Saravanan666/pypi-org huggingface dataset:
Package: pahelee
# PaheleeMaze generator using recursive depth first search algorithm[![Demo](https://i.imgur.com/gy9hrI2.gif)](https://github.com/psuzn/pahelee)## What is it?**Pahelle** is a maze generator which impliments [Depth-first search Recursive backtracker](https://en.wikipedia.org/wiki/Maze_generation_algorithm) in OpenGL (python)Installation / Usage--------------------**Installation**To install use pip:$ pip install paheleeOr clone the repo:$ git clone https://github.com/psuzn/pahelee.git$ python setup.py install**Usage**just run```shell$ pahelee```This will open a GLUT window of size 60*60 with 15 row and 15 columns (grid size) and after completition it will save a png image in your home directory.## Todos- [x] basic implimentation of algorithm.- [x] Upload this pakage to PyPI.- [ ] commandline arguments for different variables (like: no of grid rows).## SupportThis is in  alpha version and might cause some problem. so you can help me make it stable.Please [open an issue](https://github.com/pahelee/romoNepali/issues/new) for support.## ContributingPlease contribute using [Github Flow](https://guides.github.com/introduction/flow/). Create a branch, add commits, and [open a pull request](https://github.com/psuzn/pahelee/compare/).
Package: paho-socket
paho-socketThis package features a thin layer built on top ofpaho-mqttallowing for connections with unix socket
brokers. The package was built with my other projects in mind, and at the moment it only has
features I've found useful so far. To be more precise, it allows:Connecting to unix socket based brokers supporting plaintext MQTT protocol.Following features are not supported:TLS over unix socket,MQTT over websocket over unix socket.All the base features of paho-mqtt are still available using the package provided class.The package is based on paho-mqtt 1.5.1, and since it utilizes non public methods and attributes of
the original client it may not work with other versions.InstallationPackage can be installed from PyPI:pip install paho-socketConnection establishingThe package provides a class that inherits from the paho-mqttClient. It's interface is extended
withsock_connectandsock_connect_asyncmethods, which should be used in the similar manner as
those intended for creation of TCP/IP based connections. Once the connection is established, all the
paho-mqtt feature work as in the original package, including reconnects.TestsTo perform test activities, it is necessary to install dependencies contained inrequirements-dev.txt.python setup.py develop
pip install -e '.[dev]'To run automatic tests, run the following commandpytest --cov=paho_socketLicenseThis code is released under theEPL.
Package: pahotoolkit
Paho's MQTT toolkitWelcome to Paho's MQTT Toolkit.For who? and why?This toolkit if for those who have gone through the documentation of thePaho's MQTT library and its examplesand, like me, have felt that the API is somehow off and complicated to onboard applications.I, personally, like Paho's MQTT library and I don't wish to replace it in anyway. However, I want to write applications that in a way, are familiar to the frameworks I use, e.g., Flask or Sanic.
And therefore, abstracting the underlying implementation by using a solid configuration base with a simplified API.What's the base configuration?The base configuration of this toolkit uses Paho's event loop implementation by using thestart_loopandstop_loopmethods and creating a single entrypoint method that accepts different configuration parameters,start_async.Simple UsageFor more examples refer to the examples directory.importtimefrompaho.mqtt.clientimportClientfrompahotoolkitimport(init,start_async,stop_async,subscribe,json_message,)frommy_settingsimport(HOST,PORT,USERNAME,PASSWORD,LOG_CONFIG)@subscribe('/temperature')defhandle_temperature(mqtt_client:Client,userdata,message):print(f'Got{message}')@subscribe('/my/topic')@json_message()# by default, non-json messages in the topic are ignoreddeflisten_my_topic(payload:dict,*args,**kwargs):# todo: do something with the payloadpass@on_connect()defclient_connected(mqtt_client:Client,*args,**kwargs):"""Publish something on_connect."""mqtt_client.publish('/clients','Paho\'s MQTT toolkit message!')# log_config => https://docs.python.org/3/library/logging.config.html#logging.config.dictConfig# the default MQTT Client uses the default's MQTT Client constructor# https://github.com/eclipse/paho.mqtt.python#client-1defmain():client:Client=start_async(host=HOST,port=PORT,# port default to 1883 if not givenusername=USERNAME,password=PASSWORD,# optional fieldsinitialize=False,log_config=LOG_CONFIG)# optional fieldsinit(client)# only required if initialize=False, else by default is automatically calledwhileTrue:time.sleep(1)# or do something in this thread...if__name__=='__main__':try:main()finally:stop_async()ContactArnulfo Solisarnulfojr94@gmail.com
Package: pahotoolkitfork
Paho's MQTT toolkitWelcome to Paho's MQTT Toolkit.For who? and why?This toolkit if for those who have gone through the documentation of thePaho's MQTT library and its examplesand, like me, have felt that the API is somehow off and complicated to onboard applications.I, personally, like Paho's MQTT library and I don't wish to replace it in anyway. However, I want to write applications that in a way, are familiar to the frameworks I use, e.g., Flask or Sanic.
And therefore, abstracting the underlying implementation by using a solid configuration base with a simplified API.What's the base configuration?The base configuration of this toolkit uses Paho's event loop implementation by using thestart_loopandstop_loopmethods and creating a single entrypoint method that accepts different configuration parameters,start_async.Simple UsageFor more examples refer to the examples directory.importtimefrompaho.mqtt.clientimportClientfrompahotoolkitimport(init,start_async,stop_async,subscribe,json_message,)frommy_settingsimport(HOST,PORT,USERNAME,PASSWORD,LOG_CONFIG)@subscribe('/temperature')defhandle_temperature(mqtt_client:Client,userdata,message):print(f'Got{message}')@subscribe('/my/topic')@json_message()# by default, non-json messages in the topic are ignoreddeflisten_my_topic(payload:dict,*args,**kwargs):# todo: do something with the payloadpass@on_connect()defclient_connected(mqtt_client:Client,*args,**kwargs):"""Publish something on_connect."""mqtt_client.publish('/clients','Paho\'s MQTT toolkit message!')# log_config => https://docs.python.org/3/library/logging.config.html#logging.config.dictConfig# the default MQTT Client uses the default's MQTT Client constructor# https://github.com/eclipse/paho.mqtt.python#client-1defmain():client:Client=start_async(host=HOST,port=PORT,# port default to 1883 if not givenusername=USERNAME,password=PASSWORD,# optional fieldsinitialize=False,log_config=LOG_CONFIG)# optional fieldsinit(client)# only required if initialize=False, else by default is automatically calledwhileTrue:time.sleep(1)# or do something in this thread...if__name__=='__main__':try:main()finally:stop_async()ContactArnulfo Solisarnulfojr94@gmail.com
Package: pahud-cdk-github-oidc
cdk-github-oidcInspired byaripalo/aws-cdk-github-oidc, this construct library allows you to create aGithub OpenID Connect Identity Providertrust relationship with theProviderconstruct as well as federated IAM roles for one or multiple Github repositories.This construct is still inexperimentalstage and may have breaking changes. However, we aim to make this library as simple as possible.Sampleimport{Provider}from'@pahud/cdk-github-oidc';//createanewproviderconstprovider=newProvider(stack,'GithubOpenIdConnectProvider')//createanIAMrolefromthisproviderprovider.createRole('demo-role',//sharingthisroleacrossmultiplerepositories[{owner:'octo-org',repo:'first-repo'},{owner:'octo-org',repo:'second-repo'},{owner:'octo-org',repo:'third-repo'},])Import the providerEach AWS account can only have one GitHub OIDC identity provider. To import the existing one, useProvider.fromAccount()://importtheproviderconstprovider=Provider.fromAccount(stack,'GithubOpenIdConnectProvider')//createaiamrolefromtheimportedproviderprovider.createRole(...)Workflow samplename:demoon:workflow_dispatch:{}jobs:deploy:name:Upload to Amazon S3runs-on:ubuntu-latestenv:AWS_REGION:us-east-1permissions:id-token:write# needed to interact with GitHub's OIDC Token endpoint.contents:readsteps:-name:Checkoutuses:actions/checkout@v2-name:Configure AWS credentialsuses:aws-actions/configure-aws-credentials@masterwith:role-to-assume:${{ secrets.AWS_ROLE_ARN_TO_ASSUME }}aws-region:${{ env.AWS_REGION }}-name:Sync files to S3run:|aws s3 sync ./ s3://${{ secrets.AWS_BUCKET }}Projects using this librarypahud/gitpod-workspacepahud/github-codespacepahud/vscodeReferenceConfiguring OpenID Connect in Amazon Web Servicesfrom GitHub Docsaripalo/aws-cdk-github-oidcbyAri Palo
Package: pahud-cdktf-aws-ecs
cdktf-aws-ecsCDKTF construct library for Amazon ECS.UsageThe following sample creates:A new VPCAmazon ECS clusterAutoscaling Group capacity providerAutoscaling Group with Launch Template# Example automatically generated from non-compiling source. May contain errors.frompahud.cdktf_aws_ecsimportCluster# create the clustercluster=Cluster(stack,"EcsCluster")# create the ASG capacity with capacity providercluster.add_asg_capacity("ASGCapacity",max_capacity=10,min_capacity=0,desired_capacity=2)Existing VPC subnetsTo deploy in any existing VPC, specify thevpcSubnets.# Example automatically generated from non-compiling source. May contain errors.cluster.add_asg_capacity("ASGCapacity",vpc_subnets=["subnet-111","subnet-222","subnet-333"])Bottlerocket supportTo create cluster capacity with Bottlerocket machine image:# Example automatically generated from non-compiling source. May contain errors.cluster.add_asg_capacity("BRCapacity",machine_image=BottleRocketImage(stack))
Package: pahud-cdktf-aws-eks
cdktf-aws-eksCDKTF construct library for Amazon EKS.UsageThe following sample creates:A new VPCAmazon EKS cluster(control plane)The default nodegroup with the clusterThe 2nd nodegroup with spot instances# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826frompahud.cdktf_aws_eksimportCluster# create the cluster and the default nodegroupcluster=Cluster(stack,"demo-cluster",version=KubernetesVersion.V1_21,scaling_config={"min_capacity":1})# create the optional 2nd nodegroupcluster.add_node_group("NG2",scaling_config={"min_capacity":1,"max_capacity":10,"desired_capacity":5},capacity_type=CapacityType.SPOT,instance_types=["t3.large","c5.large","m5.large"])Existing VPC subnetsTo deploy in any existing VPC, specify theprivateSubnetsandpublicSubnets(if any).# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826Cluster(stack,"demo-cluster",private_subnets=["subnet-111","subnet-222","subnet-333"],public_subnets=["subnet-444","subnet-555","subnet-666"],version=KubernetesVersion.V1_21)
Package: paiagym
PAIAGymA platform to run Gymnasium or PettingZoo games with AI.Installationpip install paiagymUsageInstall a game:paiagym install <game_name>Uninstall a game:paiagym uninstall <game_name>Run the inferencing with given information by the environment variable:paiagym runRun the inferencing with given script path:paiagym run <game_name> -i <script_path>Run the training with given information by the environment variable:paiagym trainRun the training with given script path:paiagym train <game_name> -i <script_path>List all added games:paiagym lsList all available games:paiagym ls -aList games in development:paiagym ls -m devList games in production:paiagym ls -m prodUsage for ContainerYou can checkout the Dockerfile for the Docker container.To build the Docker image:docker build -t paiagym:base . --no-cacheIf you are using Linux server, run before starting the container (install and config X server with NVIDIA Driver):sudo sh display.shdisplay.sh can be found atdisplay.sh.To start the container:docker run -it --rm --gpus all -v /tmp/.X11-unix:/tmp/.X11-unix paiagym:base
Package: paichat
Python Open AI CLI Client [Un-official]Published on pypi:https://pypi.org/project/paichat/SupportedA Terminal Tool in PythonPrerequisitespipx. If you don't have pipx installed, you can install like this:python3 -m pip install --user pipxpython3 -m pipx ensurepathYou'll also need to have your own OpenAi apikey:Go tohttps://beta.openai.com/Select you profile menu and go to Manage API KeysSelect + Create new secret keyCopy generated keyInstall:pipx install paichatUsage:paichat --helpUsage:paichat[OPTIONS]╭─Options───────────────────────────────────────╮
│--helpShowthismessageandexit.│
╰─────────────────────────────────────────────────╯Contribute:Dev:Start a virtualenv using poetry:poetry shellIfpoetry shelldoes not activate the virtualenv, then use this:source $(poetry env info --path)/bin/activateWhen you're done, exit virtualenv shell:deactivate
Package: paicli
paicliA CLI tool forOpenPAI, which supports basic opperations like listing up jobs, submitting a new job, suspending a running job, and executing ssh into a running container.How to installOne can install paicli fromPyPI.$ pip install paicliMotivating examplesOne can utilize and combine the paicli subcommands to achieve several practical operations.1. Ssh into a running containerWhen you want to ssh into a running container, all you have to do is just typepai ssh:$ pai sshThen, you can choose a container from your running job list.
There is no need to download the key and specify the host explicitly.
You can also give a job name of your running job as argument:$ pai ssh example_job_0012. Submit multiple jobs with one lineOne can usepai submitand some template engine likeenvsubst.$headtemplate.json-n2{"jobName":"example_job_${JOBID}"$foriin`seq13`;docatexample.json|JOBID=$ienvsubst|paisubmit;done3. Stop multiple jobs with one lineOne can combinepai jobsandpai stop.$paijobs-usotetsuk-sRUNNING|grepexample_job|awk'{print $1}'|xargspaistop4. Ssh into multiple running containers and run the same commandOne can combinepai jobsandpai ssh. In this example, it shows python processes in multiple jobs with one line.$paijobs-usotetsuk-sRUNNING|grepexample_job|awk'{print $1}'|xargs-n1paissh-c"ps -aux | grep python"5. Show all tensorboard URLs in running jobsOne can combinepai jobsandpai hostto show all tensorboard URLs. One can use some browser extention to open all URLs (e.g.,OpenList)$paijobs-usotetsuk-sRUNNING|grepexample_job|xargs-n1paihost|greptensorboard|awk'{printf "http://%s:%s\n",$2,$4}'http://10.0.0.1:9999
http://10.0.0.2:9999
http://10.0.0.3:9999Basic usageFor more detailed usage, one can use--helpoption for each subcommand likepai config --help.$ pai --help
Usage: pai [OPTIONS] COMMAND [ARGS]...

  A CLI tool for OpenPAI.

Options:
  --help  Show this message and exit.

Commands:
  config  Write your configuration to a file.
  host    Show host information of the specified job.
  jobs    Show job list.
  ssh     SSH into a running container.
  stop    Stop a running job.
  submit  Submit your job.
  token   Generate a new access tokenConfigurationBefore utilizing paicli subcommands, you have to initialize your configuration about your PAI environment.Config file: To initialize your config file, runpai config. Then your config file will be located in$HOME/.paicli. You should set yourhost,port, andusername. You can use--profileoption to use multiple PAI environments.Access token: Before submitting/stopping a job, you should issue your access token by executingpai tokenand entering your password. You can skip entering password everytime if you write your password directly to your config file (not recommended).AuthorSotetsu KOYAMADA
Package: paida
PAIDA is pure Python scientific analysis package.The main features are:- Pure Python! (so works on both Python and Jython)- AIDA (Abstract Interfaces for Data Analysis) support- Creating/Plotting the histogram, ntuple, profile and cloud- Function fitting (parameter optimization) with constraints and its parabolic and asymmetric errorsevaluation- XML based storing- Matrix calculationPAIDA web site:http://paida.sourceforge.net/AIDA project has other implementations like Java (JAIDA), C++ etc. (Open Scientist and Anaphe).For more information about AIDA itself, please refer tohttp://aida.freehep.org/
Package: paideia-contracts
paideia-contractsPaideia ErgoScript smart-contracts.DetailsThis repository contains the ErgoScript smart-contracts used within the Paideia project, along with their respective EIP-6 protocol specifications.Python LibraryThis repository can also be installed as a python library and used within your own project:pip install paideia-contractsOverall ArchitecturePaideia consists of a number of interacting modules.PaideiaThe origin of all DAO's that use Paideia lies in the Paideia module, ensuring the DAO's that are created are verified and living up to the requirements. DAO's created through the Paideia module will get official tokens during the creation process to proof Paideia was used to create the DAODAOOnce the DAO is created the DAO module is the core of following modules. The treasury is handled here and any underlying modules need to be created in accordance with this module to be a verified part of the DAO. For example a configuration box to be used in smart contracts (either standard Paideia contracts or contracts made by the DAO itself) can proof its validity with a token distributed by the DAO module.Profit sharingIf the DAO uses profit sharing this will hook into the staking module so profits are automatically distributed to stakers.StakingThe staking system will be similar to the existing staking setup, enhanced with extra rewards from the profit sharing module and will be key to the voting setupProposalsProposals can be initiated by DAO members and have 1 or more actions related to them that will be performed if the proposal passes. A proposal can have different types of voting and will depend on settings stored in a config box. For example a config could be the % quorum needed for a proposal to pass. The total amount of possible votes is determined by using the Staking module (total amount staked).VotingA DAO member can participate in voting by creating a voting box with their stake key. The stake key will be stored in the vote box and instead the member will receive a vote key. This vote key can be used to vote on active proposals. The stake box belonging to the stake key will be used as a data input to determine voting power. When a vote is cast the proposal is updated accordingly and the vote box stores the proposal ID voted on to prevent double voting. By storing these ID's in a collection it will be possible to vote on multiple overlapping proposals. The stake key can only be retrieved if the vote box is not involved in any active proposals (withdrawing a vote could potentially be a DAO config).ActionAn action is something that happens if a proposal passes. This could something as simple as sending an amount of erg to an address, changing a config or updating a smart contract to a new version.ConfigOne or more config boxes can be in use by a DAO and can act as oracles for the smart contracts in use by the DAO. Standard Paideia related config will always be present, but if needed the DAO can create a new config box with an action.ContractsStaking Protocol
Package: pai-easydist
EasyDistEasyDist is an automated parallelization system and infrastructure designed for multiple ecosystems, offering the following key features:Usability. With EasyDist, parallelizing your training or inference code to a larger scale becomes effortless with just a single line of change.Ecological Compatibility. EasyDist serves as a centralized source of truth for SPMD rules at the operator-level for various machine learning frameworks. Currently, EasyDist currently supports PyTorch, Jax natively, and the TVM Tensor Expression operator for SPMD rules.Infrastructure. EasyDist decouples auto-parallel algorithms from specific machine learning frameworks and IRs. This design choice allows for the development and benchmarking of different auto-parallel algorithms in a more flexible manner, leveraging the capabilities and abstractions provided by EasyDist.One Line of Code for ParallelismTo parallelize your training loop using EasyDist, you can use theeasydist_compiledecorator. Here's an example of how it can be used with PyTorch:@easydist_compile()deftrain_step(net,optimizer,inputs,labels):outputs=net(inputs)loss=nn.CrossEntropyLoss()(outputs,labels)loss.backward()optimizer.step()optimizer.zero_grad()returnlossThis one-line decorator parallelizes the training step. You can find more examples in the./examples/directory.OverviewEasyDist introduces the concept of MetaOp and MetaIR to decouple automatic parallelization methods from specific intermediate representations (IR) and frameworks. Additionally, it presents the ShardCombine Algorithm, which defines operator Single-Program, Multiple-Data (SPMD) sharding rules without requiring manual annotations. The architecture of EasyDist is as follows:InstallationTo install EasyDist, you can use pip and install from PyPI:# For PyTorch userspipinstallpai-easydist[torch]# For Jax userspipinstallpai-easydist[jax]-fhttps://storage.googleapis.com/jax-releases/jax_cuda_releases.htmlIf you prefer to install EasyDist from source, you can clone the GitHub repository and then install it with the appropriate extras:gitclonehttps://github.com/alibaba/easydist.git&&cdeasydist# EasyDist with PyTorch installationpipinstall-e'.[torch]'# EasyDist with Jax installationpipinstall-e'.[jax]'-fhttps://storage.googleapis.com/jax-releases/jax_cuda_releases.htmlContributingSee CONTRIBUTING.md for details.ContributorsEasyDist is developed by Alibaba Group and NUS HPC-AI Lab. This work is supported byAlibaba Innovative Research(AIR).LicenseEasyDist is licensed under the Apache License (Version 2.0). See LICENSE file.
This product contains some third-party testcases under other open source licenses.
See the NOTICE file for more information.
Package: paigram-logger
paigram-loggerA logger for PaiGram's projects.使用方法使用默认配置frompg_loggerimportLoggerlogger=Logger()logger.info("info log")logger.success("success log")自定义配置 Loggerfrompg_loggerimportLogger,LoggerConfiglogger=Logger(LoggerConfig(width=120,keywords=['GET','PUT','POST']))logger.info("[GET] info log")logger.success("[PUT] success log")
Package: paik2json
일반 문자열(plain text)로 작성한 메모 파일을 JSON 형태로 변환하는 데 사용하는 패키지입니다. 패키지 이름 “paik2json”은 확장자가 “.paik”인 일반 텍스트를 “.json” 파일로 바꿔준다는 의미로 지었습니다.
Package: paikalta
PaikaltaFrom the place (Finnish: paikalta) we derive the name.License: MITThird party dependencies are documented in the folderthird-party.DocumentationUser and developerdocumentation of paikalta.Bug TrackerAny feature requests or bug reports shall go to thetodos of paikalta.Primary Source repositoryThe main source ofpaikaltais on a mountain in central Switzerland.
We use distributed version control (git).
There is no central hub.
Every clone can become a new source for the benefit of all.
The preferred public clones ofpaikaltaare:on codeberg- a democratic community-driven, non-profit software development platform operated by Codeberg e.V.at sourcehut- a collection of tools useful for software development.ContributionsPlease do not submit "pull requests" (I found no way to disable that "feature" on GitHub).
If you like to share small changes under the repositories license please kindly do so by sending a patchset.
You can either send such a patchset per email usinggit send-emailor
if you are a sourcehut user by selecting "Prepare a patchset" on the summary page of your fork atsourcehut.AcknowledgementsThomas Schmidtprovided the original source code as
scriptcheck.pyand
test cases intest/fixtures/upstream/as contributions to the OASIS CSAF TC repository
below/csaf_2.0/test/filenames/data.StatusExperimental.Note: The default branch isdefault.