Saravanan666/pypi-org huggingface dataset:
Package: ggpp
ggpp - An Useful g++ InterfaceTable of ContentsAboutGetting StartedUsageAbout"ggpp" is a simple g++ interface made with python3, it is useful to run C/C++ code in only one command.So, instead of:g++ example.cpp -o example./exampleAnd sometimes you don't want the compiled file. So, you also do:rm -rf exampleAll theses steps, can be reduced with ggpp to:ggpp example.cppAnd you will run your C/C++ code without (optionally) leaving a compiled file on the folder.Getting StartedInstall ggpp is very simple.PrerequisitesYou will needpython3installed on your PC.For Python 3 installation, download on theofficial page.Python 3 is already installed on the most linux distributions.Try run "python3" on your shell.As the ggpp is ag++interface, you will need it installed too.As a plus, if you want to copy the content of you C/C++ code automatically after ran it, you will needxclipinstalled on linux.For install it, you can follow thisuseful guide.On Windows, to copy the content, you don't need any prerequisites.InstallingDownload or clone this repositorygit clone https://github.com/davigsousa/ggpp.gitThen, open the repository foldercd ggppAnd run pip3 to install the package.pip3 install .Now, restart your shell and you will be able to use ggpp, try:ggpp --helpUsageYou can run your C/C++ code,just passing the filename as first argument. On Windows, you need file path, like: .\targetfile.cppggpp targetfile.cppYou can also pass arguments to the g++ just passing in the end.ggpp targetfile.cpp -Wall -HCurrently, there are two arguments to configure ggpp,they are "-c" or "--clip" to copy file content to the clipboard (xclip is required on linux),And "-nr" or "--noremove" to make ggpp do not remove the compiled file.Usage:ggpp example.cpp -c -nr
Package: ggps
ggps - gps file utilities for garmin connect and garmin devicesFeaturesParse gpx and tcx files downloaded from Garmin ConnectThe GPX parsed Trackpoint data includes additional/augmented values, including as “seq” and “elapsedtime”.The TCX parsed Trackpoint data additionally includes additional/augmented values, such as “altitudefeet”, “distancemiles”, “distancekilometers”, and “runcadencex2”.Quick startInstallation:$pipinstallggpsUse:>>>importggps>>>infile='data/activity_4564516081.gpx'>>>handler=ggps.GpxHandler()>>>handler.parse(infile)>>>trackpoints=handler.trackpoints>>>print(repr(trackpoints[-1])){
  "cadence": "86",
  "cadencex2": "172",
  "elapsedtime": "02:51:26",
  "heartratebpm": "171",
  "latitudedegrees": "35.50814299844205379486083984375",
  "longitudedegrees": "-80.83528247661888599395751953125",
  "seq": "2177",
  "time": "2020-02-17T20:06:28.000Z",
  "trackpointextension": "",
  "type": "Trackpoint"
}>>>infile='data/twin_cities_marathon.gpx'>>>handler=ggps.GpxHandler()>>>handler.parse(infile)>>>trackpoints=handler.trackpoints>>>len(trackpoints)2256>>>print(repr(trackpoints[-1])){
  "elapsedtime": "04:14:24",
  "heartratebpm": "161",
  "latitudedegrees": "44.95180849917233",
  "longitudedegrees": "-93.10493202880025",
  "seq": "2256",
  "time": "2014-10-05T17:22:17.000Z",
  "type": "Trackpoint"
}>>>infile='data/twin_cities_marathon.tcx'>>>handler=ggps.TcxHandler()>>>handler.parse(infile)>>>trackpoints=handler.trackpoints>>>len(trackpoints)2256>>>print(repr(trackpoints[-1])){
  "altitudefeet": "864.8294163501167",
  "altitudemeters": "263.6000061035156",
  "cadence": "77",
  "cadencex2": "154",
  "distancekilometers": "42.63544921875",
  "distancemeters": "42635.44921875",
  "distancemiles": "26.492439912628992",
  "elapsedtime": "04:14:24",
  "heartratebpm": "161",
  "latitudedegrees": "44.95180849917233",
  "longitudedegrees": "-93.10493202880025",
  "seq": "2256",
  "speed": "3.5460000038146977",
  "time": "2014-10-05T17:22:17.000Z",
  "type": "Trackpoint"
}Source CodeSeeggps at GitHub.Includes sample data files.ChangelogVersion 0.3.02020/02/22. Version 0.3.0,  Parsing improvements, normalize ‘cadence’ and ‘heartratebpm’ attribute names.2020/02/19. Version 0.2.1,  Upgraded the m26 and Jinga2 libraries.2017/09/27. Version 0.2.0,  Converted to the pytest testing framework.2017/09/26. Version 0.1.13, packaging.2016/11/07. Version 0.1.12, updated packaging.2016/11/07. Version 0.1.11, updated packaging.2016/11/07. Version 0.1.10, updated packaging.2016/11/07. Version 0.1.9,  updated packaging.2016/11/07. Version 0.1.8,  updated packaging.2016/11/06. Version 0.1.7,  updated description.2016/11/06. Version 0.1.6,  republished.2016/11/06. Version 0.1.5,  refactored ggps/ dir.2016/11/06. Version 0.1.4,  refactored ggps/ dir. nose2 for tests.2015/11/07. Version 0.1.3,  Added README.rst2015/11/07. Version 0.1.1   Initial release.
Package: ggpt
ggptGorgeous CLI tools using GPT.Installationpip install ggptSetupObtain an OpenAI API key athttps://beta.openai.com/account/api-keysAdd it to your shell environment variablesexport OPENAI_API_KEY=<YOUR_API_KEY_HERE>Usagedocstringgenerates automated docstring based on code changes.ggpt docstring [OPTIONS]OptionDescription--api-key TEXTOpenAI API Key.If not provided,OPENAI_API_KEYenvironment variable is used.--path PATHPath to the Git repository to search for code changes.If not provided, the current directory is used.--hash TEXTHash of the commit to review.If not provided, unstaged changes are reviewed by default.--stagedInclude only staged changes in the review.If not provided, unstaged changes are reviewed by default.https://user-images.githubusercontent.com/123562684/227761450-9297f709-3d61-448c-ad78-a04f9a5f41d8.movreviewgenerates automated code review based on code changes.ggpt review [OPTIONS]OptionDescription--api-key TEXTOpenAI API Key.If not provided,OPENAI_API_KEYenvironment variable is used.--path PATHPath to the Git repository to search for code changes.If not provided, the current directory is used.--hash TEXTHash of the commit to review.If not provided, unstaged changes are reviewed by default.--stagedInclude only staged changes in the review.If not provided, unstaged changes are reviewed by default.https://user-images.githubusercontent.com/123562684/227762097-1c42b186-014f-48fe-b116-ee5e6c39f9f7.movnamingsuggests variable names based on submitted prompt.ggpt naming PROMPT [OPTIONS]OptionDescription--api-key TEXTOpenAI API Key.If not provided,OPENAI_API_KEYenvironment variable is used.https://user-images.githubusercontent.com/123562684/227763626-d329f156-c7f6-4a46-bfee-57504882d62c.movLicenseMIT LISENCE
Package: ggpython
ggpythonTracker Network Wrapper In Python.ContentsDescriptionInstallationUsageDocumentationLicenseRequirementsRelease signatureDevelopmentAuthorDescriptionTrackerNetwork 非公式API。Discord bot 用に作成しているものをPythonライブラリとして管理している。Among its features... :ゲームの情報を取得する機能Valorant (Match, PlayableCharacter, Map, Weapon, Custom, Award, and its Summary)文字列整形機能for console standard output.for Discord text chat output.スクレイピング用のChromiumエンジンWrapperFor more information, refer to theDocumentationorSample.InstallationYou canDownloadthe latest installable version ofggpythonfor Windows (zip format).The simplest way to install the latest version from PyPI is by using:pipinstall--upgradeggpythonIf you want to install this for development purposes,you can install directly from the GitHub repository:gitclonehttps://github.com/nakashimas/ggpython.gitcdggpython
pipinstall.also following:pipinstallgit+https://github.com/nakashimas/ggpythonUsageValorantのマッチリザルトを取得できる。(最大20件)fromggpythonimportGGTrackerAPI,GAMEwithGGTrackerAPI(GAME.VALORANT)asgg:res=gg.get_match_result("Username","#tag",mode="unrated")Discord Bot用に出力結果を整形できる。(調整中)サンプル:print(convert_valorant_match_to_discord(res))# -> to paste discord plane text結果:Discordサーバに絵文字を登録しておく必要があるDocumentationDocument is available fromthere.LicenseThis project is licensed under the terms of theMIT.このプロジェクトはMITライセンスに基づいて管理されています。Requirementsseleniumchromedriver_binarychromedriver_autoinstallerYou find requirements at alsorequirements.txt.Release signatureggpython-0.0.1-py3-none-any.whlggpython-0.0.1.tar.gzDevelopmentWe would love to have you as part of the community.
If you find a bug / new-feature, feel free to write a newIssuewith arespective titleanddescriptionon this repository.If you need help with anIssueor Development, please refer to theguidelinesor askme.Authorggpythonauthors.
Package: ggq-proxy-pool
GGQ的IP代理池功能1.按照购买的代理url请求代理2.根据上报的已经被ban掉的IP从池中删除3.将质量不好的IP从池中删除4.当池中IP为0时，能够自动的挂起ip的爬虫进程，并请求新的IP放入池中。
Package: ggr-cwl-ipynb-gen
ggr-cwl-ipynb-genJupyter notebook generator to download and execute the processing files for GGR related datasets.
At this point, is not intented to cover all use cases, but to serve as a quick generator of all
related files and scripts to pre-process genomic data generated at theDuke-GCB Sequencing CoreinHARDAC.Example of usage:$ python ggr_cwl_ipynb_gen.py \
  --root-dir /path/to/rootdir \
  --metadata examples/Hong_3979_170316B1.xlsx \
  --out /path/to/output_dir \
  --forceThe information in the example metadata and configuration file should reveal what is needed to download and pre-process the samples.For a full list of options:$ python ggr_cwl_ipynb_gen.py -h
usage: Generator of Jupyter notebooks to execute CWL pre-processing pipelines [-h] -o OUT -m METADATA [-f] [-n] [--metadata-sep SEP] [--project-name PROJECT_NAME]
                                                                              [--data-from {sftp,miseq,other,dukeds,local}] [-c CONF_FILE] [-u USER] [-e USER_DUKE_EMAIL] [-r ROOT_DIR]

optional arguments:
  -h, --help            show this help message and exit
  -o OUT, --out OUT     Jupyter notebook output file name
  -m METADATA, --metadata METADATA
                        Metadata file with samples information
  -f, --force           Force to overwrite output file
  -n, --no-upload       Avoids uploading generated data to database when specified
  --metadata-sep SEP    Separator for metadata file (when different than Excel spread sheet)
  --project-name PROJECT_NAME
                        Project name (by default, basename of metadata file name)
  --data-from {sftp,miseq,other,dukeds,local}
                        Choices: sftp, miseq, other, dukeds, local
  -c CONF_FILE, --conf-file CONF_FILE
                        YAML configuration file (see examples)
  -u USER, --user USER  HARDAC User used in SLURM (default: ${USER})
  -e USER_DUKE_EMAIL, --user-duke-email USER_DUKE_EMAIL
                        Email(s) notified when execution is finished (default: ${USER}@duke.edu)
  -r ROOT_DIR, --root-dir ROOT_DIR
                        Root directory where all subfolders and files will be created (semi-required: either defined here or in conf-file)
Package: gg-release-notes
GPT Release NotesRequirements.env file -> .envpython packages requirements.txtOPTIONAL - requirements-test.txtGithub RepositoryOPENAI accountpip installationpip install gg-release-notesInstallationpython3 -m pip install -r requirements.txtDev installationpython3 -m pip install -r dev.requirements.txtUsageExample python scriptfromrelease_notes.config.env_configimportEnvConfigfromrelease_notes.config.github_configimportGithubAPIConfigfromrelease_notes.config.prompt_configimportPromptConfigfromrelease_notes.pull_requestimportProdReleasePRfromrelease_notes.versionimportReleaseVersionfromrelease_notes.generate_release_notesimportReleaseNotesfromrelease_notes.uploadimportUploadRelease,upload_release_notesconfig=EnvConfig(".env")github_api=GithubAPIConfig("DataWiz40","gg-release-example",config)prompt_config=PromptConfig(github_api)prod_release_pr=ProdReleasePR(github_api)release_version=ReleaseVersion(github_api)release_notes_instance=ReleaseNotes(env_config=config,prompt_config=prompt_config,prod_release_pr=prod_release_pr)if__name__=="__main__":generated_release_notes=release_notes_instance.create_release_notes()chosen_release=generated_release_notes.get("internal_release").get("response")upload_instance=UploadRelease(github_config=github_api,env_config=config,prod_release_pr=prod_release_pr,prod_release_version=release_version,release_notes_text=chosen_release,)upload_release_notes(prod_release_pr.issue_num,upload_instance,make_github_release=True)Example Github Actions workflowname:Generate Release noteson:pull_request:types:[labeled]branches:-mainworkflow_dispatch:jobs:test-release-notes:if:${{ github.event.label.name == 'prod-release' }}runs-on:ubuntu-lateststrategy:matrix:python-version:["3.8"]steps:-uses:actions/checkout@v2-name:Set up Python ${{ matrix.python-version }}uses:actions/setup-python@v4with:python-version:${{ matrix.python-version }}-name:Install dependenciesrun:|python3 -m pip install --upgrade pippython3 -m pip install -r requirements.txt-name:Decode base64 envrun:echo ${{ secrets.INSERT_SECRETS_KEY }} | base64 --decode > .env-name:Generate Release Notesrun:python3 ./path_to_script.py
Package: gg-scrape
gg-scrapeA little Python CLI app that provides a League of Legends champion build by scraping the web.The goal was to not have to open a browser tab (or be advertised to) to quickly check a build before a match.
The HTML is requested and parsed sequentially, so it's somewhat slow (but still faster than opening a browser).Installationpython -m pip install gg-scrapeUsageggs [OPTIONS] CHAMPION [ROLE]ScreenshotsRequirementsDepends on the anytree, beautifulsoup4, typer, and requests Python libraries.ContributionsThanks to@Mycsinafor feedback and helping to improve and expand this package's functionality!Pull requests are welcome!
Package: gg_scraper
A small script as a replacement ofthe old PHP scriptfor downloading messages stored in the black hole of the Google Groups.How to use it?This script requiresformail(1)fromprocmailpackage. Any
version is OK, so please install it from your distribution’s
repositories. Then run:pip install beautifulsoup4 PyYAML
python gg_scraper.py 'https://groups.google.com/forum/#!forum/<group_name>'BackgroundI would never start without an inspiration from thecommenton my previous post on the theme of locked down nature of Google Groups.Current bugs are filled at mybugzillaand new ones can be reported via
email (one of many of my addresses are available on myGithub page)Of course pull requests are more than welcome in the same places as
well. Currently all development is done with Python 3.3, but tests are
run onTravis-CIfor 2.6, 2.7, pypy, and 3.2 as well.
Package: ggseg
python-ggsegPython module for ggseg-like visualizations.DependenciesRequiresmatplotlib>=3.4andnumpy>=1.21.Installpip install ggsegUsageIn order to work withpython-ggseg, the data should be prepared as a
dictionary where each item is one region of a given atlas assigned with some
numeric value. The current version includes three atlases: theDesikan-Killiany (DK) atlas, theJohns Hopkins University (JHU) white-matter atlasand theFreeSurferasegatlas.Cortical ROIs (Desikan-Killiany)Cortical ROI data such as using the DK atlas may be
structured as follows:{'bankssts_left': 1.1,
 'caudalanteriorcingulate_left': 1.0,
 'caudalmiddlefrontal_left': 2.6,
 'cuneus_left': 2.6,
 'entorhinal_left': 0.6,
 ...}Then be passed to theggseg.plot_dkfunction:importggsegggseg.plot_dk(data,cmap='Spectral',figsize=(15,15),background='k',edgecolor='w',bordercolor='gray',ylabel='Cortical thickness (mm)',title='Title of the figure')The comprehensive list of applicable regions can be found in thisfolder.Subcortical regions (FreeSurferasegatlas)data={'Left-Lateral-Ventricle':12289.6,'Left-Thalamus':8158.3,'Left-Caudate':3463.3,'Left-Putamen':4265.3,'Left-Pallidum':1620.9,'3rd-Ventricle':1635.6,'4th-Ventricle':1115.6,...}ggseg.plot_aseg(data,cmap='Spectral',background='k',edgecolor='w',bordercolor='gray',ylabel='Volume (mm3)',title='Title of the figure')The comprehensive list of applicable regions can be found in thisfolder.White-matter fiber tracts (Johns Hopkins University)data={'Anterior thalamic radiation L':0.3004812598228455,'Anterior thalamic radiation R':0.2909256815910339,'Corticospinal tract L':0.3517134189605713,'Corticospinal tract R':0.3606771230697632,'Cingulum (cingulate gyrus) L':0.3149917721748352,'Cingulum (cingulate gyrus) R':0.3126821517944336,...}ggseg.plot_jhu(data_jhu,background='k',edgecolor='w',cmap='Spectral',bordercolor='gray',ylabel='Mean Fractional Anisotropy',title='Title of the figure')The comprehensive list of applicable regions can be found in thisfolder.TestsThe current development version ofpython-ggseghas a coverage rate close to 100%.
The corresponding tests can be found in thisfolder.ExamplesA Jupyter Notebook with examples can be foundthere.
Package: ggshu
This is alpha software and very subject to change!A utility package to transform dataframes intoshudata.Example(ggmap(df_cond,aes(reaction="r",color="flux",size="flux",condition="cond",y="kcat"),)# plot flux to color and size of reactions+geom_arrow()# plot kcat as histogram shows on left side of reactions+geom_hist(side="left")# plot conc to color of metabolites+geom_metabolite(aes=aes(color="conc",metabolite="m"))# plot km as density plots shows on hover on metabolites+geom_kde(aes=aes(y="km"),mets=True)).to_json("shu_data")
Package: ggsipu-result
ggsipu_resultggsipu_resultis a A python module for extraction of results from GGSIPU results pdf. It is capable of:-Extraction of Results, Subjects details from pdfs.Extract Student's photo from pdfs.Dumping the extracted data in JSON format.ExamplesGiven code prints top 5 students of 2018 batch:-fromggsipu_resultimportiter_pages,has_page_results,iter_resultsFILE="Resources/CSE_Result.pdf"results=[]forpageiniter_pages(FILE):ifhas_page_results(page):results=results+[rforriniter_results(page)ifr.batch==2018]results.sort(key=lambdax:x.cgpa,reverse=True)fori,resultinenumerate(results[:5]):print("{i}.{r}".format(i=i+1,r=result))## PROBABLE OUTPUT# 1. Result(Sem 1): [41516403218]GAURAV JAIN(2018) [CGPA: 8.92]# 2. Result(Sem 1): [41016403218]VARDAAN GROVER(2018) [CGPA: 8.77]# 3. Result(Sem 1): [40316403218]UJJWAL NEGI(2018) [CGPA: 8.73]# 4. Result(Sem 1): [40116403218]RIGVED ALANKAR(2018) [CGPA: 8.5]# 5. Result(Sem 1): [01616403218]CHAITANYA GIRI(2018) [CGPA: 8.46]For more examples please seeSample_CodefolderInstallpip install ggsipu-resultTestsggsipu_resultincludes a test suite built on the unittest framework. All tests are located in the "Tests" folder.
Tests can be run from the command line by:python-munittestTests.testsChangelogv0.3.3 (22-09-2020)Fix minor typo inResultsreprv0.3.2 (09-07-2020)Major improvement in iter_subjects, now more accurate parsingv0.3.1 (08-07-2020)fix NoneType error while searching for institution code and name initer_resultsAPI: removesubjectattribute fromMarkstoJSON: now handle case where __dict__ is not present.API: addtoDict()helper method to convert into python dict.v0.3 (08-07-2020) [Breaking Changes]API: remove unusedStudentclassAPI: mergeStudentproperties intoResult.v0.2 (04-07-2020)add support for extraction of student's photo from pdfsAPI: new functionparse_result_pdfto parse entire pdf in one go.v0.1.1 (05-06-2020)fix#1: include support for 6 digit paper codesv0.1 (initial release)LicenseAll files under the repo are licensed under GNUGPLv3
Package: gg-start
A plugin forggfor starting new branches.InstallationThis is a plugin that depends onggwhich gets automatically
installed if it’s not already available:pip install gg-startHow to developTo work on this, first run:pip install -U --editable .That installs the packageggand its dependencies. It also
installs the executablegg. So now you should be able to run:gg start --helpVersion History0.1Proof of concept
Package: ggtag
Python bindings forggtag.Installationpip install ggtagUsageTheGGTagclass provides a simple interface for creating and rendering tags. The tag can be rendered as a bitmap or encoded as byte array.Here is an example how to create a tag and encode it to byte array:tag=ggtag.GGTag()tag.text(204,122,3,"ggtag")tag.icon(212,81,34,"wifi")tag.rect(198,74,70,70)tag.rect(68,74,82,82)tag.qrcode(71,77,3,"https://ggtag.io/demo.html")tag.text(43,186,5,"https://ggtag.io")data=bytes(tag)The byte array can be programmed into a ggtag device using either:data over sound and the theggwavelibraryserial USB connection and thepyseriallibrary.Seedemo.pyfor a complete demo.
Package: gguf
ggufThis is a Python package for writing binary files in theGGUF(GGML Universal File) format.Seeconvert-llama-hf-to-gguf.pyas an example for its usage.InstallationpipinstallggufAPI Examples/Simple Toolsexamples/writer.py— Generatesexample.ggufin the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model.scripts/gguf-dump.py— Dumps a GGUF file's metadata to the console.scripts/gguf-set-metadata.py— Allows changing simple metadata values in a GGUF file by key.scripts/gguf-convert-endian.py— Allows converting the endianness of GGUF files.DevelopmentMaintainers who participate in development of this package are advised to install it in editable mode:cd/path/to/llama.cpp/gguf-py

pipinstall--editable.Note: This may require to upgrade your Pip installation, with a message saying that editable installation currently requiressetup.py.
In this case, upgrade Pip to the latest:pipinstall--upgradepipAutomatic publishing with CIThere's a GitHub workflow to make a release automatically upon creation of tags in a specified format.Bump the version inpyproject.toml.Create a tag namedgguf-vx.x.xwherex.x.xis the semantic version number.gittag-agguf-v1.0.0-m"Version 1.0 release"Push the tags.gitpushorigin--tagsManual publishingIf you want to publish the package manually for any reason, you need to havetwineandbuildinstalled:pipinstallbuildtwineThen, follow these steps to release a new version:Bump the version inpyproject.toml.Build the package:python-mbuildUpload the generated distribution archives:python-mtwineuploaddist/*TODOAdd testsInclude conversion scripts as command line entry points in this package.
Package: gguf-connector
GGUF connectorGGUF (GPT-Generated Unified Format) is a successor of GGML (GPT-Generated Model Language), it was released on August 21, 2023; by the way, GPT stands for Generative Pre-trained Transformer.This package is a simple graphical user interface (GUI) application that uses the ctransformers or llama.cpp to interact with a chat model for generating responses.Install the connector via pip (once only):pip install gguf-connectorUpdate the connector (if previous version installed) by:pip install gguf-connector --upgradeWith this version, you can interact straight with the GGUF file(s) available in the same directory by a simple command.Graphical User Interface (GUI)Select model(s) with llama.cpp connector:ggc cppSelect model(s) with ctransformers:ggc cCommand Line Interface (CLI)Select model(s) with ctransformers:ggc gSelect model(s) with llama.cpp connector:ggc gppMetadata reader (CLI only)Select model(s) with metadata reader:ggc rPDF analyzor (beta feature on CLI recently)Load PDF(s) into a model with ctransformers:ggc cpLoad PDF(s) into a model with llama.cpp connector:ggc ppMenuEnter the main menu for selecting a connector or getting pre-trained trial model(s).ggc mImport as a moduleInclude the connector selection menu to your code by:from gguf_connector import menuFor standalone version please refer to the repository in the reference list (below).Referencesmodel selector(standalone version:installable package)cgg(cmd-based tool)Resourcesctransformersllama.cppWebsitegguf.us