{ "info": { "author": "Gabriele Orlando", "author_email": "orlando.gabriele89@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)", "Operating System :: OS Independent", "Programming Language :: Python :: 2" ], "description": "### INSTALLATION ###\nShiftCrypt have been tested on Linux machines only. It may require additional installation steps in order to work on other operating systems. If you need support, please contact gabriele.orlando@vub.be or wim.vranken@vub.be.\n\nPlease note that the PyPI package have limited functionalities. For the full shiftcrypt version, that allows custom encoding schemes and retraining of the neural network, please refer to:\n```\nhttps://bitbucket.org/grogdrinker/shiftcrypt\n```\nto install the package simply run\n```\nsudo pip install shiftcrypt\n```\nThe aforementioned command will install:\n - A command line based standalone (shiftcrypt)\n - A python2 package (shiftcrypt_pkg)\n\n### DEPENDENCIES:\n\nthe following python packages are required:\n\n- python2.7\n- scipy (tested with version 0.19.1)\n- numpy (tested with version 1.14.3)\n- sklearn (tested with version 0.19.1)\n- pytorch (see https://pytorch.org/ for an easy installation guide. Tested with version 0.3.1)\n\n@@ if you experience a memory error when installing pytorch, please use the --no-cache-dir installation option (for example pip --no-cache-dir install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp27-cp27mu-linux_x86_64.whl) @@\n\nall these packages are available using pip or anaconda. See also the requirements.txt file.\n\n### USAGE ###\n\nThe tool takes as input NMR Exchange Format (NEF) or NMR-STAR v2.1 files.\n\n#### ShiftCrypt Standaolne\n```\nshiftcrypt [options] -i inputFile\n```\nto test the example input, run\n```\nshiftcrypt -i TEST_SA\n```\nThe tool should generate the results in a couple of seconds.\n@@@ please remember that if you have several missing chemical shift values, you should use the reduced model (option -m 2)\n\n- -h --> show the help\n- -i --> input nef file file\n- -o --> output file\n- -m --> model to use\n\nShiftCrypt can be used with different encoding schemes:\n- -m 1 --> the model that uses the full set of chemical shifts as described in the paper. It may fail to transform some of the residues due to missing chemical shifts data\n- -m 2 --> the model that uses only H,CA,N,CB,C chemical shifts data (H,CA,N and HA,CA,CB for Gly and Pro respectively). It is a good alternative when dealing with experiments wit ha lot of missing data\n- -m 3 --> the model that uses only N and H atoms (HA,CA,CB for Pro). It has not been used for this work\n\nWith the GIT version, it is possible to build a custom encoding scheme and to train a custom model\n\nthe output is made of two columns: the first one contains the residue name, the second one the ShiftCrypt index. \nIf the ShiftCrypt output value is equal to -10, it means that the chemical shifts for that residues were not sufficient to perform the transformation\n\n#### ShiftCrypt Python package\n\nto use the python package, import ShiftCrypt with:\n```\nfrom shiftcrypt_pkg.main_class import shiftcript\n```\nThe class has the followng args:\n- [optional] model= 1, 2 or 3 (default=2). The model to use\n\nThe class has the following Methods:\n- transform ( STAR_File ) : Calculates the ShiftCrypt values. Output: list. ith value corresponds to the ShiftCrypt value of ith residue. -10 means there are too many missing values for that specific residue\n- test() : Tests the package\nexample:\n```\nfrom shiftcrypt_pkg.main_class import shiftcript\nsci=shiftcript()\nsci.test()\n```\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://bitbucket.org/grogdrinker/shiftcryp", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "shiftcrypt", "package_url": "https://pypi.org/project/shiftcrypt/", "platform": "", "project_url": "https://pypi.org/project/shiftcrypt/", "project_urls": { "Homepage": "https://bitbucket.org/grogdrinker/shiftcryp" }, "release_url": "https://pypi.org/project/shiftcrypt/0.11/", "requires_dist": [ "numpy", "scipy", "sklearn", "torch", "torchvision" ], "requires_python": "", "summary": "Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index", "version": "0.11" }, "last_serial": 5216869, "releases": { "0.11": [ { "comment_text": "", "digests": { "md5": "a9fe7426462659bc76c55fcd27c20286", "sha256": "ccae09a7bc158a5e9de1e45bcc6acb884070ff0246fe548b2ea61cab1d4bdc19" }, "downloads": -1, "filename": "shiftcrypt-0.11-py2-none-any.whl", "has_sig": false, "md5_digest": "a9fe7426462659bc76c55fcd27c20286", "packagetype": "bdist_wheel", "python_version": "py2", "requires_python": null, "size": 1517331, "upload_time": "2019-04-25T13:58:20", "url": "https://files.pythonhosted.org/packages/33/51/1d8c3242c91d47179ada850156edadd9e94f2b84245e487b4751f5cdcaf7/shiftcrypt-0.11-py2-none-any.whl" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "a9fe7426462659bc76c55fcd27c20286", "sha256": "ccae09a7bc158a5e9de1e45bcc6acb884070ff0246fe548b2ea61cab1d4bdc19" }, "downloads": -1, "filename": "shiftcrypt-0.11-py2-none-any.whl", "has_sig": false, "md5_digest": "a9fe7426462659bc76c55fcd27c20286", "packagetype": "bdist_wheel", "python_version": "py2", "requires_python": null, "size": 1517331, "upload_time": "2019-04-25T13:58:20", "url": "https://files.pythonhosted.org/packages/33/51/1d8c3242c91d47179ada850156edadd9e94f2b84245e487b4751f5cdcaf7/shiftcrypt-0.11-py2-none-any.whl" } ] }