Saravanan666/pypi-org huggingface dataset:
Package: variations
variationsA simple interface that allows processing of images.Compatibilitypython>= 3.7InstallationRunpip install variations(optional) If you want to useStackBlurpip install pillow-stackblur(optional) If you want to useFace Detectionpip install face_recognitionUsagefromPILimportImagefromvariationsimportprocessorsfromvariations.variationimportVariationfromvariations.utilsimportprepare_imagevariation=Variation(size=(400,0),max_height=800,clip=False,upscale=False,anchor=processors.Anchor.TOP_LEFT,jpeg=dict(quality=92,),webp=dict(lossless=True,quality=90,),postprocessors=[processors.ColorOverlay('#FF0000',overlay_opacity=0.25),],)img=Image.open('source.jpg')img=prepare_image(img,draft_size=variation.get_output_size(img.size))new_img=variation.process(img)dest_path=variation.replace_extension('dest.jpg')variation.save(new_img,dest_path)OptionsTypeExamplesDescriptionsizetuplelist(640, 480)(640, 0)Thecanvassize of image. If you set the width or height to zero, the corresponding value will be automatically adjusted based on the aspect ratiomax_widthint640It specifies the maximum width in pixels.This option have meaning only when corresponding value insizeis zeromax_heightint480It specifies the maximum height in pixels.This option have meaning only when corresponding value insizeis zeroclipboolWhen set toTrue, the image can be cropped when filling the canvas.upscaleboolWhen set toTrue, the image can be upscaled when filling the canvas.anchorstrtuplelist'tr'(top right)'c'(center)(1, 1)(bottom right)Defines the anchor point.face_detectionboolUse a face detection system to find anchor point. You must installfacial recognition apito use this.formatstr'JPEG''png''WebP'Enforce output image format. Defaults to'AUTO', which means keep input format.preprocessorslist[processors.Crop(width=200, height=120, x=50, y=50)]PilKitprocessors are invoked before the main processing stagepostprocessorslist[processors.ColorOverlay('#0000FF', 0.10)]PilKitprocessors are invoked after the main processing stageAdditional options for specific formatsIt is possible to pass additionaloptionstoImage.save()method.Variation(# ...jpeg=dict(quality=80,progressive=True,),webp=dict(autoconvert=False,quality=80,),tiff=dict(compression='tiff_jpeg',))Development and TestingAfter cloning the Git repository, you should install this
in a virtualenv and set up for development:virtualenv.venvsource.venv/bin/activate
pipinstall-r./requirements.txt
pre-commitinstall
Package: varibayes
Variational Bayesian Inference ToolboxThis module is inspired by the paper ‘Black Box Variational Inference’
by Rajesh Ranganath et al. It attempts to make nearly trivial the task
of fitting a variational distribution to a user-specified log-likelihood
function without derivatives. Currently it only uses a
mean field variational distribution, but the main class
VariationalInferenceMF is flexible enough for simple subclassing in the
future. This module also contains a number of implementations of
stochastic gradient descent algorithms to be used for optimization.
Package: varify-client
#Read Me
This document should describe any tools included with the Varify client.##fetchVcf
command line to launch fetchVcf:fetchVcf.py -s sample1 sample2 -r 1:1-100000,2:1-100000##Setup
*   Go tohttp://yourvarifyinstance.chop.edu/admin/serrano/apitoken/*   Add API Token and your varify instance to the configuration file and name it varify.cfg (see the sample config included)
Package: varikn
VariKN language modeling toolkit provides tools for training n-gram
language models. Amongst the supported methods are:Absolute discountingKneser-Ney smoothingRevised Kneser pruningKneser-Ney growingThe descriptions of Revised Kneser pruning and Kneser-Ney growing can
be found in the paper Vesa Siivola, Teemu Hirsimäki and Sami Virpioja,
“On Growing and Pruning Kneser-Ney Smoothed N-Gram Models”, IEEE
Transactions on Speech, Audio and Language Processing,
15(5):1617-1624, 2007.The package provides a accurate pruning for Kneser-Ney smoothed
models. Also, it is possible to train a very high-order n-gram models
with the growing algorithm. The models can be output to arpa lm
format, which is compatible with most common other tools in the
field. You can look at the paper Vesa Siivola, Mathias Creutz and
Mikko Kurimo: “Morfessor and VariKN machine learning tools for speech
and language technology”, Proceedings of the 8th International
Conference on Speech Communication and Technology (INTERSPEECH’07),
2007 for guidelines on typical use.For help on installing the toolkit, read [INSTALL.md](INSTALL.md).For help on using the built commands, read [commands.html](commands.html).If it looks like there is interest in using the toolkit but the
documentation is inadequate, let me know the main sticking points and
I’ll try to do something about it. If you are interested in a well
supported toolkit that provides a wide range of functionality, you
might want to look at the [srilm toolkit](http://www.speech.sri.com/projects/srilm/).
Package: varints
This is a Python module which is intended to assist with variable-length
encoding integers (and lists of integers) into more compact
representations which use less memory.Notes on Memory UsageGenerally in Python, integers are stored aslongmeaning that they will use at least 32 bits. When storing many numbers
which do not require 32 bits, this would seem to be significantly
wasteful; variable length representation should be able to assist in
such cases.Unfortunately, Python 2 gives us the following>>>importsys>>>i=1>>>sys.getsizeof(i)12>>>b=bytearray([0]*4)>>>sys.getsizeof(b)29and the situation is no better in Python 3>>>importsys>>>i=1>>>sys.getsizeof(i)14>>>b=bytearray([0]*4)>>>sys.getsizeof(b)33What we can see however is that the Python overhead for bytearray is
fixed. Increasing the size of the bytearray only increases the memory
usage by the amount of bytes we’ve used:>>>importsys>>>b1=bytearray([0])>>>sys.getsizeof(b1)30>>>b10=bytearray([0]*10)>>>sys.getsizeof(b10)40So this means that currently:Memory overhead for bytearray objects is higher in Python 3 than
Python 2Using varint encoding will actuallycostus memory rather than
saving us memoryIf we consider arrays of numbers the situation is somewhat better. If we
take the example where we want to store ten zeros. A varint encoding
should mean that each zero can be stored in a single byte, meaning that
we’d end up with a bytearray with 10 elements. So …>>>importsys>>>i1=[0]*10>>>i1[0,0,0,0,0,0,0,0,0,0]>>>sys.getsizeof(i1)72>>>b1=bytearray(i1)>>>sys.getsizeof(b1)36Meaning that:There is potential for saving memory by utilising varint encoding if
we are storing arrays of integersThe amount of memory we save will depend on the numbers that are
being stored. The larger the varint representation, the smaller the
saving, hence the more numbers will need to be stored in order to
compensate for the bytearray overheadWe will incur a processing overhead in order to save this memory.
e.g.Random access to varints stored in a bytearray would be O(n) rather
than O(1)We will incur an overhead each time we want to convert to and from
varint representationSo why use varint in Python? In the case that we need a compact method
to store a list of (frequently small) numbers, and we do not generally
need random access to the numbers contained.One application is duringtree-search. Typically
we will end up with a number of nodes held in memory and not being
accessed while other nodes in the tree are being processed. If we want
to store a state associated with each node (e.g. pieces on a chess
board), then we can represent these as as list of integers and minimise
the memory usage by using varint representations.
Package: var-io
var-io模块介绍v0.2.8var-io是希望在使用jupyter环境时更方便的查看并管理变量Manage your variable in jupyter安装pip install var-io功能跟踪并查看变量保存或载入变量删除已有变量更多功能...工作模式filter:默认跟踪所有变量choose：默认不跟踪变量参数解释method:工作方式，filter或choosestrict:是否跟踪模块或callable的变量exclude_unsupported：是否屏蔽不支持的变量类型示例filter模式fromvar_ioimportVarHomevh=VarHome(method='filter')#filter模式初始化vh.vars()#查看当前追踪的变量a={"a":3,'b':4}#创建测试变量b=np.random.randn(200,20)c=pd.Series([1,2,3])vh.vars()#查看变量is_supportedtypesizememory usageaTRUEdict2240bTRUEnumpy.ndarray(200, 20)32112cTRUESeries(3,)128choose模式fromvar_ioimportVarHomevh=VarHome(method='choose')#filter模式初始化vh.vars()#查看当前追踪的变量a={"a":3,'b':4}#创建测试变量b=np.random.randn(200,20)c=pd.Series([1,2,3])vh.vars()#当前没有跟踪的变量变量管理vh.save_data('./test.ipynbdata')#保存目前跟踪的变量vh.load_data('./test.ipynbdata')#将保存的变量加载到当前命名空间中vh.del_var('a')#将变量a从当前命名空间中删除vh.reset_all()#重置vh.clear_list()#清空当前跟踪的变量列表，只跟踪之后定义的变量vh.choose_var('a')#跟踪变量avh.exclude_var('a')#屏蔽变量a
Package: variousconnector
variousconnectorhandy tools to connect the various data sourcesWhat are the Key Features?Support PostgreSQL with SSH functionality.Support SnowflakeHow to Install?Use the package managerpipto install.pipinstallvariousconnectorHow to Use?PostgreSQLfromvariousconnectorimportpostgresql_connectorpostgresql_credential={"host":"your_db_server_url","port":5432,"user":"your_user_name","pwd":"your_password","ssh_enable":True,# require SSH if using VPN"ssh_host":"your_ssh_host",# not require if ssh_enable is False"ssh_user":"your_ssh_user",# not require if ssh_enable is False"ssh_pwd":"your_ssh_password"# not require if ssh_enable is False}pg=postgresql_connector(credential=postgresql_credential)query='''select *from table_name'''query_result=pg_nossh.query(db='your_db_name',query=query)Snowflakefromvariousconnectorimportsnowflake_connectorsnowflake_credential={'account':'XXXX','user':'XXXX','pwd':'XXXX','warehouse':'XXXX'}sw=snowflake_connector(credential=snowflake_credential)query='''select *from table_name'''query_result=sw.query(db='your_db_name',query=query)'''how to know my snowflake account?if your URL is "https://abc.snowflakecomputing.com/", then your Snowflake account name is "abc".If you are in any other region (such as AWS US-EAST), then if your URL is "https://xyz.us-east-1.snowflakecomputing.com/", then your account name is "xyz"'''
Package: varius
VariusPerform computations using various versions of variablesVarius is the Latin word for "various."Installpip install variusUsage Examplesimportvariusfromvariusimport*# default variablescst=vr('cost [usd]',100)rev=vr('revenue [usd]',300)# expressions: if values are not given the expressions will be symbolicpft=ex('profit [usd]',rev-cst)pmg=ex('profit margin',pft.expr/rev)# show() will display the variables or expressions based on your python env# if in jupyter notebooks, they will be displayed as beautiful latex equations otherwise as plain textswithnote('default')asd:show(cst,rev,pft,pmg)pft.grad()pmg.grad()print(d)# new casewithnote('20% discount',copy='default')asd:rev(rev['default']*0.8)show(cst,rev,pft,pmg)pft.grad()pmg.grad()print(d)# another casewithnote('50% discount',copy='default')asd:rev(rev['default']*0.5)show(cst,rev,pft,pmg)pft.grad()pmg.grad()print(d)You will get summaries as follows:Scope version: default
  Variables:
    (cost [usd]) = 100
    (revenue [usd]) = 300
  Expressions:
    (profit [usd]) = 200
    (profit margin) = 2/3
    ∂ [(profit [usd])] / ∂ [(cost [usd])] = -1
    ∂ [(profit [usd])] / ∂ [(revenue [usd])] = 1
    ∂ [(profit margin)] / ∂ [(cost [usd])] = -1/300
    ∂ [(profit margin)] / ∂ [(revenue [usd])] = 1/900

Scope version: 20% discount
  Variables:
    (cost [usd]) = 100
    (revenue [usd]) = 240.0
  Expressions:
    ∂ [(profit [usd])] / ∂ [(cost [usd])] = -1
    ∂ [(profit [usd])] / ∂ [(revenue [usd])] = 1
    ∂ [(profit margin)] / ∂ [(cost [usd])] = -0.00416666666666667
    ∂ [(profit margin)] / ∂ [(revenue [usd])] = 0.00173611111111111
    (profit [usd]) = 140.000000000000
    (profit margin) = 0.583333333333333    

Scope version: 50% discount
  Variables:
    (cost [usd]) = 100
    (revenue [usd]) = 150.0
  Expressions:
    ∂ [(profit [usd])] / ∂ [(cost [usd])] = -1
    ∂ [(profit [usd])] / ∂ [(revenue [usd])] = 1
    ∂ [(profit margin)] / ∂ [(cost [usd])] = -0.00666666666666667
    ∂ [(profit margin)] / ∂ [(revenue [usd])] = 0.00444444444444445
    (profit [usd]) = 50.0000000000000
    (profit margin) = 0.333333333333333
Package: varlet
# VarletVarlet lets you prompt for variables at runtime, and saves them to a variables module.## Installpip install varlet## UsageIn your settings.py file add:`python from varlet import variable `whenever you declare a variable that could change depending on the environment,
use:`python # It is OK to make this True if you are in dev DEBUG =variable("DEBUG",default=False) `If this “DEBUG” variable is not defined in the variables module (somewhere in
your python path), the user is prompted to enter a Python expression to set it.When the prompt is displayed, the comments directly above the call tovariable()are displayed, and the prompt has a default value as specified by
thedefaultargument.## Implementation Detailsvarlet assumes there is avariablesmodule located somewhere in your Python
path. If it is not found, it will attempt to create one based on the location
of__main__.When a variable is set to a value, varlet will eval the value (to make sure it
is valid python), and then performast.literal_eval(repr(value))to ensure that the value
has a valid representation that can be written to a file. Therepr(value)is
then appended to the end of thevariablesmodule (along with any comments
associated with the value).If STDIN is not a tty-like interface, then a KeyError is raise if the variable
is not set in the variables module.
Package: varman
VarMan一个辅助监听变量赋值变化的类，基本用法类似dict类型应用场景示例webapp中开关量的前后台同步，前台需要显示开关状态并且可以控制开关，后台亦然。需要监听变量改变并回调相应方法的情形使用方法pipinstallvarmanimportvarmanvar=varman.VarMan()获取改变值的变量名var.a=1print(var.POPMODIFY(tag='tag1'))var.a=1print(var.POPMODIFY(tag='tag1'))var.a=2print(var.POPMODIFY(tag='tag1'))var.b=3print(var.POPMODIFY(tag='tag1'))print(var.POPMODIFY()){'a'}
set()
{'a'}
{'b'}
{'a', 'b'}设置回调函数# 对应变量“c”的回调函数@var.ONMODIFY('c')deffun(pre,nxt):print(f'pre c:{pre}, next c:{nxt}')var.c=1pre c:None, next c:1var['c']=4pre c:1, next c:4# var中任意变量改变时回调@var.ONMODIFYANYdeffunc(varname,pre,nxt):print(f'var:{varname}; pre:{pre}, next:{nxt}')var.c=1pre c:4, next c:1
var:c; pre: 4, next:1var.a=5var:a; pre: 2, next:5var.dd=[]var:dd; pre: None, next:[]# 只能监听基本变量，或变量的id改变，无法监听list、dict等类型的内部更改var.dd.append(1)主动触发（2024年1月14日）var.CALLONMODIFY('c') # 强制调用变量c的回调函数# 移除监听器var.REMOVELISTENER('c')var.c=8var:c; pre: 1, next:8# 移除对任意变量的监听器var.REMOVEANYLISTENER()var.c=10作为iter类型使用(类似dict)forkeyinvar:print(f'{key}:{var[key]}')a:5
b:3
c:10
dd:[1]# 带前缀“_”的变量将隐式调用，不会自动监听赋值状态，也不会被迭代# 不允许“__”前缀var._d=5forkeyinvar:print(f'{key}:{var[key]}')a:5
b:3
c:10
dd:[1]print(var._d)5deffun2(**params):print(params)fun2(**var){'a': 5, 'b': 3, 'c': 10, 'dd': [1]}# 实例化的同时添加变量var2=varman.VarMan(d=8,e=10)print(var2){'d': 8, 'e': 10}var2VarMan({'d': 8, 'e': 10})todo?删除变量
Package: var-mesh
Variational MeshA variational mesh implementation to optimize meshes for quantum chemistry calculations withPySCForPyFLOSIC.DocumentationTo learn how the implementation works or how to use it, take a look inside thedocumentation.InstallationThe package can be installed using pippip install var-meshAuthorsWanja Schulze- Maintainer -wangenau@protonmail.comJens Kortus- SupervisorSebastian Schwalbe- Co-worker -theonov13@gmail.comJakob Kraus- Co-worker -jakob.kraus@physik.tu-freiberg.deKai Trepte- Co-worker -kai.trepte1987@gmail.comSimon Liebing- Co-workerLicenseThis project is licensed under the Apache 2.0 License - see theLICENSEfile for details.
Package: varnish-admin-socket
A simple python library to administer Varnish over the administration socket. The library can use shared secret authentication. Almost all testing has been done against Varnish 2.1.5.Note: To use the library with Varnish 2.0.6, set compat = True (Experimental)This was heavily inspired by Tim Whitlock’s wonderful PHP library (http://github.com/timwhitlock/php-varnish).Usage:from varnish_admin_socket import VarnishAdminSocket
varnish = VarnishAdminSocket()

# Optionally set host, port, and secret
# host defaults to 127.0.0.1, port defaults to 6082, secret defaults to False
varnish.host = '127.0.0.1'
varnish.port = 6082
# You probably want to add a newline, this is usually read from a file.
varnish.secret = "123\n"
# To load the secret key from a file set secret_file
varnish.secret_file = '/etc/varnish/secret.txt'
# To use with Varnish 2.0.6 compat = True must be set
varnish.compat = True

# Connect to Varnish
varnish.connect()

# Purge Commands
varnish.purge_url('^/cached')
varnish.purge('req.http.host ~ example.com && req.url ~ ^/cached$')

# Run Stats
print varnish.stats()

# Quit and close the connection
varnish.quit()More Usage:from varnish_admin_socket import VarnishAdminSocket
# You may also set server, port, host, and secret on instantiation.
varnish = VarnishAdminSocket(host='varnish.domain.com',secret='123\n')
varnish.connect()
print varnish.stats()
varnish.quit()

# Setting auto_connect will automatically connect(), will run the first command (besides auth),
# and will then .quit()

VarnishAdminSocket(auto_connect=True,secret_file='/etc/varnish/secret.txt').purge_url('.')
Package: varnish-bans-manager
Varnish Bans Manager (VBM) is a simple server and web UI designed to ease
management of bans in complex Varnish (https://www.varnish-cache.org)
deployments. Check outhttps://github.com/allenta/varnish-bans-managerfor a detailed description of VBM, extra documentation, some script samples,
and other useful information.copyright:2012 by Allenta Consulting, see AUTHORS.txt for more details.license:GPL, see LICENSE.txt for more details.
Package: varpickler
pickle / unpickle variablesTested against Windows 10 / Python 3.10 / Anacondapip install varpicklerdecode_var(obj:Any,pickle_or_dill:str='dill',base:str='base64',str_decode:str|None=None)->AnyDecodeaPythonobjectthatwasencodedusingeitherthe'pickle'or'dill'library,andthendecodethebaseXXencodedresult.Parameters:obj(strorbytes):Theencodedobjecttobedecoded.pickle_or_dill(str,optional):Thelibrarytobeusedfordecoding.Validoptionsare'pickle'or'dill'.Defaultsto'dill'.base(str,optional):Thebaseencodingusedintheencodedobject.Defaultsto'base64'.str_decode(str,optional):ThestringencodingusedafterbaseXXencoding.IfNone,noadditionalstringdecodingisperformed.DefaultstoNone.Returns:object:Thedecodedobject.Example:importnumpyasnpobj=np.array([23,34,4])a1=encode_var(obj,pickle_or_dill="dill",base="base64",str_encode=None)a2=encode_var(obj,pickle_or_dill="dill",base="base64",str_encode="utf-8")a3=decode_var(a1,pickle_or_dill="dill",base="base64",str_decode=None)print(a3)a4=decode_var(a2,pickle_or_dill="dill",base="base64",str_decode="utf-8")print(a4)[23344][23344]encode_var(obj:Any,pickle_or_dill:str='dill',base:str='base64',str_encode:str|None=None)->str|bytesEncodeaPythonobjectusingeitherthe'pickle'or'dill'library,andthenencodetheresultusingbaseXXencoding.Parameters:obj(object):Theobjecttobeencoded.pickle_or_dill(str,optional):Thelibrarytobeusedforencoding.Validoptionsare'pickle'or'dill'.Defaultsto'dill'.base(str,optional):Thebaseencodingtobeused.Defaultsto'base64'.str_encode(str,optional):ThestringencodingtobeusedafterbaseXXencoding.IfNone,noadditionalstringencodingisperformed.DefaultstoNone.Returns:strorbytes:Theencodedobject.If'str_encode'isNone,abytesobjectisreturned.Otherwise,astringobjectisreturned.Example:obj=np.array([23,34,4])>>>a1=encode_var(obj,pickle_or_dill="dill",base="base64",str_encode=None)>>>print(a1)b'gAWVtgAAAAAAAACMCmRpbGwuX2RpbGyUjA1fY3JlYXRlX2FycmF5lJOUKIwVbnVtcHkuY29yZS5tdWx0aWFycmF5lIwMX3JlY29uc3RydWN0lJOUjAVudW1weZSMB25kYXJyYXmUk5RLAIWUQwFilIeUKEsBSwOFlGgGjAVkdHlwZZSTlIwCaTSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYolDDBcAAAAiAAAABAAAAJR0lE50lFKULg=='>>>a2=encode_var(obj,pickle_or_dill="dill",base="base64",str_encode="utf-8")>>>print(a2)'gAWVtgAAAAAAAACMCmRpbGwuX2RpbGyUjA1fY3JlYXRlX2FycmF5lJOUKIwVbnVtcHkuY29yZS5tdWx0aWFycmF5lIwMX3JlY29uc3RydWN0lJOUjAVudW1weZSMB25kYXJyYXmUk5RLAIWUQwFilIeUKEsBSwOFlGgGjAVkdHlwZZSTlIwCaTSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYolDDBcAAAAiAAAABAAAAJR0lE50lFKULg=='
Package: varscope
VarScopeUltra simple module for creating local scopes in Python.InstallationVarScope can be installed withpip:pipinstallvarscopeUsage>>>fromvarscopeimportscope>>>>>>a=1>>>withscope():# Everything defined after will only apply inside the scope...a=2...b=3...>>>a1>>>b# Not defined, because outside of scopeTraceback(mostrecentcalllast):File"<stdin>",line1,in<module>NameError:name'b'isnotdefined>>>>>>withscope()ass:# We can choose to keep some variables...a=2...b=3...s.keep("b")...>>>b3>>>withscope("a"):# We can also move variables inside scope...a=2...>>>a# Not defined, because outside of scopeTraceback(mostrecentcalllast):File"<stdin>",line1,in<module>NameError:name'a'isnotdefined>>>>>>d={}>>>withscope():# Scope can mutate object from outside...d["a"]=1...>>>d["a"]1
Package: var-sd-card-writer
SD Card Writer ToolFlash the latest OS images to SD cards, safely and easilyReport Bug·Request FeatureAboutThis tool effortlessly helps the user to flash the latest OS images directly from the FTP server to SD card devices.Copyright and LicenseCopyright 2022 Variscite LTD. Free use of this software is granted under
the terms of theBSD 3-Clause License.(back to top)
Package: varsdump
vardumpColored Python vardump for CLIa small wrapper function to output the colored variable prints to the terminal. The peculiarity of the library is that, like in PHP, the var_dump function is in the global scope. You just need to write import vardump in one startup file and then the var_dump function will be available in all modules that you connect to this script.Installationpipinstallvarsdump##Enable# app.pyimportvarsdumpimportsome_controller...var_dump(...)...##Usage# some_controller.pysome_obj={'int':123,'str':'abc','dict':{'list':[1,2,3]}}var_dump(some_obj)For update use:pip install vardump --upgrade
Package: varseries
VarSeriesAn utility to build and display variation seriesSome examplesfromvarseriesimportDiscreteVS,ContinuousVSv=DiscreteVS([1,1,1.11,1.23,2,2.3,4,5.1,5.1,5...8])v.draw_polygon()print(v.vs)# Variation series representationprint(v.acc_frequencies)# Accumulated frequencies list# etc.v1=ContinuousVS([1,1,1.11,1.23,2,2.3,4,5.1,5.1,5...8],precision=4)v1.draw_hist()v.draw_cumulate()print(v.mode)# Mode of the variation series# etc
Package: vars-gridview
vars-gridviewVARS GridViewis a tool for reviewing and correcting VARS localizations in bulk.Authors: Kevin Barnard (kbarnard@mbari.org), Paul Roberts (proberts@mbari.org)InstallFrom PyPIVARS GridView is available on PyPI asvars-gridview. To install, run:pipinstallvars-gridviewFrom sourceThis project is built withPoetry. To install from source, run (in the project root):poetryinstallRunOnce VARS GridView is installed, you can run it from the command line:vars-gridviewYou will first be prompted to log in. Enter your VARS username and password.Note: If you are not using MBARI production VARS, change the "Config server" field to point to your instance of Raziel. This setting is persisted.CreditsIcons courtesy ofFont Awesome.Copyright © 2020–2023Monterey Bay Aquarium Research Institute