style: black on src n test

src/skmiscpy/cbs.py

@@ -25,9 +25,9 @@ def compute_smd(
         A pandas DataFrame containing the columns specified in ``vars``, ``group``, and optionally ``wt_var``.
 
     vars : List[str]
-        A list of strings representing the variables names for which to calculate the SMD, where the 
-        variables should be either continuous or binary. The values of the binary variable could be 
-        either string type or numerical, they would be converted into 0 and 1 (if they are not already 
+        A list of strings representing the variables names for which to calculate the SMD, where the
+        variables should be either continuous or binary. The values of the binary variable could be
+        either string type or numerical, they would be converted into 0 and 1 (if they are not already
         0-1), where lower value converted into 0 and higher value converted into 1. To compute SMD for
         a discrete variable with more than two categories, pass that variable name in a list to the
         ``cat_vars`` parameter.
@@ -143,7 +143,9 @@ def compute_smd(
         _check_param_type({"wt_var": wt_var}, str)
 
     if cat_vars is not None:
-        if not (isinstance(cat_vars, list) and all(isinstance(v, str) for v in cat_vars)):
+        if not (
+            isinstance(cat_vars, list) and all(isinstance(v, str) for v in cat_vars)
+        ):
             raise TypeError("`cat_vars` must be a list of strings")
 
     data = _check_prep_smd_data(
@@ -152,22 +154,32 @@ def compute_smd(
 
     covariates = list(set(data.columns) - {wt_var, group})
     covariates_with_types = _classify_columns(data, covariates)
-    
+
     if not std_binary:
         if any(col_type == "binary" for col_type in covariates_with_types.values()):
-            print("For binary variables, the unstandardized mean differences are shown here. "
-                  "See 'Notes' in function documentation for details."
+            print(
+                "For binary variables, the unstandardized mean differences are shown here. "
+                "See 'Notes' in function documentation for details."
             )
 
     smd_results = []
 
     for var, var_type in covariates_with_types.items():
         if wt_var is not None:
             unadjusted_smd = _calc_smd_covar(
-                data=data, group=group, covar=var, estimand=estimand, std_binary=std_binary
+                data=data,
+                group=group,
+                covar=var,
+                estimand=estimand,
+                std_binary=std_binary,
             )
             adjusted_smd = _calc_smd_covar(
-                data=data, group=group, covar=var, wt_var=wt_var, estimand=estimand, std_binary=std_binary
+                data=data,
+                group=group,
+                covar=var,
+                wt_var=wt_var,
+                estimand=estimand,
+                std_binary=std_binary,
             )
             smd_results.append(
                 {
@@ -179,19 +191,23 @@ def compute_smd(
             )
         else:
             unadjusted_smd = _calc_smd_covar(
-                data=data, group=group, covar=var, estimand=estimand, std_binary=std_binary
+                data=data,
+                group=group,
+                covar=var,
+                estimand=estimand,
+                std_binary=std_binary,
             )
             smd_results.append(
                 {
-                    "variables": var, 
+                    "variables": var,
                     "var_types": var_type,
-                    "unadjusted_smd": unadjusted_smd
+                    "unadjusted_smd": unadjusted_smd,
                 }
             )
 
     smd_df = pd.DataFrame(smd_results)
 
-    return smd_df.sort_values(by = ['var_types', 'variables'], ascending=[False, True])
+    return smd_df.sort_values(by=["var_types", "variables"], ascending=[False, True])
 
 
 def _check_prep_smd_data(