Add _custom_model class attribute, exception in fit and p0 and bounds…

… as parameters

skgstat/Variogram.py

@@ -2,6 +2,7 @@
 Variogram class
 """
 import copy
+import inspect
 import warnings
 from typing import Iterable, Callable, Union, Tuple
 
@@ -335,6 +336,7 @@ def __init__(self,
 
         # model can be a function or a string
         self._model = None
+        self._custom_model = False
         self.set_model(model_name=model)
 
         # specify if the lag should be given absolute or relative to the maxlag
@@ -973,6 +975,7 @@ def set_model(self, model_name):
                     ) % model_name
                 )
         else:  # pragma: no cover
+            self._custom_model = True
             self._model = model_name
 
     def _build_harmonized_model(self):
@@ -1454,7 +1457,7 @@ def preprocessing(self, force=False):
         self._calc_diff(force=force)
         self._calc_groups(force=force)
 
-    def fit(self, force=False, method=None, sigma=None, **kwargs):
+    def fit(self, force=False, method=None, sigma=None, bounds=None, p0=None, **kwargs):
         """Fit the variogram
 
         The fit function will fit the theoretical variogram function to the
@@ -1563,18 +1566,34 @@ def fit(self, force=False, method=None, sigma=None, **kwargs):
             self.cof = [r, s, n]
             return
 
-        # Switch the method
-        # wrap the model to include or exclude the nugget
-        if self.use_nugget:
-            def wrapped(*args):
-                return self._model(*args)
+        # For a supported model, wrap the function depending on nugget and get logical bounds
+        if not self._custom_model:
+            # Switch the method
+            # wrap the model to include or exclude the nugget
+            if self.use_nugget:
+                def wrapped(*args):
+                    return self._model(*args)
+            else:
+                def wrapped(*args):
+                    return self._model(*args, 0)
+
+            # get p0
+            if bounds is None:
+                bounds = (0, self.__get_fit_bounds(x, y))
+            if p0 is None:
+                p0 = np.asarray(bounds[1])
+        # Else, inspect the function for the number of arguments
         else:
-            def wrapped(*args):
-                return self._model(*args, 0)
+            # The number of arguments of argspec minus one is what we initialized
+            argspec = inspect.getfullargspec(self._model)
+            nb_args = len(argspec) - 1
+            if bounds is None:
+                bounds = (0, [np.maximum(np.nanmax(x), np.nanmax(y))] * nb_args)
+            if p0 is None:
+                p0 = np.asarray(bounds[1])
 
-        # get p0
-        bounds = (0, self.__get_fit_bounds(x, y))
-        p0 = np.asarray(bounds[1])
+            def wrapped(*args):
+                return self._model(*args)
 
         # Trust Region Reflective
         if self.fit_method == 'trf':