Enhance solid_tide() function

AlexeyPechnikov · Aug 7, 2023 · c6f4fcd · c6f4fcd
1 parent d4fe840
commit c6f4fcd
Showing 1 changed file with 54 additions and 9 deletions.
diff --git a/pygmtsar/pygmtsar/SBAS_ps.py b/pygmtsar/pygmtsar/SBAS_ps.py
@@ -211,21 +211,63 @@ def slc_stack(self, dates=None, intensity=False, chunksize=None):
 
     def solid_tide(self, dates, coords, debug=False):
         """
-        Compute the tidal correction using GMTSAR binary tool solid_tide.
+        Compute the tidal correction using the GMTSAR binary tool solid_tide.
 
-        solid_tide yyyyddd.fffffff < lon_lat > lon_lat_dx_dy_dz
+        Parameters
+        ----------
+        dates : array_like
+            Dates for which to compute the tidal correction. Should be in the format yyyyddd.fffffff.
+        coords : array_like
+            Coordinates for which to compute the tidal correction. Can be a single pair of coordinates [lon, lat],
+            or a list of pairs [[lon1, lat1], [lon2, lat2], ...].
+        debug : bool, optional
+            If True, print debug information. Default is False.
 
-        coords = sbas.solid_tide(sbas.df.index, coords=[13.40076, 47.40143])
-        coords = sbas.solid_tide(sbas.df.index, coords=[[13.40076, 47.40143]])
-        coords = sbas.solid_tide(sbas.df.index, coords=[[13.40076, 47.40143], [13.40076, 47.40143]])
+        Returns
+        -------
+        pandas.DataFrame
+            DataFrame with columns 'lon', 'lat', 'dx', 'dy', 'dz', indexed by 'date'.
 
+        Examples
+        --------
+        Compute the tidal correction for a single pair of coordinates:
+
+            coords = sbas.solid_tide(sbas.df.index, coords=[13.40076, 47.40143])
+
+        Compute the tidal correction for multiple pairs of coordinates:
+
+            coords = sbas.solid_tide(sbas.df.index, coords=[[13.40076, 47.40143], [13.40076, 47.40143]])
+
+        Output:
+
+            >>> sbas.solid_tide(sbas.df.index[:3], coords=[lon, lat])        
+            lon	lat	dx	dy	dz
+            date					
+            2022-06-16	13.400758	47.401431	-0.066918	-0.004765	0.016200
+            2022-06-28	13.400758	47.401431	-0.033571	0.012279	-0.099899
+            2022-07-10	13.400758	47.401431	-0.000806	-0.007983	-0.150675
+
+            >>> sbas.solid_tide(sbas.df.index[:3], coords=[[lon, lat], [lon+1, lat+1]])
+            lon	lat	dx	dy	dz
+            date					
+            2022-06-16	13.400758	47.401431	-0.066918	-0.004765	0.016200
+            2022-06-16	14.400758	48.401431	-0.066594	-0.004782	0.010346
+            2022-06-28	13.400758	47.401431	-0.033571	0.012279	-0.099899
+            2022-06-28	14.400758	48.401431	-0.033011	0.012323	-0.100986
+            2022-07-10	13.400758	47.401431	-0.000806	-0.007983	-0.150675
+            2022-07-10	14.400758	48.401431	-0.001107	-0.007758	-0.151605
+
+        Notes
+        -----
+        This function computes the tidal correction in geographic coordinates based on the dates and coordinates provided.
+        The correction is computed by calling the GMTSAR binary tool 'solid_tide' with the date and coordinates as input.
         """
         import numpy as np
         import pandas as pd
         from io import StringIO, BytesIO
-        import os
+        #import os
         import subprocess
-    
+
         coords = np.asarray(coords)
         if len(coords.shape) == 1:
             coords = [coords]
@@ -236,7 +278,7 @@ def solid_tide(self, dates, coords, debug=False):
 
         outs = []
         for date in dates:
-            SC_clock_start, SC_clock_stop = sbas.PRM(None, date).get('SC_clock_start', 'SC_clock_stop')
+            SC_clock_start, SC_clock_stop = self.PRM(None, date).get('SC_clock_start', 'SC_clock_stop')
             dt = (SC_clock_start + SC_clock_stop)/2
             argv = ['solid_tide', str(dt)]
             #cwd = os.path.dirname(self.filename) if self.filename is not None else '.'
@@ -252,4 +294,7 @@ def solid_tide(self, dates, coords, debug=False):
 
             out = np.fromstring(stdout_data, dtype=float, sep=' ')
             outs.append(out)
-        return np.asarray(outs) if len(outs)>1 else outs[0]
+
+        return pd.DataFrame(np.asarray(outs).reshape(-1,5),
+                            columns=['lon', 'lat', 'dx', 'dy', 'dz'],
+                            index=np.repeat(dates, len(coords)))