Saravanan666/pypi-org huggingface dataset:
Package: gtl2via
Converter from AWS SageMaker Ground Truth Label Job Manifest file to VGG Image AnnotatorContextAWS SageMaker Ground Truth Label Job (GLT)https://docs.aws.amazon.com/sagemaker/index.htmlVGG Image Annotator (VIA)project:http://www.robots.ox.ac.uk/~vgg/software/via/code repo:https://gitlab.com/vgg/via/-/tree/masterWhy do we need this?The AWS SageMaker Ground Truth labelling jobs are very nice, especially when we want to out source the tedious labelling job to prepare for machine learning trainings.  It also supports the job of adjust existing boxes, where some code/algorithm is already generating the bounding boxes for certain objects. In this case, we could use the Ground Truth jobs to view existing boxes and adjust them. However, the AWS system currently do not offer an easy way to preview all the existing boxes until all the objects are manually labelled (there is a $0.08/object charge for each labelling operation and it makes business for AWS to want to encourage that).VIA offers a very powerful way to view all/multiple/selective images with their annotations and it works on any platform out of the box without much installation (just checkout the code and launch the index.html using broswer).The idea here is to take a already generated AWS SageMaker manifest file and convert it into the annotation json file used by VIA.Usageusage:gtl2via.py[-h]-iINPUT-lLABEL-oOUTPUT-sS3_BUCKET

optionalarguments:-h,--helpshowthishelpmessageandexit-iINPUT,--inputINPUTinputmanifestfileforAWSSageMakerGTLjob-lLABEL,--labelLABELusethelabelfromthemanifestfile-oOUTPUT,--outputOUTPUToutputjsonfileofVIA-sS3_BUCKET,--s3-bucketS3_BUCKETtheS3bucketusedbyAWSSageMaker(standardAWSpartitiononly,ie,notworkingforChinaorUS-Gov).ItwillbereplacedtothecorrespondinghttpsURLExamplepython gtl2via.py  -i test/defect_boxes.manifest -o test/via.json -l zerobox-quick-detection -s zerobox-publicThe input manifest file looks like this:seki@seki-Surface-Book:~/src/gtl2via$cattest/defect_boxes.manifest|jq{"source-ref":"s3://zerobox-public/test/2020-08-13/good/Camera0_202008131130183_original.png","zerobox-quick-detection":{"annotations":[{"class_id":0,"width":519,"top":896,"height":1024,"left":512}],"image_size":[{"width":1080,"depth":3,"height":1920}]},The output json looks like thisseki@seki-Surface-Book:~/src/gtl2via$cattest/via.json|jq{"https://zerobox-public.s3.amazonaws.com/test/2020-08-13/good/Camera0_202008131130183_original.png-1":{"filename":"https://zerobox-public.s3.amazonaws.com/test/2020-08-13/good/Camera0_202008131130183_original.png","size":-1,"regions":[{"shape_attributes":{"name":"rect","x":512,"y":896,"width":519,"height":1024},"region_attributes":{}}],"file_attributes":{}},Loading the annoation json file into VIA-2 and we see a result like thisKnown Limitations (ToDo)It only works with S3 in aws (Standard Regions), not for aws-cn (China Regions) or aws-us-gov (AWS GovCloud [US] Regions).It only works with one annotation type:rectNo choice of box color yet for different classes in SageMaker
Package: gtlaunch
gtlaunchlaunches Gnome Terminal with predefined tabs, runs a command
in each tab and leaves you in the shell if the command quits, so you can
jump straight to work. See the demo:PrerequisitesLinux with GnomeZSH (support for other shells is in the works)Python 2.7 or 3.3+InstallationThe recommended way to install Python packages which provide executable scripts
is to usepipsi:pipsi install gtlaunchButpipalso works:pip install gtlaunchAdd the following to your.zshrc(seethis messagefor an
explanation):if [[ $1 == eval ]]
then
    "$@"
    set --
fiConfigurationBy default,gtlaunchreads its configuration from agtlaunch.jsonfile
located in user’s home directory. This can be overridden by passing the
location of config file to--configoption:gtlaunch --config ../my-projects.jsonHere’s an example configuration file (note: JSON does not allow comments,
these are just for reference):{
    // The configuration is an object where keys are project names
    // and values store per-project settings.
    "simple_project": {
        // The simplest setup is just a list of commands under the tabs key
        "tabs": ["vim", "python", "git status"]
    },
    "more_options": {
        // prefix specifies a command that will be run before each tab's
        // command executes
        "prefix": "workon test",
        // you can specify working directory; ~ will be expanded
        "cwd": "~/Development",
        "tabs": [
            // the extended format allows more options, see below
            {
                // command is required
                "command": "vim",
                // title is optional, defaults to command
                "title": "editor"
            },
            // you can mix&match both formats
            "python",
            "git status"
        ]
    }
}ResourcesIssue trackerCI serverAuthorZbigniew Siciarz(zbigniew at siciarz dot net)Licensegtlaunch is free software, licensed under the MIT/X11 License. A copy of
the license is provided with the source code in the LICENSE file.
Package: gtmediaspace-dl
Descriptiongtmediaspace-dlis a simple tool to download lecture videos frommediaspace.gatech.edu.  It leverages the Kaltura extractor inyoutube-dlto download playlists.Installationpipinstallgtmediaspace-dlUsagePlaylist links should be in the format:https://mediaspace.gatech.edu/playlist/details/*gtmediaspace-dl *your playlist link here*Optional Arguments--embed-subs        Embed subtitle track in downloaded video files (requires ffmpeg)
Package: gtmem
OverviewA command line tool for importing Goole Takeout data into Memair. Currently the tool only imports Location History but more options will be added shortly.Installationgtmemis available on PyPIhttp://pypi.python.org/pypi/gtmemInstall viapip$ pip install gtmemOr viaeasy_install$ easy_install gtmemOr directly frommemair’sgit repo$ git clone git://github.com/memair/google-takeout-importer.git
$ cd google-takeout-importer
$ python setup.py installBasic usageGenerate a temporary access tokenDownload your location history data as JSON from Google TakeoutExtract the zip and run;$ gtmem -m 0000000000000000000000000000000000000000000000000000000000000000 -g ~/Downloads/Takeoutreplacing 00000… with your access_tokenRunning Testcoming shortly…Python compatibilityDeveloped for Python 3. May work but not tested in Python 2.
Package: gto
GTOGit Tag Ops. Turn your Git repository into an Artifact Registry:Registry: Track new artifacts and their versions for releases and significant
changes.Lifecycle Management: Create actionable stages for versions marking status of
artifact or it's readiness to be consumed by a specific environment.GitOps: Signal CI/CD automation or other downstream systems to act upon these
new versions and lifecycle updates.GTO works by creating annotated Git tags in a standard format.💡 Together withDVC, GTO serves as a backbone for Git-basedIterative Studio Model Registry.InstallationGTO requires Python 3. It works on any OS.$pipinstallgtoThis will install thegtocommand-line interface(CLI) and
make the Python API available for use in code.Getting startedTo Get Started, please head toGTO docs.ContributingContributions are welcome! Please see ourContributing Guidefor more details.Check out theDVC weekly boardto learn about what we do, and about the exciting new functionality that is
going to be added soon.Thanks to all our contributors!How to setup GTO development environmentClone this repository$gitclonegit@github.com:iterative/gto.git$cdgtoCreate virtual environment namedvenv$python3-mvenv.venv$source.venv/bin/activateInstall python libraries$pipinstall--upgradepip".[tests]"Run$pytest--basetemp=pytest-basetempThis will createpytest-basetemp/directory with some fixtures that can serve
as examples.Notably, check out this dir:$cdpytest-basetemp/test_api0/$gtoshow-vThe code that generates this folder could be foundin this fixture.To continue experimenting, callgto --helpCopyrightThis project is distributed under the Apache license version 2.0 (see the
LICENSE file in the project root).By submitting a pull request to this project, you agree to license your
contribution under the Apache license version 2.0 to this project.
Package: gtocclient
个推定制化修改版本 Owncloud SDK，提供客户方进行数据文件交互使用。FeaturesSupports connecting to ownCloud 8.2, 9.0, 9.1 and newer.General informationretrieve information about ownCloud instance (e.g. version, host, URL, etc.)Accessing filesbasic file operations like getting a directory listing, file upload/download, directory creation, etcread/write file contents from stringsupload with chunking and mtime keepingupload whole directoriesdirectory download as zipaccess files from public linksupload files to files drop link targetSharing (OCS Share API)share a file/directory via public linkshare a file/directory with another user or groupunshare a file/directorycheck if a file/directory is already sharedget information about a shared resourceupdate properties of a known shareApps (OCS Provisioning API)enable/disable appsretrieve list of enabled appsUsers (OCS Provisioning API)create/delete userscreate/delete groupsadd/remove user from groupsApp datastore app data as key/values using the privatedata OCS APIRequirementsPython >= 2.7 or Python >= 3.5requests module (for making HTTP requests)InstallationAutomatic installation with pip:$pipinstallgtocclientUsageExample for uploading a file then sharing with link:importowncloudoc=owncloud.Client('http://domain.tld/owncloud')oc.login('user','password')oc.mkdir('testdir')oc.put_file('testdir/remotefile.txt','localfile.txt')link_info=oc.share_file_with_link('testdir/remotefile.txt')print"Here is your link: "+link_info.get_link()Running the unit testsTo run the unit tests, create a config file called “owncloud/test/config.py”.
There is a config file example called “owncloud/test/config.py.sample”. All the
information required is in that file.
It should point to a running ownCloud instance to test against.You might also need to install the unittest-data-provider package:$pipinstallunittest-data-providerThen run the script “runtests.sh”:$./runtests.shBuilding the documentationTo build the documentation, you will need to install Sphinx and docutil.
Then run the following commands:$sphinx-apidoc-e-f-odocs/sourceowncloud/owncloud/test$cddocs$makehtmlYou can then find the documentation inside of “doc/build/html”.
Package: gtodo
# gtodoCommand line todo interface using pure python and click framework for easy track of your future progress.## InstalationRecommended way is to use [pip](https://pypi.org/) for installation.`bash pip install gtodo `## UsageRefer to help pagegtodo –help## ContributingPull requests are welcome. This is a very primitive project and everyone with basic python and click knowlege can use this project to train way of open source and github. Please, before pull requesting a major change open an issue first. Every new feature should have appropiate help messege.## License[MIT](LICENSE)## Click project[Click](https://github.com/pallets/click)
Package: gtodo-sprokkel78
gtodo-sprokkel78A graphical user interface in PyGTK4 for performing TODO list items on Ubuntu and other Linux distro's.
It requires Python3.10 or higher and the PyGTK apps. Developed and tested on Ubuntu 23.10.Installation on Ubuntu 23.10$sudo apt install python3 python3-dev$sudo apt install python3-gi python3-gi-cairo gir1.2-gtk-4.0 gir1.2-adw-1$python3 ./gtodo.pyAdded 'install.sh' script for system-wide installation.The startup shell script will be /usr/bin/gtodoThe application is installed in /usr/share/gtodo-sprokkel78The .desktop file is placed in /usr/share/applications/com.sprokkel78.gtodo.desktopAdded 'uninstall.sh' script for system-wide uninstallation.This will delete /usr/bin/gtodo and /usr/share/gtodo-sprokkel78,
This will also remove /usr/share/applications/com.sprokkel78.gtodo.desktopCheckhttps://www.github.com/sprokkel78/gtodofor contributing, development features and pre-releases.Funding: Paypal email:sprokkel78.bart@gmail.com
Package: gtom
This package takes an undirected unweighted scipy.sparse adjacency
matrix as an input and computes the GTOM(m) method, using m+1-step
neighbors (1). It’s highly efficient and can be used for parallel
computation by calling the function for only few nodes at a time.(1)*Gene network interconnectedness and the generalized topological
overlap
measure*,
A. M. Yip and S. Horvath, BMC Bioinformatics 2007 8:22Install$pipinstallgtomExampleRecreating Panels a and c of Figure 3 of the technical report [1]:#!pythonimportmatplotlib.pyplotasplimportnetworkxasnxfromgtomimportgtomimportscipy.sparseassprsimportnumpyasnpedges=[(0,1),(1,2),(0,3),(0,4),(0,5),(0,7),(1,3),(1,4),(1,6),(1,8),(1,9),(1,10),(5,6),(7,8)]G=nx.Graph()G.add_edges_from(edges)pos=nx.spring_layout(G)labels={n:str(n+1)forninG.nodes()}nx.draw(G,pos=pos)nx.draw_networkx_labels(G,pos=pos,labels=labels)N=G.number_of_nodes()edges=np.array(edges,dtype=int)A=sprs.csc_matrix((np.ones((edges.shape[0],)),(edges[:,0],edges[:,1])),dtype=float,shape=(N,N))A+=A.Tprint("recreate results from figure 3 in [1]")print("       |\t(i,j)=(1,2)\t(i,j)=(1,3)\t(i,j)=(2,3)")print("---------------------------------------------------------")forminrange(3):T=gtom(A,m)print(" m =%d|\t%f\t%f\t%f"%(m,T[0,1],T[0,2],T[1,2]))pl.show()
Package: gtoolkit_bridge
gtoolkit_bridgeGlamorous Toolkit Python BridgeThis library creates a server in Python that is accessible by Glamorous Toolkit and used to execute Python code
and get results back as proxies or values.Quick startimport gtoolkit_bridge as pb
args = pb.bridge_args()
pb.setup_bridge(args)
pb.run_bridge()All in oneimport gtoolkit_bridge as pb
pb.run_bridge_default()Command line$ python3 -m gtoolkit_bridge --port 9099 --pharo 0 --method msgpack --logOutputWith logging enabled (log=True).PythonBridge starting
{'port': 9099, 'pharo': 0, 'method': 'msgpack', 'log': True}
HANDLER (MsgPackSocketPlatform): loop func
PythonBridge ready
PYTHON: Start consuming commands
HANDLER (MsgPackSocketPlatform): prim handle message
HANDLER (MsgPackSocketPlatform): loop func
HANDLER (MsgPackSocketPlatform): prim handle message
DESERIALISE (bridge): pb2r9p5j02wd68kmzj1jw6zshk2
HANDLER (MsgPackSocketPlatform): loop func
PYTHON: Executing command pb2r9p5j02wd68kmzj1jw6zshk2
PYTHON: bindings: {'pharoCommandId': 'pb2r9p5j02wd68kmzj1jw6zshk2'}
PYTHON: notify(None,'pb2r9p5j02wd68kmzj1jw6zshk2')

PYTHON: Notify pb2r9p5j02wd68kmzj1jw6zshk2
PYTHON: Finished command execution
HANDLER (MsgPackSocketPlatform): prim handle message
DESERIALISE (bridge): pb2r9p5iy4fmkbyermp1kgy58qf
HANDLER (MsgPackSocketPlatform): loop func
PYTHON: Executing command pb2r9p5iy4fmkbyermp1kgy58qf
PYTHON: bindings: {'pharoCommandId': 'pb2r9p5iy4fmkbyermp1kgy58qf'}
PYTHON: import random
notify(list(map(lambda x: random.randrange(99)+1, range(1,10))), 'pb2r9p5iy4fmkbyermp1kgy58qf')
PYTHON: Notify pb2r9p5iy4fmkbyermp1kgy58qf
PYTHON: Finished command executionInfofeenk team (gt@feenk.com)https://gtoolkit.comhttps://feenk.comhttps://github.com/feenkcom/PythonBridge
Package: gtop-sprokkel78
gtopA graphical user interface in pygtk for Process and Network overview on Mac mini.
It requires Homebrew, Python3.11 and PyGObject.Install Homebrew$/opt/homebrew/bin/brew install python3 python3-pip$/opt/homebrew/bin/brew install pygobject3 gtk+3$/opt/homebrew/bin/pip3 install psutil$/opt/homebrew/bin/pip3 install gtop-sprokkel78$cd /opt/homebrew/lib/python3.11/site-packages/gtop_sprokkel78$/opt/homebrew/bin/python3 ./gtop.pyDon't forget to run the app with the python3 binary that was installed
with homebrew. It's in /opt/homebrew/bin/python3Note: You must run gtop.py from it's own directory or it won't find the css file.Checkhttps://github.com/sprokkel78/gtopfor development and pre-releases.
Package: gt-py
A Python wrapper for the GovTrack API (v2).Based off of:
- [https://github.com/cdelguercio/govtrack-python](https://github.com/cdelguercio/govtrack-python)
- [https://github.com/markgx/govtrack-node/](https://github.com/markgx/govtrack-node/)Installationpip install gt-pyUsagefrom gtpy.api import GTPY
client = GTPY()Choose an endpoint:client.role()
client.person()
client.cosponsorship()
client.vote
client.bill()
client.vote_voter()
client.committee()
client.committee_member()Get a person:>>>PERSON_ID = 400045
>>>client.person(PERSON_ID)
{‘birthday’: ‘1952-10-20’, ‘link’: ‘https://www.govtrack.us/congress/members/jeb_bradley/400045’, ‘lastname’: ‘Bradley’…Filter the results, such as getting all the bills voted by the 144th Congress (supports filtering on all [filterable fields](https://www.govtrack.us/developers/api)):>>>CONGRESS_ID = 114
>>>client.bill({‘congress’: CONGRESS_ID})Change the maximum number of results:>>>client.bill({‘congress’: CONGRESS_ID, ‘limit’: 100})
Package: gtr
===gtr===.. image:: https://travis-ci.org/nestauk/gtr.png?branch=master:target: https://travis-ci.org/nestauk/gtr.. image:: https://coveralls.io/repos/Nesta/gtr/badge.svg?branch=master&service=github:target https://coveralls.io/r/Nesta/gtrA Python interface to the Gateway To Research `GTR-2 API <http://gtr.rcuk.ac.uk/resources/GtR-2-API-v1.4.pdf>`_.Returns `requests.Response objects <http://docs.python-requests.org/en/latest/api/#requests.Response>`_.Services========- **Funds** `examples <./docs/funds.md#funds>`__, `api <http://gtr.rcuk.ac.uk/gtr/api/fund>`__- Retrieve a fund by fund id- Search for funds by- Funded project title- Fund amount- Funder organisation name- Fund type|- **Projects** `examples <./docs/projects.md#projects>`__, `api <http://gtr.rcuk.ac.uk/gtr/api/projects>`__- Retrieve project by project reference- Search for projects by- Project title- Project Abstract|- **Organisation** `examples <./docs/organisations.md#organisations>`__, `api <http://gtr.rcuk.ac.uk/gtr/api/organisations>`__- Retrieve organisation by organisation reference- Search for organisations by- Organisation name- Project title- Project abstracts|- **Persons** `examples <./docs/persons.md#persons>`__, `api <http://gtr.rcuk.ac.uk/gtr/api/persons>`__- Search for persons by:- First name- Family name- Other name- Organisation name- Project titles- Project abstracts|- **Outcomes** `examples <./docs/outcome.md#outcome>`__, `api <http://gtr.rcuk.ac.uk/gtr/api/outcome>`__- Search for organisations by:- Organisation name- Project title- Project abstractsInstallation============Get the Code------------gtr is available on `GitHub <https://github.com/nestauk/gtr>`_.You can either clone the public repository::$ git clone git://github.com/nestauk/gtr.gitDownload the `tarball <https://github.com/jamesgardiner/nestauk/tarball/master>`_::$ curl -OL https://github.com/nestauk/gtr/tarball/masterOr, download the `zipball <https://github.com/jamesgardiner/nestauk/zipball/master>`_::$ curl -OL https://github.com/nestauk/gtr/zipball/masterOnce you have a copy of the source, you can embed it in your Python package,or install it into your site-packages easily::$ python setup.py install
Package: gtracr
gtracr - AGPU-acceleratedTracking simulation forCosmicRaysgtracris a 3-D simulation package that simulates the trajectories of cosmic rays that arrive at a certain location on any location around the globe :earth_americas: . The package uses theIGRF (International Geomagnetic Reference Field) modelas the Earth's magnetic field and simulate trajectories using a 4th-order Runge Kutta numerical integration method.The main components as well as the user interface of the package is written in Python, so using this package is straighforward with minimal steps for evaluation of a trajectory. The core of the package (that is, the evaluation of the geomagnetic field and the numerical integration) is written in C++, however, and as such each trajectory is optimized to perform evaluations at around 1 ms :watch:.The code can further produce geomagnetic cutoff rigidities that either validates or invalidates a cosmic ray based on its trajectory, which is a key feature necessary to distinguish between allowed and forbidden trajectories.Note: The current version doesnotsupport GPU parallelization. This will be done in future versions, check theCHANGELOGfor more details.DependenciesPython 3 and aboveNumPySciPydatetime (for obtaining the current date)tqdmAll such dependencies will be installed with the package.Optional requirementsThese packages are required to observe plots and test different trajectory cases:matplotlib, plotlyfor plotspytest, pytest-benchmarkfor testingDocumentationCheck out thedocumentationfor more details on installing the package as well as instructions to start using this package with detailed examples.Copyright and LicenseThis project is under the BSD 3-Clause License. SeeLICENSEfor more details.
Package: g-training-pipeline
Training PipelineCheck out thisMedium articlefor more details about this module.Create Environment File~/energy-forecasting$cp.env.default.envThe commandcp .env.default .envis used to create a copy of the.env.defaultfile and name it.env. In many projects, the.envfile is
used to store environment variables that the application needs to run. The.env.defaultfile is usually a template that includes all the environment
variables that the application expects, but with default values. By copying it
to.env, you can customize these values for your own environment.Set Up the ML_PIPELINE_ROOT_DIR Variable~/energy-forecasting$exportML_PIPELINE_ROOT_DIR=$(pwd)The commandexport ML_PIPELINE_ROOT_DIR=$(pwd)is setting the value of theML_PIPELINE_ROOT_DIRenvironment variable to the current directory. In this
context,$(pwd)is a command substitution that gets replaced with the output
of thepwdcommand, which prints the path of the current directory. Theexportcommand then makes this variable available to child processes of the
current shell.In essence,ML_PIPELINE_ROOT_DIRis an environment variable that is set to the
path of the current directory. This can be useful for scripts or programs that
need to reference the root directory of the ML pipeline, as they can simply
refer toML_PIPELINE_ROOT_DIRinstead of needing to know the exact path.Install for Development~/energy-forecasting$cdtraining-pipeline&&rmpoetry.lock
~/energy-forecasting/training-pipeline$bash../scripts/devops/virtual_environment/poetry_install.sh
~/energy-forecasting/training-pipeline$source.venv/bin/activateCheck theSet Up Additional ToolsandUsagesections to
seehow to set uptheadditional toolsandcredentialsyou need to
run this project.Usage for DevelopmentRun the scripts in the following order:Start the hyperparameter tuning script:~/energy-forecasting/training-pipeline$python-mtraining_pipeline.hyperparameter_tuningUpload the best config based on the previous hyperparameter tuning step:~/energy-forecasting/training-pipeline$python-mtraining_pipeline.best_configStart the training script using the best configuration uploaded one step
before:~/energy-forecasting/training-pipeline$python-mtraining_pipeline.trainNOTE:Be careful to set theML_PIPELINE_ROOT_DIRvariable as explain in
thissection.