Saravanan666/pypi-org huggingface dataset:
Package: vr-idt
vr-idtThis package provides a VR tailored eye-tracking classification algorithm for identifying fixations and saccades in data gathered from VR headsets.BackgroundThe algorithm was initially proposed and implemented by Jose Llanes-Jurado, Javier Marín-Morales, Jaime Guixeres, and Mariano Alcañiz in their paper,Development and Calibration of an Eye-Tracking Fixation Identification Algorithm for Immersive Virtual Reality.Code from the original authors is heregithub/ASAPLableni.InstallationInstall this package usingpip(Python virtual environments).$ pip install vr-idtClassify fixationsdefclassify_fixations(df:pd.DataFrame,min_duration:float=0.15,max_angle:float=1.50,min_freq:float=30.0,time:str="time",gaze_world_x:str="gaze_world_x",gaze_world_y:str="gaze_world_y",gaze_world_z:str="gaze_world_z",head_pos_x:str="head_pos_x",head_pos_y:str="head_pos_y",head_pos_z:str="head_pos_z")->pd.DataFrame:"""Classify VR eye fixation events in eye-tracking data.Args:df: DataFrame with eye tracking data to classifymin_duration: The minimum length of a fixation in secondsmax_angle: The maximum angle of dispersion within the fixation withinmin_freq: The minimum required frequency for a fixation to be classifiedtime: df column name for time (sec) datagaze_world_x: df column name for gaze position in virtual world datagaze_world_y: df column name for gaze position in virtual world datagaze_world_z: df column name for gaze position in virtual world datahead_pos_x: df column name for head position in physical space datahead_pos_y: df column name for head position in physical space datahead_pos_z: df column name for head position in physical space dataReturns:fixation_df: Copy of original arg 'df' with 4 new fixation related columns:"fixation", "fixation_start", "fixation_end", and "fixation_duration""""Exampleimportpandasaspdfromvr_idt.vr_idtimportclassify_fixations# Load in data with eye tracking datadf=pd.read_csv("<path/to/data>")# Setup a column name mapping so algorithm knows where to look for necessary datacol_name_map={"gaze_world_x":"Gaze Pos X (world)","gaze_world_y":"Gaze Pos Y (world)","gaze_world_z":"Gaze Pos Z (world)","head_pos_x":"Head Pos X","head_pos_y":"Head Pos Y","head_pos_z":"Head Pos Z"}# Define some parametersmin_duraion=0.15max_angle=1.5min_freq=25# Run algorithm and add 4 fixation related columns to dfdf=classify_fixations(df,min_duration,max_angle,min_freq,**col_name_map)
Package: vr.launch
Deploying against Velociraptor? Need to load the config? It’s easy:parser = argparse.ArgumentParser()
yg.launch.cmdline.add_config_arg(parser)
# add your own custom args too
args = parser.parse_args()
startup_app(args.config)This module loads the config from YAML into a nice ItemsAsAttributes
dictionary, so you can get the config values as items or as attributes:args.config.foo == args.config['foo']Config is loaded usingyamlenv <https://pypi.org/project/yamlenv/>,
so any YAML can reference environment variables and other YAML files
to be interpolated.Enjoy!
Package: vroom
vroomprovides tools for identifying license plates in text.Basic usagefromvroomimportplates_parserparser=plates_parser.Parser()plates_string="Lorem ipsum SY12345 dolor sit amet, SK 1A234 consectetur adipiscing elit STA12AA"found_plates=parser.findall_plates(plates_string)print(found_plates)single_plate="SY12345"plate_info=parser.match_plate(single_plate)print(single_plate)Currently supported countriesPoland [PL] (source)
Package: vroParse
vroParsevRO XML Package ParserTable of ContentsOverviewSetupUsageSupport These ProjectsOverviewvroParseis created byJim Sadlek.vroParseis an addendum to the vRealize Build Tools and Developer Tools, and is a Python package with two executable commands,parsevroandupdatevro.This project parses code embedded in scriptable tasks from out of Workflow XML, saves them as discrete files for editing and SCC, and imports the edits back into XML for inclusion in vRO Package updates.Setuppip install vroParseThe package is hosted onPyPi.  If you do not have external access to the Internet,
you will need to do a local install on your system.To do that, obtain a clone of this repo to your local system. then, make sure
to run thesetup.pyfile, so you can install any dependencies you may need. To
run thesetup.pyfile, run the following command in your terminal.python(3) setup.py install --record filesThis will install all the dependencies listed in thesetup.pyfile. Once done
you can use theparsevroandupdatevrocommands.To manually remove this package runrm $(cat files)Usageparsevro [-h] [-v]

This script parses code from vRO Workflow XML that contain embedded Scriptable Tasks.
By: Jim Sadlek - VMware, Inc.

This must be run from the vRO Package's top-level folder, which contains a 'workflows/src/main/resources/Workflow' subfolder.
This script traverses all sub folders of the above and stores embedded code in a '.parsevro' folder.
This is usually run after a 'mvn vro:pull' command when using vRealize Build Tools, or exporting to a folder with native vRO.

-h, --help     show this help message and exit
-v, --verbose  Display verbose logging outputHere is a simple example of using thevroParsepackage to parse vRO XML package files for Scriptable Tasks' element code, and then update XML with edited code, from within Visual Studio Code or some other code editor outside of vRO.After performing amvn vro:pulloperation, run this from the root folder:parsevroAfter performing amvn vrealize:pushoperation, run this from the root folder:updatevroOther ProjectsvRealize Build Tools:vRealize Build Toolsis a Vmware Fling that provides tools to development and release teams implementing solutions based on vRealize Automation (vRA) and vRealize Orchestrator (vRO).vRealize Developer Tools:vRealize Developer Toolsis a Visual Studio Code Extension available on GitHub that provides code intelligence features and enables a more developer-friendly experience when creating vRealize content.
Package: vrprot
VRPROTABOUT VRPROTA pipeline that processes protein structures in ProteinDataBank (PDB) file format
using ChimeraX and enables them to be analyzed on theVRNetzerplatform.PROTEIN STRUCTURE ANALYSISUSAGE OF THIS PROJECTThe main purpose of this project is to serve as an easy-to-use pipeline to facilitate the processing of protein structures for presentation on theVRNetzer. It is mainly used in theProteinStructureFetch Extensionof theVRNetzer. For everyone who wants to analyze their own protein structures, with your desired highlighting and coloring, this project is the right place to start.
The program consists of five parts:PartDescriptionFetcherFetches PDB files fromAlphaFold DatabaseChimeraX ProcessorProcesses the PDB files withChimeraXGLB ConverterConverts GLB files to PLY filesPoint Cloud Samplersamples point clouds from the PLY filesColor Map Generatorgenerates the final color maps which are needed to present protein structures on theVRNetzerplatform.For easy usage of this project, we provide a one-file executable that allows using the program without further installation of Python and any dependencies. Nevertheless, aChimeraXinstallation is mandatory to use the full potential of this project.
WithoutChimeraXtheChimeraX Processorcannot work.
Package: vrpy
VRPy is a python framework for solving Vehicle Routing Problems (VRP) including:the Capacitated VRP (CVRP),the CVRP with resource constraints,the CVRP with time windows (CVRPTW),the CVRP with simultaneous distribution and collection (CVRPSDC),the CVRP with heterogeneous fleet (HFCVRP).Check out thedocsto find more variants and options.Simple example>>>fromnetworkximportDiGraph>>>fromvrpyimportVehicleRoutingProblem# Define the network>>>G=DiGraph()>>>G.add_edge("Source",1,cost=1,time=2)>>>G.add_edge("Source",2,cost=2,time=1)>>>G.add_edge(1,"Sink",cost=0,time=2)>>>G.add_edge(2,"Sink",cost=2,time=3)>>>G.add_edge(1,2,cost=1,time=1)>>>G.add_edge(2,1,cost=1,time=1)# Define the customers demands>>>G.nodes[1]["demand"]=5>>>G.nodes[2]["demand"]=4# Define the Vehicle Routing Problem>>>prob=VehicleRoutingProblem(G,load_capacity=10,duration=5)# Solve and display solution value>>>prob.solve()>>>print(prob.best_value)3>>>print(prob.best_routes){1:["Source",2,1,"Sink"]}InstallpipinstallvrpyRequirementscspyNetworkXnumpyPuLPDocumentationDocumentation is foundhere.Running the testsUnit Testscdtestspytestunittests/BugsPlease report any bugs that you find in theissuessection. Or, even better, fork the repository onGitHuband create a pull request. Any contributions are welcome.
Package: vrsjp
vrsjpThis program is to confirm the number of vaccinated people in JapanDownload the data from here（http://data.vrs.digital.go.jp/vaccination/opendata/latest/summary_by_date.csv）The information displayed in this program is the number of new vaccinations for the new coronavirus vaccine. The first inoculation, the second inoculation, and the third inoculation are displayed in different colors.
Package: vrt-generator
vrt_generatorPython class for creating vrt-annotated corpora.
Still in very early testing stage.Install by typing:pipinstallvrt_generatorUsage Example:fromvrtimportCorpus,S,TextwithCorpus("~","meinkorpus",4,"text_name")asc:withText(c,text_name="Text2")ast:withS(c)ass:s.writep("Test","TAG","TAG","Lemma")Features:Represent Corpus, Text, P and S AttributesIntegration of spacy for automatic generation of a vrt-representation of textsUsing Context Manager for xml-hierarchy representationReduces to utf8mb3 and checks formatting compatibilityIf you want to add texts that are automatically POS-Tagged with Spacy, you might look at vrt_spacy
Package: vrt-lss-packer
Veeroute Packer API.  # Description  The service is designed to calculate the placement of goods inside given packages.  ## Types of packages  ### Pallet  ![pallet](../images/packer_pallet.svg)  *Mono-pallet*  If the pallet is fully equipped with the same product, then such a pallet is called **mono-pallet**, such a pallet has the following loading rules:  1. The maximum allowable height of the pallet with the goods laid on it is taken into account. 1. The maximum allowable weight of the loaded pallet is taken into account. 1. Goods packages can only be stacked parallel to the walls of the pallet. 1. It is allowed to rotate packages around the vertical Z-axis and only at a right angle. 1. In order to equalize the height between the positions (to place another pallet on top), empty spaces can be added to the stacking by layers.  *Zone tray*  After the formation of mono-pallets, the remaining part of the goods of the same article is placed on the next pallet, which is divided into zones by means of a cardboard partition in such a way that only one item of goods is placed in each zone, while the zones have the following restrictions:  1. The zones are rectangular. 1. The ratio between the sides of the zones can be any. 1. One of the sides of any zone is always in contact with the edge of the pallet. 1. Permissible number of zones on a pallet: from 2 to 6. 1. Each zone is filled according to the rules for filling a mono-pallet. 1. The stacking height of all goods does not exceed 50 mm, so that another pallet can be placed on top of such a pallet. 1. It is allowed to form pallets with different stacking heights for different positions (height difference is more than 50 mm), if these are the top pallets in the stack.  ### Mixbox  ![mixbox](../images/packer_mixbox.svg)  If the above steps fail to place the item in this way, so that the pallet is of the required height or weight, mixboxes are used:  1. Mixbox can only be placed in one layer. 1. Different goods can be placed in one mixbox, in this case it is divided by partitions similarly to a pallet (the only difference is that the height of the packed goods is not aligned). 1. Mixboxes can be mixed with items on the same pallet. 1. One product can be packed in different mixboxes.  ### Slot  ![slot](../images/packer_slot.svg)  The loaded pallets are stacked on top of each other, the slot is where the stack of pallets is placed. The slot has no physical body - so the dimensions of the &#x60;body&#x60; of the slot are zero.  ## Coordinate system  ![coordinates](../images/packer_coordinates.svg)  To indicate the location of packages and goods, a local coordinate system is used, in which the coordinates of a particular object are indicated relative to the near left lower corner of the parent object, while:  * Horizontal plane - axes **X**, **Y** * Vertical - **Z** axis  The ratio of the axes of goods and packages:  * Width - **X** * Length - **Y** * Height - **Z**  ## Entity diagram  ![erd](../uml/packer.svg)
Package: vrtneopixel
WS2812 Matrix Emulator=======================This module can be used to emulate a WS2812 Matrix in replacement of the``neopixel.py`` module for the Raspberry Pi provided by the **rpi_ws281x** project(<https://github.com/jgarff/rpi_ws281x>).You must only change one line of your code in order to switch betweenemulator and real matrix. Replace the linefrom neopixel import *byfrom vrtneopixel import *The number of leds of your matrix is specified in the ``Adafruit_NeoPixel()``call as the first parameter. We generally use a ``LED_COUNT`` variable and twotypes are allowed:* simple integer : total number of leds in the matrix, must have an integer square root (ie. 16 -> 4x4 matrix, etc.).* tuple (rows, cols) : number of rows and columns of the matrix.----Émulateur d'écran WS2812=========================Ce module peut être utilisé pour émuler un écran de leds WS2812 Matrix enremplacement du module ``neopixel.py`` pour Raspberry Pi fourni par le projet**rpi_ws281x** (<https://github.com/jgarff/rpi_ws281x>).Vous devez seulement changer une ligne de votre code pour basculer entrel'émulateur et l'écran de leds. Remplacez la lignefrom neopixel import *parfrom vrtneopixel import *Le nombre de leds de l'écran est passé en tant que premier paramètre lors del'appel à ``Adafruit_NeoPixel()``. On utilise en général une variable ``LED_COUNT``pour laquelle deux types sont autorisés:* un entier simple : nombre total de leds composant l'écran, doit avoir une racine carrée entière (ie. 16 -> 4x4 matrix, etc.).* un tuple (rows, cols) : nombre de lignes et de colonnes de l'écran.
Package: vrt-spacy
vrt_spacyPython class for creating vrt-annotated corpora.
Still in very early testing stage.Install by typing:pipinstallvrt_spacyUsage Example:fromvrtimportCorpus,S,Textfromvrt_spacyimportAnnotatewithCorpus("~","meinkorpus",4,"text_name")asc:annotate=Annotate(c,spacymodel="de_core_news_md")annotate("Das hier ist mein Text",text_name="Text1")withText(c,text_name="Text2")ast:withS(c)ass:s.writep("Test","TAG","TAG","Lemma")Features:Represent Corpus, Text, P and S AttributesIntegration of spacy for automatic generation of a vrt-representation of textsUsing Context Manager for xml-hierarchy representationReduces to utf8mb3 and checks formatting compatibility
Package: vr.xy
vr.xy普通视频Pr制作双排vr效果，确定切割率和左右x1，x2的关系和结果脚本.Installationpipinstallvr.xyUsage-terminalvr-hvr-p0.7-l0.12-r0.18Usage-importfromxyimportH# default 1920*1080v=H()# v.width = 960# v.height = 720v.p=0.7v.left=0.12v.right=0.18v.cut()
Package: vrypy
vrypyCollection of Python utilities.vrypy.test.deepTest utilities inspired by PerlTest::Deep>>> from vrypy.test.deep import ignore, bag, super_dict_ofTheignore()for ignoring part of the structure during test comparison:>>> assert {'key': 'value'} == {'key': ignore()}
>>> assert ["value1", "value2", "value3"] == ["value1", ignore(), ignore()]Thebag(List)for order-insensitive comparison:>>> assert {'key': [1, 3, 2]} == {'key': bag([1,2,3])}
>>> assert [1,2,3,4] != bag([1,2,3])Thesuper_dict_of(Dict)for dict comparison with ignoring superfluous keys:>>> assert {'key': 'value', 'not interesting key': 'value'} \
...        == super_dict_of({'key': 'value'})
>>> assert {'key': 'value'} != super_dict_of({'expected key': 'value'})
Package: vs2dpy
VS2DPYPackage to create and read VS2DRTI models using Python.VS2DRTI was originally developed by the USGS. For more information:https://www.usgs.gov/software/vs2drti-graphical-software-package-simulating-water-flow-heat-transport-and-reactiveInstallationTo get the latest stable version install using:pip install vs2dpyTo get the development version download the GitHub code to your computer. Use cd to get to the download directory and install using:pip install -e .
Package: vsaa
vs-aaVapourSynth anti aliasing and scaling functionsWrappers for aa/scaling plugins and functions, aimed to have compatibility with vs-kernels scalers.For support you can check out theJET Discord server.How to installInstallvsaawith the following command:pipinstallvsaaOr if you want the latest git version, install it with this command:pipinstallgit+https://github.com/Jaded-Encoding-Thaumaturgy/vs-aa.git
Package: vs.alias
vs.aliasYet another “Alias” add-on for Plone 4.Functionalityvs.aliasallows you to create
internal links within a Plone site through a reference field. The functionality
is very similar to theLinkcontent-type for external links.Testedtested with Plone 4.0.XLicencevs.aliasis published under the GNU Public Licence V 2 (GPL 2)AuthorsVeit Schiele, Anne Walter, Andreas JungContactAndreas Junginfo@zopyx.comwww.zope.comVeit Schielekontakt@veit-schiele.dewww.veit-schiele.deChangelog1.2 (29/03/2011)first public release
Package: vsanimesr
AnimeSRLearning Real-World Super-Resolution Models for Animation Videos, based onhttps://github.com/TencentARC/AnimeSR.Note that the SR result and hidden state of the current frame will propagate to the next frame. Hence you may not get the same result if you jump to random frames in the editor/previewer instead of rendering linearly.DependenciesNumPyPyTorch1.13VapourSynthR55+trtrequires additional runtime libraries:CUDA Toolkit11.7cuDNN8.6TensorRT8.5For ease of installation on Windows, you can download the 7z file onReleaseswhich contains required runtime libraries and Python wheel file. Either add the unzipped directory to your systemPATHor copy the DLL files to a directory which is already in your systemPATH. Finally pip install the Python wheel file.Installationpip install -U vsanimesrUsagefromvsanimesrimportanimesrret=animesr(clip)See__init__.pyfor the description of the parameters.
Package: vsapy
vsapy - Vector Symbolic Architecture(VSA) library.This library implements the common methods used in hyperdimensional computing/Vector Symbolic Architectures. Namelybind,unbindand some bundling operations,bag,ShiftedBag,NamedBagand a hierarchical bundling
methodCSPvec. The main objective of the library is to enable a single API to cater for various flavours of VSA,
includingBinary Spatter Codes,Ternary,TernaryZero,LaihoandLaihoX(a simplifiedLaihothat
is faster and supports incremental bundling without catastrophic forgetting).A set of demo test cases are supplied to demonstrate the use of the library calls.If installing from PyPi simplyPoetry add vsapyorpip install vsapy (into your environment)Installing from sourceclone the code to a directory of your choice, say "vsapy"Installing DependanciesPoetry: the easiest way is using poetrycd vsapypoetry installpoetry shell  (to activate the environment)pip install vsapycreate an environment using your favorite environment managere.g. conda create -n vsapy39 python=3.9conda activate vsapy39pip install -r requirements.txtUsageHint: Valid values forVSaTypeare,VsaType.BSC,VsaType.Laiho,VsaType.LaihoX(fastest),VsaType.Tern,VsaType.TernZeroandVsaType.HRR(** Note, the demos listed below will not run with typeVsaType.HRR**).For examples of using the vsapy library, see the code examples in the ./tests directory. Note there are no
command-line arguments implemented for the tests at the moment. To change the type of VSA in use, edit the code changingvsa_type=VsaType.BSCas mentioned below. All of the test cases can be run by simply invoking from the command line,
e.g.,$python cspvec_sequence.py.cspvec_sequence.py: This is the most straightforward demo. Try this first. It demonstrates building a sentence as
a vsa sequence and stepping forward & backwards. Changevsa_type=VsaType.BSCin the code to change the type of VSA
used to build the representation.build_docs.py: demonstrates combining large documents into a hierarchical vsa code book. The top-level document
vector is a high-level semantic representation of the entire document. Changevsa_type=VsaType.BSCin the code to
change the type of VSA used to build the representation.load_docs.py: compares the document vectors built usingbuild_docs.pyat various levels in the
document hierarchy.Changelevels_to_extract = [0, 1],0=top-level document vectors,1=Act-level vectors,2=Scene-level vectorsand so on (Can set to any level, e.g.,levels_to_extract = [2]would compare only
Scene-level vectors).Understanding output names:OE_=Old English,NE_=New English,nf_=NoFear Shakespeare,tk_=NLTK Shakespeare,og_=Alternate Shakespeare,ham=Shakespeare's Hamlet,mbeth=Shakespeare's Macbeth.json2vsa.py: demonstrates the creation of a VSA vector from an input JSON file and shows a comparison of various
JSONs using VSA. Changevsa_type=VsaType.BSCin the code to change the type of VSA used to build the representation.
Package: vsat
Very Simple Asynchronous TasksA small library that allows asynchronous tasks to be executed.  Useful for
web applications when you need a task to run in the background.  The API
is modelled loosely onCelery.  The library makes use of the Pythonmultiprocessinglibrary to run tasks in the background.ExampleInstall the librarypip install vsatCreate and run task.In [1]: from vsat.task import task

In [2]: from vsat.worker import WorkerPool

In [3]: worker_pool = WorkerPool.init(num_workers=1)

In [4]: @task
   ...: def my_function(arg1):
   ...:     return arg1 * arg1
   ...:

In [5]: worker_pool.start()

In [6]: result = my_function.apply_async(2)

In [7]: print result.get_result(block=True)
4
Package: vsav
Save, Restore, Diff versions of filesEasily save and restore files while preserving the access
and modification times of the original file.Installationpython3 setup.py installVersion savevsav file ...Saves eachfileby making a copy.
The saved filename isfile.v.nnnwherennnis the highest version number found plus one.Version numbers consist of 3 decimal digits with leading zeros if
necessary.  The initial version number is000.The saved file retains the access and modification times of the
original file.Usevresto restore saved files.Version restorevres file ...Restores eachfilefrom the indicated version.
Iffilehas the formdest.v.nnnwherennnis a version number,
then the file is restored from this version.
Otherwise, the file is restored from its highest version.Version numbers consist of 3 decimal digits with leading zeros if
necessary.The source file was presumably created usingvsav.
The destination file retains the access and modification times
of the source file.Version Diffvdif file [diff-options] ...Compares a previous version to the current version of the file
using diff.
Arguments afterfileare passed to diff.Iffilehas the formdest.v.nnnwherennnis a version number,
then this version is compared to the current version:diff [diff-options] ... dest.v.nnn destotherwise the highest version is compared to the current version:diff [diff-options] ... file.v.nnn filewherennnis the highest version number.
Package: vsbasicvsr
BasicVSRBasicVSR: The Search for Essential Components in Video Super-Resolution and BeyondPorted fromhttps://github.com/xinntao/BasicSRDependenciesNumPyPyTorch, preferably with CUDA. Note thattorchvisionandtorchaudioare not required and hence can be omitted from the command.VapourSynthInstallationpip install --upgrade vsbasicvsrUsagefromvsbasicvsrimportBasicVSRret=BasicVSR(clip)See__init__.pyfor the description of the parameters.
Package: vsbasicvsrpp
BasicVSR++Improving Video Super-Resolution with Enhanced Propagation and Alignment, based onhttps://github.com/ckkelvinchan/BasicVSR_PlusPlus.Dependenciesmmcv>=2.0.0NumPyPyTorch>=2.0.1VapourSynth>=R60Installationpip install -U openmim
mim install "mmcv>=2.0.0"

pip install -U vsbasicvsrpp
python -m vsbasicvsrppUsagefromvsbasicvsrppimportbasicvsrppret=basicvsrpp(clip)See__init__.pyfor the description of the parameters.