hrc = hs / (alpha * (1 + epsilon) * (1 + epsilon)) #* rs_fid / (alpha * (1 + epsilon) * (1 + epsilon)) / c
res = np.vstack((omch2, daval, hrc)).T
return res
if False:
consumer = ChainConsumer()
consumer.configure_contour(sigmas=[0,1,2])
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z0"), parameters=[r"$\Omega_c h^2$", r"$\alpha$", r"$\epsilon$"], name="$0.2<z<0.6$")
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z1"), parameters=[r"$\Omega_c h^2$", r"$\alpha$", r"$\epsilon$"], name="$0.4<z<0.8$")
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z2"), parameters=[r"$\Omega_c h^2$", r"$\alpha$", r"$\epsilon$"], name="$0.6<z<1.0$")
consumer.plot(figsize="column", filename="wigglez_multipole_alphaepsilon.pdf")
#print(consumer.get_latex_table())
if True:
c = ChainConsumer()
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z0", 0.44), parameters=[r"$\Omega_c h^2$", r"$D_A(z)$", r"$H(z)$"], name="$0.2<z<0.6$")
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z1", 0.60), parameters=[r"$\Omega_c h^2$", r"$D_A(z)$", r"$H(z)$"], name="$0.4<z<0.8$")
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z2", 0.73), parameters=[r"$\Omega_c h^2$", r"$D_A(z)$", r"$H(z)$"], name="$0.6<z<1.0$")
for n, chain in zip(c.names, c.chains):
print(n)
print(chain.mean(axis=0))
print(np.std(chain, axis=0))
print("----")
c.configure_contour(sigmas=[0,1,2])
c.configure_general(bins=0.7)
c.plot(figsize="column", filename="wigglez_multipole_dah.pdf")
print(c.get_latex_table())
hrc = hs * rs_fid / (alpha * (1 + epsilon) * (1 + epsilon)) / c
res = np.vstack((omch2, daval, z/hrc)).T
return res
p1 = [r"$\Omega_c h^2$", r"$\alpha$", r"$\epsilon$"]
p2 = [r"$\Omega_c h^2$", r"$D_A(z)/r_s$", r"$cz/H(z)/r_s $"]
if False:
consumer = ChainConsumer()
consumer.configure_contour(sigmas=[0,1.3])
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z0"), parameters=p1, name="$0.2<z<0.6$")
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z1"), parameters=p1, name="$0.4<z<0.8$")
consumer.add_chain(load_directory("../bWigMpBin/bWigMpBin_z2"), parameters=p1, name="$0.6<z<1.0$")
consumer.plot(figsize="column", filename="wigglez_multipole_alphaepsilon.pdf", truth=[0.113, 1.0, 0.0])
print(consumer.get_latex_table())
if True:
c = ChainConsumer()
c.configure_contour(sigmas=[0,1,2])
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z0", 0.44), parameters=p2, name="$0.2<z<0.6$")
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z1", 0.60), parameters=p2, name="$0.4<z<0.8$")
c.add_chain(convert_directory("../bWigMpBin/bWigMpBin_z2", 0.73), parameters=p2, name="$0.6<z<1.0$")
print(c.get_latex_table())
#c.plot(figsize="column", filename="wigglez_multipole_dah.pdf")
if False:
c = ChainConsumer()
c.configure_contour(sigmas=[0,1,2])
c.add_chain(convert_directory("../bWizMpMeanBin/bWizMpMeanBin_z0", 0.44), parameters=p2, name="$0.2<z<0.6$")
c.add_chain(convert_directory("../bWizMpMeanBin/bWizMpMeanBin_z1", 0.60), parameters=p2, name="$0.4<z<0.8$")
import numpy as np
from chainconsumer import ChainConsumer
if __name__ == "__main__":
ndim, nsamples = 4, 200000
np.random.seed(0)
data = np.random.randn(nsamples, ndim)
data[:, 2] += data[:, 1] * data[:, 2]
data[:, 1] = data[:, 1] * 3 + 5
data[:, 3] /= (np.abs(data[:, 1]) + 1)
data2 = np.random.randn(nsamples, ndim)
data2[:, 0] -= 1
data2[:, 2] += data2[:, 1]**2
data2[:, 1] = data2[:, 1] * 2 - 5
data2[:, 3] = data2[:, 3] * 1.5 + 2
# If you pass in parameter labels and only one chain, you can also get parameter bounds
c = ChainConsumer()
c.add_chain(data,
parameters=["$x$", "$y$", r"$\alpha$", r"$\beta$"],
name="Model A")
c.add_chain(data2,
parameters=["$x$", "$y$", r"$\alpha$", r"$\gamma$"],
name="Model B")
table = c.get_latex_table(
caption="The maximum likelihood results for the tested models",
label="tab:example")
print(table)
