[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.

[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.