Saravanan666/pypi-org huggingface dataset:
Package: tnt2
# tnt2 v1.2.7This is a new version of tnt2 with advanced encryption using the PyCrypto module.A key is generated and stored in the directory you run the application from, and if you are backing up your system please backup your key as well in order to be able to read your notes.For queries:Visit My Website here: <link>http://www.benedict.heliohost.org</link>OREmail: benedicttshaba@gmail.com##############################################################################**INSTALLATION:**```bashgit clone git@github.com:benedict-tshaba/tnt2.git && cd tnt2chmod +x setup.pysudo ./setup.py installtnt2Alternatively:#first install Tkinter if you dont have it already.sudo apt-install python-tksudo pip install tnt2```
Package: tnt-deployment
TEONITE Deployment[![Build Status](https://drone.io/github.com/teonite/deployment/status.png)](https://drone.io/github.com/teonite/deployment/latest)We have tried various deployment tools and since Python is your main choiceregarding language/technology we used Fabric most. But as always regardingmotivation to create a new tool, not all tools meet our expectations.So we’ve created our own deployment tool based on Fabric (it uses Fabric to doall operations on the remote server).Full documentation can be found here:https://tnt.sh/public/deployment/I7HOGGFV2INL/index.html
Package: tntn
tntnPython wrapper library for tunneling appsThe main purpose is for use in my other projectBing-su/sd-webui-tunnels.Usage❯tntn--help
tntn:Pythonwrapperlibraryfortunnelingapps

Usage:tntn<app_name><args>❯tntnjprq--help
Usage:jprq<command>[arguments]Commands:auth<token>Setauthenticationtokenfromjprq.io/authtcp<port>StartaTCPtunnelonthespecifiedporthttp<port>StartanHTTPtunnelonthespecifiedporthttp<port>-s<subdomain>StartanHTTPtunnelwithacustomsubdomain--helpShowthishelpmessage--versionShowtheversionnumberseejprq❯tntnbore--help
Amodern,simpleTCPtunnelinRustthatexposeslocalportstoaremoteserver,bypassingstandardNATconnectionfirewalls.

Usage:bore.exe<COMMAND>

Commands:localStartsalocalproxytotheremoteserverserverRunstheremoteproxyserverhelpPrintthismessageorthehelpofthegivensubcommand(s)Options:-h,--helpPrinthelpinformation-V,--versionPrintversioninformationseebore
Package: tnt-python
tnt-pythontnt - Telegram notification toolFeaturesSending text messages to chatSending text documents to chatInstallationpoetry add tnt-pythonorpip install tnt-pythonExampleThis code sends a message on your behalf to the chatimportosfromtnt_python.dataimportMessageDatafromtnt_python.servicesimportNotifyerService# Your API Tokentoken=os.getenv("TG_TOKEN")data=MessageData(chat_id=os.getenv("CHAT_ID"),text="Hello world")NotifyerService().send_message(data,token)DebugMethodssend_message,send_coumentreturnsResponseTypetype dataclass.
You can outputstatus_codeor the entire response viaresultimportosfromtnt_python.dataimportMessageDatafromtnt_python.servicesimportNotifyerServicefromtnt_python.typesimportResponseTypetoken=os.getenv("TG_TOKEN")data=MessageData(chat_id=os.getenv("CHAT_ID"),text="Hello world")response:ResponseType=NotifyerService().send_message(data,token)# So you can display the entire response and the response codeprint(response.status_code,response.result)
Package: tnt-tensorflow
Transformer-in-TransformerAn Implementation of theTransformer in Transformerpaper by Han et al. for image classification, attention inside local patches.Transformer in Transformeruses pixel level attention paired with patch
level attention for image classification, in TensorFlow.PyTorch ImplementationInstallationRun the following to install:pipinstalltnt-tensorflowDeveloping tnt-tensorflowTo installtnt-tensorflow, along with tools you need to develop and test, run the following in your virtualenv:gitclonehttps://github.com/Rishit-dagli/Transformer-in-Transformer.git# or clone your own forkcdtnt
pipinstall-e.[dev]To run rank and shape tests run the following:pytest -v --disable-warnings --covUsageimporttensorflowastffromtntimportTNTtnt=TNT(image_size=256,# size of imagepatch_dim=512,# dimension of patch tokenpixel_dim=24,# dimension of pixel tokenpatch_size=16,# patch sizepixel_size=4,# pixel sizedepth=5,# depthnum_classes=1000,# output number of classesattn_dropout=0.1,# attention dropoutff_dropout=0.1,# feedforward dropout)img=tf.random.uniform(shape=[5,3,256,256])logits=tnt(img)# (5, 1000)An end to end training example for image classification on a dataset can be found in thetraining.ipynbnotebook.Pre-trained modelThe pre-trained model for TNT-S variant (reproducing the paper results, 81.4% top-1 accuracy and 95.7% top-5 accuracy on ImageNet-1K) can also be found paired with an example of inferencing with it.ModelTensorFlow HubInference Tutorialbuckettfhub.devWant to Contribute 🙋‍♂️?Awesome! If you want to contribute to this project, you're always welcome! SeeContributing Guidelines. You can also take a look atopen issuesfor getting more information about current or upcoming tasks.Want to discuss? 💬Have any questions, doubts or want to present your opinions, views? You're always welcome. You canstart discussions.Citation@misc{han2021transformer,title={Transformer in Transformer},author={Kai Han and An Xiao and Enhua Wu and Jianyuan Guo and Chunjing Xu and Yunhe Wang},year={2021},eprint={2103.00112},archivePrefix={arXiv},primaryClass={cs.CV}}LicenseCopyright 2020 Rishit Dagli

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: tnumbers
This module return all the even umbers between the given two numbers(Both inclusive).How to make most use of the module?simply run the following command “pip install tnumbers” in the command promptthen open up your project and import tevevn packagenow you are ready to use the function evens_between————-USAGE—————-import tnumbers//Prints the values between two integers a and b where a<b
a,b=2,20evens=tnumbers.evens_between(a,b)
print(evens)//output is [2,4,6,8,10,12,14,16,18,20]//similarly for odd and prime numbers
print(tnumbers.odds_between(a,b)) //output is [3, 5, 7, 9, 11, 13, 15, 17, 19]
print(tnumbers.primes_between(a,b)) //output is [2,3,5,7,11,13,17,19]Change Log0.0.1(18/03/2023)-First Release
Package: tn-weather-reporter
My very first library pythonTutorial fromhttps://www.youtube.com/watch?v=KTaLs494xgo&list=PLyb_C2HpOQSB3z_4WliKt56WyDgfXzzMz
    https://realpython.com/pypi-publish-python-package/Install requirements to build librarypip3 install wheel
    pip3 install twineBuild your packagespython3 setup.py sdist bdist_wheelCheck the distributiontwine check dist/*
    ===> If passed ==> Now we goonna published to websiteTestPyPI: Test python package publishing before publishes on Python Package Indextwine upload --repository-url https://test.pypi.org/legacy/ dist/*

- If you are using ubuntu, maybe you will get this error from commandline
....
    from keyring.util.escape import escape as escape_for_ini
ModuleNotFoundError: No module named 'keyring.util.escape'

    pip3 install --upgrade keyrings.altPyPI: Find, install and publish python packages with PPItwine upload --repository-url https://upload.pypi.org/legacy/ dist/*
Package: to4326
to4326to4326 convert bounds of satellite data such as GeoTIFF to Polygon of EPSG:4326.Depends: pyprojExamplefromto4326importtransformupper_left=[382200.000,2512500.000]lower_left=[382200.000,2279400.000]upper_right=[610500.000,2512500.000]lower_right=[610500.000,2279400.000]src_crs=32645transform.geojson_from_corner_coordinates(upper_left,lower_left,upper_right,lower_right,src_crs)result{"type":"Feature","bbox":[85.85296718933647,20.610041795245515,88.07596179098907,22.719775713801845],"properties":{},"geometry":{"type":"Polygon","coordinates":[[[85.85296718933647,22.71566587084141],[85.85471144948144,22.505128271679137],[85.85643508166952,22.29458504609756],[85.85813822451726,22.08403623614523],[85.8598210145163,21.87348188415423],[85.86148358606195,21.662922032738383],[85.86312607148135,21.452356724791283],[85.86474860106108,21.24178600348436],[85.86635130307411,21.031209912264952],[85.86793430380634,20.820628494854294],[85.8694977275825,20.610041795245515],[86.08856914891048,20.611335735650073],[86.30765561712383,20.612352097028005],[86.52675351639061,20.613090808785984],[86.7458592298015,20.613551819609576],[86.96496913971066,20.613735097472322],[87.18407962807663,20.613640629641363],[87.40318707680342,20.6132684226797],[87.6222878680816,20.612618502445013],[87.84137838472925,20.611690914085163],[88.06045501053292,20.610485722030123],[88.06192160852325,20.82107736606614],[88.0634065712253,21.031663741215127],[88.06491001527168,21.242244803670587],[88.06643205913487,21.45282050989972],[88.06797282315154,21.663390816645528],[88.06953242954742,21.873955680928745],[88.0711110024627,22.084515060049903],[88.07270866797775,22.295068911591237],[88.07432555413966,22.5056171934187],[88.07596179098907,22.71615986368381],[87.8536827240295,22.717500978966182],[87.63138882771706,22.718533183697783],[87.4090839625065,22.71925640487522],[87.18677198995947,22.71967059134279],[86.96445677235506,22.719775713801845],[86.74214217229994,22.719571764815985],[86.51983205233836,22.719058758812512],[86.29753027456248,22.71823673207974],[86.07524070022231,22.717105742760463],[85.85296718933647,22.71566587084141]]]}}noteLinear ring enclosing the pole is transformed to polar sterographic projections once.Source CRS -> polar sterographic projections (EPSG:3995, EPSG:3031) -> EPSG:4326Not support linear ring enclosing both poles.Input polygon must be less than 360deg in width and less than 180deg in height, in EPSG:4326.
Package: to7z
to7zConverts ZIP to 7z.Usage:$to7z[OPTIONS][PATH]Arguments:[PATH]: Path to working directory. Default to current dirOptions:-i, --ignore PATH: Directories to ignore  [default: ]-r, --recursive: [default: False]-f, --fetch: Return list of zips and rar found in path  [default: False]-k, --keep-original: Keep original ZIP files. Default is to delete them  [default: False]-d, --debug: [default: False]-v, --version--install-completion: Install completion for the current shell.--show-completion: Show completion for the current shell, to copy it or customize the installation.--help: Show this message and exit.
Package: toad
TOADToad is dedicated to facilitating model development process, especially for a scorecard. It provides intuitive functions of the entire process, from EDA, feature engineering and selection etc. to results validation and scorecard transformation. Its key functionality streamlines the most critical and time-consuming process such as feature selection and fine binning.Toad 是专为工业界模型开发设计的Python工具包，特别针对评分卡的开发。Toad 的功能覆盖了建模全流程，从 EDA、特征工程、特征筛选 到 模型验证和评分卡转化。Toad 的主要功能极大简化了建模中最重要最费时的流程，即特征筛选和分箱。Install and Upgrade · 安装与升级Pippipinstalltoad# to installpipinstall-Utoad# to upgradeCondacondainstalltoad--channelconda-forge# to installcondainstall-Utoad--channelconda-forge# to upgradeSource codepythonsetup.pyinstallKey features · 主要功能The following showcases some of the most popular features of toad, for more detailed demonstrations and user guidance, please refer to the tutorials.以下部分简单介绍了toad最受欢迎的一些功能，具体的使用方法和使用教程，请详见文档部分。Simple IV calculation for all features · 一键算IV:toad.quality(data,'target',iv_only=True)Preliminary selection based on criteria · 根据特定条件的初步变量筛选;and stepwise feature selection (with optimised algorithm) · 优化过的逐步回归:selected_data=toad.selection.select(data,target='target',empty=0.5,iv=0.02,corr=0.7,return_drop=True,exclude=['ID','month'])final_data=toad.selection.stepwise(data_woe,target='target',estimator='ols',direction='both',criterion='aic',exclude=to_drop)Reliable fine binning with visualisation · 分箱及可视化:# Chi-squared fine binningc=toad.transform.Combiner()c.fit(data_selected.drop(to_drop,axis=1),y='target',method='chi',min_samples=0.05)print(c.export())# Visualisation to check binning resultscol='feature_name'bin_plot(c.transform(data_selected[[col,'target']],labels=True),x=col,target='target')Intuitive model results presentation · 模型结果展示:toad.metrics.KS_bucket(pred_proba,final_data['target'],bucket=10,method='quantile')One-click scorecard transformation · 评分卡转化:card=toad.ScoreCard(combiner=c,transer=transer,class_weight='balanced',C=0.1,base_score=600,base_odds=35,pdo=60,rate=2)card.fit(final_data[col],final_data['target'])print(card.export())Documents · 文档Tutorial中文指引docsContributingCommunity · 社区We welcome public feedback and new PRs. We hold a WeChat group for questions and suggestions.欢迎各位提PR，同时我们有toad使用交流的微信群，欢迎询问加群。ContributorsDedicated byThe ESC Team
Package: toadie
toadieToadie is a microservices framework that includes everything needed to create microservices and run on a compute cluster using the ambassador pattern. Compute cluster can be Elastic Container Service, Kubernetes, or similar. Toadie supports the concepts ofserviceandtask.Service: Long-running application or workerTask: Short-running application or worker, which often requires additional compute resorces be added to clusterToadie is designed with analytic processes and smaller teams in mind.Toadie is inspired by django and rails and gummi bears.Getting StartedInstall toadie at the command prompt if you haven’t yet using eitherpiporconda:# using pip$pipinstalltoadie# using conda$condainstall-chttps://conda.anaconda.org/gonzotoadieInstall required development tools:$toadieready-systemAt the command prompt, create a new toadie project and add aservice:$toadiecreate-projectmyproject$cdmyproject$toadiecreate-servicemyservicewhere “myproject” is the project name and where “myservice” is the name of your service.Start your stack locally:$honchostartupDeploy your stack on your chosen cloud:$toadiedeploy
Package: toai
ToaiToai provides convenient classes and functions for machine learning, data science and data analysis.InstallpipinstalltoaiUsageImport most useful default imports (useful for notebooks). This will give you access tonpfor NumPy,pdfor Pandas,snsfor Seaborn and many more.fromtoai.importsimport*Import functions from a specific submodule. This will import RMSE and error rate metrics from the metrics submodule.fromtoai.metricsimportrmse,error_rate
Package: toansttlib
IntroductionThis is toanstt's libraryRepository:https://github.com/nmtoan91/pytoansttlibPypi repo:https://pypi.org/project/toansttlib/TChart usage examplefromtoansttlibimport*importpandasaspdimportosimportmatplotlib.pyplotaspltimportnumpyasnpimportshutil#datasetIds = ['Cafe_Chatbot','Dialog','Emotions_MultiTurnDiag','Question_Answer_1','Question_Answer_2','Question_Answer_3','WikiQA']datasetIds=['Dialog','Emotions_MultiTurnDiag','Question_Answer_1','Question_Answer_2','Question_Answer_3','WikiQA']#params = { "text.usetex" : True,"font.family" : "serif", "font.serif" : ["Computer Modern Serif"]}params={"text.usetex":False,"font.family":"serif"}plt.rcParams.update(params)fig,axs=plt.subplots(1,2,figsize=(8,4))fig.tight_layout(pad=2.0)fig.subplots_adjust(bottom=0.3)ys=[]ys_time=[]foriinrange(6):data=pd.read_csv('./FigureDraw/TESTING_epoch/training__home_nmtoan_DATA_CHATBOT_'+datasetIds[i]+'_glue_.csv')x=data.epoch.to_numpy()y=data.train_loss.to_numpy()ys.append(y)ys_time.append(data.train_time.to_numpy())fmta(-1)DrawSubFigure_SimpleLine(axs[0],x,ys,x_title='epochs',y_title='Loss')fmta(-1)DrawSubFigure_SimpleLine(axs[1],x,ys_time,x_title='epochs',y_title='Training time (seconds)')axs[0].legend(datasetIds,ncol=3,bbox_to_anchor=(1.6,-0.65,0.5,0.5))fileName='./FigureDraw/'+os.path.basename(__file__).replace(".py",".pdf")fileName2="C:/Users/nmtoa/Dropbox/Apps/Overleaf/Toan_ContextBasedFineTuneGPT2/Figures/"+os.path.basename(__file__).replace(".py",".pdf")plt.savefig(fileName)shutil.copyfile(fileName,fileName2)plt.show()TChart usage examplefromtoansttlibimportTCSVResultresult=TCSVResult('stage3')result.Add('Dataset',datasetID)result.WriteResultToCSV(folder="RESULT/stage3")
Package: toarray
toarraySmall Python package for converting iterables to arrays.Descriptiontoarray takes an iterable and figures out what the smallest python array type can hold the values.
returns an array if all values can fit or a list if not.Getting StartedDependenciesPython>=3.6Installingpip install toarrayExecuting programHow to run the programStep-by-step bulletsExample code to come.AuthorsContributors names and contact infoOdos Matthews:odosmatthews@gmail.comLicenseThis project is licensed under the MIT License - see the LICENSE.md file for details
Package: toast
Time Ordered Astrophysics Scalable ToolsDescriptionTOAST is asoftware frameworkfor
simulating and processing timestream data collected by telescopes. Telescopes which
collect data as timestreams rather than images give us a unique set of analysis
challenges. Detector data usually contains noise which is correlated in time as well as
sources of correlated signal from the instrument and the environment. Large pieces of
data must often be analyzed simultaneously to extract an estimate of the sky signal.DocumentationSee the full documentation here:https://toast-cmb.readthedocs.io/en/latest/
Package: toasted
toastedToast notifications library for Windows. Unlike other Windows toast libraries, Toasted is the one of the most comprehensive toast libraries. Because it supports all elements (images, progresses, texts, inputs, buttons...) that you probably never seen on a toast. Not only that, it also includes useful features found in the Notifications API.Struggling with making a GUI for your script? Say no more.Installpython -m pip install toastedExampleimportasynciofromtypingimportDictfromtoastedimportToast,Text# Create Toast with Toast(),# see docstring for all available parameters.mytoast=Toast()# Add elements.mytoast+=Text("Hello world!")# Using += operator.mytoast.data.append(Text("Hello world!"))# Or access the inner list with Toast.data.# Set up a handler.# This handler will be executed when toasted has clicked or dismissed.@mytoast.handlerdefmyhandler(arguments:str,user_input:Dict[str,str],dismiss_reason:int):# dismiss_reason will set to a value higher than or equals to 0 when dismissed,# -1 means a toast or button click.ifdismiss_reason==-1:print("Got arguments:",arguments)else:print("Toast has dismissed:",dismiss_reason)# Run show() async function.asyncio.run(mytoast.show())HighlightsRemote (HTTP) images supportNormally, Windows restricts the use of HTTP images and only allows local file paths on non-UWP applications. But to overcome the limitation, Toasted downloads HTTP images to %TEMP%, so you can now use images from web without any configuration! Downloaded images are deleted once toast has dismissed / clicked. Also, to comply with Windows API, you can enable sending system information (such asms-lang,ms-theme,ms-contrast) to remote sources as query parameters by settingadd_query_paramsproperty.Update toast content (Data binding)Properties in toast elements can have a binding/dynamic/reference value, which is done by simply putting a key surrounded with curly braces like,{myProgress}. Then, you can set a new value formyProgresskey before showing toast withshow(), and withupdate()to update toast in-place without showing a new toast.Import from JSONNotification elements and their properties can be imported with dictionaries (JSON-accepted types) withToast.from_json(), so you can add more than one element by calling a single method. See example JSON configurationshere.NotesAs you can see from screenshot, it is not possible to change "Python" title in normal ways, since Windows requires a "source application" to show notifications from. However,Toast collectionsallows to override app icon, but I'm not sure how I can implement this (or even, is it possible for a non-UWP app?), so still working on it.
Package: toastedmarshmallow-enum
Inspired byMarshmallow Enumand by ORM libraries.The Toasted Marshmallow Enum package makes it possible to dump and load Enum values based on the ultra fast
serialization thatToasted Marshmallowprovides.Installing toastedmarshmallow-enumpipinstalltoastedmarshmallow-enumUsing Toasted Marshmallow EnumUsing Toasted Marshmallow Enum in an existingMarshmallow Schemais as easy as defining the enum class attribute
as anEnumField. For example:fromenumimportEnumimporttoastedmarshmallowfrommarshmallowimportfields,Schemafromtoastedmarshmallow_enumimportEnumFieldclassLevel(Enum):LOW='0'MEDIUM='1'HIGH='2'classUserSchema(Schema):classMeta:jit=toastedmarshmallow.Jitname=fields.String()level=EnumField(Level)How it worksDump methods:classUser:def__init(name,level):self.name=nameself.level=levelschema=DummyUserSchema()user=User(name='John Doe',level=Level.HIGH)print(schema.dump(user).data)# {'name': 'John Doe', 'level': 2}print(schema.load({'name':'John Doe','level: 2}).data)# {'name': 'John Doe', 'level': 'HIGH'}
Package: toastedmarshmallow-models
Inspired byMarshmallow Modelsand by ORM libraries.The Toasted Marshmallow Models package makes it easy to define serializable classes based on the ultra fast
serialization thatToasted Marshmallowprovides.Installing toastedmarshmallow-modelspipinstalltoastedmarshmallow-modelsUsing Toasted Marshmallow ModelsUsing Toasted Marshmallow Models in an existing class requires your class to inherit
theModelclass and specify the relevantMarshmallow fields. For example:frommarshmallowimportfieldsfromtoastedmarshmallow_modelsimportModelclassEntity(Model):# Inherit Model# Define Marshmallow fieldsid=fields.Integer()name=fields.String()def__init__(self,id,name):self.id=idself.name=nameHow it worksThe Toasted Marshmallow Models package makes it easy todumpandloadmodels.Dump methods:entity=Entity(id=1,name='John Doe')print(entity.to_dict())# {"id": 1, "name": "John"}print(entity.to_json())# '{"id": 1, "name": "John"}'Load Methods:entity=Entity.from_dict({"id":1,"name":"John"})# creates an Entity instanceentity=Entity.from_json('{"id": 1, "name": "John"}')# creates an Entity instanceFeaturesValidation:entity=Entity(id='i-am-not-a-valid-int',name='John Doe')entity.validate()# throws marshmallow.ValidationError if not validGet validation errors:entity=Entity(id='i-am-not-a-valid-int',name='John Doe')entity.get_validation_errors()# returns dict(id=['Not a valid integer.'])Nested Models:classChildEntity(Model):name=fields.String()def__init__(self,name:str):self.name=nameclassParentEntity(Model):name=fields.String()# Use NestedModel to define parent-child relationshipschildren=fields.Nested(NestedModel(ChildEntity),many=True)def__init__(self,name:str,children:List[ChildEntity]):self.children=childrenself.name=nameSelf Referencing Model:classEmployee(Model):name=fields.String()# Use SelfReferencingModel to define self-referencing relationshipssubordinates=fields.Nested(SelfReferencingModel('Employee'),many=True,allow_none=True)def__init__(self,name:str,subordinates:List['Employee']=None):self.subordinates=subordinatesself.name=name
Package: toastify
This module is very lightweight and only takes about 50kb after installation.#+++++++++------EXAMPLE------++++++++++#fromtoastifyimportnotify# CALLING WITH ALL PARAMETERS :-notify(BodyText='BodyText is Required !',AppName='AppName (Optional)',AppPath='AppPath (Optional)',TitleText='TitleText (Optional)',ImagePath='optional_image_path(jpg/png/bmp/ico)')OR IF YOU ONLY WANT BODY TEXT THEN YOU CAN JUST DO :-notify('My Body Text')#+++++++++-------NOTES-------++++++++++#NOTIFICATION TIME IS SYSTEM DEAFULT (5sec)The function is blocking by default but you can use threading/asyncioStartMenu Shortcut will be created for your app. (this is a requirement set by microsoft for Toast Notifications)BodyText argument is required !! , rest of the arguments are optional.App Icon Is Set Automatically, it is same as icon of the application that the AppName shortucut in start menu is pointing.