Saravanan666/pypi-org huggingface dataset:
Package: intouch-client
DEPRECATEDThis package has been deprecated in favour ofincomfort-clientintouch-clientPython client library forIntergas InTouch-compatible boilersaccesible via aLan2RF gateway. It includes a basic CLI to demonstrate how to use the library.Porting from syncio librariesThis library is based uponhttps://github.com/bwesterb/incomfort, but usesaiohttprather than synchronous I/O (such asrequestsorhttplib).Where possible, it uses uses the same methods and properties asincomfort, but with the following differences:InTouchGatewayclass renamed from Gatewayadded kwargs:username,password(used for later versions of firmware)InTouchHeaterclass renamed from Heaterrenamed:is_burning,is_failed,is_pumping,is_tappingmoved out:room_temp,setpoint,setpoint_override,setnew/added:update,status,roomsInTouchRoomclass has been added, and some methods moved in from Heatersame name:room_temp,setpointrenamed:override,set_overridenew/added:statusBasic CLI includedThere is a very basic CLI (the output has been formatted here for readability):(venv)root@hostname:~/client_apis/intouch-client$pythonintouchclient/__init__.py${HOSTNAME}{'display_code':126,'display_text':'standby','fault_code':0,'is_burning':False,'is_failed':False,'is_pumping':False,'is_tapping':False,'heater_temp':31.22,'tap_temp':27.91,'pressure':1.23,'serial_no':'175t23072','nodenr':200,'rf_message_rssi':38,'rfstatus_cntr':0,'room_1':{'room_temp':26.4,'setpoint':19.5,'override':19.5},'room_2':{'room_temp':None,'setpoint':None,'override':19.0}}
Package: intouch-queryset-csv
Queryset CSV is a simple module to allow Django Querysets to be exported as CSV files.Installationpip install intouch-queryset-csvThen add the app to your installed appsintouch.queryset_csvUsageThere are several ways to use the Queryset csv exporter.Render to responseThe most basic usage is to call the functionqueryset_as_csv_responseThis function accepts a queryset as a parameter, along with a filename and whether the file is to be streamed.
It returns an HTTPResponse object, so should be called at the end of a view function to offer the download
to the client.Admin ActionAn action is provided for the Django admin site. There are 3 ways of including it:The functionexport_model_as_csvcan be added to your custom admin class’s actionsTheCsvExporterAdminclass can be inherited to provide the actionSettingCSV_EXPORT_ADMIN_ACTION_AVAILABLE=Truein your django settings module will make the action available to all registered admin classes.The action will simply export all objects of the selected model to csv (as a stream)Why create this code rather than use an existing CSV export project?Licensing - Other CSV export packages encountered are Licensed under the GNU GPL. The GPL is incompatible with the desired usage, so a this was written from scratch.Functionality - Existing CSV packages are not designed to handle very large querysets, the option to steam querysets is essential for models with thousands of entries.Simplicity - This package has no dependencies other than django itself, and the requirements are to be kept to a minimum in future versions (preferably solely django). It is also designed to require as little effort as possible to use.Currency - The package is written and tested using the latest versions of Python and Django. While it may work with older versions, no special effort will be expended to force compatibility with old versions.SettingsCSV_EXPORT_ADMIN_ACTION_AVAILABLE- If True, adds the CSV Export action to all admin pages by defaultQUERYSET_CSV_DATE_FORMAT- Set the format to use when outputting datetime.datetime objects, defaults to"%d-%m-%Y %H:%M:%S"Tested VersionsDjango versions 1.11-2.2 should be supported, Python 3 versions supported as per the Django releases.
Package: intoyuniot
intoyuniotis an open source ecosystem for IoT
development. Cross-platform IDE and unified debugger. Remote unit testing and
firmware updates.Get StartedProductsRegistrySolutionsDevelopment PlatformsFrameworksContributingLicenseCopyright (c) 2014-present IntoYun <support@intorobot.com>The intoyuniot is licensed under the permissive Apache 2.0 license,
so you can use it in both commercial and personal projects with confidence.
Package: intperm
This package implements a simple, configurable permutation on the set of 64-bit
integers.The permutation is based on a bitmask that maps each bit of the input to a bit
of the output. The bitmask is expanded from a random seed using aPRNG, as
described byGeorge Marsagliain his paper calledXorshift RNGs. The
permutations are thus believed to be unpredictable, provided provided that the
seed is kept secret.UsageCreate a newPermutationinstance by passing in an optional seed.>>>fromointpermimportPermutation>>>perm=Permutation(42)>>>perm.map_to(37)13750393542137160527L>>>perm.map_from(13750393542137160527)37Not providing a seed will create a random permutation:>>>perm=Permutation()>>>perm.map_from(perm.map_to(37))==37TrueUse casesUse cases may vary, but an example that I find useful is generatinghard-to-guess, random-looking tokens based on IDs stored in a database.
The IDs can be used together with the seed to decode the original ID, but theircardinalityis the same as that of the IDs themselves. When used smartly,
this can save you from having to index those tokens in the database.Another good example is randomising IDs of private objects that are available
via some sort of an API. Let’s say the user accounts on your website are
accessible via the path/user/:id, where:idis the user’s ID. Someone
could track the growth of your user base just by enumerating the URLs and
keeping track of the status codes (e.g. 403 vs. 404).Using this simple permutation, user IDs can be kept unpredictable, rendering
these kinds of attacks practically useless.See alsoThis library is also implemented inRubyandGo.
Package: intpolylib
intpolylibLibrary wchich contains operations on polynomials with integer coefficients. It includes basic operations like addition, subtraction, multiplication, division with the rest, derivative, but also factorization and finding rational roots. There are 3 represantations of polynomials: list of coefficients, dictionary of non-zero coefficients and list of points (ListPolynomial, DictPolynomial, PointsPolynomial).Installingpip install intpolylibUsageAddition>>>fromintpolylibimportListPolynomial>>>poly=ListPolynomial(x^2+x+1)>>>poly2=ListPolynomial(x+1)>>>print(poly+poly2)x^2+2x+2
Package: intra42api
Intra42ApiIntra42Api is an API wrapper.
Supports searches for Users, Campuses and Projects.InstallationNOTE: Python 3.6 or higher is required.# Windowspy-3.6-mpipinstallintra42api# Linuxpython3.6-mpipinstallintra42apiExampleimportintra42apiintra=intra42api.Intra42()intra.setToken('MY_AWESOME_UID','MY_AWESOME_SECRET')user=intra.getUser('ID_OR_LOGIN')print(f"""
        Login:{user['login']}Email:{user['email']}Full Name:{user['displayname']}Correction Points:{user['correction_point']}url:{user['url']}""")Change Log0.0.1 (13/04/2022)First Release
Package: intraCorr
Correlation for Repeated Observation within Subjects in PythonThis package applied the statistical technique for estimating the correlation for paired measures assessed on two or more ocassions for multiple subjects.
Pearson correlation is applied when each paired of measurements is independent of one another. This is not the case when you have an observation for the same subject at different times.InstallingYou can use PyPi to do so,pipinstallintraCorrContributingYou can contribute to this project by forking it through github.LicensingThe code in this project is licensed under GNU General Public License v3.0.AuthorsErika MunozJose Nandez
Package: intrag-sdk
intrag-sdkSDK não oficial do Itau Investiment ServicesInstalação$pipinstallintrag_sdkPassivoExemplo de uso docrawlerpara consultas no sitehttps://www.itaucustodia.com.br/Passivofromintrag_sdkimportItauPassivoimportpandasaspdapi=ItauPassivo()api.authenticate(user="usuario123",password="123456")# Propriedades disponíveisnome_gestor:str=api.nome_gestorcodigo_gestor:str=api.codigo_gestorfundos:pd.DataFrame=api.fundos# Download de arquivosdownloads:List[Download]|ZipFile=api.download_de_arquivos(Arquivo.ARQUIVO_DE_PERFORMANCE,data=datetime.date(2023,10,6))# Movimentações do diamovimentacoes:pd.DataFrame=api.movimentos_do_dia()# Posição de cotistascodigo_fundo:str=fundos["CDFDO"][0]posicoes:pd.DataFrame=api.posicao_cotistas(codigo_fundo=codigo_fundo)
Package: intrand
intrand.pyPython utilities that are useful in their own little ways.Installationpip:pip install --upgrade intrandUsageimportintrand# some default valuesdefaults={"preferences":{"color":"blue","size":"medium","gradient":True}}# some user input (perhaps via API)userConfig={"id":1234567890,"name":{"first":"John","middle":"James","last":"Smith"},"preferences":{"color":"gray"}}intrand.dict_utils.merge(defaults,userConfig)# merge keys present in defaults,# but missing in userConfig# into userConfigprint(userConfig)Optionally, you may choose to overwrite your existing values with the new by specifyingoverwrite=True.
Package: intreehooks
Load a PEP 517 backend from within the source tree.InPEP 517, package building
backends are not loaded from the source of the package being built. This
prevents accidentally shadowing your build system, but some packages, like
build tools, want to act as their own backend.intreehooksis a shim to work around this, so that a source tree can be
built by itself. To use it, write a pyproject.toml like this:[build-system]requires=["intreehooks"]# + any other packages required to buildbuild-backend="intreehooks:loader"[tool.intreehooks]build-backend="flit.buildapi"# Import path of your real backend
Package: intrepppid
INTREPPPIDINcorporatingTRipletError forPredictingProtein-PproteinInteractions usingDeep LearningINTREPPPID is a deep learning model for predicting protein interactions.
It's especially good at making prediction on species other than those it was trained on (cross-species prediction).How to Use INTREPPPIDHere are some quick highlights, but be sure toread the documentationfor more details!InstallingTo install INTREPPPID, clone the repository and install the dependencies usingpipand therequirements.txtfile at
the root of the repositorygitclonehttps://github.com/jszym/intrepppidcdintreppid
python-mvirtualenvvenvsourcevenv/bin/activate
pipinstall-rrequirements.txtTraining ModelsTo train INTREPPPID, simply use thetrain e2e_rnn_tripletcommand like so:python-mintrepppidtraine2e_rnn_tripletDATASET.h5spm.model310080--seed3927704--vocab_size250--trunc_len1500--embedding_size64--rnn_num_layers2--rnn_dropout_rate0.3--variational_dropoutfalse--bi_reducelast--workers4--embedding_droprate0.3--do_rate0.3--log_pathlogs/e2e_rnn_triplet--beta_classifier2--use_projectionfalse--optimizer_typeranger21_xx--lr1e-2DocumentationBe sure toread the documentationfor more details.LicenseINTREPPPIDINcorporatingTRipletError forPredictingProtein-PproteinInteractions usingDeep LearningCopyright (C) 2023  Joseph SzymborskiThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.You should have received a copy of the GNU Affero General Public License
along with this program.  If not, seehttps://www.gnu.org/licenses/.
Package: intrinioPy
DescriptionSave ticker symbols from Intrinio's stock screening API.You need to have an active subscription to Intrinio's "US Fundamentals and Stock Prices"Intrinio's screener API settings can be found athttp://blog.intrinio.com/stock-screener-api-intrinio/UsageFill in credentials:email = 'your.email@gmail.com'
password = 'your.pass' 
intrinio = Intrinio(username, password)Example
# Market-cap greater than 2 billion
intrinio.url = 'https://api.intrinio.com/securities/search?conditions=marketcap~gt~2000000000&page_size=500'Start requestsymbols = intrinio.get_stocks()Save for laterintrinio.save(symbols, "less_than_200m.p")
Package: intrinio-sdk
Welcome to the Intrinio API! Through our Financial Data Marketplace, we offer a wide selection of financial data feed APIs sourced by our own proprietary processes as well as from many data vendors. For a complete API request / response reference please view the [Intrinio API documentation](https://docs.intrinio.com/documentation/api_v2). If you need additional help in using the API, please visit the [Intrinio website](https://intrinio.com) and click on the chat icon in the lower right corner.  # noqa: E501
Package: intrinsic-dimensionality
AboutThis package includes fastfood and dense transformation wrappers for pytorch modules, primarily to reproduce results fromLi, Chunyuan, et al. "Measuring the intrinsic dimension of objective landscapes." arXiv preprint arXiv:1804.08838 (2018)- see below for info.All contributions are welcome! Please raise an issue for a bug, feature or pull request!Give this repo a star! :star:Installpip install intrinsic-dimensionalityQuick start on your classification task!importosos.environ["CUDA_VISIBLE_DEVICES"]=DEVICE_NUMimporttorchfromtorchimportnnimporttorchvision.modelsasmodelsfromintrinsicimportFastFoodWrapclassClassifier(nn.Module):def__init__(self,input_dim,n_classes):super(Classifier,self).__init__()self.fc=nn.Linear(input_dim,n_classes)self.maxpool=nn.AdaptiveMaxPool2d(1)defforward(self,x):x=self.maxpool(x)x=x.reshape(x.size(0),-1)x=self.fc(x)returnxdefget_resnet(encoder_name,num_classes,pretrained=False):assertencoder_namein["resnet18","resnet50"],"{}is a wrong encoder name!".format(encoder_name)ifencoder_name=="resnet18":model=models.resnet18(pretrained=pretrained)latent_dim=512else:model=models.resnet50(pretrained=pretrained)latent_dim=2048children=(list(model.children())[:-2]+[Classifier(latent_dim,num_classes)])model=torch.nn.Sequential(*children)returnmodel# Get model and wrap it in fastfoodmodel=get_resnet("resnet18",num_classes=YOUR_NUMBER_OF_CLASSES).cuda()model=FastFoodWrap(model,intrinsic_dimension=100,device=DEVICE_NUM)Reproducing experiments from the paperFull thread about reproducibility results is availablehere.
Note that some hyper-parameters were not listed in the paper - I raised issues on Uber's Github repohere.I am able to reproduce their MNIST results with LR=0.0003, batch size 32 for both dense and fastfood transformations
using FCN (fcn-dense, fcn-fastfood). However, not for LeNet (cnn-dense, cnn-fastfood).For CIFAR-10, with far larger resnet (Resnet-18 11mil param) vs 280k 20-layer resnet used in the paper,
results appear to be similar. FCN results in appendix (Fig S7) suggest some variation is to be expected.Cite@misc{jgamper2020intrinsic,
  title   = "Intrinsic-dimensionality Pytorch",
  author  = "Gamper, Jevgenij",
  year    = "2020",
  url     = "https://github.com/jgamper/intrinsic-dimensionality"
}

@article{li2018measuring,
  title={Measuring the intrinsic dimension of objective landscapes},
  author={Li, Chunyuan and Farkhoor, Heerad and Liu, Rosanne and Yosinski, Jason},
  journal={arXiv preprint arXiv:1804.08838},
  year={2018}
}
Package: introcs
Student Tools for Beginning PythonPython is an extremely popular language for introduction to computing courses at various
universities. However, many Python features require knowledge of advance language elements
before they can be used.  For example, the Unit Testing framework is extremely comple and
is typically inaccessible to a beginning student.  Similarly, Python has moved all string
functionality to method calls, which are confusing for a student just starting out with
functions.The purpose of this package is to provide proper masking, giving instructors more
flexibility in how they arrange their Python course.  Masking takes a complex feature and
hides it behind a simpler interface.  When the students have mastered that interface, the
instructor can remove it and allow students direct access to the complex feature.For example, this package provides non-method string functions for basic functionality
such as searching, replacing, and testing.  This allows the students to write interesting
text-manipulation functions when they are just starting out, without having to understand
the extra subtleties of method calls.  When they are ready to move on to method calls, the
students are free (and encouraged) to stop using these functions.For a complete list of modules provided by this package, see the associated documentation.
Key features include, but are not limited toMethod-free string functionsMethod-free tuple functionsSimplified unit test utilitiesSimplified web connections (similar to the classicurllib2)Simplified color model interfacesImproved graphics turtleThese packages were developed as part of the introduction to computing sequence at
Cornell University. However, they are free to all educators that might find them useful.
Package: introduce
Introduce 📱파이썬용 자기소개서 라이브러리입니다.https://pypi.org/project/introduce/https://github.com/cord0318/python_introduce다운로드 📥이 모듈은 파이썬 모든 버젼에서 사용 가능합니다.윈도우나 리눅스에서 다음과 같이 입력합니다.pip install introduce오류가 나는 경우, python -m pip install --upgrade pip 로 pip를 업데이트 해주세요.사용법 🤖importintroducemyself=introduce.MySelf("홍길동","16살","남성","코딩")# 양식 (이름, 나이, 성별, 취미)myself.introduction("Hello World!")# 자기소개 작성data=myself.check()myself.create_image(width=200,height=300,font="이순신돋움체M.ttf",font_size=15,font_color=(255,0,0),background_color=(241,241,241))# 사진 생성email=introduce.Email("example0318@gmail.com","example1234!","example0104@gmail.com")# 양식 (당신의 이메일, 당신의 이메일의 비밀번호, 메시지를 보낼 이메일)email.send(title="자기소개서",send_text=data,image="자기소개서.png")# 양식 (이메일의 제목, 보낼 메시지, 이미지)
Package: introrl
NoteThis project is in the early stages of development.IntroRLProvides A Framework For Exploring Reinforcement Learning.It is based on the textbook“Reinforcement Learning An Introduction”By Sutton & Barto.The textbook is also available inPDF format at the authors’ site.This documentation ofIntroRLis organized around the chapter structure of the Sutton & Barto textbook.Many of the examples and figures are reproduced here in order to validate theIntroRLcode.There is another site byShangtong Zhangthat was of great help to me and which covers many areas of the textbook not covered here.See the Code at:https://github.com/sonofeft/IntroRLSee the Docs at:http://introrl.readthedocs.org/en/latest/See PyPI page at:https://pypi.python.org/pypi/introrl
Package: intros-MaxEnt
The Whole Tale ProjectAuthor: Santiago Nunez-Corrales(nunezco2@illinois.edu)intros-MaxEntis a Python software package that provides an open
implementation of the MaxEnt algorithm for ecological niche modeling
as described inPhillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum
entropy modeling of species geographic distributions.
Ecological modelling, 190(3-4), 231-259.Our goal with this package is, scientifically, to help improve the
modeling landscape in ecological niche modeling by making explicit the
consequences of varying various parameters in MaxEnt based on
biologically informed hypotheses.The packaged provides facilities for the following tasks:Importing georeferenced data from taxa records for modeling species
distribution, as well as environmental variables data for locations
compliant with WGS84 latitude-longitude coordinates.Executing the MaxEnt algorithm with inspection and reparametrization
capabilities of models and outcomes.Analyzing and differentially comparing model outcomes including KML
and PNG image generation at various resolutions.Packaging of model configurations and experiments for extended
scientific replication.This package is also intended to be used in Jupyter Notebooks at The
Whole Tale environment as an example of scientific reproducibility. The
current software implementation was heavily informed byMerow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide
to MaxEnt for modeling species’ distributions: what it does, and why
inputs and settings matter. Ecography, 36(10), 1058-1069.This code is part of The Whole Tale Summer Internship 2018.
Package: introspector
Introspector[WIP] A Python library to write strongly typed code.Table of contentsIntrospectorTable of contentsIntroductionRequirementsBasic usagePython 3.10 supported typing syntaxInstrospector.strict available options:arrow_right: ignoreIntroductionIntroduce strict typing in your functions.This project is under development.RequirementsPython >= 3.10Basic usageimportintrospector@introspector.strictdeffoo(a:int,b:list[dict[str,Any]])->list[int]:# Some funny code...return[1,2,3,4]foo(42,[{'x':['hello','world'],'y':3.14,},],)When the code is executed, theinstrospector.strictdecorator will inspect the signature of thefoofunction and compare it with the values passed in its parameters.If the typing of the values does not match the signature of the function, introspector will throw aTypeErrorexception.Python 3.10 supported typing syntaxThis paclage support the following typing syntaxes:NameDescriptionExample|operatorThe Union type shortcut syntaxint | floatInstrospector.strict available options:arrow_right: ignoreA list of parameter names that will be excluded from the typing control.Notice that theignoreoption will always containsselfandcls.Example:@instrospector.strict(ignore=['b'])deffoo(a:int,b:float)->None:...The argumentbwill be ignored by the typing control.
Package: intset
IntSets are an efficient immutable representation of sets of unsigned 64-bit
integers with fast boolean operations and fast indexing of the set in sorted
order. They are designed to be particularly efficient for representing sets
with large contiguous ranges, so for example representing the set of all 64-bit
integers takes only a handful of bytes.Their behaviour and API are somehwere in between that of frozenset and that of
a sorted list of deduplicated integers.The implementation is heavily based onFast Mergeable Integer Mapsby Okasaki and Gill, but it has been adapted to support a somewhat different feature
set and a more compact representation for certain usage patterns.For usage, see theAPI documentation.
Package: int-set
int setAbstractCounts the number of integers.
整数を数えます。What does this do?Counts the number of integers that can be any integer multiple within a given start and end.
始まりと終了を指定すると、その中に含まれる任意の整数倍となる整数の数を数えます。Very Fast!!usagefromint_setimportIntSet,step# IntSet makes range like object# IntSetはrangeのようのオブジェクトを作成します。_range=IntSet(100)# start: 0 stop: 100_range=IntSet(100,1000)# start: 100 stop: 1000s=step(2)# a multiple of two（２の倍数）s=step(2,5)# a multiple of 2 or a multiple of 5（２の倍数または５の倍数）# Count multiples of 2 or 5 between 100 and 1000.# 100 ~ 1000に含まれる２の倍数または５の倍数の数を数えます。_range.count(s)# 541installpipinstallint-set
Package: intspan
intspanis asetsubclass that conveniently represents sets of integers.
Sets can be created from, and displayed as, integer spans such as1-3,14,29,92-97rather than exhaustive member listings. Compare:intspan('1-3,14,29,92-97')
[1, 2, 3, 14, 29, 92, 93, 94, 95, 96, 97]Or worse, the unsorted, non-intuitive listings that crop up with Python’s
native unordered sets, such as:set([96, 1, 2, 3, 97, 14, 93, 92, 29, 94, 95])While they all indicate the same values,intspanoutput is much more compact
and comprehensible. It better divulges the contiguous nature of segments of the
collection, making it easier for humans to quickly determine the “shape” of the
data and ascertain “what’s missing?”When iterating,pop()-ing an item, or converting to a list,intspanbehaves as if it were an ordered–in fact, sorted–collection. A key
implication is that, regardless of the order in which items are added,
anintspanwill always be rendered in the most compact, organized
form possible.The main draw is having a convenient way to specify, manage, and see output in
terms of ranges–for example, rows to process in a spreadsheet. It can also help
you quickly identify or report which items werenotsuccessfully processed in
a large dataset.There is also an orderedintspanlisttype that helps specify the
ordering of a set of elements.See the full details onRead the Docs.