Saravanan666/pypi-org huggingface dataset:
Package: redturtle.speedupui.pathbar
IntroductionThe sentence “Plone is slow” can open a very big dicussion thread (and is commonlyfalse!).
What is often true is this: “Work with Plone UI is slow”.The Plone interface need the user to click many times to do (or repeat) common tasks.
This is how the World Wide Web works: click on a link, move to a page, click on another link…Is clear to all that the presence of Javascript can speed up (sometimes dramatically) the speed
of working with CMS.What SpeedUpUI Pathbar doesThe common static breadcrumb viewlet of Plone can be replaced with a new one.Why work with static and poor-of-features links in the pathbar when we can replace those links
with a command interface, to perform directly common operation on the elements inside the pathbar?So the pathbar will change in a dropdown menu with inside common operation like:View the content (what stardard pathbar viewlet does normally)Edit the content directlyGo tofolder_contentsview of the elementAdd new content inside the pathbar element, as you are inside the element itself where you
can normally use the “Add new …” menu.All those new features are nothing special right now, but can free the user from performing
additional clicks.InstallationJust register redturtle.speedupui.pathbar in your buildout.cfgfile.[instance]
...
eggs =
    ...
    redturtle.speedupui.pathbar
...

zcml =
    ...
    redturtle.speedupui.pathbar
    redturtle.speedupui.pathbar-overrides
...TODODropdown are now done using basic Plone javascript; think about relying on jQuery Tools
(plone.app.jqtools)No test with Internet Explorer done right now!Think about add some Manager action (like/manage,/manage_security) but inside submenu
to keep clean the current UIChangelog0.2.0alpha (2009-09-12)Added real permission checking on “Edit” and “Contents” commandsIs now possible toadd new portal contentdirectly from the pathbarFixed the non-closing-bug; unique ids for every menu was missing (as is
indicated in dropdown.js doc string)Changed the pathbar separator for more usability. The arrowdown character
is a know “click-me” element of Plone UI0.1.0dev (2009-08-21)Initial release
Package: redturtle.tiles.tileoftiles
Tile that renders a tile manager.
The rendered tile manager is the one fromredturtle.tiles.management, so this add-on only works with that manager, and does not work with Mosaic.
This tile allows for more complicated layout using a rather simple tile manager.InstallationInstall redturtle.tiles.tileoftiles by adding it to your buildout:[buildout]

...

eggs =
    redturtle.tiles.tileoftilesand then runningbin/buildoutContributeIssue Tracker:https://github.com/RedTurtle/redturtle.tiles.tileoftiles/issuesSource Code:https://github.com/RedTurtle/redturtle.tiles.tileoftilesLicenseThe project is licensed under the GPLv2.ContributorsLuca Bellenghi,luca.bellenghi@redturtle.itChangelog1.0.1 (2020-03-06)Previous release was incomplete and not pushed to pypi.
[cekk]1.0 (2019-10-28)Update version to 1.0
[lucabel]1.0a2 (2019-05-08)Added text color field
[pnicolli]1.0a1 (2019-04-01)Initial release.
[]
Package: redturtle.voltoplugin.editablefooter
Add-on that allows to edit footer columns in Volto.FeaturesControl panel for plone registry to manage columns configuration.Restapi view that exposes these settings for Volto.This addon only add a registry entry where store some configuration data. You need to provide
the edit interface in your Volto theme.Volto endpointAnonymous users can’t access registry resources by default with plone.restapi (there is a special permission).To avoid enabling registry access to everyone, this package exposes a dedicated restapi route with the infos to draw the menu:@footer-columns:> curl -i http://localhost:8080/Plone/@footer-columns -H 'Accept: application/json'The response is something similar to this:[
    {
        'text': {'data': '<span>foo</span>'},
        'title': 'First column'
    },
    {
        'text': {'content-type': 'text/html', 'data': ''},
        'title': 'Second column'
    }
]Control panelYou can edit settings directly from Volto because the control has been registered on Plone and available with plone.restapi.Volto integrationTo use this product in Volto, your Volto project needs to include a new plugin:volto-editablefooter.TranslationsThis product has been translated intoItalianInstallationInstall redturtle.voltoplugin.editablefooter by adding it to your buildout:[buildout]

...

eggs =
    redturtle.voltoplugin.editablefooterand then runningbin/buildoutContributeIssue Tracker:https://github.com/RedTurtle/redturtle.voltoplugin.editablefooter/issuesSource Code:https://github.com/RedTurtle/redturtle.voltoplugin.editablefooterLicenseThe project is licensed under the GPLv2.AuthorsThis product was developed byRedTurtle Technologyteam.ContributorsRedTurtle Techonolgy,sviluppo@redturtle.itChangelog1.2.1 (2024-01-17)Handle /api (remove it) urls when absolut-ize urls.
[cekk]1.2.0 (2024-01-17)absolut-ize urls
[mamico]1.1.2 (2023-03-17)Fix requirements
[foxtrot-dfm1]1.1.1 (2021-10-27)Fix python_requires.
[nzambello]1.1.0 (2021-10-11)p.a.caching rules for rest api services.
[cekk]1.0.1 (2021-02-11)Do not break endpoint if nothing set in registry.
[cekk]1.0.0 (2020-12-15)Initial release.
[cekk]
Package: reduced-binary-coding
Python package implementing reduced binary coding.How do I install this package?As usual, just download it using pip:pipinstallreduced_binary_codingTests CoverageSince some software handling coverages sometime get slightly different results, here’s three of them:Usage examplefromreduced_binary_codingimportreduced_binary_codingreduced_binary_coding(3)# 00reduced_binary_coding(11)# 100
Package: reduce-df
This library reduce the size of any data frame by allocating onky the needed memory to every numerical column.It contains two functions: new_type and reduce** new_type(col_type,col_min,col_max):
** col_type is the current column col_type
** col_min == min value in the column
** col_max == max value in the column
Using these given, informations the function will check
and return what is the suitable data type to be set for the columns** reduce(df, verbose=True)
** This function takes as arguments
1.) The dataframedfas it is loaded from the data source
2.) If verbose = True, you will get the information about the data size reductionChange Log0.0.1 (27 / 11 / 2022)First Release
Package: reduceplus
like functools.reduce but with exception handling, args and kwargspip install reduceplusExample UsageThe function reduce_plus takes a function parameter, which should be a callable
object that accepts two arguments and performs a reduction operation.
The sequence parameter is an iterable sequence of elements that will be reduced.
The optional initial parameter provides an initial value for the reduction.
If no initial value is provided and the sequence is empty, a TypeError is raised.The args and kwargs parameters allow for additional positional and keyword arguments
to be passed to the function. The ignore_exceptions parameter determines whether exceptions
raised during the reduction process are ignored (True) or propagated (False).
By default, exceptions are ignored.The function iterates over the sequence, using the initial value or the first
element as the starting point. For each element, the function is called with the
current value, the element, and any additional arguments and keyword arguments.
If an exception is raised during the reduction process and ignore_exceptions is
False, the exception is propagated, terminating the reduction.Finally, the function returns the final reduced value.Performareductionoperationonasequenceusingthespecifiedfunction.Args:function:Acallableobjectthattakestwoargumentsandperformsareductionoperation.sequence:Aniterablesequenceofelementstobereduced.initial(optional):Aninitialvalueforthereduction.Ifnotprovided,thefirstelementofthesequenceisusedastheinitialvalue./(slash):Separatespositional-onlyargumentsfrompositionalorkeywordarguments.args(optional):Additionalpositionalargumentstobepassedtothefunction.kwargs(optional):Additionalkeywordargumentstobepassedtothefunction.ignore_exceptions(optional):IfTrue,exceptionsraisedduringthereductionprocesswillbeignoredandthereductionwillcontinue.IfFalse,theexceptionwillbepropagated,terminatingthereduction.DefaultisTrue.Returns:Thefinalreducedvalue.Raises:TypeError:Ifthesequenceisemptyandnoinitialvalueisprovided.Examples:fromreduceplusimportreduce_plusba=reduce_plus(operator.add,["dddd",222,434,"dddds"],'xxxx',ignore_exceptions=True)print(ba)xxxxddddddddsba=reduce_plus(operator.add,["dddd",222,434,"dddds"],20,ignore_exceptions=True)print(ba)676
Package: reducer
This package provides a Jupyter notebook for reducing CCD data.Documentation is at:https://reducer.readthedocs.orgPlease provide feedbackComments are very, very much welcome. Please comment bymaking a new
issue on Github(if, by
chance, a reasonably senior academic is using this, you can send
feedback by email; anyone else shouldreallycreate a github account
so you can make an issue :)).InstallationNote about IPython/Jupyter version support:Version 0.3 and higher of
reducer works with IPython 4/Jupyter. Version 0.1 works with IPython 2, and
Version 0.2.x work with IPython 3 or IPython 4/Jupyter. No improvements toreducerwill be backported to version 0.1.x/IPython 2 or to 0.2.x/IPYthon
3. Feel free to fork if you need that!You need python (2.7, or 3.4 or higher) and theSciPy
stack. The easiest way to the get the full stack
is from a distribution likeanaconda.On Windows it will be easiest to install using the Anaconda Python
distribution andconda(because everything has been compiled for you).To install usingconda:conda install -c conda-forge -c astropy reducerIf you prefer, you can install with pip in any python distribution (but may
end up compiling some of the dependencies):pip install reducerYou can, if you want, grab the source on github (there is a “Download as
ZIP” link on the right you can use if you don’t want to mess git),
change into the source directory, and runpython setup.py install.Notereducercomes with a small set of images; the download size is roughly
13MB. It is provided so you can try the notebook without needing your own
data. If you run the notebook as-is then the sample images will get
expanded to 300MB in a temporary directory.UsageThis package doesn’t magically do your reduction for you. Instead, it
creates a templatejupyter notebookthat leads
you through data reduction. When you are done
you have reduced your data andyou have a notebook that allows you or
someone else to reproduce your work.In a terminal, navigate to the directory where you want to keep the
notebook for doing your reduction (which does not have to be the same
directory where the data is, though it can be), then type:reducerThis will create a new template notebook. To open the notebook, type
in a terminal:jupyter notebookA browser window will open; the notebook you want is named “reduction.ipynb”.
Click on it, then just do what it says in the notebook and reduced data (and
someday photometry!) will be yours.Under the hoodIf you look at the source code you’ll notice pretty quickly that there
is no actualsciencecode. Think of this as the glue that brings
together a couple related packages:ccdprocfor the actual data
reduction.astropyfor lots of the underlying
structure .
Package: reduct-cli
ReductStore CLIThe ReductStore CLI is a command line client forReductStore, a time series database for
blob data. It is written in Python and uses theReductStore Client SDK for Python.FeaturesSupport for ReductStore API v1.8Easy management of buckets, tokens and replicationsAbility to check the status of a storage engineAliases for storing server credentialsExport and mirror dataRequirementsPython >= 3.9pipInstallingTo install the ReductStore CLI, simply use pip:pip install reduct-cliUsageCheck with ourdemo server:rclialiasadd-Lhttps://play.reduct.store-treductplay
rcliserverstatusplay
rclibucketls--fullplay
rcliexportfolderplay/data.--inlcudesize=bigLinksProject HomepageDocumentationReductStore Client SDK for PythonReductStore
Package: reductio
Because mapping and reducing isn’t supposed to be hard.What is reductio?Reductio is a minimalistic map-reduce framework for Python. It runs on top of
Fabric and setuptools, which you might already use to get your code onto other
machines.It has no database. It has no distributed filesystem. It uses no server other
than sshd. Because of this, it has essentially no memory requirement!Reductio is designed for disk-bound big data tasks, which many of them are. If
the pieces you need to map-reduce fit entirely in the RAM of your worker
computers, you are paying a huge premium for that. And if they don’t, a system
that tries to buffer things in RAM is going to be wasting all its effort. At
some point, you won’t see your data again unless you write it to the damn disk,
and that’s what is going to take most of the time.I created reductio out of absolute necessity, so I could start crunching my
data. You might notice its documentation is practically nonexistent at the
moment.
What Reductio does
——————
Reductio extends Fabric (http://fabfile.org). It is meant to support the
following approximate process:Set up the appropriate Python environment on all your worker machines,
including required packages, a place to keep the data, and an up-to-date
version of the code you want to run.Tell each worker machine how to contact all the other machines, so they can
send their data onward (“scatter”) when they’re done with it.Give each worker machine some Python functions to run over all the data.
These will often be “maps” or “reduces”.Collect results and group the things that belong together using Unix’s
extremely optimizedsortcommand.Examplereductio/example/fabfile.pyis in example of how to count all the letter
bigrams (pairs of adjacent letters) in the ubiquitous “words” file, and
aggregate them into a table of frequencies.Running tasksIf you have a task defined as the functiondo_stuffinmymodule.py, but
first you want to runsetupto make sure code and other things are up to
date, you would run them both with this command:fab -f mymodule setup do_stuffWhy not Hadoop?Face it, if you knew how to configure Hadoop and get your code to run with it,
you’d be doing so right now.Also, Hadoop is written for Java programmers, and Python is distinctly a
second-class citizen in its world. Hadoop seems to think all Python code
takes the form of standalone scripts with no dependencies, which perhaps
says something about what Java programmers think of Python.Reductio recognizes that none of this is going to work unless you have the
right Python setup, so it builds on tools that Python programmers already use
to deploy their code.Why not Disco?I approve of the Disco project (http://discoproject.org) and its goal of
creating a map-reduce ecosystem designed around Python, but I find it too
complex and too “magical” at the moment.It makes it difficult to understand what is going on in its internals, and yet
you have to understand its internals when something goes wrong or when you want
to do something the designers didn’t expect.
Package: reduct-py
ReductStore Client SDK for PythonThis package provides an asynchronous HTTP client for interacting with theReductStoreservice.FeaturesSupports theReductStore HTTP API v1.8Bucket managementAPI Token managementWrite, read and query dataLabelsBatching records for read and write operationsSubscription on new dataInstallTo install this package, run the following command:pip install reduct-pyExampleHere is an example of how to use this package to create a bucket, write data to it, and read data from it:importtimeimportasynciofromreductimportClient,Bucketasyncdefmain():# Create a client for interacting with a ReductStore serviceclient=Client("http://localhost:8383")# Create a bucket and store a reference to it in the `bucket` variablebucket:Bucket=awaitclient.create_bucket("my-bucket",exist_ok=True)# Write data to the bucketts=time.time_ns()/1000awaitbucket.write("entry-1",b"Hey!!",ts)# Read data from the bucketasyncwithbucket.read("entry-1",ts)asrecord:data=awaitrecord.read_all()print(data)# Run the main functionloop=asyncio.get_event_loop()loop.run_until_complete(main())For more examples, see theQuick Start.ReferencesDocumentationReductStore HTTP API
Package: reductus
This project provides tools for reflectometry reduction.reflredA python package for loading, modifying and saving reflectivity data sets.ospecredA python package for loading, modifying and saving off-specular reflectivity data sets.sansredA python package for loading, modifying and saving small-angle neutron scattering (SANS) data sets.reflwebRPC access to reduction libraries, with javascript frontend (stateless)To load data from a local store in web reduction, go to
menu->data->add source->local (must be running the server locally,
with the local datastore enabled in config)Installation and useMethod 1: pypi installpip install reductusThen start the server with:reductusMethod 2: Docker ComposeThis is the easiest way to get started.  Clone the repo, the change directories
into the repository and run:docker-compose build
docker-compose up -dThis will result in a trio of docker containers being spun up, one with a web
server for the interface (‘reflweb’), one with the backend calculation RPC
server (‘reductus’) and one with the Redis cache.Files in ./reflweb/testdata/ will be mapped into the server at /data, for
testing the local file handling. Changes to the python code can be
incorporated into the containers by stopping them, then repeating the build
and up commands above.To stop:docker-compose stopTo access the client, if using the new Docker beta navigate tohttp://localhost:8000/reflweb/web_reduction_filebrowser.htmlOn Windows 7,
if using docker-machine, you will have to get the IP of the default docker
install and use that instead of localhost, e.g.docker-machine ip defaultIn my case it was http://192.168.99.100:8000/reflweb/web_reduction_filebrowser.htmlMethod 3: Clone github repo and build, run directly in consoleClone the repo, then install (might be a good idea to make a virtualenv first),
e.g.python setup.py installThen start the server with:cd reflweb
python server_flask.py 8002and visit the pagehttp://localhost:8002/static/index.html
Package: redun
redun aims to be a more expressive and efficient workflow framework, built on
top of the popular Python programming language. It takes the somewhat contrarian
view that writing dataflows directly is unnecessarily restrictive, and by doing
so we lose abstractions we have come to rely on in most modern high-level
languages (control flow, compositiblity, recursion, high order functions, etc).
redun’s key insight is that workflows can be expressed as lazy expressions, that
are then evaluated by a scheduler which performs automatic parallelization,
caching, and data provenance logging.redun’s key features are:Workflows are defined by lazy expressions that when evaluated emit dynamic directed acyclic
graphs (DAGs), enabling complex data flows.Incremental computation that is reactive to both data changes as well as code changes.Workflow tasks can be executed on a variety of compute backend (threads, processes, AWS batch
jobs, Spark jobs, etc).Data changes are detected for in memory values as well as external data sources such as files
and object stores using file hashing.Code changes are detected by hashing individual Python functions and comparing against
historical call graph recordings.Past intermediate results are cached centrally and reused across workflows.Past call graphs can be used as a data lineage record and can be queried for debugging and
auditing.
Package: redundant
redundant is a linter for project wide technical debt.Currently, the main goal is to identify duplicate and near-duplicate code.
While redundant was built with Django projects in mind, and to that end can
analyze python, javascript, html, and css files, it should be useful for
many types of codebases.UsageSimple run redundant in the top-level directory of your project and let it
run. For larger projects it can take a while../path/to/redundant.pyYou can improve how it runs by adding a.redundantrcto your project. See
thedotredundantrcfile for an example. Excluding vendor files, especially
minimized code, can improve run time significantly.You can configure the threshold for duplicate detection by changing thediff-delta-maxsetting in the[report]section of the config.
Package: redux
[![](https://travis-ci.org/kaelzhang/python-redux.svg?branch=master)](https://travis-ci.org/kaelzhang/python-redux)
[![](https://codecov.io/gh/kaelzhang/python-redux/branch/master/graph/badge.svg)](https://codecov.io/gh/kaelzhang/python-redux)
[![](https://img.shields.io/pypi/v/redux.svg)](https://pypi.org/project/redux/)
[![](https://img.shields.io/pypi/l/redux.svg)](https://github.com/kaelzhang/python-redux)# reduxRedux in Python## Install`sh $ pip install redux `## Usage`py import redux `## License[MIT](LICENSE)
Package: red-val
RED-valRED-val (Reproducible Experiment Description Validation) is part of the Curious Containers project.
It provides functionality to implement RED-Clients namely RED-file-validation and RED-variables-resolution.AcknowledgementsThe Curious Containers software is developed atCBMI(HTW Berlin - University of Applied Sciences). The work is supported by the German Federal Ministry of Economic Affairs and Energy (ZIM project BeCRF, grant number KF3470401BZ4), the German Federal Ministry of Education and Research (project deep.TEACHING, grant number 01IS17056 and project deep.HEALTH, grant number 13FH770IX6) and HTW Berlin Booster.
Package: redvox-pandas
RedPandasDescriptionThis repository contains routines to streamline preprocessing ofRedVox API 900andAPI 1000(API M) data.
The RedVox Pandas (also known as RedPandas) pipeline is designed for integrability with other legacy and heterogeneous data sources.InstallationTo install with pip:pipinstallredvox-pandasMore installation instructions can be found in theRedPandas Installation Documentation.ExamplesA full example can be found inexamples directory.Getting startedCheck theUser Documentationto get started.You can find examples onhow to plot audio waveformsandaudio spectrograminRedVox Examples.API DocumentationCheck theAPI Documentationfor more details on RedPandas functions.DevelopmentPlease feel free to submit issues and bugs on the GitHubissue tracker.Version HistoryThe Apache LicenseNoteRedPandas depends onRedVox SDK version:For RedVox SDK >= 3.1.0, RedPandas >= 1.3.0For RedVox SDK < 3.1.0, RedPandas = 1.2.15