{ "info": { "author": "Bruno P. Kinoshita", "author_email": "brunodepaulak@yahoo.com.br", "bugtrack_url": null, "classifiers": [], "description": "PCCORA\n======\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1163385.svg\n :target: https://doi.org/10.5281/zenodo.1163385\n\n.. image:: https://travis-ci.org/kinow/pccora.svg?branch=master\n :target: https://travis-ci.org/kinow/pccora\n\n.. image:: https://coveralls.io/repos/github/kinow/pccora/badge.svg?branch=master\n :target: https://coveralls.io/github/kinow/pccora?branch=master\n\n\nPC-CORA parser for Python. Supports the format described at ``_ (accessed at 2015-12-05).\n\nThis format is used for `radiosonde data `_.\n\n A radiosonde (Sonde is French and German for probe) is a battery-powered telemetry instrument package carried into the atmosphere usually by a weather balloon that measures various atmospheric parameters and transmits them by radio to a ground receiver. (Wikipedia)\n\nThis format is produced by old `Vaisala `_ equipments. Newer data is probably available in the NetCDF.\n\nHistory\n-------\n\nI was asked by a co-worker to look at some Python code with a PC-CORA parser.\nThis co-worker also needed further analysis and processing, involving some\ndata being created as CSV, netCDF, or plotted.\n\nI decided to write a module for PC-CORA inspired by the\n`original script `_,\nbut using Python3, OO, and packaging as a Python package to be distributed\nto the `PYPI `_.\n\nThis way we could use it in scripts, or other internal applications. And it\nwould also be easier for others to find it and re-use.\n\nThe code in this repository was used on a `Doctoral Thesis\n`_ published in 2018,\nabout radiosonde, GCOS, radio occultation, and weather prediction.\n\nExample\n-------\n\n >>> from pccora import PCCORAParser\n >>> pccora_parser = PCCORAParser()\n >>> pccora_parser.parse_file('./123456789.EDT')\n >>> print(pccora_parser.get_header())\n >>> print(pccora_parser.get_identification())\n >>> print(pccora_parser.get_data())\n\nObtaining Data\n--------------\n\nThere are datasets available at the `CEDA website\n`_ (Centre for Environmental Data Archival),\nhowever, access is restricted.\n\n`NOAA's ESRL `_ (Earth System Research Laboratory)\nhas an FTP server with some data in the the old PC-CORA sounding data format.\nJust search for FTP for instructions on how to access the Physical Sciences\nDivision FTP server. Some valid files can be found at\n`/psd3/cruises/AERO_1999/RHB/balloon/Raw` (accessed 2016-01-17).\n\nRequirements\n------------\n\nPython 3.6 or superior, and the `construct library\n`_ are the minimum requirements.\n\nInstallation\n------------\n\n pip install pccora\n\nOr, to use the bleeding edge version, git clone this repository, and have a\nlook at the scripts folders for an example how to use the module from\nwithin a local folder. You may have to uninstall the pip module first.\n\n python setup.py install\n\nThe PYPI URL is ``_.\n\nLicense\n-------\n\nLicensed under the MIT License.", "description_content_type": "text/x-rst", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://github.com/niwa/pccora", "keywords": "sounding file,radiosonde,vaisala,pccora,atmosphere", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "pccora", "package_url": "https://pypi.org/project/pccora/", "platform": "any", "project_url": "https://pypi.org/project/pccora/", "project_urls": { "Documentation": "https://pypi.org/project/pccora/", "Homepage": "http://github.com/niwa/pccora", "Source": "https://github.com/kinow/pccora", "Tracker": "https://github.com/kinow/pccora/issues" }, "release_url": "https://pypi.org/project/pccora/0.3/", "requires_dist": null, "requires_python": ">=3.3", "summary": "PC-CORA sounding data files parser for Python", "version": "0.3" }, "last_serial": 4510218, "releases": { "0.0.1": [ { "comment_text": "", "digests": { "md5": "4497c2e5180a87858631bd3c44e8189e", "sha256": "b94eb9a903bad48078e8ca9a73283c9ce9aca44c4e84b6a1a8805f5cbf7418b2" }, "downloads": -1, "filename": "pccora-0.0.1-py2-none-any.whl", "has_sig": true, "md5_digest": "4497c2e5180a87858631bd3c44e8189e", "packagetype": "bdist_wheel", "python_version": "py2", "requires_python": null, "size": 4283, "upload_time": "2015-12-08T00:39:16", "url": "https://files.pythonhosted.org/packages/1a/3c/e40a24d773d4838975762aa6d67773dcd76c43483b247165cec63a9d7aa6/pccora-0.0.1-py2-none-any.whl" }, { "comment_text": "", "digests": { "md5": "0357fc0df70104b3d233a0a8adba6657", "sha256": "02f3ff7bbd8d971ca801ede0783f3bef8aeef63ace634bf7ce5e024af978fa2a" }, "downloads": -1, "filename": "pccora-0.0.1.tar.gz", "has_sig": true, "md5_digest": "0357fc0df70104b3d233a0a8adba6657", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4154, "upload_time": "2015-12-08T00:39:26", "url": "https://files.pythonhosted.org/packages/e7/57/2fadeba24a2279e9800b0ba4f59ae5e3e7b8505d249af10ebe0fc1e201fb/pccora-0.0.1.tar.gz" } ], "0.1": [ { "comment_text": "", "digests": { "md5": "7885f234494d2d8dc7f9a4cd1800b394", "sha256": "ed06c4efab8b3435250198f6ed108093025ff3b34c3d86d2d7ec13e89e6c6a0d" }, "downloads": -1, "filename": "pccora-0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "7885f234494d2d8dc7f9a4cd1800b394", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 4926, "upload_time": "2016-01-17T05:30:29", "url": "https://files.pythonhosted.org/packages/69/c7/daa2899d0b822bc1cd86278cd2e875a004db2110725b9ad3fbab7d9eab75/pccora-0.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "4fb988425a5bf2c41bfaaa8bbaf0cac7", "sha256": "2e4afaa7ee15612a3933095c01fed3437a777e84ca116ba455fe39bf9cd7b441" }, "downloads": -1, "filename": "pccora-0.1.tar.gz", "has_sig": false, "md5_digest": "4fb988425a5bf2c41bfaaa8bbaf0cac7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4657, "upload_time": "2016-01-17T05:30:35", "url": "https://files.pythonhosted.org/packages/9f/28/d09a01b32c036268db0526460b3ce77496525fb2ab968ccbf60b36415ab5/pccora-0.1.tar.gz" } ], "0.2": [ { "comment_text": "", "digests": { "md5": "7c21392ee72c0580dafa4b0ea7e54dad", "sha256": "a6edcc5a6cc55dd72555a6640ae2445c5bf567f40ece26c0b6f077344fd0bfe6" }, "downloads": -1, "filename": "pccora-0.2.tar.gz", "has_sig": false, "md5_digest": "7c21392ee72c0580dafa4b0ea7e54dad", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 5195, "upload_time": "2018-01-31T12:09:03", "url": "https://files.pythonhosted.org/packages/ab/35/e4006b955a0c56b81fb2f6a2d069e0d94a06aeec8e3065cdc82cd6657b43/pccora-0.2.tar.gz" } ], "0.3": [ { "comment_text": "", "digests": { "md5": "0d3a82756f7831ba2819957ad84c6c48", "sha256": "09a834e6ab3181687271aa9b460ff5499f24ef719a0ef8406eb96da9f3edc609" }, "downloads": -1, "filename": "pccora-0.3.tar.gz", "has_sig": false, "md5_digest": "0d3a82756f7831ba2819957ad84c6c48", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.3", "size": 8538, "upload_time": "2018-11-21T00:52:22", "url": "https://files.pythonhosted.org/packages/7f/51/0aad04d2c185ba5a802cdc814471408651a94fd11c24da95fc9690afdcbe/pccora-0.3.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "0d3a82756f7831ba2819957ad84c6c48", "sha256": "09a834e6ab3181687271aa9b460ff5499f24ef719a0ef8406eb96da9f3edc609" }, "downloads": -1, "filename": "pccora-0.3.tar.gz", "has_sig": false, "md5_digest": "0d3a82756f7831ba2819957ad84c6c48", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.3", "size": 8538, "upload_time": "2018-11-21T00:52:22", "url": "https://files.pythonhosted.org/packages/7f/51/0aad04d2c185ba5a802cdc814471408651a94fd11c24da95fc9690afdcbe/pccora-0.3.tar.gz" } ] }