def fit(argv):
initializeDetector(det, )
initializeWaveform(wf, results[wf_idx]['x'])
# Create a model object and a sampler
model = Model()
sampler = dnest4.DNest4Sampler(model,
backend=dnest4.backends.CSVBackend(".",
sep=" "))
seed = 1234
np.random.seed(seed)
# Set up the sampler. The first argument is max_num_levels
gen = sampler.sample(max_num_levels=100,
num_steps=1000000,
new_level_interval=100000,
num_per_step=10000,
thread_steps=100,
num_particles=5,
lam=10,
beta=100,
seed=seed)
# Do the sampling (one iteration here = one particle save)
for i, sample in enumerate(gen):
print("# Saved {k} particles.".format(k=(i + 1)))
# Run the postprocessing
dnest4.postprocess()
def main(idx=0):
time.sleep(120)
directory = "chan672_wfs"
os.chdir(directory)
for root, dirs, files in os.walk("."):
for wf in dirs:
index = "wf" + str(idx)
if wf == index:
os.chdir(wf)
loop = 1
while loop == 1:
dn4.postprocess(plot=False)
post = np.loadtxt("posterior_sample.txt")
if len(post) < 10:
time.sleep(120)
else:
os.chdir("../..")
return
def dnest_postprocess(self, directory):
# Moves into the result directory to postprocess, then moves back to wherever we were
# TODO: Should probably check that directories and files are valid...
old_dir = os.getcwd()
os.chdir(directory)
logZ, info, weights = None, None, None
try:
logZ, info, weights = dnest4.postprocess(plot=False)
except IndexError as e:
print(e)
print(F"There was an issue looking at the result in {directory}")
print('\033[91m' +
"I'll try to keep going but this looks serious..." +
'\033[0m')
import time
time.sleep(3)
os.chdir(old_dir)
return logZ, info, weights
import dnest4 dnest4.postprocess(single_precision=True) import display
from dnest4 import postprocess
import sys
options = sys.argv
if 'no' in options:
plot = False
else:
plot = True
if 'pvc' in options:
while True:
logz, H, logx_samples, posterior = postprocess(plot=plot)
else:
logz, H, logx_samples, posterior = postprocess(plot=plot)
if posterior.shape[0] > 5:
from display import DisplayResults
res = DisplayResults(options)
else:
print 'Too few samples, keep running the model'
Generate from the heavy-tailed distribution.
"""
a = np.random.randn()
b = np.random.rand()
t = a/np.sqrt(-np.log(b))
n = np.random.randn()
return 10.0**(1.5 - 3*np.abs(t))*n
def wrap(self, x, a, b):
assert b > a
return (x - a)%(b - a) + a
# Create a model object and a sampler
model = Model()
sampler = dnest4.DNest4Sampler(model,
backend=dnest4.backends.CSVBackend(".",
sep=" "))
# Set up the sampler. The first argument is max_num_levels
gen = sampler.sample(max_num_levels=30, num_steps=1000, new_level_interval=10000,
num_per_step=10000, thread_steps=100,
num_particles=5, lam=10, beta=100, seed=1234)
# Do the sampling (one iteration here = one particle save)
for i, sample in enumerate(gen):
print("# Saved {k} particles.".format(k=(i+1)))
# Run the postprocessing
dnest4.postprocess()
import dnest4 as dn4
import os
for root, dirs, files in os.walk("."):
for wf in dirs:
os.chdir(wf)
dn4.postprocess(plot=False)
os.chdir("..")
exit()
# Set up the sampler. The first argument is max_num_levels
gen = sampler.sample(max_num_levels=30,
num_steps=1000,
new_level_interval=10000,
num_per_step=10000,
thread_steps=100,
num_particles=5,
lam=10,
beta=100)
# Do the sampling (one iteration here = one particle save)
for sample in enumerate(gen):
pass
# Run the postprocessing to get marginal likelihood and generate posterior samples
logZdnest4, infogaindnest4, _ = dnest4.postprocess(plot=False)
postsamples = np.loadtxt('posterior_sample.txt')
print('Marginalised evidence is {}'.format(logZdnest4))
print('Number of posterior samples is {}'.format(postsamples.shape[0]))
# plot posterior samples (if corner.py is installed)
try:
import matplotlib as mpl
mpl.use("Agg") # force Matplotlib backend to Agg
import corner # import corner.py
except ImportError:
sys.exit(1)
The PostBlobby3D class is very simple. However, it is useful for organisational
purposes of the Blobby3D output. In this script, I created a PostBlobby3D
object and plotted a handful of sample attributes.
@author: Mathew Varidel
"""
import numpy as np
import matplotlib.pyplot as plt
import dnest4 as dn4
from pyblobby3d import PostBlobby3D
dn4.postprocess()
post_b3d = PostBlobby3D(samples_path='posterior_sample.txt',
data_path='data.txt',
var_path='var.txt',
metadata_path='metadata.txt',
nlines=2)
# choose a sample
sample = 0
# Plot maps for sample
fig, ax = plt.subplots(1, 4)
ax[0].set_title(r'H$\alpha$ Flux')
ax[0].imshow(np.log10(post_b3d.maps[sample, 0]),
import dnest4 dnest4.postprocess()
return norm - 0.5*chisq
# Create a model object and a sampler
model = DNest4Model()
sampler = dnest4.DNest4Sampler(model, backend=dnest4.backends.CSVBackend(".", sep=" "))
# Set up the sampler. The first argument is max_num_levels
gen = sampler.sample(max_num_levels=30, num_steps=1000, new_level_interval=10000,
num_per_step=10000, thread_steps=100, num_particles=5, lam=10, beta=100)
# Do the sampling (one iteration here = one particle save)
for sample in enumerate(gen):
pass
# Run the postprocessing to get marginal likelihood and generate posterior samples
logZdnest4, infogaindnest4, _ = dnest4.postprocess(plot=False);
postsamples = np.loadtxt('posterior_sample.txt')
print('Marginalised evidence is {}'.format(logZdnest4))
print('Number of posterior samples is {}'.format(postsamples.shape[0]))
# plot posterior samples (if corner.py is installed)
try:
import matplotlib as mpl
mpl.use("Agg") # force Matplotlib backend to Agg
import corner # import corner.py
except ImportError:
sys.exit(1)
def postprocess(argv): # Run the postprocessing dnest4.postprocess()
def postprocess(argv):
# Run the postprocessing
dnest4.postprocess()
