Saravanan666/pypi-org huggingface dataset:
Package: gura
Gura Python parserThis repository contains the implementation of aGura(compliant with version 1.0.0) format parser in Python.Installationpip install gura-parserUsageimportguragura_string="""# This is a Gura document.title: "Gura Example"an_object:username: "Stephen"pass: "Hawking"# Line breaks are OK when inside arrayshosts: ["alpha","omega"]"""# Loads: transforms a Gura string into a dictionaryparsed_gura=gura.loads(gura_string)print(parsed_gura)# {'title': 'Gura Example', 'an_object': {'username': 'Stephen', 'pass': 'Hawking'}, 'hosts': ['alpha', 'omega']}# Access a specific fieldprint(f"Title ->{parsed_gura['title']}")# Iterate over structureforhostinparsed_gura['hosts']:print(f'Host ->{host}')# Dumps: transforms a dictionary into a Gura stringprint(gura.dumps(parsed_gura))ContributingAll kind of contribution is welcome! If you want to contribute just:Fork this repository.Create a new branch and introduce there your new changes.Make a Pull Request!Or you can join to ourcommunity in Discord!TestsTo run all the tests:python -m unittest. More info inofficial Unittest docsBuildingCreate a virtual environment:python3 -m venv venvActivate it:source venv/bin/activateInstall some dependencies:pip install -r requirements.txtClean and buildrm -rf ./dist/* && python3 setup.py sdistLicenseThis repository is distributed under the terms of the MIT license.
Package: gurka_toolkits
To use (with caution), simply do:>>> from gurka import toolkits
>>>
>>> # Example rupiah formating
>>> money_idn = 45000
>>> toolkits.to_rupiah(money_idn)
'Rp. 45,000.00'
>>>
>>> # Example using autonumber
>>> toolkits.DEFAULT_PREFIX = "PRD" # PRD is a prefix code for product.
>>> toolkits.DEFAULT_OVERFLOW = 3 # Total digit autonumber
>>>
>>> toolkits.autonumber()
'PRD001'
>>>
>>> toolkits.autonumber(data=_)
'PRD002'
>>> toolkits.autonumber(data=_)
'PRD003'
>>> toolkits.autonumber(data=_)
'PRD004'
Package: gurlshortner
=============GURL Shortner=============* A tiny implementation of Google URL Shortner. It can shorten a URL and expand a shortened URL.|example:|| >>> import gurlshortner| >>> gurlshortner.shorten('http://google.com')| {u'kind': u'urlshortener#url', u'id': u'http://goo.gl/mR2d', u'longUrl': u'http://google.com/'}| >>> gurlshortner.longify('http://goo.gl/mR2d')| {u'status': u'OK', u'kind': u'urlshortener#url', u'id': u'http://goo.gl/mR2d', u'longUrl': u'http://google.com/'}===============How to install?===============$ sudo pip install gurlshortner==================Where is the code?==================Check out https://github.com/adimania/gurlshortner
Package: gurmukhiutils
Gurmukhi UtilsPython utilities library for converting, analyzing, and testing Gurmukhi strings. This project is an original work inspired by the JavaScript librarygurmukhi-utils.WIPThis library is a work in progress! Note that the API can change unexpectedly when upgrading. It will not be usingSemVeruntil version 1.0.0. Please do not use for critical projects yet.Installationpip3installgurmukhiutilspoetryaddgurmukhiutilsUsageThis is a cursory overview of the functions available. To learn more, see any function's docstring in the source code.Modulegurmukhiutils.unicodeunicode- Converts any ascii gurmukhi characters and sanitizes to unicode gurmukhi. Supports markup language ofSant Lipi.unicode_normalize- Normalizes Gurmukhi according to Unicode Standards.sort_diacritics- Orders the gurmukhi diacritics in a string according to Unicode standards.sanitize_unicode- Use single char representations of constructed characters.Modulegurmukhiutils.convertConverts text from a script to another. Currently only supports lossless Gurmukhi to Roman transliteration. Gurmukhi to Roman transcription is a WIP.Modulegurmukhiutils.removeremove- Removes substrings from string.remove_regex- Removes regex patterns from string.remove_line_endings- Attempts to remove line endings as best as possible.remove_vishrams- Removes all vishram characters.gurmukhiutils.ascii- Converts Gurmukhi to ASCII for Open Gurbani Akhar font.Example>>>fromgurmukhiutils.unicodeimportunicode>>>unicode("gurU")'ਗੁਰੂ'>>>fromgurmukhiutils.convertimportconvert>>>convert("ਗੁਰੂ")'gurū'ContributeIf you want to help, please get started with theCONTRIBUTING.mddoc.CommunityThe easiest way to communicate is viaGitHub issues. Please search for similar issues regarding your concerns before opening a new issue.Get organization updates for Shabad OS by following us onInstagramandTwitter. We also invite you to join us on our public chat server hosted onSlack.Our intention is to signal a safe open-source community. Please help us foster an atmosphere of kindness, cooperation, and understanding. By participating, you agree to abide by theContributor Covenant.If you have a concern that doesn't warrant opening a GitHub issue, please reach out to us:Bhajneet S.K., Author, Maintainer, Project Lead:@bhajneet
Package: gurobi-logtools
gurobi-logtoolsExtract information from Gurobi log files and generatepandas DataFramesor Excel worksheets for further processing. Also includes a wrapper for out-of-the-box interactive visualizations using the plotting libraryPlotly.[!NOTE]
We have renamed the project togurobi-logtools, so please also adapt the import statement accordingly:import gurobi_logtools as gltInstallationpython -m pip install gurobi-logtoolsIt is recommended to prepend thepip installcommand withpython -mto ensure that the package is installed using the correct Python version currently active in your environment.SeeCHANGELOGfor added, removed or fixed functionality.UsageFirst, you need a set of Gurobi log files to compare, e.g.,results from several model instancescomparisons of different parameter settingsperformance variability experiments involving multiple random seed runs...You may also use the providedgurobi-logtools.ipynb notebookwith theexample data setto get started.
Additionally, there is aGurobi TechTalk demonstrating how to use it (YouTube):Pandas/Plotlyparse log files:importgurobi_logtoolsasgltresults=glt.parse(["run1/*.log","run2/*.log"])summary=results.summary()nodelog_progress=results.progress("nodelog")Depending on your requirements, you may need to filter or modify the resulting DataFrames.draw interactive charts, preferably in aJupyter Notebook:final results from the individual runs:glt.plot(summary,type="box")progress charts for the individual runs:glt.plot(nodelog_progress,y="Gap",color="Log",type="line")progress of the norel heuristic (note, the time recorded here is since the start of norel, and does not include presolve + read time):glt.plot(results.progress("norel"),x="Time",y="Incumbent",color="Log",type="line")These are just examples using thePlotly Python library- of course, any other plotting library of your choice can be used to work with these DataFrames.ExcelConvert your log files to Excel worksheets right on the command-line:python -m gurobi_logtools myrun.xlsx data/*.logList all available options and how to use the command-line tool:python -m gurobi_logtools --helpRename log filesThe command line tool can also rename log files according to the parameters set and model solved in a given run. This is useful if your log files do not have a consistent naming scheme, or if multiple runs are logged per file and you want to extract the individual runs.For example:python -m gurobi_logtools --write-to-dir nicenames summary.xlsx tests/assets/combined/*.logseparates logs for individual runs in the input files and writes copies to the 'nicenames' folder with a consistent naming scheme:> ls nicenames
912-MIPFocus1-Presolve1-TimeLimit600-glass4-0.log
912-MIPFocus1-Presolve1-TimeLimit600-glass4-1.log
912-MIPFocus1-Presolve1-TimeLimit600-glass4-2.log
912-MIPFocus2-Presolve1-TimeLimit600-glass4-0.log
912-MIPFocus2-Presolve1-TimeLimit600-glass4-1.log
912-MIPFocus2-Presolve1-TimeLimit600-glass4-2.log
912-Presolve1-TimeLimit600-glass4-0.log
912-Presolve1-TimeLimit600-glass4-1.log
912-Presolve1-TimeLimit600-glass4-2.log
Package: gurobi-machines-plugin
Gurobi monitoring pluginChecks an url raise an alert if some problem is found.
Uses curl with all its power, so you can extend your check with all curl options.VERSIONInstallLinux:sudo pip3 install gurobi_machines_plugin --upgradeAlso is possible to use:sudo python3 -m pip install gurobi_machines_plugin --upgradeOn windows with python3.5:pip install gurobi_machines_plugin --upgradeFor proxies add:--proxy='http://user:passw@server:port'UsageUse the command line:> gurobi_machines_plugin --help
  usage: gurobi_machines_plugin [-h] [-u [URL]] [-e [EXTRA_ARGS]]
                                [--client_id [CLIENT_ID]]
                                [--client_secret [CLIENT_SECRET]]
                                [--outdated_minutes [OUTDATED_MINUTES]]

    optional arguments:
                        -h, --help            show this help message and exit
                        -u [URL], --url [URL]
                                                url to check
                        -e [EXTRA_ARGS], --extra_args [EXTRA_ARGS]
                                                extra args to add to curl, see curl manpage
                        --client_id [CLIENT_ID]
                                                Header client_id
                        --client_secret [CLIENT_SECRET]
                                                Header client_secret
                        --outdated_minutes [OUTDATED_MINUTES]
                                                Minutes to consider outdatedExample usageExample basic usage:> gurobi_machines_plugin --url 'https://cloud.gurobi.com/api/v2/pools/{PoolID}/machines'
                         --client_id '{client_id}' --client_secret '{client_secret}' --outdated_minute 60Nagios configExample command:define command{
    command_name  check_gurobi_machines
    command_line  /usr/local/bin/gurobi_machines_plugin --url '$ARG1$' --client_id '$ARG2$' --client_secret '$ARG3$' --outdated_minute $ARG4$  $ARG5$
}Example service:define service {
        host_name                       SERVERX
        service_description             service_name
        check_command                   check_gurobi_machines!https://cloud.gurobi.com/api/v2/pools/{PoolID}/machines!{client_id}!{client_secret}!60
        use                                         generic-service
        notes                           some useful notes
}You can use ansible role that already has the installation and command:https://github.com/CoffeeITWorks/ansible_nagios4_server_pluginsTODOUse hash passwordsAdd Unit tests?
Package: gurobimh
gurobimh: Unofficial Alternative Gurobi/Python Interface========================================================Overview--------`gurobimh` is a drop-in replacement for the `gurobipy` API bindings shipped with[Gurobi](www.gurobi.com). It offers several advantages:* `gurobimh` can be compiled for all current versions of Python; you do not needto rely on Gurobi officially supporting your desired Python version.* `gurobimh`'s performance is much better, especially when modifying models a lot (like in a hand-writtenbranch and bound solver).* `gurobimh` is free software an can be easily extended.* `gurobimh` ships a Cython `pxd` files, and the `Model` class has some fast-access `cdef` membermethods for model modifications or queries than circumvent some of the slower API parts. Thismeans that, if you are writing your algorithms in Cython, you can almost achieve the performanceof the C interface, but using a much cleaner API.Of course, there are also disatvantages:* Up to now, `gurobimh` supports only a subset of the official `gurobipy` API, in particularquadratic programming is not yet supported, and lots of parameters are missing. However thesefeatures are easy to implement once you look at how the others are, so you are welcome tocontribute. Simply put, I have only implemented the features I am using myself.* Though I have successfully verified that `gurobimh` behaves like `gurobipy` for my programs,there are probably lots of bugs, and of course there's no commercial support. Don't use inproductive environments!News----* June 2016: Large update contributed by [mikenehme](https://github.com/mikenehme), many thanks for your help!!Requirements------------The API is written in [Python](www.python.org). To compile it, you need [Cython](www.cython.org). Ofcourse, you need to have Gurobi installed, and the `GUROBI_HOME` environment variable needs to beset correctly.The current version supports Gurobi 6.5 only (due to some internal API changes in Gurobi, 6.0 is NOT supported anymore).Installation------------Install directly from the [Python Package Index](www.pypi.org) withpip install gurobimhAlternatively, download the package and type:python setup.py installBoth commands can be appended by the `--user` option which locally installs `gurobimh` for thecurrent user without needing root privileges.Usage-----Simply replace any `gurobipy` import statements with `gurobimh`. If anything goes wrong, file a bug!Contact-------Please contact [me](michaelhelmling@posteo.de) or use the GitHub features for PRs, comments, bugs etc.
Package: gurobi-modelanalyzer
Gurobi Model AnalyzerGurobi Model Analyzer is anopen-sourcepython package that provides
detailed analysis of model solutions and model characteristics.
Version 1.0 consists of a results_analyzer module that calculates
explanations of ill-conditioned basis matrices.Note: At the moment this repository is public, but final docs configuration
and packages is not yet finished.   We recommend that you hold off on using it
until version 1.0 is announced as available.DocumentationThe latest user manual is available onreadthedocs.Contact usFor questions related to using Gurobi Model Analyzer, please useGurobi's Forum.For reporting bugs, issues, and feature requests please open an issue.If you encounter issues with Gurobi or gurobipy please contactGurobi Support.InstallationDependenciesPython >= 3.9numpy>= 1.21.5    (although earlier
versions compatible with python 3.7 will probably work).Pip installationThe easiest way to install gurobi-modelanalyzer is using pip in a
virtual environment:(.venv) pip install gurobi-modelanalyzerThis will also install the numpy and gurobipy dependencies.  One of the
advanced functions makes use of matplotlib; if you haven't already installed
that and plan to use this function (matrix_bitmap), you can either install
the matplotlib package directly, or install it with the gurobi-modelanalyzer
package via "pip install gurobi-modelanalyzer matplotlib".Please note that gurobipy is commercial software and requires a
license. When installed via pip or conda, gurobipy ships with a free
license for testing and can only solve models of limited size.Then use the explainer functions.   Example usageimport gurobipy as gp
import gurobi_modelanalyzer as gma
model=gp.read("myillconditionedmodel.mps")
model.optimize()
gma.kappa_explain(model)

# row-based explanation (default)
gma.kappa_explain(model, expltype="ROWS")

# column-based explanation
gma.kappa_explain(model, expltype="COLS")

# angle-based explanation (only looks for pairs of rows or columns
# that cause ill-conditioning.
gma.angle_explain(model)Usehelp(gma.kappa_explain)orhelp(gma.angle_explain)for information
on more advanced usage.Getting a Gurobi LicenseAlternatively to the bundled limited license, there are licenses that can handle models of all sizes.As a student or staff member of an academic institution you qualify for a free, full product license.
For more information, see:https://www.gurobi.com/academia/academic-program-and-licenses/For a commercial evaluation, you canrequest an evaluation license.Other useful resources to get started:https://www.gurobi.com/documentation/https://support.gurobi.com/hc/en-us/community/topics/DevelopmentWe value any level of experience in using Gurobi Model Analyzer and would like to encourage you to
contribute directly to this project. Please see theContributing Guidefor more information.Source codeYou can clone the latest sources with the command:git clone git@github.com:Gurobi/gurobi-modelanalyzer.gitTestingSubmitting a Pull RequestBefore opening a Pull Request, have a look at the fullContributing pageto make sure your code complies with
our guidelines.
Package: gurobi-optimods
gurobi-optimods: data-driven APIs for common optimization tasksgurobi-optimodsis an open-source Python repository of implemented
optimization use cases, each with clear, informative, and pretty documentation
that explains how to use it and the mathematical model behind it.Featuresgurobi-optimodsallows users to:quickly apply optimization to solve a specific problem in their field of
interest via intuitive, data-driven APIslearn about the mathematical model behind their use-case through thorough
documentationcontribute new mods to grow the libraryInstallationpip install gurobi-optimodsDependenciesgurobipy: Python modelling interface for the Gurobi Optimizernumpy: The fundamental package for scientific computing with Pythonscipy: Fundamental algorithms for scientific computing in Pythonpandas: powerful Python data analysis toolkitgurobipy-pandas: Convenience wrapper for building optimization models from pandas dataDocumentationFull documentation forgurobi-optimodsis hosted onreadthedocs.Licensegurobi-optimodsis distributed under the terms of theApache License 2.0.Contact UsFor questions related to using gurobi-optimods please use theGurobi Community Forum.For reporting bugs, issues and feature requests, specific togurobi-optimods, pleaseopen an issue.If you encounter issues with Gurobi orgurobipyplease contactGurobi Support.
Package: gurobipy-pandas
gurobipy-pandas: Convenience wrapper for building optimization models from pandas datagurobipy-pandasis a convenient (optional) wrapper to connect pandas with gurobipy. It enables users to more easily and efficiently build mathematical optimization models from data stored in DataFrames and Series, and to read solutions back directly as pandas objects.gurobipy-pandasis aimed at experienced pandas users who are familiar with methods to transform, group, and aggregate data stored in dataframes. It expects some familiarity with optimization modelling, but does not require deep experience with gurobipy.Featuresgurobipy-pandasallows users to:create gurobipy variables tied to the index of a series or dataframeconstruct constraints row-wise using algebraic expressionsread model solutions and constraint slacks natively as pandas seriesInstallationpip install gurobipy-pandasDependenciesgurobipy: Python modelling interface for the Gurobi Optimizerpandas: powerful Python data analysis toolkitDocumentationFull documentation forgurobipy-pandasis hosted onreadthedocs.Licensegurobipy-pandasis distributed under the terms of theApache License 2.0.Contact UsFor questions related to using gurobipy-pandas please use theGurobi Community Forum.For reporting bugs, issues and feature requests, specific togurobipy-pandas, pleaseopen an issue.If you encounter issues with Gurobi orgurobipyplease contactGurobi Support.ContributorsSimon Bowly (maintainer)Robert Luce (maintainer)Irv LustigPrinceton ConsultantsRobert RandallPrinceton ConsultantsWebinarSlides for the 2023 webinars presenting this package can be found atwebinar/webinar.ipynb. The notebook will be presented as RISE slides, but can also be executed in Jupyter.
Package: gurobipy-stubs
gurobipy-stubsThis package provides type hints forgurobipy, thePython API for
Gurobi.
These stubs help you to write typed Python code, and enhance many IDE's
built-in auto completion features.ExampleWith this type stubs package installed, static type checkers such asmypywilll be able to
verify the types given in source code annotations like in this example:import gurobipy as gp

m: gp.Model = gp.Model()
v: gp.Var = m.addVar()
m.optimize()
vval: float = v.X% mypy example.py 
Success: no issues found in 1 source file
Package: guruguru
Guruguru Cliguruguru cli is a command line tool to use guruguruhttps://www.guruguru.ml/conveniently.RequirementsPython: 3.6, 3.7, 3.8requestspandastabulateInstallationInstallation is easy usingpipand will install all required libraries.pipinstallguruguruAfter install, you can usegurugurucommand from your terminal.guruguru--versionUsageAuthorizationThe first time you perform a process that requires authentication, you need to use theauthcommand to obtain authentication information.$guruguruauth-h
usage:guruguruauth[-h]{login}...

positionalarguments:{login}optionalarguments:-h,--helpshowthishelpmessageandexitSubmissionIn order to post your submission, you must haverunauth logincommandcreated a team for the competition on the guruguru website.$gurugurusubmitcreate-h
usage:gurugurusubmitcreate[-h]-cCOMPETITION--fileFILE

optionalarguments:-h,--helpshowthishelpmessageandexit-cCOMPETITION,--competitionCOMPETITIONCompetitiontosubmit(default:None)--fileFILEPathtosubmissionfile.(default:None)Competition LeaderBoardgurugurucompetitionlb-h

usage:gurugurucompetitionlb[-h]-cCOMPETITION[--private][--n_topN_TOP]optionalarguments:-h,--helpshowthishelpmessageandexit-cCOMPETITION,--competitionCOMPETITIONCompetitionIdshowLB(default:None)--privatefetchprivatelb(default:False)--n_topN_TOPmaximumnumberofteamstoshow.(default:20)FAQQ: What is the atmaCup?A: The atmaCup is an onsite data competition hosted by atma Inc.Q: How do I enter the atmaCup?A: we will send out information about the competition, so please refer to it.
Package: gurun
gurunTask automation frameworkInstallationFull installation:pipinstallgurun[full]Installing only the main framework components:pipinstallgurunYou can also install component dependencies separately. Thus, we have the cv (computer vision) version and the gui version:pipinstallgurun[cv]pipinstallgurun[gui]DevelopmentSetting up a development environmentIf you don't have a local development environment, you can follow these steps to set one up.First, if you have not already, installpoetry:pipinstallpoetryNow, initialize poetry andpre-commithooks:makeinstall&&makeinstall-pre-commitRunning testsYou can run the tests with:maketestsThis will run the tests withpytestand show information about the coverage.Formatting the codeTo format the code, you can use the command:makeformattingThis will run theblack,isortand )pyupgradecommands.If you want to just check the formatting, use the command:makecheck-formattingReleasing a new versionTo release a new version, you need to follow these steps:Update the version withpoetry version <version>and commit the changes. This project followsSemantic Versioning, so the version number should follow the format<major>.<minor>.<patch>. Alternatively, you can also use the version asmajororminororpatch, and the version number will be automatically incremented.Create a Github release with the new version number.(Optional) Publish the new version to PyPI withpoetry publish --build.