Fixed a bug that introduced an additional ghost data point when applying smoothing

deepof/data.py

@@ -210,7 +210,7 @@ class project:
             )
 
             tab_dict[key] = pd.concat([x, y], axis=1).sort_index(axis=1)
-            lik_dict[key] = lik
+            lik_dict[key] = lik.droplevel("scorer", axis=1)
 
         if self.smooth_alpha:
 
@@ -225,7 +225,7 @@ class project:
                     )
                 )
                 smooth.columns = cols
-                tab_dict[key] = smooth.iloc[1:, :]
+                tab_dict[key] = smooth.iloc[1:, :].reset_index(drop=True)
 
         for key, tab in tab_dict.items():
             tab_dict[key] = tab[tab.columns.levels[0][0]]

deepof/pose_utils.py

@@ -663,6 +663,8 @@ def tag_rulebased_frames(
                     ),
                     thickness=-1,
                 )
+            # Print frame number
+            write_on_frame("Frame " + str(fnum), corners["downleft"])
 
         if tag_dict["nose2nose"][fnum] and not tag_dict["sidebyside"][fnum]:
             write_on_frame("Nose-Nose", conditional_pos())

deepof/utils.py

@@ -427,8 +427,8 @@ def interpolate_outliers(
     exclude: str,
     lag: int = 5,
     n_std: int = 3,
-    mode: str = "and",
-    limit: int = 5,
+    mode: str = "or",
+    limit: int = 15,
 ):
     """Marks all outliers in experiment and replaces them using a univariate linear interpolation approach.
     Note that this approach only works for equally spaced data (constant camera acquisition rates).
@@ -437,9 +437,9 @@ def interpolate_outliers(
         - experiment (pd.DataFrame): dataframe with time series representing the x, y positions of a every body part
         - lag (int): size of the convolution window used to compute the moving average
         - n_std (int): number of standard deviations over the moving average to be considered an outlier
-        - mode (str): if "and" (default) both x and y have to be marked in order to call an outlier.
-        If "or", one is enough
-        - limit (int): maximum of consecutive outliers to interpolate. Defaults to 5
+        - mode (str): if "and" both x and y have to be marked in order to call an outlier.
+        If "or" (default), one is enough
+        - limit (int): maximum of consecutive outliers to interpolate. Defaults to 15
 
     Returns:
         interpolated_exp (pd.DataFrame): Interpolated version of experiment