Saravanan666/pypi-org huggingface dataset:
Package: gsxform
gsxformWavelet scattering transforms on graphs via PyTorchgsxformis a package for constructing graph scattering transforms, leveraging PyTorch
to allow for GPU based computation.
Using PyTorch,gsxformoffers the ability to more
easily build models that use both scattering transform and neural network components.gsxformis first and foremost a research project and is being continuously refined.
Behavior can potentially be unstable and consistency is not guaranteed.TODOclean up readme, add additional formattingcheck dependenciesInstallationOfficial Releasegsxformis available on PyPi:pipinstallgsxformPre-releasesThe most up-to-date version ofgsxformcan be installed via git:pipinstallgit+https://github.com/armaank/gsxform.gitLicenseThe original code of this repository is released under theBSD 3.0-Clause Licence.
Modifications, adaptations and derivative work is encouraged!CitationIf you usegsxform, please cite using theZenodo DOI
Package: gsxws
Introductiongsxws is a Python library designed to work with Apple's GSX Web Services API.
The goal is for it to support all the features of the API. Currently it supports most of them.Please consult the GSX API documentation (Testing,Production) for more details.RequirementsPython 2.7 or latercontents of requirements.pipGSX client certs and valid Apple ID with access to GSX Web ServicesInstallation:pip install gsxws... or grab the latest dev version:pip install -U -e git+git://github.com/filipp/py-gsxws.git#egg=gsxwsUsageIt goes a little something like this:# check warranty statusimportosimportgsxwsos.environ['GSX_CERT']='/path/to/gsx/client/cert.pem'os.environ['GSX_KEY']='/path/to/gsx/client/cert_private_key.pem'gsxws.connect(apple_id,sold_to)mac=gsxws.Product('70033CDFA4S')mac.warranty()# get available parts for this machinemac.parts()Check thetestsfolder for more examples.FAQQ: My script keeps prompting me for the private key passphrase - how could I automate this?A: One way would be to remove the passphrase from the key:openssl rsa -in privatekey.pem -out privatekey.nopass.pem
Package: gsyslog
# gsyslogPython package for syslog like debugging with user level programsCreate an easy to use logger facility the act similar to the Linux syslog,but can be used by any application. That way a user application can writelogs very similar to root syslog an be consistant.To check out a copy of the source:cd base-locationgit clone https://github.com/fulkgl/gsyslog.gitRun each of the following with:Python 2.7.13Run unit tests:cd base-locationpython tests\unittest_gsyslog.pyTo check the coding standards and minor quality check:cd base-locationpycodestyle pybaccarat\playingcards.pypycodestyle pybaccarat\baccarat.pypycodestyle pybaccarat\baccaratsystems.pypycodestyle bin\play_baccarat.pypylint --rcfile=\usr\local\bin\pylint2.rc pybaccarat\playingcards.pypylint --rcfile=\usr\local\bin\pylint2.rc pybaccarat\baccarat.pypylint --rcfile=\usr\local\bin\pylint2.rc pybaccarat\baccaratsystems.pypylint --rcfile=\usr\local\bin\pylint2.rc bin\play_baccarat.pyTo run the build and make distribution packages:cd base-locationpython setup.py sdist bdist_eggrem dist/* contains source and binary distribution packagesHow to publish to git and pypi.1. create project and files1.1 gsyslog\gsyslog.py and __init__.py1.2 tests\unittest_gsyslog.py and __init__.py1.3 setup.cfg1.4 setup.py1.5 README.md1.6 MANIFEST.in1.7 LICENSE (use MIT, avoid GNU)1.8 .gitignore and doxygen.cfg (optional)2. run unittest to success3. git, fulkglE743.1 on git create a new repo3.2 pycharm vcs/git/add the whole project3.2.1 vcs git tag v0.103.2.1 vcs git push, define, origin https://git.../gsyslog3.2.3 push button, change master to v0.103.2.4 check online for upload at git4. pypi userid4.1 \users\pc\.pypirc
Package: gt2-test-runner
Alternative unittest test runner with colourised output and per-test coverage support, from the
devs of getbenchmarked.io.It extends the standardunittest.TextTestRunnerclass, with a bunch of tweaks:with no verbosity set, it will print only one character for each test.  With a verbosity of 1,
one line for each test is printedif thecoloramalibrary is installed, the dot or the test outcome text is printed in coloursif thecoveragepackage is installed, and thecoverage_sourcesparameter is set on the
runner, it will measure test coverage, both for each test, and overallif verbosity is above 1, it will print coverage data for each testif requested with thererun_logparameter, tests that fail or raise an exception are collected
in the specified file, so they can be re-run laterPlanned featuresdisplay a countdown in verbose mode, so you can see how many tests are remainingcollect the output of failing/errored tests to a file to be analysed laterKnown problemsWhen verbosity is above one (thus, per-test coverage report is printed), it can’t print the
overall coverage data.  We are working on it.Right now, the library only supports Python 3, and probably won’t run in Python 2 without a lot of
tweaks.  As we use only Python 3 at getbenchmarked.io, we didn’t have the intention to support
Python 2; if you need it and do the tweaks, please send us a PR!InstallationRecommended installation is viapip, inside avirtualenvTo get it fromPyPI:$ pip install gt2_test_runnerIf you want a bleeding-edge version from GitHub:$ pip install -e git+https://github.com/GT2-io/gt2-test-runner#egg=gt2-test-runnerDownloading the package source and installing it yourself is also an option.  To do so:$ python setup.pyDependenciesThere are no hard dependencies for this pacakage.if you want colourised output, you need to install thecoloramapackageif you need coverage reports, you need to install thecoveragepackageUsageCollecting testsTo collect tests programatically, we provide thefilter_testshelper function:# Collect all tests from the 'tests' directorytest_suite=filter_tests('tests')# Filter an existing TestSuitetest_suite=filter_tests(existing_suite,selector=['test_module','test_module2.TestCase'])# Filter tests from 'tests' directorytest_suite=filter_tests('tests',selector=['test_module','test_module2.TestCase'])Measuring coverage dataTo measure coverage data, you have to pass the files to be included in the coverage report.  If
you want to collect all your source files automatically, you can use thecollect_sourceshelper
function.source_files=collect_sources('.')Running the testsTo run a test suite withGT2Runner, you can use the following example:# Collect teststests=filter_tests('tests')# Initialise the runnerrunner=GT2Runner(verbosity=1,failfast=False,rerun_log='rerun-log.txt',coverage_sources=collect_sources('.'))# Run the testsresult=runner.run(tests)# Report coverage dataresult.coverage_report(save_data=True,html_dir='htmlcov',to_stream=True)
Package: gt3x
Read Raw GT3X fileAn implementation of extract raw acceleration data from .gt3x files.This package was extracted and expanded upon from the work of Shaheen Syed and his repositorieshttps://github.com/shaheen-syed/CNN-Non-Wear-Time-Algorithmandhttps://github.com/shaheen-syed/ActiGraph-ActiWave-Analysis.  They are instrumental in this module creation.The script gt3x_functions.py contains code to extract raw acceleration data from .gt3x files. Each .gt3x file is basically a zip file containing a log.bin and a info.txt file. The log.bin is a binary file which contains the actual acceleration values. The info.txt file contains the meta-data in text form. When the script is executed, it will create a numpy file that contains the raw data and a time vector.UsageThegt3xmodule needs to be first installed.  The most up-to-date one is:pipinstall--upgradegit+https://github.com/muschellij2/gt3x.git#egg=gt3xBut can also be installed viapipfromPyPi:pipinstallgt3ximportgt3x
actigraph_acc,actigraph_time,meta_data=gt3x.read_gt3x('AI9_NEO1F16120039_2017-06-27.gt3x')Installationpip3install-rrequirements.txt
Package: gt3x-muschellij2
Read Raw GT3X fileAn implementation of extract raw acceleration data from .gt3x files.The script gt3x_functions.py contains code to extract raw acceleration data from .gt3x files. Each .gt3x file is basically a zip file containing a log.bin and a info.txt file. The log.bin is a binary file which contains the actual acceleration values. The info.txt file contains the meta-data in text form. When the script is executed, it will create a numpy file that contains the raw data and a time vector.Usageimportgt3x
actigraph_acc,actigraph_time,meta_data=gt3x.read_gt3x('AI9_NEO1F16120039_2017-06-27.gt3x')Installationpip3install-rrequirements.txt
Package: gt4sd-trainer-hf-pl
GT4SD's trainer submodule for HF transformers and PyTorch LightningTrain Language Models via HuggingFace transformers and PyTorch Lightning.Development setup & installationCreate any virtual or conda environment compatible with the specs in setup.cfg. Then run:pipinstall-e".[dev]"Perform training via the CLI commandGT4SD provides a trainer client based on thegt4sd-lm-trainerCLI command.$gt4sd-trainer-lm--helpusage: gt4sd-trainer-lm [-h] [--configuration_file CONFIGURATION_FILE]optional arguments:-h, --help            show this help message and exit--configuration_file CONFIGURATION_FILEConfiguration file for the trainining. It can be usedto completely by-pass pipeline specific arguments.(default: None)To launch a training you have two options.You can either specify the path of a configuration file that contains the needed training parameters:gt4sd-trainer-lm--training_pipeline_name${TRAINING_PIPELINE_NAME}--configuration_file${CONFIGURATION_FILE}Or you can provide directly the needed parameters as arguments:gt4sd-trainer-lm--typemlm--model_name_or_pathmlm--training_file/path/to/train_file.jsonl--validation_file/path/to/valid_file.jsonlConvert PyTorch Lightning checkpoints to HuggingFace model via the CLI commandOnce a training pipeline has been run via thegt4sd-lm-trainer, it's possible to convert the PyTorch Lightning checkpoint
to HugginFace model viagt4sd-pl-to-hf:gt4sd-pl-to-hf--hf_model_path${HF_MODEL_PATH}--training_type${TRAINING_TYPE}--model_name_or_path${MODEL_NAME_OR_PATH}--ckpt{CKPT}--tokenizer_name_or_path{TOKENIZER_NAME_OR_PATH}ReferencesIf you usegt4sdin your projects, please consider citing the following:@article{manica2022gt4sd,title={GT4SD: Generative Toolkit for Scientific Discovery},author={Manica, Matteo and Cadow, Joris and Christofidellis, Dimitrios and Dave, Ashish and Born, Jannis and Clarke, Dean and Teukam, Yves Gaetan Nana and Hoffman, Samuel C and Buchan, Matthew and Chenthamarakshan, Vijil and others},journal={arXiv preprint arXiv:2207.03928},year={2022}}LicenseThegt4sdcodebase is under MIT license.
For individual model usage, please refer to the model licenses found in the original packages.
Package: gtable
Gtable is a container for tabular or tabular-like data designed with speed in
mind. It tries to improve one some aspects of
using pandas data types Series and DataFrame as containers for simple
computations:It Reduces the overhead for column access, creation, and concatenation.It supports sparse data with bitmap indexing.It truly handles NaNs, making a difference between a NaN and a NA in its
internal representation.It provides fast transformations (filling NA values, filtering, joining…)It also relies heavily onnumpy. You can consider
gtable as a thin layer over numpy arrays.You can install gtable with a simplepip install gtable.
Gtable is an open-source project released under a BSD 3-Clause license. You
can read the full documentationhere.
Package: gtabview
gtabview: a simple graphical tabular data viewerGraphical counterpart totabview, a simple tabular data viewer
that can be used both stand-alone and as a Python module for various
files and Python/Pandas/NumPy data structures.Stand-alone usagegtabviewreads most text tabular data formats automatically:gtabview data.csv
gtabview data.txtIfxlrdis installed, Excel files can be read directly:gtabview file.xls[x]Usage as a modulegtabview.view()can be used to display simple Python types directly
in tabulated form:fromgtabviewimportview# view a fileview("/path/to/file")# view a listview([1,2,3])# view a dict (by columns)view({'a':[1,2,3],'b':[4,5,6],'c':[7,8,9]})# view a dict (by rows)view({'a':[1,2,3],'b':[4,5,6],'c':[7,8,9]},transpose=True)# view a simple list of listsview([[1,2,3],[4,5,6],[7,8,9]])# view a simple list of lists (with headers)view([['a','b','c'],[1,2,3],[4,5,6],[7,8,9]],hdr_rows=1)gtabviewincludes native support for NumPy and all features of Pandas’
DataFrames, such as MultiIndexes and level names:fromgtabviewimportview# numpy arrays up to two dimensions are supportedimportnumpyasnpview(np.array([[1,2,3],[4,5,6]]))# view a DataFrame/Series/Panelimportpandasaspddf=pd.DataFrame([[1,2,3],[4,5,6]],columns=['a','b','c'],index=['x','y'])view(df)gtabviewis designed to integrate correctly withIPython,Jupyterandmatplotlib.Whenmatplotlibis used,gtabviewwill automatically default to use
mpl’sinteractivesetting to determine the default behavior of the
data window: when interactive, calls toview()will not block, and
will keep recycling the same window.InIPythonandJupyternotebooksview()calls also default to
non-blocking behavior, while in plain Python calls will halt until the
window is closed.You can change this behavior with theview(...,wait=False)argument
for each call, or by changing the module default:import gtabview
gtabview.WAIT = FalseIn aJupyternotebook aseparatedata window will always show. The
window can be kept around or closed, but will only be refreshed when
evaluating the cell again.Separate data windows can also be opened by using theview(...,recycle=False)argument, or again by setting the globalgtabview.RECYCLEdefault. See the built-in documentation ofgtabview.viewfor more details.Requirements and installationgtabviewis available directly on thePython Package Indexand onconda-forge.gtabviewrequires:Python 3 or Python 2PyQt5, PyQt4 or PySidesetuptools (install-only)Under Debian/Ubuntu, install the required dependencies with:sudo apt-get install python3 python3-pyqt5
sudo apt-get install python3-setuptoolsThen download and install simply via pip:pip install gtabviewOr with conda:conda install -c conda-forge gtabviewYou explicitly need to installxlrdif direct reading of Excel files
is desired:pip install xlrdLicensegtabview is distributed under the MIT license (seeLICENSE.txt)Copyright(c) 2014-2021: wave++ “Yuri D’Elia” <wavexx@thregr.org>Copyright(c) 2014-2015: Scott Hansen <firecat4153@gmail.com>Latest release notesFixes an issue with column autosizing leading to a division by zero on
some systems.gtabview 0.10Fixes interactive support within IPython and Jupyter notebooks.
Package: gtaccelerate
gravitorch-accelerateOverviewPlugin to useacceleratewithgravitorch.DocumentationInstallationContributingAPI stabilityLicenseInstallationWe highly recommend installing
avirtual environment.gtacceleratecan be installed from pip using the following command:pipinstallgtaccelerateTo make the package as slim as possible, only the minimal packages required to usegtaccelerateare
installed.
To include all the dependencies, you can use the following command:pipinstallgtaccelerate[all]Please check theget started pageto see
how to install only some specific dependencies or other alternatives to install the library.
The following is the correspondinggtaccelerateversions and supported dependencies.gtaccelerateaccelerategravitorchtorchpythonmain>=0.20,<0.25>=0.0.23,<0.0.24>=2.0.0,<2.2.0>=3.9,<3.12*indicates an optional dependencyContributingPlease check the instructions inCONTRIBUTING.md.Suggestions and CommunicationEveryone is welcome to contribute to the community.
If you have any questions or suggestions, you can
submitGithub Issues.
We will reply to you as soon as possible. Thank you very much.API stability:warning: Whilegtaccelerateis in development stage, no API is guaranteed to be stable from one
release to the next.
In fact, it is very likely that the API will change multiple times before a stable 1.0.0 release.
In practice, this means that upgradinggtaccelerateto a new version will possibly break any code
that was using the old version ofgtaccelerate.Licensegtaccelerateis licensed under BSD 3-Clause "New" or "Revised" license available
inLICENSEfile.
Package: gtagora-app
gtagora-app-pyHelper app for Agora (http://www.gyrotools.com/gt/index.php/products/agora)InstallationUse the package managerpipto install gtagora-app.pipinstallgtagora-appCurrently gtagora-app supports python 3.6 and 3.7.Basic usageRunRun the app withgtagoraappSetupwhen the app is run for the first time, a setup process is started. You will be prompted for the Agora URL, your
credentials and path settings.
The setup can also be started manually by calling:gtagoraapp--setup
Package: gtarantool
Connector required tarantool version 1.6:$ pip install gtarantoolTry it example:importgeventimportgtarantoolcnt=0definsert_job(tnt):globalcntforiinrange(10000):# make io job herecnt+=1tnt.insert("tester",(cnt,cnt))tnt=gtarantool.connect("127.0.0.1",3301)jobs=[gevent.spawn(insert_job,tnt)for_inrange(10)]gevent.joinall(jobs)Under this scheme the gtarantool driver makes a smaller number of read/write tarantool socket.See benchmark results time for insert/select/delete 100K tuples on 1.5KBytes:calltarantoolgtarantoolinsert32.44824710.072774select22.3269689.305423delete33.5351889.464293In this case, your code does not contain callbacks and remains synchronous!
Package: gtasker
GTaskerGTasker is a simple command-line scheduling tool for sequential and parallel execution of CPU or single-GPU tasks.InstallationInstall from PyPI.pipinstallgtaskerOr install from GitHub.pip install git+https://github.com/suffoquer-fang/Gtasker.git@mainUsageusage: gta [-h] [-v] [commands]

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit

commands:
    start-server        Start the daemon server
    add                 Enqueue a task for execution
    remove              Remove a task from the queue
    kill                Kill a running task
    restart             Restart a task
    clean               Remove all success tasks from the queue
    follow              Follow the output of a running task
    log                 Display the output log of a task
    shutdown            Remotely shutdown the daemon
    status              Display the status of the daemonQuick StartStart the daemon serverYou have to start the daemon before usinggtaclient.Run in the current shell.gta start-serverAdd the-dor--daemonflag to run in the background.gta start-server -dAdding new tasksTo add a task:gta add lsOr a more complex command:gta add "sleep 10 && echo 'hello world' && exit 0"You can add--path {path}argument to specify the working directory for the task, which is set to current directoy by default.If the task should be executed after some certain task(s), you can add--after {after}argument to set the pre-requist tasks. The task will be executed only after all pre-tasks have been successfully completed.For GPU tasks, you can set the required GPU memory by--mem {memory}. The task will be executed on a GPU with more free memory than required.You may further set the required GPU device(s) by--gpu {gpu_devices}. The task will only be executed on the preset GPU device(s).Controlling tasksYou can kill a running task bygta kill {task_id}.To restart a killed (success / failed) task, you can simply usegta restart {task_id}and the task will be restarted as a new one. You can add--inplaceflag to restart it in place.Displaying tasksYou can usegta statusto get the current status of task queue.To look at the output log of a task, you can usegta log {task_id}orgta follow {task_id}to follow the output of a running task.ReferencesThis repo is inspired bypueue, sincerely grateful for it.
Package: gtasks-api
gtasks_apiInstallationpip install gtasks-apiUsageImport the package:fromgtasks_apiimportGtasksAPIObtain a Gtasks instance:gtasks=GtasksAPI('credentials.json','token.pickle')ifgtasks.auth_url:gtasks.finish_login(auth_code)Get all task lists:gtasks.service.tasklists().list().execute()API Documentation :https://developers.google.com/resources/api-libraries/documentation/tasks/v1/python/latest/
Package: gtasks-md
gtasks-mdGoogle Tasksis a Google Workspace tool
that has the following advantages:It has great integration with Google Calendar.There are mobile apps developed by Google for Android and iOS.It is pretty straightforward to use, there is no feature creep.All great, this works pretty well for shopping groceries or something, but what
if it was to be suitable for tracking more advanced tasks (e.g. work related)?
It is possible to add some text as note to a task but this is very limited and
not very convenient to use on desktop computer.The idea that this project represents is declarative approach to task
management. It provides an interface for manipulating Google Tasks via
Markdown[^1]. See the following demo:Supported operationsAll commands support--userflag which allows multi-user usage.authgtasks-md auth ./credentials.jsonIn order to run gtasks-md it is needed first to obtain API credentials. This can
be achieved with the following steps[^2]:Create a new project inGCPand go toGoogle
APIs.ClickENABLE APIS AND SERVICESaddGoogle Tasks API.Go toGoogle APIsand clickOAuth consent screenfrom the sidebar.ChooseExternal(Available to any user with a Google Account.) and
clickCREATE.Input your favorite name toApplication name. In theScopes for Google APIssection, clickAdd scopeand addTask API ../auth/tasks.ClickSave(DO NOTSubmit for verification).Go to theCredentialspage from the sidebar.Create a newOAuth client ID. SelectDesktop applicationfor the
application type.Download the credentials asJSONfile.Rungtasks-md authcommand with that file as argument.In case of troubles consultOAuth client ID
credentialscreation documentation for a Desktop app.view$gtasks-mdview## TODO#### My Tasks##1.[]Task1##Examplenote1.##2.[]Task2##1.[]SubTask1##Thisisanotherusefulnote.##2.[]SubTask2Downloads all task lists, parses them to Markdown format and prints to stdout.editgtasks-md editBegins with the same operations asgtasks-md viewbut later opens the text in
an editor. After user is done with entering changes, the resulting file is
parsed back to task lists and local state is reconciled with server state.reconcilegtasks-md reconcile ./tasks.mdSimilar togtasks-md editbut instead of editing the Markdown it sources the
provided file as local state and reconciles it.rollbackgtasks-md rollbackRolls back the server state to the most recent locally backuped state. Useful if
something goes wrong.InstallationInstall binary dependencies# Ubuntu$sudoaptinstallpandocpip# Fedora$sudodnfinstallpandocpipInstall thePyPI packagepipinstallgtasks-mdRungtasks-mdgtasks-md--helpDevelopmentUbuntu# 1. Install binary dependencies$sudoaptinstallpandocpip# 2. Install Python dependencies$pipinstall-rrequirements.txt# 3. Run the runner.py$./runner.py--helpNixOS (or when using Nix)# 1. Enter nix-shell (this may be done automatically when using direnv)$nix-shell# 2. Run the runner.py$./runner.py--help[^1]: Subset ofPandoc's
Markdownto be exact[^2]: Shamelessly stolen fromcalendar.vim
Package: gtauthclient
GT Auth ClientThis is the Python GT Auth Client for FastAPI. It is used to authenticate with the GT Auth API.InstallationYou can install the package using pip:pipinstallgtauthclientUsageAn example of how to use the package is shown below:fromfastapiimportFastAPI,DependsfromgtauthclientimportGTAuthClientauth=GTAuthClient(key="Your encryption key")app=FastAPI()@app.get("/v1/users",dependencies=[Depends(auth.verify_user)])asyncdeflist_users():return["John","Jane","Jack"]