From 67e36996347512eafa252dfa997b9492e7bd306f Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Mon, 23 Mar 2020 20:53:59 +0100
Subject: [PATCH] [quantile regression project] add annual maxima osbervation
from daily exponential. add sammple_r_function as argument to
rmargin_from_nb_obs
---
.../margin_model/abstract_margin_model.py | 5 +--
.../annual_maxima_observations.py | 18 +++++-----
.../daily_observations.py | 26 +++++++++++++++
.../test_spatio_temporal_observations.py | 33 +++++++++++++++++--
4 files changed, 68 insertions(+), 14 deletions(-)
create mode 100644 spatio_temporal_dataset/spatio_temporal_observations/daily_observations.py
diff --git a/extreme_fit/model/margin_model/abstract_margin_model.py b/extreme_fit/model/margin_model/abstract_margin_model.py
index ef7ae1a7..b4ac6918 100644
--- a/extreme_fit/model/margin_model/abstract_margin_model.py
+++ b/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)
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 93474165..a4dbcde9 100644
--- a/spatio_temporal_dataset/spatio_temporal_observations/annual_maxima_observations.py
+++ b/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):
diff --git a/spatio_temporal_dataset/spatio_temporal_observations/daily_observations.py b/spatio_temporal_dataset/spatio_temporal_observations/daily_observations.py
new file mode 100644
index 00000000..9483f3b3
--- /dev/null
+++ b/spatio_temporal_dataset/spatio_temporal_observations/daily_observations.py
@@ -0,0 +1,26 @@
+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)
diff --git a/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py b/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
index ed6c4d40..716a5856 100644
--- a/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
+++ b/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
@@ -1,9 +1,17 @@
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()
--
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