def getSystematicAverageTableLines(jobItem1, jobItem2):
# if you have two versions of the likelihood with the same data, and don't know which is right,
# this just crudely adds the samples with equal weight per likelihood
samps1 = loadMCSamples(jobItem1.chainRoot, jobItem=jobItem1, settings=batch.getdist_options)
samps2 = loadMCSamples(jobItem2.chainRoot, jobItem=jobItem2, settings=batch.getdist_options)
samps = samps1.getCombinedSamplesWithSamples(samps2)
if False:
import planckStyle as s
g = s.getSubplotPlotter()
g.plots_1d([samps, samps1, samps2], params=params.list(),
legend_labels=['Combined', texEscapeText(jobItem1.name), texEscapeText(jobItem2.name)])
g.export('joint_' + jobItem1.name)
return getTableLines(samps.getMargeStats())
def getSystematicAverageTableLines(jobItem1, jobItem2):
# if you have two versions of the likelihood with the same data, and don't know which is right,
# this just crudely adds the samples with equal weight per likelihood
samps1 = loadMCSamples(jobItem1.chainRoot,
jobItem=jobItem1,
settings=batch.getdist_options)
samps2 = loadMCSamples(jobItem2.chainRoot,
jobItem=jobItem2,
settings=batch.getdist_options)
samps = samps1.getCombinedSamplesWithSamples(samps2)
if False:
import planckStyle as s
g = s.getSubplotPlotter()
g.plots_1d([samps, samps1, samps2],
params=params.list(),
legend_labels=[
'Combined',
texEscapeText(jobItem1.name),
texEscapeText(jobItem2.name)
])
g.export('joint_' + jobItem1.name)
return getTableLines(samps.getMargeStats())
import planckStyle as s
g = s.getSubplotPlotter()
roots = ['base_Alens_plikHM_TT_lowl_lowE', 'base_Alens_plikHM_TTTEEE_lowl_lowE', 'base_plikHM_TTTEEE_lowl_lowE',
'base_Alens_CamSpecHM_TTTEEE_lowl_lowE']
for i, root in enumerate(roots):
samples = g.getSamples(root)
p = samples.getParams()
samples.addDerived(p.rmsdeflect ** 2, 'vardeflect', label=r'$\langle |\nabla\phi|^2\rangle\,[{\rm arcmin}^2]$')
roots[i] = samples
yparams = [u'Alens', u'vardeflect']
xparams = [u'omegabh2', u'omegach2', 'ns', u'H0', u'omegam', u'sigma8']
g.rectangle_plot(xparams, yparams, roots=roots, ymarkers=[1, None], filled=[True] * 3 + [False],
colors=g.settings.solid_colors[:3] + ['k'], ls=['-'] * 3 + ['--'],
legend_labels=[s.planckTT + r' ($\Lambda{\rm CDM}+A_L$)', s.planckall + r' ($\Lambda{\rm CDM}+A_L$)',
s.planckall + r' ($\Lambda{\rm CDM}$)'])
g.export()
import planckStyle as s
g = s.getSubplotPlotter(subplot_size=2)
g.settings.solid_colors = [('#8CD3F5', '#006FED'), ('#F7BAA6', '#E03424'),
('#B1B1B1', '#515151'), 'g']
roots = []
roots.append('base_DESlens_lenspriors_BAO')
roots.append('base_lensing_lenspriors_BAO')
roots.append('base_DESlens_lenspriors_lensing_BAO')
roots.append('base_plikHM_TTTEEE_lowl_lowE')
params = [u'H0', u'omegam', u'sigma8']
g.triangle_plot(roots,
params,
filled=True,
legend_labels=[
'DES lensing+BAO', r'$\textit{Planck}$ lensing+BAO',
r'(DES+$\textit{Planck}$) lensing + BAO', s.planckall
])
g.export()
import planckStyle as s
for dat in ['plikHM', 'CamSpecHM']:
g = s.getSubplotPlotter(subplot_size=2)
roots = []
roots.append('base_%s_EE_lowE_BAO' % dat)
roots.append('base_%s_TE_lowE' % dat)
roots.append('base_%s_TT_lowl_lowE' % dat)
roots.append('base_%s_TTTEEE_lowl_lowE' % dat)
xparams = [u'omegabh2', u'omegach2', u'theta', u'tau', u'ns', 'logA']
yparams = [u'H0', u'omegam', u'sigma8']
labels = [s.datalabel[s.defdata_EE] + '+BAO', s.datalabel[s.defdata_TE], s.datalabel[s.defdata_TT], s.planckall]
filled = True
# ranges = {'omegabh2':}
g.rectangle_plot(xparams, yparams, roots=roots, filled=filled, legend_labels=labels)
if dat == 'CamSpecHM':
for int, ax1, ax in zip(ints, axs, g.subplots.reshape(-1)):
ax.set_xlim(int[0])
ax.set_ylim(int[1])
ax.set_yticks(ax1.get_yticks())
ax.set_xticks(ax1.get_xticks())
axs = g.subplots.reshape(-1)
ints = [[ax.xaxis.get_view_interval(), ax.yaxis.get_view_interval()] for ax in g.subplots.reshape(-1)]
ax = g.get_axes_for_params('omegabh2', 'omegam')
print ax,g.get_axes_for_params('omegam', 'omegab2')
if ax:
ax.set_yticks([0.28, 0.30, 0.32, 0.34, 0.36])
g.export(tag='' if dat == 'plikHM' else dat)
import os, sys
here = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, os.path.normpath(os.path.join(here, './python')))
import planckStyle as s
from pylab import *
from getdist import plots, MCSamples
import getdist
print('Version: ', getdist.__version__)
import GetDistPlots
import planckStyle
g = planckStyle.getSubplotPlotter(chain_dir='./chains')
#g = planckStyle.getSinglePlotter(width_inch=5, chain_dir = './chains')
roots = ['Horn_GP_prior+JLA+BAO']
params = [u'binw1', u'binw2', u'binw3', u'binw4', u'H0', u'omegam', u'omegal']
#params = [u'omegam', u'omegal']
#param_3d = 'H0'
g.settings.solid_contour_palefactor = 0.8
g.triangle_plot(roots,
params,
filled=[True],
legend_labels=['Horn\_prior+JLA+BAO\_9.83'],
legend_loc='upper right')
#g.triangle_plot(roots, params, plot_3d_with_param=param_3d ,filled=[False], legend_labels=['Horn\_prior+JLA+BAO\_13'], legend_loc='upper right')
