diff --git a/spatio_temporal_dataset/coordinates/abstract_coordinates.py b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
index b269d60232718d3da23dcb1e22087a293b052326..84afada107c7fa706208564fbc39cf4e873d8e69 100644
--- a/spatio_temporal_dataset/coordinates/abstract_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
@@ -35,6 +35,8 @@ class AbstractCoordinates(object):
TEMPORAL_SPLIT = 'temporal_split'
# Coordinates columns
COORDINATES_NAMES = COORDINATE_SPATIAL_NAMES + [COORDINATE_T]
+ # Coordinate type
+ COORDINATE_TYPE = 'float64'
def __init__(self, df: pd.DataFrame, slicer_class: type, s_split_spatial: pd.Series = None,
s_split_temporal: pd.Series = None, transformation_class: type = None):
@@ -44,10 +46,8 @@ class AbstractCoordinates(object):
# Sort coordinates according to a specified order
sorted_coordinates_columns = [c for c in self.COORDINATES_NAMES if c in coordinate_columns]
self.df_all_coordinates = df.loc[:, sorted_coordinates_columns].copy() # type: pd.DataFrame
- # Check the data type of the coordinate columns
- accepted_dtypes = ['float64', 'int64']
- assert len(self.df_all_coordinates.select_dtypes(include=accepted_dtypes).columns) == len(coordinate_columns), \
- 'coordinates columns dtypes should belong to {}'.format(accepted_dtypes)
+ # Cast df_all_coordinates to the desired type
+ self.df_all_coordinates = self.df_all_coordinates.astype(self.COORDINATE_TYPE)
# Slicing attributes
self.s_split_spatial = s_split_spatial # type: pd.Series
@@ -133,7 +133,6 @@ class AbstractCoordinates(object):
# Split
def df_coordinates(self, split: Split = Split.all) -> pd.DataFrame:
- print(type(self.transformation))
df_transformed_coordinates = self.transformation.transform_df(df_coord=self.df_all_coordinates)
return df_sliced(df=df_transformed_coordinates, split=split, slicer=self.slicer)
diff --git a/spatio_temporal_dataset/coordinates/temporal_coordinates/generated_temporal_coordinates.py b/spatio_temporal_dataset/coordinates/temporal_coordinates/generated_temporal_coordinates.py
index 766cd5e409c69c94b0533a87fa42f3c71601976e..dda74952f49e6ced2f8dfd13ec8082a5b8d7cae8 100644
--- a/spatio_temporal_dataset/coordinates/temporal_coordinates/generated_temporal_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/temporal_coordinates/generated_temporal_coordinates.py
@@ -11,11 +11,9 @@ class ConsecutiveTemporalCoordinates(AbstractTemporalCoordinates):
def from_nb_temporal_steps(cls, nb_temporal_steps, train_split_ratio: float = None, start=0,
transformation_class: type = None):
df = cls.df_temporal(nb_temporal_steps, start)
- print(df.dtypes)
return cls.from_df(df, train_split_ratio, transformation_class=transformation_class)
@classmethod
def df_temporal(cls, nb_temporal_steps, start=0):
df = pd.DataFrame.from_dict({cls.COORDINATE_T: list(range(start, start + nb_temporal_steps))})
- # df = df.astype
return df
diff --git a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
index 77bfcebda3dc9564d37cea72a04da08c92c0f6b1..4f6bdc9ca930006495dc7a44d4ddcddae2573853 100644
--- a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
+++ b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
@@ -19,7 +19,8 @@ class AbstractTransformation(object):
return pd.Series(self.transform_array(s_coord.values), index=s_coord.index)
def transform_df(self, df_coord: pd.DataFrame) -> pd.DataFrame:
- return df_coord.apply(self.transform_serie, axis=1)
+ data = [self.transform_serie(r) for _, r in df_coord.iterrows()]
+ return pd.DataFrame(data, index=df_coord.index, columns=df_coord.columns)
class IdentityTransformation(AbstractTransformation):
diff --git a/test/test_experiment/test_weird_bug.py b/test/test_experiment/test_weird_bug.py
new file mode 100644
index 0000000000000000000000000000000000000000..77ad18699b2ad6489b21c1ec2b7f7f2e580aa71d
--- /dev/null
+++ b/test/test_experiment/test_weird_bug.py
@@ -0,0 +1,40 @@
+import unittest
+
+from experiment.meteo_france_SCM_study.crocus.crocus import CrocusSwe
+from experiment.meteo_france_SCM_study.visualization.study_visualization.main_study_visualizer import \
+ study_iterator_global
+from experiment.meteo_france_SCM_study.visualization.study_visualization.non_stationary_trends import \
+ ConditionalIndedendenceLocationTrendTest
+from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
+from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.uniform_normalization import \
+ BetweenZeroAndOneNormalization, BetweenMinusOneAndOneNormalization
+from utils import get_display_name_from_object_type
+
+
+class TestCoordinateSensitivity(unittest.TestCase):
+
+ def test_weird(self):
+ # todo: maybe the code does not like negative coordinates
+ # todo: maybe not that the sign of the x coordinate are all negative and the other are all positive, it is easier to find the perfect spatial structure
+ altitudes = [3000]
+ transformation_classes = [BetweenZeroAndOneNormalization, BetweenMinusOneAndOneNormalization][:]
+ for transformation_class in transformation_classes:
+ study_classes = [CrocusSwe]
+ for study in study_iterator_global(study_classes, altitudes=altitudes, verbose=False):
+ print('\n\n')
+ study_visualizer = StudyVisualizer(study, transformation_class=transformation_class)
+ study_visualizer.temporal_non_stationarity = True
+ print(study_visualizer.coordinates)
+ # trend_test = ConditionalIndedendenceLocationTrendTest(study_visualizer.dataset)
+ # # years = [1960, 1990]
+ # # mu1s = [trend_test.get_mu1(year) for year in years]
+ # # print('Stationary')
+ # # print(trend_test.get_estimator(trend_test.stationary_margin_model_class, starting_point=None).margin_function_fitted.coef_dict)
+ # print('Non Stationary')
+ # print(trend_test.get_estimator(trend_test.non_stationary_margin_model_class, starting_point=1960).margin_function_fitted.coef_dict)
+ # # print(get_display_name_from_object_type(type(transformation_2D)), 'mu1s: ', mu1s)
+ # # self.assertTrue(0.0 not in mu1s)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/test/test_spatio_temporal_dataset/test_transformation.py b/test/test_spatio_temporal_dataset/test_transformation.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c8cdedaa4023763795eb42af9a8caf3065a8016
--- /dev/null
+++ b/test/test_spatio_temporal_dataset/test_transformation.py
@@ -0,0 +1,30 @@
+import unittest
+
+import numpy as np
+
+from spatio_temporal_dataset.coordinates.temporal_coordinates.generated_temporal_coordinates import \
+ ConsecutiveTemporalCoordinates
+from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.uniform_normalization import \
+ BetweenZeroAndOneNormalization, BetweenMinusOneAndOneNormalization
+
+
+class TestTransformation(unittest.TestCase):
+
+ def test_temporal_normalization(self):
+ nb_steps = 3
+ start = 1950
+ transformation_class_to_expected = {BetweenZeroAndOneNormalization: [0.0, 0.5, 1.0],
+ BetweenMinusOneAndOneNormalization: [-1.0, 0.0, 1.0]}
+ for transformation_class, expected in transformation_class_to_expected.items():
+ temporal_coordinates = ConsecutiveTemporalCoordinates.from_nb_temporal_steps(nb_temporal_steps=nb_steps,
+ start=start,
+ transformation_class=transformation_class)
+ normalized_coordinates = temporal_coordinates.df_coordinates().iloc[:, 0].values
+ expected_coordinates = np.array(expected)
+ equals = normalized_coordinates == expected_coordinates
+ self.assertTrue(equals.all(),
+ msg="expected: {}, res:{}".format(expected_coordinates, normalized_coordinates))
+
+
+if __name__ == '__main__':
+ unittest.main()