Digital Drosophila Archive
1051 lines
124 KiB
JSON
1051 lines
124 KiB
JSON
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"description": "Brian2\n======\n\n*A clock-driven simulator for spiking neural networks*\n\nBrian is a free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible.\n\nPlease report issues at the github issue tracker (https://github.com/brian-team/brian2/issues) or in the Brian forum (https://brian.discourse.group).\n\nDocumentation for Brian2 can be found at http://brian2.readthedocs.org\n\nBrian2 is released under the terms of the `CeCILL 2.1 license <https://opensource.org/licenses/CECILL-2.1>`_.\n\nIf you use Brian for your published research, we kindly ask you to cite our article:\n\nStimberg, M, Brette, R, Goodman, DFM. “Brian 2, an Intuitive and Efficient Neural Simulator.” eLife 8 (2019): e47314. `doi: 10.7554/eLife.47314 <https://doi.org/10.7554/eLife.47314>`_.\n\n\n\n.. image:: https://img.shields.io/pypi/v/Brian2.svg\n :target: https://pypi.python.org/pypi/Brian2\n\n.. image:: https://img.shields.io/conda/vn/conda-forge/brian2.svg\n :target: https://anaconda.org/conda-forge/brian2\n\n\n.. image:: https://img.shields.io/debian/v/python3-brian/testing\n :alt: Debian package\n :target: https://packages.debian.org/testing/python3-brian\n\n.. image:: https://img.shields.io/fedora/v/python3-brian2\n :alt: Fedora package\n :target: https://packages.fedoraproject.org/pkgs/python-brian2/python3-brian2/\n\n.. image:: https://img.shields.io/spack/v/py-brian2\n :alt: Spack\n :target: https://spack.readthedocs.io/en/latest/package_list.html#py-brian2\n\n.. image:: https://img.shields.io/aur/version/python-brian2\n :alt: AUR version \n :target: https://aur.archlinux.org/packages/python-brian2\n\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5910837.svg\n :target: https://doi.org/10.5281/zenodo.5910837\n\n.. image:: https://archive.softwareheritage.org/badge/swh:1:rel:5f3d6b8e531d64530ac93ce5134194cf66a495b9/\n :target: https://archive.softwareheritage.org/swh:1:rel:5f3d6b8e531d64530ac93ce5134194cf66a495b9;origin=https://github.com/brian-team/brian2;visit=swh:1:snp:06606ee7c757a47bc7dc168d0bfa8b0a5aecb620\n\n\n.. image:: https://img.shields.io/badge/Contributor%20Covenant-v1.4%20adopted-ff69b4.svg\n :target: CODE_OF_CONDUCT.md\n :alt: Contributor Covenant\n\n.. image:: https://img.shields.io/discourse/topics?server=https%3A%2F%2Fbrian.discourse.group\n :target: https://brian.discourse.group\n :alt: Discourse topics\n\n.. image:: https://badges.gitter.im/Join%20Chat.svg\n :alt: Join the chat at https://gitter.im/brian-team/brian2\n :target: https://gitter.im/brian-team/brian2?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge\n\nQuickstart\n----------\nTry out Brian on the `mybinder <https://mybinder.org/>`_ service:\n\n.. image:: http://mybinder.org/badge.svg\n :target: http://mybinder.org/v2/gh/brian-team/brian2-binder/master?filepath=index.ipynb\n\nDependencies\n------------\nThe following packages need to be installed to use Brian 2 (cf. `setup.py <setup.py>`_):\n\n* Python >= 3.7\n* NumPy >=1.17\n* SymPy >= 1.2\n* Cython >= 0.29\n* PyParsing\n* Jinja2 >= 2.7\n* setuptools >= 24.2\n* py-cpuinfo (only required on Windows)\n\nFor full functionality, you might also want to install:\n\n* GSL >=1.16\n* SciPy >=0.13.3\n* Matplotlib >= 2.0\n\nTo build the documentation:\n\n* Sphinx (>=1.8)\n\nTo run the test suite:\n\n* pytest\n* pytest-xdist (optional)\n\nTesting status for master branch\n--------------------------------\n\n.. image:: https://github.com/brian-team/brian2/actions/workflows/testsuite.yml/badge.svg\n :target: https://github.com/brian-team/brian2/actions/workflows/testsuite.yml\n :alt: Test status on GitHub Actions\n\n.. image:: https://img.shields.io/coveralls/brian-team/brian2/master.svg\n :target: https://coveralls.io/r/brian-team/brian2?branch=master\n :alt: Test coverage\n\n.. image:: https://readthedocs.org/projects/brian2/badge/?version=stable\n :target: https://brian2.readthedocs.io/en/stable/?badge=stable\n :alt: Documentation Status\n\n\n"
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"description": "<div align=\"center\">\n <img src=\"https://pandas.pydata.org/static/img/pandas.svg\"><br>\n</div>\n\n-----------------\n\n# pandas: powerful Python data analysis toolkit\n[](https://pypi.org/project/pandas/)\n[](https://anaconda.org/anaconda/pandas/)\n[](https://doi.org/10.5281/zenodo.3509134)\n[](https://pypi.org/project/pandas/)\n[](https://github.com/pandas-dev/pandas/blob/main/LICENSE)\n[](https://codecov.io/gh/pandas-dev/pandas)\n[](https://pepy.tech/project/pandas)\n[](https://gitter.im/pydata/pandas)\n[](https://numfocus.org)\n[](https://github.com/psf/black)\n[](https://pycqa.github.io/isort/)\n\n## What is it?\n\n**pandas** is a Python package that provides fast, flexible, and expressive data\nstructures designed to make working with \"relational\" or \"labeled\" data both\neasy and intuitive. It aims to be the fundamental high-level building block for\ndoing practical, **real world** data analysis in Python. Additionally, it has\nthe broader goal of becoming **the most powerful and flexible open source data\nanalysis / manipulation tool available in any language**. It is already well on\nits way towards this goal.\n\n## Main Features\nHere are just a few of the things that pandas does well:\n\n - Easy handling of [**missing data**][missing-data] (represented as\n `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data\n - Size mutability: columns can be [**inserted and\n deleted**][insertion-deletion] from DataFrame and higher dimensional\n objects\n - Automatic and explicit [**data alignment**][alignment]: objects can\n be explicitly aligned to a set of labels, or the user can simply\n ignore the labels and let `Series`, `DataFrame`, etc. automatically\n align the data for you in computations\n - Powerful, flexible [**group by**][groupby] functionality to perform\n split-apply-combine operations on data sets, for both aggregating\n and transforming data\n - Make it [**easy to convert**][conversion] ragged,\n differently-indexed data in other Python and NumPy data structures\n into DataFrame objects\n - Intelligent label-based [**slicing**][slicing], [**fancy\n indexing**][fancy-indexing], and [**subsetting**][subsetting] of\n large data sets\n - Intuitive [**merging**][merging] and [**joining**][joining] data\n sets\n - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of\n data sets\n - [**Hierarchical**][mi] labeling of axes (possible to have multiple\n labels per tick)\n - Robust IO tools for loading data from [**flat files**][flat-files]\n (CSV and delimited), [**Excel files**][excel], [**databases**][db],\n and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]\n - [**Time series**][timeseries]-specific functionality: date range\n generation and frequency conversion, moving window statistics,\n date shifting and lagging\n\n\n [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html\n [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion\n [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures\n [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine\n [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe\n [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges\n [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced\n [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing\n [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging\n [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index\n [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html\n [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html\n [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex\n [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files\n [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files\n [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries\n [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables\n [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality\n\n## Where to get it\nThe source code is currently hosted on GitHub at:\nhttps://github.com/pandas-dev/pandas\n\nBinary installers for the latest released version are available at the [Python\nPackage Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).\n\n```sh\n# conda\nconda install pandas\n```\n\n```sh\n# or PyPI\npip install pandas\n```\n\n## Dependencies\n- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)\n- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)\n- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)\n\nSee the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.\n\n## Installation from sources\nTo install pandas from source you need [Cython](https://cython.org/) in addition to the normal\ndependencies above. Cython can be installed from PyPI:\n\n```sh\npip install cython\n```\n\nIn the `pandas` directory (same one where you found this file after\ncloning the git repo), execute:\n\n```sh\npython setup.py install\n```\n\nor for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):\n\n\n```sh\npython -m pip install -e . --no-build-isolation --no-use-pep517\n```\n\nIf you have `make`, you can also use `make develop` to run the same command.\n\nor alternatively\n\n```sh\npython setup.py develop\n```\n\nSee the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source).\n\n## License\n[BSD 3](LICENSE)\n\n## Documentation\nThe official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable\n\n## Background\nWork on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and\nhas been under active development since then.\n\n## Getting Help\n\nFor usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).\nFurther, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).\n\n## Discussion and Development\nMost development discussions take place on GitHub in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions.\n\n## Contributing to pandas [](https://www.codetriage.com/pandas-dev/pandas)\n\nAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.\n\nA detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.\n\nIf you are simply looking to start working with the pandas codebase, navigate to the [GitHub \"issues\" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out.\n\nYou can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas).\n\nOr maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!\n\nFeel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas).\n\nAs contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/pandas-dev/pandas/blob/main/.github/CODE_OF_CONDUCT.md)\n"
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"description": "# SymPy\n\n[](https://pypi.python.org/pypi/sympy)\n[](https://gitter.im/sympy/sympy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)\n[](https://zenodo.org/badge/latestdoi/18918/sympy/sympy)\n[](https://pepy.tech/project/sympy)\n[](https://github.com/sympy/sympy/issues)\n[](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project)\n[](https://numfocus.org)\n[](https://github.com/sympy/sympy/releases)\n\n[](https://sympy.org/)\n\n\nSee the [AUTHORS](AUTHORS) file for the list of authors.\n\nAnd many more people helped on the SymPy mailing list, reported bugs,\nhelped organize SymPy's participation in the Google Summer of Code, the\nGoogle Highly Open Participation Contest, Google Code-In, wrote and\nblogged about SymPy...\n\nLicense: New BSD License (see the [LICENSE](LICENSE) file for details) covers all\nfiles in the sympy repository unless stated otherwise.\n\nOur mailing list is at\n<https://groups.google.com/forum/?fromgroups#!forum/sympy>.\n\nWe have a community chat at [Gitter](https://gitter.im/sympy/sympy). Feel\nfree to ask us anything there. We have a very welcoming and helpful\ncommunity.\n\n## Download\n\nThe recommended installation method is through Anaconda,\n<https://www.anaconda.com/products/distribution>\n\nYou can also get the latest version of SymPy from\n<https://pypi.python.org/pypi/sympy/>\n\nTo get the git version do\n\n $ git clone https://github.com/sympy/sympy.git\n\nFor other options (tarballs, debs, etc.), see\n<https://docs.sympy.org/dev/install.html>.\n\n## Documentation and Usage\n\nFor in-depth instructions on installation and building the\ndocumentation, see the [SymPy Documentation Style Guide](https://docs.sympy.org/dev/documentation-style-guide.html).\n\nEverything is at:\n\n<https://docs.sympy.org/>\n\nYou can generate everything at the above site in your local copy of\nSymPy by:\n\n $ cd doc\n $ make html\n\nThen the docs will be in <span class=\"title-ref\">\\_build/html</span>. If\nyou don't want to read that, here is a short usage:\n\nFrom this directory, start Python and:\n\n``` python\n>>> from sympy import Symbol, cos\n>>> x = Symbol('x')\n>>> e = 1/cos(x)\n>>> print(e.series(x, 0, 10))\n1 + x**2/2 + 5*x**4/24 + 61*x**6/720 + 277*x**8/8064 + O(x**10)\n```\n\nSymPy also comes with a console that is a simple wrapper around the\nclassic python console (or IPython when available) that loads the SymPy\nnamespace and executes some common commands for you.\n\nTo start it, issue:\n\n $ bin/isympy\n\nfrom this directory, if SymPy is not installed or simply:\n\n $ isympy\n\nif SymPy is installed.\n\n## Installation\n\nSymPy has a hard dependency on the [mpmath](http://mpmath.org/) library\n(version \\>= 0.19). You should install it first, please refer to the\nmpmath installation guide:\n\n<https://github.com/fredrik-johansson/mpmath#1-download--installation>\n\nTo install SymPy using PyPI, run the following command:\n\n $ pip install sympy\n\nTo install SymPy using Anaconda, run the following command:\n\n $ conda install -c anaconda sympy\n\nTo install SymPy from GitHub source, first clone SymPy using `git`:\n\n $ git clone https://github.com/sympy/sympy.git\n\nThen, in the `sympy` repository that you cloned, simply run:\n\n $ pip install .\n\nSee <https://docs.sympy.org/dev/install.html> for more information.\n\n## Contributing\n\nWe welcome contributions from anyone, even if you are new to open\nsource. Please read our [Introduction to Contributing](https://github.com/sympy/sympy/wiki/Introduction-to-contributing)\npage and the [SymPy Documentation Style Guide](https://docs.sympy.org/dev/documentation-style-guide.html). If you\nare new and looking for some way to contribute, a good place to start is\nto look at the issues tagged [Easy to Fix](https://github.com/sympy/sympy/issues?q=is%3Aopen+is%3Aissue+label%3A%22Easy+to+Fix%22).\n\nPlease note that all participants in this project are expected to follow\nour Code of Conduct. By participating in this project you agree to abide\nby its terms. See [CODE\\_OF\\_CONDUCT.md](CODE_OF_CONDUCT.md).\n\n## Tests\n\nTo execute all tests, run:\n\n $./setup.py test\n\nin the current directory.\n\nFor the more fine-grained running of tests or doctests, use `bin/test`\nor respectively `bin/doctest`. The master branch is automatically tested\nby GitHub Actions.\n\nTo test pull requests, use\n[sympy-bot](https://github.com/sympy/sympy-bot).\n\n## Regenerate Experimental <span class=\"title-ref\">LaTeX</span> Parser/Lexer\n\nThe parser and lexer were generated with the [ANTLR4](http://antlr4.org)\ntoolchain in `sympy/parsing/latex/_antlr` and checked into the repo.\nPresently, most users should not need to regenerate these files, but\nif you plan to work on this feature, you will need the `antlr4`\ncommand-line tool (and you must ensure that it is in your `PATH`).\nOne way to get it is:\n\n $ conda install -c conda-forge antlr=4.11.1\n\nAlternatively, follow the instructions on the ANTLR website and download\nthe `antlr-4.11.1-complete.jar`. Then export the `CLASSPATH` as instructed\nand instead of creating `antlr4` as an alias, make it an executable file\nwith the following contents:\n``` bash\n#!/bin/bash\njava -jar /usr/local/lib/antlr-4.11.1-complete.jar \"$@\"\n```\n\nAfter making changes to `sympy/parsing/latex/LaTeX.g4`, run:\n\n $ ./setup.py antlr\n\n## Clean\n\nTo clean everything (thus getting the same tree as in the repository):\n\n $ git clean -Xdf\n\nwhich will clear everything ignored by `.gitignore`, and:\n\n $ git clean -df\n\nto clear all untracked files. You can revert the most recent changes in\ngit with:\n\n $ git reset --hard\n\nWARNING: The above commands will all clear changes you may have made,\nand you will lose them forever. Be sure to check things with `git\nstatus`, `git diff`, `git clean -Xn`, and `git clean -n` before doing any\nof those.\n\n## Bugs\n\nOur issue tracker is at <https://github.com/sympy/sympy/issues>. Please\nreport any bugs that you find. Or, even better, fork the repository on\nGitHub and create a pull request. We welcome all changes, big or small,\nand we will help you make the pull request if you are new to git (just\nask on our mailing list or Gitter Channel). If you further have any queries, you can find answers\non Stack Overflow using the [sympy](https://stackoverflow.com/questions/tagged/sympy) tag.\n\n## Brief History\n\nSymPy was started by Ondřej Čertík in 2005, he wrote some code during\nthe summer, then he wrote some more code during summer 2006. In February\n2007, Fabian Pedregosa joined the project and helped fix many things,\ncontributed documentation, and made it alive again. 5 students (Mateusz\nPaprocki, Brian Jorgensen, Jason Gedge, Robert Schwarz, and Chris Wu)\nimproved SymPy incredibly during summer 2007 as part of the Google\nSummer of Code. Pearu Peterson joined the development during the summer\n2007 and he has made SymPy much more competitive by rewriting the core\nfrom scratch, which has made it from 10x to 100x faster. Jurjen N.E. Bos\nhas contributed pretty-printing and other patches. Fredrik Johansson has\nwritten mpmath and contributed a lot of patches.\n\nSymPy has participated in every Google Summer of Code since 2007. You\ncan see <https://github.com/sympy/sympy/wiki#google-summer-of-code> for\nfull details. Each year has improved SymPy by bounds. Most of SymPy's\ndevelopment has come from Google Summer of Code students.\n\nIn 2011, Ondřej Čertík stepped down as lead developer, with Aaron\nMeurer, who also started as a Google Summer of Code student, taking his\nplace. Ondřej Čertík is still active in the community but is too busy\nwith work and family to play a lead development role.\n\nSince then, a lot more people have joined the development and some\npeople have also left. You can see the full list in doc/src/aboutus.rst,\nor online at:\n\n<https://docs.sympy.org/dev/aboutus.html#sympy-development-team>\n\nThe git history goes back to 2007 when development moved from svn to hg.\nTo see the history before that point, look at\n<https://github.com/sympy/sympy-old>.\n\nYou can use git to see the biggest developers. The command:\n\n $ git shortlog -ns\n\nwill show each developer, sorted by commits to the project. The command:\n\n $ git shortlog -ns --since=\"1 year\"\n\nwill show the top developers from the last year.\n\n## Citation\n\nTo cite SymPy in publications use\n\n> Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, Rocklin M,\n> Kumar A, Ivanov S, Moore JK, Singh S, Rathnayake T, Vig S, Granger BE,\n> Muller RP, Bonazzi F, Gupta H, Vats S, Johansson F, Pedregosa F, Curry\n> MJ, Terrel AR, Roučka Š, Saboo A, Fernando I, Kulal S, Cimrman R,\n> Scopatz A. (2017) SymPy: symbolic computing in Python. *PeerJ Computer\n> Science* 3:e103 <https://doi.org/10.7717/peerj-cs.103>\n\nA BibTeX entry for LaTeX users is\n\n``` bibtex\n@article{10.7717/peerj-cs.103,\n title = {SymPy: symbolic computing in Python},\n author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \\v{C}ert\\'{i}k, Ond\\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, Amit and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\\v{c}ka, \\v{S}t\\v{e}p\\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},\n year = 2017,\n month = Jan,\n keywords = {Python, Computer algebra system, Symbolics},\n abstract = {\n SymPy is an open-source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provides additional examples and further outlines details of the architecture and features of SymPy.\n },\n volume = 3,\n pages = {e103},\n journal = {PeerJ Computer Science},\n issn = {2376-5992},\n url = {https://doi.org/10.7717/peerj-cs.103},\n doi = {10.7717/peerj-cs.103}\n}\n```\n\nSymPy is BSD licensed, so you are free to use it whatever you like, be\nit academic, commercial, creating forks or derivatives, as long as you\ncopy the BSD statement if you redistribute it (see the LICENSE file for\ndetails). That said, although not required by the SymPy license, if it\nis convenient for you, please cite SymPy when using it in your work and\nalso consider contributing all your changes back, so that we can\nincorporate it and all of us will benefit in the end.\n"
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"description": "mpmath\n======\n\n|pypi version| |Build status| |Code coverage status| |Zenodo Badge|\n\n.. |pypi version| image:: https://img.shields.io/pypi/v/mpmath.svg\n :target: https://pypi.python.org/pypi/mpmath\n.. |Build status| image:: https://github.com/fredrik-johansson/mpmath/workflows/test/badge.svg\n :target: https://github.com/fredrik-johansson/mpmath/actions?workflow=test\n.. |Code coverage status| image:: https://codecov.io/gh/fredrik-johansson/mpmath/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/fredrik-johansson/mpmath\n.. |Zenodo Badge| image:: https://zenodo.org/badge/2934512.svg\n :target: https://zenodo.org/badge/latestdoi/2934512\n\nA Python library for arbitrary-precision floating-point arithmetic.\n\nWebsite: http://mpmath.org/\nMain author: Fredrik Johansson <fredrik.johansson@gmail.com>\n\nMpmath is free software released under the New BSD License (see the\nLICENSE file for details)\n\n0. History and credits\n----------------------\n\nThe following people (among others) have contributed major patches\nor new features to mpmath:\n\n* Pearu Peterson <pearu.peterson@gmail.com>\n* Mario Pernici <mario.pernici@mi.infn.it>\n* Ondrej Certik <ondrej@certik.cz>\n* Vinzent Steinberg <vinzent.steinberg@gmail.cm>\n* Nimish Telang <ntelang@gmail.com>\n* Mike Taschuk <mtaschuk@ece.ualberta.ca>\n* Case Van Horsen <casevh@gmail.com>\n* Jorn Baayen <jorn.baayen@gmail.com>\n* Chris Smith <smichr@gmail.com>\n* Juan Arias de Reyna <arias@us.es>\n* Ioannis Tziakos <itziakos@gmail.com>\n* Aaron Meurer <asmeurer@gmail.com>\n* Stefan Krastanov <krastanov.stefan@gmail.com>\n* Ken Allen <ken.allen@sbcglobal.net>\n* Timo Hartmann <thartmann15@gmail.com>\n* Sergey B Kirpichev <skirpichev@gmail.com>\n* Kris Kuhlman <kristopher.kuhlman@gmail.com>\n* Paul Masson <paulmasson@analyticphysics.com>\n* Michael Kagalenko <michael.kagalenko@gmail.com>\n* Jonathan Warner <warnerjon12@gmail.com>\n* Max Gaukler <max.gaukler@fau.de>\n* Guillermo Navas-Palencia <g.navas.palencia@gmail.com>\n* Nike Dattani <nike@hpqc.org>\n\nNumerous other people have contributed by reporting bugs,\nrequesting new features, or suggesting improvements to the\ndocumentation.\n\nFor a detailed changelog, including individual contributions,\nsee the CHANGES file.\n\nFredrik's work on mpmath during summer 2008 was sponsored by Google\nas part of the Google Summer of Code program.\n\nFredrik's work on mpmath during summer 2009 was sponsored by the\nAmerican Institute of Mathematics under the support of the National Science\nFoundation Grant No. 0757627 (FRG: L-functions and Modular Forms).\n\nAny opinions, findings, and conclusions or recommendations expressed in this\nmaterial are those of the author(s) and do not necessarily reflect the\nviews of the sponsors.\n\nCredit also goes to:\n\n* The authors of the GMP library and the Python wrapper\n gmpy, enabling mpmath to become much faster at\n high precision\n* The authors of MPFR, pari/gp, MPFUN, and other arbitrary-\n precision libraries, whose documentation has been helpful\n for implementing many of the algorithms in mpmath\n* Wikipedia contributors; Abramowitz & Stegun; Gradshteyn & Ryzhik;\n Wolfram Research for MathWorld and the Wolfram Functions site.\n These are the main references used for special functions\n implementations.\n* George Brandl for developing the Sphinx documentation tool\n used to build mpmath's documentation\n\nRelease history:\n\n* Version 1.3.0 released on March 7, 2023\n* Version 1.2.0 released on February 1, 2021\n* Version 1.1.0 released on December 11, 2018\n* Version 1.0.0 released on September 27, 2017\n* Version 0.19 released on June 10, 2014\n* Version 0.18 released on December 31, 2013\n* Version 0.17 released on February 1, 2011\n* Version 0.16 released on September 24, 2010\n* Version 0.15 released on June 6, 2010\n* Version 0.14 released on February 5, 2010\n* Version 0.13 released on August 13, 2009\n* Version 0.12 released on June 9, 2009\n* Version 0.11 released on January 26, 2009\n* Version 0.10 released on October 15, 2008\n* Version 0.9 released on August 23, 2008\n* Version 0.8 released on April 20, 2008\n* Version 0.7 released on March 12, 2008\n* Version 0.6 released on January 13, 2008\n* Version 0.5 released on November 24, 2007\n* Version 0.4 released on November 3, 2007\n* Version 0.3 released on October 5, 2007\n* Version 0.2 released on October 2, 2007\n* Version 0.1 released on September 27, 2007\n\n1. Download & installation\n--------------------------\n\nMpmath requires Python 2.7 or 3.5 (or later versions). It has been tested\nwith CPython 2.7, 3.5 through 3.7 and for PyPy.\n\nThe latest release of mpmath can be downloaded from the mpmath\nwebsite and from https://github.com/fredrik-johansson/mpmath/releases\n\nIt should also be available in the Python Package Index at\nhttps://pypi.python.org/pypi/mpmath\n\nTo install latest release of Mpmath with pip, simply run\n\n``pip install mpmath``\n\nOr unpack the mpmath archive and run\n\n``python setup.py install``\n\nMpmath can also be installed using\n\n``python -m easy_install mpmath``\n\nThe latest development code is available from\nhttps://github.com/fredrik-johansson/mpmath\n\nSee the main documentation for more detailed instructions.\n\n2. Running tests\n----------------\n\nThe unit tests in mpmath/tests/ can be run via the script\nruntests.py, but it is recommended to run them with py.test\n(https://pytest.org/), especially\nto generate more useful reports in case there are failures.\n\nYou may also want to check out the demo scripts in the demo\ndirectory.\n\nThe master branch is automatically tested by Travis CI.\n\n3. Documentation\n----------------\n\nDocumentation in reStructuredText format is available in the\ndoc directory included with the source package. These files\nare human-readable, but can be compiled to prettier HTML using\nthe build.py script (requires Sphinx, http://sphinx.pocoo.org/).\n\nSee setup.txt in the documentation for more information.\n\nThe most recent documentation is also available in HTML format:\n\nhttp://mpmath.org/doc/current/\n\n4. Known problems\n-----------------\n\nMpmath is a work in progress. Major issues include:\n\n* Some functions may return incorrect values when given extremely\n large arguments or arguments very close to singularities.\n\n* Directed rounding works for arithmetic operations. It is implemented\n heuristically for other operations, and their results may be off by one\n or two units in the last place (even if otherwise accurate).\n\n* Some IEEE 754 features are not available. Inifinities and NaN are\n partially supported; denormal rounding is currently not available\n at all.\n\n* The interface for switching precision and rounding is not finalized.\n The current method is not threadsafe.\n\n5. Help and bug reports\n-----------------------\n\nGeneral questions and comments can be sent to the mpmath mailinglist,\nmpmath@googlegroups.com\n\nYou can also report bugs and send patches to the mpmath issue tracker,\nhttps://github.com/fredrik-johansson/mpmath/issues\n"
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"description": "dateutil - powerful extensions to datetime\n==========================================\n\n|pypi| |support| |licence|\n\n|gitter| |readthedocs|\n\n|travis| |appveyor| |pipelines| |coverage|\n\n.. |pypi| image:: https://img.shields.io/pypi/v/python-dateutil.svg?style=flat-square\n :target: https://pypi.org/project/python-dateutil/\n :alt: pypi version\n\n.. |support| image:: https://img.shields.io/pypi/pyversions/python-dateutil.svg?style=flat-square\n :target: https://pypi.org/project/python-dateutil/\n :alt: supported Python version\n\n.. |travis| image:: https://img.shields.io/travis/dateutil/dateutil/master.svg?style=flat-square&label=Travis%20Build\n :target: https://travis-ci.org/dateutil/dateutil\n :alt: travis build status\n\n.. |appveyor| image:: https://img.shields.io/appveyor/ci/dateutil/dateutil/master.svg?style=flat-square&logo=appveyor\n :target: https://ci.appveyor.com/project/dateutil/dateutil\n :alt: appveyor build status\n\n.. |pipelines| image:: https://dev.azure.com/pythondateutilazure/dateutil/_apis/build/status/dateutil.dateutil?branchName=master\n :target: https://dev.azure.com/pythondateutilazure/dateutil/_build/latest?definitionId=1&branchName=master\n :alt: azure pipelines build status\n\n.. |coverage| image:: https://codecov.io/gh/dateutil/dateutil/branch/master/graphs/badge.svg?branch=master\n :target: https://codecov.io/gh/dateutil/dateutil?branch=master\n :alt: Code coverage\n\n.. |gitter| image:: https://badges.gitter.im/dateutil/dateutil.svg\n :alt: Join the chat at https://gitter.im/dateutil/dateutil\n :target: https://gitter.im/dateutil/dateutil\n\n.. |licence| image:: https://img.shields.io/pypi/l/python-dateutil.svg?style=flat-square\n :target: https://pypi.org/project/python-dateutil/\n :alt: licence\n\n.. |readthedocs| image:: https://img.shields.io/readthedocs/dateutil/latest.svg?style=flat-square&label=Read%20the%20Docs\n :alt: Read the documentation at https://dateutil.readthedocs.io/en/latest/\n :target: https://dateutil.readthedocs.io/en/latest/\n\nThe `dateutil` module provides powerful extensions to\nthe standard `datetime` module, available in Python.\n\nInstallation\n============\n`dateutil` can be installed from PyPI using `pip` (note that the package name is\ndifferent from the importable name)::\n\n pip install python-dateutil\n\nDownload\n========\ndateutil is available on PyPI\nhttps://pypi.org/project/python-dateutil/\n\nThe documentation is hosted at:\nhttps://dateutil.readthedocs.io/en/stable/\n\nCode\n====\nThe code and issue tracker are hosted on GitHub:\nhttps://github.com/dateutil/dateutil/\n\nFeatures\n========\n\n* Computing of relative deltas (next month, next year,\n next Monday, last week of month, etc);\n* Computing of relative deltas between two given\n date and/or datetime objects;\n* Computing of dates based on very flexible recurrence rules,\n using a superset of the `iCalendar <https://www.ietf.org/rfc/rfc2445.txt>`_\n specification. Parsing of RFC strings is supported as well.\n* Generic parsing of dates in almost any string format;\n* Timezone (tzinfo) implementations for tzfile(5) format\n files (/etc/localtime, /usr/share/zoneinfo, etc), TZ\n environment string (in all known formats), iCalendar\n format files, given ranges (with help from relative deltas),\n local machine timezone, fixed offset timezone, UTC timezone,\n and Windows registry-based time zones.\n* Internal up-to-date world timezone information based on\n Olson's database.\n* Computing of Easter Sunday dates for any given year,\n using Western, Orthodox or Julian algorithms;\n* A comprehensive test suite.\n\nQuick example\n=============\nHere's a snapshot, just to give an idea about the power of the\npackage. For more examples, look at the documentation.\n\nSuppose you want to know how much time is left, in\nyears/months/days/etc, before the next easter happening on a\nyear with a Friday 13th in August, and you want to get today's\ndate out of the \"date\" unix system command. Here is the code:\n\n.. code-block:: python3\n\n >>> from dateutil.relativedelta import *\n >>> from dateutil.easter import *\n >>> from dateutil.rrule import *\n >>> from dateutil.parser import *\n >>> from datetime import *\n >>> now = parse(\"Sat Oct 11 17:13:46 UTC 2003\")\n >>> today = now.date()\n >>> year = rrule(YEARLY,dtstart=now,bymonth=8,bymonthday=13,byweekday=FR)[0].year\n >>> rdelta = relativedelta(easter(year), today)\n >>> print(\"Today is: %s\" % today)\n Today is: 2003-10-11\n >>> print(\"Year with next Aug 13th on a Friday is: %s\" % year)\n Year with next Aug 13th on a Friday is: 2004\n >>> print(\"How far is the Easter of that year: %s\" % rdelta)\n How far is the Easter of that year: relativedelta(months=+6)\n >>> print(\"And the Easter of that year is: %s\" % (today+rdelta))\n And the Easter of that year is: 2004-04-11\n\nBeing exactly 6 months ahead was **really** a coincidence :)\n\nContributing\n============\n\nWe welcome many types of contributions - bug reports, pull requests (code, infrastructure or documentation fixes). For more information about how to contribute to the project, see the ``CONTRIBUTING.md`` file in the repository.\n\n\nAuthor\n======\nThe dateutil module was written by Gustavo Niemeyer <gustavo@niemeyer.net>\nin 2003.\n\nIt is maintained by:\n\n* Gustavo Niemeyer <gustavo@niemeyer.net> 2003-2011\n* Tomi Pieviläinen <tomi.pievilainen@iki.fi> 2012-2014\n* Yaron de Leeuw <me@jarondl.net> 2014-2016\n* Paul Ganssle <paul@ganssle.io> 2015-\n\nStarting with version 2.4.1 and running until 2.8.2, all source and binary\ndistributions will be signed by a PGP key that has, at the very least, been\nsigned by the key which made the previous release. A table of release signing\nkeys can be found below:\n\n=========== ============================\nReleases Signing key fingerprint\n=========== ============================\n2.4.1-2.8.2 `6B49 ACBA DCF6 BD1C A206 67AB CD54 FCE3 D964 BEFB`_ \n=========== ============================\n\nNew releases *may* have signed tags, but binary and source distributions\nuploaded to PyPI will no longer have GPG signatures attached.\n\nContact\n=======\nOur mailing list is available at `dateutil@python.org <https://mail.python.org/mailman/listinfo/dateutil>`_. As it is hosted by the PSF, it is subject to the `PSF code of\nconduct <https://www.python.org/psf/conduct/>`_.\n\nLicense\n=======\n\nAll contributions after December 1, 2017 released under dual license - either `Apache 2.0 License <https://www.apache.org/licenses/LICENSE-2.0>`_ or the `BSD 3-Clause License <https://opensource.org/licenses/BSD-3-Clause>`_. Contributions before December 1, 2017 - except those those explicitly relicensed - are released only under the BSD 3-Clause License.\n\n\n.. _6B49 ACBA DCF6 BD1C A206 67AB CD54 FCE3 D964 BEFB:\n https://pgp.mit.edu/pks/lookup?op=vindex&search=0xCD54FCE3D964BEFB\n\n\n"
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"description": "pytz - World Timezone Definitions for Python\n============================================\n\n:Author: Stuart Bishop <stuart@stuartbishop.net>\n\nIntroduction\n~~~~~~~~~~~~\n\npytz brings the Olson tz database into Python. This library allows\naccurate and cross platform timezone calculations using Python 2.4\nor higher. It also solves the issue of ambiguous times at the end\nof daylight saving time, which you can read more about in the Python\nLibrary Reference (``datetime.tzinfo``).\n\nAlmost all of the Olson timezones are supported.\n\n.. note::\n\n Projects using Python 3.9 or later should be using the support\n now included as part of the standard library, and third party\n packages work with it such as `tzdata <https://pypi.org/project/tzdata/>`_.\n pytz offers no advantages beyond backwards compatibility with\n code written for earlier versions of Python.\n\n.. note::\n\n This library differs from the documented Python API for\n tzinfo implementations; if you want to create local wallclock\n times you need to use the ``localize()`` method documented in this\n document. In addition, if you perform date arithmetic on local\n times that cross DST boundaries, the result may be in an incorrect\n timezone (ie. subtract 1 minute from 2002-10-27 1:00 EST and you get\n 2002-10-27 0:59 EST instead of the correct 2002-10-27 1:59 EDT). A\n ``normalize()`` method is provided to correct this. Unfortunately these\n issues cannot be resolved without modifying the Python datetime\n implementation (see PEP-431).\n\n\nInstallation\n~~~~~~~~~~~~\n\nThis package can either be installed using ``pip`` or from a tarball using the\nstandard Python distutils.\n\nIf you are installing using ``pip``, you don't need to download anything as the\nlatest version will be downloaded for you from PyPI::\n\n pip install pytz\n\nIf you are installing from a tarball, run the following command as an\nadministrative user::\n\n python setup.py install\n\n\npytz for Enterprise\n~~~~~~~~~~~~~~~~~~~\n\nAvailable as part of the Tidelift Subscription.\n\nThe maintainers of pytz and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while paying the maintainers of the exact dependencies you use. `Learn more. <https://tidelift.com/subscription/pkg/pypi-pytz?utm_source=pypi-pytz&utm_medium=referral&utm_campaign=enterprise&utm_term=repo>`_.\n\n\nExample & Usage\n~~~~~~~~~~~~~~~\n\nLocalized times and date arithmetic\n-----------------------------------\n\n>>> from datetime import datetime, timedelta\n>>> from pytz import timezone\n>>> import pytz\n>>> utc = pytz.utc\n>>> utc.zone\n'UTC'\n>>> eastern = timezone('US/Eastern')\n>>> eastern.zone\n'US/Eastern'\n>>> amsterdam = timezone('Europe/Amsterdam')\n>>> fmt = '%Y-%m-%d %H:%M:%S %Z%z'\n\nThis library only supports two ways of building a localized time. The\nfirst is to use the ``localize()`` method provided by the pytz library.\nThis is used to localize a naive datetime (datetime with no timezone\ninformation):\n\n>>> loc_dt = eastern.localize(datetime(2002, 10, 27, 6, 0, 0))\n>>> print(loc_dt.strftime(fmt))\n2002-10-27 06:00:00 EST-0500\n\nThe second way of building a localized time is by converting an existing\nlocalized time using the standard ``astimezone()`` method:\n\n>>> ams_dt = loc_dt.astimezone(amsterdam)\n>>> ams_dt.strftime(fmt)\n'2002-10-27 12:00:00 CET+0100'\n\nUnfortunately using the tzinfo argument of the standard datetime\nconstructors ''does not work'' with pytz for many timezones.\n\n>>> datetime(2002, 10, 27, 12, 0, 0, tzinfo=amsterdam).strftime(fmt) # /!\\ Does not work this way!\n'2002-10-27 12:00:00 LMT+0018'\n\nIt is safe for timezones without daylight saving transitions though, such\nas UTC:\n\n>>> datetime(2002, 10, 27, 12, 0, 0, tzinfo=pytz.utc).strftime(fmt) # /!\\ Not recommended except for UTC\n'2002-10-27 12:00:00 UTC+0000'\n\nThe preferred way of dealing with times is to always work in UTC,\nconverting to localtime only when generating output to be read\nby humans.\n\n>>> utc_dt = datetime(2002, 10, 27, 6, 0, 0, tzinfo=utc)\n>>> loc_dt = utc_dt.astimezone(eastern)\n>>> loc_dt.strftime(fmt)\n'2002-10-27 01:00:00 EST-0500'\n\nThis library also allows you to do date arithmetic using local\ntimes, although it is more complicated than working in UTC as you\nneed to use the ``normalize()`` method to handle daylight saving time\nand other timezone transitions. In this example, ``loc_dt`` is set\nto the instant when daylight saving time ends in the US/Eastern\ntimezone.\n\n>>> before = loc_dt - timedelta(minutes=10)\n>>> before.strftime(fmt)\n'2002-10-27 00:50:00 EST-0500'\n>>> eastern.normalize(before).strftime(fmt)\n'2002-10-27 01:50:00 EDT-0400'\n>>> after = eastern.normalize(before + timedelta(minutes=20))\n>>> after.strftime(fmt)\n'2002-10-27 01:10:00 EST-0500'\n\nCreating local times is also tricky, and the reason why working with\nlocal times is not recommended. Unfortunately, you cannot just pass\na ``tzinfo`` argument when constructing a datetime (see the next\nsection for more details)\n\n>>> dt = datetime(2002, 10, 27, 1, 30, 0)\n>>> dt1 = eastern.localize(dt, is_dst=True)\n>>> dt1.strftime(fmt)\n'2002-10-27 01:30:00 EDT-0400'\n>>> dt2 = eastern.localize(dt, is_dst=False)\n>>> dt2.strftime(fmt)\n'2002-10-27 01:30:00 EST-0500'\n\nConverting between timezones is more easily done, using the\nstandard astimezone method.\n\n>>> utc_dt = utc.localize(datetime.utcfromtimestamp(1143408899))\n>>> utc_dt.strftime(fmt)\n'2006-03-26 21:34:59 UTC+0000'\n>>> au_tz = timezone('Australia/Sydney')\n>>> au_dt = utc_dt.astimezone(au_tz)\n>>> au_dt.strftime(fmt)\n'2006-03-27 08:34:59 AEDT+1100'\n>>> utc_dt2 = au_dt.astimezone(utc)\n>>> utc_dt2.strftime(fmt)\n'2006-03-26 21:34:59 UTC+0000'\n>>> utc_dt == utc_dt2\nTrue\n\nYou can take shortcuts when dealing with the UTC side of timezone\nconversions. ``normalize()`` and ``localize()`` are not really\nnecessary when there are no daylight saving time transitions to\ndeal with.\n\n>>> utc_dt = datetime.utcfromtimestamp(1143408899).replace(tzinfo=utc)\n>>> utc_dt.strftime(fmt)\n'2006-03-26 21:34:59 UTC+0000'\n>>> au_tz = timezone('Australia/Sydney')\n>>> au_dt = au_tz.normalize(utc_dt.astimezone(au_tz))\n>>> au_dt.strftime(fmt)\n'2006-03-27 08:34:59 AEDT+1100'\n>>> utc_dt2 = au_dt.astimezone(utc)\n>>> utc_dt2.strftime(fmt)\n'2006-03-26 21:34:59 UTC+0000'\n\n\n``tzinfo`` API\n--------------\n\nThe ``tzinfo`` instances returned by the ``timezone()`` function have\nbeen extended to cope with ambiguous times by adding an ``is_dst``\nparameter to the ``utcoffset()``, ``dst()`` && ``tzname()`` methods.\n\n>>> tz = timezone('America/St_Johns')\n\n>>> normal = datetime(2009, 9, 1)\n>>> ambiguous = datetime(2009, 10, 31, 23, 30)\n\nThe ``is_dst`` parameter is ignored for most timestamps. It is only used\nduring DST transition ambiguous periods to resolve that ambiguity.\n\n>>> print(tz.utcoffset(normal, is_dst=True))\n-1 day, 21:30:00\n>>> print(tz.dst(normal, is_dst=True))\n1:00:00\n>>> tz.tzname(normal, is_dst=True)\n'NDT'\n\n>>> print(tz.utcoffset(ambiguous, is_dst=True))\n-1 day, 21:30:00\n>>> print(tz.dst(ambiguous, is_dst=True))\n1:00:00\n>>> tz.tzname(ambiguous, is_dst=True)\n'NDT'\n\n>>> print(tz.utcoffset(normal, is_dst=False))\n-1 day, 21:30:00\n>>> tz.dst(normal, is_dst=False).seconds\n3600\n>>> tz.tzname(normal, is_dst=False)\n'NDT'\n\n>>> print(tz.utcoffset(ambiguous, is_dst=False))\n-1 day, 20:30:00\n>>> tz.dst(ambiguous, is_dst=False)\ndatetime.timedelta(0)\n>>> tz.tzname(ambiguous, is_dst=False)\n'NST'\n\nIf ``is_dst`` is not specified, ambiguous timestamps will raise\nan ``pytz.exceptions.AmbiguousTimeError`` exception.\n\n>>> print(tz.utcoffset(normal))\n-1 day, 21:30:00\n>>> print(tz.dst(normal))\n1:00:00\n>>> tz.tzname(normal)\n'NDT'\n\n>>> import pytz.exceptions\n>>> try:\n... tz.utcoffset(ambiguous)\n... except pytz.exceptions.AmbiguousTimeError:\n... print('pytz.exceptions.AmbiguousTimeError: %s' % ambiguous)\npytz.exceptions.AmbiguousTimeError: 2009-10-31 23:30:00\n>>> try:\n... tz.dst(ambiguous)\n... except pytz.exceptions.AmbiguousTimeError:\n... print('pytz.exceptions.AmbiguousTimeError: %s' % ambiguous)\npytz.exceptions.AmbiguousTimeError: 2009-10-31 23:30:00\n>>> try:\n... tz.tzname(ambiguous)\n... except pytz.exceptions.AmbiguousTimeError:\n... print('pytz.exceptions.AmbiguousTimeError: %s' % ambiguous)\npytz.exceptions.AmbiguousTimeError: 2009-10-31 23:30:00\n\n\nProblems with Localtime\n~~~~~~~~~~~~~~~~~~~~~~~\n\nThe major problem we have to deal with is that certain datetimes\nmay occur twice in a year. For example, in the US/Eastern timezone\non the last Sunday morning in October, the following sequence\nhappens:\n\n - 01:00 EDT occurs\n - 1 hour later, instead of 2:00am the clock is turned back 1 hour\n and 01:00 happens again (this time 01:00 EST)\n\nIn fact, every instant between 01:00 and 02:00 occurs twice. This means\nthat if you try and create a time in the 'US/Eastern' timezone\nthe standard datetime syntax, there is no way to specify if you meant\nbefore of after the end-of-daylight-saving-time transition. Using the\npytz custom syntax, the best you can do is make an educated guess:\n\n>>> loc_dt = eastern.localize(datetime(2002, 10, 27, 1, 30, 00))\n>>> loc_dt.strftime(fmt)\n'2002-10-27 01:30:00 EST-0500'\n\nAs you can see, the system has chosen one for you and there is a 50%\nchance of it being out by one hour. For some applications, this does\nnot matter. However, if you are trying to schedule meetings with people\nin different timezones or analyze log files it is not acceptable.\n\nThe best and simplest solution is to stick with using UTC. The pytz\npackage encourages using UTC for internal timezone representation by\nincluding a special UTC implementation based on the standard Python\nreference implementation in the Python documentation.\n\nThe UTC timezone unpickles to be the same instance, and pickles to a\nsmaller size than other pytz tzinfo instances. The UTC implementation\ncan be obtained as pytz.utc, pytz.UTC, or pytz.timezone('UTC').\n\n>>> import pickle, pytz\n>>> dt = datetime(2005, 3, 1, 14, 13, 21, tzinfo=utc)\n>>> naive = dt.replace(tzinfo=None)\n>>> p = pickle.dumps(dt, 1)\n>>> naive_p = pickle.dumps(naive, 1)\n>>> len(p) - len(naive_p)\n17\n>>> new = pickle.loads(p)\n>>> new == dt\nTrue\n>>> new is dt\nFalse\n>>> new.tzinfo is dt.tzinfo\nTrue\n>>> pytz.utc is pytz.UTC is pytz.timezone('UTC')\nTrue\n\nNote that some other timezones are commonly thought of as the same (GMT,\nGreenwich, Universal, etc.). The definition of UTC is distinct from these\nother timezones, and they are not equivalent. For this reason, they will\nnot compare the same in Python.\n\n>>> utc == pytz.timezone('GMT')\nFalse\n\nSee the section `What is UTC`_, below.\n\nIf you insist on working with local times, this library provides a\nfacility for constructing them unambiguously:\n\n>>> loc_dt = datetime(2002, 10, 27, 1, 30, 00)\n>>> est_dt = eastern.localize(loc_dt, is_dst=True)\n>>> edt_dt = eastern.localize(loc_dt, is_dst=False)\n>>> print(est_dt.strftime(fmt) + ' / ' + edt_dt.strftime(fmt))\n2002-10-27 01:30:00 EDT-0400 / 2002-10-27 01:30:00 EST-0500\n\nIf you pass None as the is_dst flag to localize(), pytz will refuse to\nguess and raise exceptions if you try to build ambiguous or non-existent\ntimes.\n\nFor example, 1:30am on 27th Oct 2002 happened twice in the US/Eastern\ntimezone when the clocks where put back at the end of Daylight Saving\nTime:\n\n>>> dt = datetime(2002, 10, 27, 1, 30, 00)\n>>> try:\n... eastern.localize(dt, is_dst=None)\n... except pytz.exceptions.AmbiguousTimeError:\n... print('pytz.exceptions.AmbiguousTimeError: %s' % dt)\npytz.exceptions.AmbiguousTimeError: 2002-10-27 01:30:00\n\nSimilarly, 2:30am on 7th April 2002 never happened at all in the\nUS/Eastern timezone, as the clocks where put forward at 2:00am skipping\nthe entire hour:\n\n>>> dt = datetime(2002, 4, 7, 2, 30, 00)\n>>> try:\n... eastern.localize(dt, is_dst=None)\n... except pytz.exceptions.NonExistentTimeError:\n... print('pytz.exceptions.NonExistentTimeError: %s' % dt)\npytz.exceptions.NonExistentTimeError: 2002-04-07 02:30:00\n\nBoth of these exceptions share a common base class to make error handling\neasier:\n\n>>> isinstance(pytz.AmbiguousTimeError(), pytz.InvalidTimeError)\nTrue\n>>> isinstance(pytz.NonExistentTimeError(), pytz.InvalidTimeError)\nTrue\n\n\nA special case is where countries change their timezone definitions\nwith no daylight savings time switch. For example, in 1915 Warsaw\nswitched from Warsaw time to Central European time with no daylight savings\ntransition. So at the stroke of midnight on August 5th 1915 the clocks\nwere wound back 24 minutes creating an ambiguous time period that cannot\nbe specified without referring to the timezone abbreviation or the\nactual UTC offset. In this case midnight happened twice, neither time\nduring a daylight saving time period. pytz handles this transition by\ntreating the ambiguous period before the switch as daylight savings\ntime, and the ambiguous period after as standard time.\n\n\n>>> warsaw = pytz.timezone('Europe/Warsaw')\n>>> amb_dt1 = warsaw.localize(datetime(1915, 8, 4, 23, 59, 59), is_dst=True)\n>>> amb_dt1.strftime(fmt)\n'1915-08-04 23:59:59 WMT+0124'\n>>> amb_dt2 = warsaw.localize(datetime(1915, 8, 4, 23, 59, 59), is_dst=False)\n>>> amb_dt2.strftime(fmt)\n'1915-08-04 23:59:59 CET+0100'\n>>> switch_dt = warsaw.localize(datetime(1915, 8, 5, 00, 00, 00), is_dst=False)\n>>> switch_dt.strftime(fmt)\n'1915-08-05 00:00:00 CET+0100'\n>>> str(switch_dt - amb_dt1)\n'0:24:01'\n>>> str(switch_dt - amb_dt2)\n'0:00:01'\n\nThe best way of creating a time during an ambiguous time period is\nby converting from another timezone such as UTC:\n\n>>> utc_dt = datetime(1915, 8, 4, 22, 36, tzinfo=pytz.utc)\n>>> utc_dt.astimezone(warsaw).strftime(fmt)\n'1915-08-04 23:36:00 CET+0100'\n\nThe standard Python way of handling all these ambiguities is not to\nhandle them, such as demonstrated in this example using the US/Eastern\ntimezone definition from the Python documentation (Note that this\nimplementation only works for dates between 1987 and 2006 - it is\nincluded for tests only!):\n\n>>> from pytz.reference import Eastern # pytz.reference only for tests\n>>> dt = datetime(2002, 10, 27, 0, 30, tzinfo=Eastern)\n>>> str(dt)\n'2002-10-27 00:30:00-04:00'\n>>> str(dt + timedelta(hours=1))\n'2002-10-27 01:30:00-05:00'\n>>> str(dt + timedelta(hours=2))\n'2002-10-27 02:30:00-05:00'\n>>> str(dt + timedelta(hours=3))\n'2002-10-27 03:30:00-05:00'\n\nNotice the first two results? At first glance you might think they are\ncorrect, but taking the UTC offset into account you find that they are\nactually two hours appart instead of the 1 hour we asked for.\n\n>>> from pytz.reference import UTC # pytz.reference only for tests\n>>> str(dt.astimezone(UTC))\n'2002-10-27 04:30:00+00:00'\n>>> str((dt + timedelta(hours=1)).astimezone(UTC))\n'2002-10-27 06:30:00+00:00'\n\n\nCountry Information\n~~~~~~~~~~~~~~~~~~~\n\nA mechanism is provided to access the timezones commonly in use\nfor a particular country, looked up using the ISO 3166 country code.\nIt returns a list of strings that can be used to retrieve the relevant\ntzinfo instance using ``pytz.timezone()``:\n\n>>> print(' '.join(pytz.country_timezones['nz']))\nPacific/Auckland Pacific/Chatham\n\nThe Olson database comes with a ISO 3166 country code to English country\nname mapping that pytz exposes as a dictionary:\n\n>>> print(pytz.country_names['nz'])\nNew Zealand\n\n\nWhat is UTC\n~~~~~~~~~~~\n\n'UTC' is `Coordinated Universal Time`_. It is a successor to, but distinct\nfrom, Greenwich Mean Time (GMT) and the various definitions of Universal\nTime. UTC is now the worldwide standard for regulating clocks and time\nmeasurement.\n\nAll other timezones are defined relative to UTC, and include offsets like\nUTC+0800 - hours to add or subtract from UTC to derive the local time. No\ndaylight saving time occurs in UTC, making it a useful timezone to perform\ndate arithmetic without worrying about the confusion and ambiguities caused\nby daylight saving time transitions, your country changing its timezone, or\nmobile computers that roam through multiple timezones.\n\n.. _Coordinated Universal Time: https://en.wikipedia.org/wiki/Coordinated_Universal_Time\n\n\nHelpers\n~~~~~~~\n\nThere are two lists of timezones provided.\n\n``all_timezones`` is the exhaustive list of the timezone names that can\nbe used.\n\n>>> from pytz import all_timezones\n>>> len(all_timezones) >= 500\nTrue\n>>> 'Etc/Greenwich' in all_timezones\nTrue\n\n``common_timezones`` is a list of useful, current timezones. It doesn't\ncontain deprecated zones or historical zones, except for a few I've\ndeemed in common usage, such as US/Eastern (open a bug report if you\nthink other timezones are deserving of being included here). It is also\na sequence of strings.\n\n>>> from pytz import common_timezones\n>>> len(common_timezones) < len(all_timezones)\nTrue\n>>> 'Etc/Greenwich' in common_timezones\nFalse\n>>> 'Australia/Melbourne' in common_timezones\nTrue\n>>> 'US/Eastern' in common_timezones\nTrue\n>>> 'Canada/Eastern' in common_timezones\nTrue\n>>> 'Australia/Yancowinna' in all_timezones\nTrue\n>>> 'Australia/Yancowinna' in common_timezones\nFalse\n\nBoth ``common_timezones`` and ``all_timezones`` are alphabetically\nsorted:\n\n>>> common_timezones_dupe = common_timezones[:]\n>>> common_timezones_dupe.sort()\n>>> common_timezones == common_timezones_dupe\nTrue\n>>> all_timezones_dupe = all_timezones[:]\n>>> all_timezones_dupe.sort()\n>>> all_timezones == all_timezones_dupe\nTrue\n\n``all_timezones`` and ``common_timezones`` are also available as sets.\n\n>>> from pytz import all_timezones_set, common_timezones_set\n>>> 'US/Eastern' in all_timezones_set\nTrue\n>>> 'US/Eastern' in common_timezones_set\nTrue\n>>> 'Australia/Victoria' in common_timezones_set\nFalse\n\nYou can also retrieve lists of timezones used by particular countries\nusing the ``country_timezones()`` function. It requires an ISO-3166\ntwo letter country code.\n\n>>> from pytz import country_timezones\n>>> print(' '.join(country_timezones('ch')))\nEurope/Zurich\n>>> print(' '.join(country_timezones('CH')))\nEurope/Zurich\n\n\nInternationalization - i18n/l10n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPytz is an interface to the IANA database, which uses ASCII names. The `Unicode Consortium's Unicode Locales (CLDR) <http://cldr.unicode.org>`_\nproject provides translations. Python packages such as\n`Babel <https://babel.pocoo.org/en/latest/api/dates.html#timezone-functionality>`_\nand Thomas Khyn's `l18n <https://pypi.org/project/l18n/>`_ package can be used\nto access these translations from Python.\n\n\nLicense\n~~~~~~~\n\nMIT license.\n\nThis code is also available as part of Zope 3 under the Zope Public\nLicense, Version 2.1 (ZPL).\n\nI'm happy to relicense this code if necessary for inclusion in other\nopen source projects.\n\n\nLatest Versions\n~~~~~~~~~~~~~~~\n\nThis package will be updated after releases of the Olson timezone\ndatabase. The latest version can be downloaded from the `Python Package\nIndex <https://pypi.org/project/pytz/>`_. The code that is used\nto generate this distribution is hosted on Github and available\nusing git::\n\n git clone https://github.com/stub42/pytz.git\n\nAnnouncements of new releases are made on\n`Launchpad <https://launchpad.net/pytz>`_, and the\n`Atom feed <http://feeds.launchpad.net/pytz/announcements.atom>`_\nhosted there.\n\n\nBugs, Feature Requests & Patches\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nBugs should be reported on `Github <https://github.com/stub42/pytz/issues>`_.\nFeature requests are unlikely to be considered, and efforts instead directed\nto timezone support now built into Python or packages that work with it.\n\n\nSecurity Issues\n~~~~~~~~~~~~~~~\n\nReports about security issues can be made via `Tidelift <https://tidelift.com/security>`_.\n\n\nIssues & Limitations\n~~~~~~~~~~~~~~~~~~~~\n\n- This project is in maintenance mode. Projects using Python 3.9 or later\n are best served by using the timezone functionaly now included in core\n Python and packages that work with it such as `tzdata <https://pypi.org/project/tzdata/>`_.\n\n- Offsets from UTC are rounded to the nearest whole minute, so timezones\n such as Europe/Amsterdam pre 1937 will be up to 30 seconds out. This\n was a limitation of the Python datetime library.\n\n- If you think a timezone definition is incorrect, I probably can't fix\n it. pytz is a direct translation of the Olson timezone database, and\n changes to the timezone definitions need to be made to this source.\n If you find errors they should be reported to the time zone mailing\n list, linked from http://www.iana.org/time-zones.\n\n\nFurther Reading\n~~~~~~~~~~~~~~~\n\nMore info than you want to know about timezones:\nhttps://data.iana.org/time-zones/tz-link.html\n\n\nContact\n~~~~~~~\n\nStuart Bishop <stuart@stuartbishop.net>\n\n\n\n"
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