- Fix loading of correct sampling file [#145]
- calculate nomalization only on non-zero pixels
- fix typo in rescaling operation [#149]
- fixed sampling for images displayed in real and imaginary part [#152]
- enabled training and evaluation of half sized images (for 128 pixel images) [#140]
- Add naming to save path, so that the files do not get overwritten as easily [#144]
- Add normalization callback with two different techniques
- Update plotting routines for real/imag images
- Update evaluate_area and evaluate_ms_ssim for half images
- Add evaluate_ms_ssim for sampled images [#146]
- add evaluation of intensity via peak flux and integrated flux comparison [#150]
- centered bin on 1 for histogram evaluation plots
- added color to legend [#151]
- add prettier labels and descriptions to plots [#152]
- Deleted unusable functions for new source types Deleted unused hardcoded scaling [#140]
- add masked loss functions sort bundles in simulations minor adjustments in plotting scripts [#141]
- consistent use of batch_size [#142]
- Add the model name to predictions and sampling file
- Delete unnecessary pad_unsqueeze function
- Add amp_phase keyword to sample_images
- Fix deprecation warning in sampling.py
- Add image size to test_evaluation.py routines [#146]
- Outsource preprocessing steps in train_inspection.py [#148]
- Remove unused norm_path from all instances [#153]
- Deleted cropping
- updated colorbar label
- removed source_list argument [#154]
- Optimize
evaluation.utils.trunc_rvswith numba, providing functions compiled for cpu and parallel cpu computation. [#143]
- Fix loading of correct sampling file [#145]
- calculate nomalization only on non-zero pixels
- fix typo in rescaling operation [#149]
- fixed sampling for images displayed in real and imaginary part [#152]
- enabled training and evaluation of half sized images (for 128 pixel images) [#140]
- Add naming to save path, so that the files do not get overwritten as easily [#144]
- Add normalization callback with two different techniques
- Update plotting routines for real/imag images
- Update evaluate_area and evaluate_ms_ssim for half images
- Add evaluate_ms_ssim for sampled images [#146]
- add evaluation of intensity via peak flux and integrated flux comparison [#150]
- centered bin on 1 for histogram evaluation plots
- added color to legend [#151]
- add prettier labels and descriptions to plots [#152]
- Deleted unusable functions for new source types Deleted unused hardcoded scaling [#140]
- add masked loss functions sort bundles in simulations minor adjustments in plotting scripts [#141]
- consistent use of batch_size [#142]
- Add the model name to predictions and sampling file
- Delete unnecessary pad_unsqueeze function
- Add amp_phase keyword to sample_images
- Fix deprecation warning in sampling.py
- Add image size to test_evaluation.py routines [#146]
- Outsource preprocessing steps in train_inspection.py [#148]
- Remove unused norm_path from all instances [#153]
- Deleted cropping
- updated colorbar label
- removed source_list argument [#154]
- Optimize
evaluation.utils.trunc_rvswith numba, providing functions compiled for cpu and parallel cpu computation. [#143]
- train on half-sized iamges and applying symmetry afterward is a backward incompatible change models trained with early versions of radionets are not supported anymore [#140]
- fixed sampling of test data set fixed same indices for plots [#140]
- enabled training and evaluation of half sized images (for 128 pixel images) [#140]
- Deleted unusable functions for new source types Deleted unused hardcoded scaling [#140]
- added creation of uncertainty plots
changed creation and saving/reading of predictions to
dictspredictiondictshave 3 or 4 entries depending on uncertainty added scaled option toget_ifftcreated new dataset class for sampled images created option for sampling and saving the whole test dataset updated and wrote new tests [#129]
- Add and enable
towncrierin CI. [#130] - publish radionets on pypi [#134]
- Update README, use figures from the paper, minor text adjustments [#136]
- added creation of uncertainty plots
changed creation and saving/reading of predictions to
dictspredictiondictshave 3 or 4 entries depending on uncertainty added scaled option toget_ifftcreated new dataset class for sampled images created option for sampling and saving the whole test dataset updated and wrote new tests [#129]