Saravanan666/pypi-org huggingface dataset:
Package: projectscheduler
Simple SchedulerI occasionally do freelancing work and needed a simple way to build
out a notional schedule based on estimated task length. There are many
Gantt chart builders out there, but they typically require choosing
start dates for each individual task. In addition, all the online builders
make you enter the tasks in their own interface (clunky), cost money after
some short trial (too much overhead), or don’t offer an easy way to share
the schedule with someone else.Simple Scheduler asks for just a CSV of tasks, how long they’ll take,
who is going to work on each task, and any dependencies of each task.
From there it spits out an SVG with each task scheduled based on the rules:Tasks are scheduled in the order they are in the CSV.A resource (person) can only perform one task at a time.A task’s dependencies must be complete before it can begin.(by default) No work is done on weekends.InstallationpipinstallprojectschedulerUsageCreate a CSV with your tasks in the format:TaskDurationResourcesDependencyName of task 16Person 1Some other task3Person 1Some other task 312Person 2Name of task 1Where duration is given in days. Multiple resources can be separated by a “/”. (I.E, “Person1/Person2”).Anexample csvcan found found in the repository. It builds into:(We are displaying the PNG here so that GitHub displays it. TheSVGis what was actually produced.)scheduleinput.csvoutput.svgTODOMultiple dependencies for a task?CreditsPython-ganttwas the original inspiration for this project. I originally built the scheduler around it,
but didn’t like certain aspects of the API. This tool uses the rendering
code from that project.
Package: project-stats
project-stats can be used to keep track of all the projects you are involved
in.  It collects data from several on- and offline data sources (e.g. git,
github, pypi, npm) and combines them in a short report.installationpip install project-statsconfigurationSeesample.yml.The configuration is expected in./projects.yml,./.projects.yml,~/.config/projects.yml, or~/.projects.yml. The path can also be
explicitly specified with-c/--config.command line argumentsSeeproject-stats-h
Package: project-structure-extractor
Project Structure ExtractorA command-line interface (CLI) tool to extract the structure of a project into a.txtfile. It allows users to include or exclude hidden folders/files and specify files or folders to ignore.Table of ContentsInstallationDevelopment InstallationLocal InstallationUsageRequirementsContributingLicenseChangelogInstallationDevelopment InstallationFor those contributing and making changes to the codebase, it's often more efficient to install the package in 'editable' mode. This way, changes you make to the source immediately reflect in the installed package.To install in editable mode:python setup.py developThis will set up a link to your source code, so you won't have to repackage and reinstall every time you make a change.Local InstallationClean thedistdirectory to prevent old distributions:rm -r dist/*Package the project:python setup.py sdist bdist_wheelInstall the packaged distribution (Note: the version might depend on what is set insetup.py):pip install --upgrade dist/project-structure-extractor-0.1.0.tar.gzUsageAfter installation, you can run theextract-structuredirectly from the CLI.To extract the structure of a project, use:extract-structure --path path_to_project_directoryOptions available:--path(default: current directory): Path to the project directory.--hidden-included: If set, include hidden folders/files in the structure.--ignore: Comma-separated list of files or folders to ignore.For example, to extract the structure including hidden files but ignoring a specific folder:extract-structure --path ./my_project --hidden-included --ignore node_modules,buildRequirementsPython 3.xOther dependencies can be found in therequirements.txtfile.ContributingFor contributions, please create a fork, make your changes, and submit a pull request. Details on the development setup and guidelines can be found in theCONTRIBUTING.mdfile.Once pull requests are approved and merged, maintainers will handle versioning and releases. New releases on GitHub will automatically trigger package updates on PyPI via GitHub Actions.LicenseThis project is licensed under the MIT License. See theLICENSEfile for more details.ChangelogFor a detailed history of changes, seeCHANGELOG.md.0.1.0- Initial release with basic features.0.1.1- No foundational changes, more a lean towards automation builds...- ... (and so on for a few recent versions)For any issues or contributions, please open an issue or pull request.
Package: project-sync
Project Sync=======================## Introduction#### WhatA simple tool based on `rsync` for uploading / synchronizing local project(files) to remote server.#### WhoFor people who want to work on a project locally (on your laptop) and sync the filesto remote server. For example, developing locally and executing remotely.#### Why`rsync` is flexible and powerful, but flags and options are way too complex formemorizing.## Installation```$ pip install project-sync```This will install a command line tool `psync`, the usage of which is described below.## Usage1. Run `psync` under project root to generate initial config file (`.psync`).```$ cd ~/Code/demo_project$ psync```It will generate the config interactively:```You are not in a project (no .psync found)!Generate .psync to current directory (/Users/lazywei/Code/demo_project) [Y/n]? [Y]: YConfig will be generated at /Users/lazywei/Code/demo_project:---Remote path [~/remote/path]: ~/remote/pathSSH host [ssh_host]: aws_playgroundSSH username or enter '-' to skip [ssh_user]: -Files or folders to ignore (separated by space) [ ]: .git .psyncignores:- .git- .psyncremote: ~/remote/pathssh:host: aws_playgroundusername: null---Project root is now: /Users/lazywei/Code/demo_project```2. Run `psync` under any nested subfolders of the project root to sync the project.```$ psyncRunning:rsync -e ssh -ruaz --exclude .git --exclude .psync --rsync-path mkdir -p ~/remote/path && rsync /Users/lazywei/Code/demo_project aws_playground:~/remote/path--- Sync Finished ---$ ssh aws_playground "ls -a ~/remote/path/demo_project".      ..     README```3. Run `psync watch` to watch any modification under the project root and perform sync automatically.### Usage Demo![Usage Demo](demo-usage.gif)### Config OptionsThe config (`.psync`) is a YAML file. You can edit it after initial generating.- `ignores`: an array contains all the files for folders to exclude by `rsync`- `ssh`:- `host`: SSH Host- `username`: SSH username, `null` to ignore this (for example, when you set this in `~/.ssh/config` already)- `remote`: where to sync on remote server## ContributionsThis project is currently in WIP stage; any discussion, bug report and PR are more than welcome.## Licensepsync is available under the MIT license. See the LICENSE file for more info.
Package: project-system
Create and open projects.
Use multiple workstations to work on projects.Project-Systemsimplifies the management of projects within a working space.
Each project consists of a folder and a python virtual environment.
The folder is initialized for git and it is designed to correspond to a remote git repository.Quick StartCreate a new projectThis makes a new folder, git repo, and Python virtual environment for the project.project-new my-projectStart working on the projectOpen a code editor and newtmuxwindow for the project.project-workon my-projectSeeUsagefor more.Installationpip install project-systemSeeInstallationfor more.Online ResourcesDocumentationSource Code
Package: projecttemplate
ProjectTemplateA template utility for data-science projects using Python that provides a skeletal project.http://projecttemplate.netFree software: GNU General Public License v3Documentation:https://projecttemplate-python.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-06-27)First release on PyPI.
Package: project-template
This is a sample project generated bygenerator-pythonforYeoman.[1]It has
a number of features, each with its own chapter in thedocumentation:ISClicenseAll package metadata inpyproject.toml(reaching standardization inPEP 518)Scripts for common development tasks (linting, testing, building
documentation)Testing withpytest,doctests, and coverageContinuous integration on Linux and OSX withTravis CIDocumentation withSphinxandRead the Docs[1]With the exception of a few additions. Most notably, the content of
this documentation is not generated (but its boilerplate is).
Package: projecture
projectureProjecture is a project scaffolding(or a minimal bootstrap) generation
tool for various projects (see list of supported project types below).
Correctly structuring your project (i.e. some defined standard way or a
way which is widely acceptable to the related community) makes things
easy for you, for contributors and for your users.List of supported project types:pythonInstallationpipinstallprojectureUsagesprojecture installation creates a projecture_create executable in your
path. Create a new project from command line as:projecture_createpyproject-tpython-n"your name"-e"your email"-a"project generated from projecture"or from python:importprojectureprojecture.create_project('pyproject',project_type='python',author_name='your name',author_email='your_email',about='project generated from projecture',force=True)This will create pyproject dir in your current working dir.ProjectsPythonPython project generates following structure:pyproject/# your project root (project_dir)├──LICENSE# default license file with MIT license├──MANIFEST.in# file to include non-package data├──pyproject# your actual python package; will be referred as package_dir further│├──cmdline.py# command line interface script for the package│├──__init__.py│├──pyproject.py# package's main file│└──tests# dir containing package tests│├──__init__.py│├──test_pyproject.py#├──README.md# README file (markdown format)├──README.rst# restructured format README for tools like Sphinx├──requirements.txt# file to contain dependencies├──setup.py# setup tools script to package/install project
Package: project-utilities
Project-UtilitiesThe purpose of this package is to make life easier for all the developers out there who are working on a project. The package as I write this contains nothing but any contributions to the GitHub Repo would be greatly appreciated and added to the package. Some of the basic implementations I have considered adding are a file structure handling module which could make searching folders easy, and anything else in that realm. Also functions which create a release or make formatting code simple and easy.
Package: projectutils
projectutilsA small collections of modular components useful in other projects.projectutils.initTheinitmodule is helpful when you need to create a complex directory structure.
You can create objects that represent Directories and Files to create a tree.frompathlibimportPathfromprojectutils.initimportTree,Directory,Filetree=Tree([Directory("dir1"),Directory("dir2",[File("file.txt","file content")])])tree.create(Path("~/project").expanduser())# $ tree ~/project# ~/project# ├── dir1# └── dir2#     └── file.txtprojectutils.configTheconfigmodule allows you to define a configuration schema and dinamically load configurations
from multiple sources.schema.json:{"string":{"doc":"String config","format":"string","default":"DEFAULT"},"integer":{"doc":"Integer config","format":"int","default":1},"float":{"doc":"Float config","format":"float","default":1.1},"list":{"doc":"List config","format":"list","default":["a","b","c"]},"bool":{"doc":"Bool config","format":"bool","default":true},"nested.string":{"doc":"String config","format":"string","default":"DEFAULT"},}/etc/myapp/config.json:{"float":2.2,"string":"loaded from json","nested":{"string":"loaded from json"}}/etc/myapp/.env:MYAPP_CONF_LIST="e,n,v"MYAPP_CONF_BOOL="False"MYAPP_CONF_NESTED_STRING="loaded from env"app.py:importjsonfrompathlibimportPathfromprojectutils.configimportConfig,ENVSource,JSONSource,ConfigSchema# Load schemaschema=ConfigSchema.from_json_file("schema.json")# Define Sourcesenvs_prefix="MYAPP_CONF_"configs_root=Path("/etc/myapp")sources=[JSONSource(configs_root/"config.json"),ENVSource(envs_prefix,configs_root),]# Source definition dictates precedence.# In this case ENV values will override JSON values.# Load configconfig=Config(schema,sources)config.get("integer")# 1config.get("float")# 2.2config.get("bool")# Falseconfig.get("list")# ['e', 'n', 'v']config.get("nested.string")# 'loaded from env'config.get("string")# 'loaded from json'config.get("nested")# {'nested': 'loaded from env'}
Package: project-utils-2023
project-utils介绍project-utils 基于python3 的工具包软件架构使用时继承相应类即可安装教程pip3installproject-project_utils-2023使用说明安装以下格式创建工程configconfig.ini[BASE]data_url=datalog_url=logsoutput_url=outputtmp_url=tmp[MYSQL]host=port=user=password=database=[REDIS]host=port=password=db=[SYSTEM]path=datalogsoutputsrctmpexampleimportasynciofromproject_utils.web.djangoimportCeleryConfigfromnew_utilsimportsettingsclassConfig(CeleryConfig):settings=settingsmax_length=256def__init__(self,*args,**kwargs):super().__init__(*args,**kwargs)self.loop=asyncio.get_event_loop()defdjango_setting_init(self):super().django_setting_init()# self.settings.DEBUG = Falseself.add_allowed_hosts("*")self.add_app("rest_framework")self.add_app("user")self.add_middleware("middleware.my_middle.MyMiddleware")
Package: projectZ.py
Library InformationLibrary DocumentationGitHubMore infoTelegram ChannelYouTube channelDiscord ServerprojectZ.pyLibrary for working with projectZ servers, below you will see code examples, for more examples see the documentation or the examples folderLogin exampleimportprojectZclient=projectZ.Client()client.login(email='email',password='password')Async login exampleimportprojectZimportasyncioclient=projectZ.AsyncClient()asyncdefmain():awaitclient.login(email='email',password='password')if__name__=='__main__':loop=asyncio.get_event_loop()loop.run_until_complete(main())
Package: projen-types
Projen TypesAdditionalprojenproject typesGetting StartedTo create a new project, run the following command and follow the instructions:$mkdirmy-project$cdmy-project$npxprojennew--fromprojen-types<PROJECT-TYPE>🤖Synthesizingproject......Project typesmns-awscdk-app-py - AWS CDK app in Pythonmns-awscdk-app-ts - AWS CDK app in Typescript
Package: projest
ProjestMade by [Esther Vendrell]Last updated: July 2019IntroductionProjest is my first program. Consist in introduce the name of a protein and obtain information about that as the most similar protein, the differents models it has or the atoms. All the information you can obtain comes from:https://www.rcsb.org/DescriptionThe main file is protein.py. In spite of main functions are in extract_atoms_information.py - load protein pdb or load protein fasta - the rest of functions are in protein.
In this way we will have functions directly linked to protein like:
Protein("2ki5").get_similar_protein() -> We will obtain fasta file, pdb file, all the information of protein in mongodb (in the init) and the similar protein of each chain.FunctionsThere are 7 functions:get_sequence_aminoacids: in Protein. Returns a list with chain and the sequence of aminoacids (1 letter). From fasta file.get_chain_list: in Protein. Return a list with the differents chains there are.get_aminoacid_list: in Protein. Return a list with the differents aminoacids (3 letters) with the differents sequence numbers classified in chainsget_similar_protein: in Protein. Return a dictionary with differents chains and the most similar protein of that chain of the protein.general_dictionary: in Protein. Any return. It inserts in mongodb all the information classified. It is not callable.load_protein_pdb: in extract_atoms_information. Returns the pdb file of the protein. Download the pdb file of the protein in the same directory of the files.load_protein_fasta: in extract_atoms_information. Returns the fasta file of the protein.Download the fasta file of the protein in the same directory of the files.MongoDBI could have used an other structure to upload all the information to MongoDB:
Protein - Model - residue_sequence_number - atom_number - information atom.Class Model:
definit(self, model_identifier, model_dict):
self.model_identifier = model_identifier
self.chain_list = []
for chain_identifier, value in model_dict:
self.chain_list.append(Chain(chain_identifier))In this way we obtain information from mongodb. This would be easier but I haven't had enough time since I have realized.The structure of my project is:
Portein - Model - residue_name - residue_sequence_number - element_name - atom_name - information atom.This is more easy to understand the information we are getting and classified it. This is why I have used this structure. Visually is better but for obtaining this information internally it is worse.
Package: projetaai-azure
ProjetaAi AzureProjetaAi plugin to enable Kedro integration with Azure services.WipCredential assignmentBatch endpointRealtime endpointUsableBlob Gen2 credential registration:kedro credential create azureDatastore credential retrivial when running a pipeline in AzureMLPipeline conversion:kedro pipeline create azureEnvironment creation (gets updated automatically)Drafts gets updated tooScheduling (weekly):kedro pipeline azure scheduleRequires pipeline to be publishedUpdates current schedule if existsDraft publishing:kedro pipeline azure publishUpdates endpoint default pipeline after first callForwards schedules if current published existsUse--helpwith any of these commands in order to know what arguments are required
Package: projeto-02
Projeto 02 - DIO / UnimedDescriÃ§Ã£o
Esse Ã© um projeto de teste para utilizaÃ§Ã£o de pacotes para o BootCamp DIO/Unimed para CiÃªncia de Dados.InstallationUse the package managerpipto install package_namepipinstallpackage_nameUsagefrompackage_name.module1_nameimportfile1_namefile1_name.my_function()AuthorIgor MataLicenseMIT
Package: projeto-bibliotecas
Projeto BibliotecasA ideia do projeto Ã© transformar scripts com vÃ¡rias funÃ§oes em bibliotecas, possibilitando que outros usuÃ¡rios tenham acesso Ã s funcoes sem precisarem usar os scripts em siAutormatheus.lopesInstalaÃ§Ã£oInstale a biblioteca usando pippip install projeto_bibliotecasRequerimentosPandasChange Log0.0.1 (05/04/2022)First Release
Package: projetopypiALOUIA11
pacotepypipacotepypiÃ© um simples pacote para gerar palavras, em Python.DependÃªnciasPython 3.6ou posteriorPacoterandomComeÃ§ando o usoVocÃª vai precisar instalar o pacotepacotepypi, para isso basta executar:pip install pacotepypiFunÃ§Ã£ogerar_palavra_dissilaba()- Gera uma palavra dissÃ­laba.salvar_em_arquivo()- Salva as palavras geradas em um arquivo txt.
Package: proj-haystack
Project HaystackTo be definedInstallationDownload the project from Pypi:$pipinstallproj-haystackQuick startThe below example shows how to obtain an auth token from a Haystack server.fromproj_haystack.scramimportget_auth_token# define these settings specific for your use casehost_url="http://localhost:8080"username="su"password="su"# get the auth tokenauth_token=get_auth_token(host_url,username,password)print(f"Here is the auth token:{auth_token}")LicenseTo be defined
Package: proj-init
Project InitializerEverytime you want to start a project you do the basics. go to your projects folder, create a new folder for the project, do your git commands, sync the repo, add a README file and push your initial commit.That is what this simple project is for, making your life easier, and doing all that automatically for you.Setting up:Install the project.$pipinstallproj-initCreate environment variables:GIT_AUTOMATION = Github Personal Access TokenPROJECTS = Default path where to create the projects(Optional, if you don't set this variable the default path will be the current directory)Usage:proj_init [-h] [-l] [-r] [-p] [-d PATH] <repo_name>

Automate your workflow with proj_init command.

positional arguments:
  <repo_name>           Name of your repo to be created.

options:
  -h, --help            show this help message and exit
  -l, --local           Creates your repo only locally.
  -r, --remote          Creates your repo only remotelly.
  -p, --private         Creates your repo in private mode.
  -d PATH, --directory PATH
                        Path where the repo is going to be created.Happy Coding!
Package: projit
ProjitProject Integrator for Decoupled Data ScienceStatus:BetaFunctional.Projit is a utility to help data scientists manage projects that contain multiple experiments
and components that need to interact in a de-coupled manner.
Use it to define and manage project structure, properties, data, experiments & collaboration.The goal of this project is to allow data scientists and teams to work on projects in
a structured and standardized way. The projit utility allows you to establish a
project with a centralised meta-data repository. This meta-data is used by the
application and package to facilitate loosely coupled communication between
scripts for experiments, to track results and parameters.For example, projit provides a python library that can be used inside
experiments and scripts so references to training, evaluation and test data
sets can accessed without passing around and maintaing paths.In addition the project can be initialised according to a standardized layout
so that the diectory structure is familiar to all team members.This project was inspired by a combination of other projects:Cookiecutter Data ScienceThe Python CookieCutter ApplicationThe Git Source Control UtilityReleased and distributed via setuptools/PyPI/pip for Python 3.Additional details and usage instructions available in thedocumentationNotesInitial implementation is focused allowing the user to initialise a project,
and then modify it using a python package that can be called independently in
scripts across the project structure. This creates a central authority for
updating and retrieving info about data sets and experimental results.UsageYou can use this application in multiple waysUse the runner:./projit-runner.py init "Test Project"Invoke the directory as a package:python -m projit init "Test Project"Or simply install the package and use the command line application directly
as shown in the process below:InstallationInstallation from the source tree:git clone https://github.com/john-hawkins/projit
cd projit
python setup.py install(or via pip from PyPI):pip install projitNow, theprojitcommand is available:projit init "Test Project"This will initialise the current directory as a Data Science Project using the
default template. Please refer to thedocumentation for more detail on projit commands.OutputBy adding experiments and results to a projit project you can examine them.
Example output in the table below:Results on [test]experimentMAEMAPEmytest11230.4613.46mytest2nan15.86AcknowledgementsPython package built using thebootstrap cmdline templatebyjgehrcke