v0.22.1

v0.22.1

• fix for calculation of true disp to conform to the normal conventions.
  Training and predictions worked before but was rotated 180 degrees to the usual definition

v0.22.0

v0.22.0

• Add support for ctapipe coordinate transformation in the training of disp regressors.
  From now on, the configuration key disp_config.coordinate_transformatin is required and can be either FACT or CTA.

If you want to use the CTA coordinate trafo conventions, ctapipe is required, use pip install aict-tools[cta] to make sure, this is the case.

v0.21.0

v0.21.0

• Allow application of multiple cuts to the same variable in aict_apply_cuts, the old format is still supported, but new files should use a list of cuts instead of a dict:

Old:

selection:
    width: ['<=', 20] 

New:

selection:
    - width: ['<=', 20]
    - width: ['>=', 5]    # now possible 

• call tight_layout before saving plots in aict_plot_*_performance #113
• Add option for an alternative definition of the true value of disp, see #106

v0.20.4

v0.20.4

• Warning instead of error in case an empty feature generation config is given.

v0.20.3

v0.20.3

• Several fixes to make plots work with the matplotlib pgf backend

v0.20.1

v0.20.1

• Log ROC Aucs in aict_train_separation_model
• Officially support python 3.8

v0.20.0

v0.20.0

• aict-tools can now apply models stored using onnx or pmml. However, application of models stored to pickle is still the fastest option for most models.

v0.19.0

v0.19.0

• Fixing the configuration of the output names. These can now be set using output_name: in the models section
• Use onnxruntime v1.0

v0.18.3

v0.18.3

• Fix modification of input file timestamp in copy runs
• All but the apply_ scripts should now not modify their input filestamps now

v0.18.2

v0.18.2

• Fix for apply_cuts: modification date of input file is not modified anymore