[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] change reference altititudes. change cmap for the plots. change axis for the preliminary plots.

[contrasted] add massif_name_to_annual_total to abstract_study. add plot that compare side by side the total snowfall & maxima of snowfall.

[contrasting v2] fix one condition. keep all one fold fit, with at least one valid (and not one non stationary model). change xlabel for in altitude_group.py

[contrasting] improve plots. last version of the plot for the v1. improve the size of the label. improve also the process to get the legend for coherence plot

[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]  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] add histogram for model repartition & evolution of trends. improve preliminary_analysis.py

[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 a pre-computed of the max_abs value for the shape parameter & for the various moments

[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.