def ProgressPlotter(self, *args, **kwargs):
"""
Generate the ProgressPlotter object which is the son of ProgressWatcher
whose parameters are:
n_params,
interval_ms=200,
outputfiles_basename="chains/1-"
"""
return pymultinest.ProgressPlotter(self.__dim, *args, **kwargs)
# Normal Log Likelihood
modelNow = model(cube)(xdata)
return -0.5*((modelNow - ydata)**2. / yuncs**2.).sum()
# In[ ]:
if not os.path.exists("chains"): os.mkdir("chains")
# number of dimensions our problem has
# parameters = ["x", "y"]
n_params = len(parameters)
plt.figure(figsize=(5*n_params, 5*n_params))
# we want to see some output while it is running
progress = pymultinest.ProgressPlotter(n_params = n_params, outputfiles_basename='chains/2-'); progress.start()
# threading.Timer(2, show, ["chains/2-phys_live.points.pdf"]).start() # delayed opening
# run MultiNest
pymultinest.run(myloglike, myprior, n_params, importance_nested_sampling = False, resume = False, verbose = True, sampling_efficiency = 'model', n_live_points = 1000, outputfiles_basename='chains/2-')
# ok, done. Stop our progress watcher
progress.stop()
# lets analyse the results
a = pymultinest.Analyzer(n_params = n_params, outputfiles_basename='chains/2-')
s = a.get_stats()
# In[ ]:
import json
def dump():
progress = pymultinest.ProgressPlotter(n_params = n_params, outputfiles_basename=dir_output+'chains/2-'); progress.start()
threading.Timer(2, show, [dir_output+"chains/2-phys_live.points.pdf"]).start() # delayed opening
# run MultiNest
pymultinest.run(mc.multinest_call, mc.multinest_priors, n_params, importance_nested_sampling = False, resume = True,
verbose = True, sampling_efficiency = 'model', n_live_points = 1000, outputfiles_basename=dir_output+'chains/2-')
# ok, done. Stop our progress watcher
progress.stop()
# lets analyse the results
a = pymultinest.Analyzer(n_params = n_params, outputfiles_basename=dir_output+'chains/2-')
s = a.get_stats()
# store name of parameters, always useful
with open('%sparams.json' % a.outputfiles_basename, 'w') as f:
json.dump(parameters, f, indent=2)
# store derived stats
with open('%sstats.json' % a.outputfiles_basename, mode='w') as f:
json.dump(s, f, indent=2)
print()
print("-" * 30, 'ANALYSIS', "-" * 30)
print("Global Evidence:\n\t%.15e +- %.15e" % ( s['nested sampling global log-evidence'], s['nested sampling global log-evidence error'] ))
import matplotlib.pyplot as plt
plt.clf()
# run MultiNest
#pymultinest.run(mc.pymultinest_call, mc.pymultinest_priors, mc.ndim, outputfiles_basename=dir_output, resume = False, verbose = True)
#json.dump(parameters, open(dir_output+'params.json', 'w')) # save parameter names
# Here we will plot all the marginals and whatnot, just to show off
# You may configure the format of the output here, or in matplotlibrc
# All pymultinest does is filling in the data of the plot.
# Copy and edit this file, and play with it.
p = pymultinest.PlotMarginalModes(a)
plt.figure(figsize=(5 * n_params, 5 * n_params))
# plt.subplots_adjust(wspace=0, hspace=0)
for i in range(n_params):
plt.subplot(n_params, n_params, n_params * i + i + 1)
p.plot_marginal(i, with_ellipses=True, with_points=False, grid_points=50)
plt.ylabel("Probability")
plt.xlabel(parameters[i])
for j in range(i):
plt.subplot(n_params, n_params, n_params * j + i + 1)
# plt.subplots_adjust(left=0, bottom=0, right=0, top=0, wspace=0, hspace=0)
p.plot_conditional(i, j, with_ellipses=False, with_points=True, grid_points=30)
plt.xlabel(parameters[i])
plt.ylabel(parameters[j])
plt.savefig(dir_output+"chains/marginals_multinest.pdf") # , bbox_inches='tight')
show(dir_output+"chains/marginals_multinest.pdf")
for i in range(n_params):
outfile = '%s-mode-marginal-%d.pdf' % (a.outputfiles_basename, i)
p.plot_modes_marginal(i, with_ellipses=True, with_points=False)
plt.ylabel("Probability")
plt.xlabel(parameters[i])
plt.savefig(outfile, format='pdf', bbox_inches='tight')
plt.close()
outfile = '%s-mode-marginal-cumulative-%d.pdf' % (a.outputfiles_basename, i)
p.plot_modes_marginal(i, cumulative=True, with_ellipses=True, with_points=False)
plt.ylabel("Cumulative probability")
plt.xlabel(parameters[i])
plt.savefig(outfile, format='pdf', bbox_inches='tight')
plt.close()
print("Take a look at the pdf files in chains/")
#print "python cube after", [cube[i] for i in range(ndim)]
def myloglike(cube, ndim, nparams):
chi = 1.
#print "cube", [cube[i] for i in range(ndim)], cube
for i in range(ndim):
chi *= math.cos(cube[i] / 2.)
#print "returning", math.pow(2. + chi, 5)
return math.pow(2. + chi, 5)
# number of dimensions our problem has
parameters = ["x", "y"]
n_params = len(parameters)
# we want to see some output while it is running
progress = pymultinest.ProgressPlotter(n_params = n_params); progress.start()
threading.Timer(2, show, ["chains/1-phys_live.points.pdf"]).start() # delayed opening
# run MultiNest
pymultinest.run(myloglike, myprior, n_params, importance_nested_sampling = False, resume = True, verbose = True, sampling_efficiency = 'model', n_live_points = 1000)
# ok, done. Stop our progress watcher
progress.stop()
# lets analyse the results
a = pymultinest.Analyzer(n_params = n_params)
s = a.get_stats()
import json
# store name of parameters, always useful
with file('%sparams.json' % a.outputfiles_basename, 'w') as f:
json.dump(parameters, f, indent=2)
# store derived stats
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$PWD/lib
export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libgfortran.so.3
export LD_PRELOAD=/opt/local/lib/openmpi//lib/libmpi.so:$LD_PRELOAD
mpirun -np 4 python run_PyPolyChord.py
'''
# number of dimensions our problem has
n_params = mc.ndim
print '--->', mc.pam_names
mc.results_resumen(np.ones(n_params))
parameters = mc.pam_names
progress = pymultinest.ProgressPlotter(n_params=n_params,
outputfiles_basename=dir_output +
'chains/2-')
progress.start()
threading.Timer(
2, show,
[dir_output + "chains/2-phys_live.points.pdf"]).start() # delayed opening
# run MultiNest
pymultinest.run(mc.multinest_call,
mc.multinest_priors,
n_params,
resume=True,
sampling_efficiency='parameter',
evidence_tolerance=0.2,
max_iter=0,
verbose=True,
n_live_points=1000,
def run_mcmc(myloglike, myprior, nparams, parameters, **kwargs):
import matplotlib.pyplot as plt
outputfiles_basename = kwargs['outputfiles_basename']
nrows = 2
ncols = int(np.ceil(1.0 * nparams / nrows))
fig, axL = plt.subplots(nrows=nrows,
ncols=ncols,
figsize=(20, 10),
sharey=True)
axL = axL.flatten()
for i in range(nparams):
plt.sca(axL[i])
plt.title(parameters[i])
fig.set_tight_layout(True)
plt.yscale('symlog', ythresh=0.05)
plt.ylim(-1e10, -1)
# we want to see some output while it is running
progress = pymultinest.ProgressPlotter(
n_params=nparams, outputfiles_basename=outputfiles_basename, axL=axL)
progress.start()
pdffn = "{}phys_live.points.png".format(outputfiles_basename)
# delayed opening
threading.Timer(10, show, [pdffn]).start()
# run MultiNest
pymultinest.run(myloglike, myprior, nparams, **kwargs)
progress.stop()
# lets analyse the results
a = pymultinest.Analyzer(n_params=nparams,
outputfiles_basename=outputfiles_basename)
s = a.get_stats()
# store name of parameters, always useful
with open('%sparams.json' % a.outputfiles_basename, 'w') as f:
json.dump(parameters, f, indent=2)
# store derived stats
with open('%sstats.json' % a.outputfiles_basename, mode='w') as f:
json.dump(s, f, indent=2)
print()
print("-" * 30, 'ANALYSIS', "-" * 30)
print("Global Evidence:\n\t%.15e +- %.15e" %
(s['nested sampling global log-evidence'],
s['nested sampling global log-evidence error']))
plt.clf()
# Here we will plot all the marginals and whatnot, just to show off
# You may configure the format of the output here, or in matplotlibrc
# All pymultinest does is filling in the data of the plot.
# Copy and edit this file, and play with it.
p = pymultinest.PlotMarginalModes(a)
plt.figure(figsize=(5 * nparams, 5 * nparams))
#plt.subplots_adjust(wspace=0, hspace=0)
for i in range(nparams):
plt.subplot(nparams, nparams, nparams * i + i + 1)
p.plot_marginal(i,
with_ellipses=True,
with_points=False,
grid_points=50)
plt.ylabel("Probability")
plt.xlabel(parameters[i])
for j in range(i):
plt.subplot(nparams, nparams, nparams * j + i + 1)
p.plot_conditional(i,
j,
with_ellipses=False,
with_points=True,
grid_points=30)
plt.xlabel(parameters[i])
plt.ylabel(parameters[j])
plt.savefig("chains/marginals_multinest.pdf") #, bbox_inches='tight')
show("chains/marginals_multinest.pdf")
for i in range(nparams):
outfile = '%s-mode-marginal-%d.pdf' % (a.outputfiles_basename, i)
p.plot_modes_marginal(i, with_ellipses=True, with_points=False)
plt.ylabel("Probability")
plt.xlabel(parameters[i])
plt.savefig(outfile, format='pdf', bbox_inches='tight')
plt.close()
outfile = '%s-mode-marginal-cumulative-%d.pdf' % (
a.outputfiles_basename, i)
p.plot_modes_marginal(i,
cumulative=True,
with_ellipses=True,
with_points=False)
plt.ylabel("Cumulative probability")
plt.xlabel(parameters[i])
plt.savefig(outfile, format='pdf', bbox_inches='tight')
plt.close()
print("Take a look at the pdf files in chains/")
if not os.path.exists("chains"): os.mkdir("chains")
# number of dimensions our problem has
# parameters = ["x", "y"]
n_params_SEM = len(parameters_SEM)
planetName = 'HAT38'
visitName = 'visit1'
modelName = 'single_exponential_model'
outputfiles_basename = 'chains/' + planetName + '-' + visitName + '-' + modelName + '-'
start = time()
plt.figure(figsize=(5 * n_params_SEM, 5 * n_params_SEM))
# we want to see some output while it is running
progress = pymultinest.ProgressPlotter(
n_params=n_params_SEM, outputfiles_basename=outputfiles_basename)
progress.start()
# threading.Timer(2, show, ["chains/2-phys_live.points.pdf"]).start() # delayed opening
# run MultiNest
pymultinest.run(myloglike_SEM,
myprior_SEM,
n_params_SEM,
importance_nested_sampling=False,
resume=False,
verbose=True,
sampling_efficiency='model',
n_live_points=1000,
outputfiles_basename=outputfiles_basename)
# ok, done. Stop our progress watcher
progress.stop()
