Switch PyMC3 to PyMC (which is the new name starting from PyMC v4.0.0)

This MR updates the PyMC3 interface to use the new version of PyMC, which is now v4.0.0 (the version number suffix has been dropped). This is mainly a very straightforward port by replacing imports of pymc3 with pymc. The Theano library has also been renamed to aesara, so those are changed too.

The test suite runs and also the two examples, linear_regression_pymc.py and linear_regression_pymc_custom_likelihood.py, both work exactly as expected.

bilby/bilby_mcmc/sampler.py

@@ -1066,18 +1066,19 @@ class BilbyMCMCSampler(object):
         use_ratio=False,
     ):
         from ..core.sampler.base_sampler import _sampling_convenience_dump
-        self._stuff = _sampling_convenience_dump
+        self._sampling_helper = _sampling_convenience_dump
         self.beta = beta
         self.Tindex = Tindex
         self.Eindex = Eindex
         self.use_ratio = use_ratio
 
-        self.parameters = self._stuff.priors.non_fixed_keys
+        self.parameters = self._sampling_helper.priors.non_fixed_keys
         self.ndim = len(self.parameters)
 
-        full_sample_dict = self._stuff.priors.sample()
+        full_sample_dict = self._sampling_helper.priors.sample()
         initial_sample = {
-            k: v for k, v in full_sample_dict.items() if k in self._stuff.priors.non_fixed_keys
+            k: v for k, v in full_sample_dict.items()
+            if k in self._sampling_helper.priors.non_fixed_keys
         }
         initial_sample = Sample(initial_sample)
         initial_sample[LOGLKEY] = self.log_likelihood(initial_sample)
@@ -1100,7 +1101,7 @@ class BilbyMCMCSampler(object):
                 warn = False
 
             self.proposal_cycle = proposals.get_proposal_cycle(
-                proposal_cycle, self._stuff.priors, L1steps=self.chain.L1steps, warn=warn
+                proposal_cycle, self._sampling_helper.priors, L1steps=self.chain.L1steps, warn=warn
             )
         elif isinstance(proposal_cycle, proposals.ProposalCycle):
             self.proposal_cycle = proposal_cycle
@@ -1117,17 +1118,17 @@ class BilbyMCMCSampler(object):
         self.stop_after_convergence = convergence_inputs.stop_after_convergence
 
     def log_likelihood(self, sample):
-        self._stuff.likelihood.parameters.update(sample.sample_dict)
+        self._sampling_helper.likelihood.parameters.update(sample.sample_dict)
 
         if self.use_ratio:
-            logl = self._stuff.likelihood.log_likelihood_ratio()
+            logl = self._sampling_helper.likelihood.log_likelihood_ratio()
         else:
-            logl = self._stuff.likelihood.log_likelihood()
+            logl = self._sampling_helper.likelihood.log_likelihood()
 
         return logl
 
     def log_prior(self, sample):
-        return self._stuff.priors.ln_prob(sample.parameter_only_dict)
+        return self._sampling_helper.priors.ln_prob(sample.parameter_only_dict)
 
     def accept_proposal(self, prop, proposal):
         self.chain.append(prop)
@@ -1225,8 +1226,8 @@ class BilbyMCMCSampler(object):
         zerotemp_logl = hot_samples[LOGLKEY]
 
         # Revert to true likelihood if needed
-        if self._stuff.use_ratio:
-            zerotemp_logl += self._stuff.likelihood.noise_log_likelihood()
+        if self._sampling_helper.use_ratio:
+            zerotemp_logl += self._sampling_helper.likelihood.noise_log_likelihood()
 
         # Calculate normalised weights
         log_weights = (1 - beta) * zerotemp_logl