Saravanan666/pypi-org huggingface dataset:
Package: vowpalwabbit
Vowpal Wabbit is a fast machine learning library for online learning, and this is the python wrapper for the project.Installing this package builds Vowpal Wabbit locally for explicit use within python, it will not create the command-line version
of the tool (or affect any previously existing command-line installations).
To install the command-line version see the main project page:https://github.com/VowpalWabbit/vowpal_wabbitThe version of the PyPI vowpalwabbit package corresponds to the tagged version of the code in the github repo that will be used
during building and installation.
If you need to make local changes to the code and rebuild the python binding be sure to pip uninstall vowpalwabbit then rebuild
using the local repo installation instructions below.InstallationSee theinstallation instructionsUsageYou can use the python wrapper directly like this:>>>importvowpalwabbit>>>vw=vowpalwabbit.Workspace(quiet=True)>>>ex=vw.example('1 | a b c')>>>vw.learn(ex)>>>vw.predict(ex)Or you can use the included scikit-learn interface like this:>>>importnumpyasnp>>>fromsklearnimportdatasets>>>fromsklearn.model_selectionimporttrain_test_split>>>fromvowpalwabbit.sklearnimportVWClassifier>>>>>># generate some data>>>X,y=datasets.make_hastie_10_2(n_samples=10000,random_state=1)>>>X=X.astype(np.float32)>>>>>># split train and test set>>>X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=256)>>>>>># build model>>>model=VWClassifier()>>>model.fit(X_train,y_train)>>>>>># predict model>>>y_pred=model.predict(X_test)>>>>>># evaluate model>>>model.score(X_train,y_train)>>>model.score(X_test,y_test)TroubleshootingSee thetroubleshooting guideDevelopmentContributions are welcome for improving the python wrapper to Vowpal Wabbit.Check for openissuesor create one to discuss a feature idea or bug.Fork therepoon Github and make changes to the master branch (or a new branch off of master).Write a test in the python/tests folder showing the bug was fixed or feature works (recommend usingpytest).Make sure package installs and tests pass under all supported environments (this callstoxautomatically).Send the pull request.Tests can be run using setup.py:$pythonsetup.pytestDirectory Structure:python : this is where the c++ extension livespython/vowpalwabbit : this is then main directory for python wrapper code and utilitiespython/examples : example python code and jupyter notebooks to demonstrate functionalitypython/tests : contains all tests for python codeNote:neither examples nor tests directories are included in the distributed package, they are only for development purposes.
Package: vowpal-wabbit-next
py-vowpal-wabbit-nextThese are a new set of bindings forVowpalWabbit. Parity with the existing bindings is not a goal of this project as it is an opportunity for a clean slate and opportunity to rethink old decisions. The interfaces here are all subject to change so be prepared for things to change if you use this library.Installationpipinstallvowpal-wabbit-nextExampleimportvowpal_wabbit_nextasvwimportiocb_input=io.StringIO("""shared | s0:1:0.5 | a=0| a=1shared | s| a=01:0:0.5 | a=1""")workspace=vw.Workspace(["--cb_explore_adf"])withvw.TextFormatReader(workspace,cb_input)asreader:foreventinreader:print(workspace.predict_then_learn_one(event))GoalsFully typed and documentedAll artifacts automatically build in CIFocus on library usage instead of CLI behaviorSupported platformsWheels are provided for the following platforms:OS+arch: Windows x86_x64, MacOS x86_x64, MacOS arm64, Linux x86_x64Python: 3.7 (except MacOS), 3.8, 3.9, 3.10, 3.11Development informationLocal debug if using vcpkg deps# BuildexportCMAKE_TOOLCHAIN_FILE=$(pwd)/ext_libs/vcpkg/scripts/buildsystems/vcpkg.cmake
pipinstall-v.# Install dev dependnciespipinstall-rrequirements-dev.txt# Testpytest# Type checkmypy# Formatblacksrctests# Check documentationpydocstylesrcUpdate pybind11 module type stubAfter updating the native module the type stub must be updated. This can be done automatically like so (ideally automatically in future, or at least checked if it is stale):./generate_types.sh
Package: vox
AboutA wrapper aroundnoxto easily run linters.Installation$python-mpipinstallvoxDocumentationDocumentation is availablevia GitHub.TestingTo run all tests runnox. No venv is needed; nox makes all of them for us.$python-mpipinstall--usernox$gitclonehttps://peilonrayz.github.io/vox/$cdvoxvox$noxLicensevox is available under the MIT license.
Package: vox2obj
Vox2ObjConverts .vox files to .obj files.UNDER ACTIVE DEVELOPMENT so things may change very quickly.Usagefromvox2objimportconvert_to_objconvert_to_obj(in_file="in.vox",out_file="out.obj",texture_file="texture.png")Where:"in.vox" is the input .vox file"out.obj" is the output destination"texture.png" is the texture that belongs to the .vox fileIt should be a 1x255 pixel png image.You will probably have to save this separately using your voxel editor.
Package: voxa
🌐 VoxaVoxa is an easy-to-use Python package designed to translate text.🛠 InstallationTo get started with Voxa, simply run the following command:pipinstallvoxa🚀 UsageUsing Voxa is straightforward. Below is a quick example to translate an English text into French:importvoxatranslator=voxa.Translator(source='en',target='fr')translated_text=translator.translate("Hello")print(translated_text)Happy translating! 🌍
Package: vox-cards
Vox CardsThis is an easy to use dependancy free deck manager, making it easy to develope the logic of
card games without worrying about creating ways to handle the decks, cards, and hands.InstallationInstall using pip:py -m pip install vox_cardsBasic Usageimportvox_cards.deckascardsdeck=cards.Deck(2)# Pass the number of hands to construct this deck with.deck.deal()# The default number of cards to deal is 7player_1,player_2=deck.handsforcardinplayer_1:# Print all cards in the first hand.print(card.full)# Prints the full name of the card (i.e. "10 of Clubs").Keep in mind that hands are iterable, but only return the cards they hold.
With that in mind, there is also the ability for hands to draw and discard cards.# This will draw the top 2 cards for each palyer, then discard 2 cards at random.player_1.draw(2)player_1.discard(2)player_2.draw(2)player_2.discard(2)# You can also pass a card instance to hand.discard for discarding specific card(s).player_1.discard(player_1.cards[0])player_2.discard(player_2.cards[0])More complex use caseHere is a more complex use case that features the main components of this module. It doesn't use any logic, but hopefully it gives you an idea of some use cases for this project.importvox_cards.deckascardsdeck=cards.Deck(3)for_inrange(3):deck.shuffle()player_1,player_2,river=deck.handsfor_inrange(2):player_1.draw()player_2.draw()river.draw(3)river.draw()river.draw()print("\nPlayer 1's hand:")forcardinplayer_1:print(card.full)print("\nPlayer 2's hand:")forcardinplayer_2:print(card.full)print("\n\nAnd the river is:")forcardinriver:print(card.full)Here is the output for the code above:>>>pyexample.pyPlayer1's hand:QueenofSpadesJackofSpadesPlayer2's hand:6ofClubs8ofSpadesAndtheriveris:6ofHearts8ofClubs5ofHearts4ofHearts9ofSpadesLooks like player 2 wins with a 2-pair, how fitting.DocumentationThis entire project is still a WIP and has not been officially released yet.
Full documentation will come for this project whenever it has been released
in a stable state. Until then, refer to the usage examples and the github
repo for most use cases.For a full list of everything you can do, view thedocs
Package: voxceleb_luigi
# voxceleb-luigi
[![pypi version](http://img.shields.io/pypi/v/voxceleb_luigi.svg?style=flat)](https://pypi.python.org/pypi/voxceleb_luigi)Luigi pipeline to download VoxCeleb audio from YouTube and extract speaker segments.This pipeline can download both [VoxCeleb](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) and [VoxCeleb2](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html).## Installationpip install voxceleb_luigiYou need to haveffmpegandyoutube-dlinstalled. On systems withapt, you can simply run:sudo apt install ffmpeg youtube-dl## UsageDownload and unpack at least one of the metadata directories with the YouTube URLs and timestamps (VC1 [dev](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/data/vox1_dev_txt.zip)/[test](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/data/vox1_test_txt.zip), VC2 [dev](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/data/vox2_dev_txt.zip)/[test](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/data/vox2_test_txt.zip)).Start luigid, the central scheduler:luigid –backgroundThen start the worker process:luigi –module voxceleb_luigi–workers 4 voxceleb.ProcessDirectory –path /path/to/metadataThe pipeline will recursively search/path/to/metadatafor the segment files (by looking for files called like00001.txtetc.), download the audio of their source videos, and extract the speaker segments.By default, the segment audio files are stored in parallel to the metadata in directories calledwavthat get created next to thetxtdirectories. Suppose you have the metadata of the dev and test sets of VoxCeleb1 in~/vc1with paths looking like~/vc1/dev/txt/idX/videoX/XXXXX.txt. If you pass–path ~/vc1to the pipeline, segments will end up in~/vc1/dev/wav/idX/videoX/XXXXX.wav. Other output of the pipeline (full audio of videos, soft failures, dummy outputs for completed directories) are stored in./voxceleb-luigi-filesby default.## ConfigurationBoth the location where the dataset gets created and the storage directory for the pipeline can be changed through parameters in theluigi.cfg(default location is the current working directory; you can override this via theLUIGI_CONFIG_PATHenvironment variable):[voxceleb.Config]
# Required
output_dir=/where/to/store/wav/segments
pipeline_dir=/where/to/put/pipeline/stuff## Only necessary if youtube-dl, ffmpeg, and ffprobe are not in your PATH:
#ffmpeg_bin=/ffmpeg-dir/ffmpeg
#ffmpeg_directory=/ffmpeg-dir # passed on to youtube-dl via –ffmpeg-location
#youtube_dl_bin=/path/to/youtube-dl[voxceleb.ProcessDirectory]
## alternative to the –path command line option
#path=/path/to/metadataWhenoutput_diris set, the directory structure of the metadata is mirrored in this directory. In this case, thetxtdirectories are not replaced bywav, but removed from the path.
Package: voxcell
OverviewThis is a library to build circuits and atlases. It contains tools to handle“traits fields” and collections and the logic to assign them.volumetric data within NRRD filesCell collection access / writer.to build, transform and handle fields of vectors and orientations.queryingRegion hierarchy tree, such as the one available from theAllen Institute for Brain Science (AIBS):Mouse Brain Atlas Ontology’s StructureGraph(aka 1.json).InstallationInstall from PyPI:pipinstallvoxcellOr an editable install:gitclonehttps://github.com/BlueBrain/voxcellcdvoxcellpipinstall-e.ExamplesTo use the following examples, one must download an NRRD file and the Ontology Structure:curl-obrain_regions.nrrdhttp://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/annotation/ccf_2017/annotation_100.nrrdcurl-ohierarchy.jsonhttp://api.brain-map.org/api/v2/structure_graph_download/1.jsonOne can open NRRD files, and perform operations on them:importvoxcellvoxels=voxcell.VoxelData.load_nrrd('brain_regions.nrrd')print(voxels.voxel_dimensions)# prints array([100., 100., 100.], dtype=float32)One can also use theAtlasobject to load at both the atlas and the hierarchy:importnumpyasnpfromvoxcell.nexus.voxelbrainimportAtlasatlas=Atlas.open('.')brain_regions=atlas.load_data('brain_regions')rm=atlas.load_region_map()# count the number of voxels in the VIS region, and all its descendentsids=rm.find('VIS','acronym',with_descendants=True)np.count_nonzero(np.isin(brain_regions.raw,list(ids)))CitationWhen you use this software, we kindly ask you to cite the following DOI:AcknowledgementsThe development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.For license and authors, see LICENSE.txt and AUTHORS.txt respectively.Copyright © 2022 Blue Brain Project/EPFL
Package: voxcharta-my-voting-record
voxcharta-my-voting-recordOverviewGetting StartedRequirementsInstallation InstructionsVox Charta Data ExportExecutionVersioningAuthorsLicenseOverviewThis pure Python tool web scrapes data fromVox Charta'sMy Voting Records page.
It provides the data contents in two forms, a non-relational JSON file
and relational CSV file.This software was driven by the need to archive such records since Vox Charta
will be sunsetted on December 31, 2020.Getting StartedThese instructions will have the code running.RequirementsThis software is tested with 3.10.2, the latest version available during recent development.
In principle it should work with >=3.7.  It requiresBeautifulSoup4andpandas.Installation InstructionsPython and setting up an environmentUsing your preferred Python environment manager (conda,virtualenv,venv),
I recommend create a separate environment to avoid any possible conflicts with
existing software that you used. Below arecondainstructions$ (sudo) conda create -n voxcharta python=3.10.2Installation is straightforward:$ conda activate voxcharta
$ (sudo) pip install voxcharta-my-voting-recordOr from source:$ conda activate voxcharta
$ git clone https://github.com/astrochun/voxcharta-my-voting-record.git
$ cd voxcharta-my-voting-record
$ (sudo) pip install .For editable/development installation, execute(sudo) pip install -e .This is preferred over thepython setup.pymethod as certain dependencies (numpy) will fail to install during the build process.Vox Charta Data ExportBefore using this software, you will want to export your Vox Charta My Voting Records
page. Here are the steps. These are based on Chrome, so differences may occur for other browsers:Login to Vox Chartabefore you can't!Click on "Tools > My Voting Records"After the page fully loads (this can take some time), click "File > Save Page As"Change filename to "myvotingrecords"For format, select "Webpage, Complete"ExecutionThe primary script to execute isvox_runExecution requires only one argument, which is the full path
to the HTML file.$ vox_run /full/path/to/myvotingrecords.htmThis is all possible usingTyper.
The script provides rich-formatted log messages to the terminal usingRich.All contents are stored in/full/path/tounlessoutfile_prefixis specified
with a different path. Thus, without specifyingoutfile_prefixthe above execution
will generate:JSON:/full/path/to/myvotingrecords.jsonCSV:/full/path/to/myvotingrecords.csvA log file is constructed:/full/path/to/vox_run.YYYY-MM-DD.logVersioningWe useSemVerfor versioning. For the versions available,
see thereleases on this repository.AuthorsChun Ly, Ph.D. (@astrochun)LicenseThis project is licensed under theGNU GPLv3 License- see theLICENSEfile for details.
Package: voxcov
voxcov-pyPython bindings for voxcov.Quick startClonevoxcovfirst.Runmaturin develop --releaseto build and install the plugin.  Use the--releaseflag to get an optimized build.Coverage exampleImport the libraryimportvoxcovasvcCreate a map.apix=[1.0,1.0,1.0]origin=[0.0,0.0,0.0]# The maximum axis lengthsize=256vox_map=vc.Map(apix,origin,size)Add and delete spheres.# Add a sphere of radius 3.5 at (10,10,10)vox_map.add_sphere([10.0,10.0,10.0],3.5,lambdavox:print("Adding A Covered: ",vox))# Add another sphere which overlaps mostly of radius 3.5 at (11,10,10)vox_map.add_sphere([11.0,10.0,10.0],3.5,lambdavox:print("Adding B Covered: ",vox))# Delete sphere which overlaps mostly of radius 3.5 at (11,10,10)vox_map.del_sphere([11.0,10.0,10.0],3.5,lambdavox:print("Deleting B uncovered: ",vox))Blur exampleImport the libraryimportvoxcovasvcCreate a blurrer.  Note, the API is a bit clunky.  Named parameters would be nice.blurred_map=vc.BlurMap([1.084,1.084,1.084],# The cell dimensions[0,0,0],# The origin[256,256,256],# The size, 256^35.1,# Sigma for gaussian# e.g. (sigma_coef * resolution)4.0# The number of sigma to cutoff at)Stick atoms in.# We pass the center and scale of each gaussian.blurred_map.add_gaussian([3,4,5],10)blurred_map.add_gaussian([4,5,3],12)Get a numpy array out.blurred_map.to_numpy()
Package: voxelbotutils
VoxelBotUtilsVoxelBotUtils (VBU) is an extension for Novus to speed up Discord bot development. It includes a whole slew of features that are commonly used in a lot of bots so you don't need to keep writing them every time. Many features are available to you by default:Built-in error handling for all of Novus's errors.Built-in database connector.Logging using Python'slogging.Loggerclasses.Webhook sends on different events.Bot stats posting via StatsD and DataDog.A help command that doesn't look like trash.Owner-only commands.Presence auto-updating based on Twitch streamers.Website utilities.And more...Basic UsageInstall VBU via pip -pip install voxelbotutils.Create your config file via CMD -vbu create-config bot.Update your auto-generated config file inconfig/config.toml.(Optional) Add/change any cogs you wish inside of thecogs/folder.Run your bot -vbu run-bot.DocsDocumentation for the package and its usage can befound here.InstallingThe package is available via PyPi -pip install voxelbotutils. The tags on this repo can be used to keep up-to-date with different releases. The master branch is not guaranteed to be fully working, whereas PyPi releases are.
Package: voxelfarm
Voxel Farm Client PackageThis is a simple example package. You can useVoxelfarm PythonCookbookto write your content.Initialize APIThis example shows how to create a new instance of the Voxel Farm client Python API.# Import the Voxel Farm Client Libraryfromvoxelfarmimportvoxelfarmclient# The URL for the Voxel Farm APIvf_api_url=os.getenv('YOUR_VOXELFARM_API_URL')# Create instance of the Voxel Farm REST APIvf=voxelfarmclient.rest(vf_api_url)Provide CredentialsThis example shows how to provide credentials for API authentication.# Set credentialsaad_app_secrets=os.getenv('AAD_APP_SECRETS')if(aad_app_secrets!=None):vf.set_file_credentials(aad_app_secrets)Specify an HTTP proxyThis example shows how to specify an HTTP proxy to be used in all HTTP calls made by the API.# Use a proxy to debug HTTP requests using Fiddler or similar
proxies = {
  "http": os.getenv('YOUR_PROXY_URL'),
}

# Set the proxy to debug HTTP calls
vf.set_proxy(proxies)Get CRS from projectThis example shows how to retrieve the project's CRS (Coordinate Reference System).# Get the coordinate system given project IDresult=vf.get_project_crs(project)ifnotresult.success:print(result.error_info)exit()crs=result.crs
Package: voxelfuse-primitives
VoxelFuse-PrimitivesClasses for generating primitives using theVoxelFuselibrary.InstallationThe voxelfuse_primitives library can be installed using pip.pip3 install voxelfuse-primitivesThe classes needed by your script can then be imported in Python.  For example:from voxelfuse_primitives.solid import Solid
from voxelfuse_primitives.linkage import Linkage
Package: voxelgym2D
voxelgym2DA gymnasium environment for voxel/grid based reinforcement learning for path planning.Results withSB3(v1.6.2) : PPO :smile:Here are the results of training a PPO agent on theonestep-v0using the examplehere. Below you will find the episode reward and episode length over steps during training. As the agent learns, the episode reward increases and the episode length reduces are the agent learns to identify the goal and reach it in the shortest possible path.Installationpipinstallgit+https://github.com/harisankar95/voxelgym2D.gitor directly from PyPI :smile:pipinstallvoxelgym2DDevelopmentTo install the package in development mode, run the following command in the root directory of the repository:gitclonehttps://github.com/harisankar95/voxelgym2D.git~/path/to/repocd~/path/to/repo
pipinstall-e.[dev]# to aditionally install stable_baselines 3 and pytorch (optional)pipinstall-e.[dev,sb3]Usageimportvoxelgym2Dimportgymnasiumasgymenv=gym.make("voxelgym2D:onestep-v0")observation,info=env.reset(seed=123456)env.render()ExamplesThe examples can be foundhere.LicenseThis project is licensed under the terms of theMIT license.DocumentationThe documentation can be foundhere.Changelog0.1.0Initial release of voxelgym 2D environments tested with stable_baselines 3 (v1.6.2) and python 3.80.2.0Available on PyPI, sphinx documentation in work0.3.0Migration to gymnasiumAgent can now be reset to multiple start positions0.3.1License updatedDocumentation updatedTODOAdd 2D environmentsAdd documentationContributingContributions are welcome! Please open an issue or a pull request.CitationIf you find this project useful, please cite it as:@misc{Babu_2023,author={Babu, Harisankar},title={Reinforcement Learning Based Path Planning for Autonomous Flight},url={https://publica.fraunhofer.de/handle/publica/442672},year={2023},month={Jan},}ReferencesGymnasiumOpenAI GymStable Baselines 3
Package: voxelmap
voxelmapA Python library for making voxel and three-dimensional models from NumPy arrays.Installation and Local UsagepipinstallvoxelmapIt is recommended you run voxelmap using avirtualenvvirtual environment. To do so, follow the below simple protocol to create the virtual environment, run it, and install the package there:virtualenvvenvsourcevenv/bin/activatepipinstallvoxelmappython[your-voxelmap-script.py]To exit the virtual environment, simply typedeactivate. To access it at any other time again, enter with the abovesource venv...command.Just starting? Remote Usage with a Colab notebook (click below)Disclaimer: Use At Your Own RiskThis program is free software. It comes without any warranty, to the extent permitted by applicable law. You can redistribute it and/or modify it under the terms of the MIT LICENSE, as published by Andrew Garcia. See LICENSE below for more details.MIT licenseCopyright 2022 ©Andrew Garcia.
Package: voxel-model-visualizer
Voxel Model VisualizerThis code acts as a standalone tool to vizualize voxelized models stored as 4 dimensional numpy arrays (3 geometric dimensions + 1 class dimension).Package InstallationCan be done directly frompypipipinstallvoxel-model-visualizerDependenciesmodernglnumpypillowsimple-3dvizwxpythonPackage Usagefromvoxel_model_visualizerimportVoxelModelVisualizermodel=np.load('./sample/input.npy')colours=[(0.8,0,0),(0,0.8,0),(0,0,0.8)]visualizer=VoxelModelVisualizer(gif_resolution=720,static_resolution=4096,frame_count=120,fps=24,pancake_spread=5,verbose=False)visualizer.render(model,colours,output_path='./example_pancake.png',pancake=True)visualizer.render(model,colours,output_path='./example_animate.gif',animate=True)Standalone Usageusage: main.py [-h] [-a] [-p] [-o OUTPUT] filepath

Voxel Model Renderer

positional arguments:
  filepath       Model input filepath

optional arguments:
  -h, --help     show this help message and exit
  -a, --animate  Generated an Animated GIF (default: False)
  -p, --pancake  Render model as pancake slices (default: False)
  -v, --verbose  Verbose console printing (default: False)
  -o OUTPUT      Output filepath (default: ./out.png)
Package: voxelobj
voxel_obj_lib_in_pythonThis is a simple libary for creating voxel 3D-obj files in python. There is another implementation in cpp (link:https://github.com/martinpflaum/voxel_obj_lib_in_cpp)WorldGridtakes as first parameter the 3d grid size of the world. In our example the grid would be of size 4x4x4. The second parameter ofWorldGridis the size of the colorpallete. With a colorpallete of size 8, the textures you want to apply onto the object should be in a 8x8 grid. A 8x8 grid is for example 64x64 - each texture is then 8x8 pixel large. Its not that complicated just try some textures with same width and hight out.In__setitem__the first argument are the x,y,z coordinate of the cube you want to change. The second argument of__setitem__is the colorvalue of the voxel. -1 stands for void. The colorvalue 0 corresponds to the pixel value at point 0,0 in texture palette. The obj() function returns the string that contains the objfilefromvoxelobjimport*worldGrid=WorldGrid(4,8)worldGrid[1,1,1]=1worldGrid[0,1,1]=-1worldGrid[1,0,1]=-1worldGrid[1,1,0]=-1worldGrid[2,1,1]=-1worldGrid[1,2,1]=-1worldGrid[1,1,2]=-1write("output.obj",worldGrid.obj())