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        "author": "Jeremie DECOCK and Tino Michael",
        "author_email": "jd.jdhp@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Application Frameworks",
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        "description": ".. image:: https://travis-ci.org/jeremiedecock/pywi-cta.svg?branch=master\n    :target: https://travis-ci.org/jeremiedecock/pywi-cta\n\n=================================\nPyWI CTA - A CTA wrapper for PyWI\n=================================\n\nCopyright (c) 2016-2018 Jeremie DECOCK (www.jdhp.org)\n\n* Web site: http://cta.pywi.org/\n* Online documentation: http://cta.pywi.org/docs/\n* Source code: https://github.com/jeremiedecock/pywi-cta\n* Issue tracker: https://github.com/jeremiedecock/pywi-cta/issues\n* PyWI-CTA on PyPI: https://pypi.org/project/pywicta/\n* PyWI-CTA on Anaconda Cloud: https://anaconda.org/jdhp/pywicta\n\n.. Former documentation: http://sap-cta-data-pipeline.readthedocs.io/en/latest/\n\n.. Former documentation: https://jeremiedecock.github.io/pywi-cta/\n\nDescription\n===========\n\nPyWI-CTA is a ctapipe_ wrapper for PyWI_.\n\n.. warning::\n\n    This project is in beta stage.\n\nFeatures\n========\n\nThe PyWI and PyWI-CTA libraries contain:\n\n* wavelet transform and wavelet filtering functions for image multiresolution\n  analysis and filtering;\n* additional filter to remove some image components (non-significant pixels\n  clusters);\n* a set of generic filtering performance estimators (MSE, angular precision,\n  energy loss, contamination rate, ...), some relying on the scikit-image\n  Python library (supplementary estimators can be easily added to meet\n  particular needs);\n* a graphical user interface to visualize the filtering process in the wavelet\n  transformed space;\n* an Evolution Strategies (ES) algorithm known in the mathematical optimization\n  community for its good convergence rate on generic derivative-free continuous\n  global optimization problems (Beyer, H. G. (2013) \"The theory of evolution\n  strategies\", Springer Science & Business Media);\n* additional tools to manage and monitor the parameter optimization.\n\nDependencies\n============\n\n.. Highly inspired by http://docs.astropy.org/en/stable/_sources/install.rst.txt\n\nPyWI-CTA has the following strict requirements:\n\n* `Python <https://www.python.org/>`_ 3.5 or 3.6\n* `Numpy <http://www.numpy.org/>`_\n* ctapipe_ 0.6.1\n* pyhessio 2.0.1\n\nPyWI-CTA also depends on other packages for optional features:\n\n* `Scipy <https://www.scipy.org/>`_\n* `Scikit-image <http://scikit-image.org/>`_\n* `Pillow (a.k.a. PIL) <https://pillow.readthedocs.io/en/latest/>`_ to read and write many image formats (PNG, JPEG, TIFF, ...)\n* `Astropy <http://www.astropy.org/>`_ to provide Fits file format\n* `Pandas <http://pandas.pydata.org/>`_\n* `Matplotlib <http://matplotlib.org/>`_ 1.5 or later to provide plotting functionality\n* PyWI_\n* `Cosmostat iSAP Sparce2D <http://www.cosmostat.org/software/isap/>`_\n\nHowever, note that these only need to be installed if those particular features\nare needed. `pywicta` will import even if these dependencies are not installed.\n\n.. _install:\n\nInstallation\n============\n\nPyWI-CTA and its dependencies may be installed using the *Anaconda* or\n*Miniconda* package system. We recommend creating a conda virtual environment\nfirst, to isolate the installed version and dependencies from your master\nenvironment (this is optional).\n\nThe following command will set up a conda virtual environment, add the\nnecessary package channels, and download PyWI-CTA and its dependencies. The\nfile *environment.yml* can be found in this repository. \nNote this is *beta* stage software and is not yet stable enough for end-users\n(expect large API changes until the first stable 1.0 release).\n\n::\n\n    conda env create -n pywi-cta -f environment.yml\n    source activate pywi-cta\n    pip install pywicta --no-deps\n\nIf you have already installed *ctapipe* following the\n`official installation procedure <https://github.com/cta-observatory/ctapipe#installation-for-users>`_,\nyou can add PyWI-CTA to the *cta* virtual environment like this::\n\n    source activate cta\n    pip install pywicta --no-deps\n\nDevelopers should follow the development install instructions found in the\n`documentation <https://jeremiedecock.github.io/pywi-cta/developer.html#getting-started-for-developers>`_.\n\n.. note::\n\n    As *ctapipe* is not tested to work on Microsoft Windows systems, PyWI-CTA\n    does not officially support these systems neither.\n\n.. note::\n\n    The ``--no-deps`` flag is optional, but highly recommended otherwise pip\n    will sometimes try to \"help\" you by upgrading PyWI-CTA dependencies like\n    Numpy, which may not always be desired.\n\nCosmostat iSAP Sparce2D installation (optional)\n===============================================\n\n1. Download http://www.cosmostat.org/wp-content/uploads/2014/12/ISAP_V3.1.tgz (see http://www.cosmostat.org/software/isap/)\n2. Unzip this archive, go to the \"sparse2d\" directory and compile the sparse2d\n   library. It should generate two executables named ``mr_transform`` and ``mr_filter``::\n\n    tar -xzvf ISAP_V3.1.tgz\n    cd ISAP_V3.1/cxx\n    tar -xzvf sparse2d_V1.1.tgz\n    cd sparse2d\n    compile the content of this directory\n\nAn automated compilation and installation script for Linux is available\n`here <https://github.com/tino-michael/tino_cta/blob/master/grid/compile_mrfilter_pilot.sh>`_\n(author: `Tino Michael <https://github.com/tino-michael>`_).\n\n.. Also available in `utils/compile_isap_sparce2d.sh`\n\nGetting started: tutorial Notebooks\n===================================\n\nPyWI-CTA provides some Jupyter notebooks that can be used as examples or tutorials: https://github.com/jeremiedecock/pywi-cta-notebooks.\nNew users should check them in the following order:\n\n* Tutorial #0:  `Check PyWI-CTA install <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_0_check_install.ipynb>`_\n* Tutorial #1a: `Load and plot a FITS image using PyWI-CTA <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_1a_load_and_plot_fits_image.ipynb>`_\n* Tutorial #1b: `Load and plot a Simtel event using PyHESSIO <https://nbviewer.jupyter.org/github/jeremiedecock/pywi-cta-notebooks/blob/master/tuto_1b_load_simtel_event.ipynb>`_\n* Tutorial #1c: `Load and plot a Simtel image using PyWI-CTA <https://nbviewer.jupyter.org/github/jeremiedecock/pywi-cta-notebooks/blob/master/tuto_1c_load_and_plot_simtel_image.ipynb>`_\n* Tutorial #1d: `Plot Hillas parameters using PyWI-CTA <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_1d_plot_hillas_parameters.ipynb>`_\n* Tutorial #2a: `Tailcut cleaning with PyWI-CTA <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_2a_tailcut_cleaning.ipynb>`_\n* Tutorial #2b: `Plot Starlet planes with PyWI-CTA <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_2b_plot_starlet_planes.ipynb>`_\n* Tutorial #2c: `Starlet cleaning with PyWI-CTA <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_2c_starlet_cleaning.ipynb>`_\n* Tutorial #3a: `Tailcut interactive notebook <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_3a_interactive_tailcut_cleaning_with_bokeh.ipynb>`_\n* Tutorial #3b: `Starlet cleaning interactive notebook <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_3b_interactive_starlet_cleaning_with_bokeh.ipynb>`_\n* Tutorial #3c: `Wavelet Sparce2D MrTransform interactive notebook <https://nbviewer.jupyter.org/github/jeremiedecock/pywi-cta-notebooks/blob/master/tuto_3c_interactive_mrtransform_cleaning_with_bokeh.ipynb>`_\n* Tutoriel #4a: `Optimize Tailcut thresholds with exhaustive search <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_4a_optimize_tailcut_thresholds_using_bruteforce.ipynb>`_\n* Tutoriel #4b: `Optimize Starlet thresholds with exhaustive search <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_4b_optimize_starlet_thresholds_using_bruteforce.ipynb>`_\n* Tutoriel #4c: `Optimize Tailcut thresholds with Differential Evolution <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_4c_optimize_tailcut_thresholds_using_differential_evolution.ipynb>`_\n* Tutoriel #4d: `Optimize Starlet thresholds with Differential Evolution <https://mybinder.org/v2/gh/jeremiedecock/pywi-cta-notebooks/master?filepath=tuto_4d_optimize_starlet_thresholds_using_differential_evolution.ipynb>`_\n\nRecommended JupyterLab extensions for these tutorials (type the following commands in a terminal within the right conda environment)::\n\n    jupyter labextension install @jupyter-widgets/jupyterlab-manager\n    jupyter labextension install @jupyterlab/toc\n    jupyter labextension install @ijmbarr/jupyterlab_spellchecker\n\n.. PyWI Notebooks on Anaconda Cloud: https://anaconda.org/jdhp/notebooks\n\nConsole usage example\n=====================\n\nPyWI-CTA can also be used through console commands. The following is an usage example:\n\n1. Get a simtel file (e.g. from `there <https://forge.in2p3.fr/projects/cta_analysis-and-simulations/wiki/Monte_Carlo_Productions>`_)\n2. In your system terminal, from the directory that contains the sample image,\n   type the following commands (where `SIMTEL_FILE` is the path to your simtel\n   file)::\n  \n    pywicta-starlet -f common_hard_filtering -t 13.,1.5 -L mask --camid LSTCam --max-images 1 --plot SIMTEL_FILE\n    pywicta-mrtransform -f common_hard_filtering -t 13.,1.5 -L mask --camid LSTCam --max-images 1 --plot SIMTEL_FILE\n    pywicta-mrfilter -K -k -C1 -m3 -n4 -s2,4.5,3.5,3 --kill-isolated-pixels --camid LSTCam --max-images 1 --plot SIMTEL_FILE\n\n3. Type ``pywicta-starlet -h``, ``pywicta-mrtransform -h`` or ``pywicta-mrfilter -h`` to display the list of\n   available options and their documentation.\n\n.. 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