slight tidy

ocf_data_sampler/select/find_contiguous_time_periods.py

@@ -66,7 +66,7 @@ def trim_contiguous_time_periods(
     history_duration: pd.Timedelta,
     forecast_duration: pd.Timedelta,
 ) -> pd.DataFrame:
-    """Trim the contiguous time periods in-place to allow for history and forecast durations.
+    """Trim the contiguous time periods to allow for history and forecast durations.
 
     Args:
         contiguous_time_periods: DataFrame where each row represents a single time period. The 
@@ -78,9 +78,14 @@ def trim_contiguous_time_periods(
     Returns:
       The contiguous_time_periods DataFrame with the `start_dt` and `end_dt` columns updated.
     """
+    contiguous_time_periods = contiguous_time_periods.copy()
+
     contiguous_time_periods["start_dt"] += history_duration
     contiguous_time_periods["end_dt"] -= forecast_duration
-    assert (contiguous_time_periods["start_dt"] < contiguous_time_periods["end_dt"]).all()
+
+    valid_mask = contiguous_time_periods["start_dt"] <= contiguous_time_periods["end_dt"]
+    contiguous_time_periods = contiguous_time_periods.loc[valid_mask]
+
     return contiguous_time_periods
 
 
@@ -91,7 +96,19 @@ def find_contiguous_t0_periods(
         forecast_duration: pd.Timedelta,
         sample_period_duration: pd.Timedelta,
     ) -> pd.DataFrame:
-
+    """Return a pd.DataFrame where each row records the boundary of a contiguous time period.
+
+    Args:
+        datetimes: pd.DatetimeIndex. Must be sorted.
+        history_duration: Length of the historical slice used for each sample
+        forecast_duration: Length of the forecast slice used for each sample
+        sample_period_duration: The sample frequency of the timeseries
+
+
+    Returns:
+        pd.DataFrame where each row represents a single time period.  The pd.DataFrame
+            has two columns: `start_dt` and `end_dt` (where 'dt' is short for 'datetime').
+    """
     total_duration = history_duration + forecast_duration
 
     contiguous_time_periods = find_contiguous_time_periods(
@@ -106,6 +123,8 @@ def find_contiguous_t0_periods(
         forecast_duration=forecast_duration,
     )
 
+    assert len(contiguous_t0_periods) > 0
+
     return contiguous_t0_periods
 
 
@@ -192,13 +211,13 @@ def find_contiguous_t0_periods_nwp(
         # considering dropout - then the contiguous period breaks, and new starts with considering
         # dropout and history duration
         if end_this_period < dt_init + max_dropout:
-            contiguous_periods += [[start_this_period, end_this_period]]
+            contiguous_periods.append([start_this_period, end_this_period])
 
             # And start a new period
             start_this_period = dt_init + hist_drop_buffer
         end_this_period = dt_init + max_staleness
 
-    contiguous_periods += [[start_this_period, end_this_period]]
+    contiguous_periods.append([start_this_period, end_this_period])
 
     return pd.DataFrame(contiguous_periods, columns=["start_dt", "end_dt"])
 

ocf_data_sampler/select/select_time_slice.py

@@ -103,8 +103,6 @@ def select_time_slice_nwp(
         ds[channel_dim_name].values, accum_channels
     )
 
-    # TODO: Once all times have been converted to pd.Timestamp, remove this
-    t0 = pd.Timestamp(t0)
     start_dt = (t0 - history_duration).ceil(sample_period_duration)
     end_dt = (t0 + forecast_duration).ceil(sample_period_duration)