{ "info": { "author": "Open Risk", "author_email": "info@openrisk.eu", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Financial and Insurance Industry", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "transitionMatrix\n=========================\n\ntransitionMatrix is a Python powered library for the statistical analysis and visualization of state transition phenomena.\nIt can be used to analyze any dataset that captures timestamped transitions in a discrete state space.\nUse cases include credit rating transitions, system state event logs and more.\n\n* Author: `Open Risk `_\n* License: Apache 2.0\n* Code Documentation: `Read The Docs `_\n* Mathematical Documentation: `Open Risk Manual `_\n* Training: `Open Risk Academy `_\n* Development Website: `Github `_\n* Discussion: `Gitter `_\n* Production Instance: `OpenCPM `_\n\n\nFunctionality\n-------------\n\nYou can use transitionMatrix to\n\n- Estimate transition matrices from historical event data using a variety of estimators\n- Visualize event data and transition matrices\n- Characterise transition matrices\n- Manipulate transition matrices (derive generators, perform comparisons, stress transition rates etc.)\n- Access standardized datasets for testing\n\n**NB: transitionMatrix is still in active development. If you encounter issues please raise them in our\ngithub repository**\n\nArchitecture\n------------\n\n* transitioMatrix supports file input/output in json and csv formats\n* it has a powerful API for handling event data (based on pandas)\n* provides intuitive objects for handling transition matrices individually and as sets (based on numpy)\n* supports visualization using matplotlib\n\nLinks to other open source software\n-----------------------------------\n\n- Duration based estimators are similar to etm, an R package for estimating empirical transition matrices\n- There is some overlap with lower dimensionality (survival) models like lifelines\n\nInstallation\n=======================\n\nYou can install and use the transitionMatrix package in any system that supports the `Scipy ecosystem of tools `_\n\nDependencies\n-----------------\n\n- TransitionMatrix requires Python 3\n- It depends on numerical and data processing Python libraries (Numpy, Scipy, Pandas)\n- The Visualization API depends on Matplotlib\n- The precise dependencies are listed in the requirements.txt file.\n- TransitionMatrix may work with earlier versions of these packages but this has not been tested.\n\nFrom PyPi\n-------------\n\n.. code:: bash\n\n pip3 install pandas\n pip3 install matplotlib\n pip3 install transitionMatrix\n\nFrom sources\n-------------\n\nDownload the sources to your preferred directory:\n\n.. code:: bash\n\n git clone https://github.com/open-risk/transitionMatrix\n\n\nUsing virtualenv\n----------------\n\nIt is advisable to install the package in a virtualenv so as not to interfere with your system's python distribution\n\n.. code:: bash\n\n virtualenv -p python3 tm_test\n source tm_test/bin/activate\n\nIf you do not have pandas already installed make sure you install it first (will also install numpy)\n\n.. code:: bash\n\n pip3 install pandas\n pip3 install matplotlib\n pip3 install -r requirements.txt\n\nFinally issue the install command and you are ready to go!\n\n.. code:: bash\n\n python3 setup.py install\n\nFile structure\n-----------------\nThe distribution has the following structure:\n\n| transitionMatrix The library source code\n| model.py Main data structures\n| estimators Estimator methods\n| utils Helper classes and methods\n| thresholds Algorithms for calibrating AR(n) process thresholds to input transition rates\n| portfolio_model_lib Collection of portfolio analytic solutions\n| examples Usage examples\n| datasets Contains a variety of datasets useful for getting started with transitionMatrix\n| tests Testing suite\n\nTesting\n----------------------\n\nIt is a good idea to run the test-suite. Before you get started:\n\n- Adjust the source directory path in transitionMatrix/__init__ and then issue the following in at the root of the distribution\n- Unzip the data files in the datasets directory\n\n.. code:: bash\n\n python3 test.py\n\nGetting Started\n=======================\n\nCheck the Usage pages in this documentation\n\nLook at the examples directory for a variety of typical workflows.\n\nFor more in depth study, the Open Risk Academy has courses elaborating on the use of the library\n\n- Analysis of Credit Migration using Python TransitionMatrix: https://www.openriskacademy.com/course/view.php?id=38", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/open-risk/transitionMatrix", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "transitionMatrix", "package_url": "https://pypi.org/project/transitionMatrix/", "platform": "", "project_url": "https://pypi.org/project/transitionMatrix/", "project_urls": { "Homepage": "https://github.com/open-risk/transitionMatrix" }, "release_url": "https://pypi.org/project/transitionMatrix/0.4.0/", "requires_dist": null, "requires_python": "", "summary": "A Python powered library for statistical analysis and visualization of state transition phenomena", "version": "0.4.0" }, "last_serial": 4405693, "releases": { "0.4.0": [ { "comment_text": "", "digests": { "md5": "a5f9055f48e57e18ccb5d760ca2e20d2", "sha256": "10c1f0ce2697281faf8d281f41fe46a1b7893f779f2717b32cc950cb5c86f9b3" }, "downloads": -1, "filename": "transitionMatrix-0.4.0-py3.4.egg", "has_sig": false, "md5_digest": "a5f9055f48e57e18ccb5d760ca2e20d2", "packagetype": "bdist_egg", "python_version": "3.4", "requires_python": null, "size": 4123700, "upload_time": "2018-10-23T09:21:50", "url": "https://files.pythonhosted.org/packages/ac/ee/48ff6742b2f983e2040abb2f600dcde5c2d4761a8ffae6ddaeacc80bf6c9/transitionMatrix-0.4.0-py3.4.egg" }, { "comment_text": "", "digests": { "md5": "3105cb98cd3d081b6fd6779f5bd83d29", "sha256": "7daf0e66479f79b79539554ed267db5458f0090031a651419005f977a44c7284" }, "downloads": -1, "filename": "transitionMatrix-0.4.0.tar.gz", "has_sig": false, "md5_digest": "3105cb98cd3d081b6fd6779f5bd83d29", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4516759, "upload_time": "2018-10-23T09:21:55", "url": "https://files.pythonhosted.org/packages/c5/21/bc7b2f2554c6bb977dee20fe9113f60a8548212a9fff5dc9434217d20dc1/transitionMatrix-0.4.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "a5f9055f48e57e18ccb5d760ca2e20d2", "sha256": "10c1f0ce2697281faf8d281f41fe46a1b7893f779f2717b32cc950cb5c86f9b3" }, "downloads": -1, "filename": "transitionMatrix-0.4.0-py3.4.egg", "has_sig": false, "md5_digest": "a5f9055f48e57e18ccb5d760ca2e20d2", "packagetype": "bdist_egg", "python_version": "3.4", "requires_python": null, "size": 4123700, "upload_time": "2018-10-23T09:21:50", "url": "https://files.pythonhosted.org/packages/ac/ee/48ff6742b2f983e2040abb2f600dcde5c2d4761a8ffae6ddaeacc80bf6c9/transitionMatrix-0.4.0-py3.4.egg" }, { "comment_text": "", "digests": { "md5": "3105cb98cd3d081b6fd6779f5bd83d29", "sha256": "7daf0e66479f79b79539554ed267db5458f0090031a651419005f977a44c7284" }, "downloads": -1, "filename": "transitionMatrix-0.4.0.tar.gz", "has_sig": false, "md5_digest": "3105cb98cd3d081b6fd6779f5bd83d29", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4516759, "upload_time": "2018-10-23T09:21:55", "url": "https://files.pythonhosted.org/packages/c5/21/bc7b2f2554c6bb977dee20fe9113f60a8548212a9fff5dc9434217d20dc1/transitionMatrix-0.4.0.tar.gz" } ] }