def pyorbit_emcee(config_in, input_datasets=None, return_output=None):
try:
import emcee
except:
print("ERROR: emcee not installed, this will not work")
quit()
os.environ["OMP_NUM_THREADS"] = "1"
optimize_dir_output = './' + config_in['output'] + '/optimize/'
pyde_dir_output = './' + config_in['output'] + '/pyde/'
emcee_dir_output = './' + config_in['output'] + '/emcee/'
reloaded_optimize = False
reloaded_pyde = False
reloaded_emcee = False
try:
mc, population, starting_point, theta_dict = pyde_load_from_cpickle(
pyde_dir_output, prefix='')
reloaded_pyde = True
except FileNotFoundError:
pass
try:
mc, starting_point, population, prob, state, sampler_chain, \
sampler_lnprobability, _, theta_dict, sampler = \
emcee_load_from_cpickle(emcee_dir_output)
reloaded_emcee = True
except FileNotFoundError:
pass
try:
starting_point, previous_boundaries, theta_dict = starting_point_load_from_cpickle(
optimize_dir_output)
reloaded_optimize = True
except FileNotFoundError:
pass
print()
print('reloaded_optimize: ', reloaded_optimize)
print('reloaded_pyde: ', reloaded_pyde)
print('reloaded_emcee: ', reloaded_emcee)
print()
if reloaded_pyde or reloaded_emcee:
previous_boundaries = mc.bounds
if reloaded_emcee:
print('Requested steps:', mc.emcee_parameters['nsteps'])
mc.emcee_parameters['completed_nsteps'] = \
int(sampler_chain.shape[1] * mc.emcee_parameters['thin'])
print('Completed:', mc.emcee_parameters['completed_nsteps'])
pars_input(config_in, mc, input_datasets, reload_emcee=True)
print('Total:', mc.emcee_parameters['nsteps'])
""" There's no need to do anything"""
flatchain = emcee_flatchain(sampler_chain,
mc.emcee_parameters['nburn'],
mc.emcee_parameters['thin'])
mc.model_setup()
mc.initialize_logchi2()
results_analysis.print_integrated_ACF(sampler_chain, theta_dict,
mc.emcee_parameters['thin'])
""" In case the current startin point comes from a previous analysis """
mc.emcee_dir_output = emcee_dir_output
if mc.emcee_parameters['nsteps'] <= mc.emcee_parameters[
'completed_nsteps']:
print('Reference Time Tref: ', mc.Tref)
print()
print('Dimensions = ', mc.ndim)
print('Nwalkers = ', mc.emcee_parameters['nwalkers'])
print('Steps = ', mc.emcee_parameters['nsteps'])
print()
print('Original starting point of emcee:')
print()
results_analysis.results_resumen(mc,
starting_point,
compute_lnprob=True,
is_starting_point=True)
print('emcee results:')
print()
results_analysis.results_resumen(mc, flatchain)
print()
print('emcee completed')
print()
if return_output:
return mc, sampler_chain, sampler_lnprobability
else:
return
elif not sampler:
print(
'Sampler file is missing - only analysis performed with PyORBIT >8.1 cn be resumed'
)
if return_output:
return mc, sampler_chain, sampler_lnprobability
else:
return
if reloaded_emcee:
sampled = mc.emcee_parameters['completed_nsteps']
nsteps_todo = mc.emcee_parameters['nsteps'] \
- mc.emcee_parameters['completed_nsteps']
print('Resuming from a previous run:')
print('Performed steps = ', mc.emcee_parameters['completed_nsteps'])
print('Final # of steps = ', mc.emcee_parameters['nsteps'])
print('Steps to be performed = ', nsteps_todo)
print()
else:
mc = ModelContainerEmcee()
pars_input(config_in, mc, input_datasets)
if mc.pyde_parameters['shutdown_jitter'] or mc.emcee_parameters[
'shutdown_jitter']:
for dataset_name, dataset in mc.dataset_dict.items():
dataset.shutdown_jitter()
# keep track of which version has been used to perform emcee computations
mc.emcee_parameters['version'] = emcee.__version__[0]
mc.model_setup()
mc.create_variables_bounds()
mc.initialize_logchi2()
results_analysis.results_resumen(mc, None, skip_theta=True)
mc.pyde_dir_output = pyde_dir_output
mc.emcee_dir_output = emcee_dir_output
mc.emcee_parameters['nwalkers'] = mc.ndim * \
mc.emcee_parameters['npop_mult']
if mc.emcee_parameters['nwalkers'] % 2 == 1:
mc.emcee_parameters['nwalkers'] += 1
if not os.path.exists(mc.emcee_dir_output):
os.makedirs(mc.emcee_dir_output)
state = None
sampled = 0
nsteps_todo = mc.emcee_parameters['nsteps']
print('Include priors: ', mc.include_priors)
print()
print('Reference Time Tref: ', mc.Tref)
print()
print('Dimensions = ', mc.ndim)
print('Nwalkers = ', mc.emcee_parameters['nwalkers'])
print()
if mc.emcee_parameters['version'] == '2':
mc.emcee_parameters['use_threading_pool'] = False
if not mc.pyde_parameters.get('use_threading_pool', False):
mc.pyde_parameters['use_threading_pool'] = False
if not mc.emcee_parameters.get('use_threading_pool', False):
mc.emcee_parameters['use_threading_pool'] = False
if reloaded_emcee:
sys.stdout.flush()
pass
elif reloaded_pyde:
theta_dict_legacy = theta_dict.copy()
population_legacy = population.copy()
theta_dict = results_analysis.get_theta_dictionary(mc)
population = np.zeros([mc.emcee_parameters['nwalkers'], mc.ndim],
dtype=np.double)
for theta_name, theta_i in theta_dict.items():
population[:, theta_i] = population_legacy[:, theta_dict_legacy[
theta_name]]
mc.bounds[theta_i] = previous_boundaries[
theta_dict_legacy[theta_name]]
starting_point = np.median(population, axis=0)
# print(starting_point)
# print(population)
print('Using previous population as starting point. ')
print()
sys.stdout.flush()
elif mc.starting_point_flag or reloaded_optimize:
if reloaded_optimize:
print(
'Using the output from a previous optimize run as starting point'
)
theta_dict_legacy = theta_dict.copy()
starting_point_legacy = starting_point.copy()
theta_dict = results_analysis.get_theta_dictionary(mc)
for theta_name, theta_i in theta_dict.items():
starting_point[theta_i] = starting_point_legacy[
theta_dict_legacy[theta_name]]
else:
print('Using user-defined starting point from YAML file')
mc.create_starting_point()
starting_point = mc.starting_point
population = np.zeros([mc.emcee_parameters['nwalkers'], mc.ndim],
dtype=np.double)
for ii in range(0, mc.emcee_parameters['nwalkers']):
population[ii, :] = np.random.normal(starting_point, 0.0000001)
print(
'to create a synthetic population extremely close to the starting values.'
)
print()
sys.stdout.flush()
else:
""" None of the previous cases has been satisfied, we have to run PyDE """
try:
from pyde.de import DiffEvol
except ImportError:
print(
'ERROR! PyDE is not installed, run first with optimize instead of emcee'
)
quit()
if not os.path.exists(mc.pyde_dir_output):
os.makedirs(mc.pyde_dir_output)
print('Using threading pool for PyDE:',
mc.pyde_parameters['use_threading_pool'])
print('PyDE running')
sys.stdout.flush()
if mc.pyde_parameters['use_threading_pool']:
with multiprocessing.Pool() as pool:
de = DiffEvol(mc,
mc.bounds,
mc.emcee_parameters['nwalkers'],
maximize=True,
pool=pool)
de.optimize(int(mc.pyde_parameters['ngen']))
else:
de = DiffEvol(mc,
mc.bounds,
mc.emcee_parameters['nwalkers'],
maximize=True)
de.optimize(int(mc.pyde_parameters['ngen']))
population = de.population
starting_point = np.median(population, axis=0)
theta_dict = results_analysis.get_theta_dictionary(mc)
""" bounds redefinition and fix for PyDE anomalous results """
if mc.recenter_bounds_flag:
pyde_save_to_pickle(mc,
population,
starting_point,
theta_dict,
prefix='orig')
mc.recenter_bounds(starting_point)
population = mc.fix_population(starting_point, population)
starting_point = np.median(population, axis=0)
print('Boundaries redefined after PyDE output')
pyde_save_to_pickle(mc, population, starting_point, theta_dict)
print('PyDE completed')
print()
sys.stdout.flush()
if reloaded_emcee:
print('Original starting point of emcee:')
print()
results_analysis.results_resumen(mc,
starting_point,
compute_lnprob=True,
is_starting_point=True)
sys.stdout.flush()
print()
print('*************************************************************')
print()
print('Running emcee')
print()
print('emcee version: ', emcee.__version__)
if mc.emcee_parameters['version'] == '2':
print('WARNING: upgrading to version 3 is strongly advised')
print('Using threading pool for emcee:',
mc.emcee_parameters.get('use_threading_pool', False))
print()
if reloaded_emcee:
print('Using reloaded sampler')
print()
else:
sampler = emcee.EnsembleSampler(mc.emcee_parameters['nwalkers'],
mc.ndim, mc)
if mc.emcee_parameters['nsave'] > 0:
print('Saving temporary steps')
print()
niter = int(np.ceil(nsteps_todo / mc.emcee_parameters['nsave']))
for i in range(0, niter):
if mc.emcee_parameters['use_threading_pool']:
with multiprocessing.Pool() as pool:
sampler.pool = pool
population, prob, state = sampler.run_mcmc(
population,
mc.emcee_parameters['nsave'],
thin=mc.emcee_parameters['thin'],
rstate0=state,
progress=True)
else:
population, prob, state = sampler.run_mcmc(
population,
mc.emcee_parameters['nsave'],
thin=mc.emcee_parameters['thin'],
rstate0=state,
progress=True)
sampled += mc.emcee_parameters['nsave']
theta_dict = results_analysis.get_theta_dictionary(mc)
emcee_save_to_cpickle(mc,
starting_point,
population,
prob,
state,
sampler,
theta_dict,
samples=sampled)
flatchain = emcee_flatchain(sampler.chain,
mc.emcee_parameters['nburn'],
mc.emcee_parameters['thin'])
results_analysis.print_integrated_ACF(sampler.chain, theta_dict,
mc.emcee_parameters['thin'])
results_analysis.results_resumen(mc, flatchain)
print()
print(sampled, ' steps completed, average lnprob:, ',
np.median(prob))
print()
sys.stdout.flush()
# It turns out that reloading the sampler from the file will
# result in faster parallelization...
state, sampler = emcee_simpler_load_from_cpickle(emcee_dir_output)
else:
if mc.emcee_parameters['use_threading_pool']:
with multiprocessing.Pool() as pool:
sampler.pool = pool
population, prob, state = sampler.run_mcmc(
population,
nsteps_todo,
thin=mc.emcee_parameters['thin'],
rstate0=state,
progress=True)
else:
population, prob, state = sampler.run_mcmc(
population,
nsteps_todo,
thin=mc.emcee_parameters['thin'],
rstate0=state,
progress=True)
sampled += nsteps_todo
theta_dict = results_analysis.get_theta_dictionary(mc)
emcee_save_to_cpickle(mc,
starting_point,
population,
prob,
state,
sampler,
theta_dict,
samples=sampled)
flatchain = emcee_flatchain(sampler.chain,
mc.emcee_parameters['nburn'],
mc.emcee_parameters['thin'])
results_analysis.print_integrated_ACF(sampler.chain, theta_dict,
mc.emcee_parameters['thin'])
results_analysis.results_resumen(mc, flatchain)
print()
print('emcee completed')
print()
""" A dummy file is created to let the cpulimit script to proceed with the next step"""
emcee_create_dummy_file(mc)
if return_output:
return mc, sampler.chain, sampler.lnprobability
def pyorbit_optimize(config_in, input_datasets=None, return_output=None):
optimize_dir_output = './' + config_in['output'] + '/optimize/'
print()
mc = ModelContainerOptimize()
pars_input(config_in, mc, input_datasets)
mc.model_setup()
mc.create_variables_bounds()
mc.initialize_logchi2()
mc.create_starting_point()
mc.optimize_dir_output = optimize_dir_output
if not os.path.exists(mc.optimize_dir_output):
os.makedirs(mc.optimize_dir_output)
print()
print('Reference Time Tref: ', mc.Tref)
# method keyword is removed to suppress optimize warning
mc.optimize_options = mc.optimize_parameters.copy()
mc.optimize_options.pop('method')
print()
print('Scipy.optimixe.minize method: ', mc.optimize_parameters['method'])
print('Options: ')
for key_name, key_val in mc.optimize_options.items():
print(' {0:15s}: {1}'.format(key_name, key_val))
print()
results_analysis.results_resumen(mc,
mc.starting_point,
compute_lnprob=False,
is_starting_point=True)
optimize_results = optimize.minimize(
mc.
negative_log_priors_likelihood, # we minimize the negative loglikelyhood (including priors)
mc.starting_point, # initial variable parameters
method=mc.optimize_parameters['method'],
options=mc.optimize_options)
output_results = optimize_results['x']
print(optimize_results['success'])
if optimize_results['success']:
results_analysis.results_resumen(mc,
output_results,
compute_lnprob=True)
theta_dict = results_analysis.get_theta_dictionary(mc)
starting_point_save_to_cpickle(optimize_dir_output, output_results,
mc.bounds, theta_dict)
else:
print(
'Unsuccessful optimization - results have not been saved, please try again by changing method or options'
)
print()
print('Accepted options by scipy.optimize.minimize for method :',
mc.optimize_parameters['method'])
print(
'You can set them in the optimize section of the YAML input file.\n'
)
optimize.show_options(solver='minimize',
method=mc.optimize_parameters['method'])
print()
def pyorbit_emcee(config_in, input_datasets=None, return_output=None):
try:
import emcee
except:
print("ERROR: emcee not installed, this will not work")
quit()
os.environ["OMP_NUM_THREADS"] = "1"
optimize_dir_output = './' + config_in['output'] + '/optimize/'
pyde_dir_output = './' + config_in['output'] + '/pyde/'
emcee_dir_output = './' + config_in['output'] + '/emcee/'
reloaded_optimize = False
reloaded_pyde = False
reloaded_emcee_multirun = False
reloaded_emcee = False
try:
mc, population, starting_point, theta_dict = pyde_load_from_cpickle(
pyde_dir_output, prefix='')
reloaded_pyde = True
except:
pass
try:
mc, starting_point, population, _, _, sampler_chain, _, _, theta_dict, _ = \
emcee_load_from_cpickle(emcee_dir_output, prefix='MR')
reloaded_emcee_multirun = True
except:
pass
try:
mc, starting_point, population, _, _, sampler_chain, sampler_lnprobability, _, theta_dict, _ = \
emcee_load_from_cpickle(emcee_dir_output)
reloaded_emcee = True
except:
pass
try:
starting_point, previous_boundaries, theta_dict = starting_point_load_from_cpickle(
optimize_dir_output)
reloaded_optimize = True
except:
pass
print()
print('reloaded_optimize: ', reloaded_pyde)
print('reloaded_pyde: ', reloaded_pyde)
print('reloaded_emcee_multirun: ', reloaded_emcee_multirun)
print('reloaded_emcee: ', reloaded_emcee)
if reloaded_emcee:
""" There's no need to do anything"""
flatchain = emcee_flatchain(sampler_chain,
mc.emcee_parameters['nburn'],
mc.emcee_parameters['thin'])
mc.model_setup()
mc.initialize_logchi2()
results_analysis.print_integrated_ACF(sampler_chain, theta_dict,
mc.emcee_parameters['thin'])
results_analysis.results_resumen(mc, flatchain)
if return_output:
return mc, sampler_chain, sampler_lnprobability
else:
return
reloaded_mc = reloaded_pyde or reloaded_emcee_multirun
if reloaded_mc:
previous_boundaries = mc.bounds
mc = ModelContainerEmcee()
pars_input(config_in, mc, input_datasets)
if mc.pyde_parameters['shutdown_jitter'] or mc.emcee_parameters[
'shutdown_jitter']:
for dataset_name, dataset in mc.dataset_dict.items():
dataset.shutdown_jitter()
# keep track of which version has been used to perform emcee computations
mc.emcee_parameters['version'] = emcee.__version__[0]
mc.model_setup()
mc.create_variables_bounds()
mc.initialize_logchi2()
results_analysis.results_resumen(mc, None, skip_theta=True)
mc.pyde_dir_output = pyde_dir_output
mc.emcee_dir_output = emcee_dir_output
mc.emcee_parameters['nwalkers'] = mc.ndim * \
mc.emcee_parameters['npop_mult']
if mc.emcee_parameters['nwalkers'] % 2 == 1:
mc.emcee_parameters['nwalkers'] += 1
if not os.path.exists(mc.emcee_dir_output):
os.makedirs(mc.emcee_dir_output)
print()
print('emcee version: ', emcee.__version__)
if mc.emcee_parameters['version'] == '2':
print('WARNING: upgrading to version 3 is strongly advised')
print()
print('Include priors: ', mc.include_priors)
print()
print('Reference Time Tref: ', mc.Tref)
print()
print('Dimensions = ', mc.ndim)
print('Nwalkers = ', mc.emcee_parameters['nwalkers'])
if not getattr(mc, 'use_threading_pool', False):
mc.use_threading_pool = False
print()
print('Using threading pool:', mc.use_threading_pool)
print()
print('*************************************************************')
print()
if reloaded_mc:
theta_dict_legacy = theta_dict.copy()
population_legacy = population.copy()
theta_dict = results_analysis.get_theta_dictionary(mc)
population = np.zeros([mc.emcee_parameters['nwalkers'], mc.ndim],
dtype=np.double)
for theta_name, theta_i in theta_dict.items():
population[:, theta_i] = population_legacy[:, theta_dict_legacy[
theta_name]]
mc.bounds[theta_i] = previous_boundaries[
theta_dict_legacy[theta_name]]
starting_point = np.median(population, axis=0)
# print(starting_point)
# print(population)
print('Using previous population as starting point. ')
sys.stdout.flush()
print()
else:
if mc.starting_point_flag or reloaded_optimize:
if reloaded_optimize:
print(
'Using the output from a previous optimize run as starting point'
)
theta_dict_legacy = theta_dict.copy()
starting_point_legacy = starting_point.copy()
theta_dict = results_analysis.get_theta_dictionary(mc)
for theta_name, theta_i in theta_dict.items():
starting_point[theta_i] = starting_point_legacy[
theta_dict_legacy[theta_name]]
else:
print('Using user-defined starting point from YAML file')
mc.create_starting_point()
starting_point = mc.starting_point
population = np.zeros([mc.emcee_parameters['nwalkers'], mc.ndim],
dtype=np.double)
for ii in range(0, mc.emcee_parameters['nwalkers']):
population[ii, :] = np.random.normal(starting_point, 0.0000001)
print(
'to create a synthetic population extremely close to the starting values.'
)
sys.stdout.flush()
else:
try:
from pyde.de import DiffEvol
except ImportError:
print(
'ERROR! PyDE is not installed, run first with optimize instead of emcee'
)
quit()
if not os.path.exists(mc.pyde_dir_output):
os.makedirs(mc.pyde_dir_output)
print('PyDE running')
sys.stdout.flush()
de = DiffEvol(mc,
mc.bounds,
mc.emcee_parameters['nwalkers'],
maximize=True)
de.optimize(int(mc.pyde_parameters['ngen']))
population = de.population
starting_point = np.median(population, axis=0)
theta_dict = results_analysis.get_theta_dictionary(mc)
""" bounds redefinition and fix for PyDE anomalous results """
if mc.recenter_bounds_flag:
pyde_save_to_pickle(mc,
population,
starting_point,
theta_dict,
prefix='orig')
mc.recenter_bounds(starting_point)
population = mc.fix_population(starting_point, population)
starting_point = np.median(population, axis=0)
print('Boundaries redefined after PyDE output')
pyde_save_to_pickle(mc, population, starting_point, theta_dict)
print('PyDE completed')
sys.stdout.flush()
results_analysis.results_resumen(mc,
starting_point,
compute_lnprob=True,
is_starting_point=True)
if mc.use_threading_pool:
if mc.emcee_parameters['version'] == '2':
threads_pool = emcee.interruptible_pool.InterruptiblePool(
mc.emcee_parameters['nwalkers'])
else:
from multiprocessing.pool import Pool as InterruptiblePool
threads_pool = InterruptiblePool(mc.emcee_parameters['nwalkers'])
if mc.emcee_parameters['multirun'] and not reloaded_emcee_multirun:
for ii in range(0, mc.emcee_parameters['multirun_iter']):
print('emcee exploratory run #', ii, ' of ',
mc.emcee_parameters['multirun_iter'])
# sampler = emcee.EnsembleSampler(mc.emcee_parameters['nwalkers'], mc.ndim, mc,
# threads=mc.emcee_parameters['nwalkers'])
if mc.use_threading_pool:
sampler = emcee.EnsembleSampler(
mc.emcee_parameters['nwalkers'],
mc.ndim,
mc,
pool=threads_pool)
else:
sampler = emcee.EnsembleSampler(
mc.emcee_parameters['nwalkers'], mc.ndim, mc)
population, prob, state = sampler.run_mcmc(
population, mc.emcee_parameters['multirun'])
flatchain = emcee_flatchain(sampler.chain,
mc.emcee_parameters['nburn'],
mc.emcee_parameters['thin'])
results_analysis.results_resumen(mc, flatchain)
max_ind = np.argmax(prob)
starting_point = population[max_ind, :]
population = np.asarray([
starting_point + 1e-4 * np.random.randn(mc.ndim)
for i in range(mc.emcee_parameters['nwalkers'])
])
sampler.reset()
theta_dict = results_analysis.get_theta_dictionary(mc)
emcee_save_to_cpickle(mc,
starting_point,
population,
prob,
state,
sampler,
theta_dict,
prefix='MR_' + repr(ii))
emcee_save_to_cpickle(mc,
starting_point,
population,
prob,
state,
sampler,
theta_dict,
prefix='MR')
flatchain = emcee_flatchain(sampler.chain,
mc.emcee_parameters['nburn'],
mc.emcee_parameters['thin'])
results_analysis.print_integrated_ACF(sampler.chain, theta_dict,
mc.emcee_parameters['thin'])
results_analysis.results_resumen(mc, flatchain)
print('emcee exploratory runs completed')
sys.stdout.flush()
print()
print('Running emcee')
state = None
if mc.use_threading_pool:
sampler = emcee.EnsembleSampler(mc.emcee_parameters['nwalkers'],
mc.ndim,
mc,
pool=threads_pool)
else:
sampler = emcee.EnsembleSampler(mc.emcee_parameters['nwalkers'],
mc.ndim, mc)
if mc.emcee_parameters['nsave'] > 0:
print()
print(' Saving temporary steps')
niter = int(mc.emcee_parameters['nsteps'] /
mc.emcee_parameters['nsave'])
sampled = 0
for i in range(0, niter):
population, prob, state = sampler.run_mcmc(
population,
mc.emcee_parameters['nsave'],
thin=mc.emcee_parameters['thin'],
rstate0=state)
sampled += mc.emcee_parameters['nsave']
theta_dict = results_analysis.get_theta_dictionary(mc)
emcee_save_to_cpickle(mc,
starting_point,
population,
prob,
state,
sampler,
theta_dict,
samples=sampled)
flatchain = emcee_flatchain(sampler.chain,
mc.emcee_parameters['nburn'],
mc.emcee_parameters['thin'])
results_analysis.print_integrated_ACF(sampler.chain, theta_dict,
mc.emcee_parameters['thin'])
results_analysis.results_resumen(mc, flatchain)
print()
print(sampled, ' steps completed, average lnprob:, ',
np.median(prob))
sys.stdout.flush()
else:
population, prob, state = sampler.run_mcmc(
population,
mc.emcee_parameters['nsteps'],
thin=mc.emcee_parameters['thin'])
theta_dict = results_analysis.get_theta_dictionary(mc)
emcee_save_to_cpickle(mc, starting_point, population, prob, state,
sampler, theta_dict)
flatchain = emcee_flatchain(sampler.chain,
mc.emcee_parameters['nburn'],
mc.emcee_parameters['thin'])
results_analysis.print_integrated_ACF(sampler.chain, theta_dict,
mc.emcee_parameters['thin'])
results_analysis.results_resumen(mc, flatchain)
print()
print('emcee completed')
if mc.use_threading_pool:
# close the pool of threads
threads_pool.close()
threads_pool.terminate()
threads_pool.join()
""" A dummy file is created to let the cpulimit script to proceed with the next step"""
emcee_create_dummy_file(mc)
if return_output:
return mc, sampler.chain, sampler.lnprobability
def pyorbit_dynesty(config_in, input_datasets=None, return_output=None):
output_directory = './' + config_in['output'] + '/dynesty/'
mc = ModelContainerDynesty()
pars_input(config_in, mc, input_datasets)
if mc.nested_sampling_parameters['shutdown_jitter']:
for dataset in mc.dataset_dict.itervalues():
dataset.shutdown_jitter()
mc.model_setup()
mc.create_variables_bounds()
mc.initialize_logchi2()
mc.create_starting_point()
results_analysis.results_resumen(mc, None, skip_theta=True)
mc.output_directory = output_directory
print()
print('Reference Time Tref: ', mc.Tref)
print()
print('*************************************************************')
print()
import dynesty
# "Standard" nested sampling.
sampler = dynesty.NestedSampler(mc.dynesty_call, mc.dynesty_priors,
mc.ndim)
sampler.run_nested()
results = sampler.results
# "Dynamic" nested sampling.
dsampler = dynesty.DynamicNestedSampler(mc.dynesty_call, mc.dynesty_priors,
mc.ndim)
dsampler.run_nested()
dresults = dsampler.results
from dynesty import plotting as dyplot
# Plot a summary of the run.
rfig, raxes = dyplot.runplot(results)
# Plot traces and 1-D marginalized posteriors.
tfig, taxes = dyplot.traceplot(results)
# Plot the 2-D marginalized posteriors.
cfig, caxes = dyplot.cornerplot(results)
from dynesty import utils as dyfunc
# Extract sampling results.
samples = results.samples # samples
weights = np.exp(results.logwt - results.logz[-1]) # normalized weights
# Compute 5%-95% quantiles.
quantiles = dyfunc.quantile(samples, [0.05, 0.95], weights=weights)
# Compute weighted mean and covariance.
mean, cov = dyfunc.mean_and_cov(samples, weights)
# Resample weighted samples.
samples_equal = dyfunc.resample_equal(samples, weights)
# Generate a new set of results with statistical+sampling uncertainties.
results_sim = dyfunc.simulate_run(results)
""" A dummy file is created to let the cpulimit script to proceed with the next step"""
nested_sampling_create_dummy_file(mc)
if return_output:
return mc
else:
return
def pyorbit_multinest(config_in, input_datasets=None, return_output=None):
output_directory = './' + config_in['output'] + '/multinest/'
mc = ModelContainerMultiNest()
pars_input(config_in, mc, input_datasets)
if mc.nested_sampling_parameters['shutdown_jitter']:
for dataset_name, dataset in mc.dataset_dict.items():
dataset.shutdown_jitter()
mc.model_setup()
mc.create_variables_bounds()
mc.initialize_logchi2()
mc.create_starting_point()
results_analysis.results_resumen(mc, None, skip_theta=True)
mc.output_directory = output_directory
os.system("mkdir -p " + output_directory + mc.nested_sampling_parameters['base_dir'] + "/clusters")
#os.system("mkdir -p " +polychord_dir_output + "chains/clusters")
print()
print('Reference Time Tref: ', mc.Tref)
print()
print('*************************************************************')
print()
'''
On Linux system (BASH):
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$PWD/lib
export LD_PRELOAD=/usr/lib/openmpi/lib/libmpi.so:$LD_PRELOAD
export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libgfortran.so.3
mpirun -np 4 python run_PyPolyChord.py
on Mac:
export LD_LIBRARY_PATH=$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
'''
# parameters = mc.get_theta_dictionary()
if 'nlive' in mc.nested_sampling_parameters:
nlive = mc.nested_sampling_parameters['nlive']
elif 'nlive_mult' in mc.nested_sampling_parameters:
nlive = mc.ndim * mc.nested_sampling_parameters['nlive_mult']
print(' Sampling efficiency: ', mc.nested_sampling_parameters['sampling_efficiency'])
print(' N live points:', nlive)
import pymultinest
mnest_kwargs = dict(n_live_points=nlive, outputfiles_basename=output_directory + './')
for key_name, key_value in mc.nested_sampling_parameters.items():
if key_name in mc.pymultinest_signature:
mnest_kwargs[key_name] = key_value
print('Including priors to log-likelihood calculation (must be False):', mc.include_priors)
pymultinest.run(LogLikelihood= mc.multinest_call, Prior=mc.multinest_priors, n_dims=mc.ndim, **mnest_kwargs)
nested_sampling_save_to_cpickle(mc)
analyzer = pymultinest.Analyzer(mc.ndim, outputfiles_basename=output_directory)
stats = analyzer.get_stats()
samples = analyzer.get_equal_weighted_posterior()[:, :-1]
result = dict(logZ=stats['nested sampling global log-evidence'],
logZerr=stats['nested sampling global log-evidence error'],
samples=samples,
)
nested_sampling_save_to_cpickle(mc, 'result')
print()
print('MultiNest COMPLETED')
print()
#result = pymultinest.solve(LogLikelihood=mc.multinest_call, Prior=mc.multinest_priors,
# n_dims=mc.ndim, outputfiles_basename=output_directory + './',
# n_live_points=1000, sampling_efficiency=0.3, multimodal=True,
# verbose=True, resume=True)
print('evidence: %(logZ).1f +- %(logZerr).1f' % result)
print(result['logZ']//np.log(10.00), result['logZerr']//np.log(10.00))
if return_output:
return mc
else:
return
def pyorbit_dynesty(config_in, input_datasets=None, return_output=None):
mc = ModelContainerDynesty()
pars_input(config_in, mc, input_datasets)
if mc.nested_sampling_parameters['shutdown_jitter']:
'Jitter term not included for evidence calculation'
print()
for dataset_name, dataset in mc.dataset_dict.items():
dataset.shutdown_jitter()
mc.model_setup()
mc.create_variables_bounds()
mc.initialize_logchi2()
mc.create_starting_point()
results_analysis.results_resumen(mc, None, skip_theta=True)
mc.output_directory = './' + config_in['output'] + '/dynesty/'
if not os.path.exists(mc.output_directory):
os.makedirs(mc.output_directory)
nthreads = mc.nested_sampling_parameters['nthreads']
if 'nlive_mult' in mc.nested_sampling_parameters:
nlive = mc.ndim * mc.nested_sampling_parameters['nlive_mult']
else:
nlive = mc.nested_sampling_parameters['nlive']
print('Number of live points:', nlive)
print('Number of threads:', nthreads)
print()
print('Reference Time Tref: ', mc.Tref)
print()
print('*************************************************************')
print()
try:
import dynesty
except ImportError:
print("ERROR: dynesty not installed, this will not work")
quit()
# "Standard" nested sampling.
#print('Setting up the Standard Nested Sampling')
#sampler = dynesty.NestedSampler(mc.dynesty_call, mc.dynesty_priors, mc.ndim)
#print('Running Nested Sampling')
# sampler.run_nested()
#print('Getting the results')
#results = sampler.results
# print()
with multiprocessing.Pool() as pool:
# "Dynamic" nested sampling.
print('Setting up the Dynamic Nested Sampling')
print()
dsampler = dynesty.DynamicNestedSampler(
mc.dynesty_call,
mc.dynesty_priors,
mc.ndim,
nlive=nlive,
pool=pool,
queue_size=nthreads,
use_pool={'prior_transform': False},
wt_kwargs={'pfrac': 0.0})
print('Running Dynamic Nested Sampling')
dsampler.run_nested()
print()
print()
print('Getting the results')
results = dsampler.results
#taken from dynesty/dynesty/results.py but without the nlive point causing an error
res = ("niter: {:d}\n"
"ncall: {:d}\n"
"eff(%): {:6.3f}\n"
"logz: {:6.3f} +/- {:6.3f}".format(results.niter,
sum(results.ncall), results.eff,
results.logz[-1],
results.logzerr[-1]))
print()
print('Summary\n=======\n' + res)
print()
""" A dummy file is created to let the cpulimit script to proceed with the next step"""
nested_sampling_create_dummy_file(mc)
nested_sampling_save_to_cpickle(mc)
dynesty_results_save_to_cpickle(mc.output_directory, results)
if return_output:
return mc
else:
return
def pyorbit_polychord(config_in, input_datasets=None, return_output=None):
output_directory = './' + config_in['output'] + '/polychord/'
mc = ModelContainerPolyChord()
pars_input(config_in, mc, input_datasets)
if mc.nested_sampling_parameters['shutdown_jitter']:
for dataset_name, dataset in mc.dataset_dict.items():
dataset.shutdown_jitter()
mc.model_setup()
mc.create_variables_bounds()
mc.initialize_logchi2()
mc.create_starting_point()
results_analysis.results_resumen(mc, None, skip_theta=True)
mc.output_directory = output_directory
# os.system("mkdir -p " + output_directory + "/clusters")
# os.system("mkdir -p " +output_directory + "chains/clusters")
print()
print('Reference Time Tref: ', mc.Tref)
print()
print('*************************************************************')
print()
settings = PolyChordSettings(nDims=mc.ndim, nDerived=0)
settings.file_root = 'pyorbit'
settings.base_dir = output_directory
for key_name, key_value in mc.nested_sampling_parameters.items():
if hasattr(settings, key_name):
setattr(settings, key_name, key_value)
if 'nlive_mult' in mc.nested_sampling_parameters:
setattr(settings, 'nlive',
mc.ndim * mc.nested_sampling_parameters['nlive_mult'])
if 'num_repeats_mult' in mc.nested_sampling_parameters:
setattr(settings, 'num_repeats',
mc.ndim * mc.nested_sampling_parameters['num_repeats_mult'])
if 'include_priors' in mc.nested_sampling_parameters:
mc.include_priors = mc.nested_sampling_parameters['include_priors']
output = pypolychord.run_polychord(mc.polychord_call,
nDims=mc.ndim,
nDerived=0,
settings=settings,
prior=mc.polychord_priors,
dumper=dumper)
paramnames = [('p%i' % i, r'\theta_%i' % i) for i in range(mc.ndim)]
paramnames += [('r*', 'r')]
output.make_paramnames_files(paramnames)
nested_sampling_save_to_cpickle(mc)
print()
print('PolyChord COMPLETED')
print()
""" A dummy file is created to let the cpulimit script to proceed with the next step"""
nested_sampling_create_dummy_file(mc)
if return_output:
return mc
else:
return
def pyorbit_getresults(config_in, sampler, plot_dictionary):
try:
use_tex = config_in['parameters']['use_tex']
except:
use_tex = True
if use_tex is False:
print(' LaTeX disabled')
if plot_dictionary['use_getdist']:
from getdist import plots, MCSamples
# plt.rc('font', **{'family': 'serif', 'serif': ['Computer Modern Roman']})
plt.rcParams["font.family"] = "Times New Roman"
plt.rc('text', usetex=use_tex)
sample_keyword = {
'multinest': ['multinest', 'MultiNest', 'multi'],
'polychord': ['polychord', 'PolyChord', 'polychrod', 'poly'],
'emcee': ['emcee', 'MCMC', 'Emcee']
}
if sampler in sample_keyword['emcee']:
dir_input = './' + config_in['output'] + '/emcee/'
dir_output = './' + config_in['output'] + '/emcee_plot/'
os.system('mkdir -p ' + dir_output)
mc, starting_point, population, prob, state, \
sampler_chain, sampler_lnprobability, sampler_acceptance_fraction, _ = \
emcee_load_from_cpickle(dir_input)
pars_input(config_in, mc, reload_emcee=True)
if hasattr(mc.emcee_parameters, 'version'):
emcee_version = mc.emcee_parameters['version'][0]
else:
import emcee
emcee_version = emcee.__version__[0]
mc.model_setup()
""" Required to create the right objects inside each class - if defined inside """
theta_dictionary = results_analysis.get_theta_dictionary(mc)
nburnin = int(mc.emcee_parameters['nburn'])
nthin = int(mc.emcee_parameters['thin'])
nsteps = int(sampler_chain.shape[1] * nthin)
flat_chain = emcee_flatchain(sampler_chain, nburnin, nthin)
flat_lnprob = emcee_flatlnprob(sampler_lnprobability, nburnin, nthin,
emcee_version)
flat_BiC = -2 * flat_lnprob + mc.ndim * np.log(mc.ndata)
lnprob_med = common.compute_value_sigma(flat_lnprob)
chain_med = common.compute_value_sigma(flat_chain)
chain_MAP, lnprob_MAP = common.pick_MAP_parameters(
flat_chain, flat_lnprob)
n_samplings, n_pams = np.shape(flat_chain)
print()
print('emcee version: ', emcee.__version__)
if mc.emcee_parameters['version'] == '2':
print('WARNING: upgrading to version 3 is strongly advised')
print()
print(' Reference Time Tref: {}'.format(mc.Tref))
print()
print(' Dimensions = {}'.format(mc.ndim))
print(' Nwalkers = {}'.format(mc.emcee_parameters['nwalkers']))
print()
print(' Steps: {}'.format(nsteps))
results_analysis.print_integrated_ACF(sampler_chain, theta_dictionary,
nthin)
if sampler in sample_keyword['multinest']:
plot_dictionary['lnprob_chain'] = False
plot_dictionary['chains'] = False
plot_dictionary['traces'] = False
dir_input = './' + config_in['output'] + '/multinest/'
dir_output = './' + config_in['output'] + '/multinest_plot/'
os.system('mkdir -p ' + dir_output)
mc = nested_sampling_load_from_cpickle(dir_input)
mc.model_setup()
mc.initialize_logchi2()
results_analysis.results_resumen(mc, None, skip_theta=True)
""" Required to create the right objects inside each class - if defined inside """
theta_dictionary = results_analysis.get_theta_dictionary(mc)
data_in = np.genfromtxt(dir_input + 'post_equal_weights.dat')
flat_lnprob = data_in[:, -1]
flat_chain = data_in[:, :-1]
# nsample = np.size(flat_lnprob)
n_samplings, n_pams = np.shape(flat_chain)
lnprob_med = common.compute_value_sigma(flat_lnprob)
chain_med = common.compute_value_sigma(flat_chain)
chain_MAP, lnprob_MAP = common.pick_MAP_parameters(
flat_chain, flat_lnprob)
print()
print(' Reference Time Tref: {}'.format(mc.Tref))
print()
print(' Dimensions: {}'.format(mc.ndim))
print()
print(' Samples: {}'.format(n_samplings))
if sampler in sample_keyword['polychord']:
plot_dictionary['lnprob_chain'] = False
plot_dictionary['chains'] = False
plot_dictionary['traces'] = False
dir_input = './' + config_in['output'] + '/polychord/'
dir_output = './' + config_in['output'] + '/polychord_plot/'
os.system('mkdir -p ' + dir_output)
mc = nested_sampling_load_from_cpickle(dir_input)
# pars_input(config_in, mc)
mc.model_setup()
mc.initialize_logchi2()
results_analysis.results_resumen(mc, None, skip_theta=True)
""" Required to create the right objects inside each class - if defined inside """
theta_dictionary = results_analysis.get_theta_dictionary(mc)
data_in = np.genfromtxt(dir_input + 'pyorbit_equal_weights.txt')
flat_lnprob = data_in[:, 1]
flat_chain = data_in[:, 2:]
# nsample = np.size(flat_lnprob)
n_samplings, n_pams = np.shape(flat_chain)
lnprob_med = common.compute_value_sigma(flat_lnprob)
chain_med = common.compute_value_sigma(flat_chain)
chain_MAP, lnprob_MAP = common.pick_MAP_parameters(
flat_chain, flat_lnprob)
print()
print(' Reference Time Tref: {}'.format(mc.Tref))
print()
print(' Dimensions: {}'.format(mc.ndim))
print()
print(' Samples: {}'.format(n_samplings))
print()
print(' LN posterior: {0:12f} {1:12f} {2:12f} (15-84 p) '.format(
lnprob_med[0], lnprob_med[2], lnprob_med[1]))
MAP_log_priors, MAP_log_likelihood = mc.log_priors_likelihood(chain_MAP)
BIC = -2.0 * MAP_log_likelihood + np.log(mc.ndata) * mc.ndim
AIC = -2.0 * MAP_log_likelihood + 2.0 * mc.ndim
AICc = AIC + (2.0 + 2.0 * mc.ndim) * mc.ndim / (mc.ndata - mc.ndim - 1.0)
# AICc for small sample
print()
print(' MAP log_priors = {}'.format(MAP_log_priors))
print(' MAP log_likelihood = {}'.format(MAP_log_likelihood))
print(' MAP BIC (using likelihood) = {}'.format(BIC))
print(' MAP AIC (using likelihood) = {}'.format(AIC))
print(' MAP AICc (using likelihood) = {}'.format(AICc))
MAP_log_posterior = MAP_log_likelihood + MAP_log_priors
BIC = -2.0 * MAP_log_posterior + np.log(mc.ndata) * mc.ndim
AIC = -2.0 * MAP_log_posterior + 2.0 * mc.ndim
AICc = AIC + (2.0 + 2.0 * mc.ndim) * mc.ndim / (mc.ndata - mc.ndim - 1.0)
print()
print(' MAP BIC (using posterior) = {}'.format(BIC))
print(' MAP AIC (using posterior) = {}'.format(AIC))
print(' MAP AICc (using posterior) = {}'.format(AICc))
if mc.ndata < 40 * mc.ndim:
print()
print(
' AICc suggested over AIC because NDATA ( {0:12f} ) < 40 * NDIM ( {1:12f} )'
.format(mc.ndata, mc.ndim))
else:
print()
print(
' AIC suggested over AICs because NDATA ( {0:12f} ) > 40 * NDIM ( {1:12f} )'
.format(mc.ndata, mc.ndim))
print()
print(
'****************************************************************************************************'
)
print(
'****************************************************************************************************'
)
print()
print(
' Confidence intervals (median value, 34.135th percentile from the median on the left and right side)'
)
planet_variables = results_analysis.results_resumen(mc,
flat_chain,
chain_med=chain_MAP,
return_samples=True)
print()
print(
'****************************************************************************************************'
)
print()
print(
' Parameters corresponding to the Maximum a Posteriori probability ( {} )'
.format(lnprob_MAP))
print()
results_analysis.results_resumen(mc, chain_MAP)
print()
print(
'****************************************************************************************************'
)
print()
# Computation of all the planetary variables
planet_variables_med = results_analysis.get_planet_variables(
mc, chain_med[:, 0])
star_variables = results_analysis.get_stellar_parameters(
mc, chain_med[:, 0])
planet_variables_MAP = results_analysis.get_planet_variables(mc, chain_MAP)
star_variables_MAP = results_analysis.get_stellar_parameters(mc, chain_MAP)
if plot_dictionary['lnprob_chain'] or plot_dictionary['chains']:
print(' Plot FLAT chain ')
if emcee_version == '2':
fig = plt.figure(figsize=(12, 12))
plt.xlabel('$\ln \mathcal{L}$')
plt.plot(sampler_lnprobability.T, '-', alpha=0.5)
plt.axhline(lnprob_med[0])
plt.axvline(nburnin / nthin, c='r')
plt.savefig(dir_output + 'LNprob_chain.png',
bbox_inches='tight',
dpi=300)
plt.close(fig)
else:
fig = plt.figure(figsize=(12, 12))
plt.xlabel('$\ln \mathcal{L}$')
plt.plot(sampler_lnprobability, '-', alpha=0.5)
plt.axhline(lnprob_med[0])
plt.axvline(nburnin / nthin, c='r')
plt.savefig(dir_output + 'LNprob_chain.png',
bbox_inches='tight',
dpi=300)
plt.close(fig)
print()
print(
'****************************************************************************************************'
)
print()
if plot_dictionary['full_correlation']:
corner_plot = {
'samples':
np.zeros(
[np.size(flat_chain, axis=0),
np.size(flat_chain, axis=1) + 1]),
'labels': [],
'truths': []
}
i_corner = 0
for var, var_dict in theta_dictionary.items():
corner_plot['samples'][:, i_corner] = flat_chain[:, var_dict]
corner_plot['labels'].append(re.sub('_', '-', var))
corner_plot['truths'].append(chain_med[var_dict, 0])
i_corner += 1
corner_plot['samples'][:, -1] = flat_lnprob[:]
corner_plot['labels'].append('ln-prob')
corner_plot['truths'].append(lnprob_med[0])
if plot_dictionary['use_getdist']:
print(' Plotting full_correlation plot with GetDist')
print()
print(' Ignore the no burn in error warning from getdist')
print(' since burn in has been already removed from the chains')
plt.rc('text', usetex=False)
samples = MCSamples(samples=corner_plot['samples'],
names=corner_plot['labels'],
labels=corner_plot['labels'])
g = plots.getSubplotPlotter()
g.settings.num_plot_contours = 6
g.triangle_plot(samples, filled=True)
g.export(dir_output + "all_internal_variables_corner_getdist.pdf")
print()
else:
# plotting mega-corner plot
print('Plotting full_correlation plot with Corner')
plt.rc('text', usetex=False)
fig = corner.corner(corner_plot['samples'],
labels=corner_plot['labels'],
truths=corner_plot['truths'])
fig.savefig(dir_output + "all_internal_variables_corner_dfm.pdf",
bbox_inches='tight',
dpi=300)
plt.close(fig)
plt.rc('text', usetex=use_tex)
print()
print(
'****************************************************************************************************'
)
print()
if plot_dictionary['chains']:
print(' Plotting the chains... ')
os.system('mkdir -p ' + dir_output + 'chains')
for theta_name, ii in theta_dictionary.items():
file_name = dir_output + 'chains/' + repr(
ii) + '_' + theta_name + '.png'
fig = plt.figure(figsize=(12, 12))
plt.plot(sampler_chain[:, :, ii].T, '-', alpha=0.5)
plt.axvline(nburnin / nthin, c='r')
plt.savefig(file_name, bbox_inches='tight', dpi=300)
plt.close(fig)
print()
print(
'****************************************************************************************************'
)
print()
if plot_dictionary['traces']:
print(' Plotting the Gelman-Rubin traces... ')
print()
"""
Gelman-Rubin traces are stored in the dedicated folder iniside the _plot folder
Note that the GR statistics is not robust because the wlakers are not independent
"""
os.system('mkdir -p ' + dir_output + 'gr_traces')
step_sampling = np.arange(nburnin / nthin,
nsteps / nthin,
1,
dtype=int)
for theta_name, th in theta_dictionary.items():
rhat = np.array([
GelmanRubin_v2(sampler_chain[:, :steps, th])
for steps in step_sampling
])
print(' Gelman-Rubin: {0:5d} {1:12f} {2:s} '.format(
th, rhat[-1], theta_name))
file_name = dir_output + 'gr_traces/v2_' + repr(
th) + '_' + theta_name + '.png'
fig = plt.figure(figsize=(12, 12))
plt.plot(step_sampling, rhat[:], '-', color='k')
plt.axhline(1.01, c='C0')
plt.savefig(file_name, bbox_inches='tight', dpi=300)
plt.close(fig)
print()
print(
'****************************************************************************************************'
)
print()
if plot_dictionary['common_corner']:
print(' Plotting the common models corner plots')
plt.rc('text', usetex=False)
for common_name, common_model in mc.common_models.items():
print(' Common model: ', common_name)
corner_plot = {
'var_list': [],
'samples': [],
'labels': [],
'truths': []
}
variable_values = common_model.convert(flat_chain)
variable_median = common_model.convert(chain_med[:, 0])
if len(variable_median) < 1.:
continue
"""
Check if the eccentricity and argument of pericenter were set as free parameters or fixed by simply
checking the size of their distribution
"""
for var in variable_values.keys():
if np.size(variable_values[var]) == 1:
variable_values[var] = variable_values[var] * np.ones(
n_samplings)
else:
corner_plot['var_list'].append(var)
corner_plot['samples'] = []
corner_plot['labels'] = []
corner_plot['truths'] = []
for var_i, var in enumerate(corner_plot['var_list']):
corner_plot['samples'].extend([variable_values[var]])
corner_plot['labels'].append(var)
corner_plot['truths'].append(variable_median[var])
""" Check if the semi-amplitude K is among the parameters that have been fitted.
If so, it computes the correpsing planetary mass with uncertainty """
fig = corner.corner(np.asarray(corner_plot['samples']).T,
labels=corner_plot['labels'],
truths=corner_plot['truths'])
fig.savefig(dir_output + common_name + "_corners.pdf",
bbox_inches='tight',
dpi=300)
plt.close(fig)
print()
print(
'****************************************************************************************************'
)
print()
if plot_dictionary['dataset_corner']:
print(' Dataset + models corner plots ')
print()
for dataset_name, dataset in mc.dataset_dict.items():
for model_name in dataset.models:
variable_values = dataset.convert(flat_chain)
variable_median = dataset.convert(chain_med[:, 0])
for common_ref in mc.models[model_name].common_ref:
variable_values.update(
mc.common_models[common_ref].convert(flat_chain))
variable_median.update(
mc.common_models[common_ref].convert(chain_med[:, 0]))
variable_values.update(mc.models[model_name].convert(
flat_chain, dataset_name))
variable_median.update(mc.models[model_name].convert(
chain_med[:, 0], dataset_name))
corner_plot['samples'] = []
corner_plot['labels'] = []
corner_plot['truths'] = []
for var_i, var in enumerate(variable_values):
if np.size(variable_values[var]) <= 1: continue
corner_plot['samples'].extend([variable_values[var]])
corner_plot['labels'].append(var)
corner_plot['truths'].append(variable_median[var])
fig = corner.corner(np.asarray(corner_plot['samples']).T,
labels=corner_plot['labels'],
truths=corner_plot['truths'])
fig.savefig(dir_output + dataset_name + '_' + model_name +
"_corners.pdf",
bbox_inches='tight',
dpi=300)
plt.close(fig)
print(' Dataset: ', dataset_name, ' model: ',
model_name, ' corner plot done ')
print()
print(
'****************************************************************************************************'
)
print()
if plot_dictionary['write_planet_samples']:
print(' Saving the planet variable samplings to files (with plots)')
samples_dir = dir_output + '/planet_samples/'
os.system('mkdir -p ' + samples_dir)
for common_ref, variable_values in planet_variables.items():
for variable_name, variable in variable_values.items():
rad_filename = samples_dir + common_ref + '_' + variable_name
fileout = open(rad_filename + '.dat', 'w')
for val in variable:
fileout.write('{0:f} \n'.format(val))
fileout.close()
fig = plt.figure(figsize=(10, 10))
plt.hist(variable, bins=50, color='C0', alpha=0.75, zorder=0)
perc0, perc1, perc2 = np.percentile(variable,
[15.865, 50, 84.135],
axis=0)
plt.axvline(planet_variables_med[common_ref][variable_name],
color='C1',
zorder=1,
label='Median-corresponding value')
plt.axvline(planet_variables_MAP[common_ref][variable_name],
color='C2',
zorder=1,
label='MAP-corresponding value')
plt.axvline(perc1,
color='C3',
zorder=2,
label='Median of the distribution')
plt.axvline(perc0,
color='C4',
zorder=2,
label='15.865th and 84.135th percentile')
plt.axvline(perc2, color='C4', zorder=2)
plt.xlabel(re.sub('_', '-', variable_name + '_' + common_ref))
plt.legend()
plt.ticklabel_format(useOffset=False)
plt.savefig(rad_filename + '.png',
bbox_inches='tight',
dpi=300)
plt.close(fig)
print()
print(
'****************************************************************************************************'
)
print()
if plot_dictionary['plot_models'] or plot_dictionary['write_models']:
print(' Computing the models for plot/data writing ')
bjd_plot = {'full': {'start': None, 'end': None, 'range': None}}
kinds = {}
P_minimum = 2.0 # this temporal range will be divided in 20 subsets
for key_name, key_val in planet_variables_med.items():
P_minimum = min(key_val.get('P', 2.0), P_minimum)
for dataset_name, dataset in mc.dataset_dict.items():
if dataset.kind in kinds.keys():
kinds[dataset.kind].extend([dataset_name])
else:
kinds[dataset.kind] = [dataset_name]
bjd_plot[dataset_name] = {
'start': np.amin(dataset.x),
'end': np.amax(dataset.x),
'range': np.amax(dataset.x) - np.amin(dataset.x),
}
if bjd_plot[dataset_name]['range'] < 0.1:
bjd_plot[dataset_name]['range'] = 0.1
bjd_plot[dataset_name][
'start'] -= bjd_plot[dataset_name]['range'] * 0.10
bjd_plot[dataset_name][
'end'] += bjd_plot[dataset_name]['range'] * 0.10
if dataset.kind == 'Phot':
step_size = np.min(bjd_plot[dataset_name]['range'] /
dataset.n / 10.)
else:
step_size = P_minimum / 20.
bjd_plot[dataset_name]['x_plot'] = \
np.arange(bjd_plot[dataset_name]['start'], bjd_plot[dataset_name]['end'], step_size)
if bjd_plot['full']['range']:
bjd_plot['full']['start'] = min(bjd_plot['full']['start'],
np.amin(dataset.x))
bjd_plot['full']['end'] = max(bjd_plot['full']['end'],
np.amax(dataset.x))
bjd_plot['full']['range'] = bjd_plot['full']['end'] - bjd_plot[
'full']['start']
else:
bjd_plot['full']['start'] = np.amin(dataset.x)
bjd_plot['full']['end'] = np.amax(dataset.x)
bjd_plot['full']['range'] = bjd_plot['full']['end'] - bjd_plot[
'full']['start']
bjd_plot['full']['start'] -= bjd_plot['full']['range'] * 0.10
bjd_plot['full']['end'] += bjd_plot['full']['range'] * 0.10
bjd_plot['full']['x_plot'] = np.arange(bjd_plot['full']['start'],
bjd_plot['full']['end'],
P_minimum / 20.)
for dataset_name, dataset in mc.dataset_dict.items():
if dataset.kind == 'RV':
bjd_plot[dataset_name] = bjd_plot['full']
bjd_plot['model_out'], bjd_plot[
'model_x'] = results_analysis.get_model(mc, chain_med[:, 0],
bjd_plot)
bjd_plot['MAP_model_out'], bjd_plot[
'MAP_model_x'] = results_analysis.get_model(
mc, chain_MAP, bjd_plot)
if plot_dictionary['plot_models']:
print(' Writing the plots ')
for kind_name, kind in kinds.items():
for dataset_name in kind:
try:
error_bars = np.sqrt(
mc.dataset_dict[dataset_name].e**2 +
bjd_plot['model_out'][dataset_name]['jitter']**2)
except ValueError:
error_bars = mc.dataset_dict[dataset_name].e
fig = plt.figure(figsize=(12, 12))
# Partially taken from here:
# http://www.sc.eso.org/~bdias/pycoffee/codes/20160407/gridspec_demo.html
gs = gridspec.GridSpec(2, 1, height_ratios=[3.0, 1.0])
# Also make sure the margins and spacing are apropriate
gs.update(left=0.3,
right=0.95,
bottom=0.08,
top=0.93,
wspace=0.15,
hspace=0.05)
ax_0 = plt.subplot(gs[0])
ax_1 = plt.subplot(gs[1], sharex=ax_0)
# Adding minor ticks only to x axis
minorLocator = AutoMinorLocator()
ax_0.xaxis.set_minor_locator(minorLocator)
ax_1.xaxis.set_minor_locator(minorLocator)
# Disabling the offset on top of the plot
ax_0.ticklabel_format(useOffset=False)
ax_1.ticklabel_format(useOffset=False)
ax_0.scatter(
mc.dataset_dict[dataset_name].x,
mc.dataset_dict[dataset_name].y -
bjd_plot['model_out'][dataset_name]['systematics'] -
bjd_plot['model_out'][dataset_name]
['time_independent'],
color='C0',
zorder=4,
s=16)
ax_0.errorbar(
mc.dataset_dict[dataset_name].x,
mc.dataset_dict[dataset_name].y -
bjd_plot['model_out'][dataset_name]['systematics'] -
bjd_plot['model_out'][dataset_name]
['time_independent'],
yerr=error_bars,
color='C0',
fmt='o',
ms=0,
zorder=3,
alpha=0.5)
ax_0.plot(bjd_plot[dataset_name]['x_plot'],
bjd_plot['model_x'][dataset_name]['complete'],
label='Median-corresponding model',
color='C1',
zorder=2)
ax_0.plot(
bjd_plot[dataset_name]['x_plot'],
bjd_plot['MAP_model_x'][dataset_name]['complete'],
label='MAP-corresponding model',
color='C2',
zorder=1)
ax_0.set_ylabel('Same as input data')
ax_0.legend()
ax_1.scatter(
mc.dataset_dict[dataset_name].x,
mc.dataset_dict[dataset_name].y -
bjd_plot['model_out'][dataset_name]['complete'],
color='C0',
zorder=4,
s=16)
ax_1.errorbar(
mc.dataset_dict[dataset_name].x,
mc.dataset_dict[dataset_name].y -
bjd_plot['model_out'][dataset_name]['complete'],
yerr=error_bars,
color='C0',
fmt='o',
ms=0,
zorder=3,
alpha=0.5)
ax_1.axhline(0.0, color='k', alpha=0.5, zorder=0)
ax_1.set_xlabel('Time [d] (offset as the input data)')
ax_1.set_ylabel('Residuals (wrt median model)')
plt.savefig(dir_output + 'model_' + kind_name + '_' +
dataset_name + '.png',
bbox_inches='tight',
dpi=300)
plt.close(fig)
if plot_dictionary['write_models']:
for prepend_keyword in ['', 'MAP_']:
print(' Writing the ', prepend_keyword, 'data files ')
plot_out_keyword = prepend_keyword + 'model_out'
plot_x_keyword = prepend_keyword + 'model_x'
file_keyword = prepend_keyword + 'model_files'
if prepend_keyword == '':
planet_vars = planet_variables_med
# star_vars = star_variables # leaving here, it could be useful for the future
chain_ref = chain_med[:, 0]
elif prepend_keyword == 'MAP_':
planet_vars = planet_variables_MAP
# star_vars = star_variables_MAP
chain_ref = chain_MAP
dir_models = dir_output + file_keyword + '/'
os.system('mkdir -p ' + dir_models)
for dataset_name, dataset in mc.dataset_dict.items():
for model_name in dataset.models:
if getattr(mc.models[model_name], 'systematic_model',
False):
continue
fileout = open(
dir_models + dataset_name + '_' + model_name +
'.dat', 'w')
phase = np.zeros(dataset.n)
tc_folded = np.zeros(dataset.n)
phase_plot = np.zeros(
np.size(bjd_plot[dataset_name]['x_plot']))
tc_folded_plot = np.zeros(
np.size(bjd_plot[dataset_name]['x_plot']))
for common_ref in mc.models[model_name].common_ref:
if common_ref in planet_vars:
if 'P' in planet_vars[common_ref]:
phase = (dataset.x0 /
planet_vars[common_ref]['P']) % 1
phase_plot = (
(bjd_plot[dataset_name]['x_plot'] -
mc.Tref) /
planet_vars[common_ref]['P']) % 1
if 'Tc' in planet_vars[common_ref]:
tc_folded = (dataset.x - planet_vars[common_ref]['Tc']
+ planet_vars[common_ref]['P'] / 2.) \
% planet_vars[common_ref]['P'] \
- planet_vars[common_ref]['P'] / 2.
tc_folded_plot = (bjd_plot[dataset_name]['x_plot'] - planet_vars[common_ref][
'Tc']
+ planet_vars[common_ref]['P'] / 2.) \
% planet_vars[common_ref]['P'] \
- planet_vars[common_ref]['P'] / 2.
else:
tc_folded = dataset.x0 % planet_vars[
common_ref]['P']
tc_folded_plot = (bjd_plot[dataset_name]['x_plot'] - mc.Tref) % \
planet_vars[common_ref]['P']
fileout.write(
'descriptor BJD Tc_folded pha val,+- sys mod full val_compare,+- res,+- jit \n'
)
try:
len(bjd_plot[plot_out_keyword][dataset_name]
[model_name])
except:
bjd_plot[plot_out_keyword][dataset_name][model_name] = \
bjd_plot[plot_out_keyword][dataset_name][model_name] * np.ones(dataset.n)
bjd_plot[plot_x_keyword][dataset_name][model_name] = \
bjd_plot[plot_x_keyword][dataset_name][model_name] * np.ones(dataset.n)
for x, tcf, pha, y, e, sys, mod, com, obs_mod, res, jit in zip(
dataset.x, tc_folded, phase, dataset.y,
dataset.e, bjd_plot[plot_out_keyword]
[dataset_name]['systematics'],
bjd_plot[plot_out_keyword][dataset_name]
[model_name], bjd_plot[plot_out_keyword]
[dataset_name]['complete'], dataset.y -
bjd_plot[plot_out_keyword][dataset_name]
['complete'] + bjd_plot[plot_out_keyword]
[dataset_name][model_name], dataset.y -
bjd_plot[plot_out_keyword][dataset_name]
['complete'], bjd_plot[plot_out_keyword]
[dataset_name]['jitter']):
fileout.write(
'{0:f} {1:f} {2:f} {3:f} {4:f} {5:f} {6:1f} {7:f} {8:f} {9:f} {10:f} {11:f} {12:f}'
'\n'.format(x, tcf, pha, y, e, sys, mod, com,
obs_mod, e, res, e, jit))
fileout.close()
if getattr(mc.models[model_name], 'systematic_model',
False):
continue
if getattr(mc.models[model_name], 'jitter_model',
False):
continue
fileout = open(
dir_models + dataset_name + '_' + model_name +
'_full.dat', 'w')
if model_name + '_std' in bjd_plot[plot_x_keyword][
dataset_name]:
fileout.write(
'descriptor BJD Tc_folded phase mod,+- \n')
for x, tfc, pha, mod, std in zip(
bjd_plot[dataset_name]['x_plot'],
tc_folded_plot, phase_plot,
bjd_plot[plot_x_keyword][dataset_name]
[model_name], bjd_plot[plot_x_keyword]
[dataset_name][model_name + '_std']):
fileout.write(
'{0:f} {1:f} {2:f} {3:f} {4:f} \n'.format(
x, tcf, pha, mod, std))
fileout.close()
else:
fileout.write(
'descriptor BJD Tc_folded phase mod \n')
for x, tcf, pha, mod in zip(
bjd_plot[dataset_name]['x_plot'],
tc_folded_plot, phase_plot,
bjd_plot[plot_x_keyword][dataset_name]
[model_name]):
fileout.write(
'{0:f} {1:f} {2:f} {3:f}\n'.format(
x, tcf, pha, mod))
fileout.close()
if getattr(mc.models[model_name], 'model_class',
False) == 'transit':
"""
Exceptional model writing to deal with under-sampled lightcurves, i.e. when folding the
the light curve from the model file is not good enough. Something similar is performed later
with the planetary RVs, but here we must keep into account the differences between datasets
due to limb darkening, exposure times, etc.
"""
variable_values = {}
for common_ref in mc.models[model_name].common_ref:
variable_values.update(
mc.common_models[common_ref].convert(
chain_ref))
variable_values.update(
mc.models[model_name].convert(
chain_ref, dataset_name))
fileout = open(
dir_models + dataset_name + '_' + model_name +
'_transit.dat', 'w')
x_range = np.arange(-variable_values['P'] / 2.,
variable_values['P'] / 2.,
0.01)
delta_T = variable_values['Tc'] - dataset.Tref
y_plot = mc.models[model_name].compute(
variable_values, dataset, x_range + delta_T)
fileout.write('descriptor Tc_folded mod \n')
for x, mod in zip(x_range, y_plot):
fileout.write('{0:f} {1:f} \n'.format(x, mod))
fileout.close()
fileout = open(dir_models + dataset_name + '_full.dat',
'w')
fileout.write('descriptor BJD mod \n')
for x, mod in zip(
bjd_plot[dataset_name]['x_plot'],
bjd_plot[plot_x_keyword][dataset_name]
['complete']):
fileout.write('{0:f} {1:f} \n'.format(x, mod))
fileout.close()
for model in planet_vars:
try:
RV_out = kepler_exo.kepler_RV_T0P(
bjd_plot['full']['x_plot'] - mc.Tref,
planet_vars[model]['f'], planet_vars[model]['P'],
planet_vars[model]['K'], planet_vars[model]['e'],
planet_vars[model]['o'])
fileout = open(
dir_models + 'RV_planet_' + model + '_kep.dat',
'w')
fileout.write('descriptor x_range m_kepler \n')
for x, y in zip(bjd_plot['full']['x_plot'], RV_out):
fileout.write('{0:f} {1:f} \n'.format(x, y))
fileout.close()
x_range = np.arange(-1.50, 1.50, 0.001)
RV_out = kepler_exo.kepler_RV_T0P(
x_range * planet_vars[model]['P'],
planet_vars[model]['f'], planet_vars[model]['P'],
planet_vars[model]['K'], planet_vars[model]['e'],
planet_vars[model]['o'])
fileout = open(
dir_models + 'RV_planet_' + model + '_pha.dat',
'w')
fileout.write('descriptor x_phase m_phase \n')
for x, y in zip(x_range, RV_out):
fileout.write('{0:f} {1:f} \n'.format(x, y))
fileout.close()
except:
pass
print()
print(
'****************************************************************************************************'
)
print()
if plot_dictionary['veuz_corner_files']:
print(' Writing Veusz-compatible files for personalized corner plots')
# Transit times are too lenghty for the 'tiny' corner plot, so we apply a reduction to their value
variable_with_offset = {}
veusz_dir = dir_output + '/Veuz_plot/'
if not os.path.exists(veusz_dir):
os.makedirs(veusz_dir)
all_variables_list = {}
for dataset_name, dataset in mc.dataset_dict.items():
variable_values = dataset.convert(flat_chain)
for variable_name, variable in variable_values.items():
all_variables_list[dataset_name + '_' +
variable_name] = variable
for model_name in dataset.models:
variable_values = mc.models[model_name].convert(
flat_chain, dataset_name)
for variable_name, variable in variable_values.items():
all_variables_list[dataset_name + '_' + model_name + '_' +
variable_name] = variable
for model_name, model in mc.common_models.items():
variable_values = model.convert(flat_chain)
for variable_name, variable in variable_values.items():
all_variables_list[model.common_ref + '_' +
variable_name] = variable
# Special treatment for transit time, since ti can be very long but yet very precise, making
# the axis of corner plot quite messy
if variable_name == 'Tc':
offset = np.median(variable)
variable_with_offset[model.common_ref + '_' +
variable_name] = offset
all_variables_list[model.common_ref + '_' +
variable_name] -= offset
for common_ref, variable_values in planet_variables.items():
for variable_name, variable in variable_values.items():
# Skipping the variables that have been already included in all_variables_list
if common_ref + '_' + variable_name in all_variables_list:
continue
all_variables_list[common_ref + '_' + variable_name] = variable
if variable_name == 'Tc':
offset = np.median(variable)
variable_with_offset[common_ref + '_' +
variable_name] = offset
all_variables_list[common_ref + '_' +
variable_name] -= offset
text_file = open(veusz_dir + "veusz_offsets.txt", "w")
for variable_name, offset_value in variable_with_offset.items():
text_file.write('{0:s} {1:16.9f}'.format(variable_name,
offset_value))
text_file.close()
n_int = len(all_variables_list)
output_plan = np.zeros([n_samplings, n_int], dtype=np.double)
output_names = []
for var_index, variable_name in enumerate(all_variables_list):
output_plan[:, var_index] = all_variables_list[variable_name]
output_names.extend([variable_name])
plot_truths = np.percentile(output_plan[:, :], [15.865, 50, 84.135],
axis=0)
n_bins = 30 + 1
h5f = h5py.File(veusz_dir + '_hist1d.hdf5', "w")
data_grp = h5f.create_group("hist1d")
data_lim = np.zeros([n_int, 2], dtype=np.double)
data_edg = np.zeros([n_int, n_bins], dtype=np.double)
data_skip = np.zeros(n_int, dtype=bool)
sigma_minus = plot_truths[1, :] - plot_truths[0, :]
sigma_plus = plot_truths[2, :] - plot_truths[1, :]
median_vals = plot_truths[1, :]
for ii in range(0, n_int):
if sigma_minus[ii] == 0. and sigma_plus[ii] == 0.:
data_skip[ii] = True
continue
sigma5_selection = (output_plan[:, ii] > median_vals[ii] - 5 * sigma_minus[ii]) & \
(output_plan[:, ii] < median_vals[ii] + 5 * sigma_plus[ii])
data_lim[ii, :] = [
np.amin(output_plan[sigma5_selection, ii]),
np.amax(output_plan[sigma5_selection, ii])
]
if data_lim[ii, 0] == data_lim[ii, 1]:
data_lim[ii, :] = [
np.amin(output_plan[:, ii]),
np.amax(output_plan[:, ii])
]
if data_lim[ii, 0] == data_lim[ii, 1]:
data_skip[ii] = True
continue
data_edg[ii, :] = np.linspace(data_lim[ii, 0], data_lim[ii, 1],
n_bins)
veusz_workaround_descriptor = 'descriptor'
veusz_workaround_values = ''
for ii in range(0, n_int):
if data_skip[ii]:
continue
x_data = output_plan[:, ii]
x_edges = data_edg[ii, :]
for jj in range(0, n_int):
if data_skip[jj]:
continue
y_data = output_plan[:, jj]
y_edges = data_edg[jj, :]
if ii != jj:
hist2d = np.histogram2d(x_data,
y_data,
bins=[x_edges, y_edges],
density=True)
hist1d_y = np.histogram(y_data, bins=y_edges, density=True)
Hflat = hist2d[0].flatten()
inds = np.argsort(Hflat)[::-1]
Hflat = Hflat[inds]
sm = np.cumsum(Hflat)
sm /= sm[-1]
x_edges_1d = (x_edges[1:] + x_edges[:-1]) / 2
y_edges_1d = (y_edges[1:] + y_edges[:-1]) / 2
h2d_out = np.zeros([n_bins, n_bins])
h2d_out[0, 1:] = x_edges_1d
h2d_out[1:, 0] = y_edges_1d
h2d_out[1:, 1:] = hist2d[0].T * 1. / np.amax(hist2d[0])
h2d_list = h2d_out.tolist()
h2d_list[0][0] = ''
csvfile = veusz_dir + '_hist2d___' + output_names[
ii] + '___' + output_names[jj] + '.csv'
with open(csvfile, "w") as output:
writer = csv.writer(output, lineterminator='\n')
writer.writerows(h2d_list)
hist1d = np.histogram(x_data, bins=x_edges)
hist1d_norm = hist1d[0] * 1. / n_samplings
x_edges_1d = (x_edges[1:] + x_edges[:-1]) / 2
data_grp.create_dataset(output_names[ii] + '_x',
data=x_edges_1d,
compression="gzip")
data_grp.create_dataset(output_names[ii] + '_y',
data=hist1d_norm,
compression="gzip")
# data_grp.create_dataset(output_names[ii]+'_val', data=median_vals[ii])
# data_grp.create_dataset(output_names[ii]+'_val_-', data=sigma_minus[ii])
# data_grp.create_dataset(output_names[ii]+'_val_+', data=sigma_plus[ii])
# data_grp.attrs[output_names[ii]+'_val'] = median_vals[ii]
veusz_workaround_descriptor += ' ' + output_names[ii] + ',+,-'
veusz_workaround_values += ' ' + repr(
median_vals[ii]) + ' ' + repr(sigma_plus[ii]) + ' ' + repr(
sigma_minus[ii])
text_file = open(veusz_dir + "veusz_median_sigmas.txt", "w")
text_file.write('%s \n' % veusz_workaround_descriptor)
text_file.write('%s \n' % veusz_workaround_values)
text_file.close()
print()
print(
'****************************************************************************************************'
)
print()
