change alpha confidence uncertainty to 80% uncertainty interval when i run the main code.
create 3 qqplots overlaid, one for each altitude.
remove the exclusion of some bootstrap samples (in particular we remove the threshold equal to 2000)

[contrasting] add plot to compare the size of confidence interval between the pointwise approach and the piecewise elevational-temporal approach. bootstrap level is set to 100

[contrasting] implement bootstrap confidence interval both for pointwise stationary models and for elevation-temporal models. accelerate bootstraping with multiprocessing & storing of fitted functions only (storing the estimator was impossible).

[contrasting] improve plots. add comma to the name of the models. change altitude to elevation in several plots.

[contrasting] for preliminary_analysis.py from now we use fevd fit method & fit on series with more than 90% of non zero maxima. improve coherence plot

[contrasting] add general test for test_gev_spatio_temporal_polynomial_extremes_mle.py. refactor a bit

[contrasting]  fix test_gev_spatio_temporal_polynomial_extremes_mle.py for computing the confidence interval. improve coherence curve. remove sensitivity checks in one fold fit. add significant percentages for histograms of models selected. deactivate some tests for speed purpose

[contrasting] add xlabel, remove 300m from the lower altitude group. add condition that we consider only altitude for which the massif has less than 10% of non zero annual maxima. return period is changed to 100.

[contrasting] consider also the stationary model for selection. consider a level of altitude only if at least 2 altitudes are present & the reference altitude is present.

[contrasting] improve pointwise_gev_study_visualizer.py with a linear regression against the altitude. modify the _maxima_gev depending on the fit method

[contrasting] add pointwise_gev_study_visualizer.py and add confidence interval for the gev parameters for is_mev fit method

[contrasting] add temporal covariate argument to the margin model class, so that we could use the global temperature as covariate.

[contrasting] display individual model fitted with all data (300-4800). Display only the best non-stationary model if it passes the Anderson fit test.

[contrasting] add default pval equal to zero in gnFit (to avoid the program to crash when called by python). refactor anderson test. add anderson test by default for the one fold analysis.

[contrasting] harmonize gumbel altitudinal models. create plot_total_aic.py. remove exception for model with infinite aic.

[contrasting] add quadratic & cross terms Gumbel altitudinal models & fix a recurrent bug that happenning when checking the AIC value

[contrasting] add Gumbel altitudinal models. But so far only the first three models are passing tests (models with quadratic & cross terms are crashing)

This includes models that combine some quadratic aspect & a cross term.
Remove random test

the selection for the model with minimal aic only takes into account models with with finite aic (there are often several models with infinite aic)