Saravanan666/pypi-org huggingface dataset:
Package: offer
Quicklyoffera file for download on LAN.Usagepip install offerThen you can use it from the CLI:offer ~/path/file.extIt’ll try to host the file on port80by default and will fall back on port8000silently if it is not run with the correct permissions. You can provide
a custom port via the--portargument.Then from another computer/tablet/phone, you can either point the browser at
the IP address printed out or usecurl.Note about cURLRemember to use the-Lflag with cURL to follow redirects.
Package: office31
Splits for Office31 domain adaptation tasksThis repository contains the revised protocol for creating Office31 splits for few shot domain adaptation.Contrary to the usual splits, we define an independent test split here (split using a hardcoded seed), and let the train-val split vary according to a user-defined random seed.Installationpipinstalloffice31UsageGetting the splits is a simple as:fromoffice31importoffice31train,val,test=office31(source_name="webcam",target_name="amazon",seed=1,same_to_diff_class_ratio=3,image_resize=(240,240),group_in_out=True,# groups data: ((img_s, img_t), (lbl_s, _lbl_t))framework_conversion="tensorflow",office_path=None,#automatically downloads to "~/data")The function automatically downloads and unpacks the data if necessary. It then creates the splits using theDataset Ops library.
Depending on your choice of machine learning library, the dataset can be converted to Tensorflow or PyTorch (assuming either is pre-installed) using Dataset Ops.
Package: officebots
OfficeBots -- Python APIThis package implements the OfficeBots API in Python, making it easy to
create and control a robot in the OfficeBots game.InstallationFrom pypi> pip3 install officebotsFrom sources> python3 setup.py install --userUsageCheck examples in theexamples/directory. You can start with the simplecmdline.py.ROS supportThis Python package also provides a complete ROS bridge to the simulator,
supporting:/cmd_velto move the robotlaser scan simulation (/scan)tf framesROS4HRI implementation (with persons and faces tracking, and speech simulation
via the in-game chat)simulated navigation (via/move_base_simple/goal)image streaming to the robot's tablet (by publishing images to/screen/raw_image)simulated robot speech (/say)
Package: officeextractor
officeextractorTest StatusVersion InfoCompatibilityStyleAboutofficeextractoris a Python library to extract media files like images, audio and video from office documents (Microsoft Office & LibreOffice).Supported File TypesSupportedFile TypesSupported Media FormatsMicrosoft Worddocx, docm, dotm, dotximagesMicrosoft Excelxlsx, xlsb, xlsm, xltm, xltximagesMicrosoft PowerPointpotx, ppsm, ppsx, pptm, pptx, potmimages, video & audioLibreOffice Writerodt, ottimagesLibreOffice Calcods, otsimagesLibreOffice Impressodp, otp, odgimages⚠NOTE:Microsoft Office 2003 files (doc, dot, xls, xlt, ppt, pot) are not supported.Installationpip install officeextractorUsage>>> import officeextractor

>>> officeextractor.extract(src=("File1.docx", "Folder/File2.xlsx"), dest="Path/To/Output/Folder")

4 media files extracted from File1.docx:
- 2 jpeg
- 1 gif
- 1 png

1 media file extracted from Folder/File2.xlsx:
- 1 pngParametersofficeextractor.extract(src, dest, log=True)src :str, list of str or tuple of strEither a single file (string) or several files (list/tuple of strings) as relative or full path.dest :strOutput directory as relative or full path. If the directory doesn't exist, it will be created.log :bool, optionalWhether logging should be actived or not. If True, print a summary of the extraction. Default is True.Release NotesCan be found here on GitHubLicenceGNU General Public License v3.0
Package: officekit
officekitA Wagtail app providing models for people, teams, addresses and other office-related entities. Please bear with us while we prepare more detailed documentation.Compatibilityofficekit' major.minor version number indicates the Wagtail release it is compatible with. Currently this is Wagtail 4.1.xInstallationInstall usingpip:pipinstallofficekitAddofficekitto yourINSTALLED_APPSsetting:INSTALLED_APPS=[# ...'officekit'# ...]
Package: officeparser
# officeparser.pyofficerparser.py is a python script that parses the format of OLE compound documents used by Microsoft Office applications.Some useful features of this script include* macro extraction* embedded file extraction* format analysisExecute the script with the --help option to view all available options.## useful options__--extract-macros__This extracts all macro code that can be detected in an office document.  Use the -o or --output-dir option to specify the directory to store the files.
Package: officialeye
An AI-powered generic document-analysis tool.Examples·Getting started·DocumentationWhat is OfficialEye?OfficialEye is an advanced tool designed to extract information from flat documents, such as passports or application forms, through image analysis. Leveraging state-of-the-art symbolic AI techniques, OfficialEye empowers users to effortlessly transform raw images into standardized canonical forms, facilitating the seamless extraction and processing of crucial information.Key featuresExample-driven and user-friendlyOfficialEye boasts an intuitive and example-driven approach. As a user, all you need to do is provide an image of a sample document and articulate which parts contain information you are interested in, and which can be used to recognize similar documents. The tool automatically generalizes from your example, making it a user-friendly solution for various document recognition tasks.Powerful template systemTemplates encapsulate the nuances of document analysis and processing. Multiple document types are effortlessly managed by employing distinct templates, each configurable through a potent YAML-based scripting language. It allows you to express the contents of your documents comprehensively yet concisely, ensuring maintainability even for complex documents with numerous entries.Highly modular and customizableOfficialEye prioritizes modularity and customization. Each step of the image analysis process is separated and explained. The template scripting language allows users to switch between implementations of every component on-the-fly, without the need for recompilation. Inject custom implementations easily, tailoring the tool to your specific needs.Consistency-driven detectionOut-of-the box, OfficialEye's supports an innovative consistency-driven detection approach, finely tuned for flat documents. Imagine the human ability to detect formatted documents at a glance: with an innate understanding of how a document appears, our brain effortlessly filters out incongruent elements. OfficialEye mirrors this by operating under the assumption that the document in the image is a flat surface and leveraging this inherent knowledge to significantly enhance the accuracy of the results.VisualizationsOfficialEye offers a debugging mode wherein integrated algorithms generate graphical visualizations, providing users with a transparent view of the tool's internal processes. In case of issues, these visualizations serve as a diagnostic aid, swiftly pinpointing areas for improvement and thus enhancing your document analysis experience.Free and open sourceSay goodbye to proprietary alternatives. OfficialEye is committed to the principles of freedom and transparency. Released under the GNU GPL-3.0 license, it provides afree and open-sourcealternative, ensuring accessibility for all users.Whether you're a developer, data scientist, or enthusiast, OfficialEye is your go-to solution for accurate and efficient flat document analysis. Experience the future of document analysis with OfficialEye!
Package: officy
officycommon python code for office use. like dir,file,etc.办公用途的常用 python 代码，比较多涉及到文本、文件、文件夹、网页等。Install:pipinstallofficyor :pipinstall-rrequirements.txt如果您有需要，可以 clone 到本地，比如，在/work-space>下执行git clone https://github.com/liujuanjuan1984/officy.git。完成后，cd officy后所在目录/路径，请添加到PYTHONPATH环境变量中。How to use:fromofficyimportDir,JsonFileDir(".").black()JsonFile("temp.json").read({})
Package: offlinedatasci
OfflineDataSciThis package helps you download and configure common tools for teaching and doing data science without an internet connection.
This includes:Installers for data science languages: Currently R and PythonInstallers for common data science IDEs: Currently RStudioPartial local mirrors of package repositories: Currently CRAN (for R) and PyPI (for Python)Locally browseable clones of data science teaching websites: Currently Data Carpentry and Software CarpentryStatusEarly stage experimentInstallationUsing pip:From PyPIpipinstallofflinedatasciFrom GitHub (latest development version)pipinstallgit+https://git@github.com/carpentriesoffline/offlinedatasci.gitFor local development:Clone the repository and from the root directory run:pipinstall.On macOS make sure wheel package is installed first.UsageDownload and setup everythingofflinedatasciinstallall/install/pathCreate just the local CRAN mirror with basic data science packagesofflinedatasciinstallminicran/install/pathAdd packages to repository mirrors:To add packages beyond those included in the basic data science teaching focused mirrors useadd-packagesThe command structure isofflinedatasci add-packagesfollowed by the language you want to add packages to, followed by the names of the packages, followed by the path of the mirror.
The path should be the same as where the mirror was originally setup, so the same install path you used for setup offlinedatasci.For example, to add thesf,terra, andstarsgeospatial packages to the CRAN mirror:offlinedatasciadd-packagesrsfterrastars/install/pathDeveloper docsCreating a releaseIncrement the version numbers inpyproject.toml,__init__.py, anddocs/conf.pyCommit and push the changes to GitHubCreate a tag for the new versionPush the tag to GitHub (git push upstream <tag_name>)Make sure thebuildpackage is installedMake sure your PyPI credentials are stored in~/.pypircBuild the source distribution:python -m build --sdistBuild the universal wheel:python -m build --wheelUpload the new release to PyPI:twine upload dist/*
Package: offline-docs
offline-docsQuick access to Python library docs, cached for offline availability.Usage$ offline-docs --help


Usage: offline-docs [OPTIONS] COMMAND [ARGS]...

  Quick access to Python library docs, cached for offline availability.

Options:
  --help  Show this message and exit.

Commands:
  clean   Remove all downloaded docs from local cache.
  python  Open docs for the current Python version in browser.Related ProjectsAn overview of similar projects, and howoffice-docsis different:ZealMission statement, according tozealdocs.org:Zeal is an offline documentation browser for software developers.Compared to zeal,offline-docs:provides a command-line interface,shows docs in the system's default browser,matches the versions of docs to the installed versions.RoadmapThis section gives an outlook on howoffline-docscould develop in the future.Download docs fromreadthedocs.org.
Package: offline-evaluation
Offline policy evaluationImplementations and examples of common offline policy evaluation methods in Python. For more information on offline policy evaluation see thistutorial.Installationpip install offline-evaluationUsagefrom ope.methods import doubly_robustGet some historical logs generated by a previous policy:df = pd.DataFrame([
	{"context": {"p_fraud": 0.08}, "action": "blocked", "action_prob": 0.90, "reward": 0},
	{"context": {"p_fraud": 0.03}, "action": "allowed", "action_prob": 0.90, "reward": 20},
	{"context": {"p_fraud": 0.02}, "action": "allowed", "action_prob": 0.90, "reward": 10},
	{"context": {"p_fraud": 0.01}, "action": "allowed", "action_prob": 0.90, "reward": 20},     
	{"context": {"p_fraud": 0.09}, "action": "allowed", "action_prob": 0.10, "reward": -20},
	{"context": {"p_fraud": 0.40}, "action": "allowed", "action_prob": 0.10, "reward": -10},
 ])Define a function that computesP(action | context)under the new policy:def action_probabilities(context):
    epsilon = 0.10
    if context["p_fraud"] > 0.10:
        return {"allowed": epsilon, "blocked": 1 - epsilon}    
    return {"allowed": 1 - epsilon, "blocked": epsilon}Conduct the evaluation:doubly_robust.evaluate(df, action_probabilities)
> {'expected_reward_logging_policy': 3.33, 'expected_reward_new_policy': -28.47}This means the new policy is significantly worse than the logging policy.  Instead of A/B testing this new policy online, it would be better to test some other policies offline first.See examples for more detailed tutorials.Supported methodsInverse propensity scoringDirect methodDoubly robust (paper)
Package: offlineimap-notify
offlineimap-notifyWrapper for add notification sending to OfflineIMAP.Run OfflineIMAP after adding notification sending to its UIs.  When an account
finishes syncing, messages synced to the local repository will be reported
using D-Bus (through pynotify) or a fallback notifier command.Quick StartCallofflineimap-notifyinstead ofofflineimapofflineimap-notifyUsageFor configuration options and usage checkDocsLicense
Package: offline-judge
Offline JudgeAn offline equivalent of an online judge in Python. Unfortunately, it uses many Unix-exclusive features. This is in theory compatible with Unix-likes, however, it is only tested on Linux 4.12.3.Installation$ pip install offline-judgeUsage$ judge --help
usage: judge [-h] [--no-ansi] [--full-paths] [--no-summary]
             [--no-resource-usage]
             [--only-verdicts {AC,RTE,MLE,OLE,TLE,RE,WA} [{AC,RTE,MLE,OLE,TLE,RE,WA} ...]]
             [--exclude-verdicts {AC,RTE,MLE,OLE,TLE,RE,WA} [{AC,RTE,MLE,OLE,TLE,RE,WA} ...]]
             test_cases time_limit memory_limit executable
             [{standard,floats,identical}]

An quick offline judging tool.

positional arguments:
  test_cases            Directory that contains the test cases, where the
                        input is in a file ending with `.in` and the
                        corresponding output in a file with the same name
                        ending with `.out`.
  time_limit            Time limit in seconds. Decimals are accepted. Note
                        that this limit can be bypassed by catching SIGXCPU.
  memory_limit          Memory limit in one of "B", "K", "M", "G", "T". This
                        must be one continuous string, for example "5M" is
                        valid, however, "5 M" is not. Keep in mind that this
                        has to be a multiple of the architecture's page size.
  executable            The executable to run. It is executed through the
                        execve system call. Therefore, scripts starting with
                        "#!/bin/sh" will work, though it is a questionable
                        language choice. Additional languages can be supported
                        through helper scripts.
  {standard,floats,identical}
                        Checker to be used to compare the correct output and
                        the executable output. (default: standard)

optional arguments:
  -h, --help            show this help message and exit
  --no-ansi             Disable ANSI output.
  --full-paths          Use full case paths instead of batch headings and
                        padding.
  --no-summary          Do not output the final summary.
  --no-resource-usage   Do not output time and memory usage.
  --only-verdicts {AC,RTE,MLE,OLE,TLE,RE,WA} [{AC,RTE,MLE,OLE,TLE,RE,WA} ...]
                        Only display cases with the verdicts specified.
  --exclude-verdicts {AC,RTE,MLE,OLE,TLE,RE,WA} [{AC,RTE,MLE,OLE,TLE,RE,WA} ...]
                        Do not display cases with the verdicts specified.
Package: offlinerllib
OfflineRL-LibOfflineRL-Lib provides unofficial and benchmarked PyTorch implementations for selected OfflineRL algorithms, including:In-Sample Actor Critic (InAC)Extreme Q-Learning (XQL)Implicit Q-Learning (IQL)Decision Transformer (DT)Advantage-Weighted Actor Critic (AWAC)TD3-BCTD7For Model-Based algorithms, please checkOfflineRL-Kit!Benchmark ResultsWe benchmark and visualize the result via WandB. Click the following WandB links, and group the runs via the entrytask(for offline experiments) orenv(for online experiments).Available RunsOffline:TD7:chart_with_upwards_trend:XQL:chart_with_upwards_trend:InAC:chart_with_upwards_trend:AWAC:chart_with_upwards_trend:IQL:chart_with_upwards_trend:TD3BC:chart_with_upwards_trend:Decision Transformer:chart_with_upwards_trend:Online RunsSAC:chart_with_upwards_trend:TD3:chart_with_upwards_trend:TD7:chart_with_upwards_trend:XSAC:chart_with_upwards_trend:Citing OfflineRL-LibIf you use OfflineRL-Lib in your work, please use the following bibtex@misc{offinerllib,
  author ={Chenxiao Gao},
  title ={OfflineRL-Lib: Benchmarked Implementations of Offline RL Algorithms},
  year ={2023},
  publisher ={GitHub},
  journal ={GitHub repository},
  howpublished ={\url{https://github.com/typoverflow/OfflineRL-Lib}},}AcknowledgementsWe thankCORLfor providing finetuned hyper-parameters.
Package: offline_sandbox
## Decorator to return a default value for a function when offlineDecorate your functions with a default value and set OfflineSandbox.SANDBOX to your Online/Offline status```import OfflineSandboxOfflineSandbox.SANDBOX = False@OfflineSandbox.offline_sandbox_value('mundo')def greet():return 'hola'print(greet())OfflineSandbox.SANDBOX = Trueprint(greet())```You can also respond given the availability of a specific URL```@OfflineSandbox.offline_sandbox_value('mundo', url='https://im-online.herokuapp.com/')def greet_with_url():return 'hola'print(greet_with_url())```
Package: offlineslides
Offline SlidesHacky program to make a jupyter
notebook into offline reveal.js slideshow. Can also output a html version.
Will create a .slides.offline.html next to
the notebook as well as a ext/ folder including all of the required files.
You need to copy both of those for the presentation to work.Usageas command line toolofflineslides [-h] [--html] [--no-download] [--verbose] notebook_path

positional arguments:
  notebook_path      Path to the .ipynb notebook

optional arguments:
  -h, --help         show this help message and exit
  --html             Export as offline standalone html
  --no-download, -n  Do not download files if ext folder exists
  --verbose, -v      Output more info if trueas libraryfromofflineslidesimportexport_to_offlineexport_to_offline('my_notebook.ipynb')
Package: offlinetb
offlinetbis a small utility module for serializing python tracebacks for later examination. Its simple premise is:>>> from offlinetb import distill
>>> try:
...    some_code()
... except:
...    offline_tb = distill()distillreturns a Pythonic data structure describing the exception caught. It holds, among else:The exception object caught, its type, value representation, and data membersThe traceback itself, frame by frameEach frame contains its filename, function name, line number, sample lines before and after, the faulty line itself, and the locals. Each local has its data members, value representation and name distilled.Distilled tracebacks use only simple datatypes (numbers, strings, lists, dictionaries), guaranteeing their ability to be serialized to other formats (e.g. JSON).The original purpose of offlinetb was to display tracebacks caught via a web app, and an example for this usage is included; under therendering/directory you can find a sample Javascript, CSS and HTML that displays a given JSON traceback in a<div>.
Package: offline-translator
Offline TranslatorDescriptionOffline Translator GUI using Opus-MT.InstallationFirst, you need to installPython.Then you need to installpipxfor installoffline-translatoras an application.pipinstallpipxpipxensurepathpipxinstalloffline-translatoroffline-translatorDevelopmentContributingContributions are very welcome. Tests can be run withpoe check, please
ensure the coverage at least stays the same before you submit a pull request.SetupYou need to installPoetryandGitfor work with this project.gitclonehttps://github.com/Dashstrom/offline-translatorcdoffline-translator
poetryinstall--all-extras
poetryrunpoesetup
poetryshellPoePoe is available for help you to run tasks.test           Run test suite.
lint           Run linters : ruff linter, ruff formatter and mypy.
format         Run linters in fix mode.
check          Run all checks : lint, test and docs.
cov            Run coverage for generate report and html.
open-cov       Open html coverage report in webbrowser.
docs           Build documentation.
open-docs      Open documentation in webbrowser.
setup          Setup pre-commit.
pre-commit     Run pre-commit.
clean          Clean cache filesCommitIf the linting is not successful, you can't commit.
For forcing the commit you can use the next command :gitcommit--no-verify-m'MESSAGE'How to add dependencypoetryadd'PACKAGE'Ignore illegitimate warningsTo ignore illegitimate warnings you can add :# noqa: ERROR_CODEon the same line for ruff.# type: ignore[ERROR_CODE]on the same line for mypy.# pragma: no coveron the same line to ignore line for coverage.# doctest: +SKIPon the same line for doctest.Uninstallpipuninstalloffline-translatorLicenseThis work is licensed underMIT.
Package: offloading
OffloadingOffloading tasks using processesTable of ContentsInstallationLicenseUsageInstallationpip install offloadingLicenseoffloadingis distributed under the terms of theMITlicense.Usagefromoffloadingimportoffload,Taskdefget_result(x):returnx*2future=Task.run(get_result,2)res=future.result(timeout=1)assertres==4@offloaddefheavy_processing(x):returnx*2# blocking operationres=heavy_processing(10)assertres==20Check outtestsfor more.
Package: offpele
About| [![License](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![Python](https://img.shields.io/badge/python-3.6%2C%203.7-blue.svg)](https://martimunicoy.github.io/offpele) [![Release](https://img.shields.io/github/release/martimunicoy/offpele.svg?include_prereleases)](https://github.com/martimunicoy/offpele/releases/) |:—— | :——- |Status| [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/martimunicoy/offpele.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/martimunicoy/offpele/context:python) [![Test](https://github.com/martimunicoy/offpele/workflows/Test/badge.svg)](https://github.com/martimunicoy/offpele/actions?query=workflow%3ATest) [![codecov](https://codecov.io/gh/martimunicoy/offpele/branch/master/graph/badge.svg)](https://codecov.io/gh/martimunicoy/offpele) |Installation| [![Conda](https://img.shields.io/conda/v/martimunicoy/offpele.svg)](https://anaconda.org/martimunicoy/offpele) [![PyPI](https://img.shields.io/pypi/v/offpele)](https://pypi.org/project/offpele/) |# Open Force Field to PELE
Theoffpele(Open Force Field to PELE) is a Python package that builds [PELE](https://pele.bsc.es/pele.wt)-compatibleforce field templates using the [Open Force Field toolkit](https://github.com/openforcefield/openforcefield).## Documentation
The documentation for theoffpelepackage is available at [GitHub Pages](https://martimunicoy.github.io/offpele).
Package: offprem
OffpremOffprem will query AWS for all VPC's a profile has access to.
The vpc_id, region, and associated tags of each discovered VPC is stored in a configuration file, which can then be leveraged to automatically create STS tokens and boto3 sessions.InstallationRequires Python 3.9+:python-mpipinstalloffpremFeaturesSave the VPC ID and region for all VPCs an account/role has access to.Supports roles, and accounts with Multi-Factor Authentication enabled.Automatically create STS tokens and boto3 sessions to interact with VPCs.UsageRead thetutorial.Local Development and TestingDevelopmentDevelopment and testing should be done in a virtual environment.$gitclonehttps://github.com/Fauxsys/offprem.git
$cdoffprem
$python-mvenvvenv--promptoffprem
$sourcevenv/bin/activate(offprem)$python-mpipinstall-UpipInstall offprem locally.(parserconfig)$python-mpipinstall-e".[test]"TestingYou can test any changes locally with pytest.(offprem)$python-mpytest--cov=offpremYou can also test offprem as an installed package.(offprem)$python-mtoxBuilding a wheel(offprem)$python-mpipinstallbuild(offprem)$python-mbuild--wheelThere should now be a wheel in thedistdirectory.(offprem)$ls-1dist
offprem-0.0.2b0-py3-none-any.whl