Saravanan666/pypi-org huggingface dataset:
Package: gammarer.aws-waf-geo-restriction-rule-group
AWS WAF(v2) GEO Restriction Rule GroupThis is an AWS CDK Construct for Geo Restriction Rule Group on WAF V2ResourcesThis construct creating resource list.WAF V2 RuleGroupInstallTypeScriptnpminstall@gammarer/aws-waf-geo-restriction-rule-group# oryarnadd@gammarer/aws-waf-geo-restriction-rule-groupPythonpipinstallgammarer.aws-waf-geo-restriction-rule-groupC# / .NetdotnetaddpackageGammarer.CDK.AWS.WafGeoRestrictionRuleGroupJavaAdd the following to pom.xml:<dependency><groupId>com.gammarer</groupId><artifactId>aws-waf-geo-restriction-rule-group</artifactId></dependency>Exampleimport{WafGeoRestrictRuleGroup}from'@gammarer/aws-waf-geo-restriction-rule-group';newWafGeoRestrictRuleGroup(stack,'WafGeoRestrictRuleGroup',{scope:Scope.GLOBAL,//GLOBAL(CloudFront)orREIGONAL(ApplicationLoadBalancer(ALB),AmazonAPIGatewayRESTAPI,anAWSAppSyncGraphQLAPI,oranAmazonCognitouserpool)allowCountries:['JP'],//alpha-2countryandregioncodesfromtheInternationalOrganizationforStandardization(ISO)3166standard});LicenseThis project is licensed under the Apache-2.0 License.
Package: gammarer.aws-waf-ip-rate-limit-rule-group
AWS WAF(V2) IP Rete Limit Rule GroupThis is an AWS CDK Construct for IP Rate Limit Rule on WAF V2ResourcesThis construct creating resource list.WAF V2 RuleGroupInstallTypeScriptnpminstall@gammarer/aws-waf-ip-rate-limit-rule-group# oryarnadd@gammarer/aws-waf-ip-rate-limit-rule-groupPythonpipinstallgammarer.aws-waf-ip-rate-limit-rule-groupC# / .NetdotnetaddpackageGammarer.CDK.AWS.WafIpRateLimitRuleGroupJavaAdd the following to pom.xml:<dependency><groupId>com.gammarer</groupId><artifactId>aws-waf-ip-rate-limit-rule-group</artifactId></dependency>Exampleimport{Scope,WafRateLimitRuleGroup}from'@gammarer/aws-waf-ip-rate-limit-rule-group';newWafIpRateLimitRuleGroup(stack,'WafIpRateLimitRuleGroup',{name:'rate-limit-rule-group',scope:Scope.REGIONAL,rateLimitCount:3000,//default1000});LicenseThis project is licensed under the Apache-2.0 License.
Package: gammarer.aws-waf-ip-restriction-rule-group
AWS WAF(V2) IP Restriction Rule GroupThis is an AWS CDK Construct for IP Restriction Rule Group on WAF V2ResourcesThis construct creating resource list.WAF V2 RuleGroupInstallTypeScriptnpminstall@gammarer/aws-waf-ip-restriction-rule-group# oryarnadd@gammarer/aws-waf-ip-restriction-rule-groupPythonpipinstallgammarer.aws-waf-ip-restriction-rule-groupC# / .NetdotnetaddpackageGammarer.CDK.AWS.WafIpRestrictionRuleGroupJavaAdd the following to pom.xml:<dependency><groupId>com.gammarer</groupId><artifactId>aws-waf-ip-restriction-rule-group</artifactId></dependency>Exampleimport{WafIpRestrictRuleGroup}from'@gammarer/aws-waf-ip-restriction-rule-group';declareconstallowIpSet:waf.CfnIPSet;newWafIpRestrictRuleGroup(stack,'WafIpRestrictRuleGroup',{scope:Scope.GLOBAL,//GLOBAL(CloudFront)orREIGONAL(ApplicationLoadBalancer(ALB),AmazonAPIGatewayRESTAPI,anAWSAppSyncGraphQLAPI,oranAmazonCognitouserpool)allowIpSetArn:allowIpSet.attrArn,});LicenseThis project is licensed under the Apache-2.0 License.
Package: gammasimtools
Simulation tools and applications for theCherenkov Telescope Array (CTAO).License:BSD-3Useful links:DocumentationSource codeIssue trackerCitationsimtools provides:libraries for simulation model management and model database interfacetools for the preparation and configuration of simulation productionsapplications for simulation model parameter derivation and validationstandardized interfaces and data products independent of the underlying simulation software (e.g.,CORSIKA,sim_telarray)I/O and reporting tools for the MC simulation model parameter and production databasesimtools is one part of the CTAO Simulation Pipeline, which consist of the following components:CORSIKAair shower simulation code and thesim_telarraytelescope simulation codeworkflowsfor setting, derivation and validation of simulation model parameterssimulation model parameter and input data schemadatabases, especially the model parameter databasesimtools is under rapid development with continuous changes and additions planned.
Please contact the developers before using it:simtools-developer@desy.deQuickstartWarningThe pip-installation of simtools provides limited functionality only
and is not as well tested as the conda/mamba installation.Install simtools with pypi (recommended for users):pipinstallgammasimtoolsInstall simtools with mamba (recommended for developers):gitclonehttps://github.com/gammasim/simtools.gitcdsimtoolsmambaenvcreate-fenvironment.ymlcondaactivatesimtoolspipinstall-e.For more detail see theGetting_Startedsection of the manual.AuthorsRaul R Prado (DESY)Orel Gueta (DESY)Tobias Kleiner (DESY)Victor B. Martins (DESY)Gernot Maier (DESY)Citing this SoftwarePlease cite this software if it use used for a publication, see theZenodo recordandCITATION.cff.AcknowledgementsThis project is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 460248186 (PUNCH4NFDI).
Package: gamma-spectroscopy
IntroductionThis is a data acquisition app for gamma spectroscopy. The Qt app requires a 5000-series PicoScope (e.g. the 5242D) for data acquisition.Gamma spectroscopy of nuclear sources is commonly performed using either custom hardware (somewhat cheaper, easier to use, but less versatile) or modular hardware like NIM-crates (more expensive, harder to use, but more versatile). For the undergraduate physics lab at the Vrije Universiteit Amsterdam and the University of Amsterdam we require the versatility of modular hardware, but would like the ease-of-use of custom solutions. This way, students can focus on their research instead of working through a bunch of manuals.Typically, a gamma detector (e.g. a NaI-scintillator/PMT or HPGe-detector) is connected to an amplifier which is connected to a multi-channel analyzer (MCA). Proprietary software must be used to read out the MCA. For more elaborate setups, a single-channel analyzer or pulse shaper is added to the mix. This can easily result in a time-consuming quest to find the cause of a very common situation: “it doesn’t work.”We’ve decided to replace all NIM modules with a single PicoScope device. This is a digital oscilloscope which is connected over USB to a PC or laptop. Using the SDK it is easy to interface with the device and to write custom software for use by our students.Screenshot of the GUI showing a plot of the gamma spectrum of a nuclear source (sodium-22, black line). Another detector at the opposite side of the source picks up coincident annihilation radiation gammas.InstallationIn this section we will cover the installation of the gamma-spectroscopy package as well as the prerequisites (see below). On Windows, first make sure that the Anaconda distribution is installed and run all command-line commands from the Anaconda Prompt application.PrerequisitesPicoSDKPython wrappers for PicoSDKNumPyPyQt5PyQtGraphTo install the PicoSDK C libraries, follow the instructions athttps://github.com/picotech/picosdk-python-wrappers.Gamma-spectroscopyYou can install the latest release of this package directly from PyPI:$pipinstallgamma-spectroscopyor, alternatively, you can install the latest development version from GitHub:$pipinstallgit+https://github.com/davidfokkema/gamma-spectroscopyRunning the applicationYou can start the application GUI directly from the command-line (On Windows, first start the Anaconda Prompt application to get to the command-line):$gamma-spectroscopyWe will look into ways to include gamma-spectroscopy in Anaconda Navigator or as a stand-alone application so you don’t first have to start a console.
Package: gammaspotter
gammaspotterThegammaspotterapplication is designed to identify radioactive sources in gamma spectra.Features-InstallationFeaturesMeasurement CalibrationThe calibrate tab lets the user calibrate a gamma spectrum by comparing known energies to peak energies.Data Visualization:The analyze tab offers an interactive visualization to help you gain insights into your data.Statistical Analysis:You can perform statistical analysis on your calibrated data using built-in functions and algorithms. This includes peak detection, peak fitting and isotope match detection.Report Generation:Once you have completed your analysis, the analyze tab enables you to generate reports summarizing your findings. You can access visualizations and statistical results.InstallationYou can easily installgammaspotterusingpip. First, ensure you have Python and pip installed. Then, open a terminal or command prompt and execute the following command:pipinstallgammaspotterThis command will download and install the latest version of gammaspotter fromPyPI.You can now start the application by running thegammaspottercommand in a terminal.
Package: gamma-viewer
Gamma ViewerJupyter Notebook javascript extension for viewing Translator Standard messages in an analytic UI.Installationpipinstallgamma_viewerjupyternbextensionenable--pygamma_viewerUsagefromgamma_viewerimportGammaViewerfromIPython.displayimportdisplayimportjsonwithopen('translator_standard_message.json','r')asf:res=json.load(f)view=GammaViewer(props={"data":res})display(view)
Package: gammelmath
MathPyA small parser for arithmetic expressionsThe goal is to parse expressions like(1+2)*3and evaluate them.It also parses an identifiers likex+2.
The resulting object then needs values for those to evaluate itself.CLIThe package can be executed as script.
It will evaluate an expression and accepts any number of key-value-pairs to use for variables.~$python3-mexpr_parserx^2+yx=2y=15Basic UsageThe main API provides 3 functions (each takes an expression):parseas the name says, parses the expression an returns a syntax treeevaluatecreates and immidiently evaluates the resulting syntax tree. No unknown can be used!functiontakes an expression containing the unknownxand returns a function>>>fromexpr_parserimport*>>>evaluate("(1+2)*3")9>>>f=function("x^2")>>>f(4)16Advanced UsageThe Parser objectThe basic API is just a shortcut for instancing a Parser object and calling its methods.A Parser object contains the information required for parsing an expression:What brackets are usedWhich operators are usedThese can be controled with:Parser.add_brackets(opening, closing)Parser.add_operator(operator)The static methoddefault()instances an object and populates it with the default brackets and operators.
This method is used to provide the basic API's parser.Custom OperatorsAn operator object requires the following parameters:symbol: sthe operator's string representationpriority: integer for determening the operation's execution orderbinary: method for using the operator between two values x and yunary: method for using the operator in front of a single value xTheOperatorclass provides the@Operator.handle_callablesdecorator to extend the binary method to work with functions as well as numbers.fromexpr_parser.operators.baseimportOperatorMod=Operator(symbol="%",priority=20binary=Operator.handle_callables(lambdax,y:x%y))The basic math operators +, -, *, /, ^ are implemented inexpr_parser.operators.default.Inexpr_parser.operators.diceis another example for a custom operator.
It's the dice operator known from tabletop-roleplays,
where2d6means roll a 6-sided die 2-times.
Package: gamornet
Galaxy Morphology Network (GaMorNet)GaMorNet is a Convolutional Neural Network based on AlexNet to classify galaxies morphologically. GaMorNet does not need a large amount of training data (as it is trained on simulations and then transfer-learned on a small portion of real data) and can be applied on multiple datasets. Till now, GaMorNet has been tested on ~100,000 SDSS g-band galaxies and ~20,000 CANDELS H-band galaxies and has a misclassification rate of <5%.DocumentationPlease read thedetailed documentationin order to start using GaMorNetPublication & Other DataYou can look at thisApJ paperto learn the details about GaMorNet’s architecture, how it was trained, and other details not mentioned in the documentation.We strongly suggest you read the above-mentioned publication if you are going to use our trained models for performing predictions or as the starting point for training your own models.All the different elements of the public data release (including the new Keras models) are summarized in thePDR Usage GuideAttribution Info.Please cite the above mentioned publication if you make use of this software module or some code herein.@article{Ghosh2020,
  doi ={10.3847/1538-4357/ab8a47},
  url ={https://doi.org/10.3847/1538-4357/ab8a47},
  year ={2020},
  month = jun,
  publisher ={American Astronomical Society},
  volume ={895},
  number ={2},
  pages ={112},
  author ={Aritra Ghosh and C. Megan Urry and Zhengdong Wang and Kevin Schawinski and Dennis Turp and Meredith C. Powell},
  title ={Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in$\sim$100, 000{SDSS}and$\sim$20, 000{CANDELS}Galaxies},
  journal ={The Astrophysical Journal}}Additionally, if you want, please include the following text in the Software/Acknowledgment section.This work uses trained models/software made available as a part of the Galaxy Morphology Network public data release.LicenseCopyright 2020 Aritra Ghosh & contributorsDeveloped byAritra Ghoshand made available under aGNU GPL v3.0license.Getting Help/ContributingIf you have a question, please first have a look at theFAQ Section. If your question is not answered there, please send me an e-mail at thisaritraghsh09+gamornet@xxxxx.comGMail address.If you have spotted a bug in the code/documentation or you want to propose a new feature, please feel free to open an issue/a pull request onGitHub.
Package: gamornet-cpu
Galaxy Morphology Network (GaMorNet)GaMorNet is a Convolutional Neural Network based on AlexNet to classify galaxies morphologically. GaMorNet does not need a large amount of training data (as it is trained on simulations and then transfer-learned on a small portion of real data) and can be applied on multiple datasets. Till now, GaMorNet has been tested on ~100,000 SDSS g-band galaxies and ~20,000 CANDELS H-band galaxies and has a misclassification rate of <5%.DocumentationPlease read thedetailed documentationin order to start using GaMorNetPublication & Other DataYou can look at thisApJ paperto learn the details about GaMorNet’s architecture, how it was trained, and other details not mentioned in the documentation.We strongly suggest you read the above-mentioned publication if you are going to use our trained models for performing predictions or as the starting point for training your own models.All the different elements of the public data release (including the new Keras models) are summarized in thePDR Usage GuideAttribution Info.Please cite the above mentioned publication if you make use of this software module or some code herein.@article{Ghosh2020,
  doi ={10.3847/1538-4357/ab8a47},
  url ={https://doi.org/10.3847/1538-4357/ab8a47},
  year ={2020},
  month = jun,
  publisher ={American Astronomical Society},
  volume ={895},
  number ={2},
  pages ={112},
  author ={Aritra Ghosh and C. Megan Urry and Zhengdong Wang and Kevin Schawinski and Dennis Turp and Meredith C. Powell},
  title ={Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in$\sim$100, 000{SDSS}and$\sim$20, 000{CANDELS}Galaxies},
  journal ={The Astrophysical Journal}}Additionally, if you want, please include the following text in the Software/Acknowledgment section.This work uses trained models/software made available as a part of the Galaxy Morphology Network public data release.LicenseCopyright 2020 Aritra Ghosh & contributorsDeveloped byAritra Ghoshand made available under aGNU GPL v3.0license.Getting Help/ContributingIf you have a question, please first have a look at theFAQ Section. If your question is not answered there, please send me an e-mail at thisaritraghsh09+gamornet@xxxxx.comGMail address.If you have spotted a bug in the code/documentation or you want to propose a new feature, please feel free to open an issue/a pull request onGitHub.
Package: gampy
gampyGampy provides experimental constructs to improve your Python programs.PipelineWhile functional programming is a great paradigm to create data pipelines, some operations remains hard to express:wrap every function of a data pipeline with an external function (e.g. logging, safe exception handling ...)concatenate and transform data pipelines with algebra operators (e.g. convert and compare a pipeline ...)apply thecomposeandpartialoperators on a data pipeline to create a single callable functionGampy Pipeline is a data structure created to address these problems.Here is an example of a simple pipeline:fromgampy.structuresimportPipelinepipeline=Pipeline([(map,[lambdax:x+1],{}),(filter,[lambdax:x%2==0]),(list,)])Eachstepof the pipeline is represented as a 3-tuple:(function, arguments, keyword arguments). Whilefunctionis mandatory,argumentsandkeyword argumentwill be replaced bylist()anddict()respectively if they are missing. This structure allows the creation ofunevaluated expression, that can be further transformed prior to their execution.The most interesting operations over a pipeline are()(call) and@(matmul).Callconverts the pipeline into a single function. This process is divided in two steps:functools.partialis applied on each step arguments to create a single function per stepfunctions are composed two by two withfunctools.reduceto create a single function per pipeline>>>f=pipeline()>>>f(range(10))30Matmulappliesan adviceto each function of the pipeline. This allows the expression of cross concern aspects.In the snippet below, any exception raised by a pipeline function will returnNone.fromgampy.advicesimportexceptionalsafepipe=pipeline@exceptional(None)An advice is similar to aparametrized decorator, which create a function that takes a function and replaced it by a new function. The purpose is to extend the behavior of the original function.defexceptional(x:Any=None,on:Type[Exception]=Exception)->Advice:"""Return x when f raises an exception."""defadvice(f):@wraps(f)defwrapped(*args,**kwargs):try:returnf(*args,**kwargs)excepton:returnxreturnwrappedreturnadvice
Package: gamsapi
gamsapi: powerful Python toolkit to manage GAMS (i.e., sparse) data and control GAMS solvesWhat is it?gamsapiis a Python package that includes submodules to control GAMS, manipulate and
transfer data to/from the GAMS modeling system (through GDX files or in-memory objects).
This functionality is available from a variety of different Python interfaces including
standard Python scripts and Jupyter Notebooks. We strive to make it assimpleas
possible for users to generate, debug, customize, and ultimately use data to solve
optimization problems -- all while maintaining high performance.Main FeaturesHere are just a few of the things thatgamsapidoes well:Seamlessly integrates GAMS data requirements into standard data pipelines (i.e., Pandas, Numpy)Link and harmonize data sets across different symbolsClean/debug databeforeit enters the modeling environmentCustomize the look and feel of the data (i.e., labeling conventions)Bring data to GAMS from a variety of different starting pointsSend model output to a variety of different data endpoints (SQL, CSV, Excel, etc.)Automatic data reshaping and standardization -- will work to translate your data formats into the Pandas DataFrame standardControl GAMS model solves and model specificationWhere to get itThe source code is currently available with any typicalGAMS system.
No license is needed in order to usegamsapi.  A license is necessary in order to solve GAMS models.A freedemo licenseis available!DependenciesInstallinggamsapiwill not install any third-party dependencies, as such, it only contains basic functionality.
Users should modify this base installation by choosingextrasto install -- extras are described in thedocumentation.# from PyPI (with extra "transfer")pipinstallgamsapi[transfer]# from PyPI (with extras "transfer" and "magic")pipinstallgamsapi[transfer,magic]# from PyPI (include all dependencies)pipinstallgamsapi[all]DocumentationThe official documentation is hosted ongams.com.Getting HelpFor usage questions, the best place to go to isGAMS.
General questions and discussions can also take place on theGAMS World Forum.Discussion and DevelopmentIf you have a design request or concern, please write tosupport@gams.com.
Package: gamspec
GamutGamut (GAMma Utils) is a python-based gamma spectrum analysis program.Submodules:
-PeakRegion: basic objects, including:Region,Calibration.
-Spectrum:Spectrum,SimulatedSpectrumandsimuspecs.
-Operator:Operator,Pipe,Node,FlowandPipiNetobjects.-operators:
- Smoother
- PeakSearcher
- AreaCalculator
- OtherOpeartor
- BasicOperator
Package: gamspy
GAMSPy: Algebraic Modeling Interface to GAMSInstallationpipinstallgamspyWhat is it?gamspyis a mathematical optimization package that combines the power of the high performance GAMS execution system
and flexibility of the Python language. It includes all GAMS symbols (Set, Alias, Parameter, Variable, and
Equation) to compose mathematical models, a math package, and various utility functions.Design PhilosophyGAMSPy makes extensive use of set based operations -- the absence of any explicit looping, indexing, etc., in native Python.
These things are taking place, of course, just “behind the scenes” in optimized, pre-compiled C code.Set based approach has many advantages:Results in more concise Python code -- avoids inefficient and difficult to read for loopsClosely resembles standard mathematical notationEasier to readFewer lines of code generally means fewer bugsMain FeaturesHere are just a few of the things thatgamspydoes well:Specify model algebra in Python nativelyCombines the flexibility of Python programming flow controls and the power of model specification in GAMSTest a variety of solvers on a model by changing only one lineDocumentationThe official documentation is hosted onGAMSPy Readthedocs.Getting HelpFor usage questions, the best place to go to isGAMS.
General questions and discussions can also take place on theGAMS World Forum.Discussion and DevelopmentIf you have a design request or concern, please write tosupport@gams.com.
Package: gamspy-base
GAMSPy BaseGAMSPy Base is a subset of full GAMS installation which can be found at:https://gams.com/download/.How to use it in other GAMS APIsControl:from gams import GamsWorkspace
from gamspy_base import directory
ws = GamsWorkspace(system_directory=directory)Magic:%reload_ext gams.magic
from gamspy_base import directory
gams.reset(directory)GamsInteractive:from gamspy_base import directory
from gams.magic import GamsInteractive
gams = GamsInteractive()
gams.reset(directory)Transfer:import gams.transfer as gt
from gamspy_base import directory
m = gt.Container(system_directory=directory)Connect:from gams.connect import ConnectDatabase
from gamspy_base import directory
cdb = ConnectDatabase(directory)GDX Expert-Level:import gams.core.gdx as gdx
from gamspy_base import directory
rc = gdx.gdxGetReadyD(directory, 255)License AgreementBy downloading this software, you agree to theGAMS license agreement.
Package: gamtools
[![Build Status](https://travis-ci.org/pombo-lab/gamtools.svg?branch=master)](https://travis-ci.org/pombo-lab/gamtools) [![Documentation Status](https://readthedocs.org/projects/gamtools/badge/?version=latest)](http://docs.gam.tools/en/latest/?badge=latest) [![Code Climate](https://codeclimate.com/github/pombo-lab/gamtools/badges/gpa.svg)](https://codeclimate.com/github/pombo-lab/gamtools) [![Coverage Status](https://coveralls.io/repos/github/pombo-lab/gamtools/badge.svg?branch=master)](https://coveralls.io/github/pombo-lab/gamtools?branch=master)GAMtoolsGAMtools is a software package for mapping, processing, analyzing and
performing quality checks on GAM data.The official source code repository and issue tracker are on GitHub:https://github.com/pombo-lab/gamtoolsYou can read the documentation at:http://docs.gam.tools/