Saravanan666/pypi-org huggingface dataset:
Package: pacssim
This is a project done inPACS Labaiming to develop a performance simulator for serverless computing
platforms. Using this simulator, we can calculate Quality of Service
(QoS) metrics like average response time, the average probability of
cold start, average running servers (directly reflecting average cost),
a histogram of different events, distribution of the number of servers
throughout time, and many other characteristics.The developed performance model can be used to debug/improve analytical
performance models, try new and improved management schema, or dig up a
whole lot of properties of a common modern scale-per-request serverless
platform.ArtifactsPyPi PackageGithub RepoReadTheDocs
Documentation(PDF)Examples(MyBinder Jupyter
Lab)RequirementsPython 3.6 or abovePIPInstallationInstall using pip:pipinstallpacssimUpgrading using pip:pipinstallpacssim--upgradeFor installation in development mode:gitclonehttps://github.com/pacslab/pacssimcdpacssimpipinstall-e.And in case you want to be able to execute the examples:pipinstall-rexamples/requirements.txtUsageA simple usage of the serverless simulator is shown in the following:frompacssim.ServerlessSimulatorimportServerlessSimulatorasSimsim=Sim(arrival_rate=0.9,warm_service_rate=1/1.991,cold_service_rate=1/2.244,expiration_threshold=600,max_time=1e6)sim.generate_trace(debug_print=False,progress=True)sim.print_trace_results()Which prints an output similar to the following:100%|██████████| 1000000/1000000 [00:42<00:00, 23410.45it/s]
Cold Starts / total requests:    1213 / 898469
Cold Start Probability:          0.0014
Rejection / total requests:      0 / 898469
Rejection Probability:           0.0000
Average Instance Life Span:      6335.1337
Average Server Count:            7.6612
Average Running Count:           1.7879
Average Idle Count:              5.8733Using this information, you can predict the behaviour of your system in
production.DevelopmentIn case you are interested in improving this work, you are always
welcome to open up a pull request. In case you need more details or
explanation, contact me.To get up and running with the environment, run the following after
installingAnaconda:condaenvcreate-fenvironment.ymlcondaactivatesimenvpipinstall-rrequirements.txtpipinstall-e.After updating the README.md, use the following to update the README.rst
accordingly:bash.travis/readme_prep.shExamplesSome of the possible use cases of the serverless performance simulator
are shown in theexamplesfolder in our Github repository.LicenseUnless otherwise specified:MIT (c) 2020 Nima Mahmoudi & Hamzeh KhazaeiCitationYou can find the paper with details of the simultor inPACS lab
website. You can use the
following bibtex entry for citing our work:@software{mahmoudi_nima_2020_3906617,author={Mahmoudi, Nima and
                  Khazaei, Hamzeh},title={{PACSSIM: A Performance Simulator for Serverless
                   Computing Platforms}},month=jun,year=2020,publisher={Zenodo},version={0.1.3},doi={10.5281/zenodo.3906617},url={https://doi.org/10.5281/zenodo.3906617}}
Package: pacswg
PACS Workload GeneratorThis is a workload generator designed especifically to perform load
testing for several applications. The internal use case of this workload
generator is for load testing AWS Lambda functions, but it can be used
for other purposes. All you have to do is give it aworkerfunction
and the number of requests per second you want to achieve on average and
it will do the rest for you.Pypi:https://pypi.org/project/pacswg/Github Repository:https://github.com/nimamahmoudi/pacswgDocumentation:https://pacswg.readthedocs.io/UsageCheck out theusage section in our
documentations.InstallationInstall using pip:$pipinstallpacswgUpgrading:pipinstallpacswg--upgradeFor installation in development mode:git clone https://github.com/nimamahmoudi/pacswg
cd pacswg
pip install -e .DevelopmentNote:README.rst will be overwritten from time to time using
converters that convert markdown to rst format. Don’t modify it since
the changes might be overwritten.
Package: pact-im
Pact IM Python SDKFor full documentation visithttps://pact-im.github.io/sdk-python.IntroductionWelcome to the Pact API! You can use our API as a single messaging API. You can view code examples in the dark area to the right.This API was designed by way Implement only required. Feel you free to contact with support if you are sure what make sense to append additional methods.
Package: pactools
This package provides tools to estimatephase-amplitude coupling (PAC)in neural time series.In particular, it implements thedriven auto-regressive (DAR)models presented in the reference below [Dupre la Tour et al. 2017].Read more in thedocumentation.InstallationTo installpactools, use one of the following two commands:Latest stable version:pip install pactoolsDevelopment version:pip install git+https://github.com/pactools/pactools.git#egg=pactoolsTo upgrade, use the--upgradeflag provided bypip.To check if everything worked fine, you can do:python -c 'import pactools'and it should not give any error messages.Phase-amplitude coupling (PAC)Among the different classes of cross-frequency couplings,
phase-amplitude coupling (PAC) - i.e. high frequency activity time-locked
to a specific phase of slow frequency oscillations - is by far the most
acknowledged.
PAC is typically represented with a comodulogram, which shows the strenght of
the coupling over a grid of frequencies.
Comodulograms can be computed inpactoolswith more
than 10 different methods.Driven auto-regressive (DAR) modelsOne of the method is based on driven auto-regressive (DAR) models.
As this method models the entire spectrum simultaneously, it avoids the
pitfalls related to incorrect filtering or the use of the Hilbert transform
on wide-band signals. As the model is probabilistic, it also provides a
score of the modelgoodness of fitvia the likelihood, enabling easy
and legitimate model selection and parameter comparison;
this data-driven feature is unique to such model-based approach.We recommend using DAR models to estimate PAC in neural time-series.
More detail in [Dupre la Tour et al. 2017].AcknowledgmentThis work was supported by the ERC Starting Grant SLAB ERC-YStG-676943 to
Alexandre Gramfort, the ERC Starting Grant MindTime ERC-YStG-263584 to Virginie
van Wassenhove, the ANR-16-CE37-0004-04 AutoTime to Valerie Doyere and Virginie
van Wassenhove, and the Paris-Saclay IDEX NoTime to Valerie Doyere, Alexandre
Gramfort and Virginie van Wassenhove,Cite this workIf you use this code in your project, please cite
[Dupre la Tour et al. 2017]:@article{duprelatour2017nonlinear,
    author = {Dupr{\'e} la Tour, Tom and Tallot, Lucille and Grabot, Laetitia and Doy{\`e}re, Val{\'e}rie and van Wassenhove, Virginie and Grenier, Yves and Gramfort, Alexandre},
    journal = {PLOS Computational Biology},
    publisher = {Public Library of Science},
    title = {Non-linear auto-regressive models for cross-frequency coupling in neural time series},
    year = {2017},
    month = {12},
    volume = {13},
    url = {https://doi.org/10.1371/journal.pcbi.1005893},
    pages = {1-32},
    number = {12},
    doi = {10.1371/journal.pcbi.1005893}
}
Package: pactor
A minimalist actor framework in Python.Free software: MIT licenseInstallationpip install pactorRunning the ExampleClone the repo locally, then run the following commands:pipenv install
pipenv run python -m pactorTo exit, press q.DocumentationThe actor model is a computational model that is useful for concurrent execution.  See:https://en.wikipedia.org/wiki/Actor_model.pactor is a minimalist implementation of the actor model in Python, using multiprocessing.The actor model stipulates that actors only interact with each other through messaging.  pactor implements that
restriction by building a proxy around an actor class and converting method calls into messages.To wrap a pickleable class as an Actor, simply create an Actor with an instance of the target class, as follows:classMyActor:def__init__(self,name):self.name=namedefsome_method(self):...actor_instance=Actor(MyActor())The Actor class provides a couple of key capabilities:.proxyA proxy object that has methods that mirror those on the wrapped class.  Calling a method on the proxy will generate a message to the actor process with the provided parameters..close()Signals that the actor process should discontinue processing messages and terminate..join()Blocks the calling thread until the actor process terminates.Additionally, the actor class itself is enhanced with an enqueue method that can be used to send messages to itself.Consider this simple example of a Monitor:classMonitor:def__init__(self,name,aggregator):self.name=nameself.aggregator=aggregatorself.status=0defread_status(self):self.status=fetch_status()self.aggregator.update_status(self.name,self.status)self.enqueue(self.read_status)# queue up another readAnd an Aggregator:classAggregator:defupdate_status(self,target_name,status):print('Status update for%s:%s'%(target_name,status))These could be used as follows:defmain():aggregator=Actor(Aggregator('aggregator'))primary_mon=Actor(Monitor('primary',aggregator.proxy))secondary_mon=Actor(Monitor('secondary',aggregator.proxy))primary_mon.read_status()secondary_mon.read_status()aggregator.join()This simple example highlights several critical points:Each Actor class will actually run in a separate processOne Actor can be passed to another Actor using the .proxy memberInvoking a method on an Actor proxy does not directly invoke that method on the calling thread, but instead is wrapped as a message and passed to the actor process.DevelopmentTo run the all tests run:toxChangelog0.0.1 (2019-12-10)First release on PyPI.
Package: pact-state-provider
> Simple server to provide an endpoint used by pact to generate a provider stateUsageInstallationInstall the package:pip install pact_state_providerClientEntrypointpact-state-provider:Usage: pact-state-provider [OPTIONS]

  Start the state provider server on the specified host and port.

Options:
  --base-module TEXT  Module containing the state providers.
  --host TEXT         Host for the endpoint. Default: 127.0.0.1
  --port INTEGER      Port for the endpoint. Default: 1235
  --log-level TEXT    Log Level Name (DEBUG, INFO, ...). Default: INFO
  --help              Show this message and exit.TL;DRExample:$ pact-state-provider --base-module my_provider.states$ http http://127.0.0.1:1235 consumer=TestConsumer state="user exists" --json
DEBUG: Importing my_provider.states
DEBUG: Getting function "user_exists"
-> "my_provider.states.user_exists('TestConsumer')" is calledNot Long Enough;Will ReadWhen verifying contracts withPacta
state provider is called for the particular consumer with the state requested. A consumerTestConsumerwith a contract starting withgiven('user exists')requests the
configured endpoint with the payload:{
    'consumer': 'TestConsumer',
    'state': 'user exists'
}In order to execute the specific provider functions without having to implement a dedicated
endpoint on the provider or a dedicated application with provider specific codepact-state-providerprovides a simple http server endpoint which calls an existing
module function based on the request payload.In the given example a function calleduser_exists(invalid characters are translated to
underscores) will be executed on the specified base module (--base-moduleparameter).This gives the user to maintain the state provider code in the actual provider codebase, but
easily have an endpoint to use with pact.DevelopmentMakefileThis project uses a Makefile for various tasks. Some of the available tasks
are listed below.make clean- Clean build artifacts out of your projectmake test- Run Testsmake plain-test- Run Tests without rebuilding the projectmake sdist- Build a Python source distributionmake docs- Build the Sphinx documentationmake lint- Get a codestyle report about your codemake plain-lint- Get a codestyle report without rebuilding the projectmake- Equivalent tomake test lint docs sdistA New VersionCherrypick all wanted commits onto the support branch (f.e. support/1.0)Push the commitsIncrement the version insetup.pyand update the changelog and commitTag the commit with the new version (f.e. v1.0.8)Push the version commitTravis will build the new version and upload it to Pypi automatically when finding a tag on the support branch.
Package: pact-testgen
Pact Test GeneratorGenerate Python test cases from Pact files, for easier provider verification.Free software: MIT licenseDocumentation:https://pact-testgen.readthedocs.io.FeaturesTest Pact contracts against your Python providers via unit tests. Get test isolationfor free.pact-testgencreates test cases from your Pact files, with placeholders for defining provider states.Getting StartedInstall with pippython -m pip install pact-testgenGenerate test filesGenerate aprovider_states.pyandtest_pact.pyfiles in your tests directory:pact-testgen /tests/dir -f /path/to/pactfile.jsonFor more details, see theUsagesection of the documentation.Fill in the generated provider states fileIn your tests directory (passed as the first argument topact-testgen), you’ll see a file namedprovider_states.py. It will contain set up
functions matching the provider states defined in your pact file.Before continuing, complete these functions so that they create the required states.Run your testsRun your test suite as normal, being sure to check the the test runner has picked up
your newtest_pact.pyfile.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.LogoAdmiranda Urbis Venetæfrom the British Library’s King’s Topographical Collection.Verification of test responses in generated test code is powered bypactman.History0.6.0 (2022-10-30)Test against Python 3.10, 3.110.5.0 (2022-02-04)Adds Pact Broker support.0.4.3 (2021-09-20)Fix file handling for merge provider state file option.0.4.2 (2021-09-18)Improve handling of request data.0.4.1 (2021-09-17)Fix test method name missingtest_.0.4.0 (2021-09-16)Adds option to merge changes to provider state file (Python 3.9 only).Include structured provider state parameters from Pact v3 in provider
state function names.Support null provider state, i.e. pact.given(None).0.3.0 (2021-09-03)Provider state setup functions now raise NotImplementedError by default.Format output files with target line length option.Improve output to console, add quiet option.0.2.1 (2021-09-01)Fix test client not setting content type.0.2.0 (2021-09-01)Improve output for failed test cases.0.1.2 (2021-08-25)Fix bump2version config0.1.1 (2021-08-24)Fix templates missing from distributed package.0.1.0 (2021-08-23)First release on PyPI.
Package: pactus
pactusStanding fromPath Classification Tools for Unifying Strategies,pactusis a Python library that allows testing different classification methods on
several trajectory datasets.It comes with some built-in models and datasets according to the
state-of-the-art in trajectory classification. However, it is implemented in an
extensible way, so the users can build their own models and datasets.NOTE: Built-in datasets don't contain the raw trajectoy data. When a
dataset is loaded for the first time it downloads the necessary data
automatically.InstallationMake sure you have a Python interpreter newer than version 3.8:❯python--version
Python3.8.0Then, you can simply install pactus from pypi using pip:pipinstallpactusGetting startedThis is quick example of how to test a Random Forest classifier on the Animals dataset:frompactusimportDataset,featurizersfrompactus.modelsimportRandomForestModelSEED=0# Load datasetdataset=Dataset.animals()# Split data into train and test subsetstrain,test=dataset.split(0.9,random_state=SEED)# Convert trajectories into feature vectorsft=featurizers.UniversalFeaturizer()# Build and train the modelmodel=RandomForestModel(featurizer=ft,random_state=SEED)model.train(train,cross_validation=5)# Evaluate the results on the test subsetevaluation=model.evaluate(test)evaluation.show()It should produce an output as the following:General statistics:

Accuracy: 0.885
F1-score: 0.849
Mean precision: 0.865
Mean recall: 0.850

Confusion matrix:

Cattle  Deer    Elk     precision
================================
75.0    0.0     0.0     100.0
25.0    80.0    0.0     66.67
0.0     20.0    100.0   92.86
--------------------------------
75.0    80.0    100.0ℹ️ Notice that by setting the random state to a fixed seed, we ensure the
reproducibility of the results. By changing the seed value, results may be
slightly different due to the stochastic processes used when splitting the
dataset and training the model.Available datasetsSee the wholelist of datasetscompatible with pactusContributingFollow the guidlines frompactus documentation
Package: pacurses
PacursesA curses graphical interface to thePulseAudiosound system
for the terminal.InstallationPulseAudioIn the end,pacursesis only a graphical interface which is usingpacmdto communicate with thePulseAudiodaemon. Therefore make sure that both are
installed and running on your machine.ArchLinuxUbuntu...PacursesInstall it globally withpip:$pipinstallpacursesExecute always the most recent version withpipx:$pipxrunpacurses
Package: pacwrap
pacwrapProvides single interface to several common Linux package managers.FeaturesFully typed with annotations and checked with mypy,PEP561 compatibleAdd yours!Installationpipinstallpython-pacwrapUsagepacwrap helppacwrap--help
Usage:pacwrap[OPTIONS]COMMAND[ARGS]...ProvidessingleinterfacetoseveralcommonLinuxpackagemanagers.

Options:-d,--debugincrementdebuglevel-o,--outTEXTspecifyoutputfile-q,--quiet/--no-quietspecifyquietmode-r,--refresh/--no-refreshspecifyrefreshsynchronizedpackagerepositorydata-t,--test/--no-testspecifytestmode-v,--verboseincrementverbositylevel-V,--versionshowversionandexit-h,--helpShowthismessageandexit.

Commands:fileDisplayspackageifanythatincludetheFILE.findSearchesrepositoriesforPACKAGE.infoDisplayinformationaboutPACKAGE.installInstallsPACKAGE.listListsfilesinPACKAGEorinstalledpackageswhennoPACKAGE...uninstallUnistallsPACKAGE.pacwrap file helppacwrapfile--help
Usage:pacwrapfile[OPTIONS]FILENAMEDisplayspackageifanythatincludetheFILE.

Options:-h,--helpShowthismessageandexit.pacwrap find helppacwrapfind--help
Usage:pacwrapfind[OPTIONS]PACKAGESearchesrepositoriesforPACKAGE.

Options:--names-only/--no-names-onlyspecifysearchnamesonlyifpackagersupportsit-h,--helpShowthismessageandexit.pacwrap info helppacwrapinfo--help
Usage:pacwrapinfo[OPTIONS]PACKAGEDisplayinformationaboutPACKAGE.

Options:-h,--helpShowthismessageandexit.pacwrap install helppacwrapinstall--help
Usage:pacwrapinstall[OPTIONS]PACKAGEInstallsPACKAGE.

Options:-h,--helpShowthismessageandexit.pacwrap list helppacwraplist--help
Usage:pacwraplist[OPTIONS][PACKAGE]ListsfilesinPACKAGEorinstalledpackageswhennoPACKAGEspecified.

Options:-h,--helpShowthismessageandexit.pacwrap uninstall helppacwrapuninstall--help
Usage:pacwrapuninstall[OPTIONS]PACKAGEUnistallsPACKAGE.

Options:-h,--helpShowthismessageandexit.DocumentationStableLatestLicenseMITCreditsThis project was generated withwemake-python-package. Current template version is:3d9ad64bcbf7afc6bee7f2c9ea8c923d579b119c. See what isupdatedsince then.
Package: paczekfiller
Pączek fillerThis tool tries to fill in a space next tocookiecutter, giving the user ability to
reuse functionality stored in a single file across different projects.Setup a folder with files you reuse in your project.
Set thePACZEK_FILLINGSenvironment variable to point that folder.
Runpaczek- will display list of files from which you can choose a file
that will be put in your current directory.
If the file ends with.tplyou will be asked for values that will be put inside
the template.Please remember, redundancy can be removed and files can me merged
using git.Requirements[fzf](https://github.com/junegunn/fzfFuzzy Search for command lineUsageThere are two commands:paczekpaczekfiller <template_filepath> <target_filepath>paczekpaczekis a script that usesfzfto prodive you with a list of template files, from your template files folder.If the file has extensiontpl, then it is passed topaczekfiller.paczekfillerdetects Jinja2 variables in that
file, asks you to provide values for then. Finally, it substitutes the variables with the value and saves the
file in your current directory with the the file name withouttpl.If the file is without.tpl, thenpaczekcopies it to your current folder, usingcppaczekfillerpaczekfillerfills out a jinja2 template file and saves it under given target filepath.<template_filepath>is an absolute path.configEnvironment variables used by commandpaczek:PACZEK_FILLINGSstores path to folder with template filestemplatesVariables are extracted from templates. Variables names are
used in user prompt. The "_" are changed into spaces (" ").So, for a variable{{Some_variable}}, script will prompt user with
this:Some variable:Installationpip install paczekfillerLicenceMIT type.Authorspaczekfillerwas written byKris Urbanski <kris@whereibend.space>_.
Package: pad4pi
pad4piAn interrupt-based Python 2/3 library for readingmatrixkeypadkey presses using Raspberry Pi GPIO pins.pipinstallpad4piTested on a Raspberry Pi B+ using a4x3 matrix keypadbut it should work with 4x4 and other sizes.Usagefrompad4piimportrpi_gpioKEYPAD=[[1,2,3],[4,5,6],[7,8,9],["*",0,"#"]]ROW_PINS=[4,14,15,17]# BCM numberingCOL_PINS=[18,27,22]# BCM numberingfactory=rpi_gpio.KeypadFactory()# Try factory.create_4_by_3_keypad# and factory.create_4_by_4_keypad for reasonable defaultskeypad=factory.create_keypad(keypad=KEYPAD,row_pins=ROW_PINS,col_pins=COL_PINS)defprintKey(key):print(key)# printKey will be called each time a keypad button is pressedkeypad.registerKeyPressHandler(printKey)When your program exits, callkeypad.cleanup()to ensure the Raspberry Pi’s GPIO pins are reset.LicenseLicensed underGNU Lesser General Public License Version 3(LGPL v3).
Package: pada
padapada is an onomatopoeia, like the sound we make when building blocks.a ligthweight feature manage frameworkload_data: load datafeature definition: metadata, input data, transformassemble: constructpada command line generates the project directoryproject
|---definition
|  |-feats_def.py
|---load_data.py
|---main.py
|---url.pybase introduceload_data.pydefine a function nameddata(), return your data which is a pd.DataFramedefinitionyou should define your feature in this directoryurl.pyfind your feature definition byurl()functionmain.pyrun your feature engeering byrun()functionHistory0.1.0 (2022-03-01)First release on PyPI
Package: padasip
This library is designed to simplify adaptive signal
processing tasks within python
(filtering, prediction, reconstruction).
For code optimisation, this library uses numpy for array operations.Also in this library is presented some new methods for adaptive signal processing.
The library is designed to be used with datasets and also with
real-time measuring (sample-after-sample feeding).Tutorials and DocumentationEverything is on github:http://matousc89.github.io/padasip/Current FeaturesData PreprocessingPrincipal Component Analysis (PCA)Linear Discriminant Analysis (LDA)Adaptive FiltersThe library features multiple adaptive filters. Input vectors for filters can be
constructed manually or with the assistance of included functions.
So far it is possible to use following filters:LMS (least-mean-squares) adaptive filterNLMS (normalized least-mean-squares) adaptive filterLMF (least-mean-fourth) adaptive filterNLMF (normalized least-mean-fourth) adaptive filterSSLMS (sign-sign least-mean-squares) adaptive filterNSSLMS (normalized sign-sign least-mean-squares) adaptive filterRLS (recursive-least-squares) adaptive filterGNGD (generalized normalized gradient descent) adaptive filterAP (affine projection) adaptive filterGMCC (generalized maximum correntropy criterion) adaptive filterOCNLMS (online centered normalized least-mean-squares) adaptive filterLlncosh (least lncosh) adaptive filterVariable step-size least-mean-square (VSLMS) with Angâ€™s adaptation.Variable step-size least-mean-square (VSLMS) with Benvenisteâ€™s adaptationVariable step-size least-mean-square (VSLMS) with Mathewsâ€™s adaptationDetection ToolsThe library features two novelty/outlier detection toolsError and Learning Based Novelty Detection (ELBND)Learning Entropy (LE)Extreme Seeking Entropy (ESE)