MNT: change bilby MCMC to use glasflow instead of nflows

Closes #729 (closed)

I also took this chance to switch the tests to use pytest-requires which will mark tests that require glasflow as skipped rather than passing when it is not installed.

This will also require a change to the images, so we should be slighlty careful when merging this.

test/bilby_mcmc/test_proposals.py

@@ -11,6 +11,7 @@ from bilby.bilby_mcmc.chain import Chain, Sample
 from bilby.bilby_mcmc import proposals
 from bilby.bilby_mcmc.utils import LOGLKEY, LOGPKEY
 import numpy as np
+import pytest
 
 
 class GivenProposal(proposals.BaseProposal):
@@ -164,36 +165,32 @@ class TestProposals(TestBaseProposals):
         else:
             print("Unable to test GMM as sklearn is not installed")
 
+    @pytest.mark.requires("glasflow")
     def test_NF_proposal(self):
         priors = self.create_priors()
         chain = self.create_chain(10000)
-        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")
+        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)
 
+    @pytest.mark.requires("glasflow")
     def test_NF_proposal_15D(self):
         ndim = 15
         priors = self.create_priors(ndim)
         chain = self.create_chain(10000, 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")
+        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 __name__ == "__main__":