS4uJi0trTlLr6WyspL9+/eTnJwcGc8qEhYMBtn5n+UM/uwQlp2fAadWdLpoIkydoBWdRKRtME0oKa2ax1FwsipwHC+EghNVoaPS0/DTk9yYmRmYHao+Qh2qhY6UJExqDwerPg/Fkf8xSf9cjJmWgm/UUMzMDCwnirBvyscoLsV79eWERgyuM7AAkc+rbw8fv66wc7aPSXRUrtpA8bMvYmnfjqSLJmHt0ongkWNU/Gc1oZNFpN/xXdyTx9b5XL/fz6JFi7j00kux25u27HJjr091p6IJli1bxu7du7npppsigQIgNTWVhx56iG9/+9u89NJL/PznPz+j427dupWUlJRol9sgj8fDkSNHcLvdChVSg72sgs4btzFk134MwDQMTgzsy9ER5xNwu+DT2iuuiIgkvGQ7JHeGnp0jm6weH46SMpwlZdX+LMdZXIa90oNRUYmxvxL2H651uKDNii8tBW9aCr605FN/puBNT8GfnISrsJj+C9+n8Nws9l8wEtMwquaXJGVCtyn0XJVL+3+8xUflJVRkVF34VX/fOBxSTv+8MeqbVH/6Pg19nD7sq/pz6jpufaGlvsdOr7O1sx0toOOL/6RyUD+KLpvy5UiAvt2g1zdp985yzGf+yK6yYgKdM2s9PxAIsHv3brZs2dKo1eEaUlZW1riam3SWNm758uUATJ8+vdZj4W0rVqw44+Oe6T/2aDj9nRERq9dHp7xP6Lh9F5Zg1cTIk726cWTMEHzpp0KvflZERCICTjuBjhlUdMyo9ZjFHzgVNMojfzpLy3AUl+Eor8AaCOI+WYz7ZHGt54YsBiGrFdNiELRZyfxoN77UZHwpSZF9Cob0I/3wcXrkfcKREQO/fLJZ6xOM6ttq/DdunvY4Vcv6AoRMwDz137755WNmtc9DZo3nVf1h1vy8+p+nPg9HgRqR4LS6DQwMg2r7Vgs6VAsap7ZZqoWN6p/X3Lf6c8w6txvGl7WElzk+/VxV+4e/D5z2eY1PTn1vG9oXME3cH+/GtNvwt0sled2WUy/KQtn44WCxUHhpDo59h0j+MJ+iy6dyumhe1zX2+QoVTbBrV1Vjr7qGN2VkZJCZmRnZpy5erxev98s1J0pKSoCqoSaBwOlTw5pXMBgkFApFVtqQBGaaWAJBQjZrnROqjWCQjh/v4ZytH2Pz+gEo7dyBNZ1S6TAyu+odpmCwpasWEWnVghYDf7tUytul1nrMCIZwlJWfChrlkcBR9WcFllAIS6jquqDjzs8bPE/6vi9I3/dFs7wGaXnpqzZFPg/ZbRSNHhL5umzoeaSt30rBjEm1fp+HryOjcT0ZbOTvfIWKJigurno3IT09vc7H09LSOHjwYL3Pf+KJJ5g/f36t7evWrcPlckWnSJFT2pV7GHD4JD1PlGALmQQsBvs6pPFx1/YUJbvANOlZUMKw/cdJORUmit0OtvTsxKGMFDAMTnz66VecRUREmsQCtHNAu/ZAewzTJKXSxxVbP2dXp3R8dhuplT5SvD7cvpoXjNZQCEfQpMJmBcuXF5l1vVVoGvU8blTfVvNC1Tztfahq779H/jDretyovq32m1m1j/vl8RquMZqvgciFeaxfg2Ga9Cko4Wiam1LXl3MWQxYLG1evjnzd63gxEwNB1q1cRdBa99D19evX17n9THg8Dc8VClOoiKGf/OQn3H333ZGvS0pK6NGjB+PHjyc5uWW7D3u9Xo4ePYrb7U6oMYlSJWP3AXrnf4wv2c3RYQPwpibjLC2n+6f76JO/lyND+pN26CjJJ4oA8LldHB5xPif6ZZFqsdA/FOLTTz+lf//+mnMjItLSTJPQtn0kde9KybABeIDjdezWZevHnJP3KTuv/3pCL+1d19yQuuaPNPTY6Z+ffu1z+va65pXU9Xj1x6pPkm9ojsnpxzWA0O/+gXVgf4Jjhka2Wy0WJlWbH5G+NpfQnqOMv2BynXcq1q9fz7hx45o8p6K8vLxR+ylUNEH4DkX4jsXpwrPl6+N0OnE6nbW2W63WJv8AnKlAIFBjSTtJHK4TRfReuTkyuY9qf7+lPbvS5701nJNfNdk6aLdxbOh5HB/Sj5DdxukLxIaXORQRkZZV2Lc7HT/dx/Hh59f4fzwiFKLjp/so7NsDazNfQ9Q34buhi/3GBIGwr7rAr/55XRPH67qQr34R39C26sePxvazVTnwXNrt/Bxvzti6l2sPBknJ/4TKgf2wNbC6k81ma/I1ZWN/7ytUNEF4LsWuXbsYOXJkjccKCwspKChgwoQJZ3zcWCzJpuXgElen7Z/hT3ZzIGdUpGGOvayCLpt2kPHp3sj0Mm+7VHZ/fUrVik7UvGGsnw8RkdgqGNKf9rv2k7VyMwdyRoHF8uXFeTBI1srN2MsrOXp+H4LBYKPfzT/988aq6535ui6yT7/Ar28VqNPfuW9ohaczWTGqtSofk03S9l1kLFpRc/UngGCQdouWYy2roPzUnYzTRfP3dmOfr1DRBDk5OTzxxBMsWbKEb3/72zUeW7JkSWSfMzVs2DD1qZDoCIUwXnoDc/pkhmRnV23b+RnGa//C8FeNxTWHD8JMT8W5ZjPnjx5V5y3zYDCI3+9nyJAhulMhItIEp6/K81Uf4QVUjJ49qbA6yPjnYtodP4l35FBC7dOxFhbj2LQNo6QMz9WXkzV8EFD/kqzVL+jraq5X17v3X3Ws+h6rvl3OXGVKOsazL5LyxXGSLp6MtXNHgkePU/H+KkKFxaTfOZdzGuhT8cUXXzB8+PCo9KloDIWKJrjooovo06cPL7/8MnfeeSfDhg0DqprfPfroo9hsNubMmRPTGqWN8wcw/H7MzPaRTca7yzH8AczePTBnXQK9esDGPIzl6zH9fnCokZ2ISHVnEgCAL4NAeOJvtc+h9rCc8LbqF/lWq7VGd23DMLBccg7moAEE3l+FZdWHkY7ajvEjcV4ylfY9u3/lhb+0Hu7JY7H16ErFO+9TtnDxlx21J40m6bKL466jtkJFE9hsNl544QVmzJjB5MmTueaaa0hLS2PhwoXs2bOHxx57jP79+8e6TGnL7DZMux0KTlZ9fbII4/P9mIaB+d2rod2pO2InCqv2a+K7GSIi8aKlg0D1AFAjCNRzF6ChuwMNSk+HwedjhkKYPj+G06GwkMDsvXqQ/oObSLvtxrj/+1aoaKKpU6eyevVqHn74YV599VV8Ph+DBg3i0Ucf5brrrot1edLWWSwwYjDGulzMaZMhd3vV9r49vwwUwSDG2s0wcnBCrxYiIvHvTANA+ONsg4DNZosEgnAYaJYg0AwMiwXDVXuxF0lMreHvW6EiCsaMGcPixYtjXYZIncwp4zE25mG8/CYcOlK1beSp5jnBIMbf3oDiUsyc8bErUkRaraYEgeqr+pimWeeFP3y5NGd4dcTqXzcUBL5qroCIRI9ChUii694F8/pZGH9ZiBEKVTU8Mk14d3nVHYriUszrZ0H3LrGuVERayJkGgNNXDap+Z+Bsg8DZ3A1QEBCJXwoVIm3BqKGYn+3DWLMRAMurb1fNoRg5uOoOhQKFSKtlmibBYDDyUT0QVN+nsUEgPCyo+sX/2QYBrSYo0nYoVIi0BaaJsWtP1afXzcQcNggcds2hEGklwsEhFAoRCAQIhUKRuwpAZE6A3W7H4XAoCIhIi1OoEGkLDh7BOFaAabfB0PPBqWVjReJNfcEhfJchHBLsdjtOpxOHwxHplhseYqQ+MiISKwoVIm2Akbut6pNB/eFUx2wRaXnVg8Ppw5UUHESkNVOoEEl0oRBsrgoVkVWfRKTZNDY42Gw2kpOTFRxEJCEoVIgkur0HMAqLMV1OGKhmjCLREF4pqa4J0goOItIWKVSIJDhj86mGd0MGVE3OFpFG+argUH251KSkJBwOB3a7PbJNwUFE2hKFCpFEFgzClqpQoaFPIrUpOIiIRIdChUgi27UHo7QcMzkJBvSNdTUiMRGN4FC9l4OIiNSmUCGSwIxTE7QZPgj0bqoksPqCQ5iCg4hI81KoEElU/gDk7QTAHDE4xsWINF1DwcE0zUgDOKvVGgkO4cAQHqZktVoVHEREmoFChUii2rkLo9KD2S4N+vaMdTUijaLgICLSOilUiCSoL4c+DQaLJbbFiJymemhoKDi43W6cTqeCg4hInFOoEElEXh9s/wTQqk8SO6cHB9M0Ix8KDiIiiUWhQiQRbfsYw+fHzGwPWV1jXY0ksLruOJimCVArOIQnR5/eAE7BQUSk9VOoEElAkaFPI4eALtikiaoHhkAgUOdyrAoOIiJtm0KFSKIpr4CdnwEa+iSN19jgkJKSouAgIiK1KFSIJJr8nRjBIGbXznBOp1hXI3GkemCoPs9BwUFERJpKoUIkwRibqoY+6S6FBAIBvF4vwWAQAMMwIuHA6XTidDprNYBTcBARkbOhUCGSSEpKYdeeqs9HKFS0RX6/PxIkbDYbLpeLlJSUyF0HBQcREWkOChUiiWTLDgzTxOzVHTIzYl2NtADTNPH7/fh8PoLBIHa7naSkJFJTU3G5XDgcDgUIERFpdgoVIgkkvOqThj4ltnCQ8Hq9hEIhHA4HKSkpJCcn43a7sdvtChIiItKiFCpEEsWJQow9BzANo6qLtiQU0zTx+Xx4vV5M08ThcJCWllYjSIiIiMSKQoVIogj3pujXC9JTY1qKREcoFMLn8+H3+zFNE7vdTrt27UhOTsblcilIiIhI3FCoEEkQRu52AExN0G7VwkHC5/MB4HA4yMjIICkpCZfLhc2m/7ZFRCT+6LeTSCI4chzj0BFMqxWGDYx1NXKGgsFgJEhYLBYcDgeZmZm43W5cLhdWqzXWJYqIiDRIoUIkARib86s+Ob8vJCfFthhplGAwiNfrxe/3Y7FYcDqddOrUKRIkLBZLrEsUERFpNIUKkdbONCPzKcyRQ2NcjDQkEAhE5kiEG9CFhzY5nU4FCRERabUUKkRauwNfYBw/iWm3w5DzYl2NnCbcQyIQCGC1WnG73XTo0AG3243T6dTSryIikhAUKkRaucjQp8HngdMZ22IE0zQJBAI1ulq73W5SUlJwuVwKEiIikpAUKkRas1AIwqs+qeFdzJzejM5ut5OcnBwJEupqLSIiiU6hQqQ1+3w/RlEJpssJA8+NdTVtSrgZnc/ni3S1Tk1NrREkRERE2gqFCpFWzAg3vMseCGqE1uzqakaXnp6uZnQiItLmKVSItFbBIGzdAWjoU3Oqqxld9a7WakYnIiKiUCHSen3yOUZZBWZKMvTvHetqEkooFIr0kABwOp20b98+EiTUjE5ERKQmhQqRVioy9Gn4INBFbpOFm9EFAgEMw8DpdJKenq6u1iIiIo2gUCHSGvn9kL8T0NCnpqjejC7c1TojI0NdrUVERM6QQoVIa7RjF4bHi5mRDr17xLqaViXcQyLcjC48tCncjE5BQkRE5MwpVIi0QkbuqaFPIwaDLoK/UriHRLgZncvlIjU1Vc3oREREokShQqS18Xhh+yeAhj7VJ9yMzufzEQwG1YxORESkmSlUiLQ22z7G8AcwO3WA7ufEupq4cXpXa4fDQUpKSiRI2O12BQkREZFmolAh0soYm/KrPhk5BNr4RXK4q7XX68U0TRwOB+np6SQlJeF2u9WMTkREpIUoVIi0JuUV8PFuAMwRbXPoU11drTMyMkhKSlJXaxERkRjRDE+gvLycv/71r1x11VX0798ft9tNu3btyMnJ4e9//3udz1m+fDmGYdT7sX79+hZ+FdImbP0IIxTC7N4FunSMdTUtJhQK4fF4KCkpoaysDICMjAy6d+9Oz5496dy5M6mpqQoUIiIiMaI7FcCqVau4/vrr6dChAxdddBGzZ8/m2LFjLFy4kGuvvZa1a9fy7LPP1vncnJwcpkyZUmt79+7dm7lqaYuMzVVDn9rCBO1gMBi5I2EYBg6Hg8zMTDWjExERiUMKFcA555zD3/72N771rW/VeKfz5z//OWPHjuU3v/kNN9xwA6NHj6713ClTpvDII4+0YLXSZhWXwGf7qj5P0KFP4a7W1ZvRtWvXTs3oRERE4px+QwPZ2dlce+21tYZOdO7cme9973sArFixIhaliXwpdzuGaWL27gHt28W6mqgJBAJUVFRQXFxMZWUldrudTp06kZWVRVZWFh06dCApKUmBQkREJI7pTsVXCAcNm63ub9WuXbt45plnqKiooGfPnkybNo3MzMyWLFHaCGNzVcM7c+TQGFfSdOEeEoFAINKMrkOHDpGu1lr6VUREpHVRqGhAMBjkz3/+M4ZhcPHFF9e5z8svv8zLL78c+drtdjN//nzuu+++rzy+1+vF6/VGvi4pKQGqLrj8fn8Tqz8zgUCAYDBIMBjENM0WPbc0QsFJ7PsOYRoGwewBEAy26OmDp84XPMvzmqZJIBCo0dXa7XaTkZFRqxldIBCIWt0iIiJtUfg6MhrXk409hkJFAx566CG2bdvGd7/7XQYPHlzjsY4dO/LLX/6Syy+/nKysLIqKivjggw+YN28e999/P2lpaZGhU/V54oknmD9/fq3tS5YsISkpKaqvRVq3rtt20QMo6dKBj3d9GrM68vLyYnZuEREROTNLly5t8jEqKioatZ9hJtDb0pmZmZw4caLR+3/wwQd1rtwE8Ic//IHvfe97DB8+nJUrV5KSktKoY27fvp2RI0eSkZHB4cOHGxwHXtedih49elBQUEBaWlqjX0c0eDweDhw4QHJyssauxyHbk89hfHGMwLevwBw3vMXPHwwGycvLIzs7u8FVl8LN6Hw+X6SrtdvtJjk5GafTicPhaMGqRURE2ia/38/SpUuZNm1ak5dbLykpITMzk+Li4gavTxPqTsU111xDaWlpo/fv0qVLndtfeuklbr31VoYMGcLSpUsbHSgABg8ezNixY1m1ahWfffYZ/fv3r3dfp9OJ0+mstd1ut7f4evuBQACr1YrValWoiDeHj2J8cQzTasUyfBDEcCnV8M9IdXU1o2vfvj3JyclqRiciIhJD0bimbOzzEypU1NdL4ky8+OKL3HLLLQwcOJD//Oc/dOjQ4YyPEZ6o3djbRSINMXKrJmgzsB8kuWNbzCnhIOHz+QBwOBy0a9cuEiTqW9hAREREEpN+81fz4osvcvPNN3P++eezbNkyOnY8847FgUCA3NxcDMMgKyurGaqUNsU0IbLqU+x7U1RWVhIMBrFYLDgcjhp3JNSMTkREpO1SqDjlj3/8I7fccgsDBgxg2bJldOrUqcH9161bx7hx42osfRkIBLjvvvvYt28fl1xyCe3bt2/usiXR7TuEUVCI6bDD4PNa/PThZnThuT9Wq5X27durq7WIiIjUoFABLFu2jFtuuQXTNLngggt47rnnau0zbNgwZs6cGfn6mmuuwTAMJkyYQLdu3SgqKmLlypV88sknZGVl8bvf/a4FX4EkqnBvCoYMAGfLTHIOBAKRORIWiwWXy0VqaiqfffYZ3bt3r3MekIiIiLRtChXA/v37I70Zfv/739e5z4033lgjVNx22228++67LF++nIKCAmw2G+eeey4PPvgg99xzDxkZGS1RuiSyUAhytwPNP/Qp3EMiPGHf6XTWuCMR7h2hSfwiIiJSF4UKYM6cOcyZM+eMnjNv3jzmzZvXPAWJAOzeh1FSiul2wYBzo354v99foxld+I6Ey+VSV2sRERE5I1ENFT/96U8ZNmwY3/zmNwEoLS3l4MGDDBgwQBcoImcoMvRp2ECwN+2fqmmaBINBfD5fpGu6zWYjOTmZlJSUWl2tRURERM5EVEPFggULmDZtGgDl5eUMHjyYAwcOcP755/Puu+/So0ePaJ5OJHEFg7BlBwDmiDMf+mSaJoFAAL/fTyAQiPSPsNvttGvXLtIjxW63K0iIiIhIk0V1gHRBQQF9+vQB4I033sBut3Po0CHGjRvHj3/842ieSiSxfbwbo6ISMzUF+vf+yt3DnazLy8spLi6mtLQUv9+P3W4nMzOTHj16kJWVRVZWFpmZmaSmpurOhIiIiERNVO9UdOvWjYMHD9KjRw9effVV5s6dyznnnMM999zDRRddFM1TiSS0yNCnEYOgjsnRoVAociciGAwCVR0vXS4XSUlJkTsRNptNwUFERESaXVRDxXe+8x3uuusuZs2axeLFi3nyySerTmKzUVxcHM1TiSQunw/ydwJfrvoUCoXw+/2REGGxWLDZbCQlJZGUlITD4cDhcGC322NZuYiIiLRRUQ0VDz30EKZp8uabb/LII48wYMAAADZt2qTu0iKNtWMXhtdHKCOdsswMgsXFkRCRkpJSI0TYbFrATURERGIvqlckVquV+fPnM3/+/BrbDx48yLXXXhvNU4kklGAwGLkT4V6fiwMIDhtIaloabrcbp9OJw+FQB2sRERGJS1ENFYcPH+bOO+9k5cqVhEIhsrOzmTNnDvfdd5/GdYtUEwgEIp2rTdPEarVit9tJdzjh0z0AdPraRTjPOSfGlYqIiIh8taiGihtuuIFjx47x9NNPk5yczJYtW7j//vt56aWXWLRoES6XK5qnE2k1wpOq/X5/JEQ4HA7at28f6RHhcDjwrlxPcSCAtVsXHH16xrpsERERkUaJaqhYv349a9asITs7G4BZs2bxwAMPcOmll3Lvvffym9/8JpqnE4lLdfWIsNls2O12UlNTcbvdkUnVltNWdqpc/SEA7kljdXdPREREWo2ohopBgwZFlrcMc7vdPPvss0yePFmhQhLS6SECiISIcKO58J2IhoJCqLgUX95HALgmjWmR2kVERESiocmh4oYbbiA7O5uhQ4cyb9487rnnHt58803S0tIi+wSDQQ19koRhmmaN5V1N08ThcOB0OsnIyIiEiDPtVu1ZtwlCIWx9e2Lr2rkZX4GIiIhIdDU5VCQnJ7Nw4ULmz59PeXk5hmHQs2dPrr/+erKzswkGgzz33HM89dRT0ahXpMWFG835fD6CwSCGYWC323G73bjd7siciKY2mqtcsxEA90TdpRAREZHWpcmh4rnnnot8/vnnn5OXl0d+fj55eXksWrSIPXv24HQ6efbZZ7n++uubejqRZldfo7nk5ORIj4hwt+poCZ44iX/nLgBcE0dH7bgiIiIiLSGqcyr69OlDnz59uPLKKyPbysrK2LZtG3l5edE8lUjUVO8REQqFsFgs2O32Fm0051mzEUwT+/n9sGa2b7bziIiIiDSHZm/Hm5KSwvjx4xk/fnxzn0qkUeoKEQ6Hg/T09MjKTC3daK5y9amhT5qgLSIiIq1Q1EPFa6+9xqJFi/B6vQwZMoRvfetbnHvuudE+jUij1dUjIrwyk8vlikysPn151xar7/BRArv3gsWCa/yomNQgIiIi0hRRvYp68sknuemmm/D7/bjdbv79738zcOBA5s+fH83TiNQrvLxrRUUFpaWllJSU4PV6sVqttG/fnm7dupGVlUXPnj3p3Lkz6enpuFyumAUKAM+aqt4UjqHnY0lPjVkdIiIiImcrqncq/u///o9XX32VSy+9NLJt/fr1XH311XTr1o2bb745mqcTqbfRnMPhOKMeEbFimma1hnca+iQiIiKtU1RDxcmTJxk4cGCNbePGjeO3v/0t8+bNU6iQJgv3iAgHCcMwajSaCy/veqY9ImIlsO8gwYNfgN2Gc8zwWJcjIiIiclaiGirGjRvHa6+9xn333Vdj++DBg9m7d280TyVtRLhHRHh5VwC73V6j0Vx4edfWECJO5zl1l8I5YgiW5KQYVyMiIiJydqIaKn7xi18wdepUCgsLuf322+natSumafLHP/6Rfv36RfNUkqBO7xERbjSXlJRUY3lXu90e61KbzDTNqqVkAfeksTGuRkREROTsRTVUjBw5kqVLl/K9732PJ598kvT0dHw+Hw6Hg4ULF0bzVJIg6ms015I9ImLFv+tzgscKMFxOnCOHxLocERERkbMW9Su1sWPHsnXrVnbs2MGOHTsifSoyMjKifSppheprNJeamhrpEeF0Olu0R0SseFadGvo0ZhiG0xnjakRERETOXqNDRceOHRk5ciTDhw9n5MiRjBgxgj59+tS7/6BBgxg0aFBUipTWKxAIEAgE8Pl8NXpExLLRXDwwgyE8azcB4NLQJxEREWnlGh0q/H4/S5YsYcmSJZEJsenp6YwYMYIRI0ZEgobmTrRtdTWaczgctG/fPrIyUywbzcUL345PCBUVY6Qk4xw68KufICIiIhLHGh0qioqK2L17N5s3b2bz5s3k5uaSm5vLsmXLWLZsWSRopKamMmzYsEjIuO6665qteImt+npEhIczhbtV2+32Nh8iThdueOcaNxLDnnjzRURERKRtOaOrmb59+9K3b1+uuuqqyLY9e/aQm5vL5s2b2bRpE1u3bmXlypWsWrUKwzAUKhLI6SECqNEjIt4bzcUL0x/As34zAC41vBMREZEE0OS3SHv37k3v3r2ZPXs2gUCApUuX8vTTT7Ns2bJo1CcxFG40F16ZyTTNyETqcI+I1tRoLl54t27HLKvAkpGOY2D/WJcjIiIi0mRNDhVer5f33nuP119/nbfeeovi4mJM0yQ7O5vZs2dHo0ZpQXX1iHC73bjd7siciNbaaC5ehHtTuCaMxrBqWJiIiIi0fmcVKioqKli0aBGvv/4677zzDuXl5QCMHj2a2bNnM3v27AZXhpL4Ew4QoVCI5OTkSI+IcLdqiQ7T68W7cSugoU8iIiKSOBp9tVhaWspbb73F66+/zrvvvovH48EwDCZNmsTs2bOZNWsW3bp1a85apRk5nU66du2KzWZTiGhGnk15mB4v1s4dsffrHetyRERERKKi0VePnTp1wufzYbfbmTJlCrNmzWLmzJl06tSpOeuTFmIYBi6XK9ZlJLxwwzvXxNEaQiYiIiIJo9Ghwuv1YhgGF154IRdffDH9+/fXRajIGQiVV+Ddsh3Q0CcRERFJLI0OFYMHD+bjjz/m3Xff5b333ots79OnT43mdyNGjKB9+/bNUqxIa+bZkAuBALYeXbH37B7rckRERESiptGhIj8/H6/XS15eXqQvRW5uLtu3b2f37t289tprkeEcWVlZkaDxwAMPNFvxIq2JZ/WpoU+6SyEiIiIJ5oxm5DqdTsaMGcOYMV9eFPn9fvLz82sEjW3btvGvf/2LN954Q6FCBAgWl+DbthNQqBAREZHE0+Rlfux2OyNHjmTkyJHccsstAAQCAXbs2MHmzZubXKBIIvCs3QQhE/u5vbB10eIGIiIikliaZe1Qm81GdnY22dnZzXF4kVZHQ59EREQkkamdr0gzCx4/gf/jz8AwcE0YHetyRERERKJOoUKkmXnWbATAPrAf1g4ZMa5GREREJPoUKkSaWeWpoU/uSWNjXImIiIhI81CoEGlGgUNHCOzZD1YrrvEjY12OiIiISLOISqhYuXIln376aTQOJZJQKldvAMCZPRBLakqMqxERERFpHlEJFVOmTOHJJ5+MxqFEEoZpmnhWV82ncE3Uqk8iIiKSuKI2/Mk0zWgdKibmzJmDYRh1fgwYMKDO54RCIX7zm98wdOhQ3G43HTt25KqrrmLXrl0tXL3Eo8CeAwQPHwGHHefY4bEuR0RERKTZNEufitbshz/8Ie3atauxLTMzs859b731Vp5//nkGDhzIHXfcwdGjR3nllVdYsmQJa9euZeDAgS1QscQrT3jo08ihWNyuGFcjIiIi0nwUKk7zox/9iF69en3lfh988AHPP/88kydPZunSpTidTgBuuOEGpk2bxm233caKFSuauVqJV2YoROWppWTdGvokIiIiCU6rP52l559/HoDHHnssEigALrroImbMmKHJ622c/9PdhApOYrhdOEcMiXU5IiIiIs1KdypO884771BaWorT6WTo0KFMmTIFq9Vaa7/ly5eTnJzMxIkTaz02Y8YM3n33XVasWEH//v3rPZfX68Xr9Ua+LikpAcDv9+P3+6PwaiRWylesB8AxOpuAxYBW/vcZ/nnUz6WIiEj8i+bv7cYeQ6HiNLfffnuNr/v378/f//53RowYEdlWXl7OF198weDBg+sMHP369QP4ygnbTzzxBPPnz6+1fcmSJSQlJZ1N+RIPQiFGrFyHHcizQ/GiRbGuKGqWLl0a6xJERESkkaLxe7uioqJR+ylUnJKTk8MVV1zBmDFjyMzMZO/evfz+97/n2WefZfr06eTn59O1a1cAiouLAUhPT6/zWGlpaTX2q89PfvIT7r777sjXJSUl9OjRg+nTp0eOIa2PL38npZ53MFKTmXDT9Ri22sGztfH7/SxdupRp06Zht9tjXY6IiIg0IJq/t8Mjab5KQoWKzMxMTpw40ej9P/jgA6ZMmQLATTfdVOOxAQMG8Ktf/YqkpCR+/vOf86tf/Ypf/vKX0SwXp9NZYz5GmN1u14VbK1axfjMArvGjcCTYqk/62RQREWk9ovF7u7HPT6hQcc0111BaWtro/bt06fKV+8ydO5ef//znrFmzJrItfIeivjsR4URX350MSVym349nfS4A7kljY1yNiIiISMtIqFDx7LPPRv2Y4R4V1ceTJScnc84557Bnzx6CwWCteRXhuRThuRXSdni3bMesqMTSPgP7+efGuhwRERGRFhGVJWXbt29PampqNA4VdzZsqGpgdnrvipycHMrLy2vcwQh77733IvtI2+JZ/SEAromjMCxasVlERETahqhc9RQUFPC///u/0ThUTBw5coTdu3fX2n7o0CHuvPNOoGpoVXX/9V//BcBPf/pTfD5fZPt//vMf3nvvPS644IIGl5OVxBOq9ODZmAdo6JOIiIi0LQk1/Olsffzxx1x44YVMmjSJAQMG0L59e/bu3cvbb79NeXk5N954I1dddVWN50ydOpWbb76ZF154geHDh3PZZZdx9OhRXnnlFdLS0njuuedi9GokVryb8sDnw9qlE7a+PWNdjoiIiEiLUagA+vbty9y5c/nwww/55z//SWlpKenp6UyYMIG5c+dy9dVX1/m83//+9wwdOpTf//73PPPMM6SkpPD1r3+dxx9/XHcp2qDI0KdJYzAMI8bViIiIiLQchQqgR48ePP/882f8PIvFwh133MEdd9zRDFVJaxIqLcO7dTsA7kljYlyNiIiISMvSTFKRKPBsyIVAEFvP7th6dI11OSIiIiItKiqh4ujRo9E4jEir5Vm9Eaga+iQiIiLS1kQlVHTt2pXjx49H41AirU6wsBjfjo8BcE0cHeNqRERERFpeVEKFaZqYphmNQ4m0Op61GyFkYu/fB1vnjrEuR0RERKTFaU6FSBNVX/VJREREpC2KWqh44403+Pjjj3XHQtqUwNHj+D/9HCwGrgmjYl2OiIiISExEbUnZO+64g0AggNvtZujQoQwfPjzyMWTIEBwOR7ROJRI3PGuqJmg7Bp2HNaNdbIsRERERiZGohYrPP/+ckydPsnXrVrZs2cKWLVt4+eWXKS4uxmazcf7555OXlxet04nEBQ19EhEREYlSqDAMA7vdzpAhQxgyZAjXX3995LE9e/aQm5vL1q1bo3EqkbgROHCYwL6DYLPiGjsi1uWIiIiIxExUQkVD8yh69+5N7969mT17djROJRI3Kk/dpXBmD8KSmhLjakRERERiJyoTtd966y3S09OjcSiRVsE0zS+HPk0eG+NqRERERGIrKqHisssuw+l0EggE+H//7/9x+eWXc+WVV1JQUBCNw4vEncDufQSPHAOHA+eo7FiXIyIiIhJTUe1Tcffdd/OHP/yBCy64gHfffZeSkhIA5s+fzz/+8Y9onkokpirXnLpLMWooFrcrxtWIiIiIxFZUQ8Urr7zCn/70J+6//35sti+na4wYMYJnn302mqcSiRkzFIosJeuapKFPIiIiIlENFR6Phy5dutTaPmDAAHbu3BnNU4nEjH/nZ4ROFGIkuXGOGBzrckRERERiLqqh4uKLL2bhwoW1tvt8PoLBYDRPJRIzkaFPY4dj2O0xrkZEREQk9qLW/A7gl7/8JWPHjsUwDEzTxDAMAoEATz75JMOGDYvmqURiwgwE8KzbBGjok4iIiEhYVENFnz59WLlyJbfeeisVFRXk5ORQWFiIw+Hg7bffjuapRGLCt+1jzJIyLOmpOIYMiHU5IiIiInEhqqGisrKS888/nxUrVvDZZ5+Rn5+P3W5n/PjxZGZmRvNUIjFRuXoDAK7xozCs1hhXIyIiIhIfohoqbrzxRl599VUAzj33XM4999xoHl4kpkyfH++GLQC4Jo2JcTUiIiIi8SOqE7UPHDjAo48+Wmu7x+PhO9/5TjRPJdLivLn5mJUeLJntsZ/XN9bliIiIiMSNqN6p+Oc//8mYMWMYOnQo3/jGNwA4fPgw3/jGN7BYoppfRFpc5eqqVZ/cE0dj6OdZREREJCKqV0bdunXj1VdfZe7cuezYsYP169czcuRIBg4cyMqVK6N5KpEWFar04N2cD2jok4iIiMjpmnynYu7cuWRnZ0c+Jk6cyM9//nOmT59OcXEx8+fP55577olGrSIx4/1wC/j8WLt2xtY7K9bliIiIiMSVJt+pcDgcvPLKK3z961+nQ4cO9OzZk7fffhuPx8N1113HFVdcEY06RWLKc2rok2vSGAzDiHE1IiIiIvGlyXcqnnvuucjnu3fvJi8vj7y8PCwWC0uWLOH5558nKSmJQYMGsWHDhqaeTqTFhUrL8OZ9BIB7ooY+iYiIiJwuqhO1+/btS9++fZk1a1ZkW0lJCVu3biU/Pz+apxJpMZ51myEYxNa7B7bu58S6HBEREZG40+ThT3feeScejweoChCnS0tL44ILLuD2229v6qlEYqL60CcRERERqa3JdypOnjxJZWUlLpeLdu3a0bNnT4YOHUp2dnbkz379+kWjVpEWFzxRiO+jTwFwT1CoEBEREalLk0PFX//618jnO3fuJD8/n7y8PLZu3cqCBQs4cOCA5lRIq+VZtwlME/t5fbF26hDrckRERETiUlTnVJx33nmcd955fOtb34psKy4uZuvWrWzbti2apxJpEV8OfRob40pERERE4ldUQ0Vd0tPTycnJIScnp7lPJRJVgSPH8O/aAxYD14SRsS5HREREJG5FtaN2ffbt28eNN97YEqcSiZrwXQrHkPOxtkuPcTUiIiIi8atFQkVZWVmNuRcirYFnzUYAXOpNISIiItKgFgkVIq2Nf/8hAvsPgc2Ga9yIWJcjIiIiEteiEip+/OMf8+qrr/Lpp59G43AiMedZVbVSmXP4YCzJSTGuRkRERCS+RWWi9uLFi/nVr35FIBAgOTmZ7Oxshg8fzogRIxg+fDihUCgapxFpEaZpfjn0SQ3vRERERL5SVEJFXl4ePp+Pbdu2sXXrVnJzc9m0aRMvvvgilZWVGIYRjdOItAj/Z3sIHj2O4XTgHJUd63JERERE4l7UlpR1OBwMHz6cjIwMLrnkErp164ZpmnzyySfk5uaydevWaJ1KpFmFV31yjh6GxeWMcTUiIiIi8S9qE7WffvppMjMz6devH1lZWXTu3Jl7772Xjh07cu211/LUU09F61QizcYMhjT0SUREROQMReVOxR//+EceeOABbrvtNnJycqisrGTz5s38+c9/5u9//zuLFi1i2LBh0TiVSLPy7fyUUGExRnISzmGDYl2OiIiISKsQlVDx61//mieeeIK77747su3aa6/l8ccf54477mDatGls376dzp07R+N0Is0mPPTJNW4Eht0e42pEREREWoeoDH/atWsXX//612ttd7lcPP/880yYMIGf/exn0TiVSLMx/QE86zYD4Jo0NsbViIiIiLQeUQkVaWlpeDyeeh+/6667eOedd6JxKpFm48v/CLOsHEu7NByDzot1OSIiIiKtRlRCxaRJk3j11VfrfbxXr14cOXIkGqdqFnv37sUwjAY/rFZrjecsX768wf3Xr18fo1cjZ6syPPRpwigMq5rNi4iIiDRWVOZU3HfffUydOpXRo0dzxRVX1Hp827ZtdOzYMRqnahbt2rXj4YcfrvOxTZs28c477zBjxow6H8/JyWHKlCm1tnfv3j2aJUozM71evB9uATT0SURERORMRSVUjB8/nqeeeoorr7ySb3/72/zgBz8gOzsbh8PBmjVr+NGPfsQ3v/nNaJyqWbRr145HHnmkzsfCc0VuvvnmOh+fMmVKvc+V1sO7eRumx4ulYwfs/fvEuhwRERGRViVqze/uvPNO+vfvzz333MOkSZMiw4BM02TKlCk8/vjj0TpVizl8+DCLFy+mU6dOdU5El8QRHvrknjhaHeBFREREzlDUQgXAJZdcwiWXXMKmTZvYsmULfr+f7OxsJk6cGM3TtJg//elPBINBbrjhBuz1LC+6a9cunnnmGSoqKujZsyfTpk0jMzOzhSuVpgiVV+DNzQfANVlDn0RERETOVFRCxYkTJ0hLS4tceI8aNYpRo0ZF49AxY5omL774IgBz586td7+XX36Zl19+OfK12+1m/vz53HfffV95Dq/Xi9frjXxdUlICgN/vx+/3n23pcoY86zaBP4C1WxfMrp31va9D+Hui742IiEj8i+bv7cYeIyqh4pZbbiEzM5M//OEPkW1bt25l4cKFdOrUiRtuuIG0tLRonKrFrFixgt27dzNp0iQGDBhQ6/GOHTvyy1/+kssvv5ysrCyKior44IMPmDdvHvfffz9paWl873vfa/AcTzzxBPPnz6+1fcmSJSQlJUXttUjDznt/Pe2AfZnpHFq8ONblxLWlS5fGugQRERFppGj83q6oqGjUfoZpmmZTT9atWzf+/Oc/c9FFFwGwf/9+Bg4ciN/vJxgM0qdPHzZs2EBGRkZTT9WgzMxMTpw40ej9P/jggzpXbgK4/vrr+etf/8pLL73EnDlzGn3M7du3M3LkSDIyMjh8+DAWS/1Lk9Z1p6JHjx4UFBS0uhDWWoVKSim89ccQCtHuV/OxntMp1iXFJb/fz9KlS5k2bVq9QwFFREQkPkTz93ZJSQmZmZkUFxc3eH0alTsVJ0+e5Nxzz418/ac//Yn27duTn5+P3+/nsssu4+mnn+axxx6Lxunqdc0111BaWtro/bt06VLn9qKiIl5//XXS0tK46qqrzqiGwYMHM3bsWFatWsVnn31G//79693X6XTidDprbbfb7bpwayEVG/MgFMLWtyeurG6xLifu6WdTRESk9YjG7+3GPj8qoeKcc87hxIkT9OzZE4D33nuPa665hnbt2gEwf/587r333mYPFc8++2xUjvPyyy9TWVnJDTfccFbDkMITtRt7u0hiJ7Lq06QxMa5EREREpPWKStvgKVOm8Nvf/haAL774go0bNzJ9+vTI4wMHDmTfvn3ROFWL+OMf/wjU35uiIYFAgNzcXAzDICsrK9qlSRQFT5zEv3MXAK4Jo2NcjYiIiEjrFZU7Ff/93//N8OHD+fDDDyksLKRz58415iocO3YMt9sdjVM1u61bt5Kbm8vQoUMbXMFq3bp1jBs3rkZPg0AgwH333ce+ffu45JJLaN++fUuULGfJs2YjAPbz+2HN1N+ViIiIyNmKSqjo1asXH374Ib/+9a8pKirihz/8IVarNfL4Bx980ODcgnjS2LsU11xzDYZhMGHCBLp160ZRURErV67kk08+ISsri9/97nctUa40gYY+iYiIiERH1Jrf9evXLzIE6nTbt2/nW9/6VrRO1Ww8Hg9/+9vfcDqdfOc732lw39tuu413332X5cuXU1BQgM1m49xzz+XBBx/knnvuafaVrqRpAoePEti9DywWXONbd08VERERkVhrdKjo2LEjI0eOZPjw4YwcOZIRI0bQp0+fRj33z3/+81kX2JJcLhcnT55s1L7z5s1j3rx5zVyRNBfPmqq7FI6h52NJT41xNSIiIiKtW6NDhd/vZ8mSJSxZsiQyjyA9PZ0RI0YwYsSISNDo169fsxUrEg2maWrok4iIiEgUNTpUFBUVsXv3bjZv3szmzZvJzc0lNzeXZcuWsWzZskjQSE1NZdiwYZGQcd111zVb8SJnI7DvIMGDX4DdhnPM8FiXIyIiItLqndGcir59+9K3b98aDeH27NlDbm4umzdvZtOmTWzdupWVK1eyatUqDMNQqJC44zl1l8I5YgiW5DPvQyIiIiIiNTV5onbv3r3p3bs3s2fPJhAIsHTpUp5++mmWLVsWjfpEoso0zchSsu5JY2NcjYiIiEhiaHKo8Hq9vPfee7z++uu89dZbFBcXY5om2dnZzJ49Oxo1ikSN/9PPCR4rwHA5cY4cGutyRERERBLCWYWKiooKFi1axOuvv84777xDeXk5AKNHj2b27NnMnj270StDibQkz+oNADjHDMdwOmJcjYiIiEhiaHSoKC0t5a233uL111/n3XffxePxYBgGkyZNYvbs2cyaNYtu3bo1Z60iTWIGQ3jWbgLApVWfRERERKKm0aGiU6dO+Hw+7HY7U6ZMYdasWcycOZNOnTo1Z30iUePb8QmhohKMlGScQwfGuhwRERGRhNHoUOH1ejEMgwsvvJCLL76Y/v3743K5mrM2kagKD31yjR+JYY9aM3kRERGRNq/RV1aDBw/m448/5t133+W9996LbO/Tp0+N5ncjRoygffv2zVKsyNky/QE863MBcE3U0CcRERGRaGp0qMjPz8fr9ZKXlxfpS5Gbm8v27dvZvXs3r732WqQBXlZWViRoPPDAA81WvEhjebduxyyvwJKRjmNg/1iXIyIiIpJQzmgMiNPpZMyYMYwZ8+U7vX6/n/z8/BpBY9u2bfzrX//ijTfeUKiQuBBueOeaOBrDaolxNSIiIiKJpckDy+12OyNHjmTkyJHccsstAAQCAXbs2MHmzZubXKBIU4U8XrwbtwJa9UlERESkOTTLbFWbzUZ2djbZ2dnNcXiRM+LdnIfp9WHt3BH7ub1jXY6IiIhIwtE4EEl4nlXVhj6dmvcjIiIiItGjUCEJLVRegXfLdgBck8fGuBoRERGRxKRQIQnNsyEXAgFsPbpiz1LHdxEREZHmoFAhCS2y6pMmaIuIiIg0G4UKSVjBomJ823YCChUiIiIizUmhQhKWZ+1mCJnY+/XG1qVTrMsRERERSVgKFZKwPGu+XPVJRERERJqPQoUkpODxE/g//gwMA9dEDX0SERERaU4KFZKQKk/dpXAM7I+1fbvYFiMiIiKS4BQqJCF5Vm8ENEFbREREpCUoVEjCCRw6QmDPfrBacY0fGetyRERERBKeQoUknMrVGwBwZg/EkpoS42pEREREEp9ChSQU0zTV8E5ERESkhSlUSEIJ7DlA8PBRcNhxjhke63JERERE2gSFCkkonvDQp5FDsbhdMa5GREREpG1QqJCEYYZCVK6pWvXJraFPIiIiIi1GoUIShv/T3YQKTmK4XTiHD4l1OSIiIiJthkKFJAzPqqoJ2s6xwzGcjhhXIyIiItJ2KFRIQjCDQTzrNgHgnjQ2xtWIiIiItC0KFZIQfNs+JlRcipGWgmPIgFiXIyIiItKmKFRIQvCsOdWbYtxIDJstxtWIiIiItC0KFdLqmX4/nvW5gIY+iYiIiMSCQoW0et7c7ZgVlVjaZ2A//9xYlyMiIiLS5ihUSKsXGfo0cRSGRT/SIiIiIi1NV2DSqoUqPXg25gEa+iQiIiISKwoV0qp5N+WBz4e1SydsfXvGuhwRERGRNkmhQlo1z6oNALgmjcEwjBhXIyIiItI2KVRIqxUqLcObtwMA96QxMa5GREREpO1SqJBWy7MhFwJBbD27Y+vRNdbliIiIiLRZChXSanlWn1r1SXcpRERERGIqYUPFypUruffee5k6dSrp6ekYhsGcOXMafE4oFOI3v/kNQ4cOxe1207FjR6666ip27dpV73M2btzIpZdeSkZGBsnJyYwZM4aXX345yq9GThcsLMK3/RMAXBNHx7gaERERkbbNFusCmsuLL77IggULSEpKIisri5KSkq98zq233srzzz/PwIEDueOOOzh69CivvPIKS5YsYe3atQwcOLDG/suXL2fGjBk4HA6+/e1vk56ezsKFC7nuuuvYu3cvDzzwQHO9vDbPs3YTmCb2/n2wde4Y63JERERE2rSEvVNx++23s337dkpKSnjppZe+cv8PPviA559/nsmTJ5Obm8tTTz3FggULeOeddygpKeG2226rsX8gEODmm2/GMAxWrlzJ888/z//7f/+PvLw8Bg0axMMPP9zgHQ5pGg19EhEREYkfCRsqRo0axaBBg7BarY3a//nnnwfgsccew+l0RrZfdNFFzJgxg5UrV/Lpp59Gti9btozdu3dz7bXXMnz48Mj21NRUHnroIQKBQKPCjJy5wNHj+D/9HCwGrgka+iQiIiISawkbKs7U8uXLSU5OZuLEibUemzFjBgArVqyosT/A9OnTa+0f3lZ9f4kez5qNADgGnYc1Iz3G1YiIiIhIws6pOBPl5eV88cUXDB48uM47G/369QOoMZwp/Hn4seoyMjLIzMz8yuFPXq8Xr9cb+To878Pv9+P3+8/8hbQRlaca3tnHj9T3qYWEv8/6fouIiMS/aP7ebuwxFCqA4uJiANLT637XOy0trcZ+jX3OwYMHGzzvE088wfz582ttX7JkCUlJSV9deBvkLipl6P5DhCwGK4oLCC5aFOuS2pSlS5fGugQRERFppGj83q6oqGjUfnEdKjIzMzlx4kSj9//ggw+YMmVK8xUUZT/5yU+4++67I1+XlJTQo0cPpk+fHgkyUlPFq29RCTiHDWbGlTNjXU6b4ff7Wbp0KdOmTcNut8e6HBEREWlANH9vN2YFVYjzUHHNNddQWlra6P27dOlyVucJ322ofieiuvA3s/pdicY8p767GGFOp7PGpPAwu92uC7c6mKaJb+0mAJIuGKfvUQzoZ1NERKT1iMbv7cY+P65DxbPPPtsi50lOTuacc85hz549BIPBWvMq6po/UX2exciRI2vsX1hYSEFBARMmTGjmytuWwO59BI8cA4cD56jsWJcjIiIiIqdo9adTcnJyKC8vZ82aNbUee++99yL7VN8fquY/nC68rfr+0nSVq6smaLtGZ2Nxu2JcjYiIiIiEKVSc8l//9V8A/PSnP8Xn80W2/+c//+G9997jggsuoH///pHtF110EX369OHll19m69atke2lpaU8+uij2Gw25syZ01LlJzwzFMKzpmrok2uiGt6JiIiIxJO4Hv7UFKtXr+aFF14A4Pjx45Ft4Qv9AQMG8OMf/ziy/9SpU7n55pt54YUXGD58OJdddhlHjx7llVdeIS0tjeeee67G8W02Gy+88AIzZsxg8uTJXHPNNaSlpbFw4UL27NnDY489ViOESNP4d35G6GQhRpIb54jBsS5HRERERKpJ2FDx2WefsWDBghrbdu/eze7du4GqoUnVQwXA73//e4YOHcrvf/97nnnmGVJSUvj617/O448/XmdAmDp1KqtXr+bhhx/m1VdfxefzMWjQIB599FGuu+665ntxbVBk6NO4ERiaKCwiIiISVxI2VMyZM+eMhx9ZLBbuuOMO7rjjjkY/Z8yYMSxevPgMq5MzYQYCeNZvBjT0SURERCQeaU6FxD3fto8xS8qwpKfiGDIg1uWIiIiIyGkUKiTuRYY+jR+FcdpyvyIiIiISewoVEtdMrw/vhi0AuCZr6JOIiIhIPFKokLjm3bINs9KDJbM99v59Y12OiIiIiNRBoULiWuXqDwFwTxyNYdGPq4iIiEg80lWaxK1QRSXezfkAuCaNjXE1IiIiIlIfhQqJW96NW8Hnx9q1M7bePWJdjoiIiIjUQ6FC4pbn1NAn16QxGIYR42pEREREpD4KFRKXQqVlePM+AsCtoU8iIiIicU2hQuKSZ91mCAax9c7C1q1LrMsRERERkQYoVEhc+nLo0+gYVyIiIiIiX0WhQuJO8EQhvo8+BcA9UQ3vREREROKdQoXEHc/ajWCa2Aeci7Vjh1iXIyIiIiJfQaFC4k71VZ9EREREJP4pVEhcCRw5hv+zvWAxcI0fGetyRERERKQRFCokroTvUjiGnI+1XXqMqxERERGRxlCokLiioU8iIiIirY9ChcQN//5DBA4cBpsN19gRsS5HRERERBpJoULihmfVBgCcwwdjSU6KcTUiIiIi0lgKFRIXTNPEs2YjAK7JGvokIiIi0pooVEhc8O/aQ/DocQyXE+fI7FiXIyIiIiJnQKFC4oJnTdUEbeeobCwuZ4yrEREREZEzoVAhMWcGQ18OfdKqTyIiIiKtjkKFxJzvo08JFRZjJCfhHDY41uWIiIiIyBlSqJCYCw99co0bgWG3xbgaERERETlTChUSU6Y/gGfdZgBck8bGuBoRERERORsKFRJT3vyPMMvKsbRLxzHovFiXIyIiIiJnQaFCYsqzuqrhnWvCKAyrfhxFREREWiNdxUnMmF4v3g+3Alr1SURERKQ1U6iQmPFu3obp8WLtlIm9f59YlyMiIiIiZ0mhQmKmMjz0aeJoDMOIcTUiIiIicrYUKiQmQuUVeHO3ARr6JCIiItLaKVRITHg/3AL+ANbu52Dr2T3W5YiIiIhIEyhUSExUrq5qeOeeNEZDn0RERERaOYUKaXGh4lJ8+TsBcE3U0CcRERGR1k6hQlqcZ90mCIWw9e2JrWvnWJcjIiIiIk2kUCEtrvrQJxERERFp/RQqpEUFC07i37kLqFpKVkRERERaP4UKaVGetRsBsJ/fD2uH9jGuRkRERESiQaFCWpSGPomIiIgkHoUKaTGBw0cJ7N4HFguu8aNiXY6IiIiIRIlChbQYz6m7FI7sgVjSU2NcjYiIiIhEi0KFtAjTNKlcvQEAtyZoi4iIiCQUhQppEYF9BwkeOgJ2G86xI2JdjoiIiIhEUcKGipUrV3LvvfcydepU0tPTMQyDOXPm1Lv/6tWrueeeexg5ciQdOnTA5XIxYMAA5s2bR1FRUZ3P6dWrF4Zh1Plx6623Ns8La6U8q6ruUjhHDMWS5I5xNSIiIiISTbZYF9BcXnzxRRYsWEBSUhJZWVmUlJQ0uP83v/lNCgoKmDRpEjfccAOGYbB8+XKeeuopXn/9ddauXUunTp1qPS89PZ0f/ehHtbaPGqWJyGGmaVK5pmopWa36JCIiIpJ4EjZU3H777dx3330MGDCAjRs3Mn78+Ab3v+uuu7jhhhs455xzIttM0+QHP/gBzz33HPPnz+e3v/1tree1a9eORx55JNrlJxT/p58TOn4Cw+XEOXJorMsRERERkShL2OFPo0aNYtCgQVit1kbtP2/evBqBAsAwDB566CEAVqxYEfUa2wrPqQnazjHDMZyOGFcjIiIiItGWsHcqosVutwNgs9X9rfJ6vSxYsIBDhw6RkZHBhAkTyM7ObskS45oZDOJZuwnQ0CcRERGRRKVQ8RVefPFFAKZPn17n40eOHKk1AfySSy7hL3/5C5mZmQ0e2+v14vV6I1+H5334/X78fn8Tqo4f/m0fEyoqwUhJxhjYL2FeV1sT/nvT35+IiEj8i+bv7cYeQ6GiAVu3bmX+/Pl06tSJ+++/v9bj3/3ud8nJyWHQoEE4nU4++ugj5s+fz+LFi7niiitYs2YNhmHUe/wnnniC+fPn19q+ZMkSkpKSovpaYqX32jw6AUfO6cD6JUtiXY400dKlS2NdgoiIiDRSNH5vV1RUNGo/wzRNs8lnayaZmZmcOHGi0ft/8MEHTJkypdb29evXM378eG688Ub+9Kc/NepYe/bsYfLkyRQUFLB48WKmTp3aqOeFQiFycnJYvXo1b7/9Npdddlm9+9Z1p6JHjx4UFBSQlpbWqPPFM9Pvp/DWeZjllaQ9dBf2Qf1jXZKcJb/fz9KlS5k2bVpkSKCIiIjEp2j+3i4pKSEzM5Pi4uIGr0/j+k7FNddcQ2lpaaP379KlS1TOu2/fPqZOncrx48d5/fXXGx0oACwWCzfddBOrV69mzZo1DYYKp9OJ0+mstd1utyfEhZtn6w7M8kosGem4h5yPYU3YdQHajET52RQREWkLovF7u7HPj+tQ8eyzz7b4Offu3cvUqVM5fPgwr732GpdffvkZHyM8l6Kxt4sSlWf1hwC4Jo5WoBARERFJYHEdKlra3r17mTJlCocPH+aVV17hG9/4xlkdZ8OGqiVUe/XqFcXqWpeQx4t341YAXFr1SURERCSh6e3jU8KB4tChQ/zjH//gyiuvbHD/jz76iKKiolrbV69ezf/8z//gdDqZNWtWM1Ub/7yb8jC9PqydO2I/t3esyxERERGRZpSwdypWr17NCy+8AMDx48cj28LLvw4YMIAf//jHkf2nTJnCvn37GDduHPn5+eTn59c6ZvXO2a+++ipPPfUUF110Eb169cLpdLJ9+3aWLFmCxWLhd7/7HVlZWc33AuNcjaFPDayAJSIiIiKtX8KGis8++4wFCxbU2LZ79252794NQE5OTo1QsW/fPqBqpaj169fXeczqoWLq1Kns3LmT3NxcVqxYgcfjoXPnzlx99dXcddddjBnTdof8hMor8G7ZDoBr8tgYVyMiIiIizS1hQ8WcOXNqNaVryJmurJuTk0NOTs4ZVtU2eNbnQiCALasb9qxusS5HRERERJqZ5lRI1HnWfDn0SUREREQSn0KFRFWwqBjftp2AVn0SERERaSsUKiSqPGs3Q8jE3q83ti6dYl2OiIiIiLQAhQqJKs/qqh4dukshIiIi0nYoVEjUBI+dwP/JbjAMXBM0n0JERESkrVCokKipXFs1QdsxsD/W9u1iW4yIiIiItBiFComaSMM7DX0SERERaVMUKiQqAge/ILDnAFituMaPjHU5IiIiItKCFCokKipP9aZwZg/EkpoS42pEREREpCUpVEiTmaapoU8iIiIibZhChTRZYM9+goePgsOOc8zwWJcjIiIiIi1MoUKaLHyXwjlyKBa3K8bViIiIiEhLU6iQJjFDISrXbATAraFPIiIiIm2SQoU0if+T3YQKTmK4XThHDI11OSIiIiISAwoV0iSRoU9jh2M47DGuRkRERERiQaFCzpoZDOJZtwkA96SxMa5GRERERGJFoULOmm/bx4SKSzHSUnAMGRDrckREREQkRhQq5Kx5Vm8AwDV+FIbNFuNqRERERCRWFCrkrJh+P54NWwBwT9SqTyIiIiJtmUKFnBVv7nbMikosHTKwn39urMsRERERkRhSqJCzEhn6NHE0hkU/RiIiIiJtma4G5YyFKj14NuUDGvokIiIiIgoVcha8m/LA58PapRO2vj1jXY6IiIiIxJhChZwxz6pTQ58mjcEwjBhXIyIiIiKxplAhZyRUWoY3bwcA7kka+iQiIiIiChVyhjwbciEQxNazO7YeXWNdjoiIiIjEAYUKOSOe1R8CVUOfRERERERAoULOQLCwCN/2TwCFChERERH5kkKFNJpnzSYwTezn9cXWKTPW5YiIiIhInFCokEbzrDk19Gni6BhXIiIiIiLxRKFCGiVw9Dj+Tz8Hi4FrgkKFiIiIiHxJoUIaxbNmIwCOQQOwZqTHuBoRERERiScKFdIoX676pLsUIiIiIlKTQoV8pcCBwwT2HQSbFde4kbEuR0RERETijEKFfKXKU3cpnMMGY0lJjnE1IiIiIhJvFCqkQaZp4lm9AVBvChERERGpm0KFNCiwex/BI8fB4cA5KjvW5YiIiIhIHFKokAZVhu9SjM7G4nbFuBoRERERiUcKFVIvMxSq6qKNhj6JiIiISP0UKqRe/p2fETpZiJHkxjl8cKzLEREREZE4pVAh9YoMfRo3AsNuj3E1IiIiIhKvFCqkTmYggGfdZkBDn0RERESkYQoVUidf/k7M0jIs6ak4Bg+IdTkiIiIiEscUKqROlWuqGt65xo/CsFpjXI2IiIiIxLOEDRUrV67k3nvvZerUqaSnp2MYBnPmzKl3/+XLl2MYRr0f69evr/N5Gzdu5NJLLyUjI4Pk5GTGjBnDyy+/3EyvqmWYXh/eDVsAcE3W0CcRERERaZgt1gU0lxdffJEFCxaQlJREVlYWJSUljXpeTk4OU6ZMqbW9e/futbYtX76cGTNm4HA4+Pa3v016ejoLFy7k/7d390FR3fcexz8HFlYwLMpAHojgAxBxyJhSjRmNaHJ9IA/+YW5HW4KdcGNaE4gpk9QQMd4VMZpEp2ZqtGa0Wjsd27EztGNrNUKUB29MJFof0jQOMKDmctOICmyy4WHD3j8sRMLjemB3hfdrhj8453fO73tWxt9+9nd+Z9PT01VTU6Pc3Fyzl+ETzafOyf11kwIiIxR0T5yvywEAAICfG7Kh4vnnn9eKFSuUmJio8vJyTZ8+vV/HPfTQQ1qzZk2f7Vwul5555hkZhqHS0lIlJydLkux2u6ZPny673a5FixYpISHBzGX4RPutTyEP3i8jYMhOZgEAAGCADNl3jFOnTlVSUpICB2k9wJEjR1RVVaUnn3yyI1BIUlhYmFavXi2Xy6Xdu3cPSt+Dqc35tZpPnpUkjZj5gI+rAQAAwK1gyM5U3KyKigr98pe/lNPp1NixYzVv3jxFRkZ2aVdcXCxJmj9/fpd97dtKSkoGtdbB0HzitNTSqsDoO2UZH+PrcgAAAHALIFR8x969ezsttA4JCVFeXp5WrFjRqV1FRYUkdXt70+jRoxUZGdnRpifNzc1qbm7u+L193Udra6taW1tv+hrMcJZdX5AePGOKXC6XT2qA/2n/e/TV3yUAAOi/gRy3+3sOQsW/RUVFaePGjVqwYIFiY2NVX1+vo0ePKicnRy+//LJsNpuWLVvW0b6hoUGSFB4e3u35bDabPvvss1773LBhg/Ly8rpsP3z4sEJDQ01czc2xNDUr+cwnCpD0QatTTX/7m9drgH8rLCz0dQkAAKCfBmLcdjqd/Wrn16EiMjJSV65c6Xf7o0ePdvvkpv5ISkpSUlJSx++hoaFKT0/XfffdpylTpshut+snP/mJAgZw4fLKlSv14osvdvze2NiomJgYzZ8/XzabbcD66Q93W5ua3i2R0+1W4LgY/UfaYq/2D//W2tqqwsJCzZs3T0FBQb4uBwAA9GIgx+3+PkHVr0NFWlqaHA5Hv9vfeeedA17DvffeqwceeEBlZWWqrKzUPffcI+nbGYr2GYvvamxs7HEWo53VapXVau2yPSgoyGtv3FprLsn510J9/T8fSS0tkiQjMED6388VNI41FejMm3+bAADAnIEYt/t7vF+Hii1btvi6BEnqWKh94/RP+1qKiooKTZkypVP7a9euqa6uTjNmzPBekTfh67IP1bBllwIiRin0kYfk3H9YktR2rVFXXl6n8OVPKySFJ0ABAACgd0P2kbIDxeVy6dSpUzIMQ7GxsR3bZ8+eLen6+ofvat/W3sYftdZcUsOWXRqRMk1Rb7+mwIhRkqSgxHhF/WqDRqRMU8OWXWqtueTbQgEAAOD3CBX/dvz4cbnd7k7bXC6XVqxYoQsXLig1NVUREREd++bMmaMJEyZo7969On36dMd2h8Oh/Px8WSwWZWRkeKl6zzn/WqiAiFEKf+4pGRaLmo5d/8K7ETOnybBYFP7cUwoYHS7ngSIfVwoAAAB/59e3P5lx7Ngx7dy5U5J0+fLljm3tb/QTExP1yiuvdLRPS0uTYRiaMWOG7r77btXX16u0tFTnz59XbGystm/f3un8FotFO3fuVGpqqlJSUpSWliabzaaCggJVV1dr3bp1Hesv/I27rU1f/89Huu0/H5Vhscj1f/9Sa2WNFGBoxIypkiTDYlHo3BR9WXBQtswMGYbh26IBAADgt4ZsqKisrNSePXs6bauqqlJVVZWk67cm3RgqnnvuOR06dEjFxcWqq6uTxWJRfHy8Vq1apZdeekmjR4/u0sfDDz+sY8eOyW63a9++fWppaVFSUpLy8/OVnp4+uBdogrulVWppUeCdt0uS2hq/lGV8rAJstykw/NunTgXeEXV98XZLi9TNgnIAAABAGsKhIiMjw6Pbj3JycpSTk+NxP9OmTdPBgwc9Ps6XjOAgKThY33z+hSQpeGKcIjf9t9w3fBGfJH3zr8tScPD1HwAAAKAHrKkYhoyAAIU8OFXO947JfcO3Zhs3zEa4XS45i8oUMvN+bn0CAABArwgVw1Tognlqu1qvhl/t6RQspOuBomHbHrVda1Do43N9VCEAAABuFUP29if0LmhcjMKXP62GLbvU8vF5hc5NUeAdUfrmX5flLCpT27UGhS9/mi/AAwAAQJ8IFcNYSMoDssREy3mgSF8WHLy+IDs4WCEz71fo43MJFAAAAOgXQsUwFzQuRuFZ/yXbc0/J3dIqwxrMGgoAAAB4hFABSdcXbxsjeGwsAAAAPMdCbQAAAACmECoAAAAAmEKoAAAAAGAKoQIAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmGLxdQH4ltvtliQ1Njb6uBKgs9bWVjmdTjU2NiooKMjX5QAAgF4M5Ljd/r60/X1qTwgVfsThcEiSYmJifFwJAAAA8C2Hw6Hw8PAe9xvuvmIHvKatrU21tbUKCwuTYRimznX//fervLx8gCobWP5Qmy9q8Eafg9VHY2OjYmJidOnSJdlstgE/P4Ynf/i/YKgarq/trXzd/l67P9Tn7Rq81d9g9DOQ47bb7ZbD4VB0dLQCAnpeOcFMhR8JCAjQmDFjBuRcgYGBfvvmzx9q80UN3uhzsPuw2Ww+/7fD0OEP/xcMVcP1tb2Vr9vfa/eH+rxdg7f6G8x+Bmrc7m2Goh0LtYeorKwsX5fQI3+ozRc1eKNPf3htgf7i73XwDNfX9la+bn+v3R/q83YN3urPH17bgcDtTwD61NjYqPDwcDU0NPj8kyoAANA7X4zbzFQA6JPVapXdbpfVavV1KQAAoA++GLeZqQAAAABgCjMVAAAAAEwhVAAAAAAwhVABAAAAwBRCBYABU1BQoHnz5ikiIkKGYaimpsbXJQEAgB5s2LBBU6dOVVhYmO644w4tXrz4psduQgWAAfPVV18pJSVFr732mq9LAQAAfSgpKdHy5cv14Ycf6tChQ6qvr9ejjz4ql8vl8bl4+hOAAffpp59q0qRJqq6u1rhx43xdDgAA6IdLly4pNjZWZ86c0eTJkz06lpkKYJj53e9+p2XLlmnq1KmyWq0yDEO/+c1vej2mvLxcjz32mEaPHq2RI0dq2rRp2rt3r3cKBgBgGPPmuN3Q0CBJioiI8LhOi8dHALilvfrqq7pw4YIiIyN111136cKFC722Ly4uVmpqqoKDg/WjH/1I4eHhKigoUHp6umpqapSbm+ulygEAGH68NW63tbXppZde0mOPPaYxY8Z4XCczFcAws3PnTtXU1Ojy5ct69tlne23rcrn0zDPPyDAMlZaWaseOHdq0aZPOnDmjpKQk2e12VVRUeKlyAACGH2+M2263W8uWLVN1dXWfsyA9IVQAw8zcuXM1duzYfrU9cuSIqqqq9OSTTyo5Oblje1hYmFavXi2Xy6Xdu3cPVqkAAAx7gz1uu91uZWZmqqioSO+9956ioqJuqk5ufwLQo+LiYknS/Pnzu+xr31ZSUuLNkgAAQA88HbfdbreysrJ04MABlZSUKCYm5qb7JlQA6FH7FGlCQkKXfaNHj1ZkZGSnadSrV6/q4sWLHc+4/uSTT1RfX6/Y2NibWvQFAAD6z9NxOzMzU3/4wx/0l7/8RSEhIfr8888lXV+oHRwc7FHf3P4EoEftT4EIDw/vdr/NZutoI0n79+9XcnKynnjiCUnS448/ruTkZO3fv3/wiwUAYJjzdNzevn276uvrlZKSorvuuqvj5/333/e4b2YqAAyYjIwMZWRk+LoMAADQDwP5dXXMVADoUfsnHTd+qnGjxsbGHj8NAQAA3uXLcZtQAaBH7fdkdvf4uWvXrqmurq7b+zYBAID3+XLcJlQA6NHs2bMlSYcPH+6yr31bexsAAOBbvhy3CRUAejRnzhxNmDBBe/fu1enTpzu2OxwO5efny2KxsIYCAAA/4ctx23AP5AoNAH5v586dOnbsmCTp3LlzOnXqlB588EHFx8dLkhYuXKiFCxd2tD969KhSU1NltVqVlpYmm82mgoICVVdXa926dVq1apUvLgMAgGHhVhm3CRXAMJORkaE9e/b0uN9ut2vNmjWdtp04cUJ2u13Hjx9XS0uLkpKSlJ2drfT09EGuFgCA4e1WGbcJFQAAAABMYU0FAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFEIFAMBrnE6n1q9fr+9///u67bbbNGLECI0ZM0YpKSlauXKlqqqqOtrW1NTIMAwZhqEFCxZ0e77i4mIZhqFnn3222+Paf4KCgnT33Xdr8eLF+uijjwb9OgFguLH4ugAAwPDgcDg0c+ZMnT17VvHx8VqyZIlGjRqlS5cu6R//+Idef/11xcXFKS4ursuxBw4cUGlpqWbNmtXv/uLi4rRkyRJJ0ldffaWTJ0/qj3/8o/785z+rqKjIo3MBAHpHqAAAeMVbb72ls2fPaunSpdqxY4cMw+i0v7q6Ws3NzV2OGzdunC5evKicnBwdP3683/3Fx8drzZo1nba9/vrrWrlypVavXq2SkpKbug4AQFfc/gQA8Ir2QPD88893CRSSNH78eCUmJnbZPnHiRP34xz/WBx98oIKCAlM1LF26VJJ08uRJj45zOp1au3atEhISZLVaFRcXpy1btuj999+XYRhavXq1qboA4FZHqAAAeEVERIQkqbKy0uNj165dK6vVqtzcXH3zzTema7FY+j9R73A4NHv2bNntdo0dO1bZ2dmaPHmyXnjhBa1du1aS9L3vfc90TQBwKyNUAAC8YtGiRZKuzxa88sorOnLkiK5du9avY2NjY5WVlaXz58/r17/+9U3X8M4770iSZs6c2e9jMjIy9Pe//12///3vVVRUpDfeeEN/+tOflJ+fr3fffVcSoQIADLfb7fZ1EQCA4WHjxo1au3atvvzyy45tcXFxeuSRR/Szn/1MCQkJHdtramo0fvx4paam6tChQ7p69aomTJigkSNHqqKiQqGhoSouLtbDDz+sZcuWafv27Z2O++5C7fLycpWUlOj2229XcXGxJk2a1Ge9R44c0Zw5c5SRkaHdu3d32vfZZ58pJiZGNptN9fX13d7SBQDDBTMVAACvWbFihWpra7Vv3z5lZ2dr5syZunjxorZu3arJkydr//79PR4bERGhnJwc1dbW6q233uqzr6qqKuXl5SkvL0+bNm3qCBRlZWX9ChSStHXrVhmGoVWrVnVbjyTdd999BAoAwx6hAgDgVWFhYVq0aJE2b96ssrIyXb58WZmZmWpqatLSpUvV0tLS47HZ2dmKjo7Wm2++qStXrvTaT2pqqtxut9xut7744gtt3LhRdXV1WrhwYaeZkt4UFRVp4sSJio+P77KvtrZWErc+AYBEqAAA+Fh4eLjefvttjR07VnV1dTp37lyPbUNCQrRmzRo1NDRo/fr1/e4jKipKP//5z5Wbm6t//vOfevXVV/s8pr6+Xo2NjYqJiel2f2FhoSRCBQBIhAoAgB8wDEOhoaH9avv0008rMTFRW7du1cWLFz3qJzc3V9HR0dq2bZtqamp6bRsUFCRJ3c6INDU16Re/+IUkKTk52aMaAGAoIlQAALzinXfeUXl5ebf7CgoK9Omnn2rUqFG69957ez1PYGCg1q9fr+bm5o5HuvZXSEiIcnJy1Nraqvz8/F7bjhw5UrGxsTp9+rQ+/vjjju1NTU1asmSJKisrFRQUpKSkJI9qAIChiFABAPCKgwcPatq0aUpISFBGRoZyc3P1wgsvaNasWfrBD34gwzC0bds2Wa3WPs/1xBNPaPr06aqqqvK4jp/+9KeKjo7Wb3/72z6Pf/HFF9XW1qZZs2YpMzNT2dnZmjRpkhwOh4KDgzVp0iQFBwd7XAMADDWECgCAV7zxxht68803NX78eJWWlmrz5s3asWOHamtr9dRTT+nEiRNKS0vz6Hw3Y8SIEVq5cqVcLpfy8vJ6bbt8+XLl5eUpNDRUu3btUmFhobKysrR582a1tLSwngIA/o3vqQAAwEP79u3TD3/4Q23evFnZ2dm+LgcAfI6ZCgAAPHTmzBlJPPkJANoRKgAA8NDp06clESoAoB23PwEA4KExY8bIYrH0+VhaABguCBUAAAAATOH2JwAAAACmECoAAAAAmEKoAAAAAGAKoQIAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJjy/y9ROgGc0LwMAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(q, Nq-catNq, color=color_list[9], marker='o', ls='-', mfc='none', ms=7, label='$N_{SOLikeT}-N_{obs}$')\n", + "plt.fill_between(q, -np.sqrt(catNq), np.sqrt(catNq), alpha=0.2, color='gray', label='$\\pm\\sqrt{N_{obs}}$')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{obs}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_SNRbased-inj_obs_diff_ex2zbins.pdf')\n", + "plt.savefig('0Nq_SNRbased-inj_obs_diff_ex2zbins.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "432660c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chi2 : 21.20431647685156\n", + "dof : 6\n" + ] + } + ], + "source": [ + "obs = catNq\n", + "exp = Nq\n", + "\n", + "chi2 = (np.power(obs - exp, 2) / exp).sum()\n", + "\n", + "print(\"chi2 : \", chi2)\n", + "print(\"dof : \", len(exp))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f76264f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chi2 : 25.02280668624213\n", + "dof : 18\n" + ] + } + ], + "source": [ + "obs = catNz[2:]\n", + "exp = Nz[2:]\n", + "\n", + "chi2 = (np.power(obs - exp, 2) / exp).sum()\n", + "\n", + "print(\"chi2 : \", chi2)\n", + "print(\"dof : \", len(exp))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9436d2ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(q, np.abs(Nq/catNq-1), color=color_list[9], marker='o', ls='--', mfc='none', ms=7, label='fractional error')\n", + "plt.fill_between(q, 0, np.sqrt(catNq)/catNq, alpha=0.2, color='gray', label='$\\sqrt{N_{obs}}$')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('fractional error', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "#plt.xlim(0, 2.0)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_SNRbased-inj_obs_frac_ex2zbins.pdf')\n", + "plt.savefig('0Nq_SNRbased-inj_obs_frac_ex2zbins.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4a45ccf1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5.30884444, 9.44060876, 16.78804018, 29.85382619, 53.08844442,\n", + " 94.40608763])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6dc85d3f", + "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": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/soliket/clusters/notebooks/CCL_nemo_vs_SOLikeT_binned_injection.ipynb b/soliket/clusters/notebooks/CCL_nemo_vs_SOLikeT_binned_injection.ipynb new file mode 100644 index 00000000..b8f12400 --- /dev/null +++ b/soliket/clusters/notebooks/CCL_nemo_vs_SOLikeT_binned_injection.ipynb @@ -0,0 +1,939 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c42b0b2f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from soliket import BinnedClusterLikelihood\n", + "from cobaya.model import get_model\n", + "import camb\n", + "from astropy.io import fits\n", + "from astropy import table\n", + "from astLib import astWCS\n", + "import math\n", + "from nemo import completeness, MockSurvey\n", + "\n", + "import sys\n", + "sys.path.append('../')\n", + "import nemo_mocks\n", + "import imp\n", + "imp.reload(nemo_mocks)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "72955ee8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing clusters.py Binned Clusters\n", + "Running injection based selection function.\n", + "Using averaged Q from source injection.\n", + "Running completeness with down-sampled selection function inputs.\n", + "Total number of clusters in catalogue = 5738.\n", + "SNR cut = 5.0.\n", + "Number of clusters above the SNR cut = 3169.\n", + "The lowest redshift = 0.01\n", + "The highest redshift = 1.96\n", + "The lowest SNR = 5.00.\n", + "The highest SNR = 51.99.\n", + "Reading in full Q function.\n", + "Initial number of tiles = 280.\n", + "Number of tiles after removing the tiles with zero area = 264. \n", + "Reading in full RMS table.\n", + "Number of RMS values = 40672.\n", + "Down-sampling RMS and Q function using 50 bins.\n", + "Number of down-sampled RMS = 50.\n", + "Number of down-sampled Q funcs = 50.\n", + "/Users/eunseonglee/.local/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3430: RuntimeWarning: Mean of empty slice.\n", + " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Number of redshift bins = 20.\n", + "Number of SNR bins = 6.\n", + "Number of redshift points for theory calculation = 200.\n", + "Number of mass points for theory calculation = 530.\n", + " Total predicted 2D N = 3201.2172766399517\n", + "Number of clusters in redshift bin 0: 96.77418063341094.\n", + "Number of clusters in redshift bin 1: 358.86003198943274.\n", + "Number of clusters in redshift bin 2: 472.2329584094609.\n", + "Number of clusters in redshift bin 3: 486.51160696245086.\n", + "Number of clusters in redshift bin 4: 435.9029472638175.\n", + "Number of clusters in redshift bin 5: 361.9233039390036.\n", + "Number of clusters in redshift bin 6: 285.92270392094196.\n", + "Number of clusters in redshift bin 7: 215.16277694057007.\n", + "Number of clusters in redshift bin 8: 157.33595388567755.\n", + "Number of clusters in redshift bin 9: 110.92709271728684.\n", + "Number of clusters in redshift bin 10: 75.30174628331966.\n", + "Number of clusters in redshift bin 11: 50.168868495127114.\n", + "Number of clusters in redshift bin 12: 33.34705053250085.\n", + "Number of clusters in redshift bin 13: 22.19467729924078.\n", + "Number of clusters in redshift bin 14: 14.594384776440062.\n", + "Number of clusters in redshift bin 15: 9.512987878857173.\n", + "Number of clusters in redshift bin 16: 6.2194533261531575.\n", + "Number of clusters in redshift bin 17: 4.085145806814587.\n", + "Number of clusters in redshift bin 18: 2.6690947265487077.\n", + "Number of clusters in redshift bin 19: 1.5703108528968366.\n", + "------------\n", + "Number of clusters in snr bin 0: 2020.4019192914282.\n", + "Number of clusters in snr bin 1: 948.2673991613391.\n", + "Number of clusters in snr bin 2: 195.55239949620054.\n", + "Number of clusters in snr bin 3: 33.005490168715475.\n", + "Number of clusters in snr bin 4: 3.7638848203224153.\n", + "Number of clusters in snr bin 5: 0.2261837019460672.\n", + "Total predicted 2D N = 3201.2172766399517.\n", + "Theory N calculation took 0.178 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " ::: 2D ln likelihood = 153.01177780782223\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-153.01177781])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.261, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='/Users/eunseonglee/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 5.,\n", + " 'method': 'injection',\n", + " 'whichQ': 'injection',\n", + " 'resolution': 'downsample',\n", + " 'dwnsmpl_bins': 50,\n", + " 'save_dwsmpld': False,\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " 'zmin': 0.,\n", + " 'zmax': 2.,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " 'Mmin': 5e13,\n", + " 'Mmax': 1e16,\n", + " 'dlogM': 0.01\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "acdf74cd", + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "172aa5f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 3201.2172766399517\n", + "Number of clusters in redshift bin 0: 96.77418063341094.\n", + "Number of clusters in redshift bin 1: 358.86003198943274.\n", + "Number of clusters in redshift bin 2: 472.2329584094609.\n", + "Number of clusters in redshift bin 3: 486.51160696245086.\n", + "Number of clusters in redshift bin 4: 435.9029472638175.\n", + "Number of clusters in redshift bin 5: 361.9233039390036.\n", + "Number of clusters in redshift bin 6: 285.92270392094196.\n", + "Number of clusters in redshift bin 7: 215.16277694057007.\n", + "Number of clusters in redshift bin 8: 157.33595388567755.\n", + "Number of clusters in redshift bin 9: 110.92709271728684.\n", + "Number of clusters in redshift bin 10: 75.30174628331966.\n", + "Number of clusters in redshift bin 11: 50.168868495127114.\n", + "Number of clusters in redshift bin 12: 33.34705053250085.\n", + "Number of clusters in redshift bin 13: 22.19467729924078.\n", + "Number of clusters in redshift bin 14: 14.594384776440062.\n", + "Number of clusters in redshift bin 15: 9.512987878857173.\n", + "Number of clusters in redshift bin 16: 6.2194533261531575.\n", + "Number of clusters in redshift bin 17: 4.085145806814587.\n", + "Number of clusters in redshift bin 18: 2.6690947265487077.\n", + "Number of clusters in redshift bin 19: 1.5703108528968366.\n", + "------------\n", + "Number of clusters in snr bin 0: 2020.4019192914282.\n", + "Number of clusters in snr bin 1: 948.2673991613391.\n", + "Number of clusters in snr bin 2: 195.55239949620054.\n", + "Number of clusters in snr bin 3: 33.005490168715475.\n", + "Number of clusters in snr bin 4: 3.7638848203224153.\n", + "Number of clusters in snr bin 5: 0.2261837019460672.\n", + "Total predicted 2D N = 3201.2172766399517.\n", + "Theory N calculation took 0.177 seconds.\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5dc97195", + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "19bd8faa", + "metadata": {}, + "outputs": [], + "source": [ + "bin_params = info['likelihood']['soliket.BinnedClusterLikelihood']['binning']\n", + "\n", + "zbins = np.arange(bin_params['z']['zmin'], bin_params['z']['zmax'] + bin_params['z']['dz'], \\\n", + " bin_params['z']['dz'])\n", + "\n", + "logqmin = bin_params['q']['log10qmin']\n", + "logqmax = bin_params['q']['log10qmax']\n", + "dlogq = bin_params['q']['dlog10q']\n", + "\n", + "# TODO: I removed the bin where everything is larger than qmax - is this ok?\n", + "qbins = 10**np.arange(logqmin, logqmax+dlogq, dlogq)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "327ab1e2", + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig_pred = {\n", + " 'predSNRCut': 5,\n", + " 'path2truthcat': '/Users/eunseonglee/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'injection',\n", + " 'QSource': 'injection'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8005ef66", + "metadata": {}, + "outputs": [], + "source": [ + "nemoNz = nemo_mocks.get_nemo_pred(mockconfig_pred, zbins)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "a882481d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))\n", + "\n", + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], marker='o', alpha=0.4, label='SOLikeT inj pred')\n", + "plt.plot(z, nemoNz, color=color_list[3], marker='o', linestyle='--', alpha=1, label='Nemo inj pred')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[9], fmt='o', ms=7, mfc='white', zorder=0, capsize=5, capthick=1, ls='none', alpha=0.8, label='obs catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.xlim(0, 2.0)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_inj.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f920a39a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chi2 : 22.575206910641096\n", + "dof : 20\n" + ] + } + ], + "source": [ + "obs = catNz\n", + "exp = Nz\n", + "\n", + "chi2 = (np.power(obs - exp, 2) / exp).sum()\n", + "\n", + "print(\"chi2 : \", chi2)\n", + "print(\"dof : \", len(exp))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3334b681", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chi2 : 2.4761471351259305\n", + "dof : 6\n" + ] + } + ], + "source": [ + "obs = catNq\n", + "exp = Nq\n", + "\n", + "chi2 = (np.power(obs - exp, 2) / exp).sum()\n", + "\n", + "print(\"chi2 : \", chi2)\n", + "print(\"dof : \", len(exp))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "256fee19", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(catNz)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e50086c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3202.339645878253" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nemoNz.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "10202382", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3201.217276639952" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Nz.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8cffc405", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3169.0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catNz.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b0b6f473", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z, np.abs(Nz/nemoNz-1), color=color_list[3], marker='o', ls='--', mfc='none', ms=7, label='fractional error')\n", + "#plt.fill_between(z, -np.sqrt(catNz)/catNz, np.sqrt(catNz)/catNz, alpha=0.2, color='gray', label='$\\pm\\sqrt{N_{obs}}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('fractional error', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "#plt.xlim(0, 2.0)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_inj_frac.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ce222c48", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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p8MADD+CBBx647OsvvPACXnjhhS5dS6fTQa/Xm7aLjY2NhSAI2LdvH6699tp25z7++ONQKpV4/vnncfLkSTQ2Nl52pqC6uhqFhYWmGYqamhoUFBTgqaeeQlRUFObNm4ejR4+aHnJVKhWqqqoQHBzcaTJjlJubi5qaGklmKHo67qdOnUJDQ4MpyfD09MTUqVORmJiI1NRULFq0CEDXxsA4rpfOIKWlpUGtVmPIkCHtjg0bNgxKpRKurq7QaDTQ6/WmZWIHDx7E0aNH8d///rfb42BJXPJEVmGcoegf5g0AiBwzEABw6vDZy76HiIiop+RyuWiF1FK755578Prrr+Pw4cOmZU833HADQkNDce+99wIAJk2ahGnTpuGBBx7Apk2bkJeXh6SkJNxyyy1ITEzE5s2b4evri5SUFMjlcgiCgOPHj5t+5efnA2h9oHVyckJERESHr5966inMmDEDc+fONdVkJCQkQK1W44EHHsDx48dx6tQpbNq0CcuXL2/3PaSkpEChUCAmJsZq42au5ORkuLq6msYCaF329P7776O2ttaUHHVlDNLS0uDo6NiuID01NRXDhw9vtywqLS3NlHQYr7ts2TLk5eXhp59+wh133IHnnnvusgX1UmFCQVZRapqhaE0oIsYEAwByjrIwm4iI6Eri4uLwww8/4Prrr8fQoUOxdOlSTJ8+HYcPHzbtxAQAmzZtwi233IKHH34YUVFR+L//+z8EBQXh+PHjptqAlJQUGAwGjBo1CjExMaZfxk/o09PTMWzYMFPBtvETc+PXa9euhUwmM33i7+vri59++gn5+flISEjA2LFj8eqrr3Z44E1JSUF0dDQcHR0tPVyiSUlJ6VC8PmfOHJSXl8PZ2dmUHHRlDNLT0zF8+PB2BemXJg+XHjPODnl7eyMxMRE//vgjhg4diqeffhrPP/88XnzxRQt+1z0jEwQR+shTt9TW1sLDwwM1NTUd1hdeiVarxZYtW3DDDTdApVJZMELxvTBrDXJTz+HPn96J0TcMQXODBg9GvAqD3oB3jv0ZPgM8rRaLPY+jreAYmo9jaD6OofmsPYbNzc3Iy8tDWFiYXT1YXonBYEBtbS3c3d0vu2sSXRnH0HydjWFXf956+lx6Kf6ukVWUti158muboXB0USNkeGtxHLePJSIiIrJfTCjI4uqrGtFQ3brW0i/Ey3ScdRRERERE9o8JBVlcaX4VAMCzvxscXS7ufhA5trWO4hRnKIiIiIjsFhMKsrjf7/BkFNE2Q1GQWYLmBttq0EJEREREXcOEgizu0h4Ul+oX6IF+AzwgGAScPnZOitCIiIiIyExMKMjiygrabxl7qcjRrbMUOUdZR0FERERkj5hQkMX9vgfFpUx1FIdZR0FE1Fdwx3oiy7PmzxkTCrI4U0IRdvmEIudoIQx6g1XjIiIi6zI29dJqtRJHQtT7GX/OLm2mZylMKMiimhs0qC6rB9D5DMXAIX5wcFajqa4FRScvWDs8IiKyIpVKBQcHB9TU1HCWgsiCBEFATU0NHBwcrNK0UmnxO1CfZtzhydXLCS6eTh1eVygVGDxqADL35OLUkbMIHtrf2iESEZEV+fj44Ny5cygqKoKHhwdUKhVkMpnUYfWYwWCARqNBc3Mzuzz3EMfQfMYxbGpqgl6vR01NDerr6xEUFGSV+zOhIIsqLWjtQdHZ7IRR5JiByNyTi5wjhZh+7xhrhUZERBJwd3cHAJSXl+PcOfvf4U8QBDQ1NcHJycmuEyMpcQzN9/sxdHBwQFBQkOnnzdKYUJBFleZVAAD8rpRQmAqzudMTEVFf4O7uDnd3d2i1Wuj1eqnDMYtWq8Xu3btxzTXXWGVpSW/EMTSfcQynTJlitWVOl2JCQRZl7JLt30lBttHgUQMgk8lQVlCF6rI6ePq5WSs8IiKSkEqlsvsHSIVCAZ1OB0dHR7v/XqTCMTSfcQylSCYAFmWThRl3eLrSDIWzuyMGRPsBAHKOcPtYIiIiInvChIIsyliU3dmWsZeKHNva4O4klz0RERER2RUmFGQxOo0O5edqAAD9Q72ueG7EmLZ+FJyhICIiIrIrTCjIYi4UVkMwCHBwVsPD1/WK50aNaZ2hyEsvhqaJDY+IiIiI7AUTCrIYU4fsUK+rbgPnG+IFTz9X6LV65Kadt0Z4RERERCQCJhRkMaVdrJ8AAJlMhoi2WYqcI6yjICIiIrIXTCjIYrqyw9OlLvajYB0FERERkb1gQkEWY+pB0cWEImJ06wzFqaOFEATBYnERERERkXiYUJDFGJc8dXWGImxEAFSOStRXNqL4TIUlQyMiIiIikfTqhOLzzz/HQw89hNGjR8PBwQEymQyfffZZt69jMBjw7rvvYsSIEXBycoKvry/uuOMO5OTkiB90L2HQG1BW0DpD0ZUaCgBQqpUIHxkEgNvHEhEREdmLXp1Q/P3vf8eaNWtQUFCAgICAHl9n8eLFWLJkCfR6PZYsWYIbbrgBmzZtwpgxY3DixAkRI+49Ks7XQq/VQ6lWoF+ge5ffF2lc9sQGd0RERER2oVcnFB999BHy8/Nx4cIFLF68uEfX2LVrF9auXYvJkycjOTkZb7zxBtatW4fNmzejtrYWDz/8sMhR9w7GgmzfYC/IFV3/Y2YqzOZOT0RERER2oVcnFNOnT0dISIhZ11i7di0A4J///CccHBxMx6+77jrMmjULu3fvxqlTp8y6R29UZtwytov1E0YRowcAAM7nlKOuslH0uIiIiIhIXL06oRBDUlISXFxcMHHixA6vzZo1CwDw22+/WTssm1eSf7GpXXe49XNBwGAfAEDOUdZREBEREdk6pdQB2LKGhgYUFxdj+PDhUCgUHV6PiIgAgKsWZ7e0tKClpcX0dW1tLQBAq9VCq9V2OR7jud15j1RKclt3afIJ9ux2vINHBaH4dDlOHspHzLRw0WOzp3G0VRxD83EMzccxNB/H0HwcQ/NxDM1nzhiKMe5MKK6gpqYGAODh4dHp6+7u7u3Ou5xXX30VK1as6HB8+/btcHZ27nZcO3bs6PZ7rO10RgEA4OyF09iy5UK33tvk0Lo71KFt6XAe0Sx6bEb2MI62jmNoPo6h+TiG5uMYmo9jaD6Oofl6MoaNjeYvMWdCYQXPPvssli5davq6trYWAwcOxMyZM01JSVdotVrs2LEDM2bMgEqlskSoohAEARufzAAA3Hj7TPiH9+vW+89HluPof1ej+mwzZk6fBaW64+yQOexlHG0Zx9B8HEPzcQzNxzE0H8fQfBxD85kzhsaVM+ZgQnEFxpmJy81AGH8DLjeDYeTg4NCuoNtIpVL16Aenp++zluqyOrQ0aiGTyxAQ5gOlqnt/zAZG9YerlxPqq5pw/mQ5BsUPsEictj6O9oBjaD6Oofk4hubjGJqPY2g+jqH5ejKGYow5i7KvwMXFBQEBAcjLy4Ner+/wurF2wlhLQa1K81uXLPkEeUCp7n7OKpfLEcF+FERERER2gQnFVUyZMgUNDQ3Yt29fh9e2bdtmOocuMvag8OvmlrGXMvWj4E5PRERERDaNCUWb8vJyZGdno7y8vN3xRYsWAWjtuq3RaEzHf/31V2zbtg3XXHMNIiMjrRqrrTP1oAjreUIRMcY4Q1EIQRBEiYuIiIiIxNerayg++ugj7N27FwCQkZFhOpaUlAQAmDt3LubOnQsAePfdd7FixQosW7YMy5cvN11j2rRpWLhwIT766CPExcXhxhtvRGlpKb755hu4u7vjgw8+sOa3ZBdK8nrWg+JSg0YGQaGUo7q0DhfOVsMvpOfXIiIiIiLL6dUJxd69e7Fu3bp2x/bt22davhQaGmpKKK7kww8/xIgRI/Dhhx9i5cqVcHV1xc0334yXX36ZsxOduDhD0b3dnS6ldlIhNCYAZ1LOIedIIRMKIiIiIhvVqxOKzz77DJ999lmXzl2+fHm7mYlLyeVyLFmyBEuWLBEvuF6s1JhQmJkERI4NxpmUczh15Cwm3jZCjNCIiIiISGSsoSBRNVQ3ob6qCYB5RdnAJYXZR1iYTURERGSrmFCQqIyzE5793eDoojbrWsbC7MKsUjTWWa5jNhERERH1HBMKElWpCAXZRl793eAb7AXBIOD0sSKzr0dERERE4mNCQaIqFWHL2EtFts1S5HDZExEREZFNYkJBojImFH4hIiUUY9kxm4iIiMiWMaEgUZXmVwEA/EWboWgtzD59rAgGvUGUaxIRERGReJhQkKiMNRTm7vBkNCDaD05uDmhu0OBsVqko1yQiIiIi8TChINE0N2hQXVoHQLwZCrlCjsGjBgDgsiciIiIiW8SEgkRTVtC63MnF0wkunk6iXTeChdlERERENosJBYmmzLjDk0jLnYyixrDBHREREZGtYkJBojFtGStCD4pLDRo1ADK5DOWF1agsrhX12kRERERkHiYUJBqxe1AYObk6IHhofwBc9kRERERka5hQkGjE3uHpUpFjjcueWJhNREREZEuYUJBoxO5BcamI0cYGd5yhICIiIrIlTChIFDqNDuVF1QAsO0NRcLwYLY0a0a9PRERERD3DhIJEcaGwGoJBgIOTCp5+rqJf32eAB7wC3KHXGZCbel706xMRERFRzzChIFEYlzv1D/OGTCYT/foymQyRY4zLnlhHQURERGQrmFCQKErzKgBYZrmTkSmh4E5PRERERDaDCQWJouySGQpLiWhrcJdztBAGg8Fi9yEiIiKirmNCQaIoybNMU7tLhQz3h4OTCg3VTTifU26x+xARERFR1zGhIFGUFRgTin4Wu4dSpUB4XBAANrgjIiIishVMKMhsBr0BZQVtS54sOEMBXFpHwcJsIiIiIlvAhILMVllcC51GD4VKgX5BHha9l7EfBWcoiIiIiGwDEwoyW2lb/YRfsCfkCsv+kRo8agAAoPhMBWrLGyx6LyIiIiK6OiYUZLbS/Lb6CQvu8GTk6uWMoChfANw+loiIiMgWMKEgsxkTCr8QyycUABBp2j6WdRREREREUmNCQWYrtUIPiktFmDpmc4aCiIiISGpMKMhspaYeFNZJKKLaCrPz0s5D26Kzyj2JiIiIqHNMKMgsgiCgzIo1FMb7uPdzgbZFh/z0Yqvck4iIiIg6x4SCzFJ7oQHNDRrIZDL4DvS0yj1lMtnFZU/sR0FEREQkKSYUZJaSttmJfgM8oHJQWu2+kWONCQXrKIiIiIikxISCzGJa7mSl+gmjiLadnk4dPgtBEKx6byIiIiK6iAkFmeViQbaXVe8bNiIASrUCteUNKCuosuq9iYiIiOgiJhRkFms2tbuU2lGFsBGBAFpnKYiIiIhIGkwoyCzW3jL2UqyjICIiIpIeEwoyS2nbciMpEopL6yiIiIiISBpMKKjHGmqaUF/ZCADws3INBQBEjm6doTh38gIaapqsfn8iIiIiYkJBZijLb52d8PRzhaOLg9Xv7+Hniv5h3hAEAaePFVn9/kRERETEhILMUNJWP+EnwXIno0hjgzsueyIiIiKSBBMK6rEyiXZ4ulRkWx1FDguziYiIiCTBhIJ6TKoeFJeKaJuhOJ18DnqdXrI4iIiIiPoqJhTUY6UFxoSin2QxBEX5wtnDES2NGpzNLJUsDiIiIqK+igkF9ZgtzFDI5XJEjDb2o2AdBREREZG1MaGgHmlp1KCqpA6AtDUUAC4mFIdZR0FERERkbUwoqEfK2hraOXs4wtXLWdJYIse2FWYfZUJBREREZG1MKKhHjMud/CWenQCAQXFBkCvkqDhXg4pzNVKHQ0RERNSnMKGgHrlYkC19QuHookbIcH8A7EdBREREZG1MKKhHSm2gqd2lTA3u2I+CiIiIyKqYUFCPlOa31lBIXZBtFNHW4I47PRERERFZFxMK6pGLW8baRkIRObZ1huJsZimaG1okjoaIiIio72BCQd2m0+hQXlQNwHZmKPoFeqBfkAcMegPOJJ+TOhwiIiKiPoMJBXVbeVENBIMABycVPP1cpQ7H5GIdBZc9EREREVkLEwrqttL8iwXZMplM4mguMvajYGE2ERERkfUwoaBuu1g/4SVxJO1FtM1QnD5aBIPBIHE0RERERH0DEwrqNuMMha3UTxgFD+0PB2c1Gmubce7kBanDISIiIuoTmFBQt9laDwojhVKBwfFBAFhHQURERGQtTCio20oLWntQ+NtYQgFcrKPIYR0FERERkVUwoaBuMegNKMu3zRkK4JLC7MNMKIiIiIisgQkFdUtlcS10Gj0UKgX6BblLHU4Hg0cNgEwmQ2l+JWrK6qUOh4iIiKjXY0JB3VKa37rcyXegJxRKhcTRdOTs7ogB0X4AgFNHOUtBREREZGlMKKhbSvMqANjeDk+XMm4fe+owC7OJiIiILI0JBXVLWdsMRX8brJ8wYmE2ERERkfX0+oTiyJEjuOGGG+Dl5QUXFxeMHTsWX375ZZffn5SUBJlMdtlfBw8etGD0tqck3zab2l0qqm2GIi/9PDTNWomjISIiIurdlFIHYElJSUmYNWsW1Go17rzzTnh4eCAxMRELFixAfn4+nnvuuS5fa8qUKZg6dWqH4wMGDBAxYttXZmpq10/iSC7PN8QLHr6uqLlQj7y084gaFyJ1SERERES9Vq9NKHQ6HRYuXAiZTIbdu3cjLi4OALBs2TIkJCRg2bJluP322xEREdGl602dOhXLly+3YMS2TxAEU1M7W56hkMlkiBw7EEc2Z+HUkUImFEREREQW1GuXPO3cuRNnzpzB3XffbUomAMDNzQ0vvPACdDodPv30UwkjtD+15Q1obtBAJpPBN9h2EwqAhdlERERE1tJrZyiSkpIAADNnzuzwmvHYb7/91uXr5eTkYOXKlWhsbERISAhmzJgBHx8fUWK1F6Vty528g9yhcrDtPzqRY9oKs48WQhAEyGQyiSMiIiIi6p1s+6nQDDk5OQDQ6ZImLy8v+Pj4mM7pii+//LJdMbeTkxNWrFiBp59++qrvbWlpQUtLi+nr2tpaAIBWq4VW2/WiYeO53XmPmM6fvgAA8AvxkiyGrhowxBdKBwXqKhpRdKoU/uEXaz6kHsfegGNoPo6h+TiG5uMYmo9jaD6OofnMGUMxxl0mCIJg9lVs0MyZM7Fjxw7k5ORg8ODBHV4fNGgQioqK2j3odyYzMxM///wzbrrpJgQHB6O6uhq7du3CM888g3PnzmH16tV46KGHrniN5cuXY8WKFR2Of/nll3B2du7eNyah45vO48RPJQif3A+j77H9uoSdb5xE+ekGjLk3BGETbbeInIiIiEgqjY2NuPvuu1FTUwN3d/ceXYMJxVUSiss5fvw4Ro0aBS8vL5w/fx5y+eXLUTqboRg4cCDKy8u79Run1WqxY8cOzJgxAyqVqkdxm+PDJT/gwIbjuOO5a3HDIxOsfv/u+vaVndjy/n5cc9dI/OlfN5mOSz2OvQHH0HwcQ/NxDM3HMTQfx9B8HEPzmTOGtbW18PHxMSuh6LVLnjw8PAAANTU1nb5eW1trOqcnhg8fjnHjxmHPnj04ffo0IiMjL3uug4MDHBwcOhxXqVQ9+sHp6fvMVVZQDQAIGORrFz/w0eNCsOX9/Th97Fyn8Uo1jr0Jx9B8HEPzcQzNxzE0H8fQfBxD8/VkDMUY8167y5OxdqKzOomqqiqUl5d3ecvYyzEWZTc2Npp1HXtRVmDsQWG7XbIvFdm209P5UxdQX9U3fo+IiIiIrK3XJhRTpkwBAGzfvr3Da8ZjxnN6QqfTITk5GTKZDMHBwT2+jr1oqGlCXUXrQ7lfiG1vGWvk1s8FAYNbk76co0USR0NERETUO/XahOK6665DeHg4vvzyS6SmppqO19XV4aWXXoJSqcR9991nOl5eXo7s7GyUl5e3u86BAwfw+zITnU6Hp59+GgUFBZg1axa8ve3jE3tzlOVXAQA8fF3h5Npx+ZatihzNfhREREREltRrayiUSiU++ugjzJo1C5MnT8Zdd90Fd3d3JCYmIi8vD//85z/b1T28++67WLFiBZYtW9auI/Zdd90FmUyGCRMmICgoCNXV1di9ezdOnjyJ4OBgrF69WoLvzvqMPSj8bLhDdmcixw7Eb1+nIOdoodShEBEREfVKvTahAIBp06Zh7969WLZsGb799ltoNBoMGzYML730EhYsWNClazz88MPYunUrkpKSUF5eDqVSicGDB+P555/HX/7yF3h52dcDdk+V5rUmFP52Uj9hFNHW4O5MyjnotHooVQqJIyIiIiLqXXp1QgEAY8eOxc8//3zV85YvX95uZsLomWeewTPPPGOByOyLcYbCXgqyjQIG94OrlxPqq5pQcLwEg+KCpA6JiIiIqFfptTUUJC7TkqcQ+0oo5HI5IlhHQURERGQxTCioS0rbirLtbckTAES0bR/LOgoiIiIi8TGhoKvSNGlRVVwLAPALtb+EInJsax3FqcNnO+zYRURERETmYUJBV1VW0Do74ezhCFcvJ4mj6b7w2EAolHJUldShvLBa6nCIiIiIehUmFHRVJW07PPUP9YZMJpM4mu5zcFYjNCYAAHDqCJc9EREREYmJCQVdVVnBxYTCXpmWPR1hYTYRERGRmJhQ0FWVmmYo7LfnhqkwmzMURERERKJiQkFXdbEHRT+JI+m5yLaE4uyJUjTVt0gcDREREVHvwYSCrso4Q+FnxzMUXv7u8B3oCcEg4PTRIuha9DAYuOMTERERkbl6fadsMo9Oq0d5UQ0A++xBcamgSF9cKKzG2/d9A73OgJ+ezsT4ucMxe1ECQob5Sx0eERERkV3iDAVdUXlRNQx6A9ROKnj2d5M6nB7bn5iBtF2nAQDeQe4Y90AobnxsIk7sy8cLMz/E/sQMiSMkIiIisk9MKOiKTMudQrzscstYACjILMHqJYkYOT0CAFBX0YiBY7ww58nJePPA45gwLwarlySiILNE4kiJiIiI7A8TCroiU0G2HW8Zu3XNAXgFuOPxtXfA0dUBzfUa1J5rAgAoVQosfGsOPP3dsG3NQYkjJSIiIrI/TCjoikxbxtpp/YTBYMDBjZmYencc1I4qDB41AABQml1nOkepUmDagngc2HgcgsBCbSIiIqLuYEJBV1SWXwXAfntQaJp00DRp4RfSmhDFXjsYAHD8h/PIOXqxJ4VfsDc0TVpomrSSxElERERkr5hQ0BWV2HkPCrWTEmonlanb96yF4xB73WDotQLevvcbFJ0sAwCUna2E2kkFtZNKynCJiIiI7A4TCrosg8GACwXGGQr7XPIkl8sxfs4wJH2ZAp1WD4VSgUdWz0e/cBc01DTj9Tv/h7L8Suz6IhkJc4bbbeE5ERERkVSYUNBlVRXXQduig0IpR78gd6nD6bHZixJQVVyLj5ZuhE6rh4OTCpMeG4SAwf1Qeb4Wz81YjaqSOsxaNF7qUImIiIjsDhvb0WUZd3jyDfaCQqmQOJqeCxnmj8Wr5mH1kkSc2J+Pa+4ciZKqWsReOxileZVoqm2B/6B+CAi3z2VdRERERFJiQkGXZepBYacF2ZeaMC8GQVG+2LbmIDa/uw+aZh3UjkWImxmFzD25KDlTgVUPfYcnP/mDXSdPRERERNbGJU90WcYZCn87rZ/4vZBh/lj0zlysPvUM5q2KxYc5z2DpZ3fh6S/+CJWDEsnbTuKTp3/i1rFERERE3cCEgi7LmFD49ZKEwkgul0HpoDAVYEePD8Fjq2+DTC5D0pfJ+P71nRJHSERERGQ/mFDQZZl6UNhpU7vuGH3DEPzpjZsAAD/8Zzd2fHpY4oiIiIiI7AMTCuqUIAgoMXbJ7mUzFJdz7T2jMf+v0wAA657dgkM/ZkocEREREZHtY0JBnaqraERzfQtkMhl8gz2lDsdqbl06BdPvGwNBEPD+I+uRuTdP6pCIiIiIbBoTCuqUcYcn70B3qB37TvdomUyGe1+5AWNuGgqdRo//3PcVCjJLpA6LiIiIyGYxoaBOleT3reVOl5Ir5HjkvXkYMiEUTXUteOPO/6GsrWM4EREREbXHhII6VZbfe3pQ9ITaUYU/f3Yngof2R3VZPV6/83+oLW+QOiwiIiIim8OEgjplXPLk3wd2eLocFw8n/PWrP8JnoCdKcivwrwVfoLmhReqwiIiIiGwKEwrqVG/tQdFdXv7ueObre+Dq7Yzc1HN4+0/fQKfRSR0WkdkMBgOaGzQwGAxSh0JERHaOCQV1yphQ9IUeFFcTONgHT3++AA5OKmQkncGaJzfyIYzsVkFmCT58YgMeCH8FD4S/jAfCX8GHT2zok5sPMKkiIhKHUuoAyPY01jajrqIRQN8syu7M4FED8MQnf8Cb93yJfevT4eHnigXLZ0kdFlG37E/MwOolifAKcMctj0+CX4g3ygoqkfRlCvZ9n47Fq+ZhwrwYqcO0uILMEmxdcwAHN2ZC06SF2kmF8XOGYfaiBIQM85c6PCIiu8OEgjowzk64+7jAydVB4mhsR+y1EXjw7blY/VgitnywH579XXHjwxOlDouoSwoyS7B6SSImzIvBwrfmQKlSmF67eclkfLR0I1YvSURQlK/VHqoNBgM0TTqonZSQy60zYc6kiohIfEwoqIOy/NYtUrncqaPJt8eipqwOX724A18u3w4PX1dMui1W6rCIrmrrmgPwCnA3JRM6jQ711U1w7+cCpUqBhW/NwYn9+di25iAWvTPXorFINUNgi0kVEVFvwISCOijJqwDA5U6Xc+MjE1FdVo+fVx/Amid+gJu3M2KvjZA6LKLLMhgMOLgxE7c8PglKlQLNDRosu34tik6WQSaXwdPPFV7+blA7KrHnuzT4BHvA298dnv7u8PJ3g1d/N7h6O4kyiyDlDMHvk6pLWTupIiLqTURPKL777jts2bIFLS0tiImJwe23347BgweLfRuyIM5QXJlMJsPdy2ai9kID9q1PxzsPfIvnE+/DoLggqUMj6pSmSQdNkxZ+Ia0/05//YyuKTpYBAASDgKqSOlSV1JnOX/9GUodrKFQKePZ3hVd/N1OS4envBq9Lkg4vfzc4ezhCJpN1Goe1ZggMegOa6lvQXK9BU10Lmupa0FjXjP2JGRh5XQR++fQwmupb0FSvgVd/N4yaFQW/UG8oVQpMWxCPTSv34sG351z2+yAiovZETShef/11vPTSS5g7dy6cnJywadMmLFu2DM8//zyWLVsm5q3Igkw7PIUwobgcuVyORW/PQW1FAzKSzuBfCz7Hsh8fQMAgH6lDI+pA7aSE2kmFsoJKHNmShV2fH4NMJsPfvrkHQdF+qG5LKHZ9fhRpO09j0h2xqCmrNyUateUN0Gv1qCiqQUVRzRXvpXJUmpILT2Py4e8Gr/7u2Pt9Gtx9XLDgxdkdZgjkChkWvDgbx/fkIvHfSbhlyaTWZKDBmBQ0X0wQGtqShNpmnCs4j8PvFqOl4WLy0NKkvWx8R3/OxtGfs9sd+/wfWzFwSH+Mvj4aCrUCmiYtNE1aODirez7oRER9iKgJxfvvv49vv/0WN9xwg+nYwYMH8Yc//AFBQUFYuHChmLcjC+GWsV2jVCvxxMd/wCvzP0Nu6nm8/of/YdnmhfDq7yZ1aETtyOVyjJ8zDDv/dwwtjRoAwE2PTsTwKYMAAF793TBwqB6fPbcZk26LxaL/zG33fp1Gh5oLDa0JRmkdqkpqW5OQ0jpT0lFdWof6qiZom3UoK6hCWUHVZeNZHP06HF3UcPdxgbZFh6Z6DZrrLzaNPFqchaNbsrrxHdZ3elShUsDJzQFOLmo4uqpRdPIC+od6ITQmEI6uaji6qHE2sxTZBwtQmFWKwqzS1jfKgK9e2oExNwxB1PiQDskPERG1J2pCUVlZiaFDh7Y7Nn78eLz33nt45plnmFDYAU2TFpXnawEA/UO9JI7G9jm5OuCpzxdgxc0fozSvEm/c9Tle+OF+OLs7Sh0aUTszF47D7m9SAQEIifHHbc9MM72m0+qx9s8bUV1Sh1mLxnd4r1KtRL8gD/QL8rjiPTRNWlSX1aOqtA7VJXWoLK41/X/FuRpkHyyAykEJbYsOzQ0aNDdoOlxDJpNBEAR4B7rD2d0RTq4OrUmBmwMcXdSt/992TO2kRPbpExg/eRxcPZwvvtb2usqh/T9xHz6xASf25ePh9+a1SxLqKhuR+sspHN6cheRt2YAA7PjkMHZ8chjOHo6ImxGJ0dcPwYhpg+Dowp3viIh+T9SEYvz48fjuu+/w9NNPtzs+fPhw5Ofni3krspCys62fKjq7O8LV21niaOyDh68r/vb1PVh+08c4m1mCt+79Cn/96o9QO6qkDo3IJHtfPiC0/n9NWQN+fHcv/IK9UXa2Eru+SEZ1SR0Wr5pnVu2C2kkFvxAv+IV0/DDCYDDggfBXcMvjkzB7UQKqS+tQW9EItaOyXRLw0/v78OOqvViZvPSqNQxarRaNW0ow8roIqFRX/3mbvSgB+75Px0dLN7ar4XDzdkbCrTE4vjsXCoUcdy+fhbOZJUjefhJ1FY3Y93069n2fDpWDEsOvCcfo66MRNzMKHr6uPRsoIqJeRtSE4rXXXsO0adNQVVWFxx57DIGBgRAEAR9//DEiIrgLjj0ozWtd7uQX6sWCxG7wC/XGX7/6I16a+ymy9ufjg8cSseTD2yFXsBk9Sa8gswRfv/wLAGDOn69BdXEdNq3ca9qyNWHOcMxaNN6iW6Ual10lfZmCm5dMRsAgHwQMan+OTqtH0pfJSJgz3CJ//4QM88fiVfOwekkiTuzPx7QF8Z0mVcZdpgx6A04dKcTRn7Nw7OdslBVUIWXHKaTsOAWZTIaIMQMx+vpojLo+Gv5h/USPl4jIXoiaUIwaNQo7duzAQw89hNdffx0eHh7QaDRQq9VITEwU81ZkIab6CW4Z222hMQFY+tmdeOPuz3H4xxNY57MF9716IxMzkpSmSYv3Hv4eOo0e8bOicPsz10Imk2Hhf26BpkkHB2eV1f6MXm6GALj6siuxTJgXg6AoX2xbc/CqSZVcIUf0+BBEjw/BguWzUJRdhqM/Z+PY1mzkpZ3HqcNncerwWXy5YjsGRPlh1PXRGDU7GuEjA7s0plI09rM1HAOi3kH0bWPHjRuH1NRUZGZmIjMzE66urkhISICXF9fj2wMmFOYZNjkcj7w3H6sWfYdfPj0CTz833Lp0itRhUR/21Us7cO7kBXj6ueLBty5uhSqXy+HoYt1djLo6Q2DppnIhw/yx6J253UqqZDIZBg7pj4FD+uPWpVNQca4Gx7Zl49jP2cjan4+ik2UoOlmGjW/vhleAO0bNjsLo64dgSEIIlOr2/9RK1djPlnAMiHoX0ROKY8eOoaSkBGFhYbjtttv4iYOdMS554g5PPTfulmGouVCPdc9twfev74Snnyum/XGU1GFRH5T6yyls//gQAOChlbfC3cdF4oi6N0NgaeYkVf2CPDDzT+Mw80/j0FDdhNRfcnB0axbSfj2NquJa/PLpEfzy6RE4uzsi9roIjL4+GiOuHYzUHTmSNfazFVI2NyQiyxA1oVi8eDHWrFkDT09PVFdXw8HBAcOGDcPIkSMRGxuLJUuWiHk7sgBTUzvOUJhl5gPjUF1Wj41v78bHT/8It34uGH19NABO8ZN11Fyox4dP/AAAmP3geIyYZjsNRnsyQ2DLXDydMPG2EZh42whomrU4sTfPtDSqtrwBBzZk4MCGDCiUcuj1BgyOH4Ala26HzwBP0zXEbOxny6zV3JCIrEvUhOLLL7/Ed999h/nz56Ourg7p6elIS0tDWloavvzySyYUNk6n1eNCYTUAzlCI4fa/XYuasnokfZmMdxd/h/vfuAnZ+/M5xU8WJwgC1jy5EbXlDRgQ7Yc//H261CF1SoplV5amdlRh5PRIjJweiT+9cRNOJxfh2NZsHN2SjZLcCgDA6WNFeGLUfzB41ADEz4rCoLgBCBnuj4VvzcGJ/fnYtuYgFr0zV9pvxEK2rjkArwB33PvqjSjKLkPB8RLUVTZg4JD+CI0J6BNjQNQbiZpQDBgwwNSHws3NDRMnTsTEiRPFvAVZUMW5Ghj0BqidVPDsz+0QzSWTyfCnf92E2vIGJG8/iTWP/wAvfzdO8ZPF/fLZEaT+cgoqByUe/eA2bmEsEblCjsgxwYgcE4w7nrsOD4S9gshxwWiua8GZlHM4fawIp48Vmc73DnSHg5MKe79Pw4jrBiM0JgB+IV52P5PZUN2EgswS5KcXY+93aXDzdsZDUa9BrzN0ONc70B0Ozq1jMOqGaISPDGKzUCI7IGpCsWzZMvz73//Gxx9/LOZlyUpMW8YG2/8/YLZCoVTglicmI3nHydYeADLgmj/EmRqEcYqfxHbu1AV8sXwbAODOv09H8ND+EkdEAKBt1kPbosOUO+Mwcf4IVJXU4ti2kzi+Oxdnj5egNL/S1FQUAFY9+B0AwNFFjeBh/ggZ7o/gof0RMjwAA6P9oHYSJ0k0GAToWvQwGASzryUIAsqLalBwvAQFmcU4e7wE+cdLUN42821Uc6EBQOtSsdDh/nD1dsbZE6UoOVPRbgze+r+vAACefq4IHRGIsBEBpv96B7qLtkyOy1CJzCdqQnHjjTdi7dq1mDJlCu69916MGzcOQ4YM4Q+onTDt8MTlTqLa+b+j6BfgDrWLGsU55Xjtzv9h2aY/wdXLGUqVglP8JBptiw7vPfw9tM06jJg2GDMXjpM6JGqjdlJC7aRCWUHr37Ne/u6Yfu8YTL93DACgsa4ZhVll2PTObqTvOo2Q4f4oyr6A5gaNaXtaI5lchsAIH4QM8zclGyHD/LvVaM+0y9IPx6Fp1uGnpzMxfu7wLi/B1Gn1OJ9TjoLjxa0JxPESFGSWoKG6qdPzfQd6Ini4P1J3nMK4W4bhzr/P6JAUNNW3oOB4CTa9sxsZv51BwCAfnD9djuqyeqT+cgqpv5wynevezwWhIwIQNiIAYbGBCI0JgM9Az24lGdxpikg8oiYU8+fPx5EjRzBy5Ei88soryMvLg4ODA4YPH46RI0dizZo1Yt6ORHZpUzsSh8FgwMGNmbjl8Um45g9xWH7TRzh/6gJWLvwWz62/DwCgVCkwbUE8Nq3ciwffnmPXxakkre9e+xUFx0vg1s8Zi96Zyw9zbMjvG/tdWowMAM5ujhgUF4TC7DJMvn0kFr0zF3qdHsWnK1CQ2fqwXnC8BGePl6C2ogHnTl7AuZMXsD8xw3QNz/5u7WYyQob7wz/Mu0ODzUt3WbrxsYk4X1WAQK8Q7PkmrdMlmI11zSg8UYr8jGJT4lCUXQadRt/h+1Qo5QiK8kPIcH+EDvdHyPAABA/rDxcPJwDAh09swIl9+fDwc+3wd52TqwMGjxqAolMXTGPQ3KBB4YlS5KWfR356MfLSz6Po5AXUVjQgfddppO86bXq/q5cTQmMCEDYisDXZiA2EX0jnTVq50xSRuERNKPbs2YNff/0VCQkJAIC6ujqkpaUhNTUVaWlpYt6KLMA4Q+HPGQrRaJp00DRp4RfijX5BHnjmq3vw7LUfIHNvHirO1ZiWPvkFe0PTpIWmSQsH595VpErWcXx3Lja/vx8A8OBbc7ju3AZ1t7GfQqnAgGg/DIj2w8T5IwC0LiuqLq1DQWapaXbgbGYJSnIrUV1ah+rSOqT9mmO6roOTCgOG9G+dxRjuDwcnFdb+eaNplyUBBmzZUocbbpiMOU9cg/cfXo8PHluPU4cLUH2hwbQcqzNObg6m2ZGQ4QEIifFHUIQvVA6Xf7To7hg4uqgRMWYgIsYMNJ2nadKiMKs1ychLL0Z+ejEKs8tQX9WE47tzcXx3rulcZ3dHhMYEtM1mtCYaLU0a7jRFJDJRE4qYmBioVBfXdbq5uWHSpEmYNGmSmLchCzH+o+HHLWNF8/tlDgOi/RAaE4Dc1HPIOpCPSbfFAgDKzlZC7aQSbV009S11lY1Y/fgGAMB1947GqNnREkdEnRGjsZ9MJoOXvzu8/N0x8roI0/HmhhYUnihDwYkS0xKkwqxStDRpcSa5CGeSi9pdJ+doId5/ZD2Con2RmVqEzP99gbMnSlFX0QgA2PHpkXbnewe6I2SYP0JjAkxJhO9lPv239BionVQYFD8Ag+IHmI5pW3Qoyi5rn2RklaKxthkn9uXhxL4807kKpRwyuQxObg448MNxhI0IQGCED+RyOZehEvWQqAnFCy+8gOXLl+Pbb7+Fs7OzmJcmCzMYDCgraO1B4c+EQjSdLXMYMiGkNaHY35pQ6LR67PoiGQlzhnO5E3WbIAj4+OkfUVVci4DBPliwfJbUIdEVWKqxn6OLQ4dP8g16A0pyK0zLlPKPFyNj1xkAQEluJUpyK4FN7a8jk8vg5uWM+upG/OH56QiNCUTIsP5w6ydeU0RLjIHKQYmw2ECExQaajum0epw7dQF5aeeRn1GMvLTzKMgsgbZZBwDY/vFhAIcBAB6+rhg5PQJxM6Mw/JpwLkMl6iZRE4pbbrkFCoUC0dHRuO222zB27FiMHDkSUVFR/IG0cVUlddA266BQytFvgIfU4fQqv5/iHzIhDJvf34+s/fmdTvETdcdvX6XgyE8noFAp8OgH87lkzg5Yq7GfXCFHYIQvAiN8kXBrDJobNHgg/GXc99qN6B/q3VaXUYyyyhJMvmEswmMHYGC0H45szsL7j67HjPvHWuzPkzXGQKlStC7HuiRBaaxtwoMRr+G6e0dDqVIgP6N1NqPmQj1++yoFv32VAqVaAf9B/aBp0uLcyTIMiOZOaURXI2pCkZWVZWpml56ejvXr16OwsBBOTk4YPnw4Dh06JObtSETGDtk+Az2hUCqucjZ1x++n+CfNHwHIWovgnxzzH9ReaLjqFD9RZ0pyK/Df538G0NpIMWxE4FXeQbbE2o39jEsw66saMeP+sRgxbTC0Wi22bNmCqTfEm5YsW3MJprXHwNHVAWonFbz83XDr0qkAAJ1Gh+yDZ5Gy4yRStp9CaX4lirLKAADPTHkfgZG+iJsRibgZkYgcM5D/RhJ1QtSEIioqClFRUbj99ttNx2pqapCamoqMjIwrvJOkVprX2sG1P5c7WcSlU/w/rznY2pMCreP99Bd/ZDJB3abT6vH+I+vR0qjB0IlhuPGRCVKHRDbuajtNAej1SzA7GwOlWonh14Rj+DXh+OOLs1F0sgwv3vIJVA5K1FU04vypCzh/6gI2v7cPLp5OGDFtMOJmRCL22sFw9eLybiIAsPiegh4eHpgyZQoee+wxS9+KzFCSxx4Ulmac4v849znMeGAsACBwsA+TCeqRDW8m4UzKOTh7OGLxqlu5RSx1yexFCagqrsVHSzdCp22/7WtfWYJ5pTHQ6wz46d19aGnQ4Jmv78HqrL/isQ9vx8TbRsDVywkN1U04sCED7z+yHouHvoEX53yCLe/vR835JgiC+c0BieyVqDMUOp0Ob7/9NpKSkqBSqbB27Vr4+PiIeQuyEGNBdv8Q9qCwNLlcjhFTBmPHx4eRdaBA6nDIDmUfLMDGd/YAABb++xbT9sNEV/P7JZjX3DkS56sqsfHUHuz+OrVLuyzZu+7uNJUwdzgS5g6HQW9AzrEipO44hZQdp1CYVYqTBwtw8mDr3+PJn7yHuBlRiJsZiSEJoVfcPvdS7NQtPf4emE/UhGLp0qXYunUrFi5ciGXLlqG2thY+Pj5YsWIFoqKicOedd4p5OxJRqWmGop/EkfQNUeODIZPJUHy6HFWldewZQF3WWNuMDx5LhGAQcM2dIzHulmFSh0R25tIlmJvf3QdNsw5qxyIkzI0xa6cpe9KTnabkCjmixgYjamww/vD8dFworEbqL6eQvC0bx/fk4sLZamz/+BC2f3wIDs5qxEwdhLgZkRg5PQKefh3/jmen7osMBgG6Fj0MBuvO8vD3QDyiJhTffPMNNmzYgAkTJuCll14yHY+Pj8drr73GhMJGCYJwSQ8KzlBYg4uHE0KG+yM/oxjZBwqQMHe41CGRnfjsb5tRXlgNvxAv/N/LN0gdDtkp4xLMe9+4AT9t/Ak3z70JanXf2iHM3J2mfAd6Ysb9YzH1j3HYtOFHDHSJQvrOM0jZcQrVpXU4uiULR7dkAQDCRwZi5IxIxM+IQkiMPw7+kGlTnbql+oTe9ED/w3FomnX46elMjJ873CoP9OyWLi5RE4rm5mb4+3f8AxAdHY2srCwxb0UiqqtoRFNdC2QyGfy45MlqohNCkJ9RjKz9eUwoqEv2fZ+OfevTIVfI8cj78+Hk6iB1SGTn5HIZlA6KXlmA3VVi7DSldFAgbmYkxt44DIIgID+jGCk7TiFl+ynkpp5Dbup55KaeR+K/kuDm7Yz6qkZETwjFk5/cCVdPJ9N1rN2pW8pP6C99oL/xsYk4X1WAQK8Q7PkmzeIP9AWZJeyWLjJR09Dp06cjMTGxw3GNRgO9Xt/JO8gWGGcnvAPdoXZkp2ZrGTIhDABYR0FdcuFsFT79208AgFv/MgURowde5R1EJAWZTIawEYGY95epeGnbIryX8RQWvT0XY24cAkcXNeoqGyEIQNa+fDw24t94/a7/YeM7u5H0xTGk7jiFyX+Ig1s/F/y4cg/0Oss9O+1PzMALMz/EiX35uOXxSXjk/fm45fFJOLEvHy/M/BD7Ey23O+elD/RvHngcc56cjJBx3pjz5GS8eeBxTJgXg9VLElGQWWKR+29dcwBeAe4dkgkApm7pnv5u2LbmoEXu3xuJOkPxr3/9C+PGjYNMJoMgCJDJZNDpdHj99dcxcuRIMW/VZUeOHMGyZctw4MABaDQaDBs2DE8++STuvvvuLl/DYDDg/fffx5o1a5CTkwNXV1dMmzYNL7/8MiIiIiwYvXWYljtxdsKqotvqKM6fuoCasnp4+LlKHRLZKIPegA8eS0RTXQsixgzEnCcmSx0SEXWRp58bptwVhyl3xaGlSYtFka8iNCYANRcacOFsFdJ3nkb6ztMd3nfgh+M48MNxOLs7wtXLCW7eznD1dm79f6/W/3fzdoKrl3PrL+/Wc9y8nK/aQ0TqT+h//0Cv1RogCAJ0Gj0Egx5/eG46ju/OxYY3f8O8p6ZC26Lr/Ffzxf/XtP2/TtN6XNPZeRo9NE1a5Kefh4unE5aOe8d0rtpBiRsfnYAbFk+AUqVgt/RuEjWhCA8Px+7du7F48WI0NjZiypQpqKqqglqtxk8//STmrbokKSkJs2bNglqtxp133gkPDw8kJiZiwYIFyM/Px3PPPdel6yxevBhr167F0KFDsWTJEpSWluKbb77B9u3bsX//fgwdOtTC34llGRMKbhlrXa5ezhg4tD/OZpYg+2ABi2vpsjat3IOTh87C0dUBj7w3n421iOyUYGh9aJ75wDhMmBeD8znlSNlxEoVZZaivakRdZRPqqxpRXVqHlkYtgNaNGBprm027MXaF2kkFV6/WZMPNmIRc8t/Dm0/AxdMJ194zGhXnauDi4QiDXoBWo4NOo8f1ixOQnnQG377yK258ZELbg7m+9WFdo4e2WQudRg9t28O78f91LXpojA/1LfqLD/iX/L+mWYeik2VwcnPA43Fvma6h1ejwnZDS7vs4svkEjmw+IervgVF9VRPqq5pMXzfXt+CrF3fg6M/ZWLzyVvgFe0PTpIWmSWuxjvG9iagJBQAMGTIEv/32G06fPo309HSoVCokJCRYfftYnU6HhQsXQiaTYffu3YiLiwMALFu2DAkJCVi2bBluv/32q84w7Nq1C2vXrsXkyZOxY8cOODi0rln+v//7P8yYMQMPP/wwfvvtN4t/P5Zk2uGJTe2sbkhCCM5mluDE/jwmFNSpM8lFWP+vJADA/a/fyJlEIjtm7FZeVlAJmUyGoEhfBEX6djhvw1tJ2PTOHrx99M9oqG5CXVUT6isbUVfZiPqqRtRXNqGuqrHtobjteFsyotcZoGnSorJJi8rztVeMZ8XNH1/x9dRfTiH1l1Nmfc+X01Tbgia0XPZ1hVIOvc4At36tMy5qByVUxl+OSijVytZjjpccN36tVlz8f4e28xxUUDkqoVDKsfLBbzHptlhcd+9o03kn9ubhi+XbkXOkEM9e+wGGTgiF2lFplY7xvYEoCcVNN92EuLg4xMfHIy4uDqGhoRg8eDAGDx4sxuV7ZOfOnThz5gzuv/9+UzIBAG5ubnjhhRdw55134tNPP8Urr7xyxeusXbsWAPDPf/7TlEwAwHXXXYdZs2Zh69atOHXqFCIjI7sdY2pqKlxdu77MRafT4cyZM0hJSYFSKV4umJtZCABoFGqQnJws2nVtlaXGsScc/A0AgJSd2YhNDpQ0lu6wpTG0V10ZQ02TDv97NAkGvQFRU4LgFKbrEz+jXcU/h+bjGJqvu2MYMckf2z89iAGTXKFQdixl1esM2PbpQURMDsSZwraHeSUg8wPc/QB3OANwBtBxm3dBEKBp1KG5ToOm2tZfzbUaNNVp0FSrRXOdBg2VzcjZVwx3PycIAJprNdA2t9ZqyJUyKFRyKFUKGAwCWuq18ApyaX0QV8lbX1MrTP//+6+VKjkUqku+VhvPU5i+litk+GHFYQybPhAjbw6DQiUHZAKOpRzFhEkJcHBWQ6GU4+DXp3D42xw8+L/pIi050rX9AqKmBCL5lyyMvCsICoMcaAI8h8lxz7vXYNt/UnA2rRypv+bAxcsBSVv3waO/7XdEN+dnub6+3uz7i/K3R1BQELZv344333wTLS0t8PT0RFxcXLskIyoqyqpr0JKSkgAAM2fO7PCa8VhXZhaSkpLg4uKCiRMndnjNmFD89ttvPUooBEHoVmdN4/ndfd/VVJ9vAAB4BDj3iU6flhrHngga1jorVFFQh4bqZjh72MeuPbY0hvaqK2O4c3UGqosb4ObrhOseHWF6H7Xin0PzcQzN190xjJ8TjqydRdj+dgpmPDGyXVKh1xmw/e1UNFQ0I35OWI9+T9TOSqidlXC/zEOwYBCwav4WDJ8ZjPF3R5nuK5fLIJNffE47+OVJHP7uNO5bc63oz2/RU4KQf6wM0x6OgUIph06ng9JFBrWL0jQzkbG1AFHXtH7QJvafzcv9Hrj5OWHuinH49q/7UHKqGg1VLVj38E5MeXA4YmYF23QthTk/y2KMrygJxYcffggA0Ov1yMrKwrFjx5CamoojR45g7dq1qKurg4uLC2prrzz1JqacnBwA6HRJk5eXF3x8fEznXE5DQwOKi4sxfPhwKBQd1ywbr32167S0tKCl5eK0nnEc9Ho9dDrdlb+RSxjP7c57rqalUYumGg0AwMVXLeq1bZUlxrGnVC4K9AtxQ0VBHc6mlWHwhACpQ+oSWxpDe3W1MTy9vxiZ288CMmDmn2OhdJRxvH+Hfw7NxzE0X3fH0CvYBTOejMWOt9NQmF6OYTMGwt3fGbUljcjcUYiGyhbMeDIWXsEuFvt9iZjkj4ztZxE3L8z0MK03AGidNG99oN9WgMhJARbZpXPEzSHI2lWEbf9JwXVLYiC03Vin00GvM+DXVRloqGzGiJtCLDIGXfk9mPzAEJzeX4LirCr8sjINOXvP47olMXDt5yh6PGIw52dZjN9jmWDBjyQyMjKQmJiI999/H+Xl5VbdOnbmzJnYsWMHcnJyOl16NWjQIBQVFbV70P+98+fPIygoCBMnTsTevXs7vL5nzx5cc801WLRokSmp6szy5cuxYsWKDsf/+c9/wtFR2j+YjaV6ZK9rgNJZhhGPsVuzFAp/acKFZC1841UYON3p6m+gXk9TZ0DWp/XQNwP9x6sRdI1t/gNGRD3XWKbHhWMaVGZpIegAmRLwHqKC7yg1nP0su/FCY5ke2f9tgPcQFUJmO0KmuPjJu6AXULC1GZVZWkT/n4vFYqk8oUX+liaoXWXoN0INBw85WmoMqEjXQFMvIPQGJ3gPtWz9wtV+DwSDgLKjGpzf0wJBDygcgAHTHeE9tHtNEG1dc3Mz/v73v6Ompgbu7u49uoboCyYPHTqExMREbNiwAWfPnsXUqVOxYsUKzJkzR+xb2Y1nn30WS5cuNX1dW1uLgQMHIiEhAS4uLl2+jk6nw8GDBzF+/HjR1rrm7C1GNlLgE+yBSZMmiHJNW2eJcTRHjqwYPyenwFDhiEmTJkkdTpfY2hjao8uNoWAQsOEfh6FvroffYA/c9teETtdZE/8cioFjaD6zxnDexZ2frN1gMKTfeex4Ow3aMm0nn9DrMOvPIxE1xYK1fZOACzNqkbYpH6f2nIdOY4BSLUfk5CDE3hIK37CePdh229V+D64BKm+vx46301CaU4OCzc1QlHvg2keGw9nLdpYpm/PnsKGhwez7i/K3x86dO01JRG1tLWbPno3ly5fjxhtvhIeHhxi36DbjfWtqajp9vba29qqxdeUal553OQ4ODu0Kuo0UCkWP/gJXKpWi/cVfV9YMAPAKcO1z/5iIOY7mCIn1A9BaR6Ft0MPJTuooANsZQ3v2+zE8uv40itIroHRQ4Ma/joKDI7crvBr+OTQfx9B85oyhSm39nYSGXRcMvzAPJG/MxdH1udC1tD5QR10TiPg54fANt/zzW0CENwL+4o3rlsRgd9JeXDNtElQqaXZVutLvgV+YJ+56azKOfH8aB744idxDpTifVYXpj45A5GTb2lClJ38OO1vW3+37mn0FtHbIDgwMxHPPPYcHH3wQarX0/wBeWt8watSodq9VVVWhvLwcEyZc+RN5FxcXBAQEIC8vD3q9vsOAX6lOoytkMlm3Po0wnt/d911JdXFrVuoZ5NKrpu+uxBLjaA4XL0f0C3ZDxdk6nMusRMRE2/rLqTO2Nob2qLMxLD1djb3rsgAA0xbHwHsglyFeCf8cmo9jaD57HkO/QZ6YvTQes56Mk2SWxEiukEPpIIdcLrfZMVQoFRh/ZxTCx/pj65vJuJBbi59ePYqofUG47tERcHKX9tnXnD+HYoy5KAlFXFwcMjMz8fjjj2P58uXtdniKj4+XpJv0lClT8Oqrr2L79u2488472722fft20zlduc7XX3+Nffv24Zprrmn32rZt27p8nc6MHDmyW2vVtFotiouLERcXJ1oGv6UurTWWhGGIj48V5Zq2zhLjaK60687jl0+PQFOqQHx8vNThXJUtjqG9+f0YtjRq8NUTa2DQCRh9wxDc++xcm/2H1Vbwz6H5OIbm4xiaz67GMB6Yfstk/PCf3dj4zh6c3H0OJVk1eODfN2PU7GjJwjJnDMXYNEmUhbnHjh1DfX09kpOT8cYbbyAqKgr79u3DwoULER0dDQ8Pjx4/dPfUddddh/DwcHz55ZdITU01Ha+rq8NLL70EpVKJ++67z3S8vLwc2dnZKC8vb3edRYsWAQD+/ve/Q6PRmI7/+uuv2LZtG6655poebRlrK0xdstnUTlJDEkIBACf250saB0nnyxe34/ypC/Dyd8PCN29hMkFEZKOUaiVue+ZarNiyEIGRvqi5UI+37v0Kq5ckoqGm6eoX6IVEq/RTKpWIjY3F/fffj1WrVmHv3r2ora3FiRMn8MEHH2DcuHFi3arL8Xz00UcwGAyYPHkyFi1ahKeeegqxsbHIzMzE8uXL2yUC7777LoYMGYJ333233XWmTZuGhQsXYs+ePYiLi8Nf//pX3Hvvvbjxxhvh7u6ODz74wKrfl5g0l3TRZEIhLWNCUXiiFPVVjdIGQ1aXvP0kfvn0CADgoZW3ws3b9psoERH1deEjg/Dyjodw46MTIZPJsOfbNDwz5X2k7zotdWhW1+WEwtfXF7Nnz8azzz6L77//Hrm5uVd9j0wmQ1RUFO6++2688cYbZgXaE9OmTcPevXsxadIkfPvtt3j//ffRr18/fP7553j++ee7fJ0PP/wQK1euhEwmw8qVK7F582bcfPPNOHz4MIYOHWrB78Cyys5WAQCc3Bzg1o8PMFLy8HNFYIQPBEFA9sGzUodDVmAwCNC16FFVWoe1T24EAFy/OAExUwZJHBkREXWV2lGFu/8xEy9s+hP6h3mjqrgWr9/5P3z81CY01V++NUFv0+UaCq1Wi+3bt2P79u2mqXgPDw9TncSoUaMkq5e4krFjx+Lnn3++6nnLly/H8uXLO31NLpdjyZIlWLJkicjRSassvzWh6B/mzeUVNmDIhFCczylH9oF8jL5eunWYZFkFmSXYuuYADv5wHJpmHTbI0yEYBPiHe+MPz02XOjwiIuqBqLHBeOXXh/HNy79g+8eHsPN/x5CRdAaL3pmLoRPDpA7P4rqcUFRXV+PMmTM4duwYjh07huTkZCQnJ2Pnzp3YuXOn6YHUzc0NI0eONCUYCxYssFjwZJ7SvAoAXO5kK4YkhOLXdUeRxTqKXmt/YgZWL0mEV4A7bnxsIpL3ZqDgYGsdU1lBNY5szsKEeTESR0lERD3h6KLGva/cgNE3DMGaJ3/AhcJqvDzvM8xaOA5/eH46HJyl3wXVUrq1y9OgQYMwaNAg3HHHHaZjeXl5SE5OxrFjx3D06FGkpqZi9+7d2LNnD2QyGRMKG1Za0DZDwYTCJkRPCAUAFBwvQUNNE1w82DW7NynILMHqJYmYMC8GC9+ag6KTpdj49m4AwD3/nI389GKsXpKIoChfhAzzlzhaIiLqqWGTwvBa0iP4Ytk27Pr8GLZ9dAhpO0/joZVzETkmWOrwLMLsouywsDDMnz8fr7zyCrZs2YJ169bh2muvhSAIEARBjBjJQkrzjDs8eUkcCQGAV383BAzqB0EQcJJ1FL3O1jUH4BXgjoVvzYFgELD6sQ0w6ASMuHYwZi0cj4VvzYGnvxu2rTkodahERGQmJ1cHLHzzFvz1qz/CK8AdJbkVePGWT/DVS9uhadZ2ON9gMKC5QQODwSBBtOYzO6FoaWnBpk2bcO+998LPzw833XQTdu7cidjYWKxYsUKMGMlCTFvGhvWTOBIyGtI2S5F1IF/SOEhcBoMBBzdmYurdcVCqFDi48TgKs8rg4KbEA2/eBJlMBqVKgWkL4nFg43F+GENE1EvEXhuB1397BJPviIVgEPDTu/vw95kfIi/tPIDW2esPn9iAB8JfwQPhL+OB8Ffw4RMbUJBZInHk3dOjxnaNjY3YsmUL1q9fj82bN6OhobXb8pgxYzB//nzMnz8f4eHhogZK4tLr9CgvrAYA+HGGwmZEJ4Ri5/+OsY6il9E06aBp0sIvpHV54eljRQCA0ARvePi6ms7zC/aGpkkLTZO2V6+1JSLqS1w8nLB41TyMvmEoPn5qE86dvIB/XL8W8bOikLL9JLwC3HHL45PgF+KNsoJKJH2Zgn3fp2Pxqnl2U1fX5YSirq4OP/74I9avX4+tW7eiubkZMpkMkyZNwvz58zFv3jwEBQVZMlYSUUVRDfQ6A1SOSnj5u0kdDrUxzlDkZxSjsbYZzu6O0gZEolA7KaF2UqGsoHVWMDe19ZMp71CXdueVna2E2kkFtZONd4olIqJuG319NKLGDsSnf9uMQ5sycXRLFlw8HfHkJ39A2IhA03k3L5mMj5ZutKu6ui4vefLz88M999yDzZs3Y/Lkyfjggw9w/vx5JCUlYcmSJUwm7ExJ23InvxAvyOWi9TckM3kHuKN/mDcEg4CTh1hH0VvI5XKMnzMMSV+moLmhBWdPtE5le4Vc7P+i0+qx64tkJMwZzm2ciYh6Kbd+Lnh87R0YMiEUMrkMDdXNWHbDR9j49m7odXoAgFKlsLu6ui4/Sba0tDbnuPbaazFz5kxERkbC0ZGfntqriwXZ3OHJ1rCOoneavSgBVcW1WLXoe+g0erh4OsHFp3VZk06rx9o/b0R1SR1mLRovcaRERGRJBoMBZ1LO4caHJyB+VhT0Wj2+ffVXrLjpY9RcqAcAu6ur6/KSp+HDhyM7Oxtbt27Ftm3bTMfDw8PbNbaLj4+HtzcfUm2dqSCbCYXNGTIhFElfJCNrX77UoZCIQob5Y/GqeXj/0fUAABdPR5w9XIWNOXuw++tUVJfUYfGqeXYxtU1ERD1nrKsLHuaPO1+Ygb3fpeG/z/8MuUION++LM9f2VFfX5YQiPT0dLS0tSEtLM/WdSE5OxvHjx3HmzBl89913pmn64OBgU5Lx3HPPWSx46jnTDEUYEwpbMyQhFEBbHUVdM5zdOBPYW0yYF4NDPx7H0S3ZKC+qQdnHVVA7FiFhbgxmLRrPZIKIqA+4tK5OJpNh8h0jMXRSGPRaA+SKi4uH7Kmurlu7PDk4OGDs2LEYO3as6ZhWq0V6enq7JCMjIwMbNmzADz/8wITCRpXls6mdreoX5AG/EC+UFVTh1OFCjLwuQuqQSEQXzlYDAB5571ac15zGzXNvglpt2588ERGReC6tq7t5yWQoVQr0C/Rod4691dWZXY2rUqkwatQoPPjgg1i9ejUOHz6Muro6pKSkYO3atWLESCIzGAwoLeAMhS0zzlJkc/vYXkXTpEVhVhkAICwuEEoHhV38Q0FEROIy1tV9tHQjdFp9u9fssa6uR30ornpRpRKxsbGIjY21xOXJTNWl9dA26yBXyNEvyOPqbyCrGzIxFL99ncLC7F6mILMEBr0BHr6u8A5wB1KljoiIiKRgrKtbvSQRJ/bnY9qCePgFe6PsbCV2fZFsd3V1FkkoyLYZ6yd8B3pCqVJIHA11JrpthiI39TyaG1rg6OIgbUAkCmNn1LDYQM5MEBH1cRPmxSAoyhfb1hzEppV7oWnSQu2kQsKc4XZXV8eEog8y7fDE5U42y3egJ3wHeuJCYTVOHSnEiKmDpQ6JRJDbllCEjwy8yplERNQXhAzzx6J35mLhf26BpkkHB2eVXX7gxI5mfZBxhsIv1EviSOhKoo39KLh9bK+Rl3pxhoKIiMhILpfD0UVtl8kEwISiTzLOUPhzhyebxgZ3vUtzQwvO5VwAwBkKIiLqXURJKHbv3o1Tp06JcSmyAmNC4ceEwqYZd3rKTTmH5gaNtMGQ2fIzSiAYBHgHusPTz03qcIiIiEQjSkIxdepUvP7662JciixMEISLPShYQ2HTfIM90W+AB/Q6A04fLZQ6HDJTXuo5AEA4lzsREVEvI9qSJ0EQxLoUWVB9ZSMaa5sBAH7BrKGwZTKZzDRLcYL9KOxebhrrJ4iIqHdiDUUfU9o2O+Ed6G4Xrdz7OmMdRTbrKOxenmmHpyCJIyEiIhIXE4o+xrjDU3/WT9gFY0JxOvkcWhpZR2GvGmqaUHymAgAQNiJA4miIiIjExYSijynJb32oYUJhH/xCvOAd6A69Vo/Tx4qkDod6KD+9GADgG+wFt34uEkdDREQkLiYUfYypS3aIp7SBUJdcWkeRxToKu8WGdkRE1JsxoegjCjJL8OETG7A/MQMAsOHN3/DhExtQkFkicWR0NexHYf/yWJBNRES9GBOKPmB/YgZemPkhTuzLh9pRCQCYclccTuzLxwszPzQlGWSbTHUUx4qgadJKGwz1SG5bh2xuGUtERL0RE4periCzBKuXJGLCvBj8c8dDaGlsfSC98+8z8OaBxzFhXgxWL0nkTIUN6x/mDS9/N+g0epxOZh2FvamraMCFs627q4WyIJuIiHohJhS93NY1B+AV4I6Fb81BxbkaAIBbP2c4uztCqVJg4Vtz4Onvhm1rDkocKV2OTCZDtLGOgsue7E5eW0F2wKB+cPFwkjgaIiIi8TGh6MUMBgMObszE1LvjoFQpOt0yVqlSYNqCeBzYeJzNCW3YkAkhAICsffnSBkLdltvWIZv1E0RE1FuJklB4e3vDzc1NjEuRiDRNOmiatPALaU0gyvLbEoqw9lvG+gV7Q9Ok5fp8GzZkQhgA4HRyETTN/H2yJ6aGdkwoiIiol1KKcZHy8nIxLkMiUzspoXZSoaygNZGY9eB4xM2MglzRPo8sO1sJtZOKnbNtWMCgfvDwdUXNhXqcSTln2kqWbJ9xy9gwdsgmIqJeikueejG5XI7xc4Yh6csU6LR6qB1VGBDlh8DBPqZzdFo9dn2RjIQ5wyGTySSMlq5EJpOZdnvKZh2F3aguq0Pl+VrI5DKExvhLHQ4REZFFMKHo5WYvSkBVcS0+WroROq2+3Ws6rR5r/7wR1SV1mLVovEQRUlexjsL+GLeLDYrwhaOLg8TREBERWYYoS57IdoUM88fiVfOwekkiTuzPx7QF8fAL9kbZ2Urs+iIZ1SV1WLxqHkKG8dNTW2eso8g5VgRtiw4qB/742jpTQzt2yCYiol6MTyR9wIR5MQiK8sW2NQexaeVeaJq0UDupkDBnOGYtGs9kwk4ERvjA3ccFteUNyE09h6hxIVKHRFfBhnZERNQXiJJQlJaWon///mJciiwkZJg/Fr0zFwv/cws0TTo4OKtYM2FnjHUUhzZlImt/PhMKGycIwsUZCiYURETUi4lSQxEYGIgLFy6IcSmyMLlcDkcXNZMJOxWd0FZHsT9f2kDoqirP16LmQj0USjlnAYmIqFcTJaEQBIFN0YisYKixjuJIIXQancTR0JUYt4sdEO3HLZmJiKhX4y5PRHYkKMoXbv2c0dKkNT2wkm3KY4dsIiLqI0RLKH744QdkZ2dzpoLIgmQyGaLbmtpx2ZNtMyZ84dzhiYiIejnREoolS5Zg2LBhcHNzw4QJE/Doo4/io48+wrFjx6DRaMS6DVGfN4R1FDbv0oLs8Fh2yCYiot5NtG1jc3NzUVlZidTUVKSkpCAlJQVffvklampqoFQqMWTIEKSlpYl1O6I+y9iP4tThQui0eihVCokjot+7UFCF+qomKNUKDBziJ3U4REREFiVKQiGTyaBSqRATE4OYmBjcc889ptfy8vKQnJyM1NRUMW5F1OcNiPaFq5cT6quakJd2HhGjB0odEv2OcblT8FB/KNVs90NERL2baLs8XU5YWBjmz5+Pl156SYxbEfV5crncVEeRfSBf0lioc6yfICKivkSUhOLHH3+Eh4eHGJcioi4w1VEcKJA4EupMXiob2hERUd8hSkJx4403wsHBATqdDv/+979x00034dZbb0V5ebkYlyei34meEAoAOHmwAHqdXtpgqB2DwYC8dGNBNhMKIiLq/UTtQ7F06VKsWbMG11xzDbZu3Yra2loAwIoVK/D111+LeSuiPi14aH+4eDqhuUGD/IwSqcOhS5TkVqKprgVqJxWConylDoeIiMjiRE0ovvnmG3z22Wf461//CqXyYiFifHw8Vq1aJeatiPo0uVyOqHHBAICs/XkSR0OXMi53ChnuD4WSO3AREVHvJ2pC0dzcDH9//w7Ho6OjkZWVJeatiPq8oW3LnrL2s47CluSmtXbI5nInIiLqK0RNKKZPn47ExMQOxzUaDfR6rvMmEpOpjuJQAQx6g7TBkImxoV3YSDa0IyKivkHUDdL/9a9/Ydy4cZDJZBAEATKZDDqdDq+//jpGjhwp5q2I+ryQYf5wdndEY20zCo6XcEchG2DQG5CfXgyAMxRERNR3iDpDER4ejt27d2PTpk1obGzElClT4OXlhc2bN+O1114T81ZEfZ5ccbGO4gTrKGzCuVMX0NKkhaOLGgGD+kkdDhERkVWIOkPR1NSEIUOG4LfffsPp06eRnp4OlUqFhIQE+Pj4iHkrIgIwZGIoUnacQvb+Atz48ESpw+nzjA3twmIDIVeI+nkNERGRzRI1obj33nvx7bffAgAGDx6MwYMHi3l5IvqdIcaO2Qdb6yj4ECutvDQ2tCMior5H1KePwsJCvPTSSx2ONzc3449//KOYtyIitG5N6uTmgMbaZpw9USp1OH1ebiob2hERUd8jakLx/fffY/Xq1di4caPp2Pnz5zF58mTk5OSIeSsiAqBQKi72o9jHOgop6bR6nM1sbTIYNpIJBRER9R2iJhRBQUH49ttv8cADDyAzMxMHDx7EqFGjMHToUOzevVvMWxFRmyHGfhQH2I9CSkXZZdC26ODs4Yj+od5Sh0NERGQ1ZtdQPPDAA4iNjTX9mjhxIl555RXMnDkTNTU1WLFiBf7yl7+IESsRdaJdHYXBALmcdRRSyE292NBOJpNJHA0REZH1mP3koVar8c033+Dmm29Gv379EBISgp9++gnNzc1YsGABbrnlFjHiJKLLCB0RAEcXNRqqm1CYVSZ1OH0WC7KJiKivMnuG4oMPPjD9/5kzZ5CWloa0tDTI5XJs374da9euhbOzM4YNG4ZDhw6Zezsi+h2FUoHIccFI33kaWfvzETLMX+qQ+iTjlrHh7JBNRER9jKjbxg4aNAiDBg3CvHnzTMdqa2uRmpqK9PR0MW9FRJcYOiHUlFDMfnC81OH0OZpmrWl2iDMURETU15i95Onxxx9Hc3MzgNbk4ffc3d1xzTXX4LHHHjP3VkR0GdG/q6Mg6yo8UQq9Vg+3fs7wGeAhdThERERWZXZCUVlZiaamJgCAp6cnwsLCMGfOHPzjH//A999/L+l2sSUlJVi4cCECAgLg6OiIyMhIvPjii9BoNN26jkwmu+yv1157zULRE3VdWGwgHJzVqK9sRFH2BanD6XMuXe7EgmwiIuprzF7y9Pnnn5v+PysrC+np6UhLS0NqairWrVuHwsJCSWooSkpKMG7cOBQWFmLu3LmIjIzE3r17sWzZMhw4cACbN2/u1m44ISEhuO+++zocnzRpkohRE/WMUqVA5NiByEg6g+wD+Qge2l/qkPqUPDa0IyKiPkzUGoqoqChERUXh9ttvNx2rqalBamoqMjIyxLzVVT3zzDM4e/Ys3n//fTz88MMAAEEQcP/992PdunVYt24d7r///i5fLzQ0FMuXL7dQtETmG5IQioykMzixPx8zHxgndTh9Si53eCIioj7M4hvWe3h4YMqUKVatoairq8M333yD8PBwLF682HRcJpPh1VdfhVwux9q1a60WD5E1DJkYCgDIPpAPQRCkDaYPaWnUoOhka0F2ODtkExFRH2SVDlgFBQW49957rXErAMCBAwfQ0tKCGTNmdFjPHBAQgJiYGBw6dMhUTN4V1dXV+Oijj/DKK69g7dq1ktaGEHUmPDYQaicV6ioace4k6yisJf94CQSDAM/+bvDyd5c6HCIiIqsTdcnT5dTX1+Pzzz/HunXrrHE708N+REREp69HREQgLS0Nubm5GDp0aJeumZaWhgcffND0tUwmw4IFC/Dhhx/C2dn5iu9taWlBS0uL6WvjblharRZarbZL9zeef+l/qWd67TjKgIjRA5C5Jw/H955B/0FeFrtVrx3DHjh9rBAAEBYbwJ9nK+MYmo9jaD6Oofk4huYzZwzFGHerJBTWVlNTA6B1uVVn3N3d2513NU899RRuv/12REREQCaTISUlBc899xw+//xz6HQ6fPXVV1d8/6uvvooVK1Z0OL59+/arJiOd2bFjR7ffQx31xnGUebfOuu1KPIgWX8t3ze6NY9hdh37OBwBoHOuwZcuWbr+fY2g+jqH5OIbm4xiaj2Novp6MYWNjo9n3FSWh+Nvf/ob4+HiMHDkSkZGRYlwSAODj44OKiooun79r1y5MnTpVtPsb/etf/2r39bRp0/Drr78iNjYWX3/9Nf7+979j2LBhl33/s88+i6VLl5q+rq2txcCBAzFz5kxTctMVWq0WO3bswIwZM6BSqbr/jRCA3j2Og33O4vjG/6K2QIvrr7/eYluY9uYx7K49b3wAAJh12xSMuHZwl9/HMTQfx9B8HEPzcQzNxzE0nzlj2Fkfue4SJaH4+eef8Z///Ac6nQ4uLi6IjY1FXFwc4uPjERcX1+NGW3fddRfq6uq6fL6/vz+AizMTl5uBMA7c5WYwusLZ2Rl33XUXXnrpJezbt++KCYWDgwMcHBw6HFepVD36wenp+6i93jiOkaNDoHJUora8ARfyaxAU6WvR+/XGMeyOxrpmlJypBAAMjh/In2eJcAzNxzE0H8fQfBxD8/VkDMUYc1ESirS0NGg0GmRkZCA1NRXJyck4evQoPvnkEzQ1NfX4U9JVq1b16H3G2onLFU7n5ORALpcjPDy8R9c38vHxASDOVBGRGFQOSkSOHojMvXnIOpBv8YSir8tPL4YgCOg3wAMevq5Sh0NERCQJ0Woo1Go14uLi4OXlhdmzZyMoKAiCIODkyZNITk5GamqqWLe6qvHjx8PBwQE7duyAIAjtEpri4mJkZGRg3LhxcHR0NOs+xkZ9oaGhZl2HSEzRE0JbE4p9+Zh+7xipw+nV8i7pkE1ERNRXibZt7JtvvgkfHx9EREQgODgY/fv3x1NPPQVfX1/cfffdeOONN8S61VW5u7vjD3/4A3Jzc7F69WrTcUEQ8Oyzz8JgMLTbsQlonWXIzs7G2bNn2x1PSUnpdAbiu+++w1dffQUfHx9Mnz7dMt8IUQ8MmRAKgP0orCGXHbKJiIjEmaH4+OOP8dxzz+Hhhx/GlClT0NTUhGPHjuG///0vvvrqK2zZsgUjR44U41Zd9tprr2HXrl149NFH8csvvyAyMhJ79uzBvn37MGvWrA59MQ4fPoxp06ZhypQpSEpKMh1/55138MMPP+C6665DcHAwBEFAcnIy9uzZA0dHR6xbtw6urlzqQLZjUFwQVA5KVJfVo/hMBQIH+0gdUq9lmqFgQkFERH2YKAnF22+/jVdffbXdTkZ33303Xn75ZSxZsgQzZszA8ePH0b9/fzFu1yUBAQE4dOgQ/v73v2Pz5s346aefEBwcjBUrVuCZZ56BXN61yZk5c+aguroaycnJ2Lp1K3Q6HYKCgvDAAw/gqaeeQnR0tIW/E6LuUTuqMHjUAGTtz0f2/nwmFBbSUN2E0vzWguwwJhRERNSHiZJQ5OTk4Oabb+5w3NHREWvXrkVZWRlefPFFvPfee2LcrssCAgLw8ccfd+ncqVOndro85NZbb8Wtt94qdmhEFjVkQiiy9ucj60A+rv2/0VKH0yvlts1O9A/1hounk8TREBERSUeUGgp3d3c0Nzdf9vU///nP2Lx5sxi3IqIuMNZRZO1nHYWl5KaeA8DZCSIiIlESikmTJuHbb7+97OuhoaEoKSkR41ZE1AWD4wdAqVagqqTOtCyHxGWqn4hjQkFERH2bKAnF008/jX/961/YtGlTp69nZGTA15f74RNZi9qptY4CALL25UsbTC+VayrI5paxRETUt4mSUCQkJOCNN97ArbfeigULFmD//v1oaGiAVqtFUlISnnzySdx2221i3IqIumhIQigAIOtAvqRx9EY1F+pRUVQDmUyGkBh/qcMhIiKSlGiN7R5//HFERkbiL3/5CyZNmgSZTAaZTAZBEDB16lS8/PLLYt2KiLogOiEUwG+mOoqedqynjozLnQIG94Ozm3kNMomIiOydaAkFAMyePRuzZ8/G0aNHkZKSAq1Wi9jYWEycOFHM2xBRF0SMHgCFSoHK87W4UFAFv1BvqUPqNYzLnViQTUREJFJCUVFRAXd3d6hUKgDA6NGjMXo0t6okkpKDsxqD4oJw6vBZnNifz4RCRHnGDtkjWT9BREQkSg3Fgw8+iEcffbTdsdTUVPzjH//Au+++i9raWjFuQ0TdZNw+Npt1FKIyFWSP5AwFERGRKDMUhw4dwn//+1/T12fPnsWkSZOg1Wqh1+uxcuVKHDp0CF5eXmLcjoi6aMiEUGx8ezdO7M+XOpReo6qkFtWldZDJZQgZxoJsIiIiUWYoKisrMXjwYNPXn332Gby9vVFaWori4mJ4enrizTffFONWRNQNEaMHQqGUo6KoBhfOVkkdTq9wJqV1dmJAlB8cnNUSR0NERCQ9URKKgIAAVFRUmL7etm0b7rrrLnh6esLX1xcrVqzAhg0bxLgVEXWDo4vatM6fsxTiyEtr7ZAdzoJsIiIiACIlFFOnTsV7770HACguLsaRI0cwc+ZM0+tDhw5FQUGBGLciom5iHYW4ctkhm4iIqB1REop//OMfSExMRExMDMaMGYP+/ftj6tSpptfLysrg5OQkxq2IqJuMCQU7ZptPEARTD4owdsgmIiICIFJCERoaisOHD+Oaa67BlClTsH79eigUCtPru3btQmRkpBi3IqJuihw7EHKFHBcKq1FeVC11OHatvKgGdRWNUKgUCB7aX+pwiIiIbIJoje0iIiJMy55+7/jx47j99tvFuhURdYOjiwPCYgNxJrkIWQcKMPl2T6lDslu5qa31EwOH+EHlIGpfUCIiIrvV5X8RfX19MWrUKMTFxWHUqFGIj49HeHh4l9576ZayRGR9QyeEtiYU+/Iw+fZYqcOxW6aGdizIJiIiMulyQqHVarF9+3Zs374dMpkMAODh4YH4+HjEx8ebkoyIiAiLBUtEPRM9IRQ/vrsX2Qe4OYI5Lja0Y/0EERGRUZcTiurqapw5cwbHjh3DsWPHkJycjOTkZOzcuRM7d+40JRlubm4YOXKkKcFYsGCBxYInoq4x1lGU5lei4nwN+gV6SB2S3WlXkM0O2URERCbdWgQ8aNAgDBo0CHfccYfpWF5eHpKTk3Hs2DEcPXoUqamp2L17N/bs2QOZTMaEgsgGOLs5IjQmALmp55C9vwATbxshdUh2pzS/Eo21zVA5KDEgyk/qcIiIiGyG2bs8hYWFYf78+XjllVewZcsWrFu3Dtdeey0EQYAgCGLESEQiGDIxFABwYn+etIHYqdy2DtnBw/yhVCmucjYREVHfYfY2JS0tLdi2bRvWr1+PH3/8ETU1NRAEAbGxsZg/f74YMRKRCIYkhGLze/tYR9FDucYO2VzuRERE1E6PEorGxkZs2bIF69evx+bNm9HQ0AAAGDNmDObPn4/58+d3eQcoIrKOqHHBkMllKMmtQEluBfxCvSCXi9KKpk/IY0E2ERFRp7qcUNTV1eHHH3/E+vXrsXXrVjQ3N0Mmk2HSpEmYP38+5s2bh6Ag/kNLZKsuFFbDxcMR9VVN+EvCSqidVBg/ZxhmL0pAyDB/qcOzaQa9AfnpxQC4ZSwREdHvdTmh8PPzg0ajgUqlwtSpUzFv3jzMnTsXfn4sTiSydfsTM7B6SSLUjioAwNBJYRg6MRRJX6Zg3/fpWLxqHibMi5E4SttVfKYCzQ0aODipEBjhI3U4RERENqXL6x1aWloAANdeey1mzpyJyMhIODo6WiwwIhJHQWYJVi9JxIR5MXho5a0AgKqSOty6dCrePPA4JsyLweoliSjILJE4Uttl7JAdOiIAcgWXiREREV2qyzMUw4cPR3Z2NrZu3Ypt27aZjoeHh7drbBcfHw9vb2+LBEtE3bd1zQF4Bbhj4Vtz0NKogUwmQ/HpclSV1sGrvxsWvjUHJ/bnY9uag1j0zlypw7VJuW0dssO43ImIiKiDLicU6enpaGlpQVpamqnvRHJyMo4fP44zZ87gu+++MzW3Cw4ONiUZzz33nMWCJ6IrMxgMOLgxE7c8PglKlQJKDyeEDPdHfkYxsg8UIGHucChVCkxbEI9NK/fiwbfnmH6O6SIWZBMREV1et3Z5cnBwwNixYzF27FjTMa1Wi/T09HZJRkZGBjZs2IAffviBCQWRhDRNOmiatPALuThrGDlmIPIzinEmpQgJc4cDAPyCvaFp0kLTpIWDs1qqcG2SXqdH/vG2gmxuGUtERNSB2X0oVCoVRo0ahVGjRuHBBx8EAOh0OmRmZuLYsWNmB0hEPad2UkLtpEJZQaXpWGjbsh3jrkUAUHa2EmonFdROKqvHaOvOnbwAbbMOTm4O6B/G5ZxERES/Z5HqQqVSidjYWPzpT3+yxOWJqIvkcjnGzxmGpC9ToNPqAVzc9jQvvRgGgwE6rR67vkhGwpzhXO7UCWNBdtiIQPbtICIi6gT/dSTq5WYvSkBVcS0+WroROq0egRE+UDup0FzfgnOnLmDtnzeiuqQOsxaNlzpUm5TbVj8RxuVOREREnTJ7yRMR2baQYf5YvGoeVi9JxIn9+Zi2IB5eAW4oza3EP2/9DE21zVi8ah6b212GsSB7EAuyiYiIOsWEgqgPmDAvBkFRvti25iA2rdwLTZMWAODh64Lnvr+XycRlaFt0KMgsBcAtY4mIiC6HS56I+oiQYf5Y9M5cfJz7HP70r5sBAG7ezkwmrqAouwx6rR6uXk7wDfaUOhwiIiKbxISCqI+Ry+WIGhsMoHWnJ4PBIHFEtuuMsSA7NpAF60RERJfBhIKoDwqM8IGDkwrNDRqUnKmQOhybldfWITucy52IiIguiwkFUR8kV8gREhMA4OIuRtRRLjtkExERXRUTCqI+KmxEa0KRx4SiU5omLYqyywBwy1giIqIrYUJB1EeFtX3qzoSicwWZJTDoDfDwdYV3gLvU4RAREdksJhREfZRxhiI/o/XBmdrLZUE2ERFRlzChIOqjAgf7wMFZjZZGDc6fLpc6HJuTZ6qf4HInIiKiK2FCQdRHyRVyhMa09qDISy+WOBrbw4JsIiKirmFCQdSHGbs/57Ut76FWzQ0tOH+qddYmLDZA4miIiIhsGxMKoj7MlFBwhqKd/IwSCIIA70B3ePq5SR0OERGRTWNCQdSHhY1oTSgKMopZmH0JY0E2G9oRERFdHRMKoj4sYFA/OLqo0dKkxfkcFmYb5bZ1yA5jQkFERHRVTCiI+rBLO2bnpbMfhZFph6c4FmQTERFdDRMKoj7OuKzH+Kl8X9dQ04SS3AoAF5eEERER0eUxoSDq44wPzZyhaJXfVqDuG+wFN29niaMhIiKyfUwoiPq4sLbGbQXHS6DX6SWORnqmgmw2tCMiIuoSJhREfZx/uDccXR2gYWE2gIsN7ViQTURE1DVMKIj6OLlcjrARrYXZxofpviwvlR2yiYiIuoMJBREhtC2hyO/jCUVdRQMuFFYDgCnJIiIioitjQkFECI9t/TS+r89QGDuGBwzqB2d3R4mjISIisg9MKIgIYbGtn8YXZPbtwuwzKa0F2ayfICIi6jomFESE/mHecHJzgLZZh3MnL0gdjmRMDe2YUBAREXUZEwoiglwuR6ipY3axxNFIJ5cdsomIiLqNCQURAbi4zKevNrirKq1DVXEtZHIZQob7Sx0OERGR3WBCQUQALi7zyU3tmwmFcblTUIQvHF0cJI6GiIjIfjChICIAF2cozp4ogU7b9wqzjR2yw9ghm4iIqFuYUBARAMAv1AvO7o6thdmn+l5hNguyiYiIeqbXJhS7d+/GU089hWnTpsHDwwMymQz33Xdfj6+3bds2TJ06Fe7u7nBzc8PUqVOxbds28QImkli7wuw+1o9CEATTUi92yCYiIuqeXptQfPLJJ3jzzTdx+PBhBAaa94njF198gdmzZyMzMxP33nsv7r//fmRnZ2P27Nn44osvRIqYSHrG5T59LaGoPF+L2vIGKJRyBA/tL3U4REREdqXXJhSPPfYYjh8/jtraWnz66ac9vk5VVRUee+wx+Pj4IDk5GatWrcLKlSuRkpICf39/PPbYY6iqqhIxciLphI3omwmFcbvYAdF+UDupJI6GiIjIvvTahGL06NEYNmwYFAqFWdf57rvvUF1djSVLlmDgwIGm4wEBAXjyySdRXV2N7777ztxwiWxC+EhjYXZpnyrMzmWHbCIioh7rtQmFWJKSkgAAM2fO7PDarFmzAAC//fabNUMishi/EC84ezhC26JD0ckyqcOxGlNDO+7wRERE1G1MKK4iJycHABAREdHhNeMx4zlE9k4mk11c9tRH+lEIgnBxhycWZBMREXWbUuoAbF1NTQ0AwMPDo8NrLi4uUCgUpnMup6WlBS0tLaava2trAQBarRZarbbLsRjP7c57qCOO45WFDO+PzD25OJNahEl/GNHpOb1pDMsKqtBQ3QSlWgH/Qd5W+5560xhKhWNoPo6h+TiG5uMYms+cMRRj3G06ofDx8UFFRUWXz9+1axemTp1quYB66NVXX8WKFSs6HN++fTucnZ27fb0dO3aIEVafx3HsXJW+dZOB1N3Z8N1y5XN7wxiePVIJAHAPdMD2X6y/FXRvGEOpcQzNxzE0H8fQfBxD8/VkDBsbG82+r00nFHfddRfq6uq6fL6/v7/oMRhnJmpqatCvX792rzU0NECv13c6e3GpZ599FkuXLjV9XVtbi4EDB2LmzJlwd3fvcixarRY7duzAjBkzoFJxJ5qe4jheWdmwKhxY8x7qilswc/osKNUdNzboTWP49bFfAORj5DVDcMMN11vtvr1pDKXCMTQfx9B8HEPzcQzNZ84YGlfOmMOmE4pVq1ZJHQIiIiJw9OhR5OTkdEgorlRfcSkHBwc4ODh0OK5SqXr0g9PT91F7HMfOBQ7yhYunExqqm1CaW2VqdteZ3jCGBRklAIBBcQMk+V56wxhKjWNoPo6h+TiG5uMYmq8nYyjGmLMo+yqmTJkCoHV50u8ZO2UbzyHqDVoLs1uTiNzUcxJHY1kGgwF56cUAgHBuGUtERNQjTCjaNDY2Ijs7G2fPnm13/I477oCHhwdWrVqFwsJC0/Hi4mK8/fbb8PT0xO23327tcIksyrTTU9vDdm9VkluJ5voWqJ1UCIrylTocIiIiu2TTS57MsXfvXnz00UcAgAsXLpiO3XfffQCA6Oho/O1vfzOdf/jwYUybNg1Tpkwx9Z4AAC8vL7z77ru45557EB8fjzvvvBNyuRzffPMNSktL8b///Q9eXl5W+76IrCFsZN/omG2cgQkZ7g+F0rwmmERERH1Vr00oTp8+jXXr1rU7dubMGZw5cwZA6zKlSxOKK/njH/8IHx8fvPrqq/jss88AAPHx8Vi3bp2puR1Rb2KcoTh7ohTaFh1UDr3zrwpjrw0udyIiIuq53vmUAOC+++4zzUZ0xdSpUyEIwmVfnz17NmbPni1CZES2zzfYE65eTqivakJRdhnCeukDt7FDdhgb2hEREfUYayiIqAOZTIbQEb172ZNep0dBBguyiYiIzMWEgog6ZXzIzu2lCcX5nHK0NGnh6KJGwOB+V38DERERdYoJBRF1yrjMKS+9dyYUuW31E2GxgZDL+VchERFRT/FfUSLqlLEXRWFWGbQtOomjEV9uWusOT721PoSIiMhamFAQUad8BnrC1dsZeq0ehVmlUocjKoPBgDPHigCwfoKIiMhcTCiIqFPtOmb3kjqKgswSfPjEBjwQ9rKpad+hHzNRkFkicWRERET2iwkFEV2W8dP73rDT0/7EDLww80Oc2JePyXfGAQBUDkrkpZ3HCzM/xP7EDIkjJCIisk9MKIjosoz1Bfltn+bbq4LMEqxekogJ82Lw5oHHETrMHwAQNS4Ybx58AhPmxWD1kkTOVBAREfUAEwoiuixjQlGYVQpNs1biaHpu65oD8Apwx8K35kCpUlxsaBcbCKVKgYVvzYGnvxu2rTkocaRERET2hwkFEV1WvyAPuPVzhl5nQGFWmdTh9IjBYMDBjZmYencclCoFACDnSCEAILytQ7ZSpcC0BfE4sPE4BEGQLFYiIiJ7xISCiC5LJpNd7EfRts2qvdE06aBp0sIvxBsAUJpfiaKTZZAr5Bg6MdR0nl+wNzRNWmia7HcmhoiISApMKIjoisJGGBMK+6yjUDspoXZSoaygEgBwdEsWAGBIQghcvZxN55WdrYTaSQW1k0qSOImIiOwVEwoiuiLjTk/2unWsXC7H+DnDkPRlCnRaPY60JRSjbxhiOken1WPXF8lImDMcMplMqlCJiIjsEhMKIroi45KncyfL7HY50OxFCagqrsX7j3xvqp8YdX00gNZkYu2fN6K6pA6zFo2XMkwiIiK7pJQ6ACKybd6B7nDv54LaigacPVGKwaMGSB1St4UM88fiVfPw/qPrAQBe/m7I3l+AsrOV2PVFMqpL6rB41TyEtG0nS0RERF3HGQoiuiKZTIawkW11FOn2uewJACbMizElQ7XlDXj/0fXYtHIvhk8Kx0vbH8KEeTESR0hERGSfOENBRFcVNiIAab/mIC/VfhOKxtpm5LbF/8rOh+EzwBMOzirWTBAREZmJCQURXVVYbGu/BnueoUj9JQd6rR6BET4YEOUndThERES9Bpc8EdFVhcUGAACKTl6w28Lsoz+37e50/ZCrnElERETdwYSCiK7KO8AdHr6uMOgNKMgskTqcbtM0a5H2aw6A9tvFEhERkfmYUBDRVbV2zG6dpbDHZU/Hd+eiuUED70B3hLcVmBMREZE4mFAQUZcY+1Hk2WGDO2N37NGzo1mETUREJDImFETUJWEjjAlFscSRdI9ep0fy9pMAgFGsnyAiIhIdEwoi6hLjUqGik2VosaPC7FOHC1FX0QgXTydEJ4RIHQ4REVGvw4SCiLrEy98dnn6uEAwCCk+USh1OlxmXO8XPjIRSpZA4GiIiot6HCQURddnFOgr7WPYkCAKOcLtYIiIii2JCQURdZkwo8jPsI6HIzyhGRVEN1E4qxEwdJHU4REREvRITCiLqMmNhdn66fSQUxuVOI6YNhoOzWuJoiIiIeicmFETUZcZeFOdzyqFr0UsczdUd/TkbAJvZERERWRITCiLqMi9/d3j2d4NgEFBd2CR1OFdUkleBouwyKJRyxM+IlDocIiKiXosJBRF1S3hbHUVlQaPEkVyZcbnTkAmhcPF0kjgaIiKi3osJBRF1S2jbsqcqm08ouNyJiIjIGphQEFG3hMcGAbDthKKqtA45RwsBAKNmR0scDRERUe/GhIKIusVYmF1X0ozmBo3E0XTu2NbW2YlB8QPgHeAucTRERES9GxMKIuoWTz83ePm7QRCAs5klUofTKWP9xJgbODtBRERkaUwoiKjbQke0zlLkp9teQtFQ04QTe/MAsH6CiIjIGphQEFG3GROKvPTzEkfSUeqOHOh1BgRG+iJgkI/U4RAREfV6TCiIqNtCY1oTioIM25uhOPJz23Kn67nciYiIyBqYUBBRt4WO8AcAFJ8uR1N9i8TRXKRp0iL91xwAXO5ERERkLUwoiKjbPHxd4eSlgiAABcdtZ5Yi47czaGnSol+QB8LaGvARERGRZTGhIKIe8Qp2BgDkpZ6TOJKLjrYtdxo1OxoymUziaIiIiPoGJhRE1CPeoW0JRXqxxJG00uv0SN5+CgCXOxEREVkTEwoi6hHjDEVumm3s9JR98CzqKxvh6u2M6PHBUodDRETUZzChIKIe8QppTShKzlSgsa5Z4miAY23LneJnRkKhVEgcDRERUd/BhIKIesTRXQXvQHcIgiD59rGCIODo1mwAwOjrudyJiIjImphQEFGPhdlIg7v89GJUFNXAwVmNmCmDJI2FiIior2FCQUQ9FmJMKCSuoziypXW504hrB0PtpJI0FiIior6GCQUR9ViYjSQUR03dsbnciYiIyNqYUBBRj4W2JRTFEhZmF58px7mTF6BQyjFyRoQkMRAREfVlTCiIqMfcvJ3Rb4AHgNY6BikcbVvuNHRSGFw8nCSJgYiIqC9jQkFEZgmPDQQgXWH2kS1tuzuxmR0REZEkmFAQkVnCRrQlFKnWTygqi2txJrkIADBqVpTV709ERERMKIjITGEjjTMU1l/ydKyt98TgUQPg5e9u9fsTEREREwoiMpNxhqIktwKNtdYtzDbWT3C5ExERkXSYUBCRWdy8neE70BOAdWcpGqqbkLU/HwAwhgkFERGRZJhQEJHZQo2F2WnnrHbP5B2noNcZMCDKD/7h/ax2XyIiImqPCQURmc2001Oa9WYoLi53irbaPYmIiKgjJhREZLYwU0JhnZ2eWho1SN91GgDrJ4iIiKTGhIKIzBbW1jG7NL8SDTVNFr9fxm+50DRp4TPQE6ExARa/HxEREV0eEwoiMpurlzN8g70AWKdj9tGf25Y7zY6GTCaz+P2IiIjo8phQEJEowmJbZwpyLbzsSa/TI2X7SQBc7kRERGQLmFAQkSjCrVRHkX2gAPVVTXDr54yoccEWvRcRERFdXa9NKHbv3o2nnnoK06ZNg4eHB2QyGe67774eXUsmk13212uvvSZu4ER2ytjgztIJhXF3p/iZUZAreu1fYURERHZDKXUAlvLJJ59g3bp1cHZ2RnBwMGpra826XkhISKcJyaRJk8y6LlFvYdzpqaygCg3VTXDxdBL9HoIg4OjWbABc7kRERGQrem1C8dhjj+Hpp59GdHQ0jhw5goSEBLOuFxoaiuXLl4sTHFEv5OLpBL8QL5QVVCEv7f/bu/OgKu97j+OfAx4O4BYVKWoAcUmMx0irptlqwDGKWhuzqA2xRoySJp1mJq0xbZLeWqedm6V3erN0Okl1oo5cNYtLOpnUaK7iEquJF6ON2dzAFqKiolCJCJzf/QOhIojwLOccOO/XDH/wbL/f8/U538PX5/k9vxINyxjoeBuHPynR6ZJy+eJjNOyOAY4fHwAAtF2HfV5g1KhR8vv9io6ODnVXgIjRMB/FPncee6p/3Cl97CDFxHpdaQMAALRNh71D4bQzZ85o8eLFOnHihHr37q3MzEwNHjw41N0Cwkpael/t+st+19701PC62Ik87gQAQLigoGilvXv3Kjc3t+F3j8ejGTNm6LXXXlN8fHwIewaEj3+/6cn5uShKDpSq5MBJRXuj9Z1x1zl+fAAAYA0FRSs88cQTmjZtmgYPHiyPx6M9e/bo6aefVl5enmpqarRy5coW96+qqlJVVVXD7/UDxKurq1VdXd3qftRv25Z90BRxtO9KMex3Q29JUunRMpWdOKsuPZwrtne9u1+SdMNtqfLGRbf7fz+uQ/uIoX3E0D5iaB8xtM9ODJ2Iu8cYY2wfxSUJCQk6depUq7ffvHmzMjMzmyzfuXOnbr31Vs2aNUtLly51pG+VlZVKT0/XwYMH9emnn8rv919x29/85jdauHBhk+UrVqzg7gY6nPd+tV//OlGlOx4fpKSh3Rw77gf/+YVOF1Zq5IxkDczo7dhxAQCIZJWVlXrggQd09uxZdetm7Xs7rO9QZGdnq6KiotXbJyUludibxuLj45Wdna3f/va3+vDDD1ssKJ566in9/Oc/b/i9vLxcycnJGj9+fJv+4aqrq7Vx40aNGzdOXi8DUq0ijva1FMPCd8/ro798psTYZE2adLsj7Z0uKdebhQXyeKSZ8+7TNYldHDluKHEd2kcM7SOG9hFD+4ihfXZiaHdqBSnMC4pXXnkl1F1oUUJCgqS6yq4lPp9PPp+vyXKv12vpg2N1PzRGHO1rLoaDvnOtPvrLZyr69Lhj8d37wcG6Y49KVu9+PRw5ZrjgOrSPGNpHDO0jhvYRQ/usxNCJmHfY18YGw65duyTVzVEBoE7/izNmFzr4pqfdf62bzO4mJrMDACDsUFBcVFlZqS+++EJHjx5ttHzPnj3N3oF46623tHLlSiUkJOjOO+8MVjeBsJc2vI8kqfQfZ1RxuuW7d63xr7JKfb6jUBKzYwMAEI7C+pEnO7Zv367FixdLkkpLSxuW5eTkSJKGDBmiX/7ylw3bf/TRRxozZowyMjKUn5/fsPyll17SunXrNHbsWKWkpMgYo4KCAm3btk2xsbFatmyZunRp/89zA06J7xarpAG9dOzwKR3ZV6LhmYNsHW/Pxq8UqA0o+YZv6Vv9ezrUSwAA4JQOW1AcPHhQy5Yta7Ts0KFDOnTokCQpIyOjUUFxJVOmTNGZM2dUUFCg9evXq6amRv369dOcOXP0xBNPaMiQIa70H2jP0ob3qSsoPrFfUNTPjj1qEp81AADCUYctKHJychruRrRGZmammnuD7j333KN77rnHwZ4BHV/at/vqb+s+1ZF99sZRVFVe0L78uv8E4HEnAADCE2MoADgu7eLA7MM2B2bvyz+kC99Uq3fyNUr1B++10AAAoPUoKAA4rv/Fgdmn/nlW5SfPWT7Ovx93ukEej8eRvgEAAGdRUABwXHzXWPUZ2EuSLD/2VFNdq4INX0ricScAAMIZBQUAV6Sl1z32dMTiY09f7ChU5dnz6tars667KdnJrgEAAAdRUABwhd2C4uO/1j3uNCLrekVFk6oAAAhXfEsDcEVDQbHv6zbvGwgE9H8XZ8fmcScAAMIbBQUAV/S/sY88Ho9OFZ/V2dJ/tWnfw3tKVHasQrGdY+QfneZSDwEAgBMoKAC4Iq6LT0kNA7Pbdpei/u1O6WMHKybW63jfAACAcygoALhmgIVxFMYY7b44fuImHncCACDsUVAAcE3axfko2lJQFH9Vqq8PnVKnmGil3znYra4BAACHUFAAcE3at/tJattcFPWDsf2jByi+a6wr/QIAAM6hoADgmv43Jsnj8eh0SbnOnmjdwOz6x51GTRziZtcAAIBDKCgAuCa2s099BidIat1dilPFZ3X4kxJ5PB6NzKKgAACgPaCgAOCq+nEUh1sxjqL+7sR1301W98QurvYLAAA4g4ICgKva8qan3e8xmR0AAO0NBQUAV6W1sqCoOF2pL3YWSZJGTaSgAACgvaCgAOCq1GFJ8kR5VHasQmXHK664XcGGLxWoDSjFn6TE1B5B7CEAALCDggKAq2I7+9S3fmB2C3cp6mfH5u1OAAC0LxQUAFyXNvziY09XeNPT+XMX9PcthyQxfgIAgPaGggKA6xrGUXzSfEGxb/NBVZ+vUe+UHkoZ+q1gdg0AANhEQQHAdQ0Fxb6vm13f8LjTpCHyeDxB6xcAALCPggKA61L9dQOzzxyvUNmx8kbrai7UaM/GryRJN/G4EwAA7Q4FBQDXxXaOUb/BvSVJR/Y2vkvx2Y5CVZafV7eEzho8KjkU3QMAADZQUAAIirRvNz8wu/5xp5EThigqmpQEAEB7w7c3gKBIG95HknT4koHZgUBA/7f+S0m83QkAgPaKggJAUAxI7ydJKtxXImOMJOlQQbHOHK9QbBef/N9LC2X3AACARRQUAIIixf+tuoHZJ/6lsmN1M2bv/mvd407fuXOwvL5OoeweAACwiIICQFD44mN07fWJkupmzDbGaPd7X0jicScAANozCgoAQZOWXjeO4sjeEhV/Wapjh0/J6+uk9LGDQ9wzAABgFQUFgKCpn+Du8N5i/W3dp5Ik/+gBiuviC2W3AACADTy0DCBo6guHvf97UHv/96AkqfLsNyraf0yp/qRQdg0AAFjEHQoAQbFjzd+16GfvNF7okU4Wn9V/jH9NO9b8PTQdAwAAtlBQAHBd0f5jevWxNbrt3huVfENiw/Lrb07Vf3/0uG6790a9+tgaFe0/FsJeAgAAKygoALhu/Z//ph59umnuH6Y0zEchSTdNHKJO3mjN/cMUXZPUVe//eWcIewkAAKygoADgqkAgoJ3v7FfmA99RJ2+00r7dt2Fd/etiO3mjNWbGCP3tnU8bJr0DAADtAwUFAFdd+KZGF76pVmJqT0l1b3WK9kbrhtv6q3dKj4btElN66sI31brwTXWougoAACzgLU8AXBUT10kxcV6dKDotSeo7KEH/9eFj6tIjrtF2J46eVkycVzFx3lB0EwAAWMQdCgCuioqK0i1T/MpfsUc11bWSpMTUHorvFtuwTU11rTb/T4FunTJMHo8nVF0FAAAWUFAAcN2Eh29V2dflWvzzdxqKino11bVa9LN3dOZYhbIeviVEPQQAAFbxyBMA16X6k/TIK/fq1cfW6LMdhRozY4QSU3rqxNHT2vw/BTpzrEKPvHIvk9sBANAOUVAACIrb7r1R/a7vrff/vFN/eXm7LnxTrZg4r26dMkxZD99CMQEAQDtFQQEgaFL9SXr4pbs197/v0oVvauSL9zJmAgCAdo6CAkDQRUVFKbZzTKi7AQAAHMCgbAAAAACWUVAAAAAAsIyCAgAAAIBlFBQAAAAALKOgAAAAAGAZBQUAAAAAyygoAAAAAFhGQQEAAADAMgoKAAAAAJZRUAAAAACwjIICAAAAgGUUFAAAAAAso6AAAAAAYBkFBQAAAADLKCgAAAAAWNYp1B2IRMYYSVJ5eXmb9quurlZlZaXKy8vl9Xrd6FpEII72EUP7iKF9xNA+YmgfMbSPGNpnJ4b1f4/W/31qBQVFCFRUVEiSkpOTQ9wTAAAAoO7v0+7du1va12PslCOwJBAIqKSkRF27dpXH42n1fuXl5UpOTtY//vEPdevWzcUedmzE0T5iaB8xtI8Y2kcM7SOG9hFD++zE0BijiooK9e3bV1FR1kZDcIciBKKionTttdda3r9bt2584BxAHO0jhvYRQ/uIoX3E0D5iaB8xtM9qDK3emajHoGwAAAAAllFQAAAAALCMgqId8fl8WrBggXw+X6i70q4RR/uIoX3E0D5iaB8xtI8Y2kcM7Qt1DBmUDQAAAMAy7lAAAAAAsIyCAgAAAIBlFBQAAAAALKOgCJKPP/5YkyZNUo8ePdS5c2d997vf1YoVK9p0jEAgoD/+8Y8aPny44uLi1Lt3b02fPl0HDhxwtd1wYfdctm/frnnz5mnkyJHq1auXYmNjNWTIEP3iF7/QmTNnmt2nf//+8ng8zf488sgjDp1Z8NiNYX5+/hXj4fF4tHPnTlfaDSd2zyUzM7PFGHo8Hi1fvrzRPh3pOszLy9OPf/xjjRo1Sj6fTx6PR0uXLm3zcSI5HzoRw0jPh07EMNLzoRMxjPR8WFxcrBdffFHjx49XSkqKYmJilJSUpPvuu0+7du1q07FCnROZ2C4I8vPzlZWVpZiYGN1///3q3r271qxZoxkzZqiwsFBPP/10q47zyCOPaNGiRRo6dKgee+wxHT9+XG+88YY2bNigHTt2aOjQoa60Gw6cOJepU6fq5MmT+t73vqcHH3xQHo9H+fn5euGFF7R69Wrt2LFDiYmJTfbr3r27Hn/88SbLR40a5cSpBY2T10NGRoYyMzObLG9uwkauw8ZycnKajV11dbWeffZZRUVFaezYsU3Wd5Tr8Fe/+pWKioqUkJCgPn36qKioyNJxIjkfOhHDSM+HTl2HUuTmQydiGOn58JVXXtHzzz+vgQMHaty4cUpMTNSBAwe0bt06rVu3TitXrtT06dNbdayQ50QDV1VXV5uBAwcan89nCgoKGpaXl5cbv99vOnXqZL766qurHmfTpk1Gkhk9erQ5f/58w/IPPvjAeDwec8cdd7jSbjhw6lyee+45U1JS0mhZIBAwjz76qJFkfvKTnzTZJzU11aSmpto+h1BzKoabN282ksyCBQuC2m44cPtc3n77bSPJ/OAHP2iyrqNch8YYs3HjRlNYWGiMMebZZ581ksySJUvadIxIzofGOBPDSM6HxjgTw0jOh8Y4E8MriZR8uHr1arN169Ymy7du3Wq8Xq/p2bNnoxx3JeGQEykoXPb+++8bSWb27NlN1q1atcpIMk899dRVj5OdnW0kmS1btjRZN2HCBCPJfPnll463Gw7cPpeSkhIjyfj9/ibrOkriciqGbf0C5TpsvfrP8bp165qs6yjX4eWs/hESyfnwck7/IRcJ+fBywSoouA5bLxLz4eXGjx9vJJmPP/74qtuGQ07kkSeX5efnS5LGjx/fZF39si1btrTqOJ07d9btt9/eZF1WVpbWr1+vLVu26LrrrnO03XDg9rl4vV5JUqdOzX8cqqqqtGzZMhUXF6tHjx667bbblJ6ebrm9UHA6hgcOHNDLL7+syspKpaamaty4cUpISHC93VBy81z++c9/asOGDUpKStL3v//9ZrfpCNehUyI5H7otEvKh0yIxH7qJfFjnap/FS4VDTqSgcFn9YJjBgwc3WdejRw8lJCS0OGBGks6dO6evv/5aw4YNU3R0dJP19ce+9DhOtBsu3D6X119/XVLzHyxJOnbsmHJychotmzBhgpYvX97sl0Y4cjqGK1asaDRwKy4uTgsXLtT8+fNdbTeU3DyXJUuWKBAIKCcn54pfHh3hOnRCpOdDt0VCPnRaJOZDN5EPpaNHj+qDDz5QUlKSbrzxxha3DZecyFueXHb27FlJdQOImtOtW7eGbewc49LtnGo3XLh5Lp988okWLlyoxMREPfnkk03WP/TQQ8rPz1dpaanKy8u1c+dOTZw4UevXr9ddd90l004mmncqhr1799bvf/97ff755zp37pyKi4uVl5ennj176sknn9Rrr73mSrvhwK1zMcZoyZIlkqQ5c+Y0u01HuQ6dEOn50E2Rkg+dEsn50C3kw7oB6TNnzlRVVZVeeOGFZouES4VLTuQOBSLWkSNHNHnyZNXW1mrVqlXN/q/Gr3/960a/33zzzXr33XeVkZGh7du367333rviLdmOyO/3y+/3N/weHx+vGTNmKD09XSNHjtSCBQuUm5urqCj+r6K1Nm3apCNHjigjI0ODBg1qdhuuQ7iNfNh25EPnRXo+DAQCeuihh7R161bl5uZq5syZoe5Sq3GVu6y++rtSpVdeXn7FCrEtx7h0O6faDRdunEtRUZHGjBmj0tJSvf322xozZkyr942KitLs2bMlSR9++GGb2g0Vt6+HYcOG6eabb9bx48d18ODBoLUbTG6dy+LFiyVJc+fObdN+7fE6dEKk50M3RFo+dFsk5EO3RHI+NMYoNzdXeXl5+tGPfqRXX321VfuFS06koHBZc8+u1SsrK9PJkyebfYbtUp07d1afPn105MgR1dbWNlnf3LNwTrQbLpw+l8LCQmVmZqqkpERvvvmmJk+e3OY+1f/vXWVlZZv3DYVgXA/NxYTrsGVlZWVau3atrrnmGt13331t7lN7uw6dEOn50GmRmA+DoaPnQzdEcj4MBAKaM2eOXn/9dWVnZ2vp0qWtvrMVLjmRgsJlGRkZkqQNGzY0WVe/rH6bqx3n3LlzzVbe77//fpPjONVuOHDyXOq/PIuLi/XGG29oypQplvpUP4Nl//79Le0fbG5fDzU1NSooKJDH41FKSkrQ2g0mN84lLy9PVVVVmjFjhuLi4trcp/Z2HTolkvOhkyI1H7otEvKhGyI1HwYCAc2dO1dLlizRD3/4Qy1fvvyq4yYuFxY5sU0vmUWbVVdXmwEDBhifz2f27NnTsPzSyUMufTdwaWmp+fzzz01paWmj41w6aUlVVVXD8pYmLWlLu+HMqRgeOXLEpKammk6dOpnVq1dftd39+/ebsrKyJsu3bdtmYmNjjc/nM0VFRZbPK5iciuGOHTtMIBBocuzHH3/cSDITJkyw1W44cyqGl0pPTzeSGk0sdLmOdB1e7mrvricfXp3VGEZyPryc1RhGcj68nNUYXioS82Ftba3Jyckxksy0adNMdXV1i9uHc06koAiCTZs2Ga/Xa7p06WJyc3PNvHnzTFpampFkfve73zXadsGCBVecKGfu3LlGkhk6dKiZP3++efDBB43P5zPdu3c3+/fvt9VuuHMihqmpqUaSueWWW8yCBQua/bn8OHFxcWby5Mnmpz/9qZk3b57JysoyHo/HREdHm0WLFrl81s5yKob9+/c3DzzwgJk/f77Jzc01119/vZFkUlJSGmZNtdpuuHPqs2yMMbt37zaSzIgRI1pss6Ndh4sWLTKzZs0ys2bNMiNGjDCSzO23396wbO3atQ3bkg+b50QMIz0fOhXDSM6HTn2WjYncfFgfly5duphnnnmm2c/hpX/wh3NOpKAIkl27dpkJEyaY7t27m7i4ODNq1CiTl5fXZLuWLpba2lrz8ssvG7/fb3w+n+nVq5eZOnVqi1Vka9ttD+zGUNJVfy6Vn59vpk+fbgYNGmS6du1qvF6vufbaa839999vdu3a5eapusZuDJ977jmTmZlp+vbta2JiYkx8fLwZPny4eeaZZ8zp06dtt9seOPFZNsaYRx991Egyf/rTn1psr6Ndh7NmzWrxM3hpvMiHzXMihpGeD52IYaTnQ6c+y8aQD1v6ufSuTzjnRI8x7fyFvQAAAABChkHZAAAAACyjoAAAAABgGQUFAAAAAMsoKAAAAABYRkEBAAAAwDIKCgAAAACWUVAAAAAAsIyCAgAAAIBlFBQAAAAALKOgAAAAAGAZBQUAAAAAyygoAAAAAFhGQQEAAADAMgoKAEC74ff75fF4rvjz/PPPh7qLABBxOoW6AwAAtFZ2drZqamoaLauqqtKLL76oqqoqjR49OkQ9A4DI5THGmFB3AgAAK86fP6+7775bGzdu1OLFizV79uxQdwkAIg53KAAA7VJlZaXuuusu5efna+nSpZo5c2aouwQAEYmCAgDQ7pw7d06TJ0/Wtm3btHz5cmVnZ4e6SwAQsSgoAADtSkVFhSZNmqSdO3dq1apVmjp1aqi7BAARjYICANBulJeXa8KECdq9e7feeust3X333aHuEgBEPAoKAEC7cObMGWVlZWnv3r1as2aNJk+eHOouAQBEQQEAaAdOnz6tcePG6bPPPtPatWs1ceLEUHcJAHARr40FAIS9rKwsbdiwQWPGjNEdd9zRZH3fvn318MMPh6BnAAAKCgBAWAsEAuratasqKyuvuM20adP05ptvBrFXAIB6FBQAAAAALIsKdQcAAAAAtF8UFAAAAAAso6AAAAAAYBkFBQAAAADLKCgAAAAAWEZBAQAAAMAyCgoAAAAAllFQAAAAALCMggIAAACAZRQUAAAAACyjoAAAAABgGQUFAAAAAMsoKAAAAABY9v/tqjjr+/MjvAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z, Nz-nemoNz, color=color_list[3], marker='o', ls='-', mfc='none', ms=7, label='$N_{SOLikeT}-N_{Nemo}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{Nemo}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_inj_diff.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "fa5c44fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.24734182, 1.27135587, 0.35030915, 0.11697549, -0.73329531,\n", + " -1.78200942, -1.11513709, -0.08128759, 0.21560377, 0.19418965,\n", + " 0.20537936, 0.13966325, 0.10029325, 0.1400242 , 0.09843118,\n", + " 0.03589748, 0.02008318, 0.02607307, 0.03254172, -0.11011861])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Nz-nemoNz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e101b03", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7a5ff34e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 97.02152245, 357.58867612, 471.88264925, 486.39463147,\n", + " 436.63624258, 363.70531336, 287.03784101, 215.24406453,\n", + " 157.12035012, 110.73290307, 75.09636693, 50.02920525,\n", + " 33.24675729, 22.0546531 , 14.49595359, 9.4770904 ,\n", + " 6.19937014, 4.05907273, 2.63655301, 1.68042947])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nemoNz" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "661d70ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 96.77418063, 358.86003199, 472.23295841, 486.51160696,\n", + " 435.90294726, 361.92330394, 285.92270392, 215.16277694,\n", + " 157.33595389, 110.92709272, 75.30174628, 50.1688685 ,\n", + " 33.34705053, 22.1946773 , 14.59438478, 9.51298788,\n", + " 6.21945333, 4.08514581, 2.66909473, 1.57031085])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Nz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b99aab5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "45999123", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z, np.abs(Nz/catNz-1), color=color_list[9], marker='o', ls='--', mfc='none', ms=7, label='fractional error')\n", + "plt.fill_between(z, 0, np.sqrt(catNz)/catNz, alpha=0.2, color='gray', label='$\\sqrt{N_{obs}}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('fractional error', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "#plt.xlim(0, 2.0)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_inj_obs_frac.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a408b2e5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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20cMJHy2GYBBVW4eWkZ7gkQkhhGgLzWLpdM+IZHHzzTczfvx4Lr30Uvr27ctHH33EAw88wNChQ7nlllsAmD17Npdeeim33norv/rVr5gwYQJHjhzh8ccfZ/Pmzaxbt44+ffqwfv16LBYLSin27NkTu0ZGRgZDhw6loKAAt9vNqFGjAJr9++6776agoICFCxeyadMm3G43s2bNwm63c9ttt/HDH/4Qh8PBxx9/zI4dO3jwwQdj59+5cydWq7XZnf1kN2vWLBwOB8uWLePWW29l7969fPvb3+a+++6LJW7v3r0bp9PJM888w8yZM7HZbHznO99hzJgx3HzzzTz66KPk5uYybdo0IpEIjz32GOeffz7nn39+gj+75mTLkxBxEDlZAYBtYP9YECF5FEIIIRJh8uTJvPTSS1x99dWMHTuWJUuWcMUVV/Dvf/87VokJ4JVXXuHaa6/l9ttvZ8yYMXzlK19h4MCB7NmzJ5YbsHPnTnRdZ+rUqUyYMCH2J3qHfteuXYwbNy6WsF1QUNDs37/5zW/QNC12x79Pnz789a9/5ciRI8yaNYsZM2bwyCOPnFFBaufOnZx//vlnXcVINjk5OaxevZpXX32VsWPHcs899/CjH/2oWa5IdH6WLFnCVVddxcUXX8wFF1zA2rVrsVqtNDQ0sHz5cvLy8rj00kupq6vj9ddf7/LE9HPRlBl94EWL/H4/Xq8Xn8/XrGnMuYRCIdasWcM111yD3W6P4wi7t0TOY/m9PyP0yUGy7v4WNS+8SvhoMdn334lz8vguHYcZ5OvRHDKP5pB57LxUncOGhgYOHTrEsGHDkuJNpa7r+P1+PB5Pq9WOxLnJPJqjpXls6/dMR9+vNiX/c0LEQXTLk7VPLyy9jKoNskIhhBBCiO5IAgohTKZCIfRKo/qCpU8vrDlZAOjSi0IIIYQQ3ZAEFEKYLFJm5E/gcGDxZGLJaVyhKJfSsUIIIYTofiSgEMJkkdLodqccNE07tUIhW56EEEII0Q1JQCGEyZrmTwBYGgOKiDS3E0IIIUQ3JAGFECaLBRR9ext/N255khUKIYQQQnRHElAIYbJI2WkrFL2yANB91ahwOFHDEkIIIYSICwkohDDZqRyKxoAiMwNsVlAKvcqfyKEJIYQQQphOAgohTHYqhyIHAM1iwZKdZTwneRRCCCGE6GYkoBDCRCoSQW8sD2vt0zv2eKzSU3lVAkYlhBBCCBE/ElAIYSK9ogp0HWxWLNne2ONS6UkIIYQQ3ZUEFEKYKJY/0SsHzXLq20sqPQkhhBCiu5KAQggTnd6DIsoqKxRCCCGE6KYkoBDCRLGSsX2bBxSWXtEVCl+Xj0kIIYQQIp5siR6AEN2JrFAIIYToLkKhEA6HI9HD6BClVKKH0KPICoUQJoqUlgFg6X3aCkVj2Vi9okp+yAkhhEgJP/3pT/nggw9QSsXtTyQSobKykkgkYup5RdeSgEIIE0VOVgAtrFA0dstWDQFUXX1XD0sIIYRot+3btzN16tRED0OkAAkohDCJ0vVWcyg0pxMtPQ2QSk9CCCGSX1FREbm5uWialuihiBQgAYUQJtF91RAKg0XD2piE3dSpPIqqrh2YEEII0U6vvvoqn/vc5xI9DJEielRAsWLFCjRNQ9M0tm7d2uIxfr+fJUuWMGTIEJxOJ0OGDGHJkiX4/f4uHq1INZGTjfkT2VlotjPrHVhivSgkMVsIIURye+utt7j88ssTPQyRInpMQPHRRx/xwAMPkJ6e3uoxtbW1zJ07lyeeeIIxY8Zw1113MXbsWJ544gnmzp1LbW1tF45YpJrWKjxFWXOMztmyQiGEECKZ1dTUYLfbcTqdiR6KSBE9IqCIRCLccsst5OXlcf3117d63IoVK8jPz2fp0qWsW7eOn//856xdu5YHHniA/Px8VqxY0YWjFqkmFlD0bTmgsEi3bCGEiKtIJEIoFErKP5FIJNHT02ZvvPEGCxYsaPX5iRMnomkaTqcTn+/M/kpKKbKysujVq+Xfh6L76RF9KB599FEKCgrYsWMHv/jFL1o8RinFs88+S0ZGBg888ECz5+69916eeuopfvvb3/Lggw9KgpJokR6t8NS7tRWKLAAi5bLlSQghzBaJRCgqKiIUCnXJ9XRdp66ujsrKSiyWc9+ftdvt5ObmYrVa4zKeefPmcfjwYQ4fPtzpc61Zs4af//znLT7X0NDARx99BEAwGOTVV1/ly1/+crNjDhw4gM/n44orruj0WERq6PYrFHv27GH58uXcf//9jBs3rtXj9u/fT0lJCRdffPEZ26JcLheXXHIJxcXFfPrpp/EeskhR0R4UrW15inbLli1PQghhPl3XCYVCWCwWHA5H3P84nc7YtqBzHWuxWAiFQui6nuhpOidd16msrKRPnz4tPl9QUEA4HObKK6/EbrezatWqM47Zvn07AFOmTInrWEXy6NYrFOFwmK9+9atccMEF/PCHPzzrsfv37wdg1KhRLT4ffXz//v2tHhMIBAgEArF/RxO5o8udbRU9tqvusnRXXT2P4caAQuVktXhN5ckAjG7ZqfR/K1+P5pB5NIfMY+el6hyGQiGUUui63uIb8+jjdrs9bqsATSmlsNlsWK3Wc+5cUEoRDodbHbuZOnv+d999lwsvvLDV82zbtg2Ayy+/HF3Xef311/H7/WRkZJxxzOTJk885nmgTuuj/reiYluZR13WUUoRCobN+T5jxs6BbBxQ/+9nPKCgo4P3338dut5/12OgeQK/X2+LzHo+n2XEteeSRR1i+fPkZj69bt460tLS2Djtm/fr17X6NOFOXzKNSTDteihV498M9NBQfPuMQe30DUwC9yseaf/4T2rBEnkzk69EcMo/mkHnsvFSbQ5vNRv/+/ampqSEYDJ7xfCgUoq6ujlAohK2FSnvx0paCLeFwmFAohN/vP+f7kbZoaTvSwYMH8fl83HfffWc8d/vtt7f6/uZ0f//73/nyl7/canXL999/H4AxY8Zgs9lYv349q1atapaj+sEHHwAwevToNlfJrK6ubtNx4uyazmMwGKS+vp6NGzcSDodbfU1dXV2nr9ttA4qCggJ+8pOfcPfdd3fZktu9997LkiVLYv/2+/0MGjSIBQsWxAKStgiFQqxfv5758+eb8oOnp+rKedRraqn84z8BuHTxQjSH44xjlK5TsepfaLrOlRfNjuVUJDv5ejSHzKM5ZB47L1XnsKGhgcLCQjIyMnC5XGc8HwqFqKysxOl0dklAoZSitraW9PT0c65QhMNhAoEAHo/HlDl/9NFH2/Xcf/7nfzZ7H7J582b++7//m3/84x9nHHvo0CGmTZvW6vn37NkDwOzZs5k+fTp33303a9eu5ZZbbokds2vXLrxeL3l5eW1avamuriYzM1NyVDuhpXlsaGjA7XZzySWXtPg9E2VGa4RuG1DccsstjBgxggcffLBNx0cj99ZWIKKTfbYI3+l0tlhizW63d+gHSEdfJ5rrinkMVRpfNxZvJo6zlCa2ZHvRyyux+Kux92t5f2qykq9Hc8g8mkPmsfNSbQ4jkQiapmGxWFpMgo4+Hu031VXacr2m425LAve5RLe3NNXWpOytW7fy5JNP8sorr3DkyBGGDRsWe+7TTz9lzJgxrY4xGAyyZ88ehg8fTk5ODmAEFmvWrCEQCOB2uzl48CCVlZVceumlbdp6Ft2eo2ka3/72t6mpqeH5558/5+tEc03nMfr/F/1+ONf3uhk/B1Jrz0U7FBQU8PHHH+NyuWLf7Jqm8dxzzwEwa9YsNE3jpZdeAprnSLTkXDkWomc7VTK291mPs0rpWCGEEAk0c+ZMXnzxRUaNGsUrr7zS7LmXX375rN2xd+3aRSgUarbzY/HixdTW1vLGG28AsGPHDqBjCdm7du0iLy+v3a8TiddtVyhuvfXWFh/fuHEj+/fv59prr6VPnz4MHToUMAKFAQMGsHnz5tgSZlRDQwMbN25kwIABjBw5siuGL1JMLKBopWRslCVaOlYCCiGEEAm0cOFCXn75Zb73ve/FHtu8eTN33nlnq6+JBgtTp06NPbZo0SLuvPNOVq1axcKFC2MVnpoe0xZKKXbv3s2yZcva9TqRHLrtCsWzzz7b4p+LLroIMPIdnn32WSZNmgQYS0S33XYbNTU1PPTQQ83O9cgjj1BZWcltt90m+/tEi6IBhaWVkrFR1l5ZgKxQCCGESKxrr72WTZs2UVlp9EaqqKjA6/WedZtSS+Vgc3NzmT59Oq+++irBYPCsJWO3b9/OFVdcQUZGBoMGDWqWXH7w4EFqamqoqalhxowZpKenc/nll1NUVBQ7JhwO8+CDDzJixAhcLhcDBw5sMQlddL1uG1B0xNKlS5k0aRIrVqxgwYIF3HvvvVxzzTU89NBDTJo0iaVLlyZ6iCJJRUobVyjOEVBYsrOM4yukuZ0QQgjzbNiwoV1N7S666CKys7NZs2YNYDSzu+aaa876mta2My1evBifz8ebb77Jzp07yczMZPTo0c2Oef/995k3bx5XXHEFu3bt4plnnuHhhx/mb3/7G2BsVbdYLDz55JM8/fTTvPvuu5SWlnLHHXfEzvHwww+zZs0annvuOfbt28fzzz8fuzEsEqvbbnnqiPT0dDZs2MDy5cv5+9//zoYNG+jfvz933XUXy5YtO6PhnRBRp3IozrFCEc2hKK+K95CEEEJ0U20tOBN15513kpWV1ewxi8XCZz7zGV5++WVuuukm3njjDf73f/+31XOEQiF2797N4MGD6d27eb7g4sWL+cEPfsBjjz1GWVkZc+bMOWNHx3/+53/y7W9/O9YXbPjw4SxcuJC1a9dy9dVXs3v3bjIyMnjppZfIzjZ+V95zzz3NqmeuX7+eRYsWMXv2bACGDBnSrnkQ8dPjAoqVK1eycuXKVp/3er08/vjjPP744103KJHyYgHFuVYoGrc8yQqFEEKIjmqp59XZfPWrXz0joAC47rrr+MpXvkJNTQ2BQIDMzMxWz7F3714CgUCLW5lGjBhBXl4eb731FnBm/sSePXvYtWsXr732WrPHnU5nrI9HQUEBN998cyyYAONGb9Nmd5/5zGe4//77ee+997jhhhtYuHBhs4Z6InFky5MQnaTXN6BqjB+I5woopMqTEEKIzlJKtetPtADN6RYsWEAoFGLZsmXMnTv3rNc8W24EGKsUUacfs3fvXrxeL7m5uc0e//DDD5kwYQIAu3fvPqPC086dO5s99qMf/Yhdu3Zx4YUXsmzZMsaOHXvWhsOi6/S4FQohzBZdndDS07Ckuc96bLTKk6pvQK9vwOJuvdGMEEKI9jtbR2AzKaUIh8OEw+E2NbZLRmlpaVxxxRU8+eSTHDhw4KzH3nrrra1W0AT48Y9/zI9//OMWn8vIyCAYDBKJRGJJ31u3bmXbtm2sXLmS6upqDh06RCQSib2mvr6eZ5999oyu4GPHjmXs2LF87WtfY+DAgRQVFbW5C7iIHwkohOiktuZPAFjcLrQ0N6quHr2iCsvA/vEenhBC9AgWiwW73U4oFCIYDMb9erquEwqFCAQCbWpWZ7fbTWlqZ7Zrr72WoqIiBg8eHLdrzJo1C4fDwbJly7j11lvZu3cv3/72t7nvvvsYPXo069evx+l08swzzzBz5kxsNhvf+c53GDNmDDfffDNgdADPzc1l2rRpRCIRHnvsMc4//3zOP//8uI1btJ0EFEJ0kt7GHhRRlmwvkbp6IhWV2CSgEEIIU1itVnJzc5vtuY8nXdfx+/14PJ42BQoWi6VNnaO72rXXXhv3ojM5OTmsXr2au+66i8cee4yhQ4fyox/9iG9+85vous6ePXsYN24c3/3ud7nqqquor6/nxhtv5LHHHovNWUNDA8uXL+fo0aN4vV4uu+wyXn/99aSc055IAgohOilSWgacO38iypqTTaT4uFR6EkIIk1mt1i57g6nrOna7PWlXHtqqX79+3HjjjXG/zmWXXUZBQUGLz916663cddddWCwWbrnllhaPWbZsmTS9S2Kp+x0gRJJoa4WnKKn0JIQQQojuRAIKITopcrICaFsOBTSp9FRZFa8hCSGEEEJ0GQkohOikdq9QNFZ6isiWJyGEEEJ0AxJQCNEJKhhCrzJqYLc9hyILkF4UQgghhOgeJKAQohMiZY09KJwOtMy2deuMrVBIDoUQQgghugEJKITohFPbnXqfs7FR1KkcCh8q0jXlDYUQQggh4kUCCiE6IRpQWPrktPk1liwPWDTQdXS/P15DE0KIbkkpleghCJESuvJ7RQIKITohUtq+hGwAzWrFkuUFJI9CCCHaKtpfIhQKJXgkQqSG6PdKV/RmkYBCiE5ob4WnKGssj6LK5BEJIUT3ZLfbcTqd+Hw+WaUQ4hyUUvh8PpxOJ3a7Pe7Xk07ZQnRCLKDo27tdr7NkZwGgl0tithBCtFXv3r0pLi6mqKgIr9eL3W5vc/6a2XRdJxgM0tDQkNKdshNN5tEc0Xmsr68nEong8/moqalh4MCBXXJ9CSiE6IQOr1D0MhKzZYVCCCHazuPxAFBWVkZxcXFCx6KUor6+HrfbnbCgpjuQeTTH6fPodDoZOHBg7Hsm3iSgEKKDVDiM3lj6tb0BhSXWi0JWKIQQoj08Hg8ej4dQKEQkEknYOEKhEBs3buSSSy7pki0l3ZXMozmi8zh37twu2+bUlAQUQnRQpKIKdAU2m1G5qR0kh0IIITrHbrcn9A2o1WolHA7jcrnkjXAnyDyaIzqPiQgmQJKyhegwPbrdqXcOWjv3fVqivSgkoBBCCCFEipOAQogO6mj+BIC1V5ZxDgkohBBCCJHiJKAQooM60oMiKlrlSdXWoQIBM4clhBBCCNGlJKAQooNOlYxtf0ChpbnRXE7jPOVVZg5LCCGEEKJLSUAhRAd1ZsuTpmmxSk8RqfQkhBBCiBQmAYUQHdSZgALAGk3MrvSZNiYhhBBCiK4mAYUQHaB0nUhZBQCWDgYUsV4U0i1bCCGEEClMAgohOkCv8kM4DBZLrOt1e0kvCiGEEEJ0BxJQCNEB0e1OlpwsNKu1Q+eQHAohhBBCdAcSUAjRAZ3Nn4AmORSyQiGEEEKIFCYBhRAdECktAzoXUFiize0kh0IIIYQQKUwCCiE64FQPit4dPkdshaLKh9J1U8YlhBBCCNHVJKAQogNObXnK6fA5LFke0DQIR9Cra8wamhBCCCFEl5KAQogO0E8aJWM7s+VJs9mweDON80m3bCGEEEKkKAkohGgnpRSRk53PoQCp9CSEEEKI1CcBhRDtpKprUIEgANbenQsopNKTEEIIIVKdBBRCtFOsB0WWF81h79S5LNLcTgghhBApTgIKIdrJjB4UUdEu27pseRJCCCFEipKAQoh2ipR2vsJTlCU7yzinrFAIIYQQIkVJQCFEO5nRgyLK2rjlSZfmdkIIIYRIURJQCNFOZm55sjRueZIVCiGEEEKkKgkohGinSFljUrYZORSNKxSqpjZWOUoIIYQQIpVIQCFEO53Koeh8QKGlp4HDYZy30tfp8wkhhBBCdDUJKIRoB72uHlVbB5gUUGjaqTwKqfQkhBBCiBQkAYUQ7RDNn9Ay0rG4XaacU3pRCCGEECKVSUAhRDuYmZAdJSsUQgghhEhlElAI0Q6x/Im+5gUUlpzGSk/lVaadUwghhBCiq0hAIUQ7xGWFolcWICsUQgghhEhNElAI0Q56XLY8Na5QSJUnIYQQQqQgCSiEaIdoDwozAwpLjheQbtlCCCGESE0SUAjRDpHSMgCsfXqbds5YDkVlFUop084rhBBCCNEVJKAQoo1UIIjuqwbA2ifHtPNas7OMD0JhVHWNaecVQgghhOgKElAI0UaRsgoANJcTLSPdtPNqdhsWT6ZxDelFIYQQQogUIwGFEG3UtMKTpmmmntsSq/RUZep5hRBCCCHiTQIKIdoolj/R17z8iahYpScpHSuEEEKIFCMBhRBtFI8eFFGWxjwKWaEQQgghRKqRgEKINoqWjLWYmJAdFW1uJ92yhRBCCJFqunVAUVVVxR133MGsWbPo378/TqeTgQMHctlll7Fq1aoWS3T6/X6WLFnCkCFDcDqdDBkyhCVLluD3+xPwGYhkEjlpJGXHZYUiJwuQbtlCCCGESD3dOqAoKyvjd7/7Henp6SxcuJDvf//7XH311ezdu5fPf/7zfPOb32x2fG1tLXPnzuWJJ55gzJgx3HXXXYwdO5YnnniCuXPnUltbm6DPRCSDePSgiDqVQ1Fl+rmFEEIIIeLJlugBxNOwYcOoqqrCZmv+aVZXVzNz5kx+85vf8L3vfY9x48YBsGLFCvLz81m6dCmPPvpo7Phly5bx0EMPsWLFCpYvX96ln4NIDiocRq+sAuK9QlFl+rmFEEIIIeKpW69QWK3WM4IJgMzMTK688koAPv30UwCUUjz77LNkZGTwwAMPNDv+3nvvJTs7m9/+9rfSybiHipRXgq7AbsPizTT9/NZexgqF7q9GhUKmn18IIYQQIl66dUDRmoaGBt566y00TWPs2LEA7N+/n5KSEi6++GLS05s3LXO5XFxyySUUFxfHAhDRs0RKGys89e6FZjH/20bLSAe7EfxGKn2mn18IIYQQIl669ZanqKqqKn75y1+i6zqlpaWsWbOGwsJCli1bxqhRowAjoABi/z5d0+NaOyYQCBAIBGL/jiZyh0IhQu246xw9tj2vEWcycx6Dx0sBsPTOidv/iyXbi15aTrC0DJXtjcs1OkK+Hs0h82gOmcfOkzk0h8yjOWQezdGZeTRj7ntMQNE098Fut/OLX/yC73//+7HHfD7jrrDX2/IbOY/H0+y4ljzyyCMt5lisW7eOtLS0do97/fr17X6NOJMZ8ziwYB+5QHF9Le+uWdP5QbXgAhQeYPu/3qbiwL64XKMz5OvRHDKP5pB57DyZQ3PIPJpD5tEcHZnHurq6Tl+3RwQUQ4cORSlFJBKhsLCQv/71r/zoRz/ivffe44UXXmgxz6Ij7r33XpYsWRL7t9/vZ9CgQSxYsCAWkLRFKBRi/fr1zJ8/H7vdbsrYeiIz57HmaBkBYMiUPC645hpzBnia6v0lBEsryBs6DPc1l8flGh0hX4/mkHk0h8xj58kcmkPm0Rwyj+bozDya0RqhRwQUUVarlaFDh/LDH/4Qq9XK0qVL+c1vfsPtt98eW5lobQUiOtmtrWAAOJ1OnE7nGY/b7fYOfZN09HWiOTPmUZUZ/SEc/frG7f/E1juHIEBVdVL+v8vXozlkHs0h89h5MofmkHk0h8yjOToyj2bMe49MygZYsGABABs2bAA4I5fidOfKsRDdW+RkY1J2X/NLxkZZpbmdEEIIIVJQjw0oSkpKAGLbnUaNGsWAAQPYvHnzGQ3sGhoa2LhxIwMGDGDkyJFdPlaRWErXiZTHr0t2lEWa2wkhhBAiBXXrgCI/P7/FLUwVFRXcd999AFx99dUAaJrGbbfdRk1NDQ899FCz4x955BEqKyu57bbb0DQt/gMXSUWv9EE4AhZLrAFdPMgKhRBCCCFSUbfOoVi5ciXPPvssl156KUOGDCE9PZ0jR47w2muvUVNTw+LFi7nxxhtjxy9dupRXXnmFFStWsHPnTqZOnUpBQQFr165l0qRJLF26NIGfjUiUyMkyACy9stGs1rhdp+kKhVJKglchhBBCpIRuHVB8/vOfx+fzsXXrVjZu3EhdXR05OTnMnj2br3zlK3zxi19s9qYtPT2dDRs2sHz5cv7+97+zYcMG+vfvz1133cWyZcvOaHgneobIycbtTnHMn4BTKxQEQ6jaOqPZnRBCCCFEkuvWAcXs2bOZPXt2u17j9Xp5/PHHefzxx+M0KpFqYgnZccyfANAcdrTMDFR1DZGKKiwSUAghhBAiBXTrHAohzBALKHrHN6AAsDZ2yNbLJY9CCCGEEKlBAgohziFSauRQWPv2jvu1LL2k0pMQQgghUosEFEKcQ1dteQKp9CSEEEKI1CMBhRBnoZQ6lZTdJyfu15NeFEIIIYRINRJQCHEWyl8DwSAA1t7xDyhOrVBUxf1aQgghhBBmkIBCiLOI9aDIyUKz2+N+vWjjvIhseRJCCCFEipCAQoiz6Mr8CQBr45YnWaEQQgghRKqQgEKIs+jKkrEAll5ZAOi+alQ43CXXFEIIIYToDAkohDiLUysU8c+fALBkZoDNCkqhV/q65JpCCCGEEJ0hAYUQZxEpbQwouqAHBYBmsWDJzjKuXVnVJdcUQgghhOgMCSiEOIuuzqGAJpWeyqu67JpCCCGEEB0lAYUQZ5GIgEIqPQkhhBAilUhAIUQr9No6VF09AJYuXaGQSk9CCCGESB0SUAjRiujqhObJwOJydtl1rbJCIYQQQogUIgGFEK1IxHYnAEuvxhUKyaEQQgghRAqQgEKIVnR1D4qo2AqFVHkSQgghRAqQgEKIViRshaKxbKxeUYVSqkuvLYQQQgjRXhJQCNGKUz0ouniForFbtmoIxJLChRBCCCGSlQQUQrQiUSsUmtOJlp4GSKUnIYQQQiQ/CSiEaIWeoIACpNKTEEIIIVKHBBRCtEAFAuj+aiAxAYVFelEIIYQQIkVIQCFEC2I9KNLcse1HXSmaRxGRgEIIIYQQSU4CCiFaEDlZAYC1dw6apnX59ZtWehJCCCGESGYSUAjRgkQlZEfFcijKJYdCCCGEEMlNAgohWhALKPr2Tsj1o92yZcuTEEIIIZKdBBRCtCBSWgYkfoVClypPQgghhEhyElAI0YJEb3mKVXny+VGRSELGIIQQQgjRFhJQCNGCaFK2pU9OQq5v8WaCxQK6Qq/yJ2QMQgghhBBtIQGFEKdRoTB6ZRWQuBwKzWLBku0FpLmdEEIIIZKbBBRCnCZSXgFKgcOBxZOZsHFYpbmdEEIIIVKABBRCnCaWP5GgHhRRlljp2KqEjUEIIYQQ4lwkoBDiNIlOyI6KdsuObr8SQgghhEhGElAIcZpTPSgSG1BEKz1JDoUQQgghkpkEFEKcJlKaJCsU2VkA6LLlSQghhBBJTAIKIU6jJ8mWJ0vjlidZoRBCCCFEMpOAQojTJE0OhVR5EkIIIUQKkIBCiCZURCdSbqwIJD6HIgsAVd+AXt+Q0LEIIYQQQrRGAgohmtArqyASAasVS1ZWQsdicbvQ0tzGuGSVQgghhBBJSgIKIZqIbXfqlY1mTfy3R6wXheRRCCGEECJJJf4dkxBJJFnyJ6Kk0pMQQgghkp0EFEI0ESktAxKfPxEllZ6EEEIIkewkoBCiicjJCgAsybJCIZWehBBCCJHkJKAQoolk2/J0KoeiKqHjEEIIIYRojQQUQjSRbAGFrFAIIYQQItlJQCFEI6UUkbJoQNE7waMxWHK8gORQCCGEECJ5SUAhRCPd54dgCDQNa6/sRA8HaLJCUelDRfQEj0YIIYQQ4kwSUAjRKLrdyZKdhWa3JXg0BkuWBywa6Dq635/o4QghhBBCnEECCiEa6Y0Vnqx9chI8klM0qxVLlrHtSXpRCCGEECIZSUAhRKNTPSiSI38iyhqt9FRZldBxCCGEEEK0RAIKIRolW4WnKEs0j6JcErOFEEIIkXySY6O4EHFQX18PwLFjx3C5XNjtdqxW6xl/LBYjrk7WgMIqvSiEEEIIkcQkoBDdTiQSobKykrIyYwtTXV0d9fX16PqpKklNgwmbzYbdbkc7XgpAMDMNrb4eq9WKzWaLBRyJEm1up0vpWCGEEEIkIQkoRLdSV1dHeXk5NTU1OBwOANLT07FarbFjlFLouo6u60QiEQKBAPV1dWSWVaABpeEg6ujRWMBhtVpxOBzY7XZsNlssGIl+bLFY0DQtbp+TrFAIIYQQIpmZGlDcf//9TJo0ic9//vMAVFdXU1RUxPnnnx/XN1xCRFclKioqUEqRmZmJUqrFYzVNiwUFdrvdeLCuHi0QBCBj0EB0my0WcITDYQKBQLMVDk3TsFgssYDDbrfH/jQNNpoGHB39HjiVQ1HVodcLIYQQQsSTqQHFc889x/z58wGora1l/PjxFBYWcsEFF/D6668zaNAgMy8nBGB8rZWXl1NbW4vb7Y6tTEQikbafpPHuv8pIB4cDC8S2Q7VEKUUkEokFHaFQ6IzrRYONpisYTQORaJAR/Tj6ePS46B/lNIKecEUlNTU1zc7V9E9bHhNCCCGEMJupAUVZWRnDhw8H4KWXXsJut1NcXMz999/PD3/4Q/70pz+ZebmzKi4u5sUXX2TNmjV8/PHHHD9+nJycHC6++GKWLl3KhRdeeMZr/H4/Dz74IKtWreL48eP079+fxYsX8+CDD+LxeLps7KJtwuEwVVVVzVYlOpzvEN1O1Li96Fw0TWs12IBT26qiQYdSKrZicvrHpz/W9HFN06AhQBZAXT2FBw6iOR3NxhH9u7WPARwOBx6Ph7S0tLOOWwghhBCivUx9ZzFw4ECKiooYNGgQL7zwArfeeivnnXce3//+97n88svNvNQ5PfXUUzz66KOMGDGC+fPn07dvX/bv389LL73ESy+9xF/+8hduuOGG2PG1tbXMnTuX/Px85s+fz5e+9CUKCgp44oknePvtt3n33XdJT0/v0s9BtK62tpaysjLq6uqarUp0WDsDinNpuq2q0zIVyuFACwbxKg0ag9tzBSZN/66rq6O6uhqn04nH4yE9PR2XyyWrFkIIIYToNFMDii9/+cvcddddLFq0iLVr1/Loo48aF7HZ8Pl8Zl7qnGbMmMHGjRuZM2dOs8c3bdrE5Zdfzu233851112H0+kEYMWKFeTn57N06dLYuAGWLVvGQw89xIoVK1i+fHmXfg7iTE1XJYDOrUo0ocUCCm+nz2U6TYOsTCgtB58f+vZqfLjt25icTidKKQKBACdPnqSiooL09HQyMzPPSFoXQgghhGgPU+th/vjHP+bKK6/k5Zdf5sEHH+T8888HYNu2bQwePNjMS53TokWLzggmAObMmcOll15KRUUFu3fvBow7uM8++ywZGRk88MADzY6/9957yc7O5re//W2rSb6ia9TW1lJcXMzJkydxOBxkZGSYV9K1wgh4lUkrFKbzNm65q/J3+BSapuFyufB6vbhcrth8HjlyhLKyMhoaGuRrXAghhBDtZuoKhdVqZfny5WfcyS8qKuLGG28081KdEq3sE91Lvn//fkpKSrjyyivP2Nbkcrm45JJLePnll/n0008ZNWpUl4+3pwuHw1RUVFBZWYmmaXg8HvO36pi85cl0WY0Bha/alNPZbDYyMjJaXLWI5lrIqoUQQggh2sLUgKKkpIQ77riDjRs3ous6eXl5fPWrX+Wee+5Jmr3aR48e5c0336R///5MmDABMAIKoNVgIfr4/v37zxpQBAIBAoFA7N9+v3E3ORQKEQqF2jzG6LHteU13FN37X1FR0SxXomn51rOJVl1qS7UnW2PTuEiWB9pTHaqLWDwZWAFV6UM3eXzRcrfhcBi/309lZSUulyu2HSr6vdvTvx47S76vzSHz2Hkyh+aQeTSHzKM5OjOPZsy9qQHFV77yFUpLS3nsscdIT09n586dLF26lN///vesWbMGl8tl5uXaLRQKcfPNNxMIBFixYkXsDmw0v8PrbXn/fLTC07nyQB555JEW8yzWrVtHWlpau8e7fv36dr9GnKmgoOCsz1tCYabX1hvHHj1M5HhxVwyrXfr5qxgKVB05yqc7diRkDPL1aA6ZR3PIPHaezKE5ZB7NIfNojo7MY11dXaeva2pAsXXrVjZv3kxeXh5g5DHcd999XHPNNdx99938z//8j5mXaxdd1/n617/Oxo0b+cY3vsHNN99s+jXuvfdelixZEvu33+9n0KBBLFiwoF1lZ0OhEOvXr2f+/PmnGq/1EKevSqSlpXV4DiKRCAUFBeTl5Z19+87xk8a1XU7yZp5ZTjgZaFYXfLCXbM3KlClTuuy64XCYmpoaDh48yPnnn09WVhYZGRmdr6rVA/Xk72szyTx2nsyhOWQezSHzaI7OzGN0R01nmBpQjBs37oztJW63m6eeeoo5c+YkLKBQSvGNb3yD559/ni9/+cv83//9X7PnoysTra1ARCe6tRWMKKfTGasa1VR0S0l7dfR1qSocDlNZWRnLlcjOzjZlq9w5y7dGE517ZSdv3kB2FgCar7pLxxjt+g1GUndFRQV+vz+Wa+F2u5N3zpJUT/u+jheZx86TOTSHzKM5ZB7N0ZF5NGPeOx1QfOUrXyEvL4+JEyfygx/8gO9///u8/PLLze7IRyKRhG130nWd2267jd///vd86UtfYuXKlWdUBmqaI9GSc+VYiM5RSsX6StTX13dqVaJDkj0hG5onZes6mFXdqh2iwUMoFKK6uhq/34/L5Yr1tWgpmBZCCCFE99fpgCI9PZ3Vq1ezfPlyamtr0TSNIUOGcPPNN5OXl0ckEuGZZ55hxYoVZoy3XZoGE//xH//BH//4xxbvpo4aNYoBAwawefNmamtrm1V6amhoYOPGjQwYMICRI0d25fB7hFAo1GxVIi4VnM4hqXtQRHkyUJqGpuuo2jrIzEjYUKJ3P3RdJxAIcOLECex2e6yvRVpamnnlfIUQQgiR9DodUDzzzDOxjw8ePEhBQQG7du2ioKCANWvWcOjQIZxOJ0899VRc8hZao+s6t956KytXruQLX/gCzz//fKtbMzRN47bbbuOhhx7ioYceatbY7pFHHqGyspLvfve7SVOpqjtI+KpEU9EeFI3bipKS1QqZ6eCvgarqhAYUURaLBbfbjdvtJhQK4fP58Pl8smohhBBC9DCm5lAMHz6c4cOHc/3118ceq6mpYffu3eestGO2hx56iJUrV5KRkcHo0aP5yU9+csYxCxcuZNKkSQAsXbqUV155hRUrVrBz506mTp1KQUEBa9euZdKkSSxdurRLx9+dhUIhKioqqKqqwmKxJGRVopnKKuPvXlmJG0NbeDONgMLnh0HnJXo0zTRdtWhoaIitWmRkZJCRkSGrFkIIIUQ3ZmpA0ZKMjAxmzZrFrFmz4n2pZg4fPgwYAc1Pf/rTFo8ZOnRoLKBIT09nw4YNLF++nL///e9s2LCB/v37c9ddd7Fs2bIzGt6J9lNKUVNTQ3l5eeJXJZoqrzL+TuYcCjC6ZRce61S37HizWCyxEsnBYBCfz0dVVRUul4usrCzS09OT4/9cCCGEEKYxPaB48cUXWbNmDYFAgAkTJvCFL3whIbkHK1euZOXKle16jdfr5fHHH+fxxx+Pz6B6sKRblYgNLIzmb+w+ncxbniCWmK35/KgED6UtHA5HrBFhQ0MDx44dw+FwkJmZSWZmJi6XKzm+BoQQQgjRKabuQXj00Uf52te+RigUwu1288orrzB27NgWm72J7k3pOnpDAD0Sobq6mqKiIioqKnC73c26LydcVWP+hN0OGe1vPtiVlDfT+MBXndiBtFN01cLj8WCxWKioqKCwsJBjx45RU1PT5s7nQgghhEhOpq5Q/O///i8vvPAC11xzTeyxrVu38h//8R8MHDiQ2267zczLiSQUOlxI3T/XU795GwSDYLcTnDAGffZ0PCOHJk8gERWt8NQrC5JtbKeLlo5N4i1PZ6NpWqxXSzgcjpWedbvdse1Q0Z4XQgghhEgdpq5QVFRUMHbs2GaPzZw5k6effponnnjCzEuJJFS/6X3Kl/6EwJ59pF9/FZGbF1F/yXTshwpJ+5/n0LbvTvQQzxQNKLKTuGRsVIquULTEZrORmZlJRkYGwWCQkpISjh49SllZGYFAINHDE0IIIUQ7mBpQzJw5kxdffPGMx8ePHx9LkhbdU+hwIb6nfodrzgz6/M9P0a+ci3/MMOzXLkAtuxOmTUT742ooOp7ooTajpUJTuyhvaq9QtMRiscS6bgOcPHmSo0ePcvz4cWpra1EqFbJFhBBCiJ7N1P0FP//5z7n00kuprKzkO9/5DgMGDEApxW9/+1vpMt3N1f1zPZacLLy330JYKcrLy7Hb7bFSoerG69D2H0J7ZwvqpuvPcbYuFO1BkQoBRTQpu64eFQyBo/tUS9I0DZfLhcvlatbTIi0tDa/XS3p6eqt9ZIQQQgiRWKauUEydOpX169ezZs0aBg0aRE5ODpmZmTz99NP88pe/NPNSIokoXad+8zbSLp+NZrNRWVmJXvAh6U//EUpOGAdZraiLpsL2PZBMd51TaYXC7TKSx6FbbHtqjd1uj3Xcrq+vp7i4mKNHj1JRUUEwGEz08IQQQghxGtMzIC+88ELy8/PZu3cve/fujfWhyM7ONvtSIkmoYAiCQaz9+1JXV0dVVRXpO/eiHS2GV9ejvvll48Be2WihECoUAocjsYOOSqWAQtMgKxNOVhjN7frkJHpEcWW1WsnIyEApFWuWV1lZSUZGBpmZmbjd7uRL8hdCCCF6oDYHFH369GHq1KlMnjyZqVOnMmXKFIYPH97q8ePGjWPcuHGmDFIkN81hB4eD8LET+CoGAmBp3Eqk7fkEVVoOfXtBeaVxhz1ZGptFIqfyEVIhoAAjj+JkRbdeoTidpmm43W7cbjfBYJDKykqqqqrIyMjA4/GQlpYm26GEEEKIBGpzQBEKhVi3bh3r1q2L3RX0er1MmTKFKVOmxIIMyZXoeTSLBffF06h9cxPVY4eT6fVCeeWp5zdsQS2+Gu297TB1fPKUZ/VVo+k6ymoFT0aiR9M20UpP3Sgxuz2izfLC4TC1tbVUV1dLF24hhBAiwdocUFRVVXHgwAG2b9/O9u3b2bFjBzt27OCtt97irbfeigUZmZmZTJo0KRZg3HTTTXEbvEge9ivnUffOVjJeeRPLwivRgqFTT27dgVZbB75q1NxZiRvk6ZqWjLWYmk4UPynWLTtebDYbGRkZLXbh9ng8OJ1O2Q4lhBBCdJF25VCMGDGCESNGcMMNN8QeO3ToEDt27GD79u1s27aN/Px8Nm7cyKZNm9A0TQKKHkApRbUnndrFV5G+6nX45LDxeJobrBa06lrUzr2oryyG3P6JHWxTsfyJFOhB0Uh5M9EAqnrOlqeziXbhVkoRDAapqKg4YzuUJVWCRSGEECJFdTope9iwYQwbNozFixcTDodZv349jz32GG+99ZYZ4xMpoK6uDp/Ph3PWVNTwoWir1kB1jVHeNLq3PSMdJidZTk0qJWRHRbtl+3rmlqfWSBduIYQQInE6/Rs2EAjwxhtvsGrVKl599VV8Ph9KKfLy8li8eLEZYxRJTNd1KioqUEoZ+9dz+6MuGIn26WHUlPGomxbCg0+gVdegCj6CKeMTPeQYLZV6UERFm9v1oKTs9op24dZ1nfr6ekpKSnA6nXi9XjIzM3EkS4UxIYQQopvoUEBRV1fHmjVrWLVqFa+99hq1tbUATJ8+ncWLF7N48eKzVoAS3Yff76empobMzMzYY1o0Ibtfb6M87OzpsHYD2tvvoZIooKCyyvg7pQKKxnn2VRv9PCRPoFXRLtxKKQKBAKWlpVRVVZGVlYXH45EEbiGEEMIkbQ4oqqurefXVV1m1ahWvv/46DQ0NaJrG7NmzWbx4MYsWLWLgwIHxHKtIMsFgkPLychwOR/N96mVGQKF6Gb1H1OwZsH4T2uEi1KFCGDYoEcM9U3mV8XcKBhRaOIyqrTO2komzatqFu6GhgdLSUnw+XyywkK1QQgghROe0+Tdp3759CQaD2O125s2bx6JFi1i4cCF9+/aN5/hEklJKUVlZSTAYxOPxNH+yonGFojGgwJMBUyfC+zuNErLJEFDoOlQaW57IzkroUNrFZkNlpKPV1BqJ2RJQtIvL5cLpdBIIBDhx4gQ+n4+cnBwyMjKkl4UQQgjRQW0ufxIIBAC47LLLWLBgAaNHj8blcsVtYCK5RTtip6WlNS/PqevQmJsQCygANa+xXGz+h6eSoROppta4y69pkO059/HJJCu67UkSszsiumLh8XiIRCKUlJRQWFiI3+9H1/VED08IIYRIOW1eoRg/fjwff/wxr7/+Om+88Ubs8eHDhzdrbDdlyhRycnLiMliRHCKRCBUVFWiaduY+9Cr/qWZx3lN5FeT2R40ahrb/EGz6N+q6BV076NNFg5osD6TanWmvB4qOS2J2J2maRlpaWqyXRXFxMRkZGbGqUFJuVgghhGibNgcUu3btIhAIUFBQEOs7sWPHDvbs2cOBAwd48cUXY3eqBw8eHAsy7rvvvrgNXiRGS4nYMdGE7Jwzm8WpS2cZAcXmbXDVXHA6u2C0rYjmT2SnTg+KmGjp2B7aLdts0V4Wuq5TV1dHbW1ts8BCGuQJIYQQZ9eubESn08mMGTOYMWNG7LFQKMSuXbuaBRm7d+/mH//4By+99JIEFN1MtHmY0+ls+Q5u+Wn5E02NG43qk4N2sgL1fj5ccmFcx3pWqVjhqZHyGM3tenq3bLNZLJZY9+3TA4sztvYJIYQQIqbT5U3sdjtTp05l6tSpfOMb3wAgHA6zd+9etm/f3ukBiuShlKKiooJgMIjX2/Kdfa3sLAGFxYKaOwvt76+hbdiKmj39jFWMrqLF8jyyEnL9TpEViriKBhaRSISamhpqamrweDxkZWXhcrkksBBCCCFOE5d3czabjby8PL7+9a/H4/QiQWpra/H5fKSnn6WyUHnzkrFnmDkJ5XahnSyHD/fHYZRt1JhDoVKpwlNU014UIm6sViuZmZm43W78fj+FhYWcOHGChoaGRA9NCCGESCqSdSjapGki9lnr9p9tyxMYeRMXTQVAe3uLyaNsh2hSdgpueYqtUEiVpy4R7bztdDqprKyksLCQ0tLSWOU7IYQQoqeTgEK0ic/no7a2lrS0tLMfGE12bi2gANQlF6IsFrRPDkLxcfMG2VZKdYuAQqupg1A4wYPpOex2O16vF7vdTnl5OUVFRZSVlREMBhM9NCGEECKhJKAQ5xQIBKisrGw9ETsqGELzN27DOVtuQk4W5F0AgLZhq2njbLO6erRA45vAVKzylOZGRVeJ/LLtqas5HA68Xi8Wi4WTJ09SWFhIeXk5oVAo0UMTQgghEkICCnFW0Y7YoVDo3I0Mo3kJLiekn30lQ13a2Ohu2y6orjFhpO0QHWdmBjjsZz82GWlakzwK2faUKE6nE4/Hg6ZplJaWUlhYSEVFBeGwrBoJIYToWUwJKDZu3Mgnn3xixqlEkokmYp9zqxM06UGRZbzpPZthg1FDc9HCYdj0QafH2S6x7U4puDoRFQ0oqmSFIpGadt1WSnHixAkKCwvx+XxEIpFED08IIYToEqYEFPPmzePRRx8141QiiUQiEcrLy8+diB11roTs06h5xiqF9u6/uzYXIJXzJ6KkdGxS0TQNt9uNx+MhEolQUlJCUVERfr8fXdcTPTwhhBAirkzb8qSUtNjqbnw+H3V1dW1bnQC0dgYUTBqLyvKgVdfC9t0dHGX7xXpQpHJA4W1MzJYciqSiaRppaWlkZmYSDAYpLi6muLiY6upqCSyEEEJ0W5JDIVrU5kTsphorPLXag+J0ViuqsVu2tmGLUX2pK0RzKFI4oFBZ0S1PskKRjCwWC+np6WRmZlJfX09xcTHHjxsVzSR5WwghRHcjAYU4Q9OO2OdMxG4qtkKR1fbXXDwN5bCjFR+H/YfaNc4O6w5bnrzSiyIVRLtup6enU1tbC0BRUVGsMlRtba0EGEIIIVJeGzbGi56mpqYGv99/9o7YLYkGFL1z2v6aNDdcOBk2/Rvt7S2o0cPbd82O6BYBhSRlp5Jo1+3oxw0NDbEAw26343Q6SU9Px+Fw4HQ625azJIQQQiQJ+a0lmmlzR+zT1dWj1TcYH7fzjbqaOxNt079h7ydQWg59e7Xr9e3SEECrqzc+TuWAomm3bKXOXVVLJA2Hw4HVagWM1cBQKER9fT3V1dVomobdbsflcpGWlobT6cThcEiAIYQQIqnJbynRTFVVFXV1dXg8nva9sHF1QmWmg9PRvtf2640aNxpt7yfwzlbUFz7Tvte3RzR/Is0NLmf8rhNvjSsUWiiMqqs/Z98PkZw0TcPhcOBwGN8zuq4TDoepra3F7/djsViw2Wy43W7S0tJiKxjRgEQIIYRIBhJQiJiGhgaqqqpwuVxo7b3j3d4KT6dR82YZAcXWnfCZy4ytUPHQHXpQANjtqPQ0tNo68FVLQNFNWCyWFgOMmpoafD4fFoulxRUMCTCEEEIkkgQUAmjeEbvdqxPQpKldxwIKxgxHndcX7Vgpast2uHx2x85zLrGSsR0cZzLxZkJtnbHtaUC/RI9GxEFLAUYoFDojwEhLS8PtdseOlQBDCCFEV5IqTwIg9galrT0nTqc1loxtV4WnZifQUJc2Nrp7532IU5dhrbLK+CDVVyhAErN7IIvFgtPpJCMjA6/XS3p6Opqm4ff7KSkp4ejRoxw5coTjx4/j8/mor6+X/hdCCCHiTlYoBOFwmPLycqxWa8eTP6M5FB3c8gTAtImoV95Eq/Shdn0Ek8d3/FytifbKyM4y/9xdLUtKx/Z00QDD6TTygXRdJxgM4vP5qKysjK1wuN1u3G53bItUm3vLCCGEEG1gym+VnJycWElEkXqidzI7ujoBNCkZ24mAwm6H2dMB0N7e0vHznE1shSIrPufvStFu2dLcTjSyWCy4XC4yMzPxer2kpaWhlMLn88VWMI4dO0ZDQ0OihyqEEKIbMWWFoqyszIzTiARoaGigsrKyY4nYUboeu/Pf0aTsKDVnOry5Ce1QIepwIQwd1KnznSGalN3RrVlJRGV50MBIyhaiBVarFavVGmtQGYlEqKmpob6+npycHLxer+RbCCGE6DRZ9+7Boh2xw+FwbMtEh1TXoIXDKE2D7E7mJngyYcoEALS3t3buXKcLhdD8NcbH3WKFonFVULY8iTaKNtizWCycOHGCY8eOUV9fn+hhCSGESHESUPRgHe6IfbrodqdsL5hwtzOanE3+Xqj0dfp8MY3nUg5H/MrSdqXGLU+SlC3aK7otqra2lqKiIsrLy4nEqRCCEEKI7k8Cih6qaSJ2p7c8dLbC0+lyz0ONGoqm62gb3zfnnNC8B0V36Cyd1bhCUVMbt6pYovuyWCxkZmZis9koLS2lpKSEurq6RA9LCCFECpKAooeqqqrqfCJ2VCeb2rVEXXqR8cF72yEQNOeksR4UWeacL9HS01BWK5pSkkchOszpdJKZmUldXR1FRUWUlZURDocTPSwhhBApxJSA4sSJE2acRnSR+vp6qqqqcLvdHU/EbkIzo2Ts6caNRvXOQaurh3/nm3JKLbZCkWXK+RLOYpE8CmGK6GqFw+Hg5MmTFBcXU1tbi1Iq0UMTQgiRAkwJKAYMGMDJkyfNOJWIs2hH7EgkEuu+22ll5q9QYLGg5s0EQNuwxagk1VmNAYXqLgEFNAkoZIVCdJ7D4SAzM5NAIEBRUREnT56U1QohhBDnZEpAoZSSO1kporq6Gr/fb85Wp6g4bHkC4MLJKJcTrbQcPtzf+fN1txUKONXcThKzhUksFgsZGRm4XC7Ky8spKiqipqZGfsYLIYRoleRQ9CDRRGybzWZe7flIBKKN1cwOKFxOuGgq0LhK0VndMaBoXKHQZMuTMJndbsfj8RAMBikuLqa0tJRQKJToYQkhhEhCpgUUL730Eh9//LHcxUpiVVVVNDQ04HabWDK1woemFMpuA0+GeedtpC65EKVpaPsOQkkncnUikVPbgrpRQKFipWMloBDm0zQttlpRUVFBUVER1dXV8nNeCCFEM6Z0ygb47ne/Szgcxu12M3HiRCZPnhz7M2HCBPP264sOqa+vp7Ky0rRE7JjodqecrPiUYu2VDXljIX8v2oYtqBsXduw8VX40XUfZrJDZyb4bySS65UlWKEQcRVcr6urqKCkpwev1kpOTIz/XhRBCACYGFAcPHqSiooL8/Hx27tzJzp07+fOf/4zP58Nms3HBBRdQUFBg1uVEO+i6TkVFBbqum/8GIF75E02oS2eh5e+FD3bB566AzA6shES3O2V7jepI3UU0KVtyKEScaZpGeno64XCYyspK6urq6N27N5mZmebepBBCCJFyTAkoNE3DbrczYcIEJkyYwM033xx77tChQ+zYsYP8/HwzLiU6oKamhurq6s53xG6B1gUBBcMGoQYPRDtajHr3A7j60vafo7v1oIiKrVBUg1Ldo2GfSGo2mw2Px0N9fX1staJXr16yWiGEED2YaVWeWjNs2DAWL17Mww8/bMalRDuFQiHzE7GbikcPitNpGurSWcaHmz6AUAfKWFZWGX93t4AimpQdDEJDIMGDET2FpmmkpaWRlpaGz+ejsLAQn8+HbkZ5ZyGEECnHlIDi1Vdfxev1mnEqYbLKykrzE7Gb6ooVCoDJ41BZHrTqGtixu90v18qrgG7WgwLA4UC5XcbHkpgtulh0tQLg2LFjHD9+nEBAAlshhOhpTAkoPvOZz+B0OgmHw/z3f/83n/3sZ7n++uspKysz4/Sig+rq6vD5fOYnYjfV+EY97gGF1YqaMwNoLCHb3ioz3bFkbJQkZosEc7vdpKen4/f7KSoqoqqqSlYrhBCiBzE1O3XJkiX8+te/5pJLLuH111/H7zfe4Cxfvpy//vWvZl6qTZ5//nm++c1vMm3aNJxOJ5qmsXLlylaP9/v9LFmyhCFDhuB0OhkyZAhLliyJfR6pJK6J2FGBAFpNrfFxr6z4XKOpi6eh7Ha0ouPw6eH2vba7bnkCScwWScFqteLxeNA0jWPHjnHs2DEaGhoSPSwhhBBdwNSA4m9/+xsrV65k6dKl2Gyn8r2nTJnCU089Zeal2uT+++/n17/+NUeOHOG8884767G1tbXMnTuXJ554gjFjxnDXXXcxduxYnnjiCebOnUttbW0XjdocdXV11NbWxiUROya6jcjtgrQ4balqKj0NLpwEgPZ2Oxrd6Qoqu2lSNsgKhUgqLpeLzMxMqqurKSoqit3YEEII0X2ZGlA0NDTQv3//Mx4///zz+eijj8y8VJs8++yzHD58mJMnT/Ktb33rrMeuWLGC/Px8li5dyrp16/j5z3/O2rVreeCBB8jPz2fFihVdNGpz6LqOUgpLPEukRvMnesd5u1MTau5M44M9++BkedteVF2DFo6gLJZTd/O7k8bmdppPVihEcrBYLHg8HiwWCydOnKCkpIT6+vpED0sIIUScmPpu84orrmD16tVnPB4MBolEImZeqs3jGTJkyDmPU0rx7LPPkpGRwQMPPNDsuXvvvZfs7Gx++9vfSnfY03VVQnZT/fugxo5CUwrtna1teokWzZ/I8kA8Kl0lmIpteZIVCpFcoqsVtbW1FBUVUV5enpDfBUIIIeLLtMZ2AL/4xS+48MIL0TQNpRSaphEOh3n00UeZNGmSmZcy1f79+ykpKeHKK688Y4uQy+Xikksu4eWXX+bTTz9l1KhRCRpl8umSHhQtUJfOQvtwP2zZCddcdu7tVrHtTt20EplseRJJzGKxkJmZSSAQoLS0lLq6OjweD3a7HYfD0Wx7rBBCiNRk6k/y4cOHs3HjRr71rW9RV1fH3LlzqaysxOFw8M9//tPMS5lq//79AK0GC9HH9+/ff9aAIhAINCuZGE3mDoVChEKhNo8nemx7XnO6cDhMJBKJ691Aa1klGqBne9G78q7jyKFo/fugHT+J/t429EsvavGw6Oeuor0ysrzd8+5oZrqx1Fjlj8vnFz1nt5y7LtTT59Fms5GWlkZNTQ0+nw+LxYLNZsPhcOByuXA6nbEA42xbNc34+djTyRyaQ+bRHDKP5ujMPJox96YGFPX19VxwwQW88847fPrpp+zatQu73c6sWbPo3bu3mZcylc9n3MFurZdGtM569LjWPPLIIyxfvvyMx9etW0daWlq7x7V+/fp2v6YrTSgqIQ3YX1mGb8eOLr12n2HnMfz4ScJvvkt+pgPO8gakYv8B+gHHAvUUdfE4u4K9voEpANU17Ni27axz0RkFBQVxOW9PI/NojmT/+ZgKZA7NIfNoDplHc3RkHuvq6jp9XVMDiltuuYUXXngBgJEjRzJy5EgzT5/07r33XpYsWRL7t9/vZ9CgQSxYsCAWlLRFKBRi/fr1zJ8/H7vd3qGxVFdXc+zYsXZdt12Uwva3NwAYMWM69OvigHH8BNSuT3HW1jHVloaaNPaMQyKRCAUFBfS2GF/m/cadT98pU7p2nF1BV6hV/0LTdaaMHH1qC5RJovOYl5cXn27rPYTM47mFw+HYn2hlqOjWqOgqhqZpvPPOO536+djTmfE7Rsg8mkXm0RydmUcz2iOYGlAUFhby8MMP8+Mf/7jZ4w0NDdx22208//zzZl7ONNGVidZWIKITfa5u4E6nE6fTecbjdru9Q98kHX0dGNsLrFZr/N641NSiBYIAWPvkdH2ys9sKs6fDG+9gfWcrauqEVg+1NCYrW3olYJxdwYpRvarSh7W6Nm45LXH9eupBZB5bZ7Vam/0MVUoRCoUIh8P4fL5m5WfLy8vJyMjA4XDEtkrFrYFnN9WZ3zHiFJlHc8g8mqMj82jGvJsaUPz9739nxowZTJw4keuuuw6AkpISrrvuuviWL+2kpjkSLTlXjkWPVNaYl+DNhAT9AFBzZsCb76IdKkQdLoKhuS0cpLp3l+yoxoBCErNFd6JpWixgiAoGjRsZNTU11NTUoGkaNpsNu92O2+3G5XLhcDiw2+0SuAkhRBcxNaAYOHAgL7zwAtdddx0jR46kurqa66+/ngULFvDrX//azEuZatSoUQwYMIDNmzef0QyuoaGBjRs3MmDAgB63heusElThqRlvJkwZDx8UoG3YgvrqF844xBYIogUbk42y47T9Kxk09qKQbtmiu4sGCRkZGVitVpRShMNhQqFQrNeFpmnY7XZcLhdutzsWYNjtdlnFEEKIOOj0ssGtt97Kk08+yTvvvENVVRUXX3wxP/vZz1iwYAFXXHEFd999N88991yLW4GShaZp3HbbbdTU1PDQQw81e+6RRx6hsrKS2267TX4RNZUMAQWgohWedu49VR62CUet8QZDeRK3ktIlsoxeFJqsUIgeJho8pKWl4fF48Hg8pKeno2kaNTU1nDhxgsLCQo4ePcqRI0coLS3F7/fHVjqEEEJ0XqdXKBwOB3/729+4//77qa2tJTc3l7y8PBoaGrjpppu49tprzRhnhzz77LO8++67AOzevTv22IYNGwBYuHAhCxcuBGDp0qW88sorrFixgp07dzJ16lQKCgpYu3YtkyZNYunSpYn4FJJWrFlcr6xEDgMGnYcaORTt08Ow6d+oa+c3e9pZ09idt7v2oGikvB40AOmWLQQWi6VZTptSikgkQigUoqKiAqUUDoeD9PR0MjIycLvdsj1KCCE6odMBxTPPPBP7+MCBAxQUFFBQUIDFYmHdunX85je/IS0tjXHjxvH+++939nLt8u677/Lcc881e2zz5s1s3rwZgKFDh8YCivT0dDZs2MDy5cv5+9//zoYNG+jfvz933XUXy5YtO6PhXY8X7e2Q4BUKADVvlhFQbN4GV84F56n91s7axlJo3Tl/Ak5VdpJu2UKcIZpnYbPZcLvdsWTvqqoqqqqqcDqdZGZmkp6ejsvlktVoIYRoJ1NzKEaMGMGIESNYtGhR7DG/309+fj67du0y81JtsnLlSlauXNnm471eL48//jiPP/54/AbVXSTJlicAJoxB9c5GK6tEfVBgVH9qdGqFIisxY+sqHmPLkyRlC3FuTZO9dV0nGAxSVlZGeXk5brcbj8eD2+1O6q26QgiRTDqdQ3HHHXfQ0NAAtFzH1uPxcMkll/Cd73yns5cSyULXoaIxXyEZAgqLBTV3JgDa21uM8TVyNK5QqO4eUERXKGTLkxDtYrFYcLlceDwe0tLSCAaDHDt2jMLCQkpKSqiuriYcDid6mEIIkdQ6vUJRUVFBfX09LpeLrKwshgwZwsSJE8nLy4v9LeVWu5kqP1okgrJaTW+i1mEzp6BeewuttAz10acwbjTQg1YooknZDQFUQwBccmdViPayWq2kpaUBRpOo6upqfD4fTqeTjIwMMjIycLlcSV0GXQghEqHTAUXTZnUfffQRu3btoqCggPz8fJ577jkKCwsTlkMh4iS63SnbC8nyi9XlhFlT4O0tRgnZaEBR20MCCqcT5XKiNQSMPIr+fRI9IiFSWrTMrFKKQCBARUUFlZWVzVYzHA6H5FsIIQQm51CMGTOGMWPG8IUvnOoH4PP5yM/Pj1VZEt1AeZXxd6IrPJ1GzZ0JG7aifXwAVXICvJnYoj0ounmVJ8BYLTp+0tj2JAGFEKbQNA2Xy4XL5SISiRAIBDh+/Dg2m420tDQyMzNJS0vDZjP116kQQqSUuP8E9Hq9zJ07l7lz58b7UqKLaMmUkN1Ur2zIuwDyP0TbsBXmGMnZKt0NPSG50pvZGFBIYrYQ8XD6lqja2lr8fj8OhyNWJcrtdsuWKCFEj9MlP/WOHDnCLbfc0hWXEl0hiUrGnk7Nm2V88EEB2tFi4+OsHrA6AU26ZUtAIUS82e12MjIy8Hg8WCwWKioqKCoq4ujRo1RUVNDQ0IBSKtHDFEKILtElAUVNTU2zXAuR4pJ1hQJg+GBU/z5o4TDWv75qPFZyAu35f0DR8cSOLd680W7ZUulJiK6iaRpOpzPWoTscDse6cxcXF+Pz+QiFQokephBCxJVs+hTtl8wBxfbdcKIMgGiqpBoyEG3/IbQPClA3L4JpExM3vjhSWY3dsmWFQoiEsFgssS1R4XCYuro6qqurcTgcZGRkxLZESVduIUR3Y0pA8cMf/pApU6YwadIkRo8ebcYpRbIKhU7dAU+2gKLoONofV8PUCahPDqL5awBQk8fB3Jlof34Z7Y+rUf37Qm7/BA82DqJbniSHQoiEs9lsZGRkNOvKXVlZGVvNSEtLk67cQohuw5SAYu3atTzxxBOEw2HS09PJy8tj8uTJTJkyhcmTJ6M3aTQmUlxjQzvlcEBGWoIH05y2YQtkeVBfvh7+tRnt1TcBUNlZaFYr6sbrjJWKd7agbro+waONA2luJ0TSOb0rdyAQoLS0FKvVitvtJjMzE7fbLSVohRApzZSAoqCggGAwyO7du8nPz2fHjh1s27aN3/3ud9TX18sPye4ktt0pC5Lp/1XXYcce1II5YLXCxdNQr29AC4VRfXKMY6xW1EVT0dZtghsXJtf4zdCYQ4G/xpgPqTQjRFKxWCy43W7cbjfhcJhAIEBNTU2sBG1GRkYsuBBCiFRiWg6Fw+Fg8uTJZGdnc9VVVzFw4ECUUuzbt48dO3aQn59v1qVEIiVr/kQojBYKoXo3Bg/paURu+yKFOwvIPa/vqeN6ZRvHhULQ3X5pZ6ajNA1N11HVtacCDCFE0rHZbLHeFeFwuFkJ2qbBhfS3EEKkAtNuYT722GP07t2bUaNGMXjwYPr168fdd99Nnz59uPHGG1mxYoVZlxIJFOtB0TvJAgq7DWW3Q1lF7CE1ZgSlo4c2P6680jjObu/a8XUFqxU8GcbHkkchRMqI5lt4PB6sVit+v5+ioiKOHDnCiRMnqKmpIRKJJHqYplK6jt4QQMmWaCG6BVNuffz2t7/lvvvu4/bbb2fu3LnU19ezfft2/vCHP/CXv/yFNWvWMGnSJDMuJRItWXtQWCwwZTzalh2o+Y3bnk4XiaC9tx2mju9+252ivB4jh6LKD4MHJno0Qoh2aJpvoZQiGAw2S+aOduV2uVwp2zwvdLiQun+up37zNggGweHAffE00j47H/vQQYkenhCig0wJKH75y1/yyCOPsGTJkthjN954Iz/96U/57ne/y/z589mzZw/9+vUz43IikZJ1yxNGUzvtgwK0P7+MuvG65k9GImh/egl81ai5sxIyvi6R5YGjxZKYLZKTrkMoDHab5PicQ7S/hdPpRNd1gsEgJ0+exGKxNKsU5XQ6UyZPsX7T+/ie+h2WnCwyFl2NtX9fIsdLqfvXu9RvfB/vd7+Oe86FiR6mEKIDTAko9u/fz+c+97kzHne5XPzmN7+htLSUhx56iKefftqMy4lEKq8y/k7CgILc/qibF6H9cTXa/kNYZk6mV7UPy8lqtK07jWDi5kXds2RsVKy5nR/p0SuSRtFxowrbjj1GDpPdDlPGG53tu/P3o0ksFgsulwuXy4Wu6zQ0NHDixImUqhQVOlyI76nf4ZozA+/tt6A1yQ1Jv/5qfM88h++p32EbNEBWKoRIQaYEFB6Ph4aGhlafv+uuu/jqV78qAUWqq29Aq6s3Pu6VldChtGraRFT/vmjvbMHy5ruMDIVRdpvRm2Ju93/zoryZjc3tZIVCJIltu4z+MFke1II5RuGEsgq0LTu6fbPJeDi9eV5DQ0OzSlHR4MKeZHlidf9cjyUnKxZMqGAIVd+AxZuJZrPhvf0Wgnv2Uffam3j/62uJHq4Qop1MCShmz57NCy+8wIQJE1p8fujQoRw/ftyMS4lEiuZPZKSB05ngwZxFbn/UTdcTueFz5H/wAZNmTMfaUyqlZElzO5FEos0mp000tiE2yW1S8+d0/2aTcda0UlQoFGpWKSo9PT3WmTvRlaKUrlO/eRsZi66OBRPlP3qE8OFCMr50PekLr0Kz2Ui7Yg41q9fi+fZXk3alRQjRMlM2sd5zzz384he/4JVXXmnx+d27d9OnTx8zLiUSKYnzJ1pk0dDttu6bgN2SaLfsKgkoROLFmk1Gg4naOig6ZjzZ2GwSbybaO1sSO9BuwG63N6sU5fP5KC4u5ujRo5SWllJbW5uwSlEqGIJgEGt/o4R3zV9fInzwKOiKmj+tpmrF/6LX1mHt18dI1A4GEzJOIUTHmRJQzJo1ixUrVnD99ddz00038d5771FbW0soFGLDhg3ceeedfP7znzfjUiKRUi2g6ImkW3by0hWWUBj0HpLdEm02OWtKbGVCe/4fWB59Bj49bBzT2GyS7XtA9ZB5ibNopajMzEwyMzPRNI3KykoKCws5evQoZWVl1NfXd+2YHHZwOIgcLyX40X5qX1kHgHvBXLDZCHyQT/k9DxP86FOjP1B36xEkRA9g2jroHXfcwejRo/n+97/P7Nmz0TQNTdNQSjFv3jx++tOfmnUpkSBamQQUSS+alF3fgAoEwSm/mBOuMSHZtmM300Nh1N/Xw5QJ3T8h+fRmk7oO+w8BoO3Ygxo51Hi8OzebTLCWKkWVlZWhN/Z+qKysxOv14ozzFlbNYsF98TTq3txE3VubQSncl16E95s3k3bFHKr++/+InDhJ/boN2M8fKdudhEhBpm6svOqqq7jqqqvYtm0bO3fuJBQKkZeXx8UXX2zmZUSiJGsPCnGKy4lyONCCQWOVom+vRI+oZ2uSkKxfMZtD1T6GZXqxvJ/f/ROST282WVGFFmjcyrJnH3zhM8Z2xO7cbDKJNK0UFWzcUlRaWorf7yc9PT2WzG1tqYePCdI+O5/6DVtAKbRe2WR+7YsA2EcMJedn91K+9GH0iipCH3+K7+mVeG67EU1uiAiRMkwJKMrLy/F4PLGqEtOmTWPatGlmnFokk4oq4+9krfAkjDdoWZlQWm4kZktAkTinJSTrQPmOHQyZMgXtyrndPyH59GaTxacKc2iVPtSxUujXu/s3m0xC0aDB6/Wi6zp+vx+fzxfrb5Genm56fwu9yh/b1qaFw9St+RfWfn2InDhJ3Zub0H1+nBdNI7B1O/VvvUvo0BGy7r4dW2PehRAiuZkSUHzjG9+gd+/e/PrXv449lp+fz+rVq+nbty9f+cpX8Hg8ZlxKJIpSyd2DQpzi9RgBhSRmJ9QZCclNE2IbE5K1/YfQ3tmCuun6xA00jpo2myT7tN8BBR+inazo/s0mk5zdbsdut6PrOoFAgNLS0lgJWo/HY0qVKL2mFt/TKwFwXTwdzemgZvXaU52yZ08n7TNXYB86iMCuj/A98WvChwopv+dhvN/9Oq4Zk034TIUQ8WRKQPH+++/zhz/8Ifbvo0ePMnv2bEKhEJFIhCeffJL333+f7Gx5I5qyqmuMfc6aBtneRI9GnI2Ujk28aELygjlGMFFaju3Xf2JQnyyYMsU4pjEhWVu3CW5c2D3v0DdpNonVqAGivJlovmq0NzaCUt2/2WSKsFgsuN1u3G434XA4VoK26aqFy+Xq0KqF/3d/Ra+oxHpeP7z/9VU0pxPP7beggiE0Z/NmfM6JF9Drvx+g6rH/I7TvAFWPPk36wqvIuPF6tDhtxxJCdJ4pVZ4qKioYOXJk7N8rV64kJyeHEydOcOzYMbKysnjsscfMuJRIlGiFpywP9JSeDqkq1i1bKj0lTGNCMr1zIBRC+93f0E6U0feTI82rGTUmJBMKJW6s8TZtIuqebxlboGjydRmJoP7rK903hySF2Wy2WAlagLKyMgoLCykuLsbn8xFqx9drw/s7aHhnC1g0vHd8Ha0xAVyzWLC4Wt5WZe2VTc5D95D22SsAqH3pdSqXP06k0mfCZyeEiAdTAorzzjuP8vLy2L/feOMNvvSlL5GVlUWfPn1Yvnw5//jHP8y4lEgUqfCUMlR0hUK6ZSdOk4RkbfXraI35A7ZgCJq+KeopCcm9smIJ2fpDS1AD+xsd3SvkDWIy0zQNl8uFx+PB5XJRV1dHSUlJrLdFXV0d6izlfiM+P/7/+yMA6QuvxjF6RNuvbbPh+doX8S75JprLSXDvPsrveYjgh590+vMSQpjPlIBi3rx5PP300wAcO3aMDz74gAULFsSeHzt2LEeOHDHjUiJRpAdF6mhcoZAtTwkUTUjesBXt3Q9QmoZKcwPEggsikZ6TkHzsBNAY7GZnwYTzAdD27kvgoER7NF21sFgsVFRUUFhYSFFREVVVVbHKUVFKKfz/90d0fzW2Iblk3PC5Dl3XffF0ej16P7ZBA9ArfVQs+29qX37jrIGMEKLrmRJQPPDAA6xevZoJEyYwffp0+vXrx7x582LPl5aW4na7zbiUSBCtMSFbSYWn5CfdspOCmniB0Rka4PKLUePHAKAVHTeCiT+91HMSkouMgIIB/QBic8FHn0IonKBBiY6I9rbweDykpaXR0NDAsWPHKCws5MSJE9TW1qLrOg3vbCXw751gs+K941a0TqzC2XLPI+fnP8J1yYWg61T/4UWqfvEMevT7SwiRcKZshh86dCj//ve/+eUvf0lVVRXf+973mtWyfvvttxk9erQZlxKJIisUqaNpt2xdj+1dF10oFEJb+xYaoAC274b+fQDQdu5B+3e+EUz0kIRkraRxVWZg4+c66DyUJwPNX4M6cBjOH9nqa0XyslqtpKeno5QiFApRVVVFZWUlrvoArmf/BEDGDddiHzqo09eyuJx477gNx5iR+H//VwLv76D8SBFZ99xuyvmFEJ1jWnbtqFGjYtueTrdnzx6+8IUvmHUpkQgSUKQOTwZK09B0HVVTB56MRI+ox9FWv45WdByVkYb6yufRduxG27bLeK60HGZONlYmekAwAcR6UKhoQGGxwLjRsGUH2p59KAkoUpqmaTgcDhwOB3okAiv/DvUNRAYNoPrCPKipMaVpnqZppF11KbYRQ4zu2sdLKb/3Ebz/eRPuS6WBrhCJ1OaAok+fPkydOpXJkyczdepUpkyZwvDhw9v02qYlZUUKikRObZ/pLQFF0rNaITMd/DXGKoUEFF1rxx60dz8AQH1lMVwwEnXBSCKfnY/9/hVogH79VZDWQ7aB6jqUlBofDzwVQKnxY9C27IA9n8Dia7p/HkkPYXlvO5b9h1F2O+Ebr6O+pgZfTY2pTfMco4bT+78foOpXzxLcuQff//ye4L4DeL7+JTRHNy9wIESSanNAEQqFWLduHevWrYv9IPB6vUyZMoUpU6bEgoxRo0bFbbAiQSp9xt1umw0y5c1pSvB6GgMKPww6L9Gj6TlOlqP95WUAozv0BU1+Hma4achw46qpN+7YjxqWoEF2sbJKtGAQZbdBn5xTj48ZgbLZ0MorUcdPwnnSETnlnSxHe+kNANS187Hnnocdzmial56eTmZmJmlpaR1etbBkZpB93x3U/v01al54hfr1GwkdOGx01+7Xx8RPSgjRFm0OKKqqqjhw4ADbt29n+/bt7Nixgx07dvDWW2/x1ltvxYKMzMxMJk2aFAswbrrpprgNXnSR6HannCzZj58qvJlQiCRmd6VQCO13L6A1BFDDB6M+c9kZh9TleI2AovBYzwkoolWtzutrrJ5FOR0wehh8uB92fywBRarTdbTn/4EWDKFGDYNLZsSeOr1pXnV1NT6fD6fTidPpxGazYbfbsVqtLf5pjWaxkHHD57CPGkbVr54lfPAo5UsfxnvHbbimSn8TIbpSu3IoRowYwYgRI7jhhhtijx06dIgdO3awfft2tm3bRn5+Phs3bmTTpk1omiYBRXfQWOEJqfCUOhoTszWfHymu2DW0f7yBVnTMyJv46heav3luVJftJefocbTi4z3m/yWWkD3gzHwRNX4M2of70fZ+glpwSRePTJjqrc1oB4+iXE7Ul69v9eaTzWYjMzMTpRSBQID6+noikUizMrCapmG1WrFYLFgsFux2e+xPSwGHc/J4ev/ix1Q99v8I7T9I1c+eJH3RNWR8cSGaVW6CCdEVOp2UPWzYMIYNG8bixYsJh8OsX7+exx57jLfeesuM8YkkoElCdspR3kyjcZg0t+saO/agbfo3AOrmxZDtbfGw2pzGClxFx7pqZIlX3NiDYmALCejjGqv/HSqEmlrISO/CgQnTlJxAe834na8WXW2sZp9DtGleS3Rdj/2JRCKEQiEikcgZr48GHVarFYfDge3Or2NdtZbIW5upXb2GwL4DeL53K/ac7E7lbAghzq3TAUUgEOCNN95g1apVvPrqq/h8PpRS5OXlsXjxYjPGKBKtMaBQElCkjljpWNnyFHen502MbT2PrC678f/l+Emj/4LdtEJ7ySu65WlgvzOfy8kyumYXH0d9uB9mTOrSoQkThMNof1yNFo6gxo+GmZM7fcroykRrlFIopYhEIui6TigUIhAIoOs66rKZ2Ht5SX9pHeG9+yi752FCNy/CMnIoDoejQ1urhBDn1qHfZnV1daxZs4ZVq1bx2muvUVtbC8D06dNZvHgxixcvbnMFKJECoisUUuEpdXib9KIQ8dOGvImmguluVJoLra7BSETu7gnzdfVoFVXGxy2tUACMHw3Fx43ysRJQpBztjXeMrX5pbtQXr+uSal2apqFpWutBxyUz0UcOw/Lbv2EpLcPxv38keM2l+C6agt4YjABY0LDoEaxOF1a7DafTaax02GxYrdbY31arVVY4hDiHNgcU1dXVvPrqq6xatYrXX3+dhoYGNE1j9uzZLF68mEWLFjFw4MB4jlUkSiyHQgKKlOHNNP6WpOy4iuVNpLeeN9H8BRpq4Hlo+w8Z2566e0BR0rjdKdvbaplcNX4M2hsbja7Z4TDYesCqTXdxpBjWbQJA/cfnTv3cSQLagH6oe74Jf3kZbccenP/8F47i46gbFxqVxzZsgR270UJhlN1GeOIF1Fw8lXC/3oCxCtJ09cJut8cSyKOBRtO8DyF6ujb/5O7bty/BYBC73c68efNYtGgRCxcupG9fqczRrQWCaNU1xscSUKSOaFJ2XT0qGAKpzW6+nXub5E0sajVv4nRqYD/Yfwit+Fj3T8xuzJ9ocbtT1OCBqMx0tOpa1IEjMGZE14xNdE4whPbHVUZJ8SkTYMr4RI/oTC4n6qtfQA0fbDSb3LkXDhw18nWyPKgFl6B650BZBbYtO7Dt3Gt8L0+biFIqlsOh6zp1dXVUVxsrvkqpZqsjxcXFpKWlYbfbW1zdEKInaHNAEQgE0DSNyy67jCuuuILRo0e3mlAlupHG7QrK7eo5jbi6A7cLZbejhULGtqem9f9F552sQPvzSwCoK2afSi5ug1hyctHxOAwsuWix/ImzdASPds3eutPY9iQBRUrQ/vkm2okylCcDdcNnEj2c1mkazJ2JGjwAfvMXNH81yqKhrrkULjyV76Hmz0H788tof1yN6t8XLbf/WQMCXdcJBoOA8f4oEAjEcjuiOSAWiwWbzRbrIt50dSP6t2ylEt1FmwOK8ePH8/HHH/P666/zxhtvxB4fPnx4s8Z2U6ZMISdH3rx0G7EKT1kJHYZoJ02DrEw4WWEkZktAYZ5QGO33fzuVN/HZy9v18lhAUXzc6CLdnXu7NAYULVZ4akKNH4O2dSfs2QeLrpau2clu/yHYsBXA2EKUnpbY8bTFsMEwahhq18do4TDa8/9AHS4yqlLZbWC1om68Dm3/IbR3tqBuuv6sp4uWswXOaNDXNGE8HA7HEsajz52+lcput8cCkGh+SGf/CNHV2hxQ7Nq1i0AgQEFBQazvxI4dO9izZw8HDhzgxRdfjH0RDx48OBZk3HfffXEbvOgCsaZ2st0p5Xg9jQFFghOzdf1URaNu8OZZe+kNtMJ25E2crl9vo0N0QwBVUQW9u2mwp+twrNT4uIUeFM2cPwJls6KVVaJOlEF/6XSctBoCRgM7pVCzprZrdS6hdB1270MtmAMR3Ugmf/cD2PUxjByCGj4Ehg9GzZyM9uZmuHFhhwNbTdOwtZIL1NJWKl3XW8zHOP2xpsFCSwHE6Y81XSmJBitnC1wSJRwOA1BfX4+u67HVG5Fa2pX95nQ6mTFjBjNmnOqAGQqF2LVrV7MgY/fu3fzjH//gpZdekoAixWlS4Sl1RUvHJioxu+h4Y+LjHrRQCGW3w5TxqHmzIPccbzCTVf5etI3vA+3Lm2jGajW6QheWGInZ3TWgOFlx6v/9XCtkTqfROfyjT41VCgkokpb2j9fRKqpQOVmoRVclejhtFwobX499eqGmTUQNG2SUu/VXGz+jduwBMALbcAT++SZq1HAYmgsup2nDiPbP6Mgb5miAEd1a1dK/W/vT9LhkE+0xUlhYiMPhkK1iKarT5TTsdjtTp05l6tSpfOMb3wCMaHPv3r1s37690wMUCdZY4Ul6UKSgxoorCemWvW0X2h9XNyY+zoklPmpbdqB9UBBLfEwpZRVof3oJaH/exBly+0NhCVrRcdSkceaML9lE8ycG9G3TypQaNwbto0+NPIorZsd5cKJD9n6C9p7xe119+XpT32jHnd1mBLdlFca/x41GPbQEdbgIDh5FO3gUDhWiNQQA0NZtQlu3CaVpRg7Q8MGo4YNh+OCO3UgwQdOViO4kGlBkZhq/s9q6Vaxp1a2mAUd3m59UEZf6fDabjby8PPLy8uJxetGVoj98JaBIOQnrll103Agmpk1E3Xhdsy1Bpyc+psxKRSh8qt/EsEHtzps4nRp4nvF/U9x9O2a3KSG7qQlj4O+vwcGjUFuXGvvye5LaOrQ/NzZwnDfLWFFKJRYLTBmPtmWH0YDSagWHA0YPh9HDjZsuoRA8+ISxxbd3thFoVFQZK4lFx06tTmZ7YfhgLENzSQvUGtupOrpFp5ttCe2M6OpNS9pSdctms2GxWGINDB0OR7NVjejzIj6k4LdonVKxKk8SUKSgBHXL1jZsMVYmbrzO+AV5shwaApB7XrsTH5OF9vIbaIUlRvOur93Q8TcPUbk9oNJTSWNC9rnyJ6JyslAD+qGVnDC6Zk+XG1LJRHvxNaNCUr/eqM9dkejhdIiaNwvtgwK0P798xs0OIhG0v7wCNXWo278S+x5VVf5TKxgHjxpNGCt9sH031u27mQCoN9+HobmnVjCG5hrb+M6mO24JjaNzbRWLBhuRSIS6ujoikQhKqVh+iNVqjSXSR7dSnR5sSN5G50hAIVpXVx9b/iUnK6FDER3gTUAORW09bN9l1H3/zV/gcBFabR0AavQwo5NunxzURVPR1m3qVOJjl8n/EO2dTuZNnG6A0ZdBq/KjamohI73z50w2belBcbpxo6HkBNqeT1ASUCSPnXvQtu9GWSyoLy9K3b42uf1RNy8ycif2H0JdNNW4WVZeaWzl8lUb3+NN39BneYw3+tE+G4FAbJsUB46iDh7B2hCAjw+gfXwAAGWxnLlNKnqDB7rnltAEiyabRytvNaXreizgCAQCsUT46NaoaLCRDAGF1+slOzs1b+BKQCFa15iQrTwZqfsLpCeL/gLzVxurTWa/cdd1OH7S2Hd8pAgOF8GxUjRA++RQ7DBlswKa8dgjTxv137O9xl25UMjYdpCsypr0m7j8Yhg/xpzzul2oPjloJyuMXIPu1nuhts64iwtt3/IEqAlj0NZvgo/2QyTS+ZUg0Xn+arS//dP4eP4c4+57Kps20egz8c4WI08iujowdTxqbhtWB5xO4/t1zAgikQg7tm1nSv+BWI8UxVYxtEqfUXShsATtncbyujlZRoCRk4X25rswdQLqpoXdY0tokmvaE+R0TUv8RnM5EiUQCJCWlrpbPSWgEK2L9aBIzWi5x/NkAKCFI6jKKsjydm6PbnWt8QvycBEcLoQjxWiB4BmHKYABfVGzpsHQgTDwPGOV5K8vo31yCO3ldUYzLJsNWriblDRCYbTfv4BW32DkTZi9zWPgeUZZ36Jj3S+gKDFWJ1ROFrjb0QB1SC4qIw2tps7omj16eHzGJ9pGKbS/vIxWW4fKPQ911dxEj8gcuf2N7ZZfug4VChs3zDp6w8WiGW/8hwxEXXIhAKrS12Sb1BEoPmHkYlRUEb2K2v0x2v/7k7GCMXWiUQktRbeEprKzlfjtatHyuakqOWZRJCcJKFLb8TKj30E4jGXZE+3boxsOG/v7DxdhPVxI3r4D2GtePeMw5XTAkIHGG8Fhg2BILtor643GV3Omn7r71icH9Z2vorbuRFu9Fs1fgwK0V99EXTUvKVfAtJfXoR1tzJvoSL+Jc1C5/dHy9xqVnkw9cxKIbXdq5x1WiwXGjoZ/5xvbniSgSKz3dxr/DzarsQ0nSd54mcZiAWccVkizvcYKxNQJxr/rG1BHitAOHIE3NoLFYmwn/uhTtI8+Ra3dYKycXHkJ9O2dWltChWjUzX46CDNpZRJQpKzoHt3G30X65ReDy9nyHl2loNJn5DscLjS2LhUeQ2tytyR6j1n17wNDB6GG5sKwQUa/gNNWPVpNfNQ0mJEHH36Cyv/QGNr6TZC/F/Wl65KrakzBh6e2Kty8KD45RE07Znczpyo8tSN/opEaPwbt3/mNXbNTqM9Bd1NRhbZqLQDqM5fF8n5EB7hdcP5I1LDBWF5/B/3G61Dn9TVWMfbsM3Iv/p0PHxQYN30G9k+NLaFCNCEBhWhdNIdCAorU0qRsKzW18OF+6NMLLp5m7NH942q0P64y7phVNAYS/jNLy6r0NBiaiz5kIJ8E6xl52VysmW1IHm5L4uMtn0c57Ggv/BPtZAXak79HzZqKWrgA0txxmJR2KKs81W/CzLyJ0+WeZ/x9ogyCoaRcpemwxoBCtXeFAoyu2VYr2slyVGkZ9O1t8uDEOem60Q27sUwyl12c6BF1D9FeGBVVMGMSDBqAmjsTdaQY7fUNaHv2wfbdsH230f+itPzUzwkhkpwEFKJ1jU3t6JWVyFGIdmpatlV78TXjsT0fG3v1Dxca+3mVQtuwNfaaWFWSoY1bl4bmGh2cNQ09EsG/YwektWMvfBsTH9WoYfDKerR3P0Dbsh32foK64TOQN9bUOWmzcJO8iaG58S2P6c08lS9wvBQGD4zftbpSJALHSo2POxJQuF0waih8fAB274PLJaDocpv+bdwMcNiNFTqp3W+OlnphgJF/8c2bUIXHjMBi10dG/sqjz6AmXmDkrgwakNixC3EOElCIluk6VFYZH8sKRerQddixB7XA+GUVbW6n7fmk2WHK5YRAEHXtfBg+CHIHmH+HvC2Jj24X6j8+h5o6wUj+LC1He/avqLyxqC9cc6r0bRfRXl6PdrTYvH4TZ72YZtx9/PgAFB7rPgHFyQq0cBjlcHT4Z4caNwbt4wNoe/cZq0Si65SWob28HgB13ZXG6qYwzVl7YQzoC06HcYNn9DDYdxBt10douz5CjRttBBZDByVu8EKchQQULfjggw9YtmwZW7ZsIRgMMm7cOO68805uvPHGRA+t6/iqjepAFkvz+tkiuYXCxmpA7xzj35PGoXbsMbYRDWvMfRiSC58exvKHVahLZsR/j25bEh9HDkX98Nvwxjuw/l20gg/hk4Oo6xbARVO7JjGx4CNjdQdQX76+a3qvDOxv1K8v7kaJ2dH8iQF9O35ne/xoWLUGDhyFuvrEb4PrKSIRY6tiKIQaMxxmT0v0iLqftvbCmDbRKMv9xjuwfTfa3k/Q9n6COn8E6up5MHxIoj8TIZqRgOI0GzZs4Morr8ThcPDFL34Rr9fL6tWruemmmzh8+DD33XdfoofYNaIVnnK8Ugs+lUT36JZVGP8+ry/qR98987jySuO4ZCrbarejPnsFTBoPf3kJ7WgJ2l9fQW3fZTTE6xvHO6VllWh/+gcA6rKLYML58btWEyr3PCM5vehYl1yvK5xKyO5EDf3eOaj+fdCOnzS6ZkuTr67xr81oh4tQbpexuihbneKjrb0w+vdB3fJ5uPpSWLcRPigwVu4+PoAaNcwILEYOlUpQIilIQNFEOBzmtttuQ9M0Nm7cyOTJkwFYtmwZs2bNYtmyZXzhC19g1KhRCR5pF4gFFLLdKaW0tke3qUjEuBM2dXxy/iLK7Y/6/n+iNmxFe+1faPsPw8+fRl19KVx2kfkB7ul5E9fON/f8ZxOr9HTC2K7WHd7AdSYhu6nxY+D4SeOurAQU8Vd0HG3N2wCoxdeY0xFetK49vTD69jJWTa+aa1TGez/f6FWx/xBqxBCj9PaY4cn581z0GBJQNPHWW29x4MABvva1r8WCCYDMzEx+/OMf88UvfpHf//73/OxnP2vXefPz88nIyGjz8eFwmAMHDrBz584ON1ypqanh5MmT7bpuU/0+3Ed/oMKiKNq9u0PnSLRIJMKBAwew2+1Ye9Aqiyu3N6P/nU/V0yspnDut+ZtUXWfQO9vIrvLzycDeNLTh/zZh89gnE8ei+eRu2k5m8Qm0V9ZTv/kDCi+ZRn0f8wLdAVvy6XO0mLDTziezJhL68EPTzt1Ui/Oo60ywWrAEg3z83laC3sy4XLsrXXCkCAdwoKGWuk787EhPszMSiOz+iL0FBbGv4576fW2m0+dQi0QY9Y9/4Y5E8A0dwGG3BVL0535XSsjX4vjh2If2p2/+x+R8fAjLgSNoTz9Hbd8cTkwZS/Wg/ikXWMj3tKG2thaPx0NOTk6HXt+Z9441NTUdumZTElA0sWHDBgAWLFhwxnPRx9555512n1cphVJt3yEdPb69rzv9HE3/bi9HdS0AgYy0Dp8j0cyYx1RUn+PlyLzpDNnwARnHTlI+ZihBTwYOfw299h3GXlvPkXnTqc/xGj0oziGR8xjITOPA1bPJ3n+UgVsLcJdXMeqlNymdMJrjU8ca3bY7wXO4hD679wNwdO50ghlpbZqTjmhxHjWN+hwv6ScrcZ+sJODp2A2AZGFtCOCorQegPtvTqa+Xmj45hJ0ObIEgacfLqD2vD9Bzv6/NdPoc9t++F3eFj7DLQeHsKUY+j8ztOSXqazGY7qbo4smcmHQ+fXfto9dHB0kvrWD46+9S1zub41MuwD/4vJQJLOR72mDG+76OnsOMeZeAoon9+403Fi1tacrOzqZ3796xY1oSCAQIBAKxf/v9fsCIvtvTUj16bGfasEcikdifjrD7jWi1Id3d4XMkmq7rzf7uScqHDaTOk0HfDz+lX/4+LJEIutVKxfCBlI4dSX0vr1Hesw2SYR7LRuRSNaA3g7buJudQEf12fYL3UDFHL55M9YA+HTqno7qWwe98AMCJcSOpzO3X5jnpiNbmsa4xoHCWVRIZmtqlIdNOGlslA5nphKyWTs+nL7cfvQ4Uknm4GH9f465dMnw9pjo9EsEa0dEjEdJLK+hbsA+AIxdNIuCwx/X7oDtJ9NdixOXg6IwJlIwfRb89++nz8SHSyioZvu496nK8HJs0hqohA5I+sEj0PCYLXdfb/X6xqc68dzTjfZ4EFE34fD4AvN6W9456PB6Kiopaff0jjzzC8uXLz3h8y5YtuFztqOHfaOvWrec+KE7OrzTm4pOKMspDtQkbhxk++eSTcx/UXfVJh96jsOqKiEUzfrGcPGb8aadkmMc952Uy0JnL9IPHSa+uZfTr7/JpXy87h/YjaGv7UrlFV8zfcxhbMERZhou3Mm3oH38cx5Gfcvo8RkIN9AH0o8V8nJHay/3nl5QDcMJu4WMT5rPOqjMHcB84ysee5gUEkuHrMdVk1TZwfkkFQ8r9TNcV4Q8+Qdc0NAWH+njY2lANXfR90J0kw9fiHo8d5+ThXFBSwehjlaRV+Bjx1r+pcjvZM6gXR3t5jGZ5p1Oq+e+HBEqGeewOOvLesaGhodPXlYDCRPfeey9LliyJ/dvv9zNo0CBmzZpFenobOgw3CofDbN26lZkzZ3Y4h6K2tpaysrJ2XTdKi0RIe+8jAM4bP5Y+bmeHxpBouq7zySefMHr0aCzdIdk1QZJxHj+ZFmLg9g/p89FBRpb6GFLdwNGZeVQNbdvduNz3d9O7poGww86xa+YyOiMt7mNubR7Tc8rh4HH6BCKcf37XVJeKlyEntwNgGzLQlM/FEgyh9pfgrQ+SNyCXgCcjKb8eU0H2gUKG7fqYYLqb43ljOFLjJ6+yjoyyKhSgjR7O+WOGJXqYKSUZvxYbJsDehgB9PzxAv70HyKoPMPuTEhq8fo7ljaFieC5YLLjLffT98FNyDha3vILdhZJxHhOhrq6OzMxMsrM7liPYmfeOtbWdv3EsAUUT0ZWJ6ErF6fx+f6urFwBOpxOn88w331artUOBgc1m63BAYbVaY3/ay1FThwZEbFZUuhtrki+XnovFYunRiV5mSap5dFspmT0F38jBDNq0HVdVNSPe/je+IQMoungyofTT+hYohSUcQbdZ8Rw5Rr+9nwJwdN50It5MuvKzOn0eg71zUICjvgFnIES4PR3Jk0xahbHNM9Ar25yvFbeVmvN6k1lykuyiE5yccOrnb1J9PSY5V3kVwzZup3LkYI5eMpWIUtRv3U5GWRUANQP6MuS9Ahr69aahV1ZCx5qKku5rMT2N0ukTKJ84ht57P6XPnv24fDUM27idAfkfUz2gL732HSaU7ubEpDEEPBk4G3Psen1ayJF506kaObjLh51089jFop9/R9/3RXXkvaMZ8y4BRRPR3In9+/czderUZs9VVlZSVlbGRRdd1O7zapqG1o435dHj2/u608/R9O/2cFXXARDMTEdL4bsFZsyjSO55rDuvD58snk/fnR/TN/8jvEdKyCgppeTCiVRcMBxXhY8+u/eTdaCw8S6cJZZsenLCKKqHDqSrPqPW5lE57AS8mbh81aRV+Kg+PRhKFbqOq9IIKBp6Z5n2teIfMoDMkpN4jh6nbOKYpP56TFZ993xKKN1N4dxpaBYL1voGZh0wtj2WXTCc4osnc8Ff19J3z6cUzpue4NGmjmT/WtRdTkqnjqNswmgjsNj1CU5/LU7/ISI2Kyfyzqfy/GEoq/F7vnTyBQx6ZxtDNnxAIMfbZcFlss9jVzHjfV9Hz2HGvEtA0cTcuXN55JFHWLduHV/84hebPbdu3brYMe01adIkPJ62d5sOhUIcO3aMyZMnY+9g4zG/309JSUm7rhvjM/bSuQb2Z8KECR26fjKIRCKEQiEmTJjQo+96dFZKzOOkSVByAvWXl7EeLmLQuzvI/eSo0eAvy4O6ai56thftn/9Cq/KjgF6TJtCrC7++zzaP2raPYMcehtpdkKrfcyUnsOg6yuVkzKwLzeup0X8gbCkg43gZE0aOIuKwJ//XYzLRdbTfv4RaMIcJeXlGv5Nn/4olEEL1yibn1i+R43TCsUqy120i6ztfS/he+lSREj8bo6ZOgUAA9eTvofAY1nCEQZt3kPvhAdQVs2HmFKMXxvjxsPyXjC4uQ82b0yVDS4p51HUIhcFuS1g/oOrqarKzs+nbt2+HXt+Z947RIkKdkbq3n+Pg8ssvZ/jw4fz5z38mPz8/9nh1dTUPP/wwNpuNr371qwkbX1fRok3teklTO5FCBvRD3XUb+uJrUDYbWmkZKIWaORnmz0ErOWEEEy4n5F2A9qeXoOh4okcNnGoCpyXJeDqk5ITx94B+5v5C7pOD6tcbTdfho0/NO29PEQqjhULQOweUQnvhn1h2f4xusRD58vUQ3abbK9s4LhRK7HhF/NjtcOwk6spL0BdfjfJkolX6sLz4Gtqyx2Ht21DfgLpoKmzf0zNKBxcdR3v+H2h3/xTL3T9Bu/unaM//I2l+N6QSWaFowmaz8eyzz3LllVcyZ84cvvSlL+HxeFi9ejWHDh3iJz/5CaNHj070MOOvMaBQ0iVbpBqLBebNhANHUHv3oYXCaGveRn1QgHayAgB18yIYNxpt+S/R3tlidKtNtNzzjL+L2199K1lojR2yGdDP/JOPHwMnytD27IO8C8w/f3dmt6HsdiirQFv7NtrmbShN49M5Uxg2bNCp48orjeM6uCouUkBjcKn69YFpE1EXT0Nt2YH2r81oFVXGz8r178LwQcZxoRA4HIkedfxs24X2x9XGKvaCOajeOcb3yZYdaB8UGL8rpk1M9ChThgQUp7n00kt59913WbZsGS+88ALBYJBx48bx8MMPc9NNNyV6eF0jukLRWwIKkYJ0HfZ+gppv/ILQVq09FUzMmwUTjTek6qKpaOs2wY0LE7/FI9dYoaC0HAJBcKbgL/HGgCK62mImNX4M2r82w95PjP9f0XYWC0wZj/b2FrQ6o+mg/vlrqHRbiNV0ikTQ3tsOU8cn/ntBxE+T4NL4tx0uudAILPI/RPvXu2iFx2DfQaPy1x9WG9uhhuYmdNhxUXTcCCamTUTdeB002Wql5s9B+/PLaH9cjerf99TPZ3FWElC0YMaMGaxduzbRw0ic8irjb9nyJFJR9C5cn17GL4vzRxpL+ZEI6rr5p45r3OKRFHfhPJkoTwaavwZ17AQMHXTu1ySb6ApFHAIKhg1CpbnR6urRDrfeC0i0TA3oi/b+TuPjBZegXzwNduwwnoxEjO1/vmrU3FmJG6SIv2hwuWUHav6cU2+irVaYOgE1ZTzqowNov/kzWjgMBR+iFXyIGjkEdflsGDsqYfkFZtM2bDFWJm68DnzVsOcTcNhg+BBjm+WN16HtP5Q8q9gpQAIK0Vx9A1qtUeVJAor/396dR0lVnvkD/7636lZ1VXUtve8NogICQkQUI7INIEbRmDGaoFHRgNEk/E5+ySQzmWTG4+8kY5I5c05GnTgGgzFoopmITmJiWFQ2FyIhLrgAYrM1TdM0dFV3V9d6398ft29RvUF39a2u7fs5pw/dtd23Xqpv11Pv+zwP5aT+n8K5XZC3LBt4u2zb4lFXDQQ+1vfu5lpA0dmlB0NCALWpJRSelcWiv5nZ9S7E+/uAulLzj5GvPj4I8YeXIQD9/+etd6BYFJR1+qG0dUK8+Tc9mLj97/lJbAGQCz4N8dY7EL/+3wGfzEPTIHa9A2gatLu/oG8x3PUuxMeHID4+BFldAfl3c/RtQGoOv33UNGD3e5AXjIf46S8GfEgh3cXA+Y2Q1RUQu94DvnA9MMpSroWAM0R99a5OSJcTKMrNhnZU4Ib6FC5ZNm7xqK8BPvwY4mgLci4Vsrk3Ibu85EySr8nktEkQu96F8v4+oO6KtBwj7xxrhfi5/mmzvHgy5DXzIbb/BcrmHbggGoNUrfon0/M/zWCiUNRXQ97+9xDr1kPsb9ITsMtKgPbT+jnRCC4vmQp5yVRg2SJg65vAa7sgjrdB/PoFyBdfhpw/G7jqMsCZQ2WuA53A2x9A7HpXz6/rLfIghQAmNOpJ6IebITq79NsZ9/vOg8CEBsgJ44DzG/UtYGk6z+UyBhTUV6LCky+jwyAajbN+CpelWzxkXbX+B6w5B6uLpHO7k+GiCyAVBeJ4G+ydo+/qmvdOdUD87FcQPSHICY2QK24GbCrkbZ9D/Jbr8fZbb+FTl18GCz95LTyzpkNWV0JsfQNi43Z966eqApdOGxhclnghb1wKLJ0P+fouiC1vQnQEIP6wGXLjNuDKS/XctFJfxp7OWXV261u3du/RV+uSKldJn0ffynXJFMDbW2I/GoU8fAw4cEhfvWtr16uf7f0EYu8n+v0URZ+jCeMgzx+nByOe4tGPVZOQ4QikpuVkDzCeSagvloylfDDcT+Gy6VPZRKWnVn1JPof+oIg0JmQnOB36p4P7D8J3tDV9x8kHXd0Q//UrCH8nZE0l5Fdu03sMGBQBTbVmz+ocjb36aj03YPlnIaMx/fVxtteDowhYdBXk/Csg/7pHT+BuOQG8+gawdScwcxrkojlnzmOZ1B0E3v1QDyL2Neklp3vJcXWQM6fp25wONQNzL+v7gZOqAueP01chdrwFzP4UtL+bowcYnxzW/z3tBw4fAw4f03MxAD1n7/zG3lUMPQ9j2L9fR49DbHkDnt3vQUZjaLXZ4JgzC85lS6Dm0PZXBhTUhzjFgILyxEg+hcsGFaWQNhtEJAJ5oh2orsj0iIbP6EGRzoACvdue9h9ECQOKoYUjEI89DXHiJGSJF/K+23NrWwqNLUUZWVU5qxWY/SnIy2dAfvixHljsa9JzLXa9Czlpgv6p/+TzxzZg7QkB734Esfs94KMDfYOIhhrImdOAS6Yl3tvIo8ch/v2/z72KveBKvRR2bRXk3Mv1+57qAD45DHHgEPDJYaDlBERbu76a8WZv8QO3S1/BmNCoBxj11YNvv00qXRuePxuOhjq4esIIvrwDPdt2wrv6bjjmzk7XrJmKAQX1ZfSgYEBB+WCkn8JlkqIAdVVA0xHgaEvuBBSxGHC8Tf8+HT0okk2bBDy/Ae7WdsRDYcDlTO/xck08DrH2WYiDRyGdDsiv3gGUeDM9KspHQgBTLoScciHkkWN6Wee/vQ/RuzVI1lXrKxYzpw3+RtoMoTCwZ6++EvHhfohYPHGVrK06E0RUlp25XErE43HEK0ogvrAM9mdfBPYdQPjSi6GV+qCc6oBt13sQnd3ovukaRN0OwO/ve1yLAC4cp38BED0hWA4fg/VQs/7VfBzC2Gr1zgf6cW0qYvU1iI2r078aamA51QH3uvWITJ+M4OeuBiwWFFdUoLi8HK7PfQb+R5+E/+G1sDbU5sRKBQMK6uskVygoD430U7hMqasGmo5ANB+HzJWGSq0nIeJxSEdR+vdRV5ZDVpRBaWuH9tEB4NKL03u8XCKlXjv/g/2Qqqpvc8qVoJRyW0OtnqNz/WJgyxvA67shmo9D/Oo5yD9shlxwBXDlrKELvWgSSjQGaBI4V+wRjgDv79ODiA/2QURjiatkdQXkJdOgXTIF8fJSxONxaJqGeGcnpJSQUkJRFCiKAovFAuWyGRDjG2DZthPK9reASBSwqbDOngnb0vnwNNYNfw4mnWl6LCNRxA8eQXzfJ/rX/iaI7iDUTw5D/eSwfiNFAZxFgE2F58rLUFpSBsXnga23hLmwWuG9705E9uxF8I+b4f3aXcMfS4YwoKAzpAROdejfM6AgGnOyvkZPzD6aQx2zkztkj8Hqjzb1Qli2tOvVnhhQJIg/bIb4y9uQigJ51816oijRWCorgbzpWuCaBZA73oLYuhPitB/i+Q2Qf94KzJmlBxdGAnRv7oB193u4LBqD/N0mYObFepJ38pbUSBT4YD/E3/boKxKRaOIqrbwU0emTEJ42EVplOSSgBwzRKCwWC+x2O+x2O1RVhdVqhcViSfxrsViAceOAWZdAahpkJApht0GM9jzmAlDiAy7Rz09S0xA72oLoh/sR+Wg/Ih9+DK2tHejSS/SHf/YkwgAs1ZWQ8z8N9ZbrAehBhXPxXHStfwmer64Y/bjSjAEFndHZDRGJ6iXUuExONPaMHISjx/UAP8v/gACAGKP8CYOcOgnY8qb+SXyOJa+nzZY3ITZtBwDI5TcAF0/O8ICooLmcelWov7sS8q13IV5+DeLESWDzDj2Je9Z0vcfDHzYDPg+0xVehqdOP89xeKDvfhnjrHcjln4XmsEPs3gNlzz6ISCTx8PESL6LTJ0N+agpQXwOb3Q6X3Q6r1TogaFCGeX4QigKRplL5QlGgNtZBbayDc+kCAECs+ThO/p/vwzZ9CjR/ALHDzYgfPwEt0NnnvpaqCiAS0b+yvFQtAwo6w6jw5HXndtMaolxVWwkpBERXN2SgS/9dzHZGhad050/0khMaELOpsHYHIQ8e5Sfxf30PYv1LAABt2SLgipkZHhBlA9lbHtXY6jPYz/0vS4tPXQRMnwzLRx/DtnUnLAePAjv/BgFA8xQj/PdLETmvAe1796Ls/POhlvng+NOrUJ5+vs8bVFnihTJrOmxXzIT1/PF9Vhyy/ZP7wVhqKgGbDbYpF6L45uuhdQcR3XsASr9to/HWNsBm07+yHN810hksGUuUWTYbUFWuJzkfbcmpgGKsVihgsaCjtgLlB49B7NmrV1EpVHsP6KWRpYScNxu4el6mR0QjpGkaNE0765v+oa47GyFE4o228f3ZLkv7m/JPTUX8U1OhHTwK66+eA077oQS64Hj8Wdjra3Ce3Qrf7zZDBHvO3MemomjRXDiuuhy2iRNysjfDUISiwDFnFoIv74Drc5+B4nLCPrPvFk4ZiyG4eTscV12WE0ETAwo6gwEFUebV15wJKKZOPPftMynQCdHZrW+TrK0cs8N21Feh/OAxYM9e4IYlY3bcrHKkBeLxZ/SE+EumQt70mZzYIldopJR6YnA8nvgyAgjjjXzyp+zGZYqiJP7t/33yZWZ8jel81Nej9dGn4LxuEWQsjp5XX4NytAXG2UPxeVB0xaWQkSh6duyE98vLc+LNdCqcy5agZ9tO+B99Et777oRIajIpYzH4f/YktNN+OK9bnMFRDh8DCkoQ7R0AWDKWKJNkXTXErnf1Sk+ZHsy5NPfmT1SUjemSfEddJaQiIFpOQLafLrwPQU6egnh0HUQoDHnheZC338RckgwyAoT+QQOARCBgJAE7HA7YbDaoqprY698/ODD2/efjG2kZiQKRCNQLJ8AxdzbcX/gsOje8ik/efQ+TbroBjulTISwKera9iZ5XduRE7kCq1PEN8K6+G/6H1yKyZy+ci+fCUlWBeGsbgpu3Qzvth3f13TlRMhZgQEHJ2k/p/xbaH2eibFKflJid7RLbncYmf8IQt9sgz2vUG0vt2QfMz43GT6bo7IL42a8gOrv0Wv+rljPnbQz0X2WIx+OIxfSSpd3d3bDZbFAUBXa7PREwDFZVKB+DhJEQNhWw2RA/fgIAoHjdcH7uMzhsl5g2bTKERQ+mcil3YDQcc2fD2lCL4B83o2v9S3oAZbPBcdVlcF63OGeCCYABBSXrXaFAmS+ToyAqbHU1+r8nT+mNm9JUecQMwkjIHqv8iSRy6oXAgUMQ7++FLJSAIhSGePQpiLZTkKU+vQu2oyjTo8oLxgpDcuBgbFcyVg6M1QSn0wlVVaEoCg4cOICGhgbYe6sMDbeqUKHqnzuQvM3HkGu5A6Oljm+A92t3wXPfneaVrs0ABhSki8eB073dILlCQZQ5bhekzwPREYA8dhyYMC7TIxraWCdkJ9GmToLl95uB/U1ZH3iZIhaD+MUzEEeOQRY7Ib92R24k7WeJ5IDBCBqM7403b0aZUavVOujWJONf4/bRqN4PoaioCKqqZuy55Zr+uQPJcjF3wCzpLF07FhhQkK4jAKFpkFYL/0gRZVpdNdAR0Lc9ZWtAEY0BrSf17zMQUKCyDLKiVP+0fu8BYMaUsR/DWNE0iKdfgPjoAKTNBnnv7UBleaZHlTWSE5+NICG5ehKAPsnMRsMzVVUHDRiMnAZKj/65A/aFc1DWchTB5/6E8Kuv5VzuAOkYUJDOqPBU6mNyH1Gm1VcD7++DOJrFidmtbfqHEI4iwOcZ++MLAUydBGx5Qy8fm8cBhXhhI8Sud/Uu2Cu/AIyry/SQxlRysJAcNBjBQnLis5HHYAQLyfkLxhcDhsxLzh3o+d8/44JIFD22PXBcdXnO5Q6QjgEF6RL5E9zuRJRpsq4GAgCaWzI9lKElb3fK0JszOW0SxJY3gPf3AfnaNfvlHRCvvg4AkF/6HHDRhRkekLmSVxOSgwYAfVYXjEBAVdUBSc/9vxgs5AYjd8Cx8lZs+MOLWHrD9bDleRJ2PmNAQQAAwQpPRNnDqPR07ISe32SxZHY8gxAZzJ9IOL8Rssiu98I43Azk26eaf3kbygsbAQDajVcDl83I8IDMEYvFEAqFEI/HB2xFMvIRjK1Ig60uUH4RigJNtTIQzHEMKEiX6EHhy+gwiAhAWYn+RjkUhjzRDtSMXdO4YevtQZGJCk8JVqv+if3f9ujbnvIpoPhgP8TTLwAA5MIrgUVXZXY8oySlRDgcRjgchsVigdPphMfj6RM4cCsSUe5iqE86dskmyh6KAtT29nY4moXbnqQEjmWmB8WAoUzr7Sa+Z19Gx2Gqg0chfvGsnqMyazrkjVdnekQpi0aj6OzsRCAQAABUVFSgoaEBdXV18Hg8iWpK3KpElNu4QkE6BhRE2aW+BvjkMMTRFshs2+oS6IToCkIKAVRnePVk6kRIIfTO4qc69MISuezESYjHnoKIRCAnnw952405lxuiaRrC4TAikQisVivcbjfcbjecTicsWbh9j4hGjwEFAZEoRKBL/54BBVFWkPXVemJ2NnbMNsZUVQ7YMlx/3+UEzmsAPjmsJ2fPvTyz4xkNfyfEf/1KD9YaayFXflHf1pUjIpEIQqEQpJQoKipCZWUlXC4X7HY7Vx+I8lzunKkofU51AABkkR1wOjI7FiLSGR2zm4/rW4yy6Q3ZMT1/ArUZzJ9IIqdNgvjksJ5HkasBRU8I4tF1EKc6ICtKIe/9EmDP/iZXmqYhFAohGo1CVVV4vV643W44HA6uRhAVEAYUBJxMqvCUTW9aiApZTQWkokB0ByE7AkCJN9MjSjAqPMkM508kTJsE/H4TsK8JCEcAe46VnoxGIdb8Wt+25S6G/OodgLs406MakpQS0WgU4XAYUko4HA6UlZXB6XTCngNBEBGZjwEFsQcFUTZSVaC6Ql8NONqSVQEFsqFkbLLqCsiyEoj203rX7OkXZXpEZ6dpeqdxVf8TLH61HmL/QcgiO+R9twPlpRke4ODi8ThCoRBisRhsNht8Ph+Ki4vhcDhYzpWowDGgIIhEQrYvo+Mgon7qqvWAovk4cPHkTI9GF40CJ9r177MloBBCX6XY+qa+7SlbA4qjx/VGfLv3QESjkKoKlHohWk9CWi2QK5cDDTWZHmUfUkpEIhGEw2EIIeBwOFBRUQGXywVVzXD+DBFlDQYUlKjwJLlCQZRVZH01xFvvQBw9DpnpwRiOt+nlTF1OwOvO9GgS5LSJEFvfzN6u2bvehVi3HvB5IK+eC1leCryxG8q+TyAByDmXAZMmZHqUCcnN52w2G0pLSxOrEUywJqL+GFAQcIolY4myUiIxO4t6URjbnWqrsivn6oLxejPAQBfkkRZgXF2mR3TG0eN6MDFrOuStn9U7n7/xVyj7PtGvH1cHsf0vkFfMPNMlPQOM5nORSASKoiSazzkcDq5GENFZZdlHOJQRzKEgyk69by7FydNATyjDg9GJbMufMFitwOQLAABiz94MD6YvseUNfWXCCCbe+wjiN78HAMglcyH/70rA64bY+kZGxheLxdDV1ZVoPldeXt6n+RyDCSI6FwYUhS7YA2G8UWEOBVF2cTkhjWTs5izpR5Go8JRlAQWSu2ZnUUChacDuPZCfnnkmmHjitxBSQl5xCeT1iwGLBfLKS4G/7tFLBI+Rnp4e+P1+hMNhuFwu1NfXo7GxEeXl5dzaREQjwi1Phe5kb/6Euxiw5VipRaJCUF8DnPbrzeQuGJ/ZsUgJNPf2oMjCgAJTertmH22BPO3PjspY0ZiegF1WAmzaDvGHzXowMW0S5BdvOLNtrKxEv100mrZzsZFg3dPTAwBQFAVlZWVsPkdEo8YVikLHCk9E2a33jbvIhjyKjgBEsAdSUfSSttnG7QLG1+vfv78vs2MxqFZI1Qrx8mtQfr9JDybmXq53wU5u/NZ+Wq/6ZPL2IiOI6OzsRCAQgKZp8Hr1QKu+vh5lZWUoKipiMEFEo8IVikLXzoRsomwm66shAH2FItOMbVdV5YkeCtlGTpsE0XRELx971WWZHg7Q2Q3YbXrTOiEgb74O6N/NOx6HeP2vwKXTTEt071+lye12JzpYy95tVexkTURmyc6/CDRmBAMKouxW31vp6fgJIBbTk48zJVsTspNNmwT8YTOw7xMgEsnsVs7Dx/QO2F1BvezvxAnAlZf2vU08DvH0C4C/E3L+p0d1uHg8jnA4jGg0CqvVCqfTmQgibEnzEI1GR3UcIqL+GFAUOqMHRTkDCqKsVOqDdBRB9IQgj5/MaFlRcUzPn5B1VRkbwznVVEKW+iBOdUDu/SRzDQH/tgdi3fN6XkR1BeSVl0K8sBHigZ/qCdhlJUD7aX1lwt8Jefvfp/R/q2lan1KvRUVFiaRqm83GrUxENCYYUBQ6o2RsKQMKoqwkhL4i8PFBvR9FBgOKnFihMLpmb9sJ8f4+yLEOKDQN4s9bIF7aAgCQUy6EXHEz4CiCvHACxNY3IDZuP9Mp+9Jp+srECP5fNU1DJBJBJBKBEAJ2ux2VlZVwOp3MhyCijGBAUcg0jU3tiHJBvR5QiKPHIWdnaAyRKHCiXf++NosDCvR2zd62Uy8fK+XYNeALRyCeWg/x9gf6OBbNgbxhyZmu3fXVkLd9Dlj+WchoDLCpwx6bkVwdiUQgpYTNZktUaCoqKoKSbZ3BiaigMKAoZIEuiFhcr9hS4sn0aIhoCLKuRk/MzmSlp5YTeoWiYhfgKc7cOIbjgvMgbTYIfyfk0RagoTb9xzzth/j503rQZ7VAfuEG4IpLBr+togD24eV2RCIRhMNhaJoGm80Gn88Hl8sFh8PBpGoiyhoMKAqZkZDt8/QtX0hE2cXYDnP0+Nh+4p4ssd2pKjPHHwnVCkw+H3j3Q+C9vekPKJoOQ6x5BqKzC9Lt0kvCThiX8sMlV2hSVbVPhSZrJpPyiYiGwDNTIWOFJ6LcUF0BabHoidmnOjLyOyuO5UD+RBJ58SSIdz/Uy8deuzB9B9r5NsQz/6uv9tZVQ95zK1DqG/HDDLdCExFRNmJAUcgYUBDlBqsVqKnQVyiOHs/M72zvCoXM8vyJBKNr9pFjkP4A4DV5W6emQfx+E8TLrwEA5PSLIO/4e8BuH8FDDKzQVFZWBofDwc7VRJRTGFAUMNFb4YklY4lyQF0NcPQ4RHML5IyLxvbYUgLNrb3jyJGAwlMMjKsDDh4F9uwD5swy77F7QhBP/g6itxu3vGY+5GcWnkm+PgspZSKIYIUmIsoXDCgKWfsp/V+uUBBlPVlfDbETmemYfdqvb7eyWIDq8rE/forktEkQB4/q5WPNCihOnoJ47GmI422QqlWv2nTpxee8WzQaRSgUgqZpsNvtKC0tTSRXs0ITEeU6BhSFzOhBUebL5CiIaDiMlYHmDAQUxjGryjPbqXukpk4CXnwZ+OiAXvbWpo7u8fY3QTz+DESwB9Lr1vMlGuuGvHlyXgSTq4kon/GMVqhiMaAjoH/PFQqi7FdfAwB6B+hgD+B0jN2xc6Gh3WDqqiBLvBCn/ZD7m4CpE1N/rNd2Qfz2RQhNg2ysg7xn+aB5Gf2bzjkcjkTnavsI8iuIiHIJA4pCddqv15RXVcCd5TXliUjvtFxWAtF+GjjaAkycMGaHFr35E7KuasyOaQoh9CBix1t6tadUAop4HGL9n/VGeQDkrOmQyz/bZ7VDSpnY0gQAdrsd5eXliaZzzIsgonzHjZuFKlHhyZf9NeWJSFeX1I9iLOXqCgX0PAoAZ7pmj0SwB+LRdYlgQrt+MeQdNyWCiVgshu7ubgQCAcTjcfh8PtTX16OxsTGxKsFggogKAVcoClUif4LbnYhyhayv1nsrNB/HCN8apy4cAU72FnDIwYACE8+DtKkQHQHI5uOJrWPndLwN4ue/hmhrh7TZIO+8CZh+kV7qNRRCJBKBxWKBw+GAx+NhvwgiKmgMKAqUYA8KotxjvBk+2jJ2x2xp1bdHuotzc3ukqgKTzgfe+0hfpRhOQPHBfohf/o9e2arUB23VckQryxDu7ISUkqVeiYj6YUBRqE7qAYVkhSei3GG8GT7eBkRjgDoGp/BE/4kcy59IIi+eBPHeR3oexTULznJDCWx5A+L5DRBSQjuvAd233oCYowi2eBxerxfFxcVwOBywWCxjNn4iomzHgKJQcYWCKPf4PJBOh1629PgJoKE27YcUOZw/kTBFT8YWh5ohA52Axz3wNrGYXsXpjd0AgPDMaQh/bimcHr3Uq9PphKqOsuwsEVGeYkBRqE4xoCDKOUIA9dXAviY9MXsMAgojIVvmckDhdUM21kEcbta7Zl96sb6609tQTga6gMd/A6XpCKQQiN2wGK5rF6GquBh2u51bmoiIzoEBRSEKhyG6gvr3DCiIcktdDbCvCaK5Jf2J2VICx4wtTzkcUACQjbUQh5shnvk9xG/+F1JVoX1qCsIXjEPRS1ugdASAIjucX78L7tkz2b2aiGgE8vaMuW3bNvzDP/wDFi5cCK/XCyEEVqxYcdb7aJqGRx55BNOnT4fD4UBFRQVuueUW7N+/f2wGPVZ6KzxJpwNwFGV2LEQ0IrJ+DEvHtndAhMKQVoveJTtX7XoX4vW/6t8rAqGbr0No3mUQH+yH4ze/h9IRgFJdgfIffx/eT89iMEFENEJ5e9Zcu3Yt/uM//gN/+ctfUFs7vG0B9957L1avXo14PI7Vq1fj2muvxe9//3tcdtll+OCDD9I84jHE/Ami3GUkZjcfBzQtvccy8ieqK4AcTEKOx+OINB2GWLcekemTobldEHENFp8HxV4vlO4gjM1M3q+ugHW4JWWJiKiPvA0ovv71r2PPnj0IBAJ44oknznn7V199FWvWrMHcuXOxe/du/OQnP8GTTz6JP/7xjwgEArjvvvvGYNRjxKgpz4CCKPdUlUNarRCh8Jl+MulyrDegqM3+7U5Gt+pgMIhAIAC/349gMAjrtr8APg/c994B+6wZAADbs39AfP1LAADH0gVQyksR2vJ6JodPRJTT8jagmDVrFqZOnTrs0n5r1qwBAPzgBz+A3W5PXL5o0SIsXboU27Ztw759+9Iy1rEmEk3tfJkcBhGlwmIBair175vT249C9JaMlVlYMjYejyMUCqGrqwt+vx+dnZ2IxWJQVRXl5eWoq6tDY0MDrO9+CPfV81FaUQ7Xp2cB6E3CtljguedL8N7zJTgXz0XPjrcgR9pJm4iIAORxQDFSW7Zsgcvlwpw5cwZct3TpUgDA1q1bx3pY6dFu9KDgCgVRTurNoxDpzqPIkpKxmqYhEokkVh8AoKenB0IIuN1u1NTUoKGhAY2NjWhsbER5eTk8Hg/sigWIRGGp1gMw+7TJUEp9EO5ilPzL/4Vz6QIAgKWqAohE9C8iIhoxVnkC0N3djZaWFkybNm3QFY0LL7wQAM6ZnB0OhxEOhxM/G3/4otEootHosMdj3HYk9+kvFoshHo8jHo8PuM7aG1BoJV7IQa7PF8ZzH2wOaPg4j+Ywcx6V2ipYAMijLen7fwmFofZuj4xXVwJj+P8fi8UQi8UQjUahaRqEEFBVFTabDUVFeiGJmpoaOByOAQnUsVgs8b0UAGwqIs3HYY1GAUXA9x/3AwIQRUWJc2zkWCtgUxEVAmIU591cYcbfGOI8moXzaI7RzKMZc8+AAoDf7wcAeL3eQa/3eDx9bjeUBx98EA888MCAyzdu3Ain0znicW3atGnE9zknKTGrrR0WAO+3HkOoJ2D+MbLMO++8k+kh5AXOoznMmMfi7gCmAog2Hcbbu3ePflCDHaPtFKYCiDiK8Ld9H6XlGKMx3BXjCQ3V8PxpM7baNchBqjcJTcOMP70Cf0M1dr70ktnDzGpp+RtTgDiP5uA8miOVeQwGg6M+blYHFOXl5Whvbx/27V999VUsWLAgfQM6h+9+97v45je/mfg5EAigoaEBV199dSIoGY5oNIpNmzZhyZIlKXdm7ezsREtLy8DjdnXDEnsRUgBTrroSsGb1S2BU4vE43nnnHcyYMWPYuTQ0EOfRHKbOYygM/Pk12IMhzJw4GSge+QcW56K8tgsAYB1Xj5kzZ5r2uPF4PLFqq2kaFEWB1WqFzWaDw+GAzWaDzWaD1WodtHzrSM+PsanT4f/nBzH38Em47rkdwnpm7mUsjq7H1iESCuPCe+7ARePqTXue2cyMvzHEeTQL59Eco5lHY0fNaGT1u8nly5ejs7Nz2Levrk5tn6+xMjHUCoQx0UOtYBjsdnufhG6Dqqop/ZKkej8AsFqtsFgsA9+4nO59jl4PLIOMNR8NOg80YpxHc5gyjy4nZEUpRNspWFpOAJPPN2dwSUTLCf3f+mpT/t+llAgGg9A0DXa7HR6PB0VFRYmtTNYRfrgx3POjesF5wOovw//wWkQ/2A/n4rmwVFUg3tqG4Obt0E774V39ZTguOC/Vp5azRvM3hs7gPJqD82iOVObRjHnP6oDi4YcfHpPjuFwu1NTUoKmpCfF4fMAfTyN3wsilyGlGhadSXyZHQUSjVVcDtJ3SKz2lIaAwErKlCQnZRjlXh8OB8vJyuFwuCCHOfUeTOObOhrWhFsE/bkbX+pf05GubDY6rLoPzusVQxzeM2ViIiPJRVgcUY2n+/Pl45pln8Nprr2HevHl9rtuwYUPiNjmPTe2I8oKsr4Z4+32Io8dherFTTQOO6SVjR1PhSUqJ7u5uSClRVlaG0tLSEa9EmEUd3wDv1+6C5747ISNRCLttTIMaIqJ8xrKxve655x4AwPe//31EkkoHvvzyy9iwYQPmzZuHiRMnZmp4KRmsprpgQEGUH4w3+s1pKB3b3gERjkBarUBlWUoPEYlEEAgEYLfbUV9fj4qKiowFE8mEokApsjOYICIyUebP7mmyY8cOPP744wCAtra2xGUrVqwAAEyePBn/9E//lLj9woULsXLlSjz++OO45JJLcN1116G1tRXPPvssPB4PHn300TF/DqNRVFQEm82GUCiUKLMIgD0oiPJFQ43+b+tJIBIFbCbuPTaClJoKvZHeCGiahu7ubgghUFFRAZ/PlxWBBBERpU/enuU//vhjPPnkk30uO3DgAA4cOABA376UHFAAwGOPPYbp06fjsccew0MPPYTi4mJcf/31+OEPf5hzqxM2mw1lZWVoaWmBzWY7Uy3FyKEoZ0BBlNM8bshiF0RXN2TLCWBcnWkPLVJsaBeJRNDT04Pi4mKUlZWlVC6biIhyT94GFCtWrEisRgyXoihYvXo1Vq9enZ5BjTG3243Ozk50d3fD7Xbr+6JPdehXcoWCKLcJoXfM/ugAcLTF1IDCyJ8YbkK2sSqhKAoqKyvh8/lYEYyIqIAwhyKPWSwWlJaWAujtgtgRgNA0SIsF8LozPDoiGrXeN/zC7DwK4/Fqq85503A4jM7OTrhcLtTX16OsrIzBBBFRgcnbFQrSOZ1O+Hw+nDp1Cp6Tp/QLS73AIA2jiCi3yPoaCEBfoTBLT+hM8YazrFAYqxIWiwVVVVXwer0MJIiIChQDijwnhEBJSQm6u7sRO94GC8DtTkT5or43Mbu5Vd/SaMYHBcZ2J58HcA2eAxEKhRAOh+HxeFBaWgqHwzH64xIRUc7ix9QFwGazobS0FFpbu34BAwqi/FBZBqmqEJEIYKxAjlbz0P0n4vE4AoEApJSorq5GTU0NgwkiImJAUSg8Hg/sXUEALBlLlDcUBait1L8/ak4ehTg2eP5ET08Purq64PF4UFdXh5KSkjPV44iIqKDxr0GBUBQFqr8TABD3MSGbKG/0bnsSZuVR9CZkGxWe4vE4/H4/AKC2thbV1dV9e9sQEVHBYw5FAZG9WyJ6nA64pGSnWKI8IOuq9cRsMyo9aVpSydgq9ASDiMVi8Pl8KC0thd1uH/0xiIgo7zCgKBAyHIF2Wv+U0VpdgVAoxL3PRPnASMw2Y8vTydMQkSikakXArsKuKKitrYXb7eYHEERENCRueSoQ8d6EbFFkR2lDPSKRCDRNy/CoiGjUaqsghYAIdAKBrlE9lOzdNhWvLENJWRnq6+vh8XgYTBAR0VkxoCgQ8RMnAQCWynJ4PB643W50d3dneFRENGp2G1BRpn/fnHoeRSwWQ6TpsP6QE8ajqqoKNpvNjBESEVGeY0BRIBIBRVUFFEVBaWkphBCIRCIZHhkRjVp9b4nXFLY9SSnR3d2Nnp4eONo7AADOiRO4KkFERMPGgKJAxFqNgKIcwJkO2j09PZBSZnJoRDRKRkUmMcKAIhqNIhAIQFVV1NbWQjneBgBQxzWYPkYiIspfTMouEPFW/Y2CpbI8cVlJSQm6urrQ09MDp3PwjrhElAMajI7Zw9vyZKxKSClRVlaG0tJSKOEIuntzrazj69M1UiIiykNcoSgQyTkUBlVVUVZWhmg0ygRtolxW1xtQnGgHwmffxhiJRBAIBGC321FXV4eKigpYrVbEDh4FACjlpVBc/ICBiIiGjwFFgUjOoUjmdruZoE2U6zzFkJ5iCCkTfSSSxWIxBINBBAIBRCIRVFRUoK6uDsXFxYlcieihIwAAdTy3OxER0chwy1MB0LqDkN1BAIDFqAbTy0jQ7unpQSQSYVUXolxVVw0EPgaajyPWUINoNIpYLAYpJSwWC2w2G3w+H5xO56BbHGOH9BUKKwMKIiIaIQYUBSDem5CteNxQHEUDrnc6nfB6vWhvb4eqqqzuQpRjYrEYUF0B24cfI/rJIUQumQJVVeF2u+FwOGCz2WCz2c76ux07aKxQMH+CiIhGhgFFAYif6E3I7rfdKVlJSUmidCQTtImyWzweRzQaRSQSSaxAFPV2zHa0+1Hd2HjOACKZjGuIHj4GALCOY0BBREQjw4CiAAyWkN2fkaB97NgxxONxWCyWsRoeEZ2DEUBEo1FIKaEoCmw2G0pLS1FUVAS73Q7F5Ub7uvXQjrbAZh3ZSmO8pRWIRCDsNliqKtP4TIiIKB8xoCgA8X49KIZSXFwMt9uNrq4uuN3usRgaEQ0iOYDQNA0WiwWqqsLn8/XZwqQoZ+pqyOoqiCI7ZCiMeMtxWOtrh328qJE/0VgPYWGtDiIiGhkGFAUgNkgPisEYCdrBYJAJ2kRjSNM0RCKRRABhrEB4vV44HA7Y7fYBAUR/wqLAOq4e0b0HEG06MqKAwsifYP8JIiJKBQOKAjCcLU8Gh8OBkpIStLW1MUGbKE00TUusQMTjcSiKAlVVEwGEzWbTtzGdJYAYjHV8A6J7D+gBwtzZw75fosIT8yeIiCgFDCjynJQS8d7ut+fa8mTw+Xzo7OxkgjaRiWKxGADA7/dDVVWoqori4mI4nc7ECsRoc5fU8xrRAyDadGRE94seZA8KIiJKHQOKPKd1+IFIFFAELOWlw7qP1WplgjaRSeLxOILBIOLxOACguroaLpfLlACiP/U8PSCINR2GlHJYK4xaZxe09tMAuEJBRESpYfZdnkv0oCgrhbAOP350u93weDzsoE2UIk3T0NXVhWAwiOLiYtTX62/WjW1N6QjUrQ11gCKgBTqhnfYP6z5GQralshyK02H6mIiIKP8xoMhz8d6EbOsw8ieSCSFQUlICq9WKSCSSjqER5SUjkOjq6oLT6URdXR1qa2vhcKT/zbqw22Ct1ftRxJoOD+s+sYPMnyAiotFhQJHnEgnZw8yfSOZwOODz+dDT0wMppdlDI8ormqahu7sbnZ2dcDgciUCiuLh4TIsbWHu3PRl5EecSO2RUeGL+BBERpYYBRZ4bSYWnwRh174PBoJnDIsobUkoEg0F0dnbCZrOhrq4OdXV1cLvdI67SZAYjsTo2zMRsY8uTypKxRESUIiZl57lEU7sUAwqr1YrS0lImaBP1I6VEKBRCJBJBUVERKioqUFxcnPHfEet5jQCGt0Ih43HEDjfr9+MKBRERpYgBRZ6LJbY8VaT8GEaCdiAQgMfjMWtoRDkrFAohHA7DbrejqqoKHo8H1hEUPUgnY4Ui3tIKrScExVE05G3jx1qBaAyiyJ7yhw5ERETc8pTHZCwGrf0UgNRyKAxCCJSWlsJqtSIcDps1PKKcEw6H4ff7IaVEZWUlGhoaEr8b2ULxuqGUlgA4kx8xFGMVwzquHiID27OIiCg/8C9IHoufPAVoErCpUHzeUT1WUVERSkpKEAqFmKBNBScSicDv90PTNFRUVKChoQFlZWVQVTXTQxuU0Y/iXA3u2CGbiIjMwIAijyXyJyrKTKkyY9TPZ4I2FYpoNIpAIIBoNIqysjLU19ejvLwcNpst00M7KyMfInaOPAp2yCYiIjNkzzo9mW60FZ76MzpoNzc3IxaLZdU2DyIzxWIx9PT0QAgBn88Hn8+HoqKhcxGyjWokZnOFgoiIxgDfEeYxI6CwjiIhu7/i4mJ4vV74/X4maFPeicfjiRU4j8eTCCTGso+EGYxeFLHDRyFjMYhBgn8t0AntVId+ewYUREQ0Cgwo8thoS8YOxuig3d3dnahyQ5TrNE1DMBiElBLFxcXw+XxwOp05F0gYLJXlEI4iyJ4QYsdaoTbWDbiN0X/CUl1x1kpQRERE58Icijw2mi7ZZ5OcoK1pmqmPTTSWNE1DV1cXurq64HQ6E92tXS5XzgYTACAU5UweRdPhQW8TS1R4Yv4EERGNDgOKPBY/0QYAsFSat+XJYHyCywRtykXGikRnZyeKiooSgURxcXFOBxLJjETroRrcRQ+yQzYREZmDW57ylNYTgubvBGD+CgUAWCyWRAdtJmhTrpBSoqenB9FoFA6HA5WVlSguLoaShz0YjI7ZsSESs40eFVyhICKi0eK7wDwVb2sHAAiXE4rLmZZjFBcXw+PxoKOjA17v6PpcEKWTlBLhcBjhcBhFRUUoLy+H2+2GxWLJ9NDSJtGL4uARSCn7rLzIWAyxIy0AACtXKIiIaJQYUOSpeKux3cn81QmD0UE7GAwiFArlVFlNKgyapqGnpwexWAx2ux1VVVXweDwFsaJmra8FLBbIzi5o7adhKS9NXBc71grEYhCOorSeI4iIqDDk3zo/ATC/B8VQ7HY7SkpKEA6HmaBNWSMWi6GzsxNdXV2w2Wyora1FY2MjSktLCyKYAABhU2GtqwYARPslZp9JyK7Pm5wRIiLKHAYUeepMhSfzE7L783q9TNCmjDO2NQUCAYTDYbjdbtTX16OhoQFer7dgAolkiTyKfonZMXbIJiIiEzGgyFOx48aWp7K0H8tisaCsrAxSSsRisbQfjyiZUbEpEAhA0zSUl5ejoaEBNTU1eZtwPVyJPIp+idlRdsgmIiITFd5HdnkuevAIgi9uQuSv7wIAOn/5W8QOHIRz2ZK0fhrpcrng9XrR0dHBDto0JmKxWKIXipFo7XK5oKpqpoeWNazjh1qh6A0ouEJBREQmYECRR3q274T/4bVQSn2ARQFicTivnofQW++gZ9tOeFffDcfc2Wk5dnIHbSZoUzpFIhGEQiEIIRKVxpxOZ15XbEqV0WMi3toGrTsIxeVE3B+A1uEHhIB1kA7aREREI1W4ewHyTPTgEfgfXouiuZej7N++C8TiAAD37Tej4pEfomju5fA/vHbIJldmYII2pYvRP8Lv9yMej6OkpASNjY2ora3N+/Kvo6G4i6H0VncyViWM1QpLdSWUInvGxkZERPmDAUWeCL64CUqpD9777oR26jQAQCnxQthUCKsV3vvuhFLiRfCPm9M6Dq/XC5fLxQRtMkU8HkdXVxcCgQAURUFVVRUaGhpQVVUFh8PBCkXDoPYmZkcP6pWezmx3Yv4EERGZgwFFHpCahp7XdsG56CoIqxWx1oEVnoTVCufiuejZ8RaklGkbi9FBmwnaNBrRaBSdnZ0IBoNwOByoq6tLlH212WyZHl5OsfYmZhsrE0ZCNis8ERGRWZhDkQdkJApEIrBUV+oXxONQykrO/NzLUlUBRCL6lz19Wx2MBO3Tp0+zgzYNW3I3a1VV4fV64Xa74XQ6uRIxCmpvYrZR6Sm5BwUREZEZGFDkAWFTAZsN8eMnAACOeVfAMe+KASsR8dY2wGbTv9I5nt4O2t3d3QgGg3A6nWk9HuW2/t2sKysrUVxcDHsag95CklihONIMrSeEWHMLAEAdxxUKIiIyB7c85QGhKHDMmYXgyzsgk7YZJX+qK2MxBDdvh+Oqy8bk016bzYaKigoIIRKJtETJhupmXVZWxmDCRJaKMginA4jFEd65G4jFIVxOKBWlmR4aERHlCQYUecK5bAm0Ux3wP/pkn6AC0IMJ/8+ehHbaD+d1i8dsTB6PB/X19fB6vejq6kJPT8+YHZuyU3I361AoxG7WY0AIkVil6NnyBgB9uxO3kRERkVn41ztPqOMb4F19N/wPr0Vkz144F8+FpaoC8dY2BDdvh3baD+/qu8c8EdNut6O6uhpOpxPt7e3w+/0oLi5mmc8Co2kaQqEQotEobDYbSktL4fF4YLfb+cZ2DKjnNSL6/j5E3vsQANh/goiITMWAIo845s6GtaEWwT9uRtf6l/Tka5sNjqsug/O6xRmr6qIoCnw+HxwOB9rb2xEIBKCqKhwOR0bGQ2MnGo0iFApBSslu1hmkFLv6/NyzeRsQDsO5bAmrPRER0ajl5Zan7u5uPPXUU7jlllswceJEOBwO+Hw+zJ8/H7/5zW+GvJ+maXjkkUcwffp0OBwOVFRU4JZbbsH+/fvHcPSjo45vgPdrd6Hq6UdQ+fR/oerX/wXv1+7KijcNxmpFTU0NADC3Ik8ZSdZ+vx+RSKTPtiafz8dgYoz1bN+Jrt/+oc9ljoVzEN6zF+3f+QF6tu/M0MiIiChf5OUKxfbt23H77bejrKwMixYtwk033YQTJ05g/fr1uPXWW/H666/j4YcfHnC/e++9F2vWrMGUKVOwevVqtLa24tlnn8XGjRvx+uuvY8qUKRl4NqkRigKRhV1wFUWB1+tFUVERTp06Bb/fz9WKPJG8GsFqTdkhevAI/A+vRdFVlyP02l+AuAYoAp67vghYFPgffRL+h9fC2lCbFR86EBFRbsrLgKKmpgZPP/00br755j6fhv7bv/0bZs+ejUceeQR33HEHLrvsssR1r776KtasWYO5c+di06ZNiTdBd9xxB5YsWYL77rsPW7duHfPnkq/651YEAgE4nU4m5eYYTdMQiUQQDodhtVrhdrsTvSOYJ5N5wRc3QSn1wfu1FYgdaUas6QgsNVUQdr10tPe+OxHZsxfBP26G92t3ZXi0RESUq/Jyy9OMGTNw6623DthaUVVVha985SsAMCA4WLNmDQDgBz/4QZ9PVBctWoSlS5di27Zt2LdvX5pHXliEEPB6vairq4PX60UwGEQwGExrJ28yRywWQ1dXF7q6uiCEQGVlJRoaGlBTUwO3281gIgtITUPPa7vgXHQVhNWaWIFQkxraCasVzsVz0bPjLf7eERFRyvIyoDgbI8jo/0n4li1b4HK5MGfOnAH3Wbp0KYCBQQiZIzm3QlEUBAIBxPqVvqXMk1IiFArB7/cjFArB5XKhrq4u0TuiqKiIFZuyiIxEgUgElupKAIBj8TxYqivgWDyvz+0sVRV6AYdIJBPDJCKiPFBQ+0vi8Th+9atfQQiBxYvP9GPo7u5GS0sLpk2bNugnqxdeeCEAnDM5OxwOIxwOJ34OBAIA9L3l0Wh02OM0bjuS++QDY8vT6dOn4ff7YbVa4XA4Un6TaiR8M/F7dIzXdEdHBxwOB0pLS+FyuWCz2SCEgKZp0DQtw6PMfmP9ey0FAJuKSPNxWKNRiPPHwffT/zdgDJFjrYBNRVQIiBw45xTq+dFMnENzcB7NwXk0x2jm0Yy5L6iA4l/+5V/w3nvv4e6778a0adMSl/v9fgCA1+sd9H4ej6fP7Yby4IMP4oEHHhhw+caNG+F0Okc83k2bNo34PjTQO++8k+kh5IWmpqZMDyEvjOXv9YSGanj+tBlb7RqkMnBBWmgaZvzpFfgbqrHzpZfGbFxm4Plx9DiH5uA8moPzaI5U5jEYDI76uFkdUJSXl6O9vX3Yt3/11VexYMGCQa/7+c9/jgcffBCXXHIJ/vM//9OkEfb13e9+F9/85jcTPwcCATQ0NODqq69OBCXDEY1GsWnTJixZsqSgS2xGIpFRrVbE43G88847mDFjBvf0D1MsFkMoFEI8Hofdbofb7Ybdbse2bdsK/vU4Wpn4vY5NnQ7/Pz+IuYdPwnXP7RDWM78HMhZH12PrEAmFceE9d+CipNyKbMbz4+hxDs3BeTQH59Eco5lHY0fNaGR1QLF8+XJ0dnYO+/bV1dWDXv7EE0/g3nvvxcUXX4xNmzahuLi4z/XGysRQKxDGRA+1gmGw2+2DlshUVTWlX5JU75cvVFWF0+mEx+PByZMn0d3dDZfLNeJKUBaLhQHFWUgpEQ6HEYlEoCgK3G43PB5PYguasRRa6K9Hs4zlPKoXnAes/jL8D69F9IP9cC6eC0tVBeKtbQhu3g7ttB/e1V+G44LzxmQ8ZuLrcfQ4h+bgPJqD82iOVObRjHnP6oBisF4RI7V27VqsWrUKU6ZMwcsvv4yysrIBt3G5XKipqUFTUxPi8fiAN59G7oSRS0FjRwgBj8eT6FvR0dEx6twK0vVfjTC6WDO5Or845s6GtaEWwT9uRtf6l/Tka5sNjqsug/O6xew/QUREo5bVAcVorV27FitXrsRFF12EV155BRUVFUPedv78+XjmmWfw2muvYd68vlVQNmzYkLgNZYbNZkNVVVWfvhWprFYUOmM1IhwOQ1EUuFyuPqsRlJ/U8Q3wfu0ueO67EzIShbDbGDQSEZFp8rZs7C9+8QusXLkSkydPxiuvvILKysqz3v6ee+4BAHz/+99HJKl84ssvv4wNGzZg3rx5mDhxYlrHTGdnrFbU1dWhpKSEfStGIB6Po6urC4FAAFJKlJWVobGxEXV1dfB4PAwmCoRQFChFdgYTRERkqrx8F/HKK69g1apVkFJi3rx5ePTRRwfc5lOf+hRuvPHGxM8LFy7EypUr8fjjj+OSSy7Bddddh9bWVjz77LPweDyDPgZlhrFa4XK5cPLkyZxbrZBSJoKg4X4/nMuSL09+wyilhKIoiXyUXJorIiIiyn55+a7i8OHDiTdWjz322KC3ufPOO/sEFMZtp0+fjsceewwPPfQQiouLcf311+OHP/whVyeyjBACbre7T26F8aY5E5++apqGeDze519N0yClTIzH+N74Ofnf5MsHu0wIAUVRoChK4nLj++TL+n8Z97VarcyNICIiorTIy4BixYoVWLFixYjvpygKVq9ejdWrV5s/KEoLVVVRWVkJp9OZttUKKWUiQEgOGpJXBoQQsFgsUBQFqqrCZrPBZrPBarUO+obfuM9wLyMiIiLKVnkZUFBhGWq1YrASvoORUvZZVYjH4wO6ayuKkggY7HY7bDYbVFWF1WpNlKU1vpRBGogRERER5SsGFJQ3klcrjEpQABJbj/pvSTIkbyeyWCyw2+2JOs5GkJAcOHDFgIiIiOgMBhSUV5JXK06cOAEA6O7uhs1mg6IosFqtcDqdg64uJG9PIiIiIqLhYUBBeUlV1UTfkYaGBtjtdnbMJiIiIkoDBhSUt4yVhqKiIlPayhMRERHRQMweJSIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilDGgICIiIiKilFkzPYB8JqUEAAQCgRHdLxqNIhgMIhAIQFXVdAytIHAezcF5NAfn0Rycx9HjHJqD82gOzqM5RjOPxvtU431rKhhQpFFnZycAoKGhIcMjISIiIiIaWmdnJ7xeb0r3FXI04QidlaZpOHbsGNxuN4QQw75fIBBAQ0MDjhw5Ao/Hk8YR5jfOozk4j+bgPJqD8zh6nENzcB7NwXk0x2jmUUqJzs5O1NbWQlFSy4bgCkUaKYqC+vr6lO/v8Xj4y2UCzqM5OI/m4Dyag/M4epxDc3AezcF5NEeq85jqyoSBSdlERERERJQyBhRERERERJQyBhRZyG634/7774fdbs/0UHIa59EcnEdzcB7NwXkcPc6hOTiP5uA8miPT88ikbCIiIiIiShlXKIiIiIiIKGUMKIiIiIiIKGUMKIiIiIiIKGUMKEz21ltv4dprr0VJSQlcLhcuv/xy/PrXvx7RY2iahkceeQTTp0+Hw+FARUUFbrnlFuzfvz+tx80mo30+O3bswLe+9S1ceumlKCsrQ1FRESZPnox//Md/REdHx6D3GT9+PIQQg37de++9Jj2zsTXaedyyZcuQcyKEwJtvvpmW42aT0T6XBQsWnHUOhRBYt25dn/vk22vxqaeewle+8hXMmjULdrsdQgj88pe/HPHjFPq50Yx55LnRnHnkudGceSz082NzczN++tOf4uqrr0ZjYyNsNhuqq6tx0003YefOnSN6rEyfH9nYzkRbtmzB0qVLYbPZ8MUvfhFerxfr16/HbbfdhoMHD+Kf//mfh/U49957L9asWYMpU6Zg9erVaG1txbPPPouNGzfi9ddfx5QpU9Jy3GxhxvP5/Oc/j5MnT+Kqq67CHXfcASEEtmzZgp/85Cd47rnn8Prrr6OysnLA/bxeL77xjW8MuHzWrFlmPLUxZebrYv78+ViwYMGAywdr3JhPr0cznsuKFSsGnbtoNIoHH3wQiqJg0aJFA67Pp9fi97//fRw6dAjl5eWoqanBoUOHUnqcQj83mjGPPDea93oECvfcCJgzj4V+fnz44Yfx4x//GOeffz6WLFmCyspK7N+/Hy+88AJeeOEF/OY3v8Ett9wyrMfK+PlRkimi0ag8//zzpd1ul7t3705cHggE5NSpU6XVapX79u075+O88sorEoCcO3euDIVCics3b94shRBy3rx5aTlutjDr+fzoRz+Sx44d63OZpmnyvvvukwDkV7/61QH3GTdunBw3btyon0M2MGseX331VQlA3n///WN63GyQ7ufyu9/9TgKQ119//YDr8um1KKWUmzZtkgcPHpRSSvnggw9KAPKJJ54Y0WMU+rlRSnPmsdDPjVKaM4+FfG40mDGPQymU8+Nzzz0nt23bNuDybdu2SVVVZWlpaZ/z3VCy4fzIgMIkGzZskADkXXfdNeC6Z555RgKQ3/3ud8/5OMuXL5cA5NatWwdcd80110gAcu/evaYfN1uk+/kcO3ZMApBTp04dcF0+naTMmseR/tHMp9djup+L8fv8wgsvDLgun16L/aX6xqPQz439mf0GrlDOjf2NVUDB1+PIFOr5MdnVV18tAci33nrrnLfNhvMjtzyZZMuWLQCAq6++esB1xmVbt24d1uO4XC7MmTNnwHVLly7Fn//8Z2zduhUTJ0409bjZIt3PR1VVAIDVOvhLPxwO48knn0RzczNKSkpw5ZVXYsaMGSkfL1PMnsf9+/fjoYceQjAYxLhx47BkyRKUl5en/biZlM7ncvToUWzcuBHV1dW47rrrBr1NvrwWzVLo58Z0K5Rzo9kK8dyYbjw/6s71O5ksG86PDChMYiS9XHjhhQOuKykpQXl5+VkTYwCgu7sbLS0tmDZtGiwWy4DrjcdOfhwzjptN0v181q5dC2DwXyIAOH78OFasWNHnsmuuuQbr1q0b9I9EtjJ7Hn/961/3SdRyOBx44IEH8O1vfzutx82kdD6XJ554ApqmYcWKFUP+sciX16IZeG5Mv0I5N5qtEM+N6cbzI3D48GFs3rwZ1dXVuPjii89622w5P7LKk0n8fj8APVFoMB6PJ3Gb0TxG8u3MOm42Sefzefvtt/HAAw+gsrIS3/nOdwZcf/fdd2PLli1oa2tDIBDAm2++ic985jP485//jBtuuAEyh5rKmzWPFRUV+Pd//3d8+OGH6O7uRnNzM5566imUlpbiO9/5Dh577LG0HDcbpOu5SCnxxBNPAAC+/OUvD3qbfHotmoHnxvQqpHOjWQr53JhOPD/qCem33347wuEwfvKTnwwaJCTLlvMjVyioIDQ1NWHZsmWIx+N45plnBv0E41//9V/7/Dx79my8+OKLmD9/Pnbs2IE//elPQy6/5qupU6di6tSpiZ+dTiduu+02zJgxA5deeinuv/9+rFq1CorCzyaG65VXXkFTUxPmz5+PCy64YNDb8LVIY4XnxtTw3JgehX5+1DQNd999N7Zt24ZVq1bh9ttvz/SQho2vdJMYUd5QEV0gEBgyEhzJYyTfzqzjZpN0PJ9Dhw5h4cKFaGtrw+9+9zssXLhw2PdVFAV33XUXAOC1114b0XEzKd2vi2nTpmH27NlobW3Fxx9/PGbHHUvpei6PP/44AGDlypUjul+uvhbNwHNjehTiuTHdCuHcmE6FfH6UUmLVqlV46qmn8KUvfQn//d//Paz7Zcv5kQGFSQbbo2Y4ffo0Tp48OehetWQulws1NTVoampCPB4fcP1ge97MOG42Mfv5HDx4EAsWLMCxY8fw29/+FsuWLRvxmIxP7ILB4Ijvmylj8boYbF7y6fWYjudy+vRpPP/88/D5fLjppptGPKZcfC2agedG8xXquXEs5Pu5MV0K+fyoaRq+/OUvY+3atVi+fDl++ctfDnt1K1vOjwwoTDJ//nwAwMaNGwdcZ1xm3OZcj9Pd3T1ohL1hw4YBj2PWcbOFmc/H+IPZ3NyMZ599Fp/97GdTGpPRrXL8+PEp3T8T0v26iMVi2L17N4QQaGxsHLPjjqV0PJennnoK4XAYt912GxwOx4jHlIuvRbMU+rnRTIV8bky3Qjg3pkuhnh81TcPKlSvxxBNP4Atf+ALWrVt3zryJ/rLi/DiiIrM0pGg0KidMmCDtdrv829/+lrg8uUlIcg3gtrY2+eGHH8q2trY+j5PcnCQcDicuP1tzkpEcN9uZNY9NTU1y3Lhx0mq1yueee+6cx33//ffl6dOnB1y+fft2WVRUJO12uzx06FDKz2usmTWPr7/+utQ0bcBjf+Mb35AA5DXXXDOq42Yzs+Yw2YwZMySAPo2E+su312J/56pXz3Pj8KQ6j4V+buwv1Xks5HPjYFKdx2SFeH6Mx+NyxYoVEoC8+eabZTQaPevts/n8yIDCRK+88opUVVUWFxfLVatWyW9961vyvPPOkwDkD37wgz63vf/++4dsirNy5UoJQE6ZMkV++9vflnfccYe02+3S6/XK999/f1THzQVmzOO4ceMkAHnFFVfI+++/f9Cv/o/jcDjksmXL5Ne//nX5rW99Sy5dulQKIaTFYpFr1qxJ87M2n1nzOH78eHnrrbfKb3/723LVqlVy0qRJEoBsbGxMdElN9bjZzqzfaSml3LVrlwQgZ86cedZj5uNrcc2aNfLOO++Ud955p5w5c6YEIOfMmZO47Pnnn0/clufGoZkxjzw3mjePhXxulNK832spC/f8aMxLcXGx/N73vjfo72PyG/5sPj8yoDDZzp075TXXXCO9Xq90OBxy1qxZ8qmnnhpwu7O9KOLxuHzooYfk1KlTpd1ul2VlZfLzn//8WaPF4R43V4x2HgGc8yvZli1b5C233CIvuOAC6Xa7paqqsr6+Xn7xi1+UO3fuTOdTTavRzuOPfvQjuWDBAllbWyttNpt0Op1y+vTp8nvf+548derUqI+bC8z4nZZSyvvuu08CkD/72c/Oerx8fC3eeeedZ/1dTJ4znhuHZsY88txozjzy3Gje77WUhXt+PNcc9l/1yebzo5Ayxwv2EhERERFRxjApm4iIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIiIiIUsaAgoiIstrUqVMhhBjy68c//nGmh0hEVNCsmR4AERHR2SxfvhyxWKzPZeFwGD/96U8RDocxd+7cDI2MiIgAQEgpZaYHQURENFyhUAg33ngjNm3ahMcffxx33XVXpodERFTQuEJBREQ5IxgM4oYbbsCWLVvwy1/+Erfffnumh0REVPAYUBARUU7o7u7GsmXLsH37dqxbtw7Lly/P9JCIiAgMKIiIKAd0dnbi2muvxZtvvolnnnkGn//85zM9JCIi6sWAgoiIslogEMA111yDXbt24X/+539w4403ZnpIRESUhAEFERFlrY6ODixduhTvvPMO1q9fj2XLlmV6SERE1A8DCiIiykqnTp3CkiVL8MEHH+D555/HZz7zmUwPiYiIBsGysURElJWWLl2KjRs3YuHChZg3b96A62tra3HPPfdkYGRERJSMAQUREWUdTdPgdrsRDAaHvM3NN9+M3/72t2M4KiIiGgwDCiIiIiIiSpmS6QEQEREREVHuYkBBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQpY0BBREREREQp+/8Eah3x4fMQUwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(z, Nz-catNz, color=color_list[9], marker='o', ls='-', mfc='none', ms=7, label='$N_{SOLikeT}-N_{obs}$')\n", + "plt.fill_between(z, -np.sqrt(catNz), np.sqrt(catNz), alpha=0.2, color='gray', label='$\\pm\\sqrt{N_{obs}}$')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{obs}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nz_inj_obs_diff.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15517214", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3d96e0f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(catNz)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "62d867ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([115., 341., 465., 441., 435., 360., 287., 210., 177., 105., 85.,\n", + " 56., 34., 16., 12., 8., 7., 7., 4., 4.])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catNz" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "95bebcbb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], marker='o', alpha=0.8, label='SOLikeT inj pred')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[9], fmt='o', ms=7, mfc='white', zorder=0, capsize=5, capthick=1, ls='none', alpha=1, label='obs catalogue')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.xlim(0, 2.0)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_inj.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2dd40f37", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(q, Nq-catNq, color=color_list[9], marker='o', ls='-', mfc='none', ms=7, label='$N_{SOLikeT}-N_{obs}$')\n", + "plt.fill_between(q, -np.sqrt(catNq), np.sqrt(catNq), alpha=0.2, color='gray', label='$\\pm\\sqrt{N_{obs}}$')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{obs}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_inj_obs_diff.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d9329825", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.axhline(y=0, lw=5, c='k', alpha=0.2)\n", + "plt.plot(q, np.abs(Nq/catNq-1), color=color_list[9], marker='o', ls='--', mfc='none', ms=7, label='fractional error')\n", + "plt.fill_between(q, 0, np.sqrt(catNq)/catNq, alpha=0.2, color='gray', label='$\\sqrt{N_{obs}}$')\n", + "plt.xlabel('SNR $q$', fontsize=14)\n", + "plt.ylabel('fractional error', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "#plt.xlim(0, 2.0)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('0Nq_inj_obs_frac.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17db1b19", + "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": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned-Q_injection.ipynb b/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned-Q_injection.ipynb new file mode 100644 index 00000000..1a6fa031 --- /dev/null +++ b/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned-Q_injection.ipynb @@ -0,0 +1,1936 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from soliket import BinnedClusterLikelihood\n", + "from cobaya.model import get_model\n", + "import camb\n", + "from astropy.io import fits\n", + "from astropy import table\n", + "from astLib import astWCS\n", + "import math\n", + "from nemo import completeness, MockSurvey\n", + "\n", + "import sys\n", + "sys.path.append('../')\n", + "import nemo_mocks\n", + "import imp\n", + "imp.reload(nemo_mocks)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.81]\n" + ] + } + ], + "source": [ + "h = 0.68\n", + "\n", + "#Set up a new set of parameters for CAMB\n", + "pars = camb.CAMBparams()\n", + "#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency\n", + "pars.set_cosmology(H0=100.*h, ombh2=0.049*h**2, omch2=(0.31-0.049)*h**2, mnu=0.0, omk=0, tau=0.055)\n", + "pars.InitPower.set_params(As=0.81**2/0.8104862**2*2.022662e-9, ns=0.965, r=0)\n", + "pars.set_for_lmax(2500, lens_potential_accuracy=0);\n", + "\n", + "#calculate results for these parameters\n", + "results = camb.get_results(pars)\n", + "\n", + "#Note non-linear corrections couples to smaller scales than you want\n", + "pars.set_matter_power(redshifts=[0.], kmax=2.0)\n", + "\n", + "#Linear spectra\n", + "results = camb.get_results(pars)\n", + "kh, z, pk = results.get_matter_power_spectrum(minkh=1e-4, maxkh=1, npoints = 200)\n", + "s8 = np.array(results.get_sigma8())\n", + "print(s8)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/user/SOLikeT/soliket/clusters/notebooks'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binnedclusterlikelihood] Number of redshift bins = 28.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Running injection based selection function. Currently using one average Q function.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Number of rms bins = 50.\n", + "Number of rms bins = 50.\n", + "Number of rms bins = 50.\n", + "Number of rms bins = 50.\n", + "Number of rms bins = 50.\n", + "Number of rms bins = 50.\n", + "Number of rms bins = 50.\n", + "Number of rms bins = 50.\n", + "Number of rms bins = 50.\n", + "Number of Q functions = 1.\n", + "Number of Q functions = 1.\n", + "Number of Q functions = 1.\n", + "Number of Q functions = 1.\n", + "Number of Q functions = 1.\n", + "Number of Q functions = 1.\n", + "Number of Q functions = 1.\n", + "Number of Q functions = 1.\n", + "Number of Q functions = 1.\n", + "Using one averaged Q function for optimisation\n", + "Using one averaged Q function for optimisation\n", + "Using one averaged Q function for optimisation\n", + "Using one averaged Q function for optimisation\n", + "Using one averaged Q function for optimisation\n", + "Using one averaged Q function for optimisation\n", + "Using one averaged Q function for optimisation\n", + "Using one averaged Q function for optimisation\n", + "Using one averaged Q function for optimisation\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Number of redshift points for theory calculation 68.\n", + "Number of redshift points for theory calculation 68.\n", + "Number of redshift points for theory calculation 68.\n", + "Number of redshift points for theory calculation 68.\n", + "Number of redshift points for theory calculation 68.\n", + "Number of redshift points for theory calculation 68.\n", + "Number of redshift points for theory calculation 68.\n", + "Number of redshift points for theory calculation 68.\n", + "Number of redshift points for theory calculation 68.\n", + " Total predicted 2D N = 3159.5264180402437\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n", + "Theory N calculation took 0.8170828819274902 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " ::: 2D ln likelihood = 185.30914187983865\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-185.30914188])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.68\n", + "\n", + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.26, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.0,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='/Users/user/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14 #*0.68\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 5.,\n", + " #'single_tile_test': \"no\",\n", + " 'mode': 'injection',\n", + " 'Qmode': 'downsample',\n", + " 'dwnsmpl_bins': 50,\n", + " 'save_dwsmpld': False,\n", + " 'average_Q': True\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 5e13, #*0.68,\n", + " 'Mmax': 1e16, #*0.68,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + " Total predicted 2D N = 3159.5264180402437\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 0: 82.41112194969105.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 1: 355.55637958369795.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 2: 466.6294729781794.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 3: 480.55083808031964.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 4: 431.5554609232919.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 5: 358.5121917013736.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 6: 283.4278857351987.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 7: 213.52608232771593.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 8: 156.18187417840974.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 9: 110.17807335983895.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 10: 74.85633334084758.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 11: 49.875983821611506.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 12: 33.14163223938807.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 13: 22.04775633119285.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 14: 14.502139856699754.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 15: 9.456141263710114.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 16: 6.176183449179629.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 17: 4.049987655357622.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 18: 2.6455187280184678.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 19: 1.696217866477965.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 20: 1.0498317432751083.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 21: 0.6290151224112677.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 22: 0.37018849129251064.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 23: 0.21521003854045398.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 24: 0.12787061169489994.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 25: 0.07733265951490445.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 26: 0.048426818716174824.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "Number of clusters in redshift bin 27: 0.03126718459768236.\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "------------\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 0: 1995.493110390781.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 1: 935.0015602415671.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 2: 192.65372226330737.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 3: 32.467558684454126.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 4: 3.690288132954083.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Number of clusters in snr bin 5: 0.22017832718009372.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Total predicted 2D N = 3159.5264180402437.\n", + "Theory N calculation took 0.774738073348999 seconds.\n", + "Theory N calculation took 0.774738073348999 seconds.\n", + "Theory N calculation took 0.774738073348999 seconds.\n", + "Theory N calculation took 0.774738073348999 seconds.\n", + "Theory N calculation took 0.774738073348999 seconds.\n", + "Theory N calculation took 0.774738073348999 seconds.\n", + "Theory N calculation took 0.774738073348999 seconds.\n", + "Theory N calculation took 0.774738073348999 seconds.\n", + "Theory N calculation took 0.774738073348999 seconds.\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "bin_params = info['likelihood']['soliket.BinnedClusterLikelihood']['binning']\n", + "\n", + "\n", + "zbins = np.arange(bin_params['z']['zmin'], bin_params['z']['zmax'] + bin_params['z']['dz'], \\\n", + " bin_params['z']['dz'])\n", + "\n", + "logqmin = bin_params['q']['log10qmin']\n", + "logqmax = bin_params['q']['log10qmax']\n", + "dlogq = bin_params['q']['dlog10q']\n", + "\n", + "# TODO: I removed the bin where everything is larger than qmax - is this ok?\n", + "qbins = 10**np.arange(logqmin, logqmax+dlogq, dlogq)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig_pred = {\n", + " 'predSNRCut': 5,\n", + " 'path2truthcat': '/Users/user/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'injection',\n", + " 'QSource': 'injection'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "predNz = nemo_mocks.get_nemo_pred(mockconfig_pred , zbins)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], label='SOLikeT inj pred')\n", + "plt.plot(z, predNz, color=color_list[0], linestyle='--', label='Nemo inj pred')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xlim(0, 2)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3204.8743062182716" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predNz.sum()\n", + "#Nz.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz/predNz, 'r-')\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}/N_{Nemo}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "#plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz-predNz, 'r--')\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{SOLikeT}-N_{Nemo}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "#plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], label='prediction')\n", + "plt.plot(z, predNz, color=color_list[0], linestyle='--', label='nemo prediction')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(z, Nz_truth, yerr=np.sqrt(Nz_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(z, Nz_mock, yerr=np.sqrt(Nz_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xlim(0, 2)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.semilogx(q, catNq/Nq, color=color_list[12])\n", + "plt.semilogx(q, Nq_truth/Nq, color=color_list[8])\n", + "plt.semilogx(q, Nq_mock/Nq, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, catNz/Nz, color=color_list[12])\n", + "plt.plot(z, Nz_truth/Nz, color=color_list[8])\n", + "plt.plot(z, Nz_mock/Nz, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "h = 0.68\n", + "\n", + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.26, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.0,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14*0.68\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 7.,\n", + " 'single_tile_test': \"no\",\n", + " 'mode': 'injection',\n", + " 'Qmode': 'full',\n", + " 'dwnsmpl_bins': 50,\n", + " 'save_dwsmpld': False,\n", + " 'average_Q': True\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 5e13*0.68,\n", + " 'Mmax': 1e16*0.68,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.binned_clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bin_params = info['likelihood']['soliket.BinnedClusterLikelihood']['binning']\n", + "\n", + "\n", + "zbins = np.arange(bin_params['z']['zmin'], bin_params['z']['zmax'] + bin_params['z']['dz'], \\\n", + " bin_params['z']['dz'])\n", + "\n", + "logqmin = bin_params['q']['log10qmin']\n", + "logqmax = bin_params['q']['log10qmax']\n", + "dlogq = bin_params['q']['dlog10q']\n", + "\n", + "# TODO: I removed the bin where everything is larger than qmax - is this ok?\n", + "qbins = 10**np.arange(logqmin, logqmax+dlogq, dlogq)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig = {\n", + " 'predSNRCut': 7,\n", + " 'path2truthcat': '../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'fast',\n", + " 'QSource': 'fit'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a 'true' mock - use the truth catalog, get true_SNR by looking up noise in the selFn dir\n", + "mode = 'without_Q'\n", + "truthTab = nemo_mocks.make_truth_mock(mode, mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "truth_cat, zarr, qarr = nemo_mocks.bin_catalog(truthTab[truthTab['true_SNR']>7], zbins, qbins, SNR_tag='true_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mockTab = nemo_mocks.make_nemo_mock(mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mock_cat, zarr, qarr = nemo_mocks.bin_catalog(mockTab[mockTab['fixed_SNR']>7], zbins, qbins, SNR_tag='fixed_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_truth = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_truth[i] = truth_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_truth = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_truth[i] = truth_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_mock = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_mock[i] = mock_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_mock = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_mock[i] = mock_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], label='prediction')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig_pred = {\n", + " 'predSNRCut': 7,\n", + " 'path2truthcat': '../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'injection',\n", + " 'QSource': 'injection'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predNz = nemo_mocks.get_nemo_pred(mockconfig_pred, zbins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], label='prediction')\n", + "plt.plot(z, predNz, color=color_list[0], linestyle='--', label='nemo prediction')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(z, Nz_truth, yerr=np.sqrt(Nz_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(z, Nz_mock, yerr=np.sqrt(Nz_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xlim(0, 2)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.semilogx(q, catNq/Nq, color=color_list[12])\n", + "plt.semilogx(q, Nq_truth/Nq, color=color_list[8])\n", + "plt.semilogx(q, Nq_mock/Nq, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, catNz/Nz, color=color_list[12])\n", + "plt.plot(z, Nz_truth/Nz, color=color_list[8])\n", + "plt.plot(z, Nz_mock/Nz, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cobaya", + "language": "python", + "name": "cobaya" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned-Q_injection_boris.ipynb b/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned-Q_injection_boris.ipynb new file mode 100644 index 00000000..75c6bef0 --- /dev/null +++ b/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned-Q_injection_boris.ipynb @@ -0,0 +1,2596 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from soliket import BinnedClusterLikelihood\n", + "from cobaya.model import get_model\n", + "import camb\n", + "from astropy.io import fits\n", + "from astropy import table\n", + "from astLib import astWCS\n", + "import math\n", + "from nemo import completeness, MockSurvey\n", + "\n", + "import sys\n", + "sys.path.append('../')\n", + "import nemo_mocks\n", + "import imp\n", + "imp.reload(nemo_mocks)\n", + "\n", + "plt.rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"sans-serif\",\n", + " \"font.sans-serif\": [\"Helvetica\"]})" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.81]\n" + ] + } + ], + "source": [ + "h = 0.68\n", + "\n", + "#Set up a new set of parameters for CAMB\n", + "pars = camb.CAMBparams()\n", + "#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency\n", + "pars.set_cosmology(H0=100.*h, ombh2=0.049*h**2, omch2=(0.31-0.049)*h**2, mnu=0.0, omk=0, tau=0.055)\n", + "pars.InitPower.set_params(As=0.81**2/0.8104862**2*2.022662e-9, ns=0.965, r=0)\n", + "pars.set_for_lmax(2500, lens_potential_accuracy=0);\n", + "\n", + "#calculate results for these parameters\n", + "results = camb.get_results(pars)\n", + "\n", + "#Note non-linear corrections couples to smaller scales than you want\n", + "pars.set_matter_power(redshifts=[0.], kmax=2.0)\n", + "\n", + "#Linear spectra\n", + "results = camb.get_results(pars)\n", + "kh, z, pk = results.get_matter_power_spectrum(minkh=1e-4, maxkh=1, npoints = 200)\n", + "s8 = np.array(results.get_sigma8())\n", + "print(s8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# injection-based completeness" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binnedclusterlikelihood] Number of redshift bins = 28.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing clusters.py Binned Clusters\n", + "Running Q-fit completeness with full analysis. No downsampling.\n", + "Considering full map.\n", + "Total number of clusters in catalogue = 3169.\n", + "SNR cut = 5.0.\n", + "Number of clusters above the SNR cut = 3169.\n", + "The highest redshift = 1.9649999999999999\n", + "The lowest SNR = 5.000186060313553.\n", + "The highest SNR = 51.98994565380555.\n", + "Number of mass points for theory calculation 106.\n", + "Reading full Q function.\n", + "Number of tiles = 280.\n", + "Number of Q functions = 1.\n", + "Using one averaged Q function for optimisation\n", + "Reading in full RMS table.\n", + "Number of tiles = 264. \n", + "Number of sky patches = 40672.\n", + "Entire survey area = 13631.324739141011 deg2.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "Number of SNR bins = 6.\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + " Total predicted 2D N = 3121.2007428429893\n", + "Number of clusters in redshift bin 0: 49.90381986184185.\n", + "Number of clusters in redshift bin 1: 351.32891268699217.\n", + "Number of clusters in redshift bin 2: 467.59999617925683.\n", + "Number of clusters in redshift bin 3: 479.5947950965884.\n", + "Number of clusters in redshift bin 4: 429.5180054930908.\n", + "Number of clusters in redshift bin 5: 357.4171633678316.\n", + "Number of clusters in redshift bin 6: 284.2461420345068.\n", + "Number of clusters in redshift bin 7: 215.3543286031283.\n", + "Number of clusters in redshift bin 8: 155.9218599165633.\n", + "Number of clusters in redshift bin 9: 108.92521440754562.\n", + "Number of clusters in redshift bin 10: 74.2914348314808.\n", + "Number of clusters in redshift bin 11: 50.016190926329564.\n", + "Number of clusters in redshift bin 12: 33.42008408263418.\n", + "Number of clusters in redshift bin 13: 22.208840936437944.\n", + "Number of clusters in redshift bin 14: 14.665906797602725.\n", + "Number of clusters in redshift bin 15: 9.612512981278181.\n", + "Number of clusters in redshift bin 16: 6.252618639639257.\n", + "Number of clusters in redshift bin 17: 4.0346278967459375.\n", + "Number of clusters in redshift bin 18: 2.5894810178253764.\n", + "Number of clusters in redshift bin 19: 1.6548868703831667.\n", + "Number of clusters in redshift bin 20: 1.0500955433601031.\n", + "Number of clusters in redshift bin 21: 0.6560120571205891.\n", + "Number of clusters in redshift bin 22: 0.4014632964984661.\n", + "Number of clusters in redshift bin 23: 0.24076799836569945.\n", + "Number of clusters in redshift bin 24: 0.14127535479264722.\n", + "Number of clusters in redshift bin 25: 0.08169480801322461.\n", + "Number of clusters in redshift bin 26: 0.04642241831816623.\n", + "Number of clusters in redshift bin 27: 0.026188738817480527.\n", + "------------\n", + "Number of clusters in snr bin 0: 1978.6425989981892.\n", + "Number of clusters in snr bin 1: 919.1393520492203.\n", + "Number of clusters in snr bin 2: 187.6720828045029.\n", + "Number of clusters in snr bin 3: 31.7312710732445.\n", + "Number of clusters in snr bin 4: 3.7661846075608656.\n", + "Number of clusters in snr bin 5: 0.2492533102713547.\n", + "Total predicted 2D N = 3121.2007428429893.\n", + "Theory N calculation took 0.13488101959228516 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " ::: 2D ln likelihood = 221.9875054932342\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-221.98750549])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.68\n", + "\n", + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.26, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.0,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "# path2data ='/Users/boris/Work/CLASS-SZ/SO-SZ/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14#*0.68\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 5.,\n", + " 'single_tile_test': \"no\",\n", + " 'mode': 'injection',\n", + " 'Qmode': 'full',\n", + " 'dwnsmpl_bins': 50,\n", + " 'save_dwsmpld': False,\n", + " 'average_Q': True\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 5e13*0.68,\n", + " 'Mmax': 1e16*0.68,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 3121.2007428429893\n", + "Number of clusters in redshift bin 0: 49.90381986184185.\n", + "Number of clusters in redshift bin 1: 351.32891268699217.\n", + "Number of clusters in redshift bin 2: 467.59999617925683.\n", + "Number of clusters in redshift bin 3: 479.5947950965884.\n", + "Number of clusters in redshift bin 4: 429.5180054930908.\n", + "Number of clusters in redshift bin 5: 357.4171633678316.\n", + "Number of clusters in redshift bin 6: 284.2461420345068.\n", + "Number of clusters in redshift bin 7: 215.3543286031283.\n", + "Number of clusters in redshift bin 8: 155.9218599165633.\n", + "Number of clusters in redshift bin 9: 108.92521440754562.\n", + "Number of clusters in redshift bin 10: 74.2914348314808.\n", + "Number of clusters in redshift bin 11: 50.016190926329564.\n", + "Number of clusters in redshift bin 12: 33.42008408263418.\n", + "Number of clusters in redshift bin 13: 22.208840936437944.\n", + "Number of clusters in redshift bin 14: 14.665906797602725.\n", + "Number of clusters in redshift bin 15: 9.612512981278181.\n", + "Number of clusters in redshift bin 16: 6.252618639639257.\n", + "Number of clusters in redshift bin 17: 4.0346278967459375.\n", + "Number of clusters in redshift bin 18: 2.5894810178253764.\n", + "Number of clusters in redshift bin 19: 1.6548868703831667.\n", + "Number of clusters in redshift bin 20: 1.0500955433601031.\n", + "Number of clusters in redshift bin 21: 0.6560120571205891.\n", + "Number of clusters in redshift bin 22: 0.4014632964984661.\n", + "Number of clusters in redshift bin 23: 0.24076799836569945.\n", + "Number of clusters in redshift bin 24: 0.14127535479264722.\n", + "Number of clusters in redshift bin 25: 0.08169480801322461.\n", + "Number of clusters in redshift bin 26: 0.04642241831816623.\n", + "Number of clusters in redshift bin 27: 0.026188738817480527.\n", + "------------\n", + "Number of clusters in snr bin 0: 1978.6425989981892.\n", + "Number of clusters in snr bin 1: 919.1393520492203.\n", + "Number of clusters in snr bin 2: 187.6720828045029.\n", + "Number of clusters in snr bin 3: 31.7312710732445.\n", + "Number of clusters in snr bin 4: 3.7661846075608656.\n", + "Number of clusters in snr bin 5: 0.2492533102713547.\n", + "Total predicted 2D N = 3121.2007428429893.\n", + "Theory N calculation took 0.16041111946105957 seconds.\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,5))\n", + "\n", + "plt.plot(q, Nq, color='k', label=r'$\\mathrm{SOLikeT}$',marker='o')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color='b', fmt='o', ms=3, capsize=5, \\\n", + " capthick=1, ls='none', label=r'$\\mathrm{SIMS}$')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.legend(frameon=False,fontsize=23,loc=1)\n", + "plt.grid(which='both',alpha=0.2)\n", + "label_size =15\n", + "plt.xscale('log')\n", + "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", + "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", + "ax.xaxis.set_ticks_position('both')\n", + "ax.yaxis.set_ticks_position('both')\n", + "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", + "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", + "plt.xlabel(r'$q$',size=25)\n", + "plt.ylabel(r'$N(q)$',size=25)\n", + "fig.tight_layout()\n", + "plt.savefig('nq_dr5_sims_injection-based.jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,5))\n", + "\n", + "# plt.plot(q, Nq, color='k', label=r'$\\mathrm{SOLikeT}$',marker='o')\n", + "plt.errorbar(q, Nq-catNq, yerr=np.sqrt(catNq), color='b', fmt='o', capsize=3, \\\n", + " capthick=1, ls='none')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.legend(frameon=False,fontsize=23,loc=4)\n", + "plt.grid(which='both',alpha=0.2)\n", + "label_size =15\n", + "plt.xscale('log')\n", + "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", + "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", + "ax.xaxis.set_ticks_position('both')\n", + "ax.yaxis.set_ticks_position('both')\n", + "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", + "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", + "plt.xlabel(r'$q$',size=25)\n", + "plt.ylabel(r'$\\mathrm{SOLikeT}-\\mathrm{SIMS}$',size=25)\n", + "fig.tight_layout()\n", + "plt.savefig('nq_dr5_simsinjection-based-diff.jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,5))\n", + "plt.plot(z, Nz, color='k', label=r'$\\mathrm{SOLikeT}$',marker='o')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color='b', fmt='o', capsize=3, \\\n", + " capthick=1, ls='none', label=r'$\\mathrm{SIMS}$')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.legend(frameon=False,fontsize=23,loc=1)\n", + "plt.grid(which='both',alpha=0.2)\n", + "label_size =15\n", + "plt.xlim(0,1.7)\n", + "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", + "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", + "ax.xaxis.set_ticks_position('both')\n", + "ax.yaxis.set_ticks_position('both')\n", + "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", + "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", + "plt.xlabel(r'$z$',size=25)\n", + "plt.ylabel(r'$N(z\\,|\\,q>5)$',size=25)\n", + "fig.tight_layout()\n", + "plt.savefig('nz_dr5_simsinjection-based.jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,5))\n", + "# plt.plot(z, Nz, color=color_list[0], label=r'$\\mathrm{SOLikeT}$',marker='o')\n", + "plt.errorbar(z, Nz-catNz, yerr=np.sqrt(catNz), color='b', fmt='o', capsize=3, \\\n", + " capthick=1, ls='none')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.legend(frameon=False,fontsize=23,loc=1)\n", + "plt.grid(which='both',alpha=0.2)\n", + "label_size =15\n", + "plt.xlim(0,1.7)\n", + "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", + "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", + "ax.xaxis.set_ticks_position('both')\n", + "ax.yaxis.set_ticks_position('both')\n", + "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", + "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", + "plt.xlabel(r'$z$',size=25)\n", + "plt.ylabel(r'$\\mathrm{SOLikeT}-\\mathrm{SIMS}$',size=25)\n", + "fig.tight_layout()\n", + "plt.savefig('nz_dr5_simsinjection-based_diff.jpeg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Q-based completeness" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binnedclusterlikelihood] Number of redshift bins = 28.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing clusters.py Binned Clusters\n", + "Initializing clusters.py Binned Clusters\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Considering full map.\n", + "Considering full map.\n", + "Total number of clusters in catalogue = 3169.\n", + "Total number of clusters in catalogue = 3169.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "Number of clusters above the SNR cut = 3169.\n", + "Number of clusters above the SNR cut = 3169.\n", + "The highest redshift = 1.9649999999999999\n", + "The highest redshift = 1.9649999999999999\n", + "The lowest SNR = 5.000186060313553.\n", + "The lowest SNR = 5.000186060313553.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "Number of mass points for theory calculation 106.\n", + "Number of mass points for theory calculation 106.\n", + "Reading full Q function.\n", + "Reading full Q function.\n", + "Number of tiles = 280.\n", + "Number of tiles = 280.\n", + "Reading in full RMS table.\n", + "Reading in full RMS table.\n", + "Number of tiles = 264. \n", + "Number of tiles = 264. \n", + "Number of sky patches = 40672.\n", + "Number of sky patches = 40672.\n", + "Downsampling RMS and Q function using 5 bins.\n", + "Downsampling RMS and Q function using 5 bins.\n", + "Number of downsampled sky patches = 5.\n", + "Number of downsampled sky patches = 5.\n", + "Number of Q functions = 5.\n", + "Number of Q functions = 5.\n", + "Entire survey area = 13631.324739141117 deg2.\n", + "Entire survey area = 13631.324739141117 deg2.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dowsampled rms bin 0\n", + "areas of tiles in bin [1.37804228e-04 1.35708109e-04 1.39484767e-04 ... 9.68504603e-05\n", + " 1.04826478e-04 1.06973574e-04]\n", + "names of tiles in bin ['2_2_3' '2_3_4' '2_2_3' ... '1_2_6' '1_3_7' '1_3_6']\n", + "dowsampled rms bin 1\n", + "areas of tiles in bin [1.41732649e-04 1.43840834e-04 1.41941422e-04 ... 1.34858792e-04\n", + " 1.67240856e-06 1.37371561e-04]\n", + "names of tiles in bin ['1_10_3' '3_3_3' '2_1_2' ... '1_14_7' '1_14_4' '1_14_4']\n", + "dowsampled rms bin 2\n", + "areas of tiles in bin [7.36284480e-05 7.32053173e-05 1.05684735e-04 ... 1.14506186e-04\n", + " 1.07748733e-04 1.33562051e-04]\n", + "names of tiles in bin ['1_0_4' '1_0_3' '1_3_2' ... '1_4_11' '1_3_12' '1_8_5']\n", + "dowsampled rms bin 3\n", + "areas of tiles in bin [6.83094144e-06 2.82529237e-04 1.41521862e-04 ... 1.27670753e-04\n", + " 1.34522306e-04 1.36766707e-04]\n", + "names of tiles in bin ['1_2_11' '1_10_2' '2_1_0' ... '1_6_15' '1_9_13' '1_9_12']\n", + "dowsampled rms bin 4\n", + "areas of tiles in bin [1.35826334e-04 1.30065993e-04 1.33114850e-04 1.00595612e-05\n", + " 1.84485640e-05 1.05483044e-04 1.35098821e-04 1.35525697e-04\n", + " 1.28948875e-04 1.06294090e-05 2.53008811e-07 1.41850307e-04\n", + " 1.31103240e-04 1.36032102e-04 1.35826334e-04 1.34819689e-04\n", + " 1.41264619e-04 1.34402148e-04 1.41439784e-04 1.36766707e-04\n", + " 1.30065993e-04 1.35826334e-04 1.35098821e-04 1.34794930e-04\n", + " 1.41850307e-04 1.35359661e-04 1.36484130e-04 1.34522306e-04\n", + " 1.33991170e-04 4.60550675e-05 1.41595801e-04 1.37030768e-04\n", + " 1.34402148e-04 1.33991170e-04 1.35169464e-04 1.32591945e-04\n", + " 1.30065993e-04 1.37711511e-04 1.35169464e-04 1.34402148e-04\n", + " 1.00595612e-05 1.36766707e-04 1.35525697e-04 1.36032102e-04\n", + " 1.01249453e-05 1.33562051e-04 1.25044519e-04 1.35525697e-04\n", + " 1.84485640e-05 1.05483044e-04 1.36438771e-04 1.30994452e-04\n", + " 1.37711511e-04 1.30560736e-04 1.41264619e-04 1.34794930e-04\n", + " 1.04140579e-04 1.37030768e-04 1.36766707e-04 1.34794930e-04\n", + " 4.79247995e-06 1.33562051e-04 1.34794930e-04 1.34402148e-04\n", + " 4.88641649e-06 1.35098821e-04 1.00595612e-05 1.37030768e-04\n", + " 1.84485640e-05 1.05483044e-04 1.34206710e-04 1.31103240e-04\n", + " 2.83700615e-04 1.35525697e-04 1.06294090e-05 7.06744029e-06\n", + " 1.35169464e-04 1.30065993e-04 1.32430250e-04 1.34206710e-04\n", + " 1.76692871e-06 1.36484130e-04 1.37711511e-04 1.34402148e-04\n", + " 1.36766707e-04 9.91298631e-05 1.79106979e-05 1.35098821e-04\n", + " 1.41264619e-04 1.41850307e-04 1.35098821e-04 1.30254315e-05\n", + " 1.29553641e-04 1.28948875e-04 1.30065993e-04 1.30560736e-04\n", + " 1.35826334e-04 1.41264619e-04 1.41732649e-04 1.28948875e-04\n", + " 1.34206710e-04 1.15150625e-04 1.28460240e-04 2.70070294e-04\n", + " 7.27779963e-05 1.30560736e-04 9.98954340e-05 1.41264619e-04\n", + " 2.29051111e-05 1.37503202e-04 1.30065993e-04 1.60575299e-06\n", + " 1.35863583e-04 1.36183075e-04 1.35525697e-04 1.37178450e-04\n", + " 6.01677072e-05 1.34794930e-04 7.27779963e-05 1.36032102e-04\n", + " 1.30994452e-04 1.28460240e-04 1.30065993e-04 1.35525697e-04\n", + " 1.28578531e-04 1.36183075e-04 1.36041198e-04 1.33991170e-04\n", + " 1.41595801e-04 3.35373663e-06 1.41264619e-04 1.34794930e-04\n", + " 1.01249453e-05 1.34819689e-04 3.00838536e-05 7.57334637e-05\n", + " 1.25044519e-04 1.14525556e-05 4.72107951e-05 1.17939429e-05\n", + " 1.36484130e-04 4.97061890e-06 7.27779963e-05 1.34206710e-04\n", + " 1.36484130e-04 1.41732649e-04 1.33991170e-04 1.36766707e-04\n", + " 3.33577335e-06 1.41264619e-04 1.35863583e-04 3.34820190e-06\n", + " 2.98743167e-07 1.41439784e-04 1.33562051e-04 1.36183075e-04\n", + " 1.30065993e-04 1.89320947e-07 1.35098821e-04 1.35602177e-04\n", + " 1.30560736e-04 1.30560736e-04 1.34522306e-04 1.30560736e-04\n", + " 1.28948875e-04 1.34402148e-04 1.37503202e-04 1.33991170e-04\n", + " 1.16518472e-05 1.28046137e-04 4.66419903e-05 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+ " 1.30994452e-04 1.28948875e-04 2.45348838e-07 2.29429003e-05]\n", + "names of tiles in bin ['1_9_12' '1_7_14' '1_8_14' '1_8_15' '1_8_15' '1_8_15' '1_9_13' '1_8_15'\n", + " '1_7_14' '1_10_12' '1_10_13' '1_10_12' '1_10_12' '1_9_12' '1_9_13'\n", + " '1_9_11' '1_10_11' '1_8_14' '1_10_11' '1_9_12' '1_7_14' '1_9_12' '1_9_12'\n", + " '1_8_14' '1_10_12' '1_9_13' '1_9_13' '1_9_12' '1_8_14' '1_10_13'\n", + " '1_10_11' '1_9_12' '1_8_14' '1_8_14' '1_8_14' '1_8_14' '1_7_14' '1_9_11'\n", + " '1_8_14' '1_8_14' '1_8_15' '1_9_13' '1_8_14' '1_9_12' '1_8_15' '1_8_13'\n", + " '1_8_15' '1_8_13' '1_8_15' '1_8_15' '1_8_14' '1_8_14' '1_9_12' '1_7_12'\n", + " '1_10_11' '1_8_14' '1_9_13' '1_9_13' '1_9_13' '1_8_13' '1_8_14' '1_8_15'\n", + " '1_8_13' '1_8_13' '1_8_13' '1_9_13' '1_8_15' '1_9_13' '1_8_15' '1_8_15'\n", + " '1_9_12' '1_10_12' '1_10_12' '1_8_15' '1_10_12' '1_10_13' '1_8_14'\n", + " '1_7_13' '1_10_13' '1_9_12' '1_10_13' '1_9_12' '1_9_11' '1_8_14' '1_9_13'\n", + " '1_7_1' '1_7_1' '1_9_13' '1_10_12' '1_10_12' '1_9_12' '1_7_1' '1_7_15'\n", + " '1_7_13' '1_7_14' '1_7_14' '1_9_12' '1_10_12' '1_10_11' '1_7_13' '1_9_11'\n", + " '1_5_15' '1_7_13' '1_9_12' '1_5_15' '1_7_14' '1_9_13' '1_10_12' '1_5_15'\n", + " '1_9_12' '1_7_13' '1_7_12' '1_9_11' '1_9_13' '1_8_13' '1_8_14' '1_5_15'\n", + " '1_8_13' '1_5_15' '1_9_12' '1_8_15' '1_7_14' '1_7_12' '1_8_14' '1_6_15'\n", + " '1_9_13' '1_8_14' '1_8_13' '1_10_12' '1_9_12' '1_10_11' '1_8_13' '1_8_15'\n", + " '1_9_13' '1_5_15' '1_8_15' '1_8_15' '1_5_15' '1_8_15' '1_8_15' '1_9_12'\n", + " '1_9_12' '1_5_15' '1_9_13' '1_9_13' '1_10_11' '1_8_14' '1_9_12' '1_9_13'\n", + " '1_10_12' '1_9_12' '1_9_12' '1_6_15' '1_10_11' '1_8_14' '1_9_12' '1_7_14'\n", + " '1_10_13' '1_9_13' '1_9_12' '1_7_13' '1_7_13' '1_9_13' '1_7_14' '1_7_13'\n", + " '1_8_15' '1_9_13' '1_8_13' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_14'\n", + " '1_8_15' '1_7_13' '1_8_14' '1_9_12' '1_9_12' '1_8_13' '1_9_13' '1_9_13'\n", + " '1_8_14' '1_7_13' '1_9_13' '1_9_13' '1_10_11' '1_9_13' '1_9_13' '1_8_14'\n", + " '1_9_13' '1_9_13' '1_10_12' '1_8_15' '1_7_13' '1_10_12' '1_10_13' '1_7_1'\n", + " '1_7_1' '1_7_1' '1_8_14' '1_8_14' '1_9_13' '1_9_13' '1_9_13' '1_7_13'\n", + " '1_9_12' '1_9_13' '1_6_15' '1_10_12' '1_8_14' '1_9_13' '1_9_13' '1_8_15'\n", + " '1_8_14' '1_9_11' '1_8_14' '1_9_13' '1_9_13' '1_7_13' '1_10_13' '1_10_12'\n", + " '1_10_13' '1_10_12' '1_9_13' '1_8_14' '1_8_15' '1_8_14' '1_8_15' '1_7_14'\n", + " '1_8_15' '1_8_14' '1_7_13' '1_7_13' '1_8_14' '1_9_12' '1_9_13' '1_8_14'\n", + " '1_8_14' '1_8_15' '1_8_15' '1_9_13' '1_8_13' '1_9_12' '1_10_12' '1_10_11'\n", + " '1_8_15' '1_8_15' '1_9_12' '1_8_15' '1_8_15' '1_7_14' '1_9_13' '1_10_12'\n", + " '1_9_13' '1_9_13' '1_7_14' '1_8_13' '1_10_12' '1_8_13' '1_9_12' '1_9_12'\n", + " '1_10_12' '1_8_14' '1_7_14' '1_8_13' '1_8_13' '1_7_13' '1_10_12' '1_7_1'\n", + " '1_7_1' '1_7_1' '1_10_12' '1_8_14' '1_8_15' '1_9_12' '1_8_14' '1_8_14'\n", + " '1_8_13' '1_8_14' '1_9_13' '1_8_14' '1_9_13' '1_8_14' '1_8_15' '1_8_15'\n", + " '1_9_12' '1_9_14' '1_8_15' '1_9_12' '1_8_15' '1_9_13' '1_8_14' '1_8_15'\n", + " '1_7_13' '1_7_14' '1_9_13' '1_9_12' '1_8_14' '1_9_12' '1_9_13' '1_9_13'\n", + " '1_8_15' '1_8_14' '1_8_1' '1_9_13' '1_9_13' '1_7_15' '1_9_12' '1_9_13'\n", + " '1_8_15' '1_5_15' '1_10_12' '1_5_15' '1_10_12' '1_5_15' '1_9_13' '1_8_13'\n", + " '1_9_13' '1_8_15' '1_8_14' '1_8_15' '1_8_14' '1_10_13' '1_8_15' '1_8_14'\n", + " '1_8_15' '1_8_15' '1_8_13' '1_9_13' '1_8_1' '1_8_1' '1_8_14' '1_8_14'\n", + " '1_8_15' '1_8_14' '1_10_12' '1_8_15' '1_8_14' '1_8_14' '1_9_12' '1_8_15'\n", + " '1_7_14' '1_8_15' '1_7_13' '1_9_13' '1_9_12' '1_7_15' '1_10_13' '1_8_14'\n", + " '1_9_13' '1_10_13' '1_10_12' '1_8_14' '1_10_13' '1_9_12' '1_8_15'\n", + " '1_8_15' '1_10_13' '1_8_13' '1_8_1' '1_9_13' '1_7_14' '1_7_15' '1_10_13'\n", + " '1_8_14' '1_8_14' '1_9_12' '1_9_13' '1_10_13' '1_8_1' '1_8_1' '1_9_13'\n", + " '1_10_13' '1_10_13' '1_9_13' '1_8_15' '1_9_13' '1_10_13' '1_8_15'\n", + " '1_7_14' '1_8_14' '1_9_13' '1_8_15' '1_8_15' '1_8_14' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_8_14' '1_8_14' '1_7_13' '1_9_13' '1_8_14' '1_8_14' '1_7_14'\n", + " '1_8_15' '1_8_14' '1_10_12' '1_8_13' '1_8_14' '1_8_14' '1_8_15' '1_8_14'\n", + " '1_8_14' '1_8_15' '1_8_14' '1_8_0' '1_8_14' '1_7_14' '1_8_14' '1_7_13'\n", + " '1_8_15' '1_9_13' '1_8_13' '1_10_12' '1_8_14' '1_8_14' '1_8_15' '1_9_13'\n", + " '1_9_13' '1_8_14' '1_8_15' '1_8_14' '1_8_1' '1_8_15' '1_8_15' '1_9_13'\n", + " '1_9_14' '1_9_12' '1_9_12' '1_9_13' '1_9_13' '1_8_13' '1_9_13' '1_9_14'\n", + " '1_7_1' '1_7_1' '1_7_1' '1_8_14' '1_8_15' '1_8_15' '1_8_14' '1_9_13'\n", + " '1_7_1' '1_8_15' '1_8_14' '1_8_15' '1_8_14' '1_8_15' '1_8_15' '1_8_15'\n", + " '1_8_13' '1_8_14' '1_8_15' '1_9_13' '1_8_15' '1_9_12' '1_8_13' '1_8_14'\n", + " '1_8_14' '1_9_13' '1_7_1' '1_7_1' '1_7_1' '1_8_14' '1_8_14' '1_7_1'\n", + " '1_8_14' '1_8_15' '1_7_14' '1_8_1' '1_9_13' '1_9_13' '1_7_13' '1_8_14'\n", + " '1_8_15' '1_9_13' '1_8_15' '1_8_15' '1_8_1' '1_8_14' '1_7_15' '1_8_15'\n", + " '1_9_13' '1_8_15' '1_9_13' '1_7_14' '1_9_13' '1_7_13' '1_9_13' '1_8_15'\n", + " '1_8_15' '1_8_15' '1_8_14' '1_8_14' '1_8_14' '1_8_14' '1_8_14' '1_7_15'\n", + " '1_8_15' '1_8_15' '1_9_12' '1_8_15' '1_9_13' '1_8_15' '1_7_15' '1_7_14'\n", + " '1_8_15' '1_10_12' '1_8_14' '1_8_14' '1_7_1' '1_7_1' '1_7_1' '1_9_13'\n", + " '1_8_14' '1_8_14' '1_8_14' '1_7_13' '1_7_15' '1_8_14' '1_8_15' '1_8_15'\n", + " '1_7_1' '1_8_14' '1_7_1' '1_7_1' '1_9_13' '1_9_13' '1_7_15' '1_8_14'\n", + " '1_9_13' '1_8_14' '1_8_15' '1_8_1' '1_8_15' '1_8_15' '1_8_15' '1_7_13'\n", + " '1_8_15' '1_8_15' '1_8_14' '1_8_15' '1_8_15' '1_9_13' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_8_15' '1_7_15' '1_8_1' '1_8_15' '1_7_1' '1_7_15' '1_8_15'\n", + " '1_8_15' '1_8_1' '1_8_1' '1_7_14' '1_8_15' '1_8_15' '1_8_15' '1_8_1'\n", + " '1_8_15' '1_8_15' '1_8_14' '1_9_13' '1_8_1' '1_8_15' '1_8_1' '1_8_1'\n", + " '1_8_1' '1_8_1' '1_8_14' '1_8_14' '1_8_15' '1_9_14' '1_8_15' '1_8_15'\n", + " '1_8_14' '1_8_14' '1_8_1' '1_8_15' '1_8_15' '1_8_15' '1_8_1' '1_8_1'\n", + " '1_7_15' '1_8_1' '1_8_15' '1_8_15' '1_7_15' '1_8_15' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_7_1' '1_7_15' '1_7_15' '1_7_1' '1_8_15' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_8_15' '1_7_15' '1_8_15' '1_8_15' '1_8_15' '1_7_15' '1_8_1'\n", + " '1_7_15' '1_8_15' '1_7_15' '1_7_15' '1_7_15' '1_7_1' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_7_15' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_8_15' '1_7_15' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_15'\n", + " '1_7_15' '1_8_15' '1_8_15' '1_8_15' '1_7_15' '1_8_15' '1_7_15' '1_8_15'\n", + " '1_8_15']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 2655.235152027773\n", + " Total predicted 2D N = 2655.235152027773\n", + "Number of clusters in redshift bin 0: 16.57543307915122.\n", + "Number of clusters in redshift bin 0: 16.57543307915122.\n", + "Number of clusters in redshift bin 1: 282.5042034254235.\n", + "Number of clusters in redshift bin 1: 282.5042034254235.\n", + "Number of clusters in redshift bin 2: 403.9819373254358.\n", + "Number of clusters in redshift bin 2: 403.9819373254358.\n", + "Number of clusters in redshift bin 3: 420.7981698344607.\n", + "Number of clusters in redshift bin 3: 420.7981698344607.\n", + "Number of clusters in redshift bin 4: 377.99923859217466.\n", + "Number of clusters in redshift bin 4: 377.99923859217466.\n", + "Number of clusters in redshift bin 5: 312.7435583019378.\n", + "Number of clusters in redshift bin 5: 312.7435583019378.\n", + "Number of clusters in redshift bin 6: 244.93828351145845.\n", + "Number of clusters in redshift bin 6: 244.93828351145845.\n", + "Number of clusters in redshift bin 7: 183.30955576838954.\n", + "Number of clusters in redshift bin 7: 183.30955576838954.\n", + "Number of clusters in redshift bin 8: 132.5209454476309.\n", + "Number of clusters in redshift bin 8: 132.5209454476309.\n", + "Number of clusters in redshift bin 9: 93.13957117791418.\n", + "Number of clusters in redshift bin 9: 93.13957117791418.\n", + "Number of clusters in redshift bin 10: 63.93148545517453.\n", + "Number of clusters in redshift bin 10: 63.93148545517453.\n", + "Number of clusters in redshift bin 11: 43.03637262013746.\n", + "Number of clusters in redshift bin 11: 43.03637262013746.\n", + "Number of clusters in redshift bin 12: 28.506931272676837.\n", + "Number of clusters in redshift bin 12: 28.506931272676837.\n", + "Number of clusters in redshift bin 13: 18.62505743041836.\n", + "Number of clusters in redshift bin 13: 18.62505743041836.\n", + "Number of clusters in redshift bin 14: 12.027017716421547.\n", + "Number of clusters in redshift bin 14: 12.027017716421547.\n", + "Number of clusters in redshift bin 15: 7.689380886295687.\n", + "Number of clusters in redshift bin 15: 7.689380886295687.\n", + "Number of clusters in redshift bin 16: 4.8736802438727675.\n", + "Number of clusters in redshift bin 16: 4.8736802438727675.\n", + "Number of clusters in redshift bin 17: 3.0653283043364628.\n", + "Number of clusters in redshift bin 17: 3.0653283043364628.\n", + "Number of clusters in redshift bin 18: 1.9151956364039289.\n", + "Number of clusters in redshift bin 18: 1.9151956364039289.\n", + "Number of clusters in redshift bin 19: 1.1899698454917087.\n", + "Number of clusters in redshift bin 19: 1.1899698454917087.\n", + "Number of clusters in redshift bin 20: 0.7359814872599325.\n", + "Number of clusters in redshift bin 20: 0.7359814872599325.\n", + "Number of clusters in redshift bin 21: 0.45341015797542333.\n", + "Number of clusters in redshift bin 21: 0.45341015797542333.\n", + "Number of clusters in redshift bin 22: 0.27819486183592107.\n", + "Number of clusters in redshift bin 22: 0.27819486183592107.\n", + "Number of clusters in redshift bin 23: 0.16992212786921576.\n", + "Number of clusters in redshift bin 23: 0.16992212786921576.\n", + "Number of clusters in redshift bin 24: 0.10331422801948859.\n", + "Number of clusters in redshift bin 24: 0.10331422801948859.\n", + "Number of clusters in redshift bin 25: 0.06255226977380746.\n", + "Number of clusters in redshift bin 25: 0.06255226977380746.\n", + "Number of clusters in redshift bin 26: 0.03774294924290486.\n", + "Number of clusters in redshift bin 26: 0.03774294924290486.\n", + "Number of clusters in redshift bin 27: 0.022718070590300603.\n", + "Number of clusters in redshift bin 27: 0.022718070590300603.\n", + "------------\n", + "------------\n", + "Number of clusters in snr bin 0: 1609.2679103594542.\n", + "Number of clusters in snr bin 0: 1609.2679103594542.\n", + "Number of clusters in snr bin 1: 846.5269312767326.\n", + "Number of clusters in snr bin 1: 846.5269312767326.\n", + "Number of clusters in snr bin 2: 170.51430471548272.\n", + "Number of clusters in snr bin 2: 170.51430471548272.\n", + "Number of clusters in snr bin 3: 26.123092914495054.\n", + "Number of clusters in snr bin 3: 26.123092914495054.\n", + "Number of clusters in snr bin 4: 2.6599042723871094.\n", + "Number of clusters in snr bin 4: 2.6599042723871094.\n", + "Number of clusters in snr bin 5: 0.1430084892218731.\n", + "Number of clusters in snr bin 5: 0.1430084892218731.\n", + "Total predicted 2D N = 2655.235152027773.\n", + "Total predicted 2D N = 2655.235152027773.\n", + "Theory N calculation took 1.0382637977600098 seconds.\n", + "Theory N calculation took 1.0382637977600098 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " ::: 2D ln likelihood = 428.2197276229895\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-428.21972762])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.68\n", + "\n", + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.26, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.0,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "# path2data ='/Users/boris/Work/CLASS-SZ/SO-SZ/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14#*0.68\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut' : 5.,\n", + " 'single_tile_test' : \"no\",\n", + " 'mode': 'Qfit',\n", + " 'Qmode' : 'downsample',\n", + " 'dwnsmpl_bins' : 5,\n", + " 'save_dwsmpld' : True,\n", + " 'average_Q' : False\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 5e13*0.68,\n", + " 'Mmax': 1e16*0.68,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 2655.235152027773\n", + " Total predicted 2D N = 2655.235152027773\n", + "Number of clusters in redshift bin 0: 16.57543307915122.\n", + "Number of clusters in redshift bin 0: 16.57543307915122.\n", + "Number of clusters in redshift bin 1: 282.5042034254235.\n", + "Number of clusters in redshift bin 1: 282.5042034254235.\n", + "Number of clusters in redshift bin 2: 403.9819373254358.\n", + "Number of clusters in redshift bin 2: 403.9819373254358.\n", + "Number of clusters in redshift bin 3: 420.7981698344607.\n", + "Number of clusters in redshift bin 3: 420.7981698344607.\n", + "Number of clusters in redshift bin 4: 377.99923859217466.\n", + "Number of clusters in redshift bin 4: 377.99923859217466.\n", + "Number of clusters in redshift bin 5: 312.7435583019378.\n", + "Number of clusters in redshift bin 5: 312.7435583019378.\n", + "Number of clusters in redshift bin 6: 244.93828351145845.\n", + "Number of clusters in redshift bin 6: 244.93828351145845.\n", + "Number of clusters in redshift bin 7: 183.30955576838954.\n", + "Number of clusters in redshift bin 7: 183.30955576838954.\n", + "Number of clusters in redshift bin 8: 132.5209454476309.\n", + "Number of clusters in redshift bin 8: 132.5209454476309.\n", + "Number of clusters in redshift bin 9: 93.13957117791418.\n", + "Number of clusters in redshift bin 9: 93.13957117791418.\n", + "Number of clusters in redshift bin 10: 63.93148545517453.\n", + "Number of clusters in redshift bin 10: 63.93148545517453.\n", + "Number of clusters in redshift bin 11: 43.03637262013746.\n", + "Number of clusters in redshift bin 11: 43.03637262013746.\n", + "Number of clusters in redshift bin 12: 28.506931272676837.\n", + "Number of clusters in redshift bin 12: 28.506931272676837.\n", + "Number of clusters in redshift bin 13: 18.62505743041836.\n", + "Number of clusters in redshift bin 13: 18.62505743041836.\n", + "Number of clusters in redshift bin 14: 12.027017716421547.\n", + "Number of clusters in redshift bin 14: 12.027017716421547.\n", + "Number of clusters in redshift bin 15: 7.689380886295687.\n", + "Number of clusters in redshift bin 15: 7.689380886295687.\n", + "Number of clusters in redshift bin 16: 4.8736802438727675.\n", + "Number of clusters in redshift bin 16: 4.8736802438727675.\n", + "Number of clusters in redshift bin 17: 3.0653283043364628.\n", + "Number of clusters in redshift bin 17: 3.0653283043364628.\n", + "Number of clusters in redshift bin 18: 1.9151956364039289.\n", + "Number of clusters in redshift bin 18: 1.9151956364039289.\n", + "Number of clusters in redshift bin 19: 1.1899698454917087.\n", + "Number of clusters in redshift bin 19: 1.1899698454917087.\n", + "Number of clusters in redshift bin 20: 0.7359814872599325.\n", + "Number of clusters in redshift bin 20: 0.7359814872599325.\n", + "Number of clusters in redshift bin 21: 0.45341015797542333.\n", + "Number of clusters in redshift bin 21: 0.45341015797542333.\n", + "Number of clusters in redshift bin 22: 0.27819486183592107.\n", + "Number of clusters in redshift bin 22: 0.27819486183592107.\n", + "Number of clusters in redshift bin 23: 0.16992212786921576.\n", + "Number of clusters in redshift bin 23: 0.16992212786921576.\n", + "Number of clusters in redshift bin 24: 0.10331422801948859.\n", + "Number of clusters in redshift bin 24: 0.10331422801948859.\n", + "Number of clusters in redshift bin 25: 0.06255226977380746.\n", + "Number of clusters in redshift bin 25: 0.06255226977380746.\n", + "Number of clusters in redshift bin 26: 0.03774294924290486.\n", + "Number of clusters in redshift bin 26: 0.03774294924290486.\n", + "Number of clusters in redshift bin 27: 0.022718070590300603.\n", + "Number of clusters in redshift bin 27: 0.022718070590300603.\n", + "------------\n", + "------------\n", + "Number of clusters in snr bin 0: 1609.2679103594542.\n", + "Number of clusters in snr bin 0: 1609.2679103594542.\n", + "Number of clusters in snr bin 1: 846.5269312767326.\n", + "Number of clusters in snr bin 1: 846.5269312767326.\n", + "Number of clusters in snr bin 2: 170.51430471548272.\n", + "Number of clusters in snr bin 2: 170.51430471548272.\n", + "Number of clusters in snr bin 3: 26.123092914495054.\n", + "Number of clusters in snr bin 3: 26.123092914495054.\n", + "Number of clusters in snr bin 4: 2.6599042723871094.\n", + "Number of clusters in snr bin 4: 2.6599042723871094.\n", + "Number of clusters in snr bin 5: 0.1430084892218731.\n", + "Number of clusters in snr bin 5: 0.1430084892218731.\n", + "Total predicted 2D N = 2655.235152027773.\n", + "Total predicted 2D N = 2655.235152027773.\n", + "Theory N calculation took 0.9957540035247803 seconds.\n", + "Theory N calculation took 0.9957540035247803 seconds.\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(8,5))\n", + "\n", + "plt.plot(q, Nq, color='k', label=r'$\\mathrm{SOLikeT}$',marker='o')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color='b', fmt='o', capsize=3, \\\n", + " capthick=1, ls='none', label=r'$\\mathrm{SIMS}$')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.legend(frameon=False,fontsize=23,loc=1)\n", + "plt.grid(which='both',alpha=0.2)\n", + "label_size =15\n", + "plt.xscale('log')\n", + "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", + "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", + "ax.xaxis.set_ticks_position('both')\n", + "ax.yaxis.set_ticks_position('both')\n", + "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", + "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", + "plt.xlabel(r'$q$',size=25)\n", + "plt.ylabel(r'$N(q)$',size=25)\n", + "fig.tight_layout()\n", + "plt.savefig('nq_dr5_simsQ-based.jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + } + ], + "source": [ + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(8,5))\n", + "\n", + "# plt.plot(q, Nq, color='k', label=r'$\\mathrm{SOLikeT}$',marker='o')\n", + "plt.errorbar(q, Nq-catNq, yerr=np.sqrt(catNq), color='b', fmt='o', capsize=3, \\\n", + " capthick=1, ls='none')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.legend(frameon=False,fontsize=23,loc=4)\n", + "plt.grid(which='both',alpha=0.2)\n", + "label_size =15\n", + "plt.xscale('log')\n", + "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", + "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", + "ax.xaxis.set_ticks_position('both')\n", + "ax.yaxis.set_ticks_position('both')\n", + "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", + "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", + "plt.xlabel(r'$q$',size=25)\n", + "plt.ylabel(r'$\\mathrm{SOLikeT}-\\mathrm{SIMS}$',size=25)\n", + "fig.tight_layout()\n", + "plt.savefig('nq_dr5_simsQ-based-diff.jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,5))\n", + "plt.plot(z, Nz, color='k', label=r'$\\mathrm{SOLikeT}$',marker='o')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color='b', fmt='o', capsize=3, \\\n", + " capthick=1, ls='none', label=r'$\\mathrm{SIMS}$')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.legend(frameon=False,fontsize=23,loc=1)\n", + "plt.grid(which='both',alpha=0.2)\n", + "label_size =15\n", + "plt.xlim(0,1.7)\n", + "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", + "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", + "ax.xaxis.set_ticks_position('both')\n", + "ax.yaxis.set_ticks_position('both')\n", + "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", + "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", + "plt.xlabel(r'$z$',size=25)\n", + "plt.ylabel(r'$N(z\\,|\\,q>5)$',size=25)\n", + "fig.tight_layout()\n", + "plt.savefig('nz_dr5_simsQ-based.jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,5))\n", + "# plt.plot(z, Nz, color=color_list[0], label=r'$\\mathrm{SOLikeT}$',marker='o')\n", + "plt.errorbar(z, Nz-catNz, yerr=np.sqrt(catNz), color='b', fmt='o', capsize=3, \\\n", + " capthick=1, ls='none')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.legend(frameon=False,fontsize=23,loc=1)\n", + "plt.grid(which='both',alpha=0.2)\n", + "label_size =15\n", + "plt.xlim(0,1.7)\n", + "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", + "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", + "ax.xaxis.set_ticks_position('both')\n", + "ax.yaxis.set_ticks_position('both')\n", + "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", + "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", + "plt.xlabel(r'$z$',size=25)\n", + "plt.ylabel(r'$\\mathrm{SOLikeT}-\\mathrm{SIMS}$',size=25)\n", + "fig.tight_layout()\n", + "plt.savefig('nz_dr5_simsQ-based_diff.jpeg')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "bin_params = info['likelihood']['soliket.BinnedClusterLikelihood']['binning']\n", + "\n", + "\n", + "zbins = np.arange(bin_params['z']['zmin'], bin_params['z']['zmax'] + bin_params['z']['dz'], \\\n", + " bin_params['z']['dz'])\n", + "\n", + "logqmin = bin_params['q']['log10qmin']\n", + "logqmax = bin_params['q']['log10qmax']\n", + "dlogq = bin_params['q']['dlog10q']\n", + "\n", + "# TODO: I removed the bin where everything is larger than qmax - is this ok?\n", + "qbins = 10**np.arange(logqmin, logqmax+dlogq, dlogq)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig = {\n", + " 'predSNRCut': 5,\n", + " 'path2truthcat': '/Users/boris/Work/CLASS-SZ/SO-SZ/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + "# 'path2truthcat': '../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'fast',\n", + " 'QSource': 'fit'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: We don't have true_fixed_y_c or true_Q - we reconstruct those here.\n" + ] + } + ], + "source": [ + "# Make a 'true' mock - use the truth catalog, get true_SNR by looking up noise in the selFn dir\n", + "mode = 'without_Q'\n", + "truthTab = nemo_mocks.make_truth_mock(mode, mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "truth_cat, zarr, qarr = nemo_mocks.bin_catalog(truthTab[truthTab['true_SNR']>5], zbins, qbins, SNR_tag='true_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "mockTab = nemo_mocks.make_nemo_mock(mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "mock_cat, zarr, qarr = nemo_mocks.bin_catalog(mockTab[mockTab['fixed_SNR']>5], zbins, qbins, SNR_tag='fixed_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_truth = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_truth[i] = truth_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_truth = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_truth[i] = truth_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_mock = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_mock[i] = mock_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_mock = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_mock[i] = mock_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))\n", + "\n", + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], label='prediction',marker='o')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "\n", + "# plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='truth catalogue')\n", + "# plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + "# capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AOgAEBgbqspbJIK0vPP2fKjR5thKHf/6dXTsPsXtXKFv/2gtAnryBVHj4Ufz2+IAFD28PKvcviXcOD/z9/fU7klQjY8aMXLhwIVHaCj9/iSsXrnJo80/keSxp78fRv39/Pv30U6ZNm0aBAgWYNm0a9evXZ9++feTIkYNLly4B0Lt3b8aMGUNQUBBvv/02DRs25LvvviNdunT079+fAwcOsGzZMrJnz87x48c5d+5cnJ/Hl19+SbNmzejVqxfTpk3jxo0bfP3115w/f57r16/zzz//MGDAAIoUKcIff/zB0KFDadasGWvXriVTpkwsWrSIl19+mZ07d5I5c2Z8fHy4cOEC586do2PHjhQvXpwrV64wbtw4GjVqxK5du/D29o5e/+XLl7lw4QKXLl2iXr16lClTho0bN/LXX3/RvXt3WrVqFV2YJ06cyLx583jnnXcoVqwYs2bNYsmSJZQsWTL6/d28eZNr167FeL/Xr1/nxo0b0dNGjBjBf//7X8aNG8dDDz3Ezp076dixI2nTpqV+/fj7rDdv3kzw39aVK1fub7tprXXkAVwEgm97HgRYoPodyw0Bfor6uSCwB/iOyMMHQxK6vrJly1pxTu98wxP0EEktDh06lCjtHNl93PYpMML2zjfcDnj4LXtk9/FEaTc2Fy9etF5eXnb+/PnR027cuGELFixoBw8ebK21duPGjRawixYtil7mwoULNmPGjHbWrFnWWmubNGlig4ODE7zeKlWq2ObNmyd4+dDQUAvY3377LUams2fP3vX9eXh42M2bN0dPA+zy5cuttdbOnDnTZsiQwf7zzz/R82+1/fPPP1trrc2RI4cdPXp09PyIiAj78MMP2xo1akRPq1Gjhu3atWuMdbdu3do2atQoOoePj4/dtGlTjGV69OhhGzRocNf3f3u+u4nv7xDYbeOoiSnxIj93nn5gbk2z1v5qrS1rrS1prX3U6hx/EUnlDm8/ho2I3OzduH6Tw9uPJd26Dh/m+vXrVK1aNXpamjRpqFy5MocOHYqxbOXKlaN/9vf3p0SJEtHLdO7cmY8++ohSpUrRt29fvvnmm3jXu2/fPp544ok45+/du5enn36afPnykT59esqVixygfvz48Thfc+v9vPTSSxQqVIgMGTIQGBhIREREnK8LDQ2lZMmSMUbSV6lSBQ8PDw4dOsT58+c5deoUFSpUiJ5vjKF8+fLx5rjToUOHuHLlCvXr18ff3z/68f7773P48OF7aiuppJjd/sA54CaQ447pAcDp+23UGNMEaFK4cOEHiCYP6s6BfEf3/MZPWw6z8/D3vL9kKT4+3owe1w9r7QOdMiiS2hSqlA/jYbARFk+vNBSqlC/J1mWjTu2O7f/Yvfy/a9CgAceOHWPNmjVs2LCBRo0a0bRpU+bOnXvPmcLDw6lXrx516tRh4cKFBAQEcO7cOapVq8a1a9fifW2TJk3IlSsXM2bMIFeuXHh6elKsWLE4Xxff9uX26Xf7LDw8PKI/y1uuX///s5wiIiKHtK1cuZK8efPGWM7LyyvetpNLiun5W2uvEblLv+4ds+oC2x6g3ZXW2g4ZM2Z8kHiSyPKXzUO9HjV5Y2p3vt68kKBc2enWaThNn3mVI7/+7nQ8kWSTv2wegh4JJEvuTHRa3JL8ZfMk2boKFy6Mt7d3jAF+N2/e5Ntvv6VYsWIxlt2+fXv0z+Hh4Rw8eDDG+eTZsmWjZcuWzJs3j9mzZzN//nyuXr0a63pLly7Nhg0bYp33448/cu7cOUaNGkX16tV55JFHOHPmTIxlbh2/v3nzZvS0P/74g9DQUAYNGkSdOnUoWrQoFy5ciPeS5MWKFePAgQMxjqdv27aNiIgIihYtSsaMGcmRIwc7d+6Mnm+tZdeuXTHayZ49OydPnowx7cCBAzHWkzZtWo4dO0bhwoVjPPLlS7ovd/ciWXv+xhh/4FYX3APIa4x5DPjTRp7KNxFYaIzZCWwFOhE5FmB6cuaU5PVI0YKMGN2JY7/+xZvD36V65ZfoN7A9nbu9hJdXSto5JZI0fNKnxSd92iQt/AB+fn507tyZgQMHki1bNgoUKMCkSZM4ffo0Xbp0ibHsyJEjyZ49O0FBQYwYMQJvb29eeuklAIYMGUKZMmUoXrw4N27c4JNPPqFgwYKkTZs21vUOHjyYJk2aULhwYV566SWstaxfv56OHTuSN29e0qZNy7Rp0+jatSuhoaG88cYbMV6fL18+jDGsWrWKJk2a4OvrS+bMmcmWLRuzZs0iT548hIWF0a9fPzw9495mtGjRgqFDh9KqVStGjBjBX3/9RceOHXnuuee4tXe4R48ejB07liJFilCsWDFmzJjByZMnyZkzZ3Q7tWvXpmfPnnz++ec8/PDDzJgxg99++438+fMDkD59evr27Uvfvn2x1lK9enUuXrzI9u3b8fDwoEOHDvf8u0t0cQ0GSIoHUJPI4/d3PubdtkwX4Chwlcg9AdUTY90a8Jeybdy40Vpr7Ymw07Z1i/42W4YKtkaVl+ye3QedDSYSh8Qa8Gette82m2ffbTYv0dqLz5UrV2yPHj1sQECA9fb2thUrVowxQO7WALjPPvvMlihRwnp7e9vSpUvbnTt3Ri8zcuRIW6xYMevr62szZ85sGzRocNfP47PPPrNlypSx3t7eNmvWrLZJkyb28uXL1lprly5dagsWLGjTpk1ry5cvb9euXWuB6O2CtdaOGDHC5siRwxpjbOvWra211m7YsMEWL17cpk2b1hYvXtyuXbvW+vn52blz50a/jtsG/Flr7XfffWdr165tfXx8bKZMmWzr1q3t33//HT3/+vXrtkePHjZjxow2U6ZMtlevXrZ169a2fv360ctcu3bNdunSxWbNmtVmzZrVvvHGGzEG/FkbOVBw6tSptmjRotbb29tmy5bN1qlTx65fv/6uv6PkGPCXai/vm1C3HfN/5eeff3Y6jsThzsv7rv7iGwb2G8epk+d4pVMzXhvcUTcVkhTFVS/vK/9WpkwZqlatyjvvvJMs60uOy/u6/D5Va+1KYGW5cuVecTqLJFzDxjWoVr0sI0e8z6zpH7FqZQhjJ/TnyfqPOx1N5IHFdcXLO6fripfJ79ixY6xbt44aNWpw48YNZs6cyYEDB5g5c6bT0RJVihnwJ3Kn9Bn8GTO+H6vWzyJ9ej9aNO9Du9aDOHVK1/cWkaTh4eHBggULqFChApUqVWL79u2sWbMm+vRDV+HyPX9J/cpXKMGGTQuYNmURE8fNIWTjDoYO78bLrZ/Gw0PfXyX1UY8+5cqTJ0+slzt2NTrmL8nCzns1QcuZ4Knxzj/8y3H69BzN1s17qVi5FBOnvEaRh93j7oqSsiTmMX+R27ncLX2dYHWev0spVDgvn658jynvvs5PoUeo9XhLxr39AVevxn8xEBER+X8uX/zF9RhjeOnlJmzbvYzGT9Vi7OhZ1Hq8Jd9u2+d0NBGRVEHFXxxh7f8/7lf27FmYMftNPlwxiStXrvJUg0706TGa838nzp3WRERclQb8SbK4/Vh++A9HOPnmFPwCLZf+9CTHwG7sOnssxnn+96JO3Sps3v4hY0fNZPp7S1m7ZjOjx/ahydO1dZ8ASZG+f6JngpYrsWFykuYQ9+XyxV839kkZYtvYXT5rgJv8+uoUsgLfv/nf+97Y+fn5MvytHjz3/JP0enU07VoPol6DaowZ349cuQMfJLqIiMtx+eKvi/y4l1Kli7J+4xxmvr+MMaNmUrXiCwx6oxPtXnmeNGnSOB1PBPh3j/7X3pFXjis4sbsDaZKGMYbly5fz/PPPOx3lro4ePUqBAgXYtWuXy53PHxeXL/6SMty5sQv/4QjhB37Br1Rh/IoX+NflfR+Ep6cnXbq3oFGTWvTrPYbBAyby8UfrmDj1NYo/+lCirEMktapZsyaPPvoo06ZNS5T2hg0bxooVKzh48GCitPcg5s2bR7du3bh48aLTUVI8DfgTR/gVL0DAS3XxK5505+jnyx/Eso8nM/2DERw/doI6NVrz5rB3uXz5SpKtU8RV3H5/enE9Kv7i0owx/KdpPbbuWkbTFxowddICqlduwTcbd979xSIuJjg4mG+++YZ3330XYwzGGI4ePUpISAjGGFavXk2FChXw9vZm3bp1DBs2jEcffTRGG/PmzcPf3z/65+HDh/PDDz9Etzdv3rzoZf/880+aNm2Kn58fBQsWZNGiRXfNOH/+fEqUKEHatGkJDAwkODg4et7EiRMpWbIkfn5+5MqVi/bt2/P3338DkTcHa9OmDeHh4dFZhg0bBsCiRYsoX7486dOnJyAggKZNmxIWFhZvjk2bNlGxYkV8fHwIDAykV69eXLv2/9cTCQ8Pp1WrVvj7+xMYGMjo0aNp3LhxjLz58+dn/PjxMdqtWbMm3bp1i35+7do1BgwYQO7cufHz86N8+fJ89dVXd/2cHpSKv7iFLFkyMvXdN/jk83cxBp5/pjvdOo/gjz/+djqaCDfDr3Dt9F+E/3AkSdczZcoUKleuTJs2bTh58iQnT54kT5480fMHDBjAyJEj+fHHH6lYseJd22vevDl9+vTh4Ycfjm6vefPm0fNHjBjB008/zYEDB2jevDlt27bl2LFjcbY3Y8YMOnbsSJs2bfjuu+9YvXo1xYsXj57v4eHB5MmT+eGHH1iyZAk7d+6ke/fIcRJVqlRh8uTJpEuXLjpL3759gcgCO3z4cA4cOMAXX3zBuXPnePHFF+PMERYWRoMGDShdujT79u1j9uzZfPjhh7z22mvRy/Tp04dvvvmGTz/9lK+//poDBw6wefPmu35md2rTpg3ffPMNS5Ys4fvvv6d169Y0b96cAwcO3HNb9ySue/26ygNoAswsXLhwgu6NLM64/b7dSe3Spct25PD3bI4sle3DBZ60Hy1dbSMiIpJt/eIa7nb/+oS6ePBX+90TPe13tXvY7xv0tRcP/poo7calRo0atmvXrjGmbdy40QJ2xYoVMaYPHTrUFi9ePMa0uXPnWj8/v3iXsdZawA4cODD6+fXr162vr69duHBhnNly5cplBwwYkOD3smbNGuvt7W1v3rwZa7a4hIaGWsD+9ttv1lprjxw5YgG7a9cua621gwYNsoUKFYpu91bb3t7eNjw83F64cMF6eXnZDz/8MHr+xYsXbaZMmWzr1q2jp+XLl8+OGzcuxrpv//x/+eUXa4yxx44di7FMo0aNbOfOnRP0GcT3dwjstnHURpfv+Vtd3lfu4Ovrw+AhndmwaQH5C+SiS4dhNHuuB0ePxL8bUCQphB/4JfpqV/b6zcjnDknske4lS5aM/tnT05Ps2bNz5syZWJc9c+YMYWFhPPHEE3G29/XXX1O3bl1y585N+vTpee6557h27RqnTp2KN8fevXt5+umnyZcvH+nTp49+n8ePH491+dDQUCpXrhzjxmGPP/44165d45dffuHw4cNcv36dChUqRM/38/P71yGSu9m7dy/WWooVK4a/v3/0Y926dRw+fPie2rpXLl/8ReJSrHhhVq2fxdvj+7J71/dUr/wi06Ys5MaNG05HEzfiV6owRF2MyniliXzuVBY/vxjPPTw8bu1BjXYvAwG9vLxiPDfGEBEREeuyd67nTseOHaNRo0YULVqU5cuXs2fPHubMmQMQ41j8ncLDw6lXrx7p0qVj4cKF7Nq1i7Vr18b7OmttnBcIM8ZEZ73bRcTu9vlFRERgjGHXrl3s378/+rFr167o95ZUVPzFraVJk4Z2rzRl646l1KhVgeFDplG3Zhv27w11Opq4Cb/iBfApFIRXjiwUGNclSc+AAfD29ubmzZsJWjZ79uycPn06RgHbv3//fbcXn8DAQHLlysWGDRtinb97926uXbvGpEmTqFy5MkWKFOHEiRN3zfLjjz9y7tw5Ro0aRfXq1XnkkUfi3PtwS7Fixfj2229jfFHZsmUL3t7eFCpUiMKFC+Pl5cXOnf8/cPjSpUv/Ot0xe/bsnDx5Mvr5lStX+PHHH6Ofly5dGmstp06donDhwtGPQoUKkStXrngzPigVfxEgKFcgC5aMY+7Ctzl79k/qPdGW11+bxMWLl5yOJm4gjZ8P3oGZk7zwQ+QI9J07d3L06FHOnTsXZ08cIkem//nnn4waNYrDhw8ze/ZsVqxY8a/2jh07xt69ezl37hxXr16972yDBw9m8uTJTJo0if/973/s37+fCRMmAPDQQw8RERHB5MmTOXLkCB9++CGTJ0/+V5YrV67w5Zdfcu7cOS5dukTevHlJmzYt06ZN49dff2XVqlW88cYb8ebo0qULJ06coEuXLoSGhrJq1SoGDhxIt27dSJcuHf7+/rRt25YBAwawYcMGDh06RPv27aN78rfUrl2bxYsXExISwg8//EDbtm1j9PyLFClCixYtCA4OZsWKFfz666/s3r2bqVOn8sknn9z355ggcQ0GcLVH2bJl7zpwQpyTnAP+7ub83xdsv15jbLYMFexjxZ+y69dtcTqSpECJNeDPWmsP95pqD/eammjtxeenn36ylSpVsr6+vhawR44ciR7wd/bs2X8tP336dJs3b16bLl0627x5czt58uQYg+quXLli//Of/9hMmTJZwM6dO9daGzngb/ny5THaim0A3J0++OADW7RoUevl5WUDAwNtmzZtoudNmTLFBgUFWR8fH1u7dm27bNmy6PdwS6dOnWzWrFktYIcOHWqttXbp0qW2YMGCNm3atLZ8+fJ27dq1Foje7tw54M9aa7/55htboUIF6+3tbQMCAmzPnj3tlStXoudfuHDBvvzyyzZdunQ2ICDAjh492tauXdt26tQpepnz58/bF154wWbIkMEGBQXZd999918DLq9du2aHDh1qCxQoEP2eGzRoYHfv3h3v53TL/Q74M/Yux1lcRbly5ezu3budjiFxSMwr/CWWHdsP0PvVUfzvp6M881wdRr7dm8DArE7HkhQiNDSUokWLJkpbrnh5X3dz9epV8uXLR79+/ejTp88DtXXhwgXSp0+foGXj+zs0xuyx1sY6itPlL++rG/vI/apYqRRfb17ItCkLmThuLhs37GDYyFdp0bKJ7hYoDySuu/rdOV139Uu59u3bR2hoKBUqVODChQuMGTOGCxcuxLjOQUrm8sf8rU71kweQNq03ffq345ttiyn2aGF6dX+LZxp34Zef475QiYi4h4kTJ1K6dGlq167N6dOn2bRpE7lz53Y6VoK4fM9fJDEUfigf//3iPZYs+oJhr0+lRpUW9O7Xhu49W+Ht7XX3BkRuox596le6dGlS86Fkl+/5iyQWDw8PXm71FFt3LaNh4xq8/dZMaldryc4d3zkdTUTknqj4i9yjwMCszJr7Fos/mkB4+GUaPfkK/XuP5Z/zuo2oiKQOKv4i9+nJeo+zefuHdOr6IvPnfkqVCs354vONTscSEbkrFX+RB+Dvn443R/Vk3YY5ZM+ehTYtB9K6RX9OhJ12OpqISJw04E8kETxWpijrN85lxnsfMnb0LKpWfIHXh3YhuO1zpEmTxul4ksLYea8maDkTPDWJk4i7Us9fJJF4eXnSrUdLNn37IeXKPcrAvuNpXK8Dh35w7i5tIiKxUc9fJJHlL5CLjz6dysfL1/H6wEk8Ub0V3Xu2one/Nvj4pHU6nqQAd/bo7ZrI56ZBwvYIpEbGGJYvX87zzz/vdJS7yp8/P926daNv375OR0kyLt/zN8Y0McbMPH/+vNNRxI0YY3i+WX227lrGf5rVZ9L4udSo0oItm1LvecEiqcnRo0cxxqTqc/GTkssXf13hT5yUNWsmpr0/hI8/m0ZEhOXZJl15teub/PmnvoyKiHNcvviLpATVa5Zn07eLebVXKz76cA1VyzfnkxXrcJcba0nKUbNmTTp37kyfPn3IkiUL2bNnZ8qUKVy9epWuXbuSKVMm8ubNy8KFC2O87vvvv6dOnTr4+vqSJUsWgoODuXOP6vz58ylRogRp06YlMDCQ4ODgOHOMGTOGbNmysWPHjjiX2b59O7Vr18bPz4+MGTPyxBNPcOLECQDWrl1LtWrVyJw5M1myZKFevXqEhoZGv7ZAgcjbI5cvXx5jTPSNw3bt2sWTTz5JtmzZyJAhA48//jjffvttvJ/Z8ePHefbZZ0mfPj3p06fnueee4/fff4+xzOjRowkMDMTf359WrVoxfPhw8ufPHz0/ODiYxo0bx3jNsGHDePTRR2NMmzt3LuXLl8fHx4ciRYowadKkeG+7fL9U/EWSia+vD28M68qGTfPJmy+Iju2G8MLzvTh+7ITT0cRp1y5D+J/YM0eSZXWLFy8mffr07Nixg4EDB9KzZ0+eeeYZihQpwu7du2ndujXt27ePLrSXLl2ifv36+Pv7s3PnTj799FO2bdtG27Zto9ucMWMGHTt2pE2bNnz33XesXr2a4sWL/2vd1lr69u3LO++8wzfffEPFihVjzXjgwAFq1apF4cKF2bp1K9u3b6dZs2bcuHEDgPDwcHr27MnOnTsJCQkhY8aMNGnShGvXrgGwc+dOIPJLwsmTJ/nkk0+AyDvmtWzZks2bN7Nz504ee+wxGjZsyLlz52LNYa3lmWee4fTp03z99dds3LiREydO8Mwzz0R/eV+6dCnDhw/nrbfeYu/evRQtWpSJEyfe8+9l1qxZDBo0iMGDBxMaGsqECRMYM2YM77333j23dVdx3evX1R5ly5a9632RxTm37qvtLm7cuGFnTl9m8wXVtHlzVLfvTl1kr1+/7nQsuQfx3Uf9XkSc/tVGzH3VRsztbiMW9LYRp39NlHbjUqNGDVupUqX/X39EhM2WLZtt0qRJ9LRr165ZLy8vu3z5cmuttTNnzrQZMmSw//zzT/QyGzdutID9+eefrbXW5sqVyw4YMCDO9QJ26dKlNjg42D700EP2yJEj8eZ86aWXbMWKFRP8vi5evGg9PDzs5s2brbXWHjlyxAJ2165d8b4uIiLC5siRwy5cuDB6Wr58+ey4ceOstdauX7/eenh4xMh7+PBha4yxX375pbXW2kqVKtmOHTvGaLdu3bo2X7580c9bt25tGzVqFGOZoUOH2uLFi0c/z5Mnj12wYEGMz3nSpEm2aNGiceaP7+8Q2G3jqInq+Ys4IE2aNLzSsRlbdyylWo3yDH19KvVqt+XA/h+djibJ7dTPQNThn4ibUc+TVsmSJaN/NsYQEBBAiRIloqd5eXmROXNmzpw5A0TeM75kyZIx7jFfpUoVPDw8OHToEGfOnCEsLIwnnngi3vX27duXkJAQtmzZEmOXeGz27dsXb3uHDx/mpZdeolChQmTIkIHAwEAiIiI4fvx4vO2eOXOGjh07UqRIETJmzEj69Ok5c+ZMnK8LDQ0lKCgoRt6CBQsSFBTEoUOHAPjxxx+pUKFCjNfFtUcjLmfPnuW3336jY8eO5MyZE39/f/z9/Rk4cCCHDx++p7YSQsVfxEG5cgey8MNxzJ4/ilOnzvFkrTYMHTyF8PDLTkeT5JLjIcBE/uyRJup50vLyinknSmNMrNNuHWu21mKMibUtY0yCx67UrVuXU6dOsXr16rsue7c2mzRpwtmzZ5kxYwY7duxg3759eHp6Ru/2j0vr1q3ZtWsXkyZNYtu2bezfv5/cuXPH+bq7vffYfo6Nh4fHv97T9evXo3++9VlPnz6dLVu2sH//fvbv38/Bgwf54Ycf4m37fqj4izjMGMNTzzzBtp3LaNn6ad6btoRqlV5kw1fxD0IS12ACCkDmIPDPAvW6RT5PYYoVK8aBAwe4cOFC9LRt27YRERFB0aJFCQwMJFeuXGzYsCHedho2bMjy5cvp3Lkz8+fPj3fZMmXK8PXXX8c6748//iA0NJRBgwZRp04dihYtyoULF6LHAwB4e3sDcPPmzRiv3bJlC927d6dRo0YUL16c9OnTc/LkyXjfe1hYGEePHo2e9uuvv3LixAmKFSsGwCOPPBI9xuCWO59nz579X+vZv39/9M+3PsPDhw9TqFAhChcuHOOR2FT8RVKIjJnSM37yQD5fMx0fn7S88J+edGo/hLNn/3Q6miQ1b1/wy5IiCz9AixYt8PPzo1WrVnz//fds2rSJjh078txzz0UXpsGDBzN58mQmTZrE//73P/bv38+ECRP+1Vbjxo1Zvnw5nTp1YsGCBXGus1+/fuzbt48OHTpw4MABfvrpJz744AOOHz9O5syZyZYtG7NmzeKXX37hm2++oVOnTnh6/v916wICAvD19WXdunWcPn06+syEIkWKsGjRIg4dOsSuXbt44YUXor8oxKZOnTqUKlWKFi1asGfPHnbv3k2LFi0oU6YMtWvXBqBHjx7MmzePOXPm8PPPPzN27Fh27NgRY29A7dq12bdvH3PmzOGXX35h7NixbN26Nca6hg0bxtixY5k2bRo//fQTBw8eZMGCBYwePToBv6V7o+IvksJUrlKajVsW0v+1V1j52ddUKdecJYtW6rRAcUy6dOlYt24d//zzDxUqVODpp5+mcuXKzJkzJ3qZzp078+677zJr1iweffRR6tevH+fu6saNG/PRRx/RsWPHOL8APPbYY3z11Vf8+OOPVKpUiYoVK7J06VK8vLzw8PBg2bJlfPfddzz66KN07dqVN998k7Rp//8Kmp6enkydOpUPPviAoKAgnn76aQDmzJnDxYsXKVu2LC+88AJt27aNd/yBMYb//ve/ZM+enZo1a1KrVi1y5MjBf//73+ji/sILL/DGG28wcOBASpcuzcGDB+nUqRM+Pj7R7dSrV4+hQ4cyePBgypYty9GjR+nSpUuMdbVv3545c+awbNkySpUqRbVq1Zg5c2b0aYuJybjLBqVcuXJWV3pKuUJCQqLPw5X/97+fjtC7x2h2fHuAx6uVZfyUgRQqlNfpWELkQLCiRYsmSlvucHlfd/Pss89y48YNVq5cec+vvXDhQozBlfGJ7+/QGLPHWlsutnm6tr9IClbk4QJ8vno6i+Z/xoHh3/PeE/Pu+poJR4ckfTB5IHHd1e/O6bqrX+pw6dIl3n//ferXr4+npycff/wxn332GR9//LHT0eKk3f4iKZyHhwet2jzrdAwRiYMxhjVr1lC9enVKly7NsmXLWLhwIc8+m3L/37p8z98Y0wRokhSjJUWS060e/bo1m+nfZyyl/ylC1gyZaD/rRYpW1d93aqIevWvx9fXlq6++cjrGPXH5nr/VjX3ExdRrUI2lMyaS1TMjNjyCGS0W8dXSLU7HEpFUxOWLv4grOvHdaSByd6PB8Hav6UybsjBJbgAicXOXAdOSMj3I35/L7/YXcRV98o+IdboHhlJpi3Bk0hH6TRpJ322dyBkUkMzp3I+XlxeXL18mXbp0TkcRN3X58uV/XZkxodTzF3ExNaq0YPUX3zgdw+UFBAQQFhbGpUuXtAdAkpW1lkuXLhEWFkZAwP190VfPXySVSMgpfId/Oc6Bdodp3aI/rYKfYcSonvj5+SZDOveTIUMGAE6cOBHjGu0iD+rKlSsxLhAUGy8vLwIDA6P/Du+Vir+ICylUOC+rv/yAt9+aybQpC9m2dR/TPxhBqccecTqaS8qQIcN9b3xF4hISEkLp0qWTdB3a7S/iYry9vRgyvCuffD6N8PBLNKjTjncmazCgiPw/FX8RF/V49XKEbF3Mk/UfZ8TQaTz/dHdOnjjjdCwRSQFU/EVcWJYsGZm78G0mvTOYPbsPUqNKC1atDHE6log4TMVfxMUZY3i51VN8vXkhefMFEfzyAHq/Oorw8MtORxMRh6j4i7iJW4MBX+3VikULPueJ6q04sC/U6Vgi4gAVfxE34u3txRvDIgcDXrp0mQZ122swoIgbUvEXcUOPVy/HN1sXU69BtejBgCfCTjsdS0SSic7zF0kl4roH/J0Sese4zFkyMmfBaJYsWsmgAROpUeVlJr0ziMZP1XqQmCKSCqjnL+LGjDG0aPkUX29eQP4CuWjTciC9umswoIirU89fJJU4uDDmd/VCDSNI4w2/bTFcPmeip5cIvve2CxXKy6r1sxgzaibvTF7It9v2MeODEZQqXfQBU4tISpTqev7GmDzGmBBjzCFjzAFjzHNOZxJJbr7ZLD5ZwMsfCtS1+GZ78BvL3BoM+OnKdzUYUMTFpcae/w2gp7V2vzEmANhjjFlrrb3kdDCRpFRiw+Ton+1367F7vsAYwNNQaHAjTMknE2U9VauV5Zuti+nT821GDJ3G1199y7szhhKUKzBR2hcR56W6nr+19qS1dn/Uz2eAv4BsjoYSSWZXLvthb4KNAHvDcuWyX6K2nzlLRmbPH8WUd19n795D1KjyMis/+zpR1yEizkn24m+MqW6M+dwYE2aMscaY4FiW6WKMOWKMuWKM2WOMqRZHW+UAL+C3JI4tkqJcOHyJI18aTh8wHNmQhguHE3/HlzGGl15uEj0YsG2r1+jZ7S0uXtRONpHUzond/v7AQWBB1CMGY0xzYArQBdgS9e8aY0wxa+3x25bLGvX6dtbaBz/gKZLC3X6qn99ZOPOX4fIfYDwiyBm2EjtvJZDwU/0SqlChyCsDjhk1k6mTFkQNBnyTx8poMKBIapXsPX9r7Wpr7SBr7QogtpFEvYF51tpZ1tpQa2134CTQ+dYCxpi0wKfAaGvttmQJLpKCpMseOdAvsJSlQF1LuuxJuz4vL09eH9qFT1e+y5UrV2lQtx1TJy3g5s2bSbtiEUkSKWrAnzHGGygLjL9j1nqgStQyBpgHfG2tXXiX9joAHQACAwMJCQlJ5MSSWC5evKjfz11kXZiw7+p/5A9J0hwjx3Rm5nuf8Oawd/lkxRq692pO1myZknSdIu4kObaHxsk95saYi0A3a+28qOdBQBhQw1q76bblhgAtrLUPG2MeBzYB393WVEtr7ffxratcuXJ29+7dif0WJJGEhIRQs2ZNp2NIAllr+XDxF7zWfwLeXl5MnPoaTZ6u7XQsEZeQWNtDY8wea2252Oal1NH+d34jMbemWWu3WGs9rLWP3faIt/CLSOK6fTBggYK5NRhQJJVJacX/HHATyHHH9ADgvu46YoxpYoyZef78+QfNJiJ3uHVlwJ59glmyaCVPVG/F/r26TbBISpeiir+19hqwB6h7x6y6wH0N7LPWrrTWdsiYMeODxhORWHh5eTJ4SGc+/eK96MGAUybO12BAkRTMifP8/Y0xjxljHotaf96o53mjFpkIBBtj2htjihpjpgBBwPTkzioiCVf18TJ8s3UxDRvXZOTw9/jP0910m2CRFMqJnn85YF/UwxcYHvXzCABr7TKgJ/A6sB94HGhorT3mQFYRuQeZMmfgg3lvMeXd19m3N1RXBhRJoZw4zz/EWmtieQTftsx71tr81tq01tqyt4/8v1c65i+SvG4NBty4eWH0YMAeXUdqMKBICpKijvknBR3zF3FGwUJ5WLV+Fr36tuHDxV/wRPVW7NtzyOlYIoIbFH8RcY6XlyeD3ujEf1e9z9Wr12j4ZHsmT5inwYAiDlPxF5EkV6VqaUK2LKJRk1q8NeJ9nnuqG2G/azCgiFNcvvjrmL9IypApcwZmzR3J1PfeYP++UGpWfZnP/7vB6Vgibsnli7+O+YukHMYYXmzRmI2bF1KwUB7atR7Eq13f1GBAkWTm8sVfRFKegoXy8MW6mfTq24ali1dRu1pLDQYUSUYq/iLiiNsHA167dl2DAUWSkYq/iDhKgwFFkp/LF38N+BNJ+W4NBnzn/SEc2P8jNaq04LNPNRhQJKm4fPHXgD+R1MEYwwsvNeLrTQsoVDgv7YM1GFAkqbh88ReR1OXWYMDe/dqwbMlqaldryd49PzgdS8SlqPiLSIrj5eXJa6934r+r3uP69Rs0evIVJo3XYECRxKLiLyIpVuUqkYMBGz9Vi1FvajCgSGJR8ReRFC1jpvTMnDOSadOHRg8G/O8nXzkdSyRVc/nir9H+IqmfMYbmLzZk4+aFFH4oL6+0GUz3LiO4eCHc6WgiqZLLF3+N9hdxHQUK5mbl2sjBgB99uIZa1VppMKDIfXD54i8iruX2wYA3btygYd1XmDhurgYDitwDFX8RSZVuDQZ86pnajB45nWebdOX33045HUskVVDxF5FUK2Om9MyY/SbTpg/luwM/UbPqyxoMKJIAKv4ikqrdPhjwoSL5NBhQJAFU/EXEJRQomJvP18ygT/+20YMB9+w+6HQskRTJ5Yu/TvUTcR9eXp4MHNwxejBgoyc7aDCgSCxcvvjrVD8R93NrMODTzz7B6JHTeaZxFw0GFLmNp9MBRESSQsZM6Zn+wQieqFuZAX3HUaNqC57xqJGg1044OiSJ04k4y+V7/iLivowxNHuhIRs3L6JIkfxOxxFJMdTzFxGXl79ALlauncGEsXOYOG4uefMFMf2D4ewYtweALstaO5xQJHmp5y8ibsHT05MBgzrw2er3uXnzJo2e7MBvv53CYp2OJpLsVPxFxK1UqvwYGzcv5Olnn+D4sRMc/P5nTp0653QskWSl4i8ibufWlQEL5stDxMUImtd6lR9Df3U6lkiyUfEXEbd0dM9vXD51GX8PX0pffoiX6/Vhy6bdTscSSRYq/iLilg5vP4aNiDze7+nhScEMQTR7rgfLl61xOJlI0nP54q8r/IlIbApVyofxMAB4eqdhyLuvUrFSKbp0GMaEsbOxVgMBxXW5fPHXFf5EJDb5y+Yh6JFAsuTORKfFLXm0xsMs+2QKzV5owNtvzaRnt7e4fv2G0zFFkoTO8xcRt+WTPi0+6dOSv2weALy9vZg2fSh58uZkwtg5nAg7zZwFo0mfwd/hpCKJy+V7/iIi98IYw8DBHZk8bTBbNu+hcf2OnAg77XQskUSlnr+IuI0++UckaPqEo0No0fIpcuUKpE2rgdR7oh1LPppIiZJFkiOmSJJTz19EJA41a1dk1bpZeHgYmjToyNdfbXc6kkiiiLfnb4xJY63VjbBFxCXcz936ihUvzNqvZvNis9681Kw34yYNoGXrp5MgnUjyuVvP/6IxZocx5j1jTDtjzGPGGB0qEBG3kjMogJWrp1O9Znl6vzqKUW9O16mAkqrdrfi3AzYBjwDjgb3ABWPMLmPMdGPMK8aYskkdUkTEaekz+LN42QRebvU0k8bPpfMrQ7l69ZrTsUTuS7y9eGvtEmDJrefGmIeAskDpqH+bA+nv1o6IiCvw8vJk4tTXyJsviFFvvs/Jk2eZv2gMmTJncDqayD25pwF/1tqfgS+AA8AFIC1wJglyiYikSMYYevUNZvoHI9i983saPvkKx46ecDqWyD1JUPE3xmQwxrQ0xnwGnAVGA8eAJ4FcSZhPRCRF+k/Tenz06VTOnP6DBnXasm/PIacjiSRYvMXfGNPaGPMFkb37YcBPQE1rbT5rbU9r7RarUS8i4qaqPl6G1V/OwtfXh2cad2bt6k1ORxJJkLv1/OcCpYAeQFFrbX9r7Y6kj5V4dGMfEUlKRR4uwJoNsynycAFatxjABzOXOx1J5K7uVvxDAD/gfSJH+e81xswyxnQyxpQ3xngnecIHpBv7iEhSCwjIyn9XvU/delV5rd94hg6eQkREhNOxROIUb/G31ta21mYBCgMtgfVAfmAksIOoLwRJHVJEJKXz8/Nl/uIxtOvQlPemLaF98GAuX77idCyRWCXoFD1r7a/Ar8BHt6YZY/ID5YAySZJMRCSVSZMmDaPH9iFfviCGvj6VUyfPsuDDcWTLltnpaCIx3Pe1/a21R621K6y1gxIzkIhIamaMoXO3l5g9fxTff/c/GtZtz+HDx52OJRKDLs4jIm7Dzns1QcuZ4KkPvK4mT9cmR87stHyhLw3rtGfh0vFUqFjygdsVSQy6q5+ISBIpX6EEq7/8gIyZ0vNck6589ukGpyOJAOr5i4gbObgwZn+nQN3IEflHvow5vURw4q2zYKE8rPlqNi1f7Ef74EH8/lt3unRvgTEm8VYico/U8xcRSWJZs2bi48/eocnTtRn2xjsM6DuOGzduOB1L3Jh6/iLiNkpsmBzj+aVpA6Kmj0nydfv6+vDBvLcYMWQa776zmLDfTzNzzkj8/HyTfN0id1LPX0QkmXh4eDBs5KuMGd+Pr9Zv45lGnTl9+g+nY4kbUvEXEUlmbV95ngVLxvK/n47QoE47/vfTEacjiZtR8RcRt+XhcRNPr+vYM8lffOs1qMZnq6Zz5cpVGtZ9ha2b9yR7BnFfKv4i4pbsmSN4+17D0/sGrJvmyBeAx8oUZe1XswnMkZWmz77K8mVrkj2DuCcVfxFxT6d+BsAYIOJm9PPkljdfEKvWzaJCpZJ06TCMCePmoDulS1JT8RcR95TjIbBgLVg8Ip87JFPmDCz7eArPN6vP2yNn0Kv7KK5f16mAknR0qp+IuKVLZ+HUV4Z02eHSn4Yc5cAvwLk8adN6897MYeTNl5OJ4+YSFnaaOfNHkT6Dv3OhxGWlup6/MeZzY8xfxpgVTmcRkdQr/MAvXDptOHfQcOmUJfzAL05HwhjDa693YtI7g9n8zW6aNOjIibDTTscSF5Tqij8wCWjldAgRSd38ShXGpIn82Xilwa9UYWcD3eblVk/x4fKJHDt2gvp12vHDQWfGI4jrSnXF31q7EbjgdA4RSd38ihcgqKoXWYqlocC4LvgVL+B0pBhqPVGJlWtmANC4fgc2btjucCJxJcla/I0x1aN224cZY6wxJjiWZboYY44YY64YY/YYY6olZ0YRcR8+WT3I/LBniiv8tzxaoghrv5pNvnxBvNi0N4sXfu50JHERyd3z9wcOAj2Ay3fONMY0B6YAo4DSwDZgjTEmb3KGFBFJKYJyBbJyzQyq1ShHz25vMerN6ToVUB5YshZ/a+1qa+0ga+0KICKWRXoD86y1s6y1odba7sBJoHNy5hQRSUnSZ/BnyUcTadHyKSaNn0uXDsO4evWa07EkFUsxp/oZY7yBssD4O2atB6rcZ5sdgA4AgYGBhISEPEhESUIXL17U70eSXZmo2+qmlr+9p/9ThZsRV1m6eC2hh/5H39da4u+fzulYksiSY3tonNp9ZIy5CHSz1s6Leh4EhAE1rLWbbltuCNDCWvtw1POvgFKAH/An0NRa++3d1leuXDm7e/fuRH8fkjhCQkKoWbOm0zHExdl5ryZoORM8NYmTPJjly9bQo+tIChTMzYfLJ5E3X5DTkSQRJdb20Bizx1pbLrZ5KXG0/53fRszt06y1day12a216ay1uRNS+EVEXEnT5g1Y/ulUTp/6g/p12rF/b6jTkSSVSTG7/YFzwE0gxx3TA4D7vsqFMaYJ0KRw4ZRzDq+IOOPOHv2vvd8BoODE7k7EeSBVq5Vl1fqZvNi0N0836sSsuW/xZP3HnY4lqUSKKf7W2mvGmD1AXWD5bbPqAh8/QLsrgZXlypV75QEjikgq9/0TPRM0vcSGyUmeJTE8/EhB1nw1mxbNetPyxX6MHtuHtq8873QsSQWS+zx/f2PMY8aYx6LWnTfq+a1T+SYCwcaY9saYosaYKUAQMD05c4qIpBaBgVn5bPV06taryoC+4xj2+lQiImI7mUrk/yV3z78csPG258OjHvOBYGvtMmNMVuB1ICeR1wRoaK09lsw5RcQFpZYe/b3y8/Nl/uIxDBowkXffWczx4yd5d8ZQfH19nI4mKVSyFn9rbQiRA/jiW+Y94L3EWqeO+YuIO0iTJg1vj+tLvnxBDH19KqdOnWPhh+PImjWT09EkBUqJo/0TlbV2pbW2Q8aMGZ2OIiKSpIwxdOnegg/mjeK7/T/SoE47fj38m9OxJAVy+eIvIuJunn72CT7+fBrn/75Agzrt2LnjO6cjSQqj4i8i4oIqVirF6q8+IEPG9DzXpCsrP/va6UiSgrh88TfGNDHGzDx//rzTUUREklWhQnlZ89UHlCz1MO1aD+L9aUt0UyAB3KD465i/iLizbNky8/Hn02jUpCZDBk/htf4TuHnzptOxxGEuX/xFRNydr68Ps+ePoku3l5g9cznBLw8gPPxfd1UXN6LiLyLiBjw8PBj+Vg9Gj+vL+rVbeaZRZ06f/sPpWOKQFHN5XxERSXrtOzQld+5AOrR9nYZ12vHhikkUebjAPbXRJ/+IBC034eiQ+4koycDle/4a8CciElP9htX5bNV0Ll++QsO6r7B1y16nI0kyc/mev27sIyLyb6XLFmPNV3N4sWkvmj7TnanvvcHzzeon6LV39ujfaz4fgC7LWid6TkkaLt/zFxGR2OXLH8Tq9bMoX7EknV8ZyqTx83QqoJtQ8RcRcWOZMmfgo0+m8J+m9Rj15vv0fnU016/fcDqWJDGX3+0vIiLxS5vWm/dnDSdvviAmjZ9LWNhp5swfhX96P6ejSRJRz19ERDDGMOiNTkycOohNIbto3KAjJ0+ccTqWJBGXL/4a7S8iknAtWz/Nko8mcvRIGPXrtOOHgz87HUmSgMsXf13eV0Tk3tSuU4mVa2YQEWFpXL8DGzdsdzqSJDKXL/4iInLvSpQswtqvZpM3bxAvNevN4oWfOx1JEpGKv4iIxCpX7kBWrpnB49XK0rPbW4weOV2nAroIFX8REYlThoz+LFk+iRdfbszEcXPp1mk4165ddzqWPCAVfxERiZeXlydTpr3OwNc78tHSNTR/rgfn/74QPf/Khav8FXaeo3t+czCl3AsVfxERuStjDH36teW9mcPYsf0Ajeq9wm/HT3J0z2+c+PE0f/7+N9NbLNQXgFTC5Yu/TvUTEUk8TZs34KNPpnDy5Fnq12nHlk93YSMixwHcuH6Tw9uPOZxQEsLli79O9RMRSVyPVy/HqnWz8Pb2YsKcuWAip3t6paFQpXzOhpMEcfniLyIiie+RogVZ89VsshTOzJ/Xz5M2szedFrckf9k8TkeTBFDxFxGR+5IjRzY+Wz0dnww+HD93it8unnY6kiSQir+IiNw3f/90FC1aEF+ftLR+ub8uB5xKqPiLiMgD8fT0pFjxwvj7p+PFpr0I+117AFI6FX8REXlgadN6s3TFZC5evMQLz/eMcR0ASXlU/EVEJFEUK16YBYvHcviX47Rq0Z+rV685HUnioOIvIiKJ5vHq5Zg2fQjbtuyla8fhREREOB1JYuHpdAAREXEtzz1fjxNhZxg+ZBpBuQIY8VYPpyPJHVy++BtjmgBNChcu7HQUERG30fXVlwkLO8P705YQlCuATl1edDqS3Mbld/vrCn8iIsnPGMPI0T1p1KQmQwZN4bNPNzgdSW7j8sVfRESckSZNGt6fNZzyFUvQpcNQtm3d53QkiaLiLyIiScbX14dFH44nX/5ctHqxHz+G/up0JEHFX0REkljmLBlZumISaX28eeH5npw8ccbpSG5PxV9ERJJc3nxBLF0xmb//vsCLTXvxz/mLTkdyayr+IiKSLEqULMK8hW/z049HaNNyINeuXXc6kttS8RcRkWRTs3ZFJk97nU3f7KJH15G6CJBDXP48fxERSVmav9iQE2FnGPXm++QMCmDI8K5OR3I7Kv4iIpLsevZpzYmw07wzeQG5cgfQ7pWmTkdyKyr+IiKS7IwxvD2+L6dOneW1fhPIkSM7jZrUdDqW29AxfxERcUSaNGmYMXskZcsVp1P7Iezc8Z3TkdyGev4iInJP7LxXYzzv3ODW9D0xppvgqXdtK106HxYtm0DDuu15uXkfVn/5AYUfypdoWSV2Lt/zN8Y0McbMPH/+vNNRREQkFlmzZmLZx1Pw9ExD8+d6cPr0H05Hcnku3/O31q4EVpYrV+4Vp7OIiLiCO3v0l6YNACBdtzH33Wb+ArlY8tEknm7cmRef78Xnq9/HP73fA+WUuLl8z19ERFKHx8oUZfb8URz64Rfath7E9es3nI7kslT8RUQkxahTtwoTpgxk44bt9Hp1FNZapyO5JJff7S8iIqlLi5ZPcSLsDGNHzyJXrgBee72T05Fcjoq/iIikOH0HtONE2BkmjptLUFAArds+53Qkl6LiLyIiKY4xhnGT+nPq1Fn69xlHjpzZqdegmtOxXIaO+YuISIrk6enJrLlvUbLUw7zSZjB7dh90OpLLUPEXEZEUy98/HUuWTyQwMBstmvXh8OHjTkdyCSr+IiKSomXPnoWln0wG4IX/9OLs2T+dDeQCVPxFRCTFK1QoL4s/msDpU2dp0awP4eGXnY6Uqqn4i4hIqlC23KPMmvsWB/b/SPvgQdy4oYsA3S8VfxERSTXqNajG2An9+Gr9Nvr1GquLAN0nneonIiKpSuu2zxEWdoZJ4+eSK3cAfQe0dzpSqqPiLyIiqc5rr3fkxIkzjBk1i5xBAbRo+ZTTkVIVFX8REUl1jDFMmjqI06fO0afH2wTmyEadulWcjpVqpLpj/saYhsaYn4wxPxtjujidR0REnOHl5cncBaMpVrww7VoPYv/eUKcjpRqpqvgbYzyBKUAdoBTQzRiT09lUIiLiFP/0fixZPpGsWTPxUrNeHD0S5nSkVCFVFX+gAnDIWvubtfYS8CnQ2OFMIiLioBw5srHs48ncuHGT5v/pwR9//O10pBQvWYu/Maa6MeZzY0yYMcYaY4JjWaaLMeaIMeaKMWaPMeb2OzkEAb/d9vx3IFcSxxYRkRTuoSL5Wbh0PCfCzvBy8z5cunTF6UgpWnL3/P2Bg0AP4F+XZzLGNCdyt/4ooDSwDVhjjMl7a5FY2tRJniIiDrp+4QbXz1/l8o6tjuaoWKkU0z8YwZ7dP9Cp/RvcvHnT0TwpWbIWf2vtamvtIGvtCiAilkV6A/OstbOstaHW2u7ASaBz1PwwIM9ty+cGTiRpaBERidPlHVvxz36d9Dkj8P5+meNfABo1qcmosX1Ys2oTr/WboIsAxSHFnOpnjPEGygLj75i1Hrh1/sZOoLgxJg9wDniWyMF/cbXZAegAEBgYSEhISCKnlsRy8eJF/X5EUqGCB7aSOz2YqK7k2e0h/Hr5uqOZChfJztPP1WDu7I+5ei2cZ5+v5Wiee5Uc28MUU/yBbEAa4PQd008TVeCttTeMMb2ADUTutZhirY2z52+tnQnMBChXrpytWbNmEsSWxBASEoJ+PyKpz2VfL+z3ywCwEZC9Uk3yVqzqcCqoXr06Xp7DWbJwLVUfr0izFxo6HSnBkmN7mJKK/y137qMxt0+z1q4EViZrIhERiZVvxaqc//oTiLiJd52m+KaAwg/g4eHBlHdf5/Spc/ToOpKAgKzUrF3R6VgpRko61e8ccBPIccf0AP69N0BERFIIr/SeeGVMm2IK/y3e3l7MWzSGhx8pQJtWA/n+u/85HSnFSDHF31p7DdgD1L1jVl0iR/3fF2NME2PMzPPnzz9IPBERSYUyZPTnw+WTyJgxPS8835Pfjp90OlKKkNzn+fsbYx4zxjwWte68Uc9vnco3EQg2xrQ3xhQ1xkwh8tz+6fe7TmvtSmtth4wZMz5wfhERSX1yBgWwdMVkrl65RvP/9OSvP9UZTO6efzlgX9TDFxge9fMIAGvtMqAn8DqwH3gcaGitPZbMOUVExIU8UrQg85eM5djRMFq+1I8rV646HclRyX2ef4i11sTyCL5tmfestfmttWmttWWttZuSM6OIiLimqo+X4d0Zw9jx7QE6vzLUrS8ClGKO+ScVHfMXEZFbnnmuDm+O6skXn2/kjUGT3fYiQC5f/HXMX0REbtep64t06vois6Z/xHvvLHY6jiNS4nn+IiIiSWr4yFc5eeIsw954h5xB2Xnu+XpOR0pWKv4iIuJ2PDw8mDZ9CGfO/EG3TiPInj0r1WqUczpWsnH53f465i8iIrHx8UnLgsVjKVQ4L61f7s8PB392OlKycfnir2P+IiISl0yZM7B0xWT8/NLxYtNehP3uHheUdfniLyIiEp9cuQNZumISFy9e4oXne3L+7wtOR0pyKv4iIuL2ij/6EPMXjeXwL8dp1aI/V69eczpSklLxFxERAarVKMc77w9h25a9dO04nIiICKcjJRmXH+1vjGkCNClcuLDTUUREJIX7T9N6nAg7w4ih0wjKFcCIt3o4HSlJuHzPXwP+RETkXnTr8TLtOjTl/WlLmP7eh07HSRIu3/MXERG5F8YY3nq7F6dOnGXIoCnkzBnA088+4XSsROXyPX8REZF7lSZNGt7/YDjlK5agS4ehbNu6z+lIiUrFX0REJBa+vj4s/HA8efMF0erFfvwY+qvTkRKNir+IiEgcsmTJyLKPJ5PWx5sXnu/JyRNnnI6UKFy++OvyviIi8iDy5gviw+WT+PvvC7zYtBf/nL/odKQH5vLFX6P9RUTkQZUs9TBzF4zmpx+P0KblQK5du+50pAfi8sVfREQkMdR6ohKT3hnMpm920aPryFR9ESCd6iciIpJAL7zUiBNhZxg9cjo5gwIYMryr05Hui4q/iIjIPejVN5gTYad5Z/ICcuUOoN0rTZ2OdM9U/EVERO6BMYa3x/fl1KlzvNZvAjlyZKdRk5pOx7onOuYvIiJyjzw9PZk5ZyRlyhajU/sh7NzxndOR7onLF3+d6iciIkkhXTofFi2bQM6g7LzcvA+//HzM6UgJ5vLFX6f6iYhIUsmWLTPLPp6Cp2camj/Xg9On/3A6UoK4fPEXERFJSgUK5mbxsomcO/cXLz7fi4sXwp2OdFcq/iIiIg+odNlifDB/FId++IW2rQdx/foNpyPFS8VfREQkEdR9sirjJw9k44bt9Hp1FNZapyPFSaf6iYiIJJKXWz3FibDTjHv7A3LlCuC11zs5HSlWKv4iIiKJqN/A9pwIO8PEcXMJCgqgddvnnI70Lyr+IiIiicgYw7hJAzh16iz9+4wjR87s1GtQzelYMeiYv4iISCLz8vLkg3mjKFGyCK+0Gcye3QedjhSDir+IiEgS8PdPx5LlEwkMzEaLZn04fPi405GiuXzx1xX+RETEKQEBWVn68WSstbzwn16cPfun05EANyj+usKfiIg4qVDhvCz+aAKnT52lRbM+hIdfdjqS6xd/ERERp5UrX4KZc0ZyYP+PtA8exI0bzl4ESMVfREQkGdRvWJ0x4/vx1fpt9Os11tGLAOlUPxERkWQS3O45wsJOM3nCPHLlDqDvgPaO5FDxFxERSUaD3ujEiRNnGDNqFjmDAmjR8qlkz6DiLyIikoyMMUyaOogzp87Rp8fbBObIRp26VZI1g475i4iIJDNvby/mLnybosUK0a71IPbvDU3W9av4i4iIOMA/vR8frphE1qyZeKlZL44eCUu2dav4i4iIOCRHjmwsXTGJ69dvMnnCvGRbr475i4iIOKjIwwVYuXYGBQrmTrZ1qviLiIg47JGiBZN1fdrtLyIi4mZcvvjrxj4iIiIxuXzx1419REREYnL54i8iIiIxqfiLiIi4GRV/ERERN6PiLyIi4mZU/EVERNyMir+IiIibUfEXERFxMyr+IiIibkbFX0RExM0Ya63TGZKFMeYscCyWWRmBpL72b1KtIzHbTYy2HqSNbMC5B1y/PJjk+L/gtNTwHp3MmFzrdodtYkrYHuaz1maPdY611q0fwMzUuo7EbDcx2nqQNoDdTv8tuPsjOf4vOP1IDe/RyYzJtW532Cam9O2hdvvDylS8jsRsNzHaSo7PUpKOO/z+UsN7dDJjcq3bHbaJKfpvzW12+0vKZozZba0t53QOERGnJcf2UD1/SSlmOh1ARCSFSPLtoXr+IiIibkY9fxERETej4i8iIuJmVPxFRETcjIq/pGjGmM+NMX8ZY1Y4nUVExEnGmDzGmBBjzCFjzAFjzHP33ZYG/ElKZoypBfgDra21zzudR0TEKcaYnECgtXa/MSYA2AM8bK29dK9tqecvKZq1diNwwekcIiJOs9aetNbuj/r5DPAXkZcCvmcq/pJkjDHVo3bbhxljrDEmOJZluhhjjhhjrhhj9hhjqjkQVUQkySXmNtEYUw7wAn67nywq/pKU/IGDQA/g8p0zjTHNgSnAKKA0sA1YY4zJm5whRUSSSaJsE40xWYEFQDt7n8fudcxfkoUx5iLQzVo777ZpO4DvrLWv3DbtZ2CFtfa126bVjHqtjvmLiEu4322iMSYt8CUwy1q78H7Xr56/OMIY4w2UBdbfMWs9UCX5E4mIOCch20RjjAHmAV8/SOEHFX9xTjYgDXD6jumngRy3nhhjvgKWAw2NMb8bYyonX0QRkWSTkG1iVaA58IwxZn/Uo8T9rMzzvmOKJI47jzuZ26dZa+skbxwREUfFuU201m4hkTrt6vmLU84BN7mtlx8lgH9/8xURcXXJuk1U8RdHWGuvEXmBirp3zKpL5AhXERG3kdzbRO32lyRjjPEHCkc99QDyGmMeA/601h4HJgILjTE7ga1AJyAImO5AXBGRJJWStok61U+STNQpehtjmTXfWhsctUwXoD+Qk8jzX3tZazclU0QRkWSTkraJKv4iIiJuRsf8RURE3IyKv4iIiJtR8RcREXEzKv4iIiJuRsVfRETEzaj4i4iIuBkVfxERETej4i8iIuJmVPxFRETcjIq/SApljJlnjPnCXdb7oFJrbhEn6MY+IilXDyLv5Z3iGGNCgIPW2m5OZ7lNiv28RFIaFX+RFMpae97pDKmJPi+RhNNufxEHGWOqG2O2G2MuGmPOG2N2GGMejZoXYze2McbPGLMgatnTxpjXjDFfGGPm3bZMiDHmPWPMKGPMOWPMGWPMeGOMR9T8+saYzcaYv4wxfxpj1hljit5j5nlADaCrMcZGPfIbY9IaYyZHZbsS9b4eT0B78WaOWuaubd/+ecX3uUbNN8aY/saYw8aYy8aY740xLycgaxFjzJdRGQ4bYxoYY64aY564h49QxHEq/iIOMcZ4Ap8BW4BSQEVgCnAzjpdMILLoPgvUjnpNtViWawHcAKoA3YCeQPOoeX7AZKACUBM4D6w0xnjfQ/QewLfAXCJvO5oT+A0YG7WetkBp4HtgrTEmZwLajC8z99J2Aj/XkUA7oCtQDBgNzDDGNIoroDHmIWAX8APwKPAq8AHgDRxIwHsUSTF0S18RhxhjsgB/ADWttd/EMn8ekM1a29gY4w/8CbSy1i6Nmu8H/A58dtu9wEOAtNbayre18yVwzFrbPpZ1+AH/ADWstVvuXG882UO47Zh/VDt/Ae2ttQuipqUB/gd8aK19/S5txZk5oW3fyg20Iv7P1Q84Bzxprd182/TJQBFrbcM4cq4DzlhrW942bTZQz1qbO673J5ISqecv4hBr7Z/APGCdMWaVMaa3MSZPHIsXAryAnbe9Phw4GMuy393x/AQQAGCMKWSMWRK1y/of4DSR24G8sa3UGNMiatf5rUdsexpuz7f1tnw3idxDUCwBbcWZOSFt3y4Bn2sxwIfIPQfReYDOUeuK7XPIAzwJTLpj1jXU65dUSMVfxEHW2jZE7pbeBDwF/M8YUy+WRW+NYk/Irrrrd66G//+/vhLIDnSMWm9pIne3x7Xb/3Pgsdseu+NYLr58t6bF11Z8mRPSdsyJ8X+ut9ptckee4kQW+NiUIfKwwZ1ftkoC++N4jUiKpeIv4jBr7QFr7RhrbU0gBGgdy2K/EFkgK9yaYIxJR+Sx5wQxxmQFigKjrLVfWWtDgfTEc9aPtfaCtfaX2x6Xo2ZdA9Lcke8aED0IL2rXfGXg0F3aupu7th1H9rg+10PAVSDfHXl+sdYei6O5CCK3l163ZahK5BiF/Ql8HyIphk71E3GIMaYAkT3wz4EwoCCRPcn371zWWnvRGDMHGGOMOQecBF4nsiAldODOX0Qe637FGPMbkAsYR2TP/14dBSoYY/IDF4kcj/A+8HZUviNALyAQeO8+2o9mrQ03xiS47bt9rtbaC8aY8cB4Y4whcu+AP1AJiLDWzowlxh4iv4C8bYyZBJQAxkTN025/SXVU/EWccwkoAiwncqDaaWAx/19U7tSXyNH6nxNZcCcRWQCvJGRl1toIY0xzYCqRu69/AfoAH99H9vHAfCJ70b5AAWBA1Ly5QCZgH1DfWnvyPtq/0720nZDP9Y2o6X2J/FLwD5E9+LGxrdxae8IY047IswLaAF8S+cVjFJGfo0iqotH+IqmUMSYtcAwYZ62d4HQed2OMGUbkGQNVnM4icq/U8xdJJYwxpYk8Zr+TyGP1A6L+XeZkLjdWEu3yl1RKA/5EUpfeRO7y/prIXf7VrbW/OxvJbZVCg/0kldJufxERETejnr+IiIibUfEXERFxMyr+IiIibkbFX0RExM2o+IuIiLgZFX8RERE3o+IvIiLiZlT8RURE3Mz/Ae+jqGE1fVddAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], label='prediction')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig_pred = {\n", + " 'predSNRCut': 5,\n", + "# 'path2truthcat': '../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2truthcat': '/Users/boris/Work/CLASS-SZ/SO-SZ/SOLikeT/soliket/clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + "\n", + "\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'injection',\n", + " 'QSource': 'injection'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "predNz = nemo_mocks.get_nemo_pred(mockconfig_pred , zbins)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], label='prediction')\n", + "plt.plot(z, predNz, color=color_list[0], linestyle='--', label='nemo prediction')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(z, Nz_truth, yerr=np.sqrt(Nz_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(z, Nz_mock, yerr=np.sqrt(Nz_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xlim(0, 2)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], label='prediction')\n", + "plt.plot(z, predNz, color=color_list[0], linestyle='--', label='nemo prediction')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(z, Nz_truth, yerr=np.sqrt(Nz_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(z, Nz_mock, yerr=np.sqrt(Nz_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xlim(0, 2)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.semilogx(q, catNq/Nq, color=color_list[12])\n", + "plt.semilogx(q, Nq_truth/Nq, color=color_list[8])\n", + "plt.semilogx(q, Nq_mock/Nq, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, catNz/Nz, color=color_list[12])\n", + "plt.plot(z, Nz_truth/Nz, color=color_list[8])\n", + "plt.plot(z, Nz_mock/Nz, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing binned_clusters_test.py\n", + "Initializing binned_clusters_test.py\n", + "Initializing binned_clusters_test.py\n", + "Considering full map.\n", + "Considering full map.\n", + "Considering full map.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "Reading data catalog.\n", + "Reading data catalog.\n", + "Reading data catalog.\n", + "Total number of clusters in catalogue = 5738.\n", + "Total number of clusters in catalogue = 5738.\n", + "Total number of clusters in catalogue = 5738.\n", + "SNR cut = 7.0.\n", + "SNR cut = 7.0.\n", + "SNR cut = 7.0.\n", + "Number of clusters above the SNR cut = 1227.\n", + "Number of clusters above the SNR cut = 1227.\n", + "Number of clusters above the SNR cut = 1227.\n", + "The highest redshift = 1.935\n", + "The highest redshift = 1.935\n", + "The highest redshift = 1.935\n", + "Number of redshift bins = 28.\n", + "Number of redshift bins = 28.\n", + "Number of redshift bins = 28.\n", + "Number of mass bins for theory calculation 106.\n", + "Number of mass bins for theory calculation 106.\n", + "Number of mass bins for theory calculation 106.\n", + "The lowest SNR = 7.005231990769159.\n", + "The lowest SNR = 7.005231990769159.\n", + "The lowest SNR = 7.005231990769159.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "The highest SNR = 51.98994565380555.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Loading files describing selection function.\n", + "Loading files describing selection function.\n", + "Loading files describing selection function.\n", + "Reading Q as a function of theta.\n", + "Reading Q as a function of theta.\n", + "Reading Q as a function of theta.\n", + "/Users/andrina/opt/miniconda3/envs/actxdes_venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3438: RuntimeWarning: Mean of empty slice.\n", + " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n", + "Reading RMS.\n", + "Reading RMS.\n", + "Reading RMS.\n", + "Entire survey area = 13631.324739141011 deg2.\n", + "Entire survey area = 13631.324739141011 deg2.\n", + "Entire survey area = 13631.324739141011 deg2.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nz for higher resolution = 249\n", + "0 38.130629066286886\n", + "1 937.2165352071047\n", + "2 193.03116141340737\n", + "3 32.54368983255846\n", + "4 3.70733083479444\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 0: 35.55932691568533.\n", + "Number of clusters in redshift bin 0: 35.55932691568533.\n", + "Number of clusters in redshift bin 0: 35.55932691568533.\n", + "Number of clusters in redshift bin 1: 158.7682526557981.\n", + "Number of clusters in redshift bin 1: 158.7682526557981.\n", + "Number of clusters in redshift bin 1: 158.7682526557981.\n", + "Number of clusters in redshift bin 2: 199.54759560218025.\n", + "Number of clusters in redshift bin 2: 199.54759560218025.\n", + "Number of clusters in redshift bin 2: 199.54759560218025.\n", + "Number of clusters in redshift bin 3: 193.48091207341525.\n", + "Number of clusters in redshift bin 3: 193.48091207341525.\n", + "Number of clusters in redshift bin 3: 193.48091207341525.\n", + "Number of clusters in redshift bin 4: 165.55410737690264.\n", + "Number of clusters in redshift bin 4: 165.55410737690264.\n", + "Number of clusters in redshift bin 4: 165.55410737690264.\n", + "Number of clusters in redshift bin 5: 133.1729391014308.\n", + "Number of clusters in redshift bin 5: 133.1729391014308.\n", + "Number of clusters in redshift bin 5: 133.1729391014308.\n", + "Number of clusters in redshift bin 6: 101.91189780989897.\n", + "Number of clusters in redshift bin 6: 101.91189780989897.\n", + "Number of clusters in redshift bin 6: 101.91189780989897.\n", + "Number of clusters in redshift bin 7: 72.84167161695382.\n", + "Number of clusters in redshift bin 7: 72.84167161695382.\n", + "Number of clusters in redshift bin 7: 72.84167161695382.\n", + "Number of clusters in redshift bin 8: 49.659037121658066.\n", + "Number of clusters in redshift bin 8: 49.659037121658066.\n", + "Number of clusters in redshift bin 8: 49.659037121658066.\n", + "Number of clusters in redshift bin 9: 33.3460944079634.\n", + "Number of clusters in redshift bin 9: 33.3460944079634.\n", + "Number of clusters in redshift bin 9: 33.3460944079634.\n", + "Number of clusters in redshift bin 10: 22.36129653720879.\n", + "Number of clusters in redshift bin 10: 22.36129653720879.\n", + "Number of clusters in redshift bin 10: 22.36129653720879.\n", + "Number of clusters in redshift bin 11: 14.468373100983884.\n", + "Number of clusters in redshift bin 11: 14.468373100983884.\n", + "Number of clusters in redshift bin 11: 14.468373100983884.\n", + "Number of clusters in redshift bin 12: 9.216549125770031.\n", + "Number of clusters in redshift bin 12: 9.216549125770031.\n", + "Number of clusters in redshift bin 12: 9.216549125770031.\n", + "Number of clusters in redshift bin 13: 5.869437593993785.\n", + "Number of clusters in redshift bin 13: 5.869437593993785.\n", + "Number of clusters in redshift bin 13: 5.869437593993785.\n", + "Number of clusters in redshift bin 14: 3.677261877157774.\n", + "Number of clusters in redshift bin 14: 3.677261877157774.\n", + "Number of clusters in redshift bin 14: 3.677261877157774.\n", + "Number of clusters in redshift bin 15: 2.2366111714520613.\n", + "Number of clusters in redshift bin 15: 2.2366111714520613.\n", + "Number of clusters in redshift bin 15: 2.2366111714520613.\n", + "Number of clusters in redshift bin 16: 1.3255416716939048.\n", + "Number of clusters in redshift bin 16: 1.3255416716939048.\n", + "Number of clusters in redshift bin 16: 1.3255416716939048.\n", + "Number of clusters in redshift bin 17: 0.7713227907041049.\n", + "Number of clusters in redshift bin 17: 0.7713227907041049.\n", + "Number of clusters in redshift bin 17: 0.7713227907041049.\n", + "Number of clusters in redshift bin 18: 0.4487501393098355.\n", + "Number of clusters in redshift bin 18: 0.4487501393098355.\n", + "Number of clusters in redshift bin 18: 0.4487501393098355.\n", + "Number of clusters in redshift bin 19: 0.2650626033158881.\n", + "Number of clusters in redshift bin 19: 0.2650626033158881.\n", + "Number of clusters in redshift bin 19: 0.2650626033158881.\n", + "Number of clusters in redshift bin 20: 0.15536725709697824.\n", + "Number of clusters in redshift bin 20: 0.15536725709697824.\n", + "Number of clusters in redshift bin 20: 0.15536725709697824.\n", + "Number of clusters in redshift bin 21: 0.0912850721984939.\n", + "Number of clusters in redshift bin 21: 0.0912850721984939.\n", + "Number of clusters in redshift bin 21: 0.0912850721984939.\n", + "Number of clusters in redshift bin 22: 0.054732620360473196.\n", + "Number of clusters in redshift bin 22: 0.054732620360473196.\n", + "Number of clusters in redshift bin 22: 0.054732620360473196.\n", + "Number of clusters in redshift bin 23: 0.032016965823877565.\n", + "Number of clusters in redshift bin 23: 0.032016965823877565.\n", + "Number of clusters in redshift bin 23: 0.032016965823877565.\n", + "Number of clusters in redshift bin 24: 0.018019682619010265.\n", + "Number of clusters in redshift bin 24: 0.018019682619010265.\n", + "Number of clusters in redshift bin 24: 0.018019682619010265.\n", + "Number of clusters in redshift bin 25: 0.010017639880946786.\n", + "Number of clusters in redshift bin 25: 0.010017639880946786.\n", + "Number of clusters in redshift bin 25: 0.010017639880946786.\n", + "Number of clusters in redshift bin 26: 0.005500984652405427.\n", + "Number of clusters in redshift bin 26: 0.005500984652405427.\n", + "Number of clusters in redshift bin 26: 0.005500984652405427.\n", + "Number of clusters in redshift bin 27: 0.0030788202711509887.\n", + "Number of clusters in redshift bin 27: 0.0030788202711509887.\n", + "Number of clusters in redshift bin 27: 0.0030788202711509887.\n", + "Total predicted 2D N = 1204.85206033638.\n", + "Total predicted 2D N = 1204.85206033638.\n", + "Total predicted 2D N = 1204.85206033638.\n", + "Theory N calculation took 0.4842839241027832 seconds.\n", + "Theory N calculation took 0.4842839241027832 seconds.\n", + "Theory N calculation took 0.4842839241027832 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 0.22271398222826888\n", + "\r", + " Total predicted 2D N = 1204.85206033638\n", + "\r", + " ::: 2D ln likelihood = 143.02361707382096\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-143.02361707])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.68\n", + "\n", + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.26, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.0,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14*0.68\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 7.,\n", + " 'single_tile_test': \"no\",\n", + " 'mode': 'injection',\n", + " 'dwnsmpl_bins': 50,\n", + " 'save_dwsmpld': False,\n", + " 'average_Q': False\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 5e13*0.68,\n", + " 'Mmax': 1e16*0.68,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.binned_clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 38.130629066286886\n", + "1 937.2165352071047\n", + "2 193.03116141340737\n", + "3 32.54368983255846\n", + "4 3.70733083479444\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 0: 35.55932691568533.\n", + "Number of clusters in redshift bin 0: 35.55932691568533.\n", + "Number of clusters in redshift bin 0: 35.55932691568533.\n", + "Number of clusters in redshift bin 1: 158.7682526557981.\n", + "Number of clusters in redshift bin 1: 158.7682526557981.\n", + "Number of clusters in redshift bin 1: 158.7682526557981.\n", + "Number of clusters in redshift bin 2: 199.54759560218025.\n", + "Number of clusters in redshift bin 2: 199.54759560218025.\n", + "Number of clusters in redshift bin 2: 199.54759560218025.\n", + "Number of clusters in redshift bin 3: 193.48091207341525.\n", + "Number of clusters in redshift bin 3: 193.48091207341525.\n", + "Number of clusters in redshift bin 3: 193.48091207341525.\n", + "Number of clusters in redshift bin 4: 165.55410737690264.\n", + "Number of clusters in redshift bin 4: 165.55410737690264.\n", + "Number of clusters in redshift bin 4: 165.55410737690264.\n", + "Number of clusters in redshift bin 5: 133.1729391014308.\n", + "Number of clusters in redshift bin 5: 133.1729391014308.\n", + "Number of clusters in redshift bin 5: 133.1729391014308.\n", + "Number of clusters in redshift bin 6: 101.91189780989897.\n", + "Number of clusters in redshift bin 6: 101.91189780989897.\n", + "Number of clusters in redshift bin 6: 101.91189780989897.\n", + "Number of clusters in redshift bin 7: 72.84167161695382.\n", + "Number of clusters in redshift bin 7: 72.84167161695382.\n", + "Number of clusters in redshift bin 7: 72.84167161695382.\n", + "Number of clusters in redshift bin 8: 49.659037121658066.\n", + "Number of clusters in redshift bin 8: 49.659037121658066.\n", + "Number of clusters in redshift bin 8: 49.659037121658066.\n", + "Number of clusters in redshift bin 9: 33.3460944079634.\n", + "Number of clusters in redshift bin 9: 33.3460944079634.\n", + "Number of clusters in redshift bin 9: 33.3460944079634.\n", + "Number of clusters in redshift bin 10: 22.36129653720879.\n", + "Number of clusters in redshift bin 10: 22.36129653720879.\n", + "Number of clusters in redshift bin 10: 22.36129653720879.\n", + "Number of clusters in redshift bin 11: 14.468373100983884.\n", + "Number of clusters in redshift bin 11: 14.468373100983884.\n", + "Number of clusters in redshift bin 11: 14.468373100983884.\n", + "Number of clusters in redshift bin 12: 9.216549125770031.\n", + "Number of clusters in redshift bin 12: 9.216549125770031.\n", + "Number of clusters in redshift bin 12: 9.216549125770031.\n", + "Number of clusters in redshift bin 13: 5.869437593993785.\n", + "Number of clusters in redshift bin 13: 5.869437593993785.\n", + "Number of clusters in redshift bin 13: 5.869437593993785.\n", + "Number of clusters in redshift bin 14: 3.677261877157774.\n", + "Number of clusters in redshift bin 14: 3.677261877157774.\n", + "Number of clusters in redshift bin 14: 3.677261877157774.\n", + "Number of clusters in redshift bin 15: 2.2366111714520613.\n", + "Number of clusters in redshift bin 15: 2.2366111714520613.\n", + "Number of clusters in redshift bin 15: 2.2366111714520613.\n", + "Number of clusters in redshift bin 16: 1.3255416716939048.\n", + "Number of clusters in redshift bin 16: 1.3255416716939048.\n", + "Number of clusters in redshift bin 16: 1.3255416716939048.\n", + "Number of clusters in redshift bin 17: 0.7713227907041049.\n", + "Number of clusters in redshift bin 17: 0.7713227907041049.\n", + "Number of clusters in redshift bin 17: 0.7713227907041049.\n", + "Number of clusters in redshift bin 18: 0.4487501393098355.\n", + "Number of clusters in redshift bin 18: 0.4487501393098355.\n", + "Number of clusters in redshift bin 18: 0.4487501393098355.\n", + "Number of clusters in redshift bin 19: 0.2650626033158881.\n", + "Number of clusters in redshift bin 19: 0.2650626033158881.\n", + "Number of clusters in redshift bin 19: 0.2650626033158881.\n", + "Number of clusters in redshift bin 20: 0.15536725709697824.\n", + "Number of clusters in redshift bin 20: 0.15536725709697824.\n", + "Number of clusters in redshift bin 20: 0.15536725709697824.\n", + "Number of clusters in redshift bin 21: 0.0912850721984939.\n", + "Number of clusters in redshift bin 21: 0.0912850721984939.\n", + "Number of clusters in redshift bin 21: 0.0912850721984939.\n", + "Number of clusters in redshift bin 22: 0.054732620360473196.\n", + "Number of clusters in redshift bin 22: 0.054732620360473196.\n", + "Number of clusters in redshift bin 22: 0.054732620360473196.\n", + "Number of clusters in redshift bin 23: 0.032016965823877565.\n", + "Number of clusters in redshift bin 23: 0.032016965823877565.\n", + "Number of clusters in redshift bin 23: 0.032016965823877565.\n", + "Number of clusters in redshift bin 24: 0.018019682619010265.\n", + "Number of clusters in redshift bin 24: 0.018019682619010265.\n", + "Number of clusters in redshift bin 24: 0.018019682619010265.\n", + "Number of clusters in redshift bin 25: 0.010017639880946786.\n", + "Number of clusters in redshift bin 25: 0.010017639880946786.\n", + "Number of clusters in redshift bin 25: 0.010017639880946786.\n", + "Number of clusters in redshift bin 26: 0.005500984652405427.\n", + "Number of clusters in redshift bin 26: 0.005500984652405427.\n", + "Number of clusters in redshift bin 26: 0.005500984652405427.\n", + "Number of clusters in redshift bin 27: 0.0030788202711509887.\n", + "Number of clusters in redshift bin 27: 0.0030788202711509887.\n", + "Number of clusters in redshift bin 27: 0.0030788202711509887.\n", + "Total predicted 2D N = 1204.85206033638.\n", + "Total predicted 2D N = 1204.85206033638.\n", + "Total predicted 2D N = 1204.85206033638.\n", + "Theory N calculation took 0.4829540252685547 seconds.\n", + "Theory N calculation took 0.4829540252685547 seconds.\n", + "Theory N calculation took 0.4829540252685547 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 0.22271398222826888\n", + "\r", + " Total predicted 2D N = 1204.85206033638\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "bin_params = info['likelihood']['soliket.BinnedClusterLikelihood']['binning']\n", + "\n", + "\n", + "zbins = np.arange(bin_params['z']['zmin'], bin_params['z']['zmax'] + bin_params['z']['dz'], \\\n", + " bin_params['z']['dz'])\n", + "\n", + "logqmin = bin_params['q']['log10qmin']\n", + "logqmax = bin_params['q']['log10qmax']\n", + "dlogq = bin_params['q']['dlog10q']\n", + "\n", + "# TODO: I removed the bin where everything is larger than qmax - is this ok?\n", + "qbins = 10**np.arange(logqmin, logqmax+dlogq, dlogq)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig = {\n", + " 'predSNRCut': 7,\n", + " 'path2truthcat': '../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'fast',\n", + " 'QSource': 'fit'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: We don't have true_fixed_y_c or true_Q - we reconstruct those here.\n" + ] + } + ], + "source": [ + "# Make a 'true' mock - use the truth catalog, get true_SNR by looking up noise in the selFn dir\n", + "mode = 'without_Q'\n", + "truthTab = nemo_mocks.make_truth_mock(mode, mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "truth_cat, zarr, qarr = nemo_mocks.bin_catalog(truthTab[truthTab['true_SNR']>7], zbins, qbins, SNR_tag='true_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "mockTab = nemo_mocks.make_nemo_mock(mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "mock_cat, zarr, qarr = nemo_mocks.bin_catalog(mockTab[mockTab['fixed_SNR']>7], zbins, qbins, SNR_tag='fixed_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_truth = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_truth[i] = truth_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_truth = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_truth[i] = truth_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_mock = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_mock[i] = mock_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_mock = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_mock[i] = mock_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], label='prediction')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig_pred = {\n", + " 'predSNRCut': 7,\n", + " 'path2truthcat': '../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'injection',\n", + " 'QSource': 'injection'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "predNz = nemo_mocks.get_nemo_pred(mockconfig_pred, zbins)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bmzFjxvD6669TtWpVKlasyMqVKzl//jylS5fOss6010yZMgVXV1dat27N5cuXCQ8PZ+TIkfj7++Pu7s66desICQnBzc0ty1s98/o+FESSPAghhABg+fLlvPrqq0ycOJEzZ87g6+tL06ZNGT9+/D3V++yzz3Ljxg0mTpxIVFQU1apVY9WqVXe80wKgTZs21KpVi3bt2hEXF8djjz3GnDlz7viaoKAg/vzzT6ZMmUKXLl1ISEigXLlyPPDAA7i6ugIwbtw4Lly4wNChQwEYMGAA/fv35/Dhw9nW+/rrr+Pj48PLL7/MmTNnCAgIYODAgQA4OTmxZMkSZs2axcyZM2nVqhVhYWFWex8KImXNazkFXbVq1fTRo0ftHYZNLO2zkpiYGKauG2PvUGwiLCwsw9SxhY20z3EcPnyYGjVqZNh248YNvL297RSR7dmjfaGhoURHR7N69WqbHqcwfHZZ/U6mUUqFa60bW/uYMuZBCCGEELkiyYMQQgghciXfkgel1BSl1E6l1HWl1CWl1E9KqdqZyiil1Ayl1DmlVLxSKkwpVStTGVel1JtKqWilVKxS6n9KqTL51Q4hhBC2t2LFCptfshB5l589D22BpUBLoD2QDPyulPJNV2YiMA54BmgCXAR+U0qlvyC1CHgM6Au0AooBq5VSRhvHX+CZtImzZ6LsHYYQQohCLt+SB611Z631cq31Aa31fmAA4AfcB+ZeB2As8IbWepXW+gAwCPAG+qWWKQ48CUzQWv+mtd6dWk9doGN+taWgSbu3+Pq1WBrU7sGj3Ubx2Sf/48b1m3aOTAghRGFkz1s1vTEnL1dTn1cAAoFf0wporeOVUpsw91a8BzQCnDOVOa2UOpxaJvvJyQuZcSGzstw+oMRDcAB2H9jL7hf3cq3tLd5850Xc3d3yOUIhhBCFlT2Th8XAXuCv1Odpq4lk7nePAoLTlUkBorMok+VqJEqpYcAwMC9ektW9t4XZ8YgTbN9unklt44bdBAWVonLVslaZFjY/3bx5s1B/dtI+x1G8eHFu3LiRYVtKSspt27Iyo87CHB1jxv7bV+C0p5y2zxEVhrYlJCTk+/lll+RBKbUAuB+4X2udeT7PzBNPqCy23VZldmW01suAZWCe56Gw3Gve9kRbAIY/+SI/fr+ewNKlGDTkIY79c4GfV28k9mYcpYP8eLR1J3xKBFKjZiVGDHmdy5djqFipLL36dOHx3g8SUiH4zgcqIArTPAFZkfY5jsOHD982L4C15wooaPMOFIa5ELJTGNrm5uZGgwYN8vWY+Z48KKUWAk8A7bTWx9PtupD6byCQfmkzf/7rjbgAGIFSwKVMZTbZJOACLCrqMv/7YT0pKSk8+VQv6tQvy3PjRjA3LoF1azbz3bfreP+9r1n61udUqRrCgNBH8PR0J2zDdma/9j6zX3ufV2c/z7ARfezdFCGKhPknpmd4vrTPSgBGfTXIHuEIkWf5Os+DUmox5sGP7bXWRzLtjsScHHRKV94N8x0VW1M3hQNJmcqUAWqkK1NkfLryB5KTU3Bxcab/wIct2z083Hj0sU588sU8Dv7zC/MXTcbP35dF81fw6qx3iI+7xYTJQxk7LpQ2bZsAsGXTLgb2m8BPP/5BQsItezVJCGEnYWFhKKWIjs58VdgxKaX49ttv7R1GoZVvPQ9Kqbcx3xnxCHBVKZU2RuGm1vqm1lorpRYB05RSR4B/gBeAm8DnAFrra0qpD4G5SqmLwGVgAfA38Ht+taWg6NylFQvmfsTjvbvg63v7IiwAPr7FGTj4UQYOfpSzZ6L44bvf+Pbrdcx94wMMBgN7dx+iZ6/OaJNmd/gh1vy8ieLFvXn4kfY83udBmreoh8Egc4kJIfJfWFgY7dq149KlS5QqVcre4Yh08vOyxajUfzOvaToTmJH68xzAHXgb8AG2Aw9ordOPZnkO8xwRX6WWXQ8MzGLsRKG3ZUs4iYnJDHnq8RyVDy4TwNPP/h9PP/t/HD1ynO++/ZXvvvmVZ0e9jKurC50630f1GhWJPH6GVd/+yto1m/n78E+SPAghhMggP+d5UNk8ZqQro7XWM7TWpbXWblrrNqnzPaSvJ0Fr/YzWuqTW2kNr3V1rffq2AxZyr856h7eXfEaTZnWpV796rl9frXpFprwwgh17V7Hm9w8YEPoI2//ay7zZH/Lbr3/SrXtbJk5+CqUUycnJXItx7NHIQhRECTducfXsNU6E2/6/sFu3bjF27FgCAgJwc3OjefPmbNmy5bZy27Zto379+ri5udGoUaMMy0Vfu3aNAQMG4O/vj5ubGxUrVmTRokV3PO7PP/9Ms2bNcHd3p2TJknTv3p2EhAQAPv30U5o0aYK3tzf+/v706tWLs2fPAnDixAnatWsHmO+UU0oRGhoKwNq1a2nVqhU+Pj74+vrSuXPnO66ICbB//346duyIu7s7vr6+hIaGcu3aNcv+5ORknnvuOXx8fPDx8eG5555j5MiRGQb6tm3bltGjR2eoNzQ0lG7dulmea62ZM2cOlSpVwt3dnTp16vDpp5/eMTZHJH9SOqAjh4+zaP4KLpy/xNBhOet1yI5SisZN6vD6nHH8fWQ1X61aTJeHWvPz6o1MeH42dWt05/5mfXn26ZetFL0QAuBE+GnOHYniypkY3u3/ic0TiIkTJ/LVV1/x0UcfsWfPHurUqUOXLl04f/58hnLjx49n9uzZ7Nq1i4oVK9K1a1fi4uIAeOGFF9i/fz+rV6/myJEjfPTRRwQHZ3/H1tq1a+nRowedOnUiPDycDRs20KZNG0wmEwCJiYnMnDmTffv2sXr1aqKjo+nbty8AZcuWZdWqVQAcPHiQ8+fPs3jxYgBiY2MZO3YsO3bsICwsjOLFi9O9e3cSExOzjCMuLo4uXbrg5eXFjh07+P7779m6dStDhgyxlJk3bx4rVqzggw8+YNu2bZhMJj7//PNcv88vvPACH374IW+//TaHDh1iypQpDB8+nJ9//jnXdRVoWusi86hataouDCY8N1v7l2imq1V8QN+6lWjZvmHDBqsdIy4uXv/w3e/6gXaDdYBPC12qWFO9bs1mq9WfW9ZsW0Ek7XMchw4dum3b9evXc13P729t1s+Xn6mfLz9Tj6s4S//+lu3Or5s3b2pnZ2e9cuVKy7bk5GRdsWJFPW3aNK21+TMC9Keffmopc+PGDV28eHH95ptvaq217t69uw4NDc3xcVu2bKn79OmT4/KHDx/WgD59+nSGmC5dunTX9hkMBr1583/vIaC/+eYbrbXWy5Yt08WKFcvwOaXVvWfPHq211oGBgfr111+37DeZTLpatWq6TZs2lm1t2rTRTz/9dIZjDxo0SHft2tUSh5ubm960aVOGMmPGjNEPPvhgDt+F3MvqdzINsEvb4PtUeh4czI3rN/nqi58xmTSDn3wMFxdnmxzH3d2NHo92YOVns3Fzd8HDw50pE+YRF5dgk+MJUdRUal4eZTBP1ubkbKRS8/I2O1ZERARJSUncd999lm1Go5EWLVpw6NChDGVbtGhh+dnLy4s6depw5Ij55riRI0fy9ddfU69ePcaPH8/GjRvveNw9e/bQoUOHbPfv3r2bHj16UL58eby9vWncuDEAp06dumt7+vXrR6VKlShWrBgBAQGYTKZsX3f48GHq1q2bYT6Hli1bYjAYOHr0KNeuXePChQs0bdrUsl8pRZMmTe4YR2aHDh0iISHB0suR9njnnXeIiIjIVV0FnSQPDuarL9cQF5eA0Whg0OBHbX68wNJ+TH1xJHFx8Zw6dZ7FC1bY/JhCFAUhjcoSVD0A3zIlGPHZAEIalbXZscx/gJLlzLK5mW32wQcf5OTJk4wfP57o6Gi6du3K4MGD8xRTbGwsnTt3xsPDg08++YSdO3eydu1agGwvP6Tp3r07ly5d4r333mP79u3s2bMHJyenbF+ntc62nem33+29MBgMlvcyTVJSkuXntMsxP/30E3v37rU8Dh48yK+//kphIsmDgzEYDDg5OdHt4fYElvbLl2MOGfoYdetVw83NlTU/byI5OTlfjitEYefm7YpPcHGbJg4AlStXxsXFJcMAyZSUFP766y9q1qyZoey2bdssP8fGxnLgwAGqVatm2VaqVCkGDBjAihUr+PDDD1m5ciW3bmU9N0yDBg1Yvz7zDXZmR44cITo6mtdee43WrVtTvXp1Ll68mKGMi4uLJdY0ly9f5vDhw0ydOpWOHTtSo0YNbty4ccf/l2rWrMm+ffsyTEO9detWTCYTVatWpXjx4gQGBrJjxw7Lfq01O3fuzFCPn5/fbWNE9u3bl+E4rq6unDx5ksqVK2d4lC9vu54le7Dn2hYiD5ydnUhOTubJexwomRtOTk7MWziZB9oPpsV99XFykl8bIRyJp6cnI0eOZPLkyZQqVYoKFSqwcOFCoqKiGDVqVIayr7zyCn5+fgQFBTFr1ixcXFzo1asXANOnT6dhw4bUqlWL5ORkvvvuOypWrIirq2uWx502bRrdu3encuXK9OvXD601v/76K8OHD6dcuXK4urry1ltv8fTTT3P48GFefPHFDK8vX748Sil+/vlnunfvjru7Oz4+PpQqVYr333+fsmXLcvbsWSZMmHDH/5f69+/PSy+9xMCBA5k1axZXr15l+PDh9OzZk0qVKgEwZswY5syZQ9WqValZsybvvfce58+fp3Tp0pZ62rdvz9ixY/nf//5HtWrVeO+99zh9+jQhISGAeVrx8ePHM378eLTWtG7dmps3b7Jt2zYMBgPDhg3L9WdXYNliIEVBfTj6gMmtf+7WrVv2021a9tMmk+m2/bYelDZx3BztX6K53vrnbr13z2GbHiuzwjTgLivSPsdhrQGTWmv9du8V+u3eK+41pBxJSEjQY8aM0f7+/trFxUU3a9YswwDDtAGEP/74o65Tp452cXHRDRo00Dt27LC075VXXtE1a9bU7u7u2sfHRz/44IN3HKyntdY//vijbtiwoXZxcdElS5bU3bt31/Hx8Vprrb/88ktdsWJF7erqqps0aaLXrl2rgQy/L7NmzdKBgYFaKaUHDRqktdZ6/fr1ulatWtrV1VXXqlVLr127Vnt6eurly5dbXke6AZNaa/3333/r9u3bazc3N12iRAk9aNAgHRMTY2lbUlKSHjNmjC5evLguUaKEfu655/SgQYN0ly5dLHUkJibqUaNG6ZIlS+qSJUvqF198McOASa3NAy2XLFmia9SooV1cXHSpUqV0x44d9a+//pq7DywX7DFgUml9tzWnCo9q1arpo0eP2juMPDl7JoqGdXpgMmkWLJ7CgNBHbitj68WHrsXcoEWT3txKSMTdw42/dn6FdzEvmx0vvcK0sFJWpH2O4/Dhw9SoUSPDtpwurjQuZFaOjpF5DQx7KwyLR2XnTm1r2LAh9913H2+++WY+R5U7Wf1OplFKhWutG1v7mDLmwUGsXP49JpPG29uTnr062yWG4iW8eeX1sVy/fpOLUdHMeeMDu8QhhBDWdvLkSZYtW8bRo0c5ePAgY8aMYd++fQwaJIuWZUUuXjuAW7cSWbn8e5SC/xvUA09Pd7vF8uhjD/D5J6vZ+udu3n/3K57o15VatavYLR4hHElB61EQ/zEYDHz88cdMmDABk8lEzZo1WbNmjeX2UZGRJA8O4Kcf/+DK5RgAQof0tGssSilmL5hA6+b9MBqNTHh+DqvXvifrXwghHFrZsmWznK5bZE3+x3cAv67dgtFooEOnFlSsZNtbunKiUqVyjHl+EImJSSQk3OL6tZv2DkkIIUQ+kp4HB9DlwVZ8v+o3hg7vbfNj5XRA16tHJvHNV2u4eSMWN/esb9MSQghROEnPQwGXkpLChx98S0iFMrTv0Nze4Vi4ubkyd8EkIo+f4dWZ7/DJih/sHZIQQoh8Ij0PBdjVK9do0bg3ly/H8PJrY/NlXEHmAV1L+6wEYNRXt484btu+GT0ff4Bl736F1pqatSvTqHFtm8cohKPa32FsjsrVWb/IpnEIca+k56EA++Kz1Vy+HIObmwtP9Otq73CyNOu1sXh6uePs7MSE52ZnmEZWCCFE4SQ9DwWUyWTig2XfoJSi9xMPUcKnmL1DylJAQEmmTR/J5PHz2P/3Pyz/8DuGDutl77CEKJAy9ygcf948+VDFBc/YIRoh8k56Hgqo9b//xelT59FaM+Sp/FvHIi9Ch/SkXv3qOLs48+qspURFXbZ3SEIIO1FK8e2339o7jBw5ceIExYoVY9euXfYOxeFI8lBAfbjsG4xGA81a1CvwkzAZjUbmL5pMclIywcGBuDhLh5YQBU3btm0ZPXq01eqbMWMGtWsXjDFOK1aswMsrf6bKF2aSPBRQbdo2JSXFxFPD+9g7lByp16AGQ4f34p+jkZyIPGvvcIQQeZSUlGTvEIQDkOShgPpj/TYCS/vxULc29g4lx6ZMG45/QElGDZvBU4OnkZj4339C40Jm5eghRFGSEptAYtRVYg9G2vQ4oaGhbNy4kbfffhulFEopTpw4QVhYGEopfvnlF5o2bYqLiwvr1q3Lslch/V/3K1asYObMmRw8eNBS34oVKyxlr1y5Qq9evfD09KRixYp8+umnd41x5cqV1KlTB1dXVwICAggNDbXsW7BgAXXr1sXT05Pg4GCGDh1KTEwMYF50bfDgwcTGxlpimTFjBgCffvopTZo0wdvbG39/f3r16sXZs3f+42bTpk00a9YMNzc3AgICeO6550hMTLTsj42NZeDAgXh5eREQEMDrr79Ot27dMsQbEhLCvHnzMtSbuecnMTGRSZMmUaZMGTw9PWnSpAnr1q276/tUUEjyUMDExSUwfOiLhP2xndAhj+LsQJcAvIt58crrz3Hs2El++O533nnrc3uHJESBFXswkoSIcyRduELkhKU2TSAWL15MixYtGDx4MOfPn+f8+fOULfvfbLWTJk3ilVde4ciRIzRr1uyu9fXp04dx48ZRrVo1S319+vzXSzpr1ix69OjBvn376NOnD0OGDOHkyZPZ1vfee+8xfPhwBg8ezN9//80vv/xCrVq1LPsNBgOLFi3i4MGDfP755+zYsYNnnjEPMm3ZsiWLFi3Cw8PDEsv48eMB8xf0zJkz2bdvH6tXryY6Opq+fftmG8fZs2d58MEHadCgAXv27OHDDz/kiy++YMqUKZYy48aNY+PGjXz//ff88ccf7Nu3j82bN9/1Pcts8ODBbNy4kc8//5z9+/czaNAgunfvzr59+3Jdl13YYp3vgvqoWrVqtmueFxSffvyjLlWsqQ7waaEvXIjO1Ws3bNhg9Xje7r1Cv917RY7Lm0wm3evRZ3WAbwtdxr+VPnXynFXqtUXbChJpn+M4dOjQbduuX7+e63qiPvtV/91+jPnR8Tkd9dmv1ggvW23atNFPP/10hm0bNmzQgP72228zbH/ppZd0rVq1LM+vX7+uly9frj09PbMtkwbQkydPtjxPSkrS7u7u+pNPPsk2tuDgYD1p0qQct2XNmjXaxcVFp6SkaK31bbFl5/DhwxrQp0+f1lprHRkZqQG9c+dOrbXWU6dO1ZUqVbLUm1a3i4uLjo2N1Tdu3NDOzs76iy++sOy/efOmLlGihB40aJBlW/ny5fXcuXMzHDv9+3/s2DGtlNInT57MUKZHjx565MiROX4f0mT1O5kG2KVt8H0qPQ8FiNaaD977GoNB8UjPjgQElLR3SLmmlGL2vAkYDQaSkpJ4YcpCe4ckRIHkWa8yKAWAcjaan9uJtVeOrFu3ruVnJycn/Pz8uHjxYpZlL168yNmzZ+nQoUO29f3xxx906tSJMmXK4O3tTc+ePUlMTOTChQt3jGP37t306NGD8uXL4+3tbWnnqVOnsix/+PBhWrRokWFCvvvvv5/ExESOHTtGREQESUlJNG3a1LLf09Mz1wNHd+/ebZ5Yr2ZNvLy8LI+ff/6ZiIiIXNVlL5I8FCDhuw5wYP+/mEyaocMdd66EChXL8PyEwaSkmPhl9UY2rN9m75CEKHA8a1XArVIQzoG+VJg7Cs9aFewXi6dnhucGgwHzH63/yc1ASmdn5wzPlVKYTKYsy2Y+TmYnT56ka9eu1KhRg2+++Ybw8HA++ugjgAxjETKLjY2lc+fOeHh48Mknn7Bz507Wrl17x9dprVGpCV1mSilLrNmVSXO3989kMqGUYufOnezdu9fyOHz4sKVtBZ0kDwXIh8u+xWBQ1K5T1eGneR49ZgAVK5XF17c4DRvXuvsLhCiCjJ5uuAT45Evi4OLikuMZYP38/IiKisrwBbh3794813cnAQEBBAcHs379+iz379q1i8TERBYuXEiLFi2oWrUq586du2ssR44cITo6mtdee43WrVtTvXr1bHs/0tSsWZO//vorQ6KzZcsWXFxcqFSpEpUrV8bZ2ZkdO3ZY9sfFxXHgwIEM9fj5+XH+/HnL84SEBI4cOWJ53qBBA7TWXLhwgcqVK2d4BAcH3zHGgkKShwIkKSkJk0kzbGSfu2a2BZ2rqwvzFk7mypVrvPPW5zJttRB2FhISwo4dOzhx4gTR0dHZ9gSA+c6AK1eu8NprrxEREcHHH39828RPISEhnDx5kt27dxMdHc2tW7fyHNu0adNYtGgRCxcu5J9//mHv3r3Mnz8fgCpVqmAymVi0aBGRkZF88cUXLFq06LZYEhIS+O2334iOjiYuLo5y5crh6urKW2+9xfHjx/n555958cUX7xjHqFGjOHfuHKNGjeLw4cP8/PPPTJ48mdGjR+Ph4YGXlxdDhgxh0qRJrF+/nkOHDjF06FBLT0Ka9u3b89lnnxEWFsbBgwcZMmRIhp6HqlWr0r9/f0JDQ/n22285fvw4u3btYt68eXz33Xd5fh/zkyQPBUhycgq+vsV5pGdHe4diFa3aNKZXny4smr+SRnUfJeJY1tcZhRC2N378eFxcXKhZsyZ+fn7ZXvcHqFGjBu+88w7Lli2jbt26/PHHH0ydOjVDmccee4yHHnqIDh064OfnxxdffJHn2EaOHMnbb7/N+++/T+3atenSpQsHDx4EzOMnFi9ezIIFC6hZsyYffPDBbbdBtmzZkhEjRtC3b1/8/PyYM2cOfn5+rFy5kh9++IGaNWsyc+ZMFixYcMc4goODWbNmDXv27KF+/foMGTKEvn378tprr1nKzJs3j1atWvHwww/Trl076tatS+PGjXFzc7OUmTJlCu3bt6dHjx488MAD3H///TRs2DDDsZYvX87gwYOZOHEi1atXp1u3bmzatIny5cvn+X3MT+pu15sKk2rVqumjR4/aO4zbJCcn8/NPG3lq8DRGjxnA9JlP56mesLAw2rZta9XYFjy0jPjrCfRf/Cghjcre/QWZXLx4meYNexEXF899rRrx7Q9vopS642qdWbFF2woSaZ/jOHz4MDVq1Miw7caNG3h7e9/1tY66qmZO2+eI7rVtt27donz58kyYMIFx48ZZMbKcy+p3Mo1SKlxrbd0RsUjPQ4Gwbs0Whoaas/rQIY/aOZr/nAg/zbkjUVw5E8O7/T/hRPjpXNfh71+S6TNHk5JiYlPYTn78PuvrmkII4Qj27NnD559/zrFjx9izZw+DBg3ixo0bGea5KAocZwaiQizt9sxOne+nXPkge4djEbHtJNpk7plKTkohYtvJPPU+DBz8CF989hP79h7lhckL6NiphbVDFcIhFLQeBZE3CxYs4OjRozg5OVG/fn02bdpEmTJl7B1WvpLkwc7+ORrJls3hADw1vLedo8moUvPyKINCmzROzkYqNc/btTiDwcC8RVPo0HogUVGX+erLX6wcqRBC5I8GDRrIKpzIZQu7++iDVSilqFChDK3bNrF3OBmENCpLUPUAfMuUYMRnA/LU65CmTt2qDBtp7tarW6+atUIUQghhB5I82JHJZGLNzxvRWjNs1BMF8vZMN29XfIKL31PikGby1GGUDvJjwvNzuHUrEU3RGawrhBCFiVy2sKGcrBLZkSZ86/kHfZ54MB8isi8vb09efeN5hgycQtlipahYqZy9QxJCCJEHkjwUAE/064p3MS97h5Evuj3cjg6dWqC3aU6cOMP1azcpVrxotF0IveLZHJVToUtsHIkQ90aSBxuaf2J6hufp5zb4c3M4g/pP4tq1G/z51Jf2CM8ulFK8MXcCM1svxDnFyOIXl/PikmfsHZYQQohckOTBTj5Y9g3Xb9zkvvsbUbWa/RbEsYsrJnyNxdBaE/1jNHt7HKR+B1n/QhR+mXsU9Brzc/VgznokhCgoZMCkHZw/d5FfVm9Ep65jUdREbDsJmHshDCj++OovO0ckhLAVpdRt62IUVCEhIbdNfS2yJsmDHaxc/j0mk4nA0qV4oMt99g4n36XNHwFgQhNcL9DOEQkhCosTJ06glJK5GGxMkod8ZtKa5R+sAmDosN44ORW9K0dp80f4lCnOwWKRvP3p55w9c8HeYQkhhMghSR7ymVLQsHEtnJ2d6D/wYXuHYzdu3q74Bpdg9MyBHD4UQcM6j/LvPyfsHZYQ+SsxHmKvoC9G2vxQbdu2ZeTIkYwbNw5fX1/8/PxYvHgxt27d4umnn6ZEiRKUK1eOTz75JMPr9u/fz8MPP4y7uzu+vr6EhoZy7dq1DGVWrlxJnTp1cHV1JSAggNDQ0GzjmD17NqVKlWL79u3Zltm2bRvt27fH09OT4sWL06FDB86dOwfA2rVradWqFT4+Pvj6+tK5c2cOHz5seW2FCuYxZE2aNEEpZVmQbefOnTzwwAOUKlWKYsWKcf/99/PXX3e+ZHrq1CkeffRRvL298fb2pmfPnpw5cyZDmddff52AgAC8vLwYOHAgM2fOJCQkxLI/NDSUbt26ZXjNjBkzqF27doZty5cvp2bNmri5uVG1alUWLlx4x2XT7U2Sh3yWkmJi65976NmrM6VK+dg7HLt7+JH21KxVCa01r856x97hCJFv9MVIuHoObl6BdW/lSwLx2Wef4e3tzfbt25k8eTJjx47lkUceoWrVquzatYtBgwYxdOhQyxd1XFwcXbp0wdPTkx07dvD999+zdetWhgwZYqnzvffeY/jw4QwePJi///6bX375hVq1bh8ArbVm/PjxvPnmm2zcuJFmzZplGeO+ffto164dlStX5s8//2Tbtm307t2b5ORkAGJjYxk7diw7duwgLCyM4sWL0717dxITEwHYsWMHYE4yzp8/z3fffQeYV88cMGAAmzdvZseOHdSvX5+HHnqIy5cvZxmH1ppHHnmEqKgo/vjjDzZs2MC5c+d45JFHSFuN+ssvv2TmzJm8+uqr7N69mxo1atx12e+svP/++0ydOpVZs2Zx+PBh5s+fz+zZs1m6dGmu68o3Wusi86hataq2pznd3tGjak7TpYo11XvCD1m9/g0bNli9zrd7r9Bv915h03r/+P0vXapYU12qWFO9d3fW74st2laQSPscx6FDt/+OXr9+Pdf1mPat06blz5gfK8Zo07511ggvW23atNHNmzf/7/gmky5VqpTu3r27ZVtiYqJ2dnbW33zzjdZa62XLlulixYrps2fPWsps2LBBA/rff//VWmsdHBysJ02alO1xAf3ll1/q0NBQXaVKFR0ZGXnHOPv166ebNWuW43bdvHlTGwwGvXnzZq211pGRkRrQO3fuvOPrTCaTDgwM1MuWLbNsK1++vJ47d67WWutff/1VGwyGDPFGRERopZT+7bfftNZaN2/eXA8fPjxDvZ06ddLly5e3PB80aJDu2rVrhjIvvfSSrlWrluV52bJl9ccff5yhzMKFC3WNGjXu0nqzrH4n0wC7tA2+T6XnIR9duRxDTMwN6tWvRv2GWa+9XhS1bd+M5i3qo5Ri1ktv2zscIfJHYBUgdUp6gzH1uW3VrVvX8rNSCn9/f+rUqWPZ5uzsjI+PDxcvXgTg8OHD1K1bF29vb0uZli1bYjAYOHToEBcvXuTs2bN06NDhjscdP348YWFhbNmyJUOXflb27Nlzx/oiIiLo168flSpVolixYgQEBGAymTh16tQd67148SLDhw+natWqFC9eHG9vby5evHjbZYg0hw8fJigoKEO8FStWJCgoiEOHDgFw5MgRmjZtmuF12fWoZOfSpUucPn2a4cOH4+XlZXlMnjyZiIiIXNWVn4reaD07un7tJgDDR/W1cyQFi1KKGa88Q5cOT7L1z92cPnWesuVK2zssIWxK+VdA+wRBUjy0HoTyt/18L87OzhljUCrLbWnX2rXW2a65o5SydN/fTadOnfjiiy/45Zdf7jgeIu2Yd9K9e3eCg4N57733CA4OxsnJiZo1a1ouW2Rn0KBBREVFsXDhQkJCQnB1daVDhw7Zvu5ubc/q56wYDIbb2pSUlGT5Oe29fvfdd2nZsuUd6ypIJHnIR3FxCQB079HezpFkL7v1ODJvzzx75r1q1Lg2XR5sxaZNu/DwdLdq3UIUWC7u4OKeL4lDXtSsWZOPPvqIGzduWHoftm7dislkokaNGgQEBBAcHMz69evp1KlTtvU89NBD9OzZk169eqGUYtCgQdmWbdiwIX/88UeW+y5fvszhw4d5++23adeuHQC7d++2jIcAcHFxASAlJSXDa7ds2cKSJUvo2rUrAFFRUZw/f/6ObT979iwnTpyw9D4cP36cc+fOUbNmTQCqV6/Ojh07GDx4sOV1aWMu0vj5+bF3794M29I/T3sPIyIiGDhwYLbxFDRy2SIfJScnYzQacHNztXcoBdILM0aREH+LRfNXcPlyjL3DEaLI69+/P56engwfPpz9+/ezadMmhg8fTs+ePalcuTIA06ZNY9GiRSxcuJB//vmHvXv3Mn/+/Nvq6tatG9988w0jRozg448/zvaYEyZMYM+ePQwbNox9+/Zx9OhRPvjgA06dOoWPjw+lSpXi/fff59ixY2zcuJERI0ZkuOXd398fd3d31q1bR1RUlOXOkKpVq/Lpp59y6NAhdu7cyRNPPGFJNLLSsWNH6tWrR//+/QkPD2fXrl3079+fhg0b0r69+Q/AMWPGsGLFCj766CP+/fdf5syZw/bt2zP0RrRv3549e/bw0UcfcezYMebMmcOff/6Z4VgzZsxgzpw5LFy4kKNHj3LgwAE+/vhjXn/99Rx8SvYhPQ/55ObNOLTWuLpm/8taEFi7RyE3qlWvSJ9+D7HsnS9Z/+tWtuz4EoNB8lsh7MXDw4N169bxzDPP0LRpU9zc3OjRoweLFy+2lBk5ciQuLi7Mnz+fSZMm4evry0MPPZRlfd26dePrr7+md+/eAFn+pV2/fn1+//13pk6dSvPmzXF1daVx48Z07doVg8HAV199xbPPPkvt2rWpXLky8+fP57HHHrO83snJiSVLljBr1ixmzpxJq1atCAsL46OPPmLYsGE0atSIoKAgZsyYwaVLl7Jtu1KKH374gWeffdZyu2fHjh158803LcnBE088wfHjx5k8eTJxcXH07NmTESNG8OOPP1rq6dy5My+99BLTpk0jLi6O/v37M2rUKP73v/9ZygwdOhRPT0/mzp3LlClTcHd3p1atWowePToHn5J9qJxesyoMqlWrpo8ePWqXY2uteTJkIj6e3jzzyWBCGpW1+jHCwsIsv+QFXfpFwtI7c/oCjev1JCUlhXfen8njvbsAjtW2vJD2OY7Dhw9To0bGAc/pu/XvxFFX1cxp+xyRtdv26KOPkpyczE8//WS1Ou8mq9/JNEqpcK11Y2sfU3oe8sn+sKOUwAvi4N3+nzDiswE2SSAKqsz/aY58MG17eIbtZUKXMHR4L95b+iUvz3ibhx/pgItLxgFdQghREMTFxfHOO+/QpUsXnJycWLVqFT/++COrVq2yd2g2J8lDPvnm7Z8tPycnpRCx7WSRSh5yY+y4UFYu/55zZy/y+Sc/EfpkT3uHJIRVFLQeBXFvlFKsWbOG1157jfj4eKpUqcInn3zCo48+au/QbE6Sh3xy6EIE5QkAwMnZSKXm5e0cUf7K/J/mmflTASgz7rXbypYq5cOzYwcw5/UPWPbulwwaUvhPRCGE43F3d+f333+3dxh2ka+j0ZRSrZVS/1NKnVVKaaVUaKb9K1K3p39sy1TGVSn1plIqWikVm1pfmfxsR178E32SKynX8QkuXuQuWeTFyKf74eNTjJIyhbcQQhQ4+d3z4AUcAD5OfWTld2BAuueZZ/BYBPQA+gKXgQXAaqVUI611CgWQ1prL0TGYPEz4lilRJBOHzPNEjAxNyHJ72t0eXt6ejJ88lGmTFvD7uj8xqewngMluborM7HkniShc7jSBkBD5yV43PeRrz4PW+het9VSt9bdAdsuF3dJaX0j3uJK2QylVHHgSmKC1/k1rvRtzolEX6GjzBuTR5csxJCUl4ywD/3Jl0OBHKVMmgNABk1n11Xp7hyMEYJ6lMT4+3t5hCAFAfHz8bbOE5oeCOObhfqXURSAG2AhM01pfTN3XCHAGfk0rrLU+rZQ6DLQE1uVzrDkSc/U6RqMB9yI8OVTmv/r1GvMYiPkzsr91zdXVhSkvjuDp4TP56X+biYq6TEBAybvWnd1toEJYg7+/P2fPniU4OBh3d3fpgRB2obUmPj6es2fPEhAQkO/HL2jJw1rgOyASCAFeAf5IvSRxCwgEUoDoTK+LSt13G6XUMGAYmKcJDQsLs0ngdxIXl0BKigkXVydiYmJsFsPNmzft0r68qBcTA8C+u8Rb0s+NwKBSXDgXzbixLzN0+CN3rTsmtW5HeS/AsT67vChs7TMajdy4ccOSOBT2yxiFuX2O3DatNbGxsZw9ezbfj12gkget9Zfpnu5XSoUDJ4GumJOK7Cggyws/WutlwDIwTxJlj4lq/t5nnpiqhE9xSpQoYbPJchxpIh695m+AHMU7b4Ez//fEeH5ft53X3phIufJBdyx/6J2TOa67oHCkzy4vpH2OrTC3rzC3zZYK9Ny/WutzwBkgba3aC4ARKJWpqD/m3ocC6dWZSwFkTYs8eqDL/VSsFExKiokVH31v73CEEKLIK9DJg1KqFBAMpC19Fg4kAZ3SlSkD1AC25nuAOXTo4DGAIj3m4V4opQh9sjsAJYoXzilyhRDCkeT3PA9eSqn6Sqn6qccul/q8XOq+eUqpFkqpEKVUW+An4CLwPYDW+hrwITBXKdVRKdUA+AT4G/MtngWO1pro6Ku4u7tiNBrtHY7DqlGrAh0faMmSRR9z7myB7WQSQogiIb97HhoDe1If7sDM1J9nYR4IWQf4EfgHWAkcBVporW+kq+M5zOMfvgL+BG4C3QvqHA8XL14hOTkFvyzuEhC5M236SK5du0HTBo+zJ/yQvcMRQogiK7/neQjTWqssHqFa63itdWettb/W2kVrXT51++lMdSRorZ/RWpfUWntorbtnLlOQHI84BUBISLCdI3F8tetU5eFHOnDrViLTX1h89xcIIYSwiQI95qEwSEkxd4jUql3ZzpEUDi+8NAqDQbFt6142b9xl73CEEKJIkuTBxkqV8gWgfoOs11ovshLjIfYK+mJkrl5WoWIZ+g/sAcCLUxfabWpWIYQoygrUPA+FTdqaCwN9uvLn+O23bU9T1NZc0Bcj4eo5QMO6t9CdR6P8K+T49ROnPMVXn6/m4IFj7Nyxn6bN6touWCGEELeRngeR/y78i2VOL1NK6vOcCwwsxYin+wEyd4YQQtiD9DzY0LzIFwkqdR9Go5EzFzfbO5yCI7AKlklBDcbU57nz7NiBfLz8B16d9Q4rP5udIYlIuHGL+OsJnAg/XSRXMBVCCFuTngcbunD+kvk2TX9fe4dSoCj/CuATBF6+kMtLFmmKl/BmzPMD+eP3v2ha/zESE5MAOBF+mnNHorhyJoZ3+3/CifACeyOOEEI4LEkebOh4hPmLq7zcpnk7F3fw9M1T4pDmyWG98PEtzvnzl/h4uXna6ohtJ9Em8yWR5KQUIradtEq4Qggh/iPJgw39888JAGrUrGjfQAopd3c3Xpg+EoA3Xn2P2Nh4KjUvjzKYV8hzcjZSqXl5e4YohBCFkiQPNuTp4Q5AnbrV7BxJ4dVvQHeCgwO4du0m7y39gpBGZQmqHoBvmRKM+GyAjHkQQggbkOTBhnxLlQCgUuVy9g2kEHNycmLWa2MAeGvxp9y6lYibtys+wcUlcRBCCBuR5MGG/t53FDBPbCRsp3uP9lSvURFPLw97hyKEEEWCJA82YjKZmPPaMpydnfDzk7stbEkpxSuvP8eF85dY8dF3mGTWSSGEsCmZ58FGzp6JIiXFhH9gKZRS9g4nx/SKZ3NUToUusXEkudOmXVNatW7MzBff5InynalcWQZKCiGErUjyYCOW2zTLB9mk/qy+5NsAesV3GbYVtC95W3pxxtM80H4wF6MuU6ZMoL3DEUKIQkuSBxs5dsw8v0CNGpXsHEnuZE429Brzc/Vgznok7KlBo5p0fKAlequJE5Fn7R2OEEIUWpI82Mj+1MGStepUtUn9WX3Jx8TE4NO3aC2yldmsV8cwo/1CLl+OYU/4IRo0qmnvkIQQotCRAZM2UqWqeebESpXkTov8VKVqCP7+JQF47ZV37RyNEEIUTtLzYCNeXuYJoipUlLkGIPuBmJm3W2OMRvmQIKIvXcXV1XjPdQkhhLid9DzYQEpKCn/9uQcXF2dKB/nZO5wix8XZmTJlA1i3ZjN/bd1DSkqKvUMSQohCRXoebOD0qQus+vZXAgJLYTBIfgb5f9dHUHAApS760vvRMbw+Zxz/N6hHvh5fCCEKM/lms4G02zTL2eg2TXF3RoOBKS8MJyHhFjNfeovY2Hh7hySEEIWG9DzYwPGIUwBUryGraeaHcSGzst6xHQb6dAVgeq25zD9RtO9EEUIIa5GeBxs4sP8fAGrVqmznSER6ly5dsXcIQghRKEjPgw0cOXwcgIqV5E6L/HCnHgWtNQ92HEr4rgP4Li3FtJdG5WNkQghROEnPgw20adcUkNs0CwKlFLPnjQcgWe66EEIIq5CeBxtISTHh5GSkTNkAe4dy7xLjISkefTES5V8hz9Xs7zA2R+XqrF+U52Nkp16DGjzeuwsfvPcN/zewB5Uql7P6MYQQoiiRngcri46+yp+bdxMU7I+TUz7mZonxuCbHoS9GWq1KfTESrp6Dm1dg3VtWrTu/TX1xBCkpKbRp2Z+dO/bbOxwhhHBo0vNgZbvDD7Jr534aNq6Vb8dM+5J3Q5u/5DuPvqdeAosL/wLa/LMpxfw8j/Vm7lE4/vybAFRc8Mw9BJhzZcuVZvCTj7Hs3a+Y+Pwc/tj8sUMtlS6EEAWJ9DxY2fFj5jkequfnapqpX/IK/vuSt4bAKkDqF6zBmPrccU2c8hQeHm4c2P8Pv6zeaO9whBDCYUnyYGWHDh0DoHoNK/zln1OpX/IarPolr/wrgE8QePmCtXoz7Kh4CW8mTh0GwJSJ80lKSrZzREII4ZgkebCytNs0K1XKv0F5aV/yCUYP63/Ju7iDp6/DJw5pnhreG39/X86fu8hv67bYOxwhhHBIkjxY2akT5wCoUDGfl+J2ceeWk0eh+ZK3FRcXZ15543kAYmJu2DkaIYRwTJI8WFnPXp0BWdeiIHukZ0caNa7F66+8J7NOCiFEHsjdFlZ29co1ypQNxNXVxabHyTxvQoVOJpyz2G6LeRMcnVKKGa88S/cuw2lY5xF27v2OwMBS9g5LCCEchvQ8WNGB/f+w7a+9lC0baO9QxF00b1GfNu2akhB/i1kvvWXvcIQQwqHcsedBKWXUWsucvjm0KWwnZ05f4L77G9r8WJl7FOLemkRycjJ11s+3+bELizfmjqdlkz58+9Uaxj4/iKrVZLyIEELkxN0uW9xUSv0NhKd7HNBayz1uWUi708KRl+LO6nIIQOS8jNsLw+WQylXK80T/rnzx6WomjZvL96uX2jskIYRwCHe7bPEksAmoDswDdgM3lFI7lVLvKqWeUko1snWQjuLwoQgAQirk850WIs9enPE0Li7O/LklnOMRp+0djhBCOIQ79jxorT8HPk97rpSqAjQCGqT+2wfwvls9RcVJe92maUVZXQ4xb59t1eOkxCaQcjOe2IOReNay3+UCPz9fnn1uEPNmf0BUVLQsoy6EEDmQqy99rfW/SqnzmHssqgKuwEVbBOZoEhJuERNzHZCeh7uJPRhJQsQ50JrICUupMHfUPSUQ97pi5zNjB/DZJz8y88U3+WrVYoqX8AZgXMisHNU7/8T0HJUTQojCIkd3WyiliimlBiilfgQuAa8DJ4EHgGAbxucw3Nxc6fVEF/z8ffH0dLd3OAVa7L5joM0LbumkFPNzO/LwcGPytOGE7zpIiya9SUxMsms8QghR0N3tbotBQC+gI3AWWAW8prXeng+xOZxTJ887fLe3XvFshufuXllvV6FL8nwMz3qVQSnQGuVsND+/B9ZYsbNP34eYP/tDTp06z4fLvmHk6H639Sgs7bMSgFFfDbqneIUQwtHd7bLFcsxJwxhgudY60fYhOabV/9vA33uP0K1H+3w5Xn58yduKZ60KuFUKIuVmPGWnDrDrmIc0RqOR2Qsm0vfx53jj1ffoP+BhihX3sndYQghRIN0teQgD6gPvAEuUUgfJeNvmPkkozNb+som4uIR8XRDLFjInG3n5Kz4njJ5uGD3dCkTikKZDxxY0bFSL3eEHmTv7A15+bay9QxJCiALpbndbtAdQSlUEGgMNMd9l8SjgCyQppQ5qrW0/K1IBlzbHQ37daZHVl3xMTAwNP3oxX45fGCmlmLdoMu1bDeCTFT8wbfpI3Nxc7R2WEEIUODkaMKm1Pq61/lprPVlr3UlrXQqoCPQH1to0Qgdx8sRZACrInRYOrU7dqnR/pANJSclcuiiLZgkhRFbyPD+D1voEcAL41lrBOKqbN+MsyzuHVLDPzScpsQkYrtl/3gR7yDzOo0LdrLfndPzHy6+O4be1W3jt5Xd4fc54SvgUs0qcQghRWMjCWFZwMeoybm6ueHl52OWLJm3eBENMHJETlhJ7MDLfYyhMgssE8NSI3nz79Tr69X7e3uEIIUSBIzNDWkHFSmVp2qwuN2/G2uX4afMmKP6bN6Eo9T5k7lFImxXTY3TeZ8Uc+3woHy77hp079hO2YTtt2zW7pxiFEKIwkZ4HK4mMPEOFivaZ4yFt3gQNVpk3Ib2U2AQSo64Wud6MYsW9mDRtOAATnpuNyWSyc0RCCFFwSPJgBa+/8h6nT52325oWafMmmEp43PNUz+mlXQ5JunClSF4OGTqsF35+vpyIPMs3X62xdzhCCFFgSPJgBb//+idg3wWxjJ5umIq7W/VyRUGbRjqnEi6buHo0+Z6THRcXZ16fOw6AxQtWknDjFlfPXuNEuKy+KYQo2iR5sIITabdp2umyha1YppHG+pdDbCX2YCTn/kziyqEUq/SWPPxIB+rWr466CucOR3HlTAzv9v9EEgghRJEmAybv0Y3rN7l+7Sbg2EtxZ8Wa00hnt/Jl5u3ZrXyZU7H7jqFTzD9bY/CoUorX54xj0sNvoN3NvTDJSSlEbDtJSKPClSwKIURO5WvPg1KqtVLqf0qps0oprZQKzbRfKaVmKKXOKaXilVJhSqlamcq4KqXeVEpFK6ViU+uz27f28QjzX6Bubi6ULFnCXmHYjNHTDZcAH4e5e8OzXmWU0fyztXpLmjarS0iTsujUSzhOzkYqNS9/z/UKIYSjyu+eBy/gAPBx6iOzicA4IBQ4CkwHflNKVdNa30gtswjoAfQFLgMLgNVKqUZap/3NmX9uJSbh7e1JYGk/VGoXv7jdvfYo5JRnrQoE3edMfLSJEiOsN3j0hcVP83KbxbgoJwa88bj0OgghirR87XnQWv+itZ6qtf4WyHDvmzJ/844F3tBar9JaHwAGAd5Av9QyxYEngQla69+01ruBAUBdzMuG57umzeri5+9LjZqV7HF4kQW3kgZ8qjlZtbekUqVylAwoQawpgVffetfSCyGEEEVRQRowWQEIBH5N26C1jgc2AS1TNzUCnDOVOQ0cTlcmXyUnJ3Pq5LlCN95B3K58hWCMRgPhOw+w5udN9g5HCCHspiANmAxM/Tcq0/YoIDhdmRQgOosygWRBKTUMGAbg5+dHWFiYNWK1eGnaeyQnp3Ar8abV686NYjExpKSkWD2GYjExAHZtG8DNmzl/fxvrRJycNLvXruK6W0nrxXDjBgGBJeEavP7qUjy8rNf7kJv2OSJpn2MrzO0rzG2zpYKUPKTJ/D+yymJbZtmW0VovA5YBVKtWTbdt2/Ze48vg1ImZAHR5sCP33W+/lcmP/28/MTExWLt9x/+3H4CGVq43t8LCwnLUNn0xEh1pHvrS4NJW6Dwa5W+dyxeH3jlJ8RLFqedenbNno2jYoDHFintZpe6cts9RSfscW2FuX2Fumy0VpOThQuq/gUD6m+j9+a834gJgBEoBlzKVyfd+5KtXrnHzZhwAFfP5skVWtz46Z7E9vwYqFhgX/gVSp6cwpZifWyl5AFAo5i+aTKd2g3npxSVMmjqMwMBSVqtfCCEcQUEa8xCJOTnolLZBKeUGtAK2pm4KB5IylSkD1EhXJt8cP27OcVxcnAiQL5CCIbAKkDoxpsFoeW5N9RrUoG//rny68kfGjn7F6vULIURBl689D0opLyDtxnsDUE4pVR+4orU+pZRaBExTSh0B/gFeAG4CnwNora8ppT4E5iqlLvLfrZp/A7/nZ1vgvzkegssEYjDkbx6WVY+CdL+B8q/ArXgXDEYTzl2td8kis+mznmHVN7+y/re/+HNzOPe1amST4wghREGU35ctGgMb0j2fmfpYiXluhzmAO/A24ANsBx5IN8cDwHNAMvBVatn1wEB7zPHg5+eLl5cHVaqG5PehbSa/ZoK0JZPJiMlkxMVGiQNAyZIleOGlUbw4dRGjR85i595VODkVpKuAQghhO/k9z0OY1lpl8QhN3a+11jO01qW11m5a6zap8z2kryNBa/2M1rqk1tpDa9099XbNfNe6bROSk1OoXLmcPQ4v7OypEb0pW640Z05f4P13v7Z3OEIIkW/kT6V7cOrkORISbhWqBbEKco9CdjL3ilToZMpye27bNi5k1l23t6MhH/MzP3z3OyNH98tV/UII4agK0oBJh6K1ps19/wdASIXgu5QWhdljvR7gwP5/LGNghBCisJOehzyKjr5KXGw8UPiW4nY0mXsU9Jol5u2Tn72neuefmJ6jchcuRLN2zRaeffpl5i2cTPUaFe/puEIIUdBJz0Mepf2VaTQaCC7jb+dohD0FBpbiuXGhbP9rH08OmiLrXgghCj1JHvIoLXkICg6QUfaCkaP74R9Qkn+OnuDLz3+2dzhCCGFTkjzkUeTxMwBUriJ3WghwcXHmzaUvAjB10gLLzKNCCFEYyZ/MedSseV2cnZ2oVLm8vUMp8vSKrMc2ZN6uQpfYNI72HVvQ8r4GbP1zDy/PeJvZ8ybY9HhCCGEvkjzkUd361UlKSpaluAux7CbMyiz9gM0335lO0waPsW3rXpvEJIQQBYEkD3mgtWbzxl0AkjwUALbuUciNcuWDGD/pSWa/9j5bNu3i/taN7R2SEEJYnSQPeRAVdZnhT5qvb1eoIMlDYZX5FtDjz78JQMUFz9zxdU8/+398/slPPD1iJovefIF2HZrZKkQhhLALGTCZB2l3WiilKFuutJ2jEQWNu7sbM18dw7mzFxk5bDq3biXaOyQhhLAqSR7yIC15KF26FK6uLnaORhRE3R5uR9361bkcHcOCuR/ZOxwhhLAquWyRB8cjTqOUonLlEHuHIgoopRTvfTCLlk36sGThxwx+8jECS/sB2a+Z8RObMjzP6QyXQgiR36TnIQ+OR5xCKUXFyjIttche5SrlGTCwB8nJKYwZ/Yq9wxFCCKuRnoc8eKJvV37+KUwWxBJ3NfPVZ/n+u9849s9JUlJSMBqNt/UoLO2zkpiYGKauG2OnKIUQInckeciDtO5nWRCrcMs8yVSFullvv9Otol7ensxbNInhT07n809+YkDoI9YOUwgh8p1ctsilG9dv8svqMEDmeBA58+hjD9C8ZX2mTV7IR+9/a+9whBDinknPQy7t23uEhfNXAFA+RC5bFGaZexSuzJxAfLSJEiNG41mrQs7rUYrX54yj3f0DmD5tMT0ff4ASPsWsHa4QQuQb6XnIpbTbNP38fPHwcLNzNCK/xB6M5NyfSVw5lELkhKXEHozM1etr16nKI4914tatRCaOm2OjKIUQIn9I8pBLabdpVpLVNIuU2H3H0Cnmn3VSCrH7juW6jrnzJ+Lq6sL3q37j4IF/rRyhEELkH0kecun48dMYDAYqymDJIsWzXmWU0fyzcjbiWa9yruso4VOMF2aMAmDYkBetGZ4QQuQrSR5y6di/5lvuZLBk0eJZqwJB9znjW9NIhbmjcjXmIb1hI/pQoUIZLl26ws0bsVaOUggh8ockD7k0ftJQAEJkQawix62kAZ9qTnlOHAAMBgPvfDCTq1euMX+OedrqhBu3iL+cwInw09YKVQghbErutsglZ2fzW5aTnof9HcbmqM7MqzeKwq1R49r07d+Nt9/8jNizsZiOJKFNmnf7f8KIzwYQ0kguiQkhCjZJHnLheMRpfvjudyBnS3HXHmCydUjCQb0482m++Xotu37ZTwO3agAkJ6UQse2kJA9CiAJPLlvkwsYNO/jfD+vx8SlOseJe9g5H5DODIQUn5yT0xdzdppkVPz9fnp8wmPOJ0Wg0AE7ORio1L3/PdQshhK1Jz0MuWO60qJSz8Q6ZJxmKe2sSAB6jZ1s9NmFb+mIkLu6J5ifr3kJ3Ho3yz/vYB4DnxoWy/INVXIm/jr+PDyNWyCULIYRjkJ6HXIg4dgqD0UDFSvIffJFzwTwvg1KAKcXy/F44OTmxdNkMknQyF29coXxDGYQrhHAMkjzkQsSxUyQnJcuCWEVRYBUAtAYMRsvze9W2XTNKlPAmOTmFA/tl4ighhGOQ5CGHkpOTOXniHCALYhVFyr8CifEuJCc6gRUuWaRXtVoFjE5Gnhw0hQN//2O1eoUQwlZkzEMOGY1GFr89jdEjZhFSQRbEKgoy32pboZP57pmjfRdn2H6vt9o6OztRpow/v+z/k07tBvPXrq/ld0wIUaBJz0MOKaW4euU6gFy2EFbn6enOiKefIDk5mcd6jCYpKdneIQkhRLak5yGHNm7YwferfsO7mCe+vsXtHY7IB5l7FPQa890zdSY/a5PjzXj5WcL+2MGRw8cZN+Z1liyV9S+EEAWTJA85tHbNZvbtPUyt2lVQSuWpjoTLJuKjTeiDkfc0xbFwbJkvh7RK/ffQA+NY6hkAjQPg6GV+Wb2Rh7q1yff4hBDibuSyRQ4djziN0WjM8yWL2IORXD2aiMGQzIU33iL24L1PNCQKt5XLv7d3CEIIkSXpecih4xGnSEpKzvOdFrf27CCkvUYZQJuSuLZnh/Q+FFErInwzPO8SZB5Ls/ZcsQzb/7j6M99+vZbHe3fJt9iEECInpOchB5KSkjl96jxa6zwnD54BoAz/PTwDrBykKHSaNKvDs0+/wkvTFt+9sBBC5CPpeciB8+cuWsY55DV5cKnXFH36T/MkQ0YnXOo1tWKEwhb0iqwHRmbennka8ruZf2J6hufHn3+TmJgY5m/NOEDyzOkLNGv4GEvf+pwOD7SkdZsmd617XMisPMUghBC5IT0POVCufBCzXhsD5P02TeVfgcQE8yRD6sFnrDrJkCicypQN5K13ZwAwsO8Erl65Zt+AhBAilfQ85NDJE+fw8HAjIKBknuswmYyYTEZcJHFwCLntUbCFRx/rxLo1m1n1zTr6PDaWdX98dMe7fTL3KCztsxKAUV8NsmmcQoiiRXoecuD9975m7S+bCalQJs+3aQqRV0uWvkhwmQD27D7Er2u32DscIYSQ5CEnfl2zmQsXLlGhgqxpIfKfi4szP6x+B08vdxbMXU5iYpK9QxJCFHGSPORARMRpkhKTZEEsYRMpsQkYrsXfce6PkArBLHn7RXaHH2T4kBe4eSM2HyMUQoiMJHm4i1u3Ejlz+gImk5bFioTVxR6MJCHiHIaYOCInLL1jAvHwIx14vHdnVv8UxsB+E/MvSCGEyEQGTN7FicgzaK2B3N9pkd2qjJm33+uqjMJxxe47BlqjAJ2UQuy+Y3ecPGzhm9PYGLaTzZt28d7SLxg+qm/+BSuEEKmk5+Eurl+PxbdkCSDvczwIkR3PepVBKTSgnI3m53fg5ubKD6uXYjAYmD5tCUcOR+RPoEIIkY70PNxFk6Z16P9/3XnvnS8JCvbP1WtrDzDlarsoejxrVcCtUhCx0VepNOupHE1ZXrVaBV594zmmTJzPo92fZt+hn3Bxcc6ybMKNW8RfT+BE+GlCGslS8kII65DkgZzNyveE5wMYjcZ8iEYUNUZPN0xJ7rla62To8N6sXbOZjRt2sGXTLtp3bHFbmRPhpzl3JApt0rzb/xNGfDZAEgghhFVI8mBDBWGSIVF4rfxsDp3bD2b0iJms/WM55cqVzrA/YttJtMk8Xic5KYWIbScleRBCWIUkD9x5Vr56NbsTFXWZJ5963B6hCZEtT093PljxGh1aD6BFo178ufNLQkL+G5dTqXl5lEGhTRonZyOVmpe3Y7RCiMJEBkzeQVxcAufOXiQlOSXPa1oIYUvVa1Rk/OSnSExM4pGHRpGUlGzZF9KoLEHVA/AtU0IuWQghrEqShzs4EXnG8rPM8SAKqrHPD6Jps7qcPRvFs6NezrDPzdsVn+DikjgIIaxKkoc7iDh2yvKz3KYpCiqlFF+tWoS3tyfffr2W//243t4hCSEKORnzcAdu7q4Elwng/LlLlC1b+u4vECIHMk8SBuCcxfbcTB7m5e3Jl6sW0a3zU7z80tt0694Og0H+NhBC2EaBSh6UUjOAlzJtjtJaB6buV6n7hwE+wHbgaa31QVvE0+mB+2jarC57wg9lex+9ELllq3k+mjarS9SiZuYnH49FAyMfND/VK8IzlJU7gYQQ96Ig/mlyFCid7lEn3b6JwDjgGaAJcBH4TSnlbc0AEm7c4urZa0SGnyby+BkZ7yCEEEKkU6B6HlIla60vZN6Y2uswFnhDa70qddsgzAlEP+A9axw8/cQ6ix/7kKiESzToV9MaVQsB3P5Xv16zhJiYGHz6Ts/mFTl34JOMfw+kracS+VvG7XVC7/lQQogirCD2PFRUSp1VSkUqpb5USlVM3V4BCAR+TSuotY4HNgEtrXXw9BPrKA0ldXEZLCmEEEKkU9B6HrYDocARwB94AdiqlKqFOXEAiMr0migg2+sKSqlhmMdI4OfnR1hY2B0DuOZ6HfMSh2BCcyHpMjdjY+76uoLg5s2bDhFnXhTmttWLiSElJcUq7fspwjfD82kdLgOwItP27vn8Xhbmzw+kfY6sMLfNlgpU8qC1XpP+uVJqG3AcGARsSyuW6WUqi23p61wGLAOoVq2abtu27Z2DaAsnvrtA9IWr/HLiD6JTYuj+cBeq16h459cVAGFhYdy1fQ6qMLdNr/mbmJgYq7Sv7Yn/6tBac3nuGJKTktkVdJQ/Nn9st/VZCvPnB9I+R1aY22ZLBfGyhYXW+iZwEKgCpI2DCMxUzJ/beyPuiZu3K9oNolNiACgfEmTN6oXIF0opPDzccXZx5tDBY4wYOj3DDJRCCJFXBTp5UEq5AdWB80Ak5gSiU6b9rYCt1j62h4cblSqXIyjYH3d3N2tXL8R/EuNxTY5DX4y0etUGQwrexYy8/fIgfvjud2pVeZC/9x21+nGEEEVLgUoelFLzlFJtlFIVlFLNgG8BT2Cl1loDi4DJSqmeSqnawArgJvC5tWMpWbIEviVLyGBJYVP6YiRcPYdbShyse8uqCYS+GImLeyJOLsn08olg3rQnuHr1Op3bD2HbX3utdhwhRNFToJIHoAzwBea5Hr4DbgHNtdYnU/fPARYAbwO7MM8D8YDW+oa1A0lKTiYy4lSGVQqFsLoL/wIaBWBKSX1uzbpBKXPdA7vU4qVZz5CcnMwjXUfyx+9/We9YQogipUAlD1rrJ7TWQVprF611sNb6Ma31oXT7tdZ6hta6tNbaTWvdRmt9wNpxJKeksGPb30RHx0jPg7CtwCqAMo/4NRhTn1uxbg1ag8YAgVUYPeb/mLdwEikpJp54/DnWrd1sveMJIYqMAnW3hb2MC5l127ZA55IM9OnKqSUnGbfEvH/+iXufxEeI9JR/BbRPEAnXr+LeeTjKv4LV6o67BBd+V3j4QdwVRWBj8PSHQUN64uXtyahhM3hxymLq1KlKUHCA1Y4rhCj8ClTPgxBFkos7t5w8rJo4AMTuO0ZclCL6gCLugiZ23zHLvsd6deb71Uu5dOky3ToPY9XX66x6bCFE4SY9D9zeozBv9gfMfu19ACLP/IGXt6c9whLinnjWq4wygk4B5WzEs17lDPtb3teA7358m4cfGsGIp6Zz8uRZnp8wxE7RCiEciSQPWTgecRoPD3c8Pd0kcRAOy7NWBYLucyY+2kSJEaPwrHV7z0aDRjX535p36dr5KV5/5T1u3IjlpVnP3FYuq0t7WZFLe0IUDXLZIgs9Hu1IYJAfIRXL2jsUIe6JW0kDPtWcskwc0jRoWJP1mz7Gzd2VtxZ/yvjn3sjHCIUQjkh6HrLQ+cFWTBw3R+60EEVGjRqV2LT1c9q3GsDKj77n/vsb8chjlvnYbutRWNpnJQCjvhqUr3EKIQoGSR6yEB+fwLmzF6koyYOwAb3i2du2lchie+alu/NSt7tX1tuzqrtCxTL8ufMrenZ/mqdHzMTVzYUuD7VGKZXrOIQQhZtctsjCyRPnAAipIMmDKFqCgvxZ89sH1K5TlUH9J9Gz+9OkpKTYOywhRAEjPQ9ZiDx+GkAuWwibyOqvfmut7Je57uPPvwlAxQW3D4LMjo9vcVb9+CatWvRjy+ZwHuw4lJ9/fR9nZ/nvQghhJv8bZOFE5FkAKkjPg3Aw+zuMzdH2OusX3bEeL29P/tzxFe1bDWDP7kO0bzWA38JW4ObmCkDCjVvEX0/gRPhpQhrJwGIhihq5bJGFyONnKFGiGD6+xe0dihB24+Hhxqa/Pqd2nSocOXyc1s37kZKSwonw05w7EsWVMzG82/8TToSftneoQoh8Jj0PWYg8fpqQCsH2DkOIXLtbj0Juubg481vYCnp0HcmObX8za/pbtApqiDZpAJKTUojYdlJ6H4QoYiR5yEKrNk1wcjLaOwwhCgQnJyd+WvMeUybOZ+lbn3PyvrOUUl5oDU7ORio1L2/vEIUQ+UyShyw8+9xAe4cgRIFiMBh4Y+54XFycefftL3i4RGvKBQUx6K3HpddBiCJIxjwIIXJEKcXLr42l3/91JyE5kX9Pn2Tt9i1ore0dmhAin0nyIITIlcVvv0D5kCBA8+KURbRq3o/4+AR7hyWEyEeSPAghcq1MmUAaNalNzVqVOXrkOJ3bDyF81wF7hyWEyCeSPAgh8sTVxYWNWz/j0y/nce3aDbp0eJJGdR/l2D8n7B2aEMLGZMCkEOKusluSO217R5qAD3x88mdaNn2CocN78eobz8u6GEIUUtLzIISwmg9WvIqbmyvvv/s1tat1Ze/ew/YOSQhhA9LzIIS4q8xLct9J5wdbMWTAZH77dSvdOw9n9vwJlA72smF0Qoj8JsmDEOKuslszI7M66xfh5ubK598sZNPGncx+bRljnn6FChWDWbCkGPe3amTbQIUQ+UKSByHEXdUeYMr1a1q3acL9rRqx4sPvmDxhHo92G0WXh1rz4cpXcXFxsUGUQoj8ImMehBA2YzAYGPLU47w692n8/HxY+8smqpR/gJ9/2mDv0IQQ90B6HoQQd6VCl2R4HvfWJAA8Rs/O0eurVCnLwX/XMGXCPD76YBWh/zeZvv274fxLzmanzM2YCyGE7UnPgxAiXyileGPeBLZs/4IGDWvyxWer7R2SECKPpOdBCJFrCZdNxEeb0Acj8axVIVevrVqtAr9uWM7WP/fwzMiZnDp5njp1q/LFt4tY9aw5oRj11SBbhC2EsBJJHoQQuRJ7MJKrRxPx8IMLb7xF4OTRuU4gAFre14Aff36X3j3HsP/vf6hXoztDavQguEygVeLUK57NUbnMl2SEEHcnly2EELlya88OQtprAuppQtokcWvPjjzXVaZsIFt3fsXMV55FKTh54hy7dx3kz83hslqnEAWY9DwIIXLFMwDUaVCG/57fq1HP9OfxPl146b4FmG6ZGNpjKmXqlub/BvXg/wY+jNFozHWd42eUyvB8ZGgMAO+sKJFh+/zQPAYtRBEmyYMQIldc6jVFn/4TrQGjEy71mlql3rjTcXimuKENmq6+97P93EHGj32Dl6Yt5snhvRg34Uk8PNxyXF/mOzTOzJ+a5XYhRO7JZQshRK4o/wokJriQnOiEevAZlH/uxztkJWLbSbQp9VKFhucHD6Lzg/cTH3+LJQs+plLZDjw9fAaXLl3JU/0uTil4uSWiL0ZaJV4hijJJHoQQuWYyGUlOcrZa4gBQqXl5lMG8CqeTs5EGD9Tm0y/nc+T4WgaEPoLBoPj6yzU0rN2DiePmEHn8TI7r1hcjKVksnmIeibDuLUkghLhHctlCCHFXmde2qNDJlOX2OusX5fkYIY3KElQ9gPjrCfRf/CghjcoC4ONTnAWLp/Da7Of5de0W1v/2F5+u/JHlH6yidp2qvPrG87S8v8GdK7/wLwpQCnRKMurCv2DFxEeIokaSByGE3WS+nfK53qk/7J+L3v/fdhW6BDc3Vx5+pAMPP9KBp4b3pm/v5zmw/x96dB1B2XKBTJs+ip6PP4BS6rbjnI8pRslkMBogxaS5HFOMIBu2S4jCTpIHIcRdZbcwVl4WzLKG2nWrsv/Iajas38b0qYs5cuQ4I4ZO541XlzFu4hB6Pv4ARx+cYCn/91U3rngWo2L5ZI6fcMI37gfq+nwJ3FtviRBFlSQPQgi7yXw75bRhlwF4dVnJDNuzu52yXYfmbN7enIMH/uWVmUs5eyaKZ0bO4sWpi/iuUi1LuUD3ZP4+587JM84YgJpBCdZshhBFjiQPQoi7KuizMNaqXYUvvlmI1pr1v//FkAGTab/rL4xGAw91a8urbzxHwNw5nDjnTO2JoyzjKYQQeSPJA7cP+sqOdG8KYV2Z51y49vo4MKXwylcdcG92X67rU0rRsVNLDv27Bs9VU1K3xsKvrxBYD2rVS8xyPIUQInckeRBCFAjx2//Eyy8JZQC9/yviIU8JBICXtyeZJ7eOuwSxF8AzEDz87jlcIYo0SR64vUfh+PNvAlBxwTN2iEaIoin5n324Gv6b9jr5n32Qx+QBMo6n8HNN4qHgGwCkaFh3zptLt5wB+GNeD7p2b8vgJx+jUuVyd603qwW32gB6xXcZtkmPhijMJHkQQhQITlXrofcfAUCbzM/vRfpLIhc//42oD382H8do4OFRdfj4zCnW/LyRc6cv8t7SL3lv6ZdUq16Bps3q8ljvzrRo2QCDQebREyIrkjxkISU2gZSb8cQejMzTUsNCiNxzb3Yf1/74DkwpuHTsledLFlnxrFc5dYYojXI2Ur5jE96o1Zs35o7n8uUYwv7YQXT0Fdb9splPVv7IJyt/xMXFmQYNazJgUA+6P9LBsq5G5h4FvWYJMTEx+PSVNTNE0SHJQyaxByNJiDgHWhM5YSkV5o6SBEKIfOLs7QQ4WTVxAPCsVQG3SkGk3Iyn7NQBGc7pkiVL8FivBwAYNqIPq/+3gZXLv2fHtr/Zvm0f27ftY8zoV+n4QEs6P9iK+h9vyFB3pYdMeLjAsT5jiI/+b4IqGWAtCjNJHjKJ3XcM83KBoJNSiN13TJIHIWwk8/gBd6+st1tj/IDR0w2jp9sdz2elFN17tKd7j/Zordm75zAfvf8tV69e59DBY6xbs5k/Grf4L95SGjdf888VOmkifyNDAiFEYSXJQyaZuzc961W2d0hCiDzI7hbsnK7HoZSiQcOavPmO+XKE1prVP21g2Kx3iTh2Cq01z3YMYhplzf9lGMF1YCNKt+2Fp6e7FVsiRMEjyUMmd+reFEJYlyPdkaCUovvD7en+cHvi4hL48fvf2fXzT2gTaAVJySaGTV3JrhNv4+3tQfmQMjRsVJN2HZrTqnVjipfwtkvcWd0dkhVH+iyE/UnyQPZ/oRx/dnGG53INUwjHYcvz1cPDjb79u9HU3Y9jX36OdyDcuGCkc4NOxDjv5syZCxzY/w8H9v/Dxyt+ACAkJBj/gJKUKRtI63ZN6dipBQEBpe58ICEKKEkesN/iPkIIx5P+L/mon5zxvKpIvAwmDU19LjFmdCAQSMzDL/Pnlt3s2nGAEj7e7P/7H9at3cyO7X/z3be/AuDi4kzZcqV59LFO1K1Xjdp1qlKmbGCWK4Pm1W13h/w4G5LiofUglCxLLvJIkgchhMgj/womblwx/2zS5udpfHyL0+3hdnR7uJ1lW0LCLXbt2M/vv25l5479RBw7xcWoyyyYu5wLC5vAH+ZymWfHTO9eLi/oi5Fw9Zz5COveQnceLQmEyBNJHsh4MuqLkfDLIkCD0Rnk5BJCpHPgk/QTR2muJxm5ZVK4GjROe1I4sMe8v07o7a91c3Pl/taNub914wzb4+IS4OuJOTp+nerdCAwsRbnyQZZJrerUq4avb/G791hc+BdLamJKMT+X/99EHkjykJmcXEKIXPB1TbnnOjw83BiXaXnyscOv4u6m+XyVNyfPOFu2X74ZzoXzl9i753CG8u4e7ig0Pj7FCSxdivIhwVSrXpGzb5+2lClfJomnh5hvKEtKMvHeqM2cPLMNuH2RspyQwZhFlyQPmQVWARSgwWBMfS6EEGZZDcQMCwujbdu2VjtG+TJJlPZPQSkYPuga760sbkkgzkX/SWJiEkcPH2d3+EG01iTcSmT/3//w69otREVFc/ZsFOG7DgJwcXGzDHWfjzISG6fw9NA8M/SaZfvxiNP4B5TEy8vDau0QhZckD9whe05Jgl8WWq4/SvYshLCV0EpXLD+7V1MoBQYDGDU8VPcm8a7/jYRwcXGmTr1q1KlXLcu64uMTOHzoOHvCDwKbLdtPnHbivZXFSU4BJ6M5MQkpmwxAs4aPW8o5Ozvh6uaKp6c7TZrWQRlSCFu/n1sJtwgqE0CFkGAqVCxL+cffsCQb1rzkKz0a/xkXMitH5fLSc3QvHDZ5UEqNAiYApYGDwFit9eY7v0oIIQqm9Hd97dnhDKYUNKA0XMdAgwFJOa7L3d2Nho1q0rBRTaCXZfvxt7eQlGwelZlsUqyNbkidttWJi01gwuQ6/LllN1evXOPatRvcvBnH1SvX2Lnjb65cuUZS4qYsj+Xm5oqfvy9DmhVn1H3FUEqRnJTI56/PZd1ZN+5v3ZgSJbyJi4vHlGKipJ8P/n6++PmXJCCgJMWKe2E0GvP0nhUktvqST59UXkxwYt05b1I0GBV0DrqBv1tyruqzFodMHpRSfYDFwChgS+q/a5RSNbXWp3JdXxHIXoUQjsMz0cTxXxVepeHmefAslvfbybP7UtMmzbEfj3Psx+PAnb/UNmzYQNUqNdn/91FOnjjLmTNRnD93iffb3siyvJNBMbCeEwPrJQPb8H96+x1j9EidkTMlOQVnZyf+V/2/FVVL1dYE1NcoBSYTXNyniD5gHhi6LupD6jWojpurK1euXsNoMODt7YF3MW+8i3lSzNuTYsW9cHV1KRQrpF6Id8LXJZlA92Si4p24EO8kyUMuPQ+s0Fq/n/r8GaVUF2AkMMV+YQkhRN5kvotDa0V8tPmZuqw5EJn9XRy54eeaRKB7Mhfinbh0y/nuL8A8u2ZwmQCCywRk2J758sKZCAMpiWB0gTKV/kt4wv/+nkMHj3Hs2CmuXrnG1avXuRZzg/j4BBo0rMnNG7Fs37aPM6cvcOtWYoZemDMRBnSKBgNgguRiyrK//ZhlOW532p0oSikMBgMGo+LJ2hV4oGpxFr71LR/ujyQ5ORmj0YjRaMBgMFCyZAmq16yEs5MT+/8+itYao5MRZycnjE5GSpf2o0bNyjg7G7naOh6j0YCzsxPON51odCGOmpUSOXzCDR5qTGTMWZyMRua8/j4GowEno5HA0n4EBweg0Rw/dgqD0YizsxNORiNGJyOl/Hz4McI3w2f3aL3rqZOSwff7irH7ivmy0VOHj2N0MuLkZMSgFEaj0bISrC04XPKglHIBGgHzMu36FWiZ/xEJIYT1WWueqPTd3taWPuG5cstIMecUDMo858XxDc6WO1HqhAZRrnxQjutNn5ScPmQk4ai29MJcSjFaEpMPV75GmbKBJCQkEr7zAJcuXSE2Np74+ARuxd/C2cWZmrUqk5Bwi82bwvlfHxdLvWciDASVN6EM0MIEj9eoYqm3zITdaK25fu0mV69eJzk5magLlzGZbu8B+n7Vb0DGgal7djhTr30SygANmiWxb/cW/q+p+bKT/5g798Jklr7eqxFQvDwoAwSYYFi1a/hUMu/zb943V/XeK4dLHoBSgBGIyrQ9CuiYubBSahgwDMDPz4+wsDBbx2c3N2/eLLTtK8xtA2mfo7NK+158JEfFcnuckjksd6d6s2tf+rpvmRQGBQb13/Oc1J2VkumSkqRbcNNZERdtTkqSkv5LWoq9aOT6jUsA1KlfFiibbZ1NW1SBE99Znqckmr+EleG/52k+++aVu8ZoMplISUkBFCkpJriw2rKvjH9yhrrL+P93aWHUM70wmUwkp5jw9naneAlvkhOTOX78LCkpKZhMmpQUEyaTCR/fYqT/qvMMyBizZ7qOoBb31UVrjdbanORoCAwuxfKVO+7alrxQWt9pLrOCRykVBJwFWqcfIKmUegnoq7Wunt1rq1Wrpo8ePZoPUdqHtW8XK0gKc9tA2ufoHKV9sQcjiZywFJ2UgnI2UmHuqBwt/peT9kV8t53rb35h6Xko9kxfKvVsdsfX5FTEd9u5GLYf/7Z1rFbnma9XU/r6rygDaBOcL/YAZXp3s0rd8dv/xGX/V5a6E+v0wb3Zffdcr74YiV7zJuhkUE6oB5+56x0tSqlwrXXjOxbKA0fseYgGUoDATNv9ub03QgghRCrPWhWoMHcUsfuO4VmvslVXDa7UsxkRYPUv+bS6rVkfQJne3TjzNdz8extedZtbLXEAcG92H/FA8j/7cKpazyqJA2BOFB58xjx5YWAVu85+7HDJg9Y6USkVDnQCvkm3qxOwyj5RCSGEY/CsVcGqSUN6tviSt6UyvbsR5u9lk14j92b3gZWShvSUf4UCMeuxwyUPqRYAnyildgB/AiOAIOBdu0YlhBBCFAEOmTxorb9SSpUEXsA8SdQB4CGt9Un7RiaEEEIUfg6ZPABorZcCS+0dhxBCCFHUOP6UW0IIIYTIV5I8CCGEECJXJHkQQgghRK5I8iCEEEKIXJHkQQghhBC5IsmDEEIIIXJFkgchhBBC5IokD0IIIYTIFYdbVfNeKKVuAIV3WU3zcuXR9g7CRgpz20Da5+ikfY6rMLcNoJrW2tvalTrsDJN5dNQWS5MWFEqpXYW1fYW5bSDtc3TSPsdVmNsG5vbZol65bCGEEEKIXJHkQQghhBC5UtSSh2X2DsDGCnP7CnPbQNrn6KR9jqswtw1s1L4iNWBSCCGEEPeuqPU8CCGEEOIeSfIghBBCiFxx2ORBKTVKKRWplEpQSoUrpVrdpXwdpdRGpVS8UuqsUmq6UkplKtMmta4EpdRxpdQI27bijvHmuH1KqbZKqR+VUueVUnFKqb+VUkOyKKOzeFS3fWuyjDk37QvJJvYumco56uc3I5v2aaWUf2qZAvH5KaVaK6X+l3oOaaVUaA5e4zDnXm7b52jnXh7a5zDnXh7a5jDnXWosU5RSO5VS15VSl5RSPymlaufgdbY5/7TWDvcA+gBJwFNADeBN4CZQLpvyxYALwNdAbeAx4AYwLl2ZCkBsal01UutOAh5zgPZNBV4B7gMqAiOBZKBfujJtAQ3UBALTPYwO0L6Q1Ng7Z4rdpZB8fl6Z2hUIhAEbCtrnBzwEvAY8DsQBoXcp72jnXm7b52jnXm7b5zDnXh7a5jDnXWos64DBqedRHeD71HPL9w6vsdn5l6+Nt+KbuB14P9O2f4HXsyk/ErgOuKfb9gJwlv8Gjc4G/s30ug+Avwp6+7Kp42tgVbrnaSdBKQf8/NL+A2t8hzoLzecHlAVSyPoLyO6fX7qYbubgP2iHOvdy275sXldgz708fH4Ode7dy2fnKOdduti8UuPtfocyNjv/HO6yhVLKBWgE/Jpp169Ay2xe1gLYrLWOT7dtHRCE+eRIK5O5znVAY6WU873EnBt5bF9WigFXs9i+K7WLdb1Sql0ew8yze2zfd0qpi0qpP5VSj2faV5g+vyeBGGBVFvvs+vnlgcOce1ZUIM+9e1Tgzz0rcLTzzhvz0IOsftfS2Oz8c7jkAfM85EYgKtP2KMzdSVkJzKZ82r47lXFKPWZ+yUv7MlBKdQM6kPH+3vOYs9DHgJ6Y1/hYr5Rqfa8B51Je2ncTGA/0xtw1uR74Sin1f+nKFIrPTyllAIYAH2utb6XbVVA+v9xypHPvnhXwcy8vHOncyzMHPe8WA3uBv+5QxmbnnyOvbZF5ggqVxba7lc+8PSdl8ktu22cupNR9wOfAs1rrHZbKtD5KxkXB/lJKhWD+j2HTPUebezlun9Y6GpifbtMupVQpYCLw6V3qzGp7fsjT5wc8iLn79IMMlRW8zy83HO3cyxMHOvdyzEHPvbxwqPNOKbUAuB+4X2udcpfiNjn/HLHnIRrzdZ7Mf8X5c3v2lOZCNuVJ95rsyiQDl/MUad7kpX0AKKXuB9YA07XW7+TgWNuBKnkJ8h7kuX2ZZI7d4T+/VMOArVrrgzkoa4/PL7cc6dzLMwc596yloJ5798Jhzjul1EKgL9Bea338LsVtdv45XPKgtU4EwoFOmXZ1ArZm87K/gFZKKbdM5c8BJ9KV6ZhFnbu01kn3EnNu5LF9pHajrQFmaq0X5fBw9TF3y+WbvLYvC/XJGLtDf34ASqkgoCvwfg4PV598/vzywGHOvbxylHPPiupTAM+9vHKk804ptRjohzlxOJKDl9ju/LP3iNE8jjLtAyQCQzHfWrIY87W58qn7XwfWpytfHHN29SXm21V6Yh6BmtXtKotS6xyaegx73eqXm/a1TY19LhlvJ/JLV2Ys8AjmjLlWah0a6OkA7RuE+YSpAVTD3GWYCDxXGD6/dK97AbgGeGSxr0B8fphHeNdPfcQB01N/LpfNZ+do515u2+do515u2+cw515u25budQX+vEuN5e3Uc6d9pt81r3Rl8u38y9fGW/mNHIU5c7qF+S+91un2rQBOZCpfB/M1qgTMWeNLpN6qkq5MG2B3ap2RwAhHaF/qc53FI32ZicAxIB64AmwGHnKQ9g0CDqX+gl8HdgH/l0WdDvn5pW5TqTEvzaa+AvH58d+ta5kfK+7QNoc593LbPkc79/LQPoc59/L4u+kQ511qLFm1TQMzMv0+Zm6jTc4/WRhLCCGEELnicGMehBBCCGFfkjwIIYQQIlckeRBCCCFErkjyIIQQQohckeRBCCGEELkiyYMQQgghckWSByGEEELkiiQPQgghhMgVSR6EEEIIkSuSPAghbEIpNVEppbN4zLJ3bEKIeyPTUwshbEIp5Q14pts0HugPtNJaH7NPVEIIa5DkQQhhc0qpScCzmJcSPmrveIQQ98bJ3gEIIQo3pdQUYDTQTmv9j73jEULcO0kehBA2o5SaBowA2silCiEKD0kehBA2oZR6EXgKaKu1jrB3PEII65HkQQhhdak9DmOAh4FYpVRg6q4YrXWC/SITQliDDJgUQliVUkoBMUCxLHZ31Fqvz9+IhBDWJsmDEEIIIXJFJokSQgghRK5I8iCEEEKIXJHkQQghhBC5IsmDEEIIIXJFkgchhBBC5IokD0IIIYTIFUkehBBCCJErkjwIIYQQIlckeRBCCCFErvw/+g/ImBHM3IkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], label='prediction')\n", + "plt.plot(z, predNz, color=color_list[0], linestyle='--', label='nemo prediction')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(z, Nz_truth, yerr=np.sqrt(Nz_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(z, Nz_mock, yerr=np.sqrt(Nz_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xlim(0, 2)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.semilogx(q, catNq/Nq, color=color_list[12])\n", + "plt.semilogx(q, Nq_truth/Nq, color=color_list[8])\n", + "plt.semilogx(q, Nq_mock/Nq, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, catNz/Nz, color=color_list[12])\n", + "plt.plot(z, Nz_truth/Nz, color=color_list[8])\n", + "plt.plot(z, Nz_mock/Nz, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "actxdes_venv", + "language": "python", + "name": "actxdes_venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_Q_fit.ipynb b/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_Q_fit.ipynb new file mode 100644 index 00000000..20b1d930 --- /dev/null +++ b/soliket/clusters/notebooks/DR5-compare-SOLikeT-mocks-sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_Q_fit.ipynb @@ -0,0 +1,7055 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from soliket import BinnedClusterLikelihood\n", + "from cobaya.model import get_model\n", + "import camb\n", + "from astropy.io import fits\n", + "from astropy import table\n", + "from astLib import astWCS\n", + "import math\n", + "from nemo import completeness, MockSurvey\n", + "\n", + "import sys\n", + "sys.path.append('../')\n", + "import nemo_mocks\n", + "import imp\n", + "imp.reload(nemo_mocks)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.81]\n" + ] + } + ], + "source": [ + "h = 0.68\n", + "\n", + "#Set up a new set of parameters for CAMB\n", + "pars = camb.CAMBparams()\n", + "#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency\n", + "pars.set_cosmology(H0=100.*h, ombh2=0.049*h**2, omch2=(0.31-0.049)*h**2, mnu=0.0, omk=0, tau=0.055)\n", + "pars.InitPower.set_params(As=0.81**2/0.8104862**2*2.022662e-9, ns=0.965, r=0)\n", + "pars.set_for_lmax(2500, lens_potential_accuracy=0);\n", + "\n", + "#calculate results for these parameters\n", + "results = camb.get_results(pars)\n", + "\n", + "#Note non-linear corrections couples to smaller scales than you want\n", + "pars.set_matter_power(redshifts=[0.], kmax=2.0)\n", + "\n", + "#Linear spectra\n", + "results = camb.get_results(pars)\n", + "kh, z, pk = results.get_matter_power_spectrum(minkh=1e-4, maxkh=1, npoints = 200)\n", + "s8 = np.array(results.get_sigma8())\n", + "print(s8)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binnedclusterlikelihood] Number of redshift bins = 28.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing clusters.py Binned Clusters\n", + "Running Q-fit completeness with downsampling selection function inputs.\n", + "Considering full map.\n", + "Total number of clusters in catalogue = 3169.\n", + "SNR cut = 5.0.\n", + "Number of clusters above the SNR cut = 3169.\n", + "The highest redshift = 1.9649999999999999\n", + "The lowest SNR = 5.000186060313553.\n", + "The highest SNR = 51.98994565380555.\n", + "Number of mass points for theory calculation 106.\n", + "Reading full Q function.\n", + "Number of tiles = 280.\n", + "Reading in full RMS table.\n", + "Number of tiles = 264. \n", + "Number of sky patches = 40672.\n", + "Downsampling RMS and Q function using 50 bins.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dowsampled rms bin 0\n", + "areas of tiles in bin [1.37804228e-04 1.35708109e-04 1.39484767e-04 1.39166769e-04\n", + " 1.39709222e-04 1.40863925e-04 1.40114071e-04 1.36206561e-04\n", + " 1.38025979e-04 3.36132502e-06 1.35708109e-04 1.35708109e-04\n", + " 1.36206561e-04 1.39709222e-04 1.38425949e-04 1.38806231e-04\n", + " 1.40921151e-04 1.36206561e-04 1.40498990e-04 1.35708109e-04\n", + " 1.35708109e-04 1.39484767e-04 1.38425949e-04 1.36206561e-04\n", + " 1.39709222e-04 1.35708109e-04 1.40863925e-04 1.39709222e-04\n", + " 3.40231679e-06 1.35708109e-04 1.36206561e-04 3.32211124e-06\n", + " 3.40231679e-06 1.37585405e-04 4.98316685e-06 1.39709222e-04\n", + " 1.36685639e-04 1.40114071e-04 1.35708109e-04 1.40114071e-04\n", + " 1.35708109e-04 1.36206561e-04 3.36132502e-06 1.36206561e-04\n", + " 3.40231679e-06 1.36685639e-04 1.40114071e-04 3.40231679e-06\n", + " 1.40498990e-04 1.36685639e-04 1.36206561e-04 1.35708109e-04\n", + " 1.36685639e-04 1.36206561e-04 1.35708109e-04 1.40114071e-04\n", + " 3.36107511e-06 1.39709222e-04 1.35708109e-04 1.35708109e-04\n", + " 1.36206561e-04 1.40114071e-04 1.35190353e-04 1.35708109e-04\n", + " 1.39484767e-04 3.40231679e-06 1.36206561e-04 1.41141473e-04\n", + " 1.38025979e-04 1.35708109e-04 1.37585405e-04 1.35708109e-04\n", + " 3.40231679e-06 1.35708109e-04 3.40231679e-06 3.40231679e-06\n", + " 1.38425949e-04 1.36206561e-04 1.41529797e-04 1.37145276e-04\n", + " 1.35190353e-04 1.40863925e-04 3.40231679e-06 3.40231679e-06\n", + " 1.39709222e-04 1.40498990e-04 1.42119479e-04 1.37145276e-04\n", + " 3.40231679e-06 1.36206561e-04 1.41529797e-04 1.39709222e-04\n", + " 1.39709222e-04 1.36206561e-04 1.39709222e-04 1.39709222e-04\n", + " 1.36685639e-04 1.39709222e-04 1.38806231e-04 1.35708109e-04\n", + " 1.40114071e-04 1.40498990e-04 1.35708109e-04 1.40114071e-04\n", + " 1.40114071e-04 1.40498990e-04 1.38025979e-04 3.30995387e-06\n", + " 1.36206561e-04 1.40498990e-04 1.35708109e-04 1.40498990e-04\n", + " 1.36206561e-04 1.36685639e-04 1.41834725e-04 1.39709222e-04\n", + " 1.40498990e-04 1.36206561e-04 1.36685639e-04 1.38025979e-04\n", + " 1.39709222e-04 5.00069412e-06 1.40498990e-04 1.40114071e-04\n", + " 3.33379608e-06 1.40863925e-04 1.36685639e-04 1.40114071e-04\n", + " 1.40114071e-04 1.38025979e-04 1.36685639e-04 3.36107511e-06\n", + " 1.38425949e-04 1.39709222e-04 1.39709222e-04 1.35190353e-04]\n", + "names of tiles in bin ['2_2_3' '2_3_4' '2_2_3' '2_2_3' '2_2_3' '2_2_3' '2_2_3' '2_3_3' '2_2_3'\n", + " '2_2_3' '2_3_4' '2_3_4' '2_3_3' '2_2_3' '2_2_3' '2_2_3' '2_1_2' '2_3_4'\n", + " '2_2_3' '2_3_4' '2_3_4' '2_2_3' '2_2_5' '2_3_4' '2_2_3' '2_3_3' '2_2_3'\n", + " '2_2_3' '2_2_4' '2_3_4' '2_3_4' '2_3_4' '2_2_4' '2_3_3' '2_3_6' '2_2_3'\n", + " '2_3_4' '2_2_3' '2_3_4' '2_2_3' '2_3_3' '2_3_3' '2_2_4' '2_3_4' '2_2_5'\n", + " '2_3_3' '2_2_5' '2_2_5' '2_2_3' '2_3_4' '2_3_4' '2_3_4' '2_3_4' '2_3_4'\n", + " '2_3_4' '2_2_5' '2_3_3' '2_2_4' '2_3_4' '2_3_6' '2_3_4' '2_2_3' '2_3_4'\n", + " '2_3_6' '2_2_3' '2_2_4' '2_3_6' '2_1_2' '2_2_5' '2_3_3' '2_3_3' '2_3_6'\n", + " '2_2_3' '2_3_6' '2_2_3' '2_2_3' '2_2_3' '2_3_4' '2_2_3' '2_3_3' '2_3_4'\n", + " '2_2_3' '2_2_5' '2_2_3' '2_2_4' '2_2_3' '2_2_3' '2_3_4' '2_2_4' '2_3_5'\n", + " '2_2_3' '2_2_3' '2_2_4' '2_3_6' '2_2_3' '2_2_4' '2_3_4' '2_2_5' '2_2_3'\n", + " '2_3_6' '2_2_5' '2_2_3' '2_3_6' '2_2_6' '2_2_4' '2_2_3' '2_2_4' '2_3_4'\n", + " '2_3_4' '2_2_3' '2_3_6' '2_2_6' '2_3_5' '2_3_4' '2_2_3' '2_2_5' '2_2_4'\n", + " '2_3_6' '2_3_4' '2_2_3' '2_2_5' '2_3_6' '2_2_4' '2_2_4' '2_3_4' '2_2_3'\n", + " '2_3_6' '2_2_6' '2_2_3' '2_2_4' '2_3_3' '2_3_3' '2_2_3' '2_2_6' '2_2_4'\n", + " '2_3_4']\n", + "dowsampled rms bin 1\n", + "areas of tiles in bin [0.00014011 0.00013571 0.00013519 ... 0.00013571 0.00013948 0.00014011]\n", + "names of tiles in bin ['2_2_3' '2_3_4' '2_3_4' ... '2_3_5' '2_2_7' '2_2_7']\n", + "dowsampled rms bin 2\n", + "areas of tiles in bin [1.71710780e-06 5.21392696e-06 1.41681874e-04 8.44060667e-06\n", + " 1.39709222e-04 1.36685639e-04 3.40231679e-06 1.40407196e-04\n", + " 1.42862710e-04 1.42384017e-04 1.41834725e-04 1.42384017e-04\n", + " 1.37585405e-04 1.40125873e-04 3.36107511e-06 1.39484767e-04\n", + " 1.36685639e-04 2.85725421e-04 1.43247738e-04 3.36132502e-06\n", + " 1.40921151e-04 1.40921151e-04 1.40498990e-04 1.42119479e-04\n", + " 1.37804228e-04 1.41529797e-04 1.43065399e-04 1.37585405e-04\n", + " 1.37585405e-04 3.36107511e-06 1.41834725e-04 1.42384017e-04\n", + " 1.42384017e-04 3.40231679e-06 1.37585405e-04 1.37585405e-04\n", + " 1.37585405e-04 5.21392696e-06 1.40498990e-04 1.40125873e-04\n", + " 1.40114071e-04 1.41341719e-04 1.35708109e-04 5.15110856e-06\n", + " 1.39484767e-04 1.41821734e-04 5.21392696e-06 1.41141473e-04\n", + " 1.39709222e-04 1.40457896e-04 3.40231679e-06 5.15110856e-06\n", + " 1.37145276e-04 1.37585405e-04 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1.40668547e-04\n", + " 1.41529797e-04 1.42862710e-04 5.18348319e-06 1.41529797e-04\n", + " 1.40125873e-04 1.42862710e-04 3.42456575e-06 5.21392696e-06\n", + " 1.35190353e-04 1.42384017e-04 1.37145276e-04 1.41521862e-04\n", + " 5.21392696e-06 1.41821734e-04 3.36107511e-06 1.41141473e-04\n", + " 5.21392696e-06 1.42384017e-04 1.42384017e-04 1.41141473e-04\n", + " 1.71710780e-06 1.42119479e-04 1.40668547e-04 2.74895977e-05\n", + " 1.71710780e-06 1.13183442e-04 8.37613559e-08 1.33541691e-04\n", + " 1.40407196e-04 1.37145276e-04 1.41834725e-04 1.42119479e-04\n", + " 1.36206561e-04 1.42384017e-04 1.41521862e-04 1.41521862e-04\n", + " 3.36107511e-06 1.37585405e-04 5.21392696e-06 1.73804813e-06\n", + " 1.39824619e-04 1.41141473e-04 1.37585405e-04 1.37585405e-04\n", + " 1.41821734e-04 5.21392696e-06 1.25636794e-07 1.41141473e-04\n", + " 1.38025979e-04 1.40019435e-04 1.40125873e-04 1.41821734e-04\n", + " 3.39431144e-06 1.41341719e-04 1.41834725e-04 3.39431144e-06\n", + " 1.35472773e-04 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1.42639701e-04\n", + " 1.40019435e-04 1.41189728e-04 1.40863925e-04 3.43094018e-06\n", + " 1.34653369e-04 1.41681874e-04 5.21392696e-06 3.45906669e-06\n", + " 1.38487679e-04 1.42027999e-04 1.35708109e-04 1.37585405e-04\n", + " 3.36107511e-06 1.15406399e-05 1.67250337e-06 1.41341719e-04\n", + " 1.35190353e-04 5.17103851e-06 1.41941422e-04 1.40498990e-04\n", + " 1.39202600e-04 1.71710780e-06 1.43247738e-04 1.41681874e-04\n", + " 1.41821734e-04 1.43409702e-04 1.41529797e-04 3.29732569e-06\n", + " 1.35049707e-04 1.34653369e-04 1.38226219e-04 1.36685639e-04\n", + " 1.35190353e-04 1.43408933e-04 1.37585405e-04 1.36685639e-04\n", + " 5.17762908e-06 8.19835371e-08 1.41821734e-04 1.71710780e-06\n", + " 1.42100004e-04 5.07827675e-06 5.15110856e-06 1.40114071e-04\n", + " 1.41341719e-04 1.38487679e-04 1.41681874e-04 1.41821734e-04\n", + " 1.37585405e-04 1.43551267e-04 1.35420713e-04 1.37145276e-04\n", + " 1.40941147e-04 1.40668547e-04 1.34659491e-04 1.40921151e-04\n", + " 1.41529797e-04 3.36132502e-06 1.34653369e-04 3.40231679e-06\n", + " 2.77119233e-04 1.40498990e-04 1.34653369e-04 1.43551267e-04\n", + " 1.40351997e-04 1.34653369e-04 1.37698998e-04 1.40668547e-04\n", + " 1.37145276e-04 1.34443149e-04 1.40941147e-04 1.41681874e-04\n", + " 1.42862710e-04 1.41821734e-04 1.37469613e-04 1.41529797e-04\n", + " 1.43551267e-04 3.32033325e-06 1.17075236e-05 1.35190353e-04\n", + " 1.41341719e-04 1.42150160e-04 1.34653369e-04 1.42088007e-04\n", + " 1.36685639e-04 2.84176015e-04]\n", + "names of tiles in bin ['2_1_5' '2_2_6' '2_1_3' '2_2_6' '2_2_6' '2_3_5' '2_2_4' '2_2_6' '2_1_3'\n", + " '2_2_7' '2_2_4' '2_2_3' '2_3_7' '2_2_6' '2_3_3' '2_2_5' '2_3_5' '2_1_3'\n", + " '2_1_3' '2_2_7' '2_1_3' '2_1_5' '2_2_6' '2_2_4' '2_2_7' '2_2_6' '2_1_3'\n", + " '2_3_6' '2_3_3' '2_3_3' '2_2_6' '2_2_5' '2_2_4' '2_2_5' '2_3_6' '2_3_4'\n", + " '2_3_5' '2_2_6' '2_2_7' '2_2_3' '2_2_7' '2_1_3' '2_3_6' '2_2_3' '2_2_3'\n", + " '2_1_4' '2_2_6' '2_1_2' '2_2_5' '1_11_8' '2_2_3' '2_2_5' '2_3_4' '2_3_7'\n", + " '1_11_8' '2_1_6' '2_2_6' '2_1_2' '2_1_4' '2_2_7' '2_2_6' '2_3_6' '2_2_6'\n", + " '2_2_7' '2_3_7' '2_1_3' '2_1_4' '2_1_4' '2_2_5' '2_2_7' '2_1_5' '2_2_6'\n", + " '2_1_4' '2_1_5' '2_2_7' '1_11_7' '2_2_5' '2_2_6' '2_2_4' '2_3_4' '2_2_6'\n", + " '2_3_7' '2_2_7' '1_11_8' '2_2_3' '2_2_6' '2_3_3' '2_1_3' '2_2_3' '2_1_3'\n", + " '2_2_4' '2_3_5' '2_3_4' '2_2_3' '2_2_7' '2_1_3' '2_2_3' '2_2_4' '2_3_5'\n", + " '2_3_5' '2_1_3' '2_3_6' '2_3_8' '2_2_4' '2_2_6' '2_2_3' '2_3_6' '2_3_8'\n", + " '2_1_6' '2_1_4' '2_2_5' '2_1_5' '1_11_8' '2_1_3' '2_1_5' '2_2_5' '2_2_4'\n", + " '2_1_4' '2_2_4' '2_3_8' '2_3_8' '2_1_4' '2_2_3' '2_3_7' '2_3_7' '2_2_7'\n", + " '2_1_5' '1_11_8' '2_3_5' '2_3_7' '2_1_3' '2_2_4' '2_2_6' '2_2_3' '2_3_5'\n", + " '2_2_4' '2_1_3' '2_2_7' '2_2_6' '2_1_4' '2_2_3' '2_2_6' '2_3_5' '2_1_6'\n", + " '2_2_7' '2_3_5' '2_3_7' '2_2_7' '2_2_5' '2_1_4' '2_3_5' '2_2_5' '2_3_8'\n", + " '2_3_8' '2_1_3' '2_2_7' '2_2_2' '2_2_6' '2_1_5' '2_1_6' '2_2_6' '2_2_5'\n", + " '2_3_6' '2_2_7' '2_2_6' '2_3_4' '2_3_5' '2_3_5' '2_3_4' '2_3_4' '2_3_5'\n", + " '2_3_8' '2_1_3' '2_3_8' '2_2_3' '1_11_7' '2_2_7' '2_3_7' '1_11_8'\n", + " '1_11_8' '2_3_5' '1_11_8' '2_1_6' '2_1_3' '2_3_7' '2_3_7' '2_1_3' '2_1_4'\n", + " '2_2_6' '2_2_7' '2_3_7' '2_1_5' '2_3_5' '2_2_5' '2_1_4' '2_1_3' '2_1_3'\n", + " '2_3_8' '2_3_7' '2_3_7' '2_2_2' '1_11_8' '2_2_3' '2_2_7' '2_3_8' '2_1_4'\n", + " '2_1_5' '2_3_7' '2_1_4' '2_2_2' '2_2_7' '2_1_4' '2_2_4' '2_1_2' '1_11_8'\n", + " '2_3_5' '2_1_6' '2_2_4' '2_1_4' '1_11_7' '2_2_3' '2_1_6' '2_1_4' '2_3_3'\n", + " '2_1_2' '2_2_5' '2_1_5' '2_3_5' '2_1_2' '2_2_7' '2_3_3' '2_2_6' '2_2_4'\n", + " '2_1_5' '2_1_2' '2_2_5' '1_11_7' '2_1_4' '2_2_7' '2_1_3' '2_1_3' '2_1_3'\n", + " '2_1_5' '2_3_7' '2_2_6' '2_1_2' '2_3_5' '2_1_4' '2_1_6' '2_2_3' '2_1_5'\n", + " '2_2_7' '2_3_7' '2_1_4' '2_1_3' '2_2_7' '2_1_3' '2_1_5' '2_1_4' '2_2_5'\n", + " '2_2_3' '2_3_4' '2_1_3' '2_1_2' '2_1_4' '2_2_6' '2_1_5' '2_2_6' '2_2_5'\n", + " '2_2_6' '2_2_3' '2_1_3' '2_1_5' '2_1_5' '2_1_3' '2_1_6' '2_1_4' '2_2_6'\n", + " '2_3_7' '2_2_7' '2_3_3' '2_3_7' '2_2_3' '2_2_5' '2_3_3' '2_1_4' '2_2_6'\n", + " '2_1_5' '2_1_4' '2_1_6' '2_3_7' '2_2_6' '2_3_4' '2_2_7' '2_2_7' '2_2_6'\n", + " '2_1_6' '2_1_5' '2_1_6' '2_1_6' '1_10_7' '2_3_8' '2_2_6' '2_1_2' '1_11_7'\n", + " '2_1_4' '2_2_7' '2_1_4' '2_2_3' '2_2_7' '2_1_3' '2_2_7' '2_1_4' '1_11_8'\n", + " '2_2_4' '2_1_3' '2_2_7' '2_2_6' '2_2_7' '2_3_5' '2_2_7' '2_1_6' '2_2_2'\n", + " '2_2_6' '1_11_8' '2_2_7' '2_1_3' '2_1_3' '2_1_6' '2_2_7' '2_2_5' '2_2_6'\n", + " '2_1_4' '2_2_7' '2_2_7' '2_3_3' '2_1_4' '1_11_7' '2_1_4' '2_3_4' '2_1_3'\n", + " '2_3_8' '2_2_7' '2_3_5' '2_3_8' '2_3_4' '2_3_8' '2_2_4' '2_2_7' '2_1_6'\n", + " '2_1_6' '2_2_7' '2_2_7' '2_1_5' '2_2_5' '2_2_6' '2_3_3' '2_2_7' '2_3_5'\n", + " '2_1_5' '2_2_4' '2_1_5' '2_3_7' '2_1_4' '2_2_6' '2_2_6' '2_2_6' '2_1_5'\n", + " '2_1_6' '2_2_6' '2_2_6' '2_3_8' '2_1_2' '2_3_8' '2_1_4' '2_3_7' '2_2_7'\n", + " '2_3_3' '2_2_6' '2_2_7' '2_3_7' '2_2_5' '2_1_4' '2_1_5' '2_3_6' '2_3_7'\n", + " '2_2_6' '2_1_6' '2_2_6' '2_1_4' '2_3_4' '2_3_5' '2_1_4' '2_2_4' '2_2_6'\n", + " '2_1_3' '2_2_7' '2_3_8' '2_2_7' '2_1_5' '2_2_6' '2_1_6' '2_2_2' '2_2_4'\n", + " '2_3_3' '2_1_5' '2_1_3' '2_1_4' '2_3_5' '2_1_6' '2_1_4' '2_1_4' '2_1_5'\n", + " '2_1_2' '2_3_4' '2_1_5' '2_1_4' '2_1_5' '2_2_6' '2_1_6' '2_2_6' '2_2_7'\n", + " '2_3_6' '2_1_3' '2_3_5' '2_2_6' '2_1_6' '2_2_2' '2_3_7' '2_2_7' '2_3_8'\n", + " '2_2_7' '1_11_8' '2_1_5' '1_11_7' '2_3_7' '2_1_5' '2_3_7' '2_2_6' '2_1_5'\n", + " '2_2_5' '2_1_2' '2_2_7' '2_2_3' '1_11_7' '2_2_3' '2_2_7' '2_1_6' '2_1_6'\n", + " '2_2_6' '2_1_4' '1_10_4' '2_2_7' '2_1_6' '2_1_4' '2_2_7' '2_2_2' '1_11_7'\n", + " '2_2_4' '2_3_7' '2_3_5' '2_2_4' '2_1_5' '2_2_7' '1_11_8' '2_1_5' '1_11_7'\n", + " '2_2_7' '1_11_7' '2_3_7' '1_10_7' '2_2_3' '2_1_4' '2_1_5' '2_1_3' '2_2_6'\n", + " '2_2_3' '2_1_3' '2_1_4' '2_2_6' '2_2_7' '2_1_4' '2_1_3' '2_1_3' '2_2_5'\n", + " '2_1_6' '2_2_7' '1_10_7' '2_2_6' '2_3_6' '2_2_4' '2_2_7' '2_2_6' '2_1_6'\n", + " '2_3_7' '2_3_7' '2_3_6' '2_1_6' '2_1_3' '2_1_3' '2_2_7' '2_1_5' '2_3_5'\n", + " '2_3_7' '2_1_5' '2_1_3' '2_1_6' '2_3_3' '2_2_5' '2_3_8' '2_1_5' '2_1_6'\n", + " '2_2_2' '2_1_5' '2_1_6' '2_3_7' '2_1_4' '2_2_7' '2_2_3' '2_1_3' '2_2_7'\n", + " '2_2_7' '2_2_2' '2_3_5' '2_3_7' '2_3_5' '2_1_2' '2_2_7' '2_2_7' '1_11_8'\n", + " '2_2_6' '2_2_5' '1_10_7' '2_2_6' '2_1_6' '2_3_7' '2_1_5' '2_2_5' '2_1_3'\n", + " '1_10_7' '2_1_5' '2_3_7' '2_3_4' '2_1_5' '2_3_8' '2_1_6' '2_1_4' '2_1_4'\n", + " '2_1_3' '2_2_7' '1_10_7' '2_2_7' '2_2_7' '2_3_3' '2_1_5' '2_1_2' '2_3_7'\n", + " '2_1_3' '1_10_4' '2_3_6' '2_3_6' '2_1_6' '2_1_5' '2_2_7' '2_1_6' '2_3_4'\n", + " '2_1_5' '2_1_5' '2_3_8' '1_11_8' '2_3_8' '2_2_6' '2_2_6' '2_1_6' '2_1_4'\n", + " '2_1_5' '2_3_5' '1_11_7' '2_3_5' '2_2_6' '2_1_5' '2_1_4' '2_1_6' '2_1_3'\n", + " '1_11_8' '2_2_7' '2_1_6' '2_2_7' '2_1_5' '2_1_4' '2_1_3' '2_1_4' '2_2_7'\n", + " '2_2_7' '1_10_7' '2_1_5' '2_2_7' '2_1_2' '2_3_7' '2_2_8' '1_10_4' '2_3_5'\n", + " '2_2_6' '2_2_7' '2_1_6' '2_1_6' '2_3_7' '1_11_8' '1_11_7' '2_3_7' '2_3_8'\n", + " '2_1_3' '2_1_5' '2_1_4' '2_1_5' '2_3_7' '2_2_7' '2_1_6' '3_2_0' '1_10_7'\n", + " '1_11_8' '2_1_3' '2_2_7' '2_1_4' '2_2_3' '2_1_3' '2_3_8' '2_2_7' '2_3_7'\n", + " '2_3_6' '2_3_7' '2_2_2' '2_1_5' '2_1_5' '2_2_8' '2_3_8' '2_1_5' '2_3_7'\n", + " '2_2_3' '1_11_7' '2_3_8' '2_1_5' '2_2_4' '2_3_7' '2_1_5' '2_3_7' '2_2_7'\n", + " '1_10_4' '1_10_7' '2_3_8' '3_3_0' '2_2_7' '1_10_7' '2_2_2' '2_1_3'\n", + " '2_1_5' '2_1_3' '2_1_6' '1_11_7' '2_1_2' '2_1_4' '2_1_3' '2_1_5' '2_1_5'\n", + " '2_2_7' '2_1_6' '2_1_5' '2_3_8' '2_3_7' '2_1_6' '2_1_4' '1_10_7' '2_1_4'\n", + " '2_3_8' '2_3_8' '1_11_7' '2_1_2' '2_1_3' '1_11_8' '1_10_4' '2_1_5'\n", + " '2_1_6' '2_1_6' '1_10_7' '2_2_7' '2_2_7' '2_2_7' '2_3_3' '2_2_7' '2_1_7'\n", + " '2_1_4' '2_3_7' '3_3_0' '1_11_7' '2_3_4' '2_2_7' '2_2_8' '2_1_7' '2_2_7'\n", + " '2_1_5' '2_3_7' '1_10_4' '2_1_4' '2_2_7' '2_2_3' '2_3_5' '2_2_7' '2_2_6'\n", + " '2_1_3' '2_1_4' '1_11_8' '1_11_7' '2_1_5' '2_3_8' '2_1_6' '1_11_8'\n", + " '2_2_2' '1_10_4' '2_3_8' '2_1_6' '2_1_6' '2_3_3' '2_2_8' '2_1_5' '2_3_7'\n", + " '2_1_6' '2_2_8' '2_3_8' '2_2_6' '2_2_7' '2_3_8' '2_2_7' '2_3_6' '3_3_0'\n", + " '2_3_8' '2_1_3' '2_1_5' '2_2_8' '2_3_7' '2_1_5' '2_3_7' '2_1_2' '2_1_6'\n", + " '1_11_8' '2_3_7' '2_1_5' '2_2_4' '2_1_6' '2_2_8' '2_2_8' '2_1_6' '2_1_6'\n", + " '2_1_5' '2_1_4' '1_11_7' '3_2_0' '1_11_7' '2_3_6' '2_3_7' '2_1_4'\n", + " '1_10_7' '2_2_8' '2_3_7' '2_3_7' '2_1_6' '2_3_8' '2_3_3' '2_3_7' '2_1_6'\n", + " '2_3_3' '2_1_6' '1_11_7' '2_3_7' '2_2_4' '2_1_6' '2_3_5' '2_1_5' '2_2_3'\n", + " '2_1_5' '1_11_8' '2_3_2' '2_3_3' '2_1_6' '2_3_7' '2_2_8' '2_1_6' '2_2_8'\n", + " '2_3_3' '2_2_6' '2_1_7' '2_3_4' '2_3_2' '2_3_7' '2_2_8' '1_10_7' '2_3_8'\n", + " '2_3_4' '2_1_6' '2_1_6' '2_1_4' '2_3_8' '2_2_6' '2_1_6' '1_11_7' '2_1_6'\n", + " '2_3_4' '2_1_6' '2_3_4' '2_2_8' '2_2_3' '2_2_7' '1_10_4' '2_1_6' '1_10_7'\n", + " '2_3_8' '2_2_7' '2_1_7' '2_1_4' '1_11_7' '1_11_8' '2_2_6' '2_1_5' '2_1_4'\n", + " '2_3_3' '2_2_8' '2_3_6' '2_2_6' '2_1_4' '2_2_8' '2_2_7' '2_2_6' '2_2_8'\n", + " '1_10_7' '2_2_7' '2_1_3' '2_1_4' '2_2_5' '2_1_6' '1_11_8' '2_3_3' '2_2_6'\n", + " '2_2_6' '2_3_7' '2_2_7' '2_3_5' '3_3_0' '2_3_6' '2_3_7' '2_1_7' '2_2_7'\n", + " '2_2_7' '2_1_2' '2_1_7' '3_3_0' '2_3_7' '2_3_8' '3_3_0' '2_1_7' '2_1_7'\n", + " '2_2_7' '2_2_8' '2_1_6' '2_2_7' '2_3_8' '2_2_7' '2_2_7' '2_3_8' '2_1_4'\n", + " '2_1_2' '2_1_2' '2_1_5' '2_1_4' '2_3_4' '2_3_3' '2_3_3' '2_1_6' '2_3_8'\n", + " '2_3_8' '2_2_7' '2_2_6' '2_3_6' '2_1_5' '2_2_7' '2_1_3' '2_3_8' '1_10_7'\n", + " '2_3_3' '2_3_7' '2_3_5' '2_3_8' '2_3_5' '2_1_5' '2_3_7' '2_1_6' '2_1_3'\n", + " '2_2_7' '2_1_7' '2_1_6' '2_1_6' '2_1_6' '2_1_4' '2_1_6' '2_2_7' '2_3_3'\n", + " '2_2_8' '2_3_5' '2_1_7' '2_3_7' '2_3_7' '3_2_0' '2_3_7' '2_3_7' '2_1_5'\n", + " '2_2_5' '2_1_6' '2_1_6' '1_11_7' '2_2_7' '2_2_7' '2_2_7' '2_1_5' '2_3_8'\n", + " '2_1_5' '2_1_5' '2_3_3' '2_1_5' '2_2_8' '2_3_3' '2_3_8' '2_2_7' '2_2_8'\n", + " '2_1_4' '2_2_2' '2_2_7' '2_3_4' '2_2_7' '2_1_7' '2_2_7' '2_3_6' '2_1_6'\n", + " '1_11_7' '2_3_4' '2_1_7' '2_2_5' '2_3_7' '2_2_8' '2_3_8' '2_1_2' '2_1_6'\n", + " '2_1_5' '2_2_8' '2_2_7' '2_1_6' '2_2_8' '2_3_8' '2_3_5' '2_1_5' '1_11_7'\n", + " '2_3_5' '1_10_4' '2_3_3' '1_10_8']\n", + "dowsampled rms bin 3\n", + "areas of tiles in bin [1.36737828e-04 1.41681874e-04 1.38806231e-04 1.39709222e-04\n", + " 3.40231679e-06 1.34653369e-04 1.41821734e-04 5.12655632e-06\n", + " 1.35049707e-04 3.27934148e-06 3.44735901e-06 1.34653369e-04\n", + " 1.41970373e-04 1.42027999e-04 1.41595801e-04 1.37113472e-04\n", + " 6.87420248e-06 1.36685639e-04 1.46745612e-04 3.40231679e-06\n", + " 1.41681874e-04 1.35049707e-04 1.71710780e-06 1.66689804e-06\n", + " 1.41521862e-04 1.40941147e-04 3.27934148e-06 5.21392696e-06\n", + " 5.15110856e-06 1.41948762e-04 1.40457896e-04 1.34653369e-04\n", + " 1.34653369e-04 1.46745612e-04 1.43247738e-04 1.45594990e-04\n", + " 1.42619018e-04 1.40280250e-04 5.03361239e-06 5.21392696e-06\n", + " 1.40490283e-04 1.37894360e-04 1.42150160e-04 1.43551267e-04\n", + " 2.74968099e-04 1.41681874e-04 8.19774417e-08 1.35708109e-04\n", + " 3.27934148e-06 1.55554475e-04 1.35190353e-04 1.43247738e-04\n", + " 1.35420713e-04 1.43409702e-04 1.71710780e-06 1.41681874e-04\n", + " 1.34659491e-04 1.41341719e-04 1.40941147e-04 1.41529797e-04\n", + " 1.34653369e-04 3.36132502e-06 1.65497694e-06 1.34653369e-04\n", + " 1.38119440e-04 1.39618040e-04 1.42088007e-04 1.34653369e-04\n", + " 1.22975306e-07 1.35708109e-04 6.84913150e-06 2.83465297e-04\n", + " 1.42044804e-04 1.35190353e-04 1.42152686e-04 1.36685639e-04\n", + " 3.44247495e-06 1.40490283e-04 1.35190353e-04 1.36342735e-04\n", + " 1.40136342e-04 1.42135966e-04 1.40443872e-04 1.37145276e-04\n", + " 1.38025979e-04 3.48404127e-06 1.41681874e-04 1.37113472e-04\n", + " 1.36685639e-04 1.39484767e-04 1.38226219e-04 3.44735901e-06\n", + " 1.16273893e-05 1.35420713e-04 1.37585405e-04 1.47929088e-04\n", + " 1.41821734e-04 1.71710780e-06 1.41948762e-04 1.41341719e-04\n", + " 1.37698998e-04 1.41821734e-04 1.41341719e-04 1.36206561e-04\n", + " 1.48485344e-04 1.38070109e-04 1.38226219e-04 5.07827675e-06\n", + " 1.36342735e-04 1.35708109e-04 5.18860003e-06 1.38362667e-04\n", + " 1.34653369e-04 1.43672415e-04 5.07628452e-06 1.42044804e-04\n", + " 1.40941147e-04 1.36414262e-04 3.36107511e-06 3.36107511e-06\n", + " 1.42384017e-04 1.36737828e-04 1.37585405e-04 3.36648728e-06\n", + " 1.41821734e-04 1.42027999e-04 1.37145276e-04 1.22966163e-07\n", + " 1.37484050e-04 1.36708169e-04 1.34653369e-04 1.38025979e-04\n", + " 3.41770422e-06 6.92397179e-06 1.40490283e-04 1.39709222e-04\n", + " 1.41970373e-04 1.34653369e-04 1.41821734e-04 5.08829017e-06\n", + " 3.36107511e-06 5.16371242e-06 1.37113472e-04 1.42162786e-04\n", + " 1.41681874e-04 1.41941422e-04 1.65497694e-06 1.36206561e-04\n", + " 1.42975739e-04 1.43672415e-04 1.46168027e-04 1.41595801e-04\n", + " 1.34653369e-04 1.41948762e-04 1.41189728e-04 3.36107511e-06\n", + " 2.71544915e-04 1.33011255e-04 1.37484050e-04 1.41948762e-04\n", + " 1.37237607e-04 1.36737828e-04 4.21725204e-04 1.41732649e-04\n", + " 1.47929088e-04 1.41941422e-04 1.47349147e-04 1.67250337e-06\n", + " 1.39954046e-04 1.35708109e-04 2.85238036e-04 1.42639701e-04\n", + " 1.41595801e-04 1.39202600e-04 1.35708109e-04 1.41264619e-04\n", + " 1.39945987e-04 1.40351997e-04 1.39795985e-04 1.41834725e-04\n", + " 1.42088007e-04 3.48404127e-06 1.41821734e-04 1.41948762e-04\n", + " 1.36342735e-04 6.28210169e-08 1.35321912e-05 1.34653369e-04\n", + " 1.42162786e-04 3.44305255e-06 1.37145276e-04 1.43551267e-04\n", + " 1.42044804e-04 1.38953077e-04 1.36685639e-04 3.32033325e-06\n", + " 1.42027999e-04 3.45906669e-06 1.43247738e-04 1.69616746e-06\n", + " 1.42162786e-04 1.38362667e-04 1.42152686e-04 3.32033325e-06\n", + " 1.36685639e-04 1.48485344e-04 1.36737828e-04 1.36342735e-04\n", + " 1.38953077e-04 1.42150160e-04 1.34443149e-04 3.46198590e-06\n", + " 1.41850307e-04 1.36982630e-04 1.37145276e-04 1.40863925e-04\n", + " 1.37145276e-04 1.38898779e-04 1.44998641e-04 1.37113472e-04\n", + " 1.42135966e-04 5.07679142e-06 1.37698998e-04 1.38487679e-04\n", + " 3.40231679e-06 3.44305255e-06 1.42088007e-04 1.13183442e-04\n", + " 2.74895977e-05 1.40490283e-04 1.34659491e-04 1.40351997e-04\n", + " 1.39954046e-04 2.41076268e-05 1.41948762e-04 5.06436400e-06\n", + " 1.41439784e-04 1.40351997e-04 1.42119479e-04 1.43551267e-04\n", + " 1.41850307e-04 1.42027999e-04 1.34653369e-04 3.32033325e-06\n", + " 1.43672415e-04 1.36206561e-04 1.64866285e-06 1.43819166e-04\n", + " 1.71710780e-06 5.21392696e-06 1.40351997e-04 1.34659491e-04\n", + " 1.37698998e-04 5.08829017e-06 1.40941147e-04 1.34853144e-05\n", + " 1.38487679e-04 1.37237607e-04 1.35772458e-04 3.48404127e-06\n", + " 1.41521862e-04 1.36414262e-04 1.34653369e-04 3.44305255e-06\n", + " 1.36123689e-04 1.47349147e-04 4.71405338e-06 1.43551267e-04\n", + " 1.41821734e-04 1.41948762e-04 1.34653369e-04 1.41595801e-04\n", + " 5.15110856e-06 1.71710780e-06 3.28422852e-06 1.36685639e-04\n", + " 1.41948762e-04 1.39528672e-04 1.36414262e-04 1.34653369e-04\n", + " 5.04973092e-06 1.35772458e-04 1.41732649e-04 1.34653369e-04\n", + " 5.02547177e-06 1.43409702e-04 1.42100004e-04 1.40941147e-04\n", + " 1.42152686e-04 1.41948762e-04 1.13183442e-04 2.74895977e-05\n", + " 1.36123689e-04 1.38226219e-04 1.47349147e-04 1.41534308e-04\n", + " 1.42384017e-04 1.41821734e-04 1.40019435e-04 1.34653369e-04\n", + " 1.40114071e-04 3.44247495e-06 1.41732649e-04 1.39484767e-04\n", + " 1.37484050e-04 1.41941422e-04 3.27934148e-06 1.44206371e-04\n", + " 1.40457896e-04 1.41948762e-04 1.38417021e-04 1.35420713e-04\n", + " 1.41850307e-04 1.37894360e-04 1.41821734e-04 3.45565546e-06\n", + " 8.37613559e-08 5.02547177e-06 1.41189728e-04 1.41732649e-04\n", + " 1.37585405e-04 2.77119233e-04 1.38119440e-04 1.37484050e-04\n", + " 1.47929088e-04 1.35049707e-04 1.34653369e-04 1.38070109e-04\n", + " 5.02547177e-06 1.16682863e-05 1.38119440e-04 3.44305255e-06\n", + " 1.41876722e-04 1.48485344e-04 1.36685639e-04 1.38362667e-04\n", + " 1.14842750e-05 1.36685639e-04 1.36737828e-04 1.41876722e-04\n", + " 1.42639701e-04 1.46745612e-04 1.41850307e-04 1.35190353e-04\n", + " 1.35708109e-04 4.92634278e-06 1.39709222e-04 1.34443149e-04\n", + " 1.41732649e-04 1.41948762e-04 2.71661907e-05 1.43672415e-04\n", + " 1.35772458e-04 1.34653369e-04 1.34653369e-04 1.11851872e-04\n", + " 1.35049707e-04 1.41941422e-04 1.42135966e-04 1.41439784e-04\n", + " 1.38953077e-04 4.92634278e-06 1.41732649e-04 3.48404127e-06\n", + " 3.44305255e-06 5.08829017e-06 1.42975739e-04 1.88455191e-07\n", + " 1.41941422e-04 1.41595801e-04 3.44305255e-06 1.38070109e-04\n", + " 1.37698998e-04 1.36414262e-04 1.37894360e-04 3.32211124e-06\n", + " 1.41948762e-04 1.41595801e-04 1.40187713e-04 1.34443149e-04\n", + " 1.34653369e-04 1.43551267e-04 1.40092201e-04 1.46745612e-04\n", + " 1.41264619e-04 1.39202600e-04 1.46745612e-04 1.42384017e-04\n", + " 1.41850307e-04 1.36342735e-04 1.41439784e-04 1.37698998e-04\n", + " 1.41948762e-04 1.41970373e-04 1.18230602e-05 1.69616746e-06\n", + " 5.21392696e-06 5.13684862e-06 1.37113472e-04 3.44305255e-06\n", + " 1.73804813e-06 1.55467692e-04 3.45175272e-06 1.41970373e-04\n", + " 1.55230996e-04 1.38681140e-04 1.41941422e-04 3.44305255e-06\n", + " 1.38070109e-04 1.66689804e-06 7.19095828e-05 1.67522712e-06\n", + " 1.39709222e-04 1.42027999e-04 1.41876722e-04 1.36206561e-04\n", + " 1.36123689e-04 1.37469613e-04 1.38694913e-04 1.41821734e-04\n", + " 1.34653369e-04 1.37145276e-04 1.40351997e-04 1.40490283e-04\n", + " 3.42456575e-06 1.92974516e-04 1.34653369e-04 1.37484050e-04\n", + " 1.35189297e-05 1.43672415e-04 1.37237607e-04 1.40921151e-04\n", + " 1.42027999e-04 3.45565546e-06 1.40136342e-04 1.34053132e-04\n", + " 1.34653369e-04 1.39528672e-04 5.14641027e-06 1.42088007e-04\n", + " 1.41850307e-04 1.41821734e-04 1.36737828e-04 1.73804813e-06\n", + " 1.41948762e-04 1.48485344e-04 3.45175272e-06 1.36123689e-04\n", + " 1.38953077e-04 3.44305255e-06 1.45594990e-04 1.38362667e-04\n", + " 1.34443149e-04 2.74939226e-04 5.18860003e-06 3.32033325e-06\n", + " 1.38119440e-04 5.07688163e-06 1.35420713e-04 1.35264761e-05\n", + " 1.42088007e-04 1.40351997e-04 1.38119440e-04 1.42088007e-04\n", + " 1.40460392e-04 1.35708109e-04 5.12655632e-06 1.38070109e-04\n", + " 3.35574159e-06 1.40351997e-04 1.42619018e-04 1.41941422e-04\n", + " 1.37894360e-04 1.34653369e-04 1.48485344e-04 1.38070109e-04\n", + " 2.83700615e-04 1.40941147e-04 1.37698998e-04 1.37585405e-04\n", + " 1.38417021e-04 5.19297884e-06 1.14842750e-05 1.38681140e-04\n", + " 1.40921151e-04 2.77389826e-04 1.41264619e-04 1.36708169e-04\n", + " 1.41595801e-04 1.36982630e-04 1.67787080e-06 2.41076268e-05\n", + " 1.42100004e-04 1.41595801e-04 3.43710124e-06 1.41850307e-04\n", + " 1.42150160e-04 1.42119479e-04 1.34653369e-04 1.48485344e-04\n", + " 5.08829017e-06 1.39484767e-04 2.75677543e-04 5.21392696e-06\n", + " 3.36107511e-06 4.83666906e-06 1.71710780e-06 1.47349147e-04\n", + " 1.37484050e-04 1.41941422e-04 1.46745612e-04 1.41941422e-04\n", + " 1.38681140e-04 1.36685639e-04 1.34653369e-04 5.11553483e-06\n", + " 1.37469613e-04 1.44998641e-04 1.36123689e-04 1.41941422e-04\n", + " 1.40863925e-04 1.42384017e-04 1.42027999e-04 5.09146716e-06\n", + " 1.67522712e-06 1.38070109e-04 1.49526448e-04 1.38362667e-04\n", + " 1.36708169e-04 1.43551267e-04 1.73804813e-06 1.40351997e-04\n", + " 1.42088007e-04 1.41941422e-04 1.46717662e-04 1.34653369e-04\n", + " 1.42044804e-04 1.54718392e-04 1.36123689e-04 1.36685639e-04\n", + " 1.38226219e-04 5.08829017e-06 1.46745612e-04 1.37484050e-04\n", + " 1.41732649e-04 3.35045423e-06 1.47243804e-04 5.02547177e-06\n", + " 1.40136342e-04 1.38953077e-04 1.36206561e-04 1.41941422e-04\n", + " 1.37469613e-04 1.43672415e-04 1.37145276e-04 1.41439784e-04\n", + " 5.02547177e-06 6.28210169e-08 1.42150160e-04 1.40092201e-04\n", + " 1.43672415e-04 3.44735901e-06 1.41595801e-04 1.38226219e-04\n", + " 1.41686334e-04 1.46745612e-04 1.38362667e-04 1.67522712e-06\n", + " 1.41876722e-04 1.36206561e-04 1.36123689e-04 1.40443872e-04\n", + " 1.41941422e-04 1.38119440e-04 1.36685639e-04 1.34704556e-05\n", + " 3.36107511e-06 1.11851872e-04 1.40443872e-04 8.75279269e-05\n", + " 1.41686334e-04 1.36206561e-04 3.40231679e-06 8.86220259e-05\n", + " 1.36685639e-04 2.71661907e-05 1.64866285e-06 1.47929088e-04\n", + " 1.36123689e-04 5.21392696e-06 1.46717662e-04 1.43408933e-04\n", + " 3.48404127e-06 1.41941422e-04 1.37113472e-04 1.41948762e-04\n", + " 3.40231679e-06 1.36414262e-04 3.36107511e-06 1.40351997e-04\n", + " 1.34653369e-04 1.41948762e-04 1.42027999e-04 1.37484050e-04\n", + " 1.43409702e-04 3.30995387e-06 1.37894360e-04 1.41439784e-04\n", + " 1.36206561e-04 1.47929088e-04 1.37469613e-04 1.37113472e-04\n", + " 1.41941422e-04 1.43672415e-04 1.38417021e-04 1.41439784e-04\n", + " 1.37894360e-04 1.42152686e-04 1.38681140e-04 1.40187713e-04\n", + " 1.43551267e-04 1.43672415e-04 1.43672415e-04 1.64866285e-06\n", + " 1.41341719e-04 1.34653369e-04 1.35772458e-04 1.41970373e-04\n", + " 1.42162786e-04 1.34443149e-04 9.92274681e-05 3.33379608e-06\n", + " 1.38425949e-04 1.49017826e-04 1.47929088e-04 1.47929088e-04\n", + " 1.41439784e-04 1.34653369e-04 1.42119479e-04 1.49526448e-04\n", + " 1.40408338e-04 1.36206561e-04 1.47349147e-04 5.08829017e-06\n", + " 1.64211426e-06 3.45175272e-06 1.41439784e-04 1.42384017e-04\n", + " 1.38417021e-04 1.41941422e-04 1.36982630e-04 1.42135966e-04\n", + " 1.49017826e-04 1.40408338e-04 1.44998641e-04 1.41264619e-04\n", + " 1.49017826e-04 1.41941422e-04 1.42088007e-04 6.76609559e-06\n", + " 1.41821734e-04 6.86188036e-06 6.92397179e-06 1.49017826e-04\n", + " 1.67522712e-06 1.36708169e-04 1.42135966e-04 3.28422852e-06\n", + " 1.67522712e-06 1.41941422e-04 1.44206371e-04 9.80640283e-05\n", + " 1.36414262e-04 1.72264125e-05 1.41264619e-04 5.08829017e-06\n", + " 3.29732569e-06 1.36708169e-04 1.43672415e-04 1.34653369e-04\n", + " 1.43409702e-04 1.42150160e-04 1.37585405e-04 1.43672415e-04\n", + " 9.80640283e-05 1.40353795e-04 7.21031856e-05 1.38417021e-04\n", + " 1.40457896e-04 1.47929088e-04 1.38694913e-04 1.41521862e-04\n", + " 1.40353795e-04 1.41941422e-04 8.86220259e-05]\n", + "names of tiles in bin ['2_2_8' '2_1_6' '2_2_7' '2_2_7' '2_2_7' '2_3_5' '2_1_5' '2_2_7' '2_2_8'\n", + " '2_2_8' '2_1_4' '2_3_6' '1_11_8' '1_10_4' '1_10_4' '2_2_8' '2_1_5'\n", + " '2_3_3' '3_3_0' '2_2_7' '2_1_6' '2_2_8' '2_1_6' '2_3_6' '2_1_6' '2_3_8'\n", + " '2_2_8' '2_2_7' '2_2_7' '1_10_7' '1_11_8' '2_3_6' '2_3_6' '3_3_0' '2_1_2'\n", + " '3_3_0' '2_3_8' '1_11_7' '2_3_5' '2_2_7' '2_3_8' '2_1_7' '1_11_8' '2_1_5'\n", + " '2_1_7' '2_1_6' '2_3_3' '2_3_3' '2_2_8' '1_10_8' '2_3_7' '2_1_6' '2_2_8'\n", + " '2_1_6' '2_1_6' '2_1_6' '2_2_8' '2_1_2' '2_3_8' '2_2_7' '2_3_4' '2_2_7'\n", + " '2_3_5' '2_3_5' '2_2_8' '2_1_7' '1_10_7' '2_3_6' '2_2_7' '2_3_2' '2_2_3'\n", + " '1_10_4' '1_11_8' '2_3_7' '1_11_7' '2_3_7' '2_1_6' '2_3_8' '2_3_7'\n", + " '2_2_8' '2_3_8' '1_11_7' '1_11_8' '2_3_7' '2_2_7' '2_3_8' '2_1_4' '2_2_8'\n", + " '2_3_7' '2_2_7' '2_1_7' '2_1_3' '2_3_8' '2_2_8' '2_3_7' '3_3_0' '2_1_5'\n", + " '2_1_6' '1_10_7' '2_1_6' '2_1_7' '2_1_6' '2_1_5' '2_3_2' '3_3_0' '2_1_7'\n", + " '2_1_7' '2_2_2' '2_2_8' '2_3_3' '2_1_6' '2_1_7' '2_3_5' '2_1_3' '1_11_7'\n", + " '1_11_8' '2_3_8' '2_2_8' '2_3_5' '2_3_7' '2_2_5' '2_2_8' '2_3_5' '2_2_5'\n", + " '2_1_4' '1_10_4' '2_3_7' '2_3_5' '2_1_7' '2_2_8' '2_3_6' '2_2_2' '2_2_6'\n", + " '2_1_4' '2_3_8' '2_2_7' '1_11_7' '2_3_3' '2_1_5' '2_2_8' '2_3_7' '2_1_5'\n", + " '2_2_8' '2_3_8' '2_1_6' '2_1_3' '2_3_4' '2_3_7' '3_2_0' '2_1_3' '3_3_0'\n", + " '1_10_4' '2_3_5' '1_10_4' '2_3_8' '2_3_7' '2_2_8' '2_3_5' '2_1_7'\n", + " '1_10_4' '2_2_8' '2_2_8' '2_3_8' '1_10_4' '3_3_0' '2_1_3' '3_3_0' '2_3_6'\n", + " '2_1_7' '2_3_2' '2_3_8' '2_1_4' '1_10_4' '2_1_7' '2_3_2' '1_10_4' '2_1_5'\n", + " '1_11_8' '2_1_7' '2_2_2' '1_10_4' '2_3_8' '2_1_6' '1_10_4' '2_2_8'\n", + " '2_1_2' '1_10_8' '2_3_5' '2_3_8' '2_3_8' '2_3_3' '2_1_6' '1_11_7' '2_2_8'\n", + " '2_3_2' '2_2_8' '1_10_7' '2_1_4' '2_1_5' '2_1_7' '2_3_8' '2_1_7' '1_11_8'\n", + " '2_2_8' '2_3_3' '3_3_0' '2_2_8' '2_2_8' '2_2_8' '1_11_7' '2_2_8' '2_1_3'\n", + " '1_10_4' '2_2_8' '2_3_3' '2_2_7' '2_3_7' '2_1_5' '3_3_0' '2_2_8' '1_11_7'\n", + " '1_11_7' '2_1_7' '2_3_8' '2_2_7' '2_3_8' '1_10_4' '2_3_8' '2_3_8' '2_3_8'\n", + " '2_2_8' '1_11_8' '2_1_7' '3_3_0' '1_10_7' '2_2_7' '1_10_4' '1_11_4'\n", + " '2_2_2' '2_1_6' '1_10_4' '1_10_4' '2_3_4' '2_2_8' '2_1_3' '2_3_3' '2_3_6'\n", + " '3_2_0' '2_1_4' '2_2_5' '1_11_4' '2_2_8' '2_1_7' '2_2_8' '2_3_8' '1_10_4'\n", + " '2_3_8' '2_2_8' '2_2_8' '2_3_8' '2_1_6' '2_2_8' '2_3_5' '2_3_8' '2_2_8'\n", + " '3_3_0' '2_2_8' '2_1_6' '2_1_6' '1_10_4' '2_3_4' '1_10_4' '2_2_5' '2_1_6'\n", + " '2_3_4' '2_3_3' '1_10_4' '2_3_8' '2_2_8' '2_3_6' '2_2_7' '2_2_8' '1_10_4'\n", + " '2_3_6' '2_2_8' '2_1_6' '1_11_8' '2_3_8' '1_11_7' '1_10_8' '2_3_8'\n", + " '2_3_8' '2_2_8' '2_1_7' '3_3_0' '2_1_5' '2_2_3' '2_1_6' '2_3_8' '2_3_4'\n", + " '2_2_2' '2_1_3' '1_10_4' '2_2_2' '2_1_7' '2_1_3' '2_2_8' '3_2_0' '1_11_7'\n", + " '1_10_7' '2_2_8' '2_2_8' '1_10_4' '2_1_7' '2_1_5' '2_1_6' '2_1_5' '2_2_8'\n", + " '2_3_8' '1_10_4' '2_3_3' '2_1_7' '2_2_8' '2_1_7' '3_3_0' '2_2_8' '2_3_5'\n", + " '2_1_7' '2_2_8' '2_3_8' '2_2_8' '2_3_8' '1_11_7' '3_3_0' '2_3_2' '2_1_7'\n", + " '3_2_0' '2_3_3' '2_2_8' '1_11_8' '2_1_2' '3_3_0' '1_10_4' '2_3_7' '2_3_2'\n", + " '2_3_6' '2_2_2' '2_2_8' '1_10_4' '1_10_4' '2_3_8' '2_1_3' '2_2_8' '2_3_6'\n", + " '2_3_6' '2_3_8' '2_2_8' '2_1_4' '1_11_8' '1_10_4' '2_2_8' '2_3_5'\n", + " '1_10_4' '2_3_8' '2_3_8' '2_2_8' '3_2_0' '2_2_7' '2_1_4' '1_10_4' '2_3_8'\n", + " '2_1_7' '2_1_7' '2_2_8' '2_1_7' '2_3_3' '1_10_4' '1_10_4' '1_11_8'\n", + " '2_2_8' '2_3_5' '2_1_2' '2_1_7' '3_3_0' '1_10_4' '2_1_7' '3_3_0' '2_2_2'\n", + " '1_10_4' '2_2_8' '1_10_4' '2_1_7' '1_10_4' '1_11_7' '1_11_8' '2_1_7'\n", + " '2_2_2' '2_2_7' '2_2_8' '2_3_8' '2_1_4' '1_10_4' '2_1_6' '1_11_8'\n", + " '1_10_4' '1_11_4' '2_1_3' '2_3_8' '2_1_7' '2_3_4' '3_2_0' '2_1_7' '2_2_7'\n", + " '1_10_4' '1_11_7' '2_3_3' '2_2_8' '2_2_8' '2_2_8' '2_1_6' '2_3_4' '2_3_3'\n", + " '1_11_4' '2_3_8' '2_2_6' '2_2_8' '2_3_3' '2_1_7' '1_10_8' '2_1_4' '2_2_8'\n", + " '2_1_2' '1_10_4' '2_1_3' '2_3_8' '2_3_3' '2_3_6' '2_3_8' '2_2_7' '1_10_4'\n", + " '1_10_4' '2_1_6' '2_2_8' '2_1_6' '1_10_7' '3_3_0' '2_1_4' '2_2_8' '2_2_8'\n", + " '2_3_8' '3_3_0' '2_1_7' '2_2_8' '2_2_8' '2_1_5' '2_2_8' '2_2_8' '1_11_7'\n", + " '2_2_8' '1_10_4' '1_10_7' '1_11_7' '2_2_8' '1_10_4' '1_11_7' '2_3_2'\n", + " '2_2_2' '2_1_7' '2_3_3' '1_11_4' '2_3_8' '2_1_3' '2_1_7' '2_3_4' '3_3_0'\n", + " '2_1_7' '1_10_4' '2_3_8' '2_1_7' '2_3_7' '2_2_8' '2_1_2' '3_2_0' '1_11_8'\n", + " '2_1_2' '2_2_8' '1_10_4' '2_2_8' '1_10_4' '2_2_8' '2_3_5' '3_3_0'\n", + " '1_11_7' '1_10_4' '2_1_6' '1_10_4' '1_11_8' '2_2_2' '2_3_5' '3_3_0'\n", + " '2_2_8' '2_2_7' '2_2_8' '2_2_3' '2_3_3' '2_3_8' '2_1_2' '3_3_0' '2_1_7'\n", + " '2_1_3' '3_3_0' '2_1_2' '1_11_7' '2_3_7' '2_3_5' '2_2_7' '2_2_8' '3_3_0'\n", + " '2_2_8' '2_1_4' '2_2_7' '2_2_2' '1_10_8' '2_2_7' '2_1_7' '2_1_7' '3_3_0'\n", + " '2_1_7' '2_2_8' '2_1_6' '2_1_2' '1_11_4' '1_10_4' '2_1_6' '3_3_0' '2_3_5'\n", + " '1_11_8' '1_10_4' '2_2_8' '2_3_3' '2_1_7' '2_2_8' '3_3_0' '2_1_7'\n", + " '1_10_4' '2_1_7' '3_3_0' '2_2_8' '2_3_8' '2_2_8' '2_3_2' '2_1_3' '2_2_8'\n", + " '2_1_4' '2_3_7' '1_10_4' '2_2_8' '2_1_5' '1_11_4' '2_1_7' '2_1_3' '2_1_6'\n", + " '1_10_4' '2_1_7' '2_3_8' '3_3_0' '2_1_7' '2_1_7' '1_11_8' '2_3_3' '2_2_8'\n", + " '1_11_7' '2_1_5' '2_2_8' '2_3_2' '1_10_4' '2_3_7' '2_3_8' '1_11_7'\n", + " '1_1_6' '2_3_8' '2_3_3' '2_2_2' '1_1_6' '2_3_3' '2_3_8' '2_3_5' '3_3_0'\n", + " '2_2_8' '2_2_2' '3_3_0' '3_2_0' '2_3_8' '2_1_4' '2_2_8' '1_10_7' '2_2_2'\n", + " '2_2_8' '2_3_7' '1_11_4' '2_3_5' '1_10_4' '1_10_4' '2_1_7' '2_1_4'\n", + " '2_3_3' '2_1_7' '1_10_4' '2_3_3' '3_3_0' '2_2_8' '2_2_8' '2_1_3' '2_1_4'\n", + " '2_2_8' '1_10_4' '2_1_7' '1_11_7' '1_11_4' '1_11_7' '2_1_4' '2_1_5'\n", + " '2_1_5' '2_3_4' '2_1_2' '2_3_7' '2_2_8' '1_11_8' '2_3_8' '2_2_8' '1_2_7'\n", + " '2_3_3' '2_2_2' '3_3_0' '3_3_0' '3_3_0' '1_10_4' '2_3_4' '2_2_2' '3_3_0'\n", + " '1_11_7' '2_3_3' '3_3_0' '2_2_8' '2_3_4' '2_1_3' '1_10_4' '2_2_2' '2_2_8'\n", + " '2_1_3' '2_2_8' '1_11_7' '3_3_0' '1_11_7' '3_3_0' '1_10_4' '3_3_0'\n", + " '2_1_4' '1_10_8' '1_10_7' '2_1_5' '2_2_3' '2_1_5' '3_3_0' '2_1_7' '2_2_8'\n", + " '1_11_7' '2_3_3' '2_1_7' '2_1_5' '3_2_0' '1_2_7' '2_2_8' '3_2_0' '1_10_4'\n", + " '2_2_8' '2_3_7' '2_2_8' '2_1_4' '2_3_7' '2_1_2' '1_11_4' '2_3_3' '2_1_5'\n", + " '1_2_7' '1_11_7' '3_2_0' '2_2_8' '1_11_4' '3_3_0' '2_2_8' '2_1_2'\n", + " '1_11_7' '2_1_4' '1_1_6']\n", + "dowsampled rms bin 4\n", + "areas of tiles in bin [1.49526448e-04 1.38487679e-04 1.38119440e-04 1.43819166e-04\n", + " 5.06702579e-06 1.42088007e-04 1.41834725e-04 1.42150160e-04\n", + " 1.41595801e-04 1.42862710e-04 1.04631204e-04 1.41941422e-04\n", + " 1.42862710e-04 1.46745612e-04 3.40231679e-06 1.41948762e-04\n", + " 1.42027999e-04 1.41941422e-04 1.42150160e-04 1.36708169e-04\n", + " 1.46168027e-04 1.41941422e-04 1.34653369e-04 1.41529797e-04\n", + " 1.41264619e-04 1.42044804e-04 1.41941422e-04 1.42100004e-04\n", + " 1.37237607e-04 1.43672415e-04 1.41732649e-04 1.37894360e-04\n", + " 1.41595801e-04 5.17103851e-06 1.37698998e-04 1.40114071e-04\n", + " 7.14878695e-05 1.38953077e-04 1.41948762e-04 1.43672415e-04\n", + " 1.42639701e-04 1.42027999e-04 9.32791077e-05 1.49017826e-04\n", + " 1.42027999e-04 1.71710780e-06 1.40457896e-04 1.35321912e-05\n", + " 1.35190353e-04 1.41264619e-04 1.41850307e-04 1.40351997e-04\n", + " 1.34653369e-04 1.40863925e-04 1.38694913e-04 1.37469613e-04\n", + " 1.43672415e-04 1.40351997e-04 1.67522712e-06 1.35095531e-05\n", + " 1.41341719e-04 4.09887209e-08 1.37237607e-04 1.34653369e-04\n", + " 1.35708109e-04 1.41941422e-04 1.00377474e-04 4.18806779e-08\n", + " 1.40408338e-04 9.92274681e-05 1.41732649e-04 1.43065399e-04\n", + " 1.42088007e-04 1.42027999e-04 1.14842750e-05 1.41264619e-04\n", + " 1.42100004e-04 8.86220259e-05 9.32791077e-05 2.71661907e-05\n", + " 5.13684862e-06 1.11851872e-04 1.45594990e-04 1.41941422e-04\n", + " 1.42150160e-04 2.41076268e-05 7.17044667e-05 1.41941422e-04\n", + " 5.04161810e-06 1.35708109e-04 1.41264619e-04 1.42384017e-04\n", + " 1.42044804e-04 1.47746372e-04 1.38119440e-04 3.43710124e-06\n", + " 1.38226219e-04 1.42975739e-04 1.03563508e-04 3.36107511e-06\n", + " 1.16959998e-05 1.46745612e-04 1.41732649e-04 1.37937598e-04\n", + " 1.41850307e-04 1.40351997e-04 1.38487679e-04 1.05684735e-04\n", + " 1.36206561e-04 1.34653369e-04 1.37145276e-04 1.40280250e-04\n", + " 1.91391436e-04 1.42152686e-04 1.49017826e-04 1.40490283e-04\n", + " 2.78689865e-04 1.42088007e-04 1.37937598e-04 1.42044804e-04\n", + " 1.91778596e-04 1.41876722e-04 1.00377474e-04 1.43672415e-04\n", + " 1.38070109e-04 9.92274681e-05 1.36982630e-04 1.47349147e-04\n", + " 1.38417021e-04 1.41850307e-04 8.86220259e-05 1.41941422e-04\n", + " 1.41948762e-04 3.45906669e-06 9.56974786e-05 1.36982630e-04\n", + " 9.20508969e-05 1.41876722e-04 1.34653369e-04 1.05907195e-04\n", + " 9.56331801e-05 1.33011255e-04 9.92455226e-05 1.38694913e-04\n", + " 1.41941422e-04 1.38953077e-04 9.68873120e-05 1.67522712e-06\n", + " 9.44946899e-05 1.34653369e-04 1.41821734e-04 1.34653369e-04\n", + " 1.37237607e-04 1.40351997e-04 6.76323805e-06 1.41948762e-04\n", + " 1.43672415e-04 1.41595801e-04 1.03563508e-04 9.44946899e-05\n", + " 1.41941422e-04 1.06723959e-04 1.41948762e-04 1.36206561e-04\n", + " 1.40460392e-04 1.34653369e-04 8.86220259e-05 1.41439784e-04\n", + " 1.91686531e-04 1.04826478e-04 1.00377474e-04 1.42088007e-04\n", + " 5.02547177e-06 1.42027999e-04 1.37145276e-04 1.04826478e-04\n", + " 1.42150160e-04 1.43672415e-04 1.38417021e-04 3.34500673e-06\n", + " 1.42100004e-04 1.46745612e-04 8.86220259e-05 1.41970373e-04\n", + " 1.34653369e-04 1.42639701e-04 1.49526448e-04 1.38362667e-04\n", + " 1.42150160e-04 1.42088007e-04 1.42088007e-04 1.40351997e-04\n", + " 8.86220259e-05 7.51197777e-05 1.41521862e-04 1.38417021e-04\n", + " 9.80546290e-05 1.41850307e-04 1.42152686e-04 9.20508969e-05\n", + " 1.34653369e-04 1.41141473e-04 1.97988754e-04 1.40443872e-04\n", + " 1.42027999e-04 1.67250337e-06 9.20508969e-05 1.48485344e-04\n", + " 1.05907195e-04 1.41948762e-04 1.06723959e-04 9.56974786e-05\n", + " 1.40351997e-04 1.42088007e-04 1.41850307e-04 1.39579712e-04\n", + " 1.41948762e-04 1.40351997e-04 1.41941422e-04 1.03731569e-04\n", + " 9.68873120e-05 1.41850307e-04 9.56974786e-05 1.42150160e-04\n", + " 9.68873120e-05 1.41941422e-04 1.41970373e-04 1.05907195e-04\n", + " 9.68873120e-05 1.49017826e-04 1.40353795e-04 8.75279269e-05\n", + " 9.44946899e-05 8.75279269e-05 1.02481791e-04 1.35708109e-04\n", + " 8.86220259e-05 9.20508969e-05 1.41970373e-04 9.56974786e-05\n", + " 1.41439784e-04 1.39618040e-04 5.19297884e-06 1.44570487e-04\n", + " 9.68873120e-05 1.40019435e-04 9.20508969e-05 1.40921151e-04\n", + " 9.92274681e-05 1.41941422e-04 1.67522712e-06 1.87682060e-04\n", + " 9.31599438e-05 8.86220259e-05 1.41941422e-04 1.41941422e-04\n", + " 1.04826478e-04 9.68873120e-05 2.41076268e-05 1.09062742e-04\n", + " 1.47929088e-04 1.41521862e-04 1.41970373e-04 9.92274681e-05\n", + " 1.41850307e-04 1.40460392e-04 1.34653369e-04 1.04826478e-04\n", + " 9.80640283e-05 9.68504603e-05 1.04631204e-04 3.46198590e-06\n", + " 1.42152686e-04 2.77517310e-04 1.67522712e-06 1.41821734e-04\n", + " 1.41141473e-04 9.31599438e-05 1.42100004e-04 1.40460392e-04\n", + " 1.42088007e-04 9.44946899e-05 1.14842750e-05 1.34653369e-04\n", + " 1.42152686e-04 1.37937598e-04 1.41850307e-04 9.92455226e-05\n", + " 1.49526448e-04 6.90350544e-06 9.56974786e-05 1.00377474e-04\n", + " 1.41876722e-04 1.97839542e-04 1.35190353e-04 9.80640283e-05\n", + " 3.84854368e-05 6.75946486e-06 1.14506186e-04 1.06973574e-04\n", + " 1.01586842e-04 1.41941422e-04 1.59788388e-04 1.04631204e-04\n", + " 1.43672415e-04 1.41141473e-04 1.02481791e-04 1.41941422e-04\n", + " 9.20508969e-05 1.41850307e-04 9.44946899e-05 1.40498990e-04\n", + " 9.56331801e-05 8.75279269e-05 9.44029523e-05 1.73804813e-06\n", + " 1.38953077e-04 1.07748733e-04 1.40921151e-04 1.35190353e-04\n", + " 1.42135966e-04 5.15110856e-06 4.92634278e-06 9.68873120e-05\n", + " 9.80546290e-05 1.05907195e-04 1.06973574e-04 1.01586842e-04\n", + " 1.40668547e-04 1.42152686e-04 7.47632242e-05 1.41970373e-04\n", + " 9.92274681e-05 1.05684735e-04 1.07748733e-04 1.42135966e-04\n", + " 1.41850307e-04 8.86220259e-05 1.37484050e-04 1.38694913e-04\n", + " 1.42044804e-04 1.37937598e-04 1.00422980e-04 9.80640283e-05\n", + " 1.37711511e-04 1.42088007e-04 1.49526448e-04 1.42027999e-04\n", + " 1.39579712e-04 9.32791077e-05 1.71710780e-06 3.32211124e-06\n", + " 9.31599438e-05 9.32791077e-05 1.35708109e-04 7.20357243e-05\n", + " 1.40280250e-04 1.04631204e-04 1.43065399e-04 1.07748733e-04\n", + " 1.42044804e-04 1.42150160e-04 1.40280250e-04 3.32211124e-06\n", + " 1.41941422e-04 1.34653369e-04 1.14842750e-05 1.42088007e-04\n", + " 1.40443872e-04 1.18308644e-05 1.40351997e-04 1.42862710e-04\n", + " 1.41439784e-04 6.89471801e-06 1.41264619e-04 1.42975739e-04\n", + " 1.42027999e-04 9.32791077e-05 1.42044804e-04 9.80640283e-05\n", + " 1.41970373e-04 1.07748733e-04 1.37698998e-04 1.42100004e-04\n", + " 1.43247738e-04 1.06723959e-04 1.41141473e-04 9.20508969e-05\n", + " 7.57256045e-05 9.44029523e-05 1.40407196e-04 1.38681140e-04\n", + " 1.38362667e-04 1.40408338e-04 1.42044804e-04 3.40231679e-06\n", + " 1.06723959e-04 9.44946899e-05 1.41850307e-04 1.41941422e-04\n", + " 4.09887209e-08 7.60103548e-05 1.00377474e-04 1.38487679e-04\n", + " 1.04631204e-04 9.68873120e-05 1.06973574e-04 1.05684735e-04\n", + " 1.39709222e-04 1.41970373e-04 1.07748733e-04 9.68873120e-05\n", + " 2.38206312e-05 1.38681140e-04 1.34653369e-04 7.17306998e-06\n", + " 1.42152686e-04 9.92274681e-05 1.34653369e-04 1.03563508e-04\n", + " 1.00377474e-04 1.42135966e-04 1.43672415e-04 1.91394957e-04\n", + " 1.36685639e-04 1.42135966e-04 1.41732649e-04 1.43409702e-04\n", + " 1.05684735e-04 1.14947998e-05 3.35574159e-06 8.75279269e-05\n", + " 1.03563508e-04 1.06723959e-04 1.08025470e-04 1.00238010e-04\n", + " 1.34653369e-04 1.41948762e-04 1.41970373e-04 1.09062742e-04\n", + " 9.90303227e-05 1.40457896e-04 1.35095531e-05 9.44946899e-05\n", + " 5.05187844e-06 1.42044804e-04 2.79283380e-06 1.35190353e-04\n", + " 1.41970373e-04 1.04826478e-04 1.42150160e-04 1.37585405e-04\n", + " 1.13109769e-04 9.44946899e-05 5.10396119e-06 2.51273588e-07\n", + " 3.34500673e-06 1.41681874e-04 1.89900948e-04 1.42044804e-04\n", + " 1.02622616e-04 1.42150160e-04 1.18308644e-05 1.01386199e-04\n", + " 1.42027999e-04 9.80640283e-05 1.14506186e-04 1.40280250e-04\n", + " 5.21392696e-06 1.73804813e-06 1.20538134e-05 9.80640283e-05\n", + " 3.40231679e-06 1.00422980e-04 5.21392696e-06 1.41970373e-04\n", + " 5.25603574e-06 1.41439784e-04 6.65428170e-06 1.08025470e-04\n", + " 8.19774417e-08 1.40921151e-04 7.25620297e-06 1.40280250e-04\n", + " 1.44206371e-04 1.40353795e-04 1.43551267e-04 1.41876722e-04\n", + " 9.92455226e-05 1.37145276e-04 9.56331801e-05 1.71710780e-06\n", + " 1.05907195e-04 1.35708109e-04 1.40125873e-04 1.38226219e-04\n", + " 1.41850307e-04 1.00377474e-04 1.42975739e-04 1.41850307e-04\n", + " 1.41595801e-04 1.13109769e-04 9.92274681e-05 1.08025470e-04\n", + " 7.08896590e-06 9.32791077e-05 9.68873120e-05 1.42100004e-04\n", + " 9.44946899e-05 1.40460392e-04 1.42135966e-04 1.02481791e-04\n", + " 1.37937598e-04 9.44946899e-05 1.42135966e-04 1.42088007e-04\n", + " 1.18458466e-05 9.20508969e-05 1.40351997e-04 1.41264619e-04\n", + " 1.42152686e-04 9.80546290e-05 6.74917374e-06 1.43672415e-04\n", + " 1.41521862e-04 1.41948762e-04 3.30995387e-06 1.13109769e-04\n", + " 9.32791077e-05 1.35708109e-04 1.02622616e-04 1.36032102e-04\n", + " 3.36107511e-06 1.06723959e-04 1.42150160e-04 5.20604676e-06\n", + " 1.37804228e-04 9.44946899e-05 9.44029523e-05 1.42152686e-04\n", + " 1.41341719e-04 1.36206561e-04 1.41141473e-04 1.40280250e-04\n", + " 9.80640283e-05 1.41970373e-04 1.08025470e-04 1.09062742e-04\n", + " 1.42100004e-04 8.75279269e-05 1.05684735e-04 7.54257370e-05\n", + " 1.03731569e-04 1.91483109e-04 9.32791077e-05 7.54287298e-05\n", + " 1.12201126e-04 1.35190353e-04 1.42100004e-04 9.32791077e-05\n", + " 1.43672415e-04]\n", + "names of tiles in bin ['3_3_0' '2_3_8' '2_2_8' '3_2_0' '1_11_7' '1_10_4' '2_2_2' '1_11_4'\n", + " '1_10_3' '2_1_2' '1_3_8' '2_1_5' '2_1_2' '3_3_0' '2_2_2' '1_10_7'\n", + " '1_10_4' '2_1_4' '1_11_7' '2_2_8' '3_3_0' '2_1_4' '2_3_7' '2_2_2'\n", + " '1_10_4' '1_11_7' '2_1_6' '1_11_7' '2_2_8' '2_1_4' '1_10_3' '2_1_7'\n", + " '1_10_4' '2_1_5' '2_1_7' '2_2_2' '3_2_0' '2_2_8' '1_10_3' '2_1_6' '2_1_2'\n", + " '1_10_3' '1_2_7' '3_3_0' '1_10_4' '2_1_2' '1_11_4' '1_10_4' '2_3_3'\n", + " '1_10_4' '1_10_3' '1_11_4' '2_3_7' '2_2_2' '2_2_8' '2_2_8' '2_1_6'\n", + " '1_11_7' '2_1_7' '1_10_4' '2_1_2' '2_3_5' '2_2_8' '2_3_5' '2_3_3' '2_1_5'\n", + " '1_2_7' '2_1_3' '1_11_8' '1_2_7' '1_10_3' '2_1_2' '1_10_4' '1_10_3'\n", + " '3_2_0' '1_10_3' '1_11_7' '1_1_6' '1_2_7' '2_3_8' '2_2_2' '2_3_8' '3_3_0'\n", + " '2_1_5' '1_11_4' '3_3_0' '3_2_0' '2_1_6' '2_2_7' '2_3_2' '1_10_4' '2_2_3'\n", + " '1_11_7' '3_3_0' '2_2_8' '2_1_3' '2_1_7' '3_2_0' '1_3_8' '2_3_3' '1_11_5'\n", + " '3_3_0' '1_10_3' '2_3_8' '1_10_7' '1_11_4' '2_3_8' '1_3_7' '2_3_2'\n", + " '2_3_7' '2_3_2' '1_11_8' '2_2_8' '1_11_7' '3_3_0' '2_3_8' '2_1_7'\n", + " '1_10_7' '2_3_8' '1_11_7' '2_2_8' '1_11_8' '1_2_7' '2_1_5' '2_1_7'\n", + " '1_2_7' '2_2_8' '3_3_0' '2_2_8' '1_10_7' '1_1_7' '2_1_4' '1_10_3' '2_1_6'\n", + " '1_2_7' '2_2_8' '1_2_7' '1_11_7' '2_3_6' '1_3_8' '1_2_7' '2_3_7' '1_2_7'\n", + " '2_2_8' '2_1_5' '2_2_8' '1_2_7' '2_1_7' '1_2_7' '2_3_7' '2_1_2' '2_3_7'\n", + " '2_2_8' '1_11_4' '1_10_7' '1_10_4' '2_1_4' '1_10_3' '1_3_8' '1_2_7'\n", + " '2_1_5' '1_3_7' '1_10_3' '2_3_3' '1_11_4' '2_3_7' '1_1_6' '1_10_3'\n", + " '2_2_8' '1_3_8' '1_2_7' '1_10_4' '2_2_8' '1_10_3' '2_3_2' '1_3_8'\n", + " '1_11_4' '2_1_6' '2_2_8' '2_3_3' '1_11_8' '3_3_0' '1_1_7' '1_11_5'\n", + " '2_3_7' '2_1_2' '3_3_0' '2_1_7' '1_11_8' '1_10_4' '1_10_3' '1_11_8'\n", + " '1_1_6' '3_3_0' '2_1_2' '2_2_8' '1_2_7' '1_10_3' '1_11_4' '1_2_7' '2_3_7'\n", + " '2_1_2' '2_3_8' '1_11_5' '1_10_4' '2_3_4' '1_2_7' '3_3_0' '1_3_8'\n", + " '1_10_8' '1_3_8' '1_2_8' '1_11_4' '1_10_3' '1_10_7' '2_3_8' '1_10_3'\n", + " '1_11_5' '2_1_5' '1_3_8' '1_2_8' '1_10_3' '1_2_7' '1_11_4' '1_2_7'\n", + " '2_1_6' '1_11_6' '1_3_8' '1_2_8' '3_3_0' '1_11_8' '1_1_6' '1_2_7' '1_1_7'\n", + " '1_3_8' '2_3_3' '1_1_6' '1_2_8' '1_11_6' '1_2_7' '1_10_4' '2_1_7' '2_1_6'\n", + " '3_2_0' '1_2_7' '2_3_8' '1_2_7' '2_1_2' '1_2_8' '2_1_5' '2_1_7' '1_2_7'\n", + " '1_2_7' '1_1_6' '2_1_6' '2_1_5' '1_3_8' '1_2_8' '3_3_0' '1_3_8' '3_3_0'\n", + " '2_1_2' '1_11_7' '1_2_7' '1_10_3' '1_11_4' '2_3_3' '1_3_8' '1_2_7'\n", + " '1_2_8' '1_3_8' '2_1_4' '1_11_8' '2_3_8' '2_1_7' '2_1_2' '2_1_2' '1_2_7'\n", + " '1_11_7' '1_11_7' '1_10_7' '1_2_7' '3_2_0' '2_3_7' '1_11_4' '2_3_8'\n", + " '1_10_7' '1_2_7' '3_3_0' '2_1_2' '1_2_7' '1_2_7' '1_11_5' '2_3_8' '2_3_2'\n", + " '1_2_7' '2_3_8' '1_10_7' '1_4_8' '1_3_8' '1_2_8' '2_1_4' '2_3_8' '1_3_7'\n", + " '2_1_6' '2_1_2' '1_3_8' '2_1_5' '1_2_7' '1_10_7' '1_2_8' '2_2_2' '1_2_8'\n", + " '1_1_6' '1_2_7' '2_1_2' '2_2_8' '1_3_7' '2_1_2' '2_3_2' '1_11_7' '2_2_2'\n", + " '2_3_7' '1_2_7' '1_2_8' '1_3_7' '1_3_7' '1_2_7' '2_2_2' '1_11_4' '3_3_0'\n", + " '1_11_7' '1_2_7' '1_3_8' '1_3_8' '1_11_8' '1_10_7' '1_1_6' '2_1_7'\n", + " '2_2_8' '1_11_7' '2_3_8' '1_2_7' '1_2_8' '1_9_4' '1_10_4' '3_3_0'\n", + " '1_10_7' '2_3_8' '1_2_7' '2_1_2' '2_3_2' '1_2_8' '1_2_8' '2_3_3' '3_2_0'\n", + " '1_11_7' '1_3_8' '2_1_2' '1_3_7' '1_11_7' '1_11_7' '1_11_7' '2_3_7'\n", + " '2_1_6' '2_3_7' '3_2_0' '1_10_8' '1_11_4' '1_11_6' '1_11_7' '2_1_2'\n", + " '1_10_4' '2_1_2' '1_10_3' '3_2_0' '1_10_8' '1_2_8' '1_11_6' '1_2_8'\n", + " '1_11_8' '1_3_8' '2_1_7' '1_11_5' '2_1_2' '1_3_7' '2_1_2' '1_2_7' '3_3_0'\n", + " '1_2_8' '2_2_2' '1_11_4' '2_1_7' '1_11_5' '1_11_8' '2_2_2' '1_3_8'\n", + " '1_2_7' '1_10_7' '2_1_6' '2_3_4' '3_3_0' '1_2_8' '2_3_8' '1_3_7' '1_2_7'\n", + " '1_3_8' '1_3_7' '2_2_2' '1_11_7' '1_3_7' '1_2_8' '3_3_0' '1_11_4' '2_3_6'\n", + " '1_2_8' '1_11_4' '1_2_7' '2_3_7' '1_3_8' '1_2_7' '1_11_5' '2_1_6' '1_2_8'\n", + " '2_3_2' '1_11_6' '1_10_3' '2_1_2' '1_3_8' '2_3_8' '2_3_7' '1_1_6' '1_3_7'\n", + " '1_3_7' '1_3_8' '1_3_7' '2_3_7' '1_10_7' '1_11_5' '1_3_8' '1_3_8'\n", + " '1_11_4' '1_10_8' '1_2_7' '1_3_8' '1_11_7' '1_4_9' '2_3_2' '1_11_5'\n", + " '1_3_7' '1_11_4' '2_3_2' '1_4_8' '1_2_7' '1_3_8' '2_2_3' '2_3_7' '2_1_2'\n", + " '2_2_8' '1_11_5' '1_3_7' '1_11_4' '1_11_5' '1_3_8' '1_10_3' '1_2_7'\n", + " '1_4_9' '1_11_5' '2_2_3' '2_1_2' '3_3_0' '1_2_7' '2_2_2' '1_2_8' '2_2_2'\n", + " '1_11_6' '1_3_8' '1_10_3' '1_2_8' '1_3_8' '2_3_7' '2_1_2' '1_2_8'\n", + " '1_11_5' '3_2_0' '1_11_7' '2_1_1' '1_11_7' '1_2_8' '2_3_2' '1_2_7'\n", + " '2_1_2' '1_3_8' '2_3_3' '2_2_2' '2_1_7' '1_10_8' '1_2_8' '3_2_0' '1_10_4'\n", + " '1_10_4' '1_4_9' '1_2_7' '1_3_7' '1_2_8' '1_2_7' '1_2_7' '1_11_5' '1_2_8'\n", + " '1_11_5' '1_11_5' '1_3_8' '2_3_8' '1_2_8' '1_11_4' '1_10_3' '1_11_5'\n", + " '1_2_8' '1_11_4' '1_10_4' '1_11_6' '1_2_8' '1_10_3' '2_1_5' '2_1_1'\n", + " '1_10_4' '2_3_2' '1_4_8' '1_2_7' '2_3_3' '1_3_8' '1_9_4' '2_3_2' '1_3_8'\n", + " '1_11_5' '1_3_8' '2_2_2' '1_2_7' '1_2_8' '1_11_5' '2_1_2' '2_3_2' '2_1_2'\n", + " '1_11_5' '1_2_8' '1_11_5' '1_3_8' '1_3_8' '1_11_7' '1_1_6' '1_3_8'\n", + " '3_3_0' '1_3_7' '2_2_8' '1_2_8' '3_3_0' '1_4_9' '2_3_3' '1_11_6' '1_2_7'\n", + " '2_1_2']\n", + "dowsampled rms bin 5\n", + "areas of tiles in bin [4.99911174e-06 1.06973574e-04 1.40351997e-04 1.41941422e-04\n", + " 1.42100004e-04 4.83074745e-06 1.18230602e-05 1.42152686e-04\n", + " 1.41970373e-04 1.40457896e-04 1.42135966e-04 1.04631204e-04\n", + " 1.42044804e-04 1.07432591e-04 1.00422980e-04 1.38479436e-04\n", + " 1.00377474e-04 1.67522712e-06 1.09384559e-04 1.41970373e-04\n", + " 1.38681140e-04 1.41732649e-04 1.05684735e-04 9.56974786e-05\n", + " 1.05684735e-04 1.03563508e-04 1.41595801e-04 9.92274681e-05\n", + " 1.00377474e-04 1.05684735e-04 1.00377474e-04 1.13586325e-04\n", + " 1.41970373e-04 9.90303227e-05 1.40351997e-04 1.04631204e-04\n", + " 5.21392696e-06 1.42152686e-04 1.43408933e-04 1.43247738e-04\n", + " 1.41439784e-04 9.32791077e-05 3.35574159e-06 1.09754382e-04\n", + " 1.13109769e-04 9.90303227e-05 1.04631204e-04 1.42088007e-04\n", + " 1.41264619e-04 1.38479436e-04 7.00390207e-06 1.41821734e-04\n", + " 3.36132502e-06 1.14842750e-05 1.39166769e-04 1.43409702e-04\n", + " 1.64211426e-06 9.68873120e-05 1.39579712e-04 9.92274681e-05\n", + " 1.13586325e-04 1.01386199e-04 9.80640283e-05 1.02481791e-04\n", + " 9.68873120e-05 8.86220259e-05 1.42100004e-04 1.01586842e-04\n", + " 1.42135966e-04 1.07748733e-04 6.72688660e-06 1.42150160e-04\n", + " 1.02481791e-04 1.02622616e-04 1.05684735e-04 8.86220259e-05\n", + " 1.44911456e-04 1.48593185e-04 1.02481791e-04 1.41341719e-04\n", + " 1.41341719e-04 9.92455226e-05 8.86220259e-05 1.41970373e-04\n", + " 9.68504603e-05 9.92455226e-05 1.05684735e-04 1.35708109e-04\n", + " 9.92274681e-05 1.07748733e-04 1.04631204e-04 1.40457896e-04\n", + " 1.00377474e-04 9.32791077e-05 1.69616746e-06 1.35190353e-04\n", + " 1.13109769e-04 1.43672415e-04 1.05907195e-04 1.42150160e-04\n", + " 1.01386199e-04 9.68873120e-05 1.41681874e-04 1.06723959e-04\n", + " 1.06723959e-04 1.41970373e-04 9.56974786e-05 1.44570487e-04\n", + " 1.42027999e-04 1.13586325e-04 1.02481791e-04 1.06973574e-04\n", + " 5.01257132e-06 1.09062742e-04 4.94566824e-06 1.08025470e-04\n", + " 8.75279269e-05 1.38806231e-04 1.08415914e-04 1.03563508e-04\n", + " 1.12201126e-04 1.35190353e-04 1.41970373e-04 1.41876722e-04\n", + " 1.44379076e-04 1.42044804e-04 1.04631204e-04 1.40280250e-04\n", + " 1.40460392e-04 1.41970373e-04 6.88494989e-06 1.43409702e-04\n", + " 1.38681140e-04 1.38681140e-04 1.03563508e-04 1.48485344e-04\n", + " 1.47349147e-04 1.41970373e-04 9.56331801e-05 1.05684735e-04\n", + " 1.39824619e-04 1.42135966e-04 1.09062742e-04 9.90303227e-05\n", + " 1.41941422e-04 1.40408338e-04 1.00377474e-04 6.75477654e-06\n", + " 1.07432591e-04 1.38681140e-04 8.19774417e-08 9.90303227e-05\n", + " 1.42152686e-04 1.08758919e-04 1.46118583e-04 1.03731569e-04\n", + " 1.35708109e-04 1.42152686e-04 1.01586842e-04 1.42150160e-04\n", + " 9.20508969e-05 1.40460392e-04 1.41970373e-04 1.41876722e-04\n", + " 1.42150160e-04 1.42027999e-04 1.00377474e-04 1.35190353e-04\n", + " 1.14506186e-04 5.21392696e-06 1.41876722e-04 1.42100004e-04\n", + " 1.42100004e-04 1.41341719e-04 1.42384017e-04 5.07628452e-06\n", + " 1.38479436e-04 1.06973574e-04 1.34653369e-04 1.09384559e-04\n", + " 1.40280250e-04 1.04631204e-04 1.42152686e-04 5.15535295e-06\n", + " 9.80546290e-05 1.40353795e-04 1.41521862e-04 1.40353795e-04\n", + " 5.08829017e-06 1.40280250e-04 1.10734986e-04 1.42100004e-04\n", + " 1.42152686e-04 9.20508969e-05 1.04826478e-04 1.42100004e-04\n", + " 1.06973574e-04 1.37894360e-04 2.65265657e-06 1.07432591e-04\n", + " 1.40351997e-04 1.42044804e-04 1.13109769e-04 1.01386199e-04\n", + " 9.92274681e-05 9.44946899e-05 1.41681874e-04 9.56974786e-05\n", + " 8.86220259e-05 2.83753444e-04 1.40187713e-04 1.35190353e-04\n", + " 1.42135966e-04 1.42044804e-04 8.86220259e-05 1.42100004e-04\n", + " 1.42044804e-04 1.41970373e-04 9.80640283e-05 1.03563508e-04\n", + " 1.90417180e-04 1.18230602e-05 9.68504603e-05 9.56974786e-05\n", + " 1.40443872e-04 1.03563508e-04 1.46745612e-04 1.43065399e-04\n", + " 9.80640283e-05 1.40353795e-04 1.11277293e-04 1.00422980e-04\n", + " 1.40443872e-04 1.67787080e-06 1.40210429e-04 1.42152686e-04\n", + " 1.08025470e-04 1.41876722e-04 6.91789002e-06 1.41876722e-04\n", + " 1.42150160e-04 9.44946899e-05 1.43551267e-04 1.04631204e-04\n", + " 1.40351997e-04 9.32791077e-05 1.42150160e-04 1.40187713e-04\n", + " 1.42150160e-04 1.42152686e-04 1.40408338e-04 1.00377474e-04\n", + " 1.38479436e-04 2.41537372e-06 1.41970373e-04 9.20508969e-05\n", + " 1.41970373e-04 1.42027999e-04 1.40460392e-04 1.40443872e-04\n", + " 1.18460571e-05 9.68873120e-05 9.90303227e-05 9.90303227e-05\n", + " 1.40280250e-04 1.22975306e-07 1.03731569e-04 1.47243804e-04\n", + " 1.42135966e-04 3.44247495e-06 1.40353795e-04 9.92274681e-05\n", + " 1.03731569e-04 1.42152686e-04 1.41970373e-04 1.08025470e-04\n", + " 1.42135966e-04 1.97665619e-04 1.41821734e-04 1.42150160e-04\n", + " 1.09754382e-04 1.07748733e-04 1.41970373e-04 1.00238010e-04\n", + " 1.01386199e-04 1.03731569e-04 1.42044804e-04 1.42150160e-04\n", + " 2.85951478e-04 1.06723959e-04 1.42135966e-04 1.13109769e-04\n", + " 1.05907195e-04 2.62801787e-06 1.41876722e-04 1.00377474e-04\n", + " 1.40187713e-04 1.10734986e-04 1.42152686e-04 1.42088007e-04\n", + " 1.40353795e-04 1.42100004e-04 1.12201126e-04 1.42088007e-04\n", + " 1.97722060e-04 9.32791077e-05 1.42044804e-04 1.03731569e-04\n", + " 1.13586325e-04 1.40408338e-04 1.40460392e-04 9.80546290e-05\n", + " 9.32791077e-05 9.80640283e-05 1.42135966e-04 1.18370670e-05\n", + " 8.86220259e-05 1.42152686e-04 9.32791077e-05 1.12201126e-04\n", + " 1.07432591e-04 1.40351997e-04 8.86220259e-05 1.18416670e-05\n", + " 1.18416670e-05 1.41821734e-04 1.03563508e-04 1.14506186e-04\n", + " 1.40187713e-04 1.40457896e-04 1.41264619e-04 1.41876722e-04\n", + " 1.42027999e-04 1.40498990e-04 1.41970373e-04 1.08415914e-04\n", + " 1.42150160e-04 1.08758919e-04 1.42100004e-04 1.10338395e-04\n", + " 1.08415914e-04 1.18460571e-05 1.41970373e-04 1.42135966e-04\n", + " 9.20508969e-05 9.20508969e-05 1.03563508e-04 1.07748733e-04\n", + " 1.71710780e-06 9.80640283e-05 1.07748733e-04 1.18370670e-05\n", + " 1.07432591e-04 1.42150160e-04 1.05684735e-04 5.01257132e-06\n", + " 1.42135966e-04 1.34653369e-04 1.40408338e-04 1.02481791e-04\n", + " 1.41732649e-04 9.56974786e-05 1.12651087e-04 1.09384559e-04\n", + " 9.20508969e-05 9.20508969e-05 1.40457896e-04 5.07037047e-06\n", + " 1.42044804e-04 1.08758919e-04 1.06723959e-04 1.11700598e-04\n", + " 9.20508969e-05 3.41547072e-06 1.12651087e-04 6.83094144e-06\n", + " 1.40443872e-04 1.14506186e-04 1.04631204e-04 2.41076268e-05\n", + " 1.37585405e-04 1.42100004e-04 1.02622616e-04 1.40280250e-04\n", + " 9.44946899e-05 1.40187713e-04 3.58653499e-06 9.90303227e-05\n", 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1.01386199e-04\n", + " 1.42044804e-04 9.20508969e-05 5.14641027e-06 1.40408338e-04\n", + " 9.56974786e-05 1.41876722e-04 1.07748733e-04 1.42100004e-04\n", + " 1.09384559e-04 1.40443872e-04 1.42152686e-04 1.41876722e-04\n", + " 1.42088007e-04 9.20508969e-05 1.10338395e-04 1.41850307e-04\n", + " 1.46118583e-04 1.41941422e-04 9.80546290e-05 9.90303227e-05\n", + " 1.37937598e-04 1.41876722e-04 1.07748733e-04 1.12201126e-04\n", + " 1.18458466e-05 6.74306802e-06 1.09384559e-04 1.42150160e-04\n", + " 1.42150160e-04 1.40408338e-04 1.42152686e-04 1.42100004e-04\n", + " 1.10338395e-04 1.42150160e-04 1.35708109e-04 1.40457896e-04\n", + " 2.84176015e-04 1.10338395e-04 1.13109769e-04 1.42135966e-04\n", + " 1.40443872e-04 1.42135966e-04 1.08415914e-04 1.41970373e-04\n", + " 1.41970373e-04 1.42150160e-04 1.42135966e-04 1.41876722e-04\n", + " 1.40443872e-04 1.41970373e-04 1.41970373e-04 1.42100004e-04\n", + " 1.40187713e-04 1.38681140e-04 1.42152686e-04 1.42088007e-04\n", + " 1.42088007e-04 3.33379608e-06 1.40457896e-04 1.09062742e-04\n", + " 1.42044804e-04 1.41970373e-04 1.40351997e-04 1.42135966e-04\n", + " 1.34983475e-05 5.07688163e-06 1.41948762e-04 1.42135966e-04\n", + " 1.46118583e-04 1.40351997e-04 1.11700598e-04 2.82508342e-04\n", + " 1.08758919e-04 1.42135966e-04 1.44379076e-04 1.42044804e-04\n", + " 1.09384559e-04 6.73522782e-06 1.42088007e-04 1.41948762e-04\n", + " 1.10338395e-04 1.40351997e-04 1.42152686e-04 1.00238010e-04\n", + " 6.87420248e-06 1.01386199e-04 9.92274681e-05 1.41595801e-04\n", + " 6.76609559e-06 1.08415914e-04 1.11277293e-04 1.39579712e-04\n", + " 1.42044804e-04 1.43551267e-04 1.41850307e-04 1.40353795e-04\n", + " 1.09062742e-04 5.07302872e-06 1.41681874e-04 1.38479436e-04\n", + " 1.43247738e-04 1.08758919e-04 1.10734986e-04 1.41595801e-04\n", + " 1.41876722e-04 1.40280250e-04 1.42100004e-04 1.44379076e-04\n", + " 1.04826478e-04 1.42044804e-04 1.09754382e-04 1.41948762e-04\n", + " 5.19297884e-06 1.42088007e-04 2.84271933e-04 1.34653369e-04\n", 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1.41970373e-04\n", + " 1.07748733e-04 2.84200008e-04 1.40351997e-04 1.00238010e-04\n", + " 1.05684735e-04 1.41876722e-04 6.74265720e-06 1.42044804e-04\n", + " 1.42044804e-04 1.98360867e-04 9.44029523e-05 1.14506186e-04\n", + " 1.41341719e-04 1.42152686e-04 1.11700598e-04 1.42152686e-04\n", + " 9.44946899e-05 1.02622616e-04 1.41732649e-04 1.41681874e-04\n", + " 3.43710124e-06 1.42044804e-04 1.46118583e-04 9.56331801e-05\n", + " 4.18806779e-08 1.42150160e-04 1.13586325e-04 1.42100004e-04\n", + " 1.08025470e-04 2.82508342e-04 1.11700598e-04 1.15410544e-04\n", + " 1.40351997e-04 1.41850307e-04 1.11700598e-04 1.41876722e-04\n", + " 8.73992507e-05 1.40443872e-04 1.12651087e-04 1.08415914e-04\n", + " 1.41521862e-04 1.08415914e-04 2.55198059e-06 1.40351997e-04\n", + " 1.42152686e-04 9.68873120e-05 1.07432591e-04 1.37711511e-04\n", + " 1.43551267e-04 9.90303227e-05 2.72440482e-06 1.42088007e-04\n", + " 1.38681140e-04 1.42100004e-04 1.10338395e-04 1.09754382e-04\n", + " 1.40408338e-04 1.09062742e-04 1.43247738e-04 1.37894360e-04\n", + " 1.42027999e-04 2.75423021e-04 1.41732649e-04 1.40457896e-04\n", + " 1.00377474e-04 1.42152686e-04 1.35321912e-05 1.35190353e-04\n", + " 1.07432591e-04 1.42152686e-04 1.01386199e-04 1.43819166e-04\n", + " 1.40351997e-04 1.46118583e-04 1.46118583e-04 1.41876722e-04\n", + " 1.42152686e-04 1.00238010e-04 1.10338395e-04 1.42135966e-04\n", + " 1.41732649e-04 1.42135966e-04 1.00377474e-04 1.11277293e-04\n", + " 3.44735901e-06 1.14003098e-04 3.34500673e-06 1.47349147e-04\n", + " 1.42135966e-04 1.41970373e-04 2.40813898e-06 9.68504603e-05\n", + " 1.38362667e-04 1.40280250e-04 1.12651087e-04 9.32791077e-05\n", + " 1.46118583e-04 5.07628452e-06 1.40187713e-04 1.01586842e-04\n", + " 9.92274681e-05 1.46118583e-04 1.44570487e-04 1.42135966e-04\n", + " 9.31599438e-05 1.41876722e-04 1.07432591e-04 1.42135966e-04\n", + " 1.43065399e-04 1.42027999e-04 8.86220259e-05 1.42088007e-04\n", + " 1.40351997e-04 1.08758919e-04 1.46168027e-04 1.14003098e-04\n", 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1.41876722e-04\n", + " 6.76323805e-06 8.63202476e-05 1.14506186e-04 1.10734986e-04\n", + " 1.40443872e-04 1.40457896e-04 1.49017826e-04 1.13586325e-04\n", + " 1.08415914e-04 1.37711511e-04 1.38681140e-04 1.13586325e-04\n", + " 1.03563508e-04 1.42100004e-04 1.42135966e-04 5.06702579e-06\n", + " 1.12651087e-04 1.02481791e-04 1.35826334e-04 1.09384559e-04\n", + " 1.15410544e-04 1.05907195e-04 1.41970373e-04 1.41732649e-04\n", + " 1.01386199e-04 1.41521862e-04 1.02622616e-04 1.40351997e-04\n", + " 1.08415914e-04 5.07500015e-06 1.02622616e-04 1.12201126e-04\n", + " 1.42152686e-04 1.42135966e-04 2.77039818e-06 1.39641690e-06\n", + " 1.40408338e-04 1.40460392e-04 1.42044804e-04 1.42088007e-04\n", + " 1.40187713e-04 1.07432591e-04 1.09384559e-04 1.42044804e-04\n", + " 9.44946899e-05 1.03563508e-04 1.00238010e-04 1.09384559e-04\n", + " 1.40351997e-04 1.14003098e-04 1.40457896e-04 1.01386199e-04\n", + " 1.40351997e-04 8.37613559e-08 1.11700598e-04 1.40443872e-04\n", + " 9.90303227e-05 1.42152686e-04 1.42088007e-04 1.12201126e-04\n", + " 1.10734986e-04 1.42027999e-04 1.40408338e-04 1.41821734e-04\n", + " 1.42044804e-04 1.40353795e-04 1.41876722e-04 1.40114071e-04\n", + " 1.41876722e-04 1.43773126e-04 8.73992507e-05 1.09384559e-04\n", + " 1.15410544e-04 1.42044804e-04 8.86220259e-05 1.46118583e-04\n", + " 5.19661902e-06 1.40921151e-04 1.42044804e-04 9.20508969e-05\n", + " 1.40187713e-04 1.18458466e-05 1.40351997e-04 1.40351997e-04\n", + " 1.40457896e-04 1.41850307e-04 1.07432591e-04 5.07500015e-06\n", + " 9.44946899e-05 5.07679142e-06 1.41970373e-04 1.38681140e-04\n", + " 1.42150160e-04 1.12201126e-04 1.09384559e-04 1.41948762e-04\n", + " 1.38681140e-04 1.45229223e-04 9.68873120e-05 8.63202476e-05\n", + " 1.36685639e-04 1.41732649e-04 1.42152686e-04 9.90303227e-05\n", + " 1.42088007e-04]\n", + "names of tiles in bin ['1_3_8' '1_3_8' '1_11_5' '2_1_6' '1_11_8' '1_3_8' '1_11_5' '1_11_4'\n", + " '1_11_5' '1_11_5' '1_11_6' '1_3_8' '1_11_6' '1_4_9' '1_2_7' '2_1_7'\n", + " '1_2_7' '2_1_7' '1_4_8' '1_11_7' '1_11_7' '1_10_4' '1_3_8' '1_2_7'\n", + " '1_3_8' '1_3_8' '1_10_3' '1_2_8' '1_2_8' '1_3_8' '1_2_8' '1_4_8' '1_11_4'\n", + " '1_3_7' '1_11_5' '1_3_8' '2_2_2' '1_11_6' '3_2_0' '2_1_2' '1_10_4'\n", + " '1_2_7' '2_3_3' '1_4_8' '1_4_8' '1_3_8' '1_3_8' '1_10_5' '1_10_4' '2_1_7'\n", + " '1_2_8' '2_1_1' '2_2_2' '3_2_0' '2_2_2' '2_1_2' '2_3_5' '1_2_7' '2_3_8'\n", + " '1_2_7' '1_4_9' '1_3_8' '1_2_7' '1_3_8' '1_2_8' '1_1_7' '1_11_7' '1_2_7'\n", + " '1_11_5' '1_3_8' '1_10_3' '1_11_4' '1_3_8' '1_3_8' '1_3_8' '1_1_6'\n", + " '3_2_0' '2_1_2' '1_3_7' '2_1_2' '2_1_2' '1_2_8' '1_1_7' '1_11_4' '1_2_7'\n", + " '1_2_7' '1_3_8' '2_3_3' '1_2_8' '1_3_8' '1_3_7' '1_11_7' '1_2_7' '1_2_7'\n", + " '2_1_7' '2_3_3' '1_4_9' '2_1_6' '1_3_8' '1_11_4' '1_3_8' '1_2_7' '2_1_1'\n", + " '1_3_8' '1_3_8' '1_11_7' '1_2_8' '3_2_0' '1_10_8' '1_4_8' '1_3_8' '1_3_8'\n", + " '1_11_4' '1_3_8' '1_3_8' '1_3_8' '1_1_7' '2_2_2' '1_4_8' '1_3_8' '1_4_8'\n", + " '2_3_2' '1_11_5' '1_11_5' '3_3_0' '1_11_5' '1_3_8' '1_11_6' '1_11_6'\n", + " '1_11_4' '2_1_2' '2_1_1' '1_11_3' '1_11_6' '1_3_8' '3_3_0' '3_3_0'\n", + " '1_11_6' '1_2_8' '1_3_7' '2_2_2' '1_11_6' '1_3_7' '1_3_8' '2_1_6'\n", + " '1_11_5' '1_2_7' '1_10_3' '1_4_8' '1_11_5' '2_3_3' '1_3_8' '1_11_4'\n", + " '1_4_8' '3_3_0' '1_3_8' '2_3_3' '1_11_6' '1_2_8' '1_11_6' '1_2_8'\n", + " '1_11_5' '1_11_5' '1_11_4' '1_11_5' '1_10_4' '1_2_7' '2_3_2' '1_4_9'\n", + " '2_2_2' '1_11_5' '1_11_6' '1_11_4' '2_1_1' '2_2_2' '1_11_5' '2_1_7'\n", + " '1_3_8' '2_3_7' '1_4_8' '1_11_4' '1_3_8' '1_11_4' '1_3_8' '1_2_7'\n", + " '1_11_5' '2_1_2' '1_11_5' '2_2_8' '1_11_6' '1_4_8' '1_11_5' '1_11_5'\n", + " '1_2_7' '1_3_8' '1_11_6' '1_3_8' '2_1_7' '1_4_9' '1_4_8' '1_11_8'\n", + " '1_11_8' '1_4_8' '1_3_7' '1_2_8' '1_2_8' '2_1_2' '1_2_7' '1_1_6' '1_11_5'\n", + " '1_11_4' '2_3_2' '1_11_6' '1_11_5' '1_1_7' '1_11_5' '1_11_5' '1_11_7'\n", + " '1_2_7' '1_3_8' '2_2_8' '1_11_6' '1_2_7' '1_2_7' '1_11_4' '1_3_8' '3_3_0'\n", + " '2_1_2' '1_2_8' '1_11_6' '1_4_8' '1_2_8' '1_11_5' '2_3_4' '2_1_7'\n", + " '1_11_5' '1_3_8' '1_11_6' '1_2_8' '1_11_6' '1_11_5' '1_2_7' '2_1_2'\n", + " '1_3_8' '1_11_6' '1_2_7' '1_11_5' '1_11_6' '1_11_6' '1_11_5' '1_11_4'\n", + " '1_2_7' '2_1_7' '1_3_7' '1_11_5' '1_2_7' '1_11_5' '1_10_3' '1_11_5'\n", + " '1_11_6' '1_11_6' '1_2_7' '1_3_8' '1_3_7' '1_11_7' '2_2_2' '1_3_8'\n", + " '3_3_0' '1_11_5' '2_1_1' '1_11_5' '1_2_7' '1_3_8' '1_11_6' '1_11_6'\n", + " '1_3_8' '1_11_4' '2_3_8' '2_1_2' '1_11_5' '1_4_8' '1_3_8' '1_11_5'\n", + " '1_3_8' '1_3_8' '1_3_8' '1_11_5' '1_11_6' '3_2_0' '1_3_8' '1_11_4'\n", + " '1_4_9' '1_3_8' '1_3_7' '1_11_4' '1_2_7' '1_11_6' '1_4_8' '1_11_6'\n", + " '1_10_3' '1_11_5' '1_11_5' '1_4_8' '1_10_5' '2_3_8' '1_2_8' '1_11_5'\n", + " '1_3_8' '1_4_9' '1_11_6' '1_11_5' '1_2_7' '1_2_8' '1_2_7' '1_11_4'\n", + " '1_11_5' '1_1_7' '1_11_6' '1_2_8' '1_4_9' '1_4_8' '1_11_6' '1_1_6'\n", + " '1_11_5' '1_11_6' '2_1_1' '1_3_8' '1_4_8' '1_11_5' '1_11_6' '1_10_4'\n", + " '1_11_8' '1_10_7' '2_2_2' '1_11_6' '1_4_8' '1_11_6' '1_4_8' '1_11_5'\n", + " '1_4_8' '1_4_9' '1_11_5' '1_11_6' '1_11_5' '1_2_8' '1_2_7' '1_3_7'\n", + " '1_3_8' '2_1_2' '1_2_7' '1_3_8' '1_11_6' '1_4_8' '1_11_6' '1_3_8'\n", + " '1_11_5' '1_11_3' '2_3_3' '1_11_6' '1_3_8' '1_10_7' '1_2_7' '1_4_8'\n", + " '1_4_8' '1_2_7' '1_2_8' '1_11_5' '1_11_5' '1_11_6' '1_4_8' '1_3_8'\n", + " '1_4_8' '1_2_7' '1_2_7' '1_4_9' '1_2_8' '1_11_6' '1_4_8' '1_3_8' '3_3_0'\n", + " '2_3_2' '1_11_6' '1_3_8' '1_11_5' '1_2_7' '1_11_5' '1_2_7' '1_3_8'\n", + " '1_3_8' '1_3_8' '1_4_9' '1_4_9' '1_10_7' '1_2_8' '1_11_4' '1_11_5'\n", + " '1_2_8' '2_1_2' '1_2_7' '1_9_4' '1_3_8' '1_2_7' '1_11_4' '1_1_6' '1_11_5'\n", + " '1_11_4' '1_11_5' '1_10_7' '1_2_8' '1_2_7' '1_2_7' '1_11_6' '1_4_9'\n", + " '1_11_5' '1_3_8' '1_3_7' '1_11_4' '1_11_4' '1_10_5' '1_11_4' '1_3_8'\n", + " '1_11_4' '2_3_3' '1_11_5' '1_10_3' '1_11_5' '1_11_6' '1_11_5' '1_2_8'\n", + " '1_2_7' '2_3_8' '3_2_0' '1_3_8' '1_4_8' '1_4_9' '1_1_7' '1_4_9' '1_3_8'\n", + " '1_11_5' '1_11_6' '1_3_7' '1_11_7' '1_11_5' '1_10_8' '1_10_3' '1_4_9'\n", + " '1_3_8' '1_3_7' '1_11_4' '1_2_8' '2_2_2' '1_11_5' '1_2_7' '1_11_4'\n", + " '1_3_8' '1_11_5' '1_4_9' '1_11_5' '1_11_5' '1_11_6' '1_10_5' '1_2_8'\n", + " '1_4_9' '1_10_7' '3_3_0' '2_1_6' '1_2_7' '1_3_7' '2_3_8' '1_11_4' '1_3_8'\n", + " '1_4_8' '1_11_6' '1_2_8' '1_4_8' '1_11_6' '1_11_7' '1_11_7' '1_11_4'\n", + " '1_11_4' '1_4_8' '1_11_5' '2_3_2' '1_11_6' '1_10_5' '1_4_8' '1_4_8'\n", + " '1_11_5' '1_11_6' '1_11_5' '1_4_8' '1_11_6' '1_11_8' '1_11_5' '1_11_5'\n", + " '1_11_4' '1_11_4' '1_11_5' '1_11_4' '1_11_6' '1_11_5' '1_11_6' '1_11_5'\n", + " '1_10_5' '1_10_7' '2_3_2' '1_11_5' '1_3_8' '1_11_5' '1_11_4' '1_11_6'\n", + " '1_11_5' '1_10_8' '1_11_4' '1_10_7' '1_11_6' '3_3_0' '1_11_5' '1_4_8'\n", + " '1_11_4' '1_4_9' '1_11_4' '3_3_0' '1_11_5' '1_4_9' '1_10_3' '1_10_4'\n", + " '1_10_7' '1_4_8' '1_11_3' '1_11_5' '1_3_8' '2_1_2' '1_3_8' '1_2_7'\n", + " '1_10_4' '1_10_3' '1_4_8' '1_4_9' '2_3_8' '1_11_6' '2_1_2' '1_10_7'\n", + " '1_11_6' '1_3_8' '1_11_4' '2_1_2' '2_1_7' '2_1_1' '1_4_9' '1_4_9'\n", + " '1_10_3' '1_11_6' '1_11_6' '1_11_6' '3_3_0' '1_3_8' '1_11_6' '1_4_8'\n", + " '1_10_3' '2_1_5' '1_10_5' '1_11_4' '2_3_3' '1_11_5' '1_3_8' '2_1_6'\n", + " '1_11_6' '1_11_5' '2_1_1' '1_11_5' '1_11_6' '3_3_0' '1_10_7' '1_11_5'\n", + " '1_11_5' '1_4_8' '1_11_5' '1_11_4' '1_4_9' '1_4_8' '2_3_8' '1_11_8'\n", + " '1_10_7' '1_4_8' '1_11_7' '1_3_8' '1_11_5' '2_1_7' '1_3_8' '1_3_7'\n", + " '1_11_6' '3_3_0' '1_3_8' '1_4_9' '1_3_8' '1_11_5' '1_11_4' '1_10_3'\n", + " '1_11_4' '1_11_3' '1_3_8' '1_10_5' '2_3_6' '1_11_8' '1_3_7' '1_11_4'\n", + " '1_4_8' '1_11_5' '1_10_8' '1_2_7' '1_3_7' '1_4_8' '3_3_0' '1_4_8'\n", + " '1_10_5' '1_11_4' '1_11_5' '1_11_6' '1_4_8' '1_11_4' '3_3_0' '1_11_6'\n", + " '1_11_6' '1_3_8' '1_11_6' '1_11_6' '1_3_7' '1_3_8' '1_11_5' '1_10_3'\n", + " '1_11_4' '1_11_5' '2_3_8' '1_2_7' '1_4_8' '2_1_2' '1_11_3' '1_4_9'\n", + " '1_11_5' '1_2_8' '1_3_7' '1_10_7' '2_1_1' '2_1_1' '1_11_4' '3_3_0'\n", + " '1_2_7' '2_1_6' '1_11_6' '1_4_8' '1_11_4' '1_3_7' '1_11_6' '1_4_8'\n", + " '1_4_9' '1_11_3' '1_10_7' '1_4_8' '1_11_6' '1_1_7' '1_11_6' '1_4_8'\n", + " '1_4_8' '2_1_2' '1_4_9' '1_3_7' '1_11_6' '1_11_5' '1_2_7' '1_4_9' '1_9_4'\n", + " '2_1_2' '1_3_8' '1_4_9' '1_10_5' '1_11_5' '1_11_5' '1_4_8' '1_4_9'\n", + " '1_11_4' '1_3_7' '2_1_2' '2_1_7' '1_10_5' '1_9_4' '1_10_7' '1_11_6'\n", + " '1_2_8' '1_11_7' '1_10_5' '2_3_3' '1_4_8' '1_11_4' '1_3_8' '3_2_0'\n", + " '1_11_6' '3_3_0' '3_3_0' '1_11_6' '1_11_7' '1_3_8' '1_4_9' '1_11_4'\n", + " '1_10_7' '1_11_6' '1_2_7' '1_4_8' '2_1_1' '1_4_9' '2_3_2' '3_3_0'\n", + " '1_11_7' '1_11_4' '2_1_7' '1_2_8' '2_1_7' '1_11_4' '1_4_8' '1_2_9'\n", + " '3_3_0' '1_11_4' '1_11_7' '1_2_7' '1_2_7' '3_3_0' '3_2_0' '1_11_6'\n", + " '1_2_7' '1_11_6' '1_4_8' '1_11_5' '2_1_1' '1_10_5' '1_1_7' '1_10_3'\n", + " '1_11_6' '1_4_8' '3_3_0' '1_4_8' '1_2_7' '1_11_6' '1_11_4' '1_10_5'\n", + " '1_3_7' '1_4_8' '1_4_9' '1_3_7' '1_11_8' '1_11_6' '1_4_8' '1_4_9' '2_3_3'\n", + " '1_4_8' '1_11_3' '2_3_7' '1_10_5' '1_11_4' '1_11_7' '1_11_5' '1_11_4'\n", + " '1_11_4' '1_11_6' '1_10_7' '1_10_4' '1_11_6' '1_4_8' '1_11_5' '1_11_4'\n", + " '1_10_8' '1_11_5' '1_4_9' '1_2_8' '1_11_6' '1_11_4' '1_2_7' '1_2_7'\n", + " '2_1_2' '1_11_4' '1_10_6' '1_11_6' '1_2_7' '1_11_4' '1_11_7' '1_11_7'\n", + " '1_11_7' '1_2_9' '1_10_6' '1_4_8' '1_11_6' '1_3_8' '1_4_8' '1_11_5'\n", + " '1_4_8' '1_10_4' '1_3_8' '1_10_6' '1_2_7' '1_9_4' '1_11_6' '1_10_3'\n", + " '1_1_6' '1_4_9' '1_4_8' '1_11_3' '1_11_4' '3_3_0' '1_4_8' '1_4_8' '1_9_4'\n", + " '1_11_5' '1_4_8' '1_3_8' '1_11_4' '1_11_4' '1_11_4' '1_4_9' '1_3_7'\n", + " '1_9_4' '1_4_8' '1_4_9' '1_3_7' '1_11_7' '1_10_7' '1_3_8' '2_1_2' '1_3_8'\n", + " '1_11_7' '1_4_8' '1_11_4' '1_3_8' '1_4_8' '1_11_6' '1_11_3' '1_4_9'\n", + " '1_4_8' '1_11_6' '1_11_6' '1_11_7' '1_10_8' '1_11_4' '1_4_8' '1_4_8'\n", + " '1_11_4' '1_2_8' '1_3_8' '1_3_8' '1_4_8' '1_11_5' '1_4_8' '1_11_7'\n", + " '1_3_8' '1_11_7' '2_1_2' '1_4_9' '1_11_4' '1_3_8' '1_11_5' '1_10_6'\n", + " '1_4_8' '1_4_8' '1_10_5' '1_11_4' '2_1_2' '1_11_6' '1_11_6' '1_11_7'\n", + " '2_2_2' '1_11_8' '2_1_3' '1_1_7' '1_4_8' '1_4_8' '1_11_6' '1_1_6' '3_3_0'\n", + " '2_1_2' '2_1_1' '1_11_4' '1_2_7' '1_11_7' '1_11_4' '1_11_7' '1_11_3'\n", + " '1_11_3' '1_10_5' '1_4_8' '1_11_3' '1_2_9' '1_11_6' '1_11_6' '1_11_7'\n", + " '1_11_6' '1_4_8' '1_4_9' '1_10_5' '1_11_6' '3_2_0' '1_2_7' '1_1_6'\n", + " '2_3_2' '1_10_4' '1_11_3' '1_3_7' '1_10_6']\n", + "dowsampled rms bin 6\n", + "areas of tiles in bin [1.11277293e-04 1.35190353e-04 1.06723959e-04 1.40353795e-04\n", + " 1.41876722e-04 2.70085331e-06 1.41681874e-04 1.41521862e-04\n", + " 9.31599438e-05 1.40863925e-04 1.40353795e-04 1.38479436e-04\n", + " 1.42100004e-04 9.92274681e-05 1.11277293e-04 1.42100004e-04\n", + " 1.42088007e-04 1.42152686e-04 1.42150160e-04 1.42100004e-04\n", + " 1.42044804e-04 1.40187713e-04 9.80640283e-05 1.97693742e-04\n", + " 1.01586842e-04 1.41681874e-04 9.68873120e-05 1.42088007e-04\n", + " 1.42100004e-04 1.42027999e-04 1.42044804e-04 8.73992507e-05\n", + " 1.42088007e-04 1.40187713e-04 1.37503202e-04 1.36206561e-04\n", + " 1.40353795e-04 1.10338395e-04 1.42044804e-04 2.74758749e-06\n", + " 7.07992460e-05 3.49394315e-06 1.09384559e-04 1.42119479e-04\n", + " 1.13109769e-04 1.10338395e-04 1.42152686e-04 2.57767647e-06\n", + " 1.09384559e-04 1.41876722e-04 5.07679142e-06 9.56974786e-05\n", + " 1.40457896e-04 1.12201126e-04 1.18460571e-05 9.56331801e-05\n", + " 1.35190353e-04 1.49526448e-04 1.41439784e-04 1.41876722e-04\n", + " 5.01257132e-06 2.81489132e-06 9.56974786e-05 1.44911456e-04\n", + " 1.42100004e-04 1.40351997e-04 1.42088007e-04 1.41850307e-04\n", + " 3.46441268e-06 1.42384017e-04 1.41595801e-04 1.37503202e-04\n", + " 1.41834725e-04 1.48485344e-04 1.42027999e-04 1.05907195e-04\n", + " 1.42088007e-04 9.32791077e-05 1.40353795e-04 9.90303227e-05\n", + " 9.68504603e-05 5.01257132e-06 1.39795985e-04 1.15410544e-04\n", + " 8.73992507e-05 1.04631204e-04 1.37503202e-04 1.42027999e-04\n", + " 1.00377474e-04 1.41264619e-04 1.42088007e-04 1.14003098e-04\n", + " 1.43409702e-04 1.35321912e-05 1.40460392e-04 1.10338395e-04\n", + " 1.02481791e-04 1.10734986e-04 1.41876722e-04 1.12651087e-04\n", + " 1.37503202e-04 1.00238010e-04 1.08758919e-04 1.07432591e-04\n", + " 1.07432591e-04 1.04826478e-04 1.42088007e-04 1.42135966e-04\n", + " 1.41876722e-04 1.11277293e-04 1.09754382e-04 1.42088007e-04\n", + " 1.18416670e-05 9.56974786e-05 1.40460392e-04 3.28422852e-06\n", + " 1.42150160e-04 5.07679142e-06 9.56331801e-05 1.35190353e-04\n", + " 9.92274681e-05 1.41948762e-04 1.40351997e-04 1.40460392e-04\n", + " 9.90303227e-05 1.42044804e-04 9.68873120e-05 9.80640283e-05\n", + " 1.40443872e-04 5.07037047e-06 1.02481791e-04 1.00377474e-04\n", + " 1.04631204e-04 3.45894501e-06 1.08415914e-04 1.41732649e-04\n", + " 3.50831301e-06 1.35190353e-04 1.04826478e-04 1.41732649e-04\n", + " 1.01386199e-04 1.42088007e-04 9.31599438e-05 1.42150160e-04\n", + " 1.42150160e-04 1.42044804e-04 1.42088007e-04 1.02622616e-04\n", + " 8.86220259e-05 1.10734986e-04 1.12651087e-04 1.40408338e-04\n", + " 2.09262409e-04 9.32791077e-05 5.19661902e-06 1.36032102e-04\n", + " 9.44946899e-05 1.42150160e-04 1.14003098e-04 1.12201126e-04\n", + " 1.15410544e-04 1.12651087e-04 5.06702579e-06 1.41732649e-04\n", + " 1.40351997e-04 1.42040920e-04 1.42152686e-04 8.63202476e-05\n", + " 1.42040920e-04 1.42135966e-04 1.33541691e-04 1.34853144e-05\n", + " 8.73992507e-05 9.92274681e-05 9.80640283e-05 1.41850307e-04\n", + " 1.40280250e-04 1.10338395e-04 1.40351997e-04 8.73992507e-05\n", + " 1.36032102e-04 9.92274681e-05 1.14003098e-04 1.41850307e-04\n", + " 1.06723959e-04 9.80640283e-05 2.84055998e-04 5.07679142e-06\n", + " 1.41850307e-04 1.43773126e-04 1.34653369e-04 1.11277293e-04\n", + " 8.73992507e-05 1.18308644e-05 9.56974786e-05 1.08025470e-04\n", + " 1.18230602e-05 1.42044804e-04 9.68873120e-05 1.07432591e-04\n", + " 1.42027999e-04 1.42027999e-04 1.41595801e-04 1.10338395e-04\n", + " 1.40353795e-04 1.41264619e-04 9.80640283e-05 1.38681140e-04\n", + " 1.42027999e-04 2.52593922e-06 1.42027999e-04 8.73992507e-05\n", + " 1.11277293e-04 1.41439784e-04 1.42027999e-04 1.00377474e-04\n", + " 1.42040920e-04 1.42088007e-04 1.06973574e-04 8.86220259e-05\n", + " 8.86220259e-05 1.41821734e-04 9.32791077e-05 1.35190353e-04\n", + " 1.38681140e-04 5.20604676e-06 1.18446639e-05 1.41948762e-04\n", + " 1.42027999e-04 1.42150160e-04 1.37503202e-04 1.09754382e-04\n", + " 1.41850307e-04 1.41876722e-04 1.42088007e-04 5.21392696e-06\n", + " 5.07628452e-06 8.73992507e-05 1.42027999e-04 1.41850307e-04\n", + " 4.18806779e-08 1.12201126e-04 1.42040920e-04 1.36032102e-04\n", + " 1.43773126e-04 1.41941422e-04 1.41439784e-04 9.44029523e-05\n", + " 1.35826334e-04 1.42044804e-04 1.41876722e-04 6.74917374e-06\n", + " 9.90303227e-05 1.01386199e-04 1.42152686e-04 1.38519909e-06\n", + " 1.11700598e-04 1.40457896e-04 3.46441268e-06 1.16278284e-05\n", + " 1.44762936e-04 1.40443872e-04 1.01386199e-04 1.42088007e-04\n", + " 1.42027999e-04 1.38479436e-04 1.42152686e-04 9.80546290e-05\n", + " 9.20508969e-05 1.09062742e-04 1.41948762e-04 8.75279269e-05\n", + " 1.42027999e-04 1.13109769e-04 1.41595801e-04 1.41876722e-04\n", + " 1.41948762e-04 1.08025470e-04 1.42027999e-04 1.33846808e-06\n", + " 1.40460392e-04 1.42150160e-04 1.41439784e-04 9.44946899e-05\n", + " 9.68873120e-05 1.41850307e-04 1.05684735e-04 1.05684735e-04\n", + " 1.41439784e-04 1.41595801e-04 1.42040920e-04 9.92455226e-05\n", + " 1.37894360e-04 8.63202476e-05 1.03563508e-04 9.31599438e-05\n", + " 5.07628452e-06 1.55620199e-04 1.40280250e-04 1.03731569e-04\n", + " 1.09754382e-04 9.92274681e-05 1.42135966e-04 9.20508969e-05\n", + " 1.35189297e-05 2.49955587e-06 1.41876722e-04 1.41732649e-04\n", + " 9.20508969e-05 1.06723959e-04 5.07688163e-06 1.42088007e-04\n", + " 1.39954046e-04 9.92274681e-05 1.41948762e-04 1.08415914e-04\n", + " 1.42100004e-04 1.08025470e-04 1.06973574e-04 1.13109769e-04\n", + " 1.49017826e-04 1.41595801e-04 1.42152686e-04 1.36032102e-04\n", + " 1.41529797e-04 1.42027999e-04 1.43408933e-04 9.92274681e-05\n", + " 1.43773126e-04 1.42088007e-04 3.62810149e-06 1.41595801e-04\n", + " 1.41732649e-04 1.12201126e-04 1.74798501e-04 5.07688163e-06\n", + " 7.09428214e-05 1.42088007e-04 1.41521862e-04 1.41850307e-04\n", + " 1.02481791e-04 5.07302872e-06 9.80640283e-05 3.36107511e-06\n", + " 1.42027999e-04 1.37145276e-04 9.20508969e-05 1.42044804e-04\n", + " 1.38070109e-04 1.09384559e-04 1.37503202e-04 1.35826334e-04\n", + " 1.09384559e-04 1.40460392e-04 1.41595801e-04 1.41595801e-04\n", + " 1.41732649e-04 9.44029523e-05 1.35264761e-05 1.41821734e-04\n", + " 1.44911456e-04 9.90303227e-05 1.46745612e-04 8.73992507e-05\n", + " 1.37711511e-04 1.41439784e-04 1.41341719e-04 1.07748733e-04\n", + " 1.05684735e-04 9.56974786e-05 1.14506186e-04 8.73992507e-05\n", + " 1.00377474e-04 2.83897524e-04 1.71710780e-06 1.35095531e-05\n", + " 1.36032102e-04 1.34983475e-05 1.42027999e-04 1.41850307e-04\n", + " 1.02481791e-04 1.14003098e-04 1.40353795e-04 1.41595801e-04\n", + " 1.32632829e-06 6.97284208e-06 1.55554475e-04 1.37585405e-04\n", + " 9.32791077e-05 1.41948762e-04 1.14003098e-04 1.41948762e-04\n", + " 8.73992507e-05 1.00422980e-04 1.06723959e-04 1.41264619e-04\n", + " 1.42862710e-04 1.01586842e-04 1.15410544e-04 9.44946899e-05\n", + " 9.68504603e-05 9.90303227e-05 1.41732649e-04 1.37711511e-04\n", + " 6.76609559e-06 1.18416670e-05 1.42027999e-04 9.56974786e-05\n", + " 1.03563508e-04 1.38070109e-04 1.41948762e-04 9.56974786e-05\n", + " 1.35708109e-04 2.82879569e-04 9.32791077e-05 1.40187713e-04\n", + " 1.41850307e-04 1.42027999e-04 1.37711511e-04 6.75477654e-06\n", + " 1.42027999e-04 1.41948762e-04 1.41732649e-04 5.06702579e-06\n", + " 1.41948762e-04 1.41948762e-04 1.41732649e-04 9.92274681e-05\n", + " 9.32791077e-05 1.08758919e-04 9.44946899e-05 1.41948762e-04\n", + " 1.41850307e-04 1.41850307e-04 1.41439784e-04 8.63202476e-05\n", + " 1.42152686e-04 8.75279269e-05 1.42040920e-04 8.73992507e-05\n", + " 1.02622616e-04 1.41850307e-04 6.92882536e-06 1.13586325e-04\n", + " 6.92882536e-06 1.05684735e-04 1.01386199e-04 1.37503202e-04\n", + " 4.83074745e-06 1.14506186e-04 1.39484767e-04 1.42027999e-04\n", + " 1.41732649e-04 1.34537732e-05 1.41876722e-04 9.92274681e-05\n", + " 1.42027999e-04 1.34097232e-04 1.41439784e-04 1.41264619e-04\n", + " 1.07748733e-04 9.68873120e-05 1.04631204e-04 1.42027999e-04\n", + " 1.43773126e-04 3.35574159e-06 1.01386199e-04 1.43408933e-04\n", + " 1.42027999e-04 6.00861884e-06 1.47349147e-04 5.07302872e-06\n", + " 1.41264619e-04 1.10734986e-04 1.42040920e-04 1.07748733e-04\n", + " 1.02481791e-04 1.41264619e-04 1.42100004e-04 5.25603574e-06\n", + " 1.41948762e-04 9.20508969e-05 1.47746372e-04 1.37276278e-04\n", + " 1.14003098e-04 1.12201126e-04 9.20508969e-05 9.92455226e-05\n", + " 1.97633321e-04 1.41948762e-04 3.54448295e-06 5.05187844e-06\n", + " 1.42027999e-04 1.42040920e-04 1.41264619e-04 1.41948762e-04\n", + " 1.42040920e-04 8.73992507e-05 1.05907195e-04 1.37503202e-04\n", + " 9.44946899e-05 1.43551267e-04 1.34853144e-05 1.43773126e-04\n", + " 1.42040920e-04 2.57767647e-06 1.01386199e-04 1.41948762e-04\n", + " 1.35095531e-05 9.44946899e-05 1.42044804e-04 1.41732649e-04\n", + " 1.15410544e-04 1.42135966e-04 1.06723959e-04 5.07500015e-06\n", + " 1.41264619e-04 5.03822600e-06 1.34097232e-04 1.09062742e-04\n", + " 1.41439784e-04 1.41821734e-04 1.41595801e-04 2.83700615e-04\n", + " 1.11277293e-04 1.04826478e-04 1.46118583e-04 9.56331801e-05\n", + " 1.41264619e-04 1.41732649e-04 1.00377474e-04 1.42040920e-04\n", + " 1.06973574e-04 8.63202476e-05 1.12201126e-04 1.41439784e-04\n", + " 1.12201126e-04 1.00377474e-04 1.14003098e-04 1.41732649e-04\n", + " 1.11700598e-04 1.41850307e-04 1.07748733e-04 1.41821734e-04\n", + " 1.03563508e-04 1.05907195e-04 1.41439784e-04 1.12201126e-04\n", + " 1.42040920e-04 1.42040920e-04 1.41595801e-04 6.73522782e-06\n", + " 6.72688660e-06 1.41850307e-04 1.40092201e-04 6.74265720e-06\n", + " 1.41439784e-04 1.11277293e-04 1.42027999e-04 5.10396119e-06\n", + " 1.08415914e-04 1.41732649e-04 8.63202476e-05 1.42027999e-04\n", + " 1.14003098e-04 1.42088007e-04 1.43773126e-04 1.43773126e-04\n", + " 1.42044804e-04 1.12651087e-04 1.03563508e-04 1.41970373e-04\n", + " 1.41264619e-04 1.41264619e-04 3.45175272e-06 6.76609559e-06\n", + " 9.32791077e-05 5.05187844e-06 1.42027999e-04 1.14003098e-04\n", + " 1.41948762e-04 1.41439784e-04 1.41732649e-04 1.41732649e-04\n", + " 1.41264619e-04 1.42150160e-04 6.75946486e-06 1.35826334e-04\n", + " 1.41850307e-04 1.18370670e-05 1.02481791e-04 1.43773126e-04\n", + " 9.44029523e-05 1.34097232e-04 1.43773126e-04 1.41595801e-04\n", + " 1.42040920e-04 1.02481791e-04 9.90303227e-05 6.75477654e-06\n", + " 2.22427348e-04 1.44206371e-04]\n", + "names of tiles in bin ['1_4_8' '2_3_3' '1_3_8' '1_11_4' '1_11_7' '1_4_9' '2_1_2' '2_1_2' '1_2_8'\n", + " '2_2_2' '1_11_4' '2_1_7' '1_11_4' '1_2_8' '1_4_8' '1_11_6' '1_10_6'\n", + " '1_11_6' '1_11_5' '1_11_4' '1_11_6' '1_11_6' '1_2_8' '2_3_8' '1_2_7'\n", + " '2_1_2' '1_2_7' '1_10_6' '1_11_3' '1_10_4' '1_11_4' '1_1_6' '1_10_5'\n", + " '1_11_7' '1_9_4' '2_3_2' '1_11_4' '1_4_8' '1_11_4' '1_4_9' '3_2_0'\n", + " '3_3_1' '1_4_9' '2_2_2' '1_4_9' '1_4_9' '1_11_6' '1_3_7' '1_4_8' '1_11_5'\n", + " '1_11_5' '1_2_7' '1_11_5' '1_4_8' '1_11_4' '1_2_7' '2_3_3' '3_3_0'\n", + " '1_10_3' '1_11_6' '1_11_6' '1_4_9' '1_2_7' '3_2_0' '1_11_4' '1_11_6'\n", + " '1_10_5' '1_10_3' '2_1_3' '2_2_2' '1_10_7' '1_9_4' '2_2_2' '3_3_0'\n", + " '1_10_5' '1_3_9' '1_10_7' '1_2_9' '1_11_8' '1_3_8' '1_2_7' '1_11_3'\n", + " '2_1_7' '1_4_8' '1_1_6' '1_3_8' '1_9_4' '1_10_7' '1_2_9' '1_10_3'\n", + " '1_10_6' '1_4_8' '2_1_2' '1_10_7' '1_11_3' '1_4_8' '1_3_8' '1_4_8'\n", + " '1_11_5' '1_4_8' '1_9_5' '1_3_9' '1_4_8' '1_4_9' '1_4_8' '1_3_8' '1_10_7'\n", + " '1_11_6' '1_11_5' '1_4_8' '1_4_8' '1_10_6' '1_11_4' '1_2_8' '1_11_6'\n", + " '2_3_7' '1_11_7' '1_11_3' '1_2_9' '2_3_3' '1_2_7' '1_10_5' '1_11_6'\n", + " '1_11_6' '1_3_7' '1_11_4' '1_2_7' '1_2_9' '1_11_8' '1_11_6' '1_3_8'\n", + " '1_2_9' '1_3_7' '1_2_7' '1_4_9' '1_10_7' '3_3_1' '2_3_3' '1_3_7' '1_10_5'\n", + " '1_3_7' '1_10_6' '1_2_8' '1_11_3' '1_11_5' '1_11_4' '1_10_6' '1_3_8'\n", + " '1_1_6' '1_4_9' '1_4_8' '1_11_8' '1_3_9' '1_2_9' '2_1_3' '1_9_4' '1_2_7'\n", + " '1_11_7' '1_4_8' '1_4_10' '1_4_8' '1_4_9' '1_11_6' '1_10_8' '1_11_6'\n", + " '2_1_2' '1_11_7' '1_1_7' '2_1_3' '1_11_6' '2_3_3' '1_10_8' '1_1_6'\n", + " '1_2_8' '1_2_7' '1_10_4' '1_11_4' '1_4_9' '1_11_7' '1_1_6' '1_9_4'\n", + " '1_2_7' '1_4_9' '1_10_5' '1_3_7' '1_2_9' '1_10_6' '1_11_4' '1_10_8'\n", + " '2_1_5' '2_3_3' '1_4_8' '1_1_6' '1_11_7' '1_2_8' '1_3_8' '1_11_7'\n", + " '1_11_6' '1_2_9' '1_4_9' '1_10_5' '1_10_6' '1_10_7' '1_4_9' '1_11_4'\n", + " '1_10_4' '1_2_7' '1_11_6' '1_10_5' '1_3_7' '1_10_3' '1_1_6' '1_4_9'\n", + " '1_10_7' '1_10_8' '1_2_7' '2_1_3' '1_10_7' '1_3_9' '1_1_8' '1_1_7'\n", + " '2_1_2' '1_2_7' '2_3_2' '1_11_8' '1_3_9' '1_11_7' '1_10_5' '1_10_5'\n", + " '1_11_7' '1_9_4' '1_4_8' '1_10_6' '1_11_7' '1_10_5' '2_2_2' '1_11_3'\n", + " '1_1_6' '1_10_5' '1_10_7' '2_1_1' '1_4_9' '2_1_5' '1_9_4' '2_1_3' '2_1_2'\n", + " '1_10_4' '1_2_9' '1_9_5' '1_11_6' '1_11_8' '1_10_7' '1_3_9' '1_3_9'\n", + " '1_11_7' '1_4_8' '1_4_9' '1_11_6' '2_1_4' '3_2_0' '3_2_0' '1_11_7'\n", + " '1_3_9' '1_10_5' '1_10_6' '2_1_7' '1_11_4' '1_2_9' '1_2_9' '1_3_8'\n", + " '1_10_5' '1_1_7' '1_10_6' '1_4_8' '1_10_7' '1_11_7' '1_10_7' '1_3_7'\n", + " '1_10_5' '1_4_8' '1_11_7' '1_11_6' '1_10_7' '1_2_9' '1_2_9' '1_10_5'\n", + " '1_3_9' '1_3_7' '1_10_5' '1_10_7' '2_1_3' '1_2_7' '2_1_7' '1_1_7' '1_3_9'\n", + " '1_2_9' '1_11_6' '1_10_6' '1_11_8' '1_3_8' '1_4_8' '1_2_9' '1_11_7'\n", + " '1_2_8' '1_10_6' '1_3_7' '1_11_7' '1_10_5' '1_2_9' '1_3_8' '1_11_5'\n", + " '1_10_6' '2_1_7' '1_2_9' '1_10_5' '1_4_10' '1_11_7' '1_3_9' '1_3_9'\n", + " '1_4_8' '3_3_0' '1_10_5' '1_11_3' '1_9_4' '2_2_2' '1_10_6' '3_2_0'\n", + " '1_2_9' '2_1_4' '1_10_6' '1_2_7' '1_10_7' '1_10_4' '1_4_10' '1_1_7'\n", + " '1_11_3' '3_2_0' '1_10_6' '2_1_1' '1_10_5' '1_3_7' '1_11_3' '1_2_9'\n", + " '2_3_2' '1_10_5' '2_3_2' '1_2_8' '1_11_4' '2_1_7' '1_4_10' '1_9_5'\n", + " '1_9_4' '1_4_9' '1_11_4' '1_10_5' '1_10_7' '1_10_7' '1_2_8' '1_10_5'\n", + " '2_1_1' '3_2_0' '1_3_9' '3_3_0' '1_1_6' '1_9_5' '1_10_5' '2_1_1' '1_3_9'\n", + " '1_3_9' '1_2_9' '1_4_9' '1_1_6' '1_2_9' '1_10_5' '2_1_1' '1_10_5' '1_9_5'\n", + " '1_10_5' '1_10_5' '1_10_5' '1_3_9' '1_4_8' '1_11_6' '1_10_4' '1_4_8'\n", + " '2_2_8' '1_10_6' '2_3_2' '1_2_8' '1_10_5' '1_4_8' '1_10_6' '1_1_6'\n", + " '1_2_7' '1_3_7' '1_10_5' '2_1_1' '1_2_9' '1_4_8' '1_2_9' '1_2_9' '1_3_9'\n", + " '1_10_7' '1_9_5' '1_10_5' '1_11_7' '1_10_4' '1_2_7' '1_3_9' '2_1_7'\n", + " '1_10_5' '1_2_9' '2_3_2' '1_10_5' '1_2_9' '1_11_8' '1_10_6' '1_10_6'\n", + " '1_9_4' '1_10_4' '1_10_5' '1_10_5' '1_10_5' '1_11_5' '1_10_6' '1_10_5'\n", + " '1_10_6' '1_2_9' '1_2_9' '1_4_8' '1_2_8' '1_10_6' '1_10_5' '1_10_8'\n", + " '1_10_5' '1_1_6' '1_11_6' '1_1_8' '2_1_5' '1_1_7' '1_3_9' '1_10_5'\n", + " '2_1_4' '1_4_10' '2_1_5' '1_3_9' '1_3_7' '1_9_4' '1_3_10' '1_4_8' '2_2_2'\n", + " '1_10_6' '1_10_7' '1_10_5' '1_11_5' '1_2_9' '1_10_5' '2_3_4' '1_10_5'\n", + " '1_10_5' '1_3_8' '1_2_7' '1_3_9' '1_10_6' '2_1_4' '2_3_2' '1_3_9' '3_2_0'\n", + " '1_10_6' '3_1_0' '3_3_0' '1_11_6' '1_10_5' '1_4_8' '2_1_4' '1_3_7'\n", + " '1_3_9' '1_10_7' '1_11_3' '1_3_9' '1_10_6' '1_2_8' '3_3_0' '1_9_4'\n", + " '1_4_9' '1_4_9' '1_2_9' '1_2_9' '2_1_7' '1_10_6' '1_2_7' '1_3_10'\n", + " '1_10_6' '2_1_4' '1_10_5' '1_10_6' '2_1_4' '1_1_6' '1_3_9' '1_9_4'\n", + " '1_2_7' '2_1_2' '1_10_5' '2_1_3' '2_1_4' '1_3_8' '1_3_10' '1_10_6'\n", + " '1_10_6' '1_2_9' '1_11_3' '1_10_5' '1_4_8' '1_11_3' '1_3_9' '1_11_6'\n", + " '1_10_5' '1_9_5' '2_3_4' '1_3_9' '1_10_6' '2_1_2' '1_10_8' '1_10_5'\n", + " '1_4_10' '1_3_9' '3_3_0' '1_2_8' '1_10_5' '1_10_5' '1_2_9' '2_1_4'\n", + " '1_3_9' '1_1_7' '1_4_10' '1_10_5' '1_4_9' '1_2_8' '1_4_9' '1_10_4'\n", + " '1_4_8' '1_10_5' '1_3_9' '2_1_2' '1_3_7' '1_3_8' '1_10_5' '1_4_8' '2_1_3'\n", + " '2_1_5' '1_10_5' '1_10_4' '1_10_4' '1_10_4' '2_1_7' '1_10_7' '1_10_5'\n", + " '1_4_10' '1_10_7' '1_3_10' '1_4_9' '1_10_5' '1_1_6' '1_10_6' '1_4_8'\n", + " '1_10_5' '2_1_4' '2_1_6' '1_11_8' '1_4_10' '1_3_9' '1_11_8' '1_10_5'\n", + " '1_10_4' '2_1_1' '1_10_6' '1_2_8' '1_3_9' '1_10_6' '1_4_10' '1_10_6'\n", + " '1_10_7' '1_10_5' '1_10_6' '1_10_5' '1_11_3' '1_10_5' '1_9_5' '1_10_6'\n", + " '1_11_4' '1_3_9' '2_1_4' '1_2_9' '2_3_5' '2_1_3' '1_10_5' '2_1_6'\n", + " '1_3_10' '1_3_10' '1_10_5' '3_1_0' '3_2_0']\n", + "dowsampled rms bin 7\n", + "areas of tiles in bin [1.07432591e-04 1.00422980e-04 1.41876722e-04 1.41595801e-04\n", + " 1.41948762e-04 9.32791077e-05 1.09062742e-04 1.13109769e-04\n", + " 1.35190353e-04 1.18370670e-05 1.36032102e-04 6.76609559e-06\n", + " 1.35264761e-05 6.74917374e-06 1.13109769e-04 1.41850307e-04\n", + " 8.75279269e-05 1.34097232e-04 1.42040920e-04 1.07748733e-04\n", + " 1.14003098e-04 1.41264619e-04 1.41948762e-04 1.42088007e-04\n", + " 1.41595801e-04 2.83700615e-04 1.42040920e-04 1.34097232e-04\n", + " 1.41948762e-04 1.37379374e-06 1.55467692e-04 1.43773126e-04\n", + " 5.15535295e-06 1.41732649e-04 1.15410544e-04 1.41850307e-04\n", + " 1.41439784e-04 1.00422980e-04 9.92274681e-05 1.42027999e-04\n", + " 5.10396119e-06 1.41595801e-04 1.34704556e-05 1.37276278e-04\n", + " 1.14003098e-04 1.36220241e-06 4.90599630e-06 1.41264619e-04\n", + " 1.07432591e-04 1.47677174e-04 1.35826334e-04 8.63202476e-05\n", + " 6.74265720e-06 1.09754382e-04 9.32791077e-05 1.41850307e-04\n", + " 1.41595801e-04 1.09409909e-06 1.41850307e-04 9.56974786e-05\n", + " 1.41264619e-04 1.47929088e-04 1.00238010e-04 1.36685639e-04\n", + " 1.41264619e-04 1.41681874e-04 8.19774417e-08 1.41681874e-04\n", + " 1.41850307e-04 1.06723959e-04 4.94566824e-06 1.41264619e-04\n", + " 1.41439784e-04 1.41732649e-04 1.42152686e-04 1.35042665e-06\n", + " 1.35602177e-04 1.41595801e-04 1.02622616e-04 1.00377474e-04\n", + " 9.80640283e-05 9.68504603e-05 9.92274681e-05 9.68504603e-05\n", + " 1.38226219e-04 1.41732649e-04 1.03563508e-04 1.41595801e-04\n", + " 1.42027999e-04 1.41264619e-04 9.80546290e-05 1.04631204e-04\n", + " 1.49526448e-04 1.41439784e-04 1.00238010e-04 1.38070109e-04\n", + " 9.44946899e-05 1.41850307e-04 1.36032102e-04 1.42040920e-04\n", + " 1.41850307e-04 1.41732649e-04 1.41941422e-04 6.76323805e-06\n", + " 2.83700615e-04 1.41521862e-04 1.42040920e-04 1.41595801e-04\n", + " 4.99911174e-06 9.80640283e-05 1.37503202e-04 1.42027999e-04\n", + " 1.41850307e-04 1.41439784e-04 1.42088007e-04 1.08758919e-04\n", + " 1.42027999e-04 1.42040920e-04 1.41595801e-04 1.43672415e-04\n", + " 1.42040920e-04 1.42639701e-04 9.56974786e-05 1.41595801e-04\n", + " 1.06973574e-04 1.37711511e-04 1.11277293e-04 1.43773126e-04\n", + " 1.41595801e-04 1.07432591e-04 1.14003098e-04 1.41595801e-04\n", + " 1.10734986e-04 1.35602177e-04 1.34853144e-05 1.34097232e-04\n", + " 1.42040920e-04 1.41439784e-04 1.42040920e-04 2.83465297e-04\n", + " 1.41732649e-04 1.43773126e-04 1.07432591e-04 9.68873120e-05\n", + " 1.34097232e-04 1.41948762e-04 9.80640283e-05 1.08415914e-04\n", + " 1.42040920e-04 1.41595801e-04 1.43773126e-04 9.56974786e-05\n", + " 1.06973574e-04 1.42119479e-04 9.44946899e-05 1.18706860e-05\n", + " 1.34097232e-04 1.41439784e-04 1.41439784e-04 1.36032102e-04\n", + " 1.34097232e-04 1.41732649e-04 1.37711511e-04 1.42040920e-04\n", + " 1.06723959e-04 9.20508969e-05 1.47677174e-04 1.08025470e-04\n", + " 1.41732649e-04 1.36032102e-04 9.20508969e-05 1.43672415e-04\n", + " 1.03731569e-04 1.43840834e-04 1.03563508e-04 8.75279269e-05\n", + " 1.40351997e-04 1.42040920e-04 1.01586842e-04 1.14003098e-04\n", + " 1.10338395e-04 6.76323805e-06 1.37276278e-04 1.15410544e-04\n", + " 1.41948762e-04 9.92274681e-05 1.02622616e-04 1.38479436e-04\n", + " 9.90303227e-05 1.45523736e-04 1.15410544e-04 1.34097232e-04\n", + " 2.55198059e-06 1.07748733e-04 1.45229223e-04 1.08415914e-04\n", + " 1.13586325e-04 9.32791077e-05 1.41264619e-04 9.68873120e-05\n", + " 1.05684735e-04 1.39709222e-04 1.00422980e-04 1.07432591e-04\n", + " 1.45523736e-04 1.34097232e-04 1.37711511e-04 8.63202476e-05\n", + " 1.41948762e-04 1.41850307e-04 9.68873120e-05 1.11700598e-04\n", + " 1.42040920e-04 1.41948762e-04 1.41595801e-04 1.41595801e-04\n", + " 9.92455226e-05 1.01386199e-04 1.14506186e-04 1.42040920e-04\n", + " 1.41264619e-04 1.36032102e-04 1.41948762e-04 1.41595801e-04\n", + " 1.04631204e-04 1.41439784e-04 1.13109769e-04 9.68873120e-05\n", + " 1.37711511e-04 1.43551267e-04 1.40408338e-04 1.34653369e-04\n", + " 1.18706860e-05 1.34983475e-05 6.75946486e-06 1.37711511e-04\n", + " 1.47677174e-04 2.83191602e-04 1.42040920e-04 3.40231679e-06\n", + " 1.41439784e-04 1.37276278e-04 1.02481791e-04 1.11277293e-04\n", + " 1.41264619e-04 2.83191602e-04 1.09062742e-04 1.41732649e-04\n", + " 9.44946899e-05 1.41948762e-04 9.56974786e-05 1.41941422e-04\n", + " 1.43773126e-04 1.32461900e-04 1.35602177e-04 1.35602177e-04\n", + " 1.09754382e-04 1.41732649e-04 1.43773126e-04 1.34097232e-04\n", + " 1.40744566e-06 1.36032102e-04 1.34097232e-04 1.41595801e-04\n", + " 1.34097232e-04 1.41732649e-04 8.73992507e-05 1.46717662e-04\n", + " 1.41970373e-04 1.41264619e-04 1.41141473e-04 1.41439784e-04\n", + " 1.43773126e-04 1.35602177e-04 1.41439784e-04 9.56331801e-05\n", + " 1.35602177e-04 1.41850307e-04 1.41732649e-04 1.13109769e-04\n", + " 1.34704556e-05 1.43819166e-04 5.03060495e-06 9.92274681e-05\n", + " 1.41264619e-04 1.37276278e-04 9.20508969e-05 1.41948762e-04\n", + " 1.34097232e-04 1.05907195e-04 1.07432591e-04 9.80640283e-05\n", + " 1.41732649e-04 1.35826334e-04 1.42135966e-04 1.41264619e-04\n", + " 4.99911174e-06 1.04631204e-04 1.18706860e-05 1.43773126e-04\n", + " 1.14842750e-05 1.43408933e-04 1.43773126e-04 1.37711511e-04\n", + " 1.41439784e-04 1.41264619e-04 8.63202476e-05 4.83074745e-06\n", + " 8.86220259e-05 1.08415914e-04 6.92397179e-06 9.31599438e-05\n", + " 1.41439784e-04 1.37503202e-04 8.73992507e-05 1.42384017e-04\n", + " 1.11277293e-04 1.41595801e-04 1.41876722e-04 1.41439784e-04\n", + " 1.11277293e-04 1.42040920e-04 1.34097232e-04 1.37503202e-04\n", + " 1.47677174e-04 5.02230285e-06 4.90599630e-06 2.17687849e-05\n", + " 1.41595801e-04 1.41264619e-04 1.41439784e-04 1.41732649e-04\n", + " 2.68194465e-04 1.43251669e-04 1.88455191e-07 1.40351997e-04\n", + " 1.41595801e-04 1.41948762e-04 1.34704556e-05 2.57767647e-06\n", + " 8.73992507e-05 1.37711511e-04 1.42152686e-04 1.41834725e-04\n", + " 1.41732649e-04 1.03731569e-04 1.14842750e-05 1.39003229e-04\n", + " 1.42040920e-04 1.41264619e-04 1.42100004e-04 1.41595801e-04\n", + " 1.42040920e-04 1.47922702e-04 1.41732649e-04 1.34097232e-04\n", + " 1.41595801e-04 2.68194465e-04 9.80640283e-05 1.35826334e-04\n", + " 1.15410544e-04 2.17687849e-05 1.37276278e-04 6.75946486e-06\n", + " 1.48481822e-04 1.41595801e-04 1.36032102e-04 1.41439784e-04\n", + " 1.96556720e-04 1.41264619e-04 1.08415914e-04 5.24091473e-06\n", + " 2.49955587e-06 1.08758919e-04 4.94566824e-06 1.42040920e-04\n", + " 3.47901387e-06 1.12651087e-04 1.37276278e-04 1.41821734e-04\n", + " 2.82529237e-04 1.37711511e-04 1.34097232e-04 1.36032102e-04\n", + " 1.00377474e-04 2.41076268e-05 5.20604676e-06 1.06122826e-05\n", + " 1.09062742e-04 1.41732649e-04 1.47677174e-04 1.05684735e-04\n", + " 9.44029523e-05 1.41439784e-04 1.41439784e-04 1.35190353e-04\n", + " 1.34097232e-04 1.41732649e-04 1.41732649e-04 1.41595801e-04\n", + " 8.86220259e-05 1.42040920e-04 1.37711511e-04 1.34653369e-04\n", + " 1.41439784e-04 1.42040920e-04 2.17687849e-05 1.37711511e-04\n", + " 9.80640283e-05 1.96499463e-04 1.01386199e-04 1.41264619e-04\n", + " 1.41595801e-04 1.08025470e-04 9.56974786e-05 1.42040920e-04\n", + " 2.62801787e-06 1.37276278e-04 1.32461900e-04 9.68873120e-05\n", + " 1.41732649e-04 8.86220259e-05 5.15535295e-06 1.34097232e-04\n", + " 1.35826334e-04 3.29732569e-06 9.44946899e-05 8.63202476e-05\n", + " 1.34097232e-04 1.41595801e-04 1.07432591e-04 5.26246952e-06\n", + " 3.50195104e-06 1.06723959e-04 1.41970373e-04 1.43773126e-04\n", + " 1.40498990e-04 1.07432591e-04 1.41941422e-04 1.41595801e-04\n", + " 1.05684735e-04 1.63533210e-06 1.34097232e-04 1.41264619e-04\n", + " 1.34097232e-04 1.09384559e-04 1.34097232e-04 1.41341719e-04\n", + " 1.41439784e-04 1.43773126e-04 1.41264619e-04 1.37711511e-04\n", + " 8.73992507e-05 9.92274681e-05 9.68873120e-05 1.42040920e-04\n", + " 1.34097232e-04 3.46441268e-06 1.34983475e-05 1.41439784e-04\n", + " 1.41732649e-04 1.41948762e-04 2.17687849e-05 9.80640283e-05\n", + " 1.34097232e-04 1.47922702e-04 1.34853144e-05 1.46118583e-04\n", + " 1.47922702e-04 1.01386199e-04 1.08415914e-04 6.65428170e-06\n", + " 1.38362667e-04 1.41948762e-04 1.00422980e-04 9.56974786e-05\n", + " 1.47677174e-04 1.34053132e-04 1.41264619e-04 1.37711511e-04\n", + " 1.11700598e-04 1.41941422e-04 2.60302338e-06 1.34537732e-05\n", + " 1.41732649e-04 1.14506186e-04 1.40308714e-04 1.10734986e-04\n", + " 1.47922702e-04 6.73522782e-06 1.73804813e-06 3.35373663e-06\n", + " 1.48342672e-04 1.41595801e-04 1.08415914e-04 1.00238010e-04\n", + " 1.42027999e-04 1.34097232e-04 9.80640283e-05 1.07748733e-04\n", + " 1.42150160e-04 1.41595801e-04 1.42040920e-04 1.34097232e-04\n", + " 1.35826334e-04 3.45565546e-06 3.52212114e-06 1.47929088e-04\n", + " 1.41970373e-04 1.41595801e-04 1.41732649e-04 1.41264619e-04\n", + " 1.34097232e-04 1.02481791e-04 1.34097232e-04 6.74265720e-06\n", + " 1.02481791e-04 1.41264619e-04 1.37503202e-04 1.13586325e-04\n", + " 1.41439784e-04 1.48517045e-04 1.07748733e-04 1.13462710e-04\n", + " 1.34097232e-04 1.37503202e-04 1.48342672e-04 1.41439784e-04\n", + " 1.41732649e-04 3.45906669e-06 1.15410544e-04 1.10338395e-04\n", + " 9.92274681e-05 1.01386199e-04 6.72688660e-06 8.75279269e-05\n", + " 1.00238010e-04 1.41264619e-04 1.43773126e-04 1.41732649e-04\n", + " 1.06723959e-04 1.42088007e-04 9.56331801e-05 1.04826478e-04\n", + " 1.41948762e-04 9.31599438e-05 1.37503202e-04 1.45523736e-04\n", + " 1.35189297e-05 1.09384559e-04 1.38226219e-04 1.41941422e-04\n", + " 1.35602177e-04 1.09384559e-04 6.76323805e-06 9.80546290e-05\n", + " 1.47677174e-04 1.42040920e-04 1.34097232e-04 1.41948762e-04\n", + " 1.03731569e-04 1.41850307e-04 1.45229223e-04 1.18308644e-05\n", + " 9.56974786e-05 1.38576507e-04 1.34097232e-04 9.80640283e-05\n", + " 9.92274681e-05 1.02622616e-04 2.22797157e-04 1.48342672e-04\n", + " 1.34653369e-04 1.42040920e-04 2.17687849e-05 1.41264619e-04\n", + " 9.31599438e-05 1.06723959e-04 1.38576507e-04 1.00377474e-04\n", + " 1.36032102e-04 1.41948762e-04 1.41439784e-04 1.48517045e-04\n", + " 1.18706860e-05 1.34097232e-04 1.42040920e-04 1.36032102e-04\n", + " 1.43773126e-04 2.17687849e-05 9.92455226e-05 5.15110856e-06\n", + " 1.02481791e-04 9.56331801e-05 1.38576507e-04 1.00377474e-04\n", + " 1.37503202e-04 1.14003098e-04 9.56974786e-05 1.37711511e-04\n", + " 1.37503202e-04 1.35826334e-04 1.41439784e-04 1.37276278e-04\n", + " 1.35826334e-04 1.37367896e-04 5.25603574e-06 1.04826478e-04\n", + " 1.10338395e-04 5.19661902e-06 1.41439784e-04 1.41264619e-04\n", + " 1.41850307e-04 1.41264619e-04 1.34097232e-04 1.10338395e-04\n", + " 1.42040920e-04 1.37503202e-04 1.37503202e-04 1.41439784e-04\n", + " 1.42088007e-04 1.37711511e-04 8.75279269e-05 1.00377474e-04\n", + " 1.41439784e-04 1.38576507e-04 1.14003098e-04 1.02622616e-04\n", + " 1.41595801e-04 1.13109769e-04 1.48342672e-04 8.75279269e-05\n", + " 1.13586325e-04 1.35826334e-04 1.41850307e-04 6.74917374e-06\n", + " 1.07432591e-04]\n", + "names of tiles in bin ['1_4_10' '1_2_9' '1_11_4' '1_10_7' '1_10_6' '1_2_7' '1_3_7' '1_4_10'\n", + " '2_3_3' '1_11_7' '1_9_5' '1_10_4' '1_10_7' '1_10_4' '1_4_10' '1_10_5'\n", + " '1_1_6' '2_3_4' '2_1_6' '1_3_7' '1_4_8' '1_10_5' '1_10_6' '1_10_3'\n", + " '1_10_6' '1_10_6' '2_1_4' '2_3_6' '1_10_4' '1_4_8' '1_10_5' '2_1_3'\n", + " '1_3_9' '1_10_6' '1_4_9' '1_10_5' '1_10_5' '1_2_9' '1_2_8' '1_10_7'\n", + " '1_3_9' '1_10_5' '1_10_5' '1_9_4' '1_4_8' '1_4_8' '2_3_6' '1_10_3'\n", + " '1_4_9' '3_1_0' '1_9_4' '1_1_6' '1_10_4' '1_4_10' '1_2_8' '1_10_6'\n", + " '1_10_5' '1_1_8' '1_10_6' '1_2_8' '1_10_6' '3_3_0' '1_3_7' '2_3_2'\n", + " '1_10_4' '2_1_2' '2_3_2' '2_1_1' '1_10_5' '1_3_9' '1_3_10' '1_10_5'\n", + " '1_10_5' '1_10_8' '1_11_3' '1_4_8' '1_9_4' '1_10_5' '1_3_7' '1_2_9'\n", + " '1_2_9' '1_2_8' '1_2_8' '1_2_7' '2_1_7' '1_10_7' '1_3_8' '1_10_6'\n", + " '1_10_8' '1_10_5' '1_2_7' '1_3_7' '3_3_0' '1_10_5' '1_3_9' '2_1_7'\n", + " '1_2_8' '1_10_4' '1_9_5' '2_1_5' '1_10_5' '1_10_6' '2_1_2' '1_10_5'\n", + " '1_10_6' '2_1_1' '2_1_5' '1_10_5' '1_3_10' '1_2_8' '1_9_4' '1_10_7'\n", + " '1_10_5' '1_10_6' '1_10_8' '1_4_9' '1_10_6' '2_1_4' '1_10_6' '2_1_1'\n", + " '2_1_4' '2_1_1' '1_2_9' '1_10_7' '1_3_8' '1_9_5' '1_4_8' '2_1_5' '1_10_4'\n", + " '1_4_9' '1_4_10' '1_10_6' '1_4_10' '1_9_4' '1_10_6' '2_3_5' '2_1_4'\n", + " '1_10_7' '2_1_2' '1_10_6' '1_10_4' '2_1_5' '1_4_8' '1_2_9' '2_3_4'\n", + " '1_10_6' '1_2_9' '1_4_9' '2_1_6' '1_10_4' '2_1_4' '1_2_9' '1_3_7' '2_2_2'\n", + " '1_2_9' '3_1_0' '2_3_4' '1_10_7' '1_10_4' '1_9_7' '2_3_6' '1_10_6'\n", + " '1_9_7' '2_1_6' '1_3_9' '1_2_9' '3_1_0' '1_3_9' '1_10_3' '1_9_5' '1_2_9'\n", + " '2_1_2' '1_3_9' '3_3_1' '1_3_10' '1_1_7' '1_11_3' '2_1_3' '1_2_9'\n", + " '1_4_10' '1_4_10' '1_10_6' '1_9_4' '1_4_9' '1_10_5' '1_2_7' '1_3_9'\n", + " '2_1_7' '1_3_8' '3_2_0' '1_4_10' '2_3_5' '1_3_8' '1_3_9' '3_2_0' '1_4_9'\n", + " '1_4_9' '1_2_7' '1_10_5' '1_2_8' '1_3_8' '2_2_2' '1_2_8' '1_4_10' '3_2_0'\n", + " '2_3_6' '1_9_5' '1_1_3' '1_10_5' '1_10_6' '1_2_9' '1_4_10' '2_1_3'\n", + " '1_10_3' '1_10_5' '1_10_6' '1_2_9' '1_3_8' '1_4_10' '2_1_3' '1_10_5'\n", + " '1_9_8' '1_10_4' '1_10_5' '1_3_8' '1_10_4' '1_4_9' '1_2_8' '1_9_5'\n", + " '2_1_1' '1_11_3' '2_3_2' '3_1_0' '1_10_6' '1_10_4' '1_9_5' '3_1_0'\n", + " '1_10_6' '2_1_5' '2_2_2' '1_10_5' '1_9_4' '1_3_8' '1_4_9' '1_10_7'\n", + " '1_10_5' '1_3_9' '1_10_5' '1_2_8' '1_10_8' '1_2_7' '2_1_1' '2_1_4'\n", + " '2_3_6' '1_9_4' '1_9_5' '1_4_9' '1_10_5' '2_1_6' '2_3_4' '1_4_8' '1_9_5'\n", + " '2_3_4' '1_10_7' '2_3_7' '1_10_8' '1_1_7' '3_3_0' '1_11_4' '1_10_7'\n", + " '2_1_1' '1_10_5' '2_1_6' '1_9_4' '1_10_8' '1_2_9' '1_9_5' '1_10_6'\n", + " '1_10_6' '1_4_10' '1_10_6' '3_2_0' '1_9_5' '1_2_8' '1_10_6' '1_9_5'\n", + " '1_2_7' '1_10_6' '2_3_6' '1_3_9' '1_4_10' '1_2_9' '1_10_6' '1_9_5'\n", + " '1_11_8' '1_10_7' '1_3_9' '1_3_10' '3_1_0' '2_1_3' '3_2_0' '3_2_0'\n", + " '2_1_6' '1_9_5' '1_10_6' '1_10_7' '1_1_6' '1_3_9' '1_1_7' '1_4_8' '2_1_2'\n", + " '1_2_9' '1_10_6' '1_9_5' '1_1_6' '2_2_2' '1_4_10' '1_10_7' '1_11_8'\n", + " '1_10_5' '1_4_9' '2_1_3' '2_3_5' '1_9_6' '3_1_0' '1_9_5' '2_3_5' '3_2_0'\n", + " '1_10_5' '1_10_6' '1_10_7' '1_10_6' '2_3_6' '3_3_1' '2_2_2' '1_11_8'\n", + " '1_10_5' '1_10_7' '1_10_8' '1_3_9' '1_1_3' '1_9_8' '1_11_8' '2_2_2'\n", + " '1_10_5' '1_3_9' '3_2_0' '2_3_8' '2_1_6' '1_10_7' '1_11_8' '1_10_6'\n", + " '2_1_6' '3_1_0' '1_10_5' '2_3_7' '1_10_5' '2_3_5' '1_2_8' '1_9_5' '1_4_8'\n", + " '3_2_0' '1_9_4' '1_10_6' '1_10_5' '1_10_8' '1_9_6' '1_10_7' '2_1_7'\n", + " '1_10_5' '1_4_10' '3_3_0' '1_3_9' '1_4_10' '1_3_9' '2_1_3' '3_3_1'\n", + " '1_4_9' '1_9_4' '2_1_2' '1_10_7' '1_9_6' '2_3_5' '1_9_5' '1_2_8' '3_3_0'\n", + " '1_3_10' '3_2_0' '1_3_9' '1_10_8' '3_1_0' '1_3_10' '1_2_7' '1_10_6'\n", + " '1_10_8' '2_3_2' '2_3_6' '1_10_6' '1_10_5' '1_10_7' '1_1_7' '2_1_5'\n", + " '1_9_7' '2_3_3' '1_10_6' '2_1_4' '3_2_0' '1_9_6' '1_2_7' '2_1_7' '1_3_9'\n", + " '1_10_7' '1_10_6' '1_3_9' '1_2_9' '2_1_6' '1_3_9' '1_9_5' '2_3_5' '1_2_9'\n", + " '1_10_6' '1_1_8' '1_3_10' '2_3_5' '1_9_5' '2_3_2' '1_2_7' '1_1_6' '2_3_5'\n", + " '1_10_6' '1_4_8' '3_3_0' '1_2_7' '1_3_7' '1_11_8' '2_1_6' '2_2_2'\n", + " '1_4_10' '2_1_2' '1_10_6' '1_3_9' '2_3_4' '2_3_4' '1_10_6' '2_3_7'\n", + " '1_4_10' '2_3_6' '2_1_1' '1_10_7' '2_1_5' '1_10_6' '1_9_6' '1_1_6'\n", + " '1_2_8' '1_2_7' '2_1_3' '2_3_4' '2_1_6' '1_10_7' '1_10_6' '1_10_6'\n", + " '1_10_6' '3_2_0' '1_2_8' '2_3_4' '3_1_0' '1_10_7' '3_3_0' '3_1_0' '1_3_7'\n", + " '1_4_10' '1_2_9' '2_1_7' '1_10_6' '1_2_8' '1_2_8' '3_1_0' '2_3_2'\n", + " '1_10_6' '1_9_8' '1_4_9' '2_1_2' '1_3_8' '1_10_6' '1_10_5' '1_4_10'\n", + " '2_1_7' '1_4_9' '3_1_0' '1_10_7' '2_1_1' '1_9_6' '3_1_0' '1_10_6' '1_4_8'\n", + " '1_3_9' '1_10_3' '2_3_4' '1_2_8' '1_3_9' '1_11_8' '1_10_6' '2_1_6'\n", + " '2_3_5' '1_9_8' '2_1_1' '3_3_1' '3_3_0' '1_11_3' '1_10_7' '1_10_7'\n", + " '1_10_7' '2_3_6' '1_3_9' '2_3_5' '1_10_5' '1_3_9' '1_10_6' '1_9_6'\n", + " '1_4_10' '1_10_6' '3_1_0' '1_3_7' '1_5_11' '2_3_6' '1_9_5' '3_1_0'\n", + " '1_10_7' '1_10_7' '2_1_1' '1_4_10' '1_4_10' '1_2_9' '1_3_9' '1_10_5'\n", + " '1_1_6' '1_3_10' '1_10_7' '2_1_5' '1_10_5' '1_3_9' '1_10_8' '1_2_7'\n", + " '1_3_9' '1_10_8' '1_2_7' '1_9_7' '3_2_0' '1_10_7' '1_4_10' '2_1_7'\n", + " '2_1_1' '1_9_5' '1_4_9' '1_10_4' '1_2_8' '3_1_0' '2_1_5' '2_3_5' '1_10_7'\n", + " '1_3_9' '1_10_7' '3_2_0' '1_11_4' '1_2_9' '2_1_7' '2_3_4' '1_2_9' '1_2_8'\n", + " '1_3_10' '3_1_0' '3_1_0' '2_3_2' '2_1_5' '3_2_0' '1_10_5' '1_2_8'\n", + " '1_3_10' '2_1_7' '1_2_8' '1_9_5' '1_10_6' '1_10_7' '3_1_0' '3_1_0'\n", + " '2_3_6' '2_1_5' '1_9_7' '2_1_6' '3_2_0' '1_2_8' '2_2_2' '1_3_9' '1_2_9'\n", + " '2_1_7' '1_2_8' '1_9_5' '1_4_10' '1_2_7' '1_9_7' '1_9_5' '1_9_7' '1_10_7'\n", + " '1_9_4' '1_9_6' '2_3_8' '1_3_10' '1_3_9' '1_4_10' '2_1_6' '1_10_6'\n", + " '1_10_7' '1_10_8' '1_10_6' '2_3_6' '1_4_8' '2_1_5' '1_9_5' '1_9_8'\n", + " '1_10_6' '1_10_3' '1_9_4' '1_1_8' '1_2_8' '1_10_6' '2_1_7' '1_4_9'\n", + " '1_3_9' '1_10_6' '1_4_8' '3_1_0' '1_1_3' '1_4_9' '1_9_5' '1_10_6'\n", + " '1_10_6' '1_4_9']\n", + "dowsampled rms bin 8\n", + "areas of tiles in bin [1.34653369e-04 1.01386199e-04 1.34704556e-05 2.52593922e-06\n", + " 1.42044804e-04 1.37367896e-04 1.45229223e-04 1.01386199e-04\n", + " 1.47922702e-04 9.90303227e-05 1.37503202e-04 1.41439784e-04\n", + " 7.33417508e-05 1.13109769e-04 1.41439784e-04 6.72688660e-06\n", + " 1.36032102e-04 1.34537732e-05 1.44570487e-04 1.35826334e-04\n", + " 1.37503202e-04 1.34097232e-04 9.20508969e-05 1.42150160e-04\n", + " 1.34097232e-04 7.08896590e-06 9.56974786e-05 6.73522782e-06\n", + " 1.18706860e-05 1.34097232e-04 1.44911456e-04 9.20508969e-05\n", + " 1.03563508e-04 8.86220259e-05 1.34097232e-04 1.09754382e-04\n", + " 1.41264619e-04 1.07748733e-04 1.41732649e-04 1.01586842e-04\n", + " 1.07900309e-06 1.12201126e-04 9.68873120e-05 1.09384559e-04\n", + " 1.02481791e-04 2.17687849e-05 1.35826334e-04 1.12651087e-04\n", + " 1.10338395e-04 1.41439784e-04 1.41264619e-04 1.47922702e-04\n", + " 1.01586842e-04 1.41595801e-04 1.40308714e-04 1.41850307e-04\n", + " 1.41948762e-04 1.35826334e-04 1.96076147e-04 1.37503202e-04\n", + " 1.42152686e-04 9.32791077e-05 1.39003229e-04 1.00377474e-04\n", + " 9.80546290e-05 2.20408947e-05 1.34097232e-04 1.35826334e-04\n", + " 1.42135966e-04 1.34653369e-04 1.05684735e-04 1.37367896e-04\n", + " 1.48342672e-04 1.37711511e-04 1.45523736e-04 3.35373663e-06\n", + " 1.48342672e-04 9.90303227e-05 1.37711511e-04 9.68504603e-05\n", + " 9.92274681e-05 1.01586842e-04 2.41537372e-06 1.34097232e-04\n", + " 2.55198059e-06 8.86220259e-05 1.09384559e-04 9.90303227e-05\n", + " 1.41595801e-04 2.47283412e-06 1.48144547e-04 9.90303227e-05\n", + " 1.08758919e-04 9.80640283e-05 1.05907195e-04 1.40921151e-04\n", + " 1.41732649e-04 1.18706860e-05 2.26925419e-04 1.00377474e-04\n", + " 1.00422980e-04 1.37503202e-04 1.43251669e-04 1.41439784e-04\n", + " 1.14314405e-04 1.54718392e-04 1.40187713e-04 1.41732649e-04\n", + " 1.35095531e-05 1.48342672e-04 8.86220259e-05 1.05684735e-04\n", + " 1.41850307e-04 1.09384559e-04 8.73992507e-05 1.15150625e-04\n", + " 1.11277293e-04 1.12201126e-04 1.49017826e-04 9.56331801e-05\n", + " 1.07432591e-04 1.41732649e-04 9.80640283e-05 2.83700615e-04\n", + " 1.47922702e-04 9.68504603e-05 1.00377474e-04 1.37503202e-04\n", + " 1.07432591e-04 2.97034091e-04 2.62801787e-06 1.37503202e-04\n", + " 1.37503202e-04 1.37503202e-04 1.10734986e-04 1.48517045e-04\n", + " 1.37711511e-04 1.34097232e-04 1.33541691e-04 1.45229223e-04\n", + " 1.34097232e-04 1.37503202e-04 1.37711511e-04 1.34653369e-04\n", + " 6.74306802e-06 1.40210429e-04 1.48144547e-04 1.09384559e-04\n", + " 8.86220259e-05 1.08415914e-04 1.37711511e-04 1.35826334e-04\n", + " 9.80640283e-05 1.35602177e-04 1.14314405e-04 1.34097232e-04\n", + " 1.37276278e-04 9.68504603e-05 1.18706860e-05 1.34097232e-04\n", + " 9.44946899e-05 1.09062742e-04 1.01586842e-04 1.08758919e-04\n", + " 1.34653369e-04 8.73992507e-05 9.92455226e-05 1.41595801e-04\n", + " 1.48342672e-04 9.20508969e-05 1.42040920e-04 9.92274681e-05\n", + " 1.63533210e-06 5.19661902e-06 2.82529237e-04 1.13462710e-04\n", + " 8.86220259e-05 1.03731569e-04 1.34097232e-04 1.42027999e-04\n", + " 1.11277293e-04 1.47929088e-04 1.37145276e-04 1.37276278e-04\n", + " 1.13462710e-04 1.15150625e-04 1.36032102e-04 1.00238010e-04\n", + " 1.37711511e-04 1.42040920e-04 1.37711511e-04 1.35826334e-04\n", + " 1.11700598e-04 1.05907195e-04 1.35826334e-04 8.86220259e-05\n", + " 9.92455226e-05 1.41439784e-04 6.75477654e-06 1.14314405e-04\n", + " 1.41941422e-04 1.37367896e-04 9.44029523e-05 1.34097232e-04\n", + " 1.41439784e-04 1.42975739e-04 1.48517045e-04 8.75279269e-05\n", + " 3.27066420e-06 1.95244534e-04 1.00238010e-04 9.68873120e-05\n", + " 1.34097232e-04 1.35602177e-04 1.36032102e-04 9.44946899e-05\n", + " 1.41264619e-04 1.07748733e-04 1.06973574e-04 1.14473247e-05\n", + " 1.09754382e-04 9.80640283e-05 1.47922702e-04 1.10338395e-04\n", + " 1.48517045e-04 9.92274681e-05 1.37276278e-04 1.48517045e-04\n", + " 1.36032102e-04 1.41264619e-04 8.86220259e-05 1.41264619e-04\n", + " 1.37276278e-04 1.36032102e-04 1.36032102e-04 2.82529237e-04\n", + " 1.36032102e-04 1.48144547e-04 9.56974786e-05 1.36032102e-04\n", + " 1.34653369e-04 1.41439784e-04 9.20508969e-05 1.35602177e-04\n", + " 1.00422980e-04 1.42100004e-04 1.44406967e-04 1.02481791e-04\n", + " 1.37276278e-04 1.34097232e-04 1.40863925e-04 1.48144547e-04\n", + " 1.38226219e-04 1.37711511e-04 1.41264619e-04 1.04826478e-04\n", + " 1.41595801e-04 1.45229223e-04 9.32791077e-05 1.04826478e-04\n", + " 1.37503202e-04 1.48144547e-04 2.60302338e-06 1.49526448e-04\n", + " 3.35881733e-06 1.07748733e-04 1.37711511e-04 1.41439784e-04\n", + " 1.34653369e-04 1.02622616e-04 1.12044426e-04 1.48144547e-04\n", + " 1.34653369e-04 1.07748733e-04 1.35826334e-04 9.90303227e-05\n", + " 1.40351997e-04 9.68873120e-05 1.48144547e-04 1.43409702e-04\n", + " 1.03731569e-04 1.43819166e-04 1.02622616e-04 1.04631204e-04\n", + " 1.41264619e-04 1.37711511e-04 1.34653369e-04 1.36032102e-04\n", + " 1.00377474e-04 1.08758919e-04 1.13462710e-04 1.43672415e-04\n", + " 8.86220259e-05 1.41850307e-04 1.36032102e-04 1.12651087e-04\n", + " 1.08415914e-04 1.12201126e-04 1.41595801e-04 1.10338395e-04\n", + " 1.41264619e-04 1.45523736e-04 1.34097232e-04 9.56974786e-05\n", + " 1.12885475e-04 5.50549887e-05 1.36032102e-04 1.10734986e-04\n", + " 1.12201126e-04 2.52593922e-06 1.34653369e-04 9.32791077e-05\n", + " 9.92274681e-05 9.80640283e-05 9.20508969e-05 1.07748733e-04\n", + " 2.91047472e-04 1.01386199e-04 1.44206371e-04 1.11277293e-04\n", + " 9.44946899e-05 1.35826334e-04 1.05907195e-04 1.37711511e-04\n", + " 1.37276278e-04 1.13109769e-04 1.36206561e-04 1.41595801e-04\n", + " 1.34097232e-04 9.56974786e-05 1.41821734e-04 9.68504603e-05\n", + " 9.32791077e-05 1.35602177e-04 1.14880993e-04 1.01386199e-04\n", + " 1.05907195e-04 1.37276278e-04 1.48342672e-04 1.09062742e-04\n", + " 1.14314405e-04 1.48667639e-04 1.37711511e-04 1.09062742e-04\n", + " 1.41970373e-04 9.44946899e-05 1.37503202e-04 1.37367896e-04\n", + " 1.06723959e-04 1.03563508e-04 1.00377474e-04 5.03822600e-06\n", + " 1.41264619e-04 1.06723959e-04 1.42135966e-04 6.74265720e-06\n", + " 8.73992507e-05 1.37503202e-04 1.03731569e-04 1.42086677e-04\n", + " 1.13462710e-04 1.32461900e-04 1.37276278e-04 1.04631204e-04\n", + " 1.38576507e-04 1.37276278e-04 1.08758919e-04 1.03563508e-04\n", + " 1.45201545e-04 1.09384559e-04 9.68873120e-05 3.28422852e-06\n", + " 1.07748733e-04 1.06723959e-04 1.34653369e-04 8.86220259e-05\n", + " 8.63202476e-05 3.34820190e-06 1.34097232e-04 1.04631204e-04\n", + " 1.48517045e-04 1.04826478e-04 1.04631204e-04 1.34653369e-04\n", + " 1.35602177e-04 1.43247738e-04 1.41439784e-04 1.09384559e-04\n", + " 9.68873120e-05 1.48144547e-04 1.06973574e-04 1.34097232e-04\n", + " 1.06723959e-04 1.48667639e-04 8.75279269e-05 1.42639569e-04\n", + " 1.48667639e-04 3.36132502e-06 1.47677174e-04 8.86220259e-05\n", + " 1.49069112e-04 9.92274681e-05 1.42044804e-04 1.07432591e-04\n", + " 8.73992507e-05 7.17306998e-06 1.48517045e-04 1.00377474e-04\n", + " 1.04631204e-04 1.02481791e-04 1.34653369e-04 1.04826478e-04\n", + " 1.14842750e-05 1.15410544e-04 9.80546290e-05 1.08415914e-04\n", + " 3.34820190e-06 1.11277293e-04 1.41850307e-04 3.27066420e-06\n", + " 9.92274681e-05 1.05684735e-04 9.20508969e-05 6.72688660e-06\n", + " 1.48517045e-04 8.75279269e-05 1.14003098e-04 9.32791077e-05\n", + " 9.20508969e-05 1.35826334e-04 1.35602177e-04 1.07748733e-04\n", + " 1.05684735e-04 9.90303227e-05 2.41537372e-06 1.34097232e-04\n", + " 1.49069112e-04 1.10734986e-04 1.35826334e-04 9.80546290e-05\n", + " 1.15150625e-04 7.17306998e-06 1.35602177e-04 5.21852081e-06\n", + " 9.68873120e-05 1.11700598e-04 1.05907195e-04 1.08758919e-04\n", + " 1.02622616e-04 1.14314405e-04 1.14506186e-04 1.01386199e-04\n", + " 8.73992507e-05 1.37503202e-04 1.10338395e-04 1.40114071e-04\n", + " 1.07748733e-04 1.06723959e-04 1.08025470e-04 1.42135966e-04\n", + " 1.03563508e-04 1.08025470e-04 1.41439784e-04 1.02481791e-04\n", + " 1.48342672e-04 1.09754382e-04 6.83094144e-06 1.48144547e-04\n", + " 6.73522782e-06 1.14314405e-04 1.35190353e-04 9.80640283e-05\n", + " 9.44029523e-05 1.37711511e-04 1.39003229e-04 1.41595801e-04\n", + " 9.80546290e-05 1.42639569e-04 1.41529797e-04 1.41876722e-04\n", + " 1.07432591e-04 1.09384559e-04 1.37503202e-04 9.92274681e-05\n", + " 1.03563508e-04 1.01386199e-04 8.86220259e-05 1.32632829e-06\n", + " 8.73992507e-05 1.41850307e-04 1.10734986e-04 1.08415914e-04\n", + " 1.34097232e-04 1.22724483e-04 1.35826334e-04 1.38576507e-04\n", + " 1.37503202e-04 1.40900062e-04 1.10338395e-04 2.74758749e-06\n", + " 1.48485344e-04 8.75279269e-05 3.58653499e-06 9.90303227e-05\n", + " 8.73992507e-05 1.35826334e-04 1.03731569e-04 1.34097232e-04\n", + " 1.41504698e-04 1.13109769e-04 1.03563508e-04 1.41141473e-04\n", + " 1.04631204e-04 1.38576507e-04 2.26925419e-04 1.37367896e-04\n", + " 1.37711511e-04 1.06723959e-04 1.37276278e-04 1.39003229e-04\n", + " 9.56974786e-05 1.14003098e-04 1.49067054e-04 7.00390207e-06\n", + " 1.07748733e-04 1.48667639e-04 9.44946899e-05 9.56974786e-05\n", + " 3.28422852e-06 1.34097232e-04 1.35602177e-04 1.07900309e-06\n", + " 1.35826334e-04 9.92455226e-05 8.86220259e-05 1.42639569e-04\n", + " 8.86220259e-05 1.38479436e-04 1.37367896e-04 1.43853388e-04\n", + " 1.34653369e-04 1.97875184e-04 1.00377474e-04 1.37503202e-04\n", + " 1.09754382e-04 1.42100004e-04 1.10338395e-04 1.13462710e-04\n", + " 9.20508969e-05 6.83094144e-06 4.90599630e-06 9.80546290e-05\n", + " 1.37276278e-04 9.32791077e-05 1.13462710e-04 9.80640283e-05\n", + " 9.92274681e-05 2.38206312e-05 1.04826478e-04 1.10338395e-04\n", + " 9.68873120e-05 8.63202476e-05 1.43773126e-04 1.43672415e-04\n", + " 1.40351997e-04 1.33011255e-04 1.35602177e-04 1.48342672e-04\n", + " 9.68873120e-05 1.01386199e-04 9.92274681e-05 1.48667639e-04\n", + " 3.54448295e-06 1.05684735e-04 1.01386199e-04 1.09754382e-04\n", + " 1.38362667e-04 1.37276278e-04 1.02481791e-04 9.90303227e-05\n", + " 1.07432591e-04 1.14506186e-04 1.13462710e-04 1.00377474e-04\n", + " 1.13462710e-04 7.08896590e-06 1.08025470e-04 1.08758919e-04\n", + " 1.03563508e-04 1.13109769e-04 8.75279269e-05 1.37503202e-04\n", + " 1.14003098e-04 9.68873120e-05 9.80640283e-05 1.03731569e-04\n", + " 1.08415914e-04 1.35602177e-04 1.43672415e-04 9.92274681e-05\n", + " 1.95437268e-04 1.13586325e-04 1.07432591e-04 1.12651087e-04\n", + " 1.48932096e-04 1.03563508e-04 8.73992507e-05 9.90303227e-05\n", + " 1.03563508e-04 1.41941422e-04 2.47283412e-06 1.01586842e-04\n", + " 1.42119479e-04 1.06973574e-04 9.80640283e-05 9.44029523e-05\n", + " 9.92455226e-05 9.92455226e-05 1.37367896e-04 9.31599438e-05\n", + " 9.68873120e-05 1.39484767e-04 1.34653369e-04 9.80640283e-05\n", + " 1.37367896e-04 2.65265657e-06 1.48667639e-04 1.07748733e-04\n", + " 9.31599438e-05 1.03563508e-04 1.00422980e-04 1.37711511e-04\n", + " 1.48794430e-04 1.12651087e-04 1.42152686e-04 1.48667639e-04\n", + " 1.35602177e-04 1.12201126e-04 9.92274681e-05 2.30301250e-04\n", + " 1.00377474e-04 1.07432591e-04 9.92274681e-05 1.49067054e-04\n", + " 8.75279269e-05 1.13109769e-04 1.37276278e-04 2.77039818e-06\n", + " 1.13462710e-04 1.37030768e-04 8.86220259e-05 1.37276278e-04\n", + " 9.80640283e-05 1.41948762e-04 1.48667639e-04 1.02481791e-04\n", + " 1.02481791e-04]\n", + "names of tiles in bin ['2_3_2' '1_3_9' '1_10_7' '1_3_8' '1_11_3' '2_3_8' '3_2_0' '1_3_9' '3_1_0'\n", + " '1_3_9' '1_9_6' '1_10_6' '3_1_0' '1_4_10' '1_10_7' '1_10_6' '1_9_6'\n", + " '1_10_8' '3_2_0' '1_9_4' '1_9_7' '2_3_7' '1_2_8' '1_11_3' '2_3_4' '1_2_9'\n", + " '1_2_8' '1_10_5' '3_1_0' '2_3_4' '3_2_0' '1_2_8' '1_3_7' '1_1_6' '2_3_7'\n", + " '1_4_9' '1_10_6' '1_3_9' '1_10_6' '1_2_7' '1_1_6' '1_4_9' '1_2_8'\n", + " '1_4_10' '1_3_7' '3_2_0' '1_9_6' '1_4_10' '1_4_10' '1_10_6' '1_10_6'\n", + " '3_1_0' '1_2_8' '1_10_6' '2_1_7' '1_10_6' '1_10_8' '1_9_6' '2_1_7'\n", + " '1_9_5' '1_11_3' '1_2_9' '2_3_8' '1_2_8' '1_2_9' '3_2_0' '2_3_7' '1_9_7'\n", + " '1_11_3' '2_3_2' '1_3_9' '2_3_8' '3_1_0' '1_9_7' '3_2_0' '1_9_5' '3_1_0'\n", + " '1_3_9' '1_9_5' '1_2_8' '1_2_8' '1_2_8' '1_3_8' '2_3_5' '1_3_9' '1_1_8'\n", + " '1_4_8' '1_3_7' '1_10_7' '1_3_9' '3_1_0' '1_3_9' '1_4_9' '1_2_8' '1_3_7'\n", + " '2_1_1' '1_10_8' '3_1_0' '1_5_9' '1_2_9' '1_2_8' '1_9_7' '3_3_5' '1_10_8'\n", + " '1_5_11' '1_10_7' '1_11_4' '1_10_7' '1_10_7' '3_1_0' '1_1_3' '1_3_7'\n", + " '1_10_7' '1_4_10' '1_1_3' '1_5_11' '1_4_10' '1_4_10' '3_3_0' '1_2_8'\n", + " '1_4_9' '1_10_6' '1_2_7' '1_10_6' '3_1_0' '1_2_9' '1_2_9' '1_9_7'\n", + " '1_4_10' '3_1_0' '1_3_8' '1_9_7' '1_9_8' '1_9_8' '1_4_10' '3_1_0' '1_9_6'\n", + " '2_3_5' '2_3_2' '3_2_0' '2_3_6' '1_9_8' '1_9_8' '2_3_2' '1_2_9' '2_1_7'\n", + " '3_1_0' '1_4_8' '1_1_3' '1_4_10' '1_9_7' '1_9_6' '1_2_8' '1_9_5' '1_5_10'\n", + " '2_3_7' '1_9_8' '1_2_9' '3_1_0' '2_3_6' '1_2_8' '1_3_9' '1_2_8' '1_4_10'\n", + " '2_3_2' '1_1_4' '1_2_7' '1_10_8' '3_1_0' '1_2_7' '2_1_6' '1_2_9' '2_3_5'\n", + " '2_1_5' '1_10_7' '1_5_11' '1_1_7' '1_3_10' '2_3_5' '1_10_8' '1_4_9'\n", + " '3_3_0' '2_3_2' '1_9_7' '1_5_11' '1_5_11' '1_9_7' '1_3_9' '1_9_8' '2_1_6'\n", + " '1_9_6' '1_9_7' '1_4_10' '1_3_9' '1_9_7' '1_1_7' '1_2_8' '1_10_3'\n", + " '1_10_6' '1_5_11' '2_1_2' '2_3_8' '1_2_8' '2_3_7' '1_10_6' '3_2_0'\n", + " '3_1_0' '1_1_3' '2_3_7' '2_1_7' '1_3_9' '1_2_9' '2_3_7' '1_9_5' '1_9_8'\n", + " '1_2_9' '1_10_6' '1_3_9' '1_3_9' '2_3_8' '1_4_10' '1_2_8' '3_1_0' '1_4_9'\n", + " '3_1_0' '1_2_7' '1_9_5' '3_1_0' '1_9_6' '1_10_6' '1_1_6' '1_10_3' '1_9_5'\n", + " '1_9_6' '1_9_7' '1_10_8' '1_9_6' '3_1_0' '1_2_8' '1_9_7' '2_3_3' '1_10_7'\n", + " '1_2_9' '1_9_7' '1_2_7' '1_11_3' '3_3_1' '1_3_9' '1_9_5' '2_3_4' '2_2_2'\n", + " '3_1_0' '2_1_7' '1_9_6' '1_10_6' '1_3_9' '1_10_3' '3_2_0' '1_2_9' '1_3_9'\n", + " '1_9_6' '3_1_0' '1_3_9' '3_3_0' '1_9_6' '1_3_9' '1_9_6' '1_10_6' '2_3_3'\n", + " '1_3_9' '1_5_11' '3_1_0' '2_3_2' '1_3_10' '1_9_5' '1_3_9' '1_11_3'\n", + " '1_2_8' '3_1_0' '2_1_1' '1_3_9' '3_2_0' '1_3_9' '1_3_9' '1_10_8' '1_9_6'\n", + " '2_3_3' '1_9_6' '1_2_8' '1_4_8' '1_5_11' '2_1_2' '1_1_3' '1_10_3' '1_9_7'\n", + " '1_4_9' '1_4_10' '1_4_10' '1_10_7' '1_4_10' '1_10_6' '3_2_0' '2_3_7'\n", + " '1_2_8' '1_5_11' '3_3_5' '1_9_7' '1_4_9' '1_4_9' '1_3_9' '2_3_3' '1_2_6'\n", + " '1_2_8' '1_2_8' '1_2_9' '1_3_9' '3_2_0' '1_3_9' '3_2_0' '1_4_9' '1_2_6'\n", + " '1_9_7' '1_3_9' '1_9_7' '1_9_5' '1_4_9' '2_3_2' '1_10_7' '2_3_7' '1_2_8'\n", + " '2_1_1' '1_2_8' '1_2_8' '1_9_6' '1_5_9' '1_3_9' '1_3_9' '1_9_6' '3_1_0'\n", + " '1_3_9' '1_5_11' '3_1_0' '1_9_6' '1_3_9' '1_11_3' '1_2_7' '1_9_6' '2_3_8'\n", + " '1_3_9' '1_3_9' '1_2_8' '1_9_7' '1_10_6' '1_3_9' '1_11_3' '1_10_6'\n", + " '1_1_3' '1_9_8' '1_3_7' '3_3_1' '1_5_8' '2_3_7' '1_9_6' '1_3_9' '2_1_7'\n", + " '1_9_5' '1_4_10' '1_3_9' '3_2_0' '1_4_10' '1_2_8' '2_3_3' '1_3_9' '1_3_9'\n", + " '2_3_3' '1_1_7' '1_1_4' '1_9_6' '2_3_4' '1_3_9' '3_1_0' '1_3_9' '1_3_7'\n", + " '2_3_3' '1_9_5' '2_1_1' '1_10_7' '1_4_9' '1_2_8' '3_1_0' '1_3_9' '2_3_7'\n", + " '1_3_9' '3_1_0' '1_1_3' '3_3_1' '3_1_0' '2_2_2' '3_1_0' '1_1_4' '3_0_2'\n", + " '1_2_8' '1_11_8' '1_4_7' '1_1_3' '1_2_7' '3_1_0' '1_2_4' '1_3_9' '1_3_9'\n", + " '2_3_3' '1_3_10' '3_2_0' '1_4_10' '1_2_4' '1_4_9' '1_9_5' '1_4_10'\n", + " '1_10_8' '2_3_3' '1_2_6' '1_3_9' '1_2_6' '1_10_7' '3_1_0' '1_1_7' '1_4_9'\n", + " '1_2_8' '1_2_8' '1_9_6' '1_9_6' '1_3_9' '1_3_9' '1_3_9' '1_3_9' '2_3_7'\n", + " '3_0_2' '1_4_8' '1_9_8' '1_2_9' '1_5_11' '1_2_9' '1_9_8' '3_3_0' '1_2_9'\n", + " '1_4_9' '1_3_10' '1_4_9' '1_3_9' '1_5_11' '1_4_9' '1_3_9' '1_1_7' '1_9_6'\n", + " '1_4_9' '2_2_2' '1_3_9' '1_3_9' '1_3_9' '1_11_8' '1_3_9' '1_3_9' '1_10_7'\n", + " '1_3_9' '3_1_0' '1_4_9' '1_2_9' '3_1_0' '1_10_6' '1_5_11' '2_3_2' '1_2_6'\n", + " '1_2_6' '1_9_7' '2_3_8' '1_10_8' '1_2_8' '3_3_5' '2_2_2' '1_11_8' '1_4_9'\n", + " '1_4_9' '1_9_6' '1_2_4' '1_3_9' '1_3_9' '1_1_3' '1_4_8' '1_1_7' '1_10_8'\n", + " '1_4_9' '1_4_9' '2_3_7' '3_3_5' '1_9_7' '2_1_7' '1_9_6' '3_3_2' '1_4_9'\n", + " '1_4_10' '3_3_0' '1_1_4' '1_2_8' '1_3_9' '1_1_3' '1_9_8' '1_3_9' '2_3_6'\n", + " '3_3_1' '1_4_9' '1_3_9' '2_1_1' '1_3_9' '2_1_7' '1_5_9' '2_3_8' '1_9_8'\n", + " '1_3_9' '1_9_7' '2_3_8' '1_2_6' '1_4_9' '3_0_2' '1_2_9' '1_3_9' '3_1_0'\n", + " '1_2_8' '1_2_9' '2_3_2' '2_3_7' '1_9_7' '1_1_8' '1_9_6' '1_2_9' '1_1_4'\n", + " '3_3_5' '1_1_4' '2_1_7' '2_3_8' '2_1_4' '2_3_3' '2_3_8' '1_2_6' '1_9_8'\n", + " '1_4_8' '1_11_3' '1_4_10' '1_5_8' '1_2_8' '1_2_7' '2_3_7' '1_2_4' '1_9_8'\n", + " '1_2_7' '1_5_10' '1_2_4' '1_2_6' '3_3_0' '1_3_7' '1_4_8' '1_2_8' '1_1_3'\n", + " '2_1_2' '2_1_2' '1_11_8' '2_3_3' '1_9_8' '3_1_0' '1_2_6' '1_3_9' '1_2_6'\n", + " '3_1_0' '1_2_8' '1_3_9' '1_3_7' '1_4_10' '2_1_7' '1_9_5' '1_3_9' '1_3_9'\n", + " '1_4_10' '1_4_10' '1_5_9' '1_2_7' '1_5_8' '1_2_7' '1_3_9' '1_4_9' '1_3_9'\n", + " '1_4_10' '1_1_4' '1_9_7' '1_4_7' '1_2_4' '1_2_5' '1_3_7' '1_4_10' '1_9_6'\n", + " '2_1_2' '1_2_4' '2_1_7' '1_4_9' '1_4_7' '1_4_10' '3_0_2' '1_3_7' '1_1_6'\n", + " '1_3_9' '1_3_7' '2_1_1' '1_3_8' '1_2_6' '2_2_2' '1_3_9' '1_2_8' '1_2_9'\n", + " '1_2_6' '1_2_3' '2_3_8' '1_2_6' '1_2_4' '2_2_2' '2_3_2' '1_2_4' '2_3_8'\n", + " '1_4_10' '3_1_0' '1_3_7' '1_2_9' '1_3_9' '1_2_6' '1_9_8' '3_1_0' '1_4_10'\n", + " '1_11_8' '3_1_0' '1_9_7' '1_4_9' '1_2_6' '1_5_11' '1_2_6' '1_4_10'\n", + " '1_2_4' '3_0_2' '1_1_5' '1_4_9' '1_9_8' '1_4_10' '1_5_11' '1_9_4' '1_1_5'\n", + " '1_9_8' '1_2_6' '1_10_3' '3_1_0' '1_3_9' '1_3_7']\n", + "dowsampled rms bin 9\n", + "areas of tiles in bin [9.44946899e-05 1.36032102e-04 9.92274681e-05 1.48667639e-04\n", + " 1.04826478e-04 1.37276278e-04 1.38362667e-04 1.12201126e-04\n", + " 1.14003098e-04 2.49955587e-06 1.08758919e-04 1.15410544e-04\n", + " 1.18706860e-05 1.13462710e-04 9.20508969e-05 1.15410544e-04\n", + " 1.00377474e-04 1.42120215e-04 1.13109769e-04 1.42100004e-04\n", + " 1.41439784e-04 1.05684735e-04 1.00377474e-04 8.86220259e-05\n", + " 1.15150625e-04 8.86220259e-05 1.13462710e-04 1.49069112e-04\n", + " 5.07037047e-06 1.42150160e-04 1.42639569e-04 1.37276278e-04\n", + " 1.00377474e-04 1.37276278e-04 1.14506186e-04 9.90303227e-05\n", + " 1.13462710e-04 2.67693615e-06 1.15410544e-04 1.13109769e-04\n", + " 1.14003098e-04 1.41504698e-04 9.20508969e-05 1.14506186e-04\n", + " 9.32791077e-05 9.56974786e-05 1.09062742e-04 9.31599438e-05\n", + " 1.45229223e-04 1.42088007e-04 1.37276278e-04 8.86220259e-05\n", + " 2.81418452e-06 1.37276278e-04 1.04631204e-04 1.06973574e-04\n", + " 1.01586842e-04 1.14003098e-04 9.20508969e-05 9.80640283e-05\n", + " 1.12201126e-04 1.05684735e-04 1.49047302e-04 1.11277293e-04\n", + " 1.40900062e-04 6.91789002e-06 1.09754382e-04 9.68873120e-05\n", + " 1.37503202e-04 1.06973574e-04 1.40668547e-04 8.73992507e-05\n", + " 8.86220259e-05 9.32791077e-05 9.80640283e-05 8.86220259e-05\n", + " 7.34161182e-05 5.20604676e-06 8.75279269e-05 9.92455226e-05\n", + " 1.11277293e-04 9.44946899e-05 1.48667639e-04 9.32791077e-05\n", + " 1.49047302e-04 9.20508969e-05 1.37030768e-04 1.43251669e-04\n", + " 1.35602177e-04 9.32791077e-05 8.61646432e-05 3.32714085e-06\n", + " 1.35826334e-04 2.79283380e-06 9.92455226e-05 1.41504698e-04\n", + " 1.11277293e-04 9.80640283e-05 9.92274681e-05 1.35602177e-04\n", + " 1.41941422e-04 1.49041129e-04 7.25620297e-06 1.03731569e-04\n", + " 9.20508969e-05 1.41504698e-04 8.75279269e-05 1.13462710e-04\n", + " 1.11277293e-04 9.92274681e-05 2.17687849e-05 8.73992507e-05\n", + " 8.86220259e-05 1.49001627e-04 8.86220259e-05 1.49047302e-04\n", + " 9.92274681e-05 3.68018029e-06 8.75279269e-05 1.08415914e-04\n", + " 7.17306998e-06 1.49067054e-04 1.42639569e-04 1.05907195e-04\n", + " 1.08758919e-04 1.37276278e-04 7.00390207e-06 3.35373663e-06\n", + " 1.44911456e-04 9.92274681e-05 1.49001627e-04 1.00238010e-04\n", + " 9.44029523e-05 1.11700598e-04 9.68873120e-05 9.32791077e-05\n", + " 8.63202476e-05 9.32791077e-05 9.32791077e-05 1.14003098e-04\n", + " 1.02622616e-04 1.03563508e-04 9.20508969e-05 1.67410095e-06\n", + " 5.21392696e-06 1.37585405e-04 1.12201126e-04 1.49047302e-04\n", + " 1.10338395e-04 9.80640283e-05 1.35602177e-04 1.35602177e-04\n", + " 1.02481791e-04 2.26925419e-04 1.41681874e-04 3.35881733e-06\n", + " 9.90303227e-05 1.13462710e-04 9.80546290e-05 1.49069112e-04\n", + " 1.00238010e-04 1.43251669e-04 1.41970373e-04 1.06973574e-04\n", + " 1.18706860e-05 6.74306802e-06 1.00377474e-04 1.00377474e-04\n", + " 1.12201126e-04 1.35602177e-04 1.04631204e-04 1.67686832e-06\n", + " 9.32791077e-05 1.00238010e-04 8.86220259e-05 9.92455226e-05\n", + " 3.36107511e-06 1.36032102e-04 1.42100004e-04 1.00422980e-04\n", + " 1.39390919e-04 9.20508969e-05 1.04631204e-04 8.73992507e-05\n", + " 9.92455226e-05 9.32791077e-05 1.40457896e-04 1.13586325e-04\n", + " 1.37503202e-04 8.73992507e-05 9.80640283e-05 1.13711242e-04\n", + " 8.73992507e-05 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9.44946899e-05 1.49041129e-04 1.00377474e-04\n", + " 5.10396119e-06 9.44946899e-05 1.37030768e-04 1.14314405e-04\n", + " 8.63202476e-05 1.40900062e-04 3.46720176e-06 1.08025470e-04\n", + " 1.14506186e-04 9.68873120e-05 9.32791077e-05 1.49001627e-04\n", + " 1.05684735e-04 1.00377474e-04 1.06723959e-04 2.45603299e-05\n", + " 1.09062742e-04 1.07748733e-04 9.31599438e-05 1.12201126e-04\n", + " 9.44946899e-05 1.09384559e-04 8.86220259e-05 8.73992507e-05\n", + " 8.75279269e-05 1.14314405e-04 3.49394315e-06 9.44946899e-05\n", + " 1.48932096e-04 9.56974786e-05 1.13462710e-04 1.00377474e-04\n", + " 1.08758919e-04 1.40900062e-04 2.45603299e-05 1.08415914e-04\n", + " 1.41970373e-04 9.44946899e-05 1.08415914e-04 9.68873120e-05\n", + " 1.14003098e-04 1.49069112e-04 1.00377474e-04 1.03563508e-04\n", + " 1.14314405e-04 1.05907195e-04 1.39641690e-06 1.05684735e-04\n", + " 1.11277293e-04 1.11700598e-04 1.12201126e-04 1.48838718e-04\n", + " 1.00377474e-04 9.68873120e-05 3.54448295e-06 1.14314405e-04\n", + " 1.09062742e-04 9.44946899e-05 1.01386199e-04 1.09754382e-04\n", + " 1.38576507e-04 1.01586842e-04 1.08025470e-04 9.56974786e-05\n", + " 1.03731569e-04 1.18706860e-05 1.40900062e-04 1.13109769e-04\n", + " 9.20508969e-05 2.45603299e-05 1.09384559e-04 9.80640283e-05\n", + " 9.56974786e-05 1.42120215e-04 1.04631204e-04 1.01386199e-04\n", + " 1.00377474e-04 1.12201126e-04 1.00377474e-04 1.49526448e-04\n", + " 9.44946899e-05 9.90303227e-05 1.04826478e-04 1.42120215e-04\n", + " 1.15150625e-04 1.48144547e-04 1.49069112e-04 1.42086677e-04\n", + " 1.40900062e-04 9.80640283e-05 1.13586325e-04 1.43840834e-04\n", + " 1.00422980e-04 9.44946899e-05 9.68504603e-05 1.04826478e-04\n", + " 1.06973574e-04]\n", + "names of tiles in bin ['1_2_8' '1_9_8' '1_2_8' '3_1_0' '1_3_7' '1_9_7' '2_1_7' '1_4_8' '1_4_10'\n", + " '1_3_8' '1_4_10' '1_4_9' '3_1_0' '1_5_8' '1_2_9' '1_4_9' '1_2_6' '2_1_4'\n", + " '1_4_8' '1_11_3' '1_10_8' '1_3_9' '1_2_6' '1_1_3' '1_5_10' '1_1_5'\n", + " '1_5_9' '3_0_2' '1_11_3' '1_11_8' '3_3_4' '1_9_6' '1_2_8' '1_9_6' '1_4_9'\n", + " '1_3_4' '1_5_9' '1_4_10' '1_4_8' '1_4_9' '1_4_9' '3_3_5' '1_2_6' '1_4_9'\n", + " '1_2_6' '1_2_8' '1_3_9' '1_2_8' '3_2_0' '1_10_8' '1_9_6' '1_1_3' '3_3_5'\n", + " '1_9_7' '1_3_7' '1_3_9' '1_2_4' '1_4_8' '1_2_7' '1_2_3' '1_4_9' '1_3_9'\n", + " '3_0_2' '1_4_9' '3_3_5' '1_2_9' '1_4_10' '1_2_4' '1_9_6' '1_3_10' '2_2_2'\n", + " '1_1_3' '1_1_7' '1_2_6' '1_2_5' '1_1_6' '3_1_0' '1_3_4' '1_1_7' '1_2_8'\n", + " '1_4_10' '1_2_8' '3_1_0' '1_2_4' '3_0_2' '1_2_4' '1_9_4' '3_3_3' '1_9_6'\n", + " '1_2_8' '1_1_3' '1_2_8' '1_9_8' '1_4_10' '1_2_4' '3_3_3' '1_4_9' '1_2_3'\n", + " '1_2_6' '1_9_7' '2_1_2' '3_0_2' '1_2_9' '1_3_9' '1_2_8' '3_3_2' '1_1_5'\n", + " '1_5_10' '1_4_9' '1_2_4' '3_2_0' '1_1_8' '1_1_4' '3_0_2' '1_1_3' '3_0_2'\n", + " '1_2_3' '3_0_3' '1_1_5' '1_4_9' '1_2_4' '3_0_2' '3_3_2' '1_3_4' '1_4_10'\n", + " '1_9_8' '1_2_7' '1_9_8' '3_2_0' '1_2_4' '3_0_2' '1_3_4' '1_2_6' '1_4_7'\n", + " '1_2_8' '1_2_4' '1_1_7' '1_2_9' '1_2_3' '1_4_9' '1_3_9' '1_3_7' '1_2_6'\n", + " '1_9_4' '2_2_2' '2_3_2' '1_4_10' '3_0_2' '1_4_7' '1_2_6' '1_9_5' '1_9_8'\n", + " '1_3_7' '1_5_9' '2_1_1' '1_9_8' '1_3_9' '1_5_9' '1_2_4' '3_0_2' '1_3_9'\n", + " '3_3_5' '1_11_8' '1_3_4' '3_1_0' '1_2_7' '1_2_6' '1_2_4' '1_4_7' '1_9_8'\n", + " '1_3_9' '1_9_4' '1_2_6' '1_3_9' '1_1_8' '1_2_4' '2_3_2' '1_9_8' '1_11_8'\n", + " '1_2_4' '1_9_4' '1_2_6' '1_3_9' '1_1_3' '1_2_4' '1_2_4' '1_11_3' '1_4_10'\n", + " '1_9_7' '1_1_4' '1_2_4' '1_5_11' '1_1_3' '1_2_4' '1_5_9' '3_3_0' '1_3_9'\n", + " '1_4_10' '1_1_3' '1_4_7' '1_10_8' '1_3_5' '3_3_5' '1_4_9' '1_1_3' '1_4_9'\n", + " '3_0_2' '1_1_4' '1_10_8' '3_1_0' '1_4_7' '3_3_1' '1_9_7' '1_2_6' '3_1_0'\n", + " '1_2_3' '2_3_6' '1_2_7' '1_1_3' '2_1_1' '1_4_8' '1_2_6' '1_3_9' '3_0_2'\n", + " '1_4_10' '1_4_10' '1_1_6' '3_1_0' '3_0_2' '1_1_3' '3_3_1' '1_1_7' '1_3_7'\n", + " '2_0_4' '1_12_4' '1_4_8' '1_9_4' '3_0_2' '1_2_4' '1_5_9' '3_2_0' '2_1_1'\n", + " '1_3_9' '1_10_3' '1_9_6' '1_9_5' '1_2_5' '1_2_4' '1_1_3' '1_4_10' '1_2_6'\n", + " '1_2_4' '1_4_7' '1_3_4' '3_0_2' '1_2_5' '1_2_6' '1_4_9' '1_2_8' '1_2_3'\n", + " '1_2_3' '1_4_9' '3_0_2' '1_3_7' '1_2_3' '1_1_4' '2_3_2' '1_4_10' '1_4_10'\n", + " '1_2_6' '1_9_7' '1_3_9' '1_1_3' '1_2_3' '1_1_4' '1_1_3' '1_2_5' '1_12_8'\n", + " '1_2_4' '1_2_5' '3_3_1' '2_1_2' '1_9_7' '1_2_5' '1_2_6' '1_9_4' '1_2_3'\n", + " '1_4_9' '1_4_5' '1_2_4' '1_4_7' '1_4_7' '2_3_8' '1_2_6' '1_2_4' '3_3_3'\n", + " '1_2_5' '1_3_4' '3_3_1' '3_3_4' '1_4_9' '1_2_5' '1_2_6' '1_1_4' '1_4_7'\n", + " '1_2_5' '1_1_5' '1_1_7' '3_3_5' '2_1_7' '1_3_4' '1_3_9' '2_1_1' '1_5_9'\n", + " '1_1_3' '1_1_3' '3_3_5' '3_3_2' '1_2_6' '1_3_7' '2_1_1' '1_3_7' '1_9_7'\n", + " '1_2_5' '1_2_3' '1_9_6' '3_1_0' '1_4_10' '1_2_5' '2_3_4' '1_3_7' '1_4_10'\n", + " '3_2_0' '1_4_10' '3_1_0' '1_1_7' '3_3_3' '3_0_2' '1_2_8' '1_9_7' '1_9_4'\n", + " '1_2_8' '1_2_6' '1_3_5' '1_12_5' '1_2_5' '1_4_9' '1_4_10' '3_0_3' '1_2_5'\n", + " '1_2_3' '1_1_3' '1_11_3' '1_3_3' '3_3_2' '3_0_2' '1_1_4' '1_3_9' '1_4_7'\n", + " '1_2_5' '1_5_10' '3_0_2' '1_2_8' '3_0_2' '1_4_10' '1_5_10' '1_4_8'\n", + " '1_4_9' '2_1_4' '3_0_2' '1_9_8' '1_2_4' '1_1_3' '3_3_2' '3_3_5' '1_2_6'\n", + " '2_1_3' '1_2_4' '1_4_9' '1_4_9' '3_3_4' '3_3_2' '1_5_10' '1_1_5' '3_3_5'\n", + " '1_1_5' '1_2_4' '1_2_4' '1_2_5' '1_4_9' '1_4_10' '1_2_4' '1_4_9' '1_1_5'\n", + " '1_2_4' '3_3_3' '1_12_6' '1_4_8' '3_1_0' '1_2_6' '1_2_3' '1_3_6' '1_2_4'\n", + " '1_2_9' '1_4_9' '1_2_6' '1_5_10' '1_4_7' '1_3_10' '1_3_6' '1_2_6' '1_2_4'\n", + " '1_9_4' '3_3_0' '1_9_7' '3_0_2' '1_11_3' '3_3_1' '1_5_10' '3_0_2' '1_9_8'\n", + " '1_3_9' '3_3_0' '1_2_4' '3_1_0' '1_5_10' '1_3_5' '1_5_10' '1_12_8'\n", + " '1_11_8' '1_12_4' '1_5_11' '1_4_9' '1_2_6' '1_2_6' '1_2_4' '3_0_2'\n", + " '1_2_4' '2_3_2' '1_3_4' '1_2_8' '1_2_5' '3_0_3' '1_3_7' '1_3_4' '1_2_5'\n", + " '1_2_3' '1_4_8' '1_9_7' '3_1_0' '1_2_6' '1_4_10' '1_3_9' '1_1_3' '3_0_2'\n", + " '1_9_7' '1_3_10' '1_12_4' '1_2_6' '1_2_3' '1_3_7' '3_3_5' '1_3_5' '1_1_8'\n", + " '3_0_2' '1_3_4' '3_0_2' '1_3_7' '1_3_6' '1_3_7' '3_1_0' '3_1_0' '1_2_6'\n", + " '1_2_6' '1_4_9' '1_1_6' '1_4_10' '1_9_6' '3_3_5' '1_2_5' '1_12_4' '3_1_0'\n", + " '1_2_6' '1_11_3' '1_2_6' '1_3_7' '1_4_8' '1_1_4' '1_3_5' '1_4_7' '1_2_6'\n", + " '1_11_8' '1_4_10' '1_2_5' '1_3_7' '2_1_7' '3_0_2' '3_3_3' '3_0_2' '1_2_4'\n", + " '1_4_7' '1_4_9' '3_0_3' '1_2_6' '3_1_2' '1_12_7' '3_2_0' '1_9_6' '1_3_7'\n", + " '1_3_4' '1_2_6' '2_3_3' '1_2_6' '3_3_3' '3_1_0' '3_0_2' '3_0_2' '1_2_6'\n", + " '1_3_6' '1_2_4' '3_0_2' '1_4_7' '1_11_4' '1_5_10' '1_9_8' '2_1_5' '1_1_7'\n", + " '1_2_3' '2_1_1' '2_1_4' '1_2_4' '1_2_6' '1_3_7' '3_0_2' '1_2_4' '1_2_4'\n", + " '3_1_0' '1_5_10' '1_2_8' '3_3_3' '1_2_5' '1_1_5' '1_9_7' '1_11_3' '2_2_2'\n", + " '1_2_4' '1_2_6' '1_1_5' '1_2_4' '3_2_0' '1_1_4' '1_2_5' '1_4_10' '1_2_6'\n", + " '1_2_6' '3_0_2' '1_12_8' '3_0_2' '1_2_4' '3_0_2' '1_3_9' '1_2_5' '1_2_4'\n", + " '3_1_0' '1_2_5' '1_3_5' '2_2_2' '1_9_6' '1_5_10' '1_10_3' '3_0_3' '3_3_3'\n", + " '1_5_10' '1_3_7' '1_3_6' '3_3_0' '3_3_5' '1_12_4' '1_2_4' '2_3_2' '1_5_9'\n", + " '1_2_4' '1_4_5' '1_1_4' '3_1_0' '3_3_3' '1_2_4' '2_1_3' '3_0_2' '1_4_5'\n", + " '3_3_4' '1_1_5' '1_4_8' '1_2_3' '1_2_3' '1_2_4' '1_2_6' '3_0_2' '1_2_3'\n", + " '1_2_3' '1_2_3' '1_9_4' '1_2_4' '1_2_6' '1_2_5' '1_5_8' '1_4_7' '1_2_6'\n", + " '1_2_4' '1_2_4' '1_9_4' '1_4_8' '3_0_2' '1_3_4' '1_2_6' '1_1_3' '1_3_4'\n", + " '1_4_8' '3_3_3' '3_0_2' '1_5_10' '1_3_7' '3_1_2' '1_4_8' '1_3_4' '1_2_5'\n", + " '1_1_2' '1_2_5' '1_2_4' '3_0_2' '1_2_4' '1_3_4' '1_3_6' '1_3_4' '1_1_4'\n", + " '1_2_4' '3_3_1' '1_3_7' '1_9_6' '3_3_2' '1_4_9' '1_2_4' '1_11_8' '1_2_5'\n", + " '1_5_10' '1_2_6' '1_12_4' '1_4_10' '1_4_9' '1_2_6' '1_2_4' '1_2_4'\n", + " '1_1_2' '1_4_9' '1_3_7' '1_1_3' '1_3_7' '3_3_3' '1_2_5' '1_2_4' '3_0_3'\n", + " '3_2_0' '1_3_4' '3_3_5' '1_2_4' '1_4_10' '3_1_0' '1_2_4' '1_3_7' '3_3_5'\n", + " '3_3_3' '1_2_6' '1_12_7' '1_3_9' '2_1_1' '1_1_5' '1_12_4' '1_1_5' '1_3_7'\n", + " '1_1_4' '1_2_3' '1_1_5' '1_2_8' '1_2_4' '1_1_6' '1_9_8' '1_3_4' '3_1_0'\n", + " '1_2_7' '1_3_4' '1_4_9' '1_2_6' '1_2_4' '3_0_2' '1_3_7' '3_0_2' '1_2_4'\n", + " '1_4_5' '1_2_6' '1_9_7' '3_0_2' '3_0_2' '1_1_5' '1_2_6' '1_3_6' '3_3_5'\n", + " '1_4_7' '1_12_4' '3_0_3' '1_2_5' '1_2_5' '1_4_5' '1_4_7' '1_3_4' '1_1_3'\n", + " '1_2_5' '1_3_6' '1_4_9' '1_2_5' '3_3_2' '1_11_8' '2_1_1' '1_3_5' '1_2_6'\n", + " '1_3_4' '1_1_5' '1_4_7' '1_2_6' '1_3_5' '1_3_7' '1_3_7' '1_1_7' '3_3_3'\n", + " '1_3_4' '1_2_5' '3_0_2' '1_2_6' '1_2_4' '1_1_3' '1_3_6' '1_2_5' '1_4_5'\n", + " '1_4_10' '3_3_1' '1_2_4' '3_3_2' '1_2_5' '1_2_5' '1_2_5' '2_1_1' '3_3_2'\n", + " '1_10_8' '1_4_10' '3_0_2' '1_2_6' '1_3_4' '1_1_4' '2_1_4' '3_3_2' '1_5_9'\n", + " '3_3_4' '1_2_4' '1_2_5' '1_3_4' '1_12_7' '2_1_5' '1_3_5' '3_0_3' '1_2_4'\n", + " '1_2_6' '1_4_7' '3_3_3' '3_3_2' '3_0_2' '1_3_4' '1_11_3' '3_1_0' '2_1_7'\n", + " '1_4_5' '1_2_4' '3_0_2' '1_3_3' '1_1_4' '1_2_7' '1_2_5' '1_4_5' '3_2_0'\n", + " '1_3_9' '2_3_2' '1_2_3' '1_2_5' '3_3_4' '1_3_4' '1_2_4' '1_2_4' '1_3_4'\n", + " '3_3_4' '3_3_1' '1_9_7' '1_2_6' '1_4_9' '1_5_9' '1_2_4' '1_2_5' '1_2_5'\n", + " '3_3_5' '1_3_5' '1_3_6' '1_4_7' '1_3_10' '2_1_4' '1_1_4' '1_3_5' '1_11_3'\n", + " '3_3_3' '3_0_2' '1_2_6' '1_3_4' '1_3_7' '1_2_4' '1_4_7' '1_2_5' '1_2_4'\n", + " '1_12_7' '1_4_10' '1_12_6' '1_1_4' '1_2_4' '3_1_0' '1_3_7' '3_3_5'\n", + " '1_2_5' '1_2_4' '1_2_5' '1_3_7' '3_3_4' '1_2_3' '1_2_5' '1_1_3' '3_3_3'\n", + " '3_3_3' '3_0_3' '3_0_3' '1_3_3' '1_4_7' '1_4_7' '1_2_6' '1_3_7' '1_4_10'\n", + " '1_2_4' '1_1_4' '1_4_4' '1_10_8' '1_3_4' '1_2_6' '3_1_0' '1_2_5' '1_2_6'\n", + " '3_3_4' '1_3_4' '1_3_4' '3_3_1' '3_0_3' '1_3_7' '3_3_3' '1_2_4' '3_0_2'\n", + " '1_2_4' '1_2_5' '1_3_6' '1_3_5' '1_3_4' '2_3_2' '1_3_7' '1_5_8' '3_3_2'\n", + " '3_3_3' '3_3_4' '1_2_5' '1_1_8' '1_9_6' '1_4_5' '1_2_4' '3_0_2' '1_11_3'\n", + " '3_3_4' '1_2_5' '1_2_6' '1_1_4' '3_0_3' '1_4_5' '3_3_1' '1_4_7' '3_3_4'\n", + " '1_2_4' '3_3_1' '1_2_6' '1_2_3' '3_0_3' '1_2_4' '1_3_5' '1_2_6' '1_9_4'\n", + " '1_5_10' '1_1_8' '3_3_5' '2_0_4' '1_3_5' '1_4_10' '1_2_6' '1_2_4' '3_0_2'\n", + " '1_3_4' '1_2_6' '1_3_4' '3_1_2' '1_3_6' '1_3_4' '1_2_5' '1_4_7' '1_2_4'\n", + " '1_4_5' '1_1_4' '1_1_8' '1_1_5' '1_5_9' '3_3_5' '1_2_5' '3_0_2' '1_2_3'\n", + " '1_5_10' '1_2_5' '1_4_7' '3_3_2' '3_1_3' '1_4_10' '1_11_3' '1_2_4'\n", + " '1_4_5' '1_2_3' '1_4_7' '3_0_2' '1_2_5' '1_3_7' '1_5_10' '1_3_5' '1_4_9'\n", + " '1_3_7' '1_4_5' '1_4_8' '1_4_5' '3_0_2' '1_2_4' '1_2_5' '1_2_6' '1_5_10'\n", + " '1_3_4' '1_2_4' '1_3_7' '1_4_7' '2_1_7' '1_2_4' '1_3_4' '1_2_5' '1_3_7'\n", + " '3_1_0' '3_3_1' '1_4_6' '1_2_6' '3_1_2' '1_4_7' '1_2_6' '1_2_6' '2_1_3'\n", + " '1_3_5' '1_3_5' '1_2_4' '1_4_7' '1_2_5' '3_3_0' '1_2_6' '1_3_6' '1_3_9'\n", + " '2_1_4' '1_5_10' '3_1_0' '3_0_2' '3_3_3' '3_3_4' '1_2_4' '1_4_5' '3_3_4'\n", + " '1_2_4' '1_2_4' '1_2_6' '1_3_7' '1_3_6']\n", + "dowsampled rms bin 10\n", + "areas of tiles in bin [1.41732649e-04 1.43840834e-04 1.41941422e-04 ... 1.13586325e-04\n", + " 1.12201126e-04 8.23915048e-05]\n", + "names of tiles in bin ['1_10_3' '3_3_3' '2_1_2' ... '1_4_7' '1_4_6' '1_1_2']\n", + "dowsampled rms bin 11\n", + "areas of tiles in bin [9.15695507e-06 1.09572372e-05 1.43840834e-04 ... 3.46716688e-06\n", + " 8.51008821e-05 1.45229223e-04]\n", + "names of tiles in bin ['3_1_2' '3_1_1' '3_3_5' ... '2_1_3' '1_1_4' '3_2_0']\n", + "dowsampled rms bin 12\n", + "areas of tiles in bin [2.15602461e-05 1.41821734e-04 7.78570730e-05 ... 1.42237169e-04\n", + " 3.46720176e-06 8.26277535e-05]\n", + "names of tiles in bin ['3_0_0' '2_1_1' '1_1_1' ... '2_0_6' '2_0_6' '1_1_1']\n", + "dowsampled rms bin 13\n", + "areas of tiles in bin [2.32466401e-05 1.02728232e-05 8.61646432e-05 ... 3.50948471e-06\n", + " 3.04676891e-04 1.40477685e-04]\n", + "names of tiles in bin ['3_3_4' '3_0_2' '1_1_4' ... '2_0_2' '3_1_5' '2_0_5']\n", + "dowsampled rms bin 14\n", + "areas of tiles in bin [1.47811531e-04 1.50980876e-04 1.22430720e-05 ... 1.38701809e-04\n", + " 7.88302864e-05 3.68018029e-06]\n", + "names of tiles in bin ['3_2_5' '3_2_2' '3_1_5' ... '2_0_7' '1_1_4' '3_0_5']\n", + "dowsampled rms bin 15\n", + "areas of tiles in bin [9.32791077e-05 7.88302864e-05 1.48534242e-04 1.29873805e-04\n", + " 7.85179851e-05 1.47414645e-04 1.21346431e-05 8.66687034e-06\n", + " 1.38767970e-04 7.78570730e-05 7.88302864e-05 1.10085445e-04\n", + " 9.89736620e-07 8.49183701e-05 1.37030768e-04 1.35013233e-04\n", + " 1.08415914e-04 3.56015353e-06 8.13743258e-05 8.36606004e-05\n", + " 7.78570730e-05 1.41970373e-04 8.49183701e-05 1.52573875e-04\n", + " 1.40408338e-04 1.15971255e-04 1.41595801e-04 2.53649954e-07\n", + " 1.48991341e-04 7.88302864e-05 1.10338395e-04 1.35013233e-04\n", + " 8.49183701e-05 1.35013233e-04 8.13743258e-05 8.23915048e-05\n", + " 1.52338445e-04 1.49047302e-04 8.61646432e-05 7.85179851e-05\n", + " 8.51008821e-05 1.49067054e-04 3.61426218e-06 1.21637894e-05\n", + " 8.23915048e-05 6.97668646e-06 7.88302864e-05 1.49212384e-04\n", + " 1.03284692e-06 1.47414645e-04 1.52468366e-04 1.42023092e-04\n", + " 1.15971255e-04 4.22964476e-08 1.49047302e-04 2.77427627e-04\n", + " 2.70684298e-06 8.38699952e-05 1.38640322e-04 1.40457896e-04\n", + " 2.55418112e-06 8.38699952e-05 1.34097232e-04 1.43672415e-04\n", + " 1.35602177e-04 1.15971255e-04 1.17713819e-05 1.15971255e-04\n", + " 7.85179851e-05 1.40460392e-04 1.35013233e-04 1.11277293e-04\n", + " 1.46550133e-04 1.34486464e-04 1.48534242e-04 8.01098807e-05\n", + " 1.38713813e-04 1.38640322e-04 1.51575115e-04 1.53146699e-04\n", + " 5.37783500e-06 6.42920157e-06 1.35013233e-04 1.35013233e-04\n", + " 1.47811531e-04 1.46550133e-04 1.54305335e-04 1.47414645e-04\n", + " 1.46550133e-04 8.26277535e-05 8.61646432e-05 1.38640322e-04\n", + " 1.42219529e-04 1.15971255e-04 1.48859953e-04 1.48786768e-04\n", + " 1.38750760e-04 8.51008821e-05 5.07302872e-06 1.52184133e-04\n", + " 1.49047302e-04 8.01098807e-05 1.35013233e-04 8.61646432e-05\n", + " 8.49183701e-05 9.64380235e-06 8.01098807e-05 2.45603299e-05\n", + " 1.41141473e-04 1.70201764e-04 1.43971769e-04 1.49069112e-04\n", + " 3.42587872e-05 8.49183701e-05 7.17044667e-06 1.48932096e-04\n", + " 5.22007749e-05 5.50063131e-06 8.61646432e-05 1.46994157e-04\n", + " 1.14782342e-04 7.88302864e-05 8.23915048e-05 8.23915048e-05\n", + " 1.42106330e-04 1.48534242e-04 1.48337329e-04 1.37051400e-04\n", + " 1.49047302e-04 5.74213750e-07 1.38701809e-04 8.11112542e-05\n", + " 8.49183701e-05 8.11112542e-05 2.86995557e-07 1.47414645e-04\n", + " 1.53631382e-04 1.52005454e-04 5.48683508e-06 1.35013233e-04\n", + " 1.47414645e-04 1.35521724e-04 1.07900309e-06 8.49183701e-05\n", + " 1.48184750e-04 9.77517649e-07 1.48534242e-04 1.41919653e-04\n", + " 1.48991341e-04 1.48184750e-04 1.35013233e-04 3.38263586e-06\n", + " 1.10338395e-04 1.34486464e-04 6.72688660e-06 1.48184750e-04\n", + " 8.61646432e-05 8.11112542e-05 1.49047302e-04 7.22852437e-06\n", + " 1.15971255e-04 1.48184750e-04 1.48534242e-04 8.11112542e-05\n", + " 1.35013233e-04 2.48562375e-05 1.35013233e-04 1.02728232e-05\n", + " 1.43650380e-04 8.63202476e-05 1.03284692e-06 7.72053174e-05\n", + " 3.67503009e-06 1.41919653e-04 8.36606004e-05 8.11112542e-05\n", + " 1.49069112e-04 8.61646432e-05 1.49161830e-04 1.47414645e-04\n", + " 8.13743258e-05 7.62521653e-05 7.78570730e-05 8.11112542e-05\n", + " 1.48534242e-04 1.52184133e-04 1.48184750e-04 8.49183701e-05\n", + " 1.03563508e-04 1.35521724e-04 1.15971255e-04 6.92290989e-06\n", + " 1.34097232e-04 8.36606004e-05 1.48932096e-04 7.88302864e-05\n", + " 3.57439348e-06 2.05208174e-05 1.18453136e-05 1.88108360e-05\n", + " 1.38767970e-04 1.69151596e-05 1.43650380e-04 5.07300229e-06\n", + " 1.38701809e-04 1.15971255e-04 1.01688248e-04 1.53836845e-04\n", + " 3.34647075e-04 8.61646432e-05 8.73992507e-05 8.38699952e-05\n", + " 1.51575115e-04 1.15971255e-04 8.11112542e-05 5.49417304e-06\n", + " 1.42106330e-04 1.35013233e-04 7.78570730e-05 1.34486464e-04\n", + " 1.47811531e-04 1.52184133e-04 1.41850307e-04 8.36606004e-05\n", + " 1.35013233e-04 8.36606004e-05 3.46720176e-06 1.42027999e-04\n", + " 8.13743258e-05 7.72053174e-05 1.38765443e-04 8.36606004e-05\n", + " 1.34486464e-04 1.54173848e-04 3.68071881e-06 1.49067054e-04\n", + " 8.49183701e-05 1.35013233e-04 1.51323524e-04 1.52573875e-04\n", + " 1.43650380e-04 1.34486464e-04 1.21637894e-05 1.40500011e-04\n", + " 1.49069112e-04 1.48859953e-04 8.38699952e-05 1.49069112e-04\n", + " 1.49047302e-04 2.36906273e-05 1.69151596e-05 8.11112542e-05\n", + " 1.41919653e-04 1.49161830e-04 1.48337329e-04 2.03376497e-04\n", + " 1.38701809e-04 1.40502570e-04 1.01460046e-05 1.69151596e-05\n", + " 1.46550133e-04 3.46397785e-06 1.43755081e-04 1.50675318e-04\n", + " 8.51008821e-05 7.85179851e-05 2.23130045e-05 8.38699952e-05\n", + " 8.73992507e-05 8.36606004e-05 1.49161830e-04 1.15971255e-04\n", + " 1.49001627e-04 8.11112542e-05 1.15971255e-04 1.53146699e-04\n", + " 1.41919653e-04 1.51575115e-04 1.49161830e-04 3.33043128e-04\n", + " 2.23130045e-05 8.36606004e-05 5.36159022e-06 1.48534242e-04\n", + " 1.47414645e-04 1.48534242e-04 7.62521653e-05 8.13743258e-05\n", 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7.72053174e-05\n", + " 8.51008821e-05 2.23130045e-05 1.15971255e-04 5.85621565e-05\n", + " 7.72053174e-05 7.72053174e-05 1.47811531e-04 1.54412115e-04\n", + " 1.02728232e-05 8.23915048e-05 8.51008821e-05 1.35013233e-04\n", + " 1.15971255e-04 3.46720176e-06 7.78570730e-05 8.01098807e-05\n", + " 9.69357841e-07 1.38765443e-04 1.37051400e-04 1.02728232e-05\n", + " 8.13743258e-05 1.40161609e-04 1.52005454e-04 1.42040920e-04\n", + " 9.68012314e-06 8.26277535e-05 1.52005454e-04 8.51008821e-05\n", + " 1.35013233e-04 6.68237122e-05 5.84836586e-06 1.46994157e-04\n", + " 1.37276278e-04 8.26277535e-05 9.77517649e-07 1.35013233e-04\n", + " 5.84590709e-06 1.49991881e-04 1.33405933e-04 8.51008821e-05\n", + " 1.49047302e-04 1.35013233e-04 1.18460571e-05 1.40125873e-04\n", + " 1.35521724e-04 1.03284692e-06 9.13103102e-06 8.56245022e-06\n", + " 1.19884426e-05 1.40498990e-04 1.51802437e-04 1.28309060e-04\n", + " 8.73992507e-05 6.77583257e-06 1.49001627e-04 7.88302864e-05\n", + " 1.48859953e-04 7.24557280e-06 1.15971255e-04 8.16329432e-07\n", + " 1.41919653e-04 7.78570730e-05 1.50675318e-04 2.53351175e-07\n", + " 1.52005454e-04 1.52468366e-04 1.49067054e-04 3.38263586e-06\n", + " 1.42135966e-04 1.35013233e-04 1.40373326e-04 8.11112542e-05\n", + " 7.85179851e-05 1.15971255e-04 1.49069112e-04 1.52338445e-04\n", + " 1.52005454e-04 1.34486464e-04 1.43773126e-04 7.88302864e-05\n", + " 1.48991341e-04 1.52338445e-04 1.12201126e-04 8.49183701e-05\n", + " 1.41919653e-04 1.49161830e-04 1.35521724e-04 7.62521653e-05\n", + " 1.34486464e-04 6.49816550e-05 1.35521724e-04 9.89736620e-07\n", + " 2.23130045e-05 7.72053174e-05 1.38701809e-04 1.15971255e-04\n", + " 3.46145495e-06 1.48859953e-04 1.35521724e-04 1.67110693e-06\n", + " 1.49047302e-04 8.01098807e-05 8.61646432e-05 7.85179851e-05\n", + " 1.01688248e-04 3.46352756e-06 8.36606004e-05 1.69151596e-05\n", + " 1.41595801e-04 8.36606004e-05 4.84006157e-06 1.34097232e-04\n", + " 1.18453136e-05 5.07300229e-06 1.73719498e-04 9.32791077e-05\n", 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1.15971255e-04\n", + " 1.51575115e-04 4.35405596e-06 1.35013233e-04 8.73992507e-05\n", + " 1.41264619e-04 7.85179851e-05 5.07628452e-06 3.42587872e-05\n", + " 8.23915048e-05 1.14782342e-04 1.42169354e-04 1.38765443e-04\n", + " 8.38699952e-05 8.73992507e-05 3.40231679e-06 1.47811531e-04\n", + " 1.40485144e-04 8.49183701e-05 3.38263586e-06 1.38388533e-04\n", + " 1.35521724e-04 1.52338445e-04 7.62521653e-05 1.01717907e-06\n", + " 1.38259625e-04 3.30369846e-04 1.01717907e-06 8.51008821e-05\n", + " 1.48859953e-04 1.35521724e-04 1.49067054e-04 7.78570730e-05\n", + " 8.51008821e-05 1.69151596e-05 8.11112542e-05 1.18453136e-05\n", + " 1.35013233e-04 1.49001627e-04 1.01688248e-04 9.89736620e-07\n", + " 1.38713813e-04 1.42190828e-04 5.07300229e-06 1.51575115e-04\n", + " 2.71509877e-07 1.53401321e-04 1.96341603e-05 1.22430720e-05\n", + " 3.58764878e-05 7.78570730e-05 1.52184133e-04 7.78570730e-05\n", + " 8.23915048e-05 1.35013233e-04 1.27312789e-04 1.42040920e-04\n", + " 1.26824977e-07 1.21637894e-05 1.41919653e-04 3.75894752e-07\n", + " 2.45603299e-05 1.48859953e-04 1.33908370e-04 3.46039218e-05\n", + " 1.38750760e-04 1.07315731e-04 9.68873120e-05 3.58522333e-06\n", + " 1.54017677e-04 9.89736620e-07 1.01717907e-06 1.52184133e-04\n", + " 1.32461900e-04 1.09384559e-04 1.02622616e-04 1.43650380e-04\n", + " 1.49067054e-04 1.38767970e-04 1.35708109e-04 1.49001627e-04\n", + " 8.13743258e-05 7.85179851e-05 8.13743258e-05 1.38750760e-04\n", + " 8.63202476e-05 3.37533007e-06 1.00137351e-06 6.40411732e-06\n", + " 8.49183701e-05 1.14782342e-04 7.78570730e-05 1.83952626e-06\n", + " 1.49161830e-04 9.73213413e-07 3.42587872e-05 1.49047302e-04\n", + " 2.45318616e-05 1.15971255e-04 1.41919653e-04 2.98003254e-04\n", + " 1.35013233e-04 1.51323524e-04 1.49047302e-04 1.49041129e-04\n", + " 2.30541452e-05 1.49614110e-04 1.34097232e-04 7.78570730e-05\n", + " 1.49069112e-04 1.70996549e-06 2.05208174e-05 1.35359661e-04\n", + " 2.31174767e-05 1.48838718e-04 5.73991113e-07 7.88302864e-05\n", 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2.48274262e-05\n", + " 7.11803678e-05 7.72053174e-05 1.34486464e-04 1.35013233e-04\n", + " 1.34486464e-04 1.50345636e-04 1.48838718e-04 1.49047302e-04\n", + " 1.49047302e-04 1.49069112e-04 1.52468366e-04 1.25636794e-07\n", + " 8.11112542e-05 1.37503202e-04 1.15971255e-04 5.44609585e-06\n", + " 1.41264619e-04 8.13743258e-05 1.35521724e-04 1.41919653e-04\n", + " 1.52573875e-04 1.49069112e-04 8.36606004e-05 7.88302864e-05\n", + " 8.26277535e-05 1.49041129e-04 1.73804813e-06 8.11112542e-05\n", + " 1.90518397e-04 8.13743258e-05 1.38388533e-04 1.41919653e-04\n", + " 1.35013233e-04 1.34486464e-04 1.35521724e-04 2.05208174e-05\n", + " 1.15971255e-04 1.38559114e-04 3.67905252e-06 1.35013233e-04\n", + " 1.48838718e-04 1.48838718e-04 7.88302864e-05 1.41439784e-04\n", + " 9.89736620e-07 3.38263586e-06 8.49183701e-05 6.75066013e-06\n", + " 8.49183701e-05 1.33405933e-04 5.25618586e-05 1.38701809e-04\n", + " 1.38559114e-04 1.35521724e-04 1.41919653e-04 7.70374155e-05\n", + " 1.47414645e-04 1.05141375e-05 1.32885434e-04 1.42040920e-04\n", + " 1.88108360e-05 3.38263586e-06 1.35521724e-04 8.23915048e-05\n", + " 1.38713813e-04 1.35013233e-04 1.02728232e-05 8.49183701e-05\n", + " 9.89736620e-07 1.83751504e-06 1.51575115e-04 1.13973751e-05\n", + " 8.26277535e-05 8.01098807e-05 7.88302864e-05 1.35521724e-04\n", + " 1.70996549e-06 1.34097232e-04 7.72053174e-05 1.38765443e-04\n", + " 1.40291103e-04 2.67371898e-04 1.49047302e-04 1.84009015e-06\n", + " 1.35013233e-04 1.42106330e-04 3.67733570e-06 9.73213413e-07\n", + " 1.49069112e-04 2.05208174e-05 8.61646432e-05 1.38640322e-04\n", + " 1.49001627e-04 1.42152686e-04 1.15971255e-04 9.89736620e-07\n", + " 1.49069112e-04 4.77797315e-06 1.48932096e-04 1.05141375e-05\n", + " 1.41732649e-04 6.44648552e-07 8.93923703e-05 7.85179851e-05\n", + " 4.02761687e-07 5.25618586e-05 1.33522021e-04 9.20508969e-05\n", + " 1.33908370e-04 1.43969147e-04 1.48932096e-04 1.76793647e-05\n", + " 1.49067054e-04 1.49001627e-04 7.88302864e-05 1.42100004e-04\n", + " 8.38699952e-05 1.38713813e-04 1.33522021e-04 8.11112542e-05\n", + " 7.88302864e-05 2.71043448e-04 3.25663467e-06 3.46145495e-06\n", + " 1.41264619e-04 1.34486464e-04 8.23362328e-07 8.38699952e-05\n", + " 1.35013233e-04 1.48859953e-04 8.01098807e-05 3.38263586e-06\n", + " 1.41439784e-04 5.39321871e-06 1.54017677e-04 1.04631204e-04\n", + " 8.56245022e-06 1.49069112e-04 1.41919653e-04 8.73992507e-05\n", + " 1.51802437e-04 1.41732649e-04 1.28309060e-04 1.48838718e-04\n", + " 8.11112542e-05 1.19884426e-05 1.36011867e-04 1.47414645e-04\n", + " 7.88302864e-05 8.49183701e-05 1.37711511e-04 1.34486464e-04\n", + " 1.48932096e-04 1.34486464e-04 1.32885434e-04 1.35521724e-04\n", + " 1.43650380e-04 8.11112542e-05 1.33405933e-04 3.38263586e-06\n", + " 7.88302864e-05 1.35359661e-04 1.41919653e-04 1.15971255e-04\n", + " 1.48932096e-04 7.88302864e-05 1.38559114e-04 8.63202476e-05\n", + " 1.46994157e-04 1.41264619e-04 1.48932096e-04 1.38767970e-04\n", + " 1.83751504e-06 1.46994157e-04 1.15971255e-04 2.30541452e-05\n", + " 8.45895981e-07 1.36011867e-04 1.38559114e-04 1.48991341e-04\n", + " 2.83413190e-06 8.13743258e-05 1.41876722e-04 1.43650380e-04\n", + " 2.27796911e-05 1.35521724e-04 8.63202476e-05 1.35521724e-04\n", + " 1.38640322e-04 9.80640283e-05 9.77517649e-07 1.38750760e-04\n", + " 1.52468366e-04 1.48932096e-04 1.51802437e-04 1.32885434e-04\n", + " 1.34486464e-04 5.42139328e-06 8.26277535e-05 1.33228456e-06\n", + " 1.37711511e-04 1.35013233e-04 1.49001627e-04 1.15971255e-04\n", + " 7.78570730e-05 1.12934770e-05 2.48274262e-05 8.36606004e-05\n", + " 9.89736620e-07 7.72053174e-05 8.38699952e-05 1.35521724e-04\n", + " 1.34097232e-04 7.30482481e-06 8.13743258e-05 1.43650380e-04\n", + " 5.06686849e-07 5.43417960e-06 9.89736620e-07 1.34097232e-04\n", + " 1.48534242e-04 1.49001627e-04 1.33908370e-04 1.34486464e-04\n", + " 1.34486464e-04 4.22213702e-08 9.89736620e-07 1.41919653e-04\n", + " 1.49047302e-04 7.62521653e-05 1.48184750e-04]\n", + "names of tiles in bin ['1_2_9' '1_1_4' '3_2_3' '3_2_5' '1_0_2' '3_2_2' '3_2_5' '3_2_5' '2_0_7'\n", + " '1_1_4' '1_1_3' '3_2_5' '1_0_1' '1_1_5' '1_9_8' '1_14_7' '1_4_4' '3_3_3'\n", + " '1_1_4' '1_1_0' '1_1_3' '1_11_3' '1_1_2' '3_1_5' '1_11_3' '1_5_7'\n", + " '1_10_3' '3_0_5' '3_0_3' '1_1_0' '1_4_4' '1_14_1' '1_1_3' '1_14_3'\n", + " '1_1_3' '1_1_0' '3_1_5' '3_0_4' '1_1_7' '1_0_1' '1_1_2' '3_0_4' '3_2_1'\n", + " '3_1_3' '1_1_3' '2_0_2' '1_1_5' '3_2_2' '1_1_1' '3_2_3' '3_1_4' '2_0_6'\n", + " '1_5_5' '3_0_5' '3_0_4' '2_0_7' '3_0_5' '1_1_3' '2_0_7' '1_11_3' '3_1_5'\n", + " '1_1_4' '2_3_3' '2_1_1' '1_9_3' '1_5_8' '3_2_1' '1_5_7' '1_0_2' '1_11_3'\n", + " '1_14_5' '1_4_10' '3_2_1' '1_14_4' '3_2_3' '1_1_5' '2_0_7' '2_0_7'\n", + " '3_1_4' '3_1_4' '3_2_3' '1_14_3' '1_14_7' '1_14_5' '3_2_2' '3_2_1'\n", + " '3_1_4' '3_2_3' '3_2_3' '1_1_4' '1_1_1' '2_0_7' '2_0_2' '1_5_5' '3_2_3'\n", + " '3_2_3' '2_0_7' '1_1_3' '1_11_8' '3_1_4' '3_0_5' '1_1_4' '1_14_2' '1_1_2'\n", + " '1_1_5' '1_14_4' '1_1_0' '3_1_5' '2_1_1' '1_1_7' '2_0_2' '3_0_4' '3_0_5'\n", + " '1_1_0' '3_2_2' '3_0_3' '3_0_5' '3_1_4' '1_1_1' '3_2_2' '3_0_5' '1_1_4'\n", + " '1_1_3' '1_1_3' '2_0_6' '3_2_2' '3_2_1' '2_0_7' '3_0_5' '3_2_2' '2_0_7'\n", + " '1_1_0' '1_1_6' '1_1_4' '3_3_1' '3_2_2' '3_1_4' '3_1_5' '3_1_4' '1_14_6'\n", + " '3_2_2' '1_14_4' '1_1_3' '1_1_5' '3_2_3' '1_0_2' '3_2_2' '2_0_4' '3_0_4'\n", + " '3_2_2' '1_14_2' '2_0_7' '1_4_10' '1_14_4' '1_10_2' '3_2_2' '1_1_7'\n", + " '1_1_4' '3_0_5' '3_2_2' '1_5_8' '3_2_2' '3_2_5' '1_1_4' '1_14_5' '3_1_4'\n", + " '1_14_2' '3_0_4' '2_0_3' '1_1_1' '1_1_5' '1_0_1' '3_0_4' '2_0_4' '1_1_3'\n", + " '1_1_5' '3_0_3' '1_1_7' '3_2_3' '3_2_2' '1_1_3' '1_0_1' '1_1_4' '1_1_3'\n", + " '3_2_3' '3_1_4' '3_2_2' '1_1_4' '1_3_10' '1_14_4' '1_5_6' '2_0_3' '2_3_3'\n", + " '1_1_4' '3_0_3' '1_1_3' '3_2_1' '3_0_1' '1_14_1' '3_0_2' '2_0_7' '1_14_1'\n", + " '2_0_4' '1_14_1' '2_0_7' '1_5_5' '1_14_1' '3_1_4' '2_0_7' '1_1_1' '1_1_2'\n", + " '1_1_5' '3_1_4' '1_5_7' '1_1_3' '3_1_4' '2_0_6' '1_14_6' '1_1_5' '1_14_4'\n", + " '3_2_3' '3_1_4' '1_10_8' '1_1_0' '1_14_3' '1_1_4' '2_0_2' '1_10_3'\n", + " '1_1_4' '1_0_2' '2_0_7' '1_1_3' '1_14_4' '3_1_5' '3_0_4' '3_0_4' '1_1_0'\n", + " '1_14_6' '3_1_4' '3_1_4' '2_0_3' '1_14_3' '3_1_4' '2_0_7' '3_0_3' '3_2_3'\n", + " '1_1_0' '3_0_4' '3_0_5' '1_14_1' '1_14_1' '1_1_0' '2_0_6' '3_2_2' '3_2_2'\n", + " '1_14_1' '2_0_7' '2_0_7' '1_14_1' '1_14_1' '3_2_2' '2_0_4' '2_0_5'\n", + " '3_2_4' '1_1_4' '1_0_1' '3_2_3' '1_1_5' '1_1_2' '1_1_5' '3_2_3' '1_5_6'\n", + " '3_0_3' '1_1_3' '1_5_6' '3_1_4' '2_0_3' '3_1_4' '3_2_3' '2_0_7' '3_2_4'\n", + " '1_1_4' '3_2_3' '3_2_4' '3_2_3' '3_2_3' '1_0_1' '1_1_4' '3_0_4' '1_1_5'\n", + " '1_1_3' '1_0_2' '1_1_2' '1_14_4' '2_2_2' '3_0_4' '1_1_4' '3_1_3' '1_1_5'\n", + " '1_14_5' '1_14_3' '1_14_2' '3_0_1' '1_14_2' '3_0_1' '3_2_5' '3_2_5'\n", + " '1_9_8' '3_2_1' '2_0_7' '2_0_4' '3_2_5' '3_2_2' '1_14_2' '1_5_7' '3_0_3'\n", + " '2_0_7' '1_4_4' '3_2_2' '2_0_7' '2_0_2' '3_1_4' '1_14_4' '1_1_4' '1_5_8'\n", + " '1_5_7' '2_0_2' '1_14_1' '3_0_4' '1_1_7' '1_1_5' '1_5_5' '3_0_4' '3_1_5'\n", + " '1_14_7' '1_14_3' '1_1_4' '1_1_3' '3_0_1' '1_1_4' '3_2_2' '3_0_4' '1_1_0'\n", + " '3_0_1' '1_11_9' '3_2_2' '3_2_3' '1_0_1' '1_1_8' '3_2_3' '1_5_7' '3_0_5'\n", + " '1_0_1' '1_0_1' '3_2_3' '3_1_5' '3_0_5' '1_1_5' '1_1_2' '1_14_7' '1_5_7'\n", + " '2_0_2' '1_1_3' '1_1_0' '1_0_1' '2_0_7' '2_0_7' '3_0_5' '1_1_0' '2_0_6'\n", + " '3_1_4' '2_1_2' '1_14_2' '1_1_0' '3_1_3' '1_1_1' '1_14_5' '1_0_0' '1_0_0'\n", + " '3_2_3' '1_9_9' '1_1_3' '1_0_3' '1_14_6' '1_0_0' '3_2_2' '1_14_4' '1_1_1'\n", + " '3_0_4' '1_14_5' '1_11_9' '2_2_2' '1_14_4' '1_1_0' '3_1_4' '3_2_5'\n", + " '3_2_5' '2_2_2' '3_1_4' '3_2_5' '1_1_1' '3_1_5' '3_0_4' '1_1_0' '3_2_3'\n", + " '3_2_5' '1_5_7' '3_2_4' '2_0_6' '1_1_3' '3_2_3' '3_1_5' '3_1_4' '3_1_4'\n", + " '3_0_3' '2_0_7' '1_11_9' '1_14_7' '2_0_7' '1_1_5' '1_0_2' '1_5_7' '3_0_4'\n", + " '3_1_4' '3_1_3' '1_14_4' '2_1_2' '1_1_3' '3_0_4' '3_1_4' '1_4_10' '1_1_4'\n", + " '2_0_3' '3_2_3' '1_14_5' '1_0_2' '1_14_4' '1_1_0' '1_14_3' '1_0_1'\n", + " '3_2_4' '1_0_1' '2_0_7' '1_5_6' '2_0_6' '3_2_3' '1_14_6' '1_9_8' '3_0_4'\n", + " '1_1_5' '1_1_7' '1_0_1' '1_14_1' '3_3_2' '1_1_3' '1_14_1' '1_10_8'\n", + " '1_1_3' '1_14_2' '2_3_3' '1_14_1' '1_14_1' '1_1_2' '1_2_3' '2_3_2'\n", + " '1_1_1' '1_0_4' '3_0_4' '3_1_5' '1_9_4' '3_0_3' '3_1_4' '1_1_5' '3_2_5'\n", + " '1_1_5' '1_1_5' '3_0_3' '1_1_0' '1_1_3' '1_9_3' '1_1_4' '1_1_1' '2_3_5'\n", + " '2_3_2' '1_1_4' '1_5_11' '3_1_2' '1_2_3' '2_0_7' '1_1_5' '2_0_7' '2_3_2'\n", + " '1_1_5' '1_14_3' '1_14_3' '2_1_1' '3_0_4' '1_1_4' '1_1_4' '3_0_5' '3_2_3'\n", + " '3_1_5' '3_0_4' '3_1_5' '3_1_5' '3_2_3' '1_14_6' '1_0_1' '1_1_0' '3_2_3'\n", + " '3_2_3' '3_0_5' '1_1_0' '1_1_3' '1_1_1' '3_2_4' '3_0_5' '2_0_7' '2_0_3'\n", + " '3_1_4' '1_1_1' '1_1_4' '1_1_4' '1_5_7' '3_1_4' '3_1_5' '1_14_6' '1_1_2'\n", + " '1_10_3' '1_0_2' '1_11_8' '3_0_5' '1_1_5' '3_0_5' '2_0_2' '2_0_7' '1_1_5'\n", + " '1_1_3' '2_2_2' '3_2_3' '2_0_7' '1_1_5' '2_0_7' '1_12_3' '1_14_5' '3_1_4'\n", + " '1_0_1' '1_1_4' '1_12_4' '2_0_7' '1_1_5' '1_1_8' '3_2_2' '1_14_5' '3_0_4'\n", + " '1_1_3' '1_1_1' '1_14_1' '1_1_4' '1_14_1' '1_14_7' '3_0_4' '1_14_1'\n", + " '1_0_1' '2_0_7' '2_0_6' '1_14_1' '3_1_4' '3_2_5' '3_1_4' '3_0_1' '3_1_5'\n", + " '3_2_5' '1_1_0' '3_1_3' '1_1_4' '1_1_0' '1_14_6' '3_1_5' '2_1_1' '3_0_4'\n", + " '3_1_4' '2_0_3' '3_2_5' '3_1_5' '3_2_3' '1_14_3' '2_0_2' '2_0_7' '2_0_2'\n", + " '1_2_9' '3_2_1' '3_1_4' '1_0_3' '1_1_0' '3_1_4' '2_3_3' '1_4_10' '1_3_3'\n", + " '2_0_6' '3_0_4' '2_0_7' '2_3_2' '3_0_5' '1_1_5' '1_0_2' '1_1_0' '2_0_7'\n", + " '1_1_2' '1_12_3' '1_1_4' '1_14_1' '1_1_0' '3_0_5' '1_1_5' '3_0_4' '3_2_3'\n", + " '1_1_5' '3_0_5' '3_0_3' '3_0_0' '1_5_5' '2_0_6' '3_0_4' '1_14_7' '3_1_4'\n", + " '3_0_4' '3_0_4' '3_0_0' '3_2_3' '2_3_2' '1_1_5' '3_0_4' '3_0_1' '3_0_1'\n", + " '1_9_4' '3_0_0' '3_0_3' '3_3_2' '1_1_3' '1_1_5' '2_3_2' '3_2_2' '1_0_1'\n", + " '1_2_3' '1_0_2' '1_1_4' '1_1_1' '3_2_3' '1_14_1' '2_0_4' '3_1_4' '1_9_4'\n", + " '3_1_4' '1_1_3' '1_1_0' '3_2_3' '1_14_1' '1_14_5' '1_1_3' '1_14_2'\n", + " '3_0_1' '3_1_5' '1_14_1' '1_14_1' '1_14_1' '1_10_8' '3_0_5' '2_0_2'\n", + " '2_0_7' '1_1_5' '1_1_5' '1_5_6' '1_1_3' '2_0_3' '1_1_6' '1_1_5' '1_0_3'\n", + " '1_14_7' '1_10_3' '3_3_1' '3_0_0' '1_1_5' '1_14_5' '1_1_3' '1_9_4'\n", + " '3_0_0' '3_0_3' '1_14_2' '1_14_3' '1_0_2' '2_1_2' '3_2_1' '1_1_4' '3_1_4'\n", + " '3_0_4' '2_0_7' '1_0_3' '2_2_2' '3_0_0' '3_1_5' '1_0_2' '1_14_4' '1_14_5'\n", + " '1_14_2' '3_2_2' '3_0_4' '3_0_5' '3_0_4' '3_0_4' '3_1_3' '2_2_1' '1_1_5'\n", + " '1_9_9' '1_5_6' '3_2_3' '1_10_2' '1_1_5' '1_14_3' '2_0_4' '3_1_5' '3_0_3'\n", + " '1_1_0' '1_1_4' '1_1_5' '3_0_4' '2_1_1' '1_1_5' '2_0_7' '1_1_5' '1_12_3'\n", + " '2_0_3' '1_14_5' '1_14_2' '1_14_4' '3_0_1' '1_5_7' '2_0_7' '3_0_5'\n", + " '1_14_7' '3_0_4' '3_0_3' '1_1_0' '1_10_3' '1_0_1' '2_0_7' '1_1_5'\n", + " '1_12_4' '1_1_7' '1_14_7' '3_1_5' '2_0_7' '2_0_7' '1_14_3' '2_0_4'\n", + " '3_1_5' '3_2_3' '3_1_5' '1_14_5' '2_1_2' '3_0_2' '2_0_7' '1_14_4' '1_1_5'\n", + " '2_0_7' '1_14_6' '3_0_4' '1_1_0' '1_0_3' '3_0_3' '3_1_4' '3_3_2' '1_1_4'\n", + " '1_1_5' '1_1_5' '1_14_4' '3_0_1' '2_3_3' '1_0_2' '2_0_7' '2_0_7' '1_14_3'\n", + " '3_0_4' '3_0_3' '1_14_6' '2_0_6' '3_0_4' '1_1_4' '3_0_4' '3_0_1' '1_1_2'\n", + " '2_0_7' '3_0_4' '1_11_8' '1_5_5' '1_0_1' '3_0_4' '2_0_7' '3_0_4' '3_1_5'\n", + " '1_10_3' '1_14_1' '3_1_5' '1_0_4' '1_14_1' '3_1_5' '2_3_4' '1_2_3'\n", + " '1_14_3' '2_0_2' '3_0_5' '1_14_1' '3_0_4' '3_0_4' '1_1_5' '1_11_9'\n", + " '1_1_6' '2_0_7' '2_3_4' '1_1_3' '1_1_5' '1_14_3' '2_3_7' '2_0_5' '1_10_8'\n", + " '1_14_4' '3_0_0' '1_1_2' '1_14_3' '3_2_2' '1_1_5' '2_0_7' '1_10_8'\n", + " '3_2_3' '3_1_4' '1_3_10' '3_2_5' '3_0_5' '2_0_6' '1_1_1' '3_1_4' '1_10_8'\n", + " '3_2_5' '3_0_4' '1_1_3' '3_2_5' '1_14_1' '3_2_3' '1_1_3' '1_1_6' '1_9_9'\n", + " '1_14_5' '3_0_3' '1_14_2' '1_14_1' '1_14_3' '2_0_3' '1_1_4' '1_14_6'\n", + " '2_0_7' '1_1_5' '1_9_9' '2_0_4' '1_5_4' '3_0_3' '1_1_6' '2_0_7' '1_1_1'\n", + " '3_2_3' '1_10_3' '3_0_4' '2_0_7' '3_0_4' '3_2_2' '1_5_4' '3_0_0' '3_0_5'\n", + " '1_14_1' '2_0_7' '3_0_4' '3_0_5' '1_1_3' '1_11_3' '2_0_5' '3_0_0'\n", + " '1_14_3' '1_1_2' '1_14_4' '2_0_7' '1_2_3' '1_0_4' '2_0_7' '3_1_4' '3_0_5'\n", + " '3_1_4' '1_14_5' '1_14_3' '3_2_3' '1_1_4' '3_0_5' '1_9_3' '1_14_1'\n", + " '3_0_5' '1_5_5' '1_1_3' '1_14_3' '3_0_0' '1_1_3' '1_0_5' '1_0_2' '1_1_5'\n", + " '1_14_2' '2_3_3' '3_1_3' '1_1_4' '2_0_4' '3_0_1' '3_2_3' '1_0_1' '2_3_2'\n", + " '3_2_5' '3_0_3' '1_14_6' '1_14_4' '1_14_6' '3_0_1' '1_0_3' '2_0_6'\n", + " '3_0_4' '1_0_1' '3_2_3']\n", + "dowsampled rms bin 16\n", + "areas of tiles in bin [8.61646432e-05 1.02728232e-05 1.34486464e-04 1.26371726e-04\n", + " 1.33405933e-04 1.40291103e-04 8.11112542e-05 9.89736620e-07\n", + " 1.48859953e-04 8.73992507e-05 1.37051400e-04 1.83866785e-06\n", + " 1.49069112e-04 1.52468366e-04 1.50345636e-04 7.58821969e-05\n", + " 1.34486464e-04 7.88302864e-05 1.35013233e-04 1.35521724e-04\n", + " 1.42040920e-04 1.35521724e-04 1.46550133e-04 7.72053174e-05\n", + " 1.41919653e-04 7.78570730e-05 1.12934770e-05 1.32885434e-04\n", + " 1.49001627e-04 1.43650380e-04 8.11112542e-05 1.38259625e-04\n", + " 8.86220259e-05 1.49067054e-04 2.48274262e-05 1.54173848e-04\n", + " 1.41919653e-04 1.34486464e-04 1.39945718e-04 1.34486464e-04\n", + " 3.38263586e-06 1.38259625e-04 1.41919653e-04 8.26277535e-05\n", + " 1.48838718e-04 1.41919653e-04 1.52184133e-04 1.34097232e-04\n", + " 1.52468366e-04 1.12072053e-05 8.61646432e-05 1.48534242e-04\n", + " 4.84229754e-06 1.49001627e-04 8.05483790e-07 1.39945718e-04\n", + " 8.49183701e-05 5.25418094e-06 8.49183701e-05 1.35013233e-04\n", + " 1.48991341e-04 7.75486273e-05 1.38259625e-04 1.06973574e-04\n", + " 7.88302864e-05 1.22683605e-05 1.67137886e-05 8.73992507e-05\n", + " 1.68788365e-04 1.00477674e-04 9.89736620e-07 7.85179851e-05\n", + " 1.41876722e-04 1.42044804e-04 1.48991341e-04 1.50345636e-04\n", + " 1.36483597e-04 1.52338445e-04 7.88302864e-05 1.34486464e-04\n", + " 1.38559114e-04 8.11112542e-05 8.51008821e-05 8.23915048e-05\n", + " 1.35521724e-04 8.11112542e-05 1.08624459e-04 1.15971255e-04\n", + " 3.50259209e-05 1.48838718e-04 1.41850307e-04 8.51008821e-05\n", + " 1.00238010e-04 1.34097232e-04 1.02728232e-05 1.52468366e-04\n", + " 1.35013233e-04 1.35521724e-04 1.54412115e-04 7.85179851e-05\n", + " 1.35098821e-04 1.35521724e-04 1.49067054e-04 1.34486464e-04\n", + " 1.01688248e-04 1.00856117e-05 1.69151596e-05 1.38559114e-04\n", + " 8.49183701e-05 8.11112542e-05 2.35496116e-05 8.49183701e-05\n", + " 9.64380235e-06 1.67137886e-05 1.54305335e-04 1.40353795e-04\n", + " 7.72053174e-05 1.49001627e-04 1.00477674e-04 1.35521724e-04\n", + " 1.22189706e-08 8.61646432e-05 1.34537732e-05 1.12934770e-05\n", + " 7.85179851e-05 1.38559114e-04 3.38263586e-06 1.48838718e-04\n", + " 1.94271620e-04 7.72053174e-05 5.45714011e-06 1.35098821e-04\n", + " 1.12583827e-05 1.36011867e-04 1.05684735e-04 1.35521724e-04\n", + " 8.38699952e-05 8.36606004e-05 1.42509267e-04 1.52005454e-04\n", + " 3.67733570e-06 1.01688248e-04 8.49183701e-05 1.49041129e-04\n", + " 1.69151596e-05 1.48932096e-04 8.86220259e-05 9.89736620e-07\n", + " 7.72053174e-05 5.07300229e-06 1.49067054e-04 1.18453136e-05\n", + " 8.61646432e-05 2.23130045e-05 1.48838718e-04 1.47953794e-06\n", + " 1.15971255e-04 1.05907195e-04 1.52573875e-04 1.49067054e-04\n", + " 7.75486273e-05 2.05208174e-05 1.43650380e-04 1.62831733e-06\n", + " 8.51008821e-05 1.48932096e-04 1.48991341e-04 1.70996549e-06\n", + " 7.88302864e-05 9.44946899e-05 1.24137131e-05 7.85179851e-05\n", + " 1.54305335e-04 1.48838718e-04 1.12072053e-05 1.54412115e-04\n", + " 7.78570730e-05 7.85179851e-05 3.68002788e-06 1.43650380e-04\n", + " 1.34486464e-04 1.72329286e-04 1.15971255e-04 1.33522021e-04\n", + " 1.13973751e-05 1.42152686e-04 1.43650380e-04 1.34653369e-04\n", + " 1.32885434e-04 1.41919653e-04 9.89736620e-07 1.15971255e-04\n", + " 1.48932096e-04 1.52338445e-04 1.34486464e-04 1.18453136e-05\n", + " 8.49183701e-05 1.48838718e-04 5.07300229e-06 8.49183701e-05\n", + " 1.69151596e-05 1.49041129e-04 1.43969147e-04 1.34486464e-04\n", 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9.89736620e-07\n", + " 3.68066801e-06 1.15971255e-04 9.60617597e-06 8.23915048e-05\n", + " 1.52573875e-04 1.06376103e-06 8.23915048e-05 1.52338445e-04\n", + " 1.18458466e-05 1.48991341e-04 1.41264619e-04 8.61646432e-05\n", + " 1.02728232e-05 1.35013233e-04 1.48932096e-04 2.48274262e-05\n", + " 1.02728232e-05 1.48932096e-04 2.45603299e-05 1.34486464e-04\n", + " 7.58821969e-05 1.49041129e-04 1.15971255e-04 1.41948762e-04\n", + " 1.48932096e-04 1.09062742e-04 1.37051400e-04 2.77118228e-04\n", + " 8.16329432e-07 1.12583827e-05 1.03284692e-06 6.42920157e-06\n", + " 1.42150160e-04 4.80308799e-06 1.40076198e-04 1.18453136e-05\n", + " 7.58821969e-05 9.89736620e-07 1.69151596e-05 1.35521724e-04\n", + " 1.34486464e-04 1.48838718e-04 1.01688248e-04 5.07300229e-06\n", + " 8.63202476e-05 1.52338445e-04 1.41941422e-04 1.40863925e-04\n", + " 9.77517649e-07 1.48838718e-04 2.30541452e-05 1.83939927e-06\n", + " 8.73992507e-05 7.60587890e-07 6.92290989e-06 1.08025470e-04\n", + " 7.62521653e-05 4.85756668e-06 1.38750760e-04 1.12072053e-05\n", + " 1.49041129e-04 1.35521724e-04 1.35013233e-04 1.34486464e-04\n", + " 8.38699952e-05 1.34486464e-04 1.41919653e-04 3.38263586e-06\n", + " 1.35013233e-04 1.48932096e-04 1.42088007e-04 4.64465085e-07\n", + " 7.58821969e-05 8.38699952e-05 8.36606004e-05 6.68237122e-05\n", + " 5.84836586e-06 2.45603299e-05 5.84590709e-06 1.35521724e-04\n", + " 1.38388533e-04 1.49001627e-04 9.77517649e-07 1.48534242e-04\n", + " 1.38559114e-04 1.84035941e-06 9.89736620e-07 8.23915048e-05\n", + " 8.38699952e-05 1.41919653e-04 1.54173848e-04 7.78570730e-05\n", + " 1.47811531e-04 7.88302864e-05 1.52005454e-04 7.88302864e-05\n", + " 1.48838718e-04 1.49001627e-04 1.35521724e-04 1.22189706e-08\n", + " 8.38699952e-05 1.35521724e-04 8.51008821e-05 8.26277535e-05\n", + " 8.49183701e-05 1.35521724e-04 3.46039218e-05 1.07315731e-04\n", + " 1.03731569e-04 6.42920157e-06 1.35013233e-04 7.88302864e-05\n", + " 1.33522021e-04 7.72053174e-05 7.58821969e-05 1.43650380e-04\n", + " 2.45603299e-05 1.43650380e-04 1.52468366e-04 5.40773892e-06\n", + " 1.34486464e-04 1.38559114e-04 1.38259625e-04 1.42234579e-04\n", + " 1.41919653e-04 1.15971255e-04 1.51802437e-04 1.33908370e-04\n", + " 3.46145495e-06 1.48786768e-04 1.49067054e-04 9.92274681e-05\n", + " 1.35521724e-04 1.48838718e-04 1.33908370e-04 1.49069112e-04\n", + " 8.36606004e-05 1.38388533e-04 1.36778656e-04 8.13743258e-05\n", + " 1.23779099e-04 2.36615439e-05 1.84001394e-06 1.35521724e-04\n", + " 2.72023734e-04 1.49001627e-04 7.88302864e-05 1.41919653e-04\n", + " 1.41919653e-04 1.38388533e-04 7.85179851e-05 2.45603299e-05\n", + " 3.46145495e-06 1.48184750e-04 8.38699952e-05 7.85179851e-05\n", + " 1.42135966e-04 1.34486464e-04 1.48932096e-04 1.35521724e-04\n", + " 1.52573875e-04 1.42169354e-04 7.58821969e-05 1.42219529e-04\n", + " 8.61646432e-05 6.40411732e-06 1.35521724e-04 2.52374158e-08\n", + " 1.48932096e-04 8.73992507e-05 7.58821969e-05 1.48932096e-04\n", + " 9.89736620e-07 1.36011867e-04 7.88302864e-05 1.52184133e-04\n", + " 7.85179851e-05 8.63202476e-05 8.61646432e-05 1.42190828e-04\n", + " 1.35521724e-04 1.36011867e-04 9.77517649e-07 1.13973751e-05\n", + " 1.42190828e-04 1.34486464e-04 1.15971255e-04 1.33908370e-04\n", + " 1.18363036e-06 3.68066801e-06 5.07500015e-06 8.49183701e-05\n", + " 1.44964069e-06 1.53908255e-05 1.71001315e-06 1.49041129e-04\n", + " 1.40477685e-04 8.73992507e-05 1.34486464e-04 1.42027999e-04\n", + " 1.35013233e-04 1.48838718e-04 8.63202476e-05 1.34486464e-04\n", + " 1.34097232e-04 1.48932096e-04 8.73992507e-05 1.38388533e-04\n", + " 1.12583827e-05 1.43915581e-04 1.32885434e-04 1.38259625e-04\n", + " 1.35521724e-04 7.88302864e-05 8.23915048e-05 1.48932096e-04\n", + " 9.19119730e-06 8.01098807e-05 8.01098807e-05 8.36606004e-05\n", + " 1.41919653e-04 1.15971255e-04 1.38259625e-04 1.37503202e-04\n", + " 1.35521724e-04 1.36011867e-04 1.34486464e-04 7.49453797e-05\n", + " 1.12583827e-05 1.34486464e-04 7.88302864e-05 1.52184133e-04\n", + " 1.49001627e-04 1.52573875e-04 1.41264619e-04 9.71513336e-06\n", + " 8.36606004e-05 7.75486273e-05 3.50948471e-06 1.48838718e-04\n", + " 1.37711511e-04 1.38259625e-04 1.52573875e-04 7.72053174e-05\n", + " 1.38559114e-04 1.07900309e-06 1.34486464e-04 3.27066420e-06\n", + " 1.41439784e-04 4.89060173e-06 1.51764394e-04 1.42169354e-04\n", + " 9.68012314e-06 1.36483597e-04 1.35521724e-04 1.02728232e-05\n", + " 1.84001394e-06 1.35013233e-04 8.61646432e-05 7.62521653e-05\n", + " 1.40076198e-04 8.23915048e-05 8.49183701e-05 1.35521724e-04\n", + " 1.42190828e-04 1.38259625e-04 7.85179851e-05 8.49183701e-05\n", + " 1.49001627e-04 1.39709222e-04 1.38388533e-04 1.43953913e-04\n", + " 8.38699952e-05 8.86220259e-05 7.88302864e-05 1.34486464e-04\n", + " 1.43903127e-04 1.36206561e-04 1.41439784e-04 1.43650380e-04\n", + " 1.13973751e-05 1.34486464e-04 1.36011867e-04 1.35013233e-04\n", + " 1.48932096e-04 1.32885434e-04 1.13343222e-05 1.36685639e-04\n", + " 1.42237169e-04 1.43650380e-04 1.48991341e-04 1.35013233e-04\n", + " 1.35521724e-04 1.12583827e-05 1.12583827e-05 8.49183701e-05\n", + " 8.36606004e-05 1.49001627e-04 7.75486273e-05 1.90518782e-04\n", + " 8.01098807e-05 1.35013233e-04 8.49183701e-05 1.36483597e-04\n", + " 8.63202476e-05 8.86220259e-05 8.23915048e-05 1.84035941e-06\n", + " 1.02728232e-05 1.34486464e-04 1.33908370e-04 7.85179851e-05\n", + " 1.35521724e-04 5.84590709e-06 9.80640283e-05 6.68237122e-05\n", + " 5.84836586e-06 1.34392678e-04 1.49067054e-04 8.75279269e-05\n", + " 1.38388533e-04 8.61646432e-05 9.89736620e-07 1.41595801e-04\n", + " 1.13973751e-05 1.43971769e-04 1.36011867e-04 2.97677437e-04\n", + " 8.36606004e-05 7.72053174e-05 9.15695507e-06 8.38699952e-05\n", + " 9.92274681e-05 9.89736620e-07 1.41732649e-04 1.35013233e-04\n", + " 1.04837494e-06 1.35013233e-04 1.17420896e-04 1.35521724e-04\n", + " 1.43969147e-04 9.89736620e-07 8.38699952e-05 1.37276278e-04\n", + " 1.42106330e-04 1.48932096e-04 1.38259625e-04 1.41919653e-04\n", + " 1.35521724e-04 8.73992507e-05 1.43650380e-04 8.63202476e-05\n", + " 1.33522021e-04 1.32461900e-04 8.75279269e-05 7.72053174e-05\n", + " 1.12583827e-05 1.49991881e-04 4.87441416e-06 1.35521724e-04\n", + " 1.41439784e-04 1.33405933e-04 9.89736620e-07 8.11112542e-05\n", + " 1.43650380e-04 8.63202476e-05 1.35521724e-04 1.06723959e-04\n", + " 1.49067054e-04 1.34097232e-04 8.26277535e-05 1.48932096e-04\n", + " 1.41595801e-04 1.42152686e-04 1.49067054e-04 8.36606004e-05\n", + " 1.34486464e-04 1.36011867e-04 2.45603299e-05 8.51008821e-05\n", + " 7.58821969e-05 7.33417508e-06 1.33522021e-04 1.48991341e-04\n", + " 1.02728232e-05 8.49183701e-05 8.26277535e-05 1.35013233e-04\n", + " 1.34486464e-04 1.34486464e-04 1.48838718e-04 1.41919653e-04\n", + " 1.36011867e-04 1.41264619e-04 1.42150160e-04 1.42384017e-04\n", + " 1.36011867e-04 7.72053174e-05 8.11112542e-05 1.35521724e-04\n", + " 1.41948762e-04 1.76149953e-04 7.72053174e-05 1.38259625e-04\n", + " 7.62521653e-05 1.36011867e-04 8.38699952e-05 8.01098807e-05\n", + " 1.41919653e-04 1.36011867e-04 7.95144397e-07 7.85179851e-05\n", + " 1.12583827e-05 4.89923383e-05 8.11112542e-05 1.35521724e-04\n", + " 1.35521724e-04 1.36778656e-04 1.34486464e-04 7.58821969e-05\n", + " 1.42219529e-04 7.58821969e-05 1.35521724e-04 1.42135966e-04\n", + " 1.33522021e-04 1.50827577e-04 1.36778656e-04 1.15971255e-04]\n", + "names of tiles in bin ['1_1_1' '3_0_4' '1_14_3' '3_0_5' '1_14_6' '2_0_7' '1_1_5' '1_0_3' '3_2_2'\n", + " '1_1_2' '2_0_7' '3_0_3' '3_0_4' '3_1_4' '3_2_2' '1_0_1' '1_14_2' '1_1_0'\n", + " '1_14_7' '1_14_4' '2_1_2' '1_14_2' '3_2_2' '1_0_1' '2_0_5' '1_1_5'\n", + " '1_14_6' '1_14_3' '3_0_4' '2_0_6' '1_1_3' '1_12_4' '1_1_3' '3_0_5'\n", + " '3_0_0' '3_1_4' '2_0_3' '1_14_2' '1_12_4' '1_14_3' '2_0_7' '1_12_6'\n", + " '2_0_3' '1_1_3' '3_0_4' '2_0_5' '3_1_4' '2_3_3' '3_1_4' '1_14_5' '1_1_1'\n", + " '3_2_2' '1_14_1' '3_0_4' '1_14_1' '1_12_4' '1_1_0' '1_14_1' '1_1_5'\n", + " '1_14_7' '3_0_4' '1_0_3' '1_12_4' '1_3_3' '1_1_3' '1_14_1' '1_14_1'\n", + " '1_1_1' '1_1_6' '1_14_1' '1_0_4' '1_0_3' '1_11_9' '1_11_3' '3_0_4'\n", + " '3_2_3' '1_14_1' '3_1_4' '1_1_5' '1_14_1' '2_0_7' '1_1_5' '1_1_4' '1_1_5'\n", + " '1_14_5' '1_1_3' '2_0_2' '1_5_4' '2_0_2' '3_0_4' '1_10_3' '1_1_0' '1_3_3'\n", + " '2_3_3' '3_0_4' '3_1_4' '1_14_8' '1_14_2' '3_1_4' '1_0_3' '1_9_4'\n", + " '1_14_4' '3_0_3' '1_14_5' '1_14_1' '1_14_1' '1_14_1' '2_0_7' '1_1_6'\n", + " '1_1_4' '1_14_1' '1_1_0' '1_14_7' '1_14_1' '3_1_4' '1_11_9' '1_0_2'\n", + " '3_0_4' '1_14_1' '1_14_2' '1_0_4' '1_1_2' '1_10_3' '1_14_5' '1_0_1'\n", + " '2_0_7' '2_0_7' '3_0_4' '2_0_7' '1_0_2' '3_2_3' '1_9_4' '3_3_2' '1_14_1'\n", + " '1_3_10' '1_14_6' '1_1_6' '1_1_5' '2_0_2' '3_1_4' '3_0_5' '1_14_1'\n", + " '1_1_5' '3_0_4' '1_14_1' '3_0_3' '1_1_1' '1_0_4' '1_0_1' '1_14_1' '3_0_5'\n", + " '1_14_1' '1_1_0' '3_2_3' '3_0_4' '3_1_4' '1_5_5' '1_3_3' '3_1_4' '3_0_4'\n", + " '1_0_3' '3_0_1' '2_0_3' '2_3_4' '1_1_0' '3_0_4' '3_0_4' '3_0_1' '1_1_5'\n", + " '1_2_3' '3_0_0' '1_0_4' '3_1_4' '3_0_3' '1_14_3' '3_1_4' '1_1_5' '1_0_3'\n", + " '3_0_4' '2_0_4' '1_14_3' '1_1_7' '1_5_4' '2_3_4' '3_3_5' '1_11_3' '2_0_4'\n", + " '2_3_2' '1_14_1' '2_0_5' '1_0_4' '1_5_4' '3_0_3' '3_1_4' '1_14_4'\n", + " '1_14_1' '1_1_5' '3_0_4' '1_14_1' '1_1_6' '1_14_1' '3_0_4' '2_0_6'\n", + " '1_14_6' '1_9_4' '1_14_1' '1_1_0' '2_1_2' '1_5_6' '1_5_6' '2_0_6' '2_2_2'\n", + " '1_14_4' '1_10_2' '1_14_2' '1_1_5' '1_1_0' '3_2_5' '1_14_5' '1_1_6'\n", + " '1_1_5' '3_0_3' '1_0_3' '1_14_2' '2_0_1' '1_0_3' '1_1_6' '3_0_4' '1_1_2'\n", + " '3_0_5' '1_1_4' '1_11_8' '1_14_4' '1_14_5' '3_0_0' '3_1_4' '1_14_4'\n", + " '2_0_4' '1_1_4' '3_0_5' '1_1_0' '3_0_0' '1_14_5' '1_14_5' '2_3_8' '3_0_4'\n", + " '1_1_6' '1_14_3' '1_1_3' '1_0_2' '3_0_4' '1_1_0' '2_1_1' '1_0_1' '3_1_3'\n", + " '1_0_4' '3_0_4' '1_14_3' '2_0_5' '1_14_6' '1_14_4' '2_0_6' '3_1_4'\n", + " '1_0_1' '3_0_4' '1_5_7' '1_14_2' '1_1_5' '3_1_4' '1_1_5' '1_1_3' '3_1_4'\n", + " '1_11_9' '3_0_4' '1_10_3' '1_1_1' '3_0_4' '1_14_6' '3_0_4' '3_0_0'\n", + " '3_0_4' '3_0_4' '3_1_4' '1_14_3' '1_0_2' '3_0_3' '1_5_4' '1_10_3' '3_0_4'\n", + " '1_3_3' '2_0_7' '2_0_7' '3_2_3' '3_3_5' '1_1_4' '1_14_8' '1_11_8'\n", + " '1_14_2' '1_12_3' '1_14_1' '1_0_1' '1_0_2' '1_14_1' '1_14_6' '1_14_2'\n", + " '3_0_3' '1_14_1' '1_14_1' '1_1_1' '3_1_3' '2_1_1' '2_2_2' '1_0_1' '3_0_5'\n", + " '3_0_0' '3_0_4' '1_1_1' '3_0_5' '2_0_4' '1_3_3' '1_0_2' '1_14_2' '2_0_7'\n", + " '1_14_6' '3_0_4' '1_14_7' '1_14_8' '1_14_3' '1_1_5' '1_14_1' '2_0_6'\n", + " '2_0_7' '1_14_6' '3_0_4' '1_10_3' '3_0_2' '1_0_1' '1_1_6' '1_1_0' '1_0_0'\n", + " '1_0_0' '3_1_4' '1_0_0' '1_14_2' '1_12_3' '3_0_3' '1_0_1' '3_2_3' '2_0_7'\n", + " '3_0_4' '1_0_3' '1_1_5' '1_1_6' '2_0_6' '3_1_4' '1_1_5' '3_2_3' '1_1_6'\n", + " '3_1_3' '1_1_6' '3_0_3' '3_0_4' '1_14_2' '1_0_1' '1_1_0' '1_14_6' '1_1_2'\n", + " '1_1_5' '1_1_5' '1_14_7' '2_0_2' '2_0_2' '1_3_3' '1_14_6' '1_14_8'\n", + " '1_1_5' '2_3_6' '1_0_2' '1_0_1' '2_0_6' '3_1_3' '2_0_3' '3_1_4' '3_2_3'\n", + " '1_14_2' '2_0_7' '1_12_4' '2_0_2' '2_0_3' '1_5_4' '3_1_4' '1_14_2'\n", + " '2_0_3' '3_2_2' '3_0_4' '1_2_3' '1_14_7' '3_0_3' '1_14_5' '3_0_4' '1_1_5'\n", + " '1_12_3' '2_3_8' '1_1_3' '3_0_5' '1_1_0' '3_0_3' '1_14_5' '1_14_1'\n", + " '3_0_4' '1_1_5' '2_0_3' '2_0_4' '1_12_3' '1_0_1' '3_1_4' '2_0_4' '3_2_3'\n", + " '1_1_7' '1_0_4' '1_11_3' '1_14_4' '3_0_4' '1_14_6' '3_1_4' '2_0_1'\n", + " '1_0_1' '2_0_1' '1_1_0' '1_14_3' '1_14_5' '1_1_0' '3_0_4' '1_1_1' '1_0_2'\n", + " '3_0_4' '1_0_3' '1_14_1' '1_1_4' '3_1_4' '1_0_3' '1_1_0' '1_1_0' '2_0_2'\n", + " '1_14_2' '1_14_1' '1_0_3' '3_3_4' '2_0_1' '1_14_3' '1_5_7' '1_14_7'\n", + " '3_1_3' '3_0_5' '1_11_8' '1_1_5' '1_5_5' '3_0_3' '3_0_3' '3_0_3' '2_0_1'\n", + " '1_1_2' '1_14_3' '1_10_3' '1_14_1' '3_0_3' '1_1_1' '1_14_5' '2_3_2'\n", + " '3_0_5' '1_1_2' '1_12_3' '3_3_3' '2_0_1' '1_14_2' '1_12_4' '1_14_5'\n", + " '1_1_0' '1_1_5' '3_0_3' '3_1_4' '1_1_3' '1_1_0' '1_1_0' '2_0_5' '1_5_4'\n", + " '1_12_4' '1_9_3' '1_14_2' '1_14_4' '1_14_2' '1_0_1' '3_3_2' '1_14_3'\n", + " '1_1_4' '3_1_3' '3_0_5' '3_1_4' '1_10_8' '1_14_2' '1_1_5' '1_0_3' '2_0_2'\n", + " '3_0_3' '1_9_3' '1_12_6' '3_1_4' '1_0_1' '2_0_7' '1_1_0' '1_14_5' '2_3_3'\n", + " '1_10_3' '1_14_2' '1_0_1' '2_0_2' '1_14_4' '1_14_1' '1_14_6' '3_0_4'\n", + " '3_0_4' '1_14_8' '1_1_8' '1_0_2' '1_12_3' '1_1_3' '1_1_7' '1_14_5'\n", + " '2_0_1' '1_12_4' '1_0_4' '1_1_6' '3_0_4' '2_2_2' '1_12_3' '2_0_2' '1_1_6'\n", + " '1_1_2' '1_1_4' '1_14_2' '2_0_1' '2_3_2' '1_10_3' '2_0_5' '3_3_2'\n", + " '1_14_2' '1_14_6' '1_14_8' '3_0_4' '1_14_4' '1_14_3' '2_3_2' '2_0_1'\n", + " '2_0_5' '3_0_4' '1_14_7' '1_14_4' '3_3_5' '3_3_4' '1_1_6' '1_1_5' '3_0_5'\n", + " '1_0_3' '2_0_7' '1_1_3' '1_14_8' '1_1_0' '1_14_4' '1_1_2' '1_1_2' '1_1_0'\n", + " '3_0_3' '3_0_3' '1_14_3' '1_14_4' '1_0_4' '1_14_7' '1_0_0' '1_2_9'\n", + " '1_0_0' '1_0_0' '1_14_3' '3_0_3' '1_1_2' '1_12_9' '1_1_2' '1_0_4'\n", + " '1_10_3' '3_3_4' '2_0_1' '1_14_4' '3_0_4' '1_1_5' '1_0_0' '3_1_4' '1_1_7'\n", + " '1_2_9' '1_0_3' '1_10_8' '1_14_7' '1_1_0' '1_14_3' '1_5_4' '1_14_4'\n", + " '2_0_1' '1_0_1' '1_1_0' '1_9_9' '2_0_1' '3_0_5' '1_12_8' '2_0_4' '1_14_5'\n", + " '1_1_1' '2_0_5' '1_1_1' '2_3_6' '2_3_2' '1_1_1' '1_0_0' '3_3_2' '3_2_3'\n", + " '1_14_2' '1_14_3' '1_10_3' '1_14_8' '1_0_3' '1_1_6' '2_0_4' '1_1_8'\n", + " '1_14_2' '1_3_10' '3_0_4' '2_3_2' '1_1_5' '3_0_4' '1_10_8' '1_11_3'\n", + " '3_0_4' '1_1_5' '1_14_1' '1_14_3' '3_1_3' '1_1_7' '1_0_1' '3_1_3' '2_3_6'\n", + " '3_0_3' '3_0_4' '1_1_7' '1_1_5' '1_14_8' '1_14_5' '1_14_5' '3_0_4'\n", + " '2_0_5' '1_14_4' '1_10_3' '1_11_3' '2_2_2' '1_14_2' '1_0_1' '1_1_5'\n", + " '1_14_3' '1_10_3' '1_1_2' '1_0_2' '1_12_7' '1_0_1' '1_14_6' '1_1_7'\n", + " '1_1_5' '2_0_5' '1_14_1' '1_1_0' '1_0_2' '3_3_2' '3_0_5' '1_1_0' '1_14_7'\n", + " '1_14_5' '2_3_8' '1_14_5' '1_0_1' '2_0_1' '1_0_2' '1_14_6' '1_11_3'\n", + " '2_3_7' '1_0_1' '2_3_8' '1_5_4']\n", + "dowsampled rms bin 17\n", + "areas of tiles in bin [1.40477685e-04 3.50948471e-06 1.37030768e-04 3.67879854e-06\n", + " 7.85179851e-05 1.33522021e-04 1.35521724e-04 1.34486464e-04\n", + " 8.75279269e-05 9.89736620e-07 7.58821969e-05 1.40477685e-04\n", + " 1.41948762e-04 1.34486464e-04 1.36011867e-04 1.97947324e-06\n", + " 1.34097232e-04 8.49183701e-05 1.35521724e-04 1.41948762e-04\n", + " 7.85179851e-05 8.43998093e-07 1.35521724e-04 1.33522021e-04\n", + " 1.41850307e-04 8.86220259e-05 8.63202476e-05 1.43953913e-04\n", + " 2.61745685e-06 1.36778656e-04 7.85179851e-05 1.35521724e-04\n", + " 7.49453797e-05 7.85179851e-05 1.41264619e-04 8.49183701e-05\n", + " 1.35521724e-04 8.75279269e-05 1.34486464e-04 8.73992507e-05\n", + " 1.41732649e-04 1.34486464e-04 1.43650380e-04 1.34486464e-04\n", + " 7.58821969e-05 1.00377474e-04 1.36483597e-04 1.35521724e-04\n", + " 7.78570730e-05 7.75486273e-05 3.14091986e-07 1.13973751e-05\n", + " 1.48838718e-04 1.33522021e-04 1.35521724e-04 8.73992507e-05\n", + " 7.72053174e-05 1.12583827e-05 1.48838718e-04 1.21026782e-04\n", + " 5.84590709e-06 6.68237122e-05 5.84836586e-06 8.23915048e-05\n", + " 7.58821969e-05 7.85179851e-05 1.36011867e-04 8.49183701e-05\n", + " 1.34486464e-04 4.88495200e-06 7.49453797e-05 1.34392678e-04\n", + " 2.77984758e-07 7.85179851e-05 1.38259625e-04 3.50944940e-06\n", + " 1.42120215e-04 1.36011867e-04 1.36032102e-04 8.11112542e-05\n", + " 1.36011867e-04 1.38259625e-04 1.12583827e-05 7.72053174e-05\n", + " 7.75486273e-05 1.34486464e-04 9.68012314e-06 1.43903127e-04\n", + " 1.34486464e-04 1.12583827e-05 5.05827896e-06 1.33522021e-04\n", + " 1.42234579e-04 5.84590709e-06 1.35013233e-04 8.23915048e-05\n", + " 8.11112542e-05 7.35295784e-06 6.68237122e-05 5.84836586e-06\n", + " 9.44946899e-05 1.12583827e-05 1.34486464e-04 1.42152686e-04\n", + " 6.93269340e-06 9.77517649e-07 6.45341543e-06 1.42120215e-04\n", + " 7.72053174e-05 1.42234579e-04 2.84474339e-04 1.04826478e-04\n", + " 9.77517649e-07 1.35521724e-04 1.33908370e-04 9.74882832e-06\n", + " 8.38699952e-05 9.53152067e-07 7.34787308e-06 1.33522021e-04\n", + " 1.04837494e-06 1.42234579e-04 1.41919653e-04 7.34161182e-06\n", + " 1.33522021e-04 9.89736620e-07 9.89736620e-07 7.72053174e-05\n", + " 1.43650380e-04 8.01098807e-05 1.13973751e-05 1.13973751e-05\n", + " 1.43969147e-04 1.38259625e-04 9.89736620e-07 1.39945718e-04\n", + " 1.43953913e-04 1.41919653e-04 1.42135966e-04 1.43839334e-04\n", + " 1.33522021e-04 1.41970373e-04 8.38699952e-05 8.36606004e-05\n", + " 6.92290989e-06 8.01098807e-05 1.35013233e-04 1.07315731e-04\n", + " 1.38388533e-04 9.89736620e-07 5.44584589e-05 3.46039218e-05\n", + " 7.58821969e-05 1.36011867e-04 6.47675557e-06 2.68972927e-04\n", + " 3.37218598e-06 4.82190118e-06 1.35013233e-04 7.49453797e-05\n", + " 1.34486464e-04 1.50827577e-04 1.33522021e-04 1.41919653e-04\n", + " 7.72053174e-05 1.43903127e-04 1.39945718e-04 1.41941422e-04\n", + " 8.49183701e-05 1.41975139e-04 1.42234579e-04 1.48932096e-04\n", + " 1.34486464e-04 7.58821969e-05 8.73992507e-05 1.32885434e-04\n", + " 1.41595801e-04 1.39945718e-04 8.23915048e-05 8.23915048e-05\n", + " 6.74437195e-06 1.34486464e-04 1.33647424e-04 1.16967317e-05\n", + " 5.84590709e-06 7.72053174e-05 1.34486464e-04 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1.41850307e-04\n", + " 1.38259625e-04]\n", + "names of tiles in bin ['2_0_1' '2_0_1' '1_9_9' '3_0_4' '1_0_4' '2_3_6' '1_14_7' '1_14_2' '1_1_2'\n", + " '1_0_4' '1_0_2' '2_0_1' '1_10_3' '1_14_5' '1_14_7' '1_0_4' '2_3_2'\n", + " '1_1_6' '1_14_5' '1_10_8' '1_0_3' '2_0_2' '1_14_6' '2_3_7' '1_10_3'\n", + " '1_1_1' '1_1_1' '2_0_1' '2_0_2' '2_3_8' '1_0_3' '1_14_7' '1_0_1' '1_0_4'\n", + " '1_10_2' '1_1_6' '1_14_6' '1_1_2' '1_14_1' '1_1_0' '1_10_3' '1_14_2'\n", + " '2_0_6' '1_14_7' '1_0_2' '1_2_3' '1_14_1' '1_14_2' '1_1_6' '1_0_4'\n", + " '2_2_2' '3_3_5' '3_0_5' '2_3_5' '1_14_2' '1_1_1' '1_0_2' '3_3_2' '3_0_5'\n", + " '3_0_5' '1_0_0' '1_0_0' '1_0_0' '1_1_5' '1_0_1' '1_0_3' '1_14_3' '1_1_0'\n", + " '1_14_5' '2_3_7' '1_0_2' '1_14_5' '3_3_5' '1_0_4' '1_12_6' '2_1_2'\n", + " '2_1_2' '1_14_3' '1_9_3' '1_1_5' '1_14_1' '1_12_7' '3_3_3' '1_0_2'\n", + " '1_0_3' '1_14_6' '1_14_7' '2_0_1' '1_14_1' '3_3_5' '1_12_8' '2_3_7'\n", + " '2_0_1' '1_0_0' '1_14_7' '1_1_5' '1_1_0' '3_1_3' '1_0_0' '1_0_0' '1_2_9'\n", + " '3_3_4' '1_14_1' '1_11_9' '2_1_2' '1_0_3' '1_14_3' '2_1_1' '1_0_1'\n", + " '2_0_1' '2_0_1' '1_3_3' '1_0_0' '1_14_7' '1_14_6' '1_14_2' '1_1_6'\n", + " '1_0_1' '3_1_3' '2_3_4' '1_1_6' '2_0_1' '2_0_5' '3_1_3' '2_3_6' '1_0_3'\n", + " '1_0_4' '1_0_4' '2_0_5' '1_1_6' '3_3_3' '3_3_2' '2_0_1' '1_12_8' '1_0_2'\n", + " '1_12_4' '2_0_1' '2_0_5' '1_11_3' '2_0_1' '2_3_5' '1_11_9' '1_1_0'\n", + " '1_1_6' '2_0_5' '1_1_5' '1_14_8' '2_0_2' '1_12_9' '1_0_0' '1_1_0' '2_0_2'\n", + " '1_0_1' '1_14_4' '1_14_1' '1_14_1' '1_12_6' '1_14_4' '1_14_8' '1_0_2'\n", + " '1_14_5' '1_0_1' '2_3_5' '2_0_6' '1_0_3' '2_0_1' '1_12_6' '2_1_1' '1_1_0'\n", + " '1_14_6' '2_0_2' '3_0_4' '1_14_6' '1_0_2' '1_1_0' '1_14_6' '1_10_3'\n", + " '1_12_6' '1_1_6' '1_1_5' '1_12_7' '1_14_1' '1_0_0' '1_0_0' '1_0_0'\n", + " '1_0_2' '1_14_5' '1_0_3' '1_0_0' '1_14_4' '1_1_5' '1_12_7' '2_3_6'\n", + " '1_12_6' '3_0_4' '1_0_3' '2_1_1' '1_1_2' '1_0_2' '1_0_3' '1_14_9' '2_1_1'\n", + " '1_14_6' '2_1_2' '1_0_4' '1_0_0' '1_14_4' '1_14_2' '1_12_6' '1_14_2'\n", + " '1_14_8' '2_1_2' '2_0_1' '1_1_5' '1_0_4' '1_5_4' '1_14_8' '3_1_4' '2_3_6'\n", + " '1_14_1' '2_0_1' '1_14_7' '1_10_2' '1_11_3' '1_1_0' '1_1_1' '1_1_6'\n", + " '2_3_4' '1_12_7' '1_1_1' '1_1_2' '2_3_6' '1_12_7' '1_1_0' '3_3_3'\n", + " '1_14_6' '2_0_1' '1_1_2' '1_14_5' '2_3_5' '1_14_2' '2_0_6' '1_0_5'\n", + " '2_1_1' '1_0_4' '2_3_8' '1_0_3' '1_10_3' '2_0_1' '2_0_1' '1_0_1' '1_0_0'\n", + " '1_0_0' '1_0_0' '1_14_5' '1_0_3' '1_0_0' '1_10_8' '3_3_2' '1_11_9'\n", + " '2_0_1' '3_1_3' '1_1_1' '1_14_2' '1_12_6' '1_14_5' '1_14_6' '1_1_6'\n", + " '1_11_9' '1_12_5' '1_0_3' '1_14_1' '1_14_7' '1_0_2' '1_1_6' '1_0_4'\n", + " '3_3_3' '1_1_0' '1_0_4' '1_1_1' '2_3_4' '1_14_4' '1_0_4' '3_1_4' '2_0_1'\n", + " '1_10_3' '1_9_3' '3_3_3' '1_1_6' '1_11_3' '1_14_2' '2_3_8' '1_12_3'\n", + " '1_14_2' '1_14_2' '3_3_4' '1_1_2' '1_0_2' '1_11_3' '1_10_8' '1_1_5'\n", + " '1_0_0' '3_1_3' '2_0_2' '1_12_3' '1_0_0' '2_3_7' '2_0_1' '1_0_0' '1_0_0'\n", + " '3_3_3' '1_14_1' '3_0_3' '1_14_8' '2_3_7' '2_0_1' '1_0_4' '3_1_3'\n", + " '1_14_1' '1_0_4' '3_1_4' '1_10_3' '2_3_8' '3_0_3' '3_1_3' '1_14_7'\n", + " '1_12_3' '1_1_5' '1_14_1' '1_14_4' '1_0_1' '2_3_5' '2_3_4' '1_14_5'\n", + " '1_14_2' '1_1_5' '1_14_9' '1_9_9' '1_14_7' '3_3_0' '1_1_1' '1_1_5'\n", + " '1_14_3' '2_0_7' '2_3_6' '1_12_7' '1_14_2' '1_1_3' '1_1_2' '1_0_4'\n", + " '2_3_6' '1_14_4' '2_3_4' '1_12_6' '1_14_7' '1_0_0' '1_1_6' '1_0_3'\n", + " '1_1_5' '3_0_4' '1_0_0' '3_3_3' '1_1_5' '1_1_0' '1_14_7' '1_1_3' '1_14_7'\n", + " '1_14_6' '1_12_7' '1_14_6' '1_0_4' '1_9_9' '1_0_0' '1_14_8' '1_0_3'\n", + " '1_1_5' '1_14_7' '1_14_4' '1_1_5' '3_3_0' '2_3_6' '2_1_2' '3_0_3' '1_0_4'\n", + " '3_3_5' '1_14_5' '1_14_2' '1_0_4' '2_0_1' '3_0_3' '3_0_3' '2_3_7'\n", + " '1_14_1' '3_3_2' '1_14_1' '1_1_6' '1_0_3' '1_12_4' '2_0_2' '3_1_3'\n", + " '3_1_3' '1_0_0' '1_0_0' '1_14_9' '1_14_3' '1_14_4' '2_0_1' '2_0_2'\n", + " '1_10_8' '1_0_3' '1_1_2' '1_5_4' '1_14_4' '3_1_3' '3_3_4' '2_0_2'\n", + " '1_11_3' '1_0_4' '1_10_3' '1_10_8' '1_14_4' '1_9_9' '2_0_1' '1_1_6'\n", + " '2_0_2' '1_1_2' '1_1_6' '1_14_7' '1_11_3' '3_3_1' '1_1_6' '1_1_6' '1_0_4'\n", + " '1_14_10' '2_3_2' '1_14_7' '1_0_4' '3_3_3' '3_1_3' '1_14_10' '1_14_10'\n", + " '2_3_7' '1_12_6' '1_0_5' '1_14_8' '1_14_4' '2_0_1' '1_1_1' '2_0_1'\n", + " '1_0_1' '1_12_8' '1_0_4' '2_3_5' '1_14_8' '1_14_10' '1_0_4' '1_0_0'\n", + " '1_12_7' '1_0_4' '1_0_0' '1_0_0' '1_1_6' '2_0_1' '2_0_2' '2_0_5' '2_0_1'\n", + " '2_3_5' '1_5_6' '1_1_5' '2_1_1' '1_0_0' '1_14_1' '3_3_1' '1_14_7' '1_0_1'\n", + " '1_1_6' '1_1_6' '1_14_2' '1_0_4' '1_14_8' '1_3_10' '1_0_0' '1_0_0'\n", + " '1_1_2' '3_0_4' '1_12_5' '1_0_0' '2_0_2' '2_3_8' '1_0_0' '1_0_3' '1_1_0'\n", + " '1_0_0' '1_12_5' '1_1_6' '1_0_3' '1_0_0' '1_0_4' '1_0_4' '1_14_4' '1_0_3'\n", + " '1_0_0' '1_14_3' '1_1_6' '1_1_0' '1_0_0' '1_0_0' '1_14_9' '3_3_2'\n", + " '1_14_4' '1_1_0' '1_1_0' '2_3_7' '1_14_2' '3_1_3' '2_0_1' '2_0_1' '1_0_3'\n", + " '1_1_2' '1_14_2' '1_0_0' '1_0_0' '1_0_0' '1_0_0' '2_3_6' '1_0_3' '1_0_3'\n", + " '1_12_7' '1_14_2' '1_0_0' '1_0_0' '2_3_2' '1_1_3' '2_3_4' '3_3_3' '1_0_4'\n", + " '1_14_9' '2_3_5' '2_0_2' '2_3_6' '2_0_1' '1_0_1' '2_3_7' '1_10_8' '1_1_5'\n", + " '2_1_2' '1_0_4' '1_14_7' '1_0_4' '1_0_4' '1_14_2' '1_14_3' '1_14_8'\n", + " '2_0_1' '2_3_8' '1_0_0' '1_14_7' '1_12_7' '1_1_2' '2_0_1' '1_0_4' '1_0_3'\n", + " '3_0_3' '1_1_1' '2_0_1' '2_3_7' '1_1_5' '2_0_2' '1_12_5' '3_0_4' '1_1_5'\n", + " '1_1_1' '1_14_1' '1_0_0' '1_0_0' '1_0_0' '1_1_0' '1_0_4' '2_0_2' '1_14_1'\n", + " '2_0_1' '1_14_3' '1_14_3' '1_14_1' '1_11_3' '3_0_4' '1_0_2' '2_0_2'\n", + " '2_1_2' '1_1_2' '1_12_3' '1_14_2' '1_14_4' '2_0_1' '2_3_2' '1_14_9'\n", + " '1_14_1' '1_1_5' '1_0_5' '1_1_6' '2_3_7' '1_0_0' '1_12_7' '2_0_2' '1_1_2'\n", + " '2_2_1' '2_0_7' '1_0_4' '1_14_2' '2_3_4' '3_3_5' '1_0_4' '2_3_7' '2_0_1'\n", + " '1_1_6' '1_14_3' '1_0_4' '1_1_6' '2_0_7' '1_12_4' '1_0_4' '1_0_4' '1_1_6'\n", + " '2_0_2' '1_12_5' '2_0_1' '2_1_2' '2_0_1' '3_1_3' '1_12_3' '2_0_1' '1_0_1'\n", + " '1_1_2' '1_0_3' '1_14_7' '3_3_1' '1_1_6' '2_0_1' '1_0_2' '1_14_7' '2_3_8'\n", + " '3_3_0' '1_0_2' '1_12_6' '2_0_1' '3_1_3' '1_14_2' '1_1_6' '1_14_8'\n", + " '1_0_3' '2_1_1' '2_0_2' '1_12_8' '3_3_1' '1_9_4' '2_3_7' '2_2_1' '1_1_2'\n", + " '1_14_5' '1_14_9' '1_0_3' '1_1_6' '3_0_4' '1_14_2' '1_0_0' '1_5_4'\n", + " '1_1_5' '1_14_3' '2_0_2' '1_14_7' '1_1_2' '1_12_7' '1_1_1' '1_1_0'\n", + " '2_0_1' '1_12_4' '1_14_2' '1_1_0' '1_14_2' '1_14_8' '1_1_6' '3_3_3'\n", + " '1_1_6' '1_14_6' '1_1_0' '1_14_5' '1_14_4' '2_0_2' '1_1_5' '1_1_2'\n", + " '3_3_0' '1_1_6' '1_14_3' '1_0_4' '1_0_3' '1_14_5' '1_1_6' '1_14_3'\n", + " '1_11_3' '1_1_5' '2_1_1' '3_0_4' '1_1_5' '1_14_4' '1_14_1' '1_1_6'\n", + " '2_3_5' '1_14_3' '1_1_1' '2_0_1' '2_0_2' '1_12_3' '1_1_7' '2_0_2'\n", + " '1_14_7' '1_10_8' '1_12_7']\n", + "dowsampled rms bin 18\n", + "areas of tiles in bin [7.72053174e-05 7.85179851e-05 7.72053174e-05 1.34392678e-04\n", + " 7.72053174e-05 8.13743258e-05 7.85179851e-05 1.43915581e-04\n", + " 1.36032102e-04 1.38458198e-04 8.86220259e-05 1.42219529e-04\n", + " 7.58821969e-05 1.38259625e-04 1.43971769e-04 3.46720176e-06\n", + " 1.43915581e-04 9.71513336e-06 1.34392678e-04 9.89736620e-07\n", + " 1.42169354e-04 8.86220259e-05 1.35521724e-04 1.41264619e-04\n", + " 1.34486464e-04 1.33522021e-04 1.38406973e-04 9.77517649e-07\n", + " 1.37503202e-04 1.12583827e-05 7.72053174e-05 9.20508969e-05\n", + " 1.42169354e-04 1.38259625e-04 1.16776185e-04 1.33522021e-04\n", + " 1.42023092e-04 1.36483597e-04 8.73992507e-05 3.46716688e-06\n", + " 7.85179851e-05 1.33522021e-04 1.38406973e-04 8.75279269e-05\n", + " 1.16776185e-04 5.06299510e-06 1.43969147e-04 8.40784994e-05\n", + " 1.36011867e-04 1.13982213e-05 8.11112542e-05 1.35013233e-04\n", + " 5.77373540e-06 6.59987280e-05 5.77616382e-06 1.33522021e-04\n", + " 1.13973751e-05 1.38259625e-04 1.38458198e-04 1.33522021e-04\n", + " 1.36936848e-04 7.62521653e-05 9.77517649e-07 1.36483597e-04\n", + " 1.33522021e-04 5.35330230e-05 1.36011867e-04 1.36011867e-04\n", + " 8.49183701e-05 5.59261584e-06 1.34858792e-04 1.36011867e-04\n", + " 3.46716688e-06 1.58197408e-05 7.72053174e-05 8.86220259e-05\n", + " 3.46919925e-06 1.43971769e-04 1.42181659e-04 1.36011867e-04\n", + " 9.77517649e-07 1.37503202e-04 8.11112542e-05 3.46716688e-06\n", + " 1.12583827e-05 7.75486273e-05 1.12583827e-05 8.23915048e-05\n", + " 1.39945718e-04 1.43853388e-04 1.42106330e-04 6.49921888e-06\n", + " 8.49183701e-05 7.72053174e-05 1.39945718e-04 1.34486464e-04\n", + " 1.36011867e-04 1.34097232e-04 7.36284480e-05 1.15316482e-04\n", + " 6.93509045e-06 1.42219529e-04 1.36011867e-04 1.38458198e-04\n", + " 5.05827896e-06 8.11112542e-05 1.36483597e-04 7.85179851e-05\n", + " 1.40351997e-04 3.37218598e-06 1.36011867e-04 2.77984758e-07\n", + " 6.68237122e-05 5.84836586e-06 8.01098807e-05 5.84590709e-06\n", + " 8.36606004e-05 1.43903127e-04 1.42150160e-04 1.71227394e-05\n", + " 1.36011867e-04 7.35483977e-05 1.40188925e-04 1.35521724e-04\n", + " 1.00377474e-04 7.58821969e-05 4.62657112e-05 7.72053174e-05\n", + " 1.36483597e-04 3.50948471e-06 7.62521653e-05 1.68788365e-04\n", + " 1.34486464e-04 7.72053174e-05 1.36011867e-04 9.89736620e-07\n", + " 1.42219529e-04 9.89736620e-07 1.36483597e-04 1.33522021e-04\n", + " 8.13743258e-05 7.72053174e-05 1.36936848e-04 1.16753598e-05\n", + " 1.34486464e-04 1.35521724e-04 1.34097232e-04 1.16753598e-05\n", + " 8.63202476e-05 1.35521724e-04 9.20508969e-05 1.16776185e-04\n", + " 1.39945718e-04 1.45970231e-06 1.36778656e-04 1.36483597e-04\n", + " 7.72053174e-05 1.36011867e-04 1.42219529e-04 1.36011867e-04\n", + " 1.35521724e-04 7.45488036e-05 9.77517649e-07 7.88302864e-05\n", + " 1.31893704e-04 7.36284480e-05 1.33522021e-04 7.88302864e-05\n", + " 1.13973751e-05 1.36011867e-04 8.73992507e-05 1.38259625e-04\n", + " 1.34486464e-04 7.85179851e-05 1.34858792e-04 8.86220259e-05\n", + " 1.36011867e-04 7.78570730e-05 1.36483597e-04 9.77517649e-07\n", + " 5.17365674e-05 1.16776185e-04 6.93827214e-06 1.43971769e-04\n", + " 5.07679142e-06 3.46634670e-06 1.36011867e-04 1.35013233e-04\n", + " 1.40477685e-04 8.11112542e-05 1.35190353e-04 7.72053174e-05\n", + " 7.62521653e-05 1.48838718e-04 1.13973751e-05 1.36011867e-04\n", + " 6.40411732e-06 1.36483597e-04 1.41595801e-04 1.33522021e-04\n", + " 3.50944940e-06 1.36483597e-04 1.36011867e-04 1.43755081e-04\n", + " 1.12583827e-05 1.42120215e-04 1.12583827e-05 1.34486464e-04\n", + " 7.72053174e-05 1.36483597e-04 1.54410635e-04 1.69130092e-07\n", + " 1.32885434e-04 3.50944940e-06 9.89736620e-07 4.85756668e-06\n", + " 7.58821969e-05 1.33522021e-04 1.33522021e-04 7.58821969e-05\n", + " 1.34392678e-04 1.33522021e-04 8.73992507e-05 7.58821969e-05\n", + " 7.75486273e-05 7.58821969e-05 9.71513336e-06 1.16959998e-05\n", + " 1.35826334e-04 1.36483597e-04 1.42044804e-04 1.41732649e-04\n", + " 1.33522021e-04 7.72053174e-05 8.36606004e-05 7.62521653e-05\n", + " 1.37276278e-04 7.46586150e-06 7.62521653e-05 1.42150160e-04\n", + " 1.36778656e-04 8.23915048e-05 1.16753598e-05 1.33522021e-04\n", + " 8.73992507e-05 1.34486464e-04 7.85179851e-05 1.36011867e-04\n", + " 1.35826334e-04 1.40477685e-04 7.72053174e-05 7.72053174e-05\n", + " 1.36483597e-04 1.13973751e-05 1.36483597e-04 1.42120215e-04\n", + " 1.02728232e-05 9.32791077e-05 9.89736620e-07 1.38992379e-07\n", + " 1.16753598e-05 1.36936848e-04 1.13973751e-05 6.59987280e-05\n", + " 5.77616382e-06 7.88302864e-05 1.71227394e-05 1.71227394e-05\n", + " 9.25314224e-05 9.77517649e-07 1.47096795e-04 1.42120215e-04\n", + " 7.72053174e-05 5.77373540e-06 1.36483597e-04 9.60617597e-06\n", + " 1.45970231e-06 7.49453797e-05 8.01098807e-05 7.45488036e-05\n", + " 7.58821969e-05 1.36483597e-04 1.33522021e-04 1.36483597e-04\n", + " 7.72053174e-05 8.13743258e-05 4.62657112e-05 1.52338445e-04\n", + " 1.38259625e-04 8.75279269e-05 1.71227394e-05 7.45488036e-05\n", + " 7.35483977e-05 1.12583827e-05 1.33405933e-04 9.89736620e-07\n", + " 8.86220259e-05 1.40460392e-04 1.34858792e-04 1.43755081e-04\n", + " 1.36011867e-04 8.11112542e-05 1.36483597e-04 1.35521724e-04\n", + " 7.49453797e-05 1.14114040e-05 1.39945718e-04 1.36011867e-04\n", + " 1.43839334e-04 1.43773126e-04 8.23915048e-05 1.34486464e-04\n", + " 6.74437195e-06 1.34486464e-04 1.42181659e-04 1.38259625e-04\n", + " 1.36483597e-04 1.42120215e-04 3.46720176e-06 1.36032102e-04\n", + " 1.14314405e-04 1.36936848e-04 1.12583827e-05 1.42234579e-04\n", + " 7.49453797e-05 7.58821969e-05 1.12583827e-05 8.01098807e-05\n", + " 1.42219529e-04 1.00238010e-04 1.52468366e-04 1.34858792e-04\n", + " 7.58821969e-05 1.16776185e-04 9.89736620e-07 1.38458198e-04\n", + " 8.86220259e-05 1.35013233e-04 1.13462710e-04 1.16753598e-05\n", + " 1.34097232e-04 7.49453797e-05 8.01098807e-05 1.34486464e-04\n", + " 8.61646432e-05 1.12583827e-05 1.38458198e-04 7.09648593e-05\n", + " 1.37503202e-04 1.36011867e-04 8.73992507e-05 1.36483597e-04\n", + " 8.86220259e-05 1.47346189e-04 9.77517649e-07 8.26277535e-05\n", + " 7.62521653e-05 7.84454998e-05 1.34858792e-04 7.32053173e-05\n", + " 7.88302864e-05 7.85179851e-05 3.46716688e-06 8.86220259e-05\n", + " 1.33522021e-04 4.85756668e-06 3.46720176e-06 7.45488036e-05\n", + " 5.00311017e-06 1.36011867e-04 1.36936848e-04 7.62521653e-05\n", + " 7.49453797e-05 7.85179851e-05 1.37371561e-04 4.88495200e-06\n", + " 7.85179851e-05 8.86220259e-05 6.93569067e-06 8.11112542e-05\n", + " 8.38699952e-05 1.34486464e-04 1.42023092e-04 1.71227394e-05\n", + " 6.17637911e-05 4.62657112e-05 8.23915048e-05 1.34486464e-04\n", + " 7.72053174e-05 7.35483977e-05 5.00239092e-06 1.42181659e-04\n", + " 9.69357841e-07 1.35013233e-04 8.23915048e-05 1.41439784e-04\n", + " 1.36483597e-04 2.20312910e-06 8.13743258e-05 3.50230465e-06\n", + " 8.15368545e-07 7.18519200e-05 1.42181659e-04 7.62521653e-05\n", + " 5.06414184e-05 9.68012314e-06 1.42106330e-04 1.38992379e-07\n", + " 7.58821969e-05 7.75486273e-05 1.34392678e-04 1.33908370e-04\n", + " 7.78570730e-05 7.45488036e-05 9.89736620e-07 1.34486464e-04\n", + " 7.72053174e-05 1.52468366e-04 2.73873697e-04 1.36807965e-05\n", + " 8.61646432e-05 8.01098807e-05 1.36483597e-04 1.36807965e-05\n", + " 1.35521724e-04 1.43839334e-04 7.09648593e-05 1.38259625e-04\n", + " 1.35521724e-04 1.38259625e-04 7.72053174e-05 7.88302864e-05\n", + " 1.42100004e-04 7.23015480e-05 5.97667230e-06 9.89736620e-07\n", + " 9.44946899e-05 1.43969147e-04 1.34858792e-04 8.36606004e-05\n", + " 7.78570730e-05 4.87441416e-06 7.58821969e-05 1.36483597e-04\n", + " 1.22189706e-08 1.16776185e-04 1.36011867e-04 1.36483597e-04\n", + " 8.36606004e-05 7.72053174e-05 3.50944940e-06 1.36483597e-04\n", + " 7.72053174e-05 1.42234579e-04 7.58821969e-05 1.40443872e-04\n", + " 1.07280700e-05 1.02622616e-04 7.36284480e-05 7.49453797e-05\n", + " 1.16776185e-04 1.35472773e-04 1.42219529e-04 1.43915581e-04\n", + " 3.37797445e-07 6.47675557e-06 1.36011867e-04 9.89736620e-07\n", + " 7.62521653e-05 9.77517649e-07 5.05827896e-06 1.35013233e-04\n", + " 1.37030768e-04 1.35521724e-04 1.38259625e-04 8.36606004e-05\n", + " 1.40457896e-04 2.77984758e-07 1.36011867e-04 7.58821969e-05\n", + " 1.40460392e-04 1.07900309e-06 3.46913607e-06 1.42152686e-04\n", + " 1.38259625e-04 1.43953913e-04 4.16977137e-07 7.58821969e-05\n", + " 1.16753598e-05 4.90612718e-06 7.95072582e-05 7.45488036e-05\n", + " 1.42106330e-04 1.40188925e-04 6.96185634e-05 9.32791077e-05\n", + " 8.63202476e-05 7.36284480e-05 7.45488036e-05 1.36936848e-04\n", + " 1.12583827e-05 1.40457896e-04 1.13973751e-05 7.31578011e-06\n", + " 1.35013233e-04 7.45488036e-05 6.65420083e-05 8.26277535e-05\n", + " 1.36011867e-04 7.45488036e-05 8.13743258e-05 7.58821969e-05\n", + " 8.36606004e-05 1.36483597e-04 1.36936848e-04 1.36936848e-04\n", + " 9.74882832e-06 7.18519200e-05 8.01098807e-05 6.93753798e-06\n", + " 7.72053174e-05 1.60174875e-05 1.16776185e-04 5.66252354e-06\n", + " 1.36936848e-04 1.42106330e-04 8.86220259e-05 1.34486464e-04\n", + " 5.42021858e-05 1.34486464e-04 1.42106330e-04 9.89736620e-07\n", + " 2.71342388e-04 1.71227394e-05 8.75279269e-05 7.35483977e-05\n", + " 4.84006157e-06 9.20508969e-05 7.89413510e-05 9.25314224e-05\n", + " 1.31965608e-04 1.36011867e-04 1.38458198e-04 1.34486464e-04\n", + " 1.42023092e-04 1.36936848e-04 7.45488036e-05 1.71227394e-05\n", + " 1.13973751e-05 7.35483977e-05 1.43839334e-04 1.36936848e-04\n", + " 9.31599438e-05 1.16753598e-05 1.36011867e-04 7.85179851e-05\n", + " 7.45488036e-05 1.36936848e-04 7.72053174e-05 1.40076198e-04\n", + " 1.36011867e-04 1.42169354e-04 8.86220259e-05 7.72053174e-05\n", + " 1.33249953e-04 9.89736620e-07 1.68898723e-07 7.58821969e-05\n", + " 1.16776185e-04 1.42234579e-04 1.41876722e-04 7.88302864e-05\n", + " 5.06299510e-06 7.62521653e-05 7.49453797e-05 1.42181659e-04\n", + " 1.33522021e-04 7.58821969e-05 7.58821969e-05 1.16776185e-04\n", + " 1.35098821e-04 1.43971769e-04 9.89736620e-07 1.43953913e-04\n", + " 6.45341543e-06 1.37503202e-04 1.36936848e-04 6.93839851e-06\n", + " 1.33522021e-04 8.23915048e-05 2.77984758e-07 1.35013233e-04\n", + " 7.45488036e-05 9.68493561e-05 9.89736620e-07 1.35521724e-04\n", + " 8.86220259e-05 8.86220259e-05 9.77517649e-07 1.37371561e-04\n", + " 1.36483597e-04 7.58821969e-05 1.42169354e-04 1.34486464e-04\n", + " 7.58821969e-05 7.45488036e-05 1.36483597e-04 1.35521724e-04\n", + " 8.23915048e-05 9.92274681e-05 8.36606004e-05 8.38699952e-05\n", + " 9.53152067e-07 1.36936848e-04 6.25079332e-05 1.42150160e-04\n", + " 9.20508969e-05 3.46720176e-06 1.16776185e-04 1.36011867e-04\n", + " 1.36483597e-04 7.72053174e-05 7.85179851e-05 7.88302864e-05\n", + " 7.88302864e-05 5.06266069e-06 1.43839334e-04 8.86220259e-05\n", + " 1.38681140e-04 1.38259625e-04 7.88302864e-05 7.58821969e-05\n", + " 9.20508969e-05 8.49183701e-05]\n", + "names of tiles in bin ['1_0_4' '1_0_4' '1_0_0' '1_14_3' '1_0_4' '1_1_6' '1_0_3' '2_0_1' '1_9_3'\n", + " '2_0_7' '1_1_1' '2_0_2' '1_0_3' '1_12_6' '2_0_1' '2_0_1' '2_0_1' '1_14_7'\n", + " '1_14_6' '1_0_5' '2_0_2' '1_1_1' '1_14_8' '1_10_8' '1_14_7' '2_3_4'\n", + " '2_3_8' '1_0_4' '1_9_9' '3_3_2' '1_0_0' '1_2_2' '2_0_1' '1_12_6' '1_5_6'\n", + " '2_3_5' '2_0_1' '1_14_5' '1_1_0' '2_1_1' '1_0_3' '2_3_6' '2_3_8' '1_1_1'\n", + " '1_5_5' '1_12_3' '2_0_1' '1_1_0' '1_14_5' '2_3_8' '1_1_6' '1_14_9'\n", + " '1_0_0' '1_0_0' '1_0_0' '2_3_4' '3_3_1' '1_12_8' '2_0_7' '2_3_4' '1_14_2'\n", + " '1_0_4' '1_0_5' '1_14_5' '2_3_6' '1_0_0' '1_14_1' '1_14_5' '1_1_7'\n", + " '1_0_0' '1_14_1' '1_14_6' '2_1_2' '1_0_0' '1_0_3' '1_1_1' '2_0_1' '2_0_1'\n", + " '2_0_1' '1_14_7' '1_0_0' '1_9_3' '1_1_6' '2_1_1' '3_3_2' '1_0_4' '3_3_2'\n", + " '1_1_0' '1_12_6' '2_1_2' '2_0_1' '1_14_1' '1_1_7' '1_0_3' '1_12_8'\n", + " '1_14_7' '1_14_4' '2_3_2' '1_0_4' '1_5_7' '2_0_2' '2_0_2' '1_14_4'\n", + " '2_0_7' '1_12_5' '1_1_6' '1_14_5' '1_0_5' '1_11_8' '1_12_4' '1_14_2'\n", + " '3_3_2' '1_0_0' '1_0_0' '1_1_6' '1_0_0' '1_1_6' '2_0_2' '1_11_3' '1_14_1'\n", + " '1_14_2' '1_14_1' '2_0_7' '1_14_7' '1_2_9' '1_0_4' '1_14_1' '1_0_4'\n", + " '1_14_5' '2_0_1' '1_0_3' '1_1_7' '1_14_6' '1_0_4' '1_14_5' '1_0_5'\n", + " '2_0_1' '1_0_0' '1_14_2' '2_3_5' '1_1_6' '1_0_3' '1_14_3' '3_3_0'\n", + " '1_14_8' '1_14_8' '2_3_2' '3_3_0' '1_1_0' '1_14_8' '1_2_1' '1_5_11'\n", + " '1_12_5' '1_5_6' '2_3_8' '1_14_3' '1_0_4' '1_14_5' '2_0_1' '1_14_7'\n", + " '1_14_8' '1_0_2' '1_0_4' '1_1_5' '2_3_5' '1_0_2' '2_3_7' '1_1_6' '3_3_1'\n", + " '1_14_7' '1_1_0' '1_12_8' '1_14_7' '1_0_2' '1_14_3' '1_1_2' '1_14_6'\n", + " '1_1_5' '1_14_6' '1_0_3' '2_0_2' '1_5_6' '2_0_2' '2_0_1' '1_11_8' '2_1_1'\n", + " '1_14_5' '1_14_9' '2_0_1' '1_1_6' '2_3_2' '1_0_3' '1_0_4' '3_0_4' '3_3_2'\n", + " '1_14_1' '1_14_8' '1_14_2' '1_10_3' '2_3_7' '2_1_2' '1_14_3' '1_14_7'\n", + " '2_0_1' '3_3_1' '2_1_2' '3_3_3' '1_14_7' '1_0_3' '1_14_4' '1_0_3' '2_1_2'\n", + " '1_14_6' '2_1_1' '1_0_4' '1_14_4' '1_0_2' '2_3_5' '2_3_5' '1_0_2'\n", + " '1_14_5' '2_3_5' '1_1_0' '1_0_4' '1_0_4' '1_0_4' '1_14_4' '1_11_9'\n", + " '1_9_3' '1_14_4' '1_11_9' '1_10_3' '2_3_5' '1_0_3' '1_1_6' '1_0_4'\n", + " '1_9_3' '3_3_5' '1_0_2' '1_11_2' '2_3_8' '1_1_6' '3_3_0' '2_3_7' '1_1_2'\n", + " '1_14_6' '1_0_4' '1_14_6' '1_9_9' '2_0_1' '1_0_3' '1_0_4' '1_14_5'\n", + " '3_3_4' '1_14_4' '2_1_1' '3_0_3' '1_2_2' '1_0_3' '3_3_1' '3_3_0' '1_14_2'\n", + " '3_3_3' '1_0_0' '1_0_0' '1_1_6' '1_14_1' '1_14_1' '1_14_1' '1_0_4'\n", + " '1_14_1' '2_1_2' '1_0_3' '1_0_0' '1_14_1' '1_14_7' '1_5_10' '1_0_2'\n", + " '1_1_6' '1_0_1' '1_0_4' '1_14_2' '2_3_6' '1_14_2' '1_0_0' '1_1_6'\n", + " '1_14_1' '3_1_3' '1_12_5' '1_1_1' '1_14_1' '1_0_1' '1_14_1' '3_3_3'\n", + " '1_14_9' '1_0_0' '1_1_1' '1_11_9' '1_14_2' '2_0_1' '1_14_2' '1_1_6'\n", + " '1_14_2' '1_14_8' '1_0_4' '1_14_3' '1_12_7' '1_14_5' '2_0_1' '2_1_1'\n", + " '1_1_6' '1_14_8' '1_12_5' '1_14_6' '2_0_2' '1_12_6' '1_14_5' '2_1_1'\n", + " '2_0_2' '1_9_9' '1_5_4' '1_14_6' '3_3_4' '2_0_2' '1_0_1' '1_0_4' '3_3_1'\n", + " '1_1_7' '2_0_1' '1_3_10' '3_1_3' '1_14_1' '1_0_4' '1_5_11' '1_0_5'\n", + " '2_0_7' '1_1_1' '1_14_9' '1_5_11' '3_3_0' '2_3_2' '1_0_4' '1_1_7'\n", + " '1_14_6' '1_1_0' '3_3_1' '2_0_7' '1_0_3' '1_9_9' '1_14_7' '1_1_2'\n", + " '1_14_2' '1_1_0' '1_14_6' '1_0_0' '1_1_6' '1_0_0' '1_1_5' '1_14_5'\n", + " '1_0_3' '1_1_6' '1_0_5' '2_1_1' '1_1_0' '2_3_4' '1_14_5' '2_0_1' '1_0_1'\n", + " '1_14_10' '1_14_7' '1_14_5' '1_0_0' '1_0_3' '1_0_5' '1_14_1' '2_3_6'\n", + " '1_0_5' '1_1_2' '2_0_2' '1_1_7' '1_1_7' '1_14_8' '2_0_1' '1_14_1'\n", + " '1_14_10' '1_14_1' '1_1_6' '1_14_6' '1_0_4' '1_14_1' '1_14_10' '2_0_1'\n", + " '1_0_3' '1_14_9' '1_1_5' '1_10_8' '1_14_4' '1_14_1' '1_1_6' '1_14_1'\n", + " '1_14_1' '1_0_3' '2_0_1' '1_0_3' '1_14_10' '1_14_9' '2_0_2' '3_3_3'\n", + " '1_0_2' '1_0_5' '1_14_6' '1_14_8' '1_1_7' '1_0_4' '1_0_4' '1_14_8'\n", + " '1_0_3' '3_1_3' '1_14_2' '3_0_4' '1_1_0' '1_1_7' '1_14_5' '3_0_4'\n", + " '1_14_8' '2_0_1' '1_0_3' '1_12_5' '1_14_9' '1_12_6' '1_0_3' '1_1_5'\n", + " '1_11_3' '1_0_4' '3_3_0' '1_0_2' '1_2_2' '2_0_1' '1_14_4' '1_1_6' '1_1_7'\n", + " '1_14_4' '1_0_3' '1_14_2' '1_0_3' '1_5_11' '1_14_3' '1_14_7' '1_1_5'\n", + " '1_0_4' '2_1_1' '1_14_4' '1_0_5' '2_0_1' '1_0_2' '1_11_3' '1_1_0'\n", + " '1_3_10' '1_0_1' '1_0_3' '1_5_5' '2_3_2' '2_0_1' '2_0_1' '3_0_4' '1_14_6'\n", + " '1_14_5' '1_0_2' '1_0_1' '1_0_0' '1_12_7' '1_14_9' '1_9_9' '1_14_8'\n", + " '1_12_5' '1_1_6' '1_11_3' '3_3_3' '1_14_6' '1_0_3' '1_11_3' '1_1_1'\n", + " '2_0_1' '1_11_2' '1_12_5' '2_0_2' '3_3_0' '1_0_3' '3_3_0' '1_14_2'\n", + " '1_1_5' '1_0_3' '2_0_1' '2_0_7' '1_0_3' '1_2_2' '1_1_0' '1_0_1' '1_0_4'\n", + " '1_14_5' '3_3_3' '1_11_2' '3_3_4' '3_1_3' '1_14_9' '1_0_3' '2_3_4'\n", + " '1_1_6' '1_14_6' '1_0_1' '1_1_6' '1_0_4' '1_1_5' '1_14_6' '1_14_4'\n", + " '1_14_2' '1_14_7' '1_0_3' '1_1_6' '2_0_2' '1_0_3' '1_0_0' '1_5_11'\n", + " '1_0_0' '1_14_4' '2_0_1' '1_1_1' '1_14_8' '1_0_0' '1_14_6' '2_0_2'\n", + " '1_0_0' '1_14_6' '1_14_1' '1_1_1' '1_14_1' '1_14_7' '1_2_2' '1_1_5'\n", + " '1_14_1' '1_13_2' '1_14_7' '2_0_7' '1_14_8' '2_0_1' '1_14_4' '1_0_1'\n", + " '1_14_1' '3_3_4' '1_14_1' '2_0_2' '1_14_6' '1_2_2' '3_3_0' '1_14_5'\n", + " '1_0_5' '1_0_3' '1_14_2' '1_0_3' '1_12_9' '1_14_7' '2_0_2' '1_1_1'\n", + " '1_0_3' '1_13_2' '1_0_4' '3_0_3' '1_0_3' '1_5_10' '2_0_1' '1_11_3'\n", + " '1_1_7' '1_12_2' '1_0_5' '1_0_3' '2_0_1' '2_3_6' '1_0_4' '1_0_3' '1_5_11'\n", + " '1_9_5' '2_0_1' '1_0_0' '2_0_1' '1_14_8' '1_9_3' '1_14_4' '2_0_2' '2_3_5'\n", + " '1_1_6' '3_3_2' '1_14_9' '1_0_1' '1_14_1' '1_0_2' '1_14_8' '1_1_1'\n", + " '1_1_0' '1_0_5' '1_14_5' '1_14_3' '1_0_4' '2_0_1' '1_14_8' '1_0_3'\n", + " '1_0_2' '1_14_4' '1_14_9' '1_1_6' '1_2_3' '1_1_6' '1_1_7' '1_0_4'\n", + " '1_14_2' '1_14_10' '1_11_3' '1_2_2' '2_0_1' '1_5_6' '1_14_6' '1_14_6'\n", + " '1_0_3' '1_0_5' '1_1_6' '1_1_7' '1_14_10' '2_0_2' '1_1_1' '1_11_3'\n", + " '1_12_5' '1_1_6' '1_0_4' '1_2_3' '1_1_7']\n", + "dowsampled rms bin 19\n", + "areas of tiles in bin [5.06338861e-06 7.58821969e-05 7.75486273e-05 1.35826334e-04\n", + " 7.36284480e-05 1.36011867e-04 6.23792504e-05 1.53908255e-05\n", + " 8.86220259e-05 1.34486464e-04 1.36483597e-04 7.78570730e-05\n", + " 1.41948762e-04 1.36483597e-04 1.41850307e-04 7.62521653e-05\n", + " 8.23915048e-05 3.50948471e-06 7.45488036e-05 1.36483597e-04\n", + " 1.35521724e-04 8.73992507e-05 7.78570730e-05 3.46876899e-06\n", + " 1.36936848e-04 1.38259625e-04 5.01257132e-06 1.60174875e-05\n", + " 1.16776185e-04 5.66252354e-06 7.45488036e-05 1.38259625e-04\n", + " 1.13973751e-05 9.20508969e-05 5.42021858e-05 9.89736620e-07\n", + " 1.36483597e-04 8.75279269e-05 7.45488036e-05 1.67240856e-06\n", + " 1.12583827e-05 1.37371561e-04 7.58821969e-05 7.45488036e-05\n", + " 9.77517649e-07 1.15363675e-05 7.78570730e-05 1.36936848e-04\n", + " 1.41834725e-04 7.45488036e-05 1.36483597e-04 1.02180696e-04\n", + " 7.58821969e-05 8.36606004e-05 1.29405808e-06 1.35521724e-04\n", + " 9.89736620e-07 1.14314405e-04 7.78570730e-05 1.35521724e-04\n", + " 7.32053173e-05 8.11112542e-05 1.33522021e-04 1.67197144e-06\n", + " 8.23915048e-05 1.40351997e-04 1.38259625e-04 1.10541865e-04\n", + " 1.16776185e-04 7.88302864e-05 1.18458466e-05 1.13736330e-05\n", + " 7.49453797e-05 8.13743258e-05 1.41439784e-04 1.16753598e-05\n", + " 1.26890262e-04 1.35602177e-04 1.36483597e-04 1.40351997e-04\n", + " 1.37371561e-04 7.32053173e-05 1.36011867e-04 1.60174875e-05\n", + " 1.36936848e-04 1.41948762e-04 5.66252354e-06 8.23915048e-05\n", + " 1.36483597e-04 1.12583827e-05 7.36284480e-05 1.42150160e-04\n", + " 5.42021858e-05 1.58918196e-05 1.38458198e-04 8.26277535e-05\n", + " 1.16776185e-04 7.18519200e-05 1.43755081e-04 1.43853388e-04\n", + " 7.45488036e-05 7.18519200e-05 1.36483597e-04 9.77517649e-07\n", + " 9.89736620e-07 7.88302864e-05 6.49921888e-06 1.20541467e-07\n", + " 8.11112542e-05 1.42100004e-04 1.36936848e-04 1.36483597e-04\n", + " 9.58922372e-06 6.19877876e-05 8.75279269e-05 1.36483597e-04\n", + " 1.36936848e-04 7.45488036e-05 1.37711511e-04 1.54410635e-04\n", + " 1.33615179e-04 1.36011867e-04 9.20508969e-05 1.69131793e-07\n", + " 5.94499858e-06 9.53152067e-07 2.81300329e-07 1.36483597e-04\n", + " 7.85179851e-05 1.36483597e-04 1.00137351e-06 7.58821969e-05\n", + " 7.58821969e-05 8.86220259e-05 1.12934770e-05 1.36936848e-04\n", + " 7.45488036e-05 8.11112542e-05 1.35521724e-04 1.36766707e-04\n", + " 1.42088007e-04 7.45488036e-05 1.33506686e-04 7.23015480e-05\n", + " 7.88302864e-05 7.58821969e-05 7.58821969e-05 1.60174875e-05\n", + " 5.42021858e-05 8.49183701e-05 1.36766707e-04 4.89060173e-06\n", + " 1.33249953e-04 5.66252354e-06 1.36936848e-04 1.36936848e-04\n", + " 1.36766707e-04 7.78570730e-05 1.36936848e-04 7.45488036e-05\n", + " 8.26277535e-05 1.38259625e-04 1.37030768e-04 7.85179851e-05\n", + " 7.72053174e-05 1.36011867e-04 9.89736620e-07 7.45488036e-05\n", + " 8.75279269e-05 1.13973751e-05 7.23015480e-05 1.36936848e-04\n", + " 7.72053174e-05 1.36011867e-04 1.16776185e-04 1.36936848e-04\n", + " 1.34858792e-04 1.17738767e-05 9.89736620e-07 1.32885434e-04\n", + " 8.11112542e-05 1.42237169e-04 1.39824619e-04 7.17926123e-05\n", + " 8.36606004e-05 1.36011867e-04 7.35483977e-05 4.62657112e-05\n", + " 1.71227394e-05 7.45488036e-05 1.36483597e-04 8.75279269e-05\n", + " 1.56333595e-05 1.33249953e-04 8.01098807e-05 7.45488036e-05\n", + " 1.34486464e-04 1.10541865e-04 1.37711511e-04 1.16776185e-04\n", + " 7.36284480e-05 8.11112542e-05 1.67197144e-06 9.89736620e-07\n", + " 7.58821969e-05 9.89736620e-07 1.38458198e-04 1.36483597e-04\n", + " 5.07679142e-06 1.15150625e-04 1.34486464e-04 8.11112542e-05\n", + " 1.36483597e-04 1.34486464e-04 1.36483597e-04 7.36284480e-05\n", + " 1.43839334e-04 1.40351997e-04 1.42150160e-04 9.20508969e-05\n", + " 7.88302864e-05 1.37371561e-04 1.35521724e-04 9.20508969e-05\n", + " 9.32791077e-05 7.58821969e-05 1.31226358e-04 1.34486464e-04\n", + " 1.34097232e-04 9.89736620e-07 8.49183701e-05 8.01098807e-05\n", + " 9.89736620e-07 1.40351997e-04 8.75279269e-05 1.32885434e-04\n", + " 1.37371561e-04 1.43953913e-04 1.36483597e-04 1.35306648e-04\n", + " 7.32053173e-05 3.50948471e-06 1.37371561e-04 8.86220259e-05\n", + " 8.36606004e-05 1.36766707e-04 1.36807965e-05 1.37371561e-04\n", + " 1.36936848e-04 7.58821969e-05 1.35521724e-04 1.38259625e-04\n", + " 1.16776185e-04 3.46716688e-06 8.45650458e-08 1.32866687e-04\n", + " 1.36011867e-04 7.72053174e-05 8.11112542e-05 1.36936848e-04\n", + " 7.58821969e-05 7.45488036e-05 3.46720176e-06 1.41681874e-04\n", + " 1.37371561e-04 1.35521724e-04 1.34486464e-04 5.06338861e-06\n", + " 1.42152686e-04 8.49183701e-05 5.15099169e-06 7.85179851e-05\n", + " 1.22189706e-08 7.58821969e-05 1.14114040e-05 3.69184094e-05\n", + " 4.03221037e-05 1.39945718e-04 1.40351997e-04 1.35602177e-04\n", + " 1.34392678e-04 1.37371561e-04 1.42237169e-04 7.04887954e-05\n", + " 6.23792504e-05 7.18519200e-05 6.25079332e-05 7.88302864e-05\n", + " 5.06266069e-06 7.45488036e-05 1.16776185e-04 7.18519200e-05\n", + " 1.16753598e-05 5.15099169e-06 1.34858792e-04 1.36011867e-04\n", + " 1.45693669e-04 3.46720176e-06 7.45488036e-05 7.58821969e-05\n", + " 1.37371561e-04 1.42023092e-04 1.36483597e-04 3.69184094e-05\n", + " 4.03221037e-05 1.36011867e-04 1.36936848e-04 1.15150625e-04\n", + " 8.26277535e-05 7.49453797e-05 1.04837494e-06 7.32053173e-05\n", + " 8.23915048e-05 1.16776185e-04 1.33908370e-04 9.44946899e-05\n", + " 1.16776185e-04 1.12583827e-05 1.34486464e-04 1.36483597e-04\n", + " 7.18519200e-05 7.78570730e-05 1.41264619e-04 1.42152686e-04\n", + " 1.13736330e-05 1.42023092e-04 1.37030768e-04 9.20508969e-05\n", + " 1.18460571e-05 6.75477654e-06 1.34486464e-04 9.92274681e-05\n", + " 9.81225435e-06 7.85179851e-05 1.40460392e-04 7.18519200e-05\n", + " 1.16776185e-04 1.32866687e-04 1.36011867e-04 1.16776185e-04\n", + " 8.11112542e-05 1.16776185e-04 9.20508969e-05 1.38259625e-04\n", + " 8.01098807e-05 1.36936848e-04 1.42100004e-04 1.38259625e-04\n", + " 8.23915048e-05 7.36284480e-05 1.42152686e-04 1.42135966e-04\n", + " 5.84590709e-06 9.89736620e-07 6.68237122e-05 5.84836586e-06\n", + " 1.36936848e-04 1.36936848e-04 9.31599438e-05 1.37371561e-04\n", + " 3.46720176e-06 8.57735045e-06 1.36766707e-04 1.38388533e-04\n", + " 6.68237122e-05 5.84836586e-06 1.16776185e-04 8.26277535e-05\n", + " 7.75486273e-05 1.50827577e-04 9.20508969e-05 1.42023092e-04\n", + " 1.35306648e-04 5.84590709e-06 4.62657112e-05 1.42135966e-04\n", + " 1.99002514e-08 1.28476390e-04 1.16776185e-04 7.62521653e-05\n", + " 7.35483977e-05 1.71227394e-05 1.13973751e-05 1.42106330e-04\n", + " 1.36011867e-04 1.40187713e-04 5.07688163e-06 3.36107511e-06\n", + " 9.89736620e-07 7.75486273e-05 1.18446639e-05 3.50948471e-06\n", + " 7.09648593e-05 9.53152067e-07 1.36011867e-04 1.08025470e-04\n", + " 8.36606004e-05 1.37503202e-04 7.88302864e-05 7.36284480e-05\n", + " 1.36483597e-04 1.36483597e-04 1.35306648e-04 9.56974786e-05\n", + " 1.37371561e-04 1.37371561e-04 9.69357841e-07 7.45488036e-05\n", + " 1.69188973e-05 7.26728216e-05 4.57149289e-05 1.37371561e-04\n", + " 7.78570730e-05 5.66252354e-06 5.42021858e-05 1.60174875e-05\n", + " 1.39945718e-04 7.85179851e-05 1.31226358e-04 8.26277535e-05\n", + " 1.16776185e-04 1.42135966e-04 3.69184094e-05 4.03221037e-05\n", + " 8.13743258e-05 1.31226358e-04 9.92274681e-05 5.15099169e-06\n", + " 1.71710780e-06 7.49453797e-05 1.35521724e-04 1.16776185e-04\n", 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7.18519200e-05\n", + " 1.36936848e-04 1.35521724e-04 1.37371561e-04 9.20508969e-05\n", + " 1.36483597e-04 1.09902159e-04 1.35521724e-04 9.56974786e-05\n", + " 1.38259625e-04 7.23015480e-05 1.12583827e-05 1.04837494e-06\n", + " 7.18519200e-05 4.16185947e-06 7.04887954e-05 1.33908370e-04\n", + " 4.07701544e-07 2.73873697e-04 1.35736185e-04 9.44029523e-05\n", + " 1.37371561e-04 1.36032102e-04 1.36483597e-04 7.72053174e-05\n", + " 7.85179851e-05 1.06723959e-04 7.45488036e-05 1.07197659e-04\n", + " 8.63202476e-05 1.16776185e-04 8.87060741e-07 1.31226358e-04\n", + " 7.85179851e-05 8.11112542e-05 7.18519200e-05 1.16776185e-04\n", + " 1.42384017e-04 1.42106330e-04 1.13973751e-05 1.42027999e-04\n", + " 9.89736620e-07 6.54150290e-06 7.23015480e-05 7.88302864e-05\n", + " 7.18519200e-05 1.02180696e-04 5.66252354e-06 3.34481713e-06\n", + " 1.60174875e-05 1.36936848e-04 1.16776185e-04 1.38259625e-04\n", + " 1.36011867e-04 5.42021858e-05 1.02180696e-04 1.28476390e-04\n", + " 1.15316482e-04 7.72053174e-05 5.05566032e-05 1.12583827e-05\n", + " 1.52641567e-05 1.26890262e-04 7.62521653e-05 1.27911637e-04\n", + " 1.42152686e-04 1.35521724e-04 1.42120215e-04 1.35098821e-04\n", + " 1.36936848e-04 7.45488036e-05 8.86220259e-05 1.36936848e-04\n", + " 9.44946899e-05 1.16776185e-04 1.32866687e-04 1.35359661e-04\n", + " 8.86220259e-05 3.58129313e-07 1.43915581e-04 9.31599438e-05\n", + " 1.33249953e-04 1.32866687e-04 9.20508969e-05 7.45488036e-05\n", + " 7.88302864e-05 1.32866687e-04 1.26890262e-04 7.49453797e-05\n", + " 1.09409909e-06 1.16776185e-04 9.44029523e-05 8.23915048e-05\n", + " 1.00377474e-04 7.72053174e-05 8.88988600e-05 9.92274681e-05\n", + " 7.32053173e-05 8.13743258e-05 7.32053173e-05 7.85179851e-05\n", + " 1.37371561e-04 1.42119479e-04 9.32791077e-05 1.36483597e-04\n", + " 5.08739920e-06 1.38259625e-04 8.26277535e-05 6.93201608e-06\n", + " 1.35013233e-04 1.16776185e-04 7.32053173e-05 1.42150160e-04\n", + " 1.37371561e-04 1.32866687e-04 1.36483597e-04 1.33962315e-04\n", 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9.77517649e-07\n", + " 5.59261584e-06 1.01586842e-04 1.26890262e-04 1.38388533e-04\n", + " 9.56974786e-05 1.16776185e-04 5.42021858e-05 5.94499858e-06\n", + " 7.09648593e-05 1.60174875e-05 1.42862710e-04 1.16753598e-05\n", + " 6.01931106e-06 1.02481791e-04 5.66252354e-06 3.46720176e-06\n", + " 1.35098821e-04 1.38681140e-04 7.45488036e-05 9.56974786e-05\n", + " 7.09648593e-05 1.99726271e-05 1.24750423e-04 1.37371561e-04\n", + " 8.23915048e-05 8.49183701e-05 7.04887954e-05 9.20508969e-05\n", + " 7.18519200e-05 9.74882832e-06 7.32053173e-05 7.18519200e-05\n", + " 7.45488036e-05 4.89060173e-06 2.70613296e-04 1.36936848e-04\n", + " 7.58821969e-05 7.88302864e-05 7.58821969e-05 1.37276278e-04\n", + " 1.36936848e-04 7.23015480e-05 7.45488036e-05 1.36483597e-04\n", + " 1.36483597e-04 1.34853144e-05 1.16776185e-04 7.85179851e-05\n", + " 8.36606004e-05 7.62521653e-05 1.67240856e-06 7.09648593e-05\n", + " 9.77517649e-07 1.12583827e-05 7.75486273e-05 1.16959998e-05\n", + " 8.23915048e-05 1.35736185e-04 1.40280250e-04 1.38259625e-04\n", + " 1.02180696e-04 9.44029523e-05 7.75486273e-05 7.32053173e-05\n", + " 1.35098821e-04 8.36606004e-05 3.46600804e-06 9.56331801e-05\n", + " 1.37371561e-04 1.35306648e-04 1.32866687e-04 9.80640283e-05\n", + " 9.89736620e-07 7.88302864e-05 1.35736185e-04 1.37371561e-04\n", + " 1.37371561e-04 7.09648593e-05 1.28476390e-04 1.28476390e-04\n", + " 9.44029523e-05 7.62521653e-05 1.36483597e-04 1.26890262e-04\n", + " 1.32866687e-04 9.56331801e-05 7.32053173e-05 7.36284480e-05\n", + " 7.72053174e-05 1.36011867e-04 1.42023092e-04 4.23867084e-07\n", + " 8.26277535e-05 1.32866687e-04 1.35013233e-04 9.44946899e-05\n", + " 7.04887954e-05 2.40333325e-06 8.46627666e-06 7.23015480e-05\n", + " 1.33249953e-04 1.60174875e-05 3.47394909e-05 1.14476301e-05\n", + " 7.04887954e-05 3.37218598e-06 1.15971255e-04 2.98519422e-07\n", + " 7.45488036e-05 9.80640283e-05 1.28476390e-04 1.40498990e-04\n", + " 5.42021858e-05 5.66252354e-06 1.34858792e-04 1.67240856e-06\n", + " 1.37371561e-04]\n", + "names of tiles in bin ['1_14_10' '1_0_5' '1_0_5' '1_9_3' '1_0_3' '1_14_8' '1_14_10' '3_0_3'\n", + " '1_1_1' '1_14_8' '1_14_2' '1_1_6' '1_10_3' '1_14_7' '1_10_3' '1_0_3'\n", + " '1_1_6' '2_0_1' '1_0_3' '1_14_7' '1_14_9' '1_1_0' '1_1_7' '2_0_1'\n", + " '1_14_3' '1_12_5' '1_11_8' '1_0_0' '1_5_6' '1_0_0' '1_0_1' '1_12_3'\n", + " '3_3_1' '1_2_2' '1_0_0' '1_0_0' '1_14_3' '1_1_0' '1_0_3' '1_14_4' '3_3_4'\n", + " '1_14_1' '1_0_3' '1_0_1' '1_0_6' '3_3_0' '1_1_6' '1_14_5' '2_2_2' '1_0_3'\n", + " '1_14_7' '1_14_4' '1_0_4' '1_1_7' '1_14_4' '1_14_8' '1_0_3' '1_5_11'\n", + " '1_1_7' '1_14_7' '1_0_3' '1_1_7' '2_3_4' '1_14_2' '1_1_6' '1_11_2'\n", + " '1_12_8' '1_14_2' '1_5_6' '1_1_7' '1_11_3' '1_14_5' '1_0_4' '1_1_7'\n", + " '1_10_2' '3_3_0' '1_7_2' '1_9_3' '1_14_1' '1_11_3' '1_14_4' '1_0_3'\n", + " '1_14_3' '1_0_0' '1_14_2' '1_10_8' '1_0_0' '1_1_6' '1_14_2' '3_3_2'\n", + " '1_0_2' '1_11_2' '1_0_0' '1_14_10' '2_0_7' '1_1_7' '1_5_6' '1_0_3'\n", + " '2_0_1' '2_1_2' '1_0_1' '1_0_4' '1_14_6' '1_0_2' '1_0_5' '1_1_6' '1_14_3'\n", + " '1_14_10' '1_1_6' '1_11_3' '1_14_4' '1_14_4' '1_1_11' '1_1_11' '1_1_1'\n", + " '1_14_3' '1_14_2' '1_0_1' '1_9_3' '1_0_5' '1_13_2' '1_14_3' '1_2_2'\n", + " '2_0_2' '1_1_11' '1_0_5' '1_14_10' '1_14_4' '1_0_5' '1_14_7' '1_1_5'\n", + " '1_0_1' '1_0_3' '1_1_1' '1_14_8' '1_14_5' '1_0_3' '1_1_6' '1_14_9'\n", + " '1_9_5' '1_10_3' '1_0_3' '2_0_2' '1_0_3' '1_1_6' '1_0_3' '1_0_3' '1_0_0'\n", + " '1_0_0' '1_1_7' '1_9_5' '1_14_4' '1_13_2' '1_0_0' '1_14_5' '1_14_2'\n", + " '1_9_4' '1_1_6' '1_14_3' '1_0_2' '1_1_6' '1_12_5' '1_9_9' '1_0_6' '1_0_4'\n", + " '1_14_7' '1_0_9' '1_0_4' '1_1_0' '3_3_1' '1_0_3' '1_14_4' '1_0_4'\n", + " '1_14_6' '1_5_5' '1_14_4' '1_14_3' '2_0_2' '1_0_0' '1_14_8' '1_1_7'\n", + " '2_0_1' '2_2_2' '2_0_2' '1_1_6' '1_14_6' '1_14_1' '1_14_1' '1_14_1'\n", + " '1_0_3' '1_14_6' '1_1_2' '2_0_2' '1_13_2' '1_1_6' '1_0_4' '1_14_8'\n", + " '1_14_2' '1_9_9' '1_5_7' '1_0_1' '1_1_7' '1_14_2' '1_0_0' '1_0_3' '1_0_6'\n", + " '2_0_7' '1_14_3' '1_11_2' '1_5_4' '1_14_9' '1_1_6' '1_14_4' '1_14_8'\n", + " '1_14_5' '1_0_3' '2_0_1' '1_11_3' '1_11_2' '1_2_1' '1_1_7' '1_14_1'\n", + " '1_14_8' '1_2_2' '1_2_2' '1_0_3' '1_13_2' '1_14_9' '2_3_2' '1_0_4'\n", + " '1_1_7' '1_1_7' '1_0_6' '1_11_2' '1_1_1' '1_14_8' '1_14_1' '2_0_1'\n", + " '1_14_5' '1_14_4' '1_0_3' '2_0_1' '1_14_2' '1_1_1' '1_1_6' '1_9_6'\n", + " '3_0_3' '1_14_4' '1_14_7' '1_0_3' '1_14_8' '1_12_3' '1_5_7' '2_1_2'\n", + " '2_1_1' '1_13_2' '1_14_6' '1_0_0' '1_1_11' '1_14_7' '1_0_5' '1_0_0'\n", + " '2_0_1' '2_1_1' '1_14_3' '1_14_9' '1_14_6' '1_14_10' '1_11_3' '1_1_7'\n", + " '1_1_11' '1_0_2' '1_0_5' '1_0_4' '1_14_5' '1_1_11' '1_1_11' '1_12_5'\n", + " '1_11_3' '1_9_9' '1_14_8' '1_14_1' '2_0_1' '1_0_3' '1_14_10' '1_0_3'\n", + " '1_14_10' '1_1_7' '1_14_10' '1_0_2' '1_5_10' '1_0_3' '3_3_0' '1_1_11'\n", + " '1_14_6' '1_14_6' '1_14_8' '2_0_2' '1_0_1' '1_0_4' '1_14_6' '2_0_1'\n", + " '1_14_5' '1_1_11' '1_1_11' '1_14_8' '1_14_3' '1_5_11' '1_1_6' '1_0_3'\n", + " '1_1_5' '1_0_4' '1_1_7' '1_5_5' '1_14_9' '1_2_2' '1_5_10' '3_3_1'\n", + " '1_14_7' '1_14_2' '1_0_3' '1_1_7' '1_10_8' '1_11_2' '1_14_6' '2_0_2'\n", + " '1_9_4' '1_2_1' '1_11_3' '1_10_2' '1_14_7' '1_2_3' '1_14_2' '1_0_4'\n", + " '1_11_2' '1_0_3' '1_5_11' '1_13_2' '1_14_7' '1_5_5' '1_1_6' '1_5_7'\n", + " '1_2_3' '1_12_8' '1_1_6' '1_14_2' '1_11_9' '1_12_8' '1_1_6' '1_0_2'\n", + " '1_11_2' '1_11_3' '1_0_0' '1_0_5' '1_0_0' '1_0_0' '1_14_4' '1_14_4'\n", + " '1_2_3' '1_14_1' '2_0_1' '2_2_2' '1_9_5' '1_12_2' '1_0_0' '1_0_0'\n", + " '1_5_10' '1_1_7' '1_0_5' '1_0_3' '1_2_2' '2_0_1' '1_14_3' '1_0_0'\n", + " '1_14_1' '1_11_9' '1_14_1' '1_7_6' '1_5_5' '1_0_5' '1_14_1' '1_14_1'\n", + " '3_3_3' '2_0_1' '1_14_6' '1_11_9' '1_11_2' '2_3_2' '1_0_0' '1_0_5'\n", + " '1_11_3' '2_0_2' '1_0_3' '1_0_3' '1_14_8' '1_3_3' '1_1_6' '1_9_3' '1_1_7'\n", + " '1_0_1' '1_14_4' '1_14_5' '1_14_2' '1_2_3' '1_14_1' '1_14_2' '1_0_5'\n", + " '1_0_4' '1_14_1' '1_14_1' '1_14_1' '1_14_6' '1_1_7' '1_0_0' '1_0_0'\n", + " '1_0_0' '1_12_8' '1_0_5' '1_13_2' '1_1_7' '1_5_7' '1_11_2' '1_1_11'\n", + " '1_1_11' '1_1_7' '1_13_2' '1_2_2' '1_1_11' '2_1_1' '1_0_4' '1_14_9'\n", + " '1_5_6' '1_0_0' '1_1_5' '1_0_1' '1_9_6' '1_14_9' '1_0_1' '1_0_0' '1_0_3'\n", + " '3_3_2' '1_7_2' '1_11_2' '1_2_2' '1_0_3' '1_5_7' '1_14_3' '1_14_7'\n", + " '1_0_0' '1_0_3' '1_14_4' '1_5_6' '3_3_4' '2_0_7' '1_14_4' '1_0_3' '1_0_2'\n", + " '1_1_11' '1_14_2' '1_14_4' '1_5_10' '1_13_2' '1_1_7' '1_14_2' '1_13_2'\n", + " '1_0_4' '3_0_3' '1_1_6' '1_9_3' '1_14_7' '1_14_1' '1_14_6' '1_5_6'\n", + " '1_14_1' '1_5_5' '1_14_9' '1_0_3' '1_12_4' '1_14_1' '1_14_2' '1_14_1'\n", + " '1_9_3' '1_14_5' '1_14_1' '2_0_7' '1_14_5' '1_1_7' '1_14_9' '1_1_7'\n", + " '1_10_2' '1_14_8' '1_0_4' '1_14_6' '1_14_9' '1_14_2' '1_2_3' '1_14_4'\n", + " '2_0_2' '1_14_8' '1_2_2' '1_12_8' '1_0_4' '3_3_4' '1_1_8' '1_0_4'\n", + " '1_14_1' '1_0_3' '1_14_8' '1_14_1' '1_14_3' '1_14_1' '1_2_2' '1_14_5'\n", + " '1_9_9' '1_14_7' '1_0_2' '1_0_5' '1_3_2' '1_0_1' '1_14_3' '1_1_0' '1_5_8'\n", + " '1_0_4' '1_13_2' '1_0_5' '1_1_11' '1_0_3' '1_5_9' '2_2_1' '2_0_1' '3_3_1'\n", + " '1_10_3' '1_0_6' '1_14_1' '1_0_3' '1_1_6' '1_0_4' '1_14_4' '1_0_0'\n", + " '1_14_4' '1_0_0' '1_14_2' '1_5_4' '1_12_3' '1_14_8' '1_0_0' '1_14_4'\n", + " '1_7_6' '1_5_11' '1_0_2' '1_1_11' '3_3_1' '3_0_3' '1_7_6' '1_0_5' '1_7_6'\n", + " '1_11_2' '1_14_9' '2_1_2' '1_9_5' '1_14_4' '1_0_4' '1_1_2' '1_14_3'\n", + " '1_2_2' '1_5_9' '1_13_2' '1_9_9' '1_1_1' '2_3_4' '2_0_2' '1_2_3' '1_13_3'\n", + " '1_13_2' '1_2_3' '1_0_2' '1_1_6' '1_13_2' '1_7_6' '1_0_3' '1_1_0' '1_5_8'\n", + " '1_2_2' '1_1_7' '1_2_2' '1_0_4' '2_0_2' '1_2_3' '1_0_4' '1_1_6' '1_0_3'\n", + " '1_0_5' '1_14_7' '2_2_2' '1_2_1' '1_14_7' '1_1_11' '1_12_4' '1_1_6'\n", + " '2_0_2' '1_14_9' '1_5_5' '1_0_4' '1_11_2' '1_14_6' '1_13_2' '1_14_5'\n", + " '1_13_1' '1_2_3' '1_1_11' '1_1_11' '1_9_7' '1_10_8' '3_3_1' '1_5_7'\n", + " '1_1_7' '1_14_7' '1_1_7' '1_1_11' '1_0_3' '1_11_2' '1_14_3' '1_14_7'\n", + " '1_1_6' '1_0_4' '1_12_4' '1_2_2' '1_0_2' '1_14_7' '1_11_2' '1_9_5'\n", + " '1_1_6' '1_1_7' '1_9_3' '1_0_4' '1_2_1' '1_2_2' '1_5_7' '1_5_5' '1_5_6'\n", + " '1_13_2' '3_3_4' '1_1_7' '1_5_8' '1_1_0' '1_12_8' '1_2_3' '1_9_3' '1_1_8'\n", + " '1_12_3' '1_14_8' '1_0_5' '1_5_7' '1_5_6' '1_0_0' '1_0_0' '1_2_2' '1_1_6'\n", + " '1_12_3' '1_14_9' '1_1_7' '1_10_2' '1_14_2' '1_0_4' '1_0_3' '1_1_7'\n", + " '1_1_8' '1_0_5' '1_0_0' '1_2_3' '1_7_6' '1_12_2' '1_2_2' '1_5_7' '1_0_0'\n", + " '1_1_11' '1_0_3' '1_0_0' '2_1_1' '3_3_0' '1_1_11' '1_3_3' '1_0_0' '2_0_1'\n", + " '1_9_6' '1_11_2' '1_0_4' '1_2_2' '1_0_1' '1_1_11' '1_1_11' '1_14_2'\n", + " '1_1_7' '1_1_8' '1_0_4' '1_2_2' '1_0_4' '1_14_4' '1_0_3' '1_0_3' '1_0_3'\n", + " '1_14_5' '1_14_6' '1_14_7' '1_0_3' '1_1_7' '1_0_4' '1_9_3' '1_14_2'\n", + " '1_0_2' '1_0_3' '1_14_3' '1_14_7' '1_10_3' '1_5_8' '1_0_5' '1_1_7'\n", + " '1_0_5' '1_14_4' '1_0_3' '1_0_5' '3_3_4' '1_0_5' '1_11_3' '1_1_7'\n", + " '1_14_1' '1_11_3' '1_12_3' '1_14_4' '1_2_3' '1_0_2' '1_0_2' '1_9_4'\n", + " '1_1_6' '2_0_1' '1_2_2' '1_14_2' '1_14_3' '1_13_2' '1_2_3' '1_0_4'\n", + " '1_1_8' '1_14_2' '1_14_2' '1_14_1' '1_0_2' '1_7_2' '1_7_6' '1_2_3'\n", + " '1_0_4' '1_14_6' '1_7_2' '1_13_2' '1_2_2' '1_0_3' '1_0_4' '1_0_2'\n", + " '1_14_7' '2_0_1' '1_14_10' '1_1_6' '1_13_6' '1_14_9' '1_2_2' '1_0_1'\n", + " '1_14_10' '1_14_10' '1_0_3' '1_13_2' '1_0_0' '1_14_1' '1_14_3' '1_0_4'\n", + " '1_12_3' '1_5_11' '1_14_1' '1_0_4' '1_2_3' '1_7_4' '2_2_2' '1_0_0'\n", + " '1_0_0' '1_14_7' '1_14_4' '1_14_4']\n", + "dowsampled rms bin 20\n", + "areas of tiles in bin [7.36284480e-05 7.32053173e-05 1.05684735e-04 ... 1.28476390e-04\n", + " 9.80640283e-05 1.36766707e-04]\n", + "names of tiles in bin ['1_0_4' '1_0_3' '1_3_2' ... '1_7_4' '1_2_2' '1_9_6']\n", + "dowsampled rms bin 21\n", + "areas of tiles in bin [1.00238010e-04 7.32053173e-05 7.32053173e-05 ... 1.37145276e-04\n", + " 1.29023749e-04 6.61473056e-05]\n", + "names of tiles in bin ['1_3_2' '1_0_4' '1_0_3' ... '2_3_2' '1_7_7' '1_1_11']\n", + "dowsampled rms bin 22\n", + "areas of tiles in bin [3.36858535e-06 7.04887954e-05 7.85179851e-05 ... 7.49453797e-05\n", + " 1.37557026e-04 2.11013789e-07]\n", + "names of tiles in bin ['1_12_1' '1_0_5' '1_0_6' ... '1_0_6' '1_12_3' '2_0_1']\n", + "dowsampled rms bin 23\n", + "areas of tiles in bin [1.22771817e-04 4.64741679e-06 1.08758919e-04 ... 1.36766707e-04\n", + " 8.38699952e-05 8.87982868e-05]\n", + "names of tiles in bin ['1_6_6' '1_6_5' '1_4_3' ... '1_9_6' '1_1_10' '1_14_7']\n", + "dowsampled rms bin 24\n", + "areas of tiles in bin [8.73992507e-05 1.28578531e-04 9.89736620e-07 ... 1.37760676e-04\n", + " 7.65357311e-05 1.17565305e-04]\n", + "names of tiles in bin ['1_1_8' '1_6_6' '1_0_6' ... '1_12_3' '1_1_11' '1_5_3']\n", + "dowsampled rms bin 25\n", + "areas of tiles in bin [0.00010248 0.00012791 0.00014046 ... 0.00013733 0.0001191 0.00013489]\n", + "names of tiles in bin ['1_3_1' '1_6_3' '1_11_11' ... '1_12_1' '1_5_2' '1_13_4']\n", + "dowsampled rms bin 26\n", + "areas of tiles in bin [5.02444216e-06 1.37945675e-04 1.26730258e-04 ... 1.41264619e-04\n", + " 1.34602134e-04 1.39234550e-04]\n", + "names of tiles in bin ['1_12_0' '1_12_4' '1_7_5' ... '1_10_9' '1_13_9' '1_12_8']\n", + "dowsampled rms bin 27\n", + "areas of tiles in bin [7.32053173e-05 1.27430863e-04 1.41876722e-04 ... 1.31829957e-04\n", + " 1.67345402e-06 1.98876996e-08]\n", + "names of tiles in bin ['1_0_7' '1_7_9' '1_11_1' ... '1_13_8' '1_14_9' '2_3_2']\n", + "dowsampled rms bin 28\n", + "areas of tiles in bin [1.25002482e-04 2.71068200e-05 1.32190982e-07 ... 9.32791077e-05\n", + " 8.87921704e-06 1.34602134e-04]\n", + "names of tiles in bin ['1_6_9' '1_7_1' '1_4_0' ... '1_2_10' '1_10_12' '1_13_11']\n", + "dowsampled rms bin 29\n", + "areas of tiles in bin [1.27430863e-04 5.32733734e-06 1.32308459e-04 ... 1.14506186e-04\n", + " 1.07748733e-04 1.33562051e-04]\n", + "names of tiles in bin ['1_7_12' '1_10_12' '1_13_10' ... '1_4_11' '1_3_12' '1_8_5']\n", + "dowsampled rms bin 30\n", + "areas of tiles in bin [6.83094144e-06 2.82529237e-04 1.41521862e-04 ... 1.42100004e-04\n", + " 1.34894730e-04 3.28768123e-06]\n", + "names of tiles in bin ['1_2_11' '1_10_2' '2_1_0' ... '1_11_11' '1_13_11' '1_8_5']\n", + "dowsampled rms bin 31\n", + "areas of tiles in bin [1.34794930e-04 1.08346821e-04 1.35169464e-04 ... 1.07432591e-04\n", + " 1.59403387e-05 9.10862735e-05]\n", + "names of tiles in bin ['1_8_3' '1_10_0' '1_8_7' ... '1_4_13' '1_10_0' '1_12_12']\n", + "dowsampled rms bin 32\n", + "areas of tiles in bin [1.36041198e-04 1.27329567e-04 1.35525697e-04 ... 2.81489132e-06\n", + " 3.54448295e-06 1.34402148e-04]\n", + "names of tiles in bin ['1_8_6' '1_7_13' '1_8_2' ... '1_4_12' '1_2_12' '1_8_10']\n", + "dowsampled rms bin 33\n", + "areas of tiles in bin [0.00014203 0.0001344 0.00014144 ... 0.00014126 0.000114 0.00013536]\n", + "names of tiles in bin ['1_10_12' '1_8_11' '1_10_0' ... '1_10_9' '1_4_14' '1_9_0']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of downsampled sky patches = 50.\n", + "Number of Q functions = 50.\n", + "Entire survey area = 13631.324739140997 deg2.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "Number of SNR bins = 6.\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dowsampled rms bin 34\n", + "areas of tiles in bin [1.27329567e-04 1.36183075e-04 1.35863583e-04 1.38388533e-04\n", + " 1.36183075e-04 1.42027999e-04 1.19095690e-04 1.34402148e-04\n", + " 1.34794930e-04 1.11277293e-04 1.06723959e-04 1.33114850e-04\n", + " 6.74917374e-06 6.31235055e-05 1.11700598e-04 1.35525697e-04\n", + " 1.27329567e-04 2.81489132e-06 1.41850307e-04 1.34794930e-04\n", + " 1.17370769e-06 1.12651087e-04 1.25808641e-04 1.35863583e-04\n", + " 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1.25073347e-04 1.30994452e-04 1.42181659e-04\n", + " 1.15971255e-04 1.13462710e-04 1.17370769e-06 1.37696480e-04\n", + " 1.74420361e-06 1.16776185e-04 8.74467485e-05 1.37711511e-04\n", + " 1.30560736e-04 9.90303227e-05 1.11277293e-04 1.41876722e-04\n", + " 1.24292874e-04 5.87423370e-06 3.62810149e-06 1.28460240e-04\n", + " 1.30065993e-04 1.22522498e-05 2.93594385e-07 3.24670365e-06\n", + " 1.37557026e-04 1.88727659e-07 1.36484130e-04 1.35525697e-04\n", + " 7.53453895e-06 1.41732649e-04 1.41948762e-04 1.13462710e-04\n", + " 1.25757597e-04 1.25757597e-04 1.37760676e-04 1.35602177e-04\n", + " 1.36484130e-04 1.34794930e-04 3.28768123e-06 6.73099798e-05\n", + " 6.21697872e-05 1.26526902e-04 1.25002482e-04 1.35525697e-04\n", + " 1.26526902e-04 9.68873120e-05 8.63223218e-06 1.33562051e-04\n", + " 1.12201126e-04 1.35525697e-04 1.30994452e-04 6.55620233e-06\n", + " 1.41595801e-04 1.42181659e-04 1.41948762e-04 1.37276278e-04\n", + " 1.33562051e-04 6.02763116e-06 1.41732649e-04 1.17565305e-04\n", + " 1.25073347e-04 1.35525697e-04 1.36183075e-04 1.28948875e-04\n", + " 1.41948762e-04 1.35169464e-04 1.37711511e-04 4.97061890e-06\n", + " 1.26370834e-04 2.51529384e-04 2.35130609e-04 1.15150625e-04\n", + " 1.15834609e-05 1.25073347e-04 1.34402148e-04 1.33562051e-04\n", + " 8.99988071e-06 8.18984491e-05 1.30994452e-04 1.33991170e-04\n", + " 1.25165687e-04 1.35863583e-04 1.40353795e-04 1.35169464e-04\n", + " 1.35525697e-04 1.35525697e-04 4.91715175e-06 1.30560736e-04\n", + " 1.29227713e-04 1.42237169e-04 4.97061890e-06 1.59288579e-06\n", + " 1.13462710e-04 1.40353795e-04 3.34221386e-06 1.33562051e-04\n", + " 1.35525697e-04 1.33114850e-04 1.35826334e-04 1.30560736e-04\n", + " 1.15908674e-04 1.38336587e-05 8.64619997e-06 1.33562051e-04\n", + " 1.36183075e-04 1.35190856e-04 6.19655572e-06 1.41850307e-04\n", + " 1.34402148e-04 1.33114850e-04 1.27228034e-04 1.27911941e-04\n", + " 6.31235055e-05 1.35169464e-04 9.68873120e-05 1.25073347e-04\n", + " 1.27029392e-04 1.30560736e-04 1.37503202e-04 1.27911941e-04\n", + " 1.41948762e-04 2.49955587e-06 6.65475633e-06 1.04631204e-04\n", + " 1.33114850e-04 1.28578531e-04 3.29310475e-07 2.30301250e-04\n", + " 1.37276278e-04 1.33562051e-04 8.56637079e-05 1.06470319e-04\n", + " 1.19095690e-04 6.65891840e-06]\n", + "names of tiles in bin ['1_7_14' '1_9_0' '1_9_8' '1_12_11' '1_9_6' '1_10_12' '1_5_12' '1_8_12'\n", + " '1_8_10' '1_4_12' '1_3_13' '1_8_10' '1_10_9' '1_0_10' '1_4_12' '1_8_1'\n", + " '1_7_15' '1_4_14' '1_10_9' '1_8_9' '1_2_13' '1_4_12' '1_6_12' '1_9_0'\n", + " '1_9_7' '1_6_14' '1_6_13' '1_2_13' '1_12_11' '1_9_7' '1_7_2' '1_6_12'\n", + " '1_8_11' '1_2_13' '1_9_10' '1_8_2' '1_8_1' '1_8_12' '1_7_8' '1_8_10'\n", + " '1_9_9' '2_3_1' '1_5_12' '1_3_13' '1_3_12' '1_8_8' '1_3_14' '1_4_12'\n", + " '1_7_13' '1_10_9' '1_3_14' '1_6_12' '1_8_10' '1_2_13' '1_5_14' '1_8_12'\n", + " '1_12_12' '1_3_14' '1_8_11' '1_4_12' '1_8_2' '1_9_11' '1_4_12' '1_2_13'\n", + " '1_9_6' '1_8_10' '1_7_9' '1_9_8' '1_8_10' '1_12_10' '1_4_12' '1_6_13'\n", + " '1_8_9' '1_6_13' '1_6_12' '1_3_13' '1_4_14' '1_8_9' '1_12_10' '1_7_12'\n", + " '1_8_9' '1_6_13' '1_9_9' '1_8_1' '1_7_1' '1_6_14' '1_4_11' '1_12_11'\n", + " '1_12_11' '1_6_13' '1_8_8' '1_8_12' '1_9_10' '1_2_13' '1_2_13' '1_8_2'\n", + " '1_8_10' '2_0_0' '1_4_14' '1_5_13' '1_8_10' '1_8_12' '1_2_13' '1_3_11'\n", + " '1_8_8' '1_9_0' '1_4_13' '1_7_12' '1_8_12' '1_7_11' '1_8_10' '1_5_12'\n", + " '1_10_10' '1_4_13' '1_8_1' '1_8_2' '1_4_13' '1_2_13' '1_11_11' '1_5_14'\n", + " '1_5_14' '1_8_8' '1_9_9' '1_4_12' '1_8_10' '2_0_0' '1_8_11' '1_11_10'\n", + " '1_9_9' '1_6_12' '1_10_9' '1_12_11' '1_2_13' '1_0_10' '1_9_0' '1_11_10'\n", + " '1_4_14' '1_11_10' '1_2_13' '1_4_14' '1_4_12' '2_0_0' '1_7_1' '1_8_11'\n", + " '1_9_0' '1_6_13' '1_11_11' '1_8_9' '1_8_11' '1_8_1' '1_10_0' '1_7_2'\n", + " '1_5_13' '1_12_11' '1_10_0' '1_6_14' '1_8_9' '1_9_10' '1_8_9' '1_5_14'\n", + " '1_8_11' '1_5_12' '1_9_9' '1_4_12' '1_4_13' '1_8_11' '1_9_10' '1_3_11'\n", + " '1_5_13' '1_5_15' '1_10_9' '1_4_12' '2_0_0' '1_9_10' '1_7_12' '1_4_14'\n", + " '1_12_12' '1_8_11' '1_5_14' '1_8_9' '1_12_12' '1_8_11' '1_6_14' '1_4_13'\n", + " '1_12_10' '1_7_9' '1_4_12' '1_2_13' '1_8_9' '1_8_1' '1_6_13' '1_11_10'\n", + " '1_8_10' '1_7_11' '1_3_13' '1_2_13' '1_2_13' '1_12_12' '1_2_13' '1_11_10'\n", + " '1_9_0' '1_6_14' '1_8_2' '1_8_9' '1_3_13' '1_10_9' '1_12_12' '1_10_9'\n", + " '1_10_9' '1_8_2' '1_12_11' '1_12_12' '1_12_12' '1_9_0' '1_4_13' '1_3_13'\n", + " '1_4_12' '1_10_11' '1_9_10' '1_10_9' '1_9_7' '1_8_12' '1_8_1' '2_3_1'\n", + " '1_4_12' '1_8_11' '1_8_2' '1_8_9' '1_4_11' '2_3_1' '1_6_12' '1_5_13'\n", + " '1_12_10' '1_12_10' '1_9_0' '1_4_12' '1_8_10' '1_4_14' '1_2_13' '1_3_11'\n", + " '1_7_13' '1_8_9' '1_8_12' '1_5_12' '1_5_14' '1_4_13' '1_9_10' '1_4_12'\n", + " '1_11_11' '1_2_13' '1_8_11' '1_8_9' '1_8_10' '1_3_13' '1_2_13' '1_12_11'\n", + " '1_8_2' '1_10_9' '1_6_14' '1_4_12' '1_8_2' '1_9_10' '1_8_8' '1_12_12'\n", + " '1_12_12' '1_8_9' '1_8_9' '1_11_10' '1_12_12' '1_9_10' '1_4_14' '1_6_14'\n", + " '1_9_7' '1_4_14' '1_4_14' '1_9_6' '1_10_0' '2_0_0' '1_7_1' '1_4_13'\n", + " '1_12_10' '1_6_13' '1_7_2' '1_6_14' '1_4_12' '1_3_13' '1_5_14' '1_9_8'\n", + " '1_7_8' '1_10_0' '1_5_13' '1_8_1' '1_11_11' '1_9_10' '1_4_14' '1_12_10'\n", + " '1_9_10' '1_9_10' '1_3_14' '1_7_10' '1_8_11' '1_9_10' '1_4_12' '1_2_13'\n", + " '1_8_8' '1_5_12' '1_11_10' '1_6_14' '1_4_12' '1_12_10' '1_9_10' '1_5_13'\n", + " '1_9_8' '1_11_11' '1_2_13' '1_2_13' '1_2_12' '1_10_11' '1_5_14' '1_5_12'\n", + " '1_5_14' '1_8_9' '1_10_9' '1_5_12' '1_6_13' '1_7_12' '1_7_14' '1_9_0'\n", + " '1_12_10' '1_6_12' '1_5_14' '1_11_10' '1_6_13' '1_4_12' '1_6_14' '1_6_14'\n", + " '1_4_13' '1_8_1' '1_2_13' '1_8_9' '1_3_13' '1_7_13' '1_5_13' '1_8_1'\n", + " '1_4_14' '1_9_0' '1_8_12' '1_8_9' '1_4_13' '1_2_12' '1_4_12' '1_10_9'\n", + " '1_4_13' '1_12_12' '1_11_10' '1_4_13' '1_8_8' '1_8_12' '1_8_10' '1_6_12'\n", + " '1_10_10' '1_2_13' '1_10_9' '1_0_10' '1_2_13' '1_12_12' '1_3_14' '1_8_12'\n", + " '1_12_12' '1_10_0' '1_6_13' '1_6_12' '1_7_8' '1_8_9' '1_12_12' '1_6_13'\n", + " '1_12_11' '1_8_1' '1_4_12' '1_5_13' '1_12_10' '1_8_12' '1_8_10' '1_8_5'\n", + " '1_7_11' '1_9_8' '1_4_12' '1_8_12' '1_2_13' '1_8_11' '1_0_10' '1_11_10'\n", + " '1_9_0' '1_7_10' '1_4_14' '1_6_12' '1_6_13' '1_3_13' '1_2_13' '1_8_10'\n", + " '1_6_13' '1_5_12' '1_11_10' '1_4_13' '1_6_13' '1_4_13' '1_10_0' '1_4_12'\n", + " '1_2_12' '1_10_0' '1_12_12' '1_12_12' '1_12_10' '1_12_12' '1_8_12'\n", + " '1_12_12' '1_7_14' '1_4_13' '1_10_10' '1_11_11' '1_7_12' '1_5_12'\n", + " '1_12_12' '1_4_13' '1_10_0' '1_12_12' '1_8_2' '1_6_13' '1_2_13' '1_4_13'\n", + " '1_8_11' '1_10_9' '1_10_11' '1_12_10' '1_4_12' '1_8_10' '1_8_10' '1_3_14'\n", + " '1_4_12' '1_4_14' '1_8_12' '1_9_7' '1_8_11' '1_0_10' '1_8_10' '1_7_12'\n", + " '1_4_13' '1_3_13' '1_8_12' '1_3_14' '1_10_10' '1_9_9' '1_9_0' '1_4_12'\n", + " '1_11_10' '1_2_12' '1_8_1' '1_4_12' '1_7_10' '1_8_9' '1_2_13' '1_3_14'\n", + " '1_6_13' '1_8_9' '1_8_9' '1_9_9' '1_12_12' '1_9_7' '1_3_13' '1_4_12'\n", + " '1_8_9' '1_7_12' '1_4_13' '1_6_14' '1_7_10' '1_2_13' '1_8_1' '1_9_10'\n", + " '1_5_13' '1_8_9' '1_8_9' '1_8_1' '1_5_14' '1_3_14' '1_10_10' '1_8_11'\n", + " '1_9_10' '1_7_15' '1_8_11' '1_8_5' '1_8_1' '1_8_9' '1_2_12' '1_4_12'\n", + " '1_2_12' '2_0_0' '1_8_8' '1_6_14' '1_8_8' '1_10_9' '1_5_13' '1_8_9'\n", + " '1_8_1' '1_5_14' '1_7_12' '1_7_12' '1_0_10' '1_8_1' '1_8_9' '1_8_12'\n", + " '1_4_12' '1_5_14' '1_11_12' '1_8_9' '1_3_13' '1_8_11' '1_11_11' '1_4_13'\n", + " '1_9_10' '1_8_10' '1_12_12' '1_6_14' '1_12_12' '1_8_9' '1_2_12' '1_9_8'\n", + " '1_8_9' '1_4_13' '1_7_1' '1_10_10' '1_3_11' '1_12_10' '1_4_14' '1_8_1'\n", + " '1_9_0' '1_9_10' '1_7_12' '1_12_12' '1_8_12' '1_10_9' '1_8_1' '1_9_9'\n", + " '1_8_9' '1_4_12' '1_4_14' '1_10_9' '1_5_14' '1_9_7' '1_7_11' '1_8_12'\n", + " '1_7_2' '1_8_1' '1_8_8' '1_5_14' '1_9_10' '1_4_14' '1_5_13' '1_6_12'\n", + " '1_10_10' '1_10_10' '1_4_12' '1_8_12' '1_8_1' '1_6_13' '1_8_12' '1_9_9'\n", + " '1_6_14' '1_7_13' '1_9_10' '2_0_0' '1_8_12' '1_7_11' '1_8_1' '1_5_14'\n", + " '1_10_9' '1_2_13' '1_11_11' '1_5_15' '1_4_14' '1_5_14' '1_4_12' '1_4_14'\n", + " '1_8_2' '1_7_13' '1_6_12' '1_8_10' '1_5_12' '1_5_13' '1_6_11' '1_8_12'\n", + " '1_5_13' '1_9_10' '2_0_0' '1_7_11' '1_7_10' '1_6_13' '1_5_12' '2_0_0'\n", + " '1_9_8' '1_6_14' '1_2_11' '1_9_0' '1_6_14' '1_4_13' '1_8_10' '1_8_12'\n", + " '1_8_9' '1_5_13' '1_9_11' '1_11_10' '1_2_13' '2_0_0' '1_9_10' '1_8_12'\n", + " '1_10_10' '1_8_10' '1_4_13' '1_8_1' '1_6_13' '1_3_13' '1_3_11' '1_10_9'\n", + " '1_3_14' '1_9_10' '1_8_9' '1_5_15' '1_5_11' '1_6_14' '1_10_9' '1_3_13'\n", + " '1_11_10' '1_6_13' '1_2_12' '1_9_10' '1_5_14' '1_9_6' '1_8_11' '1_2_12'\n", + " '1_7_1' '1_8_9' '1_6_13' '1_4_12' '1_11_11' '1_7_14' '1_3_13' '1_12_10'\n", + " '1_4_12' '1_7_2' '1_8_1' '1_9_6' '1_8_11' '1_7_10' '1_4_13' '1_5_13'\n", + " '1_6_12' '1_7_14' '1_12_10' '1_5_14' '1_4_13' '1_10_12' '1_10_9' '1_8_2'\n", + " '1_8_12' '1_3_14' '1_2_12' '1_4_13' '1_10_10' '1_6_14' '1_3_13' '1_4_12'\n", + " '1_4_14' '1_9_11' '1_8_12' '1_0_9' '1_8_9' '1_5_13' '1_6_13' '1_5_12'\n", + " '1_9_10' '1_9_8' '1_8_12' '1_6_13' '1_2_12' '1_8_8' '1_7_13' '1_4_14'\n", + " '1_5_14' '1_9_0' '1_11_10' '1_3_14' '1_5_14' '1_8_12' '1_9_8' '1_4_14'\n", + " '1_5_13' '1_8_11' '1_4_14' '1_4_12' '1_3_13' '1_12_12' '1_10_12' '1_8_9'\n", + " '2_0_0' '1_12_12' '1_5_13' '1_9_7' '1_10_10' '1_2_12' '1_4_14' '1_4_14'\n", + " '1_6_13' '1_6_13' '1_10_10' '1_5_13' '1_12_11' '1_2_12' '1_3_11' '1_3_11'\n", + " '1_7_14' '1_2_12' '1_5_14' '1_2_12' '1_4_14' '1_11_11' '1_9_7' '1_9_9'\n", + " '1_10_11' '1_9_8' '1_8_10' '1_7_10' '1_8_12' '1_6_13' '1_10_10' '1_8_11'\n", + " '1_4_12' '1_7_12' '1_7_10' '1_8_9' '1_5_15' '1_6_14' '1_9_0' '1_7_11'\n", + " '1_10_0' '1_7_13' '1_6_13' '2_0_0' '1_5_15' '1_8_12' '1_12_12' '1_5_15'\n", + " '1_12_12' '1_12_12' '2_0_0' '1_3_13' '1_2_13' '1_11_10' '1_2_13' '1_5_11'\n", + " '1_9_8' '1_10_9' '1_2_13' '1_4_12' '1_2_13' '1_4_14' '1_10_10' '1_11_11'\n", + " '1_5_14' '1_6_12' '1_10_10' '1_11_11' '1_6_12' '1_5_13' '1_10_0' '1_6_11'\n", + " '1_9_11' '1_9_8' '1_6_12' '1_8_10' '1_9_11' '1_4_14' '1_8_9' '1_5_13'\n", + " '1_5_14' '1_9_10' '1_11_11' '1_9_10' '1_8_10' '1_6_13' '1_10_0' '1_9_0'\n", + " '1_7_13' '1_8_9' '1_2_13' '1_6_13' '1_8_12' '1_7_12' '1_7_11' '1_6_13'\n", + " '1_12_10' '1_8_11' '1_3_14' '1_9_0' '1_9_10' '2_3_0' '1_3_14' '1_2_13'\n", + " '1_12_10' '1_8_10' '1_8_11' '1_9_0' '1_11_11' '1_8_1' '1_8_1' '1_5_13'\n", + " '1_2_13' '1_8_10' '1_10_9' '1_11_10' '1_3_11' '1_7_12' '1_2_13' '1_9_0'\n", + " '1_5_15' '1_8_8' '1_5_14' '1_7_10' '1_4_12' '1_3_13' '1_7_14' '2_0_0'\n", + " '1_11_10' '1_12_11' '1_3_13' '1_7_10' '1_4_14' '1_8_12' '1_4_12' '1_2_13'\n", + " '1_4_12' '1_2_13' '2_0_0' '1_5_15' '1_6_14' '1_9_10' '1_8_12' '1_6_13'\n", + " '1_8_9' '2_0_0' '1_5_13' '1_5_13' '1_2_13' '1_12_12' '1_12_12' '1_5_13'\n", + " '1_2_13' '1_9_0' '1_7_1' '1_3_13' '1_4_12' '1_11_11' '1_6_13' '1_2_13'\n", + " '1_2_12' '1_7_11' '1_7_11' '1_12_12' '1_12_12' '1_8_11' '1_12_10'\n", + " '1_12_12' '1_9_9' '1_8_11' '1_6_14' '1_10_10' '1_10_9' '1_5_14' '1_7_13'\n", + " '1_7_14' '1_12_11' '1_9_10' '1_9_9' '1_8_9' '1_8_11' '1_12_12' '1_12_12'\n", + " '1_6_13' '1_6_14' '1_8_11' '1_6_13' '1_2_13' '1_12_12' '1_8_1' '1_4_12'\n", + " '1_8_11' '1_8_5' '1_8_2' '1_10_9' '2_0_0' '1_10_10' '1_9_0' '1_8_12'\n", + " '1_6_13' '1_10_10' '1_5_13' '1_6_14' '1_8_10' '1_9_6' '1_7_10' '1_10_11'\n", + " '1_8_11' '1_9_10' '1_9_10' '1_6_13' '1_6_13' '1_5_12' '1_5_13' '1_3_14'\n", + " '1_6_14' '1_8_8' '1_8_12' '1_3_14' '1_3_14' '1_8_12' '1_8_9' '1_7_12'\n", + " '1_9_7' '1_11_11' '1_8_10' '1_8_9' '1_8_11' '1_8_12' '1_7_11' '1_6_14'\n", + " '2_0_0' '1_9_7' '1_7_13' '1_5_14' '1_11_10' '1_9_11' '1_8_11' '1_8_11'\n", + " '1_8_11' '1_9_0' '1_7_1' '1_12_12' '1_12_12' '1_12_12' '1_8_9' '1_9_10'\n", + " '1_8_9' '1_6_13' '1_10_9' '1_8_10' '1_8_12' '1_6_13' '1_6_14' '1_0_9'\n", + " '1_8_10' '1_2_12' '1_6_13' '1_6_13' '1_7_10' '1_9_10' '1_6_12' '1_10_10'\n", + " '1_3_13' '1_3_14' '1_3_13' '1_8_9' '1_6_13' '1_0_10' '1_5_13' '1_9_10'\n", + " '1_8_1' '1_3_14' '1_5_15' '1_5_14' '1_3_14']\n", + "dowsampled rms bin 35\n", + "areas of tiles in bin [6.57536246e-06 1.34402148e-04 1.00238010e-04 1.05684735e-04\n", + " 1.18681891e-05 6.13497542e-06 1.27954213e-04 1.35863583e-04\n", + " 1.27228034e-04 1.30994452e-04 1.25757597e-04 3.81386107e-05\n", + " 7.99619108e-06 6.49340731e-06 5.01116494e-05 9.10963535e-06\n", + " 1.14314405e-04 1.30065993e-04 1.37030768e-04 1.19095690e-04\n", + " 1.25002482e-04 1.17247227e-05 4.98230763e-06 1.16776185e-04\n", + " 3.67879276e-06 1.41876722e-04 1.26526902e-04 1.27911941e-04\n", + " 1.33114850e-04 1.42106330e-04 6.51522199e-06 6.76323805e-06\n", + " 1.30560736e-04 8.28972106e-05 1.41732649e-04 1.09384559e-04\n", + " 1.15971255e-04 1.25165687e-04 1.34794930e-04 1.35863583e-04\n", + " 1.03563508e-04 3.32888122e-06 1.27029392e-04 1.13462710e-04\n", + " 1.41948762e-04 1.33114850e-04 1.35863583e-04 1.33562051e-04\n", + " 1.34704556e-05 1.42044804e-04 9.68873120e-05 1.15971255e-04\n", + " 1.30560736e-04 2.60302338e-06 1.42027999e-04 1.27029392e-04\n", + " 1.35863583e-04 1.34206710e-04 1.33114850e-04 1.33562051e-04\n", + " 3.32153842e-06 1.33991170e-04 1.16776185e-04 1.21292639e-04\n", + " 1.10338395e-04 1.33114850e-04 1.13109769e-04 1.41595801e-04\n", + " 1.12651087e-04 3.99504630e-06 4.39018571e-07 3.00838536e-05\n", + " 5.65046875e-07 1.25002482e-04 1.19095690e-04 1.15971255e-04\n", + " 4.75881837e-05 1.28578531e-04 1.19095690e-04 1.26730258e-04\n", + " 1.37711511e-04 1.42027999e-04 1.38259625e-04 1.12201126e-04\n", + " 2.82879569e-04 1.37711511e-04 1.30560736e-04 1.28578531e-04\n", + " 1.14525556e-05 1.28948875e-04 1.34402148e-04 1.34794930e-04\n", + " 1.03563508e-04 1.01586842e-04 7.27779963e-05 6.61100963e-06\n", + " 1.35169464e-04 1.28578531e-04 1.30560736e-04 1.36766707e-04\n", + " 1.29023749e-04 1.35863583e-04 1.41264619e-04 1.02481791e-04\n", + " 2.10145014e-08 1.33993480e-04 1.26526902e-04 9.54135338e-06\n", + " 1.25695166e-04 1.33114850e-04 1.41970373e-04 1.30560736e-04\n", + " 1.36183075e-04 1.27329567e-04 1.00638194e-05 1.27670753e-04\n", + " 1.35826334e-04 1.35042665e-06 1.38259625e-04 1.41732649e-04\n", + " 1.23927350e-04 1.30994452e-04 1.17565305e-04 1.30994452e-04\n", + " 1.18338508e-04 1.34794930e-04 1.36032102e-04 1.41876722e-04\n", + " 1.25695166e-04 2.26925419e-04 1.26526902e-04 1.17565305e-04\n", + " 1.38388533e-04 1.34402148e-04 1.36484130e-04 1.34402148e-04\n", + " 1.29553641e-04 1.66565226e-06 1.35525697e-04 1.28460240e-04\n", + " 1.36817872e-04 1.27228034e-04 1.22771817e-04 1.17565305e-04\n", + " 2.70705381e-04 1.35169464e-04 2.47283412e-06 1.30994452e-04\n", + " 1.41732649e-04 1.41595801e-04 1.05684735e-04 1.18338508e-04\n", + " 4.93152184e-06 1.29553641e-04 1.34402148e-04 1.27029392e-04\n", + " 1.43839334e-04 1.25808641e-04 1.37030768e-04 1.35190856e-04\n", + " 1.24292874e-04 1.05907195e-04 1.35625208e-04 1.25695166e-04\n", + " 1.33114850e-04 1.26730258e-04 1.36183075e-04 1.15908674e-04\n", + " 1.38336587e-05 1.61186094e-06 1.39945718e-04 1.59172766e-07\n", + " 1.03563508e-04 1.33562051e-04 1.30127670e-04 1.43755081e-04\n", + " 1.30560736e-04 8.64619997e-06 1.25808641e-04 1.27911941e-04\n", + " 1.38716833e-05 1.72318911e-06 9.23470059e-05 1.22771817e-04\n", + " 1.35863583e-04 1.24292874e-04 1.26526902e-04 1.41595801e-04\n", + " 1.41595801e-04 5.23278625e-06 3.54562252e-06 1.10338395e-04\n", + " 1.29162105e-04 1.36766707e-04 1.13462710e-04 1.41948762e-04\n", + " 5.23304861e-06 1.04631204e-04 1.10338395e-04 1.13462710e-04\n", + " 1.38331099e-04 1.35169464e-04 2.36677016e-04 1.30994452e-04\n", + " 1.34794930e-04 1.35169464e-04 1.27670753e-04 1.36183075e-04\n", + " 9.92274681e-05 1.11700598e-04 6.59363239e-06 1.04826478e-04\n", + " 1.36041198e-04 1.10338395e-04 1.34402148e-04 1.28578531e-04\n", + " 1.19095690e-04 1.29227713e-04 4.90211596e-06 1.28646238e-05\n", + " 1.33991170e-04 1.35525697e-04 3.17360189e-06 1.35169464e-04\n", + " 1.13462710e-04 1.33991170e-04 3.31374593e-06 1.37711511e-04\n", + " 9.79060376e-05 1.76895782e-05 6.57869047e-06 1.73029524e-06\n", + " 7.07759588e-05 1.07748733e-04 1.40187713e-04 1.33114850e-04\n", + " 1.14525556e-05 1.14237090e-04 1.30994452e-04 7.89985143e-06\n", + " 6.74082596e-07 1.33991170e-04 1.27954213e-04 1.34738202e-04\n", + " 2.28628811e-04 1.11277293e-04 1.35359661e-04 1.19095690e-04\n", + " 3.85785415e-06 1.09754382e-04 6.57457854e-06 1.22771817e-04\n", + " 8.64619997e-06 3.00838536e-05 1.42027999e-04 1.34402148e-04\n", + " 1.42106330e-04 7.27779963e-05 6.73522782e-06 1.15971255e-04\n", + " 1.16776185e-04 1.29227713e-04 1.38385443e-04 1.30065993e-04\n", + " 1.36032102e-04 1.15908674e-04 1.38336587e-05 1.25808641e-04\n", + " 1.33114850e-04 6.73522782e-06 1.40187713e-04 1.40187713e-04\n", + " 7.59105219e-07 1.35359661e-04 1.37178450e-04 1.37030768e-04\n", + " 1.22771817e-04 1.36032102e-04 1.10338395e-04 1.35525697e-04\n", + " 1.13586325e-04 1.35098821e-04 1.27029392e-04 1.33562051e-04\n", + " 1.29227713e-04 1.40076198e-04 1.17565305e-04 1.35525697e-04\n", + " 1.33562051e-04 1.14314405e-04 1.11700598e-04 1.72318911e-06\n", + " 1.34794930e-04 4.98230763e-06 1.16776185e-04 1.23927350e-04\n", + " 1.36037487e-04 1.26526902e-04 1.19095690e-04 1.27029392e-04\n", + " 1.00638194e-05 1.41948762e-04 1.41850307e-04 1.35525697e-04\n", + " 6.74917374e-06 1.41264619e-04 1.36032102e-04 1.35863583e-04\n", + " 1.35525697e-04 1.15150625e-04 6.59363239e-06 1.33562051e-04\n", + " 1.15908674e-04 1.38336587e-05 1.05684735e-04 1.04631204e-04\n", + " 1.13462710e-04 5.07154042e-05 1.42027999e-04 1.21292639e-04\n", + " 1.15971255e-04 8.64619997e-06 1.16776185e-04 1.34402148e-04\n", + " 1.01386199e-04 6.20889079e-06 3.72311557e-06 1.18370670e-05\n", + " 1.36484130e-04 1.34819689e-04 1.28578531e-04 1.26526902e-04\n", + " 1.33114850e-04 1.29227713e-04 3.85981120e-05 1.30994452e-04\n", + " 7.66433940e-06 1.20584386e-04 1.13586325e-04 1.34819689e-04\n", + " 1.33562051e-04 1.33114850e-04 1.18230602e-05 1.18338508e-04\n", + " 1.15971255e-04 1.28948875e-04 1.25073347e-04 1.35169464e-04\n", + " 1.11700598e-04 1.03563508e-04 9.90303227e-05 1.42169354e-04\n", + " 1.41970373e-04 1.30560736e-04 1.41876722e-04 2.36677016e-04\n", + " 1.28948875e-04 1.35863583e-04 1.17565305e-04 1.34206710e-04\n", + " 1.34522306e-04 1.13462710e-04 1.29023749e-04 2.41537372e-06\n", + " 1.59942766e-06 1.30065993e-04 1.33991170e-04 2.84055998e-04\n", + " 1.42023092e-04 1.14003098e-04 1.35525697e-04 1.33114850e-04\n", + " 1.25002482e-04 2.09262409e-04 7.27779963e-05 1.35169464e-04\n", + " 1.11277293e-04 1.28578531e-04 1.33114850e-04 3.00838536e-05\n", + " 1.26730258e-04 1.36438771e-04 3.85358606e-07 1.36183075e-04\n", + " 1.36484130e-04 1.42044804e-04 1.32591945e-04 1.25002482e-04\n", + " 1.33562051e-04 4.12837146e-06 3.34820190e-06 1.26526902e-04\n", + " 3.46720176e-06 1.14525556e-05 3.20711148e-07 1.21292639e-04\n", + " 1.15150625e-04 1.41850307e-04 9.44946899e-05 3.20911730e-07\n", + " 1.33114850e-04 1.13462710e-04 1.36032102e-04 1.41948762e-04\n", + " 5.06702579e-06 1.29912666e-05 1.34522306e-04 1.28578531e-04\n", + " 1.14237090e-04 1.34206710e-04 8.18984491e-05 1.42219529e-04\n", + " 1.04631204e-04 1.73029524e-06 8.99988071e-06 1.28578531e-04\n", + " 1.35098821e-04 1.35602177e-04 1.42044804e-04 1.27911941e-04\n", + " 1.27670753e-04 1.42027999e-04 1.30994452e-04 1.41948762e-04\n", + " 1.15971255e-04 1.33562051e-04 1.13636710e-05 1.37503202e-04\n", + " 7.62210254e-06 1.13462710e-04 1.35863583e-04 1.42027999e-04\n", + " 1.27911941e-04 1.33562051e-04 1.34794930e-04 1.08758919e-04\n", + " 5.05287802e-06 7.30716834e-05 4.78423782e-05 1.14003098e-04\n", + " 1.36183075e-04 6.22784161e-06 4.94566824e-06 5.92009655e-05\n", + " 1.38111999e-04 1.15150625e-04 6.96190133e-06 1.15971255e-04\n", + " 1.14314405e-04 1.27228034e-04 1.40477685e-04 1.37276278e-04\n", + " 1.35098821e-04 1.26526902e-04 1.01386199e-04 1.42106330e-04\n", + " 1.00638194e-05 1.17565305e-04 1.37030768e-04 1.23927350e-04\n", + " 1.30994452e-04 1.10338395e-04 1.30994452e-04 1.27911941e-04\n", + " 7.01614194e-05 1.13462710e-04 1.27228034e-04 1.05293403e-04\n", + " 1.17420896e-04 1.16657102e-05 1.15150625e-04 5.03822600e-06\n", + " 1.14314405e-04 1.33562051e-04 1.34794930e-04 1.41439784e-04\n", + " 6.59363239e-06 4.27983700e-07 6.49340731e-06 1.27329567e-04\n", + " 1.35169464e-04 1.26730258e-04 3.49490551e-05 1.35863583e-04\n", + " 1.15150625e-04 1.82820578e-06 1.35525697e-04 1.27228034e-04\n", + " 1.28948875e-04 1.15971255e-04 1.29227713e-04 1.41264619e-04\n", + " 1.40280250e-04 1.27029392e-04 1.34794930e-04 1.15908674e-04\n", + " 1.38336587e-05 1.30994452e-04 1.06470319e-04 6.74265720e-06\n", + " 1.04826478e-04 1.19095690e-04 1.25002482e-04 1.14314405e-04\n", + " 3.54562252e-06 2.57455796e-06 1.36438771e-04 1.15150625e-04\n", + " 1.17565305e-04 1.40353795e-04 1.18681891e-05 1.35359661e-04\n", + " 1.13109769e-04 1.28460240e-04 1.10338395e-04 1.12651087e-04\n", + " 1.27911941e-04 8.64619997e-06 1.36183075e-04 1.38331099e-04\n", + " 1.42181659e-04 1.28460240e-04 1.41850307e-04 1.05907195e-04\n", + " 1.17565305e-04 1.26730258e-04 1.27228034e-04 1.23566439e-04\n", + " 1.35098821e-04 1.35602177e-04 1.09384559e-04 1.02622616e-04\n", + " 1.16776185e-04 1.34819689e-04 1.35359661e-04 1.15630225e-04\n", + " 1.42027999e-04 1.05684735e-04 1.37711511e-04 1.27911941e-04\n", + " 1.14314405e-04 1.35525697e-04 1.42234579e-04 1.75139641e-06\n", + " 3.35373663e-06 1.40280250e-04 1.14314405e-04 1.02481791e-04\n", + " 1.13462710e-04 1.33114850e-04 1.42181659e-04 1.35169464e-04\n", + " 1.57196996e-06 1.37711511e-04 6.61100963e-06 1.36438771e-04\n", + " 1.25073347e-04 1.35863583e-04 1.19095690e-04 1.35169464e-04\n", + " 1.13586325e-04 1.18338508e-04 1.26526902e-04 1.28578531e-04\n", + " 6.31235055e-05 1.33562051e-04 1.15150625e-04 1.11277293e-04\n", + " 1.41732649e-04 1.14314405e-04 1.30560736e-04 5.10396119e-06\n", + " 1.25808641e-04 1.30994452e-04 1.33991170e-04 1.12651087e-04\n", + " 1.19095690e-04 1.41876722e-04 1.36484130e-04 1.16776185e-04\n", + " 1.30560736e-04 1.34522306e-04 1.02580699e-04 1.28578531e-04\n", + " 1.34402148e-04 1.25073347e-04 1.41876722e-04 1.17565305e-04\n", + " 1.28460240e-04 1.17565305e-04 1.37503202e-04 1.41264619e-04\n", + " 1.42190828e-04 1.34522306e-04 1.11277293e-04 1.35169464e-04\n", + " 1.25695166e-04 1.22771817e-04 9.90303227e-05 1.30065993e-04\n", + " 1.17322195e-04 1.40023618e-05 1.26526902e-04 1.11700598e-04\n", + " 8.75164143e-06 1.43903127e-04 1.42044804e-04 1.16776185e-04\n", + " 1.29553641e-04 1.13109769e-04 7.74569464e-06 1.35826334e-04\n", + " 1.18338508e-04 1.29553641e-04 1.14314405e-04 1.25757597e-04\n", + " 1.41876722e-04 1.09754382e-04 1.14314405e-04 1.15971255e-04\n", + " 1.41264619e-04 1.27228034e-04 1.07748733e-04 1.40477685e-04\n", + " 3.54562252e-06 1.16776185e-04 1.25002482e-04 1.34794930e-04\n", + " 1.38331099e-04 1.22771817e-04 1.18235887e-04 8.87425603e-06\n", + " 1.34738202e-04 1.25165687e-04 3.19505078e-05 1.01145609e-04\n", + " 1.30994452e-04 1.30065993e-04 1.05684735e-04 3.34820190e-06\n", + " 1.01386199e-04 1.28948875e-04 1.22771817e-04 1.10734986e-04\n", + " 4.95825722e-06 1.12651087e-04 1.25073347e-04 1.26526902e-04\n", + " 1.16776185e-04 1.38331099e-04 1.18338508e-04 1.06973574e-04\n", + " 3.54562252e-06 1.09493536e-05 1.26730258e-04 1.15630225e-04\n", + " 1.37276278e-04 7.70553863e-06 1.26370834e-04 1.15150625e-04\n", + " 1.36438771e-04 1.29553641e-04 1.33991170e-04 1.38300230e-04\n", + " 1.02481791e-04 1.14506186e-04 1.38259625e-04 1.26370834e-04\n", + " 1.37276278e-04 1.15150625e-04 1.33114850e-04 1.26730258e-04\n", + " 1.30994452e-04 1.33114850e-04 1.15971255e-04 1.37503202e-04\n", + " 1.42023092e-04 1.16776185e-04 1.35826334e-04 2.46313092e-07\n", + " 1.02622616e-04 1.33991170e-04 1.34402148e-04 1.28578531e-04\n", + " 1.17565305e-04 1.15150625e-04 1.17565305e-04 1.15150625e-04\n", + " 1.38259625e-04 1.04826478e-04 7.66433940e-06 1.35359661e-04\n", + " 1.46833018e-05 1.10734986e-04 1.17565305e-04 3.26201239e-06\n", + " 5.34220777e-05 1.43915581e-04 1.17565305e-04 1.28578531e-04\n", + " 6.54952192e-06 1.33114850e-04 3.79352700e-05 1.34206710e-04\n", + " 1.15150625e-04 1.25165687e-04 1.30560736e-04 1.36484130e-04\n", + " 1.41948762e-04 8.88066178e-06 1.35264761e-05 1.34402148e-04\n", + " 1.41264619e-04 1.42044804e-04 4.79247995e-06 1.14506186e-04\n", + " 1.33114850e-04 1.30065993e-04 1.36484130e-04 1.35169464e-04\n", + " 1.27228034e-04 1.27911941e-04 1.40187713e-04 1.35525697e-04\n", + " 1.01386199e-04 1.38259625e-04 1.36037487e-04 1.19095690e-04\n", + " 1.72318911e-06 1.19095690e-04 1.23566439e-04 1.06470319e-04\n", + " 1.18681891e-05 1.18338508e-04 1.25165687e-04 1.26370834e-04\n", + " 1.28460240e-04 1.30994452e-04 2.83465297e-04 1.09754382e-04\n", + " 1.16776185e-04 1.35525697e-04 2.87497902e-05 1.33114850e-04\n", + " 1.41850307e-04 6.87802300e-06 1.30065993e-04 1.30065993e-04\n", + " 1.25808641e-04 1.16776185e-04 1.28948875e-04 1.25808641e-04\n", + " 5.84877008e-05 1.35098821e-04 7.21913017e-05 1.25695166e-04\n", + " 6.81308332e-05 1.06723959e-04 1.33562051e-04 1.22771817e-04\n", + " 1.33991170e-04 1.05907195e-04 1.33562051e-04 1.18338508e-04\n", + " 1.08415914e-04 8.73750331e-06 1.15908674e-04 1.38336587e-05\n", + " 6.53615461e-06 6.75477654e-06 8.64619997e-06 6.51522199e-06\n", + " 6.29279554e-05 1.38259625e-04 1.15150625e-04 1.18338508e-04\n", + " 3.58775767e-04 1.34987895e-04 1.19095690e-04 1.29227713e-04\n", + " 1.41850307e-04 1.27029392e-04 1.07432591e-04 1.33562051e-04\n", + " 1.36438771e-04 5.84877008e-05 1.29227713e-04 4.92126749e-06\n", + " 1.89853292e-06 1.15150625e-04 1.28578531e-04 1.07432591e-04\n", + " 1.34794930e-04 6.20057732e-05 1.35098821e-04 7.59033378e-07\n", + " 6.87802300e-06 6.73522782e-06 1.66076921e-06 1.14314405e-04\n", + " 1.41850307e-04 1.29553641e-04 1.23566439e-04 1.34522306e-04\n", + " 1.41595801e-04 1.42237169e-04 1.25808641e-04 1.30521668e-05\n", + " 1.10338395e-04 1.10338395e-04 1.56457109e-06 1.02622616e-04\n", + " 6.31235055e-05 1.19095690e-04 1.41850307e-04 9.25949181e-05\n", + " 1.07748733e-04 1.36183075e-04 2.95468544e-07 8.06671175e-05\n", + " 1.41439784e-04 1.34794930e-04 1.02481791e-04 1.15743336e-04\n", + " 1.30994452e-04 1.18308644e-05 1.16776185e-04 1.15275916e-05\n", + " 1.27228034e-04 1.27430863e-04 1.43953913e-04 1.33114850e-04\n", + " 1.17565305e-04 1.27329567e-04]\n", + "names of tiles in bin ['1_8_2' '1_8_1' '1_3_13' '1_3_13' '1_5_15' '1_2_13' '1_7_14' '1_9_9'\n", + " '1_6_14' '1_8_8' '1_7_15' '1_2_13' '1_3_14' '1_8_2' '1_2_13' '1_3_14'\n", + " '1_5_14' '1_7_9' '1_9_11' '1_5_13' '1_6_14' '1_3_14' '1_9_7' '1_5_13'\n", + " '1_2_13' '1_11_11' '1_6_13' '1_6_13' '1_8_12' '2_0_0' '1_8_10' '1_10_11'\n", + " '1_7_10' '1_3_14' '1_10_10' '1_4_12' '1_5_13' '1_7_12' '1_8_12' '1_9_9'\n", + " '1_3_13' '1_9_10' '1_6_13' '1_5_14' '1_10_10' '1_8_9' '1_9_11' '1_8_1'\n", + " '1_10_10' '1_11_10' '1_2_12' '1_5_14' '1_7_10' '1_3_13' '1_10_12'\n", + " '1_6_14' '1_9_8' '1_9_0' '1_8_11' '1_8_9' '1_9_10' '1_8_11' '1_5_13'\n", + " '1_6_14' '1_4_14' '1_8_12' '1_4_12' '1_10_9' '1_4_14' '1_3_14' '1_3_14'\n", + " '1_5_15' '1_3_14' '1_6_13' '1_5_14' '1_5_14' '1_3_14' '1_6_13' '1_5_12'\n", + " '1_7_14' '1_9_10' '1_10_12' '1_12_11' '1_4_14' '1_10_9' '1_9_10' '1_7_1'\n", + " '1_6_13' '1_5_15' '1_7_10' '1_8_9' '1_8_11' '1_3_14' '1_2_12' '1_5_15'\n", + " '1_8_10' '1_8_9' '1_6_13' '1_7_12' '1_9_10' '1_7_15' '1_9_9' '1_10_10'\n", + " '1_3_13' '1_12_12' '1_8_1' '1_6_14' '1_3_14' '1_6_13' '1_8_11' '1_11_11'\n", + " '1_7_8' '1_9_8' '1_7_15' '1_8_1' '1_6_13' '1_9_10' '1_4_13' '1_12_12'\n", + " '1_10_10' '1_8_1' '1_8_12' '1_5_13' '1_8_2' '1_5_14' '1_8_12' '1_9_10'\n", + " '1_11_10' '1_6_14' '1_5_13' '1_6_14' '1_5_15' '1_12_10' '1_8_11' '1_9_0'\n", + " '1_8_12' '1_7_14' '1_0_10' '1_8_9' '1_7_11' '1_8_11' '1_6_13' '1_6_13'\n", + " '1_5_13' '1_9_10' '1_8_10' '1_3_13' '1_8_11' '1_10_10' '1_10_1' '1_3_13'\n", + " '1_5_14' '1_8_12' '1_7_12' '1_8_11' '1_6_14' '2_0_0' '1_6_13' '1_9_10'\n", + " '1_8_10' '1_6_13' '1_3_13' '1_8_10' '1_6_14' '1_8_1' '1_7_14' '1_9_9'\n", + " '1_12_12' '1_12_12' '1_7_2' '1_12_12' '2_3_0' '1_3_13' '1_8_1' '1_6_14'\n", + " '2_0_0' '1_7_12' '1_12_12' '1_6_13' '1_6_13' '2_0_0' '1_12_12' '1_12_12'\n", + " '1_6_14' '1_9_0' '1_6_14' '1_6_13' '1_10_9' '1_10_10' '1_12_12' '1_11_12'\n", + " '1_4_14' '1_12_12' '1_9_10' '1_5_13' '1_10_10' '1_12_12' '1_3_13'\n", + " '1_4_14' '1_5_15' '1_11_12' '1_8_1' '1_5_12' '1_8_10' '1_8_1' '1_8_11'\n", + " '1_6_13' '1_9_9' '1_2_13' '1_4_13' '1_8_12' '1_3_14' '1_8_10' '1_4_12'\n", + " '1_8_1' '1_6_13' '1_5_14' '1_6_14' '1_8_12' '1_7_1' '1_8_11' '1_8_11'\n", + " '1_6_15' '1_8_1' '1_5_15' '1_8_11' '1_9_8' '1_9_9' '1_7_1' '1_7_1'\n", + " '1_3_14' '2_0_0' '1_3_14' '1_3_13' '1_11_11' '1_8_11' '1_5_15' '2_0_0'\n", + " '1_8_11' '1_3_14' '1_6_15' '1_8_12' '1_7_12' '1_8_10' '1_5_14' '1_4_14'\n", + " '1_9_11' '1_5_13' '1_6_15' '1_4_13' '1_3_14' '1_6_14' '1_12_12' '1_5_15'\n", + " '1_10_12' '1_8_12' '2_0_0' '1_5_15' '1_10_9' '1_5_15' '1_5_14' '1_6_14'\n", + " '2_0_0' '1_7_11' '1_9_10' '1_12_12' '1_12_12' '1_6_12' '1_8_9' '1_10_11'\n", + " '1_11_11' '1_11_10' '1_4_14' '1_9_10' '1_8_8' '1_9_10' '1_6_13' '1_9_10'\n", + " '1_4_14' '1_8_11' '1_4_12' '1_9_0' '1_6_14' '1_8_9' '1_6_12' '1_12_11'\n", + " '1_5_13' '1_8_11' '1_8_11' '1_5_14' '1_4_14' '1_12_12' '1_8_12' '1_9_10'\n", + " '1_5_14' '1_8_1' '1_12_12' '1_6_12' '1_5_13' '1_6_13' '1_8_1' '1_10_10'\n", + " '1_10_10' '1_8_11' '1_10_10' '1_10_10' '1_9_9' '1_9_10' '1_8_11' '1_5_13'\n", + " '1_8_2' '1_8_9' '1_12_12' '1_12_12' '1_3_13' '1_3_11' '1_5_14' '1_2_13'\n", + " '1_10_11' '1_6_13' '1_5_14' '1_12_12' '1_5_13' '1_8_1' '1_3_13' '1_2_13'\n", + " '1_2_13' '1_11_12' '1_9_10' '1_9_9' '1_6_12' '1_6_14' '1_8_10' '1_6_14'\n", + " '1_2_13' '1_8_1' '1_6_14' '1_5_13' '1_4_14' '1_9_10' '1_8_9' '1_8_9'\n", + " '1_11_11' '1_5_13' '1_5_14' '1_7_12' '1_6_14' '1_8_12' '1_4_14' '1_3_13'\n", + " '1_3_13' '2_0_0' '1_11_11' '1_7_9' '1_11_11' '1_5_14' '1_7_11' '1_9_8'\n", + " '1_5_12' '1_9_8' '1_9_9' '1_5_12' '1_7_15' '1_3_13' '1_7_12' '1_7_11'\n", + " '1_8_11' '1_10_12' '2_0_0' '1_4_12' '1_8_1' '1_8_1' '1_6_13' '1_3_13'\n", + " '1_5_15' '1_8_10' '1_4_12' '1_6_14' '1_8_11' '1_5_15' '1_7_12' '1_8_12'\n", + " '1_3_14' '1_9_10' '1_9_10' '1_11_11' '1_8_11' '1_6_14' '1_8_9' '1_3_14'\n", + " '1_9_10' '1_6_13' '2_0_0' '1_5_15' '1_3_14' '1_6_13' '1_5_14' '1_10_9'\n", + " '1_2_12' '1_3_14' '1_8_1' '1_5_13' '1_9_10' '1_10_10' '1_11_10' '1_7_15'\n", + " '1_9_10' '1_6_14' '2_0_0' '1_9_8' '1_3_14' '2_0_0' '1_3_14' '2_0_0'\n", + " '1_3_14' '1_6_13' '1_9_0' '1_9_10' '1_11_12' '1_6_13' '1_6_13' '1_10_11'\n", + " '1_8_11' '1_10_10' '1_5_14' '1_8_11' '1_3_14' '1_9_10' '1_6_15' '1_5_13'\n", + " '1_9_8' '1_10_12' '1_6_13' '1_8_12' '1_8_12' '1_4_12' '1_12_10' '1_12_12'\n", + " '1_3_14' '1_4_14' '1_9_11' '1_6_13' '1_3_14' '1_12_12' '1_12_10' '1_5_13'\n", + " '1_12_12' '1_5_13' '1_5_13' '1_6_13' '2_0_0' '1_9_10' '1_9_10' '1_6_12'\n", + " '1_3_13' '2_0_0' '1_8_1' '1_5_13' '1_9_10' '1_8_1' '1_8_9' '1_4_14'\n", + " '1_8_11' '1_6_13' '1_5_15' '1_5_13' '1_6_13' '1_8_1' '1_5_13' '1_5_15'\n", + " '1_5_13' '1_9_10' '1_5_14' '1_8_10' '1_8_12' '1_10_10' '1_8_11' '1_3_14'\n", + " '1_8_10' '1_7_14' '1_8_11' '1_7_12' '1_5_15' '1_9_11' '1_5_13' '1_3_14'\n", + " '1_8_11' '1_6_13' '1_7_10' '1_5_13' '1_6_14' '1_10_10' '1_11_11' '1_6_12'\n", + " '1_8_9' '1_12_12' '1_12_12' '1_8_12' '1_5_15' '1_10_10' '1_3_13' '1_5_14'\n", + " '1_6_15' '1_5_13' '1_11_12' '1_3_14' '1_8_1' '1_5_15' '1_5_15' '1_11_12'\n", + " '1_5_15' '1_9_10' '1_4_12' '1_7_10' '1_4_14' '1_4_14' '1_6_12' '1_12_12'\n", + " '1_9_7' '1_11_12' '2_0_0' '1_7_9' '1_10_10' '1_3_13' '1_5_14' '1_7_14'\n", + " '1_6_13' '1_6_13' '1_9_10' '1_9_10' '1_4_12' '1_3_13' '1_5_13' '1_9_10'\n", + " '1_9_10' '2_0_0' '1_10_12' '1_3_11' '1_9_11' '1_6_14' '1_5_13' '1_8_12'\n", + " '2_0_0' '2_0_0' '1_9_10' '1_11_11' '1_5_14' '1_3_13' '1_5_14' '1_8_9'\n", + " '2_0_0' '1_8_11' '1_7_15' '1_9_10' '1_8_11' '1_8_9' '1_6_13' '1_9_10'\n", + " '1_5_14' '1_8_12' '1_4_12' '1_5_12' '1_6_14' '1_6_14' '1_0_9' '1_8_11'\n", + " '1_5_14' '1_4_14' '1_10_10' '1_5_13' '1_7_11' '1_3_14' '1_6_12' '1_8_12'\n", + " '1_8_11' '1_4_12' '1_5_13' '1_11_11' '1_9_10' '1_5_13' '1_7_12' '1_9_8'\n", + " '1_8_1' '1_6_14' '1_8_12' '1_6_15' '1_11_11' '1_5_13' '1_7_11' '1_5_14'\n", + " '1_9_10' '1_10_9' '2_0_0' '1_9_10' '1_4_14' '1_8_12' '1_6_15' '1_6_14'\n", + " '1_3_13' '1_7_11' '1_12_12' '1_12_12' '1_6_13' '1_4_14' '1_12_12' '2_0_0'\n", + " '1_11_12' '1_5_13' '1_7_12' '1_4_14' '1_6_14' '1_9_11' '1_5_13' '1_7_13'\n", + " '1_5_14' '1_7_13' '1_11_10' '1_4_14' '1_5_13' '1_5_14' '1_10_10' '1_6_13'\n", + " '1_3_13' '2_0_0' '1_11_12' '1_5_14' '1_6_14' '1_8_12' '1_11_12' '1_6_14'\n", + " '1_5_13' '1_11_12' '1_8_12' '1_7_14' '1_11_12' '1_11_12' '1_8_11' '1_7_9'\n", + " '1_3_13' '1_9_11' '1_3_13' '1_7_9' '1_6_14' '1_4_14' '1_8_13' '1_4_14'\n", + " '1_6_14' '1_6_14' '1_5_14' '1_11_12' '1_5_13' '1_3_13' '1_11_12' '1_8_1'\n", + " '1_7_12' '2_0_0' '1_9_10' '1_6_14' '1_6_13' '1_5_14' '1_8_10' '1_7_1'\n", + " '1_8_10' '2_0_0' '1_3_13' '1_4_12' '1_12_12' '1_6_14' '1_9_10' '1_5_13'\n", + " '1_8_10' '1_7_14' '1_8_11' '1_8_11' '1_5_13' '1_9_11' '2_0_0' '1_5_14'\n", + " '1_9_10' '1_3_14' '1_3_14' '1_8_9' '1_8_11' '1_6_13' '1_5_13' '1_5_13'\n", + " '1_5_15' '1_5_14' '1_12_12' '1_3_13' '1_6_15' '1_9_10' '2_3_1' '1_4_12'\n", + " '1_5_14' '2_3_1' '1_3_14' '2_0_0' '1_5_13' '1_6_14' '1_3_14' '1_8_12'\n", + " '1_3_14' '1_9_11' '1_5_13' '1_7_14' '1_7_11' '1_9_11' '1_10_11' '1_3_14'\n", + " '1_10_12' '1_8_11' '1_10_10' '1_11_11' '1_8_12' '1_4_12' '1_8_9' '1_7_11'\n", + " '1_9_10' '1_8_11' '1_6_13' '1_6_13' '1_11_11' '1_8_11' '1_3_13' '1_12_12'\n", + " '1_12_12' '1_5_14' '1_12_12' '1_5_13' '1_6_13' '1_5_15' '1_5_15' '1_5_14'\n", + " '1_7_13' '1_6_13' '1_7_10' '1_8_9' '1_10_10' '1_4_12' '1_5_14' '1_8_12'\n", + " '1_0_10' '1_8_12' '1_10_10' '1_12_12' '1_7_10' '1_7_11' '1_6_13' '1_5_13'\n", + " '1_7_11' '1_6_14' '1_12_12' '1_9_10' '1_12_12' '1_6_13' '1_12_12'\n", + " '1_3_13' '1_8_12' '1_6_14' '1_8_12' '1_3_14' '1_8_12' '1_5_13' '1_4_12'\n", + " '1_12_12' '1_12_12' '1_12_12' '1_8_2' '1_10_11' '1_12_12' '1_8_2'\n", + " '1_12_12' '1_12_10' '1_5_14' '1_5_14' '1_5_14' '1_8_1' '1_5_13' '1_6_13'\n", + " '1_10_10' '1_6_13' '1_4_14' '1_8_1' '1_8_11' '1_12_12' '1_6_13' '1_6_15'\n", + " '1_6_15' '1_5_13' '1_6_14' '1_4_14' '1_8_11' '1_12_12' '1_9_10' '1_6_15'\n", + " '1_12_12' '1_10_10' '1_9_11' '1_5_14' '1_10_10' '1_7_14' '1_6_14' '1_9_7'\n", + " '1_10_11' '2_0_0' '1_6_14' '2_3_1' '1_4_14' '1_4_14' '1_7_13' '1_3_13'\n", + " '1_0_9' '1_5_13' '1_10_10' '1_12_12' '1_3_13' '1_9_9' '1_11_12' '2_0_0'\n", + " '1_10_10' '1_8_11' '1_3_13' '1_5_13' '1_8_12' '1_11_11' '1_5_13'\n", + " '1_11_12' '1_6_13' '1_7_15' '2_0_0' '1_8_1' '1_5_14' '1_7_13']\n", + "dowsampled rms bin 36\n", + "areas of tiles in bin [1.02481791e-04 1.17565305e-04 1.12201126e-04 1.14525556e-05\n", + " 1.25165687e-04 1.37276278e-04 1.27228034e-04 1.19095690e-04\n", + " 1.15971255e-04 1.33562051e-04 8.87125070e-06 7.27779963e-05\n", + " 3.00838536e-05 1.33562051e-04 1.35826334e-04 1.34819689e-04\n", + " 1.03563508e-04 1.41850307e-04 1.03731569e-04 1.33077511e-04\n", + " 1.16776185e-04 1.23927350e-04 1.34402148e-04 7.46912134e-07\n", + " 1.25073347e-04 1.00638194e-05 1.15150625e-04 1.37503202e-04\n", + " 1.13462710e-04 1.30560736e-04 1.35098821e-04 1.25073347e-04\n", + " 3.46720176e-06 1.38733892e-05 6.64898048e-06 1.11277293e-04\n", + " 1.33562051e-04 1.37503202e-04 1.25695166e-04 2.31942510e-04\n", + " 1.34206710e-04 1.14237090e-04 1.62478944e-06 1.34402148e-04\n", + " 1.29553641e-04 1.13586325e-04 6.93768247e-05 1.40477685e-04\n", + " 1.14237090e-04 1.35602177e-04 1.06723959e-04 9.74738797e-05\n", + " 1.38259625e-04 1.35525697e-04 1.25695166e-04 1.27228034e-04\n", + " 1.29227713e-04 1.30560736e-04 1.15971255e-04 1.29227713e-04\n", + " 1.43953913e-04 1.30560736e-04 1.25695166e-04 8.64619997e-06\n", + " 1.41732649e-04 1.22771817e-04 1.36484130e-04 1.15908674e-04\n", + " 1.38336587e-05 1.34794930e-04 1.26370834e-04 1.41264619e-04\n", + " 1.11277293e-04 1.41264619e-04 1.35169464e-04 1.25808641e-04\n", + " 1.00238010e-04 1.27670753e-04 3.31374593e-06 1.34794930e-04\n", + " 1.15150625e-04 1.35169464e-04 2.82398599e-04 3.55015523e-04\n", + " 1.35826334e-04 1.41439784e-04 1.03731569e-04 1.35359661e-04\n", + " 1.30560736e-04 1.35169464e-04 1.12201126e-04 1.35169464e-04\n", + " 1.35169464e-04 1.41948762e-04 1.27670753e-04 3.08221545e-06\n", + " 1.15150625e-04 1.35169464e-04 3.50341273e-06 6.31235055e-05\n", + " 2.75015400e-04 1.18338508e-04 2.67693615e-06 7.01614194e-05\n", + " 1.42044804e-04 1.26370834e-04 1.14314405e-04 1.33114850e-04\n", + " 2.36677016e-04 3.49490551e-05 3.54562252e-06 1.16657102e-05\n", + " 1.25073347e-04 7.84955667e-05 1.30065993e-04 1.41948762e-04\n", + " 6.16443090e-06 1.07432591e-04 1.34704556e-05 1.41595801e-04\n", + " 1.35826334e-04 1.27670753e-04 1.27911941e-04 1.14314405e-04\n", + " 1.17565305e-04 1.35169464e-04 1.35525697e-04 1.13462710e-04\n", + " 5.16999983e-06 1.42181659e-04 1.29553641e-04 1.15971255e-04\n", + " 1.09043280e-04 5.16974063e-06 1.27605935e-04 1.34738202e-04\n", + " 1.17565305e-04 1.37503202e-04 7.78480202e-06 1.34402148e-04\n", + " 1.28948875e-04 1.21292639e-04 1.42219529e-04 1.22493590e-05\n", + " 1.14314405e-04 1.28578531e-04 1.17565305e-04 1.11277293e-04\n", + " 1.06723959e-04 1.34794930e-04 1.34522306e-04 1.17565305e-04\n", + " 1.11700598e-04 1.25073347e-04 1.06470319e-04 1.37030768e-04\n", + " 1.42027999e-04 1.35525697e-04 1.18681891e-05 4.99332183e-06\n", + " 1.27954213e-04 1.33562051e-04 1.15150625e-04 1.33991170e-04\n", + " 1.30994452e-04 1.42237169e-04 1.17565305e-04 1.19034871e-04\n", + " 1.15971255e-04 1.18338508e-04 1.41439784e-04 1.33114850e-04\n", + " 1.35190856e-04 1.41970373e-04 1.41850307e-04 1.30994452e-04\n", + " 1.15150625e-04 1.06973574e-04 1.42219529e-04 1.29553641e-04\n", + " 1.15150625e-04 1.18338508e-04 8.16930403e-05 3.15156464e-05\n", + " 8.96788948e-05 1.27954213e-04 1.25999541e-05 1.11886278e-04\n", + " 1.66788668e-06 1.07432591e-04 1.15150625e-04 6.21697872e-05\n", + " 1.25165687e-04 6.73099798e-05 8.63223218e-06 1.41732649e-04\n", + " 6.57536246e-06 1.15971255e-04 1.27605935e-04 6.74265720e-06\n", + " 1.30065993e-04 1.18338508e-04 5.16999983e-06 5.16974063e-06\n", + " 1.34522306e-04 1.34794930e-04 1.33077511e-04 8.87125070e-06\n", + " 1.15971255e-04 1.41850307e-04 1.61186094e-06 1.30994452e-04\n", + " 1.28948875e-04 1.41970373e-04 1.29227713e-04 1.56457109e-06\n", + " 1.36766707e-04 1.36766707e-04 1.14314405e-04 1.40353795e-04\n", + " 2.10544039e-07 3.49490551e-05 1.20682721e-05 2.58047498e-04\n", + " 1.33562051e-04 1.35602177e-04 7.01614194e-05 3.19505078e-05\n", + " 1.01145609e-04 1.16657102e-05 1.41595801e-04 1.27911941e-04\n", + " 8.87425603e-06 1.33991170e-04 1.37085992e-05 1.41876722e-04\n", + " 1.36032102e-04 1.42027999e-04 1.41850307e-04 1.41595801e-04\n", + " 1.26730258e-04 1.15971255e-04 1.25073347e-04 1.41850307e-04\n", + " 1.41264619e-04 1.07432591e-04 1.38331099e-04 1.09062742e-04\n", + " 1.26730258e-04 1.19095690e-04 1.06470319e-04 1.15971255e-04\n", + " 3.54562252e-06 1.41850307e-04 1.34794930e-04 1.18681891e-05\n", + " 1.30560736e-04 1.34819689e-04 1.34402148e-04 5.20470709e-05\n", + " 1.33114850e-04 1.15971255e-04 1.41732649e-04 1.43971769e-04\n", + " 1.17565305e-04 1.10549546e-05 6.23442029e-05 2.95835705e-06\n", + " 1.17565305e-04 6.32688080e-05 1.23927350e-04 1.35169464e-04\n", + " 1.00638194e-05 4.94522429e-06 1.34402148e-04 1.35826334e-04\n", + " 1.32591945e-04 1.33114850e-04 1.34794930e-04 1.27954213e-04\n", + " 1.16590008e-04 1.33114850e-04 1.29227713e-04 1.29553641e-04\n", + " 1.42219529e-04 1.19095690e-04 1.17565305e-04 1.37030768e-04\n", + " 1.41439784e-04 3.50948471e-06 1.37711511e-04 2.82529237e-04\n", + " 1.34206710e-04 1.29553641e-04 1.12201126e-04 1.26584463e-06\n", + " 1.42190828e-04 1.43915581e-04 1.17565305e-04 1.34402148e-04\n", + " 1.25165687e-04 1.55359860e-04 1.66444061e-06 1.21292639e-04\n", + " 1.15150625e-04 1.41948762e-04 1.41439784e-04 1.15971255e-04\n", + " 1.14545585e-04 1.42023092e-04 1.15971255e-04 1.19095690e-04\n", + " 1.26526902e-04 1.38259625e-04 6.74917374e-06 1.25165687e-04\n", + " 1.28578531e-04 1.14314405e-04 1.33562051e-04 1.34402148e-04\n", + " 1.25002482e-04 1.32591945e-04 1.17565305e-04 1.25757597e-04\n", + " 1.29227713e-04 1.48708227e-04 3.54562252e-06 1.36484130e-04\n", + " 1.21292639e-04 1.41732649e-04 1.25073347e-04 1.14314405e-04\n", + " 1.38331099e-04 1.33562051e-04 1.29553641e-04 1.41948762e-04\n", + " 1.34738202e-04 1.19095690e-04 1.36766707e-04 1.41970373e-04\n", + " 1.33562051e-04 1.36684301e-04 1.41948762e-04 1.41595801e-04\n", + " 1.65687297e-06 1.42106330e-04 1.26526902e-04 1.15150625e-04\n", + " 1.30065993e-04 1.25165687e-04 3.50341273e-06 1.13462710e-04\n", + " 5.26817913e-05 1.38259625e-04 1.27228034e-04 6.40403789e-05\n", + " 1.11897711e-05 1.40322839e-04 1.41595801e-04 2.33314204e-05\n", + " 1.19095690e-04 1.25695166e-04 1.46833018e-05 6.98981102e-05\n", + " 1.41948762e-04 2.58455427e-04 1.15971255e-04 8.87425603e-06\n", + " 1.41595801e-04 1.35190856e-04 3.19505078e-05 1.01145609e-04\n", + " 1.14003098e-04 1.55359860e-04 1.25999541e-05 1.13462710e-04\n", + " 1.19034871e-04 8.16930403e-05 3.15156464e-05 1.27911941e-04\n", + " 1.27430863e-04 1.37276278e-04 1.37711511e-04 1.10373076e-04\n", + " 3.35373663e-06 1.33114850e-04 1.26730258e-04 1.41595801e-04\n", + " 1.07432591e-04 1.28948875e-04 6.73099798e-05 1.26730258e-04\n", + " 6.21697872e-05 5.26817913e-05 8.63223218e-06 1.35525697e-04\n", + " 1.17565305e-04 6.40403789e-05 4.67088121e-06 1.35054545e-04\n", + " 1.41595801e-04 1.30994452e-04 1.41948762e-04 1.34794930e-04\n", + " 1.15971255e-04 1.35863583e-04 1.11897711e-05 1.36183075e-04\n", + " 1.34537732e-05 1.42023092e-04 1.23987414e-05 1.46833018e-05\n", + " 1.46833018e-05 1.19817739e-04 1.15971255e-04 1.63104914e-06\n", + " 1.41595801e-04 6.30176823e-05 1.41850307e-04 1.30560736e-04\n", + " 1.41732649e-04 1.41595801e-04 1.29023749e-04 1.37503202e-04\n", + " 1.27670753e-04 1.26730258e-04 1.67110693e-06 1.29553641e-04\n", + " 1.41595801e-04 1.35098821e-04 1.07748733e-04 1.25695166e-04\n", + " 1.30994452e-04 5.88351891e-05 6.40403789e-05 1.37503202e-04\n", + " 1.41439784e-04 1.43969147e-04 1.14593914e-05 1.26730258e-04\n", + " 1.37030768e-04 1.16776185e-04 1.22771817e-04 3.32153842e-06\n", + " 1.42044804e-04 3.11392081e-06 1.09384559e-04 6.72688660e-06\n", + " 1.37503202e-04 1.27228034e-04 1.41439784e-04 1.35169464e-04\n", + " 1.27911941e-04 1.30560736e-04 1.19095690e-04 1.27228034e-04\n", + " 1.41970373e-04 1.16657102e-05 4.79247995e-06 1.37503202e-04\n", + " 1.36032102e-04 1.34794930e-04 1.33114850e-04 1.23987414e-05\n", + " 7.01614194e-05 1.30254315e-05 1.34853144e-05 3.49490551e-05\n", + " 1.15971255e-04 1.13462710e-04 7.27779963e-05 2.19416356e-04\n", + " 1.14525556e-05 3.19505078e-05 1.01145609e-04 1.36183075e-04\n", + " 1.16776185e-04 8.87425603e-06 9.91298631e-05 1.79106979e-05\n", + " 8.16930403e-05 3.15156464e-05 1.25999541e-05 1.22493590e-05\n", + " 1.23566439e-04 3.00838536e-05 1.35863583e-04 1.42181659e-04\n", + " 3.31374593e-06 1.27228034e-04 1.34794930e-04 1.34819689e-04\n", + " 1.25808641e-04 1.28578531e-04 1.41595801e-04 1.35826334e-04\n", + " 1.41970373e-04 7.35635429e-06 1.34794930e-04]\n", + "names of tiles in bin ['1_3_13' '1_5_13' '1_4_12' '1_5_15' '1_7_13' '1_9_10' '1_6_14' '1_5_13'\n", + " '1_5_13' '1_8_12' '1_10_12' '1_5_15' '1_5_15' '1_8_10' '1_9_10' '1_9_10'\n", + " '1_3_13' '1_10_10' '1_3_13' '1_10_12' '1_5_14' '1_8_1' '1_8_11' '1_6_15'\n", + " '1_6_14' '1_8_1' '1_5_14' '1_9_10' '1_5_15' '1_7_10' '1_9_11' '1_6_13'\n", + " '2_0_0' '1_4_14' '1_6_15' '1_4_14' '1_8_12' '1_9_11' '1_6_13' '1_5_13'\n", + " '1_9_10' '2_0_0' '2_0_0' '1_8_11' '1_7_12' '1_4_14' '1_11_12' '2_0_0'\n", + " '2_0_0' '1_9_10' '1_3_13' '1_4_14' '1_12_12' '1_8_1' '1_6_14' '1_6_14'\n", + " '1_6_12' '1_7_11' '1_5_13' '1_6_14' '2_0_0' '1_7_11' '1_6_13' '1_12_12'\n", + " '1_10_11' '1_6_14' '1_9_9' '1_12_12' '1_12_12' '1_8_10' '1_6_13' '1_10_9'\n", + " '1_4_14' '1_10_10' '1_8_11' '1_6_14' '1_3_13' '1_6_14' '1_9_10' '1_8_11'\n", + " '1_5_14' '1_8_12' '1_11_12' '1_5_13' '1_9_10' '1_10_10' '1_3_13' '1_9_11'\n", + " '1_7_11' '1_8_11' '1_4_14' '1_8_12' '1_8_12' '1_10_11' '1_6_14' '1_6_13'\n", + " '1_5_15' '1_8_11' '1_11_12' '1_0_9' '1_11_12' '1_5_13' '1_4_12' '1_5_15'\n", + " '1_11_12' '1_6_13' '1_5_13' '1_8_12' '1_5_14' '1_5_15' '1_11_12' '1_5_15'\n", + " '1_6_13' '2_0_0' '1_7_12' '1_10_11' '1_6_13' '1_4_14' '1_10_11' '1_10_10'\n", + " '1_9_10' '1_6_14' '1_6_13' '1_5_13' '1_5_12' '1_8_12' '1_8_12' '1_5_15'\n", + " '1_12_12' '2_0_0' '1_7_13' '1_5_15' '1_6_15' '1_12_12' '1_12_12' '1_8_11'\n", + " '1_5_13' '1_9_10' '1_6_14' '1_8_12' '1_7_11' '1_6_13' '2_0_0' '1_6_15'\n", + " '1_5_13' '1_6_13' '1_5_14' '1_4_14' '1_3_13' '1_8_12' '1_9_10' '1_5_13'\n", + " '1_4_14' '1_6_14' '1_5_15' '1_9_11' '1_10_11' '1_8_12' '1_5_15' '1_9_10'\n", + " '1_7_12' '1_8_1' '1_5_14' '1_8_12' '1_8_12' '2_0_0' '1_5_13' '1_5_14'\n", + " '1_5_14' '1_5_14' '1_10_9' '1_8_1' '1_8_11' '1_11_11' '1_10_10' '1_8_12'\n", + " '1_5_14' '1_3_13' '2_0_0' '1_7_13' '1_5_15' '1_5_13' '1_6_15' '1_6_15'\n", + " '1_7_15' '1_7_13' '1_6_15' '1_9_0' '1_9_11' '1_4_14' '1_5_13' '1_12_12'\n", + " '1_7_14' '1_12_12' '1_12_12' '1_10_10' '1_8_12' '1_5_15' '1_12_12'\n", + " '1_10_9' '1_7_11' '1_5_13' '1_12_12' '1_12_12' '1_9_11' '1_8_12'\n", + " '1_10_12' '1_10_12' '1_5_14' '1_10_11' '1_7_12' '1_8_1' '1_7_12'\n", + " '1_11_11' '1_6_13' '1_7_12' '1_9_10' '1_9_11' '1_5_14' '1_11_12'\n", + " '1_12_12' '1_5_15' '1_12_12' '1_7_15' '1_8_11' '1_9_11' '1_5_15'\n", + " '1_11_12' '1_11_12' '1_5_15' '1_10_11' '1_6_13' '1_11_12' '1_8_11'\n", + " '2_3_1' '1_11_11' '1_9_11' '1_10_11' '1_10_10' '1_10_10' '1_7_13'\n", + " '1_5_15' '1_6_14' '1_10_11' '1_10_10' '1_4_14' '1_11_12' '1_3_13'\n", + " '1_7_14' '1_5_14' '1_5_15' '1_5_14' '1_11_12' '1_10_12' '1_8_11' '1_5_15'\n", + " '1_7_10' '1_9_10' '1_8_12' '1_6_15' '1_8_1' '1_5_13' '1_10_10' '2_0_0'\n", + " '1_5_14' '1_6_15' '1_0_10' '1_6_14' '1_5_15' '1_6_15' '1_8_1' '1_8_12'\n", + " '1_8_1' '1_8_12' '1_8_11' '1_9_11' '1_8_13' '1_8_13' '1_8_1' '1_7_13'\n", + " '1_5_13' '1_8_11' '1_6_13' '1_7_12' '2_0_0' '1_5_15' '1_5_13' '1_9_10'\n", + " '1_10_10' '2_0_0' '1_9_11' '1_10_10' '1_9_10' '1_7_14' '1_4_14' '2_0_2'\n", + " '2_0_0' '2_0_0' '1_5_13' '1_8_12' '1_7_13' '1_10_12' '1_9_11' '1_6_14'\n", + " '1_5_14' '1_10_11' '1_10_10' '1_5_14' '1_4_14' '2_0_0' '1_5_13' '1_5_15'\n", + " '1_6_13' '1_12_12' '1_10_11' '1_7_13' '1_6_13' '1_5_13' '1_8_12' '1_8_13'\n", + " '1_6_13' '1_8_1' '1_5_14' '1_7_15' '1_6_15' '1_10_11' '1_11_12' '1_9_11'\n", + " '1_6_13' '1_10_11' '1_6_15' '1_5_14' '1_11_12' '1_8_9' '1_7_14' '1_10_11'\n", + " '1_8_13' '1_5_15' '1_9_10' '1_11_10' '1_8_9' '1_11_12' '1_10_11'\n", + " '1_10_10' '1_9_11' '2_0_0' '1_6_13' '1_5_13' '1_7_12' '1_7_14' '1_11_12'\n", + " '1_5_13' '1_6_15' '1_12_12' '1_6_15' '1_6_15' '1_6_15' '1_5_15' '1_10_10'\n", + " '1_5_15' '1_5_13' '1_6_15' '2_3_1' '1_5_15' '1_10_11' '1_6_13' '1_5_15'\n", + " '1_11_12' '1_10_10' '1_8_12' '1_11_12' '1_11_12' '1_4_14' '1_10_11'\n", + " '1_6_15' '1_5_14' '1_5_13' '1_6_15' '1_6_15' '1_6_14' '1_7_15' '1_9_11'\n", + " '1_9_11' '1_6_15' '1_9_11' '1_8_12' '1_7_13' '1_10_10' '1_4_14' '1_7_12'\n", + " '1_12_12' '1_7_13' '1_12_12' '1_6_15' '1_12_12' '1_8_12' '1_5_15'\n", + " '1_6_15' '1_6_12' '2_0_0' '1_10_10' '1_8_11' '1_10_11' '1_8_11' '1_5_15'\n", + " '1_9_10' '1_6_15' '1_9_10' '1_10_11' '2_0_0' '1_6_15' '2_3_1' '2_3_1'\n", + " '1_5_13' '1_5_13' '2_3_1' '1_10_10' '1_0_10' '1_10_12' '1_7_1' '1_10_10'\n", + " '1_10_10' '1_7_15' '1_9_10' '1_6_13' '1_7_13' '1_9_11' '1_7_14' '1_10_10'\n", + " '1_9_10' '1_3_13' '1_6_13' '1_8_12' '1_6_15' '1_6_15' '1_9_10' '1_10_10'\n", + " '2_0_0' '1_6_15' '1_7_14' '1_9_10' '1_5_13' '1_6_13' '1_9_11' '1_11_12'\n", + " '1_6_14' '1_4_14' '1_10_10' '1_9_11' '1_6_13' '1_10_9' '1_8_13' '1_6_13'\n", + " '1_7_11' '1_5_14' '1_6_15' '1_11_11' '1_5_15' '1_8_13' '1_9_11' '1_9_11'\n", + " '1_8_12' '1_8_12' '1_6_15' '1_5_15' '1_7_1' '1_10_11' '1_5_15' '1_5_15'\n", + " '1_5_14' '1_5_15' '1_6_15' '1_5_15' '1_11_12' '1_11_12' '1_9_11' '1_5_14'\n", + " '1_11_12' '1_7_1' '1_7_1' '1_6_15' '1_6_15' '1_6_15' '1_6_15' '1_6_15'\n", + " '1_5_15' '1_9_11' '2_0_0' '1_9_11' '1_6_15' '1_8_13' '1_9_11' '1_6_14'\n", + " '1_6_15' '1_10_11' '1_9_11' '1_11_11' '1_4_14' '1_8_13']\n", + "dowsampled rms bin 37\n", + "areas of tiles in bin [1.46833018e-05 1.26730258e-04 1.35098821e-04 1.23987414e-05\n", + " 1.34522306e-04 3.46720176e-06 1.63104914e-06 1.35190856e-04\n", + " 1.85515617e-05 1.46833018e-05 1.28460240e-04 1.41850307e-04\n", + " 1.15971255e-04 1.34206710e-04 2.33552369e-04 1.10373076e-04\n", + " 1.18338508e-04 1.30560736e-04 1.32607886e-04 1.37030768e-04\n", + " 1.23927350e-04 1.13109769e-04 1.41439784e-04 1.00638194e-05\n", + " 1.41850307e-04 1.08758919e-04 1.29227713e-04 1.15150625e-04\n", + " 1.27954213e-04 1.37276278e-04 1.18681891e-05 1.16776185e-04\n", + " 3.19505078e-05 1.01145609e-04 1.06470319e-04 8.87425603e-06\n", + " 1.28578531e-04 2.53053804e-04 1.14314405e-04 1.36766707e-04\n", + " 3.33577335e-06 1.41732649e-04 1.34819689e-04 1.35525697e-04\n", + " 1.29227713e-04 8.16930403e-05 3.15156464e-05 1.25999541e-05\n", + " 1.30994452e-04 1.41439784e-04 1.41595801e-04 1.28948875e-04\n", + " 1.27029392e-04 6.46777588e-05 1.34206710e-04 1.35826334e-04\n", + " 9.61377736e-06 1.35525697e-04 3.46130502e-05 1.09574410e-04\n", + " 1.33114850e-04 1.28460240e-04 1.19095690e-04 6.11933684e-07\n", + " 1.43755081e-04 1.27228034e-04 1.42044804e-04 1.41948762e-04\n", + " 1.36032102e-04 1.27954213e-04 1.67410095e-06 1.15971255e-04\n", + " 1.34206710e-04 2.38191380e-04 1.33562051e-04 1.13462710e-04\n", + " 1.29553641e-04 1.27954213e-04 1.33114850e-04 1.30994452e-04\n", + " 1.30065993e-04 1.11700598e-04 1.33077511e-04 1.29227713e-04\n", + " 1.28578531e-04 8.87125070e-06 1.25757597e-04 1.25165687e-04\n", + " 1.12201126e-04 1.22771817e-04 1.23566439e-04 3.19505078e-05\n", + " 1.01145609e-04 1.35359661e-04 1.13586325e-04 1.35525697e-04\n", + " 1.33991170e-04 1.34794930e-04 1.41439784e-04 1.37711511e-04\n", + " 1.42234579e-04 8.87425603e-06 1.15150625e-04 1.17565305e-04\n", + " 6.32688080e-05 1.10549546e-05 1.42181659e-04 1.41850307e-04\n", + " 6.73099798e-05 1.27605935e-04 1.30560736e-04 8.63223218e-06\n", + " 1.34794930e-04 5.20470709e-05 1.35863583e-04 6.21697872e-05\n", + " 6.53319218e-05 3.01381558e-06 5.16974063e-06 3.54562252e-06\n", + " 5.16999983e-06 1.01232466e-06 1.38331099e-04 1.41850307e-04\n", + " 1.24860228e-05 1.33114850e-04 1.35098821e-04 1.28460240e-04\n", + " 1.34522306e-04 1.30560736e-04 1.07432591e-04 1.35525697e-04\n", + " 5.26817913e-05 1.41439784e-04 3.49490551e-05 7.01614194e-05\n", + " 1.17322195e-04 1.38758406e-05 1.27329567e-04 8.75164143e-06\n", + " 1.16657102e-05 8.76861012e-06 1.09384559e-04 3.15701446e-05\n", + " 9.99414950e-05 1.30560736e-04 1.11897711e-05 1.31103240e-04\n", + " 6.40403789e-05 1.43915581e-04 1.06294090e-05 1.28578531e-04\n", + " 1.05684735e-04 1.26730258e-04 1.41850307e-04 1.27329567e-04\n", + " 1.41948762e-04 8.06250769e-05 1.15971255e-04 1.41732649e-04\n", + " 1.36766707e-04 3.01381558e-06 1.26526902e-04 1.43971769e-04\n", + " 1.27670753e-04 1.42044804e-04 1.34819689e-04 1.36183075e-04\n", + " 1.35602177e-04 1.41264619e-04 4.91715175e-06 1.26730258e-04\n", + " 1.35169464e-04 9.66854460e-05 1.14003098e-04 8.76861012e-06\n", + " 1.27911941e-04 2.95835705e-06 1.27670753e-04 3.15701446e-05\n", + " 9.99414950e-05 1.17565305e-04 1.14506186e-04 1.13462710e-04\n", + " 1.09754382e-04 3.03153351e-06 1.33991170e-04 1.41439784e-04\n", + " 1.25165687e-04 1.41948762e-04 1.24140575e-04 1.35169464e-04\n", + " 1.41850307e-04 1.41439784e-04 1.08025470e-04 1.26526902e-04\n", + " 1.33991170e-04 1.36484130e-04 1.41264619e-04 1.30065993e-04\n", + " 1.26730258e-04 5.58805609e-05 1.29553641e-04 1.30065993e-04\n", + " 1.36183075e-04 7.74569464e-06 1.35625208e-04 1.23566439e-04\n", + " 1.33077511e-04 1.35169464e-04 1.41595801e-04 1.41948762e-04\n", + " 1.37276278e-04 1.35169464e-04 3.50341273e-06 8.87125070e-06\n", + " 1.33114850e-04 1.14314405e-04 1.30994452e-04 1.36684301e-04\n", + " 1.35525697e-04 1.42237169e-04 1.10338395e-04 1.26526902e-04\n", + " 2.10840424e-06 1.15150625e-04 1.46833018e-05 1.30065993e-04\n", + " 1.25695166e-04 1.34853144e-05 1.08758919e-04 1.24481474e-05\n", + " 1.33077511e-04 1.27228034e-04 8.07087868e-05 3.11359398e-05\n", + " 1.34794930e-04 8.87125070e-06 1.08415914e-04 1.35169464e-04\n", + " 2.84439057e-04 1.42234579e-04 1.41850307e-04 5.74129154e-05\n", + " 1.18681891e-05 3.46720176e-06 8.87425603e-06 3.19505078e-05\n", + " 1.01145609e-04 1.35525697e-04 1.14506186e-04 1.28460240e-04\n", + " 1.41595801e-04 1.06470319e-04 1.34402148e-04 1.15410544e-04\n", + " 1.08415914e-04 1.35863583e-04 1.25073347e-04 1.17565305e-04\n", + " 1.25165687e-04 7.01614194e-05 1.36766707e-04 1.31103240e-04\n", + " 1.34983475e-05 1.16657102e-05 8.03606140e-05 1.06294090e-05\n", + " 3.49490551e-05 1.22493590e-05 1.13109769e-04 1.09043280e-04\n", + " 4.87005548e-06 1.43969147e-04 1.29553641e-04 1.33077511e-04\n", + " 1.35863583e-04 1.24481474e-05 1.29227713e-04 8.07087868e-05\n", + " 3.11359398e-05 1.25073347e-04 8.87125070e-06 1.29553641e-04\n", + " 1.08415914e-04 2.83465297e-04 1.37030768e-04 4.94522429e-06\n", + " 5.39968409e-05 1.04234705e-04 1.41732649e-04 1.26629376e-07\n", + " 1.30254315e-05 1.59942766e-06 9.91298631e-05 1.79106979e-05\n", + " 6.24272217e-05 1.35602177e-04 4.52916197e-05 3.49490551e-05\n", + " 1.37503202e-04 1.16657102e-05 1.27228034e-04 3.32888122e-06\n", + " 1.41732649e-04 1.26730258e-04 1.29553641e-04 1.19095690e-04\n", + " 1.10734986e-04 1.16776185e-04 1.18681891e-05 1.24481474e-05\n", + " 1.08758919e-04 1.06470319e-04 8.63223218e-06 6.73099798e-05\n", + " 6.21697872e-05 1.35602177e-04 8.07087868e-05 3.11359398e-05\n", + " 1.19817739e-04 1.10373076e-04 1.41439784e-04 5.01332080e-06\n", + " 2.86289276e-07 4.11325808e-07 1.39242360e-05 1.25002482e-04\n", + " 1.23987414e-05 1.10734986e-04 2.11292704e-07 4.18403767e-06\n", + " 2.95816976e-07 1.35525697e-04 1.37030768e-04 1.28948875e-04\n", + " 1.30560736e-04 1.30994452e-04 1.28948875e-04 4.93152184e-06\n", + " 1.34206710e-04]\n", + "names of tiles in bin ['2_3_1' '1_7_14' '1_9_11' '1_6_15' '1_9_11' '2_0_0' '2_3_1' '1_8_13'\n", + " '1_4_14' '2_3_1' '1_7_12' '1_10_10' '1_5_15' '1_9_11' '1_5_13' '1_6_15'\n", + " '1_5_13' '1_7_11' '1_6_15' '1_9_11' '1_8_1' '1_4_14' '1_10_10' '1_8_1'\n", + " '1_10_11' '1_4_14' '1_6_14' '1_5_14' '1_7_13' '1_9_11' '1_5_15' '1_5_14'\n", + " '1_11_12' '1_11_12' '1_5_15' '1_11_12' '1_6_15' '1_6_15' '1_5_14'\n", + " '1_9_10' '1_9_11' '1_10_11' '1_9_11' '1_8_12' '1_6_13' '1_6_15' '1_6_15'\n", + " '1_6_15' '1_8_13' '1_10_10' '1_10_10' '1_7_11' '1_6_15' '2_0_0' '1_9_10'\n", + " '1_9_11' '1_11_12' '1_8_12' '1_11_12' '1_11_12' '1_8_1' '1_7_12' '1_5_14'\n", + " '2_0_0' '2_0_0' '1_6_15' '1_11_12' '1_10_11' '1_9_11' '1_7_14' '1_9_11'\n", + " '1_5_15' '1_9_11' '1_5_15' '1_8_12' '1_5_14' '1_7_13' '1_7_14' '1_8_12'\n", + " '1_8_13' '1_7_12' '1_4_14' '1_10_12' '1_6_13' '1_6_14' '1_10_12' '1_7_15'\n", + " '1_7_12' '1_4_14' '1_6_13' '1_6_15' '1_11_12' '1_11_12' '1_9_10' '1_4_14'\n", + " '1_8_12' '1_8_12' '1_8_13' '1_10_10' '1_9_11' '2_0_0' '1_11_12' '1_5_15'\n", + " '1_5_15' '1_6_15' '1_6_15' '2_0_0' '1_10_11' '1_12_12' '1_12_12' '1_7_12'\n", + " '1_12_12' '1_8_13' '1_6_15' '1_9_11' '1_12_12' '1_4_14' '1_6_14'\n", + " '1_12_12' '1_11_12' '1_12_12' '1_10_12' '1_11_12' '1_10_11' '1_10_12'\n", + " '1_8_13' '1_9_11' '1_7_12' '1_9_11' '1_7_12' '1_4_14' '1_8_12' '1_6_15'\n", + " '1_10_10' '1_5_15' '1_5_15' '1_12_12' '1_12_12' '1_7_15' '1_12_12'\n", + " '1_5_15' '1_11_12' '1_4_14' '1_11_12' '1_11_12' '1_7_12' '1_6_15'\n", + " '1_10_12' '1_6_15' '2_0_0' '1_10_12' '1_6_14' '1_3_13' '1_7_13' '1_10_11'\n", + " '1_7_15' '1_10_11' '1_4_14' '1_5_13' '1_10_10' '1_9_11' '1_6_13' '1_6_15'\n", + " '2_0_0' '1_6_12' '1_11_12' '1_9_11' '1_9_11' '1_9_11' '1_10_11' '1_8_13'\n", + " '1_7_14' '1_8_12' '1_5_15' '1_4_14' '1_11_12' '1_6_14' '1_6_13' '1_6_15'\n", + " '1_11_12' '1_11_12' '1_5_13' '1_4_14' '1_5_15' '1_4_14' '1_6_14' '1_8_13'\n", + " '1_10_11' '1_7_14' '1_10_11' '1_7_15' '1_8_13' '1_10_11' '1_10_10'\n", + " '1_3_13' '1_6_15' '1_8_12' '1_9_11' '1_10_10' '1_7_11' '1_7_13' '1_4_14'\n", + " '1_7_14' '1_7_12' '1_9_11' '1_6_15' '1_8_13' '1_6_15' '1_10_12' '1_8_12'\n", + " '1_10_12' '1_10_11' '1_9_10' '1_8_12' '1_11_12' '1_10_12' '1_8_12'\n", + " '1_5_14' '1_8_13' '1_11_12' '1_8_12' '2_0_0' '1_4_14' '1_6_15' '2_3_1'\n", + " '1_5_13' '2_3_1' '1_7_13' '1_6_15' '1_10_12' '1_4_14' '1_6_15' '1_10_12'\n", + " '1_6_14' '1_6_15' '1_6_15' '1_8_12' '1_10_12' '1_4_14' '1_8_13' '2_0_0'\n", + " '2_0_0' '1_10_11' '1_5_15' '1_5_15' '2_0_0' '1_11_12' '1_11_12' '1_11_12'\n", + " '1_8_14' '1_4_14' '1_7_12' '1_10_11' '1_5_15' '1_8_13' '1_4_14' '1_4_14'\n", + " '1_9_10' '1_6_15' '1_5_15' '1_7_13' '1_5_15' '1_9_10' '1_10_12' '1_10_11'\n", + " '1_5_15' '1_6_15' '1_10_12' '1_5_15' '1_6_15' '1_4_14' '1_6_15' '1_8_12'\n", + " '2_0_0' '1_7_13' '1_10_12' '1_9_11' '1_6_15' '1_6_13' '1_6_15' '1_6_15'\n", + " '1_6_15' '1_10_12' '1_7_14' '1_4_14' '1_10_11' '1_9_10' '1_8_13'\n", + " '1_10_12' '1_4_14' '1_10_10' '1_11_12' '1_7_1' '1_7_15' '1_7_1' '1_7_1'\n", + " '1_5_15' '1_9_12' '1_11_12' '1_5_15' '1_9_11' '1_5_15' '1_6_15' '1_9_11'\n", + " '1_10_11' '1_7_13' '1_7_13' '1_5_15' '1_4_14' '1_5_13' '1_5_15' '1_6_15'\n", + " '1_4_14' '1_5_15' '1_12_12' '1_12_12' '1_12_12' '1_9_12' '1_6_15'\n", + " '1_6_15' '1_5_14' '1_6_15' '1_10_11' '1_9_12' '1_5_15' '1_5_15' '1_5_15'\n", + " '1_6_15' '1_6_15' '1_4_14' '1_11_12' '1_11_12' '1_11_12' '1_8_12'\n", + " '1_9_11' '1_7_12' '1_7_13' '1_8_1' '1_7_12' '1_8_14' '1_9_11']\n", + "dowsampled rms bin 38\n", + "areas of tiles in bin [1.36183075e-04 1.36766707e-04 1.32572831e-04 1.59942766e-06\n", + " 1.37030768e-04 1.19095690e-04 1.34402148e-04 1.42044804e-04\n", + " 1.41595801e-04 1.41850307e-04 1.35863583e-04 1.30994452e-04\n", + " 3.27344987e-05 1.26740487e-05 1.16776185e-04 1.37711511e-04\n", + " 1.89795845e-07 8.44881019e-07 1.29553641e-04 1.36032102e-04\n", + " 8.16930403e-05 3.15156464e-05 1.25757597e-04 1.25999541e-05\n", + " 3.64807195e-05 2.74160602e-07 1.35359661e-04 1.41439784e-04\n", + " 1.36032102e-04 1.33114850e-04 1.17883609e-05 1.41595801e-04\n", + " 1.25073347e-04 1.43953913e-04 1.33991170e-04 1.30994452e-04\n", + " 8.47350926e-05 1.35169464e-04 1.35863583e-04 1.26730258e-04\n", + " 1.37711511e-04 1.35602177e-04 1.26730258e-04 4.74918805e-05\n", + " 2.73211723e-07 1.41264619e-04 1.37276278e-04 1.33562051e-04\n", + " 1.47106793e-07 1.29553641e-04 1.33077511e-04 1.35359661e-04\n", + " 8.87125070e-06 1.41732649e-04 1.08415914e-04 5.02230285e-06\n", + " 1.33114850e-04 1.29227713e-04 1.37276278e-04 1.33077511e-04\n", + " 1.30560736e-04 8.87125070e-06 1.41439784e-04 1.46833018e-05\n", + " 1.30994452e-04 4.88641649e-06 1.28460240e-04 1.36484130e-04\n", + " 1.36766707e-04 1.35525697e-04 1.33114850e-04 1.41732649e-04\n", + " 1.15971255e-04 1.41439784e-04 1.36484130e-04 1.43839334e-04\n", + " 1.35602177e-04 1.09384559e-04 1.41732649e-04 3.33577335e-06\n", + " 1.37276278e-04 8.07087868e-05 3.11359398e-05 1.36183075e-04\n", + " 4.91715175e-06 3.15701446e-05 9.99414950e-05 1.24481474e-05\n", + " 1.41948762e-04 1.41439784e-04 1.41595801e-04 8.76861012e-06\n", + " 4.93152184e-06 1.35602177e-04 1.27954213e-04 1.35359661e-04\n", + " 1.30065993e-04 1.34794930e-04 3.03592954e-07 1.25002482e-04\n", + " 1.35359661e-04 1.06294090e-05 1.51740503e-05 4.17269648e-07\n", + " 1.37030768e-04 1.25165687e-04 1.41732649e-04 4.79247995e-06\n", + " 1.31103240e-04 1.35863583e-04 1.35525697e-04 1.29553641e-04\n", + " 5.20470709e-05 1.33562051e-04 1.41850307e-04 1.09062535e-04\n", + " 1.34794930e-04 1.10549546e-05 1.25999541e-05 1.30560736e-04\n", + " 1.41850307e-04 1.29553641e-04 5.78266444e-05 8.16930403e-05\n", + " 3.08128221e-05 5.00366003e-06 1.30560736e-04 1.35169464e-04\n", + " 1.37276278e-04 1.36766707e-04 1.37276278e-04 1.27954213e-04\n", + " 1.35359661e-04 1.18681891e-05 1.41850307e-04 1.33562051e-04\n", + " 4.94522429e-06 1.06470319e-04 1.37276278e-04 1.35098821e-04\n", + " 1.35525697e-04 1.36766707e-04 1.34206710e-04 1.35525697e-04\n", + " 1.35169464e-04 1.33077511e-04 1.30065993e-04 8.87125070e-06\n", + " 1.36484130e-04 4.81489816e-05 1.33991170e-04 1.34402148e-04\n", + " 1.29553641e-04 1.41439784e-04 1.27670753e-04 1.30994452e-04\n", + " 1.13462710e-04 3.32888122e-06 3.19505078e-05 8.28741873e-05\n", + " 1.27954213e-04 1.30994452e-04 1.37276278e-04 4.95825722e-06\n", + " 1.37030768e-04 1.34794930e-04 8.87425603e-06 2.82879569e-04\n", + " 1.46833018e-05 1.30994452e-04 1.37276278e-04 1.63104914e-06\n", + " 1.13462710e-04 1.37711511e-04 1.26730258e-04 4.95825722e-06\n", + " 4.87005548e-06 1.41732649e-04 6.76054691e-05 1.34522306e-04\n", + " 1.17565305e-04 1.30065993e-04 6.32688080e-05 1.29227713e-04\n", + " 3.32153842e-06 1.37711511e-04 4.94522429e-06 1.36032102e-04\n", + " 1.35525697e-04 1.10549546e-05 1.41850307e-04 1.35098821e-04\n", + " 5.20470709e-05 1.33562051e-04 1.27954213e-04 1.28578531e-04\n", + " 6.14777131e-05 1.27329567e-04 1.30994452e-04 1.41732649e-04\n", + " 1.34819689e-04 1.34522306e-04 1.41439784e-04 1.09384559e-04\n", + " 1.41850307e-04 1.41264619e-04 1.41264619e-04 1.25002482e-04\n", + " 1.34819689e-04 1.40856939e-04 1.27029392e-04 1.34704556e-05\n", + " 1.33077511e-04 1.35863583e-04 4.87005548e-06 1.36484130e-04\n", + " 1.35169464e-04 8.87125070e-06 1.28578531e-04 1.35169464e-04\n", + " 1.15971255e-04 1.35863583e-04 1.28948875e-04 1.19095690e-04\n", + " 1.33077511e-04 8.87125070e-06 1.34402148e-04 1.37503202e-04\n", + " 2.69893866e-06 1.42181659e-04 1.37030768e-04 4.99332183e-06\n", + " 1.41264619e-04 2.53079255e-07 3.27810117e-06 1.36032102e-04\n", + " 1.34206710e-04 1.30560736e-04 1.41595801e-04 1.35602177e-04\n", + " 1.33991170e-04 3.30550482e-06 4.88641649e-06 1.35525697e-04\n", + " 1.41595801e-04 1.34402148e-04 1.33991170e-04 1.67686832e-06\n", + " 1.33114850e-04 1.30560736e-04 1.29553641e-04 1.28948875e-04\n", + " 1.41595801e-04 1.36484130e-04 1.29227713e-04 1.15150625e-04\n", + " 1.36817872e-04 1.41264619e-04 1.41595801e-04 8.87125070e-06\n", + " 1.33077511e-04 2.83191602e-04 1.29227713e-04 1.41439784e-04\n", + " 1.35359661e-04 1.06294090e-05 1.34522306e-04 1.41439784e-04\n", + " 1.13462710e-04 1.31103240e-04 1.41264619e-04 1.37030768e-04\n", + " 2.14966367e-04 1.37711511e-04 1.23566439e-04 1.09754382e-04\n", + " 1.35359661e-04 1.35602177e-04 1.13462710e-04 3.34221386e-06\n", + " 2.82529237e-04 8.87125070e-06 1.37503202e-04 1.08415914e-04\n", + " 1.33077511e-04 1.30065993e-04 1.33991170e-04 1.36766707e-04\n", + " 1.37030768e-04 1.35826334e-04 1.33562051e-04 1.34402148e-04\n", + " 1.33562051e-04 1.41595801e-04 1.35525697e-04 1.35525697e-04\n", + " 1.37178450e-04 1.37030768e-04 1.34402148e-04 1.33991170e-04\n", + " 1.36041198e-04 1.35169464e-04 1.33114850e-04 1.41850307e-04\n", + " 1.41439784e-04 1.34402148e-04 1.37276278e-04 1.47991505e-04\n", + " 1.06294090e-05 8.88752213e-05 3.26807731e-06 1.19095690e-04\n", + " 1.34402148e-04 1.33562051e-04 1.26730258e-04 1.36766707e-04\n", + " 1.31103240e-04 1.34794930e-04]\n", + "names of tiles in bin ['1_9_11' '1_9_11' '1_8_1' '1_7_13' '1_9_12' '1_5_15' '1_8_13' '1_11_12'\n", + " '1_10_11' '1_10_11' '1_9_11' '1_8_12' '1_6_15' '1_10_12' '1_5_13'\n", + " '1_9_11' '1_12_12' '1_10_12' '1_7_13' '1_9_13' '1_6_15' '1_6_15' '1_7_15'\n", + " '1_6_15' '1_12_12' '1_12_12' '1_9_12' '1_10_10' '1_9_12' '1_8_13' '2_0_0'\n", + " '1_10_12' '1_6_15' '2_0_0' '1_8_13' '1_8_13' '1_5_15' '1_8_12' '1_9_11'\n", + " '1_7_14' '1_9_11' '1_9_11' '1_7_13' '1_12_12' '1_12_12' '1_10_12'\n", + " '1_9_11' '1_8_14' '1_12_12' '1_7_13' '1_10_12' '1_9_12' '1_10_12'\n", + " '1_10_11' '1_4_14' '1_9_12' '1_8_13' '1_6_14' '1_9_12' '1_10_12' '1_7_12'\n", + " '1_10_12' '1_10_11' '2_3_1' '1_8_13' '1_8_12' '1_7_11' '1_9_11' '1_9_11'\n", + " '1_8_13' '1_8_13' '1_10_11' '1_5_15' '1_10_10' '1_9_10' '2_0_0' '1_9_11'\n", + " '1_4_14' '1_10_11' '1_9_12' '1_9_11' '1_6_15' '1_6_15' '1_9_11' '1_8_14'\n", + " '1_11_12' '1_11_12' '1_6_15' '1_10_11' '1_10_12' '1_10_11' '1_11_12'\n", + " '1_8_13' '1_9_11' '1_7_13' '1_9_11' '1_7_12' '1_8_13' '1_6_15' '1_6_15'\n", + " '1_9_11' '1_10_12' '1_6_15' '1_6_15' '1_9_12' '1_7_13' '1_10_11' '1_8_13'\n", + " '1_10_12' '1_9_12' '1_8_14' '1_7_14' '1_6_15' '1_8_13' '1_10_12' '1_4_14'\n", + " '1_8_13' '1_6_15' '1_6_15' '1_7_12' '1_10_11' '1_7_13' '1_6_15' '1_6_15'\n", + " '1_6_15' '1_9_12' '1_7_1' '1_8_13' '1_9_11' '1_9_11' '1_9_12' '1_7_13'\n", + " '1_9_11' '1_5_15' '1_10_11' '1_8_1' '1_8_13' '1_5_15' '1_9_11' '1_9_12'\n", + " '1_8_14' '1_9_11' '1_9_12' '1_8_13' '1_8_13' '1_10_12' '1_7_12' '1_10_12'\n", + " '1_9_11' '1_6_15' '1_8_13' '1_8_13' '1_7_13' '1_10_12' '1_6_15' '1_8_1'\n", + " '1_5_15' '1_9_12' '1_11_12' '1_11_12' '1_7_15' '1_8_13' '1_9_11' '1_8_12'\n", + " '1_9_13' '1_8_14' '1_11_12' '1_10_12' '2_3_1' '1_8_12' '1_9_13' '2_3_1'\n", + " '1_5_15' '1_9_13' '1_7_15' '1_8_14' '1_8_13' '1_10_11' '1_7_15' '1_9_12'\n", + " '1_5_15' '1_7_13' '1_6_15' '1_6_15' '1_9_12' '1_9_13' '1_8_14' '1_9_11'\n", + " '1_8_13' '1_6_15' '1_10_12' '1_9_11' '1_6_15' '1_8_13' '1_7_14' '1_6_15'\n", + " '1_6_15' '1_7_15' '1_8_13' '1_10_11' '1_9_12' '1_9_11' '1_10_12' '1_4_14'\n", + " '1_10_12' '1_10_12' '1_10_11' '1_6_15' '1_9_11' '1_9_12' '1_6_15'\n", + " '1_10_12' '1_10_12' '1_9_11' '1_8_13' '1_9_11' '1_8_13' '1_10_12'\n", + " '1_6_15' '1_8_13' '1_5_15' '1_9_12' '1_7_12' '1_5_15' '1_10_12' '1_10_12'\n", + " '1_8_13' '1_9_11' '1_10_12' '2_0_0' '1_9_13' '1_9_12' '1_10_12' '1_10_12'\n", + " '1_8_14' '1_9_12' '1_9_11' '1_7_14' '1_10_11' '1_9_13' '1_8_12' '1_8_14'\n", + " '1_8_13' '1_8_13' '1_10_12' '1_8_12' '1_8_13' '1_9_11' '1_8_13' '1_7_12'\n", + " '1_7_14' '1_7_13' '1_10_12' '1_9_12' '1_6_15' '1_5_15' '1_8_13' '1_10_11'\n", + " '1_10_12' '1_10_12' '1_10_12' '1_10_11' '1_6_15' '1_10_11' '1_9_11'\n", + " '1_10_12' '1_9_12' '1_10_12' '1_5_15' '1_10_12' '1_10_11' '1_9_11'\n", + " '1_10_12' '1_9_12' '1_6_15' '1_4_14' '1_9_12' '1_9_12' '1_5_15' '1_9_12'\n", + " '1_10_11' '1_10_12' '1_9_13' '1_4_14' '1_10_12' '1_7_12' '1_8_13'\n", + " '1_9_11' '1_9_11' '1_9_11' '1_8_13' '1_8_12' '1_8_13' '1_10_11' '1_8_12'\n", + " '1_8_13' '1_8_13' '1_9_11' '1_8_13' '1_8_12' '1_8_14' '1_8_13' '1_8_12'\n", + " '1_10_11' '1_10_11' '1_8_14' '1_9_12' '1_10_11' '1_10_12' '1_8_1'\n", + " '1_8_14' '1_5_15' '1_8_13' '1_8_13' '1_7_12' '1_9_11' '1_10_12' '1_8_14']\n", + "dowsampled rms bin 39\n", + "areas of tiles in bin [4.90211596e-06 1.36032102e-04 3.46720176e-06 1.41439784e-04\n", + " 1.06294090e-05 1.31103240e-04 1.28460240e-04 1.36766707e-04\n", + " 1.41595801e-04 1.41850307e-04 1.41264619e-04 1.36183075e-04\n", + " 1.41264619e-04 1.06294090e-05 1.35826334e-04 6.21697872e-05\n", + " 1.29227713e-04 8.63223218e-06 4.98230763e-06 5.66399224e-05\n", + " 1.31103240e-04 1.35359661e-04 1.25165687e-04 1.30560736e-04\n", + " 7.27779963e-05 1.30065993e-04 1.35359661e-04 2.89934260e-04\n", + " 1.34402148e-04 1.41850307e-04 1.41595801e-04 1.14525556e-05\n", + " 2.91619334e-07 1.36484130e-04 3.84978701e-05 1.16657102e-05\n", + " 1.37030768e-04 2.93312314e-05 1.25165687e-04 7.01614194e-05\n", + " 1.41439784e-04 1.15150625e-04 1.34402148e-04 5.76834452e-07\n", + " 1.56457109e-06 4.93152184e-06 1.37503202e-04 1.36484130e-04\n", + " 1.46833018e-05 2.32341435e-07 1.37030768e-04 7.75048038e-06\n", + " 1.35525697e-04 1.37711511e-04 1.06294090e-05 4.90211596e-06\n", + " 1.34402148e-04 1.35602177e-04 1.35169464e-04 1.37276278e-04\n", + " 1.41264619e-04 1.35098821e-04 1.31103240e-04 1.41439784e-04\n", + " 1.36766707e-04 1.37503202e-04 1.41595801e-04 1.61186094e-06\n", + " 1.37030768e-04 1.28460240e-04 1.28460240e-04 1.41264619e-04\n", + " 1.33562051e-04 1.36183075e-04 7.78480202e-06 6.73522782e-06\n", + " 1.37503202e-04 1.34819689e-04 1.30994452e-04 1.35525697e-04\n", + " 1.28948875e-04 1.33562051e-04 1.34522306e-04 1.23987414e-05\n", + " 1.41595801e-04 2.82879569e-04 1.35602177e-04 4.95825722e-06\n", + " 1.37711511e-04 3.32153842e-06 9.28632949e-05 1.35359661e-04\n", + " 1.25695166e-04 1.37276278e-04 1.35169464e-04 1.34819689e-04\n", + " 4.07269628e-05 1.37503202e-04 1.35098821e-04 1.33562051e-04\n", + " 1.03784119e-04 1.41439784e-04 1.35359661e-04 1.06294090e-05\n", + " 1.23987414e-05 1.36484130e-04 1.31103240e-04 1.33114850e-04\n", + " 1.37503202e-04 1.36766707e-04 1.37503202e-04 1.10373076e-04\n", + " 1.37711511e-04 1.34794930e-04 1.36183075e-04 1.41264619e-04\n", + " 1.33114850e-04 1.61186094e-06 1.36438771e-04 1.30994452e-04\n", + " 1.36484130e-04 1.33562051e-04 1.34794930e-04 4.91715175e-06\n", + " 1.35169464e-04 1.29553641e-04 5.03822600e-06 1.33991170e-04\n", + " 1.35098821e-04 1.37276278e-04 3.49490551e-05 1.34206710e-04\n", + " 1.29553641e-04 1.25165687e-04 7.01614194e-05 1.16657102e-05\n", + " 1.67940867e-06 1.35826334e-04 1.30065993e-04 1.36041198e-04\n", + " 1.41732649e-04 1.41439784e-04 1.37030768e-04 1.41595801e-04\n", + " 1.33114850e-04 1.26730258e-04 8.83505329e-06 1.36032102e-04\n", + " 1.33114850e-04 1.35169464e-04 1.36484130e-04 1.26730258e-04\n", + " 1.41439784e-04 1.25165687e-04 1.37711511e-04 1.26730258e-04\n", + " 1.33991170e-04 6.72688660e-06 1.27954213e-04 1.33114850e-04\n", + " 1.35359661e-04 1.35169464e-04 1.37503202e-04 1.37503202e-04\n", + " 1.35525697e-04 1.33991170e-04 1.41595801e-04 1.35602177e-04\n", + " 1.33562051e-04 6.84200165e-05 1.35359661e-04 1.41439784e-04\n", + " 1.26730258e-04 1.36183075e-04 1.36766707e-04 1.37030768e-04\n", + " 1.10373076e-04 1.31103240e-04 1.18681891e-05 1.28460240e-04\n", + " 1.30065993e-04 1.06470319e-04 1.37276278e-04 1.34402148e-04\n", + " 1.37711511e-04 1.23987414e-05 1.06294090e-05 1.33991170e-04\n", + " 1.29227713e-04 8.87125070e-06 1.20020766e-04 1.37276278e-04\n", + " 1.41595801e-04 1.30994452e-04 1.27954213e-04 1.36183075e-04\n", + " 1.35098821e-04 1.41264619e-04 9.47970000e-05 1.45042372e-05\n", + " 1.22493590e-05 1.61115830e-06 1.35098821e-04 1.41850307e-04\n", + " 1.36032102e-04 1.34819689e-04 1.35863583e-04 1.35525697e-04\n", + " 1.30994452e-04 1.09043280e-04 3.31374593e-06 1.41595801e-04\n", + " 3.15007959e-06 1.34402148e-04 1.36766707e-04 1.35169464e-04\n", + " 1.37030768e-04 1.35625208e-04 1.37030768e-04 1.36183075e-04\n", + " 1.33562051e-04 1.30560736e-04 1.34794930e-04 3.35881733e-06\n", + " 1.34206710e-04 1.41439784e-04 2.30321289e-05 1.35098821e-04\n", + " 2.78140696e-07 1.34819689e-04 1.41264619e-04 3.29681619e-06\n", + " 1.30560736e-04 1.34522306e-04 1.28460240e-04 1.34522306e-04\n", + " 1.11897711e-05 1.30994452e-04 1.35169464e-04 1.34794930e-04\n", + " 1.41439784e-04 5.26817913e-05 1.60575299e-06 1.36183075e-04\n", + " 1.35525697e-04 1.41264619e-04 1.37503202e-04 6.40403789e-05\n", + " 1.84485640e-05 1.05483044e-04 1.37030768e-04 3.31374593e-06\n", + " 1.41595801e-04 1.34819689e-04 1.00595612e-05 1.41850307e-04\n", + " 1.37276278e-04 1.35826334e-04 1.36183075e-04 1.15150625e-04\n", + " 1.41264619e-04 2.68804296e-04 1.34522306e-04 1.35863583e-04\n", + " 1.35169464e-04 1.35525697e-04 2.69639379e-04 1.33114850e-04\n", + " 1.36484130e-04 1.34522306e-04 1.26730258e-04 1.30994452e-04\n", + " 1.35525697e-04 1.37276278e-04 1.34522306e-04 9.27623848e-07\n", + " 3.32888122e-06 4.87005548e-06 1.35525697e-04 1.35359661e-04\n", + " 1.35525697e-04 1.37030768e-04 1.33114850e-04 1.17565305e-04\n", + " 1.37503202e-04 1.35169464e-04 1.27670753e-04 1.34522306e-04\n", + " 1.36766707e-04]\n", + "names of tiles in bin ['1_8_13' '1_9_13' '2_0_0' '1_10_11' '1_10_12' '1_10_12' '1_7_13' '1_9_12'\n", + " '1_10_12' '1_10_11' '1_10_10' '1_9_12' '1_10_11' '1_10_12' '1_9_13'\n", + " '1_12_12' '1_6_14' '1_12_12' '1_9_12' '1_12_12' '1_10_12' '1_9_13'\n", + " '1_7_15' '1_7_13' '1_5_15' '1_7_14' '1_9_12' '1_10_12' '1_8_13' '1_10_12'\n", + " '1_10_11' '1_5_15' '1_5_15' '1_9_11' '1_5_15' '1_5_15' '1_9_11' '1_5_15'\n", + " '1_7_15' '1_5_15' '1_10_12' '1_5_15' '1_8_13' '2_3_1' '1_7_15' '1_8_13'\n", + " '1_9_11' '1_9_12' '2_3_1' '1_10_12' '1_9_12' '1_10_12' '1_8_13' '1_9_12'\n", + " '1_10_12' '1_8_13' '1_8_1' '1_9_12' '1_8_13' '1_9_12' '1_10_12' '1_9_11'\n", + " '1_10_12' '1_10_11' '1_9_11' '1_9_13' '1_10_12' '1_7_13' '1_9_12'\n", + " '1_7_13' '1_7_14' '1_10_12' '1_8_12' '1_9_12' '1_6_15' '1_10_12' '1_9_12'\n", + " '1_9_11' '1_8_13' '1_8_13' '1_7_14' '1_8_13' '1_9_11' '1_6_15' '1_10_12'\n", + " '1_10_11' '1_9_12' '1_8_13' '1_9_13' '1_9_13' '1_6_15' '1_9_13' '1_6_15'\n", + " '1_9_12' '1_8_14' '1_9_12' '1_5_15' '1_9_12' '1_9_12' '1_8_15' '1_10_13'\n", + " '1_10_11' '1_9_12' '1_10_12' '1_6_15' '1_9_12' '1_10_12' '1_8_13'\n", + " '1_9_13' '1_9_12' '1_9_11' '1_6_15' '1_9_12' '1_8_13' '1_9_12' '1_10_12'\n", + " '1_8_13' '1_7_12' '1_8_13' '1_8_13' '1_9_11' '1_8_14' '1_8_13' '1_8_13'\n", + " '1_8_14' '1_7_15' '1_9_12' '1_8_13' '1_9_11' '1_9_13' '1_5_15' '1_9_12'\n", + " '1_7_14' '1_7_15' '1_5_15' '1_5_15' '1_9_11' '1_9_12' '1_7_14' '1_8_13'\n", + " '1_10_11' '1_10_12' '1_9_13' '1_10_12' '1_8_13' '1_7_15' '1_10_13'\n", + " '1_9_12' '1_8_13' '1_8_13' '1_9_12' '1_7_15' '1_10_12' '1_7_15' '1_9_13'\n", + " '1_7_15' '1_8_13' '1_10_12' '1_7_13' '1_8_13' '1_9_13' '1_8_13' '1_9_11'\n", + " '1_9_13' '1_8_14' '1_8_13' '1_10_12' '1_9_12' '1_8_13' '1_5_15' '1_9_12'\n", + " '1_10_12' '1_7_15' '1_9_12' '1_9_12' '1_9_12' '1_6_15' '1_10_12' '1_5_15'\n", + " '1_7_12' '1_7_14' '1_5_15' '1_9_12' '1_8_13' '1_9_12' '1_6_15' '1_10_12'\n", + " '1_8_13' '1_6_15' '1_10_12' '1_10_12' '1_9_11' '1_10_11' '1_8_13'\n", + " '1_7_14' '1_9_11' '1_9_12' '1_10_11' '1_6_15' '2_3_1' '1_6_15' '2_3_1'\n", + " '1_9_12' '1_10_12' '1_9_13' '1_9_12' '1_9_12' '1_8_13' '1_8_13' '1_6_15'\n", + " '1_9_13' '1_10_11' '2_0_0' '1_8_13' '1_9_13' '1_8_13' '1_9_13' '1_8_14'\n", + " '1_9_13' '1_9_12' '1_8_13' '1_7_13' '1_8_13' '1_9_12' '1_9_12' '1_10_11'\n", + " '1_5_15' '1_9_13' '1_5_15' '1_9_12' '1_10_12' '1_8_14' '1_7_14' '1_9_12'\n", + " '1_7_14' '1_9_11' '1_6_15' '1_8_13' '1_8_14' '1_8_13' '1_10_11' '1_6_15'\n", + " '1_7_13' '1_9_12' '1_8_13' '1_10_11' '1_9_12' '1_6_15' '1_8_15' '1_8_15'\n", + " '1_9_12' '1_9_12' '1_10_11' '1_9_13' '1_8_15' '1_10_11' '1_9_13' '1_9_11'\n", + " '1_9_11' '1_5_15' '1_10_11' '1_8_15' '1_9_12' '1_9_12' '1_8_13' '1_8_13'\n", + " '1_9_12' '1_8_14' '1_9_11' '1_9_12' '1_7_15' '1_8_15' '1_8_13' '1_9_13'\n", + " '1_9_12' '1_10_13' '1_9_13' '1_8_14' '1_8_14' '1_9_12' '1_8_13' '1_9_11'\n", + " '1_8_13' '1_5_15' '1_9_12' '1_8_14' '1_6_15' '1_9_13' '1_9_12']\n", + "dowsampled rms bin 40\n", + "areas of tiles in bin [1.35826334e-04 1.30065993e-04 1.33114850e-04 1.00595612e-05\n", + " 1.84485640e-05 1.05483044e-04 1.35098821e-04 1.35525697e-04\n", + " 1.28948875e-04 1.06294090e-05 2.53008811e-07 1.41850307e-04\n", + " 1.31103240e-04 1.36032102e-04 1.35826334e-04 1.34819689e-04\n", + " 1.41264619e-04 1.34402148e-04 1.41439784e-04 1.36766707e-04\n", + " 1.30065993e-04 1.35826334e-04 1.35098821e-04 1.34794930e-04\n", + " 1.41850307e-04 1.35359661e-04 1.36484130e-04 1.34522306e-04\n", + " 1.33991170e-04 4.60550675e-05 1.41595801e-04 1.37030768e-04\n", + " 1.34402148e-04 1.33991170e-04 1.35169464e-04 1.32591945e-04\n", + " 1.30065993e-04 1.37711511e-04 1.35169464e-04 1.34402148e-04\n", + " 1.00595612e-05 1.36766707e-04 1.35525697e-04 1.36032102e-04\n", + " 1.01249453e-05 1.33562051e-04 1.25044519e-04 1.35525697e-04\n", + " 1.84485640e-05 1.05483044e-04 1.36438771e-04 1.30994452e-04\n", + " 1.37711511e-04 1.30560736e-04 1.41264619e-04 1.34794930e-04\n", + " 1.04140579e-04 1.37030768e-04 1.36766707e-04 1.34794930e-04\n", + " 4.79247995e-06 1.33562051e-04 1.34794930e-04 1.34402148e-04\n", + " 4.88641649e-06 1.35098821e-04 1.00595612e-05 1.37030768e-04\n", + " 1.84485640e-05 1.05483044e-04 1.34206710e-04 1.31103240e-04\n", + " 2.83700615e-04 1.35525697e-04 1.06294090e-05 7.06744029e-06\n", + " 1.35169464e-04 1.30065993e-04 1.32430250e-04 1.34206710e-04\n", + " 1.76692871e-06 1.36484130e-04 1.37711511e-04 1.34402148e-04\n", + " 1.36766707e-04 9.91298631e-05 1.79106979e-05 1.35098821e-04\n", + " 1.41264619e-04 1.41850307e-04 1.35098821e-04 1.30254315e-05\n", + " 1.29553641e-04 1.28948875e-04 1.30065993e-04 1.30560736e-04\n", + " 1.35826334e-04 1.41264619e-04 1.41732649e-04 1.28948875e-04\n", + " 1.34206710e-04 1.15150625e-04 1.28460240e-04 2.70070294e-04\n", + " 7.27779963e-05 1.30560736e-04 9.98954340e-05 1.41264619e-04\n", + " 2.29051111e-05 1.37503202e-04 1.30065993e-04 1.60575299e-06\n", + " 1.35863583e-04 1.36183075e-04 1.35525697e-04 1.37178450e-04\n", + " 6.01677072e-05 1.34794930e-04 7.27779963e-05 1.36032102e-04\n", + " 1.30994452e-04 1.28460240e-04 1.30065993e-04 1.35525697e-04\n", + " 1.28578531e-04 1.36183075e-04 1.36041198e-04 1.33991170e-04\n", + " 1.41595801e-04 3.35373663e-06 1.41264619e-04 1.34794930e-04\n", + " 1.01249453e-05 1.34819689e-04 3.00838536e-05 7.57334637e-05\n", + " 1.25044519e-04 1.14525556e-05 4.72107951e-05 1.17939429e-05\n", + " 1.36484130e-04 4.97061890e-06 7.27779963e-05 1.34206710e-04\n", + " 1.36484130e-04 1.41732649e-04 1.33991170e-04 1.36766707e-04\n", + " 3.33577335e-06 1.41264619e-04 1.35863583e-04 3.34820190e-06\n", + " 2.98743167e-07 1.41439784e-04 1.33562051e-04 1.36183075e-04\n", + " 1.30065993e-04 1.89320947e-07 1.35098821e-04 1.35602177e-04\n", + " 1.30560736e-04 1.30560736e-04 1.34522306e-04 1.30560736e-04\n", + " 1.28948875e-04 1.34402148e-04 1.37503202e-04 1.33991170e-04\n", + " 1.16518472e-05 1.28046137e-04 4.66419903e-05 7.48210123e-05\n", + " 3.19498663e-06 6.74879359e-06 1.30065993e-04 1.30994452e-04\n", + " 1.36484130e-04 1.36766707e-04 1.33114850e-04 1.37276278e-04\n", + " 1.36484130e-04 1.33562051e-04 1.30065993e-04 1.36766707e-04\n", + " 1.35602177e-04 1.41439784e-04 1.36484130e-04 1.36484130e-04\n", + " 1.34402148e-04 1.37276278e-04 1.35863583e-04 1.31103240e-04\n", + " 1.34402148e-04 1.30065993e-04 1.06294090e-05 1.26290220e-05\n", + " 9.79060376e-05 1.76895782e-05 1.28646238e-05 1.35525697e-04\n", + " 1.33991170e-04 1.34819689e-04 1.36032102e-04 1.35826334e-04\n", + " 1.30065993e-04 1.35863583e-04 1.37711511e-04 1.27029392e-04\n", + " 1.41595801e-04 1.34794930e-04 1.34522306e-04]\n", + "names of tiles in bin ['1_9_12' '1_7_14' '1_8_14' '1_8_15' '1_8_15' '1_8_15' '1_9_13' '1_8_15'\n", + " '1_7_14' '1_10_12' '1_10_13' '1_10_12' '1_10_12' '1_9_12' '1_9_13'\n", + " '1_9_11' '1_10_11' '1_8_14' '1_10_11' '1_9_12' '1_7_14' '1_9_12' '1_9_12'\n", + " '1_8_14' '1_10_12' '1_9_13' '1_9_13' '1_9_12' '1_8_14' '1_10_13'\n", + " '1_10_11' '1_9_12' '1_8_14' '1_8_14' '1_8_14' '1_8_14' '1_7_14' '1_9_11'\n", + " '1_8_14' '1_8_14' '1_8_15' '1_9_13' '1_8_14' '1_9_12' '1_8_15' '1_8_13'\n", + " '1_8_15' '1_8_13' '1_8_15' '1_8_15' '1_8_14' '1_8_14' '1_9_12' '1_7_12'\n", + " '1_10_11' '1_8_14' '1_9_13' '1_9_13' '1_9_13' '1_8_13' '1_8_14' '1_8_15'\n", + " '1_8_13' '1_8_13' '1_8_13' '1_9_13' '1_8_15' '1_9_13' '1_8_15' '1_8_15'\n", + " '1_9_12' '1_10_12' '1_10_12' '1_8_15' '1_10_12' '1_10_13' '1_8_14'\n", + " '1_7_13' '1_10_13' '1_9_12' '1_10_13' '1_9_12' '1_9_11' '1_8_14' '1_9_13'\n", + " '1_7_1' '1_7_1' '1_9_13' '1_10_12' '1_10_12' '1_9_12' '1_7_1' '1_7_15'\n", + " '1_7_13' '1_7_14' '1_7_14' '1_9_12' '1_10_12' '1_10_11' '1_7_13' '1_9_11'\n", + " '1_5_15' '1_7_13' '1_9_12' '1_5_15' '1_7_14' '1_9_13' '1_10_12' '1_5_15'\n", + " '1_9_12' '1_7_13' '1_7_12' '1_9_11' '1_9_13' '1_8_13' '1_8_14' '1_5_15'\n", + " '1_8_13' '1_5_15' '1_9_12' '1_8_15' '1_7_14' '1_7_12' '1_8_14' '1_6_15'\n", + " '1_9_13' '1_8_14' '1_8_13' '1_10_12' '1_9_12' '1_10_11' '1_8_13' '1_8_15'\n", + " '1_9_13' '1_5_15' '1_8_15' '1_8_15' '1_5_15' '1_8_15' '1_8_15' '1_9_12'\n", + " '1_9_12' '1_5_15' '1_9_13' '1_9_13' '1_10_11' '1_8_14' '1_9_12' '1_9_13'\n", + " '1_10_12' '1_9_12' '1_9_12' '1_6_15' '1_10_11' '1_8_14' '1_9_12' '1_7_14'\n", + " '1_10_13' '1_9_13' '1_9_12' '1_7_13' '1_7_13' '1_9_13' '1_7_14' '1_7_13'\n", + " '1_8_15' '1_9_13' '1_8_13' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_14'\n", + " '1_8_15' '1_7_13' '1_8_14' '1_9_12' '1_9_12' '1_8_13' '1_9_13' '1_9_13'\n", + " '1_8_14' '1_7_13' '1_9_13' '1_9_13' '1_10_11' '1_9_13' '1_9_13' '1_8_14'\n", + " '1_9_13' '1_9_13' '1_10_12' '1_8_15' '1_7_13' '1_10_12' '1_10_13' '1_7_1'\n", + " '1_7_1' '1_7_1' '1_8_14' '1_8_14' '1_9_13' '1_9_13' '1_9_13' '1_7_13'\n", + " '1_9_12' '1_9_13' '1_6_15' '1_10_12' '1_8_14' '1_9_13']\n", + "dowsampled rms bin 41\n", + "areas of tiles in bin [1.36183075e-04 1.35525697e-04 1.34402148e-04 1.36032102e-04\n", + " 1.33114850e-04 1.36183075e-04 1.37503202e-04 1.28948875e-04\n", + " 8.41433647e-07 1.41439784e-04 1.26529999e-07 1.41595801e-04\n", + " 3.34117780e-05 1.37178450e-04 1.16518472e-05 1.30994452e-04\n", + " 4.66419903e-05 1.28460240e-04 7.48210123e-05 1.33114850e-04\n", + " 1.30560736e-04 1.30560736e-04 1.35169464e-04 1.34819689e-04\n", + " 1.35602177e-04 4.90211596e-06 1.34794930e-04 6.38997326e-06\n", + " 6.61100963e-06 1.36766707e-04 1.30994452e-04 1.36766707e-04\n", + " 1.41439784e-04 1.41264619e-04 1.34402148e-04 1.00595612e-05\n", + " 1.36183075e-04 1.84485640e-05 1.05483044e-04 1.28948875e-04\n", + " 1.37711511e-04 1.34537732e-05 1.35863583e-04 1.34206710e-04\n", + " 1.30560736e-04 1.33991170e-04 1.06294090e-05 1.35169464e-04\n", + " 1.35826334e-04 1.34206710e-04 1.22242355e-04 1.35525697e-04\n", + " 1.30560736e-04 1.34402148e-04 1.33562051e-04 1.28460240e-04\n", + " 1.41264619e-04 9.79060376e-05 1.76895782e-05 1.28646238e-05\n", + " 1.41439784e-04 1.36817872e-04 1.35525697e-04 1.37276278e-04\n", + " 1.33114850e-04 3.28768123e-06 1.33562051e-04 1.34794930e-04\n", + " 1.34522306e-04 1.30994452e-04 3.34221386e-06 1.30994452e-04\n", + " 4.66419903e-05 1.02484202e-05 1.35863583e-04 1.24369136e-07\n", + " 1.16518472e-05 1.37711511e-04 7.48210123e-05 1.37276278e-04\n", + " 1.35525697e-04 1.26569452e-04 1.28460240e-04 1.28460240e-04\n", + " 1.34819689e-04 1.35098821e-04 1.33562051e-04 1.36484130e-04\n", + " 1.36484130e-04 1.36766707e-04 1.34402148e-04 1.33562051e-04\n", + " 1.34402148e-04 1.35826334e-04 1.36183075e-04 1.61186094e-06\n", + " 1.37711511e-04 1.34206710e-04 1.37178450e-04 7.27779963e-05\n", + " 1.31103240e-04 1.14525556e-05 1.06294090e-05 3.00838536e-05\n", + " 1.36766707e-04 1.33114850e-04 1.35602177e-04 6.55620233e-06\n", + " 1.36438771e-04 1.35190856e-04 3.25761100e-06 1.34927841e-06\n", + " 1.00595612e-05 1.34402148e-04 1.84485640e-05 1.05483044e-04\n", + " 1.30994452e-04 1.35614772e-04 1.23927350e-04 1.00638194e-05\n", + " 1.35169464e-04 1.30994452e-04 1.16518472e-05 1.35190856e-04\n", + " 1.41439784e-04 7.48210123e-05 1.35169464e-04 1.35169464e-04\n", + " 1.37503202e-04 4.66419903e-05 1.28948875e-04 1.30994452e-04\n", + " 1.30560736e-04 1.35826334e-04 1.35826334e-04 1.27954213e-04\n", + " 3.82305482e-05 1.35525697e-04 1.37503202e-04 3.78612218e-07\n", + " 1.41264619e-04 1.30994452e-04 6.31045967e-08 1.37276278e-04\n", + " 1.28046137e-04 6.74879359e-06 1.39168921e-06 1.33562051e-04\n", + " 1.33114850e-04 1.35863583e-04 1.30065993e-04 1.60575299e-06\n", 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'1_7_14' '1_8_15' '1_8_14' '1_7_13' '1_7_13' '1_8_14'\n", + " '1_9_12' '1_9_13' '1_8_14' '1_8_14' '1_8_15' '1_8_15' '1_9_13' '1_8_13'\n", + " '1_9_12' '1_10_12' '1_10_11' '1_8_15' '1_8_15' '1_9_12' '1_8_15' '1_8_15'\n", + " '1_7_14' '1_9_13' '1_10_12' '1_9_13' '1_9_13' '1_7_14' '1_8_13' '1_10_12'\n", + " '1_8_13' '1_9_12' '1_9_12' '1_10_12' '1_8_14' '1_7_14' '1_8_13' '1_8_13'\n", + " '1_7_13' '1_10_12' '1_7_1' '1_7_1' '1_7_1' '1_10_12' '1_8_14' '1_8_15'\n", + " '1_9_12' '1_8_14' '1_8_14' '1_8_13' '1_8_14' '1_9_13' '1_8_14' '1_9_13'\n", + " '1_8_14' '1_8_15' '1_8_15' '1_9_12' '1_9_14' '1_8_15' '1_9_12' '1_8_15'\n", + " '1_9_13' '1_8_14' '1_8_15' '1_7_13' '1_7_14' '1_9_13' '1_9_12' '1_8_14'\n", + " '1_9_12' '1_9_13' '1_9_13' '1_8_15' '1_8_14' '1_8_1' '1_9_13' '1_9_13'\n", + " '1_7_15' '1_9_12' '1_9_13' '1_8_15' '1_5_15' '1_10_12' '1_5_15' '1_10_12'\n", + " '1_5_15' '1_9_13' '1_8_13' '1_9_13' '1_8_15' '1_8_14' '1_8_15' '1_8_14'\n", + " '1_10_13' '1_8_15' '1_8_14' '1_8_15' '1_8_15' '1_8_13' '1_9_13' '1_8_1'\n", + " '1_8_1' '1_8_14' '1_8_14' '1_8_15' '1_8_14' '1_10_12' '1_8_15' '1_8_14'\n", + " '1_8_14' '1_9_12' '1_8_15' '1_7_14' '1_8_15' '1_7_13' '1_9_13' '1_9_12'\n", + " '1_7_15' '1_10_13' '1_8_14' '1_9_13' '1_10_13' '1_10_12' '1_8_14'\n", + " '1_10_13' '1_9_12' '1_8_15' '1_8_15' '1_10_13' '1_8_13' '1_8_1' '1_9_13'\n", + " '1_7_14' '1_7_15' '1_10_13' '1_8_14' '1_8_14' '1_9_12' '1_9_13' '1_10_13'\n", + " '1_8_1' '1_8_1' '1_9_13' '1_10_13' '1_10_13' '1_9_13' '1_8_15' '1_9_13'\n", + " '1_10_13' '1_8_15' '1_7_14' '1_8_14' '1_9_13' '1_8_15' '1_8_15' '1_8_14'\n", + " '1_8_15' '1_8_15' '1_8_15' '1_8_14' '1_8_14' '1_7_13' '1_9_13' '1_8_14'\n", + " '1_8_14' '1_7_14' '1_8_15' '1_8_14' '1_10_12' '1_8_13' '1_8_14' '1_8_14'\n", + " '1_8_15' '1_8_14' '1_8_14' '1_8_15' '1_8_14' '1_8_0' '1_8_14' '1_7_14'\n", + " '1_8_14']\n", + "dowsampled rms bin 42\n", + "areas of tiles in bin [1.28948875e-04 1.01249453e-05 1.35359661e-04 1.34402148e-04\n", + " 1.41264619e-04 1.33991170e-04 1.33991170e-04 1.25044519e-04\n", + " 3.08219499e-05 1.37030768e-04 1.35525697e-04 6.74879359e-06\n", + " 1.34794930e-04 1.30994452e-04 1.28046137e-04 1.32591945e-04\n", + " 1.36183075e-04 1.86536180e-07 1.37503202e-04 1.37711511e-04\n", + " 3.34820190e-06 1.35863583e-04 1.33991170e-04 1.34819689e-04\n", + " 3.91336158e-06 2.14397594e-05 4.62176026e-07 3.21601214e-07\n", + " 1.33991170e-04 2.67124102e-04 1.36041198e-04 1.34738202e-04\n", + " 3.35881733e-06 1.28948875e-04 1.16518472e-05 1.33991170e-04\n", + " 1.25044519e-04 1.34794930e-04 7.48210123e-05 4.66419903e-05\n", + " 1.01249453e-05 1.33562051e-04 1.33114850e-04 7.16030539e-06\n", + " 1.49100663e-06 1.35853828e-04 1.36032102e-04 1.33562051e-04\n", + " 1.33114850e-04 1.34402148e-04 1.34522306e-04 1.30560736e-04\n", + " 9.91298631e-05 1.79106979e-05 1.34794930e-04 1.33562051e-04\n", + " 1.30254315e-05 1.33991170e-04 1.34402148e-04 1.30065993e-04\n", + " 1.33562051e-04 1.36484130e-04 1.36484130e-04 1.30065993e-04\n", + " 1.33991170e-04 1.01822388e-05 3.77206962e-05 1.86735465e-05\n", + " 1.06769423e-04 1.33114850e-04 1.33114850e-04 3.08890902e-05\n", + " 2.50089037e-04 1.36032102e-04 2.02498906e-05 1.36183075e-04\n", + " 1.30065993e-04 1.36183075e-04 1.30065993e-04 9.71423935e-05\n", + " 1.16518472e-05 4.66419903e-05 7.48210123e-05 1.33114850e-04\n", + " 1.34402148e-04 1.35190856e-04 1.30994452e-04 1.36817872e-04\n", + " 1.28460240e-04 5.68382789e-07 2.27521904e-06 1.37503202e-04\n", + " 3.64980548e-06 1.35863583e-04]\n", + "names of tiles in bin ['1_7_13' '1_8_15' '1_9_13' '1_8_13' '1_10_12' '1_8_14' '1_8_14' '1_8_15'\n", + " '1_9_13' '1_9_13' '1_8_14' '1_8_15' '1_8_14' '1_8_1' '1_8_15' '1_8_15'\n", + " '1_9_13' '1_9_14' '1_9_12' '1_9_12' '1_9_13' '1_9_13' '1_8_13' '1_9_13'\n", + " '1_9_14' '1_7_1' '1_7_1' '1_7_1' '1_8_14' '1_8_15' '1_8_15' '1_8_14'\n", + " '1_9_13' '1_7_1' '1_8_15' '1_8_14' '1_8_15' '1_8_14' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_8_13' '1_8_14' '1_8_15' '1_9_13' '1_8_15' '1_9_12' '1_8_13'\n", + " '1_8_14' '1_8_14' '1_9_13' '1_7_1' '1_7_1' '1_7_1' '1_8_14' '1_8_14'\n", + " '1_7_1' '1_8_14' '1_8_15' '1_7_14' '1_8_1' '1_9_13' '1_9_13' '1_7_13'\n", + " '1_8_14' '1_8_15' '1_9_13' '1_8_15' '1_8_15' '1_8_1' '1_8_14' '1_7_15'\n", + " '1_8_15' '1_9_13' '1_8_15' '1_9_13' '1_7_14' '1_9_13' '1_7_13' '1_9_13'\n", + " '1_8_15' '1_8_15' '1_8_15' '1_8_14' '1_8_14' '1_8_14' '1_8_14' '1_8_14'\n", + " '1_7_15' '1_8_15' '1_8_15' '1_9_12' '1_8_15' '1_9_13']\n", + "dowsampled rms bin 43\n", + "areas of tiles in bin [1.28046137e-04 1.29553641e-04 1.30560736e-04 6.74879359e-06\n", + " 1.41264619e-04 1.33114850e-04 1.35625208e-04 1.28646238e-05\n", + " 9.79060376e-05 1.75890336e-05 1.34778035e-04 1.33114850e-04\n", + " 1.34402148e-04 1.35169464e-04 1.30560736e-04 1.29553641e-04\n", + " 1.33991170e-04 1.33562051e-04 2.61988904e-04 1.30254315e-05\n", + " 1.30994452e-04 9.91298631e-05 1.79106979e-05 1.35863583e-04\n", + " 3.35373663e-06 1.28948875e-04 1.34794930e-04 1.34206710e-04\n", + " 2.67124102e-04 1.00595612e-05 1.33114850e-04 1.84485640e-05\n", + " 1.05483044e-04 1.35525697e-04 1.30560736e-04 1.01249453e-05\n", + " 1.25044519e-04 1.33562051e-04 1.33562051e-04 1.34402148e-04\n", + " 1.34522306e-04 1.16518472e-05 7.48210123e-05 4.66419903e-05\n", + " 6.74879359e-06 1.29553641e-04 1.30994452e-04 1.28046137e-04]\n", + "names of tiles in bin ['1_8_15' '1_7_15' '1_7_14' '1_8_15' '1_10_12' '1_8_14' '1_8_14' '1_7_1'\n", + " '1_7_1' '1_7_1' '1_9_13' '1_8_14' '1_8_14' '1_8_14' '1_7_13' '1_7_15'\n", + " '1_8_14' '1_8_15' '1_8_15' '1_7_1' '1_8_14' '1_7_1' '1_7_1' '1_9_13'\n", + " '1_9_13' '1_7_15' '1_8_14' '1_9_13' '1_8_14' '1_8_15' '1_8_1' '1_8_15'\n", + " '1_8_15' '1_8_15' '1_7_13' '1_8_15' '1_8_15' '1_8_14' '1_8_15' '1_8_15'\n", + " '1_9_13' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_7_15' '1_8_1' '1_8_15']\n", + "dowsampled rms bin 44\n", + "areas of tiles in bin [1.30560736e-04 1.30065993e-04 6.74879359e-06 2.56092274e-04\n", + " 1.23927350e-04 9.87971531e-06 1.30560736e-04 6.74879359e-06\n", + " 1.84485640e-05 1.05483044e-04 1.15289566e-04 1.00595612e-05\n", + " 1.33562051e-04 1.33114850e-04 1.25867190e-07 4.69307974e-05\n", + " 1.40077273e-04 2.04529556e-08 1.30994452e-04 1.33562051e-04\n", + " 1.59896895e-05 1.33562051e-04 1.33991170e-04 1.35525697e-04\n", + " 2.11959890e-05 1.30994452e-04 4.46102022e-05 1.33562051e-04\n", + " 1.35169464e-04 8.88401121e-07 7.48210123e-05 1.16518472e-05\n", + " 4.66419903e-05 1.30994452e-04 6.72733007e-05 1.27954213e-04\n", + " 1.27996627e-04 1.25044519e-04 1.01249453e-05 1.28460240e-04]\n", + "names of tiles in bin ['1_7_1' '1_7_15' '1_8_15' '1_8_15' '1_8_1' '1_8_1' '1_7_14' '1_8_15'\n", + " '1_8_15' '1_8_15' '1_8_1' '1_8_15' '1_8_15' '1_8_14' '1_9_13' '1_8_1'\n", + " '1_8_15' '1_8_1' '1_8_1' '1_8_1' '1_8_1' '1_8_14' '1_8_14' '1_8_15'\n", + " '1_9_14' '1_8_15' '1_8_15' '1_8_14' '1_8_14' '1_8_1' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_8_1' '1_8_1' '1_7_15' '1_8_1' '1_8_15' '1_8_15' '1_7_15']\n", + "dowsampled rms bin 45\n", + "areas of tiles in bin [1.35525697e-04 1.00595612e-05 1.84485640e-05 1.05483044e-04\n", + " 1.28948875e-04 1.30065993e-04 1.30065993e-04 1.00262143e-04\n", + " 1.28046137e-04 6.74879359e-06 1.33562051e-04 9.94184240e-05\n", + " 1.01249453e-05]\n", + "names of tiles in bin ['1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_7_1' '1_7_15' '1_7_15' '1_7_1'\n", + " '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_15']\n", + "dowsampled rms bin 46\n", + "areas of tiles in bin [1.30065993e-04 1.34402148e-04 3.70359194e-07 3.56769946e-05\n", + " 1.30560736e-04 1.33114850e-04 1.30560736e-04 1.30994452e-04\n", + " 2.00333252e-08 1.28948875e-04 1.30560736e-04 1.30560736e-04]\n", + "names of tiles in bin ['1_7_15' '1_8_15' '1_8_15' '1_8_15' '1_7_15' '1_8_1' '1_7_15' '1_8_15'\n", + " '1_7_15' '1_7_15' '1_7_15' '1_7_1']\n", + "dowsampled rms bin 47\n", + "areas of tiles in bin [7.48210123e-05 1.16518472e-05 4.66419903e-05 7.48210123e-05\n", + " 1.16518472e-05 4.66419903e-05 9.65659962e-05 1.30560736e-04\n", + " 7.48210123e-05 1.16518472e-05 4.31471822e-05 1.30994452e-04\n", + " 4.24943395e-05]\n", + "names of tiles in bin ['1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_7_15'\n", + " '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_7_15']\n", + "dowsampled rms bin 48\n", + "areas of tiles in bin [8.28693646e-05 2.46944186e-07 1.79781091e-05 1.05483044e-04\n", + " 1.00595612e-05 1.01685222e-04 2.63882486e-07 1.27306889e-05\n", + " 1.30994452e-04 1.28460240e-04]\n", + "names of tiles in bin ['1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_8_15' '1_7_15' '1_8_15' '1_8_15'\n", + " '1_8_15' '1_7_15']\n", + "dowsampled rms bin 49\n", + "areas of tiles in bin [1.30994452e-04 1.28948875e-04 2.45348838e-07 2.29429003e-05]\n", + "names of tiles in bin ['1_8_15' '1_7_15' '1_8_15' '1_8_15']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 2891.5647958661993\n", + "Number of clusters in redshift bin 0: 19.080651650622908.\n", + "Number of clusters in redshift bin 1: 301.0184749550533.\n", + "Number of clusters in redshift bin 2: 431.49091931439415.\n", + "Number of clusters in redshift bin 3: 451.75045554763057.\n", + "Number of clusters in redshift bin 4: 408.366680319877.\n", + "Number of clusters in redshift bin 5: 340.25918684930286.\n", + "Number of clusters in redshift bin 6: 268.4911844486312.\n", + "Number of clusters in redshift bin 7: 202.5351948330557.\n", + "Number of clusters in redshift bin 8: 147.64418624624602.\n", + "Number of clusters in redshift bin 9: 104.69021293361534.\n", + "Number of clusters in redshift bin 10: 72.54158859272803.\n", + "Number of clusters in redshift bin 11: 49.3276587657529.\n", + "Number of clusters in redshift bin 12: 33.026116735952435.\n", + "Number of clusters in redshift bin 13: 21.822096927256634.\n", + "Number of clusters in redshift bin 14: 14.258001956567597.\n", + "Number of clusters in redshift bin 15: 9.227648856548212.\n", + "Number of clusters in redshift bin 16: 5.923187642107952.\n", + "Number of clusters in redshift bin 17: 3.7746460170220213.\n", + "Number of clusters in redshift bin 18: 2.3905132377764335.\n", + "Number of clusters in redshift bin 19: 1.5059696456667688.\n", + "Number of clusters in redshift bin 20: 0.9444957026769373.\n", + "Number of clusters in redshift bin 21: 0.5900215199984119.\n", + "Number of clusters in redshift bin 22: 0.3670716710684263.\n", + "Number of clusters in redshift bin 23: 0.22735286890495157.\n", + "Number of clusters in redshift bin 24: 0.14020086009556335.\n", + "Number of clusters in redshift bin 25: 0.08612306469545285.\n", + "Number of clusters in redshift bin 26: 0.052738400351537734.\n", + "Number of clusters in redshift bin 27: 0.03221630259982323.\n", + "------------\n", + "Number of clusters in snr bin 0: 1733.4095753410438.\n", + "Number of clusters in snr bin 1: 928.9042800271336.\n", + "Number of clusters in snr bin 2: 193.90842876266584.\n", + "Number of clusters in snr bin 3: 31.56621812146314.\n", + "Number of clusters in snr bin 4: 3.5420581348928923.\n", + "Number of clusters in snr bin 5: 0.23423547899978517.\n", + "Total predicted 2D N = 2891.5647958661993.\n", + "Theory N calculation took 3.5855958461761475 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " ::: 2D ln likelihood = 327.99337926497645\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-327.99337926])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.68\n", + "\n", + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.26, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.0,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14#*0.68\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 5.,\n", + " 'single_tile_test': \"no\",\n", + " 'mode': 'Qfit',\n", + " 'Qmode': 'downsample',\n", + " 'dwnsmpl_bins': 50,\n", + " 'save_dwsmpld': False,\n", + " 'average_Q': False\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 5e13*0.68,\n", + " 'Mmax': 1e16*0.68,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " Total predicted 2D N = 2891.5647958661993\n", + "Number of clusters in redshift bin 0: 19.080651650622908.\n", + "Number of clusters in redshift bin 1: 301.0184749550533.\n", + "Number of clusters in redshift bin 2: 431.49091931439415.\n", + "Number of clusters in redshift bin 3: 451.75045554763057.\n", + "Number of clusters in redshift bin 4: 408.366680319877.\n", + "Number of clusters in redshift bin 5: 340.25918684930286.\n", + "Number of clusters in redshift bin 6: 268.4911844486312.\n", + "Number of clusters in redshift bin 7: 202.5351948330557.\n", + "Number of clusters in redshift bin 8: 147.64418624624602.\n", + "Number of clusters in redshift bin 9: 104.69021293361534.\n", + "Number of clusters in redshift bin 10: 72.54158859272803.\n", + "Number of clusters in redshift bin 11: 49.3276587657529.\n", + "Number of clusters in redshift bin 12: 33.026116735952435.\n", + "Number of clusters in redshift bin 13: 21.822096927256634.\n", + "Number of clusters in redshift bin 14: 14.258001956567597.\n", + "Number of clusters in redshift bin 15: 9.227648856548212.\n", + "Number of clusters in redshift bin 16: 5.923187642107952.\n", + "Number of clusters in redshift bin 17: 3.7746460170220213.\n", + "Number of clusters in redshift bin 18: 2.3905132377764335.\n", + "Number of clusters in redshift bin 19: 1.5059696456667688.\n", + "Number of clusters in redshift bin 20: 0.9444957026769373.\n", + "Number of clusters in redshift bin 21: 0.5900215199984119.\n", + "Number of clusters in redshift bin 22: 0.3670716710684263.\n", + "Number of clusters in redshift bin 23: 0.22735286890495157.\n", + "Number of clusters in redshift bin 24: 0.14020086009556335.\n", + "Number of clusters in redshift bin 25: 0.08612306469545285.\n", + "Number of clusters in redshift bin 26: 0.052738400351537734.\n", + "Number of clusters in redshift bin 27: 0.03221630259982323.\n", + "------------\n", + "Number of clusters in snr bin 0: 1733.4095753410438.\n", + "Number of clusters in snr bin 1: 928.9042800271336.\n", + "Number of clusters in snr bin 2: 193.90842876266584.\n", + "Number of clusters in snr bin 3: 31.56621812146314.\n", + "Number of clusters in snr bin 4: 3.5420581348928923.\n", + "Number of clusters in snr bin 5: 0.23423547899978517.\n", + "Total predicted 2D N = 2891.5647958661993.\n", + "Theory N calculation took 3.6435110569000244 seconds.\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "bin_params = info['likelihood']['soliket.BinnedClusterLikelihood']['binning']\n", + "\n", + "\n", + "zbins = np.arange(bin_params['z']['zmin'], bin_params['z']['zmax'] + bin_params['z']['dz'], \\\n", + " bin_params['z']['dz'])\n", + "\n", + "logqmin = bin_params['q']['log10qmin']\n", + "logqmax = bin_params['q']['log10qmax']\n", + "dlogq = bin_params['q']['dlog10q']\n", + "\n", + "# TODO: I removed the bin where everything is larger than qmax - is this ok?\n", + "qbins = 10**np.arange(logqmin, logqmax+dlogq, dlogq)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig = {\n", + " 'predSNRCut': 5,\n", + " 'path2truthcat': '../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'fast',\n", + " 'QSource': 'fit'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: We don't have true_fixed_y_c or true_Q - we reconstruct those here.\n" + ] + } + ], + "source": [ + "# Make a 'true' mock - use the truth catalog, get true_SNR by looking up noise in the selFn dir\n", + "mode = 'without_Q'\n", + "truthTab = nemo_mocks.make_truth_mock(mode, mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "truth_cat, zarr, qarr = nemo_mocks.bin_catalog(truthTab[truthTab['true_SNR']>5], zbins, qbins, SNR_tag='true_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "mockTab = nemo_mocks.make_nemo_mock(mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "mock_cat, zarr, qarr = nemo_mocks.bin_catalog(mockTab[mockTab['fixed_SNR']>5], zbins, qbins, SNR_tag='fixed_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_truth = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_truth[i] = truth_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_truth = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_truth[i] = truth_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_mock = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_mock[i] = mock_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_mock = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_mock[i] = mock_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], label='prediction')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "predNz = nemo_mocks.get_nemo_pred(mockconfig , zbins)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], label='prediction')\n", + "plt.plot(z, predNz, color=color_list[0], linestyle='--', label='nemo prediction')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(z, Nz_truth, yerr=np.sqrt(Nz_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(z, Nz_mock, yerr=np.sqrt(Nz_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xlim(0, 2)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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6EXkM6GiMOatym3eBfwDjROQ/QHPg/4B/6QgA5anE6Xto9EAls6fwz60ES2dYn0oBExT+5zUOIpPA4ai8CGasNy9TcXAeA+u+8usDN07k3xWH9nPwa1Nl/xXH31fVr//0+GH7rpqz6r5PeL8Hvq44ys/FVD5e+fWeQli/CJw+1jj1lmfq5D31QBxOTGJzyFuKMabxr4Pg5uxuAegA/FDl639V3t4ABgGJQOaBJ40xhSLSG3gBmAvswBr/P6KB8irVICQo3PqEVPkpyZSXwY78P09WtPZ3OyO6OQGpvB3t3we/dlhv/K3PhhY9kUCdvKdeubKt2TeLtlhzbyjb2D0PwFQOdeKr7vlB1Ty2AOhRf6mUcj/i9IGYFOuW0xMAU1JkFQM7Nx3Yqpo3t8P/7aj8msp7R5VtOPR81dfUaL+V+z64XzlsX1X2XZM35yOO6ag8Uxz+fYj1uDgOPqefKt1cUrZ1n79UCwCb2d0CoJSqJQkMszqopbSxO4pSNSahMZjQWKsfQI5+lrOTDkJWSinVsJKyYNMK69KWso0WAEoppRqWK8caYlmgk7faSQsApZRSDSuhqdVvI3+p3Um8mhYASimlGpT4BVpzW+QvsTuKV9MCQCmlVMNzZcO2DZiSXXYn8VpaACillGp4STnW/cZl9ubwYloAKKWUanhRyeAfbC2jrWyhBYBSSqkGJw4HuLIg35oWWDU8LQCUUkrZw5UNJUXWNNeqwWkBoJRSyh6uKtMCqwanBYBSSilbSHAERCRqPwCbaAGglFLKPknZsHkVpmy/3Um8jhYASiml7OPKhooy2LTS7iReRwsApZRS9onPBIeP9gOwgRYASimlbCM+fpCQqQWADbQAUEopZS9XDuzchNm9w+4kXkULAKWUUvZK0uGAdtACQCmllL0iEiEoHPJ0XYCGpAWAUkopW4mINRpg41JMRYXdcbyGFgBKKaXs58qGfXtg+3q7k3gNLQCUUkrZz5UFiM4K2IC0AFBKKWU7CQiB6GTtCNiAtABQSinlHpJyoGANZn+J3Um8ghYASiml3IMrC0wFbFphdxKvoAWAUkop9xCbDj7+2g+ggWgBoJRSyi2I0wcSm2k/gAaiBYBSSin34cqGXVsxRVvsTtLoaQGglFLKfei0wA1GCwCllFLuIzQWQqK1H0AD0AJAKaWU2zg4LfCm5ZiKcrvjNGpaACillHIvSdlQug+25NqdpFHTAkAppZR7SWwO4oC8JXYnadS0AFBKKeVWxC8QYtO0I2A90wJAKaWU+3Flwdb1mL3FdidptLQAUEop5X6ScgADG5fbnaTR0gJAKaWU+4lOAb8g7QdQj7QAUEop5XbE4QBXc8hfijHG7jiNkhYASiml3JMrG/YUws5NdidplLQAUEop5Z5cOi1wfdICQCmllFuSkCgIj9d+APXE9gJARG4VkTUisldE5olI9+Nsf66I/CQiu0Rkq4hMEJHmDZVXKaVUA3Jlw+ZVmLJSu5M0OrYWACLSHxgFPAq0BWYBX4tIylG2TwcmANMrtz8bCAS+apDASimlGlZSDpSXQsEqu5M0Ona3AAwHxhljxhhjlhhj7gA2ArccZfv2gC9wvzFmpTHmd+AxIFNEYhoksVJKqYYTnwkOp64OWA9sKwBExA/rDX3yYU9NBroc5WVzgVJgsIg4RSQUGAj8YozZWm9hlVJK2UJ8/a0iQDsC1jk7WwBiACew+bDHNwMJ1b3AGJML9Ab+BewDCoHWwIX1llIppZS9XNmwIx+zp9DuJI2Kj90BgMNneJBqHrOeEEkAXgPeBN4DQoF/A+NF5ExjTEU1rxkCDAGIj49n6tSpdZdc1bvi4mL9nSnl5YL3F9MBWPr9Z2wOSbU7jq3q8pxoZwGwFSjnyE/7cRzZKnDAbcBuY8x9Bx4QkauB9ViXDWYc/gJjzGhgNECHDh1Mr169Tjq4ajhTp05Ff2dKeTdjKuCDX8gOg5weveyOY6u6PCfadgnAGLMfmIfVpF9Vb6zRANUJwioaqjrwtd0dGpVSStUDEQckZUPeUqpp6FW1ZPeb5ghgkIgMFpEcERkFuICXAUTkMRH5rsr2E4F2IvIPEWkmIu2A17FaAOY1ZPCCVVsp3ra7IQ+plFLey5UN+3bDtg12J2k0bO0DYIz5QESigYeARGAhcL4xZm3lJolAZpXtvxeRK4H7gHuBEmA20McY06Dvxp/9axLLpq0iMTueZl3TaNY1nYyOqQSE+DdkDKWU8g6uLOs+fynEVDtVjDpBtncCNMa8CLx4lOcGVfPY+8D79RzruM675wwyOqWwYuYaZr01l2mvzcHh4yDlFBfNuqbTrEs6qW2T8fG3/UeslFIeTwLDMFFJ1nwAbc6xO06joO9OtdSkjYsmbVycfVt3SveWsmbeelbOXMOKmWv49vkZTHl2Or4BPqSflkKzLuk065pOUssEHE67r7oopZSHcuXA4h8wpXsR3wC703g8LQDqgG+AL827ZtC8awYAJYV7WTVnLStmWQXBxCesbgyB4QE0PT2NppUtBHGZ0YiIndGVUspzJGXDwm9h0wpo0truNB5PC4B6EBgeQKtzsmh1jnXNqqhgFytm5bJi5hpWzlrDgknWjFbhCaE07ZJ+sIUgIjHMzthKKeXe4tLBxw/ylmkBUAe0AGgAYXGhtL+4Ne0vbo0xhm1rd7Bi5hpWzFrDkh9WMO+T+QDEZkQfLAaadk4jKCLQ5uRKKeU+xOmLSWgK+bo8cF3QAqCBiQgxaVHEpEXR+ar2VFQYNi7ZzIpZVuvA3E/+YNbbcxGBpJaJNO2aRrMu6aSfloJ/kJ/d8ZVSyl6uHNiwGLNrGxIabXcaj6YFgM0cDiGpZQJJLRPodWNnykvLWfdHntVCMHMN08fOYeorP+H0dZDaNtkaYdA1nZRTknD6Ou2Or5RSDcuVbd3nL4WsrvZm8XBaALgZp6+T9A4ppHdI4ZyhPdm3Zz9rfllntRDMzGXyyB+Z9MyP+Af7kdEx5eCQw4TseBwO7VColGrkwuMgOFILgDqgBYCb8w/yI7tnU7J7NgVg9449rJq9tkofgpUABEcF0bRz2sEWguiUSB1hoJRqdEQE48qG3N8xFeWIQ1tCa0sLAA8THBlEm/NyaHNeDgA7NxYdLAZWzFzDHxMXAxCZFG6NMKhsIQiLC7EztlJK1Z2kbFjxE2xdC3EZdqfxWFoAeLiIxDBOu/wUTrv8FIwxFKzaVnm5YA0LJy3llw9/ByC+WezBYiDz9FQCw3QSDaWUh0rMAhFrVkAtAGpNC4BGRESIbxpDfNMYul17GhXlFeQt3nSwQ+Gc939lxrifEYfQpHWiNdywazrp7VPwDdA/BaWUZxD/IExMqtUPoO35dsfxWHrWb8QcTgdNWrto0trFmTd3pWxfGWt/23DwcsEPr8ziuxdn4uPnJK1Dk4MtBMmtXTh9dMpipZQbc2XD/EmYfXsQ/yC703gkLQC8iI+/D5mnp5F5ehp9hp/B3uJ9rP65skPhzFy+fvIHvuYHAkL9yeyUerBDYXyzWO1QqJRyL0nZ8Mc3sHEZpLW1O41H0gLAiwWE+NPizOa0OLM5ALu27mblT7msrGwhWPTtcgBCY0No2iXtYAtBVHKEjamVUgqISQXfQKsfgBYAtaIFgDooNCaYthe1pO1FLQHYvn7nwcsFK2eu4bcJCwGITo3805TFIdHBdsZWSnkhcTgxruaQvxRjjLZS1oIWAOqooppE0Kl/Wzr1b4sxhk3Ltxwccvj7l4uY/d6vALhy4g9eLkg/LYWAEH+bkyulvIIrG9b+AYUFEBFvdxqPowWAqhERITErjsSsOHpc34nysgo2zM8/2EIw881f+PHV2Th8HKSc4jp4uSC1bTI+/vpnppSqB1WnBdYC4ITpmVnVitPHQWq7ZFLbJXP27d0p3VvKmnnrWVk55PDb52cw5dnp+AX6Hhph0DWdpBYJOJw6wkApdfIkNBoTFmetDtiip91xPI4WAKpO+Ab40rxrBs27WpNylBTuZdWctQdbCCY+/h0AgeEBh6Ys7pJObEa0XrtTStWeKxtWzsaUlyJOX7vTeBQtAFS9CAwPoNU5WbQ6JwuAooJdrJiVa3UonLWGBd8sBawZCq985mKSWyXaGVcp5amSsmHpNChYA4nN7U7jUbQAUA0iLC6U9he3pv3FrTHGsG3tDpbPWM23z0/n2UvHctEDvek28DRtDVBKnZiEZuBwQt4SLQBOkF6MVQ1ORIhJi6LL1R0Y/tVNNO+WwWf//IZxN41nT2GJ3fGUUh5EfP0hLt3qCKhOyAm1AIhIC2CpMaainvJ4jC3jv2f/hi34RIfhExWGb3R45X0YPpGhiI8uUVkTIVFB3PDaFUx7bTZfPv4dI84fzdXPXkpa+yZ2R1NKeQpXNvz6JaakCAkMszuNxzjRSwAjgCwR2Q78Acw/cG+M2VbX4dzZvrWbKZq9iPKdxUc+KYIzPBjfqDB8osOs+6r/rnLv8Pdr+PBuRkToObgz6R1SeOuOj3nhr+M4794z6TWki93RlFKeICkHfv0S8pdB5ml2p/EYJ1QAGGP6AIjIMKAzEAXcA/QRkXXGmPQ6T+imku8dAIApK6dsxy5KtxVRtr2I0m2FlG0vomxbEaXbrcf2rtlI2fZdUHFkw4kjOMBqNYgK/1Ox4Bv956LBERzQ6K+Pp5yaxPAvhzD+/i+Y+Ph3rJyVS+rlsXbHUkq5u6gkCAix+gFoAVBjte0EONAYc3DyZRE5C7i2biJ5FvFx4hsbgW9sxDG3MxUVlBfutoqCA8XBwaLBut+zOJeybUWY/aVHHifAD9/I0CqXHCrvK4sEn6hwfKPDcIYFIQ7P7doRGB7AtS9czk/vzGPCvyeRO389mbHNado5ze5oSik3JeLAuLIgfxnGVCDiuefAhlTbAqBYRHKMMUsAjDHficj/6jBXoyMOBz6RofhEhkJm0lG3M8ZQsbukSotC0Z9bFLYVsW/NRornLqViz74jj+PjtI5T7SWH8EMtC5EhiNM9+ymICF2u7kBau2Revv4tXr7qLXrf2YPed3TXSYSUUtVzZcPqebA9H6KT7U7jEWpbANwEjBWRX7D6ALQGyusslRcTEZwhQThDgiA14ZjbVpTsO2aLwv6N29izcA3lRburOxA+ESGHWhQOKxiqtjI4/OyZXMPVIoEe/2xHweRdTB75I6tm53LVqEsJjw+1JY9Syo1VnRZYC4AaqVUBYIxZLCLdgEuBNkAu8Egd5lI14Aj0xz8pFv+kY18nrygto2z7Lsq2F1bbolC6vYi9q/Io27ELKswRr3eGBv25RaGaPgo+0WE4gwLq/Hv0CXBy5YiLadYlnU/+/hVPn/cKV464mOxeTev8WEopzyVB4ZhIl1UAtD7b7jgeoVYFgIjEAbcBpcaYv9dtJFXXHL4++MVH4hcfecztTHkFZYXFR21RKN1WyO4FqyjbXoQpPbLBxxHgd9w+Cj5Rlf0UTrBD42mXn0LKqS7euv1jxgx6lzNu6sJ595yB09c9L2MopWzgyoYlP2JK91nzA6hjqu0lgA+BN4G7gP+ISCtgkDHmnjpLphqcOB34Vr5pBx5jO2MM5bv2VNuScKBoKFmRR9n2JVSUVNNPwdeJT6RVGPgnxRLWpRUhHXNwBh77P2x801iGfnYDE/49mR9emcXqn9dy9bOXEdUk4uS+caVU45CUDYu+h80rIbml3WncXm0LgEBjzGsicjuAMWahiJxZh7mUGxMRfMKC8QkLhrRjz+FfXrKvSktC4REdG3f9soSd385F/HwJ7ZBFWPc2hHVuhTM0qNr9+Qb4cvmjF5DZOY0P7/+CEReMpv//+tK6T3Z9fKtKKU8SlwlOX8hbqgVADdS2ANgsIslA1QvGdX8BWHk8Z6A/zuRY/JOr76dgysvZvWA1RTPmUzh9PkWzFoLTQcipzQjr3gaRvdW+ru1FLWnSJpG3bv+YcTePp+vA07jo/t74BujyFkp5K/HxxcRn6rTANVTbs+VdwDggTkQGAOcA+hNXJ0ycTkJObUbIqc1IvPUSSpavp2j6fAqn/0H+yA+JBFZNWU549zaEdWuNX0L0wdfGpEZxx0fXMfGJ75g2dg65c9dzzfOXEZseffQDKqUat6Qc+OVTTPF2JCTK7jRuTYw5stf3MV9gzbBwI/A6cDHWEMBNwOvGmD11HbAudejQwcydO9fuGKoGjDHsy93EH29NIGpDEXtX5QMQ0Cy5shhoQ0CVYZILpyzj/XsmUFFWweWPXkC7fq3tiq6UspHZsREmPAZdrkCaN77pxKdOnUqvXr1qvL2IzDPGdKjuuRNuATDGVIjIIGPMK8D4yptSdUpECEhPpKRHNs169WJf/laKZsynaPp8No/9is1jv8I/JY6wbqcQ3r0NLc9uzt1f3cTbQz/hnaGfsmJWLpf8sw9+gfbMYaCUsklEAgSFW5cBGmEBUJdqewngRxEZZowZWZdhlDoaf1cMsX89k9i/nknplp0UzVpI4fQ/2PL+d2x5dwq+cZGEdW/DwAe7MWPyOr5/eSbrftvANc9fRkLzOLvjK6UaiIhgXNmwbgGmosKjp0avb7UtAFoAfxWRu4FZWKsCzjfGfFFnyZQ6Ct/YCKL7dSO6XzfKCosp+mkRRdPns/3zGWz7+EfSI0K4pG8Gk6ZuYWTfV7nk3+fR8S+nNvrFlJRSlZJyYOUc2LYOYtPsTuO2ajsTYF8AEQkBWlXezgK0AFANyic8hKg+nYjq04nyPXvZNWcxRTPmUzF7MRdElDJ9Wxjj7/uCxZ/9Rv9n/0JQjE4jrFSjl5gFiLU6oBYAR1XbmQA7APcCkVif/kcYY16ty2BKnShnUAARZ7Qj4ox2VOwvpXjuMhKn/cH0z5by26z1/K/L01xwbiLNLu5AaKcWOEOONd2RUspTSUAwJqYJ5C+DU8+zO47bqu0lgPeBh4BFQFvgExH5pzHmmzpLptRJcPj5EtalFWFdWnHlPeWc8t5PfPjENMZ/uYnTfhpPTlQZIe2bE96tDWFdWlmrNCqlGg9XDiyYgtlfgvhpsV+d2vaO2G6Med8Ys8AY8yZwHlCr5YBF5FYRWSMie0Vknoh0P872IiLDRGSpiOwTkY0i8nhtjq28g/g4aXVNN+6dNoysXs2YszWYmaRQtGYzeSM+YMlfH2b18OfY+smP7C/YYXdcpVRdSMoGUwEbl9udxG3VtgBYLSJ/q5wTAKAIOOE5AESkPzAKeBSrJWEW8LWIpBzjZU8DtwJ/A3KA84FpJ3ps5X1CooK4/rUB9H2oN6tW7+KL9eH4DxtI3FXnUFa4m40vfMqyAf9i5a0jKHjvW/atL7A7slKqtmLTwNffmhZYVau2lwD8gSHA7SKyCEgDPhaRVGPM2hPYz3BgnDFmTOXXd4hIH+AW4P7DNxaRLOAOoI0xZkmVp36rxfegvJDDIfQc3Jn0DinWyoJ3TeS8e86g15i/UZpXQOGMBdZcA69+yeZXv8Q/LcG6TNC9DQGZSTqSQCkPIQ4nJqE55C/BGKP/d6tR21EAlwCISDDQpsrtbRFpYoxJO94+RMQPaA88ddhTk4Gjzd7QD1gN9BGRiVgtGD8C9xpj9OOaqrGUU5MYPnEI4+//gomPf8eqn3IZMOJi4gacTdyAs9m/eQdFM+dTNGM+Be9OoeDtyfglRluLFXVrQ1BOqo4vVsrdJWXD+gWwawuE6XwghztmASAiTmPMkQu/VzLG7AZ+qrydqBjACWw+7PHNwNlHeU0GkApcAQzCWozoKeALEelsjKmoRQ7lpQLDA7j2hcv56e15THhkEk+fP5qrRl5C085p+MVHEnNpT2Iu7UnZjl3WxEMz5rPtk2lsHf8DPtFhhHVtQ3j3NgS3yUR8nHZ/O0qpw7kqVwnNW6oFQDWOuRaAiJRgDfObV+W20BhTdtIHFnEBeUAPY8z0Ko//AxhgjDlifVcRGY21DkGWMWZ55WPNgWXA6caYOdW8ZgjW5Qri4+Pbv//++ycbXTWg4uJiQkJC6v04hWuLmffSEnZvLqF5v1Sa901BHEc2GcreUnxXbMJv6Ub8Vm1GSsupCPRlf/NE9mcnUpoRB1oMKOU2Om6YxG7fUBbFN45pgU/0nHjGGWfUei2AG7A657UHBgDhwD4RWcihguBXY8y8Gqc5ZCtQDiQc9ngcR7YKHLARKDvw5l9pBVAGpABHFADGmNHAaLAWAzqRRRSU/U504YuT0efyc/j4oYnM+3QBFZuFK0deQnh8NcMD+1h3FXv3s2vuUmuNglkLCfhjHY5Af0I75RDWrY0110CQrpKtlJ3MTwUErvqFnt27IU7PXy68Ls+Jx/xpGGPeBd498LWINMMqBg4UBf2B0OPt5yj73i8i84DewIdVnuoNfHyUl80EfEQk0xizqvKxjMrjn0jnQ6WO4B/sx4ARF9O0SzqfPvw1I85/hQFPX0x2r6bVbu8I8CO8WxvCu7WhorSM3X+spGjaHxTNXEDh1N8RXx9C2mdZ/QY6t8InPLiBvyOlFEnZsGwGbFkDCc3sTuNWTuiN2xizQkQ2YnW+a441GuBkOt+NAN4SkZ+x3txvBlzAywAi8hjQ0RhzVuX23wK/AmNFZFjlYyOxPvnrOr/qpIkIHf9yKqltk3jzto8ZM+hdzri5C+fdfQZO36M37Tt8fQjtkE1oh2xcQ//CnkVrKKxcvXDX7EXkORwEn5JpjSjo2hrf2IiG+6aU8mYJzUAcVj8ALQD+pEYFgIiEYfXAvxw4B+tN/9PKf8+s7cGNMR+ISDTWrIKJwELg/CpDCROBzCrbV4jIhcCzWGP/S4ApwHDtAKjqUnzTWIZNuIEJ/57MDy/PYvXP67j62UuJSo447mvF6SC4TSbBbTJJvOVi9q7YQOH0+RRN/4P85z4m/7mPCWqRdnBEgb8rpv6/IaW8lPgFYuLSreWB219kdxy3crxOgAOBv2D1ys/Dapr/uLrOdp6gQ4cOZu5cbSjwJA3ZB+BofvtiER/e/wUOh4P+T/al9blH9E+tsb1rN1E0Yz6F0+ezd8UGAAIyXYR1a0N491PwT0vQ8cpK1THzxyT47Su44j9IgGdP+32i50QRqXUnwNex3viHAq8bY/bX+KhKNRJtL2pJkzaJvHX7x4y7aTzdBnXkovvPxsf/xDsUBaQmEJCaQNxV57B/0zaKZiygcPp8Ct6cRMEb3+CXFEt4d2viocCsFC0GlKoLSdnw20TIXw4Z7e1O4zaOdwabCpwKvAQ8WznrX9UhgX9oUaC8QUxqFHd8dB0Tn/iOaWPnkDt3Pdc8fxkxaVG13qdfQjQxl/ci5vJelG4vomimNQvhlg9/YMv73+EbG0FYt9aEdWtDcOsMxKnDC5Wqlagm4B9kLQ+sBcBBxxsFcCaAiGRi9fpvV3l/CRAFlIrIImNMu/oOqpTdfPx96PfwuWR2TuP9eyYw4sLRXP7fC2nXr9VJ79s3Kozoi7oSfVFXyop2s2v2Ioqmz2f7xNls+3Q6zvBgwrq0Jqx7G0LaNsfh5/nDmZRqKOJwYBKzIX+pTgtcxfFmAnwa+AyYWTnsbnyV59KADlhFgVJeo1XvLO7+6ibevvNj3hn6CSt/WsPF/+iDX6BvnezfJyyYyHM6EnlOR8pL9lH88xIKZ8yn8Mff2PH1bBxB/oSe3pLw7qcQelo2jkD/OjmuUo1aUhbk/go7N0Kky+40buF4HyOCgPcA/8q59z8FJhtjSowxuUAu8FG9JlTKDUUmhXPr+wOZ9MxUvn9pJmt/3cA1L1xOQrPYOj2OM9Cf8J6nEt7zVCr2l1H823KKps+35hr4/lfEz5fQTi2IH9iHgPTEOj22Uo1K1WmBtQAAjrMcsDHmFmNMMnABVmfA/wJbReRzEbleROr2bKeUB3H6Ojn/vrO48Y2rKN6+h5EXjWHO+N841siak+Hw8yGsUwuS77mCnI/+TfrTtxF1wekU/7acFUP+R97I8ZTtLK6XYyvl6SQ4EiISIH/J8Tf2EjVazswY87Mx5kFjTCvgFKwV+AYBG0RkhojcIyJJ9ZhTKbeV1SOTu78aQmq7ZMbf9wXvDPuUvcX76vWY4nQScmozXLdfRtabDxHdrxvbJ85m2bX/Ycv476nYf9LLdSjV+LiyYdMqTJn2XYcaFgBVGWNWGmOeNsb0AJKBsUA3rLUClPJKYXGh3PTW1fS5uxe/f7GIZy4aw4aFGxvk2D7hwbhuv4xmr95HcKsMNr3yOStueJzCGfPrrTVCKY/kyoaKMti86vjbeoGTWtDcGLPFGDPWGHOxMeapugqllCdyOB30vqMHt7x3Lfv3lPLspWOZ8eYvDfYmHJCaQNqjQ0h7/CbE14d1/xjLmrtfoKRywiGlvF5CU3D4WLMCqtoVACIyXEQWi8hPIjJaRO4QkV51G00pz5TZKZW7v76J5l2tRYXeuPlD9hSWNNjxQ0/LodmYe3ENvZy9azay8pan2fDke5RuL2qwDEq5I/Hxg4RMqyOgqnULwO3A+VjzAYzHGk1wdV2FUsrThUQFcf1rA7jowd4s+m45I84fzdpfG+6TuDidRPftRtZbDxJzeS92fjuX5df+l4J3plCxT69/Ki/myoadGzG7d9qdxHa1LQB+B7YaYzYZY741xjxjjBlch7mU8ngOh9Drxs7c/uEgRITn/zqOH16ZRUVFw12Xd4YEkXhzP5q99n+EtGvO5rETWX7dY+z84VftH6C8U1KOda+XAWpdADwGTBKRK0QkvS4DKdXYpLZNZvjEIbTqncWXj33La9e/R/G23Q2awT85ltR/30D6U7fhDAlk/X/eZPWwZ9mzdF2D5lDKdhGJEBimlwGofQHwNtbSvacDr4rIahGp9bLASjV2geEBXPvi5Vz6yHmsnLWGp88fzarZuQ2eI6RtM5q+dA9Jw/uzP28rq24bwfrH36Z0y84Gz6KUHUTEugywcSmmwrtXka9tAbDdGHOTMWaYMeYsY0wGcFldBlOqsRERul5zGnd+egP+wX68dOVbTB71IxXlDXsSEqeDqAs60/yNB4kdcBaFU39n2cD/svnNb6jYq/0DlBdIyoZ9e2D7eruT2Kq2BcBsEfnTNX9jzKY6yKNUo5fUMoFhnw+mXb9WTHrmR165+m2KCnY1eA5ncAAJgy+i+ev3E3Z6Swre+IZlA//Ljilzvf6TkWrkXFnWvZdfBqhtAZAJPCAia0TkAxF5UEQuqstgSjVmASH+DBhxMf2f7Mu63/N4+rxXWPrjSluy+CVGk/LwIDJG3olvdBgbHn+bVbePZPeiNbbkUaq+SUAoRCd7fUfAWhUAxpi+lc3+rYFngM3AWXUZTKnGTkTo+JdTGfr5YEJiQhgz8F2+fPxbykvLbckT3DqDzOfvIvlvV1K6tZDVd45i3SNvsH/zdlvyKFWvXDlQsAazv+Hm6HA3J1QAiMjwyvuWIuJjjCk2xsw2xrxqjBlWLwmVauQSmsUybMINnD6gHT+8PIsX+r/B9g07bckiDgeR53Qk640HiLvmXIp+WsjygY+y6bWJlO/Za0smpeqFKxtMBWxaYXcS25xoC8CvlfePAYtE5HcReUdE/k9ELqzjbEp5Dd8AX/7y2IVc/eylbFpewIgLRrNw8jLb8jgC/YkfdB7Nxz1AeI9T2PLuFJYPfJTtX8/GNHCnRaXqRVw6+Ph59WWAEyoAjDFTK+/7GmOysBYBeg7YCvSu83RKeZm2fVsx/MshRKdE8vqQD/jsn99Qts++lf384iJp8sA1ZD4/DN/4SPKeep+Vt46g+A97+isoVVfE6QMJzby6I2Bt1wL4WETCjDHFQCKwGxhWl8GU8lYxaVHc8dF19Li+E9PH/cxzl73O1lx7r8MH5aSR+dwwmjx4LeVFxawZ/jxr/zmWfflbbc2l1ElJyoFdWzFFW+xOYovajgJoaowpEpFWwCNYEwI9X3exlPJuPv4+9Hv4XK4b3Z9t63cw4sLR/Pb5QlsziQgRZ7aj+bgHiL/ufIp/WcqK6x9j4yufU17svR2plAdzZVv3XnoZoLYFQLmIOICBwOPGmKFYRYBSqg61OieLu7+6icSsON6+8xM+vP9L9peU2prJ4e9H3NXn0PyNB4k4qz1bP/yBZQP/y7YvZmLK7RnBoFSthMVCSBTk29ffxk61LQBexOoQeDnwWeVjwXURSCn1Z5FJ4dz6/kDOurUrs9/7lVH9XmXTCvubLH1jwkm+90qavjgc/yZx5I/8kBU3PcWuud55MlWe59C0wMswFd5XvB6zABARZ3WPG2NeBXoBrY0xxSLSFJhd9/GUUgBOXyfn33cWN75xJcXbdjPyojHMGf+bW6zoF9i8CRnP3EHKP66jYu9+cv/2ErkPjmHf+s12R1Pq+JJyoHQfbMm1O0mDO14LQLGIzBGRF0XkBhE5VUR8AIwxOys7AWKMWWmMGVTfYZXydtk9m3L31zeR2i6Z8fd9wbt3fcbe4n12x0JECO9xCs3H/h8JN17E7vkrWX7DE+S/8AllRQ278qFSJyShGYjDK0cDHK8AuAGYBmQDT2E1++8SkV9E5GURuVFE2td3SKXUIWFxodz01tX0Gd6L3z5fyDMXjWHd73l2xwLA4edL7BVn0fyNh4jq04ltn01n+bX/Zeun0zBl3tfEqtyf+AdBbKpXdgQ8ZgFgjHnXGHOvMeZMY0wkkAVcB3wPNAX+B8yp/5hKqaocTge97+zBLe9dS9m+Mp67bCyTRv5o2zTCh/ONCiVpeH+avnwvAc2S2fj8J6y48Ql2zVlsdzSljuTKhq3rMHu9q7XqRCcCWgF8CfwB7AL8gYJ6yKWUqoHMTqnc8/XNtO3biskjf+T5y19ny+ptdsc6KDDTRfr/biH1kcGYckPuA6NZ838vszd3o93RlDrElQ0Y2OhdHVhrVACISJiIXCMiE4AtWFMBrwXOAZLqMZ9S6jgCwwO48plLuPaFy9m6dgdPn/8KM9/6xS06CILVPyCsSyuavfY3Em+9mD1L1rLixifJG/URZYXFdsdTCmJSwC/Q6y4DHG8UwEAR+RLrU/4/gWVAL2NMqjFmmDFmhnGXs4xSXu6UC1pwz6SbyeiYyid//5oxA9+lcPMuu2Md5PD1IeayXmS9+SBRF3Vh+5ezWHbNf9jy4Q9UlNo33bFS4nBCYhbkLXWbwrkhHK8F4HXgFGAokGOMuc8Yo9f8lXJT4fGh3PjGlVz2yPms/nktT537Mn9MdK/r7j7hISTdeTnNxtxHUIs0Nr08gRU3PE7RzAVedfJVbiYpG/bshMJNdidpMMcrAKZiTfDzElbv/19FZIyI3Cwip4mIX70nVEqdEBGhyzUduPurm4hJjeTN2z7inWGfUlLoXsv5BqQlkP74zaQ9OgRxOln78GusuedFSla5x4gG5WUOTAvsRcMBjzcK4ExjTBRWj/9rgMlAGvAfrN7/u0Tk16PvQSlll9iMaG7/+HrOvasnv3+xkCf7vMyKWWvsjnWE0E4taDbmPlx3XMbe1fmsvOkpNjz9PqXb3efyhWr8JCQKwuO9qh9AjToBGmNWG2PGG2P+zxjT2xgTA2QAVwHf1GtCpVStOX0cnDO0J3d8cj1+gb68fOVbTPj3JEr32ruewOHEx0n0xd3JevNBoi/twY5JP7N84H8oeO9bKva7V1bViLmyYdNKTJl3/M3Vdi0AjDG5xpiPjDEP1GUgpVTdSzklieETh9B14GlMGzuHZy56lQ0L3W8onjM0CNetl9D8tf8j+NRmbH71S5Zf9xiFP/6u/QNU/UvKhvJSKFhtd5IGUesCQCnlWfwCfbn0X+dx4xtXUlJYwqiLX+PbF6ZTXlZhd7Qj+DeJI+2RwaQ/eSvOQH/W/Xscq4c9R8ny9XZHU41ZfFNwOCFvid1JGoQWAEp5meyeTbl38i20Pjebr5/8gRf+Oo6ta7fbHataIe2a0/SVe0m666/s21DAylueZv0T71C6tdDuaKoREl9/iM/0mn4AWgAo5YWCIgK55vnLuGrUJWxesYWnz3uF2e/96pbN7OJ0EHVhF7LeeJCY/mdS+MOvLBv4Xza/NYmKvfvtjqcaG1cW7MjH7Gn8RaYWAEp5KRGhXb/W3DPpZlLbJvPh/V8ydvD7FBW45+x8zpBAEof0pfnY+wntmEPBuK9ZPuhRdn43zy0LF+WhXDnWfX7jnxZYCwClvFykK5whb11Nv4fPZfmMNTzV52UWfOO+TaB+rhhS/3EdGc/cgTMihPWPvsWqO0ayZ3Gu3dFUYxDlgoBQyG/8/QBsLwBE5FYRWSMie0Vknoh0r+HrmonILhFxz48rSnkQh0PocX0n7vryRiJd4Yy7eTzv3zOBvbv22R3tqILbZNL0xeEk3zuA0oIdrLpjJOv++yb7N++wO5ryYCIO6zJA/jKMcb8OsnXJ1gJARPoDo4BHgbbALOBrEUk5zuv8gPeBafUeUikvktAsljs+uZ6zb+/G3E/m81Sfl1k1Z63dsY5KHA4i+3Si+RsPEnv1ORTNWMDyQY+y6fWvKC9x3+JFubmkbNhbDNsb96yUdrcADAfGGWPGGGOWGGPuADYCtxzndU8A84EP6zugUt7Gx8/Jefecye0fDsLh4+ClK97gi8emULbPfRfscQb6k3Dd+TQf9wBh3Vqz5e3JLB/4X3Z8MwdT0bg/xal6kOgd0wLbVgBUfopvjzW9cFWTgS7HeN0FwIXAnfWXTimV1r4Jd391E6cPaM/UV35iZL9XyV+y2e5Yx+QXH0nKg9eS8exQfOMi2fDke6y67Rl2z19ldzTlQSQoDCKTGn0/ALGr96yIuIA8oKcxZlqVxx8GrjLGZFXzmkRgHnCpMWa2iAwCnjfGhBzjOEOAIQDx8fHt33///br9RlS9Ki4uJiTkqL9e1UA2/76NP8Yup3RPGVmXppHZJxlxiN2xjs0Y/BZuIOj7xTiLStiX42LPWS2piAy2O5nyAOnbF5JctIKZKRdR4fCxO85BJ3pOPOOMM+YZYzpU95w7fFeHVyBSzWMHvA28ZIyZXeOdGzMaGA3QoUMH06tXr9pkVDaZOnUq+jtzA73ggmt289EDE1kwfin7cssZ8PTFRDWJsDvZsZ0BFTfuZ8uHP7Dl/e8IeOV7Yi7rReyVvXEGB9idTrkxk58Ik5fTvVkC0qSV3XEOqstzop19ALYC5UDCYY/HAUdrZzwT+IeIlIlIGfAaEFz59ZD6i6qUCokOZuDLf+GKp/qRt3gTT533Mj9/6P5z9DsC/Ii/5lyyxj1A+Bnt2PL+dyy/9j9sn/gTplz7B6ijiM8Ap2+jnhXQtgLAGLMfqzm/92FP9cYaDVCd1sCpVW4PAyWV/9YOgUrVMxHhtMtP4Z6vbyapRQIf3Ps5424aT/G23XZHOy7f2Aia/O0qMl8cjl9yHHkjPmDlzU9R/Ntyu6MpNyROX0ho1qg7Ato9CmAEMEhEBotIjoiMAlzAywAi8piIfHdgY2PMwqo3rD4EFZVf6+BfpRpIVJMIbnnvWi584GyWTF3Jk+e+zOLvPOONNCgrhYyRd5Dy8CDKd5ew5p4XyX1oDEWzF+nSw+rPkrKhqACza5vdSeqFrX0AjDEfiEg08BCQCCwEzjfGHBh4nAhk2pVPKXV0DqeDM4Z0IbtHJu8M+4zXbnif0we0o+9D5+Af7Gd3vGMSEcJ7nkpo55Zs/fhHtrz3Lbt+WoQj0J/QjjmEdWtNaMcWOEMC7Y6q7OSqHA6YvxSyutqbpR7Y3gnQGPMi8OJRnht0nNeOA8bVeSilVI0lZsczbMINfPPMVKa+MosVM9cw4JmLSW/fxO5ox+Xw8yVuwNnEXNaL3b8tp2jmAopmLaTwx98RHyfBbZsR1rU1YV1a4Rsdbndc1dDC4yE4UgsApZQ6Gh9/Hy78v7NpcWYz3hs+gRf+Mo4zb+nKOUN74uPntDvecTn8fAjt1ILQTi1wDf0Le5aupWjGfIpmLCB/5Ifkj/yQwJxUwru1Iaxra/ybxNkdWTUAEcG4siD3D0xFOeJw/7/lE6EFgFKqzmR0TOXur29iwiOT+O6FGSydupIrn7mYhOae84YpTgfBLdMJbplOwpC+7MvdRNHMBRTOmM+mMV+wacwX+KcmWC0D3VoT2LwJIm4+J4KqvaQcWDEbtq6DuHS709QpLQCUUnUqINSf/v/rS8uzs/jw/i945sIxXPC3s+h2XScc7j550GFEhID0RALSE4m7+hz2b95B0cz5FM1cyJb3v2PLu1PwjY2wioGurQluk4n4NK5PiV4vsTmIQN4SLQCUUqomWp2TRWrbJMbf/yUTHpnMou+Wc8WT/YhM8txr6X7xkcRc2pOYS3tSVribXbMXUTRjPtu/ms22z6bjDA0i9PQWhHVrQ2j7LByB/nZHVidJ/IMx0SlWP4C259sdp05pAaCUqjehsSFcP6Y/cz74jQn/nsRTfV7m0n+fR7uLW3t8s7lPeDCR53Yk8tyOVJTsY9e8ZRTNWMCu2YvYOWUu4u9LaPsswrq2JrRzK3zCdQpij5WUA/MnYfbtQfyD7E5TZ7QAUErVKxHh9Cva0bRzGu8N/4x37/qMRd8u57L/nE9wZOM4mToC/Qnv1obwbm0wZeXsXrCKohkLDo4qwOEguHUGYd2sSwV+8VF2R1YnwpUNf3wDG5dBWlu709QZLQCUUg0iJjWK28YP4odXZjHpmams+WUd/Z/sS3bPpnZHq1Pi4ySkbXNC2jYn8fZLKVm+3ioEZixg4wufsvGFTwlolkxY19aEd2uDf1qCx7eGNHqxqeAbAPlaACilVK04nA7OurUbWT0yefeuzxgz8F26XnsaF95/Nn6BvnbHq3MiQlBWCkFZKSRcfwH71hccHFFQMO5rCsZ9jZ8r5mDLQFCLNMRh9wSt6nDicGISm0PeEowxjaZg0wJAKdXgklslctcXN/L1U9/z46uzWT5jNVeOuJiUU5Psjlav/JvEEXvFWcRecRal2wopmrWQopkL2PbJNLaO/wGfyFBCu7QivGtrgts2x+Gnp2i3kZQD6+ZDUYE1QVAjoH9dSilb+Ab40Pehc6zJg+6ZwHOXjeXs27tz9u3dcfo2/qF0vtHhRF/UleiLulJeXMKunxdbrQPf/8qOiT/hCPIntGMLqxNhpxa6fLHdDkwLnLdUCwCllKoLTbukc8/XN/Ppv75h8qhpLJm6kitHXExcZozd0RqMMySQiDPbE3Fmeyr2l1L82wprJsJZCymc+tvBaYnDu7UhtEsrfKPC7I7sdSQ0GhMWaw0HbNHT7jh1QgsApZTtAsMDuHLExbQ8uzkfPTCREReM5sIHetP1mg6N5nprTTn8fAnr1IKwTi0wwyrYszi3shPhfPKeGQ8jPySoRdrBmQj9k2Ltjuw9XNmwcg6mvNRaLtjDaQGglHIbp5zfgvQOTfjg3s/59OGvWfztcvo/2Zfw+FC7o9lCnNbwweDWGSTc1Jd9azZSOMOaiXDT6M/ZNPpz/NMSCOvWhvCurQlolux1BVODSsqGpdOhYI01Q6CH0wJAKeVWwuJCGTzuSn56Zx5f/HcKT57zEpf/9wJOvbCl3dFsJSIEZLgIyHARf20f9m/aRtHMhRTNnM+Wd6ew5e3J+MYdmJa4DcFtMhBn4+9L0aASmoE4rH4AWgAopVTdExG6XN2BZl3SefeuT3nr9o9Z9O1yLv3XeQSGa2c4AL+EaGIu60nMZT0pKyym6KdFFM1cwPaJs9n2aeW0xJ1bWp0IO2TjCPCzO7LHE98ATFyG1Q+AvnbHOWlaACil3FZsRjS3f3w93784g8mjfmTVnLVc8VRfmnfNsDuaW/EJDyGqTyei+nSivGQfxXOXWjMRzlrIzsm/WNMSd8gmrFtrQk9viU+YTktca0nZ8OuXmJIiJNCzO2NqAaCUcmtOHwe97+xBdq+mvDPsU1656m26X9eJC/52Jr4Bnt8Rq645A/0J734K4d1PsaYl/mMlhQemJZ65wJqW+JTMgysY+sVF2h3Zs7isAoD8ZZB5mt1pTooWAEopj9CkjYvhE4cw8YnvmP76HJZPX8WVIy8huVWi3dHclvg4CWmfRUj7LFx3VE5LPGMBRTPns/H5T9j4/CcENm9ycCZC/1Sdlvi4opPBP9i6DKAFgFJKNQy/QF8u+WcfWpzVjPfv+ZxRF7/GucN6csbNXXH66BS6xyIOB0HZqQRlp5Iw+EL2rtt8sFVg89iv2Dz2K/ySYg8OLwzKSdVpiash4sC4siFvqcdPC6wFgFLK42R1z+TeSTfz8d+/4uunfmDx9yu4csTFxKTpKns1FZAST0BKPHEDzqZ0y06KfrKmJd768VS2jv8en6gwwrq0Iqxra4LbNsPhq28XB7myYc082JEPUZ47fbX+RpVSHikoIpBrnruMlmdn8cnfv+Lp81+h70PncPqAdh79qcwOvrERRPftRnTfbpQX72HXnCUUzpjPzm/nsv3LWTiCAwjtVDktccccnEFePhIjqXJa4PylWgAopZRd2vVrRUbHFN6/dwIfPTCRRd8u56+PX0RYXIjd0TySMySIiLPaE3FW5bTE85Zblwp+Wkjh978ivk5C2mVZxUDnVvhGed8kTRIUjolIhLwl0Oosu+PUmhYASimPF5EYxpA3r2bmm7/w5WPf8uS5L/GXxy6kTZ8cu6N5NIefL2GdWxLWuSWmvII9i9ZUzkS4gF1zFoOMJ6hl+qFpiV3es34DSTmw5EdM2X7ExzPnWNAeHkqpRsHhELoP6sjwiTcSlRTBGzd/yHt3T6CkaK/d0RoFcToIbpOJ69ZLyHr77zQdfS9x155LRcleNr0ygeXX/If8Fz7BVFTYHbVhuLKgohw2rbQ7Sa1pC4BSqlGJbxrLHZ9cz5TnpvHdCzNYNTuXAU/3I/P0NLujNRoiQmBmEoGZSda0xBu3sWX8D2z7ZBplO3aR/LerGn+nwfhMcPpalwGSW9idpla0BUAp1ej4+Dk57+4zuP3DQTh9nbw04E2+eHQKZfvK7I7WKPklRuO68zISbryIwh9+Y+0Doynf07hbXsTHzyoC8pfaHaXWtABQSjVaae2bMHziEE4f0J6po39iZL9XyV+y2e5YjZKIEHvFWSTfO4Di31ey5u4XKNuxy+5Y9SspGwo3Y3bvsDtJrWgBoJRq1PyD/bj80Qu4YewVFG/dzci+Y/j+5ZlUlHvJteoGFtmnE6n/voG9azexaugo9m/cZnek+uOq7GSa55mtAFoAKKW8Qoszm3PP5FtocVZzJj7+HS8NeJNt6z3zk5u7C+vckvQnb6W8aA+r7hxFyao8uyPVj4gECAqH/CV2J6kVLQCUUl4jJCqIgS/9hQFP9yNv8Sae7vMKP4//HWOM3dEaneCW6WSMvBNxOlh913MU/+G5veWPRkSsWQHzl3vk6ActAJRSXkVE6HDZKdzzzc0kt0rkg/s+Z9xN49mzs8TuaI1OQFoCGc8OxTc6nNy/vUzh9D/sjlT3XNmwfw9sW2d3khOmBYBSyitFJUdw83vXctGDvVkydSUj+73KpuUFdsdqdPziIskYeScBTZNY9+9xbPtipt2R6pYrCxCPHA2gBYBSyms5HEKvGztz63vXsn/3fp69ZCwLJy+zO1aj4xMeTMaTtxJ6Wg75Iz9k85vfNJrLLhIQAtFNPLIjoBYASimvl9a+CcO+uJG4zBheH/IBU56b1mjeoNyFI9Cf1H/fQETv0yh44xvyn/0Y01hGYiRlw5ZczH7PuoykBYBSSmGtJ3Db+IG0v6Q13zw9lbdu/5h9e/bbHatRER8nyX+7kpj+Z7L98xms+88bVOxvBJMzubLBVMDG5XYnOSFaACilVCXfAF8GjLiYCx84m/lfL+G5S19n+/qddsdqVESExCF9Sbi5H0XT/iD3/lco3+3hswbGpYOPv8f1A9ACQCmlqhARzhjShcFjB7Ajbycj+73Kqtm5dsdqdGL/cgbJ/3c1uxesYvXw5yjdXmR3pFoThxMSm0HeUo+6dKQFgFJKVSO7V1OGTRhMUEQgL1/9NrPemmt3pEYnsncH0v5zI/s2bGH1naPYl7/V7ki1l5QDxdtgl+d8D1oAKKXUUcRmRDP0sxvI6pHJx3//io8emEjZ/nK7YzUqoR1zyHjqVsp372X1HSMpWb7e7ki148q27vM8Z1ZALQCUUuoYAsMCuH5Mf866tSs/vTuPl696k11bd9sdq1EJykkjY9SdiJ8vq4c/T/GvntWZDkDCYiE02qP6AWgBoJRSx+FwOjj/vrO4+tlL2bBgIyP7jmHDwo12x2pUAlLiyXx2KL4JUeQ+8Ao7p/5md6QT58qGjSsw5Z4xssH2AkBEbhWRNSKyV0TmiUj3Y2zbS0QmiMhGEdkjIvNF5PqGzKuU8l5t+7bi9o+uwxh4/vLX+e2LRXZHalR8YyPIfOYOArNSWP+fN9n22XS7I50YVw6U7YMtuXYnqRFbCwAR6Q+MAh4F2gKzgK9FJOUoL+kCLAAuB1oBLwGjReTKBoirlFIkt0rkrs8Hk9w6kbfv+Jiv/vcdFRWe0/Pb3TlDg0j/3y2Edm5J/nMfs+n1rzynZ31iMxCHx1wGsLsFYDgwzhgzxhizxBhzB7ARuKW6jY0xjxpjHjLGzDTGrDbGvAR8AlzWgJmVUl4uNDaEm9+5ltMHtOO7F2fy+o3vU1Lk4WPZ3YjD34/Uf15HZJ9ObHl7MnnPjMeUu3/nS/ELhNg0j5kW2LYCQET8gPbA5MOemoz1Sb+mwgBd1Fsp1aB8/Jxc/ugFXPrIeSz9cRXPXjKWLWu22R2r0RCnk6R7riD2yt7smPgT6/41jor9pXbHOr6kbNi2HrO32O4kxyV2Na2IiAvIA3oaY6ZVefxh4CpjTFYN9nEh8CnQ1Rjz81G2GQIMAYiPj2///vvv10V81UCKi4sJCQmxO4ZSx7R1yU7mvrAYKqDdLdnEtY6yO1KjEvDzKoInLaA0JZpd/TthAvzsjnRUofu2027jVBbHnMaWkCZ1vv8TPSeeccYZ84wxHap7zh0KgB7GmOlVHv8HMMAYk32c13cFvgb+Vnkp4Lg6dOhg5s7VyTw8ydSpU+nVq5fdMZQ6ru3rdzL2xg/YtLyAC+8/m56DT0dE7I7VaOz8/lc2PPEO/k3iSHv8Znxjwu2OVC1TUQEfPABNWiPdrqrz/Z/oOVFEjloA2NkHYCtQDiQc9ngcsPlYLxSRblhv/g/X9M1fKaXqU1STCO745Dpa98nmi/9O4b27J1C61zOGg3mCiDPbkfboEPZv2s6qoaPYt77A7kjVEocDErMg3/2nBbatADDG7AfmAb0Pe6o31miAaolID6w3/38ZY0bWW0CllDpB/kF+XPvC5fQZ3ot5n8znhf7jKNzkuXPcu5uQ9lmkP30bFXv3s2roKPYsXWd3pOq5smFPIex077ki7B4FMAIYJCKDRSRHREYBLuBlABF5TES+O7CxiPTCevN/GXhHRBIqb7ENH10ppY4kIvS+swfXje5PwcqtPHPRq6z9dYPdsRqNoKwUMkcNxRHoz5q7n2fX3GV2RzpS0oFpgd17NICtBYAx5gNgGPAQ8DvQDTjfGLO2cpNEILPKSwYBQcA9WMMFD9x+aZDASilVQ63OyeKOT67HL9CXF654g58//N3uSI2Gf3Ismc8Oxc8Vw9oHR7Pz+3l2R/oTCY6EiAS3nw/A7hYAjDEvGmPSjDH+xpj2VUcEGGMGGWPSDvtaqrmlVbdvpZSyU2JWHMM+H0xGxxQ+uPdzPvvXJMrLKuyO1Sj4RoeT8cwdBLVMY/1/32Lrxz/aHenPXNmweRWmbL/dSY7K9gJAKaUas6CIQG4cdxU9ru/E9NfnMGbgO+zescfuWI2CMySQtMdvJqx7Gza++CmbXv3CfTreubKhvBQ2r7I7yVFpAaCUUvXM6eOg38Pn0v9/fVn9yzpG9XuNTcvdsxe7p3H4+ZLy90FEXdCZLe99R95T77nHrIEJTcHh49aXAbQAUEqpBtLxr6dy63vXsr+klGcvGcvCyW7Ygc0DidOB666/EnfNuez45mfWPjyWir32Nr2Ljx/EZ7p1R0AtAJRSqgGltW/CXV8MJq5pDK8P+YApz05zn2ZrDyYixA86D9edl7NrzmLW/O0lyop22xsqKRt2bsTs3mlvjqPQAkAppRpYeEIYt30wiPaXtuGbEVN587aP2LfHfTuLeZLoft1I+ftASpatY/Vdz1G6Zad9YVyVwwHz3bOlRwsApZSygW+ADwOe7sdFD/ZmwTdLee7S19m+fqfdsRqF8J6nkvbYzZQW7GDVnSPZu3aTPUEiXRAYBvlL7Dn+cWgBoJRSNhERet3YmcGvD2BH3k6e6TuGlT/l2h2rUQhp24yMEXdgSstZPfRZ9izJbfAMImK1AuQvwxj3G/6pBYBSStksu2dThk0YTEhUMK9c8zYz3/pF+wXUgcBmyWQ8OxRnaBCr73mRXXMWN3wIVxbs2w3b3G82SC0AlFLKDcRmRHPnp9eT1SOTT/7+NR89MJGy/W4wnM3D+btiyBg1FP/kOHL//io7pjTwxLEH+wG432gALQCUUspNBIYFcP2Y/px1a1dmv/crL1/1Jru2FNsdy+P5RoWSMeJ2gttksuHxd9gy/ocGO7YEhkJUMuS5Xz8ALQCUUsqNOJwOzr/vLK5+7jI2LNjIyH6vsmGhe68q5wmcwQGkPXoT4T1PZdMrE9j4ygRMRQNdl0/KhoI1mNK9DXO8GtICQCml3FDbi1py+0fXAfD85a/z2+cLbU7k+Rx+PjR58Fqi+nVj6/gf2PC/9zBlDXCZxZUNpgI2rqj/Y50ALQCUUspNJbdKZNjnN5LcOpG37/yEiU98R0W5+/Um9yTidOC64zLirzufnVN+Iffvr1JRsq9+DxqXDj5+btcPQAsApZRyY6Exwdz8zrWcPqAd3780k7E3fkBJkXs1JXsaESHu6nNIGt6f4rlLWX3vi5QV1t+sgeL0hYRmbjctsBYASinl5nz8nPzlsQu57JHzWTZtFc9eMpYtq7fZHcvjRV3QmZR/XMfelXmsHjaK/Zt31N/BkrJh1xbMrq31d4wTpAWAUkp5iC7XdODmt69m9449jOz3KkunrrQ7kscL79aG9P/dQum2ImvWwNx66nB5YDigG7UCaAGglFIeJPP0NIZNGExkUgSvXv8eP4yepZMGnaTgNplkPHMnGMOqoc+ye+Hquj9IWBwER7lVPwAtAJRSysNENYngjk+uo3WfbL589FveG/4ZpXtL7Y7l0QIzXWQ+OxSfiBDW3PsSRbPqdtSFiFiXATYux1S4xwRPWgAopZQH8g/y49oXLqfP3b2Y9+kCXuj/BoWbiuyO5dH8EqLJHDWUgPQE1v5jLNu/mVO3B3BlQ+le2JJbt/utJS0AlFLKQ4kIve/owXWj+1OwcivPXPQqufPW2x3Lo/lEhJD+9O2EtGtG3pPvUfDet3V3iSWxOYi4zWUALQCUUsrDtTonizs/vR6/IF9eHPAmP4//3e5IHs0Z6E/qf24k/Mx2bH71Sza+9FmdzBoo/kEQk+o2HQG1AFBKqUYgoXkcwyYMJqNjCh/c9zmf/WsS5WU6aVBtOXx9aHL/1URf2oNtH//I+sffoaK07OR3nJQDW9dh9tXfvAM1pQWAUko1EkERgdw47ip6XN+J6a/PYczAd9i9Y4/dsTyWOBwk3noJ8YMvpPC7eax9aAzlJztroCsbMJC/rE4yngwtAJRSqhFx+jjo9/C59H+yL6t/Wceofq+xcVmB3bE8logQN+Bsku4dQPGvK1hz9wuU7TyJFRpjUsAv0C36AWgBoJRSjVDHv5zKbe8PZH9JKc9dOpYFk+x/w/FkUX06kfqv69m7ZiOrho5i/6bazcQoDqfVGTBvqe3zN2gBoJRSjVRqu2Tu+mIwcU1jGHfTeCaP+pGKCp00qLbCurQi/clbKNtZzKo7R1GyKr92O0rKgT07oXBzneY7UVoAKKVUIxaeEMZtHwyi/aVtmPTMj7x120fs273f7lgeK7hVBpmj7gQRVt/1LLvnrzrxnRycFnhJ3YY7QVoAKKVUI+cb4MOAp/vR96HeLJi0lOcue51t6+tx4ZtGLiAtkcxnh+EbHcaa+16icMb8E3q9hERZUwPb3BFQCwCllPICIkLPwZ0Z/PoAduYXMrLvq6yctcbuWB7LLz6SjJFDCWiaxLp/vc72iT+d2A6SsmHTCkyZfVM4awGglFJeJLtnU4ZOuIHQ6GBeueZtZrz5i+2d0TyVT3gwGU/eSkiHbPJGfEDB25Nr/rN05UB5KRTUw8JDNaQFgFJKeZnY9Gju/PQGsns15dOHv+ajByZStt89FqjxNI5Af9IeGUxE7w5sfv0r8p/7GFNegwmYEpqCw2nrcEAtAJRSygsFhPpz3ZgrOOu2bsx+71deuvJNdm05ifHtXkx8nCTfdyUxfz2D7RNmsP7RN6nYf+xZA8XXH+IybJ0WWAsApZTyUg6HcP69Z3L1c5eRt3AjI/u9yoaFG+2O5ZHE4SDxpn4k3NSXwqm/k/vAK5Tv3nvsF7myYUceZo89qzhqAaCUUl6u7UUtuf2j6wB47rLX+XXCQpsTea7Yv55J8v9dxe75q1h99/OUbt919I2TKocDbrSnFUALAKWUUiS3SmTY5zeScoqLd4Z+wsQnvqOiJtey1REie59G2iOD2be+gNVDR7E/f2v1G0YlQUCIbZcBtABQSikFQGhMMDe9fQ2dr2zP9y/NZOyNH1BSdJxmbFWt0E4tyHjyVsp37bFmDVy54YhtRBzWZYD8ZRjT8MWWFgBKKaUO8vFzcvmjF3DZf85n2bRVjLr4Nbasrt28994uqEUaGaPuRHydrL7rOYp/W3HkRq5s2LsLtuc1eD4tAJRSSh2hy9UduPntq9mzs4SR/V5l6dSVdkfySAGpCdasgbGR5N7/MoU//v7nDVxZ1r0NwwG1AFBKKVWtzNPTGDZhMFHJEbx6/Xv88MosnTSoFnxjI8gYeQeBWSmse+QNtn0+4+BzEhQOkS5b+gFoAaCUUuqooppEcPvH19HmvBy+fOxb3hv+GaV77Zu+1lP5hAWT/sQthJ7egvxRH7F53NeHiqmkHChYjSnd16CZtABQSil1TP5Bflzz/GWcd88ZzPt0AS/89Q0KN9kzdt2TOQL8SP3X9UT26UTBW5PIH/mhNWugKxsqymFTw15msb0AEJFbRWSNiOwVkXki0v0427cWkR9FpERE8kTkYRGRhsqrlFLeSEQ4+/buXDemPwWrtvLMRa+SO2+93bE8jjidJN1zBbEDzmb7l7NY98g4KiKagNMX8ht2eWBbCwAR6Q+MAh4F2gKzgK9FJOUo24cBU4DNwGnAncC9wPAGCayUUl6uVe8s7vz0evyCfHlxwJvMGf+b3ZE8joiQMPhCEm+9hKLp81n70GuUR6Q3eD8Au1sAhgPjjDFjjDFLjDF3ABuBW46y/VVAEDDQGLPQGPMx8AQwXFsBlFKqYSQ0j2PYhMFkdkpl/H1f8Nk/v6G8TCcNOlExl/WkyYPXsGdRLms+LKB0UwGmeHuDHd+nwY50GBHxA9oDTx321GSgy1Fe1hmYbowpqfLYJOARIA3Qxa2VUqoBBEUEMvj1K/nysSlMe20OG5cX0P6SNnbH8kBO9p59Nts/ncaSNQFkBXxP1vWXN8iRbSsAgBjAidWcX9Vm4OyjvCYBOHw6pc1VnjuiABCRIcAQgPj4eKZOnVrLuMoOxcXF+jtTyo2Fd/fnVGnO/HErWDkr1+44HiwQgM2vzWdjRsxRt6rLc6KdBcABhw8qlWoeO9721T1uPWjMaGA0QIcOHUyvXr1qEVHZZerUqejvTCk31wtK7tir0wafpH2rVhGUkUJ4SuxRt6nLc6KdBcBWoBzrk3tVcRzZKnDApqNszzFeo5RSqp4FhgcQGB5gdwzP1qR9gx7Otk6Axpj9wDyg92FP9cYaDVCdn4DuIhJw2Pb5QG5dZ1RKKaUaK7tHAYwABonIYBHJEZFRgAt4GUBEHhOR76ps/y6wBxgnIq1E5FLg/4ARRuenVEoppWrM1j4AxpgPRCQaeAhIBBYC5xtj1lZukghkVtm+UER6Ay8Ac4EdwNNYhYRSSimlasj2ToDGmBeBF4/y3KBqHlsA9KjnWEoppVSjZvclAKWUUkrZQAsApZRSygtpAaCUUkp5IS0AlFJKKS+kBYBSSinlhbQAUEoppbyQFgBKKaWUF9ICQCmllPJC4k0z6IrIFmDtcTc8JBworKc49X2Mutzvye7rZF4fg7VwlLJXQ/xfsJsnfI92ZmyoY+s58dhO9JyYaoypfnlBY4zejnIDRnvqMepyvye7r5N5PTDX7r8DvTXM/wW7b57wPdqZsaGOrefE4762zs6Jegng2L7w4GPU5X5Pdl8N8XNU9csbfoee8D3ambGhjq3nxAbiVZcAlOcRkbnGmA5251BKKXdQl+dEbQFQ7m603QGUUsqN1Nk5UVsAlFJKKS+kLQBKKaWUF9ICQCmllPJCWgAopZRSXkgLAOWxRORzEdkhIh/ZnUUppewiIk1EZKqILBaRP0Tk0hq9TjsBKk8lImcAIcBAY8zldudRSik7iEgiEG+M+V1E4oB5QJYxZs+xXqctAMpjGWN+AHbZnUMppexkjNlojPm98t8FwA6sKYOPSQsAZQsR6VHZhJ8nIkZEBlWzza0iskZE9orIPBHpbkNUpZSqV3V5PhSRDoAvsP54x9UCQNklBFgIDAVKDn9SRPoDo4BHgbbALOBrEUlpyJBKKdUA6uR8KCLRwJvADaYG1/e1D4CynYgUA7cbY8ZVeWwOMN8Yc2OVx1YAHxlj7q/yWK/K12ofAKWUx6vt+VBE/IEpwBhjzFs1OZa2ACi3IyJ+QHtg8mFPTQa6NHwipZSyR03OhyIiwDjg+5q++YMWAMo9xQBOYPNhj28GEg58ISLfAh8C54vIBhHp3HARlVKqQdTkfNgV6A9cLCK/V95aH2/HPnUaU6m6dfj1Kan6mDHm7IaNo5RStjnq+dAYM4NafKDXFgDljrYC5VT5tF8pjiOrYKWUaszq7XyoBYByO8aY/VgTWfQ+7KneWL1flVLKK9Tn+VAvAShbiEgI0LTySweQIiKnAtuNMeuAEcBbIvIzMBO4GXABL9sQVyml6o1d50MdBqhsUTl874dqnnrDGDOocptbgfuARKwxsncZY6Y1UESllGoQdp0PtQBQSimlvJD2AVBKKaW8kBYASimllBfSAkAppZTyQloAKKWUUl5ICwCllFLKC2kBoJRSSnkhLQCUUkopL6QFgFJKKeWFtABQSimlvJAWAEq5MREZJyJfestxT5an5lbKDroYkFLubSjWut9uR0SmAguNMbfbnaUKt/15KeVutABQyo0ZYwrtzuBJ9OelVM3pJQClbCYiPURktogUi0ihiMwRkVaVz/2pSVtEgkXkzcptN4vI/SLypYiMq7LNVBF5UUQeFZGtIlIgIk+JiKPy+T4iMl1EdojIdhGZJCI5J5h5HNATuE1ETOUtTUT8RWRkZba9ld9Xtxrs75iZK7c57r6r/ryO9XOtfF5E5D4RWSUiJSKyQESurkHW5iIypTLDKhE5T0T2ichZJ/AjVMp2WgAoZSMR8QEmADOAU4BOwCig/CgveRrrjfcS4MzK13SvZrurgDKgC3A7MAzoX/lcMDAS6Aj0AgqBL0TE7wSiDwV+Al7HWp40EVgP/K/yONcDbYEFwDcikliDfR4rMyey7xr+XP8D3ADcBrQAHgNeEZELjhZQRJoBvwCLgFbAncCrgB/wRw2+R6Xchi4HrJSNRCQK2Ab0Msb8WM3z44AYY8yFIhICbAeuNca8X/l8MLABmFBl3fCpgL8xpnOV/UwB1hpjBldzjGCgCOhpjJlx+HGPkX0qVfoAVO5nBzDYGPNm5WNOYDnwnjHmoePs66iZa7rvA7mBazn2zzUY2AqcY4yZXuXxkUBzY8z5R8k5CSgwxlxT5bHXgHONMclH+/6UckfaAqCUjYwx24FxwCQRmSgiw0WkyVE2zwR8gZ+rvH43sLCabecf9nU+EAcgIpki8m5l83URsBnrXJBS3UFF5KrKZvQDt+paHKrmm1klXzlWS0GLGuzrqJlrsu+qavBzbQEEYLUgHMwD3FJ5rOp+Dk2Ac4BnDntqP/rpX3kgLQCUspkx5jqsJuppQF9guYicW82mB3q316TZrvTww3Do//sXQCxwU+Vx22I1vR/tEsDnwKlVbnOPst2x8h147Fj7Olbmmuz7zw8e++d6YL8XHZanJdabfHXaYV1COLzgagP8fpTXKOW2tABQyg0YY/4wxjxhjOkFTAUGVrPZSqw3yY4HHhCRIKxr0TUiItFADvCoMeZbY8wSIJRjjAgyxuwyxqysciupfGo/4Dws337gYMe8ymb6zsDi4+zreI6776NkP9rPdTGwD0g9LM9KY8zao+yuAuuc6VslQ1esPgu/1/D7UMpt6DBApWwkIulYn8Q/B/KADKxPlC8dvq0xplhExgJPiMhWYCPwENabUk078+zAuvZ9o4isB5KAJ7FaAE5ULtBRRNKAYqz+CS8Bj1fmWwPcBcQDL9Zi/wcZY3aLSI33fbyfqzFml4g8BTwlIoLVShACnA5UGGNGVxNjHlYR8riIPAO0Bp6ofE4vASiPowWAUvbaAzQHPsTqvLYZeIdDbyyHuwerF//nWG+6z2C9Ce6tycGMMRUi0h94FqspeyVwN/BxLbI/BbyB9Wk6EEgH/lb53OtABPAb0McYs7EW+z/ciey7Jj/Xv1c+fg9WYVCE9Un+f9Ud3BiTLyI3YI0WuA6YglV8PIr1c1TKo+goAKU8mIj4A2uBJ40xT9udx9uIyD+xRhJ0sTuLUidKWwCU8iAi0hbrGv7PWNfu/1Z5/4GdubxYG7T5X3ko7QSolOcZjtX8/T1W838PY8wGeyN5rVPQDoDKQ+klAKWUUsoLaQuAUkop5YW0AFBKKaW8kBYASimllBfSAkAppZTyQloAKKWUUl5ICwCllFLKC2kBoJRSSnkhLQCUUkopL/T/bAM3GUSxrN4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.semilogx(q, catNq/Nq, color=color_list[12])\n", + "plt.semilogx(q, Nq_truth/Nq, color=color_list[8])\n", + "plt.semilogx(q, Nq_mock/Nq, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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1rN5p0QfnEmoLsfOlfQlslUgW8Qa5U2v9eyAIoLXeDaxKWKuSiQytCyFM1lQdWzGYnsbNGU3+xFy2PC2z14ejeIP8nFJqHNC9ykDfa+ulKlmTXAhhMl9NAE8M5Vl7UkpR+sG5lL93gtqT9YlrmEgK8Qb5V4E/AgVKqbuVUn8A9iesVclEeuRCCJPFWp41koW3zAFg67My6W24iTnIO2qtXwVcD/wdMBPYDNyT2KYlCQlyIYSJtNbGgilxzFjvLmd8FhMvKWLzUzulZOswE3OQa63DwCe01m1a6ye01t/VWv+v1npYzgaTNcmFEGZqbmwh3B4ecI8cYNbV06gqr8FfOyz/XI9Y8Q6tr1NKfSWRDUlqUt1NCGGS89eQDzzIM0cZl9A2dbymGB7iDfKZwJeUUieVUo8rpb6jlLopkQ1LKhLkQgiTdJVnHeDQOtC1nnlTtW/AryWSR1wFYbTWNwMopTwYNdZnY5w3fz5xTUsisia5EMIkvo6Vz7wJ6JF7843X8EmPfFiJt7JbKfD3QDawE/ip1vp3iWxYUpEeuRDCJJ2hO5DLzzp58zwANFVJkA8n8Q6tPwY8DXwNI8ifUkpdl7BWJRtZk1wIYRJfbcc58uyBD607M9Ox2i00VcnQ+nASV48cqNVaP9bx/S6l1PPAOuCVxDQryUiPXAhhEl9NAFeWE6vdOuDXUkrhyXXLZLdhJt4eeblS6h86rikHaASG70lkCXIhhEkSUQymO2+eB59MdhtW4g3yNOB+4LhS6hVgD7BWKTUhYS1LJrImuRDCJL5qf0JmrHfy5EmPfLiJd9b6bQBKKTcwt9vtIaXUeK11ccJamAy6V3ez2c1tixBiRPHV+Cmcmp+w1/Pmezi971zCXk+Yr88gV0pZtdah3h7XWvuBdztuw5esSS6EMElTjZ9JucUJez1vnhtfjZ9wWGOxqIS9rjBPfz1yn1JqJ7Cl22231rp90FuWTGRNciGECULtYQJ1zXhyEje07s1zE24P09zQnJCZ8MJ8/QX5p4AFwCLgbiATaFVK7eZ8sG/VWm8Z1FaaTRZOEUKYoLMmeiKuIe/k6byWvNovQT5M9BnkWutHgEc6f1ZKTcEI9c5wvxPw9vc6KU/WJBdCmOB8edYEzlrvrO5W5YMpiTv3LswTUwBrrQ8ppc5gzHafijF7vXIwGpZUpEcuhDDBoAR5V711mbk+XER1+ZlSKkMp9TGl1LNAFfBD4DhwDTA23p0rpT6vlNqplGrsuL2rlLoh3tcbNBLkQggTdNZZT2SQdx9aF8NDf7PW7wXuAFYDFcCTwA+01psStP9TwD8AhzA+VNwLPKOUWqS13pmgfQyYstnRsia5EGKIdfbIvQk8R+7KcmKxKikKM4z0N7T+B4wA/zLwB611WyJ3rrV+tsdd31FK/S2wBKOGe/KQ6m5CiCHmq/FjsVlIz0hP2GtaLFKmdbjpb2i9DHADvwaalFJblVK/VUp9Vim1WCnlSFRDlFJWpdRdgAfYkKjXTRgJciHEEPPV+PHkuBJ+vbcnzyNBPoworXX/Gyk1CWOW+kLOz1rPAYLAHq31wrgboNQcjIIy6YAP+KjW+sUI292PURaWwsLCRY899ljPTfrk8/nweDzxNpMFp9+k3WJn16gr4n6NVDHQYzWSyLGKjRyv6Pl8Pvb+/jjN1S1c+W+LEvraG3+8izZfkOXfi/tPd1IZKe+rlStXbtFal/a8v79z5D8BngHe0VofAZ7o9lgxUIoR7gNxAJgPZAEfAv6klFqhtd7dfSOt9QPAAwClpaV6xYoVMe2krKyMWJ9zwf5f2wttzQN6jVQx0GM1ksixio0cr+iVlZWRTjqZxZkJP2Znnqvn0LvHhs2/xUh/X/V3jtwFPAqkKaVexFiD/DWtdbPW+hhwDPjrQBrQcd79cMePm5VSi4GvYhSjSR4OF/hqzW6FEGIE8dUEyCnKTvjrevKNMq1aa5SSMq2prs9z5Frrv9VajwNuwJj09n2gWin1nFLqk0qpwagmYMG4Pj25ONLlHLkQYkj5avx4E3jpWaeMfA+hthDNjS0Jf20x9KK6jlxr/Z7W+jta69nAPGAd8AnglFLqbaXU15VSMV9PrpT6D6XUMqVUsVJqjlLqh8AK4OFYX2vQyWQ3IcQQam8N0epvS+g15J26riWvkglvw0HM65FrrQ9rrX+itV4OjAMeBK7AqMUeq1HAQxjnydcCi4EPaK1fjuO1BpesSS6EGEJtTcbfmkTWWe/UeV26XEs+PAyoRrrWugojyB+M8/mfGMj+h5SsSS6EGEJdQZ6b+IVNpEzr8BJXkCul/g74NNAA7Oq8aa3LEte0JNN94RRZk1wIMchaGzuDfBCH1iXIh4V4e+RfAFYBLcBsYA5wD0YBmeGps0celPPkQojB19ZoFNIcjKF1d7YTZZEyrcNFvEG+HajWWvuAs8CahLUoWcnCKUKIIdTaNHg9covVgifHJT3yYSLmyW4dfgi8qpS6SylVksgGJS0JciHEEGptDOJw2klzJawS9gWkTOvwEW+QPwTsBi4DfqeUKldKvZO4ZiUhCXIhxBBqaxycS886efPd+KpkaH04iHdovVZr/Tfd71BKjUpAe5JXV5AHzG2HEGJEaG0M4h6EGeudvHluqsprBu31xdCJt0e+USn16e53aK3PJqA9yctqB4tVeuRCiCHR2hQc1B65J89NU5WPaBbOEskt3iCfBHxbKXVUKfW4Uuo7SqmbEtmwZKOUMi5BkyAXQgyBQR9az/PQ3haipal10PYhhkZcQ+ta65sBlFIejMvPZgNXAc8nrmlJSMq0CiGGgNaa1sbgoBSD6XS+upsfZ0b6oO1HDL6YeuQdhWBQSs1SStm01j6t9Uat9e+01l8ZlBYmEwlyIUQC6OYm9On9vT7e0tSKDumusB0M3vzOojAy4S3VxTq0vrXj6w+BPUqp7Uqph5VS31RK3ZjgtiUfCXIhRCJsfQFe/w06FHntBl+NcVnY4A6tS5nW4SKmofXOEqy9DK1fDbyQ4PYlF4cT/HVmt0IIkcJ0OAwnd4MOg78eMi5eDXooglzKtA4fcU12U0o9qZTK6KjsNhrwA19JZMOSkvTIhRADVX0MWpqM7/31ETfxVRuXuQ5mkLtzXCglK6ANB/HOWp+stW5USs0G/g2jMMz/JK5ZSUqCXAgxUCd2nf8+EHmEr6tHPojnyK02C+4cl6xJPgzEG+QhpZQFuBf4D631lzHCfHhzOCEUlDXJhRBx0VrDiZ1QOMm4w9d3kLuzB2/WOkiZ1uEi3iD/FcbEt9uBZzruG7yPjslCVkATQgxEwzlorIKShZDm7nVovanGj91tw+awDmpzvHluGVofBvoMcqVUxHeR1vp3wApgjtbap5SaDGxMfPOSTPc1yYUQIladw+rj54A7u9fJs75qPw6vfdCb48lzS498GOhv1rpPKbUT2NLttltr3a61ru/cSGt9GPjEYDUyacjCKUKIgTixE/KKUO4stDsbmqojbuar8ZOWMfhBbvTIJchTXX9B/ilgAbAIuBvIBFqVUrs5H+xbtdZbBrWVyUKCXAgRJx1ogOrjsLCj5IY7G84eiritvzaAwzs4y5d2583z0NYcpNXfRpp78PcnBkefQa61fgR4pPNnpdQUjFDvDPc7AW9/rzNsSJALIeJ1crfxdfwc46s7C4It6LZmVOfflg6+Gj+5YzMHvUmerqIwPtLcOYO+PzE4YprsprU+hFH0ZQfQBKQBlYPQruQkQS6EiNeJneDNh6yOFZ/d2cbXHufJw6Ew/trAkA2tAzTJuuQpLaogV0plKKU+ppR6FqjCKNF6HLgGGDuI7Ususia5ECIOuq0ZzhyEojnGSorQLcjrL9jWX9eM1gzN0Hq+VHcbDvocEldK3QvcAawGKoAngR9orTcNQduSj6xJLoSIR8U+CIegaM75+zyRe+Sd15APZY/cJ0VhUlp/57b/gBHgXwb+oLVuG/wmJS+lFFrWJBdCxOrELkj3QH7J+fucGaBUr0HuGIIg7ywBKyugpbb+htbLMAq9/BpoUkptVUr9Vin1WaXUYqXUyJvmKGVahRAx0KF2OLUXxs9GWc7/yVUWK7iyLhpa77wcLC1j8P+8Wu1WXNlOGVpPcf3NWl8FoJSaCJQCCzFmq98G5ABBpdQerfXCwW5o0pAgF0LE4txhoxpk92H1Tu4s8NdecNdQDq2DcQmaXEue2qK6bExrXQ6UA0903qeUKuZ8uI8cEuRCiFic2AU2B4yedvFj7myoPnHBXb4aPxarwu4amqt6vXluGVpPcfHWWkdrfUxr/Vet9bcT2aCkJ0EuhIhS1yIpY6ajbBGGyjvKtGod7rrLVxPAne1CWdSQtFHKtKa+uIN8xJIgF0JEq+YkBBqgaG7kx93Zxmz2lvM9Yl+Nf1CXL+1JhtZTnwR5rCTIhRDROrELlAXGzYr8uDvL+Npt5rqvxt81m3woePPctPrbaGuW5ZlTlQR5rGRNciFEtE7shMKJqPRegjlCURhfTWBIg9yTL5egpToJ8ljJmuRCiCjoxiqoP3O+tnokEcq0mtEjBykKk8okyGMla5ILIaJxsmPt8d7OjwOkuY2KkR1BHmxpp6WpFU+eawgaaPDmSZnWVCdBHitZOEUIEY0TuyB7DMqb2+smSqmumesAvlojTId0aF0WTkl5EuSxkiAXQvRDtzRBZXnfvfFO7izwdQR5zdAHuVfKtKY8CfJYSZALIfpzcg9oHbmaW0/ubAjUA+fLs3qHMMhtaTacGekytJ7CJMhjJUEuhOjPiV1GQOeM639bdzYEGtGhdnw1xhLJ7tyhO0cOxnKmci156pIgj5WsSS6E6INub4PT+y9ce7wv7mxAQ6DBlKF1kDKtqU6CPFayJrkQoi+n90Mo2PdlZ911Kwrjq/VjS7OR5h7ahSWlTGtqMy3IlVLfUkq9r5RqVEpVKaWeV0rNNqs90VJKGZegSZALISI5scsYuRs1Obrtu11L7qsO4M1zR9eTTyAZWk9tZvbIVwC/ApYCq4B2YI1SKsfENkVHyrQKISLQ4RCc3A3jZhnrjUejW3W3oS4G08mb56alqZVgS/uQ71sM3NCskxeB1vra7j8rpT4GNACXA8+b0qhoSZALISKpPAqt/uhmq3dQ9jS0w2X0yGv8XZXWhlLXteTVPnLGZQ35/sXAJNM5ci9Ge+r629B0EuRCiEhO7ASLDcbOiO15HUVhzOyRAzK8nqJM65FH8HNgO/BupAeVUvcD9wMUFhZSVlYW04v7fL6Yn9ObGfVNuIONbE7Q6yWbRB6r4U6OVWyG9fHSmksq3ieQlsvudzbG9NTZLSEcTadorHRQG6ihrKxsSI9V3YlGADa8uZHy+t4r0SWrYf2+ikJSBLlS6qfAFcAVWutQpG201g8ADwCUlpbqFStWxLSPsrIyYn1Ob/SGs3Byd8JeL9kk8lgNd3KsYjOcj5euOw3Hn8Z5yU2smLo0tudurKRl71bC7XZmLpjBihVLhvRY1U6u5+1/3U7x6BIuW7FwSPaZSMP5fRUN04fWlVI/A+4GVmmty81uT1RkaF0I0dOJXYCCcXFcfOPKxlfXAgz9NeRwfuEUGVpPTaYGuVLq58BHMEJ8v5ltiYmsSS6E6OnETsifgHJlxP5cTzY+v/Hn2JMztFXdAOzpNtK9aXIteYoy8zry/wXuw+iN1ymlRnXcPGa1KWqyJrkQohvtr4Oak9EtkhKJu1uQmzBrvXO/sgJaajKzR/45jJnqa4Ez3W5fN7FN0ZE1yYUQ3Z3oXHs8+svOLuDOxhcwisCYMbQOHWVaJchTkpnXkQ9t6aJE6qq33mJuO4QQA6J3vgYH34WFN0LJwvgrqp3YBZmFqMzC+J7vysTnNwrImDG0DkZ1tzP7K03ZtxgY0ye7pSRZOEWIlKfbmmHXGmhuhPV/gld+ga6tiP11WgNw9lD0tdUjUBYrvtY00l0WbGnm9K+8eVKmNVVJkMfDkW58laF1IVLXoY0QbIHrvghL74L6s/D8j9CbnjTCOVoVe0GH4x9W7+BrScNj4gwhb56b5sYW2lulTGuqSYrryFOOnCMXIqXpcAj2lkHhZFR+MeQXoyfMg20vwv71cHQLetFNMPlSlOqnv3NiJzgzIH/CgNrkC1jxuM0L0a4yrTV+ssdkmtYOETvpkceja2hdglyIlHRsG/jrYPaqrrtUmht12Yfhxr+HjAJ451F48Wfo6uO9vowOBeHUPhg/u//A74fPp/CkB9FaD+h14iXXkqcuCfJ4yJrkQqQsrTXsfgMyC2HczIseV7nj4ANfhmUfM8L+hZ+iNzyGbokwo/vMIWhvjf+ys258TSE8rnZoNWfmuLfbwikitcjQehyUUmip7iZEajp7CGpPwdK7eu1FK6Vg0mL0+Nmw41VjGP7YdvSC62Ha5eeXKD2xC2xpMHrKgJoUDoXxNwbxuDX46yHdO6DXi4cnvzPIpUeeaqRHHi8JciFS0+61RlBOXNzvpsrhRC2+FW75B8gdB5v+Cs//GH3uCFqH4eQuGDcDZbUPqEmB+mZ0GDzuMPjMWQCya2i9SoI81UiPPF4S5EKkHF13Gir2wYIbULbow1dljUZf83k4vgPefxpe/jnhwqloXyPWxQkYVq8xwtPjDkPAnCB3OO2kuR3SI09BEuTxcjjlOnIhUs2eN8HmgGlXxPxUpRT1aRM5ELyRg69v5OC2auy2HL5957QB/yH11Rh/SzweizG0bhJPnlvOkacgCfJ4OZym/ocTQsRGBxqgfLNxjjs9ujKorYE2yjcd5+Bb5Rx4q5xzh6oAyCjwMGbOeA5vquDYrhomLxnYOe2uHnm+x7ShdTAmvMms9dQjQR4vh0uG1oVIJXvXGYVbZq7odZNwWHNm3zkOrD/CgbeOcHTzSUJtIWxpNiZeUsQlH57PtOWTGDU1n7ZAkO8u+BF71hxg8pLiATWtK8gLMk0bWgfjPHlleY1p+xfxkSCPl5wjFyJl6GALHHgHJsxDefMueKyxsqmrx33w7fKuHuno6QUs+8QlTF02kYmLi7CnX3hOPc3tYMrSEvasOcjN/3hN/HXaMYJcWRSughw4dyju1xkoT56bI5t6v25eJCcJ8nh1rkkeCg54xqoQYpAdfNdYdnj2VRfcveGhzTz5jy8B4Ml1MXXZJKYtm8jUZRPJKOh/uHzWVVN58rsvUXmkmsLJ+XE3z1cdwJ3jwuLNgWMNRuU5E3jzPQTqmwkFQ1jtVlPaIGInQR6v7iugOSXIhUhW58uxTkLlXVhGdeOjWxkzo5A7f3QzY2aOwmKJrVc9c7UR5HvWHBxYkNf4jeVL3VmgNQQa4n6tgegsCuOr8ZM5KsOUNojYyXXk8ZIyrUKkhmPbLyrHCtBY6aNiz1nm3ziLcbNHxxziAFmjMxg7axR71hwcUBObavx4cl3gzjbuMGkibee15HIJWmqRII+XBLkQSU9rDXvWdpRjnXXBYwffOgLAtCsnDWgfs1ZP5fjWU10T1uJxvkfeGeTmTHjrqu5WJZegpRIJ8njJmuRCJL+zh6DmFMxaeVE51v3rjuDNczNm5qgB7WLm6qnosGbfm4fjfg1fdbehdTAtyM/XW5ceeSqRII+X9MiFSH6734hYjjUcCnNg/RGmLZ8U15B6d+Nmjyaj0MvetfENr7e3ttPS1Ion14VyOMFuXo0KWQEtNUmQx0vWJBciqRnlWPfCjOUXlWM9ufM0gfpmpq+YPOD9KKWYddVUDqw/Qntr7OuJ++s6qrrldhSpcWeBv3bA7YpHmtuBw2mX6m4pRoI8XtIjFyK59VGOdf+6IygFU5dNTMiuZl09lVZ/G4c3Hov5uZ3D2F1B7slOgjKt0iNPJRLk8ZI1yYVIWl3lWCdfFrEc64F1hxk/byzubFdC9jd5SQkOpz2u2etdVd06zk/jyjbtHDlImdZUJEEeJ6WUVHcTIlnt6yzHeuVFD/nrApzYXsH0Ac5W786ebmPqsonsXXvQmCkfg84FU7zde+Stfizh2IfpE8GT55EeeYqRIB8ICXIhkk5XOdaieaiMi4u0HHyrHK1JyPnx7maunkr96UbO7DsX0/O6euTdz5EDaSFz/rYYPXI5R55KJMgHQoJciORzaKPx/7JHAZhO+9cdwZXlZPzcMQnd7cyVU1CKmIfXfdV+bA4raR6HcUfHteRp7SYFeb4bf22AUHvYlP2L2EmQD4SsSS5EUtHhEOwpM8qx5hdf9Hg4rDmw7jBTl03EYk3snz9vvoei+WPZE8VlaDoUJtwWBDqKweS5zy+60hHk6e3m/G3x5HnQGvy1MryeKkZ8rXWtNYG9x0gbk4ctO8Y1hWVNciGSy7HtxqVbl34o4sNn9p2jqdrP9CsTO6zeadbqabz0ozdoONdEZuH5vyfhtiDN+0/g33UE/65yAnuOYklzMP2x752v6tbJlQko04bWM/I7yrRW+aNaOEaYb8QHefBMDeVf+jmj7r+Z/DsjD8X1StYkFyJpdJVjzSiA8bMibrO/zKi+Nm154ia6dTdz9VRe+tEb7Hl5D3NmZODfVY5/VznNB46jg8aKZmklo3HNLMa3+QDNhyvw1QTw5JyfPa+sdrTTS5ppPfLO6m5ynjxVjPggd4zJwzljAvVrNscR5HKOXIikcfawUY516V0XlWPttH/dYcbOGkVGgSehuw7WNhHYdYTwziN4nYpNP3oB7ygfWC04p44n97bluOdMwjWrBFumm2BVPfvv+h6BPeX4avwUTr5wjXTc2aT5zJvsBlLdLZWM+CAHyF5dyulfPknzkdM4J8UwAUbWJBcieeyJXI61U3NjC8e2nmLl/UsSsrvWk5VUPf4G/p1HaKuoAkCl2SkZm8/eYzD23z9C1vxJWJxpFz3Xnp+FvTAb367yrnPkF3BnkdZgTnU3WQEt9chkNyBzxQKwWqhfuzm2J3Zfk1wIYRpdfwZO7YlYjrXT4Q3HCLeHmZaA8+PhtiDH/+l3NJRtI62ogFH338ykX36Fmc/+kCXfu532ds1pnzViiHdyzZ5I465jBFvaLzxHDuDOJr09EPM16YmQ5nFgS7PJ0HoKkSAHbFkevItnUL92CzocwyUXUqZViOSw502j2mKEcqyd9q87TLo3jeKF4wa8u8o/vULriUqKvncfxf/+GfLvXIVrZjEWu42Jl0wg3ZvW7yIq7lkl+DqWC40U5FYdMuWqGKWUVHdLMRLkHbJWL6K9ugH/jhiWIpQgT3q6qRr97hPo7S+j24NmN0cMAh1ogCPvw5TI5VjBmAi3f91hplxegtVuHdD+AvuPU/XEG2Rffxne0ukXPW5zWJm2fBJ71xwkHO69R+2aXUJzyLjkzJPbo1Rs53KmPvOWM5Wh9dQhQd4hY+lsLK406tfEMLwua5InLR1oRG98Ap76dzj0Lmx/GZ77T/TZ+NeMjrkNZw+hj7xnfG2qMa5xFgmltYZtL3aUY13R63bnDldTf7pxwJedhduCnPqvR7HnZjL6b27pdbtZV0+jqdrPqZ2ne90mvXg0bbZ0gAjnyHOMrwFzgtyT76GpSoI8Vchktw6WNAeZy+fRsH4HY750O5Y0R/9Pkh550tGtAWPS094yCLXD1CUw7zqoPwMbHoNXfoGeejmU3mys/TwYbaitgM3Pwun9Fz6gFNqZadTSdud0fM0GT07XV2VPH5Q2DUdaa+M4H9oIc1ZHLMfa6cC6xFx2VvnnV2k9fpbiH/4NVk/v758ZKyZjsSr2rDlA0fyxEbdRVguh/Fw47oswtJ5lfDWxR35ye4Up+xaxkyDvJmt1KXWvvEfju3vIWrGg/yd0rkl+5iA6Ix8yR/U60UYMLt3eBvvfgp2vGyMkJYtgwfXn/7i7MtG3fMvome99E07tRl96B2rC3MS1wV9v9A4Pv2d8yFt8K4ybZRQN8tUaK1p1fq0+Bse3Q49eunY4z4d87ngoXojKKkxYG4eVbS8ZH9qmL4eFN/W56b6ywxROySd7bGbcuwvsP0HV42vJvu5SvJfM6HNbV5aTktIi9qw5xAe+3vtlre2ZWYAPZ5q68AGnlzAKi0kFp7x5bny1AcKhcMIr4InEkyDvxj13Mra8TOrXbI4uyNPcRk/q4AbjpizozALIHtNxGws5Y8CVdb78okgoHQ4ZPbIdr0CgAcbOhIU3onIvntCk7Gmw+FZ0yUJ45xF483foCfPh0g+hXPH/gdfBFti11ggVHYZZK2HuNai0jg96mZGDWOswNDddHPK+WmiqgZN7YPvL6JyxxgeTkoUoT07c7RxO9I5XYeerMHUpXPrBPv9/tQbaKH/vBFfcG/mytGiE29o59aNHsOVkMPpvex9S727m6qk8//3XqT1VT864rIjbBNNd2C1hguUVpOdmdN2vlIVWmxOnWUPreR50WOOva+66rlwkLwnybpTVQtaqRVQ/WUZ7vQ9bVt9FI5TVhv7QP0NTNdRVQO1p42vVMTi69fyGDhc6e4wR6p0Bnz0aZYti+F5EpHXYKMe57UVorIKCElh+L2pU/+dAVV4R+qa/h91rYfsrcOYAuvRWY7JUDB+4dDjE6MZyePJ1aGmCkoWw8CaUNzeq5ytlMcpxujKBkotfP9BgvI+OboUtz8GW59AFJUaoF89HOTMuftERQO9ea/y7T7oElny41+IvnY5sPEaoLTSg8+OVD71K67GzTPjB/Vg90a1hPqsjyPeuPcgV914ScZsWrKRbNYHdR/EuvrCX32p14TRxaB3AV+2TIE8BEuQ9ZK0upfqJN2go20burcv63V5ZLJBZYNyKz/fidVsz1J02bp0Bf2gjtLd1PtMYjh8zDYrnQ8Ek47WGgPbXQbAFLDawWI2vVuv5n2O8dlVrDaGgcWsPdvu+7cKfQ0GwpXWEVxY4Pf3+EY64r9P7YcvzUHsKskbDVZ+BcbNjCmFlscLca4we+YZHjVv5ZvTSu/o819rVhlN7YPOzTG04B4WT4Kr7UfkTYvpd+m2jK9Po3c9aiW6qPh/qm/4K7z2JHjUVJi40lutMiy5cUp3eu844L16yEC7/SFTvn/3rjuBw2ikpLYprn80HT1L16Fqyr72EjEtnRv28/JJc8ifmsmdN70Hur2/B5bbj33P0osdabeat5eDtKtPqZ7QpLRCxMDXIlVLLga8Di4AxwH1a6z+a2SbnpDGkTxxD3ZrNUQV5b5TDafyBLzw/uUbrsDFkWncaaiug5qQR7vvfAmcGesJcmLDAWLkpgaGuQ+1QWW6Ez6m90ND3eslXAvrPz50PeYsVrJ2hb4VwGEJtFwZ3PJQF3dkj7Qz3CD8ru1FUQ1ceha3PG6U4PTmw7GNQsmhAx0plFqCv+6Lx7/D+s/Dsf6DnXwezVhlh34OuPgGbnzHakFHA7oLLmH3d3YN+6kR582DuNcaHj7ozcHSLEervPArvPoEeO8PoqY+f3XW8hht94B1470komgvLPhb1v/uBdUeYtKQYe3rsf+7CwXZO/tcj2LK9jP7bW2N+/qyrp/LWg5toaWol3Xvxv4uvJoAn103zvuPoUAhlPf+ea7U5obECHQ4P2Yf8Tt6uhVOkKEwqMLtH7gF2A3/uuCWFrNWlnH3gOVorqkgb23fvLBZKWSAj37hNmAeADrYaAXtsGxzaBPvfhnQvesI8o6deODmu/8Q60AAVe43gPr0fgq1GCI+aYpxXdGcZs7rDofNfw8bXY0cOU1w0/vx9oc7HOr63WMDmMApwWO1g6/zquPBnq6PHYzZjJMDfAIF645x2oOP7+rPn29nzd7E7Id0DTVVGCc5Lb4epSxJWFlcpC0xdih43Czb+n9HbP7oNffndqNzxRht8tbD1BSjfbLTlsjtg6lJq1r815PMfVPZoyL4RveAGqDkB5VuM98/J3WBzoMfPNiqcFUwc0nYNJn1oE7z7uDF58MpPRPyQFUn1sVqqj9Wy7L7IPeL+VD30Gq1HzzDh+5/B6o191GPWVVMp+3/vcmD9EebdcHFv3lfjZ9SMHMIVx2k5chrn1PFdj7VYXcaci+bG87PYh4iUaU0tpga51vol4CUApdQfzWxLd1mrFnL2t89Tv3YLhR+/blD3pexpxjBhycJuob4dDm+CA52hPtcYti+c1OsfMB0OQ/VxI7gr9hiLR4DRqy1ZZPwBHD01qt7a8VoHJYtWJOx3vEhe7w/pYMuFAe/v+L65AaZcCjOuHLQep3JlwqpPo4/vMAL9hZ+gZ64ApWDvOuPr3Gtg9lWDdulaTO1VCvImQN4E9OJb4dwRo5d+bDsc34FecR+qKHGz8s2iyzcbkxPHTIMVn0RZL/yz5d9zlMDeY+TeugyL/cLH9ndcdjZ9Reznx5sPnqTykTVkXb2YjMsir6bWnwkLx+PKdrJn7cGLgjwc1vhr/WRNmg4V4N9dfkGQt9o63mP+uiEP8vSMNKwOq5RpTRFm98iTkj0/C/f8ydSv2UzBx64dsh7XRaFesdfoaR15Hw68A+kedFFHT33UZKP3WrHPCP+KfdDqN8ImvwQW3miEd/aYlJoxr+zpkJne60zvIWnDhHnoUVOMCWZ73gAUTFoMC29AubNNa1dflLIYoy2jpqAX3gSv/xrefBC9/OOokoVmNy9u+th2eOshGDUJVn3moss7w8F2Tv7wIYJnamh4Yyvjv/0x0sYXdD2+f90R8opzyJsQ22z/cLCdUz96FFu2hzGfvy3u9lttFmasnMK+Nw4Rag9jtZ0fXWtuaCYc0mQW5WIvyCKw5yh88Mqux1ttHSMA/joiTYYcTF1lWqUoTEpQZhTlj0Qp5QO+0Ns5cqXU/cD9AIWFhYsee+yxmF7f5/Ph8US/dGHa9uN4nt9Gw33LaR9n7iU/lnA7Oc3nyPefIrf5LFYdImixYwsHUUDQ4qDWWUiNcxR1zkLarQObDR/rsRrO3G31aCwEHJFniCfrsbKGg8w+t4HM1hr255VS6YlvoleixXK8cgNnmFm5kaa0bHYWXkHYcnG/I+39cjyv7KR5yRTSth9DtYfxXzuH1vkTCAU1r35hA+OXjWLOx2LrkTvX7cO1/gCNd15KcOrApnudfr+KLf+7j6XfmkfutPOXOTadDlD27c0s/Ox0pp0+ju1EDfVfvtb4MA60NNZxbe2bHMmezanMqQNqQzzW/8tWHG47l319zpDvO1bJ+v8w0VauXLlFa13a8/6U6ZFrrR8AHgAoLS3VK1asiOn5ZWVlxPKc0OIW9r26m5LaMGPviW1fg0m3t0HFPuwndxvD5uNmYs+bwCiLhVEJ2kesx2okS+ZjpYPL4Y3fMuPMFmZMnYKampjlOwci2uOlK/bB2mchbzyZ13ye5RFOZYSbWznwP2tJmzuJ2f/2OdqrGzj5X4+gXtjOmIYw/iWXEmoLc9VHlzNzRfRB2Hz4FIffeY6s1aXMuf/uWH69iFoWtbL9gQM4alwX/O5HNh6jjM1csnwx2ZWjOfPLJ1k6cx6OQqPjUPbmm9CUxqRRuUy+dEXkFx9Eh/98hoazTUn7/u4umf8fDgUp2dMLqzudjKWzaXhzG7o9eWpkK5sDNWEe6oqPohbegCooGfIZrSI1KHsaXHU/jJ0OGx5F73/L7CZFRZ85CG/8DrJGwdWf63U+QvXT62mva6Lw0zeilMKen0XJf36WUfffTNO7e9j0z/+HzW5h0mXF0e+7PcSp/3oEW4ab0QMYUu8u3ZvGpMuK2bPmwtXQfDXGGg2eXDfuWcbQeaD7ZWhKGQWn/OZdS+6Tc+QpQRKgD1mrSwk1+ml6f3//GwuRhJTNAas+DeNnw8b/Q+950+wm9UmfOwJrHwBvHlzz+V6vjw81Bah6/A28l83qCkEw6jrk37mKSb/8ChWNFgpsrdQ+9ArhYHtU+6985HVajpxm7Fc/jC0jcYVQZq2eSlV5DVXlNV33NdUY5589uW7SJ47G4kzDv7vH9eRmB3mNv88V3ERyMDXIlVIepdR8pdT8jrYUdfycFCf0vIunY81wx7YimhBJRlntsPJTxiTJ959G73zN7CZFpKuOwZrfGKeMrv08Kr33c55Vj68l7G+h8JM3RHy8xZ1BXQCmLBpD9eNvUP6ln9N6su/6Cc1HTlP50GtkXrWIjMsTe1545lXG0P6ebmuU+6r9HZ1uJ8pqxTVjwoU9cugI8vqEtiVanjwP4ZAmUC+LQiU7s3vkpcC2jpsT+JeO7//VzEZ1UjYrmSsX0LhhNyF/i9nNESJuymKF5ffCxFLY+gJ620uYNdFVa40OthhrxVceRZ/chd7/Nrz2a6NOwLVf6LP8bLCmgeqn1pO5aiHOSWMibnNg/REALvnn2yn6l0/SdqaGQ5/9CbUvvhvx99btIaOWutfFmM9/MDG/aDc547IYPb2QPa8f6LrPV+PHle3qWpTENauElvLThALd/ta4s6ClCd0eZ9GlAeheplUkN7OvIy8DTL82qq6igawxGREv08q+upTaZ9+m4a0d5Fx3qQmtEwMVDrajLBZUDKs4NTe0cGzrSY5tPYUnx8XCW+fgzk7tMqjKYkVfcY9RmGfHKxBqRy+6KWGXJ+q2ZqOwT4uv49bU7XsftPq4tL4G/vKcUVyoJ2+uEeL9XDNd+dDr6PYQhff2XuNhf9lhssdmUjg5DzUlH9f0CZz8z4ep+OnjNL2/j7FfvRNb5vmh86rH19Jy6BRF//LJC+5PpFlXT+WNX72Nvy6AO9uFvzZwwfKlrtklENYE9h7HWzrNuLPzcsdAvVFIagh1L9M6augnzYsYpMys9cFSeaSa/775d6z+4jJWffbyix53Tp+AY2we9Ws2j+ggb6ry8e4jW9j0+DbaAkEyCjx4Czxk5Hf7mu817s/3kFHgId2bZto17CFfM40b99D41k6a3tuHNcNF7q3LyLl+ScQ/1HUVDRzdfIKj75/k6OYTnD1QidagLAod1jz/wzXMuXY6l961gMlLSrBYTP/8GRdlsaCX3mWU3t29BkJB9CV9rx7WFx0KGjUMyjcbq7X1LNdrcxiV8NI94MygvsXCqOLJ4PRCmuf8Y+kecGf1W7Gt7XQ1tS9uIOcDl/VadTEUDHHwnXIW3HS+/r49L5OS//ws1X8t49zvX+TQvv9i/Dc/imfBVFqOnqHyz6+SuWIBmVcMXgGdWVdNZc0v32J/2WEW3TYXX40fT+75D4euGcVgUQT2lF8c5P66IQ9yT1eZVrmWPNmN+CDPK8llxsopvPgfa8ktymbe9RdWX1JKkbW6lMo/v0qwqh57fpY5DTXJ8W2nePtP77PjxT2EgmGmLZ9EblE2TVU+GiubKD96gqYqH+1tF8/st6fbukI9o8CLN9+DMzMdh9OOw+XA4bLjcDpIc9mN+9wO4/6Ox9NcdmxptqhDpr3BT+O7u2lcvwPf1gPoYAhbbgbZ111C26kqzv3uBSr/8ioZqxbRPm82p08HOLr5JEc3n6ThTCMAaR4HExaMY971MykpHc/4+WOpOV7He49vY/PTO9n+/B5yxmdx6Z0LWHz7vIQe66GilAV92R1Gz3xvGYRD6Mtuj3oBG63DRhW58i1GFbm2gBHEU5YYM+SdGV3h3HOFvwNlZYxevCLutp/70ysoq5WCe67pdZtjW0/R6mu7aLUzZbGQ/+FVeOZP4eQP/sLRv/81eR9eiX/bISweJ2O++KG42xWNcXPH4M33sGfNQRbdNpemaj9jZpwvfGR1p5NeMppA9wlv3YN8iMnQeuoY8UFusSju+vEt1J9p4JGvPkPW6AwmLLhwLeusqxZR+adXqH9jK/l3rjKppUMnFAyz+amdvP2n9zi54zRpHgdLP1rK5R9fTP7Ei5fo1FrT3NhCY6WvI+B9XUHf+f25Q1UceucoLU0tMS2upizqfLC7HaR70khzO0jzGl8dNoWqb0CfrUafq8auwqRnu8maN5/sS6eROauIdG86zSfqOOmZwOG1+zj9qwMEw8akI2+Ok0lLSyhZXERJaRGjpxd0nbPsNGZGIbd+7zpu+OZqdr26n02Pb+XlH7/JKz8to2BONnnB0cxYOeWCql3JTimFXnybEea71kC4Hb3krj4vZdS1FUbP++hWI1hsDmMBk4mlMGZa1PXP49VSfpr6tVvIu2Nlnx+oD6w7jMVmYfLS4oiPO6eOZ/Kvv8aZ3zxL9eNvAFD0T5/od9nigbJYFDOvmsL2F/bQ3hYyeuQ9lgh1zZpI/evvo0MdH4xdWcZXE4LcmenEardIvfUUMOKDHIye430P3MkvbnuQBz/zOF9++lPkjM/qejxtbD7OGROoX7N5WAd5w9lGNjy8hfV/2kRbY5DCyXl88N8+wKLb5pLu6b2+uVIKV6YTV6aTUVP6XwK0vbWd1kCQtkAbbc1B2vxtXd+3BoK0NbfRFui4v/P75iAtvlZa/W001/mp2VdDc32AtpZ2gmFFSCugY5iyCjh4FP7vwhnASsGoaQUsun0KefhxHzxAemMFaY1t5GYWkl2SdVGId2dPt7HwltksvGU21cdqee+Jbbz98Pv84TOPk1HgYfHt87jkwwvIKza3EmC0lFJGOVerHba/bJwzv+KjFwSy9tUa4V2+BerPgLLA2Bmw6OYLVloLh8KU/b8NNFX7+MDXVmJPT8yCNt2d/cNLWFxp5N91VZ/b7Ss7QvHCcTgz0nvdxuJMY+xXP4z3slm0na4m88r5CW5tZLNWT2XTY9s49E45zQ0teHMvDHL37BJqn3ublvIzACibHZ3uNWXmusWi8OS6ZQW0FCBB3sGT6+ZTD97NLz/4IL/75KN88cn7LvhDkL26lNO/fJLmI6d7nSmbirTWHH3/BG//6X12vbIPHdYUzM/l1q99gCmXl3QNa+tQmJYjFfh3lYMCe0E2joJs7PlZWLM8UQ9/K6Wwp9uNP/Q5/U8e0+EwoQY/wZoGfFsP0rB+B83Hj0M6pC0ZTeayRWQum4dtXAFtgSAtTa20+luN0Pe10epvo8XXSkaBh+KF43Fmnv831e0hGtbvoPqpdZz55ZOce/BFcj5wGbm3LsMx+uKRh+7yinO4/htXkb7IQkF4DJse38Ybv9nA2l+9w+QlxVx61wLmXDsjrqUzh5JSCuZ/AG2xGUvEhtrRl94OJ3YaAV5ZbmxYUGKs+Fa84KLLwurPNPLIV5/myMbjABx8q5yP/c/tFE7uY3WcGPn3HKVpw24K77u+z8lojZVNnN57luu/Ed0H7owl8S2GEq8pl0/ElmbjvSe2A1ww2Q06JrzRURgmq+NOd5aJ15J7pEeeApL7r8wQK5ycx72/uYMHPv4wf/78X/n0g3djtRu9k8yVCzj9q6epX7MZ56Sbh7xtofYwLU0tNDe00NzYQnNTC1arpeNcszHsnNZx3rmzzX0JtgTZ+uxu3v7je5zedw5nZjrLP3UZSz9Wyq4jO5iyZALNB07g33EE/47D+HeXE+7lEjzlsGPPz8Sen429IOuCkO/82eq6sHcUbm2jvbaJYG0j7bWNF35f00h7XRPBjq+Ew13PS58yjsJP3kDm8rmkjb9wYRWbw4YrK/pVyZTNStaqhWStWkhg3zGqn1pP9dPrqX5qHRlL55D7weW4507q80OKxaqYfdU0Zl89jYazjbz/1x1senwbD3/5aZyZLzN+rrFoTddLRPhea6A9hG5tI9zaRrgliG5tIycnjdKbZjJu+XTSJhResFZ1oqm5V6OtNnj/aTi+3bgza5Sx+E7JIpQ38geb3a8d4PFvPEd7Wzt3/fgWvPluHvnqM/z3zb/l9h/cyKJbB349ttaac79/EVuWh9wPXdnntgfWGx88ep4fTxYOp52pV5SwZ41xGVrPILcXZGPLy8S/5yhc3lFOw50NjZVD3VQAPHlufBLkSU+CvIcpS0u44wc38vg3nuOpf3qZ239wA0opbJkevJfMoP6NLYz69I0xXcrUm6NbTlJVXkNLYwuBhhZaGo2QDjSe/7650QjvVn9b1K9rdVi7Qj3N5TAmkTntxjlllwOrzcK+Nw8TqG9m9PQC7vjhjSy4cSbtJ87gf3sL3jffY++PXybcbKwNnja+gKwVC3DPn4x7ziSU3Uawso62yjqClXUEq+oJVtYTrKzDv+0QwZoG6FENyuJOx16QDe0hgrWNkT8UWBS2LC+2nAxsOV7SJ44xvs/NwJ7txTl1fL895Xi5ZhRT9J1igvffTM1z71D7wgYa395J+uSxeBfPwOpOx+JxGl/dTqyudOP7hgAhXwCLK53MURms/sIyVn3uCg6/e5T3nthOzfE60Bod1oSD7ei2dsJtQXQwZPwcbDeqjoU1Fxwxq5Xyoz7e3/I2Y5xvMj2vnSnzR+GdOQHntCKc04twjM5N6FUBatZKdLoH6s4Yq/DljO319YMtQZ77/uts+Mtmxs0ezT2/+GDX/ImvvXQ/D33pKR75ytOUbzrOrf987YCG2n1bDuDfcZjRn78Nq7PvJWz3rzuMN9/DmJnmrZ7Xn1mrp7F37SEAPHkXjkoppXDPLjEmvHUP8tMH0FoP+VUg3jw3Z/b3XUhHmE+CPIJLPjyf6mM1rP3VO+SV5LDy/qWAUbK16d09+HcexrMg/gsrW/1tPPMvr3QNr3VK96bhzEg3bpnp5BZld33fdX/Hz+nedHQoTGugjVZ/x3llfxutAeOccmugreMcc5DWjnPQjed8HY+3MenSIkqvLCI/1Ehg60YOPfQYusX4sGDJ85J1dSnueZNxz52EPefi4hy2LM8Fayd3p0MhgtWNHSFfR7Cy3gj9qnosdhue0mnYss8HtC03wwjsTE9CPiANhD0/i1GfuoGCe66mfs0Wap59i6rH37hgVKC7bGDvL4xKaRZXGla3E0tHyF/uTic8oY22MzW01zQYT1BAGlgyHThG5+IYPRr76FwcY/I6fs7FUZiNJc1B/ZkG3v3dBjY9tYs3TrawqaqRKRveZ4pnPW5bGKvXhXN6Ec5pRbg6wj3Sv1Us1KTF/W5z9mAlf/niU5w9UMmVn7mM67++Clva+T8lmaMy+OwjH+fVn77J2l+9w/FtFXz8fz9EwaTYh9o7e+P2wmxybrz48tDuwqEwB98qZ+ZVU5N66d6ZV03p+t6Tc/FpAtesEhrKtmNp7Kio5s6G9lZoa4ZeStYOFk9HmVYzPkSI6EmQ9+K6r6+i+ngdL/5wDblF2cy9bgYZS2ZhcaVRv2Zz3EF+avcZHvrSU1QfrWH1F67gkjsX4MxIJ92T1udEq4HQ7SFajp+j5fApmo9U0HLoFIH9m9G/f5dKIL1kNDnXXdoV3G9v38yCAawkpKxWHIXZOAqTc+3uaFjSHOTcsIScG5YYlchag4T8zYT8LYT9zYT8rYT9zezdup0p44u7PdZyfpvGAJY0O97F03CM7hbUo3OjmleQNTqTD3z3A1zzrWvZ9+Yh3n14CzvWHWZHXTqTZ+Uya3waBdXV+Las6fqgYc/Pwjm9CO8lM8m+ZjHKlrjheK017z68hWf/7TXSvWl85o8fYfqKyEPYVpuF679xFRMvmcDDX32a/775d9z+gxshM+LmvWp8ayfNB08y7u/vxuLo+8/VyR2nCdQ399qmZJFR4GX8vDGc3HH6oqF1MIIcwHayoy57Z4GcQP2QB7k3z0MoGKa5oSWm01ZiaEmQ98JiUdz9k1uoP93AI195mqzHMyiaN5bM5fNoWL+DMV+6HUta9Ot+h8Oa9b/fyEv/tRZPrpvPPvJxJi8pTni7Q/4WWspP03z4FC2HK2g+UkHrsTPooHE5i0qzG8F94xIjuOdMGrRKVsOFUgqV7sCS7sCee2EStep68gZ5+USrzcLsq43z8DUn69j06FY2PbGdQ7uqyRmXxaV33MKchXlYz1UROHCC5n3HaXxrJ1WPraHwvuvJvHL+gFfIC9Q388Q/PM+uV/czbfkk7v7JLXjz+79ca/qKyXztpb/hL198koe//BRFK0Zx+WWXRzXUrkMhzv3hRdKKCsm6uv+Rgv3rDqMsiqlXlPS7rdkW3z6PQH0z6RkXnypwThqLSndgO1lr3NF5LbmvDrKHdqKtN7+zuptPgjyJSZD3wZ5u577f3sUvbvs9D37qMb70zKfIWr2Yulfeo3HDbrJWLozqdRorfTz6tWc4+FY5s6+Zxof/8yacbgd1r28m5G/GYreh0uxYHPaOrzaUw44lzX7+q73zZ1tXL6u9ptHoYR+uMIL7SAVtFdVd+7VmunFOHofntitxTh5L+uSxpI3LH9RJU2Jw5Y7P5vpvXMU1X1nB7tf28+7DW3j5Z+t4zW5h9jXTWfrRZUz51j34Nu7l7IMvcvLf/0zVY28w6tM34CmdHtfw6JFNx3n4K0/jq/Zx03euZvmnLoupsl3W6Aw+9+jHeeWnZbzx63f4xW0P8vH/vT1iTYLu6l7fTOuJSoq+d19Up1z2rztC0bwxKVFKd+k9pSy9pzTiv4eyWXFNn0DwZMcENxOLwnRe595U5adw8tBWlhPRkyDvhzfPzaf/cDe/uO1Bfv+px/j84x/Hnp9F/ZotUQX5vjcP8djXn6XV38bt37+BS++cT8PaLZz808sEz8X5H9OiUDYbuu18OUzH6FzSJ48j+5pLSJ80FufksdjyMuW81jBlc1iZf+Ms5t84i3OHq9n46Bbe/+sOdry4l/yJucy/cRZjPngTnvpqWl8o49g3/x/ueZMZ9ekbcc0sjmofofYwr/9iPWv+5y1yi7L54lOfZPyc+HqEVruVG/7hKnzpDez+wxF+dtNvjUmWN8+OuH24rZ3KP72Cc1oRGVGUTfXXBTi5o4Krv9z3rPZk0d//S9fsEnw7DhNqbsXizDCu3zelultHmVaZuZ7UJMijUDg5n3t/cwe/vfcR/vLFp7jhygXUPb2O9npfr9Wg2lvbeeE/1vLWHzYxenoBn330g7gqz3L4/h/Revws6VPGMfYrH8Y5dbwxi7k1aHxtCxJuDZ6f3dz5c2uQcFu78XOb8bi9IAvn5HGkTxyD1SPDXiNV4eQ8bvnutVz/96vY8eJeNjy8hTW/XN9VQS/N7SCvoJiMt6rIXP9rxpdOYMaXbiRzZu+rBdeequfhrzzNsc0nKf3QPG77l+v6LAoUdVvn5XDNS1fx0Bef5KEvPcWRTce55bvXXnS9fe3z7xCsrGPc1++K6sPogfXlaA3Tr5w04DYmA/esEpTWNO87jmfhVLQrS8q0il5JkEdp6uUTuf37N/DEPzxPZtZ0ZraHaSjbRu6tyy7a9tzhKh764lOc3neOZZ+4hCs/UEzNLx6met9xHOPyKfqnT5CxbO6Az1sK0Z093U7ph+ZR+qF5tAbaOHugkjP7Kzm97xyn953laFULLT4rG1+phlf+SGaGjXELixi3YDyjpxcyZmYhOeOy2PnSPp745vNorfnoz29j4S19Xwuutaa9tpGWwxWoNLtx7X0f7+3sMZl87rF7eenHb1D2/97tmtWeX2IMtYcCLVQ+8jruBVPwLJoW1e9+YN1hXNlOxs8dHsWaXDOL0RiFcDwLp3YUhakf+nZku7BYlfTIk5wEeQwuvXMBVUdrePM3G7BNKcC1ZvMFQa61ZuOjW3n2X1/F4XJwz7+uJmvPDk59+xXs+VmM/bs7yb7uEjlHLQZdmstY/KX7ugFaa+pONXByyzGOPPkep7af5OQ7h9hTVn7+eW4Hrf42iuaN4Z5ffojcoguvPNChEK0nq2g5UkHz4Qpajhi39vrzPTbH2Hxyb7mc7GsvweqJfL7aardy07euZtIlE3j0a8/ys5t+yzVfvpJRU/MJbd5Fa62P4n+/IarfNRzWHFh/hGnLJg3alR9DzepxEirIMCq8AXiyofLYkLfDYlG4c9wS5ElOgjxG13/jKmpO1PHuy/tIbzrN+FNVpI3LN2b1fvN5dr2yn8mlY7lykib0pydoznAz6m9uIfeWy2Oa5S5EoimlyBmfRc74+cy7dT5tlXVU/uVVKl/aRINOp23mdJo8OeRMyOHKT18GwSD+3eVdVz+0HKmg5ejZrrkZym4lrXg03stmkT5pDM7J42irrKP22bc586tnOPvgS2RdtYjcW67AOWlsxDbNvGoqf/fi/Tz0pSd5/vuvd3skB+edT5A1JpPssRlkjc4ka0yG8XPH18xCL1a7ldN7z9JU7R82w+qd2sfnENh7DB0KgysbAtvROhz1KnWJ4s33yNB6kpMgj5HFovjIT2+l7kQt6/doxv15Hd5VpTz81WdoqvSx7PI8Jp7bg95to+Bj15J3x0qs7t4XbxDCLI6CbMZ97S7y7ljJuT+8ROP6bVgz3LgdEzn8yTdpO93tCogMN+mTx5J7yxVGaE8aS1pR4UXXqbsx1iVoPnSKmmffpn7NZupefBfX7BJyb7kC9MWFdbLHZvKFv95H/elGjvzmRSpe247z1lX4mjV1pxuoP93IsS2nCNQ3X/A8pSCj0Iu1ow3Tlg+vIA+OyyW85Rgtx86S7smGcAiam8AV48X4A+TNc8ua5ElOgjwO9nQ7n/rDR/nJlf/N4w/uJfi7fWRm2rlhXCN5NXXk3HYFBR+5Glu21+ymCtGv9KJCJvzzfQT2n+DcH18ygmPyWLKvu4T0ifFdAeGcMo5xX7+L0X9zM7WvbKL2+Xc4+f2/kO1O4+zxZnJuXIqj4PywvVIKj0OTvnkbC2+bz/hvXnfRa7YG2qg/3Uj96QbqzzRSV2F8rT/dwIxVU6K6rj2VtI83VtEL7CknfUGWcae/3pQgP3e4akj3KWIjQR4nb76Hj3x9CX/+/lsUeUNcmlNLwbWLKbj3OhyFqbGMpRDduaYXUfIfn03oa1q9LvLvWEneh67Et+UAB3//LFWPrKHq0bVkLJ1N7i1X4F4wBaUUlX95FcJhCj9xcYiDcd6/cHJeQldVS2bhLBe2nAwCu4+Sc/lK405/LeRPGNJ2ePKNc+RSpjV5SZAPwKQ7L+eeNzfgLBlP4SdvIL14lNlNEiIpKYsF7+IZNPnPsXT6HGqf30DtSxtpfHunsSjP6lJqX95E7s2X4xg1OAvjpBylcM0qMVZC83zQuM+EmevePA+hthAtja0XLAMskocE+QBY3elM/8t3zW6GECnFMSqXUZ+5iYJ7r6OhbBs1z77NuT+8hEp3kP/Rq81uXlJxzy6h8a0dBBuD2G0OU68lb6r2SZAnKQlyIYQpLA472ddcQvY1l9B88CQ6rAe8ettw45pt1I1v3nsMuzvb3DKt1f64VrATg0+CXAhhut6WxB3pnJPHodLs+PccJWNylmlD6wA+uZY8aQ2P6glCCDEMGQuoFBHYfdRYPMXkoXWRnCTIhRAiiblmTaT58CnC9gxobkSHgv0/KYHcOS6URcq0JjMJciGESGKu2SUQChOoDBl3BBqGdP8WqwV3jkuG1pOYBLkQQiQxd8eys4GTHUFq0vB6U5UMrScrCXIhhEhiVq+LtAmjCJR3lMw1aea6DK0nLwlyIYRIcq7ZJQQOnjHWmDdh5npGvkeG1pOYBLkQQiQ596wSwr5mWgNO8JnVI/ehtR7yfYv+SZALIUSS6ywME6hPh4AZ58g9BFvaafW1Dfm+Rf8kyIUQIsk5xuRhy/IQqMKkojByLXkykyAXQogkp5TCNXsigYpW04bWAZnwlqQkyIUQIgW4ZpXQVttCe2Mzuq15SPctZVqTmwS5EEKkAHfHeXK/CcPr3nwZWk9mEuRCCJEC0qeMQ9mtBCrVkF9L7s5xoxQ0VUmPPBlJkAshRAqw2G04p4ztmPA2tEFutVlwZUuZ1mQlQS6EECnCNWcSLbUQrq8Z8n17O64lF8nH9CBXSn1OKXVUKdWilNqilFpmdpuEECIZuWdPQocVzQcrhnzfUqY1eZka5EqpO4GfAz8AFgAbgJeVUkVmtksIIZKRa1ZHYZgjVUO+b2+elGlNVmb3yP8O+KPW+rda631a6y8CZ4C/NbldQgiRdGyZbtJy0wicaBryfcvQevKymbVjpZQDWAT8uMdDrwFLh75FQgiR/FwTc6jfeobD93xjSPfbdspOWyCN/5zzz6gh3XP/tNa8p940uxkX+dD3VjLpQysGfT+mBTmQB1iBcz3uPwes7rmxUup+4H6AwsJCysrKYtqZz+eL+TkjlRyr6Mmxio0cr+j1dqwyphXiOVtDODy0C5iMz23ndKOVId5tDJKvYeXHyzlZNvj7MTPIO/U8+irCfWitHwAeACgtLdUrVqyIaSdlZWXE+pyRSo5V9ORYxUaOV/R6PVYrgPuGuDEdLjNnt/0a6e8rM8+RVwMhYFSP+wu4uJcuhBBCiAhMC3KtdRuwBbi6x0NXY8xeF0IIIUQ/zB5a/ynwF6XUe8A7wGeBMcBvTG2VEEIIkSJMDXKt9eNKqVzgH4HRwG7geq31cTPbJYQQQqQKs3vkaK1/BfzK7HYIIYQQqcjsgjBCCCGEGAAJciGEECKFSZALIYQQKUyCXAghhEhhEuRCCCFECpMgF0IIIVKYBLkQQgiRwiTIhRBCiBSmtE6+pd/6o5SqAmKt/paHsVCL6J8cq+jJsYqNHK/oybGK3kg5VhO01vk970zJII+HUmqz1rrU7HakAjlW0ZNjFRs5XtGTYxW9kX6sZGhdCCGESGES5EIIIUQKG0lB/oDZDUghcqyiJ8cqNnK8oifHKnoj+liNmHPkQgghxHA0knrkQgghxLAjQS6EEEKksGET5EqpzymljiqlWpRSW5RSy/rZfo5Sap1SqlkpVaGU+iellBqq9poplmOllCpWSukIt+uGss1mUEotV0o91/H+0EqpT0TxnBH5vor1WI3w99W3lFLvK6UalVJVSqnnlVKzo3jeiHtvxXOsRuJ7a1gEuVLqTuDnwA+ABcAG4GWlVFEv22cArwPngMXAl4C/B/5uSBpsoliPVTfXAaO73d4YzHYmCQ+wG/gy0NzfxiP5fUWMx6qbkfi+WgH8ClgKrALagTVKqZzenjCC31sriPFYdTNy3lta65S/AZuA3/a47xDww162/1ugEXB2u+8fgQo6JgAO11scx6oY0ECp2W03+bj5gE/0s82IfV/FcazkfXX+WHiAEHBTH9vIeyv6YzXi3lsp3yNXSjmARcBrPR56DeNTXCRLgLe01t17Dq8CYzDeBMNSnMeq01NKqUql1DtKqdsHpYGpb0S+rwZI3lfgxRgdretjG3lvGaI5Vp1GzHsr5YMco8auFWPIqbtzwKhenjOql+07Hxuu4jlWPuDrwIeB64G1wONKqXsGq5EpbKS+r+Ih76vzfg5sB97tYxt5bxmiOVYj7r1lM7sBCdTzgngV4b7+to90/3AU9bHSWlcDP+l212alVB7wDeChwWleShvJ76uoyfvKoJT6KXAFcIXWOtTP5iP6vRXtsRqJ763h0COvxjhn0vNTaQEXf4LtdLaX7enjOcNBPMcqkk3AlEQ1ahgZqe+rRBlR7yul1M+Au4FVWuvyfjYf0e+tGI9VJMP6vZXyQa61bgO2AFf3eOhqjBnZkbwLLFNKpffY/jRwLNFtTBZxHqtI5gNnEtSs4WREvq8SaD4j5H2llPo58BGMYNofxVNG7HsrjmMVyXyG83vL7Nl2CZrJeCfQBnwamIFxHsWHsXYrwA+Btd22z8T4hPsYMBv4IMaM0K+Z/bsk4bG6F+M/0QxgGsa5pzbgq2b/LkNwrDwYfwDmAwHgnzq+L5L31YCP1Uh+X/1vx/tiFUYvu/Pm6baNvLfiP1Yj7r1legMS+A/+OYxPpq0Yvc7l3R77I3Csx/ZzgPVAC8YntX9mhFzGEcux6vhPsRfwd/yH2gzcY/bvMETHaQXG+ceetz/K+2pgx2qEv68iHScNfK/bNvLeivNYjcT3liyaIoQQQqSwlD9HLoQQQoxkEuRCCCFECpMgF0IIIVKYBLkQQgiRwiTIhRBCiBQmQS6EEEKkMAlyIYQQIoVJkAshhBApTIJcCCGESGES5EKIfimlvqGU0hFu/2p224QY6aREqxCiX0opL+DudtfXgY8Cy7TWh81plRACJMiFEDFSSv0D8CWMZSUPmN0eIUY6m9kNEEKkDqXUt4AvACu11gfNbo8QQoJcCBElpdR3gM8CV8pwuhDJQ4JcCNEvpdR3gc8AK7TWR8xujxDiPAlyIUSfOnriXwZuBvxKqVEdD9VrrVvMa5kQAmSymxCiD0opBdQDGREeXq21Xju0LRJC9CRBLoQQQqQwKQgjhBBCpDAJciGEECKFSZALIYQQKUyCXAghhEhhEuRCCCFECpMgF0IIIVKYBLkQQgiRwiTIhRBCiBQmQS6EEEKksP8P2/e8EDBAT9kAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, catNz/Nz, color=color_list[12])\n", + "plt.plot(z, Nz_truth/Nz, color=color_list[8])\n", + "plt.plot(z, Nz_mock/Nz, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing binned_clusters_test.py\n", + "Initializing binned_clusters_test.py\n", + "Initializing binned_clusters_test.py\n", + "Initializing binned_clusters_test.py\n", + "Initializing binned_clusters_test.py\n", + "Initializing binned_clusters_test.py\n", + "Downsampling selection function inputs.\n", + "Downsampling selection function inputs.\n", + "Downsampling selection function inputs.\n", + "Downsampling selection function inputs.\n", + "Downsampling selection function inputs.\n", + "Downsampling selection function inputs.\n", + "Considering full map.\n", + "Considering full map.\n", + "Considering full map.\n", + "Considering full map.\n", + "Considering full map.\n", + "Considering full map.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "Reading data catalog.\n", + "Reading data catalog.\n", + "Reading data catalog.\n", + "Reading data catalog.\n", + "Reading data catalog.\n", + "Reading data catalog.\n", + "Total number of clusters in catalogue = 6522.\n", + "Total number of clusters in catalogue = 6522.\n", + "Total number of clusters in catalogue = 6522.\n", + "Total number of clusters in catalogue = 6522.\n", + "Total number of clusters in catalogue = 6522.\n", + "Total number of clusters in catalogue = 6522.\n", + "SNR cut = 7.0.\n", + "SNR cut = 7.0.\n", + "SNR cut = 7.0.\n", + "SNR cut = 7.0.\n", + "SNR cut = 7.0.\n", + "SNR cut = 7.0.\n", + "Number of clusters above the SNR cut = 902.\n", + "Number of clusters above the SNR cut = 902.\n", + "Number of clusters above the SNR cut = 902.\n", + "Number of clusters above the SNR cut = 902.\n", + "Number of clusters above the SNR cut = 902.\n", + "Number of clusters above the SNR cut = 902.\n", + "The highest redshift = 1.9249999999999998\n", + "The highest redshift = 1.9249999999999998\n", + "The highest redshift = 1.9249999999999998\n", + "The highest redshift = 1.9249999999999998\n", + "The highest redshift = 1.9249999999999998\n", + "The highest redshift = 1.9249999999999998\n", + "Number of redshift bins = 28.\n", + "Number of redshift bins = 28.\n", + "Number of redshift bins = 28.\n", + "Number of redshift bins = 28.\n", + "Number of redshift bins = 28.\n", + "Number of redshift bins = 28.\n", + "Number of mass bins for theory calculation 106.\n", + "Number of mass bins for theory calculation 106.\n", + "Number of mass bins for theory calculation 106.\n", + "Number of mass bins for theory calculation 106.\n", + "Number of mass bins for theory calculation 106.\n", + "Number of mass bins for theory calculation 106.\n", + "The lowest SNR = 7.004804083787903.\n", + "The lowest SNR = 7.004804083787903.\n", + "The lowest SNR = 7.004804083787903.\n", + "The lowest SNR = 7.004804083787903.\n", + "The lowest SNR = 7.004804083787903.\n", + "The lowest SNR = 7.004804083787903.\n", + "The highest SNR = 43.010754788401286.\n", + "The highest SNR = 43.010754788401286.\n", + "The highest SNR = 43.010754788401286.\n", + "The highest SNR = 43.010754788401286.\n", + "The highest SNR = 43.010754788401286.\n", + "The highest SNR = 43.010754788401286.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Loading files describing selection function.\n", + "Loading files describing selection function.\n", + "Loading files describing selection function.\n", + "Loading files describing selection function.\n", + "Loading files describing selection function.\n", + "Loading files describing selection function.\n", + "Reading Q as a function of theta.\n", + "Reading Q as a function of theta.\n", + "Reading Q as a function of theta.\n", + "Reading Q as a function of theta.\n", + "Reading Q as a function of theta.\n", + "Reading Q as a function of theta.\n", + "Reading full Q function.\n", + "Reading full Q function.\n", + "Reading full Q function.\n", + "Reading full Q function.\n", + "Reading full Q function.\n", + "Reading full Q function.\n", + "Number of tiles = 280.\n", + "Number of tiles = 280.\n", + "Number of tiles = 280.\n", + "Number of tiles = 280.\n", + "Number of tiles = 280.\n", + "Number of tiles = 280.\n", + "Reading RMS.\n", + "Reading RMS.\n", + "Reading RMS.\n", + "Reading RMS.\n", + "Reading RMS.\n", + "Reading RMS.\n", + "Reading in full RMS table.\n", + "Reading in full RMS table.\n", + "Reading in full RMS table.\n", + "Reading in full RMS table.\n", + "Reading in full RMS table.\n", + "Reading in full RMS table.\n", + "Number of tiles = 264. \n", + "Number of tiles = 264. \n", + "Number of tiles = 264. \n", + "Number of tiles = 264. \n", + "Number of tiles = 264. \n", + "Number of tiles = 264. \n", + "Number of sky patches = 40828.\n", + "Number of sky patches = 40828.\n", + "Number of sky patches = 40828.\n", + "Number of sky patches = 40828.\n", + "Number of sky patches = 40828.\n", + "Number of sky patches = 40828.\n", + "Downsampling RMS and Q function using 50 bins.\n", + "Downsampling RMS and Q function using 50 bins.\n", + "Downsampling RMS and Q function using 50 bins.\n", + "Downsampling RMS and Q function using 50 bins.\n", + "Downsampling RMS and Q function using 50 bins.\n", + "Downsampling RMS and Q function using 50 bins.\n", + "Found empty bin.\n", + "Found empty bin.\n", + "Found empty bin.\n", + "Found empty bin.\n", + "Found empty bin.\n", + "Found empty bin.\n", + "Number of downsampled sky patches = 50.\n", + "Number of downsampled sky patches = 50.\n", + "Number of downsampled sky patches = 50.\n", + "Number of downsampled sky patches = 50.\n", + "Number of downsampled sky patches = 50.\n", + "Number of downsampled sky patches = 50.\n", + "Number of Q functions = 50.\n", + "Number of Q functions = 50.\n", + "Number of Q functions = 50.\n", + "Number of Q functions = 50.\n", + "Number of Q functions = 50.\n", + "Number of Q functions = 50.\n", + "Entire survey area = 13631.392731778147 deg2.\n", + "Entire survey area = 13631.392731778147 deg2.\n", + "Entire survey area = 13631.392731778147 deg2.\n", + "Entire survey area = 13631.392731778147 deg2.\n", + "Entire survey area = 13631.392731778147 deg2.\n", + "Entire survey area = 13631.392731778147 deg2.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nz for higher resolution = 249\n", + "0 33.706125667983315\n", + "1 887.9814791372355\n", + "2 187.7998427383542\n", + "3 31.61553810843096\n", + "4 3.827305708681894\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Theory N calculation took 38.09064507484436 seconds.\n", + "Theory N calculation took 38.09064507484436 seconds.\n", + "Theory N calculation took 38.09064507484436 seconds.\n", + "Theory N calculation took 38.09064507484436 seconds.\n", + "Theory N calculation took 38.09064507484436 seconds.\n", + "Theory N calculation took 38.09064507484436 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 0.29590081798960904\n", + "\r", + " Total predicted 2D N = 1145.2261921786753\n", + "\r", + " ::: 2D ln likelihood = 175.5827587152357\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-175.58275872])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.68\n", + "\n", + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.26, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.0,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data ='../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\\\n", + "'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits\",\n", + " 'Q_file': \"selFn/QFit.fits\",\n", + " 'tile_file': \"selFn/tileAreas.txt\",\n", + " 'rms_file': \"selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200c',\n", + " 'md_ym': '200c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 4.25e14*0.68\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 7.,\n", + " 'single_tile_test': \"no\",\n", + " 'mode': 'Qfit',\n", + " 'Qmode': 'downsample',\n", + " 'dwnsmpl_bins': 50,\n", + " 'save_dwsmpld': False,\n", + " 'average_Q': False\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 5e13*0.68,\n", + " 'Mmax': 1e16*0.68,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.binned_clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200c'}}\n", + "}\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 1.9e-05,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 33.706125667983315\n", + "1 887.9814791372355\n", + "2 187.7998427383542\n", + "3 31.61553810843096\n", + "4 3.827305708681894\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 0: 31.020261656378576.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 1: 134.12326669897055.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 2: 183.09612887969396.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 3: 185.68196834758456.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 4: 162.8217057195779.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 5: 131.20061947146078.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 6: 99.63953388864644.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 7: 72.009537802529.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 8: 50.17553788295927.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 9: 33.954271794676664.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 10: 22.711631359996225.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 11: 14.702317262593402.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 12: 9.341087463253508.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 13: 5.835463171494961.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 14: 3.590356397655985.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 15: 2.179095489354455.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 16: 1.3062713370304362.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 17: 0.7740629964729798.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 18: 0.45381063280905964.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 19: 0.2634698656124782.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 20: 0.15162215359790404.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 21: 0.0865591964369826.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 22: 0.04903135185373896.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 23: 0.027550573335238852.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 24: 0.015354367647925625.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 25: 0.008487968523740617.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 26: 0.0046550231005464664.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Number of clusters in redshift bin 27: 0.0025334254282671402.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Total predicted 2D N = 1145.2261921786753.\n", + "Theory N calculation took 37.59646916389465 seconds.\n", + "Theory N calculation took 37.59646916389465 seconds.\n", + "Theory N calculation took 37.59646916389465 seconds.\n", + "Theory N calculation took 37.59646916389465 seconds.\n", + "Theory N calculation took 37.59646916389465 seconds.\n", + "Theory N calculation took 37.59646916389465 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 0.29590081798960904\n", + "\r", + " Total predicted 2D N = 1145.2261921786753\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "Nz = np.zeros(len(z))\n", + "catNz = np.zeros(len(z))\n", + "for i in range(len(z)):\n", + " Nz[i] = Nzq[i, :].sum() \n", + " catNz[i] = catNzq[i, :].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "bin_params = info['likelihood']['soliket.BinnedClusterLikelihood']['binning']\n", + "\n", + "\n", + "zbins = np.arange(bin_params['z']['zmin'], bin_params['z']['zmax'] + bin_params['z']['dz'], \\\n", + " bin_params['z']['dz'])\n", + "\n", + "logqmin = bin_params['q']['log10qmin']\n", + "logqmax = bin_params['q']['log10qmax']\n", + "dlogq = bin_params['q']['dlog10q']\n", + "\n", + "# TODO: I removed the bin where everything is larger than qmax - is this ok?\n", + "qbins = 10**np.arange(logqmin, logqmax+dlogq, dlogq)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "mockconfig = {\n", + " 'predSNRCut': 7,\n", + " 'path2truthcat': '../../../../../data/sims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_truthCatalog.fits',\n", + " 'path2noisemap': path2data+'selFn/stitched_RMSMap_Arnaud_M2e14_z0p4.fits',\n", + " 'path2selFn': path2data+'selFn',\n", + " 'path2Qfunc': path2data+'selFn/QFit.fits',\n", + " 'relativisticCorrection': False,\n", + " 'rhoType': 'critical',\n", + " 'massFunc': 'Tinker08',\n", + " 'delta': 200,\n", + " 'applyPoissonScatter': False,\n", + " 'predAreaScale': 1.000, \n", + " 'makeMock': True,\n", + " 'selFnZStep': 0.01,\n", + " 'method': 'fast',\n", + " 'QSource': 'fit'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: We don't have true_fixed_y_c or true_Q - we reconstruct those here.\n" + ] + } + ], + "source": [ + "# Make a 'true' mock - use the truth catalog, get true_SNR by looking up noise in the selFn dir\n", + "mode = 'without_Q'\n", + "truthTab = nemo_mocks.make_truth_mock(mode, mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "truth_cat, zarr, qarr = nemo_mocks.bin_catalog(truthTab[truthTab['true_SNR']>7], zbins, qbins, SNR_tag='true_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "mockTab = nemo_mocks.make_nemo_mock(mockconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "mock_cat, zarr, qarr = nemo_mocks.bin_catalog(mockTab[mockTab['fixed_SNR']>7], zbins, qbins, SNR_tag='fixed_SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_truth = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_truth[i] = truth_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_truth = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_truth[i] = truth_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "Nq_mock = np.zeros(len(q))\n", + "\n", + "for i in range(len(q)):\n", + " Nq_mock[i] = mock_cat[:,i].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "Nz_mock = np.zeros(len(z))\n", + "\n", + "for i in range(len(z)):\n", + " Nz_mock[i] = mock_cat[i,:].sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[0], label='prediction')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(q, Nq_truth, yerr=np.sqrt(Nq_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(q, Nq_mock, yerr=np.sqrt(Nq_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "predNz = nemo_mocks.get_nemo_pred(mockconfig , zbins)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, Nz, color=color_list[0], label='prediction')\n", + "plt.plot(z, predNz, color=color_list[0], linestyle='--', label='nemo prediction')\n", + "plt.errorbar(z, catNz, yerr=np.sqrt(catNz), color=color_list[4], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='obs catalogue')\n", + "plt.errorbar(z, Nz_truth, yerr=np.sqrt(Nz_truth), color=color_list[8], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='truth catalogue')\n", + "plt.errorbar(z, Nz_mock, yerr=np.sqrt(Nz_mock), color=color_list[12], fmt='o', ms=3, capsize=5, \\\n", + " capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xlim(0, 2)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.semilogx(q, catNq/Nq, color=color_list[12])\n", + "plt.semilogx(q, Nq_truth/Nq, color=color_list[8])\n", + "plt.semilogx(q, Nq_mock/Nq, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[matplotlib.legend] *WARNING* No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(z, catNz/Nz, color=color_list[12])\n", + "plt.plot(z, Nz_truth/Nz, color=color_list[8])\n", + "plt.plot(z, Nz_mock/Nz, color=color_list[4])\n", + "# plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('$z$', fontsize=14)\n", + "plt.ylabel('$N_{sim}/N_{pred}$', fontsize=14)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "actxdes_venv", + "language": "python", + "name": "actxdes_venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/soliket/clusters/notebooks/Nz_test-CAMB-vs-CCL.ipynb b/soliket/clusters/notebooks/Nz_test-CAMB-vs-CCL.ipynb new file mode 100644 index 00000000..e50ff5a1 --- /dev/null +++ b/soliket/clusters/notebooks/Nz_test-CAMB-vs-CCL.ipynb @@ -0,0 +1,626 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from soliket import BinnedClusterLikelihood\n", + "from cobaya.model import get_model\n", + "import camb" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.81050087]\n" + ] + } + ], + "source": [ + "params = {\n", + " 'cosmomc_theta': 0.0104135,\n", + " 'ns': 0.965,\n", + " 'ombh2': 0.0226576, \n", + " 'omch2': 0.1206864, \n", + " 'As': 2.022662e-9,\n", + " 'tenToA0': 4.35e-5,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'tau': 0.055,\n", + " 'mnu': 0.0,\n", + " 'nnu': 3.046,\n", + " 'omnuh2': 0.,\n", + " 'w': -1,\n", + " \n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "#Set up a new set of parameters for CAMB\n", + "pars = camb.CAMBparams()\n", + "#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency\n", + "pars.set_cosmology(cosmomc_theta=params['cosmomc_theta'], ombh2=params['ombh2'], omch2=params['omch2'], mnu=0.0, omk=0, \\\n", + " tau=params['tau'])\n", + "pars.InitPower.set_params(As=params['As'], ns=params['ns'], r=0)\n", + "pars.set_for_lmax(2500, lens_potential_accuracy=0);\n", + "\n", + "#calculate results for these parameters\n", + "results = camb.get_results(pars)\n", + "\n", + "#Note non-linear corrections couples to smaller scales than you want\n", + "pars.set_matter_power(redshifts=[0.], kmax=2.0)\n", + "\n", + "#Linear spectra\n", + "results = camb.get_results(pars)\n", + "kh, z, pk = results.get_matter_power_spectrum(minkh=1e-4, maxkh=1, npoints = 200)\n", + "s8 = np.array(results.get_sigma8())\n", + "print(s8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing binned_clusters.py\n", + "Initializing binned_clusters.py\n", + "Downsampling selection function inputs.\n", + "Downsampling selection function inputs.\n", + "Considering full map.\n", + "Considering full map.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "2D likelihood as a function of redshift and signal-to-noise.\n", + "Reading data catalog.\n", + "Reading data catalog.\n", + "Total number of clusters in catalogue = 4195.\n", + "Total number of clusters in catalogue = 4195.\n", + "SNR cut = 5.0.\n", + "SNR cut = 5.0.\n", + "Number of clusters above the SNR cut = 2419.\n", + "Number of clusters above the SNR cut = 2419.\n", + "The highest redshift = 1.91\n", + "The highest redshift = 1.91\n", + "Number of redshift bins = 28.\n", + "Number of redshift bins = 28.\n", + "Number of mass bins for theory calculation 138.\n", + "Number of mass bins for theory calculation 138.\n", + "The lowest SNR = 5.0015351968853565.\n", + "The lowest SNR = 5.0015351968853565.\n", + "The highest SNR = 53.68491271239472.\n", + "The highest SNR = 53.68491271239472.\n", + "Number of SNR bins = 6.\n", + "Number of SNR bins = 6.\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118].\n", + "Loading files describing selection function.\n", + "Loading files describing selection function.\n", + "Reading Q as a function of theta.\n", + "Reading Q as a function of theta.\n", + "Reading in binned Q function from file.\n", + "Reading in binned Q function from file.\n", + "Reading RMS.\n", + "Reading RMS.\n", + "Reading in binned RMS table from file.\n", + "Reading in binned RMS table from file.\n", + "Number of rms bins = 5.\n", + "Number of rms bins = 5.\n", + "Number of Q functions = 5.\n", + "Number of Q functions = 5.\n", + "Entire survey area = 13211.395702126332 deg2.\n", + "Entire survey area = 13211.395702126332 deg2.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../../../../../data/DR5ClusterSearch/selFn/QFitdwsmpld_nbins=5.npz\n", + "False\n", + "False\n", + " Nz for higher resolution = 249\n", + "0 1517.2563411483504\n", + "1 696.2387993079161\n", + "2 104.11361156050228\n", + "3 9.71837256406774\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Number of clusters in redshift bin 0: 19.03005096447916.\n", + "Number of clusters in redshift bin 0: 19.03005096447916.\n", + "Number of clusters in redshift bin 1: 182.1444676952088.\n", + "Number of clusters in redshift bin 1: 182.1444676952088.\n", + "Number of clusters in redshift bin 2: 333.4504144137944.\n", + "Number of clusters in redshift bin 2: 333.4504144137944.\n", + "Number of clusters in redshift bin 3: 381.6691387352821.\n", + "Number of clusters in redshift bin 3: 381.6691387352821.\n", + "Number of clusters in redshift bin 4: 358.03974057663055.\n", + "Number of clusters in redshift bin 4: 358.03974057663055.\n", + "Number of clusters in redshift bin 5: 300.2054482705682.\n", + "Number of clusters in redshift bin 5: 300.2054482705682.\n", + "Number of clusters in redshift bin 6: 233.5140683713308.\n", + "Number of clusters in redshift bin 6: 233.5140683713308.\n", + "Number of clusters in redshift bin 7: 171.79036506921184.\n", + "Number of clusters in redshift bin 7: 171.79036506921184.\n", + "Number of clusters in redshift bin 8: 120.9148005078851.\n", + "Number of clusters in redshift bin 8: 120.9148005078851.\n", + "Number of clusters in redshift bin 9: 82.05206693444826.\n", + "Number of clusters in redshift bin 9: 82.05206693444826.\n", + "Number of clusters in redshift bin 10: 54.68523995292617.\n", + "Number of clusters in redshift bin 10: 54.68523995292617.\n", + "Number of clusters in redshift bin 11: 35.089063261567205.\n", + "Number of clusters in redshift bin 11: 35.089063261567205.\n", + "Number of clusters in redshift bin 12: 21.988133122360708.\n", + "Number of clusters in redshift bin 12: 21.988133122360708.\n", + "Number of clusters in redshift bin 13: 13.491586911763994.\n", + "Number of clusters in redshift bin 13: 13.491586911763994.\n", + "Number of clusters in redshift bin 14: 8.127363806103304.\n", + "Number of clusters in redshift bin 14: 8.127363806103304.\n", + "Number of clusters in redshift bin 15: 4.820777283159958.\n", + "Number of clusters in redshift bin 15: 4.820777283159958.\n", + "Number of clusters in redshift bin 16: 2.8242796461563424.\n", + "Number of clusters in redshift bin 16: 2.8242796461563424.\n", + "Number of clusters in redshift bin 17: 1.6398496265753406.\n", + "Number of clusters in redshift bin 17: 1.6398496265753406.\n", + "Number of clusters in redshift bin 18: 0.9477524549753394.\n", + "Number of clusters in redshift bin 18: 0.9477524549753394.\n", + "Number of clusters in redshift bin 19: 0.5482631537877283.\n", + "Number of clusters in redshift bin 19: 0.5482631537877283.\n", + "Number of clusters in redshift bin 20: 0.3196094346404393.\n", + "Number of clusters in redshift bin 20: 0.3196094346404393.\n", + "Number of clusters in redshift bin 21: 0.18916661718961245.\n", + "Number of clusters in redshift bin 21: 0.18916661718961245.\n", + "Number of clusters in redshift bin 22: 0.11449404095931863.\n", + "Number of clusters in redshift bin 22: 0.11449404095931863.\n", + "Number of clusters in redshift bin 23: 0.07128007597702109.\n", + "Number of clusters in redshift bin 23: 0.07128007597702109.\n", + "Number of clusters in redshift bin 24: 0.04581055523951839.\n", + "Number of clusters in redshift bin 24: 0.04581055523951839.\n", + "Number of clusters in redshift bin 25: 0.030403967544014563.\n", + "Number of clusters in redshift bin 25: 0.030403967544014563.\n", + "Number of clusters in redshift bin 26: 0.020776212961889706.\n", + "Number of clusters in redshift bin 26: 0.020776212961889706.\n", + "Number of clusters in redshift bin 27: 0.014539163357194384.\n", + "Number of clusters in redshift bin 27: 0.014539163357194384.\n", + "Total predicted 2D N = 2327.778950826085.\n", + "Total predicted 2D N = 2327.778950826085.\n", + "Theory N calculation took 29.33384084701538 seconds.\n", + "Theory N calculation took 29.33384084701538 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 0.44476631361709307\n", + "5 0.007059931630231669\n", + "\r", + " Total predicted 2D N = 2327.778950826085\n", + "\r", + " ::: 2D ln likelihood = 291.3318164364894\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-291.33181644])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = {\n", + " 'h': 0.68,\n", + " 'n_s': 0.965,\n", + " 'Omega_b': 0.049, \n", + " 'Omega_c': 0.26, \n", + " 'sigma8': 0.81,\n", + " 'tenToA0': 4.35e-5,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'm_nu': 0.0,\n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data = '../../../../../data/'\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': True,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"DR5_cluster-catalog_v1.1.fits\",\n", + " 'Q_file': \"DR5ClusterSearch/selFn/QFit.fits\",\n", + " 'tile_file': \"DR5ClusterSearch/selFn/tileAreas.txt\",\n", + " 'rms_file': \"DR5ClusterSearch/selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"CCL\",\n", + " 'massfunc_mode': 'ccl',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200m',\n", + " 'md_ym': '500c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 3e14\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 5.,\n", + " 'single_tile_test': \"no\",\n", + " 'mode': 'downsample',\n", + " 'dwnsmpl_bins': 5,\n", + " 'save_dwsmpld': True,\n", + " 'average_Q': False\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 1e13,\n", + " 'Mmax': 1e16,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'soliket.binned_clusters.CCL': \n", + " {'transfer_function': 'boltzmann_camb',\n", + " 'matter_pk': 'halofit',\n", + " 'baryons_pk': 'nobaryons',\n", + " 'md_hmf': '200m'}}\n", + "}\n", + "\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 4.35e-5,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1517.2563411483507\n", + "1 696.2387993079161\n", + "2 104.11361156050228\n", + "3 9.71837256406774\n", + "4 0.44476631361709307\n", + "5 0.007059931630231669\n", + " Total predicted 2D N = 2327.778950826085\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq_ccl = np.zeros(len(q))\n", + "catNq_ccl = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq_ccl[i] = Nzq[:,i].sum() \n", + " catNq_ccl[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[camb] `camb` module loaded successfully from /Users/andrina/opt/miniconda3/envs/actxdes_venv/lib/python3.7/site-packages/camb\n", + " Nz for higher resolution = 249\n", + "0 1514.66493026296\n", + "1 694.8402010528979\n", + "2 103.94954263066289\n", + "3 9.724771671699106\n", + "4 0.4476669171400141\n", + "5 0.007202742800105421\n", + " Total predicted 2D N = 2323.6343152781606\n", + " ::: 2D ln likelihood = 291.6657098152746\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-291.66570982])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = {\n", + " 'cosmomc_theta': 0.0104135,\n", + " 'ns': 0.965,\n", + " 'ombh2': 0.0226576, \n", + " 'omch2': 0.1206864, \n", + " 'As': 2.022662e-9,\n", + " 'tenToA0': 4.35e-5,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1.,\n", + " 'tau': 0.055,\n", + " 'mnu': 0.0,\n", + " 'nnu': 3.046,\n", + " 'omnuh2': 0.,\n", + " 'w': -1,\n", + " \n", + " 'C0': 2.\n", + "\n", + "}\n", + "\n", + "path2data = path2data\n", + "\n", + "info = {\n", + " 'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'verbose': False,\n", + " 'data': {\n", + " 'data_path': path2data,\n", + " 'cat_file': \"DR5_cluster-catalog_v1.1.fits\",\n", + " 'Q_file': \"DR5ClusterSearch/selFn/QFit.fits\",\n", + " 'tile_file': \"DR5ClusterSearch/selFn/tileAreas.txt\",\n", + " 'rms_file': \"DR5ClusterSearch/selFn/RMSTab.fits\"\n", + " },\n", + " 'theorypred': {\n", + " 'choose_theory': \"camb\",\n", + " 'massfunc_mode': 'internal',\n", + " 'choose_dim': \"2D\",\n", + " 'compl_mode': 'erf_diff',\n", + " 'md_hmf': '200m',\n", + " 'md_ym': '500c'\n", + " \n", + " },\n", + " 'YM': {\n", + " 'Mpivot': 3e14\n", + " },\n", + " 'selfunc': {\n", + " 'SNRcut': 5.,\n", + " 'single_tile_test': \"no\",\n", + " 'mode': 'downsample',\n", + " 'dwnsmpl_bins': 5,\n", + " 'average_Q': False\n", + " },\n", + " 'binning': {\n", + " 'z': {\n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1\n", + " },\n", + " 'q': {\n", + " # SNR setting\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25\n", + " },\n", + " 'M': {\n", + " # mass setting\n", + " 'Mmin': 1e13,\n", + " 'Mmax': 1e16,\n", + " 'dlogM': 0.05\n", + " }\n", + " }\n", + " }},\n", + " 'theory': {'camb': {'extra_args': {'num_massive_neutrinos': 0}}}\n", + "}\n", + "\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 4.35e-5,\n", + " 'B0': 0.08,\n", + " 'C0': 2.,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1514.66493026296\n", + "1 694.8402010528979\n", + "2 103.94954263066289\n", + "3 9.724771671699106\n", + "4 0.4476669171400141\n", + "5 0.007202742800105421\n", + " Total predicted 2D N = 2323.6343152781606\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(q, Nq, color=color_list[2], label='CAMB prediction, nbins=5')\n", + "plt.plot(q, Nq_ccl, color=color_list[6], label='CCL prediction, nbins=5')\n", + "plt.errorbar(q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.semilogx(q, Nq_ccl/Nq, color=color_list[6])\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N_{CCL}/N_{CAMB}$', fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "clusters_env", + "language": "python", + "name": "clusters_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/soliket/clusters/notebooks/Nz_test-binning.ipynb b/soliket/clusters/notebooks/Nz_test-binning.ipynb new file mode 100644 index 00000000..4bd9b6b1 --- /dev/null +++ b/soliket/clusters/notebooks/Nz_test-binning.ipynb @@ -0,0 +1,839 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:CAMB:Importing *auto-installed* CAMB (but defaulting to *global*).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CAMB] Importing *auto-installed* CAMB (but defaulting to *global*).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:camb:Initialized!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[camb] Initialized!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Downsampling selection function inputs.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " :::::: this is initialisation in binned_clusters.py\n", + "\r", + " :::::: reading catalogue\n", + "\r", + " Number of mass bins : 138\n", + "[soliket.binned_clusters.binned_clusters] Downsampling selection function inputs.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Loading files describing selection function.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SO for a full map\n", + " 2D likelihood as a function of redshift and signal-to-noise\n", + "\r", + " Total number of clusters in catalogue = 4195\n", + "\r", + " SNR cut = 5.0\n", + "\r", + " Number of clusters above the SNR cut = 2419\n", + "\r", + " The highest redshift = 1.91\n", + "\r", + " Number of redshift bins = 20\n", + "\r", + " Catalogue N in redshift bins\n", + "0 30.0\n", + "1 106.0\n", + "2 246.0\n", + "3 329.0\n", + "4 380.0\n", + "5 350.0\n", + "6 300.0\n", + "7 223.0\n", + "8 174.0\n", + "9 121.0\n", + "10 65.0\n", + "11 42.0\n", + "12 30.0\n", + "13 15.0\n", + "14 6.0\n", + "15 0.0\n", + "16 0.0\n", + "17 1.0\n", + "18 0.0\n", + "19 1.0\n", + "20 0.0\n", + "2419.0\n", + "\r", + " The lowest SNR = 5.00\n", + "\r", + " The highest SNR = 53.68\n", + "\r", + " Number of SNR bins = 6\n", + "\r", + " Centres of SNR bins = [ 5.30884444 9.44060876 16.78804018 29.85382619 53.08844442\n", + " 94.40608763 167.88040181]\n", + "\r", + " Edges of SNR bins = [ 3.98107171 7.07945784 12.58925412 22.38721139 39.81071706\n", + " 70.79457844 125.89254118]\n", + "\r", + " Catalogue N in SNR bins\n", + " 0 1465.0\n", + " 1 763.0\n", + " 2 161.0\n", + " 3 26.0\n", + " 4 4.0\n", + " 5 0.0\n", + " 6 0.0\n", + "[soliket.binned_clusters.binned_clusters] Loading files describing selection function.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Reading Q as a function of theta.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Reading Q as a function of theta.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Reading full Q function.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Reading full Q function.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of tiles = 280.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of tiles = 280.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Reading RMS.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Reading RMS.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Reading in full RMS table.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Reading in full RMS table.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of tiles = 263. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of tiles = 263. \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of sky patches = 102519.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of sky patches = 102519.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Downsampling RMS and Q function using 5 bins.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Downsampling RMS and Q function using 5 bins.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of downsampled sky patches = 5.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of downsampled sky patches = 5.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of Q functions = 5.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of Q functions = 5.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Entire survey area = 13211.395702126332 deg2.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Entire survey area = 13211.395702126332 deg2.\n", + " Nz for higher resolution = 291\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:soliket.binnedclusterlikelihood:Error at evaluation. See error information below.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binnedclusterlikelihood] *ERROR* Error at evaluation. See error information below.\n" + ] + }, + { + "ename": "IndexError", + "evalue": "index 140 is out of bounds for axis 1 with size 138", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRemoteTraceback\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/usr/local/anaconda3/lib/python3.8/multiprocessing/pool.py\", line 125, in worker\n result = (True, func(*args, **kwds))\n File \"/usr/local/anaconda3/lib/python3.8/multiprocessing/pool.py\", line 48, in mapstar\n return list(map(*args))\n File \"/Users/boris/Work/CLASS-SZ/SO-SZ/SOLikeT/soliket/binned_clusters/binned_clusters.py\", line 1174, in get_comp_zarr2D\n erfunc.append(get_erf_compl(y0[i,index_z,int(tile[j])-1], qmin, qmax, noise[j], qcut))\nIndexError: index 140 is out of bounds for axis 1 with size 138\n\"\"\"", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_q/j04c9lw93j75_c_z2jdfbtwm0000gn/T/ipykernel_6081/2254738176.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0mlike\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlikelihood\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'soliket.BinnedClusterLikelihood'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloglikes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Work/CLASS-SZ/SO-SZ/cobaya_sz/cobaya/model.py\u001b[0m in \u001b[0;36mloglikes\u001b[0;34m(self, params_values, return_derived, make_finite, cached)\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0minput_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameterization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m return self._loglikes_input_params(input_params, return_derived=return_derived,\n\u001b[0m\u001b[1;32m 306\u001b[0m cached=cached, make_finite=make_finite)\n\u001b[1;32m 307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Work/CLASS-SZ/SO-SZ/cobaya_sz/cobaya/model.py\u001b[0m in \u001b[0;36m_loglikes_input_params\u001b[0;34m(self, input_params, return_derived, make_finite, cached)\u001b[0m\n\u001b[1;32m 308\u001b[0m def _loglikes_input_params(self, input_params, return_derived=True, make_finite=False,\n\u001b[1;32m 309\u001b[0m cached=True):\n\u001b[0;32m--> 310\u001b[0;31m result = self.logps(input_params, return_derived=return_derived, cached=cached,\n\u001b[0m\u001b[1;32m 311\u001b[0m make_finite=make_finite)\n\u001b[1;32m 312\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_derived\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Work/CLASS-SZ/SO-SZ/cobaya_sz/cobaya/model.py\u001b[0m in \u001b[0;36mlogps\u001b[0;34m(self, input_params, return_derived, cached, make_finite)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0mdepend_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minput_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparam_dep\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minput_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcomponent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_params\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m compute_success = component.check_cache_and_compute(\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwant_derived\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mneed_derived\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m dependency_params=depend_list, cached=cached)\n", + "\u001b[0;32m~/Work/CLASS-SZ/SO-SZ/cobaya_sz/cobaya/theory.py\u001b[0m in \u001b[0;36mcheck_cache_and_compute\u001b[0;34m(self, params_values_dict, dependency_params, want_derived, cached)\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;32mif\u001b[0m \u001b[0mwant_derived\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"logp\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m \u001b[0;31m# in case of exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"logp\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_derived\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mderived\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams_values_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 130\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Computed log-likelihood = %g\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"logp\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mderived\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Work/CLASS-SZ/SO-SZ/SOLikeT/soliket/binned_clusters/binned_poisson.py\u001b[0m in \u001b[0;36mlogp\u001b[0;34m(self, **params_values)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlogp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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'params': params,\n", + " 'likelihood': {'soliket.BinnedClusterLikelihood': {\n", + " 'choose_theory': \"camb\",\n", + " \n", + " 'single_tile_test': \"no\",\n", + " 'choose_dim': \"2D\",\n", + " 'Q_optimise': \"yes\",\n", + " 'stop_at_error': True,\n", + " 'data_path': path2data,\n", + " 'cat_file': \"DR5_cluster-catalog_v1.1.fits\",\n", + " 'Q_file': \"DR5ClusterSearch/selFn/QFit.fits\",\n", + "# 'Q_file': \"DR5ClusterSearch/selFn_dwnsmpld/Q_dwnsmpld.npz\",\n", + " 'tile_file': \"DR5ClusterSearch/selFn/tileAreas.txt\",\n", + " 'mode': 'downsample',\n", + " 'compl_mode': 'erf_diff',\n", + " 'dwnsmpl_bins': 5,\n", + " 'average_Q': False,\n", + " 'rms_file': \"DR5ClusterSearch/selFn/RMSTab.fits\",\n", + " \n", + " # redshift setting\n", + " 'zmin': 0.,\n", + " 'zmax': 2.8,\n", + " 'dz': 0.1,\n", + "\n", + " \n", + " \n", + " # SNR setting\n", + " 'SNRcut': 5.,\n", + " 'log10qmin': 0.6,\n", + " 'log10qmax': 2.0,\n", + " 'dlog10q': 0.25,\n", + " \n", + " # mass setting\n", + " 'Mmin': 1e13,\n", + " 'Mmax': 1e16,\n", + " 'dlogM': 0.05,\n", + " \n", + " # mass definition\n", + " 'delta': 200.\n", + " }},\n", + "# 'rms_file': \"DR5ClusterSearch/selFn_dwnsmpld/RMS_dwnsmpld.txt\"}},\n", + " 'theory': {'camb': \n", + " {'extra_args': {'num_massive_neutrinos': 0},\n", + " #'ignore_obsolete': True #for new cobaya version\n", + " }}\n", + "}\n", + "\n", + "\n", + "# initialisation \n", + "model = get_model(info)\n", + "like = model.likelihood['soliket.BinnedClusterLikelihood']\n", + "model.loglikes({})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pk_intp = like.theory.get_Pk_interpolator((\"delta_nonu\", \"delta_nonu\"), nonlinear=False)\n", + "SZparams = {\n", + " 'tenToA0': 4.35e-5,\n", + " 'B0': 0.08,\n", + " 'scatter_sz': 0.,\n", + " 'bias_sz': 1. \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 0: 106.07846017661463.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 4921.297921860493\n", + "1 2694.5670611237156\n", + "2 540.255487553468\n", + "3 75.1119467935664\n", + "4 6.010101649443801\n", + "5 0.21751511288565423\n", + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 0: 106.07846017661463.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 1: 481.8490132857626.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 1: 481.8490132857626.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 2: 920.6043318795657.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 2: 920.6043318795657.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 3: 1139.9256523587376.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 3: 1139.9256523587376.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 4: 1158.891110783115.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 4: 1158.891110783115.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 5: 1052.701747744105.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 5: 1052.701747744105.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 6: 886.9371109833565.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 6: 886.9371109833565.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 7: 706.8838404211742.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 7: 706.8838404211742.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 8: 539.2161619079624.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 8: 539.2161619079624.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 9: 396.69556422489484.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 9: 396.69556422489484.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 10: 283.0032040766712.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 10: 283.0032040766712.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 11: 196.5489273075399.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 11: 196.5489273075399.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 12: 133.30870098594394.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 12: 133.30870098594394.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 13: 88.50635230031612.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 13: 88.50635230031612.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 14: 57.63947426644873.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 14: 57.63947426644873.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 15: 36.87867496180542.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 15: 36.87867496180542.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 16: 23.217541496072247.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 16: 23.217541496072247.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 17: 14.399965339848517.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 17: 14.399965339848517.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 18: 8.810203496279764.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 18: 8.810203496279764.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 19: 5.3639960973587915.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 19: 5.3639960973587915.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Number of clusters in redshift bin 20: 0.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Number of clusters in redshift bin 20: 0.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Total predicted 2D N = 8237.460034093574.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Total predicted 2D N = 8237.460034093574.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:soliket.binned_clusters.binned_clusters:Theory N calculation took 0.7011499404907227 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[soliket.binned_clusters.binned_clusters] Theory N calculation took 0.7011499404907227 seconds.\n" + ] + } + ], + "source": [ + "Nzq = like._get_theory(pk_intp, **SZparams)\n", + "z, q, catNzq = like.delN2Dcat\n", + "\n", + "Nq = np.zeros(len(q))\n", + "catNq = np.zeros(len(q))\n", + "for i in range(len(q)):\n", + " Nq[i] = Nzq[:,i].sum() \n", + " catNq[i] = catNzq[:,i].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "color_list = plt.cm.magma(np.linspace(0.1,0.8,13))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'q' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_q/j04c9lw93j75_c_z2jdfbtwm0000gn/T/ipykernel_5638/44843278.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'prediction, nbins=5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatNq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcatNq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapthick\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mock catalogue'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'signal-to-noise $q$'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'$N$'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'q' is not defined" + ] + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.plot(10**q, Nq, color=color_list[2], label='prediction, nbins=5')\n", + "plt.errorbar(10**q, catNq, yerr=np.sqrt(catNq), color='black', fmt='o', ms=3, capsize=5, capthick=2, ls='none', label='mock catalogue')\n", + "plt.xlabel('signal-to-noise $q$', fontsize=14)\n", + "plt.ylabel('$N$', fontsize=14)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "# plt.title('WebSkyHalos A10tSZ cat comparison', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/soliket/clusters/notebooks/cstat.ipynb b/soliket/clusters/notebooks/cstat.ipynb new file mode 100644 index 00000000..eb874e76 --- /dev/null +++ b/soliket/clusters/notebooks/cstat.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from soliket.clusters import cluster_utils\n", + "import numpy as np\n", + "import scipy.stats" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "npred = np.arange(5, 905).reshape(30, 30)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "nobs = scipy.stats.poisson.rvs(npred)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "pval, Ce, Cv, Cd = cluster_utils.gof_cash(npred, nobs)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2634276257040634" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pval" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(900.9325882032165, 42.47433288108644, 927.8109317074573)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Ce, np.sqrt(Cv), Cd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "actxdes_venv", + "language": "python", + "name": "actxdes_venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/soliket/clusters/survey.py b/soliket/clusters/survey.py deleted file mode 100644 index a9d0e472..00000000 --- a/soliket/clusters/survey.py +++ /dev/null @@ -1,211 +0,0 @@ -import os -import numpy as np - -from scipy import interpolate -import astropy.io.fits as pyfits - -# from astLib import astWCS -from astropy.wcs import WCS -from astropy.io import fits -import astropy.table as atpy - - -def read_clust_cat(fitsfile, qmin): - list = fits.open(fitsfile) - data = list[1].data - SNR = data.field("SNR2p4") - z = data.field("z") - zerr = data.field("zErr") - Y0 = data.field("y0tilde") - Y0err = data.field("y0tilde_err") - ind = np.where(SNR >= qmin)[0] - print("num clust ", np.shape(ind), qmin) - return z[ind], zerr[ind], Y0[ind], Y0err[ind] - - -def read_mock_cat(fitsfile, qmin): - list = fits.open(fitsfile) - data = list[1].data - SNR = data.field("fixed_SNR") - z = data.field("redshift") - zerr = data.field("redshiftErr") - Y0 = data.field("fixed_y_c") - Y0err = data.field("err_fixed_y_c") - ind = np.where(SNR >= qmin)[0] - return z[ind], zerr[ind], Y0[ind], Y0err[ind] - - -def read_matt_mock_cat(fitsfile, qmin): - list = fits.open(fitsfile) - data = list[1].data - # ra = data.field("RADeg") - # dec = data.field("decDeg") - z = data.field("redshift") - zerr = data.field("redshiftErr") - Y0 = data.field("fixed_y_c") - Y0err = data.field("fixed_err_y_c") - SNR = data.field("fixed_SNR") - # M = data.field("true_M500") - ind = np.where(SNR >= qmin)[0] - return z[ind], zerr[ind], Y0[ind], Y0err[ind] - - -def read_matt_cat(fitsfile, qmin): - list = fits.open(fitsfile) - data = list[1].data - z = data.field("redshift") - zerr = data.field("redshiftErr") - Y0 = data.field("fixed_y_c") - Y0err = data.field("fixed_err_y_c") - SNR = data.field("fixed_SNR") - ind = np.where(SNR >= qmin)[0] - return z[ind], zerr[ind], Y0[ind], Y0err[ind] - - -def loadAreaMask(extName, DIR): - """Loads the survey area mask (i.e., after edge-trimming and point source masking, - produced by nemo). - Returns map array, wcs - """ - areaImg = pyfits.open(os.path.join(DIR, "areaMask%s.fits.gz" % (extName))) - areaMap = areaImg[0].data - wcs = WCS(areaImg[0].header) # , mode="pyfits") - areaImg.close() - - return areaMap, wcs - - -def loadRMSmap(extName, DIR): - """Loads the survey RMS map (produced by nemo). - Returns map array, wcs - """ - areaImg = pyfits.open( - os.path.join(DIR, "RMSMap_Arnaud_M2e14_z0p4%s.fits.gz" % (extName)) - ) - areaMap = areaImg[0].data - wcs = WCS(areaImg[0].header) # , mode="pyfits") - areaImg.close() - - return areaMap, wcs - - -def loadQ(source, tileNames=None): - """Load the filter mismatch function Q as a dictionary of spline fits. - Args: - source (NemoConfig or str): Either the path to a .fits table (containing Q fits - for all tiles - this is normally selFn/QFit.fits), or a NemoConfig object - (from which the path and tiles to use will be inferred). - tileNames (optional, list): A list of tiles for which the Q function will be - extracted. If source is a NemoConfig object, this should be set to None. - Returns: - A dictionary (with tile names as keys), containing spline knots for the Q - function for each tile. - """ - if type(source) == str: - combinedQTabFileName = source - else: - # We should add a check to confirm this is actually a NemoConfig object - combinedQTabFileName = os.path.join(source.selFnDir, "QFit.fits") - tileNames = source.tileNames - tckDict = {} - if os.path.exists(combinedQTabFileName): - combinedQTab = atpy.Table().read(combinedQTabFileName) - for key in combinedQTab.keys(): - if key != "theta500Arcmin": - tckDict[key] = interpolate.splrep( - combinedQTab["theta500Arcmin"], combinedQTab[key] - ) - else: - if tileNames is None: - raise Exception( - "If source does not point to a complete QFit.fits file,\ - you need to supply tileNames." - ) - for tileName in tileNames: - tab = atpy.Table().read( - combinedQTabFileName.replace(".fits", "#%s.fits" % (tileName)) - ) - tckDict[tileName] = interpolate.splrep(tab["theta500Arcmin"], tab["Q"]) - return tckDict - - -class SurveyData: - def __init__( - self, - nemoOutputDir, - ClusterCat, - qmin=5.6, - szarMock=False, - MattMock=False, - tiles=False, - num_noise_bins=20, - ): - self.nemodir = nemoOutputDir - - self.tckQFit = loadQ(self.nemodir + "/QFit.fits") - self.qmin = qmin - self.tiles = tiles - self.num_noise_bins = num_noise_bins - - if szarMock: - print("mock catalog") - self.clst_z, self.clst_zerr, self.clst_y0, self.clst_y0err = read_mock_cat( - ClusterCat, self.qmin - ) - elif MattMock: - print("Matt mock catalog") - self.clst_z, self.clst_zerr, self.clst_y0, self.clst_y0err = read_matt_cat( - ClusterCat, self.qmin - ) - else: - print("real catalog") - self.clst_z, self.clst_zerr, self.clst_y0, self.clst_y0err = read_clust_cat( - ClusterCat, self.qmin - ) - - if tiles: - self.filetile = self.nemodir + "/tileAreas.txt" - self.tilenames = np.loadtxt( - self.filetile, dtype=np.str, usecols=0, unpack=True - ) - self.tilearea = np.loadtxt( - self.filetile, dtype=np.float, usecols=1, unpack=True - ) - - self.fsky = [] - self.mask = [] - self.mwcs = [] - self.rms = [] - self.rwcs = [] - self.rmstotal = np.array([]) - - for i in range(len(self.tilearea)): - self.fsky.append(self.tilearea[i] / 41252.9612) - tempmask, tempmwcs = loadAreaMask("#" + self.tilenames[i], self.nemodir) - self.mask.append(tempmask) - self.mwcs.append(tempmwcs) - temprms, temprwcs = loadRMSmap("#" + self.tilenames[i], self.nemodir) - self.rms.append(temprms) - self.rwcs.append(temprwcs) - self.rmstotal = np.append(self.rmstotal, temprms[temprms > 0]) - - self.fskytotal = np.sum(self.fsky) - else: - self.rms, self.rwcs = loadRMSmap("", self.nemodir) - self.mask, self.mwcs = loadAreaMask("", self.nemodir) - self.rmstotal = self.rms[self.rms > 0] - self.fskytotal = 987.5 / 41252.9612 - - count_temp, bin_edge = np.histogram( - np.log10(self.rmstotal), bins=self.num_noise_bins - ) - - self.frac_of_survey = count_temp * 1.0 / np.sum(count_temp) - self.Ythresh = 10 ** ((bin_edge[:-1] + bin_edge[1:]) / 2.0) - - @property - def Q(self): - if self.tiles: - return self.tckQFit["Q"] - else: - return self.tckQFit["PRIMARY"] diff --git a/soliket/clusters/sz_utils.py b/soliket/clusters/sz_utils.py deleted file mode 100644 index 138ac315..00000000 --- a/soliket/clusters/sz_utils.py +++ /dev/null @@ -1,410 +0,0 @@ -import numpy as np -from scipy import interpolate -# from astropy.cosmology import FlatLambdaCDM - -# from nemo import signals -from ..constants import MPC2CM, MSUN_CGS, G_CGS, C_M_S, T_CMB -from ..constants import h_Planck, k_Boltzmann, electron_mass_kg, elementary_charge - -# from .clusters import C_KM_S as C_in_kms - -rho_crit0H100 = (3. / (8. * np.pi) * (100. * 1.e5) ** 2.) \ - / G_CGS * MPC2CM / MSUN_CGS - - -def gaussian(xx, mu, sig, noNorm=False): - if noNorm: - return np.exp(-1.0 * (xx - mu) ** 2 / (2.0 * sig ** 2.0)) - else: - return 1.0 / (sig * np.sqrt(2 * np.pi)) \ - * np.exp(-1.0 * (xx - mu) ** 2 / (2.0 * sig ** 2.0)) - - -class szutils: - def __init__(self, Survey): - self.LgY = np.arange(-6, -2.5, 0.01) - self.Survey = Survey - - # self.rho_crit0H100 = (3. / (8. * np.pi) * \ - # (100. * 1.e5)**2.) / G_in_cgs * Mpc_in_cm / MSun_in_g - - def P_Yo(self, LgY, M, z, param_vals, Ez_fn, Da_fn): - H0 = param_vals["H0"] - - Ma = np.outer(M, np.ones(len(LgY[0, :]))) - - Ytilde, theta0, Qfilt = y0FromLogM500( - np.log10(param_vals["massbias"] * Ma / (H0 / 100.0)), - z, - self.Survey.Q, - sigma_int=param_vals["scat"], - B0=param_vals["B0"], - H0=param_vals["H0"], - Ez_fn=Ez_fn, - Da_fn=Da_fn - ) - Y = 10 ** LgY - - # Ytilde = np.repeat(Ytilde[:, :, np.newaxis], LgY.shape[2], axis=2) - - # ind = 20 - # print ("M,z,y~",M[ind],z,Ytilde[ind,0]) - - numer = -1.0 * (np.log(Y / Ytilde)) ** 2 - ans = ( - 1.0 / (param_vals["scat"] * np.sqrt(2 * np.pi)) * - np.exp(numer / (2.0 * param_vals["scat"] ** 2)) - ) - return ans - - def P_Yo_vec(self, LgY, M, z, param_vals, Ez_fn, Da_fn): - H0 = param_vals["H0"] - # Ma = np.outer(M, np.ones(len(LgY[0, :]))) - - Ytilde, theta0, Qfilt = y0FromLogM500( - np.log10(param_vals["massbias"] * M / (H0 / 100.0)), - z, - self.Survey.Q, - sigma_int=param_vals["scat"], - B0=param_vals["B0"], - H0=param_vals["H0"], - Ez_fn=Ez_fn, - Da_fn=Da_fn, - ) - Y = 10 ** LgY - - Ytilde = np.repeat(Ytilde[:, :, np.newaxis], LgY.shape[2], axis=2) - - numer = -1.0 * (np.log(Y / Ytilde)) ** 2 - ans = ( - 1.0 / (param_vals["scat"] * np.sqrt(2 * np.pi)) * - np.exp(numer / (2.0 * param_vals["scat"] ** 2)) - ) - return ans - - def Y_erf(self, Y, Ynoise): - qmin = self.Survey.qmin - ans = Y * 0.0 - ans[Y - qmin * Ynoise > 0] = 1.0 - return ans - - def P_of_gt_SN(self, LgY, MM, zz, Ynoise, param_vals, Ez_fn, Da_fn): - Y = 10 ** LgY - - sig_tr = np.outer(np.ones([MM.shape[0], MM.shape[1]]), self.Y_erf(Y, Ynoise)) - sig_thresh = np.reshape(sig_tr, - (MM.shape[0], MM.shape[1], len(self.Y_erf(Y, Ynoise)))) - - LgYa = np.outer(np.ones([MM.shape[0], MM.shape[1]]), LgY) - LgYa2 = np.reshape(LgYa, (MM.shape[0], MM.shape[1], len(LgY))) - - P_Y = np.nan_to_num(self.P_Yo_vec(LgYa2, MM, zz, param_vals, Ez_fn, Da_fn)) - - ans = np.trapz(P_Y * sig_thresh, x=LgY, axis=2) * np.log(10) - return ans - - def PfuncY(self, YNoise, M, z_arr, param_vals, Ez_fn, Da_fn): - LgY = self.LgY - - P_func = np.outer(M, np.zeros([len(z_arr)])) - M_arr = np.outer(M, np.ones([len(z_arr)])) - - P_func = self.P_of_gt_SN(LgY, M_arr, z_arr, YNoise, param_vals, Ez_fn, Da_fn) - return P_func - - def P_of_Y_per(self, LgY, MM, zz, Y_c, Y_err, param_vals): - P_Y_sig = np.outer(np.ones(len(MM)), self.Y_prob(Y_c, LgY, Y_err)) - LgYa = np.outer(np.ones(len(MM)), LgY) - - LgYa = np.outer(np.ones([MM.shape[0], MM.shape[1]]), LgY) - LgYa2 = np.reshape(LgYa, (MM.shape[0], MM.shape[1], len(LgY))) - - P_Y = np.nan_to_num(self.P_Yo(LgYa2, MM, zz, param_vals)) - ans = np.trapz(P_Y * P_Y_sig, LgY, np.diff(LgY), axis=1) * np.log(10) - - return ans - - def Y_prob(self, Y_c, LgY, YNoise): - Y = 10 ** (LgY) - - ans = gaussian(Y, Y_c, YNoise) - return ans - - def Pfunc_per(self, MM, zz, Y_c, Y_err, param_vals, Ez_fn, Da_fn): - LgY = self.LgY - LgYa = np.outer(np.ones(len(MM)), LgY) - - P_Y_sig = self.Y_prob(Y_c, LgY, Y_err) - P_Y = np.nan_to_num(self.P_Yo(LgYa, MM, zz, param_vals, Ez_fn, Da_fn)) - ans = np.trapz(P_Y * P_Y_sig, LgY, np.diff(LgY), axis=1) - - return ans - - def Pfunc_per_parallel(self, Marr, zarr, Y_c, Y_err, param_vals, Ez_fn, Da_fn): - # LgY = self.LgY - # LgYa = np.outer(np.ones(Marr.shape[0]), LgY) - - # LgYa = np.outer(np.ones([Marr.shape[0], Marr.shape[1]]), LgY) - # LgYa2 = np.reshape(LgYa, (Marr.shape[0], Marr.shape[1], len(LgY))) - - # Yc_arr = np.outer(np.ones(Marr.shape[0]), Y_c) - # Yerr_arr = np.outer(np.ones(Marr.shape[0]), Y_err) - - # Yc_arr = np.repeat(Yc_arr[:, :, np.newaxis], len(LgY), axis=2) - # Yerr_arr = np.repeat(Yerr_arr[:, :, np.newaxis], len(LgY), axis=2) - - # P_Y_sig = self.Y_prob(Yc_arr, LgYa2, Yerr_arr) - # P_Y = np.nan_to_num(self.P_Yo(LgYa2, Marr, zarr, param_vals, Ez_fn)) - - P_Y_sig = self.Y_prob(Y_c, self.LgY, Y_err) - P_Y = np.nan_to_num(self.P_Yo(self.LgY, Marr, zarr, param_vals, Ez_fn, Da_fn)) - - ans = np.trapz(P_Y * P_Y_sig, x=self.LgY, axis=2) - - return ans - - def Pfunc_per_zarr(self, MM, z_c, Y_c, Y_err, int_HMF, param_vals): - LgY = self.LgY - - # old was z_arr - # P_func = np.outer(MM, np.zeros([len(z_arr)])) - # M_arr = np.outer(MM, np.ones([len(z_arr)])) - # M200 = np.outer(MM, np.zeros([len(z_arr)])) - # zarr = np.outer(np.ones([len(M)]), z_arr) - - P_func = self.P_of_Y_per(LgY, MM, z_c, Y_c, Y_err, param_vals) - - return P_func - - -### -"""Routines from nemo (author: Matt Hilton ) to limit dependencies""" - - -# ---------------------------------------------------------------------------------------- -def calcR500Mpc(z, M500, Ez_fn, H0): - """Given z, M500 (in MSun), returns R500 in Mpc, with respect to critical density. - - """ - - if type(M500) == str: - raise Exception( - "M500 is a string - check M500MSun in your .yml config file:\ - use, e.g., 1.0e+14 (not 1e14 or 1e+14)" - ) - - Ez = Ez_fn(z) - - criticalDensity = rho_crit0H100 * (H0 / 100.) ** 2 * Ez ** 2 - R500Mpc = np.power((3 * M500) / (4 * np.pi * 500 * criticalDensity), 1.0 / 3.0) - - return R500Mpc - - -# ---------------------------------------------------------------------------------------- -def calcTheta500Arcmin(z, M500, Ez_fn, Da_fn, H0): - """Given z, M500 (in MSun), returns angular size equivalent to R500, with respect to - critical density. - - """ - - R500Mpc = calcR500Mpc(z, M500, Ez_fn, H0) - DAz = Da_fn(z) - - theta500Arcmin = np.degrees(np.arctan(R500Mpc / DAz)) * 60.0 - - return theta500Arcmin - - -# ---------------------------------------------------------------------------------------- -def calcQ(theta500Arcmin, tck): - """Returns Q, given theta500Arcmin, and a set of spline fit knots for (theta, Q). - - """ - - # Q=np.poly1d(coeffs)(theta500Arcmin) - Q = interpolate.splev(theta500Arcmin, tck) - - return Q - - -# ---------------------------------------------------------------------------------------- -def calcFRel(z, M500, obsFreqGHz=148.0, Ez_fn=None): - """Calculates relativistic correction to SZ effect at specified frequency, given z, - M500 in MSun. - - This assumes the Arnaud et al. (2005) M-T relation, and applies formulae of - Itoh et al. (1998) - - As for H13, we return fRel = 1 + delta_SZE (see also Marriage et al. 2011) - """ - - # Using Arnaud et al. (2005) M-T to get temperature - A = 3.84e14 - B = 1.71 - # TkeV=5.*np.power(((cosmoModel.efunc(z)*M500)/A), 1/B) # HMF/Astropy - Ez = Ez_fn(z) - TkeV = 5.0 * np.power(((Ez * M500) / A), 1 / B) # Colossus - TKelvin = TkeV * ((1000 * elementary_charge) / k_Boltzmann) - - # Itoh et al. (1998) eqns. 2.25 - 2.30 - thetae = (k_Boltzmann * TKelvin) / (electron_mass_kg * C_M_S ** 2) - X = (h_Planck * obsFreqGHz * 1e9) / (k_Boltzmann * T_CMB) - Xtw = X * (np.cosh(X / 2.0) / np.sinh(X / 2.0)) - Stw = X / np.sinh(X / 2.0) - - Y0 = -4 + Xtw - - Y1 = ( - -10.0 - + (47 / 2.0) * Xtw - - (42 / 5.0) * Xtw ** 2 - + (7 / 10.0) * Xtw ** 3 - + np.power(Stw, 2) * (-(21 / 5.0) + (7 / 5.0) * Xtw) - ) - - Y2 = ( - -(15 / 2.0) - + (1023 / 8.0) * Xtw - - (868 / 5.0) * Xtw ** 2 - + (329 / 5.0) * Xtw ** 3 - - (44 / 5.0) * Xtw ** 4 - + (11 / 30.0) * Xtw ** 5 - + np.power(Stw, 2) - * (-(434 / 5.0) + (658 / 5.0) * Xtw - - (242 / 5.0) * Xtw ** 2 - + (143 / 30.0) * Xtw ** 3) - + np.power(Stw, 4) * (-(44 / 5.0) + (187 / 60.0) * Xtw) - ) - - Y3 = ( - (15 / 2.0) - + (2505 / 8.0) * Xtw - - (7098 / 5.0) * Xtw ** 2 - + (14253 / 10.0) * Xtw ** 3 - - (18594 / 35.0) * Xtw ** 4 - + (12059 / 140.0) * Xtw ** 5 - - (128 / 21.0) * Xtw ** 6 - + (16 / 105.0) * Xtw ** 7 - + np.power(Stw, 2) - * ( - -(7098 / 10.0) - + (14253 / 5.0) * Xtw - - (102267 / 35.0) * Xtw ** 2 - + (156767 / 140.0) * Xtw ** 3 - - (1216 / 7.0) * Xtw ** 4 - + (64 / 7.0) * Xtw ** 5 - ) - + np.power(Stw, 4) - * (-(18594 / 35.0) + (205003 / 280.0) * Xtw - - (1920 / 7.0) * Xtw ** 2 + (1024 / 35.0) * Xtw ** 3) - + np.power(Stw, 6) * (-(544 / 21.0) + (992 / 105.0) * Xtw) - ) - - Y4 = ( - -(135 / 32.0) - + (30375 / 128.0) * Xtw - - (62391 / 10.0) * Xtw ** 2 - + (614727 / 40.0) * Xtw ** 3 - - (124389 / 10.0) * Xtw ** 4 - + (355703 / 80.0) * Xtw ** 5 - - (16568 / 21.0) * Xtw ** 6 - + (7516 / 105.0) * Xtw ** 7 - - (22 / 7.0) * Xtw ** 8 - + (11 / 210.0) * Xtw ** 9 - + np.power(Stw, 2) - * ( - -(62391 / 20.0) - + (614727 / 20.0) * Xtw - - (1368279 / 20.0) * Xtw ** 2 - + (4624139 / 80.0) * Xtw ** 3 - - (157396 / 7.0) * Xtw ** 4 - + (30064 / 7.0) * Xtw ** 5 - - (2717 / 7.0) * Xtw ** 6 - + (2761 / 210.0) * Xtw ** 7 - ) - + np.power(Stw, 4) - * ( - -(124389 / 10.0) - + (6046951 / 160.0) * Xtw - - (248520 / 7.0) * Xtw ** 2 - + (481024 / 35.0) * Xtw ** 3 - - (15972 / 7.0) * Xtw ** 4 - + (18689 / 140.0) * Xtw ** 5 - ) - + np.power(Stw, 6) - * (-(70414 / 21.0) + (465992 / 105.0) * Xtw - - (11792 / 7.0) * Xtw ** 2 + (19778 / 105.0) * Xtw ** 3) - + np.power(Stw, 8) * (-(682 / 7.0) + (7601 / 210.0) * Xtw) - ) - - deltaSZE = ( - ((X ** 3) / (np.exp(X) - 1)) - * ((thetae * X * np.exp(X)) / (np.exp(X) - 1)) - * (Y0 + Y1 * thetae + Y2 * thetae ** 2 + Y3 * thetae ** 3 + Y4 * thetae ** 4) - ) - - fRel = 1 + deltaSZE - - return fRel - - -# ---------------------------------------------------------------------------------------- -def y0FromLogM500( - log10M500, - z, - tckQFit, - tenToA0=4.95e-5, - B0=0.08, - Mpivot=3e14, - sigma_int=0.2, - fRelWeightsDict={148.0: 1.0}, - H0=70., - Ez_fn=None, - Da_fn=None -): - """Predict y0~ given logM500 (in MSun) and redshift. Default scaling relation - parameters are A10 (as in H13). - - Use cosmoModel (astropy.cosmology object) to change/specify cosmological parameters. - - fRelWeightsDict is used to account for the relativistic correction when y0~ has been - constructed from multi-frequency maps. Weights should sum to 1.0; keys are observed - frequency in GHz. - - Returns y0~, theta500Arcmin, Q - - """ - - if type(Mpivot) == str: - raise Exception( - "Mpivot is a string - check Mpivot in your .yml config file:\ - use, e.g., 3.0e+14 (not 3e14 or 3e+14)" - ) - - # Filtering/detection was performed with a fixed fiducial cosmology... so we don't - # need to recalculate Q. - # We just need to recalculate theta500Arcmin and E(z) only - M500 = np.power(10, log10M500) - theta500Arcmin = calcTheta500Arcmin(z, M500, Ez_fn, Da_fn, H0) - Q = calcQ(theta500Arcmin, tckQFit) - - Ez = Ez_fn(z) - - # Relativistic correction: now a little more complicated, to account for fact y0~ maps - # are weighted sum of individual frequency maps, and relativistic correction size - # varies with frequency - fRels = [] - freqWeights = [] - for obsFreqGHz in fRelWeightsDict.keys(): - fRels.append(calcFRel(z, M500, obsFreqGHz=obsFreqGHz, Ez_fn=Ez_fn)) - freqWeights.append(fRelWeightsDict[obsFreqGHz]) - fRel = np.average(np.array(fRels), axis=0, weights=freqWeights) - - # UPP relation according to H13 - # NOTE: m in H13 is M/Mpivot - # NOTE: this goes negative for crazy masses where the Q polynomial fit goes -ve, so - # ignore those - y0pred = tenToA0 * np.power(Ez, 2) * np.power(M500 / Mpivot, 1 + B0) * Q * fRel - - return y0pred, theta500Arcmin, Q diff --git a/soliket/poisson.py b/soliket/poisson.py index 0a4a0630..76b95f59 100644 --- a/soliket/poisson.py +++ b/soliket/poisson.py @@ -1,27 +1,16 @@ -import pandas as pd - from cobaya.likelihood import Likelihood - from .poisson_data import PoissonData class PoissonLikelihood(Likelihood): - name = "Poisson" - data_path = None - columns = None + name: str = "Poisson" def initialize(self): - catalog = self._get_catalog() - if self.columns is None: - self.columns = catalog.columns - self.data = PoissonData(self.name, catalog, self.columns) - - def get_requirements(self): - return {} + catalog, columns = self._get_catalog() + self.data = PoissonData(self.name, catalog, columns) def _get_catalog(self): - catalog = pd.read_csv(self.data_path) - return catalog + raise NotImplementedError def _get_rate_fn(self, **kwargs): """Returns a callable rate function that takes each of 'columns' as kwargs. @@ -33,7 +22,8 @@ def _get_n_expected(self, **kwargs): """ raise NotImplementedError - def logp(self, **params_values): - rate_fn = self._get_rate_fn(**params_values) - n_expected = self._get_n_expected(**params_values) - return self.data.loglike(rate_fn, n_expected) + def logp(self, **kwargs): + pk_intp = self.provider.get_Pk_interpolator() + rate_densities = self._get_rate_fn(pk_intp, **kwargs) + n_expected = self._get_n_expected(pk_intp, **kwargs) + return self.data.loglike(rate_densities, n_expected) diff --git a/soliket/poisson_data.py b/soliket/poisson_data.py index e1ed72d9..e4742063 100644 --- a/soliket/poisson_data.py +++ b/soliket/poisson_data.py @@ -1,5 +1,31 @@ import numpy as np import pandas as pd +import time + + +def poisson_logpdf(n_expected, catalog, columns, rate_fn, name="unbinned"): + """Computes log-likelihood of data under poisson process model + + rate_fn returns the *observed rate* as a function of self.columns + (must be able to take all of self.columns as keywords + + n_expected is predicted total number + """ + start = time.time() + + rate_densities = np.array(rate_fn(**{c: catalog[c].values for c in columns})) + assert np.all(np.isfinite(rate_densities)) + + elapsed = time.time() - start + # print("\r ::: rate density calculation took {:.3f} seconds.".format(elapsed)) + + loglike = -n_expected + np.nansum(np.log(rate_densities[np.nonzero(rate_densities)])) + + # print("\r ::: 2D ln likelihood = ", loglike) + # print("rates:",np.shape(rate_densities),rate_densities) + + return loglike + class PoissonData: @@ -11,70 +37,12 @@ class PoissonData: Catalog of observed data. columns : list Columns of catalog relevant for computing poisson rate. - samples : dict, optional - Each entry is an N_cat x N_samples array of posterior samples; - plus, should have a 'prior' entry of the same shape that is the value of the - interim prior for each sample. """ - def __init__(self, name, catalog, columns, samples=None): + def __init__(self, name, catalog, columns): self.name = str(name) - self.catalog = pd.DataFrame(catalog)[columns] self.columns = columns - if samples is not None: - for c in columns: - if c not in samples: - raise ValueError("If providing samples, must have samples \ - for all columns: {}".format(columns)) - - if "prior" not in samples: - raise ValueError('Must provide value of interim prior \ - for all samples, under "prior" key!') - - assert all( - [samples[k].shape == samples["prior"].shape for k in samples] - ), "Samples all need same shape!" - self.N_k = samples["prior"].shape[1] - self._len = samples["prior"].shape[0] - - else: - self._len = len(self.catalog) - - self.samples = samples - - def __len__(self): - return self._len - - def loglike(self, rate_fn, n_expected, broadcastable=False): - """Computes log-likelihood of data under poisson process model - - rate_fn returns the *observed rate* as a function of self.columns - (must be able to take all of self.columns as keywords, and be broadcastable - (though could make this an option)) - - n_expected is predicted total number - """ - # Simple case; no uncertainties - if self.samples is None: - if broadcastable: - rate_densities = rate_fn(**{c: self.catalog[c].values for - c in self.columns}) - else: - rate_densities = np.array( - [ - rate_fn(**{c: self.catalog[c].values[i] for c in self.columns}) - for i in range(len(self)) - ] - ) - - return -n_expected + sum(np.log(rate_densities)) - - else: - # Eqn (11) of DFM, Hogg & Morton (https://arxiv.org/pdf/1406.3020.pdf) - summand = rate_fn(**{c: self.samples[c] for - c in self.columns}) / self.samples["prior"] - l_k = 1 / self.N_k * summand.sum(axis=1) - assert l_k.shape == (self._len,) - return -n_expected + sum(np.log(l_k)) + def loglike(self, rate_fn, n_expected): + return poisson_logpdf(n_expected, self.catalog, self.columns, rate_fn, name=self.name) diff --git a/soliket/tests/data/toy_cashc.txt b/soliket/tests/data/toy_cashc.txt new file mode 100644 index 00000000..131b43ab --- /dev/null +++ b/soliket/tests/data/toy_cashc.txt @@ -0,0 +1,4 @@ +0. 100 2 +0.2 100 4 +0.4 100 5 +0.6 100 2 \ No newline at end of file diff --git a/soliket/tests/test_clusters.py b/soliket/tests/test_clusters.py index b2df1b38..3a1b64ea 100644 --- a/soliket/tests/test_clusters.py +++ b/soliket/tests/test_clusters.py @@ -1,58 +1,150 @@ +# pytest -k clusters -v tests + import numpy as np import pytest from cobaya.model import get_model -fiducial_params = { - "ombh2": 0.02225, - "omch2": 0.1198, - "H0": 67.3, - "tau": 0.06, - "As": 2.2e-9, - "ns": 0.96, - "mnu": 0.06, - "nnu": 3.046, + +params = { + 'h': 0.68, + 'n_s': 0.965, + 'Omega_b': 0.049, + 'Omega_c': 0.261, + 'sigma8': 0.81, + 'm_nu': 0., + 'tenToA0': 4.0e-05, + 'B0': 0.08, + 'C0': 2., + 'scatter_sz': 0., + 'bias_sz': 1. } -info_fiducial = { - "params": fiducial_params, - "likelihood": {"soliket.ClusterLikelihood": {"stop_at_error": True}}, - "theory": { - "camb": { - "extra_args": { - "accurate_massive_neutrino_transfers": True, - "num_massive_neutrinos": 1, - "redshifts": np.linspace(0, 2, 41), - "nonlinear": False, - "kmax": 10.0, - "dark_energy_model": "ppf", - } - }, +path = './clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5/NemoCCL_A10tSZ_DR5White_ACT-DR5/' + +lkl_common = { + 'verbose': True, + 'stop_at_error': True, + 'data': { + 'data_path': path, + 'cat_file': 'NemoCCL_A10tSZ_DR5White_ACT-DR5_mass.fits', + 'Q_file': 'selFn/QFit.fits', + 'tile_file': 'selFn/tileAreas.txt', + 'rms_file': 'selFn/RMSTab.fits' + }, + 'theorypred': { + 'choose_theory': 'CCL', + 'massfunc_mode': 'ccl', + 'compl_mode': 'erf_diff', + 'md_hmf': '200c', + 'md_ym': '200c' }, + 'YM': { + 'Mpivot': 4.25e14 + }, + 'selfunc': { + 'SNRcut': 5., + 'method': 'SNRbased', + 'whichQ': 'injection', + 'resolution': 'downsample', + 'dwnsmpl_bins': 50, + 'save_dwsmpld': False, + }, + 'binning': { + 'z': { + 'zmin': 0., + 'zmax': 2.6, + 'dz': 0.1 + }, + 'q': { + 'log10qmin': 0.6, + 'log10qmax': 2.0, + 'dlog10q': 0.25 + }, + 'M': { + 'Mmin': 5e13, + 'Mmax': 1e16, + 'dlogM': 0.05 + }, + 'exclude_zbin': 0, + } +} + +ccl_baseline = { + 'transfer_function': 'boltzmann_camb', + 'matter_pk': 'halofit', + 'baryons_pk': 'nobaryons', + 'md_hmf': '200c' } + + +info_binned = { + 'params': params, + 'likelihood': {'soliket.BinnedClusterLikelihood': lkl_common}, + 'theory': {'soliket.clusters.CCL': ccl_baseline} +} + +info_unbinned = { + 'params': params, + 'likelihood': {'soliket.UnbinnedClusterLikelihood': lkl_common}, + 'theory': {'soliket.clusters.CCL': ccl_baseline} +} + + +def test_clusters_import(): + + from soliket.clusters import BinnedClusterLikelihood + from soliket.clusters import UnbinnedClusterLikelihood + + def test_clusters_model(): - model_fiducial = get_model(info_fiducial) # noqa F841 + binned_model = get_model(info_binned) + unbinned_model = get_model(info_unbinned) def test_clusters_loglike(): - model_fiducial = get_model(info_fiducial) + binned_model = get_model(info_binned) + unbinned_model = get_model(info_unbinned) + + binned_lnl = binned_model.loglikes({})[0] + unbinned_lnl = unbinned_model.loglikes({})[0] + + assert np.isfinite(binned_lnl) + assert np.isfinite(unbinned_lnl) + + +def test_clusters_prediction(): - lnl = model_fiducial.loglikes({})[0] + binned_model = get_model(info_binned) + unbinned_model = get_model(info_unbinned) - assert np.isclose(lnl, -855.0) + binned_model.loglikes({})[0] + unbinned_model.loglikes({})[0] + binned_like = binned_model.likelihood['soliket.BinnedClusterLikelihood'] + unbinned_like = unbinned_model.likelihood['soliket.UnbinnedClusterLikelihood'] -def test_clusters_n_expected(): + binned_pk_intp = binned_like.theory.get_Pk_interpolator() + unbinned_pk_intp = unbinned_like.theory.get_Pk_interpolator() + SZparams = { + 'tenToA0': 4.0e-05, + 'B0': 0.08, + 'C0': 2., + 'scatter_sz': 0., + 'bias_sz': 1. + } - model_fiducial = get_model(info_fiducial) + Nzq = binned_like._get_theory(binned_pk_intp, **SZparams) + Ntot = unbinned_like._get_n_expected(unbinned_pk_intp, **SZparams) - lnl = model_fiducial.loglikes({})[0] + assert np.isclose(Nzq.sum(), Ntot) - like = model_fiducial.likelihood["soliket.ClusterLikelihood"] - assert np.isfinite(lnl) - assert like._get_n_expected() > 40 +# test_clusters_import() +# test_clusters_model() +# test_clusters_loglike() +# test_clusters_prediction() diff --git a/soliket/tests/test_clusters.yaml b/soliket/tests/test_clusters.yaml index 2fb84681..7970292a 100644 --- a/soliket/tests/test_clusters.yaml +++ b/soliket/tests/test_clusters.yaml @@ -1,10 +1,13 @@ debug: true +<<<<<<< HEAD +======= likelihood: soliket.ClusterLikelihood: +>>>>>>> master params: # fixed @@ -82,4 +85,8 @@ theory: stop_at_error: true sampler: - evaluate: \ No newline at end of file +<<<<<<< HEAD + evaluate: +======= + evaluate: +>>>>>>> master diff --git a/version.py b/version.py new file mode 100644 index 00000000..a6642837 --- /dev/null +++ b/version.py @@ -0,0 +1,8 @@ +# Note that we need to fall back to the hard-coded version if either +# setuptools_scm can't be imported or setuptools_scm can't determine the +# version, so we catch the generic 'Exception'. +try: + from setuptools_scm import get_version + version = get_version(root='..', relative_to=__file__) +except Exception: + version = '0.1rc1.dev101+g7bc3f87'