bp.utils.time: renamed "time_to_datetime" to "time_to_timedelta" (nam…

…ing was wrong all the time..)

src/biopsykit/questionnaires/questionnaires.py

@@ -32,7 +32,7 @@
 )
 from biopsykit.utils._datatype_validation_helper import _assert_has_columns, _assert_num_columns, _assert_value_range
 from biopsykit.utils.exceptions import ValueRangeError
-from biopsykit.utils.time import time_to_datetime
+from biopsykit.utils.time import time_to_timedelta
 
 
 def psqi(data: pd.DataFrame, columns: Optional[Union[Sequence[str], pd.Index]] = None) -> pd.DataFrame:
@@ -83,11 +83,11 @@ def psqi(data: pd.DataFrame, columns: Optional[Union[Sequence[str], pd.Index]] =
 
     # Bedtime Start: Question 1
     bed_time_start = data.filter(regex="01").iloc[:, 0]
-    bed_time_start = time_to_datetime(bed_time_start)
+    bed_time_start = time_to_timedelta(bed_time_start)
 
     # Bedtime End: Question 3
     bed_time_end = data.filter(regex="03").iloc[:, 0]
-    bed_time_end = time_to_datetime(bed_time_end)
+    bed_time_end = time_to_timedelta(bed_time_end)
 
     # Compute Hours in Bed (needed for habitual sleep efficiency)
     bed_time_diff = bed_time_end - bed_time_start

src/biopsykit/utils/time.py

@@ -19,7 +19,7 @@
     "check_tz_aware",
     "extract_time_from_filename",
     "get_time_from_date",
-    "time_to_datetime",
+    "time_to_timedelta",
     "timedelta_to_time",
 ]
 
@@ -123,8 +123,8 @@ def get_time_from_date(
     return data
 
 
-def time_to_datetime(data: pd.Series) -> pd.Series:
-    """Convert time information in a series into ``datetime.datetime`` data.
+def time_to_timedelta(data: pd.Series) -> pd.Series:
+    """Convert time information in a series into ``datetime.timedelta`` data.
 
     Parameters
     ----------
@@ -135,11 +135,14 @@ def time_to_datetime(data: pd.Series) -> pd.Series:
     Returns
     -------
     :class:`~pandas.Series`
-        series with data converted into :class:`datetime.datetime`
+        series with data converted into :class:`datetime.timedelta`
 
     """
-    col_data = pd.to_datetime(data.astype(str))
-    return col_data - col_data.dt.normalize()
+    _assert_is_dtype(data, pd.Series)
+    if np.issubdtype(data.dtype, np.timedelta64):
+        # data is already a timedelta
+        return data
+    return pd.to_timedelta(data.astype(str))
 
 
 def timedelta_to_time(data: pd.Series) -> pd.Series:

tests/test_io/test_io_sleep.py

@@ -29,7 +29,7 @@ def sleep_endpoints_dataframe_correct():
             "wake_onset": pd.to_datetime("01.01.2021 08:00"),
             "total_sleep_duration": 8 * 60,
         },
-        index=pd.DatetimeIndex(pd.to_datetime(["31.12.2020"]), name="date"),
+        index=pd.DatetimeIndex(pd.to_datetime(["31.12.2020"], format="%d.%m.%Y"), name="date"),
     )
 
 
@@ -42,7 +42,7 @@ def sleep_endpoints_dataframe_additional_cols():
             "major_rest_period_start": pd.to_datetime("31.12.2020 23:00"),
             "major_rest_period_end": pd.to_datetime("01.01.2021 08:30"),
         },
-        index=pd.DatetimeIndex(pd.to_datetime(["31.12.2020"]), name="date"),
+        index=pd.DatetimeIndex(pd.to_datetime(["31.12.2020"], format="%d.%m.%Y"), name="date"),
     )
 
 
@@ -63,15 +63,15 @@ def sleep_endpoints_dataframe_missing_cols():
             "sleep_onset": pd.to_datetime("01.01.2021 00:00"),
             "wake_onset": pd.to_datetime("01.01.2021 08:00"),
         },
-        index=pd.DatetimeIndex(pd.to_datetime(["31.12.2020"]), name="date"),
+        index=pd.DatetimeIndex(pd.to_datetime(["31.12.2020"], format="%d.%m.%Y"), name="date"),
     )
 
 
 def sleep_endpoints_dict_correct():
     return {
-        "date": pd.to_datetime("31.12.2020"),
-        "sleep_onset": pd.to_datetime("01.01.2021 00:00"),
-        "wake_onset": pd.to_datetime("01.01.2021 08:00"),
+        "date": pd.to_datetime("31.12.2020", format="%d.%m.%Y"),
+        "sleep_onset": pd.to_datetime("01.01.2021 00:00", format="%d.%m.%Y %H:%M"),
+        "wake_onset": pd.to_datetime("01.01.2021 08:00", format="%d.%m.%Y %H:%M"),
         "total_sleep_duration": 8 * 60,
     }