faster bootstraps, extreme value upsamling
  • general:
    • improved and faster bootstrap workflow
    • new plot PlotAvailability
    • extreme values upsampling
    • improved runtime environment
  • new features:
    • entire bootstrap workflow has been refactored and much faster now, can be skipped with evaluate_bootstraps=False, #60
    • upsampling of extreme values, set with parameter extreme_values=[your_values_standardised] (e.g. [1, 2]) and extremes_on_right_tail_only=<True/False> if only right tail of distribution is affected or both, #58, #87
    • minimal data length property (in total and for all subsets), #76
    • custom objects in model class to load customised model objects like padding class, loss, #72
    • new plot for data availability: PlotAvailability, #103
    • introduced (default) plot_list to specify which plots to draw
    • latex and markdown information on sample sizes for each station, #90
  • technical:
    • implemented tests on gpu and from scratch for develop, release and master branches, #95
    • usage of tensorflow 1.13.1 (gpu / cpu), separated in 2 different requirements, #81
    • new abstract plot class to have uniform plot class design
    • New time tracking wrapper to use for functions or classes
    • improved logger (info on display, debug into file), #73, #85, #88
    • improved run environment, especially for error handling, #86
    • prefix general in data store scope is now optional and can be skipped. If given scope is not general, it is treated as subscope, #82
    • all 2D Padding classes are now selected by Padding2D(padding_name=<padding_type>) e.g. Padding2D(padding_name="SymPad2D"), #78
    • custom learning rate (or lr_decay) is optional now, #71