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            "License :: OSI Approved",
            "Operating System :: MacOS",
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        "description": "===================================================\nalphaCSC: Convolution sparse coding for time-series\n===================================================\n\n\n.. image:: https://api.travis-ci.org/alphacsc/alphacsc.svg?branch=master\n    :target: https://travis-ci.org/alphacsc/alphacsc\n    :alt: Build Status\n\n.. image:: https://codecov.io/github/alphacsc/alphacsc/coverage.svg?precision=0\n\t:target: https://codecov.io/gh/alphacsc/alphacsc\n\t:alt: Test coverage\n\nThis is a library to perform shift-invariant `sparse dictionary learning\n<https://en.wikipedia.org/wiki/Sparse_dictionary_learning>`_, also known as\nconvolutional sparse coding (CSC), on time-series data.\nIt includes a number of different models:\n\n1. univariate CSC\n2. multivariate CSC\n3. multivariate CSC with a rank-1 constraint [1]_\n4. univariate CSC with an alpha-stable distribution [2]_\n\nA mathematical descriptions of these models is available `in the documentation\n<https://alphacsc.github.io/models.html>`_.\n\nInstallation\n============\n\nTo install this package, the easiest way is using ``pip``. It will install this\npackage and its dependencies. The ``setup.py`` depends on ``numpy`` and\n``cython`` for the installation so it is advised to install them beforehand. To\ninstall this package, please run one of the two commands:\n\n(Latest stable version)\n\n.. code::\n\n    pip install numpy cython\n    pip install alphacsc\n\n(Development version)\n\n.. code::\n\n\tpip install numpy cython\n\tpip install git+https://github.com/alphacsc/alphacsc.git#egg=alphacsc\n\nIf you do not have admin privileges on the computer, use the ``--user`` flag\nwith ``pip``. To upgrade, use the ``--upgrade`` flag provided by ``pip``.\n\nTo check if everything worked fine, you can run:\n\n.. code::\n\n\tpython -c 'import alphacsc'\n\nand it should not give any error messages.\n\nQuickstart\n==========\n\nHere is an example to present briefly the API:\n\n.. code:: python\n\n    import numpy as np\n    import matplotlib.pyplot as plt\n    from alphacsc import BatchCDL\n\n    # Define the different dimensions of the problem\n    n_atoms = 10\n    n_times_atom = 50\n    n_channels = 5\n    n_trials = 10\n    n_times = 1000\n\n    # Generate a random set of signals\n    X = np.random.randn(n_trials, n_channels, n_times)\n\n    # Learn a dictionary with batch algorithm and rank1 constraints.\n    cdl = BatchCDL(n_atoms, n_times_atom, rank1=True)\n    cdl.fit(X)\n\n    # Display the learned atoms\n    fig, axes = plt.subplots(n_atoms, 2, num=\"Dictionary\")\n    for k in range(n_atoms):\n        axes[k, 0].plot(cdl.u_hat_[k])\n        axes[k, 1].plot(cdl.v_hat_[k])\n\n    axes[0, 0].set_title(\"Spatial map\")\n    axes[0, 1].set_title(\"Temporal map\")\n    for ax in axes.ravel():\n        ax.set_xticklabels([])\n        ax.set_yticklabels([])\n\n    plt.show()\n\nBug reports\n===========\n\nUse the `github issue tracker <https://github.com/alphacsc/alphacsc/issues>`_ to report bugs.\n\nCite our work\n=============\n\nIf you use this code in your project, please consider citing our work:\n\n.. [1] Dupr\u00e9 La Tour, T., Moreau, T., Jas, M., & Gramfort, A. (2018).\n\t`Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals\n\t<https://arxiv.org/abs/1805.09654v2>`_. Advances in Neural Information\n\tProcessing Systems (NIPS).\n\n.. [2] Jas, M., Dupr\u00e9 La Tour, T., \u015eim\u015fekli, U., & Gramfort, A. (2017). `Learning\n\tthe Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding\n\t<https://papers.nips.cc/paper/6710-learning-the-morphology-of-brain-signals-using-alpha-stable-convolutional-sparse-coding.pdf>`_.\n\tAdvances in Neural Information Processing Systems (NIPS), pages 1099--1108.",
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        "license": "BSD (3-clause)",
        "maintainer": "Mainak Jas",
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