diff --git a/extreme_data/meteo_france_data/scm_models_data/altitudes_studies.py b/extreme_data/meteo_france_data/scm_models_data/altitudes_studies.py
index d094282551aa9acf8fdfbf676c5796626fbadd56..730fa5eab8e83cea3dd99c7273ccf8063fb0f5ac 100644
--- a/extreme_data/meteo_france_data/scm_models_data/altitudes_studies.py
+++ b/extreme_data/meteo_france_data/scm_models_data/altitudes_studies.py
@@ -18,6 +18,8 @@ from spatio_temporal_dataset.coordinates.spatio_temporal_coordinates.abstract_sp
     AbstractSpatioTemporalCoordinates
 from spatio_temporal_dataset.coordinates.spatio_temporal_coordinates.spatio_temporal_coordinates_for_climate_models import \
     SpatioTemporalCoordinatesForClimateModels
+from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
+    AbstractTemporalCoordinates
 from spatio_temporal_dataset.coordinates.temporal_coordinates.generated_temporal_coordinates import \
     ConsecutiveTemporalCoordinates
 from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
@@ -57,8 +59,17 @@ class AltitudesStudies(object):
         for altitude in massif_altitudes:
             study = self.altitude_to_study[altitude]
             for year, maxima in zip(study.ordered_years, study.massif_name_to_annual_maxima[massif_name]):
-                coordinate_values_to_maxima[(altitude, year)] = [maxima]
+                if len(massif_altitudes) == 1:
+                    coordinate_values_to_maxima[year] = [maxima]
+                else:
+                    coordinate_values_to_maxima[(altitude, year)] = [maxima]
+
         coordinates = self.spatio_temporal_coordinates(s_split_spatial, s_split_temporal, massif_altitudes)
+        # Remove the spatial coordinate if we only have one altitude
+        if len(massif_altitudes) == 1:
+            df = pd.concat([coordinates.df_temporal_coordinates(), coordinates.df_coordinate_climate_model], axis=1)
+            coordinates = AbstractTemporalCoordinates.from_df(df)
+
         observations = AnnualMaxima.from_coordinates(coordinates, coordinate_values_to_maxima)
         return AbstractDataset(observations=observations, coordinates=coordinates)
 
diff --git a/extreme_fit/function/param_function/spline_coef.py b/extreme_fit/function/param_function/spline_coef.py
index 204a8f68bc411505b23c99d81b052a64b7f3cb16..0c62b8befdd8e69f671c95c7061993060e3143c2 100644
--- a/extreme_fit/function/param_function/spline_coef.py
+++ b/extreme_fit/function/param_function/spline_coef.py
@@ -27,6 +27,9 @@ class SplineCoef(AbstractCoef):
     def nb_coefficients(self):
         return len(self.coefficients)
 
+    @property
+    def nb_params(self):
+        return self.nb_knots + self.nb_coefficients
 
 class SplineAllCoef(LinearCoef):
 
@@ -45,3 +48,7 @@ class SplineAllCoef(LinearCoef):
                                                                         spline_coef.nb_coefficients))
             formula_str = ' '.join(formula_list)
         return {self.param_name + '.form': self.param_name + ' ~ ' + formula_str}
+
+    @property
+    def nb_params(self):
+        return sum([spline_coef.nb_params for spline_coef in self.dim_to_spline_coef.values()])
\ No newline at end of file
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_evgam.py b/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_evgam.py
index f852fdd6fcef500d60e46bc45dfa51ff48b96a65..70238d0f39d198f440a5d707091d17a21ba27144 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_evgam.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_evgam.py
@@ -53,7 +53,7 @@ class ResultFromEvgam(AbstractResultFromExtremes):
 
     @property
     def nb_parameters(self):
-        return len(np.array(self.name_to_value['coefficients']))
+        return len(np.array(self.name_to_value['coefficients']))  + self.nb_knots
 
     @property
     def aic(self):
@@ -107,14 +107,20 @@ class ResultFromEvgam(AbstractResultFromExtremes):
             raise NotImplementedError
         else:
             dim, max_degree = dims[0]
-            d = self.get_python_dictionary(self.name_to_value[r_param_name])
-            smooth = list(d['smooth'])[0]
-            knots = np.array(self.get_python_dictionary(smooth)['knots'])
+            knots = self.load_knots(r_param_name)
             x = np.array(self.name_to_value["data"])[1]
             y = np.array(self.get_python_dictionary(self.name_to_value[r_param_name])['fitted'])
             assert len(knots) == 5
             x_for_interpolation = [int(knots[1]+1), int((knots[1] + knots[3])/2), int(knots[3]-1)]
-            index = [i for i, e in enumerate(x) if e in x_for_interpolation][:len(x_for_interpolation)]
+
+            # For the time covariate, the distance will be zero for the closer year
+            # For the temperature covariate, the distance will be minimal for the closer covariate
+            index = []
+            for x_to_find in x_for_interpolation:
+                distances = np.power(x - x_to_find, 2)
+                closer_index = np.argmin(distances)
+                index.append(closer_index)
+
             x = [x[i] for i in index]
             y = [y[i] for i in index]
             spline = make_interp_spline(x, y, k=1, t=knots)
@@ -123,6 +129,19 @@ class ResultFromEvgam(AbstractResultFromExtremes):
             dim_knots_and_coefficient[dim] = (knots, coefficients)
         return dim_knots_and_coefficient
 
+    def load_knots(self, r_param_name):
+        try:
+            d = self.get_python_dictionary(self.name_to_value[r_param_name])
+            smooth = list(d['smooth'])[0]
+            knots = np.array(self.get_python_dictionary(smooth)['knots'])
+        except (IndexError, KeyError):
+            knots = []
+        return knots
+
+    @property
+    def nb_knots(self):
+        return sum([len(self.load_knots(r_param_name)) for r_param_name in self.r_param_name_to_param_name.keys()])
+
     @property
     def r_param_name_to_param_name(self):
         return {
diff --git a/extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py b/extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py
index d749c0811cfbc795adb9ebe0064ec5533bb91014..48e012b6813f356150e4cf25e78f6f9559b03e90 100644
--- a/extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py
+++ b/extreme_trend/ensemble_fit/independent_ensemble_fit/one_fold_fit_merge.py
@@ -7,11 +7,12 @@ from extreme_trend.one_fold_fit.one_fold_fit import OneFoldFit
 
 class OneFoldFitMerge(OneFoldFit):
 
-    def __init__(self, one_fold_fit_list: List[OneFoldFit], massif_name, altitude_class, temporal_covariate_for_fit,
+    def __init__(self, one_fold_fit_list: List[OneFoldFit], massif_name,
+                 altitude_group, temporal_covariate_for_fit,
                  first_year, last_year, merge_function=np.median):
         assert len(one_fold_fit_list) > 0
         self.one_fold_fit_list = one_fold_fit_list
-        self.altitude_group = altitude_class()
+        self.altitude_group = altitude_group
         self.massif_name = massif_name
         self.temporal_covariate_for_fit = temporal_covariate_for_fit
         self.merge_function = merge_function
diff --git a/extreme_trend/ensemble_fit/independent_ensemble_fit/visualizer_merge.py b/extreme_trend/ensemble_fit/independent_ensemble_fit/visualizer_merge.py
index 5ed3fcda9452a796ef17399bd88e4ed846e70627..0c3c12fff32cad0a606fe5c336459d85d7b9614c 100644
--- a/extreme_trend/ensemble_fit/independent_ensemble_fit/visualizer_merge.py
+++ b/extreme_trend/ensemble_fit/independent_ensemble_fit/visualizer_merge.py
@@ -36,8 +36,8 @@ class VisualizerMerge(AltitudesStudiesVisualizerForNonStationaryModels):
             one_fold_fit_list = [v.massif_name_to_one_fold_fit[massif_name] for v in self.visualizers
                                  if massif_name in v.massif_name_to_one_fold_fit]
             if len(one_fold_fit_list) > 0:
-                one_fold_fit_merge = OneFoldFitMerge(one_fold_fit_list, massif_name,
-                                                     type(self.altitude_group), self.temporal_covariate_for_fit,
+                one_fold_fit_merge = OneFoldFitMerge(one_fold_fit_list, massif_name, self.altitude_group,
+                                                     self.temporal_covariate_for_fit,
                                                      first_year=self.study.year_min, last_year=self.study.year_max,
                                                      merge_function=self.merge_function)
                 self._massif_name_to_one_fold_fit[massif_name] = one_fold_fit_merge
diff --git a/extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py b/extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py
index 096e5e9ec9f7b65f780fdeeb2ec90b9ef6d4e5b5..dfd0943656d83525bd78d0433af37f75e121be5f 100644
--- a/extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py
+++ b/extreme_trend/ensemble_fit/visualizer_for_projection_ensemble.py
@@ -100,7 +100,7 @@ class VisualizerForProjectionEnsemble(object):
         for ensemble_fit_class in self.ensemble_fit_classes:
             for ensemble_fit in self.ensemble_fits(ensemble_fit_class):
                 visualizer_list.extend(ensemble_fit.visualizer_list)
-        compute_and_assign_max_abs(visualizer_list)
+        # compute_and_assign_max_abs(visualizer_list)
         # Plot
         if IndependentEnsembleFit in self.ensemble_fit_classes:
             self.plot_independent()
diff --git a/extreme_trend/one_fold_fit/altitude_group.py b/extreme_trend/one_fold_fit/altitude_group.py
index 60924af08cbf243e402c5adf75d287b054d94ab4..c1d01f14611a3517cde3017fa9fdf87b4c9b818e 100644
--- a/extreme_trend/one_fold_fit/altitude_group.py
+++ b/extreme_trend/one_fold_fit/altitude_group.py
@@ -136,13 +136,17 @@ class VeyHighAltitudeGroup(AbstractAltitudeGroup):
 
 class DefaultAltitudeGroup(AbstractAltitudeGroup):
 
+    def __init__(self, altitudes):
+        assert len(altitudes) == 1
+        self.altitude = list(altitudes)[0]
+
     @property
     def name(self):
-        return 'default'
+        return str(self.altitude)
 
     @property
     def reference_altitude(self):
-        return 500
+        return self.altitude
 
 
 def get_altitude_class_from_altitudes(altitudes):
@@ -160,7 +164,7 @@ def get_altitude_group_from_altitudes(altitudes):
     elif s == set(altitudes_for_groups[3]):
         return VeyHighAltitudeGroup()
     else:
-        return DefaultAltitudeGroup()
+        return DefaultAltitudeGroup(altitudes)
 
 
 def get_linestyle_for_altitude_class(altitude_class):
diff --git a/extreme_trend/one_fold_fit/altitudes_studies_visualizer_for_non_stationary_models.py b/extreme_trend/one_fold_fit/altitudes_studies_visualizer_for_non_stationary_models.py
index 7706a557c0ca297064cb2f5f3451e52643e98fe5..6b1a375d3f1c907b1e831a6893ac166ce5587941 100644
--- a/extreme_trend/one_fold_fit/altitudes_studies_visualizer_for_non_stationary_models.py
+++ b/extreme_trend/one_fold_fit/altitudes_studies_visualizer_for_non_stationary_models.py
@@ -30,6 +30,7 @@ from spatio_temporal_dataset.coordinates.temporal_coordinates.temperature_covari
 
 
 class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
+    consider_at_least_two_altitudes = True
 
     def __init__(self, studies: AltitudesStudies,
                  model_classes: List[AbstractSpatioTemporalPolynomialModel],
@@ -85,7 +86,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
                                       self.study.year_max,
                                       self.fit_method,
                                       self.temporal_covariate_for_fit,
-                                      type(self.altitude_group),
+                                      self.altitude_group,
                                       self.display_only_model_that_pass_test,
                                       self.confidence_interval_based_on_delta_method,
                                       self.remove_physically_implausible_models)
@@ -120,9 +121,10 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
     def load_condition(self, massif_altitudes):
         # At least two altitudes for the estimated
         # reference_altitude_is_in_altitudes = (self.altitude_group.reference_altitude in massif_altitudes)
-        at_least_two_altitudes = (len(massif_altitudes) >= 2)
-        # return reference_altitude_is_in_altitudes and at_least_two_altitudes
-        return at_least_two_altitudes
+        if self.consider_at_least_two_altitudes:
+            return len(massif_altitudes) >= 2
+        else:
+            return True
 
     @property
     def massif_name_to_one_fold_fit(self) -> Dict[str, OneFoldFit]:
diff --git a/extreme_trend/one_fold_fit/one_fold_fit.py b/extreme_trend/one_fold_fit/one_fold_fit.py
index 7cdfc50f48677405d7ba64cf024f4b0866da6e81..7fe04a0a7d0e061b44c798e5402e1628389d23d5 100644
--- a/extreme_trend/one_fold_fit/one_fold_fit.py
+++ b/extreme_trend/one_fold_fit/one_fold_fit.py
@@ -47,7 +47,7 @@ class OneFoldFit(object):
                  first_year, last_year,
                  fit_method=MarginFitMethod.extremes_fevd_mle,
                  temporal_covariate_for_fit=None,
-                 altitude_class=DefaultAltitudeGroup,
+                 altitude_group=None,
                  only_models_that_pass_goodness_of_fit_test=True,
                  confidence_interval_based_on_delta_method=False,
                  remove_physically_implausible_models=False,
@@ -58,7 +58,7 @@ class OneFoldFit(object):
         self.remove_physically_implausible_models = remove_physically_implausible_models
         self.confidence_interval_based_on_delta_method = confidence_interval_based_on_delta_method
         self.only_models_that_pass_goodness_of_fit_test = only_models_that_pass_goodness_of_fit_test
-        self.altitude_group = altitude_class()
+        self.altitude_group = altitude_group
         self.massif_name = massif_name
         self.dataset = dataset
         self.models_classes = models_classes
@@ -197,7 +197,11 @@ class OneFoldFit(object):
         return sorted_estimators
 
     def _compute_shape_for_reference_altitude(self, estimator):
-        coordinate = np.array([self.altitude_plot, self.last_year])
+        if isinstance(self.altitude_group, DefaultAltitudeGroup):
+            coordinate = np.array([self.last_year])
+        else:
+            coordinate = np.array([self.altitude_plot, self.last_year])
+        print(coordinate)
         gev_params = estimator.function_from_fit.get_params(coordinate, is_transformed=False)
         shape = gev_params.shape
         return shape
diff --git a/extreme_trend/one_fold_fit/utils_altitude_studies_visualizer.py b/extreme_trend/one_fold_fit/utils_altitude_studies_visualizer.py
index a6d6ae62bcada070bb355c2328546aca733703ac..9f36764eae275c53c260b80b4f912d70ac043635 100644
--- a/extreme_trend/one_fold_fit/utils_altitude_studies_visualizer.py
+++ b/extreme_trend/one_fold_fit/utils_altitude_studies_visualizer.py
@@ -5,7 +5,8 @@ from extreme_trend.one_fold_fit.altitudes_studies_visualizer_for_non_stationary_
     AltitudesStudiesVisualizerForNonStationaryModels
 
 
-def load_visualizer_list(season, study_class, altitudes_list, massif_names, model_must_pass_the_test=True, **kwargs_study):
+def load_visualizer_list(season, study_class, altitudes_list, massif_names, model_must_pass_the_test=True,
+                         do_compute_and_assign_max_abs=True, **kwargs_study):
     model_classes = ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
     visualizer_list = []
     # Load all studies
@@ -20,7 +21,8 @@ def load_visualizer_list(season, study_class, altitudes_list, massif_names, mode
                                                                       display_only_model_that_pass_anderson_test=model_must_pass_the_test
                                                                       )
         visualizer_list.append(visualizer)
-    compute_and_assign_max_abs(visualizer_list)
+    if do_compute_and_assign_max_abs:
+        compute_and_assign_max_abs(visualizer_list)
 
     return visualizer_list
 
diff --git a/projects/projected_extreme_snowfall/results/main_projections_ensemble.py b/projects/projected_extreme_snowfall/results/main_projections_ensemble.py
index 864f6f95f88a9a06a432d5378ed7d5903879eaf1..2069ed808f519b32c04d49d8d7d963ef9b3958f0 100644
--- a/projects/projected_extreme_snowfall/results/main_projections_ensemble.py
+++ b/projects/projected_extreme_snowfall/results/main_projections_ensemble.py
@@ -3,7 +3,11 @@ import time
 from typing import List
 import matplotlib
 
+from extreme_fit.model.margin_model.utils import MarginFitMethod
 from extreme_trend.ensemble_fit.together_ensemble_fit.together_ensemble_fit import TogetherEnsembleFit
+from extreme_trend.one_fold_fit.altitudes_studies_visualizer_for_non_stationary_models import \
+    AltitudesStudiesVisualizerForNonStationaryModels
+from projects.projected_extreme_snowfall.results.utils import SPLINE_MODELS_FOR_PROJECTION_ONE_ALTITUDE
 
 matplotlib.use('Agg')
 import matplotlib as mpl
@@ -41,49 +45,49 @@ def main():
     study_class = AdamontSnowfall
     ensemble_fit_classes = [IndependentEnsembleFit, TogetherEnsembleFit][:1]
     temporal_covariate_for_fit = [TimeTemporalCovariate,
-                                  AnomalyTemperatureWithSplineTemporalCovariate][0]
+                                  AnomalyTemperatureWithSplineTemporalCovariate][1]
     set_seed_for_test()
     AbstractExtractEurocodeReturnLevel.ALPHA_CONFIDENCE_INTERVAL_UNCERTAINTY = 0.2
+    scenarios = [AdamontScenario.rcp85_extended]
 
-    fast = False
-    scenarios = rcp_scenarios[::-1] if fast is False else [AdamontScenario.rcp85]
-    scenarios = rcm_scenarios_extended[::-1]
-
-    scenarios = [AdamontScenario.histo]
-    gcm_to_year_min_and_year_max = {
-        gcm: (1959, 2005) for gcm in get_gcm_list(adamont_version=2)
-    }
-
+    fast = True
     for scenario in scenarios:
         gcm_rcm_couples = get_gcm_rcm_couples(scenario)
         if fast is None:
-            massif_names = None
             gcm_rcm_couples = gcm_rcm_couples[:2]
             AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
-            altitudes_list = altitudes_for_groups[3:]
+            altitudes_list = [1800, 2100]
         elif fast:
-            massif_names = ['Vanoise', 'Haute-Maurienne']
             gcm_rcm_couples = gcm_rcm_couples[:2]
             AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
-            altitudes_list = altitudes_for_groups[:1]
+            altitudes_list = [2400]
         else:
-            massif_names = None
-            altitudes_list = altitudes_for_groups[:]
+            altitudes_list = [600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600]
 
         assert isinstance(gcm_rcm_couples, list)
 
+        altitudes_list = [[a] for a in altitudes_list]
         assert isinstance(altitudes_list, List)
         assert isinstance(altitudes_list[0], List)
+        for altitudes in altitudes_list:
+            assert len(altitudes) == 1
+        AltitudesStudiesVisualizerForNonStationaryModels.consider_at_least_two_altitudes = False
+
         print('Scenario is', scenario)
         print('Covariate is {}'.format(temporal_covariate_for_fit))
 
-        model_classes = ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
+        # Default parameters
+        gcm_to_year_min_and_year_max = None
+        massif_names = ['Vanoise']
+        model_classes = SPLINE_MODELS_FOR_PROJECTION_ONE_ALTITUDE
+
 
         visualizer = VisualizerForProjectionEnsemble(
             altitudes_list, gcm_rcm_couples, study_class, Season.annual, scenario,
             model_classes=model_classes,
             ensemble_fit_classes=ensemble_fit_classes,
             massif_names=massif_names,
+            fit_method=MarginFitMethod.evgam,
             temporal_covariate_for_fit=temporal_covariate_for_fit,
             remove_physically_implausible_models=True,
             gcm_to_year_min_and_year_max=gcm_to_year_min_and_year_max,
diff --git a/projects/projected_extreme_snowfall/results/utils.py b/projects/projected_extreme_snowfall/results/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c711d3314fe292783858c0122396cb88b6e2e03
--- /dev/null
+++ b/projects/projected_extreme_snowfall/results/utils.py
@@ -0,0 +1,16 @@
+from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \
+    NonStationaryLocationTemporalModel, NonStationaryScaleTemporalModel
+from extreme_fit.model.margin_model.spline_margin_model.temporal_spline_model_degree_1 import \
+    NonStationaryTwoLinearLocationModel, NonStationaryTwoLinearScaleModel
+
+SPLINE_MODELS_FOR_PROJECTION_ONE_ALTITUDE = [
+    StationaryTemporalModel,
+
+    # Location only non-stationarity
+    NonStationaryLocationTemporalModel,
+    NonStationaryTwoLinearLocationModel,
+
+    # Scale only non-stationarity
+    NonStationaryScaleTemporalModel,
+    NonStationaryTwoLinearScaleModel,
+]
diff --git a/spatio_temporal_dataset/spatio_temporal_observations/annual_maxima_observations.py b/spatio_temporal_dataset/spatio_temporal_observations/annual_maxima_observations.py
index c8a889b7ad3f74f64710db04c1498a6ca8b41ad5..abd109d1f96f74df3afff1e706a4b795af3fa5a3 100644
--- a/spatio_temporal_dataset/spatio_temporal_observations/annual_maxima_observations.py
+++ b/spatio_temporal_dataset/spatio_temporal_observations/annual_maxima_observations.py
@@ -20,7 +20,10 @@ class AnnualMaxima(AbstractSpatioTemporalObservations):
     def from_coordinates(cls, coordinates: AbstractCoordinates, coordinate_values_to_maxima):
         index_to_maxima = {}
         for i, coordinate_values in coordinates.df_all_coordinates.iterrows():
-            coordinate_values = tuple([int(v) for v in coordinate_values])
+            if len(coordinate_values) == 1:
+                coordinate_values = coordinate_values[0]
+            else:
+                coordinate_values = tuple([int(v) for v in coordinate_values])
             index_to_maxima[i] = coordinate_values_to_maxima[coordinate_values]
         df = pd.DataFrame(index_to_maxima).transpose()
         df.index = coordinates.index
diff --git a/test/test_extreme_trend/test_one_fold_fit.py b/test/test_extreme_trend/test_one_fold_fit.py
index 80e3e7064192bf5d514e21acf2813a83b7e3e86f..49432249cfd67c68bc1cf01449f37e3eda2497c0 100644
--- a/test/test_extreme_trend/test_one_fold_fit.py
+++ b/test/test_extreme_trend/test_one_fold_fit.py
@@ -11,7 +11,7 @@ from extreme_fit.model.margin_model.polynomial_margin_model.models_based_on_pari
 from extreme_fit.model.margin_model.polynomial_margin_model.utils import \
     ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
 from extreme_data.meteo_france_data.scm_models_data.altitudes_studies import AltitudesStudies
-from extreme_trend.one_fold_fit.altitude_group import VeyHighAltitudeGroup
+from extreme_trend.one_fold_fit.altitude_group import VeyHighAltitudeGroup, LowAltitudeGroup
 from extreme_trend.one_fold_fit.one_fold_fit import OneFoldFit
 from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_covariate_for_fit import \
     TimeTemporalCovariate
@@ -85,6 +85,7 @@ class TestOneFoldFit(unittest.TestCase):
                                   temporal_covariate_for_fit=None,
                                   only_models_that_pass_goodness_of_fit_test=False,
                                   remove_physically_implausible_models=True,
+                                  altitude_group=LowAltitudeGroup(),
                                   first_year=1959,
                                   last_year=2019
                                   )
@@ -104,7 +105,7 @@ class TestOneFoldFit(unittest.TestCase):
         one_fold_fit = OneFoldFit(self.massif_name, dataset,
                                   models_classes=self.model_classes,
                                   temporal_covariate_for_fit=AnomalyTemperatureWithSplineTemporalCovariate,
-                                  altitude_class=VeyHighAltitudeGroup,
+                                  altitude_group=VeyHighAltitudeGroup(),
                                   only_models_that_pass_goodness_of_fit_test=False,
                                   remove_physically_implausible_models=True,
                                   first_year=1959,