Adjust to new error methods and some linting

src/zfitwrapper/BaseFitter.py

@@ -61,14 +61,18 @@ class BaseFitter(ABC):
 
         assert self.model.model is not None
         counts_model = self.model.model.pdf(centers)
-        counts_model_normed = counts_model * np.sum(counts) * (float(high) - float(low)) / len(counts)
+        counts_model_normed = (
+            counts_model * np.sum(counts) * (float(high) - float(low)) / len(counts)
+        )
 
         nPars = 0
         for parameter in self.model.parameters.values():
             if parameter.floating:
                 nPars += 1
 
-        gof = np.sum((counts - counts_model_normed) ** 2 / counts_model_normed) / (len(counts) - nPars)
+        gof = np.sum((counts - counts_model_normed) ** 2 / counts_model_normed) / (
+            len(counts) - nPars
+        )
 
         return gof
 
@@ -81,10 +85,12 @@ class BaseFitter(ABC):
             name = fitparameter.latex_name
             unit = fitparameter.unit
             value = float(params[param.name]["value"])
-            lower = float(params[param.name]["minuit_minos"]["lower"])
-            upper = float(params[param.name]["minuit_minos"]["upper"])
+            lower = float(params[param.name]["errors"]["lower"])
+            upper = float(params[param.name]["errors"]["upper"])
 
-            self.model.update_fitparameter(str(param.name), value, value + lower, value + upper)
+            self.model.update_fitparameter(
+                str(param.name), value, value + lower, value + upper
+            )
 
             self.result_text.append(
                 "{name} = ${value:.3{c1}}^{{+{upper:.3{c2}}}}_{{{lower:.3{c3}}}}$ {unit}".format(
@@ -107,7 +113,12 @@ class BaseFitter(ABC):
                         name=fitparameter.latex_name,
                         value=fitparameter.value,
                         unit=fitparameter.unit,
-                        c1="e" if (abs(fitparameter.value) > 1e3 or abs(fitparameter.value) < 1e-3) else "f",
+                        c1="e"
+                        if (
+                            abs(fitparameter.value) > 1e3
+                            or abs(fitparameter.value) < 1e-3
+                        )
+                        else "f",
                     )
                 )
 
@@ -128,18 +139,20 @@ class BaseFitter(ABC):
             modelparameter = model.get_modelparameter(param.name)
 
             if modelparameter.floating and param.shuffle:
-                modelparameter.randomize(minval=param.lower, maxval=param.upper, sampler=np.random.uniform)
+                modelparameter.randomize(
+                    minval=param.lower, maxval=param.upper, sampler=np.random.uniform
+                )
 
     def _single_fit(self) -> None:
         assert isinstance(self.eval_model, zfit.core.interfaces.ZfitPDF)
         nll = self.lossfunction(model=self.eval_model, data=self.data)
 
         # minimize
-        minimizer = zfit.minimize.Minuit(use_minuit_grad=True)
-        result = minimizer.minimize(nll)
+        minimizer = zfit.minimize.Minuit(gradient=True)
+        result = minimizer.minimize(nll).update_params()
 
         # calculate errors
-        result.errors(method="minuit_minos")
+        result.errors()
         result.hesse()
         self.zfitresult = result
         # Information on all the parameters in the fit