Model/Optimize bugfix

Pop objective function argument dictionary entries when setting up Optimize
Copy objective function argument dictionary to Optimize to prevent errors when fitting in multiple stages.

pycroscopy/analysis/fit_methods.py

@@ -44,7 +44,6 @@ class Fit_Methods(object):
 
         if guess.size < 4:
             raise ValueError('Error: The Single Harmonic Oscillator requires 4 parameter guesses!')
-        # data_vec, guess = args
         data_mean = np.mean(data_vec)
 
         Amp, w_0, Q, phi = guess[:4]

pycroscopy/analysis/model.py

@@ -358,7 +358,7 @@ class Model(object):
             while self.data is not None:
                 opt = Optimize(data=self.data, guess=self.guess, parallel=self._parallel)
                 temp = opt.computeFit(processors=processors, solver_type=solver_type, solver_options=solver_options,
-                                      obj_func=obj_func)
+                                      obj_func=obj_func.copy())
                 # TODO: need a different .reformatResults to process fitting results
                 results.append(self._reformat_results(temp, obj_func_name))
                 self._get_guess_chunk()

pycroscopy/analysis/optimize.py

@@ -117,9 +117,9 @@ class Optimize(object):
         if obj_func['class'] is None:
             self.obj_func = obj_func['obj_func']
         else:
-            self.obj_func_name = obj_func.get('obj_func')
+            self.obj_func_name = obj_func.pop('obj_func')
             self.obj_func = fm.__getattribute__(self.obj_func_name)
-            self.obj_func_class = obj_func.get('class')
+            self.obj_func_class = obj_func.pop('class')
             self.obj_func_args = obj_func.values()
 
     def computeFit(self, processors=1, solver_type='least_squares', solver_options={},