Updated examples to use new group attribute

examples/datasets/_01_datasets_basics.py

@@ -266,10 +266,10 @@ def data(self) -> str:
         # Note that we need to make our checks from the least restrictive to the most restrictive (if there is only a
         # single trail, there is only just a single recording).
         if self.is_single(["participant", "recording"]):
-            return "This is the data for participant {} and rec {}".format(*self.groups[0])
+            return "This is the data for participant {} and rec {}".format(*self.group)
         # None -> single row
         if self.is_single(None):
-            return "This is the data for participant {}, rec {} and trial {}".format(*self.groups[0])
+            return "This is the data for participant {}, rec {} and trial {}".format(*self.group)
         raise ValueError(
             "Data can only be accessed when their is only a single recording of a single participant in the subset"
         )
@@ -283,7 +283,7 @@ def segmented_stride_list_(self) -> str:
         # We use assert here, as we don't have multiple options.
         # (We could also used `None` for the `groupby_cols` here)
         self.assert_is_single(["participant", "recording", "trial"], "segmented_stride_list_")
-        return "This is the segmented stride list for participant {}, rec {} and trial {}".format(*self.groups[0])
+        return "This is the segmented stride list for participant {}, rec {} and trial {}".format(*self.group)
 
     def create_index(self):
         return index

examples/datasets/_02_datasets_real_world_example.py

@@ -355,7 +355,7 @@ def data(self) -> pd.DataFrame:
         # Check that there is only a single participant in the dataset
         self.assert_is_single(None, "data")
         # Reconstruct the ecg file path based on the data index
-        p_id = self.index["participant"][0]
+        p_id = self.group.participant
         file_path = self.data_path / f"{p_id}.pk.gz"
         # We try to use the cache if enabled.
         if self.use_lru_cache:
@@ -369,7 +369,7 @@ def r_peak_positions_(self) -> pd.DataFrame:
         This includes all R-Peaks (PVC or normal)
         """
         self.assert_is_single(None, "r_peaks_")
-        p_id = self.index["participant"][0]
+        p_id = self.group.participant
         r_peaks = pd.read_csv(self.data_path / f"{p_id}_all.csv", index_col=0)
         r_peaks = r_peaks.rename(columns={"R": "r_peak_position"})
         return r_peaks

examples/datasets/datasets_final_ecg.py

@@ -70,7 +70,7 @@ def r_peak_positions_(self) -> pd.DataFrame:
         This includes all R-Peaks (PVC or normal)
         """
         self.assert_is_single(None, "r_peaks_")
-        p_id = self.index["participant"][0]
+        p_id = self.group.participant
         r_peaks = pd.read_csv(self.data_path / f"{p_id}_all.csv", index_col=0)
         r_peaks = r_peaks.rename(columns={"R": "r_peak_position"})
         return r_peaks
@@ -82,7 +82,7 @@ def pvc_positions_(self) -> pd.DataFrame:
         The position is equivalent to a position entry in `self.r_peak_positions_`.
         """
         self.assert_is_single(None, "pvc_positions_")
-        p_id = self.index["participant"][0]
+        p_id = self.group.participant
         pvc_peaks = pd.read_csv(self.data_path / f"{p_id}_pvc.csv", index_col=0)
         pvc_peaks = pvc_peaks.rename(columns={"PVC": "pvc_position"})
         return pvc_peaks