Add global minimizing strategy

src/zfitwrapper/BaseFitter.py

 from abc import ABC, abstractmethod
 import numpy as np
-from typing import Any, Mapping, Type, Union, List, TypeVar
+from typing import Any, Dict, Mapping, Optional, Type, Union, List, TypeVar
 import zfit
 import zfit.core
 
@@ -17,9 +17,13 @@ class BaseFitter(ABC):
         self,
         model: Model,
         retry: int = 10,
+        global_optimizer: str = "",
+        global_optimizer_args: Dict[Any, Any] = {},
     ) -> None:
         self.model = model
         self.retry = retry
+        self.global_optimizer = global_optimizer
+        self.global_optimizer_args = global_optimizer_args
 
         self.result_text = []  # type: List[str]
 
@@ -126,8 +130,20 @@ class BaseFitter(ABC):
         nll = self.lossfunction(model=self.eval_model, data=self.data)
 
         # minimize
-        minimizer = zfit.minimize.Minuit(gradient=True)
-        result = minimizer.minimize(nll).update_params()
+        if self.global_optimizer:
+            minimizer_global = getattr(zfit.minimize, self.global_optimizer)(
+                **self.global_optimizer_args
+            )
+            minimizer_global = zfit.minimize.NLoptStoGOV1()
+            result_global = minimizer_global.minimize(nll).update_params()
+            result_global.errors()
+            result_global.hesse()
+            print("Done with global minimization")
+            print(result_global.params)
+            nll = result_global.loss
+
+        minimizer_local = zfit.minimize.Minuit(gradient=True)
+        result = minimizer_local.minimize(nll).update_params()
 
         # calculate errors
         result.errors()
@@ -138,6 +154,8 @@ class BaseFitter(ABC):
         covariance = result.covariance(as_dict=True)
         assert isinstance(covariance, dict)
         self.model.covariance = covariance
+        self.model.gof = self.gof
+        print("Done with local minimization")
         print(params)
 
         self._save_result(params=params)