Saravanan666/pypi-org huggingface dataset:
Package: rapidoml
RapidoML: Automated Machine Learning Made SimpleRapidoML is an easy-to-use Python library that automates the process of building and optimizing machine learning models. It simplifies feature selection, hyperparameter tuning, and model selection, making it perfect for quickly prototyping and testing models. RapidoML relies on popular dependencies like scikit-learn, NumPy, TensorFlow, Keras, XGBoost, LightGBM, and CatBoost.InstallationYou can install RapidoML usingpip:pipinstallRapidoMLThis will install the required dependencies along with the RapidoML library.QuickstartFirst, import the required functions from the library:importrapidomLfromrapidoml.datasetsimportsample_datasetsLoad a sample dataset, such as Iris, Titanic, or others, and split it into training and testing sets with a 70/30 ratio. The functionsample_datasetsalso performs preprocessing, cleaning, and normalization.df_train,df_test=sample_datasets('iris')Create an instance of theAutoMLclass and train the model using the training dataset. If you want to specify the model to use, you can pass it as an argument, for example:model='XGBClassifier'. Otherwise, RapidoML will automatically pick the best model for the given dataset.automl=RapidoML.AutoML()automl.train(df_train)Evaluate the model using the testing dataset. This will return a dictionary containing evaluation metrics for the trained model, such as accuracy, precision, recall, and F1 score for classification tasks, or R2, MSE, and MAE for regression tasks.metrics=RapidoML.evaluate(automl,df_test,model_type='classifier')print(metrics)Finally, save the trained model to a pickle file for future use.automl.save()Custom DatasetsIf you want to use your own dataset, follow these steps:Load your dataset using your preferred method (e.g.,pandas.read_csv).Preprocess your dataset (cleaning, normalization, etc.).Split the dataset into training and testing sets (e.g., usingtrain_test_splitfromsklearn.model_selection).Pass the training dataset to thetrainfunction of theAutoMLinstance.Evaluate and save the model as described in the Quickstart example above.Supported ModelsRapidoML supports various models, including:DeepLearningClassifier and DeepLearningRegressorXGBClassifier and XGBRegressorLGBMClassifier and LGBMRegressorCatBoostClassifier and CatBoostRegressorYou can easily extend the library by adding more models to themodels.pyfile.ContributingIf you'd like to contribute to RapidoML, feel free to submit pull requests, report issues, or suggest new features. Your feedback is highly appreciated!SupportIf you have any questions or need help using RapidoML, please post a question oropen an issueon GitHub.
Package: rapid-orientation
rapid-orientation1. Install package by pypi.$pipinstallrapid-orientation2. Run by script.RapidOrientation has the defaultmodel_pathvalue, you can set the different value ofmodel_pathto use different models, e.g.orientation_engine = RapidOrientation(model_path='rapid_orientation.onnx')See details, forREADME_Layout.📌layout.pngsource:linkfromrapid_orientationimportRapidOrientationorientation_engine=RapidOrientation()withopen('test_images/layout.png','rb')asf:img=f.read()orientation_res,elapse=orientation_engine(img)print(orientation_res)3. Run by command line.Usage:$rapid_orientation-h
usage:rapid_orientation[-h]-imgIMG_PATH[-mMODEL_PATH]optionalarguments:
-h,--helpshowthishelpmessageandexit-imgIMG_PATH,--img_pathIMG_PATHPathtoimageforlayout.
-mMODEL_PATH,--model_pathMODEL_PATHThemodelpathusedforinferenceExample:$rapid_orientation-imglayout.png4. Result.# Return str, four types:：0 | 90 | 180 | 270
Package: rapido.souper
rapido.souperrapido.souper provides aSouper-basedpersistence layer for Rapido applications.Changelog1.0.4 (2017-02-27)Bug fixes:Fix __contains__ method on record
[ebrehault]1.0.3 (2017-02-27)Fix items() method on records
[ebrehault]1.0.2 (2015-11-17)Fixes1.0.1 (2015-11-08)More pythonic access to record items1.0 (2015-11-07)Initial release
Package: rapid-paraformer
rapid_paraformerUseInstallInstall therapid_paraformerpipinstallrapid_paraformerDownload theresources.zip(Google Drive|Baidu NetDisk)resources
├──[700]config.yaml
└──[4.0K]models├──[11K]am.mvn├──[824M]asr_paraformerv2.onnx└──[50K]token_list.pklUsefromrapid_paraformerimportRapidParaformerconfig_path="resources/config.yaml"paraformer=RapidParaformer(config_path)wav_path=["test_wavs/0478_00017.wav","test_wavs/asr_example_zh.wav",]result=paraformer(wav_path)print(result)See details forRapidASR.
Package: rapidpe-rift-pipe
# RapidPE/RIFT PipelineGenerates pipelines for running RapidPE and RIFT on HTCondor.## Changelog
[./CHANGES.md](https://git.ligo.org/rapidpe-rift/rapidpe-rift-pipe/-/blob/main/CHANGES.md)# CreditCopyright (c) 2022 Caitlin Rose, Vinaya Valsan, Daniel Wysocki, Sinead Walsh, Patrick BradyThis software may be freely distributed under the terms of the GNU General Public License v2.  See [COPYING](./COPYING) for details.
Package: rapidpro-flows
RapidPro Flows==============Python implementation of the RapidPro flow engine.Usage-----```pythonorg = Org("RW", "eng", pytz.timezone("Africa/Kigali"), DateStyle.DAY_FIRST, False)fields = [...]contact = Contact(...)flow = Flow.from_json("...")runner = Runner()run = runner.start(org, fields, contact, flow)steps = run.get_completed_steps()fields_to_create = run.get_created_fields()runner.resume(run, Input.of("Yes"))json = run.to_json()  # run state can be serialized as JSONrestored = RunState.from_json(json, flow)  # and then de-serialized when neededrunner.resume(run, Input.of(123))```
Package: rapidpro-for-rapidsms
This is theRapidProbackend forRapidSMS.  It has been created forUNICEFto facilitate transition of legacy applications designed forRapidSMSand theDjangoweb framework to the newRapidProplatform.
This component uses the officialrapidpro-pythonclient library to communicate with RapidPro.FeaturesIncoming (MO) SMS supportOutgoing (MT) SMS supportNew configuration optionsRapidPro API gateway URLRapidPro API tokenRequirementsPython 2.7+ or Python 3.3+Django >=1.7,<=1.9RapidSMS 0.18.0+rapidpro-python >=2.1.6InstallationThe rapidpro-for-rapidsms backend supports Python >= 2.7 and Django >= 1.7.  All required Python packages should be intalled automatically as long as you follow the official installation procedure described below.To install from PyPi:$ pip install rapidpro-for-rapidsmsConfigurationEdit your Django project settings (e.g.settings.py) to include the following:addrapidpro4rapidsmsto yourINSTALLED_APPS, e.g.:INSTALLED_APPS = (
    # your apps
    'rapidpro4rapidsms',
)configure the backend to use your RapidPro service and API token, e.g.:INSTALLED_BACKENDS = {
    # some other backends (optional)
    "rapidpro-backend": {
        "ENGINE": "rapidpro4rapidsms.RapidProBackend",
        # The following URL and token should be set to match your RapidPro
        "rapidpro_api_gateway_url": "your rapidpro API gateway URL, e.g. "
                                            "http://127.0.0.1:8080/api/v1"
                                            "or"
                                            "rapidpro.io",
        "rapidpro_api_token": "YOUR-RAPIDPRO-API-TOKEN"
    }
}configure your Django project to include rapidpro4rapidsms.urls, e.g.:urlpatterns = patterns(
    # your Django project URLs
    # ...
    # rapidpro4rapidsms URLs
    url(r'backends/', include('rapidpro4rapidsms.urls')),
)LICENSEThe rapidpro-for-rapidsms backend is released under the BSD License. See theLICENSE.TXTfile for more details.DevelopmentTo install:$ pip install -e .[development]To run tests:$ python setup.py testContributingIf you think you’ve found a bug or are interested in contributing to this
project check outrapidpro-for-rapidsms on Github.Development by Tomasz J. Kotarba ofSYSTEM7.
Package: rapidpro-python
RapidPro Python ClientOfficial Python client library for theRapidPro. Supports latest Python 3.Visitherefor complete documentation.Installationpip install rapidpro-pythonExamplefromtemba_client.v2importTembaClientclient=TembaClient('rapidpro.io','your-api-token')forcontact_batchinclient.get_contacts(group='Reporters').iterfetches(retry_on_rate_exceed=True):forcontactincontact_batch:print(contact.name)If you don't know your API token then visit theAPI ExplorerDevelopmentFor discussions about future development, see theRapidPro Developers Group.To run the tests:nose2 -C --coverage temba_client
Package: rapid-qc-ms
Rapid QC-MSRapid QC-MSis an all-in-one solution for automated quality control of liquid chromatography-mass spectrometry (LC-MS) instrument runs, both during and after data acquisition.It offers a fast, straightforward approach to ensure collection of high-quality data, allowing for less time investigating raw data and more time conducting experiments.Developed at theMass Spectrometry Platform of CZ Biohub San Francisco, Rapid QC-MS provides a host of key features to streamline untargeted metabolomics research, such as:Automated and user-defined quality control checksduring instrument runsRealtime updates on QC failsin the form of Slack or email notificationsInteractive data visualizationof internal standard retention time,m/z, and intensity across samplesGoogle Drive cloud syncand secure, Google-authenticated access to QC results from any deviceRequirementsRapid QC-MS was designed to run on Windows platformsbecause of its dependency onMSConvertfor vendor format data conversion andMS-DIALfor data processing and identification. However, MacOS users can still use Rapid QC-MS to monitor / view their instrument run data.In addition, Rapid QC-MS requires Python 3.8 to 3.11 and various Python packages, including:PandasSQLAlchemyPlotly DashBootstrapWatchdogGoogle APISlack APIThese are installed automatically during setup.Note:Installation of Python and various Python packages on MS instrument computers comes at no risk. For extra security and peace of mind, you can opt to install Rapid QC-MS in a virtual environment. To learn more, please read theinstallation guide.InstallationInstalling Rapid QC-MS is easy. Simply open your Terminal or Command Prompt and enter:py-mpipinstallrapid-qc-msIf you have multiple versions of python on your system, you can specifiy the version you want to use this waypy -3.# -m pip install ms-autoqcPython dependencies are installed automatically, but dependencies such as MSConvert and MS-DIAL will need to be installed manually.You can also opt to download and install Rapid QC-MS manually, or in a virtual environment if you prefer. Check out theinstallation guidefor more details.UsageTo start Rapid QC-MS, simply enter:rapidqcmsCheck out thequickstart guideto learn how easy setting up new QC jobs is.Supported instrument vendorsRapid QC-MS was designed to be a universal, open-source solution for data quality control. Because MSConvert converts raw acquired data into open mzML format before routing it to the data processing pipeline, the package will work seamlessly with data of all vendor formats.However, Rapid QC-MS has only been tested extensively on Thermo Fisher mass spectrometers, Thermo acquisition sequences, and Thermo RAW files.As such, it is expected that there may be bugs and issues with processing data of other vendor formats.If you encounter a bug, please report it byopening an issue on GitHub.We are open to collaboration! If you would like to help us develop support for Agilent, Bruker, Sciex, or Waters acquisition sequences and data files, please send an email tobrian.defelice@czbiohub.org.
Package: rapid-rest-client
REST-CLIENTSetup a client in minutesInstallationpipinstallrapid-rest-clientUsageThere are multiple options to auto-create a client. Here's using a swagger definition:fromrest_client.baseimportBaseUrlConfig,SwaggerApiConfiguration,Clientpetstore_endpoint_config:BaseUrlConfig=BaseUrlConfig('https://petstore.swagger.io/v2/')@SwaggerApiConfiguration(url='https://petstore.swagger.io/v2/swagger.json',base_url_config=petstore_endpoint_config)classSwaggerUrlConfiguredClient(Client):passswagger_url_configured_client=SwaggerUrlConfiguredClient()# Client now has all paths available, as defined in the json file at urlr=swagger_url_configured_client.find_pets_by_status(status='pending')print(r)You can also pass the swagger definition as a dict:@SwaggerApiConfiguration(definition={<swaggerdict>},base_url_config=petstore_endpoint_config)Manually creating a client:fromrest_client.baseimportBaseUrlConfig,ApiConfiguration,RequestConfig,Clientendpoint_config:BaseUrlConfig=BaseUrlConfig('https://reqres.in/api/','https://sandbox.reqres.in/api/')@ApiConfiguration(endpoints=[RequestConfig('users','list_users'),RequestConfig('users/{}','get_user'),RequestConfig('register','register_user','POST')],base_url_config=endpoint_config)classExampleClient(Client):passThis makes these methods available onExampleClientlist_usersget_userregister_userclient=ExampleClient()list_users_response=client.list_users()get_user_response=client.get_user(3)register_user_response=client.register_user(email="eve.holt@reqres.in",password="pistol")If you want to customize a method, you can simply add methods to a client. Use theendpointdecorator to do that:fromrest_client.baseimportendpoint,ClientclassExampleClient(Client):@endpoint('users','list_users')deflist_users(self,**kwargs):returnself._request(kwargs)You can also create the client from json, or a dict:fromrest_client.baseimportDictApiConfiguration,BaseUrlConfig,Clientendpoint_config:BaseUrlConfig=BaseUrlConfig('https://reqres.in/api/')@DictApiConfiguration(endpoints=[{'path':'users','name':'list_users',}],base_url_config=endpoint_config)classMyClient(Client):passAuthenticationYou can pass custom authentication through theauthentication_handlerproperty. Authentication handlers should extend AuthBase fromrequests.authFor example:fromrest_client.baseimportBaseUrlConfig,ApiConfiguration,RequestConfig,Client,BearerTokenAuthauth_endpoint_config:BaseUrlConfig=BaseUrlConfig('https://gorest.co.in/public/v1/')@ApiConfiguration(endpoints=[RequestConfig('users','register_user','POST'),],base_url_config=auth_endpoint_config)classAuthClient(Client):passauth_client=AuthClient(authentication_handler=BearerTokenAuth('token'))Please note:This is a work in progress and completely new. Contributions are very welcome.
Package: rapidrobustness
RAPID - Robustness Analysis Producing Intelligent DecisionsThe RAPID software package enables the generic guidance framework introduced in McPhail et al. (soon to be published) (see reference below) to be implemented in a user-friendly and consistent manner, including functionality to guide the user through the process of creating a custom robustness metric.
McPhail, C., Maier, H.R., Westra, S., van der Linden, L., Kwakkel, J.H. (to be published). Guidance framework and software for understanding and achieving system robustness.Getting startedPrerequisitesPython 3.xInstallingThis software package can be installed from PyPIpip install rapidrobustnessAuthorsCameron McPhail-Initial development of packageLicenseThis project is licensed under the MIT license - see theLICENSEfile for details.AcknowledgementsSupervisors and co-authors who contributed to Cameron McPhail's PhD (and therefore the concepts underpinning this package): Holger Maier, Seth Westra, and Leon van der Linden.Co-authors from several papers which were created prior to this package: Andrea Casttelletti, Matteo Giuliani, and Jan Kwakkel.Inspiration for this package was drawn from theEMA Workbench, created by Jan Kwakkel.
Package: rapidrpc
===========================pytodos===========================Command line lightweight todos... image:: https://travis-ci.org/chuanwu/PyTodos.svg?branch=master.. image:: https://badge.fury.io/py/pytodos.svg:target: https://badge.fury.io/py/pytodosInstall::pip install pytodosUsage::add: 增加任务list: 显示未完成的任务以及一个礼拜以内处理完的任务listall：显示未完成的任务以及处理完的所有任务got x: x表示未完成任务的编号，将其标记为完成.. image:: https://ooo.0o0.ooo/2017/04/19/58f729d498fa7.pngAttention::文件保存在/tmp/data.txt，请注意不要误删了，否则您的数据会丢失。
Package: rapidrsa
RapidRSASimpler to use implementation of the pycryptodome RSA algorithmExample Usefromrapidrsaimportrsarsa=rsa()e=rsa.encrypt("Example Text")d=rsa.decrypt(e)Easily Create and Verify Signaturesfromrapidrsaimportrsarsa=rsa()e=rsa.encrypt("Example Text")signature,digest=rsa.create_signature(message)ifrsa.verify_signature(signature,digest):d=rsa.decrypt(e)Required Dependences From PyPipycryptodome >= 3.15.0pycryptodome on GitHubpycryptodome on PyPiDocumentation'''Classes:rsa(key_size=2048, public_key=None, private_key=None)Can be fully functional and secure without passing any argumentsMethods:keygen(self, key_size: int) -> bytes and bytesGenerates Keys for Encryption/Decryption. The 'key_size' will determine the securityand speed of your data (bigger is more secure, but slower)encrypt(self, data: str or bytes, public_key=None) -> bytesOnly requires a public key if you don't want to use the class generated keydecrypt(self, encrypted_text: bytes, private_key=None) -> str or bytesOnly requires a private key if you don't want to use the class generated keycreate_signature(self, data: str, private_key=None) -> bytes and objectCreates a signature for later verifcation that the data hasn't been tamperedwith during transportverify_signature(self, signature: bytes, digest: object, public_key=None) -> boolVerifies the signature of the data, ensuring the data hasn't been tampered withgenerate_password(self, length=64) -> strGenerates a random password to share with the client/server for symmetric cryptography'''
Package: rapids
Contents1Introduction2Usage3Hacking3.1Development environment3.2Run test suite3.3Build and package1IntroductionREST APIs documented and sensibleBuild automatically documented REST APIs with thePyramid framework.This library is available on the Python package index under the project namerapids:https://pypi.python.org/pypi/rapidshttps://pypi.org/project/rapids/2Usage@rapids.decorators.resource('',None)classRoot(rapids.resources.Base):pass@pyramid.view.view_config(context=Root,rapids='')defroot_view(resource,request):returnpyramid.httpexceptions.HTTPNotFound()@rapids.decorators.resource('foo',Root)classFoo(rapids.resources.Base):pass@pyramid.view.view_defaults(context=Foo,rapids='')classFooView:def__init__(self,resource,request):pass@pyramid.view.view_config(request_method='GET')def_get_view(self):returnpyramid.httpexceptions.HTTPOk()@pyramid.view.view_config(request_method='POST')def_post_view(self):returnpyramid.httpexceptions.HTTPCreated()3HackingThis project makes extensive use oftox,pytest, andGNU Make.3.1Development environmentUse following command to create a Python virtual environment with all
necessary dependencies:tox --recreate -e developThis creates a Python virtual environment in the.tox/developdirectory. It
can be activated with the following command:. .tox/develop/bin/activate3.2Run test suiteIn a Python virtual environment run the following command:make reviewOutside of a Python virtual environment run the following command:tox --recreate3.3Build and packageIn a Python virtual environment run the following command:make packageOutside of a Python virtual environment run the following command:tox --recreate -e package
Package: rapids-dask-dependency
Dask MetapackageThis repository provides metapackages for pip and conda that centralize the Dask version dependency across RAPIDS.
Dask's API instability means that each RAPIDS release must pin to a very specific Dask release to avoid incompatibilities.
These metapackages provide a centralized, versioned storehouse for that pinning.
Therapids-dask-dependencypackage encodes bothdaskanddistributedrequirements.Versioning the Metapackage ItselfThis package is versioned just like the rest of RAPIDS: using CalVer, with alpha tags (trailing a*) for nightlies.
Nightlies of the metapackage should be consumed just like nightlies of any other RAPIDS package:conda packages should pin up to the minor version with a trailing.*, i.e.==23.10.*. Conda will allow nightlies to match, so no further intervention is needed.pip packages should have the same pin, but wheel building scripts must add an alpha spec>=0.0.0a0when building nightlies to allow rapids-dask-dependency nightlies. This is the same strategy used to have RAPIDS repositories pull nightly versions of other RAPIDS dependencies (e.g.cudfrequiresrmmnightlies).Strategy for Dask NightliesFor conda, nightlies are published to thedask channel.
The metapackage assumes that thedask/label/devchannel is included in a user's condarc so that the nightly will be found.
During RAPIDS development phase, Dask versions should be specified using PEP 440-compatible versions like>=2023.7.1a0so that nightlies may be picked up.
Then, at release time these versions may be pinned.For pip, dask and distributed do not publish nightly wheels.
Therefore, the only option is for this metapackage to install those dependencies from source.
To do so, the metapackage will encode dependencies in pyproject.toml as:- dask @ git+https://github.com/dask/dask.git@main
- distributed @ git+https://github.com/dask/distributed.git@mainAt release, these dependencies will be pinned to the desired versions.
Note that encoding direct URLs as above is technically prohibited by thePython packaging specifications.
However, while PyPI enforces this, the RAPIDS nightly index does not.
Therefore, use of this versioning strategy currently prohibits rapids-dask-dependency nightlies from being uploaded to PyPI, and they must be hosted on the RAPIDS nightly pip index.
Package: rapidsms-appointments
rapidsms-appointments is a reusable RapidSMS application for sending appointment
reminders. Users can be subscribed to a timeline of milestones for future appointments. Reminders
are send to the patient or staff to remind them of the appointment. Appointments
can be confirmed or rescheduled by patient or staff. It also tracks the history of confirmed
notifications and missed/made appointments.Dependenciesrapidsms-appointments currently runs on Python 2.6 and 2.7 and requires the following
Python packages:Django >= 1.3RapidSMS >= 0.11.0Celery >= 3.0.13DocumentationDocumentation on using rapidsms-appointments is available onRead The Docs.TranslationsThe translations for rapidsms-appointment are managed on ourTransifex project.
If you are interested in translating rapidsms-appointments into your native language
you can join the project and add your language.Running the TestsWith all of the dependancies installed, you can quickly run the tests with via:python setup.py testor:python runtests.pyTo test rapidsms-appointment in multiple supported environments you can make use
of thetoxconfiguration.:# You must have tox installed
pip install tox
# Build default set of environments
tox
# Build a single environment
tox -e py26-1.4.XLicenserapidsms-appointments is released under the BSD License. See theLICENSEfile for more details.ContributingIf you think you’ve found a bug or are interested in contributing to this project
check outrapidsms-appointments on Github.Development sponsored byCaktus Consulting Group, LLC.