{
    "info": {
        "author": "Kai Br\u00fcgge, Maximilian N\u00f6the, Jens Buss",
        "author_email": "kai.bruegge@tu-dortmund.de",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3 :: Only",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Astronomy",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "# aict-tools [![Build Status](https://travis-ci.org/fact-project/aict-tools.svg?branch=master)](https://travis-ci.org/fact-project/aict-tools) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3338081.svg)](https://doi.org/10.5281/zenodo.3338081) [![PyPI version](https://badge.fury.io/py/aict-tools.svg)](https://badge.fury.io/py/aict-tools)\n\n\nExecutables to perform machine learning tasks on FACT and CTA eventlist data.\nPossibly also able to handle input of other experiments if in the same file format.\n\nAll you ever wanted to do  with your IACT data in one package. This project is mainly targeted at using machine-learning for the following tasks:\n\n* Energy Regression\n* Gamma/Hadron Separation\n* Reconstruction of origin (Mono for now)\n\n# Citing\n\nIf you use the `aict-tools`, please cite us like this using the doi provided by\nzenodo, e.g. like this if using bibtex files:\n```bibtex\n@misc{aict-tools,\n      author = {N\u00f6the, Maximilian and Br\u00fcgge, Kai Arno and Bu\u00df, Jens Bj\u00f6rn},\n      title = {aict-tools},\n      subtitle = {Reproducible Artificial Intelligence for Cherenkov Telescopes},\n      doi = {10.5281/zenodo.3338081},\n      url = {https://github.com/fact-project/aict-tools},\n}\n```\n\n\n# Installation\n\nThen you can install the aict-tools by:\n```\npip install aict-tools\n```\n\nBy default, this does not install optional dependencies for writing out\nmodels in `onnx` or `pmml` format.\nIf you want to serialize models to these formats, install this using:\n\n```\n$ pip install aict-tools[pmml] # for pmml support\n$ pip install aict-tools[onnx] # for onnx support\n```\n\nIn the case of working with CTA data, you will also need to have ctapipe installed.\nIf this is not already the case, you can install it using:\n\n```\n$ pip install aict-tools[cta] # for DISP use on CTA data\n```\n\nTo install all optional dependencies, use:\n\n```\n$ pip install aict-tools[all]  # for all\n```\n\nAlternatively you can clone the repo, `cd` into the folder and do the usual `pip install .` dance.\n\n\n# Usage \n\nFor each task, there are two executables, installed to your `PATH`.\nEach take `yaml` configuration files and `h5py` style hdf5 files as input.\nThe models are saved as `pickle` using `joblib` and/or `pmml` using `sklearn2pmml`.\n\n* `aict_train_<...>`   \n  This script is used to train a model on events with known truth\n  values for the target variable, usually monte carlo simulations.\n\n* `aict_apply_<...>` \n  This script applies a given model, previously trained with `aict_train_<...>` and applies it to data, either a test data set or data with unknown truth values for the target variable.\n\nThe apply scripts can iterate through the data files in chunks using\nthe `--chunksize=<N>` option, this can be handy for very large files (> 1 million events). \n\n## Energy Regression\n\nEnergy regression for gamma-rays require a `yaml` configuration file\nand simulated gamma-rays in the event list format.\n\nThe two scripts to perform energy regression are called\n\n* `aict_train_energy_regressor`\n* `aict_apply_energy_regressor`\n\nAn example configuration can be found in [examples/config_energy.yaml](examples/config_energy.yaml).\n\nTo apply a model, use `aict_apply_energy_regressor`.\n\n## Separation\n\nBinary classification or Separation requires a `yaml` configuration file,\none data file for the signal class and one data file for the background class.\n\nThe two scripts to perform separation are called\n\n* `aict_train_separation_model`\n* `aict_apply_separation_model`.\n\nAn example configuration can be found in [examples/config_separator.yaml](examples/config_separator.yaml).\n\n\n## Reconstruction of gamma-ray origin using the disp method\n\nTo estimate the origin of the gamma-rays in camera coordinates, the \n`disp`-method can be used.\n\nHere it is implemented as a two step regression/classification task.\nOne regression model is trained to estimate `abs(disp)` and a\nclassification model is trained to estimate `sgn(disp)`.\n\nTraining requires simulated diffuse gamma-ray events.\n\n* `aict_train_disp_regressor`\n* `aict_apply_disp_regressor`\n\nAn example configuration can be found in [examples/config_source.yaml](examples/config_source.yaml).\nCurrently supported experiments: \n* FACT\n* CTA\n\n**Note: By applying the disp regressor, `Theta` wil be deleted from the feature set.** \nTheta has to be calculated from the source prediction e.g. by using `fact_calculate_theta` from pyfact.\n\n\n# Utility scripts\n\n## Applying straight cuts\n\nFor data selection, e.g. to get rid of not well reconstructable events,\nit is customary to apply so called pre- or quality cuts before applying machine learning models.\n\nThis can be done with `aict_apply_cuts` and a `yaml` configuration file of the cuts to apply. See [examples/quality_cuts.yaml](examples/quality_cuts.yaml) for an example configuration file.\n\n\n## Split data into training/test sets\n\nUsing `aict_split_data`, a dataset can be randomly split into sets,\ne.g. to split a monte carlo simulation dataset into train and test set.\n\n\n",
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