Saravanan666/pypi-org huggingface dataset:
Package: visepoint
visepointis a Python package designed to provide a simple interface to interact with thevisepointAPI and prompt models. It can be used to build applications that require AI-based text completion, code completion, and other natural language processing tasks.:Installation:Optional:Install virtualenv:[~$]-pipinstallvirtualenvOptional:Set virtual environment:[~$]-virtualenvvenv&&sourcevenv/bin/activateInstall visepoint via pip:[~$]-pipinstallvisepoint:Usage:To get started withvisepoint, you will need to obtain an API token from the visepointcheckoutcreatororcreate a checkout session using the built-ingenerate_checkout()function provided.importvisepointnew_user=visepoint.UserCreate.generate_checkout()print(new_user)NOTE: Upon completing checkout, you will be registered with visepoint and an email will be sent to you containing your api credentials.Once you have your API key, you can start using the package to interface with thevisepointAPI. Here's an example of how to use thecompletion_createfunction to prompt one of our text completion models.Synchronous methodimportvisepointdata=visepoint.Models.completion_create("visepoint","your_api_token_here","your_query_here")Asynchronous methodimportvisepointdata=awaitvisepoint.Models.completion_create_async("visepoint","your_api_token_here","your_query_here"):License:visepointis distributed under theGNU General Public License v3.0License. See theLICENSEfile for more information.
Package: viser
pip install viser•Documentationviseris a library for interactive 3D visualization + Python, inspired by
tools likePangolin,rviz,meshcat, andGradio. It's designed to support
applications in 3D vision and robotics.As a standalone visualization tool,viserfeatures include:Python API for visualizing 3D primitives in a web browser.Python-configurable GUI elements: buttons, checkboxes, text inputs, sliders,
dropdowns, and more.Ameshcatandtf-inspired coordinate frame tree.Theviser.infrabackend can also be used to build custom web applications. It
supports:Websocket / HTTP server management, on a shared port.Asynchronous server/client communication infrastructure.Client state persistence logic.Typed serialization; synchronization between Python dataclass and TypeScript
interfaces.InstallationYou can installviserwithpip:pipinstallviserTo run examples:# Clone the repository.gitclonehttps://github.com/nerfstudio-project/viser.git# Install the package.# You can also install via pip: `pip install viser`.cd./viser
pipinstall-e.# Run an example.pipinstall-e.[examples]python./examples/02_gui.pyAfter an example script is running, you can connect by navigating to the printed
URL (default:http://localhost:8080).See also: ourdevelopment docs.ExamplesPoint cloud visualizationhttps://github.com/nerfstudio-project/viser/assets/6992947/df35c6ee-78a3-43ad-a2c7-1dddf83f7458Source:./examples/07_record3d_visualizer.pyGaussian splatting visualizationhttps://github.com/nerfstudio-project/viser/assets/6992947/c51b4871-6cc8-4987-8751-2bf186bcb1aeSource:WangFeng18/3d-gaussian-splattingandheheyas/gaussian_splatting_3d.SMPLX visualizerhttps://github.com/nerfstudio-project/viser/assets/6992947/78ba0e09-612d-4678-abf3-beaeeffddb01Source:./example/08_smplx_visualizer.py
Package: viseval
Vis-eval: Visualization tool for your evaluation folderInstallation:pip install visevalUsage:viseval.Tabletakes in a list of dicts. Every element in the list indicates
a row in the HTML visualization. Each dict should contain column-name: image-path.fromvisevalimportTableeval_dir=Path("eval_results")# Vis data is a list of dictionaries.# - Each element in the list corresponds to a row in the table.# - Each key:value pair in a dictionary represents column-name: column-data.vis_data=[]vis_data.append({"gt_img":eval_results/"0_gt.png","pred_img":eval_results/"0_gt.png","gt_mesh":eval_results/"0_gt_mesh.obj","pred_mesh":eval_results/"0_pred_mesh.obj","gt_label":"0_gt_label","pred_label":"0_gt_label",})# Populate vis data with the image / mesh path, text data in your eval folder.# Once populated, save visualization table as html filetable=Table.from_list_dict(vis_data,eval_dir)html_path=table.generate()For demo, you can runpython examples/demo.py. To visualize the generated html file, run a simplepython -m http.serverfrom inside the evaluation directory and openlocalhost:8000url in any web browser:Acknowledgement:This idea and the repository is built on a tool I used inProfessor Shuran's lab.
The initial code is written viaZhenjia Xu.
Package: visgraph
Visgraph APIThe Visgraph API allows users to attach python scripts to a running session and perform changes on the graph from the web client.Link to PyPIUsageInstall withpip install visgraph.See setup.py and infection_sim.py for example usage.useasyncio.run(visgraph.connect(url, port, sid, key, title, startParams, simulate))to start the session.Get the key from the simulate tab in the client session (unique to each user, users can have multiple keys).Title is shown in the client.Start params are passed as a dict, and can be either int, string, float or boolean. Each value requires an attribute (title), type, and defaultValue.
These can be accessed from the client interface.Nodes and edges are passed to the simulate function as lists, and attributes are accessed using the 'data' key.Params are passed as a dictionary, where each key is the attribute value given in the setup dict.ContributingLicenseTODO
Package: vishop
VishopPublish your vim plugin right away!vishopis VIM script publisher client.
Easily bundle and deploy tovim.orgon command line.No web browser required!InstallationpipinstallvishopUsage# Create configuration file (vishop.json)vishopinit# Bundlevishopbuild.# Publishingvishoppublishdist/*.tar.gzFAQWhy Python?Because VIM supports python command (see:help python).
It will much easier when you develop your own VIM plugin using vishop module,
or even simple gluing in vimrc file; because it's written in Python. And alsolife is short.LicenseCopyright (C) 2020 XvezdaMIT License
Package: vishwa-ml-sdk
Welcome to vishwa.ai 👋vishwa-ml-sdkWebsite|Docs|News|Twitter|DiscordRoadmap 🚀FrameworkStatusLangchain✅LLamaIndexPlannedPyTorchPlannedSKLearnPlannedTransformersPlannedStable DiffusionNext💡 If support of any framework/feature is useful for you, please feel free to reach out to us viaDiscordor Github Discussions🔗 InstallationInstall from PyPIpipinstallvishwa-ml-sdk🧩 Usage Examplefromvishwa.mlmonitor.langchain.instrumentimportLangchainTelemetryimportosimportvishwafromvishwa.prompt_hubimportPromptClient# Enable this for advance tracking with our vishwa-ai platformvishwa.host_url="https://api.vishwa.ai"vishwa.api_key="********************"# Get from https://platform.vishwa.aivishwa.adv_tracing_enabled="true"# Enable this for automated insights and log tracing via xpulsAI platform# Add default labels that will be added to all captured metricsdefault_labels={"service":"ml-project-service","k8s_cluster":"app0","namespace":"dev","agent_name":"fallback_value"}# Enable the auto-telemetryLangchainTelemetry(default_labels=default_labels,).auto_instrument()prompt_client=PromptClient(prompt_id="clrfm4v70jnlb1kph240",# Get prompt_id from the platformenvironment_name="dev"# Deployed environment name)## [Optional] Override labels for scope of decorator [Useful if you have multiple scopes where you need to override the default label values]@TelemetryOverrideLabels(agent_name="chat_agent_alpha")@TagToProject(project_slug="defaultoPIt9USSR")# Get Project Slug from platformdefget_response_using_agent_alpha(prompt,query):agent=initialize_agent(llm=chat_model,verbose=True,agent=CONVERSATIONAL_REACT_DESCRIPTION,memory=memory)data=prompt_client.get_prompt({"variable-1":"I'm the first variable"})# Substitute any variables in promptres=agent.run(data)# Pass the entire `XPPrompt` object to run or invoke methodℹ️ Complete Usage GuidesLangchain Framework+Grafana Template🧾 LicenseThis project is licensed under the Apache License 2.0. See the LICENSE file for more details.📢 ContributingWe welcome contributions to xpuls-ml-sdk! If you're interested in contributing.If you encounter any issues or have feature requests, please file an issue on our GitHub repository.💬 Get in touch👉Join our Discord community!🐦 Follow the latest from vishwa.ai team on Twitter@vishwa_ai📮 Write to us athello@vishwa.ai
Package: visibility-graph
This python package is an implementation of the algorithm described in
the article:From time series to complex networks: The visibility graph.InstallationInstall library, perhaps within a virtualenv:$ pip install visibility_graphApplication Programming InterfacePass series as a list, visibility_graph will return anetworkXundirected graph. Nodes contain the magnitudes on their timepoints.:>>> from visibility_graph import visibility_graph
>>> series = [0.87, 0.49, 0.36, 0.83, 0.87]
>>> g = visibility_graph( series )
>>>
>>> g.nodes()
[0, 1, 2, 3, 4]
>>> g.edges()
[(0, 1), (0, 4), (1, 2), (2, 3), (3, 4)]
>>>
>>> g.nodes[1]
{'mag': 0.49}Series to edgelistA command line script is provided:$ series2edgelist.py series.csvwhereseries.csvis a file containing one time point per line. One
can specify more than one series file:$ series2edgelist.py series1.csv series2.csv seriesN.csvThe script will also read a series from the standard input:$ cat series.csv | series2edgelist.pyEdgelist will be printed to the standard output. So it might be wise
to:$ cat series.csv | series2edgelist.py > series.edgelistThe gist“In this graph, every node corresponds, in the same order, to series data, and two nodes are connected if visibility exists between the corresponding data, that is to say, if there is a straight line that connects the series data, provided that this “visibility line” does not intersect any intermediate data height.”“More formally, we can establish the following visibility criteria: two arbitrary data values (t a, y a) and (t b, y b) will have visibility, and consequently will become two connected nodes of the associated graph, if any other data (t c, y c) placed between them fulfills:”
Package: visibudget
VisiBudget helps you “see” your budget working over time.VisiBudget is a new kind of budget management system. It isn’t concerned with double-entry accounting (though it does
this behind the scenes), nor does it try to do everything for you. Instead, VisiBudget helps you to visualize the effect
of spending your money on your paycheck so you always know how much money you have and what it can be used for.If you have ever used another budget management system, you’ve probably seen that they either get very complicated very
quickly, trying to make everything “easy” for you, or they leave all the hard work up to you. VisiBudget only tries to
help you keep track of the money coming in, what account it goes into, where it goes out later and when, and how much
you have spent and have left over. Your spending is mapped out on a calendar so you can easily see how much you will
have on a given date and how far you’ve progressed towards your savings goals.InstallationTo install VisiBudget, simply usepipenv(or pip, of course):$pipenvinstallvisibudgetHow to ContributeCheck for open issues or open a fresh issue to start a discussion around a feature idea or a bug. There is agood first issuetag for issues that should be ideal for people who are not very familiar with the codebase yet.Forkthe repositoryon GitHub to start making your changes to themasterbranch (or branch off of it).Write a test which shows that the bug was fixed or that the feature works as expected.Send a pull request and bug the maintainer until it gets merged and published.Thanks!The skeleton for this project borrows heavily from work already done byKenneth Reitz. I would have had a much tougher time putting this
together if he hadn’t already done a lot of it already. If youthank mefor this project, please alsothank Kennethas well!Release History0.1.0 (2019-01-04)Initial commits, laying out the project structure.
Package: visidata
VisiData v3.0.2A terminal interface for exploring and arranging tabular data.VisiData supports tsv, csv, sqlite, json, xlsx (Excel), hdf5, andmany other formats.Platform requirementsLinux, OS/X, or Windows (with WSL)Python 3.8+additional Python modules are required for certain formats and sourcesInstallTo install the latest release from PyPi:pip3 install visidataTo install the cutting edgedevelopbranch (no warranty expressed or implied):pip3 install git+https://github.com/saulpw/visidata.git@developSeevisidata.org/installfor detailed instructions for all available platforms and package managers.Usage$ vd <input>
$ <command> | vdPressCtrl+Qto quit at any time.Hundreds of other commands and options are also available; see the documentation.DocumentationVisiData documentationPlugin Author's Guide and API ReferenceQuick reference(available withinvdwithCtrl+H), which has a list of commands and options.Intro to VisiData TutorialbyJeremy Singer-VineHelp and SupportIf you have a question, issue, or suggestion regarding VisiData, pleasecreate an issue on Githubor chat with us at #visidata onirc.libera.chat.If you use VisiData regularly, pleasesupport me on Patreon!LicenseCode in thestablebranch of this repository, including the mainvdapplication, loaders, and plugins, is available for use and redistribution under GPLv3.CreditsVisiData is conceived and developed by Saul Pwanson<vd@saul.pw>.Anja Kefala<anja.kefala@gmail.com>maintains the documentation and packages for all platforms.Many thanks to numerous othercontributors, and to those wonderful users who provide feedback, for helping to make VisiData the awesome tool that it is.
Package: visie
Visie is a Simple Initialism EnumeratorIt helps you name things with acronyms.Installationpip3 install visieExamplesBy default, visie will find initialisms and acronyms
that contain any subset of the provided words, in any order:$ visie pleasing orange home noise expeller
HONE: home orange noise expeller
HOPE: home orange pleasing expeller
NOPE: noise orange pleasing expeller
OPEN: orange pleasing expeller noise
PEHO: pleasing expeller home orange
PEON: pleasing expeller orange noise
PHEON: pleasing home expeller orange noise
PHON: pleasing home orange noise
PHONE: pleasing home orange noise expeller
PONE: pleasing orange noise expellerWrapping words in angle brackets<...>means that they must all occur, in order:$ visie 'pleasing orange home <noise expeller>'
HONE: home orange noise expeller
PHONE: pleasing home orange noise expeller
PONE: pleasing orange noise expellerWords in square brackets[...]must all occur, but can be in any order:$ visie '[pleasing orange home <noise expeller>]'
PHONE: pleasing home orange noise expellerParenthesis(...)means exactly one of the contained elements will be used:$ visie --min-length 3 'pleasing home (orange noise expeller)'
HEP: home expeller pleasing
HOP: home orange pleasing
PHO: pleasing home orange
POH: pleasing orange homeElements in curly braces{...}can occur in any order and any quantity:$ visie 'pleasing home ({orange noise} expeller)'
PHON: pleasing home orange noiseElements followed by a question mark are optional:$ visie '<diaphone is? a? [pleasing orange home noise expeller]>'
DIAPHONE: diaphone is a pleasing home orange noise expellerFinally, you can create recursive acronyms by using a period as a wildcard:$ visie '<. is? a? [pleasing orange home noise expeller]>'
DIAPHONE: d is a pleasing home orange noise expeller
WANHOPE: w a noise home orange pleasing expellerThe namevisiewas discovered this way:$ visie '<<. is? a?>? (efficient simple magical) recursive? (acronym initialism) (name word)? (generator enumerator)>'LicenseVisie is licensed and distributed under theAGPLv3license.Contact usif you’re looking for an exception to the terms.
Package: visio2img
visio2imgis a image converter. It converts from MS-Visio file (.vsd, .vsdx) to images.RequirementsPython 2.7, 3.3 and laterpywin32Microsoft VisioSetupInstallpywin32manuallyInstallvisio2imgpackage:$ pip install visio2imgAnd then,visio2imgcommand is available on your environment.UsageExecutevisio2imgcommand:$ visio2img [visio_filename.vsdx] [image_filename.png]If your visio file has multiple pages,visio2imgcommand generates image files for each page.page option-p(–page) option choices a page by page number.For example, this command-line picks up second page of visio file:visio2img.py -p 2 visio_filename.vsdx output.pngname option-n(–name) option choices a page by page name.For example, this command-line picks up a page named “circle”:visio2img.py -n "circle" visio_filename.vsdx output.pngAuthorYassu <mathyassu@gmail.com>MaintainerTakeshi KOMIYA <i.tkomiya@gmail.com>LicenseApache License 2.0
Package: visiomode
Visiomode is an open-source platform for rodent touchscreen-based visuomotor tasks. It has been designed to promote the use of touchscreens as an accessible option for implementing a variety of visual task paradigms, with low-cost and ease-of-use as a priority. Visiomode is implemented on the popular Raspberry Pi computer, and provides the user with an intuitive web interface to design and manage experiments. It can be deployed as a stand-alone cognitive testing solution in both freely-moving and head-restrained environments.InstallationRaspberry Pi OS (recommended)The recommended way to install Visiomode is by usingpipx(https://pypa.github.io/pipx/).pipxwill create an isolated python environment from which Visiomode will run, leaving the system python alone.WARNING: If you're using Pimoroni's Hyperpixel 4.0 display, you'll need Raspberry Pi OS "Buster" (https://www.raspberrypi.com/software/operating-systems/#raspberry-pi-os-legacy), as the Hyperpixel 4.0 display does not yet support the latest Bullseye version (seehttps://github.com/pimoroni/hyperpixel4/issues/155).First, make sure that your Raspberry Pi OS is up-to-date.sudoaptupdate&&sudoaptupgradeVisiomode runs on SDL 2.0. To make sure all system dependencies are present, runsudoaptinstalllibsdl2-mixer-2.0-0libsdl2-image-2.0-0libsdl2-2.0-0libsdl2-ttf-2.0-0Installpipxusingpython3-mpipinstall--userpipx
python3-mpipxensurepathAnd finally, install Visiomode usingpipx:pipxinstallvisiomodeLinux / MacOSWhile Visiomode primarily targets the Raspberry Pi OS, it can be installed on any Linux or MacOS machine, which can be useful for trying out the software before deployment, or for testing. Please note that only the Raspberry Pi OS is officially supported - your milage with any other Linux distribution or MacOS in production may vary.The recommended way to install Visiomode is viapipx(https://pypa.github.io/pipx/).pipxinstallvisiomodeUsageTo launch Visiomode, open a terminal and runvisiomodeIf running overssh, you will need to prependDISPLAY=:0to thevisiomodecommand to run the behaviour window on the primary display.DISPLAY=:0visiomodeThis will launch the behaviour window (what the animal sees). The web interface can be accessed from any machine connected on the same network as the Raspberry Pi running Visiomode athttp://<YOUR-PI-HOSTNAME>.local:5000, where<YOUR-PI-HOSTNAME>is the hostname of your Raspberry Pi. If you're unsure on what this is, runhostnamein a terminal window.UpgradingUsepipxto upgrade visiomode:pipxupgradevisiomodeContributingVisiomode is currently closed to PRs, except bugfixes. Please open an issue if you wish to contribute.Funders
Package: vision6D
Vision6DVision6D is a visualization tool to provide a convinent way to create ground truth 6D pose for 6D pose estimation related projects.InstallationInstall the package using pip:condacreate-nvision6Dpython=3.9
pipinstallvision6DUsageUse Command-Line Interface(CLI) to run the following commandvision6DContactIf you have further question, you can either open a issue on GitHub or directly connect me via emailyike.zhang@vanderbilt.edu
Package: vision-agent
🔍 Vision AgentVision Agent is a library for that helps you to use multimodal models to organize and structure your image data. Check out our discord for roadmaps and updates!One of the problems of dealing with image data is it can be difficult to organize and search. For example, you might have a bunch of pictures of houses and want to count how many yellow houses you have, or how many houses with adobe roofs. The vision agent library uses LMMs to help create tags or descriptions of images to allow you to search over them, or use them in a database to carry out other operations.Getting StartedLMMsTo get started, you can use an LMM to start generating text from images. The following code will use the LLaVA-1.6 34B model to generate a description of the image you pass it.importvision_agentasvamodel=va.lmm.get_lmm("llava")model.generate("Describe this image","image.png")>>>"A yellow house with a green lawn."WARNINGWe are hosting the LLaVA-1.6 34B model, if it times out please wait ~3-5 min for the server to warm up as it shuts down when usage is low.DataStoreYou can use theDataStoreclass to store your images, add new metadata to them such as descriptions, and search over different columns.importvision_agentasvaimportpandasaspddf=pd.DataFrame({"image_paths":["image1.png","image2.png","image3.png"]})ds=va.data.DataStore(df)ds=ds.add_lmm(va.lmm.get_lmm("llava"))ds=ds.add_embedder(va.emb.get_embedder("sentence-transformer"))ds=ds.add_column("descriptions","Describe this image.")This will use the prompt you passed, "Describe this image.", and the LMM to create a new column of descriptions for your image. Your data will now contain a new column with the descriptions of each image:image_pathsimage_iddescriptionsimage1.png1"A yellow house with a green lawn."image2.png2"A white house with a two door garage."image3.png3"A wooden house in the middle of the forest."You can now create an index on the descriptions column and search over it to find images that match your query.ds=ds.build_index("descriptions")ds.search("A yellow house.",top_k=1)>>>[{'image_paths':'image1.png','image_id':1,'descriptions':'A yellow house with a green lawn.'}]You can also create other columns for you data such asis_yellow:ds=ds.add_column("is_yellow","Is the house in this image yellow? Please answer yes or no.")which would give you a dataset similar to this:image_pathsimage_iddescriptionsis_yellowimage1.png1"A yellow house with a green lawn.""yes"image2.png2"A white house with a two door garage.""no"image3.png3"A wooden house in the middle of the forest.""no"
Package: visionary
Python client library for Google Cloud Vision APIhttps://cloud.google.com/visionInstallLibrary can be installed with pip:pip install visionaryUsageUsage examples:fromvisionaryimportGoogleCloudVision,LabelDetection,LogoDetectionAPI_KEY="dummy_api_key"client=GoogleCloudVision(API_KEY)# Ask for logo detection, 10 results maxresponse=client.annotate("dummy.jpg",LogoDetection())# Ask for label detection, 3 results maxresponse=client.annotate("dummy.jpg",LabelDetection(3))Detection params can be set explicitly:# 5 results max for logo detection and only one result for label detectionresponse=client.annotate("dummy.jpg",LogoDetection(5),LabelDetection(1))First param can be file object:uploaded_file=open("dummy.jpg")...# somewhere laterresponse=client.annotate(uploaded_file)Or URL:response=client.annotate("http://google.com/dummy.jpg")Client supports multiple images in singleannotatecallresponse=client.annotate(("dummy.jpg"),("http://google.com/dummy.jpg"),("dummy.jpg",LogoDetection(1)),)Dealing with response:ifresponse.ok:forrespinresponse.responses:foriinresp.logo_annotations:print(i.description)else:print(response.error['code'],response.error['message'],response.error['status'])
Package: vision-counter
Vision Counter is a powerful and user-friendly Python library that enables accurate object counting in images using pre-trained models. With a simple and intuitive API, Vision Counter can be easily integrated into your computer vision projects, providing reliable object counting solutions for various applications. The library comes equipped with state-of-the-art models and efficient algorithms to deliver high-performance object counting capabilities. Whether you need to count people, vehicles, or any other objects in your images, Vision Counter is the ideal choice for fast and accurate results.