pr response

Author: andped10

src/easyscience/fitting/minimizers/minimizer_bumps.py

@@ -112,8 +112,6 @@ def fit(
         minimizer_kwargs.update(engine_kwargs)
 
         if tolerance is not None:
-            if 0.1 < tolerance:
-                raise ValueError('Tolerance must be  equal or smaller than 0.1')
             minimizer_kwargs['ftol'] = tolerance  # tolerance for change in function value
             minimizer_kwargs['xtol'] = tolerance  # tolerance for change in parameter value, could be an independent value
         if max_evaluations is not None:

src/easyscience/fitting/minimizers/minimizer_dfo.py

@@ -281,7 +281,7 @@ def _prepare_kwargs(tolerance: Optional[float] = None, max_evaluations: Optional
         if max_evaluations is not None:
             kwargs['maxfun'] = max_evaluations  # max number of function evaluations
         if tolerance is not None:
-            if 0.1 < tolerance:
+            if 0.1 < tolerance:  # dfo module throws errer if larger value
                 raise ValueError('Tolerance must be equal or smaller than 0.1')
             kwargs['rhoend'] = tolerance  # size of the trust region
         return kwargs

tests/integration_tests/Fitting/test_fitter.py

@@ -170,7 +170,7 @@ def test_basic_max_evaluations(fit_engine):
         assert "Objective has been called MAXFUN times" in str(e)
 
 
-@pytest.mark.parametrize("fit_engine,tolerance", [(None, 10), (AvailableMinimizers.LMFit, 10), (AvailableMinimizers.Bumps, 0.1), (AvailableMinimizers.DFO, 0.1)])
+@pytest.mark.parametrize("fit_engine,tolerance", [(None, 10), (AvailableMinimizers.LMFit, 10), (AvailableMinimizers.Bumps, 10), (AvailableMinimizers.DFO, 0.1)])
 def test_basic_tolerance(fit_engine, tolerance):
     ref_sin = AbsSin(0.2, np.pi)
     sp_sin = AbsSin(0.354, 3.05)