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Update dynesty to save blobs and prior #102

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Sep 20, 2023
Merged

Update dynesty to save blobs and prior #102

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27 changes: 13 additions & 14 deletions cosmosis/samplers/dynesty/dynesty_sampler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,14 +5,17 @@


def log_probability_function(p):
return pipeline.likelihood(p)[0]
results = pipeline.run_results(p)
# also save the prior because we want the posterior which dynesty does not report
blob = np.concatenate([[results.prior], results.extra])
return results.like, blob

def prior_transform(p):
return pipeline.denormalize_vector_from_prior(p)

class DynestySampler(ParallelSampler):
parallel_output = False
sampler_outputs = [('log_weight', float), ("like", float)]
sampler_outputs = [('log_weight', float), ("prior", float), ("post", float)]

def config(self):
global pipeline
Expand All @@ -34,14 +37,6 @@ def config(self):
print_progress_default = logs.is_enabled_for(logs.logging.INFO)
self.print_progress = self.read_ini("print_progress", bool, print_progress_default)

# if self.mode=='dynamic':
# raise ValueError("Dynesty mode 'dynamic' not yet implemented (sorry)")

for sec,name in pipeline.extra_saves:
col = "{}--{}".format(sec,name)
print("WARNING: DYNESTY DOES NOT SUPPORT DERIVED PARAMS - NOT SAVING {}".format(col))
self.output.del_column(col)

self.converged = False


Expand All @@ -63,7 +58,8 @@ def execute(self):
update_interval = self.update_interval,
first_update = {'min_ncall':self.min_ncall, 'min_eff':self.min_eff},
queue_size = self.queue_size,
pool = self.pool
pool = self.pool,
blob=True
)

sampler.run_nested(dlogz=self.dlogz)
Expand All @@ -77,15 +73,18 @@ def execute(self):
sample = self.sample,
# update_interval = self.update_interval,
queue_size = self.queue_size,
pool = self.pool
pool = self.pool,
blob=True
)
sampler.run_nested(dlogz_init=self.dlogz)

results = sampler.results
results.summary()

for sample, logwt, logl in zip(results['samples'],results['logwt'], results['logl']):
self.output.parameters(sample, logwt, logl)
for sample, logwt, logl, derived in zip(results['samples'],results['logwt'], results['logl'], results['blob']):
prior = derived[0]
post = prior + logl
self.output.parameters(sample, logwt, prior, post, derived[1:])

self.output.final("efficiency", results['eff'])
self.output.final("nsample", len(results['samples']))
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