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