import planckStyle as s import pylab as plt g = s.getSinglePlotter() g.plot_3d('base_omegak_plikHM_TTTEEE_lowl_lowE', ['omegak', 'omegam', 'H0'], alpha_samples=True) plt.axvline(0, c='k', ls='--', color='gray', alpha=0.5, lw=0.7) g.add_2d_contours('base_omegak_plikHM_TTTEEE_lowl_lowE', 'omegak', 'omegam', filled=False, ls='--', color='k') g.add_2d_contours('base_omegak_plikHM_TTTEEE_lowl_lowE_lensing', 'omegak', 'omegam', filled=False, ls='-', color='g') # g.add_2d_contours('base_omegak_plikHM_TTTEEE_lowl_lowE_BAO_post_lensing','omegak','omegam',filled=True, alpha=0.85) g.add_2d_contours('base_omegak_plikHM_TTTEEE_lowl_lowE_BAO_post_lensing', 'omegak', 'omegam', filled=True, color='purple',
import planckStyle as s from pylab import * g = s.getSinglePlotter(plot_data='/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/ede/edepar1/plot_data/') import GetDistPlots, os #g=GetDistPlots.GetDistPlotter('/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik9/wwa/plot_data/') g.settings.setWithSubplotSize(2.0000) g.settings.param_names_for_labels = '/home/pettorin/codici/git/cosmomcplanck/chains/paper/ede/edepar1/edepar1_lowTEB_plikTT.paramnames' g.settings.legend_frac_subplot_margin=0.1 #ranges = [-2.1, 1, 0., 1.] outdir='/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/ede/edepar1/figures/' #labels=[s.planckTT,'+lensing',s.planckall, '+lensing','+BAO+HST+JLA' ] roots = ['','_BAO_JLA_HSTlow', '_WL', '_RSD','_RSD_WL'] roots = ['edepar1_lowTEB_plikTT'+root for root in roots] labels=['Planck', 'Planck + BSH', 'Planck + WL', 'Planck + RSD', 'Planck + WL + RSD'] g.settings.solid_colors=[('#8CD3F5', '#006FED'), ('#F7BAA6', '#E03424'), ('#D1D1D1', '#A1A1A1'), 'g', 'c', 'm'] # Planck: #00007f, - # Planck + priority1: #008AE6, . # Planck + WL: g (green), : # Planck + RSD: #808080, -- # Planck + WL + RSD: #E03424, : -.
import planckStyle as s from pylab import * import numpy as np import GetDistPlots, os import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from matplotlib import rc, font_manager from matplotlib.pyplot import figure, axes, plot, xlabel, ylabel, title, \ grid, savefig, show outdir='/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/ede/edepar3/figures/' g = s.getSinglePlotter(plot_data='/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/ede/edepar3/plot_data/') g.settings.setWithSubplotSize(4.0000) n_groups = 4 # Values for TT redshift = (1./0.1, 1./0.02, 1./0.005, 1./0.001 ) width = (1,1,1,1) limit2 = (0.0360, 0.0198, 0.0150, 0.0118) #prior on a1dn1 bar_width = (0.6,3.1,11,52) # Values for WL + RSD redshift_RSD_WL = (1./0.1, 1./0.02, 1./0.005, 1./0.001 ) redshift_RSD_WL = map(sum,zip(redshift_RSD_WL,bar_width)) #shift just for plotting limit2_RSD_WL = (0.0381, 0.0208, 0.0158, 0.0131) # Values for TTTEEE + BSH redshift_TTTEEE = (1./0.1, 1./0.02, 1./0.005, 1./0.001 )
import planckStyle as s from pylab import * g=s.getSinglePlotter(ratio=1) roots = ['base_nnu_yhe_planck_lowl_lowLike_highL', 'base_nnu_planck_lowl_lowLike_highL'] g.plot_2d(roots, param_pair=['nnu','thetastar'], filled=True,lims=[1.0, 6.0, 1.036, 1.047]) nnu = g.param_latex_label(roots[0], 'nnu') yhe = g.param_latex_label(roots[0], 'yheused') g.add_legend([ s.LCDM + '+'+nnu+'+'+ yhe,s.LCDM+ '+'+nnu],legend_loc='upper right',colored_text=True); text(1.2, 1.0365, s.WPhighL, color='#000000', fontsize=g.settings.legend_fontsize) g.export('neff_thetas')
import planckStyle as s from pylab import * g = s.getSinglePlotter() ranges = [0, 22, 0.76, 0.93] pair = ['zrei', 'sigma8'] g.newPlot() g.make_figure(1, xstretch=1.3) dataroots = [s.defdata_TTonly, s.defdata_allNoLowE, s.defdata_allNoLowE + '_lensing', s.defdata_allNoLowE + '_lensing_BAO'] roots = [g.getRoot('', x) for x in dataroots] g.plot_2d(roots, param_pair=pair, filled=True, lims=ranges) legends = [s.planckTT, s.NoLowLE, r'+lensing', r'+BAO'] g.add_x_marker(6.5, ls='-') c = 'gray' one = array([1, 1]) fill_between([0, 6.5], one * 0.7, one * 1, facecolor=c, alpha=0.1, edgecolor=c, lw=0) # g.add_2d_contours(g.getRoot('', s.defdata_TTonly + '_reion_BAO'), param_pair=pair, ls='-', color='red') # g.add_text(s.planckall, 0.96, 0.18, color='olive') # g.add_text(s.planckall + '+lensing', 0.96, 0.12, color='midnightblue')
import os, sys here = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.normpath(os.path.join(here, '../python/'))) from matplotlib.backends.backend_pgf import FigureCanvasPgf from matplotlib.backend_bases import register_backend register_backend('pdf', FigureCanvasPgf) import planckStyle as s from pylab import * #g = s.getSinglePlotter(plot_data='plot_data/') import GetDistPlots import planckStyle g = planckStyle.getSinglePlotter(chain_dir='./chains', ratio=.7) roots = ['BAO_ddt', 'union_ddt', 'BAO+union_ddt'] g.settings.solid_contour_palefactor = 0.8 g.plot_2d(roots, 'omegam', 'sigma8', filled=[True, True, False], colors=['#FFB300', '#8E001C', 'black']) #, lims=[0.25, 0.45, 0.6, 1.1])#0.6, 0.9]) labels = [r'BAO', 'SN', 'BAO+SN'] g.add_legend(labels, legend_loc='upper left', fontsize='small') #, colored_text=True); #plt.show()
import planckStyle as s from matplotlib.pyplot import * g = s.getSinglePlotter() ranges = [0.246, 0.37, 0.73, 0.965] pair = ['omegam', 'sigma8'] if False: for TT in [False, True]: g.newPlot() if not TT: basedat = s.defdata_allNoLowE basedatname = s.NoLowLE allname = s.planckall fname = 'Planckall' else: basedat = s.defdata_TTonly basedatname = s.planckTT fname = 'PlanckTT' allname = s.planckTTlowTEB roots = [ g.getRoot('', basedat), g.getRoot('', basedat + '_lensing'), g.getRoot('', basedat + '_lensing_BAO') ] legends = [basedatname, '+lensing', '+BAO'] g.plot_2d(roots, param_pair=pair, filled=True, lims=ranges)
import planckStyle as s from matplotlib.pyplot import * import getdist g = s.getSinglePlotter(chain_dir=[getdist.default_grid_root, r'C:\Tmp\Planck\KiDs', r'C:\Tmp\Planck\2017\fsig8']) ranges = [0.2, 0.35, 0.6, 1.05] #ranges = [0.2, 0.55, 0.4, 1.05] pair = ['omegam', 'sigma8'] omm = np.arange(0.05, 0.7, 0.01) s.plotBounds(omm, s.planck_lensing) #g.plot_2d('kids450fiducial', param_pair=pair, filled=True, lims=ranges) samples = g.sampleAnalyser.samplesForRoot('kids450fiducial', settings={'ignore_rows':0.3, 'max_scatter_points':4000}) p=samples.getParams() samples.filter((p.A < 2.5)*(p.A > 1.7) ) s8samples = g.sampleAnalyser.samplesForRoot('fsigma-vel-theta', settings={'ignore_rows':0.3}) if False: #testing putting in Jacobian # print s8samples.PCA(['omegam', 'H0', 'theta'], 'LLL', 'theta') def jacobian(H0, omegam, sigma8): #assume theta \propto omm^a*H0^b # sigma8^2 \propto As Omegam^(1.5) H0^(3.5) a = 0.15 b = 0.4 map =np.zeros((3,3))
import planckStyle as s import pylab as plt for with_KIDS in [True, False]: g = s.getSinglePlotter( chain_dir=[r'C:\Tmp\Planck\KiDs', r'C:\Tmp\Planck\2017\Dec17']) roots = [] roots.append('base_DESlens_DESpriors') roots.append('base_lensing_DESpriors') roots.append('base_DESlens_DESpriors_lensing') # roots.append('base_DES') roots.append('base_' + s.defdata_all) g.plot_2d(roots, [u'omegam', u'sigma8'], filled=True, shaded=False) g.add_2d_contours('base_DES_DESpriors', u'omegam', u'sigma8', ls='--') if with_KIDS: g.add_2d_contours('KiDS_lcdm_DESpriors', u'omegam', u'sigma8', ls=':', color='black', alpha=0.5) g.add_legend([ 'DES lensing', r'$\textit{Planck}$ lensing', r'DES lensing+$\textit{Planck}$ lensing', s.planckall, r'DES joint', 'KiDS-450' ], align_right=True) plt.ylim(None, 1.29) g.export(tag='with_KIDS')
from setup_matplotlib import * from matplotlib.ticker import MaxNLocator from matplotlib.patches import Rectangle, FancyBboxPatch import planckStyle as s from pylab import * import numpy as np import GetDistPlots, os import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from matplotlib import rc, font_manager from matplotlib.pyplot import figure, axes, plot, xlabel, ylabel, title, \ grid, savefig, show outdir='/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/pca/plots/' g = s.getSinglePlotter(plot_data='/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/pca/4bins/') g.settings.setWithSubplotSize(4.0000) # Load data wt = np.loadtxt('/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/pca/pythoncode/4bins/output/weights.txt') pca = np.loadtxt('/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/pca/pythoncode/4bins/output/w_reconstructed.txt') #f = plt.figure() #ax = f.add_subplot(1,1,1) #col = 'blue' #z0 = pca[:,0] # lower limit #z1 = pca[:,2] # upper limit #w0 = pca[:,3] # mean w #dw = pca[:,4] #for j in range(len(z0)-1): # llc = (z0[j],w0[j]-2.*dw[j]) # lower left corner
from matplotlib.ticker import MaxNLocator from matplotlib.patches import Rectangle, FancyBboxPatch import planckStyle as s from pylab import * import numpy as np import GetDistPlots, os import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from matplotlib import rc, font_manager from matplotlib.pyplot import figure, axes, plot, xlabel, ylabel, title, \ grid, savefig, show outdir = '/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/pca/plots/' g = s.getSinglePlotter( plot_data= '/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/pca/4bins/' ) g.settings.setWithSubplotSize(4.0000) # Load data wt = np.loadtxt( '/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/pca/pythoncode/4bins/output/weights.txt' ) pca = np.loadtxt( '/home/pettorin/codici/cosmomc_plots/output_getdist/clik/clik10/pca/pythoncode/4bins/output/w_reconstructed.txt' ) #f = plt.figure() #ax = f.add_subplot(1,1,1) #col = 'blue' #z0 = pca[:,0] # lower limit
import planckStyle as s from matplotlib.pyplot import * import getdist g = s.getSinglePlotter(chain_dir=[ getdist.default_grid_root, r'C:\Tmp\Planck\KiDs', r'C:\Tmp\Planck\2017\fsig8' ]) ranges = [0.2, 0.35, 0.6, 1.05] #ranges = [0.2, 0.55, 0.4, 1.05] pair = ['omegam', 'sigma8'] omm = np.arange(0.05, 0.7, 0.01) s.plotBounds(omm, s.planck_lensing) #g.plot_2d('kids450fiducial', param_pair=pair, filled=True, lims=ranges) samples = g.sampleAnalyser.samplesForRoot('kids450fiducial', settings={ 'ignore_rows': 0.3, 'max_scatter_points': 4000 }) p = samples.getParams() samples.filter((p.A < 2.5) * (p.A > 1.7)) s8samples = g.sampleAnalyser.samplesForRoot('fsigma-vel-theta', settings={'ignore_rows': 0.3}) if False: #testing putting in Jacobian