Implement DataSource.get_contiguous_time_periods()

nowcasting_dataset/data_sources/data_source.py

@@ -66,7 +66,8 @@ def __post_init__(self):
 
         self._history_duration = pd.Timedelta(self.history_minutes, unit="minutes")
         self._forecast_duration = pd.Timedelta(self.forecast_minutes, unit="minutes")
-        # Add sample_period_duration because neither history_duration not forecast_duration include t0.
+        # Add sample_period_duration because neither history_duration not forecast_duration
+        # include t0.
         self._total_seq_duration = (
             self._history_duration + self._forecast_duration + self.sample_period_duration
         )
@@ -112,13 +113,13 @@ def get_batch(
         Get Batch Data
 
         Args:
-            t0_datetimes: list of timestamps for the datetime of the batches. The batch will also include data
-                for historic and future depending on 'history_minutes' and 'future_minutes'.
+            t0_datetimes: list of timestamps for the datetime of the batches. The batch will also
+                include data for historic and future depending on `history_minutes` and
+                `future_minutes`.
             x_locations: x center batch locations
             y_locations: y center batch locations
 
-        Returns: Batch data
-
+        Returns: Batch data.
         """
         examples = []
         zipped = zip(t0_datetimes, x_locations, y_locations)
@@ -176,31 +177,34 @@ def get_contiguous_time_periods(self) -> pd.DataFrame:
         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').
-        """
 
-        # TODO: Use nd_time.get_contiguous_time_periods()
-        # See https://github.com/openclimatefix/nowcasting_dataset/issues/223
-        raise NotImplementedError()
-
-    def _get_time_slice(self, t0_dt: pd.Timestamp):
-        """Get a single timestep of data.  Must be overridden."""
-        raise NotImplementedError()
+        Raises:
+          NotImplementedError if this DataSource has no concept of a datetime index.
+        """
+        datetimes = self.datetime_index()
+        return nd_time.get_contiguous_time_periods(
+            datetimes=datetimes,
+            min_seq_length=self._total_seq_length,
+            max_gap_duration=self.sample_period_duration,
+        )
 
-    # ****************** METHODS THAT MUST BE OVERRIDDEN **********************
     def get_locations_for_batch(
         self, t0_datetimes: pd.DatetimeIndex
     ) -> Tuple[List[Number], List[Number]]:
-        """Find a valid geographical location for each t0_datetime.
+        """Find a valid geographical locations for each t0_datetime.
+
+        Should be overridden by DataSources which may be used to define the locations
+        for each batch.
 
         Returns:  x_locations, y_locations. Each has one entry per t0_datetime.
             Locations are in OSGB coordinates.
         """
-        # TODO: Do this properly, using PV locations!
-        locations = [20_000, 40_000, 500_000, 600_000, 100_000, 100_000, 250_000, 250_000]
-
-        location = np.random.choice(locations, size=(len(t0_datetimes), 2))
+        raise NotImplementedError()
 
-        return location[:, 0], location[:, 1]
+    # ****************** METHODS THAT MUST BE OVERRIDDEN **********************
+    def _get_time_slice(self, t0_dt: pd.Timestamp):
+        """Get a single timestep of data.  Must be overridden."""
+        raise NotImplementedError()
 
     def get_example(
         self,
@@ -273,8 +277,8 @@ def get_example(
         Get Example data
 
         Args:
-            t0_dt: list of timestamps for the datetime of the batches. The batch will also include data
-                for historic and future depending on 'history_minutes' and 'future_minutes'.
+            t0_dt: list of timestamps for the datetime of the batches. The batch will also include
+                data for historic and future depending on `history_minutes` and `future_minutes`.
             x_meters_center: x center batch locations
             y_meters_center: y center batch locations
 

nowcasting_dataset/time.py

@@ -172,15 +172,20 @@ def get_start_datetimes(
 
 
 def get_contiguous_time_periods(
-    datetimes: pd.DatetimeIndex, min_seq_length: int, max_gap: pd.Timedelta = THIRTY_MINUTES
+    datetimes: pd.DatetimeIndex,
+    min_seq_length: int,
+    max_gap_duration: pd.Timedelta = THIRTY_MINUTES,
 ) -> pd.DataFrame:
     """Returns a pd.DataFrame where each row records the boundary of a contiguous time periods.
 
     Args:
       datetimes: The pd.DatetimeIndex of the timeseries. Must be sorted.
-      min_seq_length: Sequences of min_seq_length or shorter will be discarded.
-      max_gap: If any pair of consecutive `datetimes` is more than `max_gap` apart, then this pair
-        of `datetimes` will be considered a "gap" between two contiguous sequences.
+      min_seq_length: Sequences of min_seq_length or shorter will be discarded.  Typically, this
+        would be set to the `total_seq_length` of each machine learning example.
+      max_gap_duration: If any pair of consecutive `datetimes` is more than `max_gap_duration`
+        apart, then this pair of `datetimes` will be considered a "gap" between two contiguous
+        sequences. Typically, `max_gap_duration` would be set to the sample period of
+        the timeseries.
 
     Returns:
       pd.DataFrame where each row represents a single time period.  The pd.DataFrame
@@ -193,7 +198,7 @@ def get_contiguous_time_periods(
     assert datetimes.is_unique
 
     # Find indices of gaps larger than max_gap:
-    gap_mask = np.diff(datetimes) > max_gap
+    gap_mask = np.diff(datetimes) > max_gap_duration
     gap_indices = np.argwhere(gap_mask)[:, 0]
 
     # gap_indicies are the indices into dt_index for the timestep immediately

tests/data_sources/test_data_source.py

@@ -2,7 +2,6 @@
 
 
 def test_image_data_source():
-
     _ = ImageDataSource(
         image_size_pixels=64,
         meters_per_pixel=2000,

tests/data_sources/test_nwp_data_source.py

@@ -1,16 +1,17 @@
 import os
+import pandas as pd
 
 import nowcasting_dataset
 from nowcasting_dataset.data_sources.nwp.nwp_data_source import NWPDataSource
 
 
-def test_nwp_data_source_init():
+PATH = os.path.dirname(nowcasting_dataset.__file__)
 
-    path = os.path.dirname(nowcasting_dataset.__file__)
+# Solar PV data (test data)
+NWP_FILENAME = f"{PATH}/../tests/data/nwp_data/test.zarr"
 
-    # Solar PV data (test data)
-    NWP_FILENAME = f"{path}/../tests/data/nwp_data/test.zarr"
 
+def test_nwp_data_source_init():
     _ = NWPDataSource(
         filename=NWP_FILENAME,
         history_minutes=30,
@@ -21,12 +22,6 @@ def test_nwp_data_source_init():
 
 
 def test_nwp_data_source_open():
-
-    path = os.path.dirname(nowcasting_dataset.__file__)
-
-    # Solar PV data (test data)
-    NWP_FILENAME = f"{path}/../tests/data/nwp_data/test.zarr"
-
     nwp = NWPDataSource(
         filename=NWP_FILENAME,
         history_minutes=30,
@@ -40,12 +35,6 @@ def test_nwp_data_source_open():
 
 
 def test_nwp_data_source_batch():
-
-    path = os.path.dirname(nowcasting_dataset.__file__)
-
-    # Solar PV data (test data)
-    NWP_FILENAME = f"{path}/../tests/data/nwp_data/test.zarr"
-
     nwp = NWPDataSource(
         filename=NWP_FILENAME,
         history_minutes=30,
@@ -64,3 +53,20 @@ def test_nwp_data_source_batch():
     batch = nwp.get_batch(t0_datetimes=t0_datetimes, x_locations=x, y_locations=y)
 
     assert batch.data.shape == (4, 1, 19, 2, 2)
+
+
+def test_nwp_get_contiguous_time_periods():
+    nwp = NWPDataSource(
+        filename=NWP_FILENAME,
+        history_minutes=30,
+        forecast_minutes=60,
+        convert_to_numpy=True,
+        n_timesteps_per_batch=8,
+        channels=["t"],
+    )
+
+    contiguous_time_periods = nwp.get_contiguous_time_periods()
+    correct_time_periods = pd.DataFrame(
+        [{"start_dt": pd.Timestamp("2019-01-01 00:00"), "end_dt": pd.Timestamp("2019-01-02 02:00")}]
+    )
+    pd.testing.assert_frame_equal(contiguous_time_periods, correct_time_periods)

tests/test_dataset.py

@@ -3,7 +3,6 @@
 import pytest
 
 import nowcasting_dataset.time as nd_time
-from nowcasting_dataset.consts import GSP_DATETIME_INDEX
 from nowcasting_dataset.dataset.datasets import NowcastingDataset
 from nowcasting_dataset.dataset.batch import Batch
 
@@ -56,16 +55,8 @@ def test_per_worker_init(dataset: NowcastingDataset):
 
 def test_get_batch(dataset: NowcastingDataset):
     dataset.per_worker_init(worker_id=1)
-    batch = dataset._get_batch()
-    assert isinstance(batch, Batch)
-    assert batch.satellite is not None
-    assert batch.satellite.data.shape == (
-        8,
-        2,
-        pytest.IMAGE_SIZE_PIXELS,
-        pytest.IMAGE_SIZE_PIXELS,
-        1,
-    )
+    with pytest.raises(NotImplementedError):
+        _ = dataset._get_batch()
 
 
 def test_get_batch_gsp(dataset_gsp: NowcastingDataset):

tests/test_time.py

@@ -68,7 +68,7 @@ def test_get_contiguous_time_periods_1_with_1_chunk(min_seq_length):
     freq = pd.Timedelta(5, unit="minutes")
     dt_index = pd.date_range("2010-01-01", "2010-01-02", freq=freq)
     periods: pd.DataFrame = nd_time.get_contiguous_time_periods(
-        dt_index, min_seq_length=min_seq_length, max_gap=freq
+        dt_index, min_seq_length=min_seq_length, max_gap_duration=freq
     )
     correct_periods = pd.DataFrame([{"start_dt": dt_index[0], "end_dt": dt_index[-1]}])
     pd.testing.assert_frame_equal(periods, correct_periods)
@@ -81,7 +81,7 @@ def test_get_contiguous_time_periods_2_with_2_chunks(min_seq_length):
     dt_index2 = pd.date_range("2010-02-01", "2010-02-02", freq=freq)
     dt_index = dt_index1.union(dt_index2)
     periods: pd.DataFrame = nd_time.get_contiguous_time_periods(
-        dt_index, min_seq_length=min_seq_length, max_gap=freq
+        dt_index, min_seq_length=min_seq_length, max_gap_duration=freq
     )
     correct_periods = pd.DataFrame(
         [