Merge pull request #67 from Stanford-NavLab/derek/navdata-updates

Derek/navdata updates

Author: kanhereashwin

data/unit_test/navdata/navdata_only_header.csv

@@ -0,0 +1 @@
+names,integers,floats,strings

data/unit_test/navdata/navdata_test_nan.csv

@@ -1,7 +1,7 @@
 names,integers,floats,strings
-ab,10,0.5,gps
+ab,10,0.5,NaN
 cde,2,nan,glonass
 alpha,45,0.3,galileo
 epsilon,67,0.99,gps
-gamma,nan,0.45,gps
+nan,nan,0.45,gps
 xyzuvw,300,0.74,galileo

gnss_lib_py/algorithms/snapshot.py

@@ -51,14 +51,9 @@ def solve_wls(measurements, weight_type = None,
 
     """
 
-    if "x_sv_m" not in measurements.rows:
-        raise KeyError("x_sv_m (ECEF x position of sv) missing.")
-    if "y_sv_m" not in measurements.rows:
-        raise KeyError("y_sv_m (ECEF y position of sv) missing.")
-    if "z_sv_m" not in measurements.rows:
-        raise KeyError("z_sv_m (ECEF z position of sv) missing.")
-    if "b_sv_m" not in measurements.rows:
-        raise KeyError("b_sv_m (clock bias of sv) missing.")
+    # check that all necessary rows exist
+    measurements.in_rows(["x_sv_m","y_sv_m","z_sv_m","b_sv_m",
+                          "gps_millis"])
 
     unique_timesteps = np.unique(measurements["gps_millis",:])
 

gnss_lib_py/parsers/android.py

@@ -47,14 +47,18 @@ def __init__(self, input_path, remove_timing_outliers=True):
         derived_timestamps = pd_df['millisSinceGpsEpoch'].unique()
         indexes = np.searchsorted(derived_timestamps, derived_timestamps)
         map_derived_time_back = dict(zip(derived_timestamps, derived_timestamps[indexes-1]))
-        pd_df['millisSinceGpsEpoch'] = np.array(list(map(lambda v: map_derived_time_back[v], pd_df['millisSinceGpsEpoch'])))
-
+        pd_df['millisSinceGpsEpoch'] = np.array(list(map(lambda v: map_derived_time_back[v],
+                                                pd_df['millisSinceGpsEpoch'])))
 
         # Correction 5 implemented verbatim from competition tips
         if remove_timing_outliers:
             delta_millis = pd_df['millisSinceGpsEpoch'] - pd_df['receivedSvTimeInGpsNanos'] / 1e6
             where_good_signals = (delta_millis > 0) & (delta_millis < 300)
             pd_df = pd_df[where_good_signals].copy()
+            if np.all(~where_good_signals):
+                warnings.warn("All data removed due to timing outliers,"
+                            + " try setting remove_timing_outliers to"
+                            + " False", RuntimeWarning)
 
         super().__init__(pandas_df=pd_df)