refactor the selection of models using minimum_match

src/dysh/spectra/core.py

@@ -254,34 +254,22 @@ def baseline(spectrum, order, exclude=None, **kwargs):
     """
     kwargs_opts = {
         #'show': False,
-        "model": "polynomial",
+        "model": "chebyshev",
         "fitter": LinearLSQFitter(calc_uncertainties=True),
         "fix_exclude": False,
-        "exclude_action": "replace",  # {'replace','append',None}
+        "exclude_action": "replace",  # {'replace','append', None}
     }
     kwargs_opts.update(kwargs)
 
-    # @todo allow minimum match here; need a tool for this, e.g.
-    # model = minmatch(kwargs_opts["model"], _valid_models)
-    # if model = None:
-    #      raise ValueError()
-    # elif model == "polynomial"
-    #  ...
-    _valid_models = ["polynomial", "chebyshev", "legendre", "hermite"]
-    model = minimum_string_match(kwargs_opts["model"], _valid_models)
+    available_models = { "chebyshev"  : Chebyshev1D,
+                         "hermite"    : Hermite1D,
+                         "legendre"   : Legendre1D,
+                         "polynomial" : Polynomial1D,
+                        }
+    model = minimum_string_match(kwargs_opts["model"], list(available_models.keys()))
     if model == None:
-        raise ValueError(f'Unrecognized input model {kwargs["model"]}. Must be one of {_valid_models}')
-    elif model == "polynomial":
-        model = Polynomial1D(degree=order)
-    elif model == "chebyshev":
-        model = Chebyshev1D(degree=order)
-    elif model == "legendre":
-        model = Legendre1D(degree=order)
-    elif model == "hermite":
-        model = Hermite1D(degree=order)
-    else:
-        # should never get here, unless we someday allow user to input an astropy.model
-        raise ValueError(f'Unrecognized input model {kwargs["model"]}. Must be one of {_valid_models}')
+        raise ValueError(f'Unrecognized input model {kwargs["model"]}. Must be one of {list(available_models.keys())}')
+    selected_model = available_models[model](degree=order)
 
     _valid_exclude_actions = ["replace", "append", None]
     if kwargs_opts["exclude_action"] not in _valid_exclude_actions:
@@ -306,7 +294,7 @@ def baseline(spectrum, order, exclude=None, **kwargs):
         # exist (they will be a list of SpectralRegions or None)
         regionlist = p._exclude_regions
     print(f"EXCLUDING {regionlist}")
-    return fit_continuum(spectrum=p, model=model, fitter=fitter, exclude_regions=regionlist)
+    return fit_continuum(spectrum=p, model=selected_model, fitter=fitter, exclude_regions=regionlist)
 
 
 def mean_tsys(calon, caloff, tcal, mode=0, fedge=0.1, nedge=None):