Saravanan666/pypi-org huggingface dataset:
Package: petab-MS
petab_MS - a Python package for handling PEtab files for multiscale systemsPEtab_MS is an extension of PEtab for multiscale systems.PEtabis a data format for specifying
parameter estimation problems in systems biology. This repository provides
thepetabPython package for reading, writing and validating PEtab files.InstallationThe PEtab library is available onpypiand the easiest way to install it is runningpip3 install petab_MSIt will require Python>=3.7.1 to run.Development versions of the PEtab library can be installed usingpip3 install https://github.com/EmadAlamoudi/libpetab-python-MS/archive/develop.zip(replacedevelopby the branch or commit you would like to install).Getting helpIf you have any question or problems with this package, feel free to post them
at our GitHubissue tracker.ContributingContributions and feedback to this package are very welcome, see ourcontribution guide.
Package: petabtests
PEtab test suiteThe PEtab test suite is a collection of test models for thePEtabparameter estimation data format.
It is intended to be used to verify and quantify PEtab-support by developers
of tools for model simulation and parameter estimation.Download and install the test suiteThe PEtab test suite can be downloaded from GitHub viagit clone https://github.com/petab-dev/petab_test_suiteThe test suite comes with all necessary files pregenerated.
In thepetabtestssubdirectory, it contains a python module for
generating the tests and evaluating results. This can be installed viacd petab_test_suite
pip3 install -e .Use the test suiteThecasessubdirectory contains a collection of enumerated tests.
Each test contains a descriptivewxyz.mdfile, and a script filewxyz.pyfile that can be used to generate all problem and solution files for the test.
The necessary files are in the same case specific folder, starting with an
underscore.
In each case folder, there is a file_wxyz.yamlcontaining the parameter
estimation problem description, and a file_wxyz_solution.yamlcontaining
information on the expected results: chi2 value, log-likelihood, simulation
table reference, and tolerances.After download, all necessary files are already pregenerated.Further, installation of the petabtests module installs a routine to recreate
the problem and solution files for all test problems. It can be invoked viacd petab_test_suite
petabtests_createEvaluate resultsTo evaluate how a tool performs on a given test problem, three metrics are
employed: Simulations, chi2 value and log-likelihood. A tool can be said to
cover a test problem if any of those values matches the ground truth values
up to some tolerance.The python module provides convenience functions for evaluation inpetabtests/evaluate.py.->Overview of passed test cases for different tools supporting PEtab
Package: petabvis
petabvisAn interactive visualization tool forPEtabmeasurement and simulation files.InstallationFrom PyPIpip3installpetabvisFrom GitHubpip3installgit+https://github.com/PEtab-dev/petabvis.git@mainFrom a local copy of the sourceFrom within the root source directory, run:pip3install.UsageRunpetabvisSelect a PEtab YAML fileOptionally select a simulation fileNavigate through the plotsSee alsopetabvis -hfor command line usage.
Package: petakita
Petakita PackageA helper pip package for PetakitaAssumptions:Assuming python is installed on your system.Installation sets up commandSituation before installation::$ petakita
bash: petakita: command not foundInstallation right from the source tree (or via pip from PyPI)::$ python setup.py install

$ pip install petakitaNow, thepetakitacommand is available::$ petakita
Welcome to Petakita!
Package: petal
:hibiscus: Petal - Flask, for gRPC services.Petal reduces the boilerplate required to write and maintain gRPC based services whilst ensuring your service
code is always in sync with your definitions. It aims to be a somewhat opinionated gRPC framework.This is very, very early code.Installation and featurespip install petalRight now:Define your service methods as simple Python functionsEnsure they are up to date with service definitions by utilizing type annotationsBuild your proto definitions and run your app with apetalcommandShare compiled protobuf definitions as Python packages.Streaming requests and responsesFuture features:Autoreloading during developmentStructured loggingSome form of plugin architectureDistributed tracingA testing clientAsyncIO supportHello world example:frompetalimportServicefromexample.protobuf.greeter_pb2importHelloReply,HelloRequestservice=Service(__name__)@service.grpc()defsay_hello(request:HelloRequest)->HelloReply:returnHelloReply(message=f'Hello{request.name}')Tutorial: Creating a petal appLets create a Hello World Petal app. First lets define an entirely useless service. Place the file below
inhello_world/protobuf/service.proto:syntax="proto3";packagehello_world.protobuf.service;serviceHelloWorld{rpcSayHello(HelloRequest)returns(HelloReply){}}messageHelloRequest{}messageHelloReply{}Then runpetal build hello_world.Next, write our service definitions. Create ahello_world/__init__.pyfile and add some imports:frompetalimportServicefromhello_world.protobuf.service_pb2importHelloReply,HelloRequestservice=Service(__name__)This imports the request and response types our service will need. Next we need to fill in theSayHellomethod:@service.grpc()defsay_hello(request:HelloRequest)->HelloReply:returnHelloReply()And finally, run the service by executingpetal run hello_world.Now if we modify the service by adding a new method:messageHelloAgain{}serviceHelloWorld{rpcSayHelloAgain(HelloRequest)returns(HelloAgain){}}And runpetal build hello_world && petal run hello_world, we get an error:$petalrunhello_world
Error:CannotfindasuitablemethodforGRPCmethodSayHelloAgain
Pleaseensureoneexistsinyourservicecode,oruseservice.grpc(name=NAME)todefineone.Petal is ensuring we have the correct Python methods defined for all our service functions. We can fix this
by defining one:fromhello_world.protobuf.service_pb2importHelloAgain@service.grpc()defsay_hello_again(request:HelloRequest)->HelloAgain:returnHelloAgain()Now runningpetal serve hello_worldwill work.
Package: petaldata
PetalData Python LibraryExport your data from cloud apps like Stripe, Hubspot, and Metabase into Pandas Dataframes withPetalData.Installationpip install --upgrade petaldataRequirementsPython 3.4+UsageimportpetaldataExample UsageSee theexamples directoryin theGitHub repo.DocumentationInteractive documentation is athttps://petaldata.app/.ReleasingUpdate CHANGELOGBump the version number insetup.py.Generate the distribution archive and upload the archive:rm dist/*
python setup.py sdist bdist_wheel
python -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*Adding a DatasetCreate apetaldata/datasets/[CLOUD_APP]directoryCreate apetaldata/datasets/[CLOUD_APP]/[DATASET_NAME (PLURAL)].pyfile.Create a class that inherits frompetaldata.datasets.abstract.Dataset. Seepetaldata.datasets.stripe.Invoicesfor an example.Create apetaldata/datasets/[CLOUD_APP]/__init__.pyfile.Import the dataset created above.Add config variables needed to use the[CLOUD_APP] API (like anAPI KEY`).Addfrom petaldata.datasets import [CLOUD_APP]topetaldata/__init__.pyQuestionsEmailderek@petaldata.app.
Package: petard
# petard> For ’tis the sport to have the engineer Hoist with his own petard…
>     - William Shakespeare, “Hamlet”Petard provides a crude Python interface for the Amazon API Gateway service.
Because the API Gateway uses a style of API that is very different than other
Amazon Web Services, it has taken much longer for the official SDK’s to provide
support for the service.Because I had an urgent need to have a Python library for API Gateway, I have
created this small hack that builds upon the mechanisms in botocore to provide
a rough interface.  I’m sure the official SDK’s will soon support API Gateway
and when they do this library will not be of much use to anyone.  But in the
meantime, if it helps you in any way you are more than welcome to it.
Package: petbenchmarks
Benchmarking procedure to test approaches on thePET-dataset(hosted onhuggingface).This is an beta version.Documentation will come soon.Example of ‘’how to benchmark an approach’’frompetbenchmarks.benchmarksimportBenchmarkApproachBenchmarkApproach(tested_approach_name='Approach-name',predictions_file_or_folder='path-to-prediction-file.json')TheBenchmarkApproachobject does all the job.
It reads the prediction file, computes score and generates a reports.Created byPatrizio Bellan.
Package: petchou-pathfinder
pathfindingTable of contenttable of contentInstallationNoteKnown issuesUseCreate an instanceSteps to find a pathDisplay the pathInstallationYou can install the package by running the following command :python3 -m pip install --upgrade PathFinderNotePlease notethat the package is currently inbetaversion and may encounterbugsorunexpected behavioursfor whichI am not responsible.Known issues :No cartesian coordinates for grid display available (only alphabetical coordinates)Grid display is not centered and might not work in every terminalThefind_pathmethod is not optimized and might take a long time to find a pathUseCreate an instanceSimply import thepathfindingobject from the package into your script and start use it.frompathfindingimportPathFinderaspf# From Asynconf 2022 - https://asynconf.fr/map="""O___O_OO__OO__VO_O_O__O___O_OOO_OO_____OOO___O___OOO_OOOOO_O__OO__X__OO_O___O__O_OO___OO______O___OO""".split("\n")pathfinder=pf(map,# map to use (list or str)"X",# start point (str)"V",# end point (str)walkable="_",# walkable tiles (str)non_walkable="O",# non walkable tiles (str)debug_mode=False# display processing informations (bool))Steps to find a pathMake a graph from the mapgraph=pathfinder.make_graph()Get the first path foundpath=pathfinder.find_path()Optimize the pathoptimized_path=pathfinder.optimize_path(path)use the pathprint(pathfinder.get_path(alphanumeric=True))print(pathfinder.get_path(alphanumeric=False))##################################################['G4','H4','I4','I5','J5','K5','L5','M5','N5','N4','O4','P4','P5','Q5','R5','R4','S4','S3','S2','R2','Q2','P2','O2','O1'][(6,3),(7,3),(8,3),(8,4),(9,4),(10,4),(11,4),(12,4),(13,4),(13,3),(14,3),(15,3),(15,4),(16,4),(17,4),(17,3),(18,3),(18,2),(18,1),(17,1),(16,1),(15,1),(14,1),(14,0)]Display the pathprint(pathfinder.show_path(show_grid=True,animate=False))################################################### displays the path on the map. If animate is True, the path will be displayed step by step and the screen will be cleared after each step
Package: petcmd
petcmdInstallationpetcmdcan be installed withpip:python-mpipinstallpetcmdYou can also download the project source and do:pipinstall.Dependenciespetcmdwas created as a lightweight package, so there are no dependencies.UsageComing soon.ExamplesComing soon.DocumentationDocumentation is available athttps://petcmd.readthedocs.io/en/latest/Testingpython-mtests
Package: petdb
PetDBPetDB is a simple and lightweight NOSQL JSON database for pet projects.
It was designed with the convenient and comfortable mongo-like API.
In this package also was implemented simple array with the same API,
that supports not only dict documents but any built-in python type.InstallationPetDB can be installed withpip:python-mpipinstallpetdbYou can also download the project source and do:pipinstall.DependenciesPetDB was created as a lightweight package, so there are no dependencies.UsageTo use this database you should only import PetDB and create an instance.
By default, it will create folder for storing collections in the current directory,
but you can provide any path:frompetdbimportPetDBdb=PetDB(os.path.join(os.getcwd(),"persistent","storage"))Next you should select the collection, if it doesn't exist, it will created automatically.
You can do it just by attribute access, by index key or use a method (it will create a new one if it doesn't exist):users=db.userssubscriptions=db["subscriptions"]payments=db.collection("payments")Examples>>>frompetdbimportPetDB>>>db=PetDB()>>>users=db["users"]>>>users.insert_many([...{"name":"John","age":28,"subscriptions":["tv","cloud"]},...{"name":"Michael","age":32,"subscriptions":["tv"]},...{"name":"Sam","age":18,"subscriptions":["music","cloud"]},...{"name":"Alex","age":24,"subscriptions":["tv","music"]},...{"name":"Bob","age":18,"subscriptions":["music"]},...])[{"name": "John", "age": 28, "subscriptions": ["tv", "cloud"], "_id": "..."}, ...]>>>users.update(...{"$append":{"subscriptions":"games"}},...{"age":{"$lt":25},"subscriptions":{"$contains":"music"}}...)>>>selection=users.filter({"age":18}).sort("name")>>>print(selection.flat("name").list())["Bob", "Sam"]>>>selection.remove()[{"name": "Bob", "age": 18, "subscriptions": ["music", "games"], "_id": "..."},{"name": "Sam", "age": 18, "subscriptions": ["music", "cloud", "games"], "_id": "..."}]>>>foruserinusers:...print(user)...{'name': 'John', 'age': 28, 'subscriptions': ['tv', 'cloud'], '_id': '...'}{'name': 'Michael', 'age': 32, 'subscriptions': ['tv'], '_id': '...'}{'name': 'Alex', 'age': 24, 'subscriptions': ['tv', 'music', 'games'], '_id': '...'}DocumentationDocumentation is available athttps://petdb.readthedocs.io/en/latest/Testingpython-mtests
Package: pete
PeteHe’s just happy to be hereVery much a work in progress. A task runner that will check on things,
and report things. Spiritually similar toIFTTT.Example>>> from pete.runner import Runner
>>> from pete.examples import StringBroadcaster, TimeChecker
>>>
>>> runner = Runner(
...     tasks=[TimeChecker()],
...     broadcasters=[StringBroadcaster()],
...     timeout=10)
>>> runner.main()
It is now 00:00:56
It is now 00:01:06
...PlansBroadcastersEmailSlackHTMLRSSTasksMBTA delaysWeather in the white mountainsWhy is the flag at half mast
Package: peter-69
PeTeR_69A small demo library that describes the input number through PeTeR.Installationpip install peter-69Get startedHow to describe your number with this library:frompeter_69importPeTeR# Instantiate a PeTeR objectpeter=PeTeR()# Call the says methodresult=peter.says(-42069)ContributorsParas GuptaTarun SharmaReuben Devanesan
Package: peter-parquet
Peter ParquetQuickly inspect parquet file in command lineWhere to get itThe source coid is currently hosted on Github at:
Binary installers for the latest version are available at the repopipinstallpeter-parquetUsagepeter-parquet --helpreturn all commands availableNon Bash usersIf you are a non Bash user, notice that Python package executables are installed to ~/.local/bin. So, make sure to add this entry to your PATH variable.
Package: peterparser
🎉 PeterParser: Your Personal Python Parser for Blockchain and DeFi Configurations or other domains 🎉🚀 IntroductionWelcome toPeterParser, a Python package that's become an integral part of my personal development workflow in blockchain and DeFi projects. While it may not be a one-size-fits-all solution, it's tailored to efficiently read and write the specific configuration files I often use in Web3 environments. It handles lists and dictionaries with ease, making my smart contract interactions smooth and hassle-free.✅ Current StateAs of now, this package does exactly what I need it to do for my specific use-cases in blockchain and DeFi projects. If you happen to work in a similar manner, you might find it useful as well.🌟 Key FeaturesPersonalized Utility: Created to support my unique workflow in blockchain and DeFi.Efficient Parsing: Skilled at reading and writing configuration files that include lists and dictionaries.Web3 Friendly: Geared to facilitate seamless smart contract interactions in Web3 environments.No External Dependencies: Works right out of the box.Open for Contributions: If you find it useful, your improvements are welcome.🚀 Quick StartOption 1: Using pippipinstallpeterparserOption 2: Cloning from GitHubAlternatively, you can clone the repository directly from GitHub to access the latest features and updates that might not yet be published on PyPI:Open a terminal window.Navigate to the directory where you want to clone the repository.Run the following command to clone the repository:gitclonehttps://github.com/deefifofun/peterparser.gitNavigate into the cloned directory:cdpeterparserInstall the package using:pipinstall.This will install the package from the source code.
Package: petersburg
petersburg==========version number: 0.0.1author: Will McGinnisOverview========A simple framework for analyzing decision processes as directed graphs.Simulating uncertain decisions------------------------------Inspired by the [St. Petersburg paradox](https://en.wikipedia.org/wiki/St._Petersburg_paradox),where the expected value of a simple game is infinity, unless reasonable contraints are set (i.e. the bank doesn't haveand infinite bankroll, you don't have infinite time to play, etc.). The idea is that we can represent decisions and thedecisions they lead to as directed acyclic graphs, wherein the nodes have payoffs, edges have costs, and at each node,the edge to progress on to is selected at random (with weights).With such a graph assembled, we can simulate the different first choices to see what the expected (simulated) outcome ofeach is.Future goals------------In the future the plan is to add things like extremely rare and extreme events at each node (i.e. unknown unknowns, orblack swans), more complex switching logic at nodes than weighted random, more complex cost and payoff models (utility),and better methods for building complex graphs.The first outcome will be more detailed metrics on the simulated outcomes than just mean (simulated expected value). Ofcourse a positive expected value with extremely high risk is different than the same expected value without. We'd loveto capture that concisely.Installation / Usage====================To install use pip:$ pip install petersburgOr clone the repo:$ git clone https://github.com/wdm0006/petersburg.git$ python setup.py installContributing============TBDExample=======Here is a simple example of simulating the St. Petersburg Paradox game, with some slight variations. In this case theentrance fee is $10, and the game only has a maximum of 10,000 flips and is played 10,000,000 times.from petersburg import Graphif __name__ == '__main__':g = Graph()# st petersburg paradox w/ $10 entrance fee and only 10000 possible flipsentrance_fee = 10gd = {1: {'payoff': 0, 'after': []}, 2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': entrance_fee}]}}nn = 3for idx in range(10000):node_id = 2 * (idx + 1)payoff = 2 ** (idx + 1)gd[nn] = {'payoff': payoff, 'after': [{'node_id': node_id, 'cost': 0, 'weight': 1}]}nn += 1gd[nn] = {'payoff': 0, 'after': [{'node_id': node_id, 'cost': 0, 'weight': 1}]}nn += 1g.from_dict(gd)outcomes = []for _ in range(10000000):outcomes.append(g.get_outcome())print('\n\nSimulated Output')print(sum(outcomes))Via simulation, the outcome of this is a profit of: $197,592,288.  This will, of course, vary depending on the run, butwill approach infinity as the number of games goes to infinity, regardless of cost-to-play.
Package: petfacts
About the petfacts CLI applicationPet facts is a simple but fun application that retrieves either dog or cat facts from two different resources.Click hereto see the cat facts api.Click hereto see the dog facts api.Why did I write it?I wrote this application to learn a python module called argparse, and this seemed like the perfect fun project to do this with.How can I get it?You can clone the repository onGitHubor install viapipby runningpip install petfactsHow to useThis is a very simple and light program, to see a random pet fact of a dog or cat variety simply runpetfactswith not arguments.If you want to specify a whether you want a dog or car fact pass in the arguments like so:petfacts --catwill return a cat fact.petfacts --dogwill return a dog fact.petfacts --helporpetfacts -hof course returns a help menu and exits.Whats next?TODO: I want to get this up on PyPi so you just have to usepipto install.TODO: Give it the ability to store facts, why?  Just because I'm learning.
Package: petfinder-sdk
petfinder-sdkA high-performance, modern client for retrieving data from the Petfinder API.The petfinder API provides access to detailed records for millions of pets, and anyone who requests an API key can access a minimum of 100,000 records per day. Petfinder-sdk provides a clean and efficient interface for accessing that data.FeaturesSupports both standard and async usage.It's fast. Even in "standard" mode the client will execute batches of requests concurrently, so you get the benefits of async execution without needing to write async code.It's efficient. To reduce duplicate requests (which count against your daily API quota), petfinder-sdk comes with a built-in requests cache.It's user-friendly. Results are provided as a pandas DataFrame, so you can immediately analyze them or export the data into a variety of formats.Getting started$pipinstallpetfinder-sdkThen, go here to get a petfinder API key:https://www.petfinder.com/developers/
Package: petisco
petisco 🍪Documentation:https://alice-biometrics.github.io/petisco/Source Code:https://github.com/alice-biometrics/petiscoWhat is petisco? 🍪petisco is a framework for helping Python developers to build clean Applications in Python.How could petisco help me? 🍪petisco provides several handy classes to help you on defining your domain with hexagonal architecture, event streaming and CQRS.Installation 💻pip install petiscoInstallation with Extraspip install petisco[fastapi,sqlalchemy,elastic,elastic-apm,rabbitmq,slack,redis]ContributeWe'd love you to contribute topetisco🥳🥳🥳🥳🥳🥳️️!For more information, check ourdocumentationContact 📬support@alicebiometrics.com
Package: petit-downloader
[Latest Version = 0.17][Installation]$pipinstallFancy_Downloader##[How to use]frompetit_downloaderimportDownloadd=Download(url=url,filename="test.zip",type="serial")#verbose requeries the Fancy_Progressbar libd.download()Download types availablebasicserialparrarelserial_parralelObjects availableDownload()DownloadContainer()Action(func,*args)UpdateCan now reload stopped download in order to continue it, use :# here you load the json data from the file or your dbd:DownloadProgressSave=...download=from_save(d)# this will resume your downloaddownload.download()