Saravanan666/pypi-org huggingface dataset:
Package: research-assistant
Research AssistantResearch Assistant is a Python 3 library featuring small utilities that make life easier.InstallationUse the package managerpipto install Research Assistant.pipinstallresearch-assistantScripts and UsageTo use a utility, simply call it from the command line. For example:paper-scraper.pywill run the paper-scraper utilityScriptDescriptionRequirementspaper-scraper.pyRun on a folder of PDF files to rename files in a AUTHOR_DATE_TITLE format. For example:jourABC_article_184505.pdfâ†’Hattwell_2020_Research Assistant.pdf. Quality of renaming dependent on metadata of PDFs.PyPDF2ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMITThanksThanks to Luke Husdell for feedback
Package: research-data-services-common
Research Data Services Common PackageThis package make the most common modules in Sciebo RDS available in one place, so we do not have to maintain them in several places.
In the RDS project, we use OAuth2 for authentication between numerious services, so we need a datastructure with methods, which supports this.So this package implement 3 basic classes (User, Service, Token), which handles standard user-password authentication.
If you need this classes with oauth2-support, you have to use the corresponding version (e.g. Service => Oauth2Service). (Notice: User does not have an oauth2-version, because token takes care of password or token and service takes care of everything else for oauth2.)UsageYou can find some examples to use this package below. If you need more, please take a look into the tests orsciebo RDS(e.g.Token Storage).fromRDSimportUseruser1=User("Max Mustermann")Installationpipinstallresearch-data-services-commonOptional dependenciesIf you want to work with flask, you can use some additional features, when it is installed.pipinstall"research-data-services-common[flask]"JSONEncoderWith flask installed, you can use the JSONEncoder for flask.fromflaskimportFlask,jsonifyfromRDSimportUtilapp=Flask(__name__)app.json_encoder=Util.get_encoder(func_name="to_dict")classStorage():defto_dict(self):return{"foo":"bar"}@app.route("/")defhello():returnjsonify(Storage())If you want to use builtin json, you do not need flask. Then you can usemonkeypatch-method.fromRDSimportUtilimportjsonclassStorage():defto_dict(self):return{"foo":"bar"}Util.monkeypatch(func_name="to_dict")print(json.dumps(Storage()))# expects: '{"foo":"bar"}'Notice: func_name defaults toto_json.Monkeypatch JSONEncoder in FlaskIf you want to monkeypatch json and flask, you can use the helper functionmonkeypatchfrom RDS.fromRDSimportUtilUtil.monkeypatch()If it runs in a flask app context, it patches the app by itself. Otherwise you have to set theapp-argument.fromflaskimportFlaskapp=Flask(__name__)@app.route('/')defhello_world():return'Hello, World!'fromRDSimportUtilUtil.monkeypatch("getDict",app=app)The first argument in the previous example, you can see how to set the method, which should be used for json encoding from your object. Themonkeypatch-method patches JSONEncoder in your python installation and your app flask.RO-Crate ParserThis helps you to work with ro-crate-metadata files. Take a look in theteststo see, how this can be used.Available ModulesUserService (Oauth2Service)Token (Oauth2Token)ROParser
Package: researchhelper
Table of ContentsWelcome to ResearchHelperSphinx, PypiWelcome to ResearchHelperThis is a personal repository of functions that I use throughout projects. Besides that, it could obviously also be useful to others! So see if it holds something for you.Sphinx, PypiThis package is not only for my functionality, but also to practice in making a package and documenting is correctly. For that I'm using Sphinx. The online hosted documentation is found on my websitehttps://www.baschatel.nl/ResearchHelper.
Package: research-learn
research-learnToolbox to simplify the design, execution and analysis of machine learning
experiments. It based onstatsmodels,scikit-learnandimbalanced-learn.DocumentationInstallation documentation, API documentation, and examples can be found on thedocumentation.Dependenciesresearch-learn is tested to work under Python 3.6+. The dependencies are the
following:numpy(>=1.1)statsmodels(>=0.9.0)scikit-learn(>=0.22)imbalanced-learn(>=0.6.0)Additionally, to run the examples, you need matplotlib(>=2.0.0) and
pandas(>=0.22).Installationresearch-learn is currently available on the PyPi’s repository and you can
install it viapip:pip install -U research-learnThe package is released also in Anaconda Cloud platform:conda install -c gdouzas research-learnIf you prefer, you can clone it and run the setup.py file. Use the following
commands to get a copy from GitHub and install all dependencies:git clone https://github.com/georgedouzas/research-learn.git
cd research-learn
pip install .Or install using pip and GitHub:pip install -U git+https://github.com/georgedouzas/research-learn.gitTestingAfter installation, you can usepytestto run the test suite:make test
Package: researchmap.py
researchmap.pyKey FeaturesModern Pythonic API usingasyncandawait.Optimised in both speed and memory.InstallingPython 3.8 or higher is requiredTo install the library without full voice support, you can just run the following command:# Linux/macOSpython3-mpipinstall-Uresearchmap.py# Windowspy-3-mpipinstall-Uresearchmap.pyTo install the development version, do the following:$ git clone https://github.com/RTa-technology/researchmap.py
$ cd researchmap.py
$ python3 -m pip install -U .Quick Exampleimportresearchmapdefmain():withopen('env/rmap_jwt_private.key','rb')asf_private:private_key=f_private.read()withopen('env/rmap_client_id.key','r')asf_id:id=f_id.read()client_id=idclient_secret=private_keyscope='read researchers'auth=researchmap.Auth(client_id,client_secret,scope)access_token=auth.get_access_token()["access_token"]req=researchmap.RequestsAdapter(access_token)payload={"format":"json","limit":100,"institution_code":"所属機関の機関コード"}print(req.get_bulk(payload))if__name__=="__main__":main()ContributingHow to localize$docs/make.batgettext
$sphinx-intlupdate-pdocs/_build/gettext-lja
$# Translate the po file.$Set-Itemenv:SPHINXOPTS"-D language=ja"$docs/make.bathtmlLinksDocumentationAPI Documents
Package: researchpal
researchpal: A Python Library for Automated Literature Review GenerationWhat is it?researchpal is a Python library that automates the process of generating academic literature reviews based on a research question. It utilizes external data sources to fetch research papers, synthesizes the findings, and generates a concise literature review. This library is particularly useful for researchers and students looking to streamline the literature review process.Features:Fetches research papers from Springer and Arxiv.Synthesizes research findings into a coherent literature review.Extracts citations and generates a references list.Supports both short(cite around 5 research papers) and long(cite around 10 research papers) literature reviews.Prerequisites:Before using researchpal, ensure you have the following prerequisites:Python 3.8 installed on your system.An API key for OpenAI (required for certain functionalities).Installation:To install researchpal, you can use pip:pip install researchpalOptions:research_question: Your research question.openai_key: Your OpenAI API key.length: The length of the literature review ("short" or "long," default is "short").Usage:Here's a basic example of how to use researchpal in your Python script:from researchpal import generate_literature_reviewresearch_question ="your_query"openai_key ="your_openai_api_key"length (by default) ="short"# can be "short" or "long"literature_review.generate_literature_review(research_question, openai_key, length)License:This project is licensed under the MIT License.Support and Feedback:For support or feedback, please contact us atinfo@researchpal.coAcknowledgments:This library makes use of research papers from Springer API and Arxiv API, and it requires an OpenAI API key for certain functionality.
Package: resellerclub
ResellerClub API in Python & Command Line Client-------------------------------------------This is a command line client for the `Resellerclub HTTP API`__. It canalso be used as a library (Python).__ http://manage.resellerclub.com/kb/answer/744For now, only a couple of DNS related functions are implemented. Pullrequests certainly welcome. Its straightforward to extend.Usage:------DNS::$ resellerclub dns example.org add A foo 8.8.8.8$ resellerclub dns example.org delete A foo 8.8.8.8$ resellerclub dns example.org list A fooInstallation & Setup-----------------::$ pip install resellerclubFirst, realize that Resellerclub requires you to whitelist your ip address(they implement this whitelist on a firewall level). This is probably no goodif you want to use this tool on your workstation, so I encourage you setupa proxy. You can find a ready-to-use docker image for such a proxy here:https://index.docker.io/u/elsdoerfer/resellerclub-api-proxy/You'll also need to determine your reseller id and your api key. The wholething is explained here:http://manage.resellerclub.com/kb/answer/753You currently need to provide this data via environment variables. I suggestyou add them to your profile:RESELLERCLUB_USER_IDYour reseller idRESELLERCLUB_API_KEYYour API KeyRESELLERCLUB_URLOptional: The base url to use for the API. Unless you provide aproxy, this will be the default server that whitelists IPs.
Package: resellerinterface-api-client-python
api-client-pythonInstallationpipinstallresellerinterface-api-client-pythonExamplefromresellerinterface_api_client_pythonimportClient# create a new clientclient=Client()# loginawaitclient.login("username","password",1234)# make a requestresponse=awaitclient.request("reseller/details",{"resellerID":"own"})print(response.getData())# response# {#     "time": 12,#     "state": 1000,#     "stateName": "OK",#     "stateParam": "",#     "reseller": {#     "resellerID": 23456,#         "parentID": 23455,#         "state": "ACTIVE",#         "company": "Acme Corp.",#         "firstname": "John",#         "lastname": "Doe",#         "street": "Mainstreet",#         "number": "1223",#         "postcode": "10115",#         "city": "Berlin",#         "country": "DE",#         "mail": "info@example.org",#         "phone": "+491234567890",#         "fax": "",#         "parents": [#         23455#     ],#         "settings": {#         "group": {#             "name": "value"#         }#     }# },#     "user": {#     "userID": 12345,#         "state": "ACTIVE",#         "username": "User",#         "password": "****",#         "settings": {#         "group": {#             "name": "value"#         }#     },#     "rightsCategory": 51,#         "rightsGroups": [#         912,#         913#     ],#         "directRights": {#         "category": {#             "group": {#                 "function": true#             }#         }#     },#     "rights": {#         "category": {#             "group": {#                 "function": true#             }#         }#     }# }# }
Package: resemble
resemble.ai APIresemble.aiis a state-of-the-art natural voice cloning and synthesis provider. Best of all, the platform is accessible by using our public API! Sign uphereto get an API token!This repository hosts a Python library for convenient usage of theResemble API.Quick startfromresembleimportResembleResemble.api_key('your_api_key')project=Resemble.v2.projects.get('project_uuid')voice=Resemble.v2.voices.get('voice_uuid')clip=Resemble.v2.clips.create_sync('project_uuid','voice_uuid','This is a test')StreamingThe Streaming API is currently in beta and is not available to all users. Please reach out toteam@resemble.aito inquire more.Streaming example:fromresembleimportResembleResemble.api_key('your_api_key')Resemble.syn_server_url('your_resemble_synthesis_server_url')# Extra configuration required for streamingforchunkinResemble.v2.clips.stream('project_uuid','vouce_uuid','This is a test'):# do something with the `chunk` of audio data, such as feeding it into a streaming audio playerpassDevelopmentThe library files are located inresemble/TestingNote that currently the test suite performs real actions against the Resemble AI platform (e.g., creating projects, retrieving clips, updating voices,et cetera).# For tests that do NOT require a voice$TEST_API_KEY=<...>TEST_BASE_URL=<...>python3-munittest# For tests that do require a voice$TEST_API_KEY=<...>TEST_BASE_URL=<...>TEST_VOICE_UUID=<...>python3-munittestPublishing new versionsYou need thebuildandtwinepip packages. (pip3 install build twine)git status: Make sure your working directory has no pending changes.Update the version attribute insetup.py.git commit: Commit this version change.Build the package:make pkg.build(optional) Publish to the test index:makepkg.publish.testPublish to the indexmakepkg.publish
Package: resemble-enhance
Resemble Enhancehttps://github.com/resemble-ai/resemble-enhance/assets/660224/bc3ec943-e795-4646-b119-cce327c810f1Resemble Enhance is an AI-powered tool that aims to improve the overall quality of speech by performing denoising and enhancement. It consists of two modules: a denoiser, which separates speech from a noisy audio, and an enhancer, which further boosts the perceptual audio quality by restoring audio distortions and extending the audio bandwidth. The two models are trained on high-quality 44.1kHz speech data that guarantees the enhancement of your speech with high quality.UsageInstallationpipinstallresemble-enhanceEnhanceresemble_enhance in_dir out_dirDenoise onlyresemble_enhance in_dir out_dir --denoise_onlyWeb DemoWe provide a web demo built with Gradio, you can try it outhere, or also run it locally:python app.pyTrain your own modelData PreparationYou need to prepare a foreground speech dataset and a background non-speech dataset. In addition, you need to prepare a RIR dataset (examples).data
├──fg│├──00001.wav
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├──bg│├──00001.wav
│└──...
└──rir├──00001.npy└──...TrainingDenoiser WarmupThough the denoiser is trained jointly with the enhancer, it is recommended for a warmup training first.python-mresemble_enhance.denoiser.train--yamlconfig/denoiser.yamlEnhancerThen, you can train the enhancer in two stages. The first stage is to train the autoencoder and vocoder. And the second stage is to train the latent conditional flow matching (CFM) model.Stage 1python-mresemble_enhance.enhancer.train--yamlconfig/enhancer_stage1.yamlStage 2python-mresemble_enhance.enhancer.train--yamlconfig/enhancer_stage2.yaml
Package: resen
Resen (REproducible Software ENvironment), is a tool that enables reproducible scientific data analysis, built using python and docker.  It is designed to make it easier for geospace researchers to share analysis and results, as well as build off of work others have done.  Resen was developed under the InGeO project, currently supported by the National Science Foundation’s Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program (Grant #1835573).  For more information about the InGeO project, please visit theInGeO website.Resen is based on the concept of portable environments, or buckets, where code can be developed and run independent of a users system.  When you start a resen bucket, it has a variety of common geospace software packages preinstalled and ready for use.  This means you have easy access to common models and datasets, and can start using them in your analysis immediately.  You can also set up your bucket to access your own datasets, locally stored on your machine.After you have completed your analysis, you can share an entire bucket with other researchers.  Within the bucket, your analysis code will always run exactly the same way, regardless of what system the bucket is on.  This means that other researchers should be able to reproduce your work and start building off of it immediately, instead of spending time configuring their system, installing new packages, and setting up file paths so their environment is compatible with your code.QuickstartInstallationResen requires both python 3 and docker to be installed.InstallPython 3InstalldockerInstall Resen with pippip install resenPlease refer to theinstallation documentationfor more detailed instructions.UsageResen is a command line tool.  To start resen, simply enterresenat a command prompt:$ resenFor a list of available commands, use thehelpcommand:[resen] >>> helpResen Workflow ExampleDocumentationComplete documentation for Resen is available athttps://resen.readthedocs.io/.
Package: resend
Resend Python SDKInstallationTo install Resend Python SDK, simply execute the following command in a terminal:pip install resendSetupFirst, you need to get an API key, which is available in theResend Dashboard.importresendimportosresend.api_key="re_yourkey"Exampleimportosimportresendresend.api_key="re_yourkey"params={"from":"onboarding@resend.dev","to":["delivered@resend.dev"],"subject":"hi","html":"<strong>hello, world!</strong>","reply_to":"to@gmail.com","bcc":"bcc@resend.dev","cc":["cc@resend.dev"],"tags":[{"name":"tag1","value":"tagvalue1"},{"name":"tag2","value":"tagvalue2"},],}r=resend.Emails.send(params)print(r)
Package: resend-client-sdk-python
Resend is the email platform for developers.SDK Installationpipinstallresend-client-sdk-pythonAuthenticationTo authenticate you need to add an Authorization header with the contents of the header being Bearer re_123456789 where re_123456789 is your API Key. First, you need to get an API key, which is available in theResend Dashboard.Authorization:Bearerre_123SDK Example Usageimportresendfromresend.modelsimportoperations,shareds=resend.Resend()s.config_security(security=shared.Security(bearer_auth="Bearer YOUR_BEARER_TOKEN_HERE",))req=operations.SendEmailRequest(request=shared.Email(from_="hello@resend.com",to="thefuture@yourcompany.com",subject="Welcome to Resend!",text="Hello, World!",),)res=s.email.send_email(req)ifres.send_email_responseisnotNone:# handle responseSDK Available Operationsemailsend_email- Send an emailContributionsWhile we value open-source contributions to this SDK, this library is generated programmatically. Feel free to open a PR or a Github issue as a proof of concept and we'll do our best to include it in a future release !SDK Generated bySpeakeasy
Package: reservauto
Reservauto API (unofficial)This is an unofficial package to interact with the official Reservauto API available for testiing with Swagger UI athttps://restapifrontoffice.reservauto.net/ReservautoFrontOfficeInstallationpipinstallreservautoUsagefromreservautoimportReservautoClientclient=ReservautoClient()# Get available branches (already loaded upon initialization)branches=client.get_branches()# Get cities for a branchcities=client.get_cities(branch_id=1)# Get stations availability for a city, location, and date rangestations_availability=client.get_stations_availability(city=cities[1],min_latitude=45.5,max_latitude=45.6,min_longitude=-73.6,max_longitute=-73.5,start_datetime=datetime.datetime(2021,1,1,12,0,0),end_datetime=dateetime.datetime(2021,1,1,13,0,0),)
Package: reserve
reserve is agenericPython server (and server library). It means it’s able to host any type of application, not only wsgi apps like thewebservers do.Requirements:sdlaunch - reserve doesn’t contain socket opening nor daemonizing code, it depends on sdlaunch for that (or alternatively you can use systemd socket activation)fdsocket - python’s sockets, when creating them from a file descriptor, require their user to provide some information unobtainable from Python, but easy to get from C. fdsocket provides that information.Usagesdlaunch -b "[::]:80" -- reserve app [args...]where:::is the IPv6 address to listen on (::means all)80is the portappis the name of application you want to serve[args...]is a list of arguments to pass to the application (optional)Application definitionApplication is a python module containing alaunch(args)callable - where args is an array of strings.It should return a request handler callable -handle(socket, client_address, server), where:socketis a newly opened socketclient_addressis a tuple containing client’s IP and portserveris a TCPServer object that you probably should not touchBundled applicationsreserve currently bundles only one reserve app -http. Together withwsgisubapplication it can be used to serve WSGI apps.The API for writing subapplications ofhttpis currently undocumented and considered a implementation detail. You should not use it, as it might change at any point in the future. Still, you may use it together withwsgi- as that is guaranteed to remain backwards compatible.Serving WSGI appssdlaunch -b "[::]:80" -- reserve app http wsgi wsgi-app-name [args...]wherewsgi-app-nameis a python module containing alaunch(args)callable (like with normal reserve app)It should return a WSGI (PEP 3333) application callable.Warning:You should not use reserve.http in production. You should serve your application as SCGI/FCGI/… instead and use a real web server as a frontend.reserve does not currently support SCGI/FCGI/…, but in the near future it will.
Package: reserved
When you let users of a webapp create their own usernames, and this name is used
in an email address or Unix user, it is important to check against some common
usernames, like ‘root’, ‘postmaster’, and ‘postfix’. This library is an attempt
for that.You should check the following:The address in your WHOIS, wich may be used for things like registering a
TLS certificate. It should really be something like hostmaster@ anyway, so
thisshould notbe an issue.If you make Unix accounts with these names, check which names occur in/etc/passwd(and possibly/etc/group).License: BSD 2-clauseResources:RFC2142Security StackExchange: What email addresses are treated as trustedPostbit reserved username list/etc/passwdand/etc/groupon my laptop (Debian testing/stretch).shouldbee’s listkwappa’s listImage viaxkcd.com
Package: reserver
Table of contentsDisclaimerOverviewInstallationUsageIssues & Bug ReportsTodoContributionAuthorsLicenseShow Your SupportChangelogCode of ConductDisclaimer⚠️The intention of this package is facilitating the reservation of package names on PyPI for legitimate and appropriate purposes. We explicitly disclaim any responsibility for the misuse or spamming of this tool, particularly in the excessive reservation of package names. Users are advised to be cautious and ensure the  legitimate use of this package to avoid potential consequences such as the suspension of their PyPI account. By using this package, users acknowledge and agree to these terms.OverviewReserver is an open source Python package that offers the ability to quickly reserve a PyPI package name. Got a notion? Before it's taken, immediately reserve the product name!PyPI CounterGithub StarsBranchmaindevCIInstallationPyPICheckPython Packaging User GuideRunpip install reserver==0.1Source codeDownloadVersion 0.1orLatest SourceRunpip install .UsageSecure your desired PyPI package name!fromreserverimportUploaderuploader=Uploader(PYPI_API_TOKEN,test_pypi=False)uploader.upload_to_pypi("CONSIDERED_NAME_FOR_YOUR_PACKAGE")Issues & bug reportsJust fill an issue and describe it. We'll check it ASAP! or send an email toinfo@openscilab.com.Please complete the issue templateYou can also join our discord serverReferencesflaticon.combox:Box icons created by Good Ware - Flaticonreserve plate:Reserved icons created by Freepik - FlaticonShow your supportStar this repoGive a ⭐️ if this project helped you!Donate to our projectIf you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do ;-) .ChangelogAll notable changes to this project will be documented in this file.The format is based onKeep a Changelogand this project adheres toSemantic Versioning.UnreleasedAddedChanged0.1- 2024-02-07AddedUploaderclassCheck package name existenceUpload template package to test.pypiUpload template package to pypiGenerate template package for a given nameHandle similiar name existance in pypiHandle issue with "-" character.egginfofile name