diff options
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/enums.py | 6 | ||||
| -rw-r--r-- | utils/plot.py | 188 |
2 files changed, 189 insertions, 5 deletions
diff --git a/utils/enums.py b/utils/enums.py index a7982f8..441a16f 100644 --- a/utils/enums.py +++ b/utils/enums.py @@ -76,3 +76,9 @@ class StatCateg(Enum): class SteeringCateg(Enum): P2_0 = 1 P2_1 = 2 + + +class Texture(Enum): + OEU = 0 + OET = 1 + OUT = 2 diff --git a/utils/plot.py b/utils/plot.py index f81f9da..5a3d1f7 100644 --- a/utils/plot.py +++ b/utils/plot.py @@ -32,12 +32,15 @@ from getdist import plots, mcsamples import ternary from ternary.heatmapping import polygon_generator +# print ternary.__file__ +# assert 0 import shapely.geometry as geometry from utils import misc as misc_utils -from utils.enums import DataType, EnergyDependance +from utils.enums import DataType, EnergyDependance, str_enum from utils.enums import Likelihood, MixingScenario, ParamTag, StatCateg +from utils.enums import Texture from utils.fr import angles_to_u, angles_to_fr @@ -595,7 +598,7 @@ 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**(bayes_K))] # Strong degree of belief + al = scales_rm[reduced_ev > np.log(10**(bayes_K))] 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 @@ -997,9 +1000,10 @@ def get_tax(ax, scale): tax.clear_matplotlib_ticks() # Set ticks - ticks = np.linspace(0, 1, 6) - tax.ticks(ticks=ticks, locations=ticks*scale, axis='blr', linewidth=1, - offset=0.03, fontsize=fontsize, tick_formats='%.1f') + # ticks = np.linspace(0, 1, 6) + # tax.ticks(ticks=ticks, locations=ticks*scale, axis='blr', linewidth=1, + # offset=0.03, fontsize=fontsize, tick_formats='%.1f') + tax.ticks() tax._redraw_labels() @@ -1156,3 +1160,177 @@ def flavour_contour(frs, ax, nbins, coverage, linewidth=2, color='black'): ) ax.scatter(points.T[0], points.T[1], marker='o', s=2, alpha=1, color=color, zorder=3) + +def plot_source_ternary(data, outfile, outformat, args): + """Ternary plot in the source flavour space for each operator texture.""" + r_data = np.full((data.shape[0], data.shape[2], data.shape[1], data.shape[3], data.shape[4]), np.nan) + for idim in xrange(data.shape[0]): + for isrc in xrange(data.shape[1]): + for isce in xrange(data.shape[2]): + r_data[idim][isce][isrc] = data[idim][isrc][isce] + r_data = ma.masked_invalid(r_data) + print r_data.shape, 'r_data.shape' + nsrcs = int(len(args.source_ratios) / 3) + + for idim, dim in enumerate(args.dimensions): + print '|||| DIM = {0}, {1}'.format(idim, dim) + for isce in xrange(r_data.shape[1]): + print '|||| SCEN = {0}'.format(str_enum(Texture(isce))) + fig = plt.figure(figsize=(8, 8)) + ax = fig.add_subplot(111) + tax = get_tax(ax, scale=nsrcs) + interp_dict = {} + for isrc, src in enumerate(args.source_ratios): + src_sc = tuple(np.array(src)*(nsrcs-1)) + print '|||| SRC = {0}'.format(src) + scales, statistic = ma.compress_rows(r_data[idim][isce][isrc]).T + print 'scales', scales + print 'statistic', statistic + + try: + tck, u = splprep([scales, statistic], s=0) + except: + interp_dict[src_sc] = -60 + continue + # sc, st = splev(np.linspace(0, 1, 10000), tck) + sc, st = splev(np.linspace(0, 1, 20), tck) + scales_rm = sc[sc >= scales[1]] + statistic_rm = st[sc >= scales[1]] + # scales_rm = sc + # statistic_rm = st + + min_idx = np.argmin(scales) + null = statistic[min_idx] + if args.stat_method is StatCateg.BAYESIAN: + reduced_ev = -(statistic_rm - null) + print 'reduced_ev', reduced_ev + al = scales_rm[reduced_ev > np.log(10**(bayes_K))] + else: + assert 0 + if len(al) == 0: + print 'No points for DIM {0} FRS {1}!'.format(dim, src) + interp_dict[src_sc] = -60 + continue + 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) + interp_dict[src_sc] = -60 + continue + lim = al[0] + print 'limit = {0}'.format(lim) + + interp_dict[src_sc] = lim + print 'interp_dict', interp_dict + print + print 'vertices', heatmap(interp_dict, nsrcs) + print + tax.heatmap(interp_dict, scale=nsrcs, vmin=-60, vmax=-30) + misc_utils.make_dir(outfile) + for of in outformat: + print 'Saving plot as {0}'.format(outfile+'_SCEN{0}.'.format(isce)+of) + fig.savefig(outfile+'_SCEN{0}.'.format(isce)+of, bbox_inches='tight', dpi=150) + print 'nsrcs', nsrcs + assert 0 + + +def texture_label(x): + if x == Texture.OEU: + return r'$\mathcal{O}_{e\mu}$' + elif x == Texture.OET: + return r'$\mathcal{O}_{e\tau}$' + elif x == Texture.OUT: + return r'$\mathcal{O}_{\mu\tau}$' + else: + raise AssertionError + + +def plot_source_ternary_1D(data, outfile, outformat, args): + """Ternary plot in the source flavour space for each operator texture.""" + sources = args.source_ratios + x_1D = [] + i_src_1D = [] + for i, s in enumerate(sources): + if s[2] != 0: continue + x_1D.append(s[0]) + i_src_1D.append(i) + i_src_1D = list(reversed(i_src_1D)) + x_1D = list(reversed(x_1D)) + print 'x_1D', x_1D + def get_edges_from_cen(bincen): + hwidth = 0.5*(bincen[1] - bincen[0]) + return np.append([bincen[0]-hwidth], bincen[:]+hwidth) + be = get_edges_from_cen(x_1D) + + r_data = np.full((data.shape[0], data.shape[2], len(i_src_1D), data.shape[3], data.shape[4]), np.nan) + for idim in xrange(data.shape[0]): + for i1D, isrc in enumerate(i_src_1D): + for isce in xrange(data.shape[2]): + r_data[idim][isce][i1D] = data[idim][isrc][isce] + r_data = ma.masked_invalid(r_data) + print r_data.shape, 'r_data.shape' + + for idim, dim in enumerate(args.dimensions): + print '|||| DIM = {0}, {1}'.format(idim, dim) + fig = plt.figure(figsize=(4, 4)) + ax = fig.add_subplot(111) + ax.tick_params(axis='x', labelsize=12) + ax.tick_params(axis='y', labelsize=12) + ax.set_xlabel(r'$x$', fontsize=18) + ax.set_ylabel(r'${\rm New\:Physics\:Scale}\:[\:{\rm log}_{10} (\Lambda_{d=6}^{-1}\:/\:{\rm GeV}^{-2})\: ]$', fontsize=18) + ax.set_xlim(0, 1) + for isce in xrange(r_data.shape[1]): + print '|||| SCEN = {0}'.format(str_enum(Texture(isce))) + H = np.full(len(x_1D), np.nan) + for ix, x in enumerate(x_1D): + print '|||| X = {0}'.format(x) + scales, statistic = ma.compress_rows(r_data[idim][isce][ix]).T + print 'scales', scales + print 'statistic', statistic + + try: + tck, u = splprep([scales, statistic], s=0) + except: + continue + # sc, st = splev(np.linspace(0, 1, 10000), tck) + sc, st = splev(np.linspace(0, 1, 20), tck) + scales_rm = sc[sc >= scales[1]] + statistic_rm = st[sc >= scales[1]] + # scales_rm = sc + # statistic_rm = st + + min_idx = np.argmin(scales) + null = statistic[min_idx] + if args.stat_method is StatCateg.BAYESIAN: + reduced_ev = -(statistic_rm - null) + print 'reduced_ev', reduced_ev + al = scales_rm[reduced_ev > np.log(10**(bayes_K))] + else: + assert 0 + if len(al) == 0: + print 'No points for DIM {0} X {1}!'.format(dim, x) + continue + if reduced_ev[-1] < np.log(10**(bayes_K)) - 0.1: + print 'Peaked contour does not exclude large scales! For ' \ + 'DIM {0} X {1}!'.format(dim, x) + continue + lim = al[0] + print 'limit = {0}'.format(lim) + + H[ix] = lim + H = ma.masked_invalid(H) + H_0 = np.concatenate([[H[0]], H]) + ax.step(be, H_0, linewidth=2, + label=texture_label(Texture(isce)), linestyle='-', + drawstyle='steps-pre') + print 'H', H + print + for ymaj in ax.yaxis.get_majorticklocs(): + ax.axhline(y=ymaj, ls=':', color='gray', alpha=0.3, linewidth=1) + for xmaj in be: + ax.axvline(x=xmaj, ls=':', color='gray', alpha=0.3, linewidth=1) + ax.legend() + misc_utils.make_dir(outfile) + for of in outformat: + print 'Saving plot as {0}'.format(outfile+'_DIM{0}.'.format(dim)+of) + fig.savefig(outfile+'_DIM{0}.'.format(dim)+of, bbox_inches='tight', dpi=150) + |