fixed star choice when there are too many clastars in ApplyRecalibrat…

…e.py

RMS/Astrometry/ApplyRecalibrate.py

@@ -132,17 +132,33 @@ def recalibrateFF(
     # If there more stars than a set limit, sample them randomly using the same seed for reproducibility
     if not ignore_max_stars and len(star_dict_ff[jd]) > config.recalibration_max_stars:
 
+        # Make a copy so that the original star dictionary is not modified
+        star_dict_ff = copy.deepcopy(star_dict_ff)
+
+        # Python 3+
         if hasattr(np.random, 'default_rng'):
+
             # Use the newer Generator-based RNG
             rng = np.random.default_rng(seed=0)
-
+
+            # Sample the stars and store them in a copy of the star dictionary
+            star_dict_ff = {jd: rng.choice(star_dict_ff[jd], config.recalibration_max_stars, replace=False)}
+
+        # Python 2
         else:
+
             # Use the older RandomState-based RNG
             rng = np.random.RandomState(seed=0)
 
-        # Sample the stars and store them in a copy of the star dictionary
-        star_dict_ff = copy.deepcopy(star_dict_ff)
-        star_dict_ff = {jd: rng.choice(star_dict_ff[jd], config.recalibration_max_stars, replace=False)}
+            # RandomState.choice requires indices for complex data types
+            indices = rng.choice(
+                len(star_dict_ff[jd]),
+                config.recalibration_max_stars,
+                replace=False
+            )
+
+            # Use the indices to select the stars
+            star_dict_ff[jd] = [star_dict_ff[jd][i] for i in indices]
 
 
     # A list of matching radiuses to try