Fix pintk bug: random models with jumps fails

src/pint/models/jump.py

@@ -1,4 +1,4 @@
-"""Phase jumps. """
+"""Phase jumps."""
 
 import astropy.units as u
 import numpy
@@ -192,6 +192,8 @@ def jump_params_to_flags(self, toas):
                     d["jump"] = str(jump_par.index)
                 log.info(f"Set up on {jump_par=} {d=}")
             log.info(f"TOAs {toas['jump']=}")
+        if "f" in toas.table.colnames:
+            toas.table.remove_column("f")
 
     def add_jump_and_flags(self, toa_table):
         """Add jump object to PhaseJump and appropriate flags to TOA tables.

src/pint/pintk/pulsar.py

@@ -26,7 +26,6 @@
 import pint.logging
 from loguru import logger as log
 
-
 plot_labels = [
     "pre-fit",
     "post-fit",
@@ -104,6 +103,7 @@ def __init__(self, parfile=None, timfile=None, ephem=None, fitter="GLSFitter"):
         # turns pre-existing jump flags in toas.table['flags'] into parameters in parfile
         self.prefit_model.jump_flags_to_params(self.all_toas)
         self.selected_toas = copy.deepcopy(self.all_toas)
+
         print("The prefit model as a parfile:")
         print(self.prefit_model.as_parfile())
         # adds extra prefix params for fitting
@@ -626,6 +626,7 @@ def random_models(self, selected):
 
         # These are the currently selected TOAs in the fit
         sim_sel = copy.deepcopy(self.selected_toas)
+
         # These are single TOAs from each cluster of TOAs
         inds = np.zeros(sim_sel.ntoas, dtype=bool)
         inds[np.unique(sim_sel.get_clusters(), return_index=True)[1]] |= True
@@ -662,6 +663,10 @@ def random_models(self, selected):
         self.faketoas1.compute_pulse_numbers(self.postfit_model)
         self.faketoas1.get_clusters(add_column=True)
 
+        if "f" in self.faketoas1.table.colnames:
+            self.faketoas1.table.remove_column("f")
+        if "f" in sim_sel.table.colnames:
+            sim_sel.table.remove_column("f")
         # Combine our TOAs
         toas = merge_TOAs([sim_sel, self.faketoas1])
         zero_residuals(toas, self.postfit_model)