Merge pull request #64 from rahil-makadia/dev

updated ca/impact searches; addressed pandas FutureWarnings

grss/fit/fit_optical.py

@@ -1,5 +1,6 @@
 """Optical observation handling for the GRSS orbit determination code"""
 import os
+from io import StringIO
 from astropy.time import Time
 import requests
 from astroquery.gaia import Gaia
@@ -36,7 +37,7 @@ def get_mpc_raw_data(tdes):
         obs_data = response.json()[0]["XML"]
     else:
         print("Error getting MPC XML data: ", response.status_code, response.content)
-    return obs_data
+    return StringIO(obs_data)
 
 def _ades_mode_check(df):
     """
@@ -137,20 +138,23 @@ def create_optical_obs_df(body_id, optical_obs_file=None, t_min_tdb=None,
     # read in the data and add extra columns if not present
     obs_df = (
         pd.read_xml(obs_data, dtype=ades_column_types)
-        # add columns if they are not present
-        .reindex(columns=ades_column_types.keys(), fill_value=np.nan)
         # compute the obsTimeMJD time from obsTime
         .assign(obsTimeMJD=lambda x:Time(x['obsTime'].to_list(),format='isot',scale='utc').utc.mjd)
     )
+    # add columns if they are not present
+    str_cols = ['trx', 'rcv', 'sys', 'selAst']
+    for col in str_cols:
+        if col not in obs_df:
+            obs_df[col] = str(np.nan)
+    for col in ades_column_types:
+        if col not in obs_df:
+            obs_df[col] = np.nan
     if verbose:
         print(f"Read in {len(obs_df)} observations from the MPC.")
     _ades_mode_check(obs_df)
     _ades_ast_cat_check(obs_df)
     # filter the data based on the time range
     obs_df.query(f"{t_min_utc} <= obsTimeMJD <= {t_max_utc}", inplace=True)
-    # # filter the data based on the observation station
-    # stn_to_remove = ('247', '270') # roving observatories
-    # obs_df.query(f"stn not in {stn_to_remove}", inplace=True)
     # reindex the data frame
     obs_df.reset_index(drop=True, inplace=True)
     # for all indices with OCC mode compute ra and dec as raStar+deltaRA and decStar+deltaDec
@@ -958,7 +962,13 @@ def get_optical_obs(body_id, optical_obs_file=None, t_min_tdb=None,
                 "for observations during the same night.")
     obs_df = create_optical_obs_df(body_id, optical_obs_file,
                                     t_min_tdb, t_max_tdb, verbose)
-    obs_df = apply_debiasing_scheme(obs_df, debias_lowres, verbose)
+    if debias_lowres is not None:
+        obs_df = apply_debiasing_scheme(obs_df, debias_lowres, verbose)
+    else:
+        print("WARNING: No debiasing scheme applied to the observations.",
+                "Setting biases to zero.")
+        opt_idx = obs_df.query("mode != 'RAD'").index
+        obs_df.loc[opt_idx, ['biasRA', 'biasDec']] = 0.0
     obs_df = apply_weighting_scheme(obs_df, verbose)
     if deweight:
         obs_df = deweight_obs(obs_df, num_obs_per_night, verbose)

grss/fit/fit_simulation.py

@@ -8,7 +8,7 @@
 
 from .. import libgrss
 from ..utils import default_kernel_path, grss_path
-from .fit_utils import get_observer_info
+from .fit_utils import get_observer_info, get_similarity_stats
 
 __all__ = [ 'FitSimulation',
 ]
@@ -712,7 +712,8 @@ def _parse_observation_arrays(self, obs_df):
             None
         """
         self.obs = obs_df
-        self.obs['selAst'].fillna('A', inplace=True)
+        sel_ast_map = {'': 'A', ' ': 'A', np.nan: 'A', 'nan': 'A'}
+        self.obs['selAst'] = self.obs['selAst'].replace(sel_ast_map)
         if self.simulated_obs is not None:
             self._add_simulated_obs()
         self.obs.sort_values(by='obsTimeMJD', inplace=True, ignore_index=True)
@@ -1893,18 +1894,16 @@ def save(self, filename):
                 f.write(f'{obs["selAst"]+obs["obsTime"]:<{widths["obsTime"]}}')
                 if obs['mode'] in {'RAD', 'SIM_RAD_DEL', 'SIM_RAD_DOP'}:
                     f.write(f'{obs["trx"]+"/"+obs["rcv"]:^{widths["stn"]}}')
-                    f.write(f'{obs["delay"]:^{widths["ra"]}.9f}')
-                    sign = "+" if obs["doppler"] >= 0 else ""
-                    f.write(f'{sign+str(obs["doppler"]):^{widths["dec"]}}')
+                    f.write(f'{obs["delay"]:^{widths["ra"]}.13g}')
+                    f.write(f'{obs["doppler"]:^+{widths["dec"]}.12g}')
                     f.write(f'{obs["sigDelay"]:^{widths["sigRA"]}.4f}')
                     f.write(f'{obs["sigDoppler"]:^{widths["sigDec"]}.4f}')
                     f.write(f'{obs["resDelay"]:^+{widths["resRA"]}.7f}')
                     f.write(f'{obs["resDoppler"]:^+{widths["resDec"]}.7f}')
                 else:
                     f.write(f'{obs["stn"]:^{widths["stn"]}}')
-                    f.write(f'{obs["ra"]:^{widths["ra"]}}')
-                    sign = "+" if obs["dec"] >= 0 else ""
-                    f.write(f'{sign+str(obs["dec"]):^{widths["dec"]}}')
+                    f.write(f'{obs["ra"]:^{widths["ra"]}.13g}')
+                    f.write(f'{obs["dec"]:^+{widths["dec"]}.12g}')
                     f.write(f'{obs["sigRA"]:^{widths["sigRA"]}.4f}')
                     f.write(f'{obs["sigDec"]:^{widths["sigDec"]}.4f}')
                     f.write(f'{obs["resRA"]:^+{widths["resRA"]}.7f}')
@@ -1951,6 +1950,21 @@ def save(self, filename):
                 if itrn.iter_number != self.n_iter:
                     f.write(subsection_full + '\n')
 
+            mean_0 = np.array(list(self.x_init.values()))
+            cov_0 = self.covariance_init
+            mean_f = np.array(list(self.x_nom.values()))
+            cov_f = self.covariance
+            md_f, md_0, b_dist, b_coeff = get_similarity_stats(mean_0, cov_0, mean_f, cov_f)
+            f.write(f'Mahalonobis distance between initial and final solution: {md_f:0.2f}')
+            f.write("\n")
+            f.write(f'Mahalonobis distance between final and initial solution: {md_0:0.2f}')
+            f.write("\n")
+            f.write(f'Bhattacharya distance between initial and final solution: {b_dist:0.4f}')
+            f.write("\n")
+            f.write(f'Bhattacharya coefficient between initial and final solution: {b_coeff:0.4f}')
+            f.write("\n")
+            f.write(subsection_full + '\n')
+
             f.write(section_full + '\n')
             f.write(f"{header_section_half} END OF FILE {header_section_half}" + '\n')
             f.write(section_full + '\n')

grss/prop/prop_parallel.py

@@ -364,6 +364,7 @@ def cluster_ca_or_impacts(full_list, max_duration=45, central_body=399):
         A tuple of close approach clusters (each cluster is a list of
         close approaches).
     """
+    start_time = time.time()
     all_clusters = []
     full_list = [ca_or_impact for ca_or_impact in full_list
                     if ca_or_impact.centralBodySpiceId == central_body]
@@ -387,4 +388,9 @@ def cluster_ca_or_impacts(full_list, max_duration=45, central_body=399):
             cluster.append(full_list[idx])
             cluster_bodies.append(bodies[idx])
     all_clusters.append(cluster)
+    end_time = time.time()
+    duration = end_time - start_time
+    mm = int(duration / 60)
+    ss = duration % 60
+    print(f'Clustering took {mm:02d} minute(s) and {ss:.6f} seconds')
     return tuple(all_clusters)

grss/prop/prop_utils.py

@@ -545,8 +545,13 @@ def plot_bplane(ca_list, plot_offset=False, scale_coords=False, n_std=3, units_k
     mtp_y = np.array([approach.mtp.y*au2units for approach in ca_list])
     mtp_nan = np.any(np.isnan(mtp_x)) or np.any(np.isnan(mtp_y))
     focus_factor = np.nanmean([approach.gravFocusFactor for approach in ca_list])
-    impact_bool = np.array([approach.impact for approach in ca_list])
-    impact_any = np.any(impact_bool)
+    try:
+        lon = np.array([approach.lon for approach in ca_list])
+        lat = np.array([approach.lat for approach in ca_list])
+    except AttributeError:
+        lon = None
+        lat = None
+    impact_any = lon is not None and lat is not None
     if len(ca_list) >= 100 or sigma_points is not None:
         if not kizner_nan:
             kizner_data = data_to_ellipse(kizner_x, kizner_y, n_std, 'kizner',

grss/version.txt

@@ -1 +1 @@
-4.0.1
+4.0.2

include/simulation.h

@@ -174,6 +174,7 @@ struct NongravParameters {
  * @brief Class for integrated bodies in a PropSimulation.
  * 
  * @param spiceId SPICE ID of the body.
+ * @param logCA Flag to indicate if close approaches are logged (default: true).
  * @param isCometary Flag to indicate if the body is initialized with a cometary state (as opposed to Cartesian state).
  * @param initState Initial state of the body.
  * @param isInteg Flag to indicate if the body is integrated.
@@ -188,6 +189,7 @@ class IntegBody : public Body {
    private:
    public:
     int spiceId = -99999;
+    bool logCA = true;
     bool isCometary = false;
     std::vector<real> initState;
     std::vector<real> initCart;
@@ -272,8 +274,10 @@ struct BPlaneParameters {
 /**
  * @brief Class for close approach parameters in a PropSimulation.
  * 
- * @param t Time of the close approach.
- * @param xRel Relative state at the close approach.
+ * @param t Time of the close approach or impact (from child class).
+ * @param xRel Relative state at the close approach or impact (from child class).
+ * @param tCA Time of the close approach.
+ * @param xRelCA Relative state at the close approach.
  * @param tMap Time of the B-plane map.
  * @param xRelMap Relative state at the B-plane map time.
  * @param dist Distance at the close approach.
@@ -302,6 +306,8 @@ class CloseApproachParameters {
    public:
     real t;
     std::vector<real> xRel;
+    real tCA;
+    std::vector<real> xRelCA;
     real tMap;
     std::vector<real> xRelMap;
     real dist;
@@ -513,7 +519,7 @@ class PropSimulation {
         std::vector<std::vector<real>> observerInfo =
             std::vector<std::vector<real>>(),
         bool adaptiveTimestep = true, real dt0 = 0.0L, real dtMax = 21.0L,
-        real dtMin = 5.0e-3L, real dtChangeFactor = 0.25L,
+        real dtMin = 1.0e-4L, real dtChangeFactor = 0.25L,
         real tolInteg = 1.0e-11L, real tolPC = 1.0e-16L);
     /**
      * @brief Get the constants used in the simulation.