Code formatting with black

mofapy2/build_model/build_model.py

@@ -296,7 +296,7 @@ def build_Sigma(self):
                     warping_open_end=self.smooth_opts["warping_open_end"],
                     warping_groups=self.smooth_opts["warping_groups"],
                     opt_freq=self.smooth_opts["opt_freq"],
-                    model_groups=self.smooth_opts["model_groups"]  # ,
+                    model_groups=self.smooth_opts["model_groups"],  # ,
                     # use_gpytorch  = self.model_opts['use_gpytorch']
                 )
             # Non-warping
@@ -312,7 +312,7 @@ def build_Sigma(self):
                     # warping_open_begin = self.smooth_opts['warping_open_begin'],
                     # warping_open_end = self.smooth_opts['warping_open_end'],
                     opt_freq=self.smooth_opts["opt_freq"],
-                    model_groups=self.smooth_opts["model_groups"]  # ,
+                    model_groups=self.smooth_opts["model_groups"],  # ,
                     # use_gpytorch  = self.model_opts['use_gpytorch']
                 )
 

mofapy2/core/BayesNet.py

@@ -234,9 +234,7 @@ def precompute(self):
         if self.options["verbose"]:
             print("ELBO before training:")
             print(
-                "".join(
-                    ["%s=%.2f  " % (k, v) for k, v in elbo.drop("total").items()]
-                )
+                "".join(["%s=%.2f  " % (k, v) for k, v in elbo.drop("total").items()])
                 + "\nTotal: %.2f\n" % elbo["total"]
             )
         else:

mofapy2/core/distributions/beta.py

@@ -46,9 +46,9 @@ def updateExpectations(self):
         E = np.divide(a, a + b)
         lnE = special.digamma(a) - special.digamma(a + b)
         lnEInv = special.digamma(b) - special.digamma(a + b)  # expectation of ln(1-X)
-        lnEInv[
-            np.isinf(lnEInv)
-        ] = -np.inf  # there is a numerical error in lnEInv if E=1
+        lnEInv[np.isinf(lnEInv)] = (
+            -np.inf
+        )  # there is a numerical error in lnEInv if E=1
         self.expectations = {"E": E, "lnE": lnE, "lnEInv": lnEInv}
 
     def sample(self, n=1):

mofapy2/core/nodes/Sigma_node.py

@@ -1075,9 +1075,9 @@ def align_sample_cov_dtw(self, Z):
                 new_val = tref[ref_idx]
                 old_val = self.sample_cov[self.warping_groups == g, 0]
                 new_sample_cov = [new_val[tg == told].item() for told in old_val]
-                self.sample_cov_transformed[
-                    self.warping_groups == g, 0
-                ] = new_sample_cov
+                self.sample_cov_transformed[self.warping_groups == g, 0] = (
+                    new_sample_cov
+                )
 
                 # # reorder by covariate value to ensure monotonicity constrains are correctly placed
                 # idx_ref_order = np.argsort(self.sample_cov[self.warping_groups == self.reference_group,0])

mofapy2/run/entry_point.py

@@ -1695,12 +1695,16 @@ def save(
             views_names=self.data_opts["views_names"],
             groups_names=self.data_opts["groups_names"],
             covariates_names=covariates_names,
-            samples_metadata=self.data_opts["samples_metadata"]
-            if "samples_metadata" in self.data_opts
-            else None,
-            features_metadata=self.data_opts["features_metadata"]
-            if "features_metadata" in self.data_opts
-            else None,
+            samples_metadata=(
+                self.data_opts["samples_metadata"]
+                if "samples_metadata" in self.data_opts
+                else None
+            ),
+            features_metadata=(
+                self.data_opts["features_metadata"]
+                if "features_metadata" in self.data_opts
+                else None
+            ),
             compression_level=9,
         )