Add python 3.10 image and testing

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)