Fixed stellarphot so it can run under current astropy (and numpy, when including previous PR)

CHANGES.rst

@@ -1,3 +1,18 @@
+1.1.5 (2023-05-31)
+------------------
+
+New Features
+^^^^^^^^^^^^
+
+Other Changes and Additions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
++ Most functions are now linked to the documentation (some still need docstrings). [#92]
+
+Bug Fixes
+^^^^^^^^^
++ Runs without errors on current numpy (1.24.3) and astropy (5.3).
+
+
 1.1.2 (2022-10-18)
 ------------------
 
@@ -10,9 +25,9 @@ Other Changes and Additions
 
 Bug Fixes
 ^^^^^^^^^
-
 + Simplify comparison notebook.
 
+
 1.1.1 (2022-10-18)
 ------------------
 

setup.cfg

@@ -13,7 +13,7 @@ github_project = stellarphot/stellarphot
 
 [options]
 install_requires =
-    astropy >=5,<5.1
+    astropy >=5
     astroquery
     ccdproc
     ginga

stellarphot/analysis/transit_fitting.py

@@ -7,7 +7,7 @@
                                       _validate_model,
                                       _convert_input)
 
-# Functions blow changed from private to public in astropy 5
+# Functions below changed from private to public in astropy 5
 try:
     from astropy.modeling.fitting import (
         fitter_to_model_params,
@@ -52,7 +52,17 @@ def __call__(self, model, *args, weights=None,
             dfunc = None
         else:
             dfunc = self._wrap_deriv
-        init_values = model_to_fit_params(model_copy)
+        init_values0 = model_to_fit_params(model_copy)
+        # This returns a tuple of (model_params, fitparam_indices, model_bounds),
+        # where model_params is a numpy array, fitparam_indices is a list, and
+        # model_bounds is a tuple of tuples.  The problem is that this doesn't
+        # convert simply into an array within scipy.optimize.leastsq, when called.
+        # So we handle model_bounds here first to the scipy.optimize.leastsq format.
+        # can handle the list of initial values we pass in.
+        init_values = np.concatenate((np.asarray(init_values0[0]).flatten(),
+                                      np.asarray(init_values0[1]).flatten(),
+                                      np.asarray(init_values0[2]).flatten()), axis=None)
+
         fitparams, cov_x, dinfo, mess, ierr = optimize.leastsq(
             self.objective_function, init_values, args=farg, Dfun=dfunc,
             col_deriv=model_copy.col_fit_deriv, maxfev=maxiter, epsfcn=epsilon,