Saravanan666/pypi-org huggingface dataset:
Package: shimmers
shimmerssimple client to upload files to / from an office 365 sharepoint site
usingwaddleto provide credential management.
named after the group of hummingbirds.pax avium.based onoffice365-rest-python-clientquick startpipinstallshimmersusageuploading a filefromwaddleimportload_configfromshimmersimportSharepointfromioimportBytesIOconf=load_config('path/to/conf.yml')sharepoint=Sharepoint(conf=conf,site_name='my_site')buff=BytesIO()buff.write('hello, shimmers!\n'.encode('utf-8'))sharepoint.upload(buff,'Documents/hello_shimmers.txt')downloading a filefromwaddleimportload_configfromshimmersimportSharepointconf=load_config('path/to/conf.yml')sharepoint=Sharepoint(conf=conf,site_name='my_site')buff=sharepoint.download('Documents/hello_shimmers.txt')st=buff.getvalue().decode('utf-8')print(st)uploading a set of dataframes as a single excel spreadsheetfromwaddleimportload_configfrompandasimportDataFramefromshimmersimportSharepointconf=load_config('path/to/conf.yml')sharepoint=Sharepoint(conf=conf,site_name='my_site')df1=DataFrame([dict(pet='sesame',type='cat'),dict(pet='peanut',type='dog')])df2=DataFrame([dict(pet='cody',owner='will'),dict(pet='kho',owner='boris')])sharepoint.upload_dataframes('Documents/pets.xlsx',df1,'names',df2,'owners')downloading an excel spreadsheet as a dataframefromwaddleimportload_configfrompandasimportDataFramefromshimmersimportSharepointconf=load_config('path/to/conf.yml')sharepoint=Sharepoint(conf=conf,site_name='my_site')df=sharepoint.download_dataframe('Documents/pets.xlsx')df1=df[df.pet=='sesame']print(len(df1.index))### downloading an excel spreadsheet as a dataframe, using a particular sheet```pythonfromwaddleimportload_configfrompandasimportDataFramefromshimmersimportSharepointconf=load_config('path/to/conf.yml')sharepoint=Sharepoint(conf=conf,site_name='my_site')df=sharepoint.download_dataframe('Documents/pets.xlsx',sheet_name='nicknames')df1=df[df.nickname=='meowcat']print(len(df1.index))
Package: shimport
shimportImport utilities for pythonOverviewInstallationUsageSimple lazy modulesFiltering module contentsFiltering for Public FunctionsFiltering using Chaining / Fluid StyleFiltering for ClassesFiltering for DataAutomatically importing submodulesOverviewImport utilities for pythonInstallationSeepypifor available releases.pip install shimportUsageSimple lazy modules>>>importshimport>>>pathlib=shimport.lazy('pathlib')>>>print(pathlib.Path('.').absolute)<boundmethodPath.absoluteofPosixPath('.')>>>>Filtering module contentsFiltering for Public FunctionsSuppose you want to retrieve just the function-definitions from a module namespace.Usingshimport.wrapperlet's you slice and dice:>>>importshimport>>>wrapper=shimport.wrapper("os.path")>>>wrapper=wrapper.prune(only_functions=True)>>>print(wrapper.namespace.keys())dict_keys(['abspath','basename','commonpath','commonprefix','dirname','exists','expanduser','expandvars','getatime','getctime','getmtime','getsize','isabs','isdir','isfile','islink','ismount','join','lexists','normcase','normpath','realpath','relpath','samefile','sameopenfile','samestat','split','splitdrive','splitext'])>>>Filtering using Chaining / Fluid StyleSome use-cases forshimportinvolve scenarios that aren't great with declarative-style development.So, there's good support forchaining (aka fluent)programming style as you can see below.  (Note that indention here follows fluent-style that shed/black should support)>>>importshimport>>>(...shimport....wrapper('os.path')....prune(only_data=True)....prune(val_predicate=lambdav:v=='/')....namespace.keys()...)dict_keys(['sep'])>>>>>>importtyping,shimport>>>(...shimport....wrapper("os.path")....prune(...exclude_private=True,...filter_module_origin=True)...)<ModulesWrapper[os.path]>>>>Filtering for ClassesGrab only the classes from the given namespace:>>>importshimport>>>namespace=shimport.wrapper('pathlib').filter(only_classes=True)>>>assert'Path'innamespace# Grab only subclasses of a given class from the given namespace#>>> plugins = shimport.wrapper('my_app.plugins').prune(...)#>>>Filtering for DataGrab only the classes from the given namespace:>>>importshimport>>>namespace=shimport.wrapper('os.path').filter(only_data=True)>>>assert'sep'innamespace>>>Automatically importing submodules
Package: shimpy
ShimpySample CLI Tool to interact with Shimmer Network usingiota-sdk.InstallUsing pippip install shimpyUsing poetrypoetry add shimpyUsageshimpy --helpCommandsinfo : Get Info about Nodeaccountnew: Generate new accountbalance: Get balance by account aliaslist: List all accountsaddressnew: Generate new address for accountlist: List all addresses in the accountnftmint: Mint new NFTsend: Send NFT to an addresssend: Send SMR to an addressContributions WelcomeUnder Development
Package: shimtax
Yes, this is a joke.  But I think it works?*rofl*A joke…  that works…Projects likeCoconutandcursed-foroffer new syntax on top of the standard Python syntax.
They achieve this by preprocessing code of their customized form down to standard Python.
One option for this preprocessing is to leverage thesource code encodingfeatures of Python.
For example, specifying# coding: coconut, after some other setup, can enable Coconut syntax for that file.
But, what if you need multiple of these syntax modifying encodings?
That’s where shimtax comes in and let’s you apply multiple other encodings.# coding: shimtax:cursed-for:coconutfor(i=0;i<10;i+=2):i|>printGiven that each encoding is offering custom syntax that the others are presumably unaware of, expect many combinations to be order dependent or to simply not work.
Those that simply operate on the code as a string are much more likely to be mixable.
Those that parse the code via a Python syntax parser are likely to fail.Setupshimtax can be set to be automatically enabled using the CLI.
This will insert a.pthfile in the platlib directory returned bysysconfig.get_path("platlib").
That file will register the shimtax encoding so you don’t have to.$.venv/bin/shimtaxregisterThe CLI can also remove the.pthfile.$.venv/bin/shimtaxunregisterIf handling this yourself is preferred, you can use the registration helper function.importshimtaxshimtax.register()
Package: shinami-python-sdk
Shinami Python SDKThis package is an asynchronous API wrapper for theShinami In-App Wallet (IAW) API.Currently, this SDK supports the IAW methods below.shinami_key_createSessionshinami_wal_createWalletshinami_wal_executeGaslessTransactionBlockshinami_wal_getWalletHow to Installpip install shinami-python-sdkHow to Useimport asyncio
import os

from shinami_python_sdk.iaw import ShinamiIawClient

# Set Shinami API key.
SHINAMI_IAW_API_TOKEN = os.environ["SHINAMI_IAW_API_KEY"]

# Initialize IAW API client.
shinami_iaw_client = ShinamiIawClient(SHINAMI_IAW_API_KEY)

# Create a session_token.
session_token = asyncio.run(shinami_iaw_client.create_session("NOT_A_SECURE_SECRET"))

# Create a wallet.
wallet = asyncio.run(shinami_iaw_client.create_wallet("walletid123", session_token))
Package: shindan-cli
shindan-cliShindanMaker (https://shindanmaker.com) CLIInstallpipinstallshindan-cliUsageCLI$ shindan -h
usage: shindan [-h] [-w] [-H] [-l] [-V] ID NAME

ShindanMaker (https://shindanmaker.com) CLI

positional arguments:
  ID             shindan page id
  NAME           shindan name

optional arguments:
  -h, --help     show this help message and exit
  -w, --wait     insert random wait
  -H, --hashtag  add hashtag `#shindanmaker`
  -l, --link     add link to last of output
  -V, --version  show program's version number and exit

$ shindan 1036646 hoge
ねこって、むしだ。

𝙐𝙉𝙄𝙌𝙇𝙊

$ shindan 1036646 huga -l
ねこって、むしだ。

𝙉𝙄𝙎𝙎𝙄𝙉
https://shindanmaker.com/1036646

$ shindan 1036646 huga -l -H
ねこって、むしだ。

𝙁𝙐𝙅𝙄𝙏𝙎𝙐
#shindanmaker
https://shindanmaker.com/1036646Libraryfromshindan_cliimportshindan# type: (int, str, optional[bool]) -> ShindanResultsshindan(1036646,'hoge',wait=False)Returns:{'results':['ねこって、むしだ。','','𝙏𝙊𝙆𝙔𝙊 𝙈𝙀𝙏𝙍𝙊'],'hashtags':['#shindanmaker'],'shindan_url':'https://shindanmaker.com/1036646'}LicenseMITSimilar ImprementationsC#misodengaku/ShindanMakerLibraryGokakakaya/goshindanLibrary + CLIhttps://pkg.go.dev/github.com/kakakaya/goshindanJavashibafu528/shindan4jLibraryhttps://jitpack.io/#shibafu528/shindan4jJavaScriptasawo/shindan-scraperLibrarystawberri/shindanLibrary (Archived)https://www.npmjs.com/package/shindanPerlLikk/WebService-ShindanMakerLibraryPHPmoroya/php-shindanmakerLibraryhttps://packagist.org/packages/moroya/php-shindanmakerPythonLe96/auto_shindanmakerBot Servertanitanin/shindan-pythonCLI (Script)Rubyosak/shindanmakerMikutterPlugingouf/shindanLibraryikaruga777/shindan-cliCLIyasuhito/shindanCLIhttps://rubygems.org/gems/shindanTypeScriptdqn/shindanmaker-jsLibrary
Package: shinden
shinden.pybs4 based web scrapping api for shinden.plRequired libraries:BeautifulSoup4requestspytest (for testing)UsageInstallationUsing pip:pip install shindenResultResults are returned usingResultobject with following attributes:title: the title of the series,tags: list of tags,ratings: dict of specific ratings,type: type of series,episodes: number of episodes,status: status of series (eg. ongoing, finished)top_score: overall scoreurl: url to the shinden.pl page of the seriescover_url: url to the cover imagesearch_url: url from which the result was extractedExampleImporting with import aliasimportshindenasshGenerating a list ofResultobjects based on first page of shinden search engineanime_list=sh.search_titles('alchemist')#    [a list of Result objects]Getting the title of the first resultanime_list[0].title#    "Fullmetal Alchemist"
Package: shineflow
Feed Forward Neural Networks using NumPyThis library is a modification of my previous one.Click Hereto check my previous library.Installation$[sudo]pip3installshineflowDevelopment Installation$gitclonehttps://github.com/Subhash3/Neural_Net_Using_NumPy.gitUsage>>>fromshineflowimportNeuralNetworkCreating a Neural Networkinputs=2outputs=1network=NeuralNetwork(inputs,outputs)# Add 2 hidden layers with 16 neurons each and activation function 'tanh'network.addLayer(16,activation_function="tanh")network.addLayer(16,activation_function="tanh")# Finish the neural network by adding the output layer with sigmoid activation function.network.compile(activation_function="sigmoid")Building a datasetThe package contains a Dataset class to create a dataset.>>>fromshineflowimportDatasetMake sure you have inputs and target values in seperate files in csv format.input_file="inputs.csv"target_file="targets.csv"# Create a dataset object with the same inputs and outputs defined for the network.datasetCreator=Dataset(inputs,outputs)datasetCreator.makeDataset(input_file,target_file)data,size=datasetCreator.getRawData()If you want to manually make a dataset, follow these rules:Dataset must be a list of data samples.A data sample is a list containing inputs and target values.Input and target values are column vector of size (inputs x 1) and (outputs x 1) respectively.For eg, a typical XOR data set looks something like :>>>XOR_data=[[np.array([[0],[0]]),np.array([[0]])],[np.array([[0],[1]]),np.array([[1]])],[np.array([[1],[0]]),np.array([[1]])],[np.array([[1],[1]]),np.array([[0]])]]>>>size=4Training The networkThe library provides aTrainfunction which accepts the dataset, dataset size, and two optional parameters epochs, and logging.defTrain(dataset,size,epochs=5000,logging=True):........For Eg: If you want to train your network for 1000 epochs.>>>network.Train(data,size,epochs=1000)Notice that I didn't change the value of log_outputs as I want the output to printed for each epoch.DebuggingPlot a nice epoch vs error graph>>>network.epoch_vs_error()Know how well the model performed.>>>network.evaluate()To take a look at all the layers' info>>>network.display()Sometimes, learning rate might have to be altered for better convergence.>>>network.setLearningRate(0.1)Exporting ModelYou can export a trained model to a json file which can be loaded and used for predictions in the future.filename="model.json"network.export_model(filename)Load ModelTo load a model from an exported model (json) file.
load_model is a static function, so you must not call this on a NeuralNetwork object!.filename="model.json"network=NeuralNetwork.load_model(filename)Todo- [ ] Data scaling
    - [x] Min Max scaler
    - [ ] Data Standardization
- [ ] Implement Cross Entropy Loss
- [ ] Linear and Relu activation function
- [ ] Ability to perform regression
- [ ] Convolution Nets
- [ ] Recurrent Nets
Package: shine-package
SHINE是基于密度泛函理论和非平衡格林函数方法的量子力学程序，采用实空间投影缀加平面波方法描述离子原子核和价电子的原子轨道基。对于栅压的输运体系，在实空间求解恰当边界条件的Poisson方程。通过围道积分格林函数和k点实空间上的并行化，使得代码具有非常准确的计算以及很高的计算效率。适合解决纳米材料的各类性质。
Package: shinigami
Shinigami was created to be simplistic and maintainableShinigami is an open source Python library allowing the user to generate and build Dockerfiles during runtime.UsageYou can install Shinigami via pip:pipinstallshinigamiQuick ExamplefromshinigamiimportShinigamidefcreate_file():Shinigami(lang_os="python",version="3.9",build=True,verbose=True,color=True).generate_dockerfile()if__name__=='__main__':create_file()If you just want to generate the Dockerfile without building the container, you can do that too. Just remove thebuildboolean from the class and you should see a Dockerfile populate in your current directory within seconds.
Package: shinkei
shinkeiA client library forsingyeong, a fully-dynamic, metadata-oriented service mesh.For more info on how to configure and run a singyeong server visit the official repository previously linked.The binaries for it are available ondocker.CreditsSpecial thanks toamy (queer)for creating and helping me understand singyeong and toRapptz (Danny)for the amazing docs addon and overall heavily inspiring this lib's design
throughdiscord.py.InstallationThe library is available on PyPi so it can be installed through pip:pipinstallshinkei-URequirementsPython 3.5.3+websockets6.0aiohttp3.3.0+These should already be handled bypip.Optionalujson(For faster json encoding/decoding)pip install shinkei[ujson] -UDocumentationThe documentation is available atread the docs.
Package: shinnosuke-gpu
#Shinnosuke-GPU : Deep learning framework
##DescriptionsBased on Cupy(GPU version)Completely realized by Python onlyKeras-like APIFor deep learning studying##FeaturesNative to PythonKeras-like APIEasy to get startCommonly used models are provided: Dense, Conv2D, MaxPooling2D, LSTM, SimpleRNN, etcSeveral basic networks ExamplesSequential model and Functional model are implementedAutograd is supported##Installation
Using pip:$ pip install shinnosuke-gpu##SupportsTwo model types:1.Sequentialfromshinnosuke.modelsimportSequentialfromshinnosuke.layers.FCimportDensem=Sequential()m.add(Dense(500,activation='relu',n_in=784))m.add(Dense(10,activation='softmax'))m.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1)m.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.)2.Modelfromshinnosuke.modelsimportModelfromshinnosuke.layers.FCimportDensefromshinnosuke.layers.BaseimportInputX_input=Input(shape=(None,784))X=Dense(500,activation='relu')(X_input)X=Dense(10,activation='softmax')(X)model=Model(inputs=X_input,outputs=X)model.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1)model.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.)Two basic class:- Layer:DenseConv2DMaxPooling2DMeanPooling2DActivationInputDropoutBatchNormalizationTimeDistributedSimpleRNNLSTMGRU (waiting for implemented)ZeroPadding2DOperations( includes Add, Minus, Multiply, Matmul, and so on basic operations for Layer and Node)####- Node:VariableConstant###OptimizersStochasticGradientDescentMomentumRMSpropAdaGradAdaDeltaAdamWaiting for implemented more###ObjectivesMeanSquaredErrorMeanAbsoluteErrorBinaryCrossEntropySparseCategoricalCrossEntropyCategoricalCrossEntropy###ActivationsReluLinearSigmoidTanhSoftmax###InitializationsZerosOnesUniformLecunUniformGlorotUniformHeUniformNormalLecunNormalGlorotNormalHeNormalOrthogonal###Regularizes
waiting for implement.###Utilsget_batches (generate mini-batch)to_categorical (convert inputs to one-hot vector/matrix)concatenate (concatenate Nodes that have the same shape in specify axis)pad_sequences (pad sequences to the same length)
Package: shino
shino简介shino 是一个免费开源的无代码化分布式爬虫框架，项目使用到 mysql、rabbitmq、redis 分别作为数据存储、消息队列和缓存。利用 grpc 技术作解耦各个模块，实现多机灵活部署。灵活组合的拓展需求。功能块种子生成器下载器解析器清洗器存储器准备mysql安装并开启rabbitmq安装并开启redis安装并开启备注：请确保你所使用的环境和resouces中的配置一致安装使用版本要求python >= 3.7.0获取项目代码gitclonehttps://github.com/hacksman/shino安装依赖pipinstalrequirements.txt运行服务# 开启服务 / debug 表示在 debug 配置环境下运行shstart.shdebug# 关闭服务shstop.sh# 重启服务shrestart.shdebug原理说明shino 原理说明使用教程shino 教程 - 知乎热榜数据抓取TODO-LISTseed modifier 中间件模式改写run.sh 运行启动脚本支持 start、restart、stop命令行模式支持更加丰富的功能debug 模式seed 运行时间修改为 10s 执行一次debug 以上级别日志输出install 脚本编写，实现 start/restart/stop 功能各个地方的参数配置化各模块功能及原理说明文档是什么原理使用说明使用说明基本 demo链式调用 demo动态 url demo解析 xpath demo解析内嵌结构 demo去掉冗余的日志支持多 headers 切换形式数据支持 update 操作支持仅本地抓取完善统计功能后端接口暴露前端界面License
Package: shinobi-client
Shinobi Python ClientA Python client for controllingShinobi(an open-source video management
solution).AboutThis package contains an (very incomplete) set of tools for interacting with Shinobi using Python.This library tries to use the (rather unique)documented APIbut it also uses
undocumented endpoints (which may not be stable).InstallationInstall fromPyPi:pipinstallshinobi-clientInstall with ability to start a Shinobi installation:pipinstallshinobi-client[shinobi-controller]Install with CLI:pipinstallshinobi-client[cli]UsageWarning: methods are generally not thread safe.PythonStart with creating the client for a particular Shinobi installation:fromshinobi_clientimportShinobiClientshinobi_client=ShinobiClient(host,port,super_user_token=super_user_token)(super_user_tokenis optional and only required for some operations.)Useruser=shinobi_client.user.get(email)# Get user details using the user's password (does not require super user token)user=shinobi_client.user.create(email,password)users=shinobi_client.user.get_all()user=shinobi_client.user.create(email,password)modified=shinobi_client.user.modify(email,password=new_password)deleted=shinobi_client.user.delete(email)API Keyapi_key=shinobi_client.api_key.get(email,password)Monitor (Camera Setup)# Setting monitors (camera setups) for the user with the given email addressmonitor_orm=shinobi_client.monitor(email,password)monitors=monitor_orm.get_all()monitor=monitor_orm.get(monitor_id)monitor=monitor_orm.create(monitor_id,configuration)modified=monitor_orm.modify(monitor_id,configuration)deleted=monitor_orm.delete(monitor_id)Shinobi ControllerStarts/Stops a temporarycontainerised installation of Shinboi. Written
for the purpose of testing but it is also installable as an extra. Requires Docker.fromshinobi_clientimportstart_shinobiwithstart_shinobi()asshinobi_client:print(shinobi_client.url)# Do things with a temporary Shinobi installationorfromshinobi_clientimportShinobiControllercontroller=ShinobiController()shinobi_client=controller.start()print(shinobi_client.url)# Do things with a temporary Shinobi installationcontroller.stop()CLIA basic auto-generated CLI is available if the package is installed with thecliextra:PYTHONPATH=.pythonshinobi_client/user.py\--host=HOST--port=PORT--super_user_token=SUPER_USER_TOKEN\getuser@example.come.g.$PYTHONPATH=.pythonshinobi_client/cli.py\--host='0.0.0.0'--port=50694--super_user_token='26dd3352-73c4-4bbd-8b09-17f2aacbd7b9'\create'user@example.com''password123'DevelopmentInstall with dev-dependencies:poetryinstall--no-root--extras"shinobi-controller cli"Run tests with:python-munittestdiscover-v-sshinobi/testsLegalGPL v3.0. Copyright 2020 Colin Nolan.I am not affiliated to the development of Shinobi project in any way.This work is in no way related to the company that I work for.
Package: shinto
shinto======-----.. contents:: **Table of Contents**:backlinks: noneInstallation------------shinto is distributed on `PyPI <https://pypi.org>`_ as a universalwheel and is available on Linux/macOS and Windows and supportsPython 2.7/3.5+ and PyPy... code-block:: bash$ pip install shintoLicense-------shinto is distributed under the terms of the`MIT License <https://choosealicense.com/licenses/mit>`_.
Package: shiny
Shiny for PythonShiny for Python is the best way to build fast, beautiful web applications in Python. You can build quickly with Shiny and create simple interactive visualizations and prototype applications in an afternoon. But unlike other frameworks targeted at data scientists, Shiny does not limit your app's growth. Shiny remains extensible enough to power large, mission-critical applications.To learn more about Shiny see theShiny for Python website. If you're new to the framework we recommend these resources:HowShiny is differentfrom Dash and Streamlit.Howreactive programmingcan help you build better applications.How touse modulesto efficiently develop large applications.Hosting applications for free onshinyapps.io,Hugging Face, orShinylive.Join the conversationIf you have questions about Shiny for Python, or want to help us decide what to work on next,join us on Discord.Getting startedTo get started with shiny follow theinstallation instructionsor just install it from pip.pipinstallshinyTo install the latest development version:# First install htmltools, then shinypipinstallhttps://github.com/posit-dev/py-htmltools/tarball/main
pipinstallhttps://github.com/posit-dev/py-shiny/tarball/mainYou can create and run your first application withshiny create, the CLI will ask you which template you would like to use. You can either run the app with the Shiny extension, or callshiny run app.py --reload --launch-browser.DevelopmentAPI documentation for themainbranch of Shiny:https://posit-dev.github.io/py-shiny/api/If you want to do development on Shiny for Python:pipinstall-e".[dev,test]"Additionally, you can install pre-commit hooks which will automatically reformat and lint the code when you make a commit:pre-commitinstall# To disable:# pre-commit uninstall
Package: shinyecharts
py-shiny-echarts: ECharts for Shiny for PythonShiny for Pythonbindings forECharts JSNoteThis package is still in an early state.Installation# Stablepipinstallshinyecharts# Devpipinstallgit+https://github.com/eodaGmbH/py-shiny-echartsBasic usagefrompandasimportDataFrame# Must always be imported, otherwise App is not foundfromshiny.expressimportuifromshinyechartsimportChart,InitOptionsfromshinyecharts.optionsimportLinefromshinyecharts.rendererimportChartRendererinit_options=InitOptions(width=600,height=400,renderer="canvas")data=DataFrame([[0,1,2,3],[1,4,5,6],[2,-2,4,9]],columns=["a","b","c","d"],)lines=(Line(x="a",y="b",tooltip=dict(trigger="axis"),legend=dict()).add_series("c").add_series("d"))@ChartRendererdefrender_dataset():returnChart(init_options,data=data).set_option(lines)shinyrundocs/examples/getting_started/basic_usage.py--reload