diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 5200c68185667faf3fb253515780c82c7829428f..be407b533520bfb55b93f626a2749fb7f1ff5e30 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -508,13 +508,13 @@ class Model(TransformerMixin, BaseEstimator):
                  tof_start: Optional[int]=None,
                  delta_tof: Optional[int]=300,
                  validation_size: float=0.05,
-                 n_nonlinear_kernel: int=5000):
+                 n_nonlinear_kernel: int=10000):
         # models
         x_model_steps = list()
         x_model_steps += [('select', SelectRelevantLowResolution(channels, tof_start, delta_tof))]
         if n_nonlinear_kernel > 0:
             x_model_steps += [('fex', Pipeline([('prepca', PCA(n_pca_lr, whiten=True)),
-                                                ('nystroem', Nystroem(n_components=n_nonlinear_kernel, kernel='rbf', gamma=None, n_jobs=-1)),
+                                                ('nystroem', Nystroem(n_components=n_nonlinear_kernel, kernel='rbf', gamma=None, n_jobs=8)),
                                                 ]))]
         x_model_steps += [
                           ('pca', PCA(n_pca_lr, whiten=True)),
@@ -527,7 +527,7 @@ class Model(TransformerMixin, BaseEstimator):
                                 ('unc', UncertaintyHolder()),
                                 ])
         #self.fit_model = FitModel()
-        self.fit_model = MultiOutputWithStd(ARDRegression(n_iter=30, verbose=True))
+        self.fit_model = MultiOutputWithStd(ARDRegression(n_iter=30, tol=1e-4, verbose=True))
 
         # size of the test subset
         self.validation_size = validation_size
diff --git a/pes_to_spec/test/offline_analysis.py b/pes_to_spec/test/offline_analysis.py
index 66cc7782a815e9f49a13465bd024ae8cadf2c896..37bdd6ce2ad4831615c239843409843f076ff9d0 100755
--- a/pes_to_spec/test/offline_analysis.py
+++ b/pes_to_spec/test/offline_analysis.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 
 import sys
+sys.path.append('.')
 sys.path.append('..')
 
 import numpy as np