output sib/spt

Use the nonwear_utils package to fix timing for sib/spt
Update orchestrator +tests to output data

Author: Asanto32

src/wristpy/core/orchestrator.py

@@ -361,8 +361,8 @@ def _run_file(
         non_wear_algorithms=nonwear_algorithm,
     )
 
-    nonwear_epoch = nonwear_utils.nonwear_array_cleanup(
-        nonwear_array=nonwear_array,
+    nonwear_epoch = nonwear_utils.synchronize_measurements(
+        data_measurement=nonwear_array,
         reference_measurement=activity_measurement,
         epoch_length=epoch_length,
     )
@@ -374,11 +374,24 @@ def _run_file(
     sleep_array = analytics.sleep_cleanup(
         sleep_windows=sleep_windows[0], nonwear_measurement=nonwear_epoch
     )
+    spt_windows = nonwear_utils.synchronize_measurements(
+        data_measurement=sleep_windows[1],
+        reference_measurement=activity_measurement,
+        epoch_length=epoch_length,
+    )
+    sib_periods = nonwear_utils.synchronize_measurements(
+        data_measurement=sleep_windows[2],
+        reference_measurement=activity_measurement,
+        epoch_length=epoch_length,
+    )
+
     results = writers.OrchestratorResults(
         physical_activity_metric=activity_measurement,
         anglez=anglez,
         physical_activity_levels=physical_activity_levels,
         sleep_status=sleep_array,
+        sib_periods=sib_periods,
+        spt_periods=spt_windows,
         nonwear_status=nonwear_epoch,
         processing_params=parameters_dictionary,
     )

src/wristpy/io/writers/writers.py

@@ -23,6 +23,8 @@ class OrchestratorResults(pydantic.BaseModel):
     physical_activity_levels: models.Measurement
     nonwear_status: models.Measurement
     sleep_status: models.Measurement
+    sib_periods: models.Measurement
+    spt_periods: models.Measurement
     processing_params: Optional[Dict[str, Any]] = None
 
     def save_results(self, output: pathlib.Path) -> None:

src/wristpy/processing/analytics.py

@@ -43,7 +43,10 @@ def run_sleep_detection(
     ) -> Tuple[List[SleepWindow], models.Measurement, models.Measurement]:
         """Sleep Detector must contain a run_sleep_detection function.
 
-        The function must return a list of SleepWindow objects.
+        The function must return a tuple that contains key sleep information, namely:
+            - a list of SleepWindow objects.
+            - a Measurement object with the SPT windows.
+            - a Measurement object with the SIB periods.
         """
         pass
 
@@ -81,8 +84,11 @@ def run_sleep_detection(
         the SPT windows and SIB periods.
 
         Returns:
-            A list of SleepWindow instances, each instance contains a sleep onset/wakeup
-            time pair.
+            A tuple that contains:
+                - a list of SleepWindow instances, each instance contains a sleep
+                    onset/wakeup time pair.
+                - a Measurement object with the SPT windows.
+                - a Measurement object with the SIB periods.
         """
         logger.debug("Beginning sleep detection.")
         spt_window = self._spt_window(self.anglez)

src/wristpy/processing/nonwear_utils.py

@@ -160,22 +160,23 @@ def get_nonwear_measurements(
     return results[0]
 
 
-def nonwear_array_cleanup(
-    nonwear_array: models.Measurement,
+def synchronize_measurements(
+    data_measurement: models.Measurement,
     reference_measurement: models.Measurement,
     epoch_length: float = 5.0,
 ) -> models.Measurement:
-    """This function is used to match the nonwear array to a reference Measurement.
+    """This function is used to match a Measurement object to a reference Measurement.
 
-    This function ensures that the nonwear array and reference Measurement times
-    are synced up. This is accomplished by first resampling the nonwear array to
+    This function ensures that a Measurement object and reference Measurement times
+    are synced up. This is accomplished by first resampling a Measurement object to
     the specified temporal resolution.
-    It also ensures that the nonwear array is a binary array, where 1 indicates
+    It also ensures that a Measurement object is a binary array, where 1 indicates
     nonwear and 0 indicates wear.
-    It then truncates the nonwear array to match the reference Measurement time points.
+    It then truncates a Measurement object to match the reference
+    Measurement time points.
 
     Args:
-        nonwear_array: The nonwear array to clean up.
+        data_measurement: The nonwear array to clean up.
         reference_measurement: The reference measurement to use for resampling.
         epoch_length: The temporal resolution of the output, in seconds.
             Defaults to 5.0.
@@ -185,7 +186,7 @@ def nonwear_array_cleanup(
         returned as a boolean (True == nonwear).
     """
     time_fix_nonwear = _time_fix(
-        nonwear_array, reference_measurement.time[-1], reference_measurement.time[0]
+        data_measurement, reference_measurement.time[-1], reference_measurement.time[0]
     )
     resampled_nonwear = computations.resample(time_fix_nonwear, epoch_length)
     binary_nonwear = np.where(resampled_nonwear.measurements >= 0.5, 1, 0)

tests/unit/test_orchestrator.py

@@ -29,6 +29,8 @@ def dummy_results() -> writers.OrchestratorResults:
         physical_activity_levels=dummy_measure,
         nonwear_status=dummy_measure,
         sleep_status=dummy_measure,
+        sib_periods=dummy_measure,
+        spt_periods=dummy_measure,
     )
 
     return dummy_results

tests/unit/test_writers.py

@@ -28,6 +28,8 @@ def dummy_results() -> writers.OrchestratorResults:
         physical_activity_levels=dummy_measure,
         nonwear_status=dummy_measure,
         sleep_status=dummy_measure,
+        sib_periods=dummy_measure,
+        spt_periods=dummy_measure,
     )
 
     return dummy_results