def run_example(directory, abc=False):
os.chdir(directory)
os.system("./main -s 0 -t 8")
if directory == "StraightLine":
print("Figure 1 in the paper has been zoomed in.")
if abc:
dn4.postprocess_abc(rng_seed=0)
else:
dn4.postprocess(rng_seed=0)
exec(open("display.py").read())
os.chdir("..")
def postprocess():
# Postprocess DNest4 output
dn4.postprocess(plot=False)
# Load the data
f = open("data.yaml")
data = yaml.load(f, Loader=yaml.SafeLoader)
f.close()
# Plot the forecast
posterior_sample = dn4.my_loadtxt("posterior_sample.txt")
amount = posterior_sample[:, -1]
amount = np.sort(amount)
n = len(amount)
indices = [int(0.1 * n), int(0.5 * n), int(0.9 * n)]
future_tips = amount
quantiles = future_tips[indices]
print("")
print(
"Assuming things keep rolling along more or less as they have been, I")
print("predict, with 90% probability, that you'll receive between")
print(np.round(quantiles[0], 2),
"and",
np.round(quantiles[2], 2),
"LBC",
end=" ")
print("over the next month.")
# plt.hist(future_tips, 500, density=True)
# plt.xlabel("Future tips over next month (LBC)")
# plt.ylabel("Probability Density")
# plt.show()
return quantiles
import dnest4.classic as dn4
dn4.postprocess()
import matplotlib.pyplot as plt
posterior_sample = dn4.my_loadtxt("posterior_sample.txt")
# Plot some curves through the data
x_data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
y_data = [1.0, 3.8, 3.7, 3.8, 2.9, 2.4, 2.0, 1.3, 0.4, -0.2]
plt.plot(x_data, y_data, "o")
for i in range(0, 10):
plt.plot(x_data, posterior_sample[i, 2:], color="g", alpha=0.2)
plt.show()
import dnest4.classic as dn4 dn4.postprocess(single_precision=True, cut=0) import display
#!/usr/bin/python
import dnest4.classic as dn4
dn4.postprocess(single_precision=True)
import display
import numpy as np
posterior_sample = dn4.my_loadtxt("posterior_sample.txt")
indices = dn4.load_column_names("posterior_sample.txt")
# Extract column by name
#print("Indices")
#print(indices)
#plt.show()
print(indices)
print(type(indices))
print("Length of the dictionanry : ", len(indices))
I = indices.get('indices')
print("Items of the dictionanry : ", indices.items())
Z = indices.get('colnames')
print("Colnames: ", Z)
print("Type of Colnames: ", type(Z))
print("Type of items : ", type(I))
print("Type of Posterior ", type(posterior_sample))
index = 'log_a[2]'
print("Index: ", I['log_a[2]'])
import corner
import dnest4.classic as dn4
import matplotlib.pyplot as plt
import numpy as np
# DNest4 postprocessing
dn4.postprocess(cut=0.0)
# Corner plot
posterior_sample = dn4.my_loadtxt("posterior_sample.txt")
corner.corner(posterior_sample, plot_contours=False, plot_density=False)
plt.show()
# Correlation matrix
plt.imshow(np.corrcoef(posterior_sample.T),
cmap="coolwarm",
vmin=-1.0,
vmax=1.0,
interpolation="nearest")
plt.show()
import dnest4.classic as dn4 dn4.postprocess()
A, P, phi = 5*rng.randn(), -0.05*np.log(rng.rand()), 2*np.pi*rng.rand()
data["y"] = A*np.sin(2*np.pi*data["t"]/P + phi) + rng.randn(data["N"])
# Create model
model = bd.Model()
# Sinusoid parameters
model.add_node(bd.Node("A", bd.Normal(0.0, 5.0)))
model.add_node(bd.Node("P", bd.Exponential(0.05)))
model.add_node(bd.Node("phi", bd.Uniform(0.0, 2.0*np.pi)))
# Data nodes
for i in range(0, data["N"]):
name = "y{i}".format(i=i)
mean = "A*sin(2*M_PI*t{i}/P + phi)".format(i=i)
model.add_node(bd.Node(name, bd.Normal(mean, 1.0), observed=True))
# Create the C++ code
bd.generate_h(model, data)
bd.generate_cpp(model, data)
# Compile the C++ code so it's ready to go
os.system("make")
os.system("nice -n19 ./main")
f.write(str(dn4.postprocess(plot=False)[1]) + "\n")
f.flush()
f.close()
import corner
import dnest4.classic as dn4
import matplotlib.pyplot as plt
import numpy as np
dn4.postprocess(rng_seed=0)
posterior_sample = dn4.my_loadtxt("posterior_sample.txt")
# Do corner plot
#colnames = dn4.load_column_names("posterior_sample.txt")["colnames"]
#corner.corner(posterior_sample, labels=colnames)
#plt.savefig("corner.png")
#plt.show()
# Corner plot for V2
# Fonts
plt.rcParams["font.family"] = "serif"
plt.rcParams["font.size"] = 16
plt.rc("text", usetex=True)
#plt.rcParams["axes.labelpad"] = 24
posterior_sample = posterior_sample[:, [0, 1, 2, 3, 6, 7, 8, 9, 10]]
fig = corner.corner(posterior_sample,
labels=[
"$A_0$ (km/s)", "$A_1$ (km/s)", "$\\phi_0$ (deg)",
"$\\phi_1$ (deg)", "$\\sigma_0$ (km/s)",
"$\\sigma_1$ (km/s)", "$\\gamma_0$", "$\\gamma_1$",
"$p_{\\rm subs}$"
],
plot_contours=False,
import dnest4.classic as dn4 dn4.postprocess(single_precision=True, cut=0) import display
import dnest4.classic as dn4 dn4.postprocess(single_precision=True) import display
def amorph_postprocess():
dn4.postprocess()
from . import display
display.display()
