NPI-3421 fix format handling of STD nodata values (formatter was not …

…getting invoked because the specified column names to use it for, were missing the underscore).

gnssanalysis/gn_io/sp3.py

@@ -73,14 +73,14 @@ def sp3_clock_nodata_to_nan(
     sp3_df: _pd.DataFrame
 ) -> None:
     """
-    Converts the SP3 Clock column's nodata values (999999 or 999999.999999 - the fractional component optional) to NaNs.
+    Converts the SP3 Clock column's nodata values (999999 or 999999.999999 - fractional component optional) to NaNs.
     See https://files.igs.org/pub/data/format/sp3_docu.txt
 
     :param _pd.DataFrame sp3_df: SP3 data frame to filter nodata values for
     :return None
     """
     nan_mask = sp3_df[("EST", "CLK")] >= SP3_CLOCK_NODATA_NUMERIC
-    sp3_df.loc[nan_mask, ("EST", "CLK")] = _np.NAN
+    sp3_df.loc[nan_mask, ("EST", "CLK")] = _np.nan
 
 
 def mapparm(old, new):
@@ -395,6 +395,8 @@ def clk_formatter(x):
     # NOTE: the following formatters are fine, as the nodata value is actually a *numeric value*,
     # so DataFrame.to_string() will invoke them for those values.
 
+    # TODO A future improvement would be to use NaN rather than specific integer values, as this is an internal
+    # only representation.
     def pos_std_formatter(x):
         # We use -100 as our integer NaN/"missing" marker
         if x <= SP3_POS_STD_NODATA:
@@ -413,10 +415,10 @@ def clk_std_formatter(x):
         "Y": pos_formatter,
         "Z": pos_formatter,
         "CLK": clk_formatter,  # Can't handle CLK nodata (Inf or NaN). Handled prior to invoking DataFrame.to_string()
-        "STDX": pos_std_formatter,  # Nodata is represented as an integer, so can be handled here.
-        "STDY": pos_std_formatter,
-        "STDZ": pos_std_formatter,
-        "STDCLK": clk_std_formatter,  # ditto above
+        "STD_X": pos_std_formatter,  # Nodata is represented as an integer, so can be handled here.
+        "STD_Y": pos_std_formatter,
+        "STD_Z": pos_std_formatter,
+        "STD_CLK": clk_std_formatter,  # ditto above
     }
     for epoch, epoch_vals in out_df.reset_index("PRN").groupby(axis=0, level="J2000"):
         # Format and write out the epoch in the SP3 format