import unittest import pandas as pd from spatio_temporal_dataset.spatio_temporal_data_handler import SpatioTemporalDataHandler class TestPipeline(unittest): def main_pipeline(self): # Select a type of marginals (either spatial, spatio temporal, temporal) # this will define the dimension of the climatic space of interest pass # Select the max stable # Define an optimization process # The algo: In 1 time, in 2 times, ..., or more complex patterns # This algo have at least main procedures (that might be repeated several times) # For each procedure, we shall define: # - The loss # - The optimization method for each part of the process def blanchet_smooth_pipeline(self): pass # Spatial marginal # NO MAX STABLE # Procedure: # Optimization of a single likelihood process that sums up the likelihood of all the terms. def padoan_extreme_pipeline(self): pass # Spatial marginal # todo: question, when we are optimizing the full Pairwise loss, are we just optimization the relations ? # or ideally do we need to add the term of order 1 def gaume(self): # Combining the 2 pass def test_pipeline_spatial(self): pass # Sample from a # Fit the spatio temporal experiment margin # Fit the max stable process def test_dataframe_fit_unitary(self): df = pd.DataFrame(1, index=['station1', 'station2'], columns=['200' + str(i) for i in range(18)]) xp = SpatioTemporalDataHandler.from_dataframe(df) if __name__ == '__main__': df = pd.DataFrame(1, index=['station1', 'station2'], columns=['200' + str(i) for i in range(18)]) xp = SpatioTemporalDataHandler.from_dataframe(df)