Saravanan666/pypi-org huggingface dataset:
Package: pyflix2
Introductionpyflix2is aBSD licensedpython module for accessing netflix API (both v1 and v2)
Netflix provides REST interfaces to access it’s catalog and various user data.
This module exposes easy to use object oriented interfaces that is inteded to make it even easier
for python programmers to use.InstallInstalling requests is simple withpip:$ pip install pyflix2or, witheasy_install:$ easy_install pyflix2Examplefrom pyflix2 import *

netflix = NetflixAPIV2( 'appname', 'key', 'shared_secret')
movies = netflix.title_autocomplete('Terminator', filter='instant')
for title in movies['autocomplete']['title']:
    print title

user = netflix.get_user('use_id', 'access_token', 'access_token_secret')
reco = user.get_reccomendations()
for movie in reco['recommendations']:
    print movie['title']['regular']NoteHereappname,keyandshared_secretneeds to be obtained from:http://developer.netflix.com/apps/mykeys.Theuser_id,access_token,access_token_secretneeds to be obtained programmatically usingget_request_tokenandget_access_tokenCommandline$ python -mpyflix2 -s 'the matrix' -xOr see help:$ python -mpyflix2 -hFeaturesSupports both V1 and V2 of netflix REST APISupports both out-of-bound (oauth 1.0a) and  vanila three legged oauth auhenticationProvides easy to use and well documented functional interface for all the API exposed by netflixThrows Exception for all kinds of error situation making it easier to integrate with other programV1 and V2 APIs are exposed using different classes, so version specific features can be used easilyInternally usesRequestsfor making HTTP callsWant any new feature? pleasefile a feature requestDocumentation:http://pyflix2.readthedocs.org/en/latest/index.htmlNote: I would like to thank Kirsten Jones for the libraryhttp://code.google.com/p/pyflix/As pyflix2 was initially inspired by pyflix.RequirementsRequiresrequestsmodule minimumv1.1.0Install latest version of requests-oauthlib:pip install-Ugit+git://github.com/requests/requests-oauthlib.gitHistory0.2.1 (2014-04-29)Adding a new method to download the entire catalog into a file.0.2.0 (2013-01-26)Issue #6: Add support for downloading full catalog in lib as well as in command lineIssue #8: Incorporate netflix api change to api-public.netflix.comIssue #9: Update codebase to work with requests v1.1.0Backward incompatible changesget_userapi signature has changed (require one more parameteruser_id)Addition ofuser_idin~/.pyflix.cfgget_access_tokenreturns additionaluser_id0.1.3 (2012-07-09)Fixed access token retrival code in __main__.pyFixed typo in sample config file0.1.2 (2012-07-06)Issue #5: Fixed circular dependency in setup.py0.1.1 (2012-07-04)Initial version
Package: pyfloat
PyFloat LibPython Library for doing math operations with floats without lossing precissionHow to use>> from pyfloat import PyFloat

>> PyFloat(2.1234e-12)
'0.0000000000021234'
>> PyFloat(-2.1234e+12)
'-2123400000000'
>> PyFloat(0.1452)
'0.1452'
>> PyFloat(PyFloat(152.455))
'152.455'
>> a = PyFloat("123451.1234551230000000004444445551122000000011")
>> b = PyFloat(-8123994.000002234100000001323400000001232112221)
>> a + b
'-8000542.8765471111000000008789554448890321122199'
>> a - b
'8247445.1234573571000000017678445551134321122221'
>> a * b
'-1002916186242.9543234169148643344158458369442274183843912848765430855693663896461975553234431'
>> a == b
'False'
>> a != b
'True'
>> a > b
'True'
>>> PyFloat(0.00239419391).round(10)
'0.0023941939'
>>> PyFloat(0.00239419391).round(7)
'0.0023942'
>>> PyFloat(-0.00239419391).round(7)
'-0.0023942'
>>> PyFloat(0.00239419391).round(4)
'0.0024'
>>> PyFloat(-1234.5678).abs()
'1234.5678'
>>> PyFloat(0.0000089778).truncate(8)
'0.00000897'
>>> PyFloat(-0.0000089778).truncate(8)
-0.00000897Testpython pytfloat_test.py
Package: pyFloatplane
# pyFloatplanepyFloatplane is a Python3 REST-Client for [Floatplane.com](https://floatplane.com) and is consideredUNOFFICIAL.Some use-cases for the library are:Video downloadsImage grabbingComment interactionPull-based Video notificationsThe exampledownload.pycan list your subscriptions and show the last _n_ videos with their respective comments.## Guidance steps### ConfigThelmg-forumselement in the config is considereddeprecatedand isn’t needed anymore.
It was used for the forum-based VideoUrl-Endpoint.### PipEnvYou can usepipenvExample commands:`bash pipenv--threeinstall pipenv run python download.py `
Package: pyflogd
pyflogd is a monitoring tool to support you when tracking down potential
file system bottlenecks. It uses the inotify kernel API.pyflogd uses a dev-friendly JSON output format. Every line will contain
one JSON object with a type and a path property. You can parse the lines
and analyse which files are accessed and written the most.RequirementsdaemondocopthashlibjsonlockfilepyinotifyschemasignalNotes on using pyflogd on UbuntuWhen pyinotify is installed via apt you will get an old version that has
a known bug regarding recursive watching. When using this version it is
not possible to track files and folders in folders that are created
after pyflogd has started. To solve this, you can runpip install--upgradepyinotify.Python 3 compatibilitypyflogd depends on python-daemon which is currently not compatible with
Python 3. Running pyflogd as daemon will not work without this, but theruncommand should work just fine.InstallationTo install pyflog run the following commands:git clone https://github.com/mkzero/pyflogd
python2 setup.py installAfter that you should be able to use thepyflodcommand from you
commandline.UsageUsage:
 pyflogd run [-f | --only-files] [-r | --recursive] [-o <file> | --outfile=<file>] <folder> ...
 pyflogd start [-f | --only-files] [-r | --recursive] [-o <file> | --outfile=<file>] <folder> ...
 pyflogd stop <folder> ...
 pyflogd -h | --help
 pyflogd -v | --version

Options:
 -h --help                 Show this screen
 -v --version              Show version
 -r --recursive            Watch a folder recursivly
 -f --only-files           Don't report events for folders
 -o FILE --outfile=FILE    Write to file instead of stdoutrunTheruncommand starts pyflogd in foreground and outputs events to
stdout when nooutfileis supplied.Example:pyflogd run --outfile=/tmp/pyflogd.log --recursive /path/to/folder1 \
           /path/to/folder2 /path/to/folder3start/stopThestartcommand starts a pyflogd daemon in the background and
outputs events to the suppliedoutfile. To stop the daemon use the
same folders as for the start command and omit all other options likeoutfileorrecursive.Example:pyflogd start --outfile=/tmp/pyflogd.log --recursive /path/to/folder1 \
           /path/to/folder2 /path/to/folder3

pyflogd stop /path/to/folder1 /path/to/folder2 /path/to/folder3
Package: pyfloip
A library for converting the questions in a FLOIP Data Package descriptor to an
ODK XForm.Getting Started$ pip install pyfloip
$ floip data/flow-results-example-1.jsonExampleReading a FLOIP results data package and generating the XML ODK XForm.fromfloipimportFloipSurveysuvey=FloipSurvey('data/flow-results-example-1.json')print(survey.xml())Testing$ pip install -r requirements.txt
$ py.test --pylintDocumentationFloipSurveyA class that converts a FLOIP results data package to an ODK XForm.
Package: pyflowcl
Bienvenido a pyflowclPyflowcl es una biblioteca de Python que proporciona una interfaz para interactuar con la API de Flow en Chile. Con pyflowcl, puedes realizar diversas operaciones, como crear pagos, obtener información de pagos, realizar reembolsos y más.InstalaciónPara instalar pyflowcl, asegúrate de tener Python y pip instalados. Luego, puedes instalar la biblioteca utilizando pip:pipinstallpyflowclUso BásicoAquí hay un ejemplo básico de cómo usar pyflowcl para crear un pago:frompyflowclimportFlowAPI
frompyflowcl.utilsimportgenera_parametrosapi=FlowAPI(api_key="tu llave flow",api_secret="tu secreto flow")parametros={"apiKey":api.api_key,"amount":10000,"currency":"CLP","subject":"Ejemplo de Pago","email":"correo@example.com","url_confirmation":"https://mi-sitio.com/confirmacion",}pago=api.objetos.call_payment_create(parameters=genera_parametros(parametros,api.api_secret))print(pago)>{"flowOrder":123456,"url":"https://www.flow.cl/app/pay.php","token":"tok_123456"}# Obtiene la URL de pagourl_pago=pago.get("url")token_pago=pago.get("url")print(f"URL de pago: {url_pago}?token={token_pago}")DocumentaciónPara obtener más información sobre cómo usar pyflowcl y todas las funcionalidades disponibles, consulta la documentación completa enhttps://mariofix.github.io/pyflowcl.Contribuir¡Tú contribución es bienvenida! Si encuentras errores, tienes sugerencias o deseas agregar nuevas características, por favor, crea un problema o envía una solicitud de extracción en el repositorio de GitHub:Repositorio de pyflowcl.LicenciaPyflowcl se distribuye bajo la Licencia MIT. Consulta el archivoLICENSEpara obtener más información.
Package: pyflow-cse-asu
Welcome to pyFlow!DescriptionIn this project we created a full deep learning framework to get the full experience of the deep learning engineer not only by using the well-known frameworks but also by developing our own framework , the framework contains Data module which help in loading the data and preprocessing it , NN module which contain layers as Dense Conv and pool , and also conation optimizer class , activation functions and Evaluation Metrics class , the framework contain utils module which help in saving and loading parameters and also contain visualization module which help in giving the full experience by plotting the results vs the training data .Product PerspectiveThis project is a simple Deep learning frame work to help understanding the neural network concepts.General CapabilitiesThe frame work is composed of several modules which will help building any efficient conventional NN model or CNN model .General ConstrainsThe user build CNN model without wasting time on difficult interfaces and get the full experience.User CharacteristicsThe user who uses this framework is interested in making Deep learning models easily and professionally.Environment DescriptionWe are using Colab notebook in our development to speed up the process using google servers .System Requirements:Functional requirements:Implement Data moduleImplement NN module to design different architecturesimplement visualization moduleimplement utils moduleThe modules must be combatable with each otherNon functional requirements:Product requirements:The Model execution speed is fast as it executes in less than 1ms per example.
It takes memory size of max 1GB.
The system should have fast response time as it responds in 10ms after failure and it should tolerate common types of faults.
Any one can get it easy using pip install.Organizational requirements :It is written in python using Colab notebook.
The delivery time is on January 25st. It should conform to IEEE and ISO standards.External requirements:The system should conform to all applicable local and international laws.
Package: pyflow-cse-asu-exp-1
Welcome to pyFlow!DescriptionIn this project we created a full deep learning framework to get the full experience of the deep learning engineer not only by using the well-known frameworks but also by developing our own framework , the framework contains Data module which help in loading the data and preprocessing it , NN module which contain layers as Dense Conv and pool , and also conation optimizer class , activation functions and Evaluation Metrics class , the framework contain utils module which help in saving and loading parameters and also contain visualization module which help in giving the full experience by plotting the results vs the training data .Product PerspectiveThis project is a simple Deep learning frame work to help understanding the neural network concepts.General CapabilitiesThe frame work is composed of several modules which will help building any efficient conventional NN model or CNN model .General ConstrainsThe user build CNN model without wasting time on difficult interfaces and get the full experience.User CharacteristicsThe user who uses this framework is interested in making Deep learning models easily and professionally.Environment DescriptionWe are using Colab notebook in our development to speed up the process using google servers .System Requirements:Functional requirements:Implement Data moduleImplement NN module to design different architecturesimplement visualization moduleimplement utils moduleThe modules must be combatable with each otherNon functional requirements:Product requirements:The Model execution speed is fast as it executes in less than 1ms per example.
It takes memory size of max 1GB.
The system should have fast response time as it responds in 10ms after failure and it should tolerate common types of faults.
Any one can get it easy using pip install.Organizational requirements :It is written in python using Colab notebook.
The delivery time is on January 25st. It should conform to IEEE and ISO standards.External requirements:The system should conform to all applicable local and international laws.
Package: pyflower
collection of modules to build distributed and reliable concurrent
systems in Python.RequirementsPython from 2.6 to 3.xA platform supported bylibuv <https://github.com/joyent/libuv>Simple exampleA simple example showing how to create a consumer and use other actor
function to feed it.importflowermessages=[]sources=[]defconsumer():# wait for coming message in the current actorwhileTrue:source,msg=flower.receive()ifnotmsg:breakprint("got message from%s:%s"%(source.ref,msg))defpublisher1(ref):# an actor sending messages to the consumermsg=['hello','world']forsinmsg:flower.send(ref,s)defpublisher2(ref):msg=['brave','new','world','']forsinmsg:flower.send(ref,s)ref_consumer=flower.spawn(consumer)flower.spawn(publisher1,ref_consumer)flower.spawn(publisher2,ref_consumer)flower.run()should return:$ python examples/actor_example.py
got message from 1: hello
got message from 2: brave
got message from 1: world
got message from 2: new
got message from 2: worldInstallationFlower requires Python superior to 2.6 (yes Python 3 is supported)To install flower using pip you must make sure you have a
recent version of distribute installed:$ curl -O http://python-distribute.org/distribute_setup.py
$ sudo python distribute_setup.py
$ easy_install pipFor now flower can only be installed from sources. To install or upgrade to the latest released version of flower:$ git clone https://github.com/benoitc/flower.git
$ cd flower && pip install -r requirements.txtLicenseflower is available in the public domain (see UNLICENSE). flower is also
optionally available under the MIT License (see LICENSE), meant
especially for jurisdictions that do not recognize public domain
works.
Package: pyflowlauncher
pyFlowLauncherpyFlowLauncher is an API that allows you to quickly create plugins for Flow Launcher!InstallationInstall via pip:python-mpipinstallpyflowlauncher[all][!IMPORTANT]
Please use the[all]flag in order to support Python versions older then3.11.UsageBasic pluginA basic plugin using a function as the query method.frompyflowlauncherimportPlugin,Result,send_resultsfrompyflowlauncher.resultimportResultResponseplugin=Plugin()@plugin.on_methoddefquery(query:str)->ResultResponse:r=Result(Title="This is a title!",SubTitle="This is the subtitle!",IcoPath="icon.png")returnsend_results([r])plugin.run()Advanced pluginA more advanced usage using aMethodclass as the query method.frompyflowlauncherimportPlugin,Result,Methodfrompyflowlauncher.resultimportResultResponseplugin=Plugin()classQuery(Method):def__call__(self,query:str)->ResultResponse:r=Result(Title="This is a title!",SubTitle="This is the subtitle!")self.add_result(r)returnself.return_results()plugin.add_method(Query())plugin.run()
Package: pyflowmeter
Python CICFlowMeter (PyFlowmeter)This project is cloned fromPython Wrapper CICflowmeterand customized to fit my need. Therefore, it is not maintained actively. If there are any problems, please create an issue or a pull request.Installationpipinstall--upgradepip
pipinstallpyflowmeterUsagefrompyflowmeter.snifferimportcreate_snifferThis function returns ascapy.sendrecv.AsyncSnifferobject.Parametersinput_file[default=None]A .pcap file where capture offline data from. If it is set to ´None´, the data will be capture frominput_interfaceinput_interface[default=None]Interface or list of interfaces (default: None for sniffing on all interfaces).server_endpoint[default=None]A server endpoint where the data of the flow will be sent. If it is set toNone, no data will be sent.verbose[default=False]Wheather or not to print a message when a new packet is read.to_csv[defalut=Fasle]Wheather or not to save the output flows as csv. The data will be saved onoutput_file.output_file[default=None]File to store the data. Ifto_csvis set toFalse, this parameter will be ignored.sending_interval[defalut=1]The frequency, in seconds, at which data will be sent to the server. Ifserver_endpointis None, this parameter will be ignored.ExamplesSniff packets real-time from interface and send the flow to a server every 5 seconds(need root permission):frompyflowmeter.snifferimportcreate_sniffersniffer=create_sniffer(server_endpoint='http://127.0.0.1:5000/send_traffic',verbose=True,sending_interval=5)sniffer.start()try:sniffer.join()exceptKeyboardInterrupt:print('Stopping the sniffer')sniffer.stop()finally:sniffer.join()Get CSV analysis from a pcap file:frompyflowmeter.snifferimportcreate_sniffersniffer=create_sniffer(input_file='path_to_the_file.pcap',to_csv=True,output_file='./flows_test.csv',)sniffer.start()try:sniffer.join()exceptKeyboardInterrupt:print('Stopping the sniffer')sniffer.stop()finally:sniffer.join()Simulate offline traffic from a file and send the data to a server:frompyflowmeter.snifferimportcreate_sniffersniffer=create_sniffer(input_file='path_to_the_file.pcap',server_endpoint='http://127.0.0.1:5000/send_traffic',)sniffer.start()try:sniffer.join()exceptKeyboardInterrupt:print('Stopping the sniffer')sniffer.stop()finally:sniffer.join()Reference:https://www.unb.ca/cic/research/applications.html#CICFlowMeter
Package: pyFlowSOM
pyFlowSOMCI / CDPackageMetaPython runner for theFlowSOMlibrary.Basic usage:importnumpyasnpimportpandasaspdfrompyFlowSOMimportmap_data_to_nodes,som# generate example input data, rows are observations (e.g. cells), columns are features (e.g. proteins)df=pd.DataFrame(np.random.rand(500,16))# alternatively, specify path to your own input datadf=pd.read_csv('path/to/som/input.csv')example_som_input_arr=df.to_numpy()# train the SOMnode_output=som(example_som_input_arr,xdim=10,ydim=10,rlen=10)# use trained SOM to assign clusters to each observation in your dataclusters,dists=map_data_to_nodes(node_output,example_som_input_arr)To put the data back into dataframes:eno=pd.DataFrame(data=node_output,columns=df.columns)eco=pd.DataFrame(data=clusters,columns=["cluster"])To export to csv:eno.to_csv('examples/example_node_output.csv',index=False)eco.to_csv('examples/example_clusters_output.csv',index=False)To plot the output as a heatmap:importseabornassns# Append results to the input datadf['cluster']=clusters# Find mean of each clusterdf_mean=df.groupby(['cluster']).mean()# Make heatmapsns_plot=sns.clustermap(df_mean,z_score=1,cmap="vlag",center=0,yticklabels=True)sns_plot.figure.savefig(f"example_cluster_heatmap.png")DevelopThe C code (pyFlowSOM/flowsom.c) is wrapped using Cython (pyFlowSOM/cyFlowSOM.cyx).Tests do an exact comparison to cluster id ground truth and an approximate comparison to node values only because of floating point differences. Randomness works in tandem to theseedflag to thesomfunction.To run the tests, use the following command:pytest
Package: pyflow-workflow-generator
PyflowPyflowis a high level Python interface to ecFlow allowing the creation of ecFlow suites in a modular and "pythonic" way.The documentation can be found athttps://pyflow-workflow-generator.readthedocs.io.InstallationTo install pyflow using conda (including ecflow):conda env create -n pyflow -f environment.ymlTo install pyflow using pip (requires a local installation of ecflow):pip install pyflow-workflow-generatorTutorialPyflow tutorials are available in the form of a Jupyter notebook:jupyter-notebook tutorials/pyflow.ipynb
jupyter-notebook tutorials/course/course.ipynbOther learning materials can be found in the tutorials folder.LicenseApache License 2.0In applying this licence, ECMWF does not waive the privileges and immunities
granted to it by virtue of its status as an intergovernmental organisation nor does it submit to any jurisdiction.
Package: pyflox
FLoXFLoX (FederatedLearning on funcX) is a Python library for serverless Federated Learning experiments.This is initial documentation that will be soon expanded.InstallationUse the package managerpipto install flox.# seems like flox is taken, will update the name soonpipinstallfloxUsageFor a full example, see thisGoogle Colab tutorial.fromflox.floximportfederated_learning# performs 5 rounds of Federated Learning train global_model on given endpoint_ids# uses 10 epochs and 100 samples from fashion_mnist dataset for trainingfederated_learning(global_model=global_model,endpoint_ids=endpoint_ids,loops=5,epochs=10,keras_dataset="fashion_mnist",num_samples=100,input_shape=(32,28,28,1))ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: pyflp
PyFLPPyFLP is an unofficial parser forFL Studioproject and preset files written in Python.CIPyPIActivityQAOtherFrom a very general point-of-view, this is the state of what is currently
implemented. Click on a link to go to the documentation for that feature.GroupFeatureIssuesArrangements🎼 Playlist🎞️ TracksChannel Rack📈 Automations🎹 Instruments📚 Layer📁 SamplerMixer🎚️ Inserts🎰 Effect slots🎶 Patterns🎛 Controllers🎵 Notes🚩 TimemarkersPluginsNative -
      8effects,
      1synthVST 2/3Project- Settings and song metadata⏬ InstallationCPython 3.8+ / PyPy 3.8+ required.python -m pip install -U pyflp▶ UsageLoada project file:importpyflpproject=pyflp.parse("/path/to/parse.flp")If you get any sort of errors or warnings while doing this, please open anissue.Savethe project:pyflp.save(project,"/path/to/save.flp")It is advised to do a backup of your projects before doing any changes.
It is also recommended to open the modified project in FL Studio to ensure
that it works as intended.Check thereferencefor a
complete list of useable features.🙏 AcknowledgementsMonad.FLParser:https://github.com/monadgroup/FLParserFLPEdit (repo deleted byauthor)✨ ContributorsThanks goes to these wonderful people:nickberry17💻zacanger🐛📖Tim📖💻🚧This project follows theall-contributorsspecification.
Contributions of any kind are welcome!Please see thecontributor's guidefor more information about contributing.📧 ContactYou can contact me either viaissuesanddiscussionsor through
email viademberto(at)proton(dot)me.© LicenseThe code in this project has been licensed under theGNU Public License v3.