Make TestOrbit.range_observations use new Observations table and Test…

…Orbit.generate_ephemeris_from_observations

thor/orbit.py

@@ -134,7 +134,7 @@ def _is_cache_fresh(self, observations: Observations) -> bool:
             return False
         elif pc.all(
             pc.is_in(
-                self._cached_observation_ids.sort(), observations.detections.id.sort()
+                observations.detections.id.sort(), self._cached_observation_ids.sort()
             )
         ).as_py():
             return True
@@ -274,7 +274,8 @@ def generate_ephemeris_from_observations(
 
     def range_observations(
         self,
-        observations: qv.Linkage[PointSourceDetections, Exposures],
+        observations: Observations,
+        propagator: Propagator = PYOORB(),
         max_processes: Optional[int] = 1,
     ) -> RangedPointSourceDetections:
         """
@@ -283,8 +284,10 @@ def range_observations(
 
         Parameters
         ----------
-        observations : qv.Linkage[PointSourceDetections, Exposures]
+        observations : `~thor.observations.observations.Observations`
             Observations to range.
+        propagator : `~adam_core.propagator.propagator.Propagator`, optional
+            Propagator to use to propagate the orbit. Defaults to PYOORB.
         max_processes : int, optional
             Number of processes to use to propagate the orbit. Defaults to 1.
 
@@ -293,41 +296,44 @@ def range_observations(
         ranged_point_source_detections : `~thor.orbit.RangedPointSourceDetections`
             The ranged detections.
         """
-        exposures = observations.right_table
-        ephemeris = self.generate_ephemeris(
-            exposures.observers(), max_processes=max_processes
+        # Generate an ephemeris for each unique observation time and observatory
+        # code combination
+        ephemeris = self.generate_ephemeris_from_observations(
+            observations, propagator=propagator, max_processes=max_processes
         )
-        # Get the light-time corrected state vector: the state vector
-        # at the time where the light reflected/emitted from the object
-        # would have reached the observer.
-        # We will use this state vector to get the heliocentric distance of
-        # the object at the time of the exposure.
-        # TODO: We could use other adam_core functionality to calculate this if need
-        # be and we may need to if we plan on mapping covariances.
-        propagated_orbit = ephemeris.left_table.aberrated_coordinates
-
-        # TODO: We could investigate using concurrent futures here to parallelize
-        # this loop
+
+        # Link the ephemeris to the observations
+        link = qv.Linkage(
+            ephemeris,
+            observations,
+            left_keys=ephemeris.id,
+            right_keys=observations.state_id,
+        )
+
+        # Do a sorted iteration over the unique state IDs
         rpsds = []
-        for propagated_orbit_i, exposure_i in zip(propagated_orbit, exposures):
+        state_ids = observations.state_id.unique().sort()
+        for state_id in state_ids:
 
-            # Select the detections that belong to this exposure
-            detections_i = observations.select_left(exposure_i.id[0])
+            # Select the ephemeris and observations for this state
+            ephemeris_i = link.select_left(state_id)
+            observations_i = link.select_right(state_id)
+            detections_i = observations_i.detections
 
             # Get the heliocentric distance of the object at the time of the exposure
-            r_mag = propagated_orbit_i.r_mag[0]
+            r_mag = ephemeris_i.ephemeris.aberrated_coordinates.r_mag[0]
 
             # Get the observer's heliocentric coordinates
-            observer_i = exposure_i.observers()
+            observer_i = ephemeris_i.observer
 
             # Create an array of observatory codes for the detections
-            num_detections = len(detections_i)
+            num_detections = len(observations_i)
             observatory_codes = np.repeat(
-                exposure_i.observatory_code[0].as_py(), num_detections
+                observations_i.observatory_code[0].as_py(), num_detections
             )
 
             # The following can be replaced with:
-            # coords = detections_i.to_spherical(observatory_codes)
+            # coords = observations_i.to_spherical(observatory_codes)
             # Start replacement:
             sigma_data = np.vstack(
                 [
@@ -357,21 +363,12 @@ def range_observations(
                     coordinates=assume_heliocentric_distance(
                         r_mag, coords, observer_i.coordinates
                     ),
+                    state_id=observations_i.state_id,
                 )
             )
 
-        # Sort ranged detections by time
         ranged_detections = qv.concatenate(rpsds)
-        table = pa.table(
-            [ranged_detections.coordinates.time.jd()],
-            names=["time_jd"],
-        )
-
-        indices = pc.sort_indices(
-            table,
-            (("time_jd", "ascending"),),
-        )
-        return ranged_detections.take(indices)
+        return ranged_detections
 
 
 def assume_heliocentric_distance(