feat: add along-track GLA12 test product

fix: add boundary case in constant time fit

Author: tsutterley

GZ/calculate_GZ_ICESat_GLA12.py

@@ -48,7 +48,7 @@
     utilities.py: download and management utilities for syncing files
 
 UPDATE HISTORY:
-    Updated 06/2024: renamed GLAH12 quality summary variable to d_qa_sum
+    Updated 06/2024: renamed GLAH12 quality summary variable to qa_sum_flg
         save output HDF5 files as trajectory type for visualization
     Written 05/2024
 """
@@ -144,7 +144,7 @@ def campaign_bias_correction(campaign: str):
     campaign: str
         ICESat campaign
     """
-    # 
+    # corrections for each laser number
     correction = dict(laser1=0.0, laser2=-0.017, laser3=0.011)
     # find the laser number from the campaign
     laser, = re.findall(r'\d', campaign)
@@ -279,7 +279,7 @@ def calculate_GZ_ICESat(base_dir, INPUT_FILE,
     d_ocElv = fid['Data_40HZ']['Geophysical']['d_ocElv'][:]
     d_ocElv[d_ocElv == fv] = 0.0
     # mask for reducing to valid values
-    quality_summary = fid1['Data_40HZ']['Quality']['d_qa_sum'][:]
+    quality_summary = fid1['Data_40HZ']['Quality']['qa_sum_flg'][:]
     # get the transform for converting to the latest ITRF
     transform = gz.crs.tp_itrf2008_to_wgs84_itrf2020()
     # transform the data to WGS84 ellipsoid in ITRF2020

grounding_zones/fit.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 u"""
 fit.py
-Written by Tyler Sutterley (05/2024)
+Written by Tyler Sutterley (06/2025)
 
 Utilities for creating models from surface elevation data
 
@@ -10,6 +10,7 @@
     scipy: Scientific Tools for Python (https://docs.scipy.org/doc/)
 
 UPDATE HISTORY:
+    Updated 06/2025: added boundary case for constant temporal fit
     Updated 05/2024: add function to build the complete design matrix
         add function to build the constraints for the least-squares fit
         add function to validate the columns in the design matrix
@@ -959,7 +960,11 @@ def _build_constraints(t_in, x_in, y_in, d_in, **kwargs):
     ub[0] = dmax + dsigma
     # time-variable constraints
     FIT_TYPE = kwargs['FIT_TYPE'].lower()
-    if (FIT_TYPE == 'polynomial') and (n_time > 1):
+    if (n_time == 1):
+        # no constraints for constant time-variable fit
+        pass
+    elif (FIT_TYPE == 'polynomial') and (n_time > 1):
+        # bounds for polynomial fit
         lb[1] = (dmin - dmax - 2.0*dsigma)/(tmax - tmin)
         ub[1] = (dmax - dmin + 2.0*dsigma)/(tmax - tmin)
     elif (FIT_TYPE == 'chebyshev'):