make format

netam/dasm.py

@@ -149,7 +149,7 @@ def prediction_pair_of_batch(self, batch):
     def predictions_of_pair(self, log_neutral_aa_probs, log_selection_factors):
         # Take the product of the neutral mutation probabilities and the selection factors.
         # NOTE each of these now have last dimension of 20
-        # this is p_{j, a} * f_{j, a} 
+        # this is p_{j, a} * f_{j, a}
         predictions = torch.exp(log_neutral_aa_probs + log_selection_factors)
         assert torch.isfinite(predictions).all()
         predictions = clamp_probability(predictions)
@@ -206,4 +206,4 @@ def build_selection_matrix_from_parent(self, parent: str):
         parent_idxs = sequences.aa_idx_array_of_str(parent)
         selection_factors[torch.arange(len(parent_idxs)), parent_idxs] = 1.0
 
-        return selection_factors
+        return selection_factors

netam/dnsm.py

@@ -417,7 +417,8 @@ def find_optimal_branch_lengths(self, dataset, **optimization_kwargs):
         # return burrito.serial_find_optimal_branch_lengths(dataset, **optimization_kwargs)
         our_optimize_branch_length = partial(
             worker_optimize_branch_length,
-            self.__class__,)
+            self.__class__,
+        )
         with mp.Pool(worker_count) as pool:
             splits = dataset.split(worker_count)
             results = pool.starmap(