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

[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.

[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 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.