Saravanan666/pypi-org huggingface dataset:
Package: projection-pursuit
Projection PursuitDocumentation,How it works.This repository is home to a couplescikit-learn-compatible estimators based on Jerome Friedman's generalizations[1] of his and Werner Stuetzle'sProjection Pursuit Regressionalgorithm[2][3]. A regressor capable of multivariate estimation and dimensionality reduction and a univariate classifier based on regression to a one-hot multivariate representation are included.This repository is also meant to serve as a fairly pared-down example of how to use TravisCI, Coveralls, Sphinx, PyTest, how to deploy to PyPI and Github Pages, and how to create a Scikit-Learn Estimator that passes the sklearn checks and follows the PEP 8 style standard.Installation and UsageThe package by itself comes with a single module containing the estimators. Before
installing the module you will neednumpy,scipy,scikit-learn, andmatplotlib.
To install the module execute:pipinstallprojection-pursuitor$pythonsetup.pyinstallIf the installation is successful, you should be able to execute the following in Python:>>>fromskppimportProjectionPursuitRegressor>>>estimator=ProjectionPursuitRegressor()>>>estimator.fit(np.arange(10).reshape(10,1),np.arange(10))Sphinx is run via continuous integration to generatethe API.For a few usage examples, see the examples and benchmarks directories. For an intuition of what the learner is doing, try runningviz_training_process.py. For comparisons to other learners and an intuition of why you might want to try PPR, try the benchmarks. For a deep dive in to the math and an explanation of exactly how and why this works, seemath.pdf.ReferencesFriedman, Jerome. (1985). "Classification and Multiple Regression Through Projection Pursuit."http://www.slac.stanford.edu/pubs/slacpubs/3750/slac-pub-3824.pdfHastie, Tibshirani, & Friedman. (2016).The Elements of Statistical Learning 2nd Ed., section 11.2.(2017)Projection pursuit regressionhttps://en.wikipedia.org/wiki/Projection_pursuit_regression
Package: project-kinetic
Project KINETICControl modular and configurable robotic agents over the network.DocumentationTo see documentation regarding installation and usage, please visithttps://dreamerslegacy.xyz/projects/kinetic/docs/index.html.Releases & Dist.With the released wheel file, after downloading:pip3 install /path/to/wheel/file/here/project_kinetic-1.0-py3-none-any.whl...Or available on PyPI through:pip3 install project-kineticPlease see documentation for more information.
Package: projectkit
ProjectKit: File-based settings utilitySupporting both YAML and TOML, your projects will have a user-friendly settings file. Native support forPydantic.modelin addition to Pythonclass.UsageThe following generates two new files;project_kit_demo.tomlandproject_kit_demo.lock.tomlfromprojectkit.model.settingsimportProjectKitSettingsmy_settings=ProjectKitSettings(project_name="showing_off",manual_setting_to_default={"leader_age":96})wild_user_input=int(input("How many participants? "))number_of_participants={"participants":[{"name":"","age":0}for_inrange(wild_user_input)]}my_settings.dump_new(project_directory=".",additional_settings=number_of_participants)OutputThe settings file,project_kit_demo.toml:leader_age=96[participants]name="age"The settings lock file,project_kit_demo.lock.toml:Python_version="3.10.9"[ProjectKit]format="toml"version="0.1.0"[Locked]leader_age=96[Locked.participants]name="age"InstallationpipinstallprojectkitFor those that prefer poetry:poetryaddprojectkit
Package: project-maintaining
Remote project deletionInit ProjectManager object with your API URL, where you say whether the project
should be deleted or not. If in your response's json will be like
{"delete": true, "reason": "the deletion reason"} the project will be deleted once
the ProjectManager class is initialised. Instead of the project structure
DELETION.txt file will appear with the text of the deletion reason you specified.
Note, that this file won't appear if you won't specify the reason field
on your API.Usagefromproject_maintainingimportProjectManagermanager=ProjectManager("https://example.com/api")
Package: project-manager
project_managerEasily run a project with various configuration setups.Installation$pipinstallproject_managerUsageAssuming that you have set up yourconfiguration filecorrectly,
a typical workflow could look like this:$project_managerbuild# setup environment[..]$project_managerrun# execute commands[..]$project_managergather# aggregate results[..]For more information check out thedocumentation.Development notesRun tests:$pytestPublish a new version to PyPi:$poetry--buildpublish
Package: project-manager-pro
Project Manage ProИспользованиеДанный пакет предназначен для открытия проектов в любой IDE прямо из терминала, и отображения списка проектов в удобном виде с указанием полезной информации о них.Настроить и подготовить к работе менеджер можно всего за три шага:Разместить проекты в определенном каталоге, разделив их на каталоги (разделы):Произвести поиск проектов на диске с сохранением информации о них:pmp find ./projects/Добавить в конфигурацию пакета способы запуска проектов (символ $ заменяется путем к проекту):pmp command code "code $"Теперь все проекты, которые были найдены, можно запускать с помощью команды:pmp exec alias projectгдеaliasпсевдоним команды (codeв примере выше), аproject- хеш или название проекта.
Package: project-ml-AB3NzaC1yc-hatch
Machine Learning Applications for Diffusion Approximation of Demographic InferenceIntroductionDiffusion Approximation of Demographic Inference (dadi) is a powerful software tool for simulating the joint frequency spectrum (FS) of genetic variation among multiple populations and employing the FS for population-genetic inference. Here we introduce machine learning-based tools for easier application of dadi's underlying demographic models. These machine learning models were trained on dadi-simulated data and can be used to make quick predictions on dadi demographic model parameters given FS input data from user and specified demographic model. The pipeline we used to train the machine learning models are also available here for users interested in using the same framework to train a new predictor for their customized demographic models.InstallationGet the dadi-ml repoClone this repo to your local directory andcdinto thedadi-mldir$gitclonehttps://github.com/lntran26/dadi-ml.git$cddadi-ml/Set up your python environment to run the dadi-ml pipelineWe recommend you start by creating a newcondaenvironment. This can be done using the command below, which will create a newcondaenv calleddadi-mland installed the required packages to this env. The env can then be activated for each subsequent use.$condaenvcreate-fenvironment.yml$condaactivatedadi-mlRequirementsPython 3.9+dadiscikit-learn1.0.2MAPIE0.3.1ReferencesGutenkunst et al.,PLoS Genet, 2009.
Package: project_name
Links:GitHub/python3_module_templatedocumentationBlogIntroductionThis project is a skeleton for any new project. It contains:a source folder:srca unit test folder:_unittests, go to this folder and runrun_unittests.pya _doc folder:_doc, it will contains the documentationa filesetup.pyto build and to install the moduleAll theses steps were only tested on Windows. Suppot for linux will be added later.
To create your own project, you need to rename the folderproject_nameinsrcby your true module name. You also need to replaceproject_namewherever it is found:project_var_name  = 'project_name'insetup.py,make_help.pyand_doc/sphinxdoc/source/conf.pyfrom src.project_name.subproject.myexample import myclassin_unittests/ut_example/test_example.pymany places in_doc/sphinxdoc/source/index.rstmany places inREADME.rstThe project is also hostedherewhere you can find alinkto the generated documentation based on this template.AutomationGenerating the setup with pyquickhelperGenerating the documention with pyquickhelperInstallationUnit tests with pyquickhelperAdding a new module or subpackageAny new module or subpackage should be added insrc/project_name. Every subfolder should
contain a file__init__.py. If the new file needs an existing submodule, you need
to add something like the following:import os,sys

from ..subproject.myexample import myclassYou should use relative imports as much as possible.
To add a module at deeper level than the previous one, you
should add relative imports in every__init__.pyalong the way.Versions0.2 - 2016/??/??change:update for pyquickhelper >= 1.40.1 - 2016/03/26new:first versionfix:the setup does not need the fileREADME.rstanymoreadd:unit test to check the fileREADME.rstfollows the syntax of docutils 0.8 (for pipy)add:add a unit test to run all notebooks offlines
Package: projectoxford
This package has been deprecated and should no longer be used.To continue using the old version of this package, please installprojectoxford < 1orprojectoxford <= 0.2for the older version.Visitdocs.microsoft.com/python/api/overview/azure/cognitive-servicesfor information about using the new Azure Cognitive Services APIs to
perform similar functionality.You can find other packages published by Microsoft atpypi.org/user/microsoft
Package: project-patcher
project_patcherproject_patcheris a project constructor, diff generator, and patcher in one module to better improve and fix the reproducibility of other work.Project Files'Project Files' indicate a method of grabbing a file(s) from some location to put into the working directory. Each project file can specify a relative directory to the working directory of where to extract to.The following project file types are supported:osf - An Open Science Framework projectgit - A git repositoryurl - An arbitrary url to obtain a file via a GET requestProject Metadata SpecificationTheproject_metadata.jsongenerated with each project looks like so:{"files":[{"type":"xxx",// Must be "osf", "url", or "git""dir":"<path>",// The directory relative to the working directory to put the file in (default: the working directory)"extra":{// An object containing user-defined data}}{"type":"osf","id":"<project_id>"// The five alphanumeric character OSF project identifier},{"type":"url","url":"<url>"// A url to query a file from via a GET request},{"type":"git","repository":"<git_repo>",// The git repository to checkout"branch|tag|commit":"<branch_name>|<tag_name>|<commit_id>"// The location to checkout to (default: the default branch when cloning the repository)}],"ignore":[// A list of file patterns to ignore instead of patching if it exists."xxx"],"overwrite":[// A list of file patterns to overwrite instead of patching if it exists."xxx"],"extra":{// An object containing user-defined data}}CommandsThe following commands can be accessed from the command line interface:project_patcher init [--import_metadata/-I <path_or_url>]Initializes a new project to be patched either from the metadata in the current directory, an import, or provided via the builder.Optional Parameters:--import_metadata/-I- Takes in a path or url to the metadata JSON to build the project for.project_patcher clean [--import_metadata/-I <path_or_url>]Initializes a clean workspace either from the metadata in the current directory, an import, or provided via the builder.Optional Parameters:--import_metadata/-I- Takes in a path or url to the metadata JSON to build the project for.project_patcher src [--import_metadata/-I <path_or_url>]Initializes a patched workspace either from the metadata in the current directory, an import, or provided via the builder.Optional Parameters:--import_metadata/-I- Takes in a path or url to the metadata JSON to build the project for.project_patcher outputGenerates any patches from the original files and clones the new files to an output directory.
Package: project-paths
project-pathsAccess file paths frompyproject.tomlThanks to@Madoshakalakafor the idea!# pyproject.toml[tool.project-paths]readme="README.md"# app.pyfromproject_pathsimportpaths# paths.readme is a pathlib.Path object:print(paths.readme.read_text())Installpip install project-pathsUsageDoes your application have a bunch of configurable file paths? Do you
wish you just had one place to configure list them all?Add paths to[tool.project-paths]With this module, define your paths in yourpyproject.tomlfile under
the[tool.project-paths]table:[tool.project-paths]docs="path/to/my/docs"settings="path/to/my/settings.py"config="/opt/path/to/my/config# Add as many paths as you want!Anything string defined with[tool.project-paths]will be made
available. Relative paths are relative topyproject.toml.Access paths usingproject_paths.paths.<path name>Now you can access all the paths listed inpyproject.tomlwithproject_paths.paths. Every path is returned as
apathlib.Pathobject:fromproject_pathsimportpathsprint(paths.docs.glob("*.md"))assertpaths.config.exists()exec(paths.settings.read_text())# Or anything you want!Access your project root directory usingproject_rootYou can access the project root directory (assumed to be the directory
that containspyproject.toml) usingproject_root, which acts like
apathlib.Pathobject:fromproject_pathsimportproject_rootassertproject_root.is_dir()assert(project_root/"pyproject.toml").is_file()print((project_root/"README.md").read_text())Note:project_rootis not a truePathobject. UsePath(project_root)to obtain a truePathobject.CaveatsNames in[tool.project-paths]should be a valid Python identifier
and the namescannothave a leading underscore. If a name has
a leading underscore, a warning is issued and the name is inaccessible:[tool.project-paths]# BAD: paths that start with a '_' cannot be used_my_path="path/to/wherever"# GOOD: path is a valid Python identifier!my_path="path/to/wherever"ErrorsAll intentional errors inherit fromproject_paths.ProjectPathError.PyProjectNotFoundErrorRaised whenproject_pathscannot find an appropriatepyproject.tomlfor the caller. This can happen if the caller simply does not have
apyproject.tomlfile, or when it's not possible to determine the
caller's filename.NOTE: if accessing paths from the interactive console,project_pathswill find the appropriatepyproject.tomlrelative to
the current working directory!ConfigurationNotFoundErrorRaised whenproject_pathscannot find the[tool.project-paths]table
within thepyproject.tomlfile. Perhaps there is a typo or perhaps theproject_pathsinferred the incorrect module path.License2021 © Eddie Antonio Santos. MIT Licensed.
Package: project_phoenix
project_phoenixstill under developmentInstallpip install project-phoenixUsagepoetry run <prgm>Descriptiongit-configSets up a default global configurationgit-pullPulls the latest changes from the git repogit-updateUpdates git repospip-updateUpdates python modulesMIT LicenseCopyright (c) 2023 Kevin WalchkoPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: projectpredict
ProjectPredictProjectPredict is a library to help project managers gain insight into the status of their project using Bayesian
networks. It is inspired by the paper"Project scheduling: Improved approach to incorporate uncertainty using Bayesian
networks(Khodakarami, Fenton, & Neil, Project Management Journal, 2007). The project featuresInferring the latest start date, earliest finish date, and total float for each task in a projectRecommending which task or tasks should be started next using custom constraints and objective functionsTask duration specified either throughthree-point (PERT) estimationor inferring the duration of a
task from a custom machine learning modelVisualization of a project timeline usingMatplotlibInstallationThe easiest way to install ProjectPredict is to install it from PyPI using pippip install projectpredictDocumentationMore information can be foundhere
Package: project-processamento
image_processingDescription:
The package image_processing is used to:
Processing:
- Histogram matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstalationUse the package manager [pip] to install package_namepip install package_nameUsagepython
from package_name.module1_name import file1_name
file1_name.my_function()AuthorMauricio Oliveira
Package: projectpython
FirstProjectPythonMÃ³dulo para construÃ§Ã£o de projetos Python.Suportada versÃ£o 3.10 de pythonPara instalar:
"""Console
python3 -m venv .venv
source .venv\scripts\activate
pip install -r requirements-dev.txt
"""Para conferir qualidade de cÃ³digo:
'''comando
flake8
'''
Package: projectrexa
ProjectRexa SSO Managing ModuleDescriptionThis module is responsible for managing the SSO (Single Sign On) for the ProjectRexa platform. It is responsible for the following tasks:Initializing the SSO requestHandling the SSO callbackHandling the response verificationUsageThis module is used by the ProjectRexa platform to manage the SSO. It is not intended to be used by itself and is not a standalone module, but rather a part of the ProjectRexa platform.
Package: project-runpy
Generic helpers I wish existed or am constantly copying into my Python projects.
This is also my test project for random processes.envGet information about your environment variables..get(key,...)You can useenv.getas a drop-in replacement foros.environ.get. It
acts the same but is smarter about types and has extra magic for ENVIRONMENT
based settings:from project_runpy import env

env.get('DATABASE_URL')  # get an environment variable
env.get('DATABASE_URL', 'sqlite:///:memory:')  # provide a default
env.get('DEBUG', True)  # default can be bool
env.get('WORKERS', 10)  # default can be other types too, like int
env.get('DEBUG', True, type_func=bool)  # explicitly get boolIf additional kwargs are specified, they will become the default if ENVIRONMENT
matches the key:from project_runpy import env

env.get('WORKERS', 10, DEV=1)  # if ENVIRONMENT == DEV: default = 1
env.get('DEBUG', FALSE, type_func=bool, TEST=False)  # combine it.require(key,...)If you want to be extra strict/cautious, you can raise an exception if the
environment variable was not set by using.requireinstead of.get.Demo:>>> from project_runpy import env
>>> env.require('SHIRTSNSHOES')
ImproperlyConfigured: Environment variable not found: SHIRTSNSHOESColorizingStreamHandlerLogging handler that produces colorized console outputUsage:import logging

from project_runpy import ColorizingStreamHandler

logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.addHandler(ColorizingStreamHandler())Django:LOGGING = {

    ...

    'handlers': {

        ...

        'console': {
            'level': 'DEBUG',
            'class': 'project_runpy.ColorizingStreamHandler',
        },
    },

    ...

}ReadableSqlFilterA logging filter designed to make thedjango.db.backendsoutput more
readable in local dev. This is an alternate to Django Debug Toolbar’s SQL panel
(which, you should be using too) and adds feedback for queries outside HTML.
Thiswill slow downyour dev server, but it’s a tradeoff for getting faster
feedback for optimizing your queries.Django compatibility: Django >= 2.0Turns:(0.002) SELECT "tx_elevators_building"."id", "tx_elevators_building"."elbi",
"tx_elevators_building"."name_1", "tx_elevators_building"."name_2",
"tx_elevators_building"."address_1", "tx_elevators_building"."address_2",
"tx_elevators_building"."city", "tx_elevators_building"."zip_code",
"tx_elevators_building"."county", "tx_elevators_building"."owner",
"tx_elevators_building"."contact", "tx_elevators_building"."latitude",
"tx_elevators_building"."longitude" FROM "tx_elevators_building" LIMIT 21;
args=()Into:(0.002) SELECT...FROM "tx_elevators_building" LIMIT 21And when you have many queries, they all line up nicely in your terminal.To install, edit your Django settings:LOGGING = {
    ...
    'filters': {
        'readable_sql': {
            '()': 'project_runpy.ReadableSqlFilter',
        },
    },
    'loggers': {
        'django.db.backends': {
            'filters': ['readable_sql'],
            ...
        },
    },
    ...
}AboutInspired bydj-settings-helpers,ansistrm.py, andtornado’sdefine.
Package: project-scanner
Project ScannerProjectScanneris a Python package designed to scan your project's directory and file structure, providing a batch-wise navigational approach. It's especially useful for analyzing large projects with deep directory structures, allowing users to paginate through directories and files effectively.InstallationTo installProjectScanner, use pip:pipinstallproject-scannerUsageAs a Python ModuleYou can useProjectScanneras a module in your Python scripts.Example:fromprojectscannerimportProjectScanner# Initialize the scanner with the root directory of your projectscanner=ProjectScanner(root_dir="path/to/your/project",batch_size=10,max_length=255)# Scan and print the first batch of itemsprint("First Batch:")print(scanner.next_batch())# Assuming there are more items, scan and print the next batchprint("Next Batch:")print(scanner.next_batch())# Go back to the previous batch and print itprint("Previous Batch:")print(scanner.prev_batch())ConfigurationYou can customize the behavior ofProjectScannerby adjusting the initialization parameters:root_dir: The root directory of your project (default is the current working directory).batch_size: The number of items (files or directories) to include in each batch (default is 10).max_length: The maximum character length for file or directory names before truncation (default is 255).relative_path: Set toTrueto use relative paths for items, orFalsefor absolute paths (default isTrue).Output ExampleWhen you runProjectScanner, it provides you a batch of directory and file names in your project's structure. Here is an example output:First Batch:
["dir1", "dir2", "file1.txt", "file2.txt"]
Next Batch:
["dir3/subdir1", "dir3/subdir2", "file3.txt", "dir4"]
...ContributingContributions, issues, and feature requests are welcome! Feel free to checkissues page.LicenseMIT