Saravanan666/pypi-org huggingface dataset:
Package: grayscale-image
grayscale-imageGrayscale image.Installpipinstallgrayscale-imageInstalled Command Utilsgrayscale-imageUsageC:\Code\grayscale-image>grayscale-image--help
Usage:grayscale-image[OPTIONS]COMMAND[ARGS]...

Options:--helpShowthismessageandexit.

Commands:fileGrayscaleanimagefile.folderGrayscale.pngand.jpgimagesinafolder.

C:\Code\grayscale-image>grayscale-imagefile--help
Usage:grayscale-imagefile[OPTIONS]SRC_FILEDST_FILEGrayscaleanimagefile.

Options:--helpShowthismessageandexit.

C:\Code\grayscale-image>grayscale-imagefolder--help
Usage:grayscale-imagefolder[OPTIONS]SRC_ROOTDST_ROOTGrayscale.pngand.jpgimagesinafolder.

Options:--helpShowthismessageandexit.Examplesgrayscale-imagefilea.pngb.pngGrayscale a.png and save to b.png.grayscale-imagefoldersrcdstGrayscale all .png and .jpg images in src folder, and save them to dst folder.Releasesv0.1.0 2020/04/05First release.
Package: grayskull
Grayskull"Skeletonr's main goal is to conquer the mysterious fortress of Castle Grayskull, from which He-Man draws his powers. If he succeeds, Skeletor would be able to conquer not only Eternia, but the whole universe."Adapted fromWikipedia. Image credits:https://he-man.fandom.comIntroductionGrayskull is an automatic conda recipe generator.The main goal of this project is to generate concise recipes
forconda-forge.
The Grayskull project was created with the intention to eventually replaceconda skeleton.Presently Grayskull can generate recipes for Python packages available on PyPI and also those not published on PyPI but available as GitHub repositories.
Grayskull can also generate recipes for R packages published on CRAN.
Future versions of Grayskull will support recipe generation for packages of other repositories such as Conan and CPAN etc..InstallationIt is possible to install this project usingpip:pipinstallgrayskullorconda, using theconda-forgechannel:condainstall-cconda-forgegrayskullIt is also possible to clone this repo and install it usingpip:gitclonehttps://github.com/conda/grayskull.gitcdgrayskull
pipinstall-e.UsageIt is pretty simple to usegrayskull. Just call it, pass the repository
(pypiorcran) and the package name.Example:grayskullpypipytestAfter thatgrayskullwill create a folder with the same name as the package
and inside this folder the generated recipe will be present (meta.yaml).Example withpytest(grayskull pypi pytest):If your package is a GitHub repository just replace the package name with the GitHub repository URL.For example:grayskullpypihttps://github.com/confluentinc/confluent-kafka-pythonYou can also generate a recipe from a local sdist archive:grayskullpypi./pytest-5.3.5.tar.gzNote that such a recipe isn't really portable as it will depend on the local path of the
sdist file. It can be useful if you want to automatically generate a conda package.Online GrayskullIt is also possible to use Grayskull without any installation. You can go to this websitemarcelotrevisani.com/grayskulland inform the name and the version (optional) of the package and it will create the recipe for you.LicenseCopyright Marcelo Duarte Trevisani and contributors, 2020-2023.Distributed under the terms of the Apache 2.0 license, grayskull is free and open source software.
Package: graystruct
Integration between structlog and graylog GELF, provided by graypy.RationaleStructlogprovides a rich tool for producing structured log messages
from applications.Graypyprovides a Python interface to emit logs in
theGELFformat accepted bygraylog.  In effect,structlogpre-processes the _inputs_ to Pythonloggingmodule, whilegraypyprocesses the outputs (LogRecordinstances), and neither expects the
other to be present.graystructprovides a small integration layer composed of two main
components that are used in conjunction with bothstructlogandgraypy.  These components minimally alter the behaviour ofstructlogandgraypyat their interface points so that they are able to cooperate
in producing structured logs.Example>>>importlogging>>>importstructlog>>>fromgraystruct.encoderimportGELFEncoder>>>fromgraystruct.handlerimportGELFHandler>>>fromgraystruct.utilsimportadd_app_context>>>structlog.configure(...logger_factory=structlog.stdlib.LoggerFactory(),...processors=[...# Prevent exception formatting if logging is not configured...structlog.stdlib.filter_by_level,...# Add file, line, function information of where log occurred...add_app_context,...# Format positional args to log as in stdlib...structlog.stdlib.PositionalArgumentsFormatter(),...# Add a timestamp to log message...structlog.processors.TimeStamper(fmt='iso',utc=True),...# Dump stack if ``stack_info=True`` passed to log...structlog.processors.StackInfoRenderer(),...# Format exception info is ``exc_info`` passed to log...structlog.processors.format_exc_info,...# Encode the message in GELF format (this must be the final processor)...structlog.processors.GELFEncoder(),...],...)>>>std_logger=logging.getLogger()>>>std_logger.setLevel(logging.WARNING)>>>gelf_handler=GELFHandler('localhost',12201)>>>std_logger.addHandler(gelf_handler)>>>logger=structlog.get_logger('some.package')# Will transmit a GELF-encoded message>>>logger.error('user.login',username='sjagoe')
Package: grbackup
grbackup is a Python library used
to save all items from your Google Reader account into different places.Usagelist subscriptions: grbackup -e email@gmail.com -p password -ls
list topics: grbackup -e email@gmail.com -p password -lt http://feed.com
list starred: grbackup -e email@gmail.com -p password -lx
list all items: grbackup -e email@gmail.com -p password -la

backup subscriptions: grbackup -e email@gmail.com -p password -bs -o json:/tmp/subscriptions.json
backup topics: grbackup -e email@gmail.com -p password -bt http://myfeed.com -o json:/tmp/myfeed.json
backup starred into MongoDB: grbackup -e email@gmail.com -p password -bx -o mongodb://localhost:27017
backup all items into Redis: grbackup -e email@gmail.com -p password -ba -o redis://localhost:6379/3
Package: grbl-gamepad
Gamepad Jog Controller for GRBLJog your GRBL-based CNC machines with a gamepad controller.Free software: MIT licenseDocumentation:https://grbl-gamepad.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-03-30)First release on PyPI.
Package: grbl-link
Grbl LinkInteract seamlessly with a Grbl CNC controller. Stream files, jog, and receive status messages from a connected Grbl device.Free software: MIT licenseDocumentation:https://grbl-link.readthedocs.io.FeaturesSend GCODE and Grbl-specific commandsBuilt-in jog APIReceive push messages sent from GrblCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-02-02)First release on PyPI.
Package: grblogtools
GRBlogtoolsExtract 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.Installationpython -m pip install grblogtoolsIt 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 providedgrblogtools.ipynb notebookwith theexample data setto get started.
Additionally, there is aGurobi TechTalk demonstrating how to use grblogtools (YouTube):Pandas/Plotlyparse log files:importgrblogtoolsasgltresults=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 grblogtools myrun.xlsx data/*.logList all available options and how to use the command-line tool:python -m grblogtools --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 grblogtools --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: gr-cli
grGerrit's command line toolIt's likegh(GitHub's CLI) but for GerritInstallationpip3installgr-cliAuthAuthentication done byHTTP credentials:https://gerrit.cloudlinux.com/settings/#HTTPCredentialsAt first launch,grwill ask for username and password that you generated at gerrit and write to~/.config/gr/conf.py. That's plain python file and you can use it however you want, e.g.:importospswd=os.popen("pass show cl/gr-cli | head -n 1").read().strip()user=os.popen("pass show cl/gr-cli | grep username").read().strip().split(" ")[-1]AUTH=(user,pswd)Usage>grch--help
Usage:main.pych[OPTIONS]COMMAND[ARGS]...Managepullrequests

Options:--helpShowthismessageandexit.

Commands:abandonAbandonchangebyIDadd-reviewersAbandonchangebyIDcheckoutCheckoutchangetonewbranchcommentCommentchangebyIDcommentsListchangecommentsbyIDcreateCreatechangefromcurrentbranchdiffShowdiffbychangeIDlistListallchangesmergeSubmitchangebyIDrebaseRebasechangetotargetbranchbyIDreviewReviewchangebyIDstatusListallCHangesviewViewchangedetailsExamplegitcheckout-btest-grechoChanges>file.txt
gitaddfile.txt
gitcommit-m'New very important patch'# push current branch to remote and create PRgrchcreate

grchlist

grchmerge[change-id]
Package: grcriddles
# GRCRiddlesStudy and examination of alphabetical and isopsephical riddles of the Ancient GreeksInstall:```bashpip install grcriddles```Use from Python/IPython console:```python# get words with length 9, isopsephy 1697, consonants 5,# and the first three syllables having 2 letters each# syllable count is going to be 4 with above parameterswords = get_database({0: 'Word', 1: 'Count', 3: 'Chars', 4: 'Isopsephy', 5: 'Syllables', 7: 'Vowels', 8: 'Mutes'})a = words[words['Isopsephy'] == 1697]a = a[a['Chars'] == 9]a = a[a['Mutes'] == 5]a[a.apply(lambda x: len(x['Syllables'][0]) == 2 and \len(x['Syllables'][1]) == 2 and \len(x['Syllables'][2]) == 2, axis=1)]```Output:```txtCount    Chars  Isopsephy          Syllables  Vowels       MutesWordΑΜΦΕΚΑΛΥΨ      1        9       1697  [ΑΜ, ΦΕ, ΚΑ, ΛΥΨ]       4           5ΛΗΛΥΘΟΤΩΝ      1        9       1697  [ΛΗ, ΛΥ, ΘΟ, ΤΩΝ]       4           5ΜΕΤΑΝΑΣΤΩ      1        9       1697  [ΜΕ, ΤΑ, ΝΑ, ΣΤΩ]       4           5ΣΥΝΩΚΙΣΘΗ     13        9       1697  [ΣΥ, ΝΩ, ΚΙ, ΣΘΗ]       4           5``````python# get words containing ΑΜΦΕΚΑΛΥ stem wordwords.filter(like="ΑΜΦΕΚΑΛΥ", axis=0)```Output:```txtCount  Chars  Isopsephy               Syllables  Vowels  MutesWordΑΜΦΕΚΑΛΥΠΤΕ       3     11       1382   [ΑΜ, ΦΕ, ΚΑ, ΛΥ, ΠΤΕ]       5      6ΑΜΦΕΚΑΛΥΠΤΟΝ      2     12       1497  [ΑΜ, ΦΕ, ΚΑ, ΛΥ, ΠΤΟΝ]       5      7ΑΜΦΕΚΑΛΥΦΘΗ       2     11       1514   [ΑΜ, ΦΕ, ΚΑ, ΛΥ, ΦΘΗ]       5      6ΑΜΦΕΚΑΛΥΨ         1      9       1697       [ΑΜ, ΦΕ, ΚΑ, ΛΥΨ]       4      5ΑΜΦΕΚΑΛΥΨΑΝ       2     11       1748   [ΑΜ, ΦΕ, ΚΑ, ΛΥ, ΨΑΝ]       5      6ΑΜΦΕΚΑΛΥΨΕ       18     10       1702    [ΑΜ, ΦΕ, ΚΑ, ΛΥ, ΨΕ]       5      5ΑΜΦΕΚΑΛΥΨΕΝ      20     11       1752   [ΑΜ, ΦΕ, ΚΑ, ΛΥ, ΨΕΝ]       5      6```### DocsDeveloper documentation: http://grcriddles.readthedocs.io/en/latest/Greek Alpha-Numeric Riddle Solver EBook: https://www.gitbook.com/book/markomanninen/isopsephical-riddles-pseudo-sibylline-oracles/details
Package: grd
grdDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-ngrd--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallgrdnbdev# activate conda environment$condaactivategrd# make sure the grd package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the grd package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/grd.gitor fromconda$condainstall-cdsm-72grdor frompypi$pipinstallgrdDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.abc,d=Literal['a','b','c'],'d'classABC_Guard(TTypeGuard):types=abcABC_Guard.istype('bz'),ABC_Guard.istype('b')(False, True)classABCD_Guard(TTypeGuard):types=Union[abc,d]ABCD_Guard.istype('b'),ABCD_Guard.istype('d')(True, True)importnumpyasnpfromtypingimportTypeAliasndarray:TypeAlias=np.ndarrayclassNPArrayGuard(TTypeGuard):types=ndarrayNPArrayGuard.istype([1,2,3]),NPArrayGuard.istype(np.array([1,2,3]))(False, True)
Package: grdwindinversion
grdwindinversionPackage to perform Wind inversion from GRD Level-1 SAR imagesFree software: MIT licenseDocumentation:https://grdwindinversion.readthedocs.io.UsageSAR_L1-to-L2_wind_processor-husage:SAR_L1-to-L2_wind_processor[-h]--input_fileINPUT_FILE[--config_fileCONFIG_FILE]--outputdirOUTPUTDIR[--verbose][--overwrite]PerforminversionfromS1(L1-GRD)SAFE,L1-RCM,L1-RS2;usingxsar/xsarseatoolsoptions:-h,--helpshowthishelpmessageandexit--input_fileINPUT_FILEinputfilepath--config_fileCONFIG_FILEconfigfilepath[ifnotprovidedwilltakeconfigfilebasedoninputfile]--outputdirOUTPUTDIR--verbose--overwriteoverwriteexisting.ncfiles[defaultisFalse]FeaturesThis Python library (based onxarray) allows to perform wind inversion from level-1 GRD (projected magnitude image).
Mission supported:Sentinel-1RCMRadarSat-2
Package: gre
$gre_GRE vocab helperinstallfrom pypiRunpip install grefrom sourceClone repo/download as zipcdto the folderRunpipenv installRunpipenv shellRunpip install .usageUsage: gre [OPTIONS] COMMAND [ARGS]...

  A handy helper to manage GRE Vocab preparation

Options:
  --debug / --no-debug
  --help                Show this message and exit.

Commands:
  add     Add a word/words to vocabulary
  export  Export vocabulary to file
  flash   Flash cards
  get     Retrieve a word, definiton from vocabulary
  nuke    Delete entire vocabulary
  remove  Remove a word/words from vocabulary
  show    Print entire vocabularyauthorSayan Goswami (c) 2018
Package: greads
Goodreads Quotes ScraperRequirementspython>=3.6InstallationpipinstallgreadsUsagegreads--helpusage: greads [-h] -a AUTHOR [-l LANGUAGE] [-n NUM_PAGES] [-o OUTPUT_FILE]
            [--enable-multiprocessing]

optional arguments:
  -h, --help            show this help message and exit
  -a AUTHOR, --author AUTHOR
                        Full name of the author you want to search
  -l LANGUAGE, --language LANGUAGE
                        Two letters string representing the language you want
                        to parse the results in (default: en)
  -n NUM_PAGES, --num-pages NUM_PAGES
                        Number of pages to iterate through (if empty, will
                        iterate through all pages)
  -o OUTPUT_FILE, --output-file OUTPUT_FILE
                        Name of the output file (default: quotes.json)
  --enable-multiprocessing
                        Enable multiprocessingExamplegreads--author"Oscar Wilde"--num-pages100# 100%|█████████████████████████████████████████| 100/100 [01:06<00:00,  1.49it/s]# Cleaning the quotes text...# Saved results to quotes.json...greads--author"Oscar Wilde"--num-pages10--language'es'# 100%|███████████████████████████████████████████| 10/10 [00:06<00:00,  1.60it/s]# Cleaning the quotes text...# Saved results to quotes.json...
Package: greasyfork-archive
Greasyfork User ArchiveScrape data from a users Greasyfork account.For a given user, for each installable script that users created, scrapes:script id
script name
script authors
daily installs
total installs
rating score
created date
updated date
script type
script version
sensitive
language
css available as js
description
url
code (the script contents)This does not scrape the script code for items that can't be installed directly, e.g.this.Examplegreasyfork_archive 106222 --output-file scraped_data.jsonThis would scrape information for the userhttps://greasyfork.org/en/users/106222InstallationTo install with pip, run:pip3 install --user greasyfork_archiveRunUsage: greasyfork_archive [OPTIONS] GREASYFORK_USER_ID

Options:
  --output-file PATH  JSON filepath to output scraped data to  [required]
  --help              Show this message and exit.Testspip3 install --user pytest vcrpy
pytest
Package: great-ai
GreatAIEasily transform your prototype AI code into production-ready software.Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases. GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software.Examplepipinstallgreat-aiCreate a new file calleddemo.pyfromgreat_aiimportGreatAI@GreatAI.createdefgreeter(name:str)->str:returnf"Hello{name}!"Start it by executinggreat-ai demo.py, find the dashboard athttp://localhost:6060.That's it. Your GreatAI service isnearlyready for production use. Many of theSE4ML best practicesare configured and implemented automatically (of course, these can be customised as well).Check out the full documentation here.Why is this GREAT?GreatAI fits between the prototype and deployment phases of your AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes:General: use any Python library without restrictionRobust: have error-handling and well-tested utilities out-of-the-boxEnd-to-end: utilise end-to-end feedback as a built-in, first-class conceptAutomated: focus only on what actually requires your attentionTrustworthy: deploy models that you and society can confidently trustWhy GreatAI?There are other, existing solutions aiming to facilitate this phase.Amazon SageMakerandSeldon Coreprovide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great.However,research indicatesthat professionals rarely use them. This may be due to their inherent setup and operating complexity.GreatAI is designed to be as simple to use as possible.Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of theSE4ML best practices, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.Learn moreCheck out the full documentation here.Findgreat-aionPyPIpipinstallgreat-aiFindgreat-aionDockerHubdockerrun-p6060:6060schmelczera/great-aiContributeContributions are welcome.Install for developmentpython3-mvenv--copies.envsource.env/bin/activate
pipinstall--upgradeflitpip
flitinstall--symlinkDevelopscripts/format-python.shgreat_aidocstestsFormat code.python3-mpytest--doctest-modules--asyncio-mode=strict.Run tests.mkdocsserveServe documentation.
Package: great_ape_safe
GreatApeSafeThis is an extension of the legendaryApe Safe, inheriting all the good stuff fromApeSafe, while adding some extra functions and making it protocol aware.Instead of (re)writing protocol specific logic for every script,GreatApeSafecontains wrappers for each protocol's specific on-chain API. On top of that, all necessary addresses are hard-coded and loaded.This prevents having to dive into documentation every time, either for figuring out the exact way to call the on-chain functions or finding deployed addresses.For example, calling theAAVE lending pool's contractdoesn't require any searching or address copypasting:>>>safe.aave.pool.paused()FalseAnd making a deposit becomes as simple as:safe.aave.deposit(usdc,usdc.balanceOf(safe))Under the hood, the protocol classAavesolves what exact aToken we are dealing with, makes the necessary approval, calls the right functions and makes an on-fork assertion of successful execution:classAave():...defdeposit(self,underlying,mantissa,destination=None):# deposit `mantissa` amount of `underlying` into aave pool# https://docs.aave.com/developers/the-core-protocol/lendingpool#depositdestination=self.safe.addressifnotdestinationelsedestinationatoken_addr=self.data.getReserveTokensAddresses(underlying)[0]atoken=self.safe.contract(atoken_addr)bal_before=atoken.balanceOf(destination)underlying.approve(self.pool,mantissa)self.pool.deposit(underlying,mantissa,destination,0)assertatoken.balanceOf(destination)>bal_beforeBy no means complete, but included platforms currently are:AaveCompoundConvexCurveInstallationgit clone git@github.com:gosuto-ai/great-ape-safe.gitpip install -r requirements-core.txtAnd copy.env.exampleto.envand add your API keys to it if needed.Runningbrownie run exampleTestingbrownie test