Saravanan666/pypi-org huggingface dataset:
Package: visualtitle
Visual TitleIntroductionCompatibilityInstallationUsageLimitationsInternalsi18nSource and issue trackingPolicy and source coding conventionsAuthorIntroductionThis add-on separates navigation title and visual title fields in Plone.
This serves the needs of marketing message whereThe author wants long, marketing pitch tuned, title for the pageFor the navigation tree, shorter, informative title is preferredThe add-on is also useful for languages preferring long words, like German and Finnish,
where the actual title does not fit well into the navigation tree and
a shortened title may be preferred.CompatibilityPlone 4+InstallationAddvisualtitlein buildout.cfg:[buildout]

eggs =
    ...
    visualtitleInstall the add-on using the add-on installer in Site Setup.NoteFor Plone 4.1 or lower: Add Dexterity extends = line in buildout.cfghttp://plone.org/products/dexterity/documentation/how-to/installUsageThe add-on currently supportsArchetypesbased content.When the add-on is activated you see title and visual title fields on Archetypes edit pages
on all Archetypes content pages.NoteDexterity support is planned, but looking for the sponsor.LimitationsThe page template must use Plone 4+content-coreslots orgeneric_title_viewmacro to render the page title.Correct example 1:<html xmlns="http://www.w3.org/1999/xhtml"
      xmlns:metal="http://xml.zope.org/namespaces/metal"
      xmlns:tal="http://xml.zope.org/namespaces/tal"
      xmlns:i18n="http://xml.zope.org/namespaces/i18n"
      metal:use-macro="context/main_template/macros/master">

    <metal:block fill-slot="content-core">
        .. page payload goes here ...
    </metal:block>
</html>Correct example 2:<h1 metal:use-macro="context/kss_generic_macros/macros/generic_title_view">
     Generic KSS Title. Is rendered with class="documentFirstHeading".
</h1>If you do like this for your own content types the visual title magic won’t take a place:<h1 class="documentFirstHeading">My title</h1>You can also access the visual title directly (not recommended):<h1 tal:content="python:context.restrictedTraverse('visualtitle')()" class="documentFirstHeading" />InternalsThis add-on overrideskss_generic_macrostemplate for Plone 4+,
adds new fields using archetypes.schemaextender and a helper view
which you can call from the code to get the visual title.i18nYep, it does.Source and issue trackinghttps://github.com/miohtama/visualtitlePolicy and source coding conventionsThe code follows Pylint policies defined inVVV policy file.The code is PEP-8 compatible for the parts where PEP-8 does not get confused.AuthorMikko OhtamaaChangelog0.3 (2014-07-25)Register the ‘static’ directory in ZCML (grok no longer does this) [djowett]Correct English on field description [djowett]Insert the visualtitle layer into all existing skins [djowett]Fix for error: AttributeError: ‘module’ object has no attribute ‘CodeView’ [djowett]0.2.2 (2012-06-25)Attempt #3 of fixing .mo distribution problems [miohtama]0.2.1 (2012-06-25)Attempt #2 of fixing .mo distribution problems [miohtama]0.2 (2012-06-25)Fixed distribution of .mo files [miohtama]Do not install as theme [miohtama]0.1 (2012-06-25)Initial release [miohtama]
Package: visual-tree
Visual Tree Python PackageIntroductionthis is a simple tool to visualize the tree structure of any recursable object in python.Installationpipinstallvisual-treeUsagefromvisual_treeimportbuild_treetree=build_tree(data_structure)# print the data_structureprint(tree)# print the tree directlytree()# get the tree as a string (member of tree)tree.tree# get the tree as a list of strings (__iter__)list(tree)Examplelook at theexample.pyfile for more examples.requirementspython needs to be installeddata structure with__iter__(better with__len__also)data structure with__bool__data structure with__str__orinstand of__iter__you can use other extending methods pass withmkchildparameterinstand of__bool__you can use other extending methods pass withvalidparameterLicenseApache License 2.0 (seeLICENSEfile)
Package: visvalingam
Simple python implementation of afamous algorithm: the Visvalingam-Wyatt simplification.InstallationUsing pip$ pip install visvalingamYou can download the source usinggit clone https://github.com/milkbread/Visvalingam-Wyatt.gitExamples executionThe whole example is located in theexample/directory$ cd exampleScript usageDummy$ python simplify.py -i <inFile> -o <outFile> -t <tolerance>Examplepython simplify.py -i in.json -o out.json -t 0.0005Helppython simplify.py -hView results in browserThis is an exemplary workflow for tests :Download the repositorySimplify your datapython simplify.py -i in.json -o out.json -t 0.0005setup local server (necessary for D3.js)python -m SimpleHTTPServer 8888open in browser:http://localhost:8888/index.htmlIf neededadjust the filenames within theexample/index.html(lines 24 & 28)evaluate the resulting data and try another thresholdImpatient to see some results?See theexample/index.htmlhere!Inspired by M.Bostocks JavaScript-Implementation:ExplanationCode
Package: visvalingamwyatt
Visvalingam-WyattA Python implementation of the Visvalingam-Wyatt line simplification algorithm.This implementation is due toEliot Hallmark. This release simply packages it as a Python module.Use>>>importvisvalingamwyattasvw>>>points=[(1,2),(2,3),(3,4),...]>>>vw.simplify(points)[(1,2),(3,4),...]Points may be anySequence-like object that (list,tuple, a custom class that exposes an__iter__method).Test different methods and thresholds:simplifier=vw.Simplifier(points)# Simplify by percentage of points to keepsimplifier.simplify(ratio=0.5)# Simplify by giving number of points to keepsimplifier.simplify(number=1000)# Simplify by giving an area threshold (in the units of the data)simplifier.simplify(threshold=0.01)Shorthands for working with geodata:importvisvalingamwyattasvwfeature={"properties":{"foo":"bar"},"geometry":{"type":"Polygon","coordinates":[...]}}# returns a copy of the geometry, simplified (keeping 90% of points)vw.simplify_geometry(feature['geometry'],ratio=0.90)# returns a copy of the feature, simplified (using an area threshold)vw.simplify_feature(feature,threshold=0.90)The command line toolvwsimplifyis available to simplify GeoJSON files:# Simplify using a ratio of points
vwsimplify --ratio 0.90 in.geojson -o simple.geojson

# Simplify using the number of points to keep
vwsimplify --number 1000 in.geojson -o simple.geojson

# Simplify using a minimum area
vwsimplify --threshold 0.001 in.geojson -o simple.geojsonInstallFionafor the additional ability to simplify any geodata layer.LicenseMIT
Package: visvis
Visvis is a pure Python library for visualization of 1D to 4D data in an
object oriented way. Essentially, visvis is an object oriented layer of
Python on top of OpenGl, thereby combining the power of OpenGl with the
usability of Python. A Matlab-like interface in the form of a set of
functions allows easy creation of objects (e.g. plot(), imshow(), volshow(),
surf()).With visvis a range of different data can be visualized by simply adding
world objects to a scene (or axes). These world objects can be anything
from plots (lines with markers), to images, 3D rendered volumes,
shaded meshes, or you can program your own world object class. If required,
these data can also be moved in time.Visvis can be used in Python scripts, interactive Python sessions (as
with IPython or IEP) and can be embedded in applications.Requirements:NumpyPyOpenglA backend GUI toolkit (PySide, PyQt4, PyQt5, wxPython, GTK, fltk)(optionally, to enable reading and writing of images) imageiousage:
import visvis as vvAll wobjects, wibjects and functions are present in the visvis
namespace. For clean lists, see vv.wibjects, vv.wobjects, or vv.functions,
respectively.For more help, see …the docstringsthe examples in the examples dirthe examples at the bottom of the function modules (in the functions dir)the online docs:https://github.com/almarklein/visvis/wikiVisvis is maintained by Almar Klein.
Package: visyn-core
visyn_coreFeaturesTelemetry: OpenTelemetry integration for the three pillar of observability: metrics, logs, and tracesInstallationgitclone-bdevelophttps://github.com/datavisyn/visyn_core.git# or any other branch you want to develop incdvisyn_core# Frontendyarninstall# Backendpython3-mvenv.venv# create a new virtual environmentsource.venv/bin/activate# active itmakedevelop# install all dependenciesLocal development# Frontendyarnstart# Backendpythonvisyn_coreTesting# Frontendyarnruntest# BackendmaketestBuildingyarn run build
Package: vit
VITVisual Interactive Taskwarrior full-screen terminal interface.For VIT 1.3,visit hereFeaturesFully-customizable key bindings(default Vim-like)Uncluttered displayNo mouseSpeedPer-column colorizationAdvanced tab completionMultiple/customizable themesOverride/customize column formattersIntelligent sub-project indentingRequirementsTaskwarriorPython3.7+pipInstallationFollow the directions inINSTALL.mdQuick startRunvit --helpfrom the command line for basic usage instructions.Runvitfrom the command line to start VIT with default config, report, and filters.While VIT is running, type:helpfollowed by enter to review basic command/navigation actions.Recommendations:VIT will suggest to install a default user config file if none exists -- it's fully commented with all configuration options, check it out.Dovit --help(know the vit command line arguments)Do:helpin vit(look over the "commands")Use an xterm terminal(for full color support)For suggestions on further tweaks, seeCUSTOMIZE.mdVIT handles task coloring differently than Taskwarrior, seeCOLOR.mdfor more detailsTroubleshooting:SeeFAQ.mdUpgradingFollow the directions inUPGRADE.mdDevelopment:Interested in the architecture, or in helping out with development? SeeDEVELOPMENT.mdIn tributeOur friend and collaborator Steve Rader passed away in May 2013.  We owe a lot to Steve for his excellent work, and so vit is preserved, maintained and continued.Taskwarrior Teamsupport@taskwarrior.org
Package: vit4elm
Applications of Vision Transformers for Exotic Lattice ModelsAboutApplications of Vision Transformers to Exotic Lattice Models. This repository contains all of the finetuned models as well as core scripts to reproduce results and experiment on new data. The experimentation suite that this repository builds on top of can be foundin this website. The goal of this repository is to provide an end-to-end experimentation and evaulation of Vision Transfomers on these types of systems.Raising issues are encouraged so we know what features to prioritize. We want to inevitably work towards predicting regions of interest using masked patch prediction, so this is our top most priority on our road map.The main goal of building our own framework (PyTorch datasets, Trainer, etc.) is to further expand upon the ideas of implementing vision transformers in these systems. This includes studying saliency and attention maps of CNN and Transformer based architectures in attempts of a deeper understanding of how these neural network systems may learn from the underlying physics of exotic lattice models.DocumentationInstallationTo install the package, you can simply run the following:pip install vit4elmNotebooks:For full-fledged documentation on the various classes that have been created within this repository, there arenotebooks to view for your discretion.To DoBigger Stuff:Head wise visualizationsAdd masker to images
Package: vitabs
Project website (with screenshots): <http://pstiasny.github.io/VITABS/>IntroductionVITABS is a guitar tab editor inspired by the legendary Vi text editor.  It is
created with convenient editing and fast keyboard entry in mind.  Keyboard
mappings are modeled after Vi and Vim.A more detailed documentation is available in the doc directory in Markdown
format.RequirementsPython 3 and curses library are required to run VITABS.
python-rtmidi is required for MIDI playback.InstallationIt’s easiest to get VITABS via the pip package manager:pip install vitabs[midi]FeaturesVi-like editing commandsMIDI playbackPlanned features include multiple tracks and plugin support.License and DisclaimerCopyright (C) 2011  Paweł StiasnyThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.Contactpawelstiasny [at] gmail . com
Package: vital
Vital Python LibraryThe Vital Python library provides access to the Vital API from applications written in Python.DocumentationAPI reference documentation is availablehere.InstallationAdd this dependency to your project's build file:pipinstallvital# orpoetryaddvitalUsagefromvital.clientimportVitalvital_client=Vital(api_key="YOUR_API_KEY",)lab_test=vital_client.lab_tests.get('order-id')print(lab_test)Async ClientThe SDK also exports an async client.fromvital.clientimportAsyncVitalimportasynciovital_client=AsyncVital(api_key="YOUR_API_KEY",)asyncdefget_lab_test()->None:lab_test=vital_client.lab_tests.get('order-id')print(lab_test)asyncio.run(get_lab_test())Handling ErrorsAll exceptions thrown by the SDK will sublcassApiError.fromvital.coreimportApiErrorfromvitalimportBadRequestErrortry:vital_client.lab_tests.get('order-id')exceptBadRequestErrorase:# handle bad request errorexceptAPIErrorase:# handle any api related errorEnvironmentsWhen you sign up to Vital you get access to two environments, Sandbox and Production.Environment URLsproductionapi.tryvital.ioproduction-euapi.eu.tryvital.iosandboxapi.sandbox.tryvital.iosandbox-euapi.sandbox.eu.tryvital.ioBy default, the SDK uses theproductionenvironment. See the snippet below
for an example on how ot change the environment.fromvital.clientimportVitalfromvital.environmentimportVitalEnvironmentvital_client=Vital(api_key="YOUR_API_KEY",environment=VitalEnvironment.Sandbox)TimeoutsBy default, the client is configured to have a timeout of 60 seconds.
You can customize this value at client instantiation.fromvital.clientimportVitalfromvital.environmentimportVitalEnvironmentvital_client=Vital(api_key="YOUR_API_KEY",environment=VitalEnvironment.Sandbox,timeout=15)Beta statusThis SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning the package version to a specific version in your pyproject.toml file. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.ContributingWhile we value open-source contributions to this SDK, this library is generated programmatically. Additions made directly to this library would have to be moved over to our generation code, otherwise they would be overwritten upon the next generated release. Feel free to open a PR as a proof of concept, but know that we will not be able to merge it as-is. We suggest opening an issue first to discuss with us!On the other hand, contributions to the README are always very welcome!
Package: vitality
VitalityThis tool is currently in active development. The API may change at any time, and many features are still in progress.Vitality is a tool for generated dynamic and interactive presentation slides from a text-based configuration file.Vitality uses a YAML configuration file to define a presentation. A sample presentation might look like the below:title:"MyPresentation"defaults:background_color:whitecolor:blackslides:-type:titletitle:My Presentationsubtitle:August 2019-First Section-type:bulletstitle:Agendabullets:-First bullet here.-Another bullet goes here.-Here's some more text.-Fourth bullet.-Next SectionOnce installed, Vitality can be run as:vitality config.yml -o presentation.htmlOpen uppresentation.htmlto view the resulting presentation! The right and left arrow keys can be used to move forward and backwards through the slides, and thezkey will return the presenter to the beginning of the presentation.
Package: vitalpy
vitalpyWelcome to vitalpy - essential tools for programming in Python!InstallationLinux / macOS (python3):python3-mpipinstallvitalpyLinux  / macOS (python2):python-mpipinstallvitalpyWindows:py-mpipinstallvitalpyDocsvitalpy documentationv.file.ls(path)#List all files in a folderv.file.create(file)# Create the specified folderv.file.write(file,content)# Write some data to a filev.file.read(file)# Get the contents of a filev.file.rm(path)# Delete a filev.file.rmdir(path)# Delete a folderv.file.mkdir(folder)# Create a folderv.file.copy(origin,destination)# Copy the origin file to the destinationv.file.append(file,content)# Append the content to the filev.file.move(origin,destination)# Change the filepath of origin to destinationv.file.rename(origin,destination)# Rename the old_name file to new_name, same as move()
Package: vitalpython
VitalPy: A Vital Signal Analysis PackageCurrently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.It provides:PPG preprocessing: Singal quality metrics, baseline extraction, etc.PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.UseVitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:pip install -r requirements.txtImport:from src.ppg.PPGSignal import PPGSignalCheck the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):signal = PPGSignal(waveform_df, verbose=1)signal.check_keypoints()Get features:signal = PPGSignal(waveform_df, verbose=0)features = signal.extract_features()Example plots for a AuroraBP signalUsed file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsvMean signalThe following figure shows the mean template computed from all templates within the signal given as input.Preprocessing stepsAll preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.PPG KeypointsExemplary PPG keypoint extraction.LicenseVitalPy is available under the General Public License v3.0.CitationIf you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:A. Cisnalet al."Robust Feature Selection for Continuous BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring,"IEEE J Biomed Health Inform, Under Review (2023).@unpublished{vitalpy,
  title={Robust Feature Selection for Continuous BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring},
  author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2023},
  note = "Under review"
}
Package: vitamin-b
VItamin_B: A Machine Learning Library for Fast Gravitational Wave Posterior Generation:star: Star us on GitHub  it helps!Welcome to VItamin_B, a python toolkit for producing fast gravitational wave posterior samples.Thisrepositoryis the official implementation ofBayesian Parameter Estimation using Conditional Variational Autoencoders for Gravitational Wave Astronomy.Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonlini, Roderick Murray-SmithOfficial Documentation can be found athttps://hagabbar.github.io/vitamin_b.Check out our Blog (to be made),PaperandInteractive Demo.Note: This repository is a work in progress. No official release of code just yet.RequirementsVItamin requires python3.6. You may use python3.6 by initializing a virtual environment.virtualenv -p python3.6 myenv
source myenv/bin/activate
pip install --upgrade pipOptionally, installbasemapandgeosin order to produce sky plots of results.For installing basemap:Install geos-3.3.3 from sourceOnce geos is installed, install basemap usingpip install git+https://github.com/matplotlib/basemap.gitInstall VItamin using pip:pip install vitamin-bTrainingTo train an example model from the paper, try out thedemo.Full model definitions are given inmodelsdirectory. Data is generated fromgen_benchmark_pe.py.ResultsWe train using a network derived from first principals:We track the performance of the model during training via loss curves:Finally, we produce posteriors after training and other diagnostic tests comparing our approach with 4 other independent methods:Posterior example:KL-Divergence between posteriors:PP Tests:
Package: vitamin-c-crawler
Vitamin_C_crawlerVitamin_C_crawleris an advanced data crawling system developed in Python, designed for efficient web scraping and data analysis. This tool specializes in collecting and analyzing web page data, with a particular focus on product names, prices, and image URLs. It offers flexibility for various data crawling scenarios, utilizing libraries likelxml,requests, andpandas.Key Features- Dynamic web page content scraping.
- Precise extraction of product information (names, prices, image URLs).
- Capability to export data in multiple formats, including CSV and JSON.
- Modular architecture, easily adaptable to specific requirements.Quick Start GuidePrerequisitesEnsure you are using Python 3.10 or newer.Havepoetryinstalled for dependency management.InstallationUsing a package managerYou can install the crawler as a package: Usingpip:pipinstallvitamin_c_crawlerOr usingpoetry:poetryaddvitamin_c_crawlerCloning the repositoryYou can also clone the repository and install the dependencies. Usingpoetry:gitclonehttps://github.com/DenKof82/vitamin_c_crawlercdvitamin_c_crawler
poetryinstallUsageAs a moduleimportvitamin_c_crawlerasvc_crawlerimportconfig# Ensure this script also has access to configif__name__=='__main__':vc_crawler.crawl_vitamin_c_products(time_limit=config.TIME_LIMIT,source=config.SOURCE_URL,download_images=config.DOWNLOAD_IMAGES)For more examples look in theexamplesdirectory.LicenseThis project is licensed under the MIT license.
Package: vitaoptimum
VitaOptimum is a library which solvers are designed to find a globally best solution of non-linear models in the possible or known presence of multiple local optima and constraints. The python package contains interface classes for the library. VitaOptimum Library efficiently handles any type of arguments to optimize including continuous, mixed-variables, combinatorial permutations, integer or binary. It can deal with any type of constraints: boundary, equality or inequality ones. The library gives freedom to express models in a language that are closer to reality, which makes the final solution closer to reality.There are many fields of their application, for example:Advanced Engineering DesignData AnalysisFinancial ForecastingPortfolio and Risk ManagementProcess ControlRobot DesignPackingLogisticsTelecommunicationsSystem BiologyComputational Physics and ChemistryBiotechnologyAdvanced StatisticsScientific ModelingArtificial IntelligenceMachine and Deep Learningetc.In order to use the python interface VO or VOPlus library is needed. It can be downloaded from the official web site (see links below). Also a license file is needed in order to use the library. The license file can be requested via emailvo@skyworkflows.comalong with a short description of your research.RequirementsVitaOptimum library 1.xPython 3.7 and higherPython packages: numpy, matplotlib, retryingLinksThe official web site:http://skyworkflows.com/voThe pip web page:https://pypi.org/project/vitaoptimum