{
    "info": {
        "author": "Maxime Woringer, Zachary Harmany",
        "author_email": "maxime.woringer@ens.fr",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
        ],
        "description": "pySPIRAL-TAP, a Python version of SPIRAL-TAP\n--------------------------------------------\n\n# Inspiration\n\nEverything comes from the code by [Zachary Harmany](http://drz.ac) (harmany@wisc.edu).\n\nOriginal source: http://drz.ac/code/spiraltap/ or https://github.com/zharmany/SPIRAL-TAP (might be more up-to-date). The algorithm is described in this paper:\n> Z. T. Harmany, R. F. Marcia, and R. M. Willett, \"This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms \u2013 Theory and Practice,\" IEEE Transactions on Image Processing, vol. 21, pp. 1084\u20131096, Mar. 2012.\n\n# Disclaimer\n\n2. *Code*: still experimental.\n\n# Install\n## Requirements\n`pySPIRALTAP` requires the following dependencies:\n- `numpy`\n- `rwt`, the Rice Wavelet Toolbox, available on this page: https://github.com/ricedsp/rwt\n- `scipy.io` (only to run the demo)\n- `pytest` (only to run the tests)\n\n## Installing `numpy`\nInstallation using your package manager (Debian/Ubuntu):\n\n```{shell}\nsudo apt-get install python-numpy\n```\n\nAlternatively, if you have `pip` installed, you can install `numpy` by typing the following:\n\n```{shell}\npip install --user numpy\n```\n\n## Installing `rwt`\nYou can install the [Rice Wavelet Toolbox](https://github.com/ricedsp/rwt) by typing the following. Before that, make sure that you have `cmake` installed (`sudo apt-get install cmake` if you use a Debian-derived distribution):\n\n```{shell}\ngit clone https://github.com/ricedsp/rwt.git\ncd rwt/python\ncmake .\nsudo make install\n```\n\nAnd then test if the installation succeeded by typing:\n\n```{shell}\npython -c \"import rwt\"\n```\n\nIf this returns nothing, the installation worked. In case it returns an error such as:\n\n```{python}\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nImportError: No module named rwt\n```\n\nThis means that the installation failed. It can be because the installation was performed using a different version of Python that is not the default version. So far, I do not have information about how to perform this installation.\n\nMore information about the installation can be found in the `INSTALL` file, or on the `rwt` [webpage](https://github.com/ricedsp/rwt).\n\n## Installing `pySPIRALTAP`\n\nInstallation can be performed using the following `shell` commands:\n\n```{shell}\ngit clone https://gitlab.com/padouppadoup/pySPIRAL-TAP.git\ncd pySPIRAL-TAP\nsudo python setup.py install\n```\n\n# Usage/Example\n\n## Demo examples\nA simple working example can be run at (in the main folder):\n\n```{shell}\npython SPIRALdemo.py\n```\n\nThis file contains one demo where a 1D signal is reconstructed using a $l1$ penalty. This demo uses `matplotlib` for plotting output.\n\nAlternatively, one can play with the [Jupyter notebook](http://jupyter.org): `SPIRALdemo.ipynb`, that feature the same demo as `SPIRALdemo.py`, but in a more fancy format.\n\n## Calling from a script\nThe pySPIRALTAP methods can be imported with `import pySPIRALTAP`.\n\n## `SPIRALTAP` function parameters\n\nHere is a canonical function call with many parameters exposed:\n\n```{python}\n    resSPIRAL = pySPIRALTAP.SPIRALTAP(y,A,              # y: measured signal, A: projection matrix\n                                              1e-6,             # regularization parameter\n                                      maxiter=100,      # min. number of iterations\n                                      miniter=5,        # max. number of iterations\n                                      penalty='canonical', # type of penalty to apply\n                                      noisetype='gaussian',# form of the log-likelihood penalty\n                                      stopcriterion=3,  # index of the termination criterion\n                                      tolerance=1e-8,\n                                      alphainit=1,\n                                      alphamin=1e-30,\n                                      alphamax=1e30,\n                                      alphaaccept=1e30,\n                                      logepsilon=1e-10,\n                                      saveobjective=True,\n                                      savereconerror=True,\n                                      savesolutionpath=False,\n                                      verbose=verbose, savecputime=True)\n```\n\n# Status\nThe methods based on RDP (*recursive dyadic partitions*) have not been implemented yet. Additionnally, the code has not been fully tested, although we did our best to provide a working product.\n\n# License\n- This software is released under the MIT license. See the `LICENSE` file for more details.\n- The `denoise_bound` code is released under the GNU GPLv2 license and was written by Copyright (2008): Amir Beck and Marc Teboulle.",
        "description_content_type": null,
        "docs_url": null,
        "download_url": null,
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://gitlab.com/padouppadoup/pySPIRAL-TAP/",
        "keywords": "compressed sensing",
        "license": "unknown",
        "maintainer": null,
        "maintainer_email": null,
        "name": "pySPIRALTAP",
        "package_url": "https://pypi.org/project/pySPIRALTAP/",
        "platform": "UNKNOWN",
        "project_url": "https://pypi.org/project/pySPIRALTAP/",
        "project_urls": {
            "Homepage": "https://gitlab.com/padouppadoup/pySPIRAL-TAP/"
        },
        "release_url": "https://pypi.org/project/pySPIRALTAP/0.4.12/",
        "requires_dist": null,
        "requires_python": null,
        "summary": "A compressed sensing library for Python",
        "version": "0.4.12"
    },
    "last_serial": 2746961,
    "releases": {
        "0.4.12": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "b949fafacc82344963bfbd34a45edb48",
                    "sha256": "0703e952d439f35f2078e054ef4cdbd50110a8710e80d6c28b66ddf3e4d8c412"
                },
                "downloads": -1,
                "filename": "pySPIRALTAP-0.4.12.tar.gz",
                "has_sig": false,
                "md5_digest": "b949fafacc82344963bfbd34a45edb48",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 18024,
                "upload_time": "2017-04-02T04:41:26",
                "url": "https://files.pythonhosted.org/packages/d4/36/fa1ef2b7a192b9db6e440c1960c6d6aac3720b95f0ab824e6154f4fbfa8d/pySPIRALTAP-0.4.12.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "b949fafacc82344963bfbd34a45edb48",
                "sha256": "0703e952d439f35f2078e054ef4cdbd50110a8710e80d6c28b66ddf3e4d8c412"
            },
            "downloads": -1,
            "filename": "pySPIRALTAP-0.4.12.tar.gz",
            "has_sig": false,
            "md5_digest": "b949fafacc82344963bfbd34a45edb48",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 18024,
            "upload_time": "2017-04-02T04:41:26",
            "url": "https://files.pythonhosted.org/packages/d4/36/fa1ef2b7a192b9db6e440c1960c6d6aac3720b95f0ab824e6154f4fbfa8d/pySPIRALTAP-0.4.12.tar.gz"
        }
    ]
}