def testFileLoadPlot(self):
samples = loadMCSamples(self.root, settings={'ignore_rows': 0.1})
g = plots.get_single_plotter(chain_dir=self.tempdir,
analysis_settings={'ignore_rows': 0.1})
self.assertEqual(
g.samples_for_root('testchain').numrows, samples.numrows,
"Inconsistent chain loading")
self.assertEqual(
g.samples_for_root('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.get_single_plotter()
g.plot_3d(samples, ['x', 'y', 'x'])
g.export(self.root + '_plot.pdf')
g = plots.get_single_plotter(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_mcmc_dr9regions():
titles = {
'bmzls': 'BASS/MzLS',
'ndecals': 'DECaLS North',
'sdecals': 'DECaLS South'
}
stg = {
'mult_bias_correction_order': 0,
'smooth_scale_2D': 0.15,
'smooth_scale_1D': 0.3,
'contours': [0.68, 0.95]
}
mc_kw = dict(names=['fnl', 'b', 'n0'],
labels=['f_{NL}', 'b', '10^{7}n_{0}'],
settings=stg)
read_kw = dict(ndim=3, iscale=[2])
mc_kw2 = dict(names=['fnl', 'b', 'n0', 'b2', 'n02'],
labels=['f_{NL}', 'b1', '10^{7}n_{0}', 'b2', '10^{7}n_{0}2'],
settings=stg)
read_kw2 = dict(ndim=5, iscale=[2, 4])
mc_kw3 = dict(names=['fnl', 'b', 'n0', 'b2', 'n02', 'b3', 'n03'],
labels=[
'f_{NL}', 'b1', '10^{7}n_{0}', 'b2', '10^{7}n_{0}2',
'b3', '10^{7}n_{0}3'
],
settings=stg)
read_kw3 = dict(ndim=7, iscale=[2, 4, 6])
xlim = None
with PdfPages('figs/fnl2d_dr9_regions.pdf') as pdf:
for r in ['bmzls', 'ndecals', 'sdecals']:
noweight = MCMC(
f'/fs/ess/PHS0336/data/rongpu/imaging_sys/mcmc/0.57.0/mcmc_lrg_zero_{r}_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
nnknown = MCMC(
f'/fs/ess/PHS0336/data/rongpu/imaging_sys/mcmc/0.57.0/mcmc_lrg_zero_{r}_nn_known_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
nnall = MCMC(
f'/fs/ess/PHS0336/data/rongpu/imaging_sys/mcmc/0.57.0/mcmc_lrg_zero_{r}_nn_all_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
# Triangle plot
g = plots.get_single_plotter(width_inch=4 * 1.5)
g.settings.legend_fontsize = 14
g.plot_2d([noweight, nnknown, nnall], 'fnl', 'b', filled=True)
g.add_legend(['No Correction', 'Conservative', 'Extreme'],
colored_text=True,
title=titles[r])
g.subplots[0, 0].set_ylim((1.23, 1.6))
add_scale(g.subplots[0, 0])
g.fig.align_labels()
pdf.savefig(bbox_inches='tight')
def test_styles(self):
tmp = rcParams.copy()
plots.set_active_style(tab10)
g = plots.get_single_plotter()
self.assertEqual(g.settings.line_styles.name, 'tab10')
plots.set_active_style(planck)
g = plots.get_single_plotter()
self.assertTrue(g.settings.prob_y_ticks)
plots.set_active_style(tab10)
g = plots.get_single_plotter()
self.assertEqual(g.settings.line_styles.name, 'tab10')
plots.set_active_style()
g = plots.get_single_plotter()
self.assertFalse(g.settings.prob_y_ticks)
g = plots.get_single_plotter(style='tab10')
self.assertEqual(g.settings.line_styles.name, 'tab10')
plots.set_active_style('planck')
plots.set_active_style()
self.assertDictEqual(tmp, rcParams)
def plot_lfs_kde(cmd_list):
g = plots.get_single_plotter(width_inch=2 * 3.464, ratio=0.75)
colors = ['k']
colors.extend(['r'] * (len(cmd_list) - 1))
ct = 0
for cmd in cmd_list:
cmd_samples = MCSamples(samples=cmd, names=['color', 'mag'])
if ct > 0:
g.settings.linewidth = 0.5
g.plot_1d(cmd_samples, 'mag', colors=[colors[ct]])
ct += 1
def Plot2D_Omegak(results_dir):
labels = [
"base_omegak_plikHM_TTTEEE_lowl_lowE",
"base_omegak_plikHM_TTTEEE_lowl_lowE_BAO"
]
datasets = [LoadDataset(label) for label in labels]
g = plots.get_single_plotter(width_inch=5)
g.settings.legend_frame = False
g.plot_2d(datasets, ['omegak', 'omegam'], filled=True)
g.add_legend(["PlikHM TTTEEE+lowl+lowE", "+BAO"], frameon=False)
g.export(str(results_dir / "2D_omegak_omegam.pdf"))
return
def Plot3D_Omegak_Omegam_H0(results_dir):
labels = [
"base_omegak_plikHM_TTTEEE_lowl_lowE",
"base_omegak_plikHM_TTTEEE_lowl_lowE_BAO"
]
datasets = [LoadDataset(label) for label in labels]
g = plots.get_single_plotter(width_inch=5)
g.settings.legend_frame = False
g.plots_3d(datasets, [['omegak', 'omegam', 'H0'], ['omegak', 'omegam', 'H0']], nx=2, legend_labels=["PlikHM TTTEEE+lowl+lowE", "+BAO"], legend_ncol=2)
#g.add_legend(["PlikHM TTTEEE+lowl+lowE", "+BAO"])
g.export(str(results_dir / '3D_omega_k_omegam_h0.pdf'))
return
def test_styles(self):
from getdist.styles.tab10 import style_name as tab10
from getdist.styles.planck import style_name as planck
from matplotlib import rcParams
tmp = rcParams.copy()
plots.set_active_style(tab10)
g = plots.get_single_plotter()
self.assertEqual(g.settings.line_styles.name, 'tab10')
plots.set_active_style(planck)
g = plots.get_single_plotter()
self.assertTrue(g.settings.prob_y_ticks)
plots.set_active_style(tab10)
g = plots.get_single_plotter()
self.assertEqual(g.settings.line_styles.name, 'tab10')
plots.set_active_style()
g = plots.get_single_plotter()
self.assertFalse(g.settings.prob_y_ticks)
g = plots.get_single_plotter(style='tab10')
self.assertEqual(g.settings.line_styles.name, 'tab10')
plots.set_active_style('planck')
plots.set_active_style()
self.assertDictEqual(tmp, rcParams)
plt.close()
from getdist import plots, MCSamples
import getdist
samples1 = MCSamples(samples=samp1.T,names=['x1','x2','x3','x4','x5','x6','x7'], labels=names, label=r'True $n(z)$, $\Delta \chi^2 = 0.0$', weights=c1.weight, settings={'boundary_correction_order':0, 'mult_bias_correction_order':1})
samples2 = MCSamples(samples=samp2.T,names=['x1','x2','x3','x4','x5','x6','x7'], labels=names, label=r'wrong $n(z)$, $\Delta \chi^2 = 3.0$ ', weights=c2.weight, settings={'boundary_correction_order':0, 'mult_bias_correction_order':1})
samples3 = MCSamples(samples=samp3.T,names=['x1','x2','x3','x4','x5','x6','x7'], labels=names, label=r'wrong $n(z)$, $\Delta \chi^2 = 7.0$', weights=c3.weight, settings={'boundary_correction_order':0, 'mult_bias_correction_order':1})
samples4 = MCSamples(samples=samp4.T,names=['x1','x2','x3','x4','x5','x6','x7'], labels=names, label=r'wrong $n(z)$, $\Delta \chi^2 = 14$', weights=c4.weight, settings={'boundary_correction_order':0, 'mult_bias_correction_order':1})
gd = plots.get_single_plotter(width_inch=6, ratio=1)
gd.settings.legend_fontsize = 16
gd.settings.fontsize = 18
gd.settings.axes_fontsize = 16
gd.settings.axes_labelsize = 20
gd.settings.axis_tick_max_labels = 15
gd.settings.linewidth = 1.5
gd.settings.legend_colored_text=True
gd.settings.axis_tick_step_groups = [[2.5, 3, 4, 6, 8],[1, 2, 5, 10]]
col0 = ['#DDA0DD', '#FA86C9','#000000','#4682b4']
#['#8B0000','#8b008b','#191970','#FF69B4','#000000']
gd.triangle_plot([samples1, samples2, samples3, samples4],['x1','x2','x3','x4','x5','x6','x7'], filled=[True,False,False,False,False,False], contour_args=[{'alpha':0.6},{'alpha':1.},{'alpha':1.},{'alpha':1.}, {'alpha':1}, {'alpha':1}],contour_colors=['#7223AD','#3775A1','#A4CD64','#FF69B4','#A4CD64']) #, param_limits={'x1':(-2.5,5), 'x2':(-5,5), 'x3':(-5,5)})
#gd.plot_2d([samples1, samples2,samples3,samples4,samples5],['x1','x2'], filled=[True,True,True,True,False], colors=['#8B0000','#8b008b','#4682b4','#FF69B4','#000000' ], lims=[-1.5,4.5,-5.5,4])
#gd.add_legend(['redMaGiC high-$z$','redMaGiC low-$z$','eBOSS ELGs','eBOSS LRGs','DES Y3'], legend_loc='lower right');
r'\mathcal{M}_{\rm c}'], \
ranges={'a_1':(0.0, 0.8), \
'iota':(0.0, np.pi), \
'q':(0.02, 0.6), \
'lambda_2':(0.0, 4000.0)}))
# snr-ordered colouring
n_targets = len(samples)
cm = mpcm.get_cmap('plasma')
cols = [mpc.rgb2hex(cm(x)) for x in np.linspace(0.2, 0.8, n_targets)[::-1]]
# generate figure and axes
n_col = 5
n_row = 8
fig, axes = mp.subplots(n_row, n_col, figsize=(20, 30))
g = gdp.get_single_plotter()
# set up points at which we want to plot the base run
i_xy_base = [[0, 1], [1, 0], [2, 1], [4, 4], [6, 4]]
# nummbers of different variants
n_spin_mag = 4
n_spin_dir = 2
n_inc = 5
n_q = 6
# @TODO
# 3 - credible intervals?
# plot spin magnitude variants
spin_mags = np.sqrt(targets['spin1x'] ** 2 + \
def plot_lf(cmd):
cmd_samples = MCSamples(samples=cmd, names=['color', 'mag'])
g = plots.get_single_plotter(width_inch=2 * 3.464, ratio=0.75)
g.plot_1d(cmd_samples, 'mag')
samp2 = np.array([
c2.samples['cosmological_parameters--omega_m'],
c2.samples['cosmological_parameters--s8']
])
samp3 = np.array([
c3.samples['cosmological_parameters--omega_m'],
c3.samples['cosmological_parameters--s8']
])
names = [r'$\Omega_{\rm m}$', r'$S_8$']
plt.close()
from getdist import plots, MCSamples
import getdist
import matplotlib.pyplot as plt
g = plots.get_single_plotter(width_inch=6, ratio=0.8)
g.settings.legend_fontsize = 12
g.settings.fontsize = 18
g.settings.axes_fontsize = 18
g.settings.axes_labelsize = 22
g.settings.axis_tick_max_labels = 15
g.settings.linewidth = 1.5
g.settings.legend_colored_text = True
g.settings.axis_tick_step_groups = [[2.5, 3, 4, 6, 8], [1, 2, 5, 10]]
samples1 = MCSamples(samples=samp1.T,
names=['x1', 'x2'],
labels=names,
label='DES Y3',
weights=c1.weight,
settings={
dirname = r'/home/nayantara/Desktop/Cosmology_Dvorkin/Project_Cosmology/Cosmo212/Results_JLA/OL2/'
names = ['omegam', 'omegal']
labels = ['\Omega_m', '\Omega_\Lambda']
for ch in range(Num_chains):
itns = np.loadtxt(dirname + 'chfinal_' + str(ch) + '.txt')
print(itns.shape)
start = int(itns.shape[0] * burnin)
matlist.append(itns[start:, :])
mat = np.vstack(matlist)
#mat[:, 1] = mat[:, 1]*100 #for hubble
samp_sne = MCSamples(samples=mat, names=names, labels=labels)
#Planck plotting (alone)
g = plots.get_single_plotter(
chain_dir=
r'/home/nayantara/Desktop/Cosmology_Dvorkin/Project_Cosmology/Datasets/Combined chains/chains_FL2'
)
roots = ['base_plikHM_TTTEEE_lowl_lowE_lensing']
pairs = [['omegam', 'H0']]
g.plots_2d(roots, param_pairs=pairs, filled=True, shaded=False)
g.export()
#Planck
names = ['omegam', 'H0']
labels = ['\Omega_m', 'H_0']
mat_pl = np.loadtxt('base_plikHM_TTTEEE_lowl_lowE_lensing_1.txt',
usecols=(29, 31)) #has 96 columns
names_pl = np.loadtxt('base_plikHM_TTTEEE_lowl_lowE_lensing.paramnames',
dtype=str,
delimiter=';') #has 94 columns
samp_pl = MCSamples(samples=mat_pl, names=names, labels=labels)
samples = mcsamples.loadMCSamples(
"{}/multinest".format(optimizer_directory))
def get_params(samples):
params = [name for name in samples.paramNames.names]
return params
# if str(name).startswith(
# "galaxies_{}".format(galaxy)
# )
params = get_params(samples=samples)
plotter = plots.get_single_plotter(width_inch=16)
plotter.triangle_plot(
samples,
filled=True,
)
plt.show()
# #print(samples.samples.shape);exit()
# fig = corner.corner(
# xs=samples.samples[:, 0:6],
# weights=samples.weights,
# bins=20,
# colors="b",
# truths=truths[0:6],
# labels=labels,
# smooth=1.0,
v["progress"].plot(x="N",
y="Rminus1",
ax=ax[1],
color=colors[i],
legend=False,
logy=True)
plt.legend(results.keys())
plt.savefig(os.path.join(chain_dir, "progress.png"))
# Distributions of alpha posteriors
from getdist import plots
samples = [v["sample"] for k, v in results.items()]
line_args = [{"ls": "-", "color": colors[i]} for i in range(len(samples))]
g = plots.get_single_plotter(ratio=1, width_inch=6)
g.plot_1d(samples, "alpha", line_args=line_args)
plt.xlabel(r"$\alpha$ [deg.]")
plt.legend(results.keys())
plt.savefig(os.path.join(chain_dir, "posteriors.png"))
# Print mean and variance values
mean_mcmc = [result["sample"].getMeans()[0] for result in results.values()]
std_mcmc = [
np.sqrt(result["sample"].getVars()[0]) for result in results.values()
]
df = pd.DataFrame(
{
"mean MCMC": mean_mcmc,
"std. MCMC": std_mcmc,
import matplotlib.pyplot as plt
# import IPython
import numpy as np
from global_tools import *
names = [
"gamma", "log_{10}(g)", "log_{10}(M[GeV])", "N_a", "N_conv", "N_pr", "N_mu"
]
labels = [
"gamma", "log_{10}(g)", "log_{10}(M[GeV])", "N_a", "N_conv", "N_pr", "N_mu"
]
gamma, log10_g, log10_M, N_a, N_conv, N_pr, N_mu, something_1, something_2, \
something_3 = Read_Data_File(os.getcwd()+'/out/likelihood/ev.dat')
samps = np.array([gamma, log10_g, log10_M, N_a, N_conv, N_pr, N_mu]).T
samples = MCSamples(samples=samps, names=names, labels=labels)
plt.figure()
g = plots.get_single_plotter()
samples.updateSettings({
'contours': [0.68, 0.90],
'fine_bins_2D': 20,
'smooth_scale_2D': 0.6
})
g.settings.num_plot_contours = 2
g.plot_2d(samples, 'log_{10}(M[GeV])', 'log_{10}(g)', shaded=True)
plt.plot(-1.94, -1.16, 'X', color='black', markersize=14)
g.export(os.getcwd() + '/out/plots/gMmarg.png')
def plot_mcmc_mocks():
stg = {
'mult_bias_correction_order': 0,
'smooth_scale_2D': 0.15,
'smooth_scale_1D': 0.3,
'contours': [0.68, 0.95]
}
mc_kw = dict(names=['fnl', 'b', 'n0'],
labels=['f_{NL}', 'b', '10^{7}n_{0}'],
settings=stg)
read_kw = dict(ndim=3, iscale=[2])
mc_kw2 = dict(names=['fnl', 'b', 'n0', 'b2', 'n02'],
labels=['f_{NL}', 'b1', '10^{7}n_{0}', 'b2', '10^{7}n_{0}2'],
settings=stg)
read_kw2 = dict(ndim=5, iscale=[2, 4])
mc_kw3 = dict(names=['fnl', 'b', 'n0', 'b2', 'n02', 'b3', 'n03'],
labels=[
'f_{NL}', 'b1', '10^{7}n_{0}', 'b2', '10^{7}n_{0}2',
'b3', '10^{7}n_{0}3'
],
settings=stg)
read_kw3 = dict(ndim=7, iscale=[2, 4, 6])
fs = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_fullsky_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
bm = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_bmzls_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
nd = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_ndecals_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
sd = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_sdecals_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
bn = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_bmzlsndecals_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw2,
read_kw=read_kw2)
joint = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_bmzlsndecalssdecals_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw3,
read_kw=read_kw3)
stats = {}
stats['Full Sky'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_fullsky_noweight_steps10k_walkers50.npz',
**read_kw)[0]
stats['F. Sky [BMzLS scaled]'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_fullskyscaled_noweight_steps10k_walkers50.npz',
**read_kw)[0]
stats['BASS/MzLS'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_bmzls_noweight_steps10k_walkers50.npz',
**read_kw)[0]
stats['DECaLS North'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_ndecals_noweight_steps10k_walkers50.npz',
**read_kw)[0]
stats['DECaLS South'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_sdecals_noweight_steps10k_walkers50.npz',
**read_kw)[0]
stats['NGC'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_ngc_noweight_steps10k_walkers50.npz',
**read_kw)[0]
stats['Joint (BMzLS+DECaLS N)'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_bmzlsndecals_noweight_steps10k_walkers50.npz',
**read_kw2)[0]
stats['Joint (DECaLS N+DECaLS S)'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_ndecalssdecals_noweight_steps10k_walkers50.npz',
**read_kw2)[0]
stats['DESI'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_desi_noweight_steps10k_walkers50.npz',
**read_kw)[0]
stats['Joint (BMzLS+DECaLS)'] = read_chain(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_zero_bmzlsndecalssdecals_noweight_steps10k_walkers50.npz',
**read_kw3)[0]
# Triangle plot
g = plots.get_single_plotter(width_inch=4 * 1.5)
g.settings.legend_fontsize = 14
g.plot_2d([bm, nd, sd, fs], 'fnl', 'b', filled=False)
g.add_x_marker(0)
g.add_y_marker(1.43)
g.add_legend(['BASS/MzLS', 'DECaLS North', 'DECaLS South', 'Full Sky'],
colored_text=True,
legend_loc='lower left')
#prsubplots(g.subplots[0, 0].get_xlim())
# g.subplots[0, 0].set_xticklabels([])
g.fig.align_labels()
g.fig.savefig('figs/fnl2dmocks_area.pdf', bbox_inches='tight')
# Triangle plot
g = plots.get_single_plotter(width_inch=4 * 1.5)
g.settings.legend_fontsize = 14
g.plot_1d([bm, bn, joint], 'fnl', filled=False)
g.add_x_marker(0)
# g.add_y_marker(1.4262343145500318)
g.add_legend([
'BASS/MzLS', 'BASS/MzLs+DECaLS North', 'BASS/MzLS+DECaLS (North+South)'
],
colored_text=True)
g.subplots[0, 0].set_xticks([-100, -75, -50, -25, 0, 25., 50.])
# g.subplots[0, 0].set_xticklabels([])
g.fig.align_labels()
g.fig.savefig('figs/fnl1dmocks_joint.pdf', bbox_inches='tight')
pstats = pd.DataFrame(stats,
index=[
'MAP [scipy]', 'MAP [chain]', 'Mean [chain]',
'Median [chain]', '16th', '84th'
]).T
bm = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_po100_bmzls_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
nd = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_po100_ndecals_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
sd = MCMC(
'/fs/ess/PHS0336/data/lognormal/v2/mcmc/mcmc_lrg_po100_sdecals_noweight_steps10k_walkers50.npz',
mc_kw=mc_kw,
read_kw=read_kw)
# Triangle plot
g = plots.get_single_plotter(width_inch=4 * 1.5)
g.settings.legend_fontsize = 14
g.plot_2d([bm, nd, sd], 'fnl', 'b', filled=False)
g.add_x_marker(100)
g.add_y_marker(1.43)
g.add_legend(['BASS/MzLS', 'DECaLS North', 'DECaLS South'],
colored_text=True,
legend_loc='lower left')
#prsubplots(g.subplots[0, 0].get_xlim())
# g.subplots[0, 0].set_xticklabels([])
g.fig.align_labels()
g.fig.savefig('figs/fnl2dmocks_po100.pdf', bbox_inches='tight')
return pstats
def testPlots(self):
self.samples = self.testdists.bimodal[0].MCSamples(12000, logLikes=True, random_state=10)
g = plots.get_single_plotter(auto_close=True)
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.new_plot()
g.plot_1d(samples, 'y', normalized=True, marker=0.1, marker_color='b')
g.new_plot()
g.plot_2d(samples, 'x', 'y')
g.new_plot()
g.plot_2d(samples, 'x', 'y', filled=True)
g.new_plot()
g.plot_2d(samples, 'x', 'y', shaded=True)
g.new_plot()
g.plot_2d_scatter(samples, 'x', 'y', color='red', colors=['blue'])
g.new_plot()
g.plot_3d(samples, ['x', 'y', 'z'])
g = plots.get_subplot_plotter(width_inch=8.5, auto_close=True)
g.plots_1d(samples, ['x', 'y'], share_y=True)
g.new_plot()
g.triangle_plot(samples, ['x', 'y', 'z'])
self.assertTrue(g.get_axes_for_params('x', 'z') == g.subplots[2, 0])
self.assertTrue(g.get_axes_for_params('z', 'x', ordered=False) == g.subplots[2, 0])
self.assertTrue(g.get_axes_for_params('x') == g.subplots[0, 0])
self.assertTrue(g.get_axes_for_params('x', 'p', 'q') is None)
self.assertTrue(g.get_axes(ax=('x', 'z')) == g.subplots[2, 0])
self.assertTrue(g.get_axes(ax=(2, 0)) == g.subplots[2, 0])
g.new_plot()
g.triangle_plot(samples, ['x', 'y'], plot_3d_with_param='z')
g.new_plot()
g.rectangle_plot(['x', 'y'], ['z'], roots=samples, filled=True)
prob2 = self.testdists.bimodal[1]
samples2 = prob2.MCSamples(12000, random_state=10)
g.new_plot()
g.triangle_plot([samples, samples2], ['x', 'y'])
g.new_plot()
g.plots_2d([samples, samples2], param_pairs=[['x', 'y'], ['x', 'z']])
g.new_plot()
g.plots_2d([samples, samples2], 'x', ['z', 'y'])
g.new_plot()
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_plot_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')
g.new_plot()
omm = np.arange(0.1, 0.7, 0.01)
g.add_bands(omm, 0.589 * omm ** (-0.25), 0.03 * omm ** (-0.25), nbands=3)
g = plots.get_subplot_plotter()
import copy
for upper in [False, True]:
g.triangle_plot([samples, samples2], ['x', 'y', 'z'], filled=True,
upper_roots=[copy.deepcopy(samples)], upper_kwargs={'contour_colors': ['green']},
legend_labels=['1', '2', '3'], upper_label_right=upper)
for i in range(3):
for j in range(i):
self.assertTrue(g.subplots[i, j].get_xlim() == g.subplots[j, i].get_ylim())
self.assertTrue(g.subplots[i, j].get_ylim() == g.subplots[j, i].get_xlim())
self.assertTrue(g.subplots[i, j].get_xlim() == g.subplots[j, j].get_xlim())
def make_single_plot(chainfile,
savefile,
chainfile2=None,
pi1=0,
pi2=3,
true_parameter_values=None,
ranges=None,
string=True,
burnin=10000):
"""Make a getdist plot"""
ticks = {}
if string:
pnames = [
r"G\mu", r"\mathrm{StMBBH}", r"\mathrm{IMRI}", r"\mathrm{EMRI}"
]
else:
pnames = [
r"T_\ast", r"\mathrm{StMBBH}", r"\mathrm{IMRI}", r"\mathrm{EMRI}",
r"\alpha"
] #, r"\beta"]
prange = None
if ranges is not None:
prange = {pnames[i]: ranges[i] for i in range(len(pnames))}
print("Sim=", savefile)
subplot_instance = gdp.get_single_plotter()
samples = np.loadtxt(chainfile)
posterior_MCsamples = gd.MCSamples(samples=samples[burnin:],
names=pnames,
labels=pnames,
label='',
ranges=prange)
if chainfile2 is not None:
samples2 = np.loadtxt(chainfile2)
posterior2 = gd.MCSamples(samples=samples2[burnin:],
names=pnames,
labels=pnames,
label='',
ranges=prange)
subplot_instance.plot_2d([posterior_MCsamples, posterior2],
pnames[pi1],
pnames[pi2],
filled=True)
subplot_instance.add_legend(["LIGO+LISA", "LIGO+LISA+TianGo"])
else:
subplot_instance.plot_2d([posterior_MCsamples],
pnames[pi1],
pnames[pi2],
filled=True)
# colour_array = np.array(['black', 'red', 'magenta', 'green', 'green', 'purple', 'turquoise', 'gray', 'red', 'blue'])
#Ticks we want to show for each parameter
if string:
ticks = {
pnames[0]: [
np.log(1e-17),
np.log(2e-17),
np.log(5e-17),
np.log(1e-16),
np.log(2e-16),
np.log(5e-16),
np.log(1e-15)
]
}
ticklabels = {
pnames[0]: [
r"$10^{-17}$", r"$2\times 10^{-17}$", r"$5\times 10^{-17}$",
r"$10^{-16}$", r"$2\times 10^{-16}$", r"$5\times 10^{-16}$",
r"$10^{-15}$"
]
}
else:
ticks = {
pnames[0]: [
np.log(1e3),
np.log(2e3),
np.log(5e3),
np.log(1e4),
np.log(2e4),
np.log(5e4)
]
} #, np.log(1e6)]}#, np.log(1e11)
#pnames[4]: [np.log(1e-4), np.log(1e-3), np.log(1e-2), np.log(0.1), 0]}
ticklabels = {
pnames[0]: [
r"$10^{3}$", r"$2\times 10^3$", r"$5\times 10^3$", r"$10^{4}$",
r"$2\times 10^4$", r"$5\times 10^{4}$"
]
} #, r"$10^{6}$"]}#, r"$10^{11}$"]},
#Place horizontal and vertical lines for the true point
ax = subplot_instance.subplots[0, 0]
ax.yaxis.label.set_size(16)
ax.xaxis.label.set_size(16)
if pnames[pi2] in ticks:
ax.set_yticks(ticks[pnames[pi2]])
ax.set_yticklabels(ticklabels[pnames[pi2]])
if pnames[pi1] in ticks:
ax.set_xticks(ticks[pnames[pi1]])
ax.set_xticklabels(ticklabels[pnames[pi1]])
ax.axhline(true_parameter_values[pi2], color='gray', ls='--', lw=2)
ax.axvline(true_parameter_values[pi1], color='gray', ls='--', lw=2)
plt.savefig(savefile)
