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Recent Releases of ctlearn

ctlearn - v0.10.2

What's Changed

  • Update help description of callbacks by @rcervinoucm in https://github.com/ctlearn-project/ctlearn/pull/233

Full Changelog: https://github.com/ctlearn-project/ctlearn/compare/v0.10.1...v0.10.2

- Python
Published by TjarkMiener 12 months ago

ctlearn - v0.10.1

What's Changed

  • Version script reupload by @rcervinoucm in https://github.com/ctlearn-project/ctlearn/pull/222
  • Update release.yml to manually trigger CD workflow by @rcervinoucm in https://github.com/ctlearn-project/ctlearn/pull/223
  • Update environment.yml with setuptools package by @rcervinoucm in https://github.com/ctlearn-project/ctlearn/pull/224
  • Update pyproject.toml with setup tools by @rcervinoucm in https://github.com/ctlearn-project/ctlearn/pull/225
  • CTLearn Docker container by @TjarkMiener in https://github.com/ctlearn-project/ctlearn/pull/231
  • Add EarlyStopping option for training by @rcervinoucm in https://github.com/ctlearn-project/ctlearn/pull/217
  • Polishing docs for v0.10.X by @TjarkMiener in https://github.com/ctlearn-project/ctlearn/pull/228

Full Changelog: https://github.com/ctlearn-project/ctlearn/compare/v0.10.0...v0.10.1

- Python
Published by TjarkMiener 12 months ago

ctlearn - v0.10.0

What's Changed

  • Update build configuration, use setuptools_scm by @maxnoe in https://github.com/ctlearn-project/ctlearn/pull/199
  • Transform back the predicted quantities into proper the space for real data by @TjarkMiener in https://github.com/ctlearn-project/ctlearn/pull/200
  • Hot fix dependencies by @rcervinoucm in https://github.com/ctlearn-project/ctlearn/pull/203
  • Allow extrapolation for the pointing by @TjarkMiener in https://github.com/ctlearn-project/ctlearn/pull/204
  • Added Exceptions to Handle Non-Existent Directories by @Olmichu22 in https://github.com/ctlearn-project/ctlearn/pull/206
  • Update run_model.py by @rcervinoucm in https://github.com/ctlearn-project/ctlearn/pull/209
  • Adopts ctapipe components and tools & defines API for CTLearn models by @TjarkMiener in https://github.com/ctlearn-project/ctlearn/pull/213
  • Fix LST1 tool based on lstchain data by @TjarkMiener in https://github.com/ctlearn-project/ctlearn/pull/214
  • Bug fixes for prediction tools by @TjarkMiener in https://github.com/ctlearn-project/ctlearn/pull/215
  • Updating metadata for 0.10.0 by @nietootein in https://github.com/ctlearn-project/ctlearn/pull/220

New Contributors

  • @maxnoe made their first contribution in https://github.com/ctlearn-project/ctlearn/pull/199
  • @Olmichu22 made their first contribution in https://github.com/ctlearn-project/ctlearn/pull/206

Full Changelog: https://github.com/ctlearn-project/ctlearn/compare/v0.9.0...v0.10.0

- Python
Published by TjarkMiener 12 months ago

ctlearn - v0.9.0

What's Changed

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

Full Changelog: https://github.com/ctlearn-project/ctlearn/compare/v0.8.0...v0.9.0

- Python
Published by TjarkMiener over 1 year ago

ctlearn - 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.

- Python
Published by TjarkMiener almost 2 years ago

ctlearn - 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.

- Python
Published by TjarkMiener almost 3 years ago

ctlearn - 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.

- Python
Published by TjarkMiener over 3 years ago

ctlearn - 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.

- Python
Published by TjarkMiener almost 4 years ago

ctlearn - 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

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

- Python
Published by TjarkMiener about 4 years ago

ctlearn - 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

- Python
Published by TjarkMiener about 4 years ago

ctlearn - 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.

- Python
Published by TjarkMiener about 5 years ago

ctlearn - 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.

- Python
Published by aribrill over 6 years ago

ctlearn - 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

- Python
Published by aribrill over 6 years ago

ctlearn - v0.3.1

Release v0.3.1

Major Features

Minor Improvements

  • Upgraded Python version to 3.7.3.
  • Upgraded TensorFlow version to 1.13.1.

Bug Fixes and Other Changes

  • Fixed typo in image mapper.

- Python
Published by aribrill over 6 years ago

ctlearn - v0.3.0

Release v0.3.0

Major Features

  • Added FACT, H.E.S.S.-I, H.E.S.S.-II, and MAGIC cameras to ImageMapper.
  • Added bilinear interpolation, bicubic interpolation, nearest neighbor interpolation, rebinning, image shifting, and axial addressing image mapping methods in ImageMapper.
  • Added support for running models using data of multiple telescope types.
  • Added use_peak_times data loading option to load peak arrival times from data files.

Minor Improvements

  • Added auto_configuration.py script to automatically change the paths in benchmark configuration files.
  • Added argument in run_multiple_configurations.py to resume from a particular run.
  • Added summarize_results.py to summarize the results of a set of runs.
  • Rationalized the metadata variables returned by DataLoader.
  • Added test_image_mapper.ipynb for testing the image mapping methods of ImageMapper.
  • Changed telescope names for compatibility with ctapipe camera names.
  • Refactored ImageMapper to implement all image mapping methods as matrix operations, so that more expensive calculations are performed only during initialization.

Bug Fixes and Other Changes

  • Fixed DivisionByZeroError in apply_cuts during HDF5DataLoader initialization.
  • Renamed internal variables in and generally cleaned up DataLoader.
  • Refactored DataLoader._load_metadata() into smaller functions for clarity and efficiency.
  • Fixed incorrect logging of array examples by class.
  • Changed model loading to use included CTLearn models by default.
  • Added contributing guidelines.
  • Made run_model.py append the CTLearn version number to config files.
  • Updated TensorFlow version to v1.12.
  • Added benchmark configuration files for CTLearn v0.3.0.
  • Removed deprecated models.

- Python
Published by aribrill almost 7 years ago

ctlearn - CTLearn v0.2.0

Release v0.2.0

Major Features

  • User-defined TensorFlow classification models with custom configuration parameters can now be imported, in addition to the Single Telescope, CNN-RNN, and Variable Input Network models provided with CTLearn.
  • Image mapping added for all CTA telescope types as well as VERITAS.
  • Data loading, data processing, and image mapping have been refactored into separate classes with methods to load HDF5 files, preprocess generic IACT data, and map telescope data to square images. Each class is defined in a separate module.
  • Configuration now uses YAML instead of INI format, allowing lists and dictionaries to be configured cleanly.

Minor Improvements

  • Benchmark configuration files and results have been produced using Single Telescope and CNN-RNN models for all CTA telescope types.
  • Package installation now uses a conda environment file to resolve dependencies, providing clean and light installation and removal.
  • Training and prediction now handled as two modes within run_model.
  • Updated run_multiple_configurations.py to allow configuration parameter combinations to be grouped together.
  • Image mapping is now configurable with options for padding and hexagonal conversion algorithm.
  • Prediction output is now NumPy-compatible and includes the run number, event number, and telescope ID (if applicable) of each example.

Bug Fixes and Other Changes

  • Renamed project to CTLearn from CTALearn.
  • Added BSD 3-Clause license.
  • TensorFlow version updated to v1.9.0.
  • Clarified telescope sorting options.
  • Fixed errors in and otherwise updated supplementary scripts.
  • Moved unsupported models to models/deprecated and will be removed in the next release.
  • Removed plot_gpu_util.py.
  • Added workaround to handle overflow error in tel_id parameter of ImageExtractor HDF5 file format.
  • Removed dependency on TensorFlow-Slim.

- Python
Published by aribrill over 7 years ago

ctlearn - OSX Support

This release updates the requirements to provide support for OSX.

- Python
Published by aribrill almost 8 years ago

ctlearn - Hands-on Session

This release includes three main improvements. First, the TensorFlow version has been updated to the most recent version v1.7. Second, the CNN-RNN model has been updated and improved and can be used for classification. Third, a script has been added for prediction so that trained models can now be applied to test data. In addition, several supplementary scripts are provided to plot ROC curves using the predicted classification values.

- Python
Published by aribrill almost 8 years ago

ctlearn - Initial Release

First pre-release version of ctalearn. This release is intended to provide a basis for code validation on different machines.

Notes

  • As with all v0.x releases, this is a development release. Future versions may include substantial revisions and breaking changes.
  • This release uses Tensorflow v1.4.1. The Tensorflow version will be upgraded in a future release.
  • The correctness and performance of the available models have not yet been fully validated. Use at your own risk.
  • If you would like to test the code on your machine, please contact the repository authors for a tarball containing a configuration file and Tensorflow checkpoints for a benchmark run.

- Python
Published by aribrill almost 8 years ago