Saravanan666/pypi-org huggingface dataset:
Package: ap_python_sdk
ApPythonSdk===================================Alternative Payments python libary sdk. Accept local payments from all over the worldInstallation-----Install gem, using pip:$ pip install ap-python-sdkIn your initializing files add line:import ap_python_sdkap_python_sdk.api_secret_key = 'Fill it with your own secret key.'ap_python_sdk.api_public_key = 'Fill it with your own public key.'Usage-----For usage and examples check `http://www.alternativepayments.com/support/api/` or sample application on our open-source repo `https://github.com/AlternativePayments/ap-python-sdk`Example of creating new customer:customer = Customer.create({'firstName': 'John','lastName': 'Doe','email': 'tempmail@mail.com','address': 'Rutledge Ave 409','city': 'Folsom','zip': '19033','country': 'US','state': 'PA','phone': '55555555555','created': '2016-03-24T15:19:10.7800694Z'});Accessing object's attributes:customer.firstName=> JohnSame goes for complex objects like Transaction.Create SEPA transaction:customer = Customer(id='cus_bd838e3611d34d598',firstName='John',lastName='Doe',email='john@doe.com',country='DE')payment = Payment(paymentOption='SEPA',holder='John Doe',iban='BE88271080782541')return Transaction.create({'customer': customer,'payment': payment,'amount': 500,'currency': 'EUR','description': 'test sepa php sdk','merchantPassThruData': 'test_sepa_123','ipAddress': '127.0.0.1'})Access customer:transaction.customer.firstNameDevelopment-----To release a new version, update the version number in `version.py`, and then run `python setup.py sdist bdist_wheel upload -r https://pypi.python.org/pypi` to create a new version of library and deploy it to `https://pypi.python.org/pypi/ap_python_sdk`Contributing-----1. Fork it ( https://github.com/AlternativePayments/ap-python-sdk/fork )2. Create your feature branch (`git checkout -b my-new-feature`)3. Commit your changes (`git commit -am 'Add some feature'`)4. Push to the branch (`git push origin my-new-feature`)5. Create a new Pull Request
Package: appyweather
AppyweatherAppyweather is a Python package that provide you with 24-hour weather forecast information for any city
or location, and it is mainly created for educational reasons.
All weather data are provided through the 'openweathermap' api service.Get your own api-key from:https://openweathermap.orgApi-key might take a few hours to be activated.InstallationUse the package managerpipto install appyweather.pipinstallappyweatherUsagefromappyweatherimportWeather# using a city nameweather=Weather(api_key='yourapikeyhere',city='Athens')# using latitude/longitude coordinates + units (optional)weather1=Weather(api_key='yourapikeyhere',lat=37.98,lon=23.72,units='metric')# get full 3-hour weather data for the next 24 hoursweather.next_24_hours()# get basic 3-hour weather data for the next 24 hoursweather1.next_24_hours_basic()Sample url for sky condition icons:http://openweathermap.org/img/wn/10d@2x.pngLicenseMIT
Package: aPRAW
aPRAWAsynchronous Python Reddit API Wrapper byDan6erbond.aPRAWis an asynchronous API wrapper written for the Reddit API that builds on the idea ofPRAWin many ways. It follows a very similar design, but adds features such as unlimited listings and, most importantly, support for asynchronous requests. This allows the library to be used in scenarios where the requests can take longer (such as with those unlimited listings and streams) and not block other tasks.Table of ContentsKey FeaturesReporting BugsPull RequestsCommunity and SupportContributingDocumentationRoadmapLinksLicenseKey FeaturesAsynchronous HTTPS requests to the Reddit API.Unlimited listings.Full OOP class design.InstallationaPRAW requires a release of Python 3.6 or newer as it uses the inbuiltasyncandawaitsyntax. You can install aPRAW via pip:pip install aprawQuickstartCreate an application on your Reddit account as per theirdocumentation. Once you have that, you can interact with aPRAW and its subclasses:importaprawimportasyncio# instantiate a `Reddit` instance# you can also supply a key to an entry within a praw.ini# file, making your login compatible with praw as wellreddit=apraw.Reddit(client_id="CLIENT_ID",client_secret="CLIENT_SECRET",password="PASSWORD",user_agent="USERAGENT",username="USERNAME")asyncdefscan_posts():# get an instance of a subredditsubreddit=awaitreddit.subreddit("aprawtest")# loop through new postsasyncforsubmissioninsubreddit.new():print(submission.title)if__name__=="__main__":# get the asyncio event looploop=asyncio.get_event_loop()# add scan_posts() to the queue and run itloop.run_until_complete(scan_posts())Due to the fact that aPRAW's code is almost entirely asynchronous, you will have to perform all network-related tasks within the asyncio event loop.Community and SupportIf you have any questions regarding aPRAW and its usage...Join the/r/aPRAWsubredditFeel free to post a question in the questions thread or make your own post if it could start a big discussion!Join theaPRAW Discord serverUse the#💬generalchat for discussion about the library and talking to other users.Use the#❓questionsto post questions. The developers will try to get back to you as quickly as possible, but other users can help as well!Use the#💡ideasif you have any ideas for the framework but don't know how to implement them, or just want to throw in the suggestion.ContributingOf course, aPRAW is open-source under the GPLv3 license and all additions and bug reports in the form of issues and pull requests are welcome! For further information, view thecontribution guidelinesand have fun coding!DocumentationThe full API documentation can be found onRead The Docs.RoadmapFull coverage of Reddit's API.PyPi release.Never-ending streams.Useful helper functions.LinksDocumentationOfficial Discord serverPyPi ReleaseLicenseaPRAW's source is provided under GPLv3.Copyright ©, RaviAnand MohabirNote by the DevelopersaPRAW's development process and code is in no way affiliated with that of PRAW. The design allows for a simple adoption when moving from PRAW to aPRAW, but the developers have set out to create a wrapper from the ground-up, without the use of a pre-existing codebase.
Package: aprdna
Use aprdna to look for periodic repetitions in sequences of DNA, in the GenBank format.InstallUse pip to install the program:$pipinstallaprdnaUsageusage: aprdna [-h] [-a NAME] [-p PERIOD] [-t TOLERANCE] [-m LENGTH] [-r THROTTLE] pathSearch for repetitions of nucleotides.positional arguments:path                  Path to Sequence fileoptional arguments:-h,--helpshow help message and exit-pPERIOD,--periodPERIODPeriodicity of the repetition-tTOLERANCE,--toleranceTOLERANCETolerance window for the repetition-mLENGTH,--lengthLENGTHMinimal repetition length-rTHROTTLE,--throttleTHROTTLEStop after so many fragments
Package: aprendepython
aprendepythonLibrería de código abierto diseñada para enseñar los fundamentos del lenguaje
Python.ContenidoEl curso se centra en temas básicos de Python, insipirado en el contenido de la
secciónLearn the Basicsdelearnpython.orgLos temas incluyen:Variables y tipos de datosOperadores aritméticos, relacionales y lógicosCondicionalesCiclosFuncionesClases y objetosInstalaciónPara utilizar aprendepython es necesario contar con Python 3.7 o superior
instalado en el sistema. Después, instala la librería utilizando pip con el
siguiente comando:pipinstallaprendepythonNota: Se puede utilizar entornos virtuales en caso de haber problemas con la
instalación global. Para más información sobre entornos virtuales visitaCreación de entornos virtuales.UsoPara comenzar a usar el curso interactivo, solo necesitas importar la librería
en un entorno de Python y llamar la funcióncomenzar():importaprendepythonaprendepython.comenzar()Al iniciar el curso por primera vez se mostrará una introducción que indica la
forma de interactuar con las lecciones y navegar por el menú.ContribucionesLas contribuciones de la comunidad son valoradas y bienvenidas, ya sea para
mejorar el contenido del curso o integrar mejoras al código. Si deseas colaborar
en el desarrollo, puedes seguir estos pasos:Realiza un fork del proyecto.Realiza cambios en el código o las lecciones.Haz un pull request a este proyecto para revisar los cambios e integrar tus
contribuciones.LicenciaEste proyecto se distribuye bajo la licencia MIT, indicando que es de código
abierto y que puede utilizarse libremente en otros proyectos. Para más detalles,
consulta el archivoLICENSE.
Package: aprep
aprepDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-naprep--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallaprepnbdev# activate conda environment$condaactivateaprep# make sure the aprep package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the aprep package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/aprep.gitor fromconda$condainstall-cdsm-72aprepor frompypi$pipinstallaprepDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: apres
Apres bindingsPython bindings for the Apres MIDI libraryInstallationCan be installed through pippip install apresUsage ExamplesLoad a SongfromapresimportMIDImidi=MIDI("/path/to/file.mid")Create a new MIDIfromapresimportMIDImidi=MIDI()Creating a songfromapresimportMIDI,NoteOnEvent,NoteOffEvent# Create an empty MIDI file.midi=MIDI()# Press midi note 64 (Middle E) on the first track (0) at the first position (0 ticks)midi.add_event(NoteOnEvent(channel=0,note=64,velocity=100),tick=0,track=0)# Release midi note 64 (Middle E) on the first track (0) one beat later (120 ticks)midi.add_event(NoteOffEvent(channel=0,note=64),wait=120,track=0)# Save it to a filemidi.save("beep.mid")
Package: aprheology
APRheologyToolkit for Anton Paar rheometer dataThe purpose of this program is to facilitate the analysis
of rheometer data of polymeric samples. The program is originally
designed to use RheoCompass csv exports as input, however, it is
developed without involvement of Anton Paar and is thus independent
from the company and its RheoCompass software.Currently, the extraction of relaxation times from frequency sweep
and stress relaxation experiments is supported. Other functionality might
be added as needed.LicenseAPRheology is available free of charge under an MIT License.
See the LICENCE file for more details.Changelog0.3.2Fixed that the program crashes when one tries to get Arrhenius data without
relaxation times (for example no cross-over in frequency sweep at any temperature). Now None values and an empty plot are returned.0.3.1Bugfixes0.3.0Included stretched exponentials for stress relaxation0.2.3Export of advanced tab now possibleBugfixes0.2.0Introduce generalised Maxwell model for stress relaxation0.1.4Bugfixes0.1.0Initial release
Package: apricatewrapper
ApricateIO-WrapperA basic python wrapper around the Apricate.io API.Example Usagefrom apricatewrapper import ApricateAPI

api = ApricateAPI()

user_res = api.users.create_user("my_user")
token = user_res["data"]["token"]

api.set_token(token)
plots_res = api.plots.my_plots()
print(plot_res)
Package: apricopt
ApricoptApricopt is a python framework for simulation-based optimisation of dynamical systems.It is agnostic with respect to the optimiser and to the simulator.Apricopt supports the PEtab format for the definition of the optimisation problem. More info about PEtabhere.Currently, we support the following simulators:COPASI. A state-of-the-art simulator for models of biological processes. We support SBML models (any level and version).RoadRunner. A fast state-of-the-art simulator of biological models.Currently, we support the following black-box optimisers:NOMAD. A state-of the art black-box solver that supports surrogate models. More infohere.SciPy.optimize. A python library that implements several algorithms for multivariate optimization. More infohere.PySwarms. A python library that implements various forms of the Particle Swarm Optimization algorithm. More infohere.Currently, we support the following white-box optimisers:COPASI. It supports several optimisation algorithms for biological processes. More infohereInfoWIPVersion 0.0.2a3Who do I talk to?Marco Esposito (author) -esposito@di.uniroma1.itLeonardo Picchiami (author) -picchiami.1643888@studenti.uniroma1.itCopyright (C) 2020-2021  Marco Esposito, Leonardo Picchiami.Distributed under GNU General Public License v3.
Package: apricopt-synthesis
APriCOPT#APriCOPT (Automatic Personalized Combination Therapy Optimizer) is a python library and standalone program for the synthesis of personalized therapies on biological models given as black-boxes, with a focus on SBML models.It requires little to no programming experience, as it usesPEtabfor describing the problem and providing all the needed information.APriCOPT natively supportsNOMADfor black-box optimization, but any solver can be used by implementing a simple API.SBML modelsIf the biological model is described in the SBML format, APriCOPT provides immediate ways to setup the therapy synthesis.Currently, we support the following simulators of SBML models:COPASI. A state-of-the-art simulator for models of biological processes. We support any level and version of SBML.InfoWIPVersion 0.0.1a1.dev1Who do I talk to?Marco Esposito (author) -esposito@di.uniroma1.itLeonardo Picchiami (author) -picchiami.1643888@studenti.uniroma1.itCopyright (C) 2020-2021  Marco Esposito, Leonardo Picchiami.Distributed under GNU General Public License v3.
Package: apricot-server
ApricotApricotis a proxy for delegating LDAP requests to an OpenID Connect backend.
The name is a slightly tortured acronym for: LDAPproxy for OpenIDConnect.UsageStart theApricotserver on port 1389 by running:pythonrun.py--client-id"<your client ID>"--client-secret"<your client secret>"--backend"<your backend>"--port1389--domain"<your domain name>"Alternatively, you can run in Docker by editingdocker/docker-compose.yamland running:docker-composeupfrom thedockerdirectory.OutputsThis will create an LDAP tree that looks like this:dn: DC=<your domain>
objectClass: dcObject

dn: OU=users,DC=<your domain>
objectClass: organizationalUnit
ou: users

dn: OU=groups,DC=<your domain>
objectClass: organizationalUnit
ou: groupsEach user will have an entry likedn: CN=<user name>,OU=users,DC=<your domain>
objectClass: organizationalPerson
objectClass: person
objectClass: top
objectClass: user
<user data fields here>Each group will have an entry likedn: CN=<group name>,OU=groups,DC=<your domain>
objectClass: group
objectClass: top
<group data fields here>OpenID ConnectInstructions for specific OpenID Connect backends below.Microsoft EntraYou will need to use the following command line arguments:--backendMicrosoftEntra--entra-tenant-id"<your tenant ID>"You will need to register an application to interact withMicrosoft Entra.
Do this as follows:Create a newApp Registrationin yourMicrosoft Entra.Set the name to whatever you choose (in this example we will useapricot)Set access toAccounts in this organizational directory only.SetRedirect URItoPublic client/native (mobile & desktop)with a value ofurn:ietf:wg:oauth:2.0:oobUnderCertificates & secretsadd aNew client secretSet the description toApricot Authentication SecretSet the expiry time to whatever is relevant for your use-caseYoumustrecord the value of this secret atcreation time, as it will not be visible later.UnderAPI permissions:Ensure that the following permissions are enabledMicrosoft Graph>User.Read.All(application)Microsoft Graph>GroupMember.Read.All(application)Select this and click theGrant admin consentbutton (otherwise manual consent is needed from each user)
Package: april3rd
2017年10月03日，hy和llt在一起了。今天是2018年04月03日，他俩在一起6个月了。期间，有过很多欢笑、感动，也有过争吵、矛盾。
就是这么巧合，04月03日，她来到这个世界。
他们从好基友到小情侣，虽然有很多磕磕绊绊，但两个人也在为了同一个未来共同努力。
他们憧憬着未来一起的生活，那一定很美好。
他命中缺木，而她正是他一直在找的人。
余生请多指教。
Package: aprile
APRILEGetting Started|Documentation|Adverse Polypharmacy Reaction Intelligent Learner and Explainer (APRILE)is an explainable framework to reveal the mechanisms underlying adverse drug reactions (ADRs) caused by polypharmacy therapy. After learning from massive biomedical data, APRILE generate a small pharmacogenomic knowledge graph (i.e. drug targets and protein interactions) as mechanistic explanation for a drug-drug interaction (DDI) which associated an ADR and a set of such interactions.APRILE is able to answer the following example questions:Why the combination use of a pair of drugs (nicotine, ondansetron) causes anxiety?When taking fexofenadine, hydroxyzineand and loratadine simultaneously, what side effects may occur, and why?Which genes are associated with the infection diseases?What are the common mechanisms among peptic ulcers (such as duodenal ulcer, gastric ulcer and esophageal ulcer)?We have demonstrated the viability of discovering polypharmacy side effect mechanisms by learning from an AI model trained on massive biomedical data (see[paper])FeaturesAPRILE predicts side effects for drug combinations and gives the prediction reasonsAPRILE delineates non-intuitive mechanistic associations between {genes, proteins, biological processes} and {symptoms, diseases, mental disorders $\in$ ADRs)Using our pre-trained model, molecular mechanisms for 843,318 (learned) + 93,966 (novel) side effect–drug pair events, spanning 861 side effects (472 diseases, 485 symptoms and 9 mental disorders) and 20 disease categories, have been suggested.InstallationPrerequisites:
Before installingaprile,PyTorchandPyTorch Geometricare required to be installed matching your hardware.We recommend using torch 1.4.0 (python3.7+cuda10.1), torch-cluster 1.5.4, torch-scatter 2.0.4, torch-sparse 0.6.1, torch-spline-cov 1.2.0 and torch-geometric 1.4.2Install the environment dependencies of APRILE usingpip:pipinstallaprileCite UsIf you found this work useful, please cite us:@article{aprile,
	title={APRILE: Exploring the Molecular Mechanisms of Drug Side Effects with Explainable Graph Neural Networks},
	author={Hao Xu and Shengqi Sang and Herbert Yao and Alexandra I. Herghelegiu and Haiping Lu and Laurence Yang},
	journal={bioRxiv preprint},
	year={2021}
}
Package: aprilgrid
AprilgridPure python version of aprilgridInspired byApriltagandKalibr. There's lot of existing python binding of apriltag. But as everyone knows, python binding causes a lot of issues. So I rewrite a pure python version of apriltag and it's also able to detect aprilgrid.Install from PyPIpip install aprilgridUsageSome examples of usage can be seen in the example/main.py file.fromaprilgridimportDetectordetector=Detector("t36h11")detector.detect(img)Kalibr uses 2 bit black border while apriltag3 uses 1 bit black border for each tag. The default of aprilgrid follows Kalibr. Please uset36h11b1if you're using tags with 1 bit black border.Developmentgitclonehttps://github.com/powei-lin/aprilgrid.gitcdaprilgrid
pipinstall-e.TODOAdd output for opencv calibrate camera.Provide pregenerated pdf and easy way to customize.Support all tag families.Accelerate and multi-threading.Robustness.Referencehttps://github.com/AprilRobotics/apriltaghttps://github.com/ethz-asl/kalibr
Package: apriltag
Apriltagapriltag marker detection
based onhttps://github.com/swatbotics/apriltagDependenciesOpenCV (optional)Exampleimportapriltagimportcv2img=cv2.imread('apriltag_foto.jpg'.cv2.IMREAD_GRAYSCALE)detector=apriltag.Detector()result=detector.detect(img)result is in the form of[DetectionBase(tag_family='tag36h11', tag_id=2, hamming=0, goodness=0.0, decision_margin=98.58241271972656, homography=array([[ -1.41302664e-01,   1.08428082e+00,   1.67512900e+01],
   [ -8.75899366e-01,   1.50245469e-01,   1.70532040e+00],
   [ -4.89183533e-04,   2.12210247e-03,   6.02052342e-02]]), center=array([ 278.23643912,   28.32511859]), corners=array([[ 269.8939209 ,   41.50381088],
   [ 269.57183838,   11.79248142],
   [ 286.1383667 ,   15.84242821],
   [ 286.18066406,   43.48323059]])),
DetectionBase(tag_family='tag36h11', ... etcMultiple options can be specified to tune the detection performance:options=apriltag.Detectoroptions(families='tag36h11',border=1,nthreads=4,quad_decimate=1.0,quad_blur=0.0,refine_edges=True,refine_decode=False,refine_pose=False,debug=False,quad_contours=True)detector=apriltag.Detector()result=detector.detect(img)Access valuestf=result[0].tag_familycx=result[0].center[0].
Package: apriltags
Apriltagapriltag marker detection
based onhttps://github.com/swatbotics/apriltagDependenciesOpenCV (optional)Exampleimport apriltag
import cv2
img = cv2.imread('apriltag_foto.jpg'.cv2.IMREAD_GRAYSCALE)
detector = apriltag.Detector()
result = detector.detect(img)result is in the form of[DetectionBase(tag_family='tag36h11', tag_id=2, hamming=0, goodness=0.0, decision_margin=98.58241271972656, homography=array([[ -1.41302664e-01,   1.08428082e+00,   1.67512900e+01],
   [ -8.75899366e-01,   1.50245469e-01,   1.70532040e+00],
   [ -4.89183533e-04,   2.12210247e-03,   6.02052342e-02]]), center=array([ 278.23643912,   28.32511859]), corners=array([[ 269.8939209 ,   41.50381088],
   [ 269.57183838,   11.79248142],
   [ 286.1383667 ,   15.84242821],
   [ 286.18066406,   43.48323059]])),
DetectionBase(tag_family='tag36h11', ... etc.
Package: april-vision
april_visionA fiducial marker system used by Student Robotics.
Usesapril tagmarkers to provide detection, pose and distance estimation for these markers.InstallationThis library requires OpenCV but the default installation does not install OpenCV. There are a few different versions of OpenCV with different install sizes, to install the default package without OpenCV, run the following command.pipinstallapril-visionTo install the lightweight headless version OpenCV install the library with the following command.pipinstallapril-vision[opencv]If you want to perform some of the more advanced features of the CLI (live view of the camera) you need the full version of OpenCV.
The CLI dependecies can be installed with the following command.pipinstallapril-vision[cli]Then the full version of OpenCV can then be installed with the following command.pipinstallopencv-python>=4.8All the versions of OpenCV (standard, headless and contrib) clash so you should only have one installed.Examplefromapril_vision.examples.cameraimportsetup_cameras# Markers 0-100 are 80mm in sizetag_sizes={range(0,100):80}# Returns a dict of index and cameracameras=setup_cameras(tag_sizes)iflen(cameras)==0:print("No cameras found")forname,camincameras.items():print(name)print(cam.see())ToolsWhen installed april_vision can be used on the command line providing the following list of useful tools. Each of the tools contain help text on correct usage accessed via the-hargument.annotate_image
annotate_video
calibrate
live
marker_generator
vision_debug
toolsfamily_detailslist_cameras