diff --git a/Analyser/FitAnalyser.py b/Analyser/FitAnalyser.py
index 17c8991..94ca740 100644
--- a/Analyser/FitAnalyser.py
+++ b/Analyser/FitAnalyser.py
@@ -300,7 +300,7 @@ class FitAnalyser():
     def _guess_2D(self, data, x, y, **kwargs):
         return self.fitModel.guess(data=data, x=x, y=y, **kwargs)
     
-    def guess(self, dataArray, x=None, y=None, guess_kwargs={}, input_core_dims=None, dask='parallelized', vectorize=True, keep_attrs=True, **kwargs):
+    def guess(self, dataArray, x=None, y=None, guess_kwargs={}, input_core_dims=None, dask='parallelized', vectorize=True, keep_attrs=True, daskKwargs=None, **kwargs):
         
         kwargs.update(
             {
@@ -308,9 +308,13 @@ class FitAnalyser():
                 "vectorize": vectorize,
                 "input_core_dims": input_core_dims,
                 'keep_attrs': keep_attrs,
+
             }
         )
 
+        if not daskKwargs is None:
+            kwargs.update({"dask_gufunc_kwargs": daskKwargs})
+
         if input_core_dims is None:
             kwargs.update(
                 {
@@ -393,7 +397,7 @@ class FitAnalyser():
     def _fit_2D(self, data, params, x, y):
         return self.fitModel.fit(data=data, x=x, y=y, params=params)
 
-    def fit(self, dataArray, paramsArray, x=None, y=None, input_core_dims=None, dask='parallelized', vectorize=True, keep_attrs=True, **kwargs):
+    def fit(self, dataArray, paramsArray, x=None, y=None, input_core_dims=None, dask='parallelized', vectorize=True, keep_attrs=True, daskKwargs=None, **kwargs):
         
         kwargs.update(
             {
@@ -403,6 +407,9 @@ class FitAnalyser():
                 'keep_attrs': keep_attrs,
             }
         )
+
+        if not daskKwargs is None:
+            kwargs.update({"dask_gufunc_kwargs": daskKwargs})
         
         if isinstance(paramsArray, type(self.fitModel.make_params())):
 
@@ -528,7 +535,7 @@ class FitAnalyser():
         res = self.fitModel.eval(x=x, y=y, **fitResult.best_values)
         return res.reshape(shape)
 
-    def eval(self, fitResultArray, x=None, y=None, output_core_dims=None, prefix="", dask='parallelized', vectorize=True, **kwargs):
+    def eval(self, fitResultArray, x=None, y=None, output_core_dims=None, prefix="", dask='parallelized', vectorize=True, daskKwargs=None, **kwargs):
         
         kwargs.update(
             {
@@ -538,6 +545,9 @@ class FitAnalyser():
             }
         )
 
+        if daskKwargs is None:
+            daskKwargs = {}
+
         if self.fitDim == 1:
 
             if output_core_dims is None:
@@ -548,14 +558,18 @@ class FitAnalyser():
                 )
                 output_core_dims = [prefix+'x']
             
+            daskKwargs.update(
+                {
+                    'output_sizes': {
+                        output_core_dims[0]: np.size(x),
+                    },
+                    'meta': np.ndarray((0,0), dtype=float)
+                }
+            )
+
             kwargs.update(
                 {   
-                    "dask_gufunc_kwargs": {
-                        'output_sizes': {
-                            output_core_dims[0]: np.size(x),
-                        },
-                        'meta': np.ndarray((0,0), dtype=float)
-                    },
+                    "dask_gufunc_kwargs": daskKwargs,
                 }
             )
 
@@ -570,16 +584,20 @@ class FitAnalyser():
                     }
                 )
                 output_core_dims = [prefix+'x', prefix+'y']
+
+            daskKwargs.update(
+                {
+                    'output_sizes': {
+                        output_core_dims[0]: np.size(x),
+                        output_core_dims[1]: np.size(y),
+                    },
+                    'meta': np.ndarray((0,0), dtype=float)
+                },
+            )
             
             kwargs.update(
                 {   
-                    "dask_gufunc_kwargs": {
-                        'output_sizes': {
-                            output_core_dims[0]: np.size(x),
-                            output_core_dims[1]: np.size(y),
-                        },
-                        'meta': np.ndarray((0,0), dtype=float)
-                    },
+                    "dask_gufunc_kwargs": daskKwargs,
                 }
             )