def testFileLoadPlot(self):
samples = loadMCSamples(self.root, settings={'ignore_rows': 0.1})
g = plots.getSinglePlotter(chain_dir=self.tempdir,
analysis_settings={'ignore_rows': 0.1})
self.assertEqual(
g.sampleAnalyser.samplesForRoot('testchain').numrows,
samples.numrows, "Inconsistent chain loading")
self.assertEqual(
g.sampleAnalyser.samplesForRoot('testchain').getTable().tableTex(),
samples.getTable().tableTex(), 'Inconsistent load result')
samples.getConvergeTests(0.95)
self.assertAlmostEqual(
0.0009368, samples.GelmanRubin, 5,
'Gelman Rubin error, got ' + str(samples.GelmanRubin))
g = plots.getSinglePlotter()
g.plot_3d(samples, ['x', 'y', 'x'])
g.export(self.root + '_plot.pdf')
g = plots.getSinglePlotter(chain_dir=self.tempdir,
analysis_settings={
'ignore_rows': 0.1,
'contours': [0.68, 0.95, 0.99]
})
g.settings.num_plot_contours = 3
g.plot_2d('testchain', ['x', 'y'])
def plot2D(rdic, par=[1, 2], tex=1):
snstyle(tex)
rdic = np.asarray(rdic)
root = list(rdic[:, 0])
lengend = list(rdic[:, 1])
rn = len(root)
Samp = []
minkaf = np.zeros(rn)
data_num = np.zeros(rn)
for i in range(rn):
savefile_name = './chains/' + root[i] + '.npy'
samples, theta_name, theta_fit, theta_fact, minkaf[i], data_num[
i] = np.load(savefile_name)
Samp.append(
MCSamples(samples=samples, names=theta_name, labels=theta_name))
pnames = Samp[0].getParamNames().names
rn = len(root)
g = plots.getSinglePlotter(width_inch=7)
#samples.updateSettings({'contours': [0.68, 0.95, 0.99]})
#g.settings.num_plot_contours = 3
g.settings.lab_fontsize = 18
g.settings.axes_fontsize = 14
g.plot_2d(Samp, pnames[par[0] - 1].name, pnames[par[1] - 1].name)
for i in range(rn):
sns.kdeplot(Samp[i].samples[:, par[0] - 1],
Samp[i].samples[:, par[1] - 1],
cmap="Blues",
shade=True,
shade_lowest=False)
g.add_legend(lengend, colored_text=True, fontsize=18)
mpl.pyplot.tight_layout()
def plot1D(self, n, colorn=0, width_inch=8, **kwargs):
g = plots.getSinglePlotter(width_inch=width_inch)
g.plot_1d(self.Samp,
self.param_names[n - 1],
ls=lss,
colors=colors[colorn:colorn + self._n],
lws=[1.5] * self._n,
**kwargs)
# g.settings.figure_legend_frame = False
ax = plt.gca()
if all(self.lengend):
leg = ax.legend(self.lengend, loc=1, fontsize=16, frameon=False)
for line, text in zip(leg.get_lines(), leg.get_texts()):
text.set_color(line.get_color())
if 'x_marker' in kwargs:
g.add_x_marker(kwargs['x_marker'], lw=1.5)
# if 'x_bands' in kwargs:
# g.add_x_bands(0,0.01)
if 'xaxis' in kwargs:
ax.xaxis.set_major_locator(plt.MultipleLocator(kwargs['xaxis']))
if 'title' in kwargs:
ax.set_title(kwargs['title'])
# plt.tight_layout()
if 'name' in kwargs:
g.export(os.path.join(outdir, '%s.pdf' % kwargs['name']))
else:
g.export(
os.path.join(outdir + ''.join(self.root) +
self.param_names[n - 1].replace('\\', '') +
'_1D.pdf'))
return g
def testFileLoadPlot(self):
samples = loadMCSamples(self.root, settings={'ignore_rows': 0.1})
g = plots.getSinglePlotter(chain_dir=self.tempdir, analysis_settings={'ignore_rows': 0.1})
self.assertEqual(g.sampleAnalyser.samplesForRoot('testchain').numrows, samples.numrows,
"Inconsistent chain loading")
self.assertEqual(g.sampleAnalyser.samplesForRoot('testchain').getTable().tableTex(),
samples.getTable().tableTex(), 'Inconsistent load result')
samples.getConvergeTests(0.95)
self.assertAlmostEqual(0.0009368, samples.GelmanRubin, 5, 'Gelman Rubin error, got ' + str(samples.GelmanRubin))
g = plots.getSinglePlotter()
g.plot_3d(samples, ['x', 'y', 'x'])
g.export(self.root + '_plot.pdf')
g = plots.getSinglePlotter(chain_dir=self.tempdir,
analysis_settings={'ignore_rows': 0.1, 'contours': [0.68, 0.95, 0.99]})
g.settings.num_plot_contours = 3
g.plot_2d('testchain', ['x', 'y'])
def plot_sampling(mcmc_config,
param_info_wod,
burned_chain,
theta_ml,
include_outlier_dist=False,
include_ml=True):
chain_tag = mcmc_config.chain_tag
param_names_wod, param_labels_wod, param_guess_wod, param_priors_wod = param_info_wod
print('\nProcessing MCMC chain...')
if include_outlier_dist:
ndim = burned_chain.shape[1]
elif not include_outlier_dist:
ndim = burned_chain.shape[1] - 3
names = param_labels_wod[0:ndim].copy()
ranges_gd = {}
for i in range(ndim):
ranges_gd[param_labels_wod[i]] = (param_priors_wod[i][1],
param_priors_wod[i][1])
gd_samples = MCSamples(samples=burned_chain[:, 0:ndim],
names=param_names_wod[0:ndim],
labels=param_labels_wod[0:ndim]) #,
#ranges=ranges_gd[0:ndim])
if ndim == 1:
fig = plots.getSinglePlotter(width_inch=5)
fig.plot_1d(gd_samples, names[0], normalized=True)
elif ndim > 1:
fig = plots.getSubplotPlotter()
fig.triangle_plot([gd_samples], filled=True)
if include_ml:
for i in range(ndim):
ax = fig.subplots[i, i]
ax.axvline(theta_ml[i], color='r', ls='-', alpha=0.75)
for i in range(ndim - 1):
for j in range(i + 1):
ax = fig.subplots[i + 1, j]
ax.plot(theta_ml[j],
theta_ml[i + 1],
'w*',
zorder=3,
markersize=5.)
plt.show()
fig_name = 'MCMC_sampling_{}'.format(chain_tag)
save_figure(mcmc_config, fig, fig_name, gd_plot=True)
# plot_file = '{}/MCMC_sampling_{}.png'.format(plots_dir, chain_tag)
# mcmc_fig.export(plot_file)
plt.close()
def testPlots(self):
self.samples = self.testdists.bimodal[0].MCSamples(12000,
logLikes=True)
g = plots.getSinglePlotter()
samples = self.samples
p = samples.getParams()
samples.addDerived(p.x + (5 + p.y)**2, name='z')
samples.addDerived(p.x, name='x.yx', label='forPattern')
samples.addDerived(p.y, name='x.2', label='x_2')
samples.updateBaseStatistics()
g.plot_1d(samples, 'x')
g.newPlot()
g.plot_1d(samples, 'y', normalized=True, marker=0.1, marker_color='b')
g.newPlot()
g.plot_2d(samples, 'x', 'y')
g.newPlot()
g.plot_2d(samples, 'x', 'y', filled=True)
g.newPlot()
g.plot_2d(samples, 'x', 'y', shaded=True)
g.newPlot()
g.plot_2d_scatter(samples, 'x', 'y', color='red', colors=['blue'])
g.newPlot()
g.plot_3d(samples, ['x', 'y', 'z'])
g = plots.getSubplotPlotter(width_inch=8.5)
g.plots_1d(samples, ['x', 'y'], share_y=True)
g.newPlot()
g.triangle_plot(samples, ['x', 'y', 'z'])
g.newPlot()
g.triangle_plot(samples, ['x', 'y'], plot_3d_with_param='z')
g.newPlot()
g.rectangle_plot(['x', 'y'], ['z'], roots=samples, filled=True)
prob2 = self.testdists.bimodal[1]
samples2 = prob2.MCSamples(12000)
g.newPlot()
g.triangle_plot([samples, samples2], ['x', 'y'])
g.newPlot()
g.plots_2d([samples, samples2], param_pairs=[['x', 'y'], ['x', 'z']])
g.newPlot()
g.plots_2d([samples, samples2], 'x', ['z', 'y'])
g.newPlot()
self.assertEqual(
[name.name for name in samples.paramNames.parsWithNames('x.*')],
['x.yx', 'x.2'])
g.triangle_plot(samples, 'x.*')
samples.updateSettings({'contours': '0.68 0.95 0.99'})
g.settings.num_contours = 3
g.plot_2d(samples, 'x', 'y', filled=True)
g.add_y_bands(0.2, 1.5)
g.add_x_bands(-0.1, 1.2, color='red')
def testPlots(self):
self.samples = self.testdists.bimodal[0].MCSamples(12000, logLikes=True)
g = plots.getSinglePlotter()
samples = self.samples
p = samples.getParams()
samples.addDerived(p.x + (5 + p.y) ** 2, name='z')
samples.addDerived(p.x, name='x.yx', label='forPattern')
samples.addDerived(p.y, name='x.2', label='x_2')
samples.updateBaseStatistics()
g.plot_1d(samples, 'x')
g.newPlot()
g.plot_1d(samples, 'y', normalized=True, marker=0.1, marker_color='b')
g.newPlot()
g.plot_2d(samples, 'x', 'y')
g.newPlot()
g.plot_2d(samples, 'x', 'y', filled=True)
g.newPlot()
g.plot_2d(samples, 'x', 'y', shaded=True)
g.newPlot()
g.plot_2d_scatter(samples, 'x', 'y', color='red', colors=['blue'])
g.newPlot()
g.plot_3d(samples, ['x', 'y', 'z'])
g = plots.getSubplotPlotter(width_inch=8.5)
g.plots_1d(samples, ['x', 'y'], share_y=True)
g.newPlot()
g.triangle_plot(samples, ['x', 'y', 'z'])
g.newPlot()
g.triangle_plot(samples, ['x', 'y'], plot_3d_with_param='z')
g.newPlot()
g.rectangle_plot(['x', 'y'], ['z'], roots=samples, filled=True)
prob2 = self.testdists.bimodal[1]
samples2 = prob2.MCSamples(12000)
g.newPlot()
g.triangle_plot([samples, samples2], ['x', 'y'])
g.newPlot()
g.plots_2d([samples, samples2], param_pairs=[['x', 'y'], ['x', 'z']])
g.newPlot()
g.plots_2d([samples, samples2], 'x', ['z', 'y'])
g.newPlot()
self.assertEquals([name.name for name in samples.paramNames.parsWithNames('x.*')], ['x.yx', 'x.2'])
g.triangle_plot(samples, 'x.*')
samples.updateSettings({'contours': '0.68 0.95 0.99'})
g.settings.num_contours = 3
g.plot_2d(samples, 'x', 'y', filled=True)
g.add_y_bands(0.2, 1.5)
g.add_x_bands(-0.1, 1.2, color='red')
def single_plot(filename, legends, limits, out):
chain1 = np.genfromtxt("like/"+filename[0])[:,:7]
chain2 = np.genfromtxt("like/"+filename[1])[:,:7]
chain1[:,3] = chain1[:,3]+(1-1./(1+0.266))*chain1[:,4]
chain2[:,3] = chain2[:,3]+(1-1./(1+zp))*chain2[:,4]
labels[3] ="w_\mathrm{p}"
names[3] = "wp"
samples1 = MCSamples(samples=chain1,names = names, labels = labels)
samples2 = MCSamples(samples=chain2,names = names, labels = labels)
samples1.fine_bins_2D = 20
samples2.fine_bins_2D = 20
g = plots.getSinglePlotter()
g.plot_2d([samples1, samples2],"wp","wa", filled=[False,True],lims=limits,colors=['red','blue'])
g.settings.legend_fontsize = 12
g.add_legend(legends)
g.export('plots/'+out)
def simpleGetdist(self, **kwargs):
"""
Lewis (2019)
arXiv:1910.13970v1 [astro-ph.IM]
"""
from getdist import plots, MCSamples, chains
smooth2d = kwargs.pop("smooth2d", 0.3)
smooth1d = kwargs.pop("smooth1d", 0.3)
burnin = kwargs.pop("burnin", 0.2)
colors = kwargs.pop(
"colors",
['red', 'blue', 'black', 'green', 'yellow', 'purple', 'gray'])
legend_labels = kwargs.pop("legend_labels", [])
filled = kwargs.pop("filled", False)
normalized = kwargs.pop("normalized", False)
shaded = kwargs.pop("shaded", False)
label = kwargs.pop("label", None)
roots = kwargs.pop('roots', [self.root])
g = plots.getSinglePlotter(chain_dir=self.chainsdir,
width_inch=10,
ratio=0.9,
scaling=2,
analysis_settings={
'smooth_scale_2D': smooth2d,
'smooth_scale_1D': smooth1d,
'ignore_rows': burnin
})
g.triangle_plot(roots,
self.listpars,
diag1d_kwargs={'colors': colors},
colors=colors,
legend_labels=legend_labels,
filled=filled,
normalized=normalized,
shaded=shaded)
self.image = "{}_getdist.png".format(self.filename)
self.saveFig(label)
def plot2D(self, pp, colorn=0, contour_num=2, width_inch=8, **kwargs):
g = plots.getSinglePlotter(width_inch=width_inch, ratio=1)
g.settings.num_plot_contours = contour_num
g.settings.axes_fontsize = 14
g.settings.lab_fontsize = 18
g.settings.legend_frame = False
g.plot_2d(self.Samp,
self.param_names[pp[0] - 1],
self.param_names[pp[1] - 1],
filled=True,
**kwargs)
if 'x_locator' in kwargs:
ax = g.get_axes()
ax.xaxis.set_major_locator(plt.MultipleLocator(
kwargs['x_locator']))
del kwargs['x_locator']
if 'y_locator' in kwargs:
ax = g.get_axes()
ax.yaxis.set_major_locator(plt.MultipleLocator(
kwargs['y_locator']))
del kwargs['y_locator']
if 'lims' in kwargs:
[xmin, xmax, ymin, ymax] = kwargs['lims']
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
del kwargs['lims']
if 'x_marker' in kwargs:
g.add_x_marker(kwargs['x_marker'], lw=1.5)
if all(self.lengend):
kwarg = kwargs.copy()
if 'name' in kwarg: del kwarg['name']
g.add_legend(self.lengend, colored_text=True, fontsize=16, **kwarg)
# plt.tight_layout()
if 'name' in kwargs:
g.export(os.path.join(outdir, '%s.pdf' % kwargs['name']))
else:
g.export(os.path.join(outdir, ''.join(self.root) + '_2D.pdf'))
return g
import getdist.plots as gplot
import os
from pylab import *
import numpy as np
from scipy.integrate import quad
import matplotlib.pyplot as plt
G=gplot.getSinglePlotter(chain_dir=r'/Users/scottpreston/Desktop/base')
roots = ['base_r_plikHM_TTTEEE_lowTEB']
pairs = [[u'ns', u'r']]
G.plots_2d(roots, param_pairs=pairs, filled=True, shaded=False)
ylim(ymax=0.25)
N = np.linspace(50.0, 60.0, 10)
n = 2/3
n_1 = 2
n_2 = 4/3
n_3 = 3
n_4 = 1
n_5 = 1.125
def f_r0(N, n): # phi 2/3
for j in range(len(N)):
return (16*n)/(4*N + n)
def ns_N0(N, n):
for k in range(len(N)):
return 1 -2*(n + 2)/(4*N + n)
def f_r1(N): #R^2 inflation
samps2 = samps1[0.1<=samps1[:,1],:]
samps3 = samps2[samps2[:,0]<=85,:]
cut_samps = samps3[50<=samps3[:,0],:]
names = ["H","m"]
labels = [r"H_0",r"\Omega_{m_0}"]
samples = MCSamples(samples=cut_samps,names = names, labels = labels,ranges={'H':(50,85),'m':(0.1,0.7)})
samples1 = MCSamples(samples=samps,names = names, labels = labels,ranges={'H':(50,85),'m':(0.1,0.7)})
'''
1D likelihoods
'''
z1 = loadtxt('flat_LCDM_L(H0).dat', unpack=True)
print('check 4')
x1 = loadtxt('flat_LCDM_H0_1D.dat', unpack=True)
g = plots.getSinglePlotter(width_inch=4)
g.plot_1d(samples, 'H', ls=['solid'])
plt.plot(x1, z1/max(z1), linestyle=':', color='blue')
g.export('flat_LCDM_L(H0)_1D_MCMC_vs_grid_my_data.pdf')
z1 = loadtxt('flat_LCDM_L(Om).dat', unpack=True)
print('check 5')
x1 = loadtxt('flat_LCDM_Om_1D.dat', unpack=True)
g = plots.getSinglePlotter(width_inch=4)
g.plot_1d(samples, 'm', ls=['solid'])
plt.plot(x1, z1/max(z1), linestyle=':', color='blue')
g.export('flat_LCDM_L(Om)_1D_MCMC_vs_grid_my_data.pdf')
'''
2D contours
g = plots.getSubplotPlotter(chain_dir= dir_name, width_inch=16,
analysis_settings={'smooth_scale_2D': -1. , 'ignore_rows': 0.2})
g.settings.axes_fontsize = 18
g.settings.lab_fontsize = 20
g.settings.alpha_filled_add =0.9
g.plots_2d(roots, param_pairs=[['omegak','w'], ['omegak','wa'], ['w','wa']], nx=3, filled=True,
legend_labels=False)
g.add_legend(['PLANCK + DR12 LOWZ+CMASS', 'PLANCK + DR12 BAO', 'PLANCK + DR12 BAO+FS'],
colored_text=True, legend_loc='upper right')
g.export('owa_dr12_jla.pdf')
roots = ['LCDM_ABfs8_PLK+DR12+ALLB']
g = plots.getSinglePlotter(chain_dir= dir_name,
analysis_settings={'smooth_scale_2D': -1., 'ignore_rows': 0.2})
g.settings.axes_fontsize = 20
g.settings.lab_fontsize = 25
g.settings.alpha_filled_add =0.9
g.add_y_marker(0)
g.add_x_marker(1)
g.plot_2d(roots, 'Afs8','Bfs8', filled=True, legend_labels=False, colors = ['blue'], alphas=[0.8])
g.add_legend(['PLANCK+BAO+FS'], colored_text=True, legend_loc='upper right')
g.export('mg.pdf')
if True:
roots = ['mnu_Alens_Afs8_PLK+DR12+ALLB', 'mnu_Alens_PLK+DR12+ALLB', 'mnu_Afs8_PLK+DR12+ALLB', 'mnu_PLK+DR12+ALLB']
g = plots.getSinglePlotter(chain_dir= dir_name,
analysis_settings={'smooth_scale_2D': -1. , 'ignore_rows': 0.2})
import matplotlib.pyplot as plt
from getdist import plots
import planckStyle
dir_name = '/Users/josevazquezgonzalez/Desktop/BOSS/BOSS_files/DR12/chains'
roots = ['owcdmdr12','OkwoCDM_PLK+BAO12','OkwoCDM_PLK+DR12']
g= plots.getSinglePlotter(chain_dir = dir_name, ratio=1., analysis_settings={'smooth_scale_2D': -1.})
g.plot_2d(roots, param_pair=['omegak','w'], filled=True, legend_labels=False, line_args={'lw':2, 'ls':'-'})
#g = plots.getSubplotPlotter(chain_dir= dir_name, width_inch=16, analysis_settings={'smooth_scale_2D': -1.})
g.settings.axes_fontsize = 20
g.settings.lab_fontsize = 25
g.settings.alpha_filled_add =0.9
g.add_y_marker(-1)
g.add_x_marker(0)
#g.plots_2d(roots, param_pairs=[['omegak','w'], ['omegak','H0'], ['w','H0']], nx=3, filled=True,
# legend_labels=False, line_args={'lw':2, 'ls':'-'})
#g.add_legend(['PLACK + DR12 LOWZ+CMASS', 'PLACK + DR12 BAO', 'PLACK + DR12 BAO+RSD'],
# colored_text=True, legend_loc='upper right')
g.export('OkwoCDM_10.pdf')
from getdist import plots
dirs = [
"../completed_runs/standard_Mnu_nEff_mn_build/",
"../completed_runs/standard_Mnu_nEff_bao_mn_build/",
"../completed_runs/standard_Mnu_nEff_lrg_mn_build/",
"../completed_runs/standard_Mnu_nEff_wigglez_mn_build"
]
roots = [
"standard_neff_summnu_planck",
"standard_neff_summnu_planck_bao_",
"standard_neff_summnu_planck_lrg_",
"standard_neff_summnu_planck_wigglez_"
]
g = plots.getSinglePlotter(chain_dir=dirs)
g.plot_2d(roots, "h", "ombh2", filled=True, lims=[0.55, 0.80, 0.020, 0.024])
g.settings.legend_fontsize = 12
g.add_legend(["Planck2015", "Planck2015+BAO", "Planck2015+LRG", "Planck2015+WiggleZ"], legend_loc="lower right")
g.export("test.pdf")
from getdist import loadMCSamples, plots
number_of_parameters = 14
samples = loadMCSamples('../output/chains/mcmc_final_output_HP',
settings={'ignore_rows': 2})
g = plots.getSinglePlotter()
g.settings.rcSizes(axes_fontsize=2, lab_fontsize=7)
g.triangle_plot(samples, filled=True)
g.export('../output/chains/triangle_figure_HP_R11_H.pdf')
bestfit = samples.getLikeStats()
means = samples.setMeans()
filebestfit = open("../output/chains/bestfit.txt", 'w')
filemeans = open("../output/chains/means.txt", 'w')
#filebestfit.write("-log(Like) = "+str(bestfit.logLike_sample)+"\n")
for index in range(number_of_parameters):
filebestfit.write(str(bestfit.names[index].bestfit_sample) + "\n")
filemeans.write(str(means[index]) + "\n")
pl.rcParams['axes.labelpad'] = '10.0'
pl.rcParams['lines.dashed_pattern'] = 3.5, 1.0
#pl.rcParams['axes.formatter.limits']=-10,10
pl.rcParams['lines.dotted_pattern'] = 1.0, 0.7
pl.rcParams['xtick.labelsize'] = '15'
pl.rcParams['ytick.labelsize'] = '15'
pl.rcParams['axes.labelsize'] = '30'
pl.rcParams['axes.labelsize'] = '30'
pl.rcParams['xtick.major.pad'] = '10'
pl.rcParams['xtick.minor.pad'] = '10'
#pl.rcParams['hatch.color'] = 'black'
pl.rc('axes', linewidth=2)
g1 = gplot.getSinglePlotter(chain_dir=r'./planck')
samples = g1.sampleAnalyser.samplesForRoot(
'base_plikHM_TTTEEE_lowl_lowE_lensing')
param = samples.getParams()
g1.settings.num_plot_contours = 2
g = plots.getSinglePlotter(width_inch=4, ratio=1)
P = param.sigma8 * (param.omegam / 0.3)**0.5
samples.addDerived(P, name=r'S80') #, label=r'c/h_rd')
k = param.H0
samples.addDerived(k, name=r'h') #, label=r'c/h_rd')
samples.updateBaseStatistics()
g.plot_2d([samples], [r'h', r'S80'], filled=True, colors=['magenta'])
x = np.linspace(0, 120, 100)
plt.fill_between(x, 0.70103627, 0.77290325, color='darkgrey', alpha='0.5')
x = np.linspace(72.61, 75.45, 100)
