{ "info": { "author": "Michael Katz", "author_email": "mikekatz04@gmail.com", "bugtrack_url": null, "classifiers": [ "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "#########################################################\ngwsnrcalc package\n#########################################################\n\n``gwsnrcalc`` is a package designed for fast signal-to-noise ratio (SNR) calculations for single gravitational wave sources using a matched filtering SNR approach. It was originally designed to support `BOWIE`_ for `Evaluating Black Hole Detectability with LISA` (`arXiv:1807.02511`_). It provides a fast SNR calculator, frequency-domain amplitude waveforms for binary black holes, and a SNR grid generator for binary black holes.\n\n.. _BOWIE: https://mikekatz04.github.io/BOWIE/\n.. _arXiv:1807.02511: https://arxiv.org/abs/\n\nThe main snr function is ``gwsnrcalc.gw_snr_calculator.snr``. It has the capability to perform calculations in parallel across processors for faster calculation.\n\nThe waveform generator (``gwsnrcalc.utils.waveforms``) creates either circular or eccentric waveforms.\n\nCircular waveforms are created with PhenomD amplitude waveforms for binary black hole inspiral, merger, and ringdown. PhenomD is from Husa et al 2016 (`arXiv:1508.07250`_) and Khan et al 2016 (`arXiv:1508.07253`_). The current waveforms returned are in units of characteristic strain.\n\nEccentric waveforms are generated according to Peters evolution only for the inspiral phase.\n\n.. _arXiv:1508.07250: https://arxiv.org/abs/1508.07250\n.. _arXiv:1508.07253: https://arxiv.org/abs/1508.07253\n\nThe snr grid generator: ``gwsnrcalc.generate_contour_data`` uses ``gwsnrcalc.gw_snr_calculator.snr`` to create SNR grids for contour plots (like those used in BOWIE).\n\nAvailable via pip and on github: https://github.com/mikekatz04/BOWIE/\n\nGetting Started\n===============\n\nThese instructions will get you a copy of the project up and running on your local machine for usage and testing purposes.\n\nPrerequisites\n=============\n\nSoftware installation/usage only requires a few specific libraries in python. All libraries are included with Anaconda. If you do not run python in an anaconda environment, you will need the following libraries and modules to run with all capabilities: Numpy, Scipy, collections, sys, json, multiprocessing, datetime, time, astropy, and h5py. All can be installed with pip. For example, within your python environment of choice:\n\n``pip install astropy``\n\nIn order to properly create waveforms with ctypes, you will need complex, gsl, and math c libraries. For installing gsl, refer to https://www.gnu.org/software/gsl/ or install it through anaconda.\n\nInstallation\n=============\n\nInstallation is done two ways:\n\n1) using pip\n\n ``pip install gwsnrcalc``\n\n This will download the all necessary packages to your current environment. It will not download the notebooks for testing and example usage.\n\n2) Clone the git repo on the command line, or downloading it from github. This is for all the modules, example jupyter notebooks, and extra files. This method will include BOWIE. To just download specific files that do not come with pip (e.g. jupyter notebook with examples), just download the files from the github.\n\n a) navigate to the directory of your choice.\n\n b) clone the git repo on the command line.\n\n ``git clone https://github.com/mikekatz04/BOWIE.git``\n\n c) run setup.py to add the modules to your environment and compile the c codes.\n\n ``python ./setup.py install``\n\nTesting and Running an Example\n==============================\n\nTo test the codes, you run the guide notebook.\n\n``jupyter notebook pyphenomd_guide.ipynb``\n\nContributing\n============\n\nPlease read `CONTRIBUTING.md`_ for details on our code of conduct, and the process for submitting pull requests to us.\n\n.. _CONTRIBUTING.md: https://gist.github.com/PurpleBooth/b24679402957c63ec426\n\nVersioning\n=============\n\nCurrent version is 1.0.0.\n\nWe use `SemVer`_ for versioning.\n\n.. _SemVer: http://semver.org/\n\nAuthors\n=======\n\n* **Michael Katz** - `mikekatz04`_\n\n.. _mikekatz04: https://github.com/mikekatz04/\n\nPlease email the author with any bugs or requests.\n\nLicense\n=======\n\nThis project is licensed under the GNU License - see the `LICENSE.md`_ file for details.\n\n.. _LICENSE.md: https://github.com/mikekatz04/BOWIE/blob/master/LICENSE\n\nAcknowledgments\n===============\n\n* Thanks to Michael Puerrer, Sebastian Khan, Frank Ohme, Ofek Birnholtz, Lionel London for authorship of the original c code for PhenomD within LALsuite.\n\n\n", "description_content_type": "text/x-rst", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/mikekatz04/BOWIE/snr_calculator_folder", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "gwsnrcalc", "package_url": "https://pypi.org/project/gwsnrcalc/", "platform": "", "project_url": "https://pypi.org/project/gwsnrcalc/", "project_urls": { "Homepage": "https://github.com/mikekatz04/BOWIE/snr_calculator_folder" }, "release_url": "https://pypi.org/project/gwsnrcalc/1.0.0/", "requires_dist": [ "numpy", "scipy", "astropy", "h5py" ], "requires_python": "", "summary": "Gravitational waveforms and snr.", "version": "1.0.0" }, "last_serial": 4607458, "releases": { "1.0.0": [ { "comment_text": "", "digests": { "md5": "9536961f38996025d5a3b51798b9dc62", "sha256": "9f31a12599c0746bf89af7ef6b3587b01bee32d4f8654770df2f2eb79adc063b" }, "downloads": -1, "filename": "gwsnrcalc-1.0.0-cp35-cp35m-macosx_10_6_x86_64.whl", "has_sig": false, "md5_digest": "9536961f38996025d5a3b51798b9dc62", "packagetype": "bdist_wheel", "python_version": "cp35", "requires_python": null, "size": 7458914, "upload_time": "2018-12-17T10:48:52", "url": "https://files.pythonhosted.org/packages/9f/8e/d2ed181a8ba2314f3abe4cac2af55870b8955ff1078654d4d11935f57db5/gwsnrcalc-1.0.0-cp35-cp35m-macosx_10_6_x86_64.whl" }, { "comment_text": "", "digests": { "md5": "a5359ef8932672ed77f424bde53b5acb", "sha256": "4999928170b021e3f5c9660bc074eec4515ce8b0479c7671117d54f0421dbf06" }, "downloads": -1, "filename": "gwsnrcalc-1.0.0.tar.gz", "has_sig": false, "md5_digest": "a5359ef8932672ed77f424bde53b5acb", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 7264415, "upload_time": "2018-12-17T10:50:09", "url": "https://files.pythonhosted.org/packages/d8/6d/cd0b0d74c3e868a7016beafca041ed2ca5114fba137e64dd48cebdcb90e7/gwsnrcalc-1.0.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "9536961f38996025d5a3b51798b9dc62", "sha256": "9f31a12599c0746bf89af7ef6b3587b01bee32d4f8654770df2f2eb79adc063b" }, "downloads": -1, "filename": "gwsnrcalc-1.0.0-cp35-cp35m-macosx_10_6_x86_64.whl", "has_sig": false, "md5_digest": "9536961f38996025d5a3b51798b9dc62", "packagetype": "bdist_wheel", "python_version": "cp35", "requires_python": null, "size": 7458914, "upload_time": "2018-12-17T10:48:52", "url": "https://files.pythonhosted.org/packages/9f/8e/d2ed181a8ba2314f3abe4cac2af55870b8955ff1078654d4d11935f57db5/gwsnrcalc-1.0.0-cp35-cp35m-macosx_10_6_x86_64.whl" }, { "comment_text": "", "digests": { "md5": "a5359ef8932672ed77f424bde53b5acb", "sha256": "4999928170b021e3f5c9660bc074eec4515ce8b0479c7671117d54f0421dbf06" }, "downloads": -1, "filename": "gwsnrcalc-1.0.0.tar.gz", "has_sig": false, "md5_digest": "a5359ef8932672ed77f424bde53b5acb", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 7264415, "upload_time": "2018-12-17T10:50:09", "url": "https://files.pythonhosted.org/packages/d8/6d/cd0b0d74c3e868a7016beafca041ed2ca5114fba137e64dd48cebdcb90e7/gwsnrcalc-1.0.0.tar.gz" } ] }