def newGraph_Confidence(result):
H_0 = [ii[0] for ii in result]
omega_m0 = [ii[1] for ii in result]
omega_q0 = [ii[2] for ii in result]
alpha = [ii[3] for ii in result]
alpha_x = [ii[4] for ii in result]
m = [ii[5] for ii in result]
names = [
"H_0", "\Omega_{m_0}", "\Omega_{Q_0}", r"\tilde{\alpha}",
r"\tilde{\alpha}_x", "m"
]
#names = ["H_0"]
labels = names
newShape = [H_0, omega_m0, omega_q0, alpha, alpha_x, m]
#newShape = [H_0]
values = np.array(newShape).T
#print(values.shape)
s1 = MCSamples(samples=values, names=names, labels=labels)
g = plots.get_subplot_plotter()
s1.updateSettings({'contours': [0.68, 0.95, 0.99]})
g.settings.colormap = "binary"
g.settings.num_plot_contours = 3
g.triangle_plot([s1], shaded=True, title_limit=1)
plt.savefig("result.pdf")
def _triangle_plot(self, these_samples, these_y, plotname):
names = self._parameters_list
labels = []
level_lines = [0.2, 0.4, 0.6, 0.8, 0.95, 0.98]
num_level_lines = len(level_lines)
g = plots.getSubplotPlotter(width_inch=9)
g.settings.num_plot_contours = num_level_lines
mcsamples = MCSamples(samples=these_samples, names=names, labels=names)
mcsamples.updateSettings({'contours': level_lines})
g.triangle_plot(
[mcsamples],
names,
# filled_compare=True,
legend_labels=labels,
legend_loc='upper right',
# filled=False,
contour_colors=['darkblue', 'green'],
# filled=True,
# contour_lws=[.2, .4, .68, .95, .98]
)
n_params = len(names)
for i in range(n_params):
for j in range(n_params):
if j > i:
continue
ax = g.subplots[i, j]
ax.axvline(these_y[j], color='black', ls='--', alpha=0.4)
if i != j:
ax.axhline(these_y[i], color='black', ls='-.', alpha=0.4)
g.export(plotname)
#data.reshape(7,7)
cov = data
ndim = data.shape[0]
mean_ = [0.01, 0.082, -2.6, -3.14, 20 * 0.53, -2.54, 1.53]
samps = np.random.multivariate_normal(mean_, cov, size=nsamp)
#samps = np.random.multivariate_normal([0]*ndim, cov, size=nsamp)
#A = np.random.rand(ndim,ndim)
#cov = np.dot(A, A.T)
#samps2 = np.random.multivariate_normal([0]*ndim, cov, size=nsamp)
#names = ['r','As','$\alpha_s$','$\beta_s$','Ad','$\alpha_d$','$\beta_d$']
#labels = ['r','As','$\alpha_s$','$\beta_s$','Ad','$\alpha_d$','$\beta_d$']
names = ['r', 'As', 'alpha_s', 'beta_s', 'Ad', 'alpha_d', 'beta_d']
#labels = ['r','As','alpha_s','beta_s','Ad','alpha_d','beta_d']
labels = ['r', 'A_s', '\\alpha_s', 'beta_s', 'Ad', 'alpha_d', 'beta_d']
samples = MCSamples(samples=samps,
names=names,
labels=labels,
ranges={
'r': (0, None),
'As': (0, None)
})
#samples2 = MCSamples(samples=samps2,names = names, labels = labels, label='Second set')
g = plots.getSubplotPlotter()
samples.updateSettings({'contours': [0.68, 0.95, 0.99]})
g.settings.num_plot_contours = 3
g.triangle_plot(samples, filled=True)
#g.triangle_plot([samples, samples2], filled=True)
g.export('output_file.pdf')
usecols=[2, 3],
sep='\s+',
skiprows=1,
names=colname)
from getdist import plots, MCSamples
labels = ['\\mathrm{X}', '\\mathrm{Y}']
samples1 = MCSamples(samples=chains1[['Separation', 'Flux_Ratio']].values,
names=colname,
labels=labels)
samples2 = MCSamples(samples=chains2[['Separation', 'Flux_Ratio']].values,
names=colname,
labels=labels)
conf = [0.683, 0.955, 0.997]
samples1.updateSettings({'contours': conf})
samples2.updateSettings({'contours': conf})
g = plots.getSubplotPlotter()
g.settings.num_plot_contours = 3
g.settings.shade_level_scale = 3
g.settings.fig_width_inch = 8.5
g.settings.linewidth = 0.3
g.plot_2d(samples1,
'Separation',
'Flux_Ratio',
filled=False,
lims=[881, 911, 807, 837]) #30 x 30 pixels
g.plot_2d(samples2,
'Separation',
'Flux_Ratio',
'''
2D contours
'''
xstep1, ystep1 = loadtxt('flat-LCDM_steps.dat', unpack=True)
xstep = int(xstep1)
ystep = int(ystep1)
print('check 6')
z2 = loadtxt('flat_LCDM_chi2_H(z)only.dat', unpack=True)
y2 = loadtxt('flat_LCDM_Omega_m0.dat', unpack=True)
x2 = loadtxt('flat_LCDM_H0.dat', unpack=True)
zmin = z2.tolist().index(min(z2))
xmin2 = x2[zmin]
ymin2 = y2[zmin]
c2 = z2[zmin]
print(c2, xmin2, ymin2, "2D best fit points with grid")
g = plots.getSinglePlotter(width_inch=4, ratio=1)
samples.updateSettings({'contours': [0.6827, 0.9545, 0.9973]})
g.settings.num_plot_contours = 3
g.plot_2d([samples],['H','m'],filled_compare=False,
line_args=[{'ls':'solid','color':'k'}])
x2_2D_mcase3 = reshape(x2, (xstep, ystep))
y2_2D_mcase3 = reshape(y2, (xstep, ystep))
z2_2D_mcase3 = reshape(z2, (xstep, ystep))
plt.contour(x2_2D_mcase3, y2_2D_mcase3, z2_2D_mcase3, [c2, c2 + 2.3, c2 + 6.17, c2 + 11.8],
colors='blue', linestyles=':')
plt.plot([xmin2, xmin2], [ymin2, ymin2], marker='+', color='blue') #Best fit parameters
g.export('flat_LCDM_H0_Om_2D_MCMC_vs_grid_my_data.pdf')
print("--- %s seconds ---" % (time.time() - start_time))
"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
# names = ["gamma", "N_a", "N_{conv}", "N_{pr}", "N_{mu}"]
# labels = ["gamma", "N_a", "N_{conv}", "N_{pr}", "N_{mu}"]
# gamma, 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, N_a, N_conv, N_pr, N_mu]).T
samples = MCSamples(samples=samps, names=names, labels=labels)
plt.figure()
g = plots.get_subplot_plotter()
samples.updateSettings({
'contours': [0.68, 0.90],
'fine_bins_2D': 40,
'smooth_scale_2D': 0.6
})
g.settings.num_plot_contours = 3
g.triangle_plot(samples, shaded=True)
#plt.plot(-2.37, -1.80, 'X', color = 'black', markersize=14)
# g.export(os.getcwd()+'/out/plots/all_param_marg.pdf')
g.export(os.getcwd() + '/out/plots/all_param_marg.png')
def plot_triangle(sim_samples,
gp_samples,
burnin_frac=0.1,
emulator=True,
gp_error=False):
if gp_error and emulator:
label_gp = 'GP (Error)'
elif emulator and not gp_error:
label_gp = 'GP (Mean)'
else:
label_gp = 'Simulator (MOPED)'
ndim = sim_samples.shape[-1]
names = ["x%s" % i for i in range(ndim)]
labels = [
r"\Omega_{m}", r"w_{0}", r"M_{B}", r"\delta M", r"\alpha", r"\beta"
]
# for the simulator
burnin = int(burnin_frac * sim_samples.shape[1])
samples_exact = sim_samples[:, burnin:, :].reshape((-1, ndim))
cut_samps = samples_exact[samples_exact[:, 0] >= 0.0, :]
samples1 = MCSamples(samples=cut_samps,
names=names,
labels=labels,
ranges={'x0': (0.0, None)})
# for the emulator
burnin = int(burnin_frac * gp_samples.chain.shape[1])
samples_emu = gp_samples.chain[:, burnin:, :].reshape((-1, ndim))
cut_samps = samples_emu[samples_emu[:, 0] >= 0.0, :]
samples2 = MCSamples(samples=cut_samps,
names=names,
labels=labels,
ranges={'x0': (0.0, None)})
# setups for plotting
sim_color = '#EEC591'
gp_color = 'Blue'
alpha_tri = 0.1
red_patch = mpatches.Patch(color=sim_color,
label='Simulator',
alpha=alpha_tri)
gp_line = Line2D([0], [0],
color=gp_color,
linewidth=3,
linestyle='--',
label=label_gp)
rec_leg = [red_patch, gp_line]
contours = np.array([0.68, 0.95])
G = plots.getSubplotPlotter(subplot_size=3.5)
samples1.updateSettings({'contours': [0.68, 0.95]})
G.triangle_plot([samples1],
filled=True,
line_args={
'lw': 3,
'color': sim_color
},
contour_colors=[sim_color])
G.settings.num_plot_contours = 2
plt.legend(handles=rec_leg,
loc='best',
prop={'size': 25},
bbox_to_anchor=(0.7, 6.0),
borderaxespad=0.)
G.settings.alpha_filled_add = alpha_tri
for i in range(0, 6):
for j in range(0, i + 1):
if i != j:
ax = G.subplots[i, j]
a, b = G.get_param_array(samples2,
['x' + str(j), 'x' + str(i)])
density = G.sample_analyser.get_density_grid(samples2, a, b)
density.contours = density.getContourLevels(contours)
contour_levels = density.contours
ax.contour(density.x,
density.y,
density.P,
sorted(contour_levels),
colors=gp_color,
linewidths=3,
linestyles='--')
ax.tick_params(labelsize=20)
ax.yaxis.label.set_size(20)
ax.xaxis.label.set_size(20)
else:
ax = G.subplots[i, j]
dense = samples2.get1DDensity('x' + str(i))
dense.normalize(by='max')
ax.plot(dense.x, dense.P, lw=3, c=gp_color, linestyle='--')
ax.tick_params(labelsize=20)
ax.yaxis.label.set_size(20)
ax.xaxis.label.set_size(20)
plt.savefig('images/triangle_plot.jpg',
bbox_inches='tight',
transparent=False)
plt.close()
