Saravanan666/pypi-org huggingface dataset:
Package: peachapisec-ci-jira
# Peach API Security CI JIRA IntegrationThis package provides the Peach API Security JIRA Integration
scripts.  The integration scripts allow populating found issues
into JIRA when running from a CI system.## InstallationThis integration is available as a pip installable package.$ pip install peachapisec-ci-jira### Offline InstallationInstall required python dependencies using our offline dependencies
folder.$ pip install –no-index –find-links ../../deps -r requirements.txt## UsagePlease see the Peach API Security User Guide for information on usage.
Package: peachapisec-cli
This is a command line interface (cli) tool for the Peach
API Security product.The cli exposes several commands useful commands available
via the installed ‘peachcli’ script.RequirementsYou will need the following prerequisites in order to use:Peach API SecurityPython 2 or 3InstallationTo install:$ python -m pip install peachapisec-cliFrom Source$ python setup.py installOffline$ pip install –no-index –find-links ../deps -r requirements.txt
$ python setup.py installUsageRunning ‘peachcli’ will provide some syntax help.See the Peach API Security User Guide for more information.
Package: peachapisec-recorder
This is a web traffic recorder.  It records web traffic and saves it in HAR format.
The HAR format is compatible with the peachrunner command.A typical use for this tool is to record the traffic generated by a unit tesitng framework or similar traffic generator that can then be used to perform security testing.InstallationInstall required python dependencies with internet connection.$ pip install peachapisec-recorderOffline InstallationPackages for offline installation are not included for this tool.
Package: peachapisec-runner
This test runner will send requests via Peach API Security to perform testing
of a target api.  There are several methods for sending requests:Burp file with recorded requests. Each request is tested one at a time.Postman collection with requests. Each request is tested one at a time.Single command line (ex: curl, python script, etc.)Text file with one-command per lineDirectory with executable scripts/programs, each executed in sequenceInstallationInstall required python dependencies with internet connection.$ pip install -r requirements.txtOffline InstallationInstall required python dependencies using our offline dependencies
folder.$ pip install –no-index –find-links ../../deps -r requirements.txt
Package: peachapisec-slack
This tool provides notification ability via
Slack, the popular messaging application.InstallationInstallation of this tool has two steps.Create the app and webhook in Slack. See documentation for steps.Install peach2slackpip install peachapisec-slackStart using the toolSyntaxpeach2slack–apihttp://192.168.1.100–api_token=xxxx –slack_webhookhttps://hooks.slack.com/services/xxxxxAll parameters can also be provided via environment variables.
This is especially usefull for the Slack webhook url, which can then
be stored in your CI secrets store.Parametersapi:Peach API Security API URL. Defaults to PEACH_API environ.api_token:Peach API Security API Token. Defaults to PEACH_API_TOKEN environ.slack_webhook:Slack webhook URL. Defaults to PEACH_SLACK_WEBHOOK environ.msg:[optional] Message template to post when no issues are found. Defaults to PEACH_MSG environ.Defaults to: Security testing completed succesfully, no issues found after {{tests_count}} tests.failmsg:[optional] Message template to post when issues are found. Defaults to PEACH_FAILMSG environ.Defaults to: A total of {{failure_count}} failures from {{tests_count}} tests were found.errormsg:[optional] Error message template to post when job did’t complete. Defaults to PEACH_ERRORMSG environ.Defaults to: Error: {{reason}}Message TemplateA message template is provided using any of the following placeholders:{{failure_count}}:Count of failured identified during job{{job_id}}:Job Identifier (GUID){{job_url}}:Url to job details. Requires ‘baseurl’ be provided.{{job_tags}}:Tags set on job{{project}}:Project name{{reason}}:Job completion reason. Succes or error message.{{tests_count}}:Count of security tests performed during testing
Package: peach-collector
peach-collectorPeach Collector client implementation in Python 3.8PeachCollectorSimplest way to send events.collector = PeachCollector("a-site-key", "an-app-id")
collector.add_event({"type":"an-event-type", foo": 666})
collector.add_event({"type":"an-event-type", "bar": 777})
await collector.send_events()PeachCollectorQueuePushes events on to a Redis queue which is drained at regular intervals.# Client
q = PeachCollectorQueue("redis://localhost")
q.push_event({"type":"an-event-type", "foo": 66})
q.push_event({"type":"an-event-type", "bar": 777})

# Scheduled Worker
collector = PeachCollector("a-site-key", "an-app-id")
q = PeachCollectorQueue("redis://localhost", collector)
q.drain()
Package: peach-partner
Peach Partner LibraryOverviewPeach Partner Libraryis a platform agnostic Python package to help integrating PeachPayments with their partners.Documentation:Source Code:https://gitlab.com/peachpayments/peach-partner-python/UsagePackage requires Python 3.9+Installation# pip$pip3installpeach-partner# poetry$poetryaddpeach-partnerResult codesfrompeach_partner.result_codesimportresult_codesresult_codes.TRANSACTION_SUCCEEDED.code=="000.000.000"result_codes.get("000.000.000").name=="TRANSACTION_SUCCEEDED"result_codes.get("000.000.000").description=="Transaction succeeded"Field validationTODOAuthenticationTODO
Package: peachpayments-partner
Peach Partner LibraryOverviewPeach Partner Libraryis a platform-agnostic Python package to help Payment Service Providers in integrating with PeachPayments.Documentation:Source Code:https://gitlab.com/peachpayments/peach-partner-python/Key termsTermDefinitionPartner APIA service provided by Peach Payments to enable Payment Service Providers to become available on the Peach PlatformPayment Service ProviderA payment service provider who integrates with the Partner APIOutbound API callAPI calls sent from Partner API to the Payment Service ProviderInbound API callAPI calls sent from Payment Service Provider to Partner APIInstallationPackage requires Python 3.9+# pip$pip3installpeachpayments-partner# poetry$poetryaddpeachpayments-partnerResult codesfrompeachpayments_partner.result_codesimportresult_codesresult_codes.TRANSACTION_SUCCEEDED.code=="000.000.000"result_codes.get("000.000.000").name=="TRANSACTION_SUCCEEDED"result_codes.get("000.000.000").description=="Transaction succeeded"AuthenticationRequests to Payment Service ProviderPeachPayments uses an authorization token (JWT) in each request made to the Payment Service Provider.
This library provides theauthentication.is_authenticatedmethod, which takes the token as an argument and theauthentication.get_keyto collect the signing_key.Theis_authenticatedmethod has only one required argument, the token. If it's called without the optionalsigning_keyit will collect the key using theget_keymethod. If it's called without the optionalaudienceit will try to use the environment variableAUTH0_AUDIENCE.The method decodes the token. If that succeeds, it returnsTrue. Otherwise, it raises anAuthErrorexception.Formatting error responsesPeachPayments requires the error responses to be formatted in a specific way. This library provides theformat_error_responsemethod, which takes a dict containing error response as an argument and returns a formatted error response.defformat_error_response(code,errors,data):Theerrorsdict might look like this:{"status":["Not a valid string."],"code":["Missing data for required field."],}Thedatadict might look like this:{"status":10}With thecodeasResultCodes.INVALID_OR_MISSING_PARAMETER, the formatted error response will look similar to this:{"result":{"code":"200.300.404","description":"invalid or missing parameter","parameterErrors":[{"value":10,"name":"status","message":"Not a valid string."},{"name":"code","message":"Missing data for required field."}]},"timestamp":"2021-08-03T16:16:30.992618Z"}FixturesThis library provides examples of valid requests and responses.An example of the recommended usage for testing:importpytestfromcopyimportdeepcopyfrompeachpayments_partner.fixturesimportDEBIT_RESPONSE@pytest.fixturedefdebit_response():returndeepcopy(DEBIT_RESPONSE)
Package: peachpayments-partner-marshmallow
PeachPayments Partner Marshmallow LibraryOverviewPeachPayments Partner Marshmallow Libraryis a platform-agnostic Python package to help Payment Service Providers in integrating with PeachPayments. This library provides functionality to validate request and response data using Marshmallow Python library.Source Code:https://gitlab.com/peachpayments/peach-partner-marshmallow/Key termsTermDefinitionPartner APIA service provided by Peach Payments to enable Payment Service Providers to become available on the Peach PlatformPayment Service ProviderA payment service provider who integrates with the Partner APIOutbound API callAPI calls sent from Partner API to the Payment Service ProviderInbound API callAPI calls sent from Payment Service Provider to Partner APIUsagePackage requires Python 3.9+Installation# pip$pip3installpeachpayments-partner-marshmallow# poetry$poetryaddpeachpayments-partner-marshmallowField validationPayment Service Provider receives a debit request from PeachPayments.# ... importsfrompeachpayments_partner_marshmallow.validatorimportvalidate_debit_request,validate_debit_responsedefdebit(db:Session,data:dict):request_validation=validate_debit_request(data)ifnotrequest_validation["valid"]:raiseHttpJSONError(request_validation["response"])# Store a transaction in a database# Prepare the response to PeachPayments in the `response_fields` dictionaryresponse_validation=validate_debit_response(response_fields)ifnotresponse_validation["valid"]:raiseException("Badly formatted response fields")returnHttpResponse(response_fields)
Package: peachpayments-partner-pydantic
PeachPayments Partner Pydantic LibraryOverviewPeachPayments Partner Pydantic Libraryis a platform-agnostic Python package to help Payment Service Providers in integrating with PeachPayments. This library provides functionality to validate request and response data using Pydantic Python library.Source Code:https://gitlab.com/peachpayments/peach-partner-pydantic/Key termsTermDefinitionPartner APIA service provided by Peach Payments to enable Payment Service Providers to become available on the Peach PlatformPayment Service ProviderA payment service provider who integrates with the Partner APIOutbound API callAPI calls sent from Partner API to the Payment Service ProviderInbound API callAPI calls sent from Payment Service Provider to Partner APIUsagePackage requires Python 3.9+Installation# pip$pip3installpeachpayments-partner-pydantic# poetry$poetryaddpeachpayments-partner-pydanticField validationScenario:Payment Service Provider written in FastAPI receives a debit request from PeachPayments.# ... importsfrompeachpayments_partner_pydantic.schemasimportDebitRequest,DebitResponse@router.post("/v1/debit",response_model=schemas.DebitResponse,)defdebit_request(*,debit_in:schemas.DebitRequest)->Any:# Store the transactiontransaction=Transaction.create_from_debit(debit_in)# Validate the debit responsedebit_response=DebitResponse(**transaction.to_debit_response_fields())returndebit_response.dict()Translating exception to PeachPayments error responseScenario:Payment Service Provider written in FastAPI receives a request with a validation error from PeachPayments.1. Write validation exception handler# app/exception_handlers.pyfromfastapiimportRequest,statusfromfastapi.exceptionsimportRequestValidationErrorfromfastapi.responsesimportJSONResponsefrompeachpayments_partner_pydantic.exception_handlersimportexception_to_responseasyncdefvalidation_exception_handler(request:Request,exc:RequestValidationError)->JSONResponse:returnJSONResponse(status_code=status.HTTP_400_BAD_REQUEST,content=exception_to_response(exc))2. Connect it to the application# app/main.pyfromfastapiimportFastAPIfromfastapi.exceptionsimportRequestValidationErrorfromapp.exception_handlersimportvalidation_exception_handlerapplication=FastAPI(exception_handlers={RequestValidationError:validation_exception_handler},)
Package: peachplumear
PeachPlumEarThis programme provides a simple user interface which helps you train your ear to musical intervals by generating and playing 'challenges'.The challenges are either 'scale' (just notes) or 'chords' (triads).
Each challenge starts on the root note of the key and plays a sequence of notes or triads from that key.You can play either just the challenge notes or add an introductory 2 bars playing the scale and root chords to introduce the key to your ear.The root note is currently always in octave 4 and by default the notes (or root notes of the chords) of the challenge will all lie within the first octave above the root note. You can allow for notes in the octave below to be included.You can also control:the numbers of intervals in the sequencethe bpm of the play backwhich intervals can be included in the challengewhether to include chord inversionswhether to change key for the next challenge or notThe user interface also allows you to play the notes of the scale over 2 octaves.
Package: peachproxy
The peach proxy module provides a wrapper around thePeach API SecurityAPI.
This module is used for by CI integrations,
test runner plugins and custom traffic generators.RequirementsYou will need the following prerequisites in order to use:Peach API SecurityPython 2 or 3InstallationTo install:$ pip install peachproxyFrom Source$ python setup.py installUsageSee the Peach API Security User Guide for more information.
Package: peachweb-api
The peach proxy module provides a wrapper around thePeach API SecurityAPI.
This module is used for by CI integrations,
test runner plugins and custom traffic generators.RequirementsYou will need the following prerequisites in order to use:Peach API SecurityPython 2 or 3InstallationTo install:$ pip install peachweb-apiFrom Source$ python setup.py installUsageSee the Peach API Security User Guide for more information.
Package: peachy
Peachy“A python3 framework that makes developing 2d games peachy keen.”Peachy is a python game development framework made to make developing games in
python a bit more streamlined.FeaturesEntity, World structureSimple architecture2d collision detection for basic shapes2d renderingResource management and loadingTiled supportCommand line utility (TODO)Project scaffoldingProfilingBuilding Executable(Some features are still under development)Modulespeachy - The core modules, contains the Engine, World, and Entity classes among others.peachy.audio - Contains sound classes (still under heavy maintenance).peachy.collision - Multiple collision detection functions for 2d shapes.peachy.fs - Short for ‘filesystem’, this module is used for loading and storing resources.peachy.geo - Geometric shapes and collision detection between them.peachy.graphics - Drawing and animation.peachy.stage - Used to load popular level editor templates (only Tiled at the moment).peachy.resources - Resource handling. Loading & management.peachy.utils - Utility functions for camera, input, etc.peachy.etc - Everything else.Building DocsPeachy is documented using Google Style Python Docstrings. Docs are held
within /docs.You can build the docs using Sphinx.Running TestsJust use pytestInstallationRequirementsPython 3Pygame (check out resources for compatible versions)PyTMX (If you intend to use peachy to load tiled stages)PyInstaller (If you intend to use Peachy for building your executables)Click (Used for the Peachy CLI tool)Install via PyPipip install peachyInstall manuallygit clonehttps://github.com/shellbotx/peachypython setup.py installResourcesRepository ~https://github.com/shellbotx/peachyTutorial ~ coming soonPeachy is licensed under the MIT license.
Package: peacockprint
PeacockPrintPretty print text in varioua replacement template and a color tuple.Free software: GNU General Public License v3Documentation:https://peacockprint.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-11-20)First release on PyPI.
Package: peacock-trame
MOOSE GUI developed using Trame from Kitware Inc.Blog postVideoLicenseFree software: Apache Software LicenseMamba setupcurl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-MacOSX-arm64.sh
bash Mambaforge-MacOSX-arm64.sh -b -p ~/mambaforge3
export PATH=$HOME/mambaforge3/bin:$PATHYou will probably have to move the code that was generated in~/.bash_profileto~/.zshrc.Then after terminal restart add INL channelconda config --add channels https://conda.software.inl.gov/publicRunning the softwaremkdir peacock-work
cd peacock-work
git clone --recursive git@github.com:Kitware/peacock.git
git clone --recursive https://github.com/idaholab/moose.gitCreate venv with mamba locallymamba install python=3.9
mamba create -n moose python=3.9 moose paraview -y
mamba activate moose
pip install peacock-trameTest application on a moose examplepeacock-trame -I ./moose/examples/ex08_materials/ex08.iRunning with language serverClone and build the moose language servergit clone git@github.com:idaholab/moose-language-support.git
cd moose-language-support
npm run compileInstall middleware packagescd /path/to/peacock/lang-server
npm iPoint to compiled language server when running apppeacock-trame -I ./ex08.i -L /path/to/moose-language-support/server/out/server.jsDevelopment setupInstalling peacock using the local filespip install -e ./peacockBuilding the client codecd vue-components
npm i
npm run build
cd -Run the application assuming the same layout as previously describedcd ./moose/examples/ex08_materials
make
peacock-trame -I ./ex08.i
Package: peak-bot
Peak BotThis voice-assistant-like AI is a young client part of the "Peaks" infrastructure.
"Peaks" are Django-based managment servers for personaly customized and trained bots, and places where they exchange/trade their commands.
Peak 63 andPeak 30are in development, and should be publised along with extented features of the client part.Table of contentsStatusGetting startedPrequisitesArch LinuxUbuntu, Debian, Linux MintFedora, CentOSWindowsInstallationUsageStatusAlpha release is now on PyPI.
Master branch runs well on Linux and Windows,
but still lacks large amount of features to be competitive with commercial AIs.
To test and/or contribute in the meantime, please follow the rest of this file...Getting startedPeak Bot is written in python3, and relies on sqlite3 to store it's data.
It currently uses Google's Speech-to-Text Client Library for python and requires internet connection.
Default input and settings are using .JSON format.PrequisitesTo satisfy the current dependencies, make sure you have python3, pip3, and portaudio installed.Arch Linuxsudo pacman -S python python-pip portaudioUbuntu, Debian, Linux Mintsudo apt-get install python3 python3-pip libportaudio2Fedora, CentOSsudo yum -y install python36 python36-setuptools portaudio-developtional:cd /usr/lib/python3.6/site-packages/python3 easy_install.py pip3WindowsDownload and install the latest python3, and portaudio releases frompython.organdportaudio.com.Pip3 script should now be automaticaly placed inside 'Scripts' directory.
If PowerShell or Command Prompt don't recognizepip3command,
runwhere pythonto find it the location of the 'Scripts',
and add the location of the pip script to a PATH.InstallationPeak Bot is now on PyPi, so you should have no problems installing it with:pip3 install --upgrade peak-botoreasy_install peak-botBefore running, you will also need to export the path to the google credentials.
On linux, add this line to your .bashrc file:export GOOGLE_APPLICATION_CREDENTIALS="/some_directory/google_speech_api_credentials.json"and run:source .bashrcUsageIf installation went well, peak-bot command should be ready...$ peak-bot <verbosity 0-6>.
Package: peakdet
peakdet: A toolbox for physiological peak detection analysesThis package is designed for use in the reproducible processing and analysis of
physiological data, like those collected from respiratory belts, pulse
photoplethysmography, or electrocardiogram (ECG/EKG) monitors.OverviewPhysiological data are messy and prone to artifact (e.g., movement in
respiration and pulse, ectopic beats in ECG). Despite leaps and bounds in
recent algorithms for processing these data there still exists a need for
manual inspection to ensure such artifacts have been appropriately removed.
Because of this manual intervention step, understanding exactly what happened
to go from "raw" data to "analysis-ready" data can often be difficult or
impossible.This toolbox,peakdet, aims to provide a set of tools that will work with a
variety of input data to reproducibly generate manually-corrected, analysis-
ready physiological data. If you'd like more information about the package,
including how to install it and some example instructions on its use, check out
ourdocumentation <https://peakdet.readthedocs.io>_!License InformationThis codebase is licensed under the Apache License, Version 2.0. The full
license can be found in theLICENSE <https://github.com/physiopy/peakdet/ blob/master/LICENSE>_ file in thepeakdetdistribution. You may also
obtain a copy of the license at:http://www.apache.org/licenses/LICENSE-2.0.Contributors ✨Thanks goes to these wonderful people (emoji key):Elizabeth DuPre💻🚇Ross Markello🐛💻📖🤔🚇🚧📆👀⚠️✅Stefano Moia👀💻🚇📆Taylor Salo🚇Mi-Xue Tan💻📓🔌Eneko Uruñuela🚇xl624💻This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: peakdetect_delta
Find positive spike-like peaks from 1-D array.examplefrompeakdetect_deltaimportpeakdetect,peakdetect_simpleedge# peak are represented as a tuple ((pvalue, pidx), (sidx, eidx))#   pvalue: value at pidx#   pidx: index of the peak#   sidx: index where y[sidx+1] - y[sidx] > rdelta#   eidx: index where y[eidx] - y[eidx-1] < fdelta#   so, y[sidx:eidx] takes the detected peak# examples of peakdetect_simpleedge# case 1.1s=[0,0,1.5,2,0,0]assertpeakdetect_simpleedge(s,rdelta=1,fdelta=-1)==[((2,3),(1,5))]# case 1.2s=[0,2,0,-2,0,-2]assertpeakdetect_simpleedge(s,rdelta=1,fdelta=1)==[((2,1),(0,3)),((0,4),(3,6))]# example of peakdetects=[0,3,0,2,0,0,-1,1,-2,-1]# same as peakdetect_simpleedgeassertpeakdetect(s,rdelta=1,fdelta=-1,minimumspace=-1)==[((3,1),(0,3)),((2,3),(2,5)),((1,7),(6,9))]# first 2 peaks are mergedassertpeakdetect(s,rdelta=1,fdelta=-1,minimumspace=0)==[((3,1),(0,5)),((1,7),(6,9))]peakdetect(s,rdelta=1,fdelta=-1,minimumspace=2)==[((3,1),(0,9))]
Package: peak-finder
Lorentzian Peak Finder (now with MaChInE lEaRnInG!)A python library for finding, fitting to, and tracking Lorentzian peaks from RUS or other measurements. Usage and documentation can be found in thewiki. Tutorials and demo data is available inreleases.Note that throughout the library, the definition of Lorentzians used is the following.Where the individual terms are as below.InstallingNote that none of the installation methods download nor install the pre-made Lorentzian models. You need to get those separately. Please seeLorentzian Modelsfor more information.Therecommendedinstallation method is frompypi.pipinstallpeak-finderBut, you can also install directly from this git repository. These releases might not always be stable.pip3installgit+https://github.com/GabePoel/ML-Peak-Tracker#egg=peak_finder--userOr, if you only want to install the deltas, you can also clone this repository locally and then install using the includedlocal_install.shscript. Navigate into the cloned repository and then run the following command.sh./peak_finder/local_install.sh