{
    "info": {
        "author": "GenTex contributors",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Information Analysis"
        ],
        "description": "[![Documentation Status](https://readthedocs.org/projects/gentex/badge/?version=latest)](https://gentex.readthedocs.io/en/latest/?badge=latest)\n[![CircleCI](https://circleci.com/gh/NPann/GenTex.svg?style=svg)](https://circleci.com/gh/NPann/GenTex)\n\nGenTex stands for General Texture analysis.  \n\nThis package provides a suite of routines that combines standard texture analysis methods \nbased on [GLCM](https://en.wikipedia.org/wiki/Co-occurrence_matrix) \nand entropy/statistical complexity analysis methods.\n\n## What is this package for?\n\nGenTex provides a number of the standard algorithms required for generating \ncomplexity/texture measure estimates from multimodal imaging data. These include:\n\n1. Generation of multidimensional feature spaces from multimodal 'image' data \n(i.e. multiple 'co-registered' 1,2,3, or 4 dimensional data sets, e.g. \nmultiple 'co-registered' time series, multimodal image data, space/time data..) \nvia the use of a set of image templates, including:  \n\n    - single voxels\n    - linear sequences in cardinal directions (ref.)\n    - notches in cardinal directions (ref.)\n    - light cones in cardinal directions and 45 degree angles (ref.)\n\n2. Clustering methods for generating discrete ('grey') levels from the constructed \nfeature space (the levels are then typically mapped to the original image space at \nthe anchor points of the templates)\n\n3. Building co-occurrence matrices from a discrete level 'image' or a pair of \ndiscrete level 'images', where the discrete level 'images' are typically generated \nvia feature space clustering of the original multimodal data sets (time series, images, \nspace/time data...)\n\n4. Estimation of various complexity/texture measures from the co-occurrence matrices.\n(Haralick measures and epsilon machine related quantities) such as:\n\n    - CM Entropy\n    - EM Entropy\n    - Statistical Complexity\n    - Energy Uniformity\n    - Maximum Probability\n    - Contrast\n    - Inverse Difference Moment\n    - Correlation\n    - Probability of Run Length\n    - Epsilon Machine Run Length\n    - Run Length Asymmetry\n    - Homogeneity\n    - Cluster Tendency\n    - Multifractal Spectrum Energy Range\n    - Multifractal Spectrum Entropy Range\n\n### Documentation\n\nThe documentation on GenTex in hosted [here](https://gentex.readthedocs.io/en/latest/topics/quickstart.html)\n\n### Installation ###\n\n``` bash\npip install gentex\n```\n\n### Who do I talk to?\n\n- Karl Young (original developer)\n- Nicolas Pannetier \n- Norbert Schuff\n\n\n### License\n\nGenTex is licensed under the terms of the BSD license.\nPlease see the License file in the GenTex distribution\n\n\n### References\n\n* K. Young, Y. Chen, J. Kornak, G. B. Matson, N. Schuff,\n'Summarizing complexity in high dimensions',\nPhys Rev Lett. (2005) Mar 11;94(9):098701.\n\n* C. R. Shalizi and J. P. Crutchfield, 'Computational\nMechanics: Pattern and Prediction, Structure and Simplicity',\nJournal of Statistical Physics 104 (2001) 819--881.\n\n* K. Young and J. P. Crutchfield, 'Fluctuation Spectroscopy',\nChaos, Solitons, and Fractals 4 (1993) 5-39.\n\n* J. P. Crutchfield and K. Young, 'Computation at the\nOnset of Chaos', in Entropy, Complexity, and Physics of\nInformation, W. Zurek, editor, SFI Studies in the Sciences\nof Complexity, VIII, Addison-Wesley, Reading, Massachusetts\n(1990) 223-269.\n\n* C. R. Shalizi and J. P. Crutchfield, 'Computational\nMechanics: Pattern and Prediction, Structure and Simplicity',\nJournal of Statistical Physics 104 (2001) 819--881.",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/NPann/GenTex",
        "keywords": "",
        "license": "",
        "maintainer": "",
        "maintainer_email": "",
        "name": "gentex",
        "package_url": "https://pypi.org/project/gentex/",
        "platform": "",
        "project_url": "https://pypi.org/project/gentex/",
        "project_urls": {
            "Homepage": "https://github.com/NPann/GenTex"
        },
        "release_url": "https://pypi.org/project/gentex/0.1.2/",
        "requires_dist": null,
        "requires_python": ">=3.7",
        "summary": "General Texture Analysis",
        "version": "0.1.2"
    },
    "last_serial": 5929403,
    "releases": {
        "0.1.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "3561610f144b693f7f31ec2055bf239c",
                    "sha256": "8599ecab60c672201b3f76a9a4f502092f19f8ada5876a80a7d5b937b15a7c19"
                },
                "downloads": -1,
                "filename": "gentex-0.1.1-py3.7-linux-x86_64.egg",
                "has_sig": false,
                "md5_digest": "3561610f144b693f7f31ec2055bf239c",
                "packagetype": "bdist_egg",
                "python_version": "3.7",
                "requires_python": ">=3.7",
                "size": 60323,
                "upload_time": "2019-10-04T02:05:50",
                "url": "https://files.pythonhosted.org/packages/12/83/e3943bcae1ec634de705046f027bcdb8546ee870816b2cde31319ebaa7a3/gentex-0.1.1-py3.7-linux-x86_64.egg"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "6af5c1f7397736a5935cde1853791379",
                    "sha256": "faf38b6bfb2c31fa8e86d01153ee671922ac8ef3a035a0b3941509fd10a4d946"
                },
                "downloads": -1,
                "filename": "gentex-0.1.1.tar.gz",
                "has_sig": false,
                "md5_digest": "6af5c1f7397736a5935cde1853791379",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.7",
                "size": 25573,
                "upload_time": "2019-10-04T02:05:52",
                "url": "https://files.pythonhosted.org/packages/db/76/dcb9119bd4011e67ec9e8b68b4bedf00a56e54990eb64eedcb5b8d317cd0/gentex-0.1.1.tar.gz"
            }
        ],
        "0.1.2": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "570537a4bc281dc956a73326bb5dff50",
                    "sha256": "ae8310f1059fb81ff99cc860cc049f79595d62a133edd98f9e18a8dded9bcf88"
                },
                "downloads": -1,
                "filename": "gentex-0.1.2-py3.7-linux-x86_64.egg",
                "has_sig": false,
                "md5_digest": "570537a4bc281dc956a73326bb5dff50",
                "packagetype": "bdist_egg",
                "python_version": "3.7",
                "requires_python": ">=3.7",
                "size": 60328,
                "upload_time": "2019-10-04T17:23:49",
                "url": "https://files.pythonhosted.org/packages/da/4d/f25311a6e15e0ff5d3955af8e7a1a33a9eb90449878090f1660e61292a78/gentex-0.1.2-py3.7-linux-x86_64.egg"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "fc50304e5cd88963bb5f18c112020efb",
                    "sha256": "a5696e2252f44382df8420a7873aa2de286cc8eefa2b126addcff14780b8f893"
                },
                "downloads": -1,
                "filename": "gentex-0.1.2.tar.gz",
                "has_sig": false,
                "md5_digest": "fc50304e5cd88963bb5f18c112020efb",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.7",
                "size": 25578,
                "upload_time": "2019-10-04T17:23:50",
                "url": "https://files.pythonhosted.org/packages/d1/96/2932365dc16f9a51b3e7082dc41c317b3166307b175ef6bbe49abbb10370/gentex-0.1.2.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "570537a4bc281dc956a73326bb5dff50",
                "sha256": "ae8310f1059fb81ff99cc860cc049f79595d62a133edd98f9e18a8dded9bcf88"
            },
            "downloads": -1,
            "filename": "gentex-0.1.2-py3.7-linux-x86_64.egg",
            "has_sig": false,
            "md5_digest": "570537a4bc281dc956a73326bb5dff50",
            "packagetype": "bdist_egg",
            "python_version": "3.7",
            "requires_python": ">=3.7",
            "size": 60328,
            "upload_time": "2019-10-04T17:23:49",
            "url": "https://files.pythonhosted.org/packages/da/4d/f25311a6e15e0ff5d3955af8e7a1a33a9eb90449878090f1660e61292a78/gentex-0.1.2-py3.7-linux-x86_64.egg"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "fc50304e5cd88963bb5f18c112020efb",
                "sha256": "a5696e2252f44382df8420a7873aa2de286cc8eefa2b126addcff14780b8f893"
            },
            "downloads": -1,
            "filename": "gentex-0.1.2.tar.gz",
            "has_sig": false,
            "md5_digest": "fc50304e5cd88963bb5f18c112020efb",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.7",
            "size": 25578,
            "upload_time": "2019-10-04T17:23:50",
            "url": "https://files.pythonhosted.org/packages/d1/96/2932365dc16f9a51b3e7082dc41c317b3166307b175ef6bbe49abbb10370/gentex-0.1.2.tar.gz"
        }
    ]
}