diff --git a/CorrMatrix_ABC/covest.py b/CorrMatrix_ABC/covest.py index d7f53ce8..c8e2e50e 100644 --- a/CorrMatrix_ABC/covest.py +++ b/CorrMatrix_ABC/covest.py @@ -188,7 +188,9 @@ def par_fish_SH(n, n_D, par): """Parameter RMS from Fisher matrix esimation of SH likelihood. """ - return [np.sqrt(alpha_new(n_S, n_D) * 2.0 * n_S / (n_S - 1.0)) * par for n_S in n] + #return [np.sqrt(alpha_new(n_S, n_D) * 2.0 * n_S / (n_S - 1.0)) * par for n_S in n] + return [np.sqrt(alpha_new(n_S, n_D) * (n_S - 1.0) / n_S) * par for n_S in n] + #return [np.sqrt((n_S - 1.0) / n_S) * par for n_S in n] def coeff_TJ14(n_S, n_D, n_P): @@ -697,7 +699,7 @@ def __init__(self, par_name, n_n_S, n_R, file_base='mean_std', yscale='linear', self.std[p] = np.zeros(shape = (n_n_S, n_R)) self.F = np.zeros(shape = (n_n_S, n_R, 2, 2)) - self.fs = 12 + self.fs = 14 self.n_D = n_D self.n_S_arr = n_S_arr @@ -1025,7 +1027,7 @@ def plot_mean_std(self, par=None, boxwidth=None, xlog=False, ylim=None, model='a linestyle = ['-', '--'] plot_sth = False - fs = 13 + fs = 14 plot_init(self.n_D, n_R, fs=fs) box_width = set_box_width(boxwidth, xlog, n) @@ -1140,10 +1142,10 @@ def plot_mean_std(self, par=None, boxwidth=None, xlog=False, ylim=None, model='a ylabel = stat_notation(which) plt.ylabel(ylabel) ax.set_yscale(self.yscale[j]) - leg = ax.legend(loc=f'{loc[which]} right', frameon=False, handlelength=1.3) + leg = ax.legend(loc=f'{loc[which]} right', frameon=False, handlelength=1.3, fontsize=12) plt.gca().add_artist(leg) if which in leg2: - ax.legend(leg2[which], lab2[which], loc=f'{loc[which]} left', frameon=False, handlelength=0.9) + ax.legend(leg2[which], lab2[which], loc=f'{loc[which]} left', frameon=False, handlelength=0.9, fontsize=12) plt.xlim(xmin, xmax) @@ -1276,7 +1278,7 @@ def plot_std_var(self, par=None, sig_var_noise=None, xlog=False, title=False, mo yy = self.fct['std_var_TJ14'](n_fine, self.n_D, par[i]) p_sym = par_symbol(p, eq=False) plot_add_legend(True, n_fine, yy, linestyle=linestyle[i], - color=color[i], label=fr'$\mathbf{{\hat F}}({p_sym})$', linewidth=2) + color=color[i], label=fr'$\mathbf{{\hat F}}({p_sym})$', linewidth=12) cols.append(self.fct['std_var_TJ14'](n, self.n_D, par[i])) names.append('TJ14({})'.format(p)) @@ -1306,7 +1308,7 @@ def plot_std_var(self, par=None, sig_var_noise=None, xlog=False, title=False, mo plt.xlabel('$n_{\\rm s}$') plt.ylabel(stat_notation('std_var')) ax.set_yscale('log') - ax.legend(loc='best', numpoints=1, frameon=False, handlelength=1.3) + ax.legend(loc='best', numpoints=1, frameon=False, handlelength=1.3, fontsize=12) #ax.set_aspect(aspect=1) # x-ticks diff --git a/results/Example_1/T2_MCMC/mean_std_fit_SH.txt b/results/Example_1/T2_MCMC/mean_std_fit_SH.txt index f58db845..3b2c8d88 100644 --- a/results/Example_1/T2_MCMC/mean_std_fit_SH.txt +++ b/results/Example_1/T2_MCMC/mean_std_fit_SH.txt @@ -1,11 +1,11 @@ -# n_S mean[a]_run00 std[a]_run00 mean[a]_run01 std[a]_run01 mean[a]_run02 std[a]_run02 mean[a]_run03 std[a]_run03 mean[a]_run04 std[a]_run04 mean[a]_run05 std[a]_run05 mean[a]_run06 std[a]_run06 mean[a]_run07 std[a]_run07 mean[a]_run08 std[a]_run08 mean[a]_run09 std[a]_run09 mean[a]_run10 std[a]_run10 mean[a]_run11 std[a]_run11 mean[a]_run12 std[a]_run12 mean[a]_run13 std[a]_run13 mean[a]_run14 std[a]_run14 mean[a]_run15 std[a]_run15 mean[a]_run16 std[a]_run16 mean[a]_run17 std[a]_run17 mean[a]_run18 std[a]_run18 mean[a]_run19 std[a]_run19 mean[a]_run20 std[a]_run20 mean[a]_run21 std[a]_run21 mean[a]_run22 std[a]_run22 mean[a]_run23 std[a]_run23 mean[a]_run24 std[a]_run24 mean[a]_run25 std[a]_run25 mean[a]_run26 std[a]_run26 mean[a]_run27 std[a]_run27 mean[a]_run28 std[a]_run28 mean[a]_run29 std[a]_run29 mean[a]_run30 std[a]_run30 mean[a]_run31 std[a]_run31 mean[a]_run32 std[a]_run32 mean[a]_run33 std[a]_run33 mean[a]_run34 std[a]_run34 mean[a]_run35 std[a]_run35 mean[a]_run36 std[a]_run36 mean[a]_run37 std[a]_run37 mean[a]_run38 std[a]_run38 mean[a]_run39 std[a]_run39 mean[a]_run40 std[a]_run40 mean[a]_run41 std[a]_run41 mean[a]_run42 std[a]_run42 mean[a]_run43 std[a]_run43 mean[a]_run44 std[a]_run44 mean[a]_run45 std[a]_run45 mean[a]_run46 std[a]_run46 mean[a]_run47 std[a]_run47 mean[a]_run48 std[a]_run48 mean[a]_run49 std[a]_run49 mean[b]_run00 std[b]_run00 mean[b]_run01 std[b]_run01 mean[b]_run02 std[b]_run02 mean[b]_run03 std[b]_run03 mean[b]_run04 std[b]_run04 mean[b]_run05 std[b]_run05 mean[b]_run06 std[b]_run06 mean[b]_run07 std[b]_run07 mean[b]_run08 std[b]_run08 mean[b]_run09 std[b]_run09 mean[b]_run10 std[b]_run10 mean[b]_run11 std[b]_run11 mean[b]_run12 std[b]_run12 mean[b]_run13 std[b]_run13 mean[b]_run14 std[b]_run14 mean[b]_run15 std[b]_run15 mean[b]_run16 std[b]_run16 mean[b]_run17 std[b]_run17 mean[b]_run18 std[b]_run18 mean[b]_run19 std[b]_run19 mean[b]_run20 std[b]_run20 mean[b]_run21 std[b]_run21 mean[b]_run22 std[b]_run22 mean[b]_run23 std[b]_run23 mean[b]_run24 std[b]_run24 mean[b]_run25 std[b]_run25 mean[b]_run26 std[b]_run26 mean[b]_run27 std[b]_run27 mean[b]_run28 std[b]_run28 mean[b]_run29 std[b]_run29 mean[b]_run30 std[b]_run30 mean[b]_run31 std[b]_run31 mean[b]_run32 std[b]_run32 mean[b]_run33 std[b]_run33 mean[b]_run34 std[b]_run34 mean[b]_run35 std[b]_run35 mean[b]_run36 std[b]_run36 mean[b]_run37 std[b]_run37 mean[b]_run38 std[b]_run38 mean[b]_run39 std[b]_run39 mean[b]_run40 std[b]_run40 mean[b]_run41 std[b]_run41 mean[b]_run42 std[b]_run42 mean[b]_run43 std[b]_run43 mean[b]_run44 std[b]_run44 mean[b]_run45 std[b]_run45 mean[b]_run46 std[b]_run46 mean[b]_run47 std[b]_run47 mean[b]_run48 std[b]_run48 mean[b]_run49 std[b]_run49 -755 0.9843335177255574 0.000821703749105363 1.0035695354715444 0.0020126273076731056 1.0039700623554664 0.0011249402465389835 1.012300042041743 0.0008309214787507842 1.02037137562254 0.0015861504694466178 0.9923209464360335 0.0006361457836263305 1.0158726576323203 0.0011943312252287314 1.00940088609085 0.0009694039562981128 1.0086701400377844 0.001292869612595579 1.026041379266252 0.0014330754603310819 1.0136028126812413 0.0015923313545857323 0.9874615469673772 0.0017682489149485428 1.0139788735127842 0.0005977104269141474 0.9892779762849873 0.002038192212603338 1.0255649468747723 0.001515422504066101 0.9816744559344127 0.0009080011175212791 1.0115908404983056 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a/results/Example_2/Euclid_dist_acf2lin/std_2mean_std_ABC.txt +++ b/results/Example_2/Euclid_dist_acf2lin/std_2mean_std_ABC.txt @@ -1,6 +1,6 @@ -# n_S sigma(sigma^2_tilt) sigma(sigma^2_ampl) TJ14(tilt) TJ14(ampl) -2 2.859957682e-06 1.847028056e-05 nan nan -5 2.467277298e-06 1.186425654e-05 nan nan -10 1.876307812e-06 8.776322349e-06 3.937050307e-07 1.100917147e-05 -20 1.509240552e-06 1.437147627e-05 3.264429663e-07 9.128322758e-06 -40 1.762607642e-06 8.17724968e-06 1.565754885e-07 4.378319471e-06 +# n_S sigma(sigma^2_tilt) sigma(sigma^2_ampl) +2 2.859957682e-06 1.847028056e-05 +5 2.467277298e-06 1.186425654e-05 +10 1.876307812e-06 8.776322349e-06 +20 1.509240552e-06 1.437147627e-05 +40 1.762607642e-06 8.17724968e-06 diff --git a/results/Example_3/Euclid_dist_acf2lin/mean_std_ABC.pdf b/results/Example_3/Euclid_dist_acf2lin/mean_std_ABC.pdf index 558d68ec..67dcd5ba 100644 Binary files a/results/Example_3/Euclid_dist_acf2lin/mean_std_ABC.pdf and b/results/Example_3/Euclid_dist_acf2lin/mean_std_ABC.pdf differ diff --git a/results/Example_3/Euclid_dist_acf2lin/std_2mean_std_ABC.pdf b/results/Example_3/Euclid_dist_acf2lin/std_2mean_std_ABC.pdf index 401fc598..d51be875 100644 Binary files a/results/Example_3/Euclid_dist_acf2lin/std_2mean_std_ABC.pdf and b/results/Example_3/Euclid_dist_acf2lin/std_2mean_std_ABC.pdf differ diff --git a/results/Example_3/Euclid_dist_acf2lin/std_2mean_std_ABC.txt b/results/Example_3/Euclid_dist_acf2lin/std_2mean_std_ABC.txt index 388dfbf2..fa071c56 100644 --- a/results/Example_3/Euclid_dist_acf2lin/std_2mean_std_ABC.txt +++ b/results/Example_3/Euclid_dist_acf2lin/std_2mean_std_ABC.txt @@ -1,6 +1,6 @@ -# n_S sigma(sigma^2_Omegam) sigma(sigma^2_sigma8) TJ14(Omegam) TJ14(sigma8) -2 1.171630964e-05 2.55991623e-05 nan nan -5 7.495316861e-06 1.374675137e-05 nan nan -10 1.442707666e-05 3.860273721e-05 7.495544903e-06 2.503703555e-05 -20 1.173516019e-05 1.668958924e-05 6.214977511e-06 2.07596132e-05 -40 1.665798398e-05 3.413116483e-05 2.980959128e-06 9.957165308e-06 +# n_S sigma(sigma^2_Omegam) sigma(sigma^2_sigma8) +2 1.171630964e-05 2.55991623e-05 +5 7.495316861e-06 1.374675137e-05 +10 1.442707666e-05 3.860273721e-05 +20 1.173516019e-05 1.668958924e-05 +40 1.665798398e-05 3.413116483e-05 diff --git a/scripts/ABC/job_ABC.py b/scripts/ABC/job_ABC.py index 57b49311..347b2339 100755 --- a/scripts/ABC/job_ABC.py +++ b/scripts/ABC/job_ABC.py @@ -649,8 +649,9 @@ def main(argv=None): yscale=['linear', 'log'], n_D=param.n_D, n_S_arr=n_S_arr, - fct= {'std': par_fish_SH, 'std_var_TJ14' : std_fish_deb_TJ14} + fct={}, ) + # fct={'std': par_fish_SH, 'std_var_TJ14' : std_fish_deb_TJ14} # Create simulations/write observation diff --git a/scripts/cov_matrix_definition/cm_likelihood.py b/scripts/cov_matrix_definition/cm_likelihood.py index aae208b0..fa15479a 100755 --- a/scripts/cov_matrix_definition/cm_likelihood.py +++ b/scripts/cov_matrix_definition/cm_likelihood.py @@ -482,7 +482,7 @@ def fit_corr_inv_true(x1, cov_true, sig2, n_jobs=3): toy_data['nobs'] = len(x1) # sample size = n_D toy_data['x'] = x1 # explanatory variable - # cov = covariance of the data! + # cov = covariance of the data y = multivariate_normal.rvs(mean=x1, cov=cov_true, size=1) toy_data['y'] = y # response variable, here one realisation @@ -491,7 +491,6 @@ def fit_corr_inv_true(x1, cov_true, sig2, n_jobs=3): toy_data['cov_inv'] = cov_inv # STAN code - # the fitting code does not believe that observations are correlated! stan_code = """ data { int nobs; @@ -516,18 +515,11 @@ def fit_corr_inv_true(x1, cov_true, sig2, n_jobs=3): """ import pystan - start = time.time() fit = pystan.stan(model_code=stan_code, data=toy_data, iter=2000, chains=n_jobs, verbose=False, n_jobs=n_jobs) - end = time.time() - - #elapsed = end - start - #print 'elapsed time = ' + str(elapsed) - return fit - def fit_corr(x1, cov_true, cov_est, n_jobs=3): """ Generates one draw from a multivariate normal distribution @@ -577,21 +569,12 @@ def fit_corr(x1, cov_true, cov_est, n_jobs=3): """ import pystan - start = time.time() fit = pystan.stan(model_code=stan_code, data=toy_data, iter=2000, chains=n_jobs, verbose=False, n_jobs=n_jobs) - # Testing: fast call to pystan - #fit = pystan.stan(model_code=stan_code, data=toy_data, iter=1, chains=1, verbose=False, n_jobs=n_jobs) - - end = time.time() - - #elapsed = end - start - #print 'elapsed time = ' + str(elapsed) - return fit -def fit_corr_SH(x1, cov_true, cov_est_inv, n_jobs=3): +def fit_corr_SH(x1, cov_true, cov_est_inv, n_S, n_jobs=3): """ Generates one draw from a multivariate student-t distribution (see Sellentin & Heavens 2015) @@ -601,6 +584,7 @@ def fit_corr_SH(x1, cov_true, cov_est_inv, n_jobs=3): input: x1, mean of multivariate normal distribution - vector of floats cov_true, square covariance matrix for the simulation cov_est_inv, inverse square covariance matrix for the fitting + n_S, number of simulations used for covariance n_jobs, number of parallel jobs output: fit, Stan fitting object @@ -610,8 +594,9 @@ def fit_corr_SH(x1, cov_true, cov_est_inv, n_jobs=3): toy_data = {} # build data dictionary toy_data['nobs'] = len(x1) # sample size = n_D toy_data['x'] = x1 # explanatory variable + toy_data['ns'] = n_S # number of simulations used for covariance - # cov = covariance of the data! + # cov = covariance of the data y = multivariate_normal.rvs(mean=x1, cov=cov_true, size=1) toy_data['y'] = y # response variable, here one realisation @@ -619,10 +604,10 @@ def fit_corr_SH(x1, cov_true, cov_est_inv, n_jobs=3): toy_data['cov_est_inv'] = cov_est_inv # STAN code - # the fitting code does not believe that observations are correlated! stan_code = """ data { - int nobs; + int nobs; + int ns; vector[nobs] x; vector[nobs] y; matrix[nobs, nobs] cov_est_inv; @@ -637,30 +622,24 @@ def fit_corr_SH(x1, cov_true, cov_est_inv, n_jobs=3): mu = b + a * x; - # Sellentin & Heavens debiasing scheme: - # Replace normal likelihood with t-distribution - # y ~ (1 + (log_normal(mu, cov_est)) / (1 + n_S))^(-n_S/2) + // Sellentin & Heavens debiasing scheme: + // Replace normal likelihood with t-distribution + // y ~ (1 + (log_normal(mu, cov_est)) / (n_S - 1))^(-n_S/2) chi2 = (y - mu)' * cov_est_inv * (y - mu); - # target += log-likelihood - target += log(1.0 + chi2/(1.0 + nobs)) * -nobs/2.0; + // target += log-likelihood + target += log(1.0 + chi2/(ns - 1.0)) * -ns/2.0; } """ import pystan - start = time.time() fit = pystan.stan(model_code=stan_code, data=toy_data, iter=2000, chains=n_jobs, verbose=False, n_jobs=n_jobs) - end = time.time() - - #elapsed = end - start - #print 'elapsed time = ' + str(elapsed) return fit - def set_fit_MCMC(fit, res, i, run): """Set mean and std of fit according to stan output res. @@ -774,7 +753,7 @@ def simulate(x1, yreal, n_S_arr, sigma_ML, sigma_m1_ML, sigma_m1_ML_deb, fish_an if re.search('SH', options.likelihood) is not None: if options.verbose == True: print('Running MCMC with Sellentin&Heavens (SH) likelihood') - res = fit_corr_SH(x1, cov, cov_est_inv, n_jobs=options.n_jobs) + res = fit_corr_SH(x1, cov, cov_est_inv, n_S, n_jobs=options.n_jobs) set_fit_MCMC(fit_SH, res, i, run) @@ -940,22 +919,22 @@ def main(argv=None): # Initialisation of results - sigma_ML = Results(tr_name, n_n_S, options.n_R, file_base='sigma_ML') - sigma_m1_ML = Results(tr_name, n_n_S, options.n_R, file_base='sigma_m1_ML', yscale='log', fct={'mean': tr_N_m1_ML}) - sigma_m1_ML_deb = Results(tr_name, n_n_S, options.n_R, file_base='sigma_m1_ML_deb', fct={'mean': no_bias}) + sigma_ML = Results(tr_name, n_n_S, options.n_R, file_base='sigma_ML', n_S_arr=n_S_arr) + sigma_m1_ML = Results(tr_name, n_n_S, options.n_R, file_base='sigma_m1_ML', yscale='log', fct={'mean': tr_N_m1_ML}, n_S_arr=n_S_arr) + sigma_m1_ML_deb = Results(tr_name, n_n_S, options.n_R, file_base='sigma_m1_ML_deb', fct={'mean': no_bias}, n_S_arr=n_S_arr) - fish_ana = Results(par_name, n_n_S, options.n_R, file_base='std_Fisher_ana', yscale='log', fct={'std': par_fish}) + fish_ana = Results(par_name, n_n_S, options.n_R, file_base='std_Fisher_ana', yscale='log', fct={'std': par_fish}, n_S_arr=n_S_arr) fish_num = Results(par_name, n_n_S, options.n_R, file_base='std_Fisher_num', yscale='log', \ - fct={'std': par_fish, 'std_var_TJK13': std_fish_biased_TJK13, 'std_var_TJ14': std_fish_biased_TJ14}) + fct={'std': par_fish, 'std_var_TJK13': std_fish_biased_TJK13, 'std_var_TJ14': std_fish_biased_TJ14}, n_S_arr=n_S_arr) fish_deb = Results(par_name, n_n_S, options.n_R, file_base='std_Fisher_deb', yscale='log', \ - fct={'std': no_bias, 'std_var_TJK13': std_fish_deb, 'std_var_TJ14': std_fish_deb_TJ14}) + fct={'std': no_bias, 'std_var_TJK13': std_fish_deb, 'std_var_TJ14': std_fish_deb_TJ14}, n_S_arr=n_S_arr) fit_norm_num = Results(par_name, n_n_S, options.n_R, file_base='mean_std_fit_norm', yscale=['linear', 'log'], - fct={'std': par_fish, 'std_var_TJK13': std_fish_biased_TJK13, 'std_var_TJ14': std_fish_biased_TJ14}) + fct={'std': par_fish, 'std_var_TJK13': std_fish_biased_TJK13, 'std_var_TJ14': std_fish_biased_TJ14}, n_S_arr=n_S_arr) fit_norm_deb = Results(par_name, n_n_S, options.n_R, file_base='mean_std_fit_norm_deb', yscale=['linear', 'log'], - fct={'std': no_bias, 'std_var_TJ14': std_fish_deb_TJ14}) + fct={'std': no_bias, 'std_var_TJ14': std_fish_deb_TJ14}, n_S_arr=n_S_arr) fit_SH = Results(par_name, n_n_S, options.n_R, file_base='mean_std_fit_SH', yscale=['linear', 'log'], - fct={'std': par_fish_SH}) + fct={'std': par_fish_SH}, n_S_arr=n_S_arr) # Data x1 = uniform.rvs(loc=-delta/2, scale=delta, size=options.n_D) # exploratory variable @@ -994,17 +973,17 @@ def main(argv=None): print('Creating plots') if options.do_fish_ana == True: - fish_ana.plot_mean_std(n_S_arr, options.n_D, par={'std': dpar_exact}) - fish_num.plot_mean_std(n_S_arr, options.n_D, par={'std': dpar_exact}) - fish_deb.plot_mean_std(n_S_arr, options.n_D, par={'std': dpar_exact}) + fish_ana.plot_mean_std(par={'std': dpar_exact}) + fish_num.plot_mean_std(par={'std': dpar_exact}) + fish_deb.plot_mean_std(par={'std': dpar_exact}) if options.do_fit_stan == True: if re.search('norm_biased', options.likelihood) is not None: - fit_norm_num.plot_mean_std(n_S_arr, options.n_D, par={'mean': options.par, 'std': dpar_exact}, boxwidth=param.boxwidth) + fit_norm_num.plot_mean_std(par={'mean': options.par, 'std': dpar_exact}, boxwidth=param.boxwidth) if re.search('norm_deb', options.likelihood) is not None: - fit_norm_deb.plot_mean_std(n_S_arr, options.n_D, par={'mean': options.par, 'std': dpar_exact}, boxwidth=param.boxwidth) + fit_norm_deb.plot_mean_std(par={'mean': options.par, 'std': dpar_exact}, boxwidth=param.boxwidth) if re.search('SH', options.likelihood) is not None: - fit_SH.plot_mean_std(n_S_arr, options.n_D, par={'mean': options.par, 'std': dpar_exact}, boxwidth=param.boxwidth) + fit_SH.plot_mean_std(par={'mean': options.par, 'std': dpar_exact}, boxwidth=param.boxwidth) dpar2 = dpar_exact**2 @@ -1013,24 +992,24 @@ def main(argv=None): else: title = False - fish_num.plot_std_var(n_S_arr, options.n_D, par=dpar2, title=title) + fish_num.plot_std_var(par=dpar2, title=title) - fish_deb.plot_std_var(n_S_arr, options.n_D, par=dpar2, title=title) + fish_deb.plot_std_var(par=dpar2, title=title) if options.do_fit_stan == True: if re.search('norm_biased', options.likelihood) is not None: - fit_norm_num.plot_std_var(n_S_arr, options.n_D, par=dpar2, sig_var_noise=param.sig_var_noise, title=title) + fit_norm_num.plot_std_var(par=dpar2, sig_var_noise=param.sig_var_noise, title=title) if re.search('norm_deb', options.likelihood) is not None: - fit_norm_deb.plot_std_var(n_S_arr, options.n_D, par=dpar2, sig_var_noise=param.sig_var_noise, title=title) + fit_norm_deb.plot_std_var(par=dpar2, sig_var_noise=param.sig_var_noise, title=title) if re.search('SH', options.likelihood) is not None: - fit_SH.plot_std_var(n_S_arr, options.n_D, par=dpar2, sig_var_noise=param.sig_var_noise, title=title) + fit_SH.plot_std_var(par=dpar2, sig_var_noise=param.sig_var_noise, title=title) - sigma_ML.plot_mean_std(n_S_arr, options.n_D, par={'mean': [options.sig2]}, boxwidth=param.boxwidth, ylim=[4.9, 5.1]) + sigma_ML.plot_mean_std(par={'mean': [options.sig2]}, boxwidth=param.boxwidth, ylim=[4.9, 5.1]) # Precision matrix trace f_mean = 1/options.sig2 - sigma_m1_ML.plot_mean_std(n_S_arr, options.n_D, par={'mean': [f_mean]}, boxwidth=param.boxwidth) - sigma_m1_ML_deb.plot_mean_std(n_S_arr, options.n_D, par={'mean': [f_mean]}, boxwidth=param.boxwidth) + sigma_m1_ML.plot_mean_std(par={'mean': [f_mean]}, boxwidth=param.boxwidth) + sigma_m1_ML_deb.plot_mean_std(par={'mean': [f_mean]}, boxwidth=param.boxwidth) ### End main program diff --git a/scripts/plot_many.py b/scripts/plot_many.py index cd2ab3eb..f9e25cb1 100755 --- a/scripts/plot_many.py +++ b/scripts/plot_many.py @@ -317,9 +317,10 @@ def plot_box(fits, n_D, par, dy, which, boxwidth=None, xlog=False, ylog=False, # xticks for n_S in n: - x_loc.append(n_S) - lab = '{}'.format(n_S) - x_lab.append(lab) + if n_S not in (974, 2094, 4502): + x_loc.append(n_S) + lab = '{}'.format(n_S) + x_lab.append(lab) # Curves for predicted values if fit.fct is not None and which in fit.fct: @@ -398,6 +399,8 @@ def plot_box(fits, n_D, par, dy, which, boxwidth=None, xlog=False, ylog=False, frac = float(n_D) / float(n_S) if frac > 100: lab = '{:.0f}'.format(frac) + elif frac < 1: + lab = '{:.1g}'.format(frac) else: lab = '{:.2g}'.format(frac) x2_lab.append(lab) @@ -429,15 +432,13 @@ def main(argv=None): # Read sampling results fit_ABC = read_fit(param.par_name, param.ABC, 'ABC', fct={'mean': no_bias}, flab={'mean': 'true value'}, verbose=param.verbose) - # fct={'std': no_bias, 'std_var': std_fish_deb_TJ14}, fit_MCMC_norm = read_fit(param.par_name, param.MCMC_norm, 'MCMC $N$', fct={'std': no_bias, 'std_var': std_fish_Gupta}, #std_fish_deb}, flab={'std': r'$\mathbf{\hat F}$', 'std_var': r'$\mathbf{\hat F}$'}, verbose=param.verbose) - #flab={'std': r'$\hat{\bm{\mathrm{F}}}$', 'std_var': r'$\hat{\bm{\mathrm{F}}}$'}, fit_MCMC_T2 = read_fit(param.par_name, param.MCMC_T2, 'MCMC $T^2$', - fct={'std': par_fish_SH}, flab={'std': r'$\mathbf{\hat F}_{T^2}$'}, verbose=param.verbose) - #fct={'std': par_fish_SH}, flab={'std': r'$\hat{\bm{\mathrm{F}}}_{T^2}$'}, verbose=param.verbose) + fct={}, flab={}, verbose=param.verbose) + #fct={'std': par_fish_SH}, flab={'std': r'$\mathbf{\hat F}_{T^2}$'}, verbose=param.verbose) mode = -1 delta = 200