Saravanan666/pypi-org huggingface dataset:
Package: pdsc
PDSC: Planetary Data System CoincidencesThe purpose of this package is to allow quick querying via Python of remote
sensing observations made of a particular location on the surface of a
planetary body, or overlapping with another observation from any supported
instrument.  Incidentally,pdscalso allows querying based on metadata and
transforming between pixel and world coordinate systems. Currently, only
several instruments from Mars orbiters are supported, but the system is
designed to be extensible to other instruments and bodies.Please refer to thedocumentationfor
instructions on installation, setup, and usage.Copyright 2019, by the California Institute of Technology. ALL RIGHTS RESERVED.
United States Government Sponsorship acknowledged. Any commercial use must be
negotiated with the Office of Technology Transfer at the California Institute
of Technology.
Package: pdscheduling
PDScheduling - a library to create schedules in PagerDutyJust generate an array with a user for every hour for the next week - and the library will push it to PagerDuty.Install:pip install pdschedulingExample:importrandomfrompdschedulingimportPagerDutypd=PagerDuty("token")users=pd.get_users()schedule=[]fordayinrange(7):user=random.choice(users)# your fancy algorithm to select a user for the dayforhourinrange(24):# btw, the week starts in UTC timezoneschedule+=[user["id"]]pd.create_or_update_schedule(name="Automatic Schedule",hours=schedule)Why library? Can I just use PagerDuty API?You can, but it will be harder. PagerDuty don't give straightforward API for this, instead you need to create schedule
with a layer for every developer with proper restriction.OptDutyThe library extracted fromhttps://optduty.com. If you need a help with library or to create scheduling system please
reach outroman@optduty.com
Package: pdscli
Thepdscommand line environment provides commands for interacting with the You Technology
Personal Data Store.  The overall command structure is fairly simple, with command groups
for each major resource in the PDS (types, scopes, etc…). All inputs and outputs are in
the form of JSON documents.Details on post-installation setup can be found inPDS Command Line Tool Setup.ThePDS Command Line Tool Tutorialshows basic usage of the tool.
Package: pds-data-upload-manager
PDS Data Upload ManagerThe PDS Data Upload Manager provides the client application and server interface for managing data deliveries and retrievals from the Data Providers to and from the Planetary Data Cloud.PrerequisitesThe PDS Data Delivery Manager has the following prerequisties:python3for running the client application and unit testsawscli(optional) for deploying the service components to AWS (TBD)User QuickstartInstall with:pip install pds-data-upload-managerTo deploy the service components to an AWS environment:TBDTo execute the client, run:pds-ingress-client.py <ingress path> [<ingress_path> ...]Code of ConductAll users and developers of the NASA-PDS software are expected to abide by ourCode of Conduct. Please read this to ensure you understand the expectations of our community.DevelopmentTo develop this project, use your favorite text editor, or an integrated development environment with Python support, such asPyCharm.ContributingFor information on how to contribute to NASA-PDS codebases please take a look at ourContributing guidelines.InstallationInstall in editable mode and with extra developer dependencies into your virtual environment of choice:pip install --editable '.[dev]'Configure thepre-commithooks:pre-commit install && pre-commit install -t pre-pushPackagingTo isolate and be able to re-produce the environment for this package, you should use aPython Virtual Environment. To do so, run:python -m venv venvThen exclusively usevenv/bin/python,venv/bin/pip, etc. (It is no longer recommended to usevenv/bin/activate.)If you havetoxinstalled and would like it to create your environment and install dependencies for you run:tox --devenv <name you'd like for env> -e devDependencies for development are specified as thedevextras_requireinsetup.cfg; they are installed into the virtual environment as follows:pip install --editable '.[dev]'ToolingThedevextras_requireincluded in this repo installsblack,flake8(plus some plugins), andmypyalong with default configuration for all of them. You can run all of these (and more!) with:tox -e lintTestsA complete "build" including test execution, linting (mypy,black,flake8, etc.), and documentation build is executed via:toxUnit testsOur unit tests are launched with command:pytestDocumentationYou can build this projects' docs with:python setup.py build_sphinxYou can access the build files in the following directory relative to the project root:build/sphinx/html/
Package: pds.deeparchive
🪐 PDS Deep Archive UtilitiesSoftware for thePlanetary Data Systemto generate Archive Information Package (AIP) and Submission Information Package (SIP) products, based uponOpen Archival Information Systemstandards.✨ FeaturesInstalling this package will give you two different executable Python programs:pds-deep-archivefor making Deep Archives of local, on-disk PDS products.pds-deep-registry-archivefor making Deep Archives of proucts in a PDS Registry server.⎆ InstallationSee the online documentation forInstallationinstructions.You should then be able to run the deep archive utilities:(pds-deep-archive) bash> pds-deep-archive --help
(pds-deep-archive) bash> pds-deep-registry-archive --help👉Note:The above commands demonstrate typical usage with a command-line prompt, such as that provided by the popularbashshell; your own prompt may appear differently and may vary depending on operating system, shell choice, and so forth.👷‍♂️ BuildSee theDevelopment Guidefor more information.📄 DocumentationInstallation and Usage information can be found in the documentation online athttps://nasa-pds.github.io/deep-archive/or the latest version is maintained under thedocsdirectory.🐈 Build Sphinx DocsTo build the Sphinx HTML documentation:$python3.9-mvenvvenv$venv/bin/pythonsetup.pydevelop$venv/bin/pythonsetup.pybuild_sphinxrunning build_sphinx…The HTML pages are in build/sphinx/html.🥖 TranslationsThis product has not been translated into any other languages than US English.👏 ContributeAll users and developers of the NASA-PDS software are expected to abide by ourCode of Conduct. Please read this to ensure you understand the expectations of our community. For information on how to contribute to NASA-PDS codebases please take a look at ourContributing guidelines.Issue Tracker:https://github.com/NASA-PDS/deep-archive/issuesSource Code:https://github.com/NASA-PDS/deep-archiveWiki:https://github.com/NASA-PDS/deep-archive/wiki💁‍♀️ SupportIf you are having issues file a bug report in Github:https://github.com/NASA-PDS/deep-archive/issuesOr you can reach us athttps://pds.nasa.gov/?feedback=true💳 LicenseThe project is licensed under the Apache License, version 2. See theLICENSE.txtfile for details.
Package: pds-doi-core
NASA PDS DOI ServiceThis tools provides services for PDS operators to mint DOIs.Prerequisitespython 3a login to OSTI serverUser Documentationhttps://nasa-pds.github.io/pds-doi-service/DevelopersGet the code and work on a branchgit clone ...
git checkout -b "#<issue number>"Install virtual envpip install virtualenv
python -m venv venv
source venv/bin/activateDeploy dependancies:pip install -r requirements_dev.txtorpip intall -e .Launch api server (to be re-worked)pds-doi-start-devTestUnit tests:python setup.py testBehavioral testingYou first need to get some reference datasets, in the project base directory:curl https://pds.nasa.gov/software/test-data/pds-doi-service/aaDOI_production_submitted_labels.zip > aaDOI_production_submitted_labels.zip
unzip aaDOI_production_submitted_labels.zipThen you can run the behavioral tests:behaveYou can also run them for a nicer reporting:behave -f allure_behave.formatter:AllureFormatter -o ./allure ./features 
allure service allureTo report to testrailTest report can be pushed to testrail:https://cae-testrail.jpl.nasa.gov/testrail/Project: Planetary Data System (PDS)
Test suite: pds-doi-serviceSet you environment:export TESTRAIL_USER=<your email in testrail>
export TESTRAIL_KEY=<your API key in tesrail>Run the tests:behaveSee the results inhttps://cae-testrail.jpl.nasa.gov/testrail/index.php?/projects/overview/168Documentation managementDesign :See in this repository:https://github.com/NASA-PDS/pds-doi-service/tree/master/docsUser documentationManaged with sphynxbrew install sphinx-doc
pip install -r requirements_dev.txt
cd docs
sphinx-build -b html source build -aBuild & ReleaseThe build and release process is managed in github actions.
Package: pds-epitome
🪐 EpitomeThis is a demonstration package that uses the PDS Python template repository.Please visit our website at:https://nasa-pds-engineering-node.github.io/epitomeIt has useful information for developers and end-users.PrerequisitesNone.User QuickstartInstall with:pip install pds.epitomeTo execute, run:pds-epitomeCode of ConductAll users and developers of the NASA-PDS software are expected to abide by ourCode of Conduct. Please read this to ensure you understand the expectations of our community.DevelopmentTo develop this project, use your favorite text editor, or an integrated development environment with Python support, such asPyCharm.ContributingFor information on how to contribute to NASA-PDS codebases please take a look at ourContributing guidelines.InstallationInstall in editable mode and with extra developer dependencies into your virtual environment of choice:pip install --editable '.[dev]'Configure thepre-commithooks:pre-commit install && pre-commit install -t pre-pushPackagingTo isolate and be able to re-produce the environment for this package, you should use aPython Virtual Environment. To do so, run:python -m venv venvThen exclusively usevenv/bin/python,venv/bin/pip, etc.If you havetoxinstalled and would like it to create your environment and install dependencies for you run:tox --devenv <name you'd like for env> -e devDependencies for development are specified as thedevextras_requireinsetup.cfg; they are installed into the virtual environment as follows:pip install --editable '.[dev]'All the source code is in a sub-directory undersrc.ToolingThedevextras_requireincluded in the template repo installsblack,flake8(plus some plugins), andmypyalong with default configuration for all of them. You can run all of these (and more!) with:tox -e lintTestsThis section describes testing for your package.A complete "build" including test execution, linting (mypy,black,flake8, etc.), and documentation build is executed via:toxUnit testsYour project should have built-in unit tests, functional, validation, acceptance, etc., tests.For unit testing, check out theunittestmodule, built into Python 3.Tests objects should be in packagestestmodules or preferably in project 'tests' directory which mirrors the project package structure.Our unit tests are launched with command:pytestIf you want your tests to run automatically as you make changes start uppytestin watch mode with:ptwBuildpip install wheel
python setup.py sdist bdist_wheel
Package: pdsimage
The Lunar Reconnaissance Orbiter  (LRO) mission has gathered some
fantastic data of the lunar surface  and, thanks toNASA, all are
available for free on the internet.However,  using them  can  be  criptic without  a  bit of  experience.Pdsimageaims  to facilitate the  extraction of knowledge  from two
set of data gathered by two experiments on board of LRO:The Lunar Orbiter Laser Altimer (LOLA) experiment which
collected high resolution topographic data of the lunar surfaceThe  Lunar  Reconnaissance  Orbiter Camera  (LROC)  experiment
which collected  images of the  lunar surface. In  particular, we
focus  on Wide  Angle Images  taken  from the  Wide Angle  Camera
(WAC)Highlighted featuresUnlike  previous python  PDS  library, this  module  allows to  easily
extract data for arbitrarily sized region at the lunar surface.It is particularly suited for  studying geological unit at the surface
of the  Moon.  It also  includes some extension  to ease the  study of
lunar impact craters and low-slope domes.FULL DOCUMENTATIONHERE
Package: pds-one
PDS (Port Data Splitter) - это программный разветлитель данных, реализованный в виде TCP сервера,
прослушивающего некоторый порт (USB, COM) и рассылающего эти данные своим клиентам (subscribers).Пример использования (Рассыльщик данных с USB на порту 2290 на Linux):
pds = PortDataSplitter(‘0.0.0.0’, 2290, port_name=’/dev/ttyUSB0’, debug=False, device_name=’USB listener’)1.4.0
Добавлен тестовый режим, при передаче test_mode=True, porDataSplitter не будет подключаться к порту, а будет
просто рассылать test_value (по умолчанию b’101010’)2.0.0
Подключен модуль GCSE (WITAPI)
Package: pdspy
pdspy: A MCMC Tool for Continuum and Spectral Line Radiative Transfer ModelingWelcome to the GitHub page for pdspy! This code is meant to fit Monte Carlo Radiative Transfer models for protostellar/protoplanetary disks to ALMA continuum and spectral line datasets using Markov Chain Monte Carlo fitting.Further capabilities (e.g. fitting spectral line data with a radiative equilibrium calculation) are being developed. If you are interested in new features, do let me know and I would be happy to either add them myself, or to work with you to add them. The documentation is currently included below, but will be ported to a more extensive, better laid out format soon.If you are interested in making use of the code, please check out thedocumentation.For more extensive details on what the code does, please see these papers:Disk Masses for Embedded Class I Protostars in the Taurus Molecular CloudHigh-precision Dynamical Masses of Pre-main-sequence Stars with ALMA and GaiaIf you have any questions about using the code (or this documentation), requests for features, or suggestions for improvement, please don't hesitate to send me an e-mail.
Package: pd-sql-loader
Pandas-sql-loaderМодуль, реализующий функциюsql_load, которая копирует функционалфункцииpandas.read_sql(), но оптимизирует потребление памяти.
Пытается проставить минимально возможный тип данных для каждого столбца.(float64 -> float16, int64 -> uint8 и т.д)Интерфейс метода полностью совместим с pandas.read_sql(),
однако присутствуют дополнительные опциональные параметры.sql_load interfacesql_load(sql,con,index_col=None,coerce_float=True,params=None,parse_dates=None,columns=None,chunksize=None,need_downcast=False,column_types=None,iterator=True)Все стандартные параметры есть в документации, опишу только новые и
chunksize:chunksize: int, default NoneЕсли задан, то:если iterator=True, тогда вернет вернет итераторесли iterator=False, тогда чанками загрузит датафреймы,
объединит их в один и вернет его как результат.need_downcast: bool, default FalseФлаг, устанавливающий нужна оптимизация памяти или нет.column_types: list, default NoneСписок numpy типов, к которым нужно привести столбцы.Если в столбце есть значение, превыщающее заданный тип, то тип будет проигнорирован.
Если длина списка меньше, чем кол-во столбцов, то список будет автоматически
расширен типами uint8.
Если не задан, то каждый столбец попытается привести к uint8.На самом дел довольно бесполезная штука. Изначально думал, что
даункаст будет сильно медленнее стандартного метода, но на деле
плюс минус одинаково.
Возможно, в следующих версиях будет приводить к данным типам насильно?iterator: bool, default TrueФлаг, устанавливающий должен ли вернуться итератор или уже собранный из
чанков DataFrame при установленном chunksize.РекомендацияДля датасетов, размер которых в полтора раза меньше чем доступная память и более,
настоятельно рекомендуется использовать загрузку чанками, тк прежде чем сдаункастить
типы, в память будет загружен DataFrame средствами самого пандас (т.е. с жирными типами)ПримерЧтобы получить готовый оптимизированный DataFrame, но загруженный чанками:frompd_sql_loaderimportsql_loaddf=sql_load(query,con,chunksize=10000,need_downcast=True,iterator=False)
Package: pds.registry
🪐 NASA PDS Registry repositoryThis repository is an umbrella for the PDS registry application. The application is composed of multiple sub-components, each of them managed in their own repository and integrated here:registry-loader: tools to load PDD4 producs to the registryopensearch: the famoua data store and search engine which performaces we praise.sregistry-sweepers: suite of script consolidating the PDS4 product descriptions in OpenSearch.registry-api: the PDS Search API service.registry-ref-data: some reference datsets that we use for our application integration tests.Users and administrator should use the documentation published onhttp://nasa-pds.github.io/registryThis repository specifically contains these PDS registry application resources:the source for the user/administrator documentation, seedocsfolderdocker compose script to start/test the full application with its required components, seehttps://github.com/NASA-PDS/registry/blob/main/docker/README.mdfor details. This also includes integration tests (indocker/postman). The integration test datasets are maintained in NASA-PDS/registry-ref-data repository.Code of ConductAll users and developers of the NASA-PDS software are expected to abide by ourCode of Conduct. Please read this to ensure you understand the expectations of our community.DevelopmentTo develop this project, use your favorite text editor, or an integrated development environment with Python support, such asPyCharm.ContributingFor information on how to contribute to NASA-PDS codebases please take a look at ourContributing guidelines.DocumentationThe project usesSphinxto build its documentation. PDS' documentation template is already configured as part of the default build. You can build your projects docs with:cd docs
make htmlThe generated documnentation can be found in directory docs/build/htmlCI/CDThe template repository comes with our two "standard" CI/CD workflows,stable-cicdandunstable-cicd. The unstable build runs on any push tomain(± ignoring changes to specific files) and the stable build runs on push of a release branch of the formrelease/<release version>. Both of these make use of our GitHub actions build step,Roundup. Theunstable-cicdwill generate (and constantly update) a SNAPSHOT release. If you haven't done a formal software release you will end up with av0.0.0-SNAPSHOTrelease (see NASA-PDS/roundup-action#56 for specifics).
Package: pds.solr-to-es
Solr to Elasticsearch MigratorThis will migrate a Solr node to an Elasticsearch index.RequirementsPython 3+elasticsearchpysolrUsageusage: solr-to-es [-h] [--solr-query SOLR_QUERY] [--solr-fields COMMA_SEP_FIELDS]
                  [--rows-per-page ROWS_PER_PAGE] [--es-timeout ES_TIMEOUT]
                  solr_url elasticsearch_url elasticsearch_index doc_typeThe following example will page through all documents on the local Solr, and submit them to the local Elasticsearch server in the indexes_indexwith a document type ofsolr_docs.solr-to-eshttp://localhost:8983/solr/<<collection_name>> http://localhost:9200 <<collection_name>>solr_docssolr_urlis the full url to your Solr,elasticsearch_urlis the url of your Elasticsearch server.elasticsearch_indexis the index you will submit the Solr documents to on Elasticsearch.doc_typeis the type of document Elasticsearch should assume you are importing.--solr-querydefaults to*:*--solr-fieldsdefaults to(i.e. all fields)--rows-per-pagedefaults to500--es-timeoutdefaults to60--es-userfor authentication in Elasticsearch--es-passwordfor authentication in Elasticsearch--es-max-retriesmaximum number of times a document will be retried when 429 is received, set to 0 for no retries on 429--es-initial-backoffnumber of seconds we should wait before the first retry. Any subsequent retries will be powers of initial_backoff * 2**retry_numberInstallRunpython setup.py installto install the script.DemoHere is an example of grabbing the over 114 thousand journal articles from Plos.org API aboutanimals.docker run -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" elasticsearch

solr-to-es --solr-query animal http://api.plos.org/search localhost:9200 es_plos solr_docs

curl http://localhost:9200/_cat/indices?vNote: that you will get an 403 Forbidden error from the script, and that is because the solr.quepid.com doesn't allow deep paging, however you will have documents in your ES cluster.
Package: pdsspect
NOTE:This is Alpha quality software that is being actively developed, use
at your own risk.  This software is not produced by NASA.Free software: BSD licenseDocumentation:https://pdsspect.readthedocs.org.FeaturesNASA PDS Image ViewerNOTE:This is alpha quality software.  It lacks many features and lacks
support for many PDS image types.  This software is not produced by NASA.InstallOn OS X you must first install the Qt UI toolkit using Homebrew
(http://brew.sh/).  After installing Homebrew, issue the following command:brew install qtInstall Using PipInstall pdsspect using pip:pip install pdsspectThen install your choice of pyside, pyqt4, or pyqt5Install for DevelopmentCreate a new virtual environment, install thepdsspectmodule with git,
and setup the PySide environment. You must install either PySide, PyQt5, or
PyQt4 as well (recommend PyQt5):Make a clone of ``pdsspect`` and change to main directory. We recommend
making a virtual environment for to install ``pdsspect`` in.

pip install -e .
pip install PyQt5Now you should be able to run thepdsspectprogram.This works on Linux as well (Ubuntu 14.04).History0.1.1 (“2017-08-21”)Make compatible to be opened by other programs like pystamps0.1.0 (“2017-08-20”)First release on PyPi
Package: pdstools
Pega Data Scientist ToolsOpen source tooling that helps data scientists working with Pega AI to analyze Pega decisioning and modeling performance.We release this under the Apache 2.0 license and welcome contributing back, preferably through pull requests, but just submitting an Issue or sending a note to the authors is fine too. Pega does not make any representation or warranty with respect to this free software.The tools help to do analysis on Pega decisioning and predictive analytics as used in solutions such as Customer Decision Hub or Process AI, pull data from the platform, easily build meaningful plots and more. Tooling exists in both R and Python, as an R package and as notebooks and scripts for Python. See theGraph Galleryfor a sample of the graphs you can build to help get insights from the platform. Or jump right into the examples inPython examples,R vignettes, orbrowse the example folder.Getting StartedFor instructions and information, see thePega Data Scientist Tools Wiki page. For further examples and the API reference, see theDocumentation.ContributingWe love feedback. If you have interesting analysis plots you would like to share, we are very interested in incoporating them. If you developed tools or library functions that may be of interest to others, consider sending us a pull request. SeeContributingfor guidance on developing the R and Python packages.
Package: pd.subversion.notify
Short package description=========================Package developed to provide possibility send notificationto xmlrpc-servers when data in svn has been updated.Package provide utility:pdsubversionnotify.pyThe program started, read update list from file andsend it to server. When file and acquired it truncated.Used:pdsubversionnotify.py  <URL XMLRPC> <EXCHANGE FILE>Sample------In subversion post-commit-hook please, write ::#!/bin/shREPOS="$1"REV="$2"export LC_ALL=ru_RU.UTF-8svnlook changed --revision $REV $REPOS|grep -v "^D" |cut -b 5-|awk '{print "https://code.dreambot.ru/svn/"$0}' >>/var/tmp/allfiles.txtpdsubversionnotify <URL XMLRPC> /var/tmp/allfiles.txt* Tue Nov 20 2007 Andrey Orlov <cray@neural.ru> 0.0.2- Bugs with "t" switch fixed* Sun Nov 11 2007 Andrey Orlov <cray@neural.ru> 0.0.1- first official release