[snow projection] improve rank & bias plots. remove dataset attribute for abstract_adamont_study.py.

[snow projection] improve drastically the loading time for the abstract_adamont_study.py based on a simple conversion to numpy array.
 remove fit_method from plot_map argument.

[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 v2] improve a lot of all my plots (i save both a transparent version in svg & a non transparent in png).

[contrasting v2] download_adamont.py all studies. Lower a bit the text for some massif. files are by default generated in svg from now on.
change group -> range. remove the elevation 3900 from the high-elevation range.

add altitude argument to adamont studies. refactor a bit. add time series display for adamont series

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