Lint fixes

src/kbmod/filters/known_object_filters.py

@@ -37,7 +37,7 @@ def __init__(self, table, mjd_col="mjd_mid", ra_col="RA", dec_col="DEC", name_co
         ------
         ValueError
             If the required columns are not present in the table.
-        
+
         Returns
         -------
         KnownObjs
@@ -64,25 +64,25 @@ def get_mjd(self, ko_idx):
         Returns the MJD of the known object at a given index.
         """
         return self.data[ko_idx][self.mjd_col]
-    
+
     def get_ra(self, ko_idx):
         """
         Returns the RA of the known object at a given index.
         """
         return self.data[ko_idx][self.ra_col]
-    
+
     def get_dec(self, ko_idx):
         """
         Returns the DEC of the known object at a given index.
         """
         return self.data[ko_idx][self.dec_col]
-    
+
     def get_name(self, ko_idx):
         """
         Returns the name of the known object at a given index.
         """
         return self.data[ko_idx][self.name_col]
-    
+
     def to_skycoords(self):
         """
         Returns a SkyCoord representation of the known objects.
@@ -106,11 +106,10 @@ def filter_rows_by_time(self, start_mjd, end_mjd):
             A new KnownObjs object filtered down withing the given time range.
         """
         new_data = self.data[(self.data[self.mjd_col] >= start_mjd) & (self.data[self.mjd_col] <= end_mjd)]
-        return KnownObjs(new_data,
-                         mjd_col=self.mjd_col,
-                         ra_col=self.ra_col,
-                         dec_col=self.dec_col,
-                         name_col=self.name_col)
+        return KnownObjs(
+            new_data, mjd_col=self.mjd_col, ra_col=self.ra_col, dec_col=self.dec_col, name_col=self.name_col
+        )
+
 
 def apply_known_obj_filters(result_data, known_objs, obstimes, wcs, filter_params, remove_match_obs=True):
     """This function takes a list of results and matches them to known objects.
@@ -131,7 +130,7 @@ def apply_known_obj_filters(result_data, known_objs, obstimes, wcs, filter_param
         filter_type, filter_params, and filter_obs.
     remove_match_obs : bool
         If True, remove observations that match to known objects from the results
-    
+
     Returns
     -------
     list(dict)
@@ -155,18 +154,22 @@ def apply_known_obj_filters(result_data, known_objs, obstimes, wcs, filter_param
     if len(result_data) == 0 or len(obstimes) == 0:
         logger.info(f"{filter_type} : skipping, no results.")
         return all_matches
-    
+
     # Skip matching known objects if there are none
     if len(known_objs) == 0:
         logger.info("Known Object Filtering : skipping, no objects to match agains.")
         return all_matches
 
-    known_obj_thresh = filter_params['known_obj_thresh']
+    known_obj_thresh = filter_params["known_obj_thresh"]
 
-    sep_thresh = filter_params['known_obj_sep_thresh'] if 'known_obj_sep_thresh' in filter_params else 1.0
+    sep_thresh = filter_params["known_obj_sep_thresh"] if "known_obj_sep_thresh" in filter_params else 1.0
     sep_thresh = sep_thresh * u.arcsec
 
-    time_sep_thresh_s = filter_params['known_obj_sep_time_thresh_s'] if "known_obj_sep_time_thresh_s" in filter_params else 1200.0
+    time_sep_thresh_s = (
+        filter_params["known_obj_sep_time_thresh_s"]
+        if "known_obj_sep_time_thresh_s" in filter_params
+        else 1200.0
+    )
 
     # First filter down our cached data to a range of possible obstimes to speed up the search
     start_mjd = max(0, min(obstimes) - 2 - time_sep_thresh_s)
@@ -182,7 +185,7 @@ def apply_known_obj_filters(result_data, known_objs, obstimes, wcs, filter_param
         # Becauase we're only using the valid obstimes, we can user this below to map back to
         # the original observation index.
         trj_idx_to_obs_idx = np.where(result_data[result_idx]["obs_valid"])[0]
-        
+
         # Now we can compare the SkyCoords of the known objects to the SkyCoords of the trajectories using search_around_sky
         # This will return a list of indices of known objects that are within sep_thresh of a trajectory
         trjs_idx, known_objs_idx, _, _ = search_around_sky(trj_skycoords, known_objs_ra_dec, sep_thresh)
@@ -191,7 +194,7 @@ def apply_known_obj_filters(result_data, known_objs, obstimes, wcs, filter_param
         matched_known_objs = {}
         for t_idx, ko_idx in zip(trjs_idx, known_objs_idx):
             # Check the time separation is witihin our threshold
-            if abs(known_objs.get_mjd(ko_idx) - valid_obstimes[t_idx])*3600 <= time_sep_thresh_s:
+            if abs(known_objs.get_mjd(ko_idx) - valid_obstimes[t_idx]) * 3600 <= time_sep_thresh_s:
                 # The name of the object that matched to this observation
                 obj_name = known_objs.get_name(ko_idx)
                 # Create an array of dimension trj_skycoords where each value is false
@@ -202,7 +205,9 @@ def apply_known_obj_filters(result_data, known_objs, obstimes, wcs, filter_param
                 # want for results filtering.
                 obs_idx = trj_idx_to_obs_idx[t_idx]
                 if obs_idx >= len(matched_known_objs[obj_name]):
-                    raise ValueError(f"obs_idx: {obs_idx}, \n t_idx: {t_idx}, \n trj_idx_to_obs_idx: {trj_idx_to_obs_idx}, \nvalid_obstimes: {valid_obstimes}\n,trjs_idx: {trjs_idx},\n known_objs_idx: {known_objs_idx}")
+                    raise ValueError(
+                        f"obs_idx: {obs_idx}, \n t_idx: {t_idx}, \n trj_idx_to_obs_idx: {trj_idx_to_obs_idx}, \nvalid_obstimes: {valid_obstimes}\n,trjs_idx: {trjs_idx},\n known_objs_idx: {known_objs_idx}"
+                    )
 
                 matched_known_objs[obj_name][obs_idx] = True
         all_matches.append(matched_known_objs)
@@ -211,8 +216,8 @@ def apply_known_obj_filters(result_data, known_objs, obstimes, wcs, filter_param
 
     # Add matches as a result column
     result_data.table[filter_params["filter_type"]] = all_matches
-    
+
     if remove_match_obs:
         result_data.update_obs_valid(new_obs_valid)
-    
+
     return all_matches