diff --git a/bilby/bilby_mcmc/proposals.py b/bilby/bilby_mcmc/proposals.py
index e7363fe8d172a1442c86610ecc5672b1c79dba9c..29c40d057a217f194576bf75964c9b331a865739 100644
--- a/bilby/bilby_mcmc/proposals.py
+++ b/bilby/bilby_mcmc/proposals.py
@@ -547,6 +547,7 @@ class GMMProposal(DensityEstimateProposal):
def _sample(self, nsamples=None):
return np.squeeze(self.density.sample(n_samples=nsamples)[0])
+ @staticmethod
def check_dependencies(warn=True):
if importlib.util.find_spec("sklearn") is None:
if warn:
@@ -593,12 +594,15 @@ class NormalizingFlowProposal(DensityEstimateProposal):
fallback=fallback,
scale_fits=scale_fits,
)
- self.setup_flow()
- self.setup_optimizer()
-
+ self.initialised = False
self.max_training_epochs = max_training_epochs
self.js_factor = js_factor
+ def initialise(self):
+ self.setup_flow()
+ self.setup_optimizer()
+ self.initialised = True
+
def setup_flow(self):
if self.ndim < 3:
self.setup_basic_flow()
@@ -699,6 +703,9 @@ class NormalizingFlowProposal(DensityEstimateProposal):
self.trained = True
def propose(self, chain):
+ if self.initialised is False:
+ self.initialise()
+
import torch
self.steps_since_refit += 1
@@ -728,6 +735,7 @@ class NormalizingFlowProposal(DensityEstimateProposal):
return theta, float(log_factor)
+ @staticmethod
def check_dependencies(warn=True):
if importlib.util.find_spec("nflows") is None:
if warn:
@@ -1094,10 +1102,6 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
]
if GMMProposal.check_dependencies(warn=warn):
plist.append(GMMProposal(priors, weight=big_weight, scale_fits=L1steps))
- if NormalizingFlowProposal.check_dependencies(warn=warn):
- plist.append(
- NormalizingFlowProposal(priors, weight=big_weight, scale_fits=L1steps)
- )
plist = remove_proposals_using_string(plist, string)
return ProposalCycle(plist)
diff --git a/test/bilby_mcmc/test_proposals.py b/test/bilby_mcmc/test_proposals.py
index 3bb70b168569edf2258c8f6b42674f53f8175a6f..84042d2d409dbaea08ecf21d02604cb82bdb45bc 100644
--- a/test/bilby_mcmc/test_proposals.py
+++ b/test/bilby_mcmc/test_proposals.py
@@ -165,29 +165,33 @@ class TestProposals(TestBaseProposals):
def test_NF_proposal(self):
priors = self.create_priors()
chain = self.create_chain(10000)
- prop = proposals.NormalizingFlowProposal(priors, first_fit=10000)
- prop.steps_since_refit = 9999
- start = time.time()
- p, w = prop(chain)
- dt = time.time() - start
- print(f"Training for {prop.__class__.__name__} took dt~{dt:0.2g} [s]")
- self.assertTrue(prop.trained)
-
- self.proposal_check(prop)
+ if proposals.NormalizingFlowProposal.check_dependencies():
+ prop = proposals.NormalizingFlowProposal(priors, first_fit=10000)
+ prop.steps_since_refit = 9999
+ start = time.time()
+ p, w = prop(chain)
+ dt = time.time() - start
+ print(f"Training for {prop.__class__.__name__} took dt~{dt:0.2g} [s]")
+ self.assertTrue(prop.trained)
+ self.proposal_check(prop)
+ else:
+ print("nflows not installed, unable to test NormalizingFlowProposal")
def test_NF_proposal_15D(self):
ndim = 15
priors = self.create_priors(ndim)
chain = self.create_chain(10000, ndim=ndim)
- prop = proposals.NormalizingFlowProposal(priors, first_fit=10000)
- prop.steps_since_refit = 9999
- start = time.time()
- p, w = prop(chain)
- dt = time.time() - start
- print(f"Training for {prop.__class__.__name__} took dt~{dt:0.2g} [s]")
- self.assertTrue(prop.trained)
-
- self.proposal_check(prop, ndim=ndim)
+ if proposals.NormalizingFlowProposal.check_dependencies():
+ prop = proposals.NormalizingFlowProposal(priors, first_fit=10000)
+ prop.steps_since_refit = 9999
+ start = time.time()
+ p, w = prop(chain)
+ dt = time.time() - start
+ print(f"Training for {prop.__class__.__name__} took dt~{dt:0.2g} [s]")
+ self.assertTrue(prop.trained)
+ self.proposal_check(prop, ndim=ndim)
+ else:
+ print("nflows not installed, unable to test NormalizingFlowProposal")
if __name__ == "__main__":
diff --git a/test/core/sampler/nessai_test.py b/test/core/sampler/nessai_test.py
index 7f6ec21a8a5d26b606e8c6f8aa3cae3ede905ca2..cbb084735ec50274b45d2dd629772c92d0d3daed 100644
--- a/test/core/sampler/nessai_test.py
+++ b/test/core/sampler/nessai_test.py
@@ -21,6 +21,7 @@ class TestNessai(unittest.TestCase):
plot=False,
skip_import_verification=True,
sampling_seed=150914,
+ npool=None, # TODO: remove when support for nessai<0.7.0 is dropped
)
self.expected = self.sampler.default_kwargs
self.expected['output'] = 'outdir/label_nessai/'
@@ -59,10 +60,13 @@ class TestNessai(unittest.TestCase):
assert self.expected["seed"] == 150914
def test_npool_max_threads(self):
+ # TODO: remove when support for nessai<0.7.0 is dropped
expected = self.expected.copy()
expected["n_pool"] = None
+ expected["max_threads"] = 1
new_kwargs = self.sampler.kwargs.copy()
new_kwargs["n_pool"] = 1
+ new_kwargs["max_threads"] = 1
self.sampler.kwargs = new_kwargs
self.assertDictEqual(expected, self.sampler.kwargs)
diff --git a/test/gw/utils_test.py b/test/gw/utils_test.py
index 7946a99c83b99958160636f482f4593103f6c7fe..a63c3ae0dde56686c5e28450f117241c864d16ba 100644
--- a/test/gw/utils_test.py
+++ b/test/gw/utils_test.py
@@ -104,7 +104,7 @@ class TestGWUtils(unittest.TestCase):
strain = gwutils.read_frame_file(
filename, start_time=None, end_time=None, channel=channel
)
- self.assertEqual(strain.channel.name, channel)
+ self.assertEqual(strain.name, channel)
self.assertTrue(np.all(strain.value == data[:-1]))
# Check reading with time limits