Saravanan666/pypi-org huggingface dataset:
Package: pdsupp-cli
Automatic Dev DocsInstallation & Use (note:Pre-Release)PyPi Index LinkInstall:pipinstallpdsupp_cliUse:pspdbeta--helpGoal:1 - User AccessibilityMove the hard work represented inPagerDuty Public Support Scriptsto an accessible, user-friendly, but low-maintenance CLI framework.Offer basic help, parsing, and apip install ...option for users.2 - Developer ConfidenceAdd unit and integration tests to the varous script conversions to make extension and maintenance feel (andbe) safer!3 - Developer AcessibilityAdd developer documentation (alongside auto-doc publication and in-IDE registration) to the scripts to make maintenance and extension (and inspiration) more fun and efficient!4 - Shared LearningOffer a learning project to share with anyone who wants to work with CLI apps, small-scale automated CI/CD processes, and modern Python idioms.General Repo characteristics:Python Skeleton RepoThis repo is derived from an in-progressPython+Poetry Skeleton.  All code there is functional.  However some knowledge of what variables to populate is still required.Supported Virtual Environoment, Dependency, and Publication Mgmgt Environment:This repo is designed for use with thePoetryenvironment.
The code it runs assumes that .venv's are local.
Set with:poetryconfigvirtualenvs.in-projecttrueCheck with:poetryconfig--listThe repo also uses pre-commit hooks (download separately, as.git/is not automatically synched) that auto-populate arequirements.txt&requirements-dev.txtso Poetry isNOTrequired for building and exploration.  (However the CI/CD scripts will use the Poetry system.)Dev-Dependencies Specifiedformatting:isort&blacklinting:pylintlsp & typechecking:pyrighttesting:pytest+coverage(viapytest-cov)auto-documentation:pdoc(not"pdoc3", which should be strongly avoided)Run Pre-Commit Hook Manuallyfrom anywhere in project:githookrunpre-commitNote:Due to runner environment caching in GitHub Workflow environment renaming needs to be accompanied by explicit cache deletion.  And possible other maintenance.
Package: pdsvex.policy
This is the policy product for pdsvex.policy.  It orchestrates the
dependencies necessary in order to run a PDS Venus Express profile server.InstallationAddpdsvex.policyto the list of eggs for your Zope 2 instance, or make it
the dependency of an egg in your Zope 2 instance.Changelog2.0.0 — UpgradesSeriously updated for Zope 2.13.21 and Plone 4.3.2.1.0.1 — 2009.7.16 — DocumentationThis release addresses an issue where documentation for the profile server
wasn’t appearing.  It does now!1.0.0 — FCSFirst customer ship.0.0.0 — UnreleasedInitial release.
Package: pdsvex.profile
This is theprofile servicefor the Planetary Data System (PDS) Venus
Express (VEX) mission.  A profile server answers metadata queries for data.
This profile server looks up and responds to metadata for scientific and other
mission data collected during VEX.InstallationThis profile server is built using theZopeapplication framework and thePlonecontent management system.  To deploy this profile server, you can
include this package in your buildout.  Alternatively, you can use thepdsvex.policypackage, which sets up a complete web site with the profile
server, online documentation, and other features.How to QueryIf you’re using theOODT Agilityor theOODTqueryclient, use the
following string as your client’s object key:http://hostname:port/instance/profwherehostnameis the name or IP address of the host where you deployed the serverportis the port number on which the server accepts HTTP requestsinstanceis the name of the Plone instance installed in the application serverprofis a fixed stringprofthat indicates acceptance of a profile queryNote that it’s common to shield the application server behind another web
server such as Apache HTTPD, NGINX, etc., which may rewrite URLs to omit one
or more parts of the object key above.  Consult your system administrator.InstallationAddpdsvex.profileto the list of eggs for your Zope 2 instance, or make it
the dependency of an egg in your Zope 2 instance.Changelog0.0.0 — UnreleasedInitial release
Package: pdsvex.theme
This is the theme product for PDS Venus Express Data’s HTTP interface.  It
lets visitors using a browser view the control panel and browse VEX profiles
in a comforting environment.This product depends onjpl.themeand, if you’re using your buildout or
setuptools properly, it’ll be used automatically.InstallationAddpdsvex.themeto the list of eggs for your Zope 2 instance, or make it
the dependency of an egg in your Zope 2 instance.Changelog2.0.0 — UpdatedModernized to work with Zope 2.13.21 and Plone 4.3.2.1.0.1 — 2009.7.16 — Cosmetic UpdateThis release is a purely cosmetic update that makes the documentation somewhat
more readable by not displaying certain unnecessary elements.1.0.0 — FCSThis was the first customer ship of the PDS VEX Theme.0.0.0 — UnreleasedInitial release
Package: pdsview
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://pdsview.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 qtCreate a new virtual environment, install thepdsviewmodule with pip,
and setup the PySide environment. You must install either PySide, PyQt5, or
PyQt4 as well (recommend PyQt5):mkvirtualenv pdsview
pip install pdsview
pip install PyQt5Now you should be able to run thepdsviewprogram.This works on Linux as well (Ubuntu 14.04).  Instructions coming soon.
Installing the proper Qt dev package and runningpyside_postinstall.pyin a similar fashion should work.Screen ShotsDefault View:Region of Interest:Composite False Color Image:View Label and make alterations to image:History0.5.1 (2017-07-24)Fixes bug in ROI creation0.5.0 (2017-05-22)Use QtPy and do not depend on PySideCreate a histogram to apply cut levels0.4.1 (2015-09-10)Updated to work with ginga 2.5.Bug fixes.0.4.0 (2015-08-30)Clicking on a point in an image now shows the image coordinates and pixel
value at that location.Right clicking and dragging on the image will now create a region of interest.
Statistics about that region of interest are shown.Composite RGB images can now be displayed by using the new Channels dialog and
checking the RGB toggle.0.3.0 (2015-08-03)Reworked to use an ImageSet and ImageStamp model to improve program flow and
permit launching from other programs.0.2.0 (2015-07-28)Cleaned up with parts rewritten.Takes files as command line arguments.Handles mutiple images.0.1.0 (2015-06-06)First release on PyPI.
Package: pdsys
pdsysPandas-powered package for systems monitoringTo InatallpipinstallpdsysBasic usageimportpdsysTo get a system utilization report (as dataframe) on local:df=pdsys.report()pdsys returns by default all process attributes information frompsutilprocess iterator:df.columns.tolist()>>>['cmdline','connections','cpu_affinity','cpu_num','cpu_percent','cpu_times.children_system','cpu_times.children_user','cpu_times.system','cpu_times.user','create_time','cwd','environ','exe','gids.effective','gids.real','gids.saved','hostname','io_counters','io_counters.read_bytes','io_counters.read_chars','io_counters.read_count','io_counters.write_bytes','io_counters.write_chars','io_counters.write_count','ionice.value','memory_full_info','memory_full_info.data','memory_full_info.dirty','memory_full_info.lib','memory_full_info.pss','memory_full_info.rss','memory_full_info.shared','memory_full_info.swap','memory_full_info.text','memory_full_info.uss','memory_full_info.vms','memory_info.data','memory_info.dirty','memory_info.lib','memory_info.rss','memory_info.shared','memory_info.text','memory_info.vms','memory_maps','memory_percent','name','nice','num_ctx_switches.involuntary','num_ctx_switches.voluntary','num_fds','num_threads','open_files','pid','ppid','status','terminal','threads','uids.effective','uids.real','uids.saved','username']You can query the output dataframe to get more insights about the system:# getting top 5 processes sorted by memory utilizationdf.sort_values(by='memory_percent',ascending=False)[['name','memory_percent']].head(5)namememory_percent104systemd-journald20.86576gunicorn4.0688675gunicorn4.0569777gunicorn4.0153674gunicorn1.92189Also, pdsys can run reports from remote systems by providing list of hosts:df=pdsys.report(hosts=['user@host1','user@host2'])df[df.memory_percent>0.9].groupby(['hostname','name']).agg({'memory_percent':'sum','pid':'count','num_threads':'sum','memory_info.rss':lambdax:sum(x)/1e6})hostnamenamememory_percentpidnum_threadsmemory_info.rss0host1Google Chrome2.13456131183.3571host1Google Chrome Helper (GPU)1.3119719112.6972host1Google Chrome Helper (Renderer)9.36998107804.8683host1Python1.084811293.1844host1Terminal1.774516152.4295host1pycharm9.88402166849.0316host2do-agent1.197911612.38227host2gunicorn14.063144145.3638host2postgres1.545041115.97039host2python33.472083435.889210host2systemd-journald21.048411217.567
Package: pdtable
pdtableThepdtablePython package offers interfaces to read, write, and manipulate StarTable data.DocumentationThe pdtable documentation is available atpdtable.readthedocs.io.ExamplesDemo: see thepdtable_demo notebookor, if you a Jupyter notebook doesn't do it for you, the notebook'spaired script.Installationpdtable is available from pypi.orgpip install pdtableand from conda-forgeconda install pdtable -c conda-forgeData and metadata: storage and accessTable blocks are stored asTableDataFrameobjects, which inherit frompandas.DataFramebut include additional, hidden metadata. This hidden metadata contains all the information from Table blocks that does not fit in a classic Pandas dataframe object: table destinations, column units, table origin, etc.Data inTableDataFrameobjects can be accessed and manipulated using the Pandas API as it the object were a vanilla Pandas dataframe, with all the convenience that this entails.The StarTable-specific metadata hidden in aTableDataFrame's metadatacanin principle be accessed directly; however a much more ergonomic interface is offered via aTablefacade object, which is a thin wrapper aroundTableDataFrame.Tablealso supports some limited data manipulation, though with the advantage of more easily supporting StarTable-specific metadata; for example, easily specifying column units when adding new columns.I/OReaders and writers are available for CSV and Excel, both as files and as streams. Parsing is efficient and, by default, lenient, though this is readily customized.Reading can also be filtered early, such that only certain block types or tables with certain names get fully parsed. This can reduce reading time substantially when reading e.g. only a few tables from an otherwise large file or stream.Directive blocks are parsed by the readers, and presented to the client code for application-specific interpretation.Import from and export to JSON is also supported.
Package: pdt-client
pdt-clientpdt-client is a client tool for Paylogic deployment tool web applicationContentspdt-clientInstallationUsagePush migrations dataCheck not reviewed migrationsApply migrationsReport deployment statusGenerate a graph of the revisionsGet not deployed case dataContactLicenseChangelog1.6.01.5.11.4.31.4.21.3.11.3.01.2.4Installationpip install pdt-clientUsageClient has single entry point console script:pdt-client --helpPush migrations datapdt-client migration-data pushCheck not reviewed migrationspdt-client migration-data get-not-reviewedApply migrationspdt-client migrateReport deployment statuspdt-client deployGenerate a graph of the revisionsThe same as the graph command of alembic offline, but enriched with the release from the PDT.pdt-client graphGet not deployed case datapdt-client case-data get-not-deployedContactIf you have questions, bug reports, suggestions, etc. please create an issue on
theGitHub project page.LicenseThis software is licensed under theMIT licensePlease refer to thelicense file© 2015 Anatoly Bubenkov, Paylogic International and others.Changelog1.6.0remove unnecessary parameters in order to support pdt 1.1+ (bubenkoff)1.5.1case-data get-revisionsmoved tocase-data get-not-deployed(bubenkoff)1.4.3Added revision to deploy command (bubenkoff)Make–casefor deploy command optional (bubenkoff)1.4.2Added case-data command and it’s get-revisions subcommand to get case revisions (bubenkoff)1.3.1Fixed graph command (hvdklauw)1.3.0Added graph command for graphing the migration dependency tree (hvdklauw)1.2.4Initial public release (bubenkoff)
Package: pdtj
PDTJ - (Python Docstring To Json)IntroductionThis project was created to solve a personal need for a flexible and simple tool to retrieve all
Python docstrings and convert them into a JSON format. The tool's design provides flexibility to
customize the parsing and documentation process, making it easy to navigate through the codebase's structure and
documentation.This project aims to provide a powerful tool for parsing and documenting Python code within a directory. The tool works
by recursively traversing a directory and identifying all Python files contained within it. It then extracts the
docstrings of these files, organizing them into a dictionary format that captures the structure and documentation of the
entire codebase. This dictionary is then written to a JSON file, making it easy to view and access the information
it contains.
Whether you're a software developer or a codebase maintainer,
this tool can help you gain a deeper understanding of your Python code and streamline the documentation
process.Moreover, this tool aims to simplify the process of generating a documentation webpage by
using the JSON file. It provides a clear overview of the codebase's structure and documentation,
allowing users to quickly navigate through the code and understand its functionality.
The project is designed to be flexible and customizable, enabling users to tailor the parsing
and documentation process to their specific needs. With this tool, you can easily generate comprehensive
documentation for your Python codebase and share it with others.How to UseInstallationTo install the tool, run the following command:pipinstallpdtjNext, create a.pdtj.yamlfile in the root directory of your project and specify the following information:name:<projectname># replace <project name> with your project's nameshelve:<shelvename># choose any name you like for <shelve name>sub-project:<sub-projectname># choose any name you like for <sub-project name>Generating the DocumentationOnce you have installed the tool and configured the.pdtj.yamlfile, you can generate the documentation by running
the following command:pdtjIf everything is set up correctly, the tool will traverse your project directory, extract the docstrings from all Python
files, and create a JSON file containing the documentation. The resulting file can be used to generate a
documentation webpage or for any other purpose.Features missingLogUnit testsLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: pdtoolbox
tradingtoolsBasic trading tools used repeatedly in algo trading applications. Setting as a package enables imports to be performed.pip install git+https://github.com/pedrostanton/toolbox.gitfunctionsFunctionDescriptionmin_incrementsfunction to get and return the number of decimal places a product quotes at or base min sizeget_min_incrementsfunction to get and return all the minimum increasements for all products on an exchangeexchange_connectionsfunction to get and return authorised client logins for trading exchangesfull_average_changesfunction to create data frame of average changes, by looping through each row and running the average changes function against each sub data set. Then updating with DataFrame.update()average_changesfunction for calculating the differences between two periods as a percentage.merge_frames_drop_colsfunction to merge together many dataframes which have calculated average gainsfull_buy_sell_signalfunction to create a dataframe of buy sell signals for a time range (index of peroids)buy_sell_signalfunction to get a buy sell signal from a single time peroid, comparing the average gains of assetsreturnsfunction for calculating returns on a backtest with buy (1) or sell signals (2)classesClassDescriptionConsumerclass to connect to rabbit mq and start consuming from an exchange or queueConsumer func: start_sms_producerfunction to make a connection to rabbit for a producer which will send sms messagesConsumer func: send_smsfunction to send a message to sms consumer (which will be sent as an SMS)InfluxDBclass to connect to Influx DB and perform functionsInfluxDB func: get_hlocreturns a High Low Open Close data set
Package: pdtools
Paradrop Developer ToolsThe Paradrop developer tools (pdtools) make it easy to interact with
the Paradrop edge and cloud APIs. Use it to configure or install software
on a Paradrop node or develop and release your own applications, called
"chutes", to the Paradrop Chute Store.pdtoolscan be used as a command
line utility and can also be imported into Python code as a library.What is Paradrop?Paradrop is a software platform for edge computing that brings the cloud
closer to home by enabling applications to exist on networking equipment
such as Wi-Fi routers.By running services closer to data sources and mobile devices,
applications can take advantage of low network latency and make efficient
use of bandwidth.  Additionally, services running on Paradrop can protect
the privacy of users by processing sensitive data near its source rather
than transmitting it over the wide area network. Some services can even
remain functional when the Internet connection is down.InstallationThe latest version is available onPyPi.
Install it withpip:pip install pdtoolsUsing the command above will installpdtoolsas a local Python
module. On Linux, you can also installpdtoolsas a global package
to make it available as a command. That means you will be able to usepdtoolsin your shell instead of typingpython -m pdtools.sudo pip install pdtoolsUsagepdtoolsincludes an extensive command line utility for interacting with
Paradrop nodes and our cloud controller. You can explore the commands
available with the-hor--helpflags.python -m pdtools --helpUse the interactive tool to begin developing a new chute:python -m pdtools chute initializeInstall a chute on a node if you know its IP address:python -m pdtools node --target <node-ip-address> install-chuteList the chutes installed on a node:python -m pdtools node --target <node-ip-address> list-chutesList chutes available in the Paradrop Chute Store:python -m pdtools store list-chutesInstall a chute on a node from the chute store. Here you must use the
name assigned to the node on paradrop.org:python -m pdtools store install-chute <chute-name> <node-name>All of the functions available through the command line are also
available for scripting in your own Python code. For example,
the following Python code queries a node for the list of chutes
installed and then makes a request to remove one of them:>>> NODE_ADDRESS = "192.0.2.1" # Use the IP address of your node.
>>> from pdtools import ParadropClient
>>> client = ParadropClient(NODE_ADDRESS)
>>> for chute in client.list_chutes():
...     print(chute['name'])
...
node-hello-world
wiki
python-hello-world
sticky-board
>>> client.remove_chute('node-hello-world')
{u'change_id': 1}Instructions for MaintainersUse the following commands in the directory containing this file and setup.py
to build the packages:python setup.py sdist
python setup.py bdist_wheel --universalUse the following command to upload the packages to PyPi:twine upload dist/*
Package: pdtransform
Installation:$ pip install pdtransformA little package with a few transformers to work with Pandas dataframes in the
Sklearn pipeline, which I found myself writing quite frequently. Example usage:frompdtransformimportDFTransform,DFFeatureUnionpipeline=Pipeline([('ordinal_to_nums',DFTransform(_ordinal_to_nums,copy=True)),('union',DFFeatureUnion([('categorical',Pipeline([('select',DFTransform(lambdaX:X.select_dtypes(include=['object']))),('fill_na',DFTransform(lambdaX:X.fillna('NA'))),('one_hot',DFTransform(_one_hot_encode)),])),('numerical',Pipeline([('select',DFTransform(lambdaX:X.select_dtypes(exclude=['object']))),('fill_median',DFTransform(lambdaX:X.fillna(X.median()))),('add_features',DFTransform(_add_features,copy=True)),('remove_skew',DFTransform(_remove_skew,copy=True)),('find_outliers',DFTransform(_find_outliers,copy=True)),('normalize',DFTransform(lambdaX:X.div(X.max())))])),])),])For more information readthis blog post.
Package: pdtrend
PDT (Photometric DeTrending Algorithm Using Machine Learning) aims to remove systematic trends in the light curves. For details about the algorithm, see [Kim et al. 2009](http://adsabs.harvard.edu/abs/2009MNRAS.397..558K). In brief, PDT finds clusters of light curves that are highly correlated using machine learning, construct one master trend per cluster and detrend an individual light curve using the constructed master trends by minimizing residuals while constraining coefficients to be positive. For details, visit the GitHub repository (https://github.com/dwkim78/pdtrend)
Package: pdtweak
pdtweakTable of ContentsInstallationUsageCommandsSupportContributingInstallationThepdtweakgit repois available asPyPi packagepip install pdtweak
pip install git+ssh://git@github.com/kmedian/pdtweak.gitUsageCheck theexamplesfolder for notebooks.CommandsInstall a virtual environmentpython3 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt(If your git repo is stored in a folder with whitespaces, then don't use the subfolder.venv. Use an absolute path without whitespaces.)Python commandsJupyter for the examples:jupyter labCheck syntax:flake8 --ignore=F401,E501 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')Upload to PyPi with twine:python setup.py sdist && twine upload -r pypi dist/*Clean upfind . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venvSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: pdu
PYTHON DISK USAGETakes a path as an argument and returns it's total disk usage recursively.INSTALLATIONpip install pduCLI USAGEusage: pdu [-h] [path]

Python Disk Usage Calculator.

positional arguments:
  path        A valid path.

optional arguments:
  -h, --help  show this help message and exitPYTHON USAGEpath='/path/to/some/file/or/directory'frompduimportduhuman_readable_size=du(path)frompduimportcalcsize_in_bytes=calc(path)frompduimportconverthuman_readable_units=convert(bytes)
Package: pducontrol
pducontrolpducontrol is a Python library to control a wide variety of PDUs
Currently we support the following models:SWH-1023J-08N1 (Dexlan, seems to be just an OEM rebadged unit)PDU81003 (CyberPower)InstallationYou can install pducontrol from source:makepip
pipinstall/dist/pducontrol-*.tar.gzor just call therefresh_package.shscriptUsageThis package provides both Python and CLI interfaces, here is how you can
use them:In PythonfrompducontrolimportPDUControlRESTpdu=PDUControlREST("192.168.0.23")print(pdu.get_all_names())In CLIpducontrol192.168.0.23get_nameallA configuration file namedpdu_settings.cfg, containing IP addresses and
credentials can be added in~/.config/pducontrol/for ease of use and
autocompletion.An example file is available inconfig.ContributingBug reports and comments are warmly welcome,
but contributions, such as pull requests and patches,
are not, for legal reasons only.
If that's bothering you, don't hesitate to tell, with enough pressure, we could
reconsider our position.Licensepducontrol is released under LGPL v3HackingExecute the program from the source directorypython3-mpducontrolGenerate a debian packagemakedebianExecute the unit testsThey are located intests/.maketestCoding styleAll python files should respect the PEP 8 coding style and shell scripts, the
shellcheck coding style.