diff options
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/gf.py | 6 | ||||
| -rw-r--r-- | utils/plot.py | 23 |
2 files changed, 19 insertions, 10 deletions
diff --git a/utils/gf.py b/utils/gf.py index 2c794d3..13d5728 100644 --- a/utils/gf.py +++ b/utils/gf.py @@ -87,8 +87,10 @@ def steering_params(args): params.sampleToLoad = gf.sampleTag.MagicTau params.use_legacy_selfveto_calculation = False - params.spline_hole_ice = False - params.spline_dom_efficiency = False + # params.spline_hole_ice = False + # params.spline_dom_efficiency = False + params.spline_hole_ice = True + params.spline_dom_efficiency = True return params diff --git a/utils/plot.py b/utils/plot.py index 91b8b4e..f81f9da 100644 --- a/utils/plot.py +++ b/utils/plot.py @@ -41,6 +41,12 @@ from utils.enums import Likelihood, MixingScenario, ParamTag, StatCateg from utils.fr import angles_to_u, angles_to_fr +bayes_K = 1. # Substantial degree of belief. +# bayes_K = 3/2. # Strong degree of belief. +# bayes_K = 2. # Very strong degree of belief +# bayes_K = 5/2. # Decisive degree of belief + + if os.path.isfile('./plot_llh/paper.mplstyle'): plt.style.use('./plot_llh/paper.mplstyle') elif os.environ.get('GOLEMSOURCEPATH') is not None: @@ -218,6 +224,7 @@ def chainer_plot(infile, outfile, outformat, args, llh_paramset, fig_text=None): ha='center', va='center') for of in outformat: + print 'Saving', outfile+'_angles.'+of g.export(outfile+'_angles.'+of) if not hasattr(args, 'plot_elements'): @@ -327,7 +334,7 @@ def plot_statistic(data, outfile, outformat, args, scale_param, label=None): ax.plot(scales_rm, reduced_ev) - ax.axhline(y=np.log(10**(3/2.)), color='red', alpha=1., linewidth=1.3) + ax.axhline(y=np.log(10**(bayes_K)), color='red', alpha=1., linewidth=1.3) for ymaj in ax.yaxis.get_majorticklocs(): ax.axhline(y=ymaj, ls=':', color='gray', alpha=0.3, linewidth=1) @@ -384,7 +391,7 @@ def plot_sens_full(data, outfile, outformat, args): null = statistic[min_idx] if args.stat_method is StatCateg.BAYESIAN: reduced_ev = -(statistic - null) - al = scales[reduced_ev > np.log(10**(3/2.))] # Strong degree of belief + al = scales[reduced_ev > np.log(10**(bayes_K))] # Strong degree of belief # al = scales[reduced_ev > 0.4] # Testing elif args.stat_method is StatCateg.FREQUENTIST: reduced_ev = -2*(statistic - null) @@ -468,7 +475,7 @@ def plot_sens_fixed_angle_pretty(data, outfile, outformat, args): 8: (-21-(en[0]*6), -21-(en[1]+en[1]*6)) } - angles = 2 + angles = 3 colour = {0:'red', 1:'blue', 2:'green'} rgb_co = {0:[1,0,0], 1:[0,0,1], 2:[0.0, 0.5019607843137255, 0.0]} @@ -588,14 +595,14 @@ def plot_sens_fixed_angle_pretty(data, outfile, outformat, args): null = statistic[min_idx] if args.stat_method is StatCateg.BAYESIAN: reduced_ev = -(statistic_rm - null) - al = scales_rm[reduced_ev > np.log(10**(3/2.))] # Strong degree of belief + al = scales_rm[reduced_ev > np.log(10**(bayes_K))] # Strong degree of belief elif args.stat_method is StatCateg.FREQUENTIST: reduced_ev = -2*(statistic_rm - null) al = scales_rm[reduced_ev > 2.71] # 90% CL for 1 DOF via Wilks if len(al) == 0: print 'No points for DIM {0} FRS {1}!'.format(dim, src) continue - if reduced_ev[-1] < np.log(10**(3/2.)) - 0.1: + if reduced_ev[-1] < np.log(10**(bayes_K)) - 0.1: print 'Peaked contour does not exclude large scales! For ' \ 'DIM {0} FRS{1}!'.format(dim, src) continue @@ -729,14 +736,14 @@ def plot_sens_fixed_angle(data, outfile, outformat, args): null = statistic[min_idx] if args.stat_method is StatCateg.BAYESIAN: reduced_ev = -(statistic - null) - al = scales[reduced_ev > np.log(10**(3/2.))] # Strong degree of belief + al = scales[reduced_ev > np.log(10**(bayes_K))] # Strong degree of belief elif args.stat_method is StatCateg.FREQUENTIST: reduced_ev = -2*(statistic - null) al = scales[reduced_ev > 2.71] # 90% CL for 1 DOF via Wilks if len(al) == 0: print 'No points for DIM {0} FRS {1}!'.format(dim, src) continue - if reduced_ev[-1] < np.log(10**(3/2.)) - 0.1: + if reduced_ev[-1] < np.log(10**(bayes_K)) - 0.1: print 'Peaked contour does not exclude large scales! For ' \ 'DIM {0} FRS{1}!'.format(dim, src) continue @@ -893,7 +900,7 @@ def plot_sens_corr_angle(data, outfile, outformat, args): print sep_arrays if args.stat_method is StatCateg.BAYESIAN: - reduced_pdat_mask = (sep_arrays[2] > np.log(10**(3/2.))) # Strong degree of belief + reduced_pdat_mask = (sep_arrays[2] > np.log(10**(bayes_K))) # Strong degree of belief elif args.stat_method is StatCateg.FREQUENTIST: reduced_pdat_mask = (sep_arrays[2] > 4.61) # 90% CL for 2 DOFS via Wilks reduced_pdat = sep_arrays.T[reduced_pdat_mask].T |