Saravanan666/pypi-org huggingface dataset:
Package: vips-utils
# Ethereum Utils[![Join the chat athttps://gitter.im/ethereum/eth-utils](https://badges.gitter.im/ethereum/eth-utils.svg)](https://gitter.im/ethereum/eth-utils)[![Build Status](https://circleci.com/gh/ethereum/eth-utils.svg?style=shield)](https://circleci.com/gh/ethereum/eth-utils)[Documentation hosted by ReadTheDocs](https://eth-utils.readthedocs.io/en/latest/)Common utility functions for codebases which interact with ethereum.> This library and repository was previously located athttps://github.com/pipermerriam/ethereum-utils.  It was transferred to the Ethereum foundation github in November 2017 and renamed toeth-utils.  The PyPi package was also renamed fromethereum-utilstoeth-utils.## Installation`sh pip installeth-utils`## DevelopmentClone the repository and then run:`sh pip install-e.[dev]eth-hash[pycryptodomex]`## DocumentationBuilding Sphinx docs locally:`sh pip install-e.[doc] cd docs make html `Docs are written in [reStructuredText](http://docutils.sourceforge.net/rst.html) and built using the [Sphinx](http://www.sphinx-doc.org/) documentation generator.### Running the testsYou can run the tests with:`sh py.test tests `Or you can installtoxto run the full test suite.### ReleasingPandoc is required for transforming the markdown README to the proper format to
render correctly on pypi.For Debian-like systems:` apt install pandoc `Or on OSX:`sh brew install pandoc `To release a new version:`sh make releasebump=$$VERSION_PART_TO_BUMP$$`#### How to bumpversionThe version format for this repo is{major}.{minor}.{patch}for stable, and{major}.{minor}.{patch}-{stage}.{devnum}for unstable (stagecan be alpha or beta).To issue the next version in line, specify which part to bump,
likemake release bump=minorormake release bump=devnum.If you are in a beta version,make release bump=stagewill switch to a stable.To issue an unstable version when the current version is stable, specify the
new version explicitly, likemake release bump=”–new-version 4.0.0-alpha.1 devnum”
Package: vipy
VIPYVIPY: Python Tools for Visual Dataset TransformationDocumentation:https://visym.github.io/vipyVIPY is a python package for representation, transformation and visualization of annotated videos and images.  Annotations are the ground truth provided by labelers (e.g. object bounding boxes, face identities, temporal activity clips), suitable for training computer vision systems.  VIPY provides tools to easily edit videos and images so that the annotations are transformed along with the pixels.  This enables a clean interface for transforming complex datasets for input to your computer vision training and testing pipeline.VIPY provides:Representation of videos with labeled activities that can be resized, clipped, rotated, scaled, padded, cropped and resampledRepresentation of images with object bounding boxes that can be manipulated as easily as editing an imageClean visualization of annotated images and videosLazy loading of images and videos suitable for distributed processing (e.g. dask, spark)Straightforward integration into machine learning toolchains (e.g. torch, numpy)Fluent interface for chaining operations on videos and imagesDataset download, unpack and import (e.g. Charades, AVA, ActivityNet, Kinetics, Moments in Time)Minimum dependencies for easy installation (e.g. AWS Lambda, Flask)Requirementspython 3.6+ffmpeg(required for videos)numpy, matplotlib, dill, pillow, ffmpeg-pythonInstallationpipinstallvipyOptional dependencies are installable as a complete package:pipinstallpip--upgradepipinstall'vipy[all]'You will receive a friendly warning if attempting to use an optional dependency before installation.Quickstartimportvipyvipy.image.owl().mindim(512).zeropad(padwidth=150,padheight=0).show()Thetutorialsanddemosprovide useful examples to help you get started.
Package: viqi-api
ViQi APILow level interface to ViQi servicesInstallpip install viqi-apiConfigurationFor command line tools create~/.config/viqi/profilesor `~/config/viqi/profiles`` (windows) with the following[default]
host=https://science.viqiai.cloud
user=myuser
password=mysecret

[science-user2]
host=https://science.viqiai.cloud
user=myuser2
password=mysecret2DocumentationService-oriented APIObject-oriented APIUsageExample ipython session:ipython
from vqapi import bisque_session
session = bisque_session(args=["--profile=science-user2"])

meta = session.service("meta")
user = meta.fetch("user")
print(user)If used in script bisque_session will read from sys.argvmyscript.py:from vqapi import bisque_session
session = bisque_session()

meta = session.service("meta")
user = meta.fetch("user")
print(user)Pass args on command line:$ python myscript.py --profile=science-user2
Package: viqubox-sdk
ViQuBox-SDKSDK for interacting with the Data Processing API.SetupRequirements:Python 3.8+API token generated from the ViQuBox front-end or given by the ViQuBox team.Usingpippython-mvenvvenv.venv/bin/activatepipinstallviqubox-sdkUsageTo create a processing request and get the jobs, use the following code:fromviqubox_sdk.clientimportAuthentication,ViquboxClientfromviqubox_sdk.modelsimport(AuthenticationOptions,AuthenticationType,InputFileConfig,MetricConfig,OutputConfig,ProcessingRequest,)input_files=InputFileConfig(authentication=AuthenticationOptions(opts={"username":"admin","password":"123"},type=AuthenticationType.PASSWORD,),connection_protocol="smb",paths=["smb://some/path/test_video_1.mp4"],)output=OutputConfig(db=True,files=False)brisque_metric=MetricConfig()brisque_metric.add_option("video","@test_video")brisque_metric.add_option("model","portrait")brisque_metric.add_option("marker_num","9000")brisque_metric.add_option("scale","400")brisque_metric.add_option("roi","1.2:0:1:1")request=ProcessingRequest(test_id="t1",output=output)request.add_files("test_video",input_files)request.add_metric("brisque",brisque_metric)client=ViquboxClient(Authentication(api_key="{REDACTED}"))req=client.processing_create(request)jobs=client.processing_get_jobs(req.id)
Package: viral
viralAcquiring and analyzing covid-19 (coronavirus) statistics dataTo install:pip install viralPypi:https://pypi.org/project/viral/What for?This module will help you get and prepare the data behind these:Country cases:https://public.flourish.studio/visualisation/1704821/Country Deaths:https://public.flourish.studio/visualisation/1705644/US State cases:https://public.flourish.studio/visualisation/1794768/US State Deaths:https://public.flourish.studio/visualisation/1794797/Country cases per capita:https://public.flourish.studio/visualisation/6812015/Country deaths per capita:https://public.flourish.studio/visualisation/6812118/US State Cases per capita:https://public.flourish.studio/visualisation/6811967/US State deaths per capita:https://public.flourish.studio/visualisation/6812165You'll need a kaggle account to get the data (or download it manually).You'll need a flourish account to make the bar graph races.Both accounts are free.
Package: viraliq
Graph Based Temporal Aggregation for Video RetrievalVIRALIQ:VIdeo RetrevALthroughImageQueriesDependenciesFFmpegFFmpeg is required for extracting frames from videos. Download links can be foundhere.Note:Windows users will have to add the FFmpeg bin folder to the PATH variable.InstallationInstall the package using pippip install viraliqCLI usageCreate embeddingsCreate video embeddingsviraliq cluster "PATH_TO_VIDEO_DIR"SearchSearch using an image queryviraliq search "PATH_TO_VIDEO_DIR" "PATH_TO_IMAGE"CitingIf you find VIRALIQ useful please cite the following paper:Srinivasan, Arvind, Aprameya Bharadwaj, Aveek Saha, and Subramanyam Natarajan. "Graph Based Temporal Aggregation for Video Retrieval." arXiv preprint arXiv:2011.02426 (2020).
Package: viraloverlay
ViralOverlay™Add styled, timed text to videos 'n GIFs!  Kinda like this:Usagevo [OPTIONS] FILEPATH OVERLAY_DATA

OVERLAY_DATA: a JSON file path OR a JSON-formatted string containing an
array of overlays, each containing key-value pairs of at least "text",
"start", and "stop". optional keys: "font_size", "font_color",
"text_position_x", and "text_position_y"

Options:
  -g, --gif             output to GIF instead of MP4
  -f, --font-path TEXT  path to the font you'd like to use.
  --help                Show this message and exit.Example$ vo <path_to_video> <json_transcript_filepath_or_string> [--gif --font]

(lots of ffmpeg output...)
Okay, I overlaid your text on <path_to_video_overlaid>.JSON Transcript FormatThejson_transcriptstring or file should look like this:[
    {
        "start": 1.03,
        "stop": 1.21,
        "text": "I"
    },
    {
        "start": 1.21,
        "stop": 1.44,
        "text": "am"
    },
    {
        "start": 1.45,
        "stop": 1.84,
        "text": "saying"
    },
    {
        "start": 1.85,
        "stop": 2.25,
        "text": "some"
    },
    {
        "start": 2.25,
        "stop": 2.97,
        "text": "words"
    }
]See sibling projectsTranscribe all the Thingsandtprofor easy, automatic generation of such transcripts.Installationpip install viraloverlay
brew install ffmpeg  # (if you don't have ffmpeg already)
Package: viral-verify
viral_verifyviralVerifyrewrite/refactor for PyPI packaging and distribution, maintainability and clarity.NOTE:BLAST+ search option has been removed. Results output table will be different than the originalviralVerify. Naive Bayes classifier training script has not been ported yet.Free software: MIT licenseDocumentation:https://viral-verify.readthedocs.io.FeaturesGene prediction withProdigalin metagenomic modeHMMer3hmmsearchfor protein domains in predicted genesNaive Bayes classification of contigs as viral/not viral based onHMMer3resultsOutput of detailed contig classification results table in CSV formatOutput of contigs based on classification into separate FASTA filesRequirementsAnHMMer3HMM database is required. For example, the latest version of Pfam-A HMM:http://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam33.1/Pfam-A.hmm.gzNOTE:Please extract any compressed HMM DB ($ gunzipPfam-A.hmm.gz)Software dependencies:Prodigalv2.6.3HMMer3v3.3Python v3.6+Python dependencies:attrsClickPandasBiopythonInstallationCondaIt’s recommended that you useCondato install the required software (ProdigalandHMMer3) and Python dependencies.$ conda env create -f environment.ymlPipIf you haveProdigalandHMMer3installed in your$PATH, and Python 3.6 or greater, you can usepipto installviral_verify:$ pip install viral_verifyUsage$ viral_verify --help
Usage: viral_verify [OPTIONS]

  HMM and Naive Bayes classification of contig sequences as either viral,
  plasmid or chromosomal.

  Requires Prodigal for gene prediction and hmmsearch from HMMer3 for
  searching for Pfam HMM profiles.

Options:
  -i, --input-fasta PATH          Input fasta file  [required]
  -o, --outdir PATH               Output directory  [required]
  -H, --hmm-db PATH               Path to Pfam-A HMM database  [required]
  -t, --threads INTEGER           Number of threads (default=16)
  -p, --output-plasmids-separately
                                  Output predicted plasmids separately?
  --prefix TEXT                   Output file prefix (default: None)
  --uncertainty-threshold FLOAT   Uncertainty threshold (Natural log
                                  probability) (default=3.0)

  --naive-bayes-classifier-table PATH
                                  Table of protein domain frequencies to use
                                  for Naive Bayes classification (default="/ho
                                  me/pkruczkiewicz/repos/viral_verify/viral_ve
                                  rify/data/classifier_table.txt")

  -v, --verbose                   Logging verbosity
  --version                       Show the version and exit.
  --help                          Show this message and exit.CreditsThe original source code, design and conception can be found atviralVerify. This is merely a rewrite for easier packaging via PyPI, adding some CI with Travis-CI and organizing the code for maintainability and clarity.This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.1 (2020-06-04)Fix PyPI release (includeclassifier_table.txtin package)0.1.0 (2020-06-03)First release on PyPI.
Package: viramp-hub
VirAmp-HubA tool that lets you manipulate/convert viral amplicon/primer scheme information.What is it for?The layout of (tiled-) amplicon schemes for amplifying and sequencing viral
genomes is typically specified through one of a number of non-standardized
scheme files. These can, generally, not be used interchangeably with downstream
analysis tools.VirAmp-Hub can convert between commonly used scheme file formats, such as used,
e.g., by theARTIC network,
theivarandcojacsuites of tools, andGalaxytools and workflows for viral sequencing
data analysis.UsageAfter installation (pip install viramp-hub), use the package via thescheme-convertcommand.For help run:scheme-convert --helpPrepare an existing primer scheme file for use with ivar:scheme-convert primer_scheme.bed --to bed --bed-type ivar -o ivar.bedGenerate an amplicon insert scheme file for cojac:scheme-convert primer_scheme.bed --to bed --bed-type cojac -o cojac_insert.bedGenerate an amplicon info file for use with ivar inside Galaxy:scheme-convert primer_scheme.bed --to amplicon-info -o info.tsvAmplicon info files are simple tab-separated files listing primers contributing
to the same amplicon on a single line. For nested primer schemes, you can
control, which primers to report in the info file.List only the inner primers:scheme-convert primer_scheme.bed --to amplicon-info -r inner -o info.tsvList only the outer primers:scheme-convert primer_scheme.bed --to amplicon-info -r outer -o info.tsvVirAmp-Hub tries to autodetect primer to amplicon assignments using the Python
re pattern:r'(?P<prefix>(.*_)*)(?P<num>\d+).*_(?P<name>L(?:EFT)?|R(?:IGHT)?)'.
If this fails for any given primer scheme, it is possible to override
autodetection by providing an amplicon info file directly, e.g.:scheme-convert primer_scheme.bed -a amplicon_info.tsv --to bed --bed-type cojac -o cojac_insert.bedor:scheme-convert primer_scheme.bed -a amplicon_info.tsv --to amplicon-info -r inner -o info.tsv
Package: virat
requirements_dev.txt we use for the testingIt makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.difference between requirements_dev.txt and requirements.txtrequirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.tox.iniWe use if for the testing in the python package testing against different version of the pythonhow tox works tox enviornment creationInstall depedencies and packagesRun commandsIts a combination of the (virtualenvwrapper and makefile)It creates a .toxpyproject.tomlit is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependenciessetup.cfgIn summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projecTesting python applicationtypes of testingAutomated testingManual testingMode of testingUnit testingIntegration testsTesting frameworkspytestunittestrobotframeworkseleniumbehavedoctestcheck with the code style formatting and syntax(coding standard)pylintflake8(it is best because it containt 3 library pylint pycodestyle mccabe)pycodestyle
Package: vireoSNP
vireo: donor deconvolution for pooled single-cell dataVireo: Variational Inference for Reconstructing Ensemble Origin by expressed
SNPs in multiplexed scRNA-seq data.The name vireo follows the theme fromcardelino(for clone deconvolution),
while the Python package name isvireoSNPto aviod name confilict on PyPI.NewsAll release notes can be found in doc/release.rst.Notebookfor subclone reconstructing with mitochrondrial mutationsInstallationVireo is available throughPyPI. To install, type the following command
line, and add-Ufor upgrading:pipinstall-UvireoSNPAlternatively, you can install from this GitHub repository for latest (often
development) version by following command linepipinstall-Ugit+https://github.com/single-cell-genetics/vireoIn either case, add--userif you don’t have the write permission for your
Python environment.For more instructions, see theinstallationmanual.Manual and examplesThe full manual is athttps://vireoSNP.readthedocs.ioIt includes more details on installation,demultiplex usage, and preprocess
withgenotypingcells.Test example data is included in this repo and demos can be found in examples/demo.sh.Also, typevireo-hfor all arguments with the version you are using.ReferenceYuanhua Huang, Davis J. McCarthy, and Oliver Stegle.Vireo: Bayesian
demultiplexing of pooled single-cell RNA-seq data without genotype reference.Genome Biology20, 273 (2019)
Package: virgil-crypto-lib
This library is designed to be a small, flexible and convenient wrapper for a variety of crypto algorithms. So it can be used in a small microcontroller as well as in a high load server application. Also, it provides several custom hybrid algorithms that combine different crypto algorithms to solve common complex cryptographic problems in an easy way. This eliminates the requirement for developers to have strong cryptographic skills.The library is available for different platforms and contains wrappers for other languages.
Package: virgodev-websocket-support
Virgodev Websocket Supportprovides a uniform system for sending and receiving websocket messagesspecsdocumentation for connecting django-channelssupport functions for making django-channels easysignal for when a message is receivedcreate a function to send messages to a usercreate a function to send messages to a groupGet StartedInstall virgodev_websocket_supportpipinstallvirgodev_websocket_supportAdd INSTALLED_APPS settingINSTALLED_APPS=("daphne",# install at the top to convert `runserver` into a websocket client"django.contrib.auth","django.contrib.contenttypes","django.contrib.sessions","django.contrib.sites","virgodev_websocket_support",...)Add ASGI_APPLICATION settingASGI_APPLICATION='virgodev_websocket_support.asgi.application'or use the asgi from your own application's asgi filefromvirgodev_websocket_support.asgiimportapplicationasappapplication=appAdd CHANNEL_LAYERS settingassign the host to the same redis instance as your cacheCHANNEL_LAYERS={"default":{"BACKEND":"channels_redis.core.RedisChannelLayer","CONFIG":{"hosts":[CACHES["default"]["LOCATION"]],"group_expiry":60*10,},},}Productioninstall and use uvicorn
Package: virheat
virHEAT is a tool to visualize vcfs as a heatmap and map mutations to respective genes.Ever wanted to have a condensed look at variant frequencies after mapping your raw reads to a viral/bacterial reference genome and compare multiple vcf files at the same time? Than virHEAT is for you. You can not only visualize the heatmap but also read in a gff3 file that lets you display genes harboring a mutation. This lightweight script was inspired bysnipitand myvariant frequency plot, getting the best visualization features of both.SARS-CoV-2 example:Installationvia pip (recommened):pipinstallvirheatvia conda:condainstall-cbiocondavirheatfrom this repo:gitclonehttps://github.com/jonas-fuchs/virHEATcdvirHEAT
pipinstall-rrequirements.txt# orpipinstall.That was already it. To check if it worked:virheat-vYou should see the current virHEAT version.Usageusage:virheat<foldercontainingvcfs><outputdir>-lor-g[additionalarguments]Arguments:positional arguments:
  input                 folder containing input files and output folder

options:
  -h, --help            show this help message and exit
  --name virHEAT_plot.pdf
                        plot name and file type (pdf, png, svg, jpg). Default: virHEAT_plot.pdf
  -l None, --genome-length None
                        length of the genome (needed if gff3 is not provided)
  -g None, --gff3-path None
                        path to gff3 (needed if length is not provided)
  -a [gene ...], --gff3-annotations [gene ...]
                        annotations to display from gff3 file (standard: gene). Multiple possible.
  -t 0, --threshold 0   display frequencies above this threshold (0-1)
  --delete, --no-delete
                        delete mutations that are present in all samples and their maximum frequency divergence is smaller than 0.5 (default: True)
  -n None, --delete-n None
                        do not show mutations that occur n times or less (default: Do not delete)
  -z start stop, --zoom start stop
                        restrict the plot to a specific genomic region.                      
  --sort, --no-sort     sort sample names alphanumerically (default: False)
  --min-cov 20          display mutations covered at least x time (only if per base cov tsv files are provided)
  -v, --version         show program's version number and exitYou need to either provide the length of your reference genome or if you want to get the sequence annotation you will need to provide the gff3 file.Additionally, you can also analyse if mutations are sufficiently covered and display non-covered cells in grey. For that first create a per base coverage tsv files for each bam file withQualimapand provide it in the same folder as the vcf files. Give them the same name as your vcf files.Important disclaimer:The code is under the GPLv3 licence. The code is WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Package: viridicle
What is Viridicle?Viridicle is a library for simulating stochastic graphical ecological models. It implements the continuous time models described in Section 6 ofDurrett and Levin 1994. The library is written in C for speed, but has a Python API for ease of use. It supports well-mixed systems, multi-dimensional periodic lattices, and arbitrary graphs, with up to 256 species, and is substantially faster than comparable software written in Python alone.Viridicle models an ecological system as a graph, where each vertex denotes a discrete site - an island, an oasis, a point on a Petri dish, etc. Each site is small enough that we can model its state by assigning it a single number specifying which species is occupying it. Traditionally, 0 denotes an unoccupied site, but neither the mathematics nor the code require this.The system changes over time as acontinuous-time Markov chain, where each directed edge in the system is aPoisson point process. That is, at any given moment, for every edge in the system, the time until the edge's vertices change to stateis exponentially distributed with some rate that depends on the state of the system. The model is implemented using theGillespie algorithm. For further details, consult thedocumentation.InstallationTo install Viridicle from the PyPi repo, run:pip install viridicleIf you want to compile from source yourself, first make sure that you have gcc installed, along with Python, including its development headers. Then, run:git clone https://github.com/TheoremEngine/viridicle.git
cd viridicle
python3 setup.py install