Saravanan666/pypi-org huggingface dataset:
Package: vivid-node
Vivid NodeVivid Node(aliasRian) is a freePythonlibrary
for next generation machine learning- and data analysis applications, that
implementcloud-assisted meta programming. Rian is part of theVivid Codeframework and actively developed at
theFrootlabOrganization.The key goal of Rian is to automate and support collaborative data science. To
achieve this goal Rian orchestrates established frameworks likeTensorFlowÂ®orKerasÂ®and
dynamically extends their capabilities by cloud based community algorithms.
Thereby Rian allows the usage of abstract defined algorithms, that are specified
with respect to their category, the used data type and an evaluation metric.
During runtime these abstract specifications are resolved cloud-sided bycurrently best fittingalgorithm, that match the specification. This allows
the separation of engineering and data science, as well as simple collaborations
between organizations without the requirement to share data.Collaborative data science using the Vivid Code frameworkFor a given algorithm category and data type, the currently best fitting
algorithms are determined by the used metric. Examples for such metrices would
be the prediction accuracies within a fixed set of gold standard samples of the
respective domain of application (e.g. latin handwriting, spoken words, TCGA
gene expression data, etc.).Current Development StatusRian currently is inPre-Alphadevelopment stage, which immediately follows
thePlanningstage. This means, that at least some essential requirements of
Rian are not yet implemented.InstallationComprehensive information and installation support is provided within theonline manual. If you already have a
Python environment configured on your computer, you can install the latest
distributed version by using pip:$ pip install rianOr alternatively:$ pip install vivid-nodeDocumentationThe documentation of the latest distributed version is available as anonline
manualand for download, given in the
formatsPDF,EPUBandHTML.ContributionContributors are very welcome! Feel free to report bugs, ideas and feature
requests to theissue tracker,
provided by GitHub. Currently, as our team still is growing, we do not provide
any Contribution Guide Lines. So, if you are interested to help or to join the
team, we would be glad, tohear about you.LicenseRian is open source software and available free for any use under theGPLv3 license:Â© 2019 The Frootlab Organization
Â© 2013-2019 Patrick Michl
Package: vivid-root
Vivid RootVivid Root(alias Aughra) is a planned directory service for theVivid Codeframework. Vivid Root
is responsible to host and deploy standards for algorithm types, interfaces and
evaluation metrices toVivid Stores.
Thereupon Vivid Root provides information about public Vivid Stores, to allow an
automatic configuration ofVivid Nodes.Vivid Root is open source, based on thePythonprogramming language and actively developed as part of theVivid Codeframework at theFrootlab Organization.Current Development StatusVivid Root currently is in earlyplanningstage. This means, that the
project's directives, requirements and goals are not yet settled and
implemented.InstallationComprehensive information and installation support is provided within theonline manual. If you already have a
Python environment configured on your computer, you can install the latest
distributed version by using pip:$ pip install vivid-dbOr alternatively:$ pip install aughraDocumentationThe latest Vivid Root documentation is available as anonline
manualand for download in the
formatsPDF,EPUBandHTML.ContributeContributors are very welcome! Feel free to report bugs and feature requests to
theissue trackerprovided by
GitHub. Currently, as the Frootlab Developers team still is growing, we do not
provide any Contribution Guide Lines to collaboration partners. However, if you
are interested to join the team, we would be glad, to receive an informalapplication.LicenseVivid Root is open source and available free for any use under theGPLv3license:Â© 2019 Frootlab Developers:
  Patrick Michl <patrick.michl@frootlab.org>
Package: vivid-store
BreaBreais a planed catalog server for algorithm storage and evaluation and
based onGIT. Brea aims to serve as an algorithm
catalog to allow the usage of abstractcurrently best fitting(CBF)
algorithms, as required by theCloud-Assisted Meta Programming(CAMP)
paradigm.Thereby Brea is required to host and deliver algorithms as well es to
cyclically evaluate and index them with respect to their corresponding metrics,
usingRian. An example for such a metric
would be the average prediction accuracy within a fixed set of gold standard
samples of the respective domain of application (e.g. latin handwriting samples,
spoken word samples, TCGA gene expression data, etc.). Consequently Brea is
also required to host or connect these samples by usingDeet.Due to this approach brea allows the implementation ofsmart analyticsprojects, that are automatically kept up-to-date by a minimum of maintenance
costs. Also brea supports scientific applications, by facilitating a local
(workgroup, lab, institution) or global publication, application and evaluation
of algorithms, e.g. developed within a PhD-position or program.Brea is open source, based on thePythonprogramming language and actively developed as part of theVivid Codeframework atFrootlab.Current Development StatusBrea currently is in latePlanningstage, which immediately is followed by
thePre-Alphastage. This means, that the projects directives, requirements
and goals are mostly settled but so far only hardly implemented.InstallationComprehensive information and installation support is provided within theonline manual. If you already have a
Python environment configured on your computer, you can install the latest
distributed version by using pip:$ pip install breaDocumentationThe latest Brea documentation is available as anonline
manualand for download in the
formatsPDF,EPUBandHTML.ContributeContributors are very welcome! Feel free to report bugs and feature requests to
theissue trackerprovided by
GitHub. Currently, as the Frootlab Developers team still is growing, we do not
provide any Contribution Guide Lines to collaboration partners. However, if you
are interested to join the team, we would be glad, to receive an informalapplication.LicenseBrea is open source and available free for any use under theGPLv3license:Â© 2019 Frootlab Developers:
  Patrick Michl <patrick.michl@frootlab.org>
Package: vivintpy
vivintpyPython library for interacting with a Vivint security and smart home system.This was built to support theVivintintegration inHome-Assistantbutshouldwork outside of it too. Currently, it can be utilized viaHACSby adding thehacs-vivintcustom repository.CreditThis was inspired by the great work done byMike Reibardto reverse engineer the Vivint Sky API andOvidiu Stateinafor the repository from which this is forked and expanded on.FeaturesIt currently has support for the following device types:alarm panelscamerasdoor locksgarage doorsswitchesbinarymultilevelthermostatswireless sensorscarbon monoxidedoor/windowfloodglass breakmotionsmoke/fireetcIn addition, it integrates with PubNub to receive real-time updates for devices. This subscription stops receiving notifications around 15-20 minutes unless a call is made to the Vivint Sky API periodically. Thismightbe related to the cookie expiration since it expires 20 minutes after the last API call was received. If another client connects, however, the notifications start to stream again for all currently connected clients.UsageSee demo.py for a demonstration on how to use this library.TODO:write a better readmewrite some documentationadd advanced support for:thermostatsadd testsSupport MeI'm not employed by Vivint, and provide this python package as-is.If you don't already own a Vivint system, please consider usingmy referal code (kaf164)to get $50 off your bill (as well as a tip to me in appreciation)!If you already own a Vivint system and still want to donate, consider buying me a coffee ☕ (or beer 🍺) instead by using the link below:
Package: vivisect
Vivisect / Vdb / VtraceA combined disassembler/static analysis/symbolic execution/debugger framework.UsagePlease see the quickstart/usage docs over at ourdocs pageInstallingFor most use cases, you should just be able to runpip install "vivisect[gui]"to get both the vivisect/vdb libraries and UI components. For other use cases, please see ourdocumentation.VersioningAll releases prior to v1.0.0 are python2 only. As of v1.0.0, vivisect/vdb/vstruct
are all python3 compatible. Please report any bugs/issues to theissue trackeror hit us up in the #vivisect room in thesynapse slackPlease see v0.x.x-support branch for the current python2 version, or pip install
the v.0.2.x version of vivisect.UpgradingDue to fun pickle shenanigans, old python2 vivisect workspaces are not typically
compatible with python3. In what will be one of (if not, the) final release of the
python2 compatible vivisect, v0.2.1 will include a conversion script that can migrate
the basicfile-based vivisect workspaces files to the msgpack-back ones, which should
be loadable in python3 vivisect.Build Status
Package: vivit-for-pytorch
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structureViViT is a collection of numerical tricks to efficiently access curvature from
the generalized Gauss-Newton (GGN) matrix based on its low-rank structure.
Provided functionality includes computingGGN eigenvalues (basic
example)GGN eigenpairs (eigenvalues + eigenvector,basic
example)1ˢᵗ- and 2ⁿᵈ-order directional derivatives along GGN eigenvectors (basic
example)Directionally damped Newton steps (basic
example)These operations can also further approximate the GGN to reduce cost via
sub-sampling, Monte-Carlo approximation, and block-diagonal approximation.Documentation:https://vivit.readthedocs.io/en/latest/Bug reports & feature requests:https://github.com/f-dangel/vivit/issuesHow does it work?ViViT uses and extendsBackPACKforPyTorch. The described functionality is
realized through a combination of existing and new BackPACK extensions and
hooks into its backpropagation.Installationpipinstallvivit-for-pytorchExamplesBasic and advanced demos can be found in thedocumentation.How to citeIf you are using ViViT, consider citing thepaper@misc{dangel2022vivit,
      title={{ViViT}: Curvature access through the generalized Gauss-Newton's low-rank structure},
      author={Felix Dangel and Lukas Tatzel and Philipp Hennig},
      year={2022},
      eprint={2106.02624},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
Package: vivlio
vivlioBusiness Ready Documents for MerakiCurrent API CoverageAlert SettingsOrganizationsOrganization DevicesOrganization Devices Management InterfacesOrganization NetworksNetwork ClientsInstallation$python3-mvenvmeraki$sourcemeraki/bin/activate(meraki)$pipinstallvivlioUsage - Environment Variable - IMPORTANTPlease export / setup this environment variable prior to running vivlio(meraki)$exportMERAKI_DASHBOARD_API_KEY=<MerakiAPIToken>Usage - In-line(meraki)$vivlioRecommended VS Code ExtensionsExcel Viewer - CSV FilesMarkdown Preview - Markdown FilesMarkmap - Mindmap FilesOpen in Default Browser - HTML FilesAlways On SandboxThis code works with the always on sandbox!https://devnetsandbox.cisco.com/RM/Diagram/Index/a9487767-deef-4855-b3e3-880e7f39eadc?diagramType=Topologyexport MERAKI_DASHBOARD_API_KEY=fd6dd87d96915f21bc0e0b3d96a866ff0e53e381(venv)$pipinstallvivlio(venv)$mkdirvivlio_output(venv)$cdvivlio_output(venv)/vivlio_output$ vivlio(venv)/vivlio_output$ code .(Launches VS Code and you can view the output with the recommended VS Code extensions)ContactPlease contact John Capobianco if you need any assistance
Package: vivludo
VivludoVivludo is a library for working with cellular automata in Python.There are many other PyPI packages for cellular automata, but most of them
were not quite what I was looking for, so I decided to make my own.  It's not
really intended to be a "product", but since I was making it anyways I figured
there was no reason not to release it.  I can't guarantee support,
documentation, and so on.This package uses a reasonably fast implementation based on convolution
(not as fast as whatever Golly uses, though).
I think it might be possible to modify it for GPU acceleration
(using PyTorch or something), but I haven't tried that.Vivludois Esperanto for "Life Game", in reference to Conway's Game of Life,
which is one of the most well-known cellular automata.Installationpip install vivludoRequires Python 3.5 or greater.UsageSome usage examples can be found in theexamplesdirectory.I may eventually add actual documentation, but for now there's just the
docstrings in the source code.LicenceThis package is released under the MIT licence, as described inLICENCE.txt.
Package: vivp
VIVPVIVP Installs Verilog Packages
Verilog Package Manager. A simple package manager for all your verilog projectsInstallingInstall with pip (python>=3.6):$python3-mpipinstallvivpTo install the very latest version [may be buggy]$pipinstallgit+https://github.com/AdityaNG/VIVP.gitCreating a Verilog Packagecd into the desired directory run the following; you will be prompted to enter your package Name, list of Authors and an optional remote URL$vivp-s.# setupTo install dependencies :$vivp-ihttps://github.com/...To remove dependencies :$vivp-ihttps://github.com/...All dependencies are stored atproject_directory/.vpackage/repos/ContributingIf you see something that you know you can help fix or implement, do contact me at :Mail :adityang5@gmail.comDiscord : to be addedPlease readContributing.TODO List(DONE) Add a run commandAdd support fom non vpackage projectsAdd .vpackage/* to .gitignoreInstall / Remove command triggers a FULL cache clear and re-download. This needs to be optimizedRecursive imports need to be implemented with a dependency tree checkMake use of version control to download a specific version of a packageUnit tests to see if package is functionalMake website on git to give introduction, show list of modules on git, look pretty, SEO :https://adityang.github.io/VIVP/LicenseThis software is released under theMIT License.
Package: vivs
VIVSVIVS (Variational Inference for Variable Selection) is a Python package to identify molecular dependencies in omics data.
Please refer to thepreprintand to the project repositoryherefor more details.InstallationCurrently, VIVS is only available on GitHub. To install the package, on Python >= 3.9, you can do the following:install jax with GPU support (seeherefor instructions)install the package using pip:pipinstallvivsAlternatively, you can clone the repository and install the package in editable mode:pipinstall-e.Basic usageData formatTo use VIVS in your project, import the data of intest in a scanpy AnnData object.
Right now, VIVS only supports assays where $X$ corresponds to gene expression counts.
In particular, make sure than the anndata contains raw counts and not normalized data.The response(s) $Y$ of interest are expected to be stored in theobsmattribute of the anndata object, either as an array or as a dataframe.VIVS may not scale to many thousands of genes.
In such a case, it is recommended to filter the genes before running VIVS, which can be done in the following way:fromvivsimportselect_genesadata=select_genes(adata,n_top_genes=2000)Model fitting and inferenceVI-VS can be initialized and trained as follows:fromvivsimportVIVSmodel=VIVS(adata,feature_obsm_key="my_obsm_key",xy_linear=False,xy_model_kwargs={"n_hidden":8})model.train_all()Once the model is trained, feature significance can be computed as follows:res=model.get_hier_importance(n_clusters_list=[100,200])Here,n_clusters_listis a list of the number of clusters to consider for the hierarchical clustering of the features.These results can be visualized usingplot_hier_importance:model.plot_hier_importance(res,theme_kwargs=dict(figure_size=(15,2))
Package: vix
VIX is a object oriented python wrapper forVMware’s VIX API.InstallingInstall and update usingpip:pip install -U vixQuick ExamplefromviximportVixHost,VixErrorhost=VixHost()try:vm=host.open_vm(r'/home/root/VirtualMachines/Debian/Debian.vmx')snapshot=vm.create_snapshot('Testing VIX','Well, this is great!',include_memory=True)print('Snapshot created!')exceptVixErrorasex:print("Something went wrong :({0}".format(ex))FeaturesThe full VIX API was wrapped, some of the supported operations include:Power on & power off VMs.Manage snapshots (with or without memory)Cloning (linked or full)Script & Command execution on guestsExecuting scripts and processesManage directories, files & processesControl VMs environmentManage shared foldersCreate screenshot of guest VMs
Package: vixtor
# [vixtor](https://github.com/imvickykumar999/vixtor/blob/master/vixtor.ipynb)## [Available functions you can try](https://github.com/imvickykumar999/vixtor/blob/master/vector/vixtor/__init__.py)## [pip install vixtor](https://github.com/imvickykumar999/vixtor/blob/master/pip%20install%20vixtor.ipynb)#### …made with love and hardwork ;)#### [Post on LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:6715639149992394752/)[![pypi](https://raw.githubusercontent.com/imvickykumar999/vixtor/master/pypi.png)](https://pypi.org/project/vixtor/)# Package : Vixtor = Vix (my nickname) + 3D_Vec.tor…this package can solve most of the questions of chapter 11, [NCERT](https://github.com/imvickykumar999/vixtor/blob/master/NCERT-Books-for-class%2012-Maths-Chapter%2011.pdf) (3D Vector) of class 12th.…checkout my linkedin post for tutorial on how to use package’s function and fully explained by solving questions.…repo on github ( username : imvickykumar999 )![plane](https://raw.githubusercontent.com/imvickykumar999/vixtor/master/vixtor.png)
Package: vixuploader
New feature Added : Now you can Upload Your Custom package on Python Package Index using Above Project !!!Update setup.py according to your package requirements :https://github.com/imvickykumar999/100th-Repository-Morsetor-python-Package/blob/main/PYPI%20python%20package/setup.pyRun this python file :https://github.com/imvickykumar999/100th-Repository-Morsetor-python-Package/blob/main/PYPI%20python%20package/RUNME.pyRun [py RUNME.py](https://github.com/imvickykumar999/100th-Repository-Morsetor-python-Package/blob/main/PYPI%20python%20package/RUNME.py)[Morsetor Tutorial](https://github.com/imvickykumar999/100th-Repository-Morsetor-python-Package/blob/master/morse%20tutorial.ipynb)[![tutorial](https://github.com/imvickykumar999/100th-Repository-Morsetor-python-Package/blob/main/screenshot.png?raw=true)](https://github.com/imvickykumar999/100th-Repository-Morsetor-python-Package/blob/master/morse%20tutorial.ipynb)[Uploading Files in Existing Repository on github using python script.](https://github.com/imvickykumar999/git-bash/tree/master)[C:UsersVickyDesktoppythonMy APPsbrythongitcmd>cd gitbash](https://github.com/imvickykumar999/git-bash/blob/master/upload.py)C:UsersVickyDesktoppythonMy APPsbrythongitcmdbefore gitbash>py upload.pyDEKH LIYA CHAL NIKAL1). git init
Initialized empty Git repository in C:/Users/Vicky/Desktop/python/My APPs/brython/gitcmd/before gitbash/.git/2). git add .Enter files name to Upload selected files (or, Press ENTER to Upload All…) :3). git commit -m “Adding files”
[master (root-commit) 0534954] Adding files
5 files changed, 232 insertions(+)
create mode 100644 __pycache__/morse.cpython-38.pyc
create mode 100644 index.html
create mode 100644 morse.py
create mode 100644 readme.md
create mode 100644 upload.py4). git remote add originhttps://github.com/…
Enter the github Username : imvickykumar999
Enter Existing Repository : git-bash5). git pull origin master
warning: no common commits
remote: Enumerating objects: 8, done.
remote: Counting objects: 100% (8/8), done.
remote: Compressing objects: 100% (8/8), done.
remote: Total 8 (delta 0), reused 8 (delta 0), pack-reused 0
Unpacking objects: 100% (8/8), 3.32 KiB | 141.00 KiB/s, done.
Fromhttps://github.com/imvickykumar999/git-bash* branch            master     -> FETCH_HEAD
* [new branch]      master     -> origin/master
fatal: refusing to merge unrelated histories6). git push origin master
Enumerating objects: 8, done.
Counting objects: 100% (8/8), done.
Delta compression using up to 8 threads
Compressing objects: 100% (7/7), done.
Writing objects: 100% (8/8), 4.30 KiB | 2.15 MiB/s, done.
Total 8 (delta 1), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (1/1), done.
Tohttps://github.com/imvickykumar999/git-bash.git+ 2e9e533…0534954 master -> master (forced update)C:UsersVickyDesktoppythonMy APPsbrythongitcmdbefore gitbash>
Package: vix-utils
VIX UtilsOverviewvix_utilsprovides some tools for preparing data for analysing  the VIX Futures and Cash Term structures.The futures can also contain a 30 day continuous maturity weighting of front two months of vix futures.VIX Futures Data downloaded fromCBOE Futures Historical Data.Vix Cash Data are downloaded fromCBOE Historical Volatility Indexes.There is an API for Python to load the data into Pandas DataFrames.  If you do your analysis in Python, use the API.Since there is no documentation yet, look at the examples in the src/vix_utils/examples folder.
There is a Jupyter Notebook vix_utils.ipynb in that folder.Important note for Juypter notebooks.You must use  async_get_vix_index_histories and async_load_vix_term_structure
rather than get_vix_index_histories and load_vix_term_structure.  There is an example Jupyter notebook "vix_utils use in Jupyter.ipynb" in the src/vix_utils/examples folder.If you do your analysis in other tools such as R or excel, you can use the command line tool vixutil.vixutil -hwill give the help.  The data are availble in record and wide formats.  Just run it and look at the excel or csv output to see what they look like.InstallationYou will need a Python 3.11 or later instalation.Install from the Python Packaging IndexInstall using pip fromThe Python Package Index:pip install vix_utilsIf you want to run the samples, install like this:pip install vix_utils[examples]The sample to load all the various data frames can be run as:
'vix_sample_load_data'The sample to plot the history of futures and cash term structures:vix_sample_plotsTo load the sample Jupyter notebook, run vix_sample_load_data to figure out where the examples folder is. Browse there with Jupyter and open a notebook.DevelopmentClone fromgithub repository.pip install -e .[test,examples]will:install vix_utils into your python environment, including any command line scripts.install the necessary prequisites for running any
tests in thetestfolder, and for running the programs in thesrc/vixutils/examplesfolder.TestingThe tests directory contains a few tests.  This project wasn't developed withTest Driven Development, unit tests have been added to isolate
and fix defects.However, new features and bug fixes should be developed withTest Driven Developmentpractices when practical.ExamplesSource is insrc/vix_utils/examples## Data Notes
These dates appear to be missing from the CBOE Data.
At some point they need to be patched in if they exist.
```
[Timestamp('2006-11-10 00:00:00'), Timestamp('2007-01-03 00:00:00'), Timestamp('2021-04-02 00:00:00'), Timestamp('2021-12-24 00:00:00')]
```
There seem to be  a few dates where spot indexes are missing, you will have to workaround by using fill feature of Pandas datafame, or skip those days, in any analysis.Developinghttps://numpydoc.readthedocs.io/en/latest/format.html