datavar = '_lensing' root = 'base_plikHM_TTTEEE_lowl_lowE' + datavar samples = g.sampleAnalyser.samplesForRoot(root) datasets = ['sdss_6DF_bao.dataset', 'sdss_MGS_bao.dataset', 'sdss_DR14_quasar_bao.dataset'] names = ['6DFGS', 'SDSS\nMGS', 'SDSS quasars'] dataredshifts = np.zeros(len(datasets)) datapoints = np.zeros(len(datasets)) dataerrs = np.zeros(len(datasets)) colors = ['g', 'm', 'r', 'darkred'] for i, dat in enumerate(datasets): if '.dataset in dat': ini = inifile.IniFile(batchjob.getCodeRootPath() + 'data/' + dat) datapoints[i], dataerrs[i] = [float(f) for f in ini.split('bao_measurement')] dataredshifts[i] = ini.float('zeff') rescale = ini.float('rs_rescale', 1.) tp = ini.string('measurement_type') if tp == 'rs_over_DV': dataerrs[i] = -0.5 / (datapoints[i] + dataerrs[i]) + 0.5 / (datapoints[i] - dataerrs[i]) datapoints[i] = 1 / datapoints[i] elif tp != 'DV_over_rs': raise Exception('error') datapoints[i] *= rescale dataerrs[i] *= rescale print(dataredshifts[i], datapoints[i], dataerrs[i]) DR12 = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/final_consensus_results_dM_Hz_fsig.dat', usecols=[0, 1])
datavar = '_lensing' samples = g.sampleAnalyser.samplesForRoot('base_plikHM_TTTEEE_lowl_lowE' + datavar) variant = None # e.g. to add another set of bands in H(z) 'base_nnu_plikHM_TTTEEE_lowl_lowE' datasets = ['sdss_6DF_bao.dataset', 'sdss_MGS_bao.dataset', 'sdss_DR14_quasar_bao.dataset'] names = ['6DFGS', 'SDSS\nMGS', 'SDSS quasars'] dataredshifts = np.zeros(len(datasets)) datapoints = np.zeros(len(datasets)) dataerrs = np.zeros(len(datasets)) colors = ['g', 'm', 'r', 'darkred'] for i, dat in enumerate(datasets): if '.dataset in dat': ini = inifile.IniFile(batchjob.getCodeRootPath() + 'data/' + dat) datapoints[i], dataerrs[i] = [float(f) for f in ini.split('bao_measurement')] dataredshifts[i] = ini.float('zeff') rescale = ini.float('rs_rescale', 1.) tp = ini.string('measurement_type') if tp == 'rs_over_DV': dataerrs[i] = -0.5 / (datapoints[i] + dataerrs[i]) + 0.5 / (datapoints[i] - dataerrs[i]) datapoints[i] = 1 / datapoints[i] elif tp != 'DV_over_rs': raise Exception('error') datapoints[i] *= rescale dataerrs[i] *= rescale print dataredshifts[i], datapoints[i], dataerrs[i] def GetBackgroundFuncs(samples):
FAPv = np.arange(0.56, 0.78, 0.003) f8v = np.arange(0.28, 0.63, 0.003) FAP, f8 = np.meshgrid(FAPv, f8v) like = (FAP - FAPbar) ** 2 * invcov[0, 0] + 2 * (FAP - FAPbar) * (f8 - f8bar) * invcov[0, 1] + (f8 - f8bar) ** 2 * invcov[1, 1] density = Density2D(FAPv, f8v, exp(-like / 2)) density.contours = exp(-np.array([1.509, 2.4477]) ** 2 / 2) return density FAPbar = 0.6725 f8bar = 0.4412 density = RSDdensity(FAPbar, f8bar, batchjob.getCodeRootPath() + 'data/sdss_DR11CMASS_RSD_bao_invcov_Samushia.txt') g.add_2d_contours(roots[0], 'FAP057', 'fsigma8z057', filled=True, density=density) # CS = contourf(FAP, f8, like, origin='lower', levels=[2.279, 5.991], colors='r') FAPbar = .683 f8bar = 0.422 density = RSDdensity(FAPbar, f8bar, batchjob.getCodeRootPath() + 'data/sdss_DR11CMASS_RSD_bao_invcov_Beutler.txt') g.add_2d_contours(roots[0], 'FAP057', 'fsigma8z057', filled=False, density=density, ls=':', alpha=0.5) g.add_2d_contours(roots[0], 'FAP057', 'fsigma8z057', filled=True, plotno=3) g.add_legend(['BOSS CMASS (Samushia et al.)', 'BOSS CMASS (Beutler et al.)', s.defplanck + '+lensing'], legend_loc='upper left')
WP = r'\textit{Planck}+WP' WPhighL = r'\textit{Planck}+WP+highL' NoLowL = r'\textit{Planck}$-$lowL' lensonly = 'lensing' HST = r'$H_0$' BAO = 'BAO' LCDM = r'$\Lambda$CDM' s = copy.copy(plots.defaultSettings) s.legend_frame = False s.figure_legend_frame = False s.prob_label = r'$P/P_{\rm max}$' s.norm_prob_label = 'Probability density' s.prob_y_ticks = True s.param_names_for_labels = os.path.join(batchjob.getCodeRootPath(), 'clik_units.paramnames') s.alpha_filled_add = 0.85 s.solid_contour_palefactor = 0.6 s.solid_colors = [('#8CD3F5', '#006FED'), ('#F7BAA6', '#E03424'), ('#D1D1D1', '#A1A1A1'), 'g', 'cadetblue', 'olive', 'darkcyan'] s.axis_marker_lw = 0.6 s.lw_contour = 1 s.param_names_for_labels = os.path.normpath( os.path.join(os.path.dirname(__file__), '..', 'clik_latex.paramnames')) use_plot_data = getdist.use_plot_data rootdir = getdist.default_grid_root or os.path.join(batchjob.getCodeRootPath(),
f8v = np.arange(0.28, 0.63, 0.003) FAP, f8 = np.meshgrid(FAPv, f8v) like = (FAP - FAPbar)**2 * invcov[0, 0] + 2 * (FAP - FAPbar) * ( f8 - f8bar) * invcov[0, 1] + (f8 - f8bar)**2 * invcov[1, 1] density = Density2D(FAPv, f8v, exp(-like / 2)) density.contours = exp(-np.array([1.509, 2.4477])**2 / 2) return density FAPbar = 0.6725 f8bar = 0.4412 density = RSDdensity( FAPbar, f8bar, batchjob.getCodeRootPath() + 'data/sdss_DR11CMASS_RSD_bao_invcov_Samushia.txt') g.add_2d_contours(roots[0], 'FAP057', 'fsigma8z057', filled=True, density=density) # CS = contourf(FAP, f8, like, origin='lower', levels=[2.279, 5.991], colors='r') FAPbar = .683 f8bar = 0.422 density = RSDdensity( FAPbar, f8bar, batchjob.getCodeRootPath() + 'data/sdss_DR11CMASS_RSD_bao_invcov_Beutler.txt')
samples = g.sampleAnalyser.samplesForRoot(root) datasets = [ 'sdss_6DF_bao.dataset', 'sdss_MGS_bao.dataset', 'sdss_DR14_quasar_bao.dataset' ] names = ['6DFGS', 'SDSS\nMGS', 'SDSS quasars'] dataredshifts = np.zeros(len(datasets)) datapoints = np.zeros(len(datasets)) dataerrs = np.zeros(len(datasets)) colors = ['g', 'm', 'r', 'darkred'] for i, dat in enumerate(datasets): if '.dataset in dat': ini = inifile.IniFile(batchjob.getCodeRootPath() + 'data/' + dat) datapoints[i], dataerrs[i] = [ float(f) for f in ini.split('bao_measurement') ] dataredshifts[i] = ini.float('zeff') rescale = ini.float('rs_rescale', 1.) tp = ini.string('measurement_type') if tp == 'rs_over_DV': dataerrs[i] = -0.5 / (datapoints[i] + dataerrs[i]) + 0.5 / ( datapoints[i] - dataerrs[i]) datapoints[i] = 1 / datapoints[i] elif tp != 'DV_over_rs': raise Exception('error') datapoints[i] *= rescale dataerrs[i] *= rescale print dataredshifts[i], datapoints[i], dataerrs[i]
import planckStyle as s from paramgrid import batchjob import GetDistPlots import pylab as plt import numpy as np g = s.getSubplotPlotter(subplot_size=4) rd_fid = 147.78 cov = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/BAO_consensus_covtot_dM_Hz.txt') pts = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/sdss_DR12Consensus_bao.dat', usecols=[0, 1]) pnames = ['DM038', 'Hubble038', 'DM051', 'Hubble051', 'DM061', 'Hubble061'] redshifts = pts[:, 0] data = pts[:, 1] planckmeans = [] def BAOdensity(p1, p2, marge=True): err = np.sqrt(cov[p1, p1]) DAv = np.arange(data[p1] - 4 * err, data[p1] + 4 * err, 4) err = np.sqrt(cov[p2, p2]) Hv = np.arange(data[p2] - 4 * err, data[p2] + 4 * err, 0.3) DA, H = np.meshgrid(DAv, Hv) v1 = data[p1] v2 = data[p2] if marge: mcov = cov[np.ix_([p1, p2], [p1, p2])] invcov = np.linalg.inv(mcov)
variant = None # e.g. to add another set of bands in H(z) 'base_nnu_plikHM_TTTEEE_lowl_lowE' datasets = [ 'sdss_6DF_bao.dataset', 'sdss_MGS_bao.dataset', 'sdss_DR14_quasar_bao.dataset' ] names = ['6DFGS', 'SDSS\nMGS', 'SDSS quasars'] dataredshifts = np.zeros(len(datasets)) datapoints = np.zeros(len(datasets)) dataerrs = np.zeros(len(datasets)) colors = ['g', 'm', 'r', 'darkred'] for i, dat in enumerate(datasets): if '.dataset in dat': ini = inifile.IniFile(batchjob.getCodeRootPath() + 'data/' + dat) datapoints[i], dataerrs[i] = [ float(f) for f in ini.split('bao_measurement') ] dataredshifts[i] = ini.float('zeff') rescale = ini.float('rs_rescale', 1.) tp = ini.string('measurement_type') if tp == 'rs_over_DV': dataerrs[i] = -0.5 / (datapoints[i] + dataerrs[i]) + 0.5 / ( datapoints[i] - dataerrs[i]) datapoints[i] = 1 / datapoints[i] elif tp != 'DV_over_rs': raise Exception('error') datapoints[i] *= rescale dataerrs[i] *= rescale print dataredshifts[i], datapoints[i], dataerrs[i]
WP = r'\textit{Planck}+WP' WPhighL = r'\textit{Planck}+WP+highL' NoLowL = r'\textit{Planck}$-$lowL' lensonly = 'lensing' HST = r'$H_0$' BAO = 'BAO' LCDM = r'$\Lambda$CDM' s = copy.copy(plots.defaultSettings) s.legend_frame = False s.figure_legend_frame = False s.prob_label = r'$P/P_{\rm max}$' s.norm_prob_label = 'Probability density' s.prob_y_ticks = True s.param_names_for_labels = os.path.join(batchjob.getCodeRootPath(), 'clik_units.paramnames') s.alpha_filled_add = 0.85 s.solid_contour_palefactor = 0.6 s.solid_colors = [('#8CD3F5', '#006FED'), ('#F7BAA6', '#E03424'), ('#D1D1D1', '#A1A1A1'), 'g', 'cadetblue', 'olive', 'darkcyan'] s.axis_marker_lw = 0.6 s.lw_contour = 1 s.param_names_for_labels = os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'clik_latex.paramnames')) use_plot_data = getdist.use_plot_data rootdir = getdist.default_grid_root or os.path.join(batchjob.getCodeRootPath(), 'main') output_base_dir = getdist.output_base_dir or batchjob.getCodeRootPath() H0_gpe = [70.6, 3.3]
d = loadtxt(prob_file) ix = 0 prob = np.zeros((alpha_npoints, alpha_npoints)) alpha_perp = np.zeros(alpha_npoints) alpha_pl = np.zeros(alpha_npoints) for i in range(alpha_npoints): for j in range(alpha_npoints): alpha_perp[i] = d[ix, 0] alpha_pl[j] = d[ix, 1] prob[j, i] = d[ix, 2] ix += 1 prob = prob / np.max(prob) return alpha_perp, alpha_pl, prob alpha_perp, alpha_pl, prob = BAOdensity(batchjob.getCodeRootPath() + 'data/sdss_DR11CMASS_consensus.dat') densityG = BAOdensityG() perp = alpha_perp * DA_fid para = H_fid / alpha_pl density = GetDistPlots.Density2D(perp, para, prob) density.contours = exp(-np.array([1.509, 2.4477]) ** 2 / 2) root = 'base_plikHM_TT_lowTEB_lensing' c = 29979.2458 def makeNew(samples):
import planckStyle as s from paramgrid import batchjob import GetDistPlots import pylab as plt import numpy as np g = s.getSubplotPlotter(subplot_size=4) rd_fid = 147.78 FAP = True if FAP: cov = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/final_consensus_covtot_dV_FAP_fsig.txt') pts = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/final_consensus_results_dV_FAP_fsig.dat', usecols=[0, 1]) else: cov = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/final_consensus_covtot_dM_Hz_fsig.txt') pts = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/sdss_DR12Consensus_final.dat', usecols=[0, 1]) pnames = ['DM038', 'Hubble038', 'fsigma8z038', 'DM051', 'Hubble051', 'fsigma8z051', 'DM061', 'Hubble061', 'fsigma8z061'] redshifts = pts[:, 0] data = pts[:, 1] planckmeans = [] def BAOdensity(p1, p2, marge=True): err = np.sqrt(cov[p1, p1]) DAv = np.arange(data[p1] - 4 * err, data[p1] + 4 * err, err / 100) err = np.sqrt(cov[p2, p2]) Hv = np.arange(data[p2] - 4 * err, data[p2] + 4 * err, err / 100) DA, H = np.meshgrid(DAv, Hv) if marge:
datavar = '_lensing' root = 'base_plikHM_TTTEEE_lowl_lowE' + datavar samples = g.sampleAnalyser.samplesForRoot(root) datasets = ['sdss_6DF_bao.dataset', 'sdss_MGS_bao.dataset', 'sdss_DR14_quasar_bao.dataset'] names = ['6DFGS', 'SDSS\nMGS', 'SDSS quasars'] dataredshifts = np.zeros(len(datasets)) datapoints = np.zeros(len(datasets)) dataerrs = np.zeros(len(datasets)) colors = ['g', 'm', 'r', 'darkred'] for i, dat in enumerate(datasets): if '.dataset in dat': ini = inifile.IniFile(batchjob.getCodeRootPath() + 'data/' + dat) datapoints[i], dataerrs[i] = [float(f) for f in ini.split('bao_measurement')] dataredshifts[i] = ini.float('zeff') rescale = ini.float('rs_rescale', 1.) tp = ini.string('measurement_type') if tp == 'rs_over_DV': dataerrs[i] = -0.5 / (datapoints[i] + dataerrs[i]) + 0.5 / (datapoints[i] - dataerrs[i]) datapoints[i] = 1 / datapoints[i] elif tp != 'DV_over_rs': raise Exception('error') datapoints[i] *= rescale dataerrs[i] *= rescale print dataredshifts[i], datapoints[i], dataerrs[i] DR12 = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/final_consensus_results_dM_Hz_fsig.dat', usecols=[0, 1])
import planckStyle as s from paramgrid import batchjob import GetDistPlots import pylab as plt import numpy as np g = s.getSubplotPlotter(subplot_size=4) rd_fid = 147.78 FAP = True if FAP: cov = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/final_consensus_covtot_dV_FAP_fsig.txt') pts = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/final_consensus_results_dV_FAP_fsig.dat', usecols=[0, 1]) else: cov = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/final_consensus_covtot_dM_Hz_fsig.txt') pts = np.loadtxt(batchjob.getCodeRootPath() + 'data/DR12/sdss_DR12Consensus_final.dat', usecols=[0, 1]) pnames = [ 'DM038', 'Hubble038', 'fsigma8z038', 'DM051', 'Hubble051', 'fsigma8z051', 'DM061', 'Hubble061', 'fsigma8z061' ] redshifts = pts[:, 0] data = pts[:, 1] planckmeans = []