[quantile regression project] add annual maxima osbervation from daily...

[quantile regression project] add annual maxima osbervation from daily exponential. add sammple_r_function as argument to rmargin_from_nb_obs

extreme_fit/model/margin_model/abstract_margin_model.py

@@ -69,11 +69,12 @@ class AbstractMarginModel(AbstractModel, ABC):
         maxima_gev = self.frech2gev(maxima_frech, coordinates_values, self.margin_function_sample)
         return maxima_gev
 
-    def rmargin_from_nb_obs(self, nb_obs: int, coordinates_values: np.ndarray) -> np.ndarray:
+    def rmargin_from_nb_obs(self, nb_obs: int, coordinates_values: np.ndarray,
+                            sample_r_function='rgev') -> np.ndarray:
         maxima_gev = []
         for coordinate in coordinates_values:
             gev_params = self.margin_function_sample.get_gev_params(coordinate)
-            x_gev = r.rgev(nb_obs, **gev_params.to_dict())
+            x_gev = r(sample_r_function)(nb_obs, **gev_params.to_dict())
             maxima_gev.append(x_gev)
         return np.array(maxima_gev)
 

spatio_temporal_dataset/spatio_temporal_observations/annual_maxima_observations.py

@@ -9,6 +9,7 @@ from spatio_temporal_dataset.coordinates.spatio_temporal_coordinates.abstract_sp
     AbstractSpatioTemporalCoordinates
 from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations \
     import AbstractSpatioTemporalObservations
+from spatio_temporal_dataset.spatio_temporal_observations.daily_observations import DailyExp
 
 
 class AnnualMaxima(AbstractSpatioTemporalObservations):
@@ -25,7 +26,8 @@ class MarginAnnualMaxima(AnnualMaxima):
     def from_sampling(cls, nb_obs: int, coordinates: AbstractCoordinates,
                       margin_model: AbstractMarginModel):
         maxima_gev = margin_model.rmargin_from_nb_obs(nb_obs=nb_obs,
-                                                      coordinates_values=coordinates.coordinates_values())
+                                                      coordinates_values=coordinates.coordinates_values(),
+                                                      sample_r_function='rgev')
         df_maxima_gev = pd.DataFrame(data=maxima_gev, index=coordinates.index)
         return cls(df_maxima_gev=df_maxima_gev)
 
@@ -35,15 +37,11 @@ class DailyExpAnnualMaxima(AnnualMaxima):
     @classmethod
     def from_sampling(cls, nb_obs: int, coordinates: AbstractCoordinates,
                       margin_model: AbstractMarginModel):
-        pass
-
-
-class DailyExp(AbstractSpatioTemporalObservations):
-
-    @classmethod
-    def from_sampling(cls, nb_obs: int, coordinates: AbstractCoordinates,
-                      margin_model: AbstractMarginModel):
-        pass
+        # todo: to take nb_obs into accoutn i could generate nb_obs * 365 observations
+        observations = DailyExp.from_sampling(nb_obs=365, coordinates=coordinates, margin_model=margin_model)
+        df_daily_values = observations.df_maxima_gev
+        df_maxima_gev = pd.DataFrame({'0': df_daily_values.max(axis=1)}, index=df_daily_values.index)
+        return cls(df_maxima_gev=df_maxima_gev)
 
 
 class MaxStableAnnualMaxima(AnnualMaxima):

spatio_temporal_dataset/spatio_temporal_observations/daily_observations.py

+import pandas as pd
+
+from extreme_fit.distribution.abstract_params import AbstractParams
+from extreme_fit.distribution.gev.gev_params import GevParams
+from extreme_fit.model.margin_model.abstract_margin_model import AbstractMarginModel
+from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from spatio_temporal_dataset.coordinates.temporal_coordinates.generated_temporal_coordinates import \
+    ConsecutiveTemporalCoordinates
+from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
+    AbstractSpatioTemporalObservations
+
+
+class DailyObservations(AbstractSpatioTemporalObservations):
+    pass
+
+
+class DailyExp(AbstractSpatioTemporalObservations):
+
+    @classmethod
+    def from_sampling(cls, nb_obs: int, coordinates: AbstractCoordinates,
+                      margin_model: AbstractMarginModel):
+        exponential_values = margin_model.rmargin_from_nb_obs(nb_obs=nb_obs,
+                                                              coordinates_values=coordinates.coordinates_values())
+        df_exponential_values = pd.DataFrame(data=exponential_values, index=coordinates.index)
+        return cls(df_maxima_gev=df_exponential_values)

test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py

 import unittest
-import numpy as np
 
+import numpy as np
 import pandas as pd
 
-from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import AbstractSpatioTemporalObservations
+from extreme_fit.distribution.abstract_params import AbstractParams
+from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel
+from extreme_fit.model.utils import set_seed_for_test
+from spatio_temporal_dataset.coordinates.temporal_coordinates.generated_temporal_coordinates import \
+    ConsecutiveTemporalCoordinates
+from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
+    AbstractSpatioTemporalObservations
+from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observations import DailyExpAnnualMaxima
+from spatio_temporal_dataset.spatio_temporal_observations.daily_observations import DailyExp
 
 
 class TestSpatioTemporalObservations(unittest.TestCase):
@@ -18,5 +26,26 @@ class TestSpatioTemporalObservations(unittest.TestCase):
         self.assertTrue(np.equal(example, maxima_frech).all(), msg="{} {}".format(example, maxima_frech))
 
 
+class TestDailyObservations(unittest.TestCase):
+
+    def setUp(self) -> None:
+        super().setUp()
+        set_seed_for_test(seed=42)
+        self.coordinates = ConsecutiveTemporalCoordinates.from_nb_temporal_steps(nb_temporal_steps=10)
+        gev_param_name_to_coef_list = {
+            AbstractParams.SCALE: [1],
+        }
+        self.margin_model = StationaryTemporalModel.from_coef_list(self.coordinates, gev_param_name_to_coef_list)
+
+    def test_exponential_observations(self):
+        obs = DailyExp.from_sampling(nb_obs=1, coordinates=self.coordinates,
+                                     margin_model=self.margin_model)
+        self.assertAlmostEqual(obs.df_maxima_gev.mean()[0], 4.692385276235156)
+
+    def test_annual_maxima_observations_from_daily_observations(self):
+        obs = DailyExpAnnualMaxima.from_sampling(1, self.coordinates, self.margin_model)
+        self.assertAlmostEqual(obs.df_maxima_gev.mean()[0], 1183.8468374768636)
+
+
 if __name__ == '__main__':
     unittest.main()