small refactoring

fink_fat/associations/association_orbit.py

@@ -94,6 +94,7 @@ def orbit_associations(
     ]["ssoCandId"]
     assert len(duplicated_id) == 0
 
+    # recompute the orbit using local or cluster mode
     new_orbit_pdf = switch_local_cluster(config, traj_to_new_orbit)
 
     # remove the failed orbits

fink_fat/command_line/cli_main/kalman.py

@@ -18,7 +18,7 @@
 from fink_fat.associations.association_orbit import orbit_associations
 from fink_fat.associations.association_kalman import kalman_association
 from fink_fat.kalman.init_kalman import init_kalman
-from fink_fat.command_line.orbit_cli import kalman_to_orbit
+from fink_fat.command_line.orbit_cli import trcand_to_orbit
 
 
 def get_default_input() -> (
@@ -332,7 +332,7 @@ def cli_kalman_associations(arguments: dict, config: dict, output_path: str):
         trajectory_df = pd.concat([trajectory_df, seeds])
         kalman_df = pd.concat([kalman_df, new_kalman_df])
 
-    trajectory_df, kalman_df, trajectory_orb, orbits = kalman_to_orbit(
+    trajectory_df, kalman_df, trajectory_orb, orbits = trcand_to_orbit(
         config, trajectory_df, trajectory_orb, kalman_df, orbits, logger, True
     )
 

fink_fat/command_line/orbit_cli.py

@@ -232,29 +232,28 @@ def switch_local_cluster(config: dict, traj_orb: pd.DataFrame) -> pd.DataFrame:
     return new_orbit_pdf
 
 
-def kalman_to_orbit(
+def trcand_to_orbit(
     config: dict,
     trajectory_df: pd.DataFrame,
     trajectory_orb: pd.DataFrame,
-    kalman_df: pd.DataFrame,
+    trparams_df: pd.DataFrame,
     orbits: pd.DataFrame,
     logger: LoggerNewLine,
     verbose: bool,
 ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
     """
-    Compute and return the orbits for the trajectories with enough points found by the kalman filters
-
+    Compute and return the orbits for the trajectories with enough points found by the prediction functions
 
     Parameters
     ----------
     config : dict
         the data from the config file
     trajectory_df : pd.DataFrame
-        trajectories found by the kalman filters
+        trajectories found by the function predictors
     trajectory_orb : pd.DataFrame
         trajectories with orbits
-    kalman_df : pd.DataFrame
-        the kalman filters parameters
+    trparams_df : pd.DataFrame
+        the fit functions parameters
     orbits : pd.DataFrame
         the orbits parameters
     logger : LoggerNewLine
@@ -265,7 +264,7 @@ def kalman_to_orbit(
     Returns
     -------
     Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame,]
-        * trajectories found by the kalman filters without those sent to the orbit fitting
+        * trajectories found by the prediction functions without those sent to the orbit fitting
         * kalman filters without those with the associated trajectories sent to the orbit fitting
         * trajectories with the ones with orbits
         * the orbits parameters with the new ones
@@ -276,7 +275,7 @@ def kalman_to_orbit(
     large_traj = traj_size[traj_size >= orbit_limit]
 
     if len(large_traj) == 0:
-        return trajectory_df, kalman_df, trajectory_orb, orbits
+        return trajectory_df, trparams_df, trajectory_orb, orbits
 
     # to be send to the orbit fitting,
     # a trajectory must have a number of point above the orbit point limit set in the config file
@@ -310,16 +309,16 @@ def kalman_to_orbit(
     trajectory_df = trajectory_df[
         ~trajectory_df["trajectory_id"].isin(new_traj_id)
     ].reset_index(drop=True)
-    kalman_df = kalman_df[~kalman_df["trajectory_id"].isin(new_traj_id)].reset_index(
+    trparams_df = trparams_df[~trparams_df["trajectory_id"].isin(new_traj_id)].reset_index(
         drop=True
     )
     failed_orbit = np.setdiff1d(large_traj.index.values, new_traj_id)
-    mask_failed = kalman_df["trajectory_id"].isin(failed_orbit)
+    mask_failed = trparams_df["trajectory_id"].isin(failed_orbit)
     with pd.option_context("mode.chained_assignment", None):
-        kalman_df.loc[mask_failed, "orbfit_test"] = (
-            kalman_df.loc[mask_failed, "orbfit_test"] + 1
+        trparams_df.loc[mask_failed, "orbfit_test"] = (
+            trparams_df.loc[mask_failed, "orbfit_test"] + 1
         )
-    return trajectory_df, kalman_df, trajectory_orb, orbits
+    return trajectory_df, trparams_df, trajectory_orb, orbits
 
 
 if __name__ == "__main__":

fink_fat/roid_fitting/init_roid_fitting.py

@@ -88,7 +88,7 @@ def init_polyast(
     night_pdf: pd.DataFrame,
 ) -> pd.DataFrame:
     """
-    Initialize kalman filters based on the seeds or tracklets.
+    Initialize fit functions based on the seeds or tracklets.
     required columns: ra, dec, jd, magpsf, fid, trajectory_id
 
     Parameters