Adjust for new extended model and lintinig

src/zfitwrapper/FitParameter.py

@@ -3,7 +3,13 @@ import numpy as np
 
 import zfit
 
-__all__ = ["Fitparameter", "dictToFitparameter", "FitCovarianceMatrix", "CovarianceInputType", "CovarianceType"]
+__all__ = [
+    "Fitparameter",
+    "dictToFitparameter",
+    "FitCovarianceMatrix",
+    "CovarianceInputType",
+    "CovarianceType",
+]
 
 
 CovarianceInputType = Dict[Tuple[zfit.Parameter, zfit.Parameter], Optional[np.float64]]
@@ -51,7 +57,12 @@ class Fitparameter:
 
 
 class FitCovarianceElement:
-    def __init__(self, parameter1: zfit.Parameter, parameter2: zfit.Parameter, value: Optional[np.float64]) -> None:
+    def __init__(
+        self,
+        parameter1: zfit.Parameter,
+        parameter2: zfit.Parameter,
+        value: Optional[np.float64],
+    ) -> None:
         self.parameter1 = parameter1.name
         self.parameter2 = parameter2.name
         self.value = value if value is None else float(value)
@@ -69,7 +80,9 @@ class FitCovarianceMatrix:
 
     def _read_covariance(self) -> None:
         for (parameter1, parameter2), value in self._zfitcovariance.items():
-            covarianceElement = FitCovarianceElement(parameter1=parameter1, parameter2=parameter2, value=value)
+            covarianceElement = FitCovarianceElement(
+                parameter1=parameter1, parameter2=parameter2, value=value
+            )
             self._covarianceElementList.append(covarianceElement)
 
     @staticmethod
@@ -102,6 +115,10 @@ class FitCovarianceMatrix:
 def dictToFitparameter(**kwargs: Any) -> Dict[str, Fitparameter]:
     fitparameterdict = {}
     for key, value in kwargs.items():
-        fitparameterdict[key] = Fitparameter(**value)
+        if not isinstance(value, list):
+            fitparameterdict[key] = Fitparameter(**value)
+        else:
+            for val in value:
+                fitparameterdict[val["name"]] = Fitparameter(**val)
 
     return fitparameterdict