Improving output csv

src/stepcount/models.py

@@ -234,18 +234,14 @@ def fn(chunk):
             X, return_step_timings=return_step_timings, **kwargs
         )
 
+        Y = pd.Series(Y, index=T)
+
         step_timings = (
             pd.Series(
                 flatten(
                     [
-                        T[i]
-                        + np.array(
-                            [
-                                pd.Timedelta(seconds=elem)
-                                for elem in batch_step_timings[i]
-                            ]
-                        )
-                        for i in range(len(batch_step_timings))
+                        [T[i] + pd.Timedelta(seconds=timing) for timing in step_timings]
+                        for i, step_timings in enumerate(batch_step_timings)
                     ]
                 )
             )
@@ -802,8 +798,8 @@ def calibrate(yp, yt, pnr=1.0, precision_tol=0.9, recall_tol=0.9):
     return results
 
 
-def flatten(l):
-    return [item for sublist in l for item in sublist]
+def flatten(lst):
+    return [item for sublist in lst for item in sublist]
 
 
 def print_report():

src/stepcount/stepcount.py

@@ -103,7 +103,9 @@ def main():
 
     # Save the timings of each step if returned
     if step_timings is not None:
-        step_timings.to_csv(f"{outdir}/{basename}-StepTimings.csv")
+        step_timings.to_csv(
+            f"{outdir}/{basename}-StepTimings.csv", header=False, index=False
+        )
 
     # Summary
     summary = summarize(Y, model.steptol)