v0.9.0

What's Changed

• fix numpy bug by @TjarkMiener in #190
• CI&CD fixes by @rcervinoucm in #192
• Properly perform direction task for any arbitrary tel pointing by @TjarkMiener in #194
• Cluster env file by @TjarkMiener in #191
• Support only ctapipe data format v6.0.0 by @TjarkMiener in #193
• Issue hotfix by @rcervinoucm in #198

Full Changelog: v0.8.0...v0.9.0

v0.8.0

CTLearn Release v0.8.0

Waveform processing
AI-Trigger application
LST-1 observation processing

Major Features

• AI-based trigger system #180

Minor Improvements

• Added the SST1M camera to the default config files
• Renamed the default CTLearn's model: mergedTRN to stackedTRN
• Added default models for calibrated waveforms and AITrigger
• Improving docs and README
• Upgrade to dl1dh v0.11.1, ctapipe v0.20.0 , pyirf to v0.11, TensorFlow v2.15 & python 3.10

Bug Fixes and Other Changes

Known Issues

• Particle classification is not working with Multitask Learning models yet.

v0.7.0

CTLearn Release v0.7.0

GitHub actions and featuring of the LSTSiPM camera

Major Features

• Fixed GitHub actions #162 @nietootein

Minor Improvements

• Added the LSTSiPM camera to the default config files #167
• Renamed the structure of CTLearn's core modules
• Get viewcone from difference of max and min stored in the file
• Improving docs and README
• Upgrade to dl1dh v0.10.10, ctapipe v0.19.0 , pyirf to v0.8, TensorFlow v2.9 & python 3.10

Bug Fixes and Other Changes

Known Issues

• Particle classification is not working with Multitask Learning models yet.

v0.6.1

CTLearn Release v0.6.1

Output (dl2-like) format handling and creation of an IRF builder using pyirf #142
Store keras model in onnx format #143

Major Features

• Speeding up the output writing by pseudo-chunk processing of the keras predictions
• Clean up CNNRNN model via TimeDistributed layer
• Enable learning rate reducer (including early stopper) callback
• Automatised class label handling for multiple particle types
• Set cleaning from the command line via a flag. Therefore default models with cleaned images can be removed.

Minor Improvements

• Store only the best model checkpoints for validation metric
• Improve installation process of TF by removing cpu/gpu mode
• Upgrade supplementary scripts to the new output format
• Upgrade to dl1dh v0.10.7, ctapipe v0.15.0 & python 3.9

Bug Fixes and Other Changes

Known Issues

• Particle classification is not working with Multitask Learning models yet.
• There is some version incompatibility (numpy and numba) when trying to run on Wilkes-3.

v0.6.0

CTLearn Release v0.6.0

Major upgrade to TensorFlow v2.8 #137
Sphinx docs #139

Major Features

• Upgrade the models to the Keras API in order to run with TF v2.8
• Enable usage of multiple GPUs via tf.distribute.MirroredStrategy
• Make CTLearn user-friendly by allowing several analysis options (like reconstruction tasks, directories, telescope types/ids, quality cuts) to be set from command line. Therefore minimum information about the model has to be included in the config file. Default CTLearn models can be constructed from the command line via default config files shipped by the installation.
• Balance the data for the classification task by default
• Add Sphinx docs and additional code meta data @nietootein

Minor Improvements

• ResNets can be constructed with the SingleCNN model.
• Store Only the best model checkpoints
• Model architecture & matrices can be plotted automatically
• Upgrade to dl1dh v0.10.5, ctapipe v0.12.0 & python 3.8

Bug Fixes and Other Changes

Known Issues

• Particle classification is not working with Multitask Learning models yet.
• There is some version incompatibility (numpy and numba) when trying to run on Wilkes-3.

v0.5.2

CTLearn Release v0.5.2

upgrade to dl1dh v0.10.4

Major Features

Minor Improvements

Bug Fixes and Other Changes

• Improve installation instructions

Known Issues

• apply_class_weights only supported for CTLearn <= v0.5.1 and dl1dh <= v0.10.2. Please balance your dataset by hand beforehand. For CTLearn v0.6.0 apply_class_weights will be supported again and automatized.

v0.5.1

CTLearn Release v0.5.1

ctapipe-stage1 migration

Major Features

• Support of the official CTA stage 1 files (the dl1dh data format is still supported but will be deprecated in the future)
• Upgrade to dl1dh v0.10.0 and ctapipe v0.10.5
• Added ResNet-RNN model
• New feature: Freeze the backbone in deep stereo models like the ResNet-RNN
• pypi installation for CTLearn

Minor Improvements

• Add CTA and MAGIC example files
• Improve input file handling in predict mode to process real data in standard convention

Bug Fixes and Other Changes

v0.5.0

Release v0.5.0

DL full event reconstruction

Major Features

• Enable energy and arrival direction regression
• Add residual blocks to construct ResNet architectures
• Modify singleCNN model to construct VGG models @LucaRomanato
• Enable attention mechanism
• Add drawio diagrams @nietootein
• Count examples by class and regression values @aribrill

Minor Improvements

• Add ability to overwrite the random seed from the command line
• Add new ctlearn mode to recursively train and predict
• Predict on several file lists at once
• cttearn as a command line tool
• Update from plotting scripts @aribrill
• Add notebook to visualize IACT regression metrics using ctaplot
• Upgrade TensorFlow version to 1.15.3

Bug Fixes and Other Changes

Remarks

• Stereo models haven't been benchmarked with this version.

Legacy Release v0.4.0

Legacy Release v0.4.0

Major Features

• Adapted data loading using DL1-Data-Handler legacy_reader to load data files in the "legacy" format of DL1DH <0.6.0.

Minor Improvements

• Added configuration files for the legacy file format to run benchmarks and training for the CTLearn ICRC 2019 conference contribution.
• Added script to rename multiple_configurations run folders.
• Added scripts used to generate plots for ICRC 2019.

Bug Fixes and Other Changes

v0.4.0

Release v0.4.0

Major Features

• Replaced DataLoader, DataProcessor, and ImageMapper with DL1DataReader from the DL1-Data-Handler package (#115, #82, #46, #73).
• Greatly revised configuration format to use DL1DH parameter names and inputs and simplify all sections.

Minor Improvements

• Simplified input_fn in run_model to remove unnecessary required parameters (#41).
• Added option to list data files directly in configuration file (#40).
• Added explicit dictionary of label names to list of class names to configuration file.
• Added load_only mode in run_model to load the data and print info without running a model.
• Simplified predict mode in run_model to iterate through the data only once.

Bug Fixes and Other Changes

• Added kludge when loading data to manually convert unsigned dtypes to the next-higher signed dtype, as TensorFlow cannot automatically perform this conversion.
• Removed scripts specific to processing and image mapping (plot_camera_image and visualize_bounding_boxes).
• Removed scripts made obsolete by load_only mode (print_dataset_metadata and print_run_metadata).
• Moved test_image_mapper notebook to DL1DH.
• Replaced direct dependencies on Astropy, OpenCV, Pillow, PyTables, and SciPy with a dependency on DL1-Data-Handler installed using pip.