Merge pull request #770 from dirac-institute/output_ra_dec

Have KBMOD append RA, dec columns if possible

src/kbmod/run_search.py

@@ -12,6 +12,7 @@
 
 from .results import Results
 from .trajectory_generator import create_trajectory_generator
+from .trajectory_utils import predict_pixel_locations
 from .wcs_utils import wcs_to_dict
 from .work_unit import WorkUnit
 
@@ -187,7 +188,14 @@ def do_gpu_search(self, config, stack, trj_generator):
         keep = self.load_and_filter_results(search, config)
         return keep
 
-    def run_search(self, config, stack, trj_generator=None, wcs=None, extra_meta=None):
+    def run_search(
+        self,
+        config,
+        stack,
+        trj_generator=None,
+        workunit=None,
+        extra_meta=None,
+    ):
         """This function serves as the highest-level python interface for starting
         a KBMOD search given an ImageStack and SearchConfiguration.
 
@@ -200,8 +208,8 @@ def run_search(self, config, stack, trj_generator=None, wcs=None, extra_meta=Non
         trj_generator : `TrajectoryGenerator`, optional
             The object to generate the candidate trajectories for each pixel.
             If None uses the default EclipticCenteredSearch
-        wcs : `astropy.wcs.WCS`, optional
-            A global WCS for all images in the search.
+        workunit : `WorkUnit`, optional
+            An optional WorkUnit with additional meta-data, including the per-image WCS.
         extra_meta : `dict`, optional
             Any additional metadata to save as part of the results file.
 
@@ -253,8 +261,11 @@ def run_search(self, config, stack, trj_generator=None, wcs=None, extra_meta=Non
         if config["save_all_stamps"]:
             append_all_stamps(keep, stack, config["stamp_radius"])
 
-        # Append the WCS information if it is provided. This will be saved with the results.
-        keep.table.wcs = wcs
+        # Append additional information derived from the WorkUnit if one is provided,
+        # including a global WCS and per-time (RA, dec) predictions for each image.
+        if workunit is not None:
+            keep.table.wcs = workunit.wcs
+            append_ra_dec_to_results(workunit, keep)
 
         # Create and save any additional meta data that should be saved with the results.
         num_img = stack.img_count()
@@ -306,6 +317,74 @@ def run_search_from_work_unit(self, work):
             work.config,
             work.im_stack,
             trj_generator=trj_generator,
-            wcs=work.wcs,
+            workunit=work,
             extra_meta=extra_meta,
         )
+
+
+def append_ra_dec_to_results(workunit, results):
+    """Append predicted (RA, dec) positions to the results.
+
+    Parameters
+    ----------
+    workunit : `WorkUnit`
+        The WorkUnit with all the WCS information.
+    results : `Results`
+        The current table of results including the per-pixel trajectories.
+        This is modified in-place.
+    """
+    num_results = len(results)
+    if num_results == 0:
+        return  # Nothing to do
+
+    num_times = workunit.im_stack.img_count()
+    times = workunit.im_stack.build_zeroed_times()
+
+    # Predict where each candidate trajectory will be at each time step.
+    xp = predict_pixel_locations(times, results["x"], results["vx"], as_int=False)
+    yp = predict_pixel_locations(times, results["y"], results["vy"], as_int=False)
+
+    # Compute the predicted (RA, dec) positions for each trajectory in global space.
+    if workunit.wcs is not None:
+        logger.info("Found common WCS. Adding global_ra and global_dec columns.")
+
+        skypos = workunit.wcs.pixel_to_world(xp, yp)
+        results.table["global_ra"] = skypos.ra.degree
+        results.table["global_dec"] = skypos.dec.degree
+
+        # Loop over the trajectories to build the original positions.
+        all_ra = []
+        all_dec = []
+        for idx in range(num_results):
+            pos_tuples = [(xp[idx, j], yp[idx, j]) for j in range(num_times)]
+            skypos = workunit.image_positions_to_original_icrs(
+                image_indices=np.arange(num_times),  # Compute for all times.
+                positions=pos_tuples,
+                input_format="xy",
+                output_format="radec",
+                filter_in_frame=False,
+            )
+
+            # We get back a list of SkyCoord, because we gave a list.
+            # So we flatten it and extract the coordinate values.
+            all_ra.append([skypos[j].ra.degree for j in range(num_times)])
+            all_dec.append([skypos[j].dec.degree for j in range(num_times)])
+
+        results.table["img_ra"] = all_ra
+        results.table["img_dec"] = all_dec
+    else:
+        logger.info("No common WCS found. Skipping global_ra and global_dec columns.")
+
+        # If there are no global WCS, we just predict per image.
+        all_ra = np.zeros((len(results), num_times))
+        all_dec = np.zeros((len(results), num_times))
+
+        for time_idx in range(num_times):
+            wcs = workunit.get_wcs(time_idx)
+            if wcs is not None:
+                skypos = wcs.pixel_to_world(xp[:, time_idx], yp[:, time_idx])
+                all_ra[:, time_idx] = skypos.ra.degree
+                all_dec[:, time_idx] = skypos.dec.degree
+
+        results.table["img_ra"] = all_ra
+        results.table["img_dec"] = all_dec

tests/test_run_search.py

@@ -0,0 +1,144 @@
+"""Test some of the functions needed for running the search."""
+
+import unittest
+
+import numpy as np
+
+from kbmod.configuration import SearchConfiguration
+from kbmod.fake_data.fake_data_creator import create_fake_times, FakeDataSet
+from kbmod.results import Results
+from kbmod.run_search import append_ra_dec_to_results
+from kbmod.search import *
+from kbmod.wcs_utils import make_fake_wcs
+from kbmod.work_unit import WorkUnit
+
+
+class test_run_search(unittest.TestCase):
+    def test_append_ra_dec_global(self):
+        # Create a fake WorkUnit with 20 times, a completely random ImageStack,
+        # and no trajectories.
+        num_times = 20
+        fake_times = create_fake_times(num_times, t0=60676.0)
+        fake_ds = FakeDataSet(800, 600, fake_times)
+
+        # Append a global fake WCS and one for each time.
+        global_wcs = make_fake_wcs(20.0, 0.0, 800, 600, deg_per_pixel=0.5 / 3600.0)
+        all_wcs = []
+        for idx in range(num_times):
+            curr = make_fake_wcs(
+                20.01 + idx / 100.0, 0.01 + idx / 100.0, 800, 600, deg_per_pixel=0.5 / 3600.0
+            )
+            all_wcs.append(curr)
+
+        fake_wu = WorkUnit(
+            im_stack=fake_ds.stack,
+            config=SearchConfiguration(),
+            wcs=global_wcs,
+            per_image_wcs=all_wcs,
+            reprojected=True,
+            per_image_indices=[i for i in range(num_times)],
+            heliocentric_distance=np.full(num_times, 100.0),
+            obstimes=fake_times,
+        )
+
+        # Create three fake trajectories in the bounds of the images. We don't
+        # bother actually inserting them into the pixels.
+        trjs = [
+            Trajectory(x=5, y=10, vx=1, vy=1, flux=1000.0, lh=1000.0, obs_count=num_times),
+            Trajectory(x=400, y=300, vx=-5, vy=-2, flux=1000.0, lh=1000.0, obs_count=num_times),
+            Trajectory(x=100, y=500, vx=10, vy=-10, flux=1000.0, lh=1000.0, obs_count=num_times),
+        ]
+        results = Results.from_trajectories(trjs)
+        self.assertEqual(len(results), 3)
+
+        append_ra_dec_to_results(fake_wu, results)
+
+        # The global RA should exist and be close to 20.0 for all observations.
+        self.assertEqual(len(results["global_ra"]), 3)
+        for i in range(3):
+            self.assertEqual(len(results["global_ra"][i]), num_times)
+            self.assertTrue(np.all(results["global_ra"][i] > 19.0))
+            self.assertTrue(np.all(results["global_ra"][i] < 21.0))
+
+        # The global Dec should exist and be close to 0.0 for all observations.
+        self.assertEqual(len(results["global_dec"]), 3)
+        for i in range(3):
+            self.assertEqual(len(results["global_dec"][i]), num_times)
+            self.assertTrue(np.all(results["global_dec"][i] > -1.0))
+            self.assertTrue(np.all(results["global_dec"][i] < 1.0))
+
+        # The per-image RA should exist, be close to 20.0 for all observations,
+        # and be different from the global RA
+        self.assertEqual(len(results["img_ra"]), 3)
+        for i in range(3):
+            self.assertEqual(len(results["img_ra"][i]), num_times)
+            self.assertTrue(np.all(results["img_ra"][i] > 19.0))
+            self.assertTrue(np.all(results["img_ra"][i] < 21.0))
+            self.assertFalse(np.any(results["img_ra"][i] == results["global_ra"][i]))
+
+        # The global Dec should exist and be close to 0.0 for all observations.
+        self.assertEqual(len(results["img_dec"]), 3)
+        for i in range(3):
+            self.assertEqual(len(results["img_dec"][i]), num_times)
+            self.assertTrue(np.all(results["img_dec"][i] > -1.0))
+            self.assertTrue(np.all(results["img_dec"][i] < 1.0))
+            self.assertFalse(np.any(results["img_dec"][i] == results["global_dec"][i]))
+
+    def test_append_ra_dec_no_global(self):
+        # Create a fake WorkUnit with 20 times, a completely random ImageStack,
+        # and no trajectories.
+        num_times = 20
+        fake_times = create_fake_times(num_times, t0=60676.0)
+        fake_ds = FakeDataSet(800, 600, fake_times)
+
+        # Append a global fake WCS and one for each time.
+        all_wcs = []
+        for idx in range(num_times):
+            curr = make_fake_wcs(
+                20.01 + idx / 100.0, 0.01 + idx / 100.0, 800, 600, deg_per_pixel=0.5 / 3600.0
+            )
+            all_wcs.append(curr)
+
+        fake_wu = WorkUnit(
+            im_stack=fake_ds.stack,
+            config=SearchConfiguration(),
+            wcs=None,
+            per_image_wcs=all_wcs,
+            reprojected=False,
+            per_image_indices=[i for i in range(num_times)],
+            obstimes=fake_times,
+        )
+
+        # Create three fake trajectories in the bounds of the images. We don't
+        # bother actually inserting them into the pixels.
+        trjs = [
+            Trajectory(x=5, y=10, vx=1, vy=1, flux=1000.0, lh=1000.0, obs_count=num_times),
+            Trajectory(x=400, y=300, vx=-5, vy=-2, flux=1000.0, lh=1000.0, obs_count=num_times),
+            Trajectory(x=100, y=500, vx=10, vy=-10, flux=1000.0, lh=1000.0, obs_count=num_times),
+        ]
+        results = Results.from_trajectories(trjs)
+        self.assertEqual(len(results), 3)
+
+        append_ra_dec_to_results(fake_wu, results)
+
+        # The global RA and global dec should not exist.
+        self.assertFalse("global_ra" in results.colnames)
+        self.assertFalse("global_dec" in results.colnames)
+
+        # The per-image RA should exist, be close to 20.0 for all observations.
+        self.assertEqual(len(results["img_ra"]), 3)
+        for i in range(3):
+            self.assertEqual(len(results["img_ra"][i]), num_times)
+            self.assertTrue(np.all(results["img_ra"][i] > 19.0))
+            self.assertTrue(np.all(results["img_ra"][i] < 21.0))
+
+        # The global Dec should exist and be close to 0.0 for all observations.
+        self.assertEqual(len(results["img_dec"]), 3)
+        for i in range(3):
+            self.assertEqual(len(results["img_dec"][i]), num_times)
+            self.assertTrue(np.all(results["img_dec"][i] > -1.0))
+            self.assertTrue(np.all(results["img_dec"][i] < 1.0))
+
+
+if __name__ == "__main__":
+    unittest.main()