Saravanan666/pypi-org huggingface dataset:
Package: sheng
The Sheng Programming Language结绳：在文字产生之前，古人们靠结绳记事、认事，此举起到了帮助人们记忆的作用。Sheng is a Chinese programming language named after 结绳 (Jie Sheng), which means tying knots in ropes. In ancient China, before the creation of characters and words, the people remembered and recognized things by tying knots in ropes, which helped them memorize.The philosophies of the Sheng grammar are interpretable, colloquial, and less punctuation marks. The compiler is implemented in Python with thePLY (Python Lex-Yacc)package which processes lexing and parsing in the phases of the compilation.Usagesheng [option] [file]Installing and RunningNote: Sheng requires Python 3.9 or laterPyPI [Recommended]Install the Sheng compiler fromThe Python Package Index (PyPI):Note: This method requires Internet access.pip install shengThen, execute commandsheng [option] [file]to run.Other install methodsBuild and installBuild the code and install the Sheng compiler from the local builds:Note: This method does not require Internet access.python3 -m pip install --upgrade pip
python3 -m pip install --upgrade build
python3 -m build
pip install ./dist/<.whl file> --force-reinstallThen, execute commandsheng [option] [file]to run.Run the compiler module via PythonRun the compiler directly without building and installing in advance:Note: This method requires Python 3.9 or later in your environment.python3 -m src [option] [file]Getting StartedThis is the Sheng code inexample/helloworld.zh:甲 赋值 "你好，世界！"
打印(甲)The first line is an assignment statement where甲is a variable name which is赋值(assign)ed a string value你好，世界！(Hello, World!) surrounded by"(double quotes). The second line is a function call statement which calls the built-in function打印(print) to output the value of the variable甲.This is the Python equivalent of the above Sheng code:x = "你好，世界！"
print(x)Compile the.zhfile viashengexecutable:sheng example/helloworld.zhYou should see the following output instdout:你好，世界！ContributingI am excited to work alongside you to build and enhance the Sheng Programming Language!BEFORE you start work on a feature/fix, please read and follow theContributor's Guideto help avoid any wasted or duplicate effort.Code of ConductThis project has adopted theContributor Covenant Code of Conduct. For more information contactluo@jiahai.cowith any additional questions or comments.
Package: shenjian
================================概述--------神箭手 Python SDK该版本的SDK依赖于第三方HTTP库 `requests <https://github.com/kennethreitz/requests>`。 请按照下述安装方法进行安装。运行环境-------------------Python 2，3安装----------通过pip安装：.. code-block:: bash$ pip install shenjian直接通过安装包安装：.. code-block:: bash$ sudo python setup.py install快速使用---------------.. code-block:: python# -*- coding: utf-8 -*-import shenjianuser_key = '你的user_key'user_secret = '你的user_secret'########shenjian.Serviceservice = shenjian.Service(user_key,user_secret)# 获得应用列表result = service.get_app_list(page=1, page_size=30)# 获得爬虫列表result = service.get_crawler_list(page=1, page_size=30)# 创建爬虫result = service.create_crawler(app_name="爬虫名字",code="爬虫代码",app_info='')########shenjian.Crawlercrawler = shenjian.Crawler(user_key,user_secret,appID)# 修改爬虫名称信息result = crawler.edit(app_name="新的名称",app_info="新的info")# 使用个人优质版代理IP，代理种类查看shenjian.proxy_typeresult = crawler.config_proxy(shenjian.proxy_type.PROXY_TYPE_BETTER)# 开启文件云托管result = crawler.config_host(shenjian.host_type.HOST_TYPE_SHENJIANSHOU)# 删除爬虫result = crawler.delete()# 设置爬虫自定义项(不同的爬虫自定义项不同，传递一个dict)result = crawler.config_custom({"img":True})#######启动爬虫########## 用2个节点启动爬虫result = crawler.start(2)#遇到爬取结果停止发现新链接，更新此数据dup_type='change',跳过继续往后爬dup_type='skip',默认为skipresult = crawler.start(dup_type='unspawn')#覆盖原爬取结果里的数据,默认是保留原数据，插入一条新版本数据change_type='insert'result = crawler.start(change_type='update')#定时启动爬虫，该例子为每天爬取一次，更多定时设置与参数详见文档http://docs.shenjian.io/develop/platform/restful/crawler.html#启动爬虫result = crawler.start(timer_type='daily',time_start='10:00',time_end='23:00')#######启动爬虫########## 停止爬虫result = crawler.stop()# 暂停爬虫result = crawler.pause()# 继续爬虫（并设置运行的节点是3个）result = crawler.resume(3)# 获取爬虫状态result = crawler.get_status()# 获取爬虫速率result = crawler.get_speed()# 增加一个运行节点result = crawler.add_node(1)# 减少一个运行节点result = crawler.reduce_node(1)# 获取爬虫对应的数据源信息result = crawler.get_source()# 获取爬虫的Webhook设置result = crawler.get_webhook()# 删除爬虫的Webhook设置result = crawler.delete_webhook()# 修改爬虫的Webhook设置(设置为新增数据发送webhook，更新数据不发送，自定义数据不发送)result = crawler.set_webhook(self,"http://www.baidu.com",data_new=True,data_updated=False,msg_custom=False)# 获取爬虫的自动发布状态result = crawler.get_publish_status()# 启动自动发布result = crawler.start_publish(publish_id)# 停止自动发布result = crawler.stop_publish()Platform: UNKNOWN
Package: shenko
ShenkoInstalling ShenkoWindows user may need to install ‘pip3’ packagage manager.Install the latest development build of Shenko using the following command:pip3 install shenkoWe are currently developing a snap package as well, should have better support in ver 0.2;snap install shenkoIf this command fails, please make sure your version of pip is up-to-date.
Come visit our website athttps://www.shenko.org/Free software: GNU General Public License v3Documentation:https://shenko.org/helpUsageWe are still in the early stages of development,
The best way to see our latest developments is to
visit www.shenko.orgFeaturesThis project is written entirely in python, and
uses the panda3d game engine.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-02-18)First release on PyPI.0.1.26 (2020-09-11)We moving everything towards python 3
Package: shennongname
shennongnameDocumentationThe documentation forshennongnameis available on the documentation website of the ShennongAlpha (ShennongDoc):English中文You can also contribute to the documentation on theShennongDocGitHub repository by submitting a pull request:English中文Systematic Nomenclature for Natural Medicinal Materials Algorithm (SNNMMA)The following code shows how to useshennongnamePython package to construct an NMM systematic name (NMMSN).fromshennongname.snnmma.algorithmimportconstruct_nmmsnfromshennongname.snnmma.modelimportNmmsnNameElement# Construct a Natural Medicinal Material Scientific Name (NMMSN)input=NmmsnNameElement.model_validate({'nmm_type':'processed','species_origins':[['Ephedra sinica','草麻黄'],'or',['Ephedra intermedia','中麻黄'],'or',['Ephedra equisetina','木贼麻黄']],'medicinal_parts':[['stem herbaceous','草质茎']],'special_descriptions':[],'processing_methods':[['segmented','段制'],'and',['aquafried honey','蜜炙制']]})construct_nmmsn(input).model_dump()The output is a Python dictionary:{'success':True,'error_msg':'Pipe: construct_nmmsn_spe_ori. Status: warning. Reason: Multiple species origins detected.','error_msg_en_zh':{'en':'Multiple species origins detected.','zh':'检测到多个物种基源。'},'nmmsn':{'nmmsn':'Ephedra equisetina vel intermedia vel sinica Stem-herbaceous Segmented and Aquafried-honey','nmmsn_zh':{'zh':'蜜炙制段制木贼麻黄或中麻黄或草麻黄草质茎','pinyin':'mì zhì zhì duàn zhì mù zéi má huáng huò zhōng má huáng huò cǎo má huáng cǎo zhì jīng'},'nmmsn_name_element':{'nmm_type':'processed','species_origins':[['Ephedra equisetina','木贼麻黄'],'or',['Ephedra intermedia','中麻黄'],'or',['Ephedra sinica','草麻黄']],'medicinal_parts':[['stem herbaceous','草质茎']],'special_descriptions':[],'processing_methods':[['segmented','段制'],'and',['aquafried honey','蜜炙制']]},'nmmsn_seq':[['Ephedra equisetina vel intermedia vel sinica','木贼麻黄或中麻黄或草麻黄'],['Stem-herbaceous','草质茎'],['',''],['Segmented and Aquafried-honey','蜜炙制段制']]}}Start ShennongName Flask ServerTheshennongnamepackage also provides a Flask server for constructing NMMSNs.1. Configure the.envfilecp.env.example.env2. Installshennongnamepackagepipinstall.3. Run the Flask server# Productiongunicorn-b0.0.0.0:5001shennongname.flask.run:app# Developmentpython3shennongname/flask/run.pyStart ShennongName Flask Server with DockerYou can also use Docker to run the Flask server.dockerbuild-tshennongname.
dockerrun-d-p5001:5001shennongname
Package: shepherd
## ShepherdShepherd provides a system for configuring and launching clusters (or flocks) of Docker containers.Feature include:YAML definition of each “flock” including environment, volumes, external networks.API and HTTP API for starting, stopping, pausing and resuming instances of each flock.User overrides, including environment variables and ‘descendant’ imagesMultiple pools for managing running flocks/Queuing with fixed pool size and queing (fixed size pool)Persistent pool for time-slicing flock execution.### Comparison to Docker ComposeThe flock format is inspired by compose and supports a limited subset of compose spec.
Unlike with Compose, which runs a single docker-compose.yml at a time,
the intent of Shepherd is to manage multiple instances of each available flock,
and to schedule their execution. Each instance is given a unique id which can be used to refer
to the instance.### Use Cases and Test SuiteThis library does not include any specific flocks and is designed to be as generic as possible.It only uses very small public Docker containers for the test suite, which can be run via:python setup.py testafter installing withpython setup.py installFor an example implementation that uses Shepherd and additional flock definitions, seehttps://github.com/oldweb-today/browser-shepherd### Other dependenciesBesides Docker, Shepherd relies on Redis to store internal state (though can be used with an in-memory ‘fake’ redis as well).
Package: sheraf
A versatile ZODB abstraction layersheraf is a wrapper library aroundZODBthat provides models management and indexation. It aims to make the use ofZODBsimple by providing ready-to-use tools and explicit tools. sheraf is currently compatible withZODB 5andpython 3.7+.You can expect sheraf to:Do few things, but do them right;Be simple enough so beginners can do a lot with a few lines;Be powerful enough and tunable for python experts;Have a simple and expressive code, that allows you to hack it if needed.Installationsheraf is compatible with Python 3.7+poetry add sheraf
# or
pip install sherafIf you need pytest fixtures for your project check outpytest-sheraf. There are alsosheraf fixtures for unittest.pip install pytest-sherafContributingBug reports and pull requests are highly encouraged!Test some code :poetry run pytestandpoetry run toxFormat code :blackGenerate documentation :poetry run tox -e docDocumentationYou can build it with the following commands, or read it onreadthedocs.poetry run tox -e doc
open build/sphinx/html/index.htmlDevelopment installationsheraf use poetry as its main build tool. Do not hesitate to checkthe documentation.poetry install --extras all
Package: sherali-adams
sherali_adamsA small library of functions with few dependencies to run k rounds of Sherali-Adams relaxation on a system of the form Ax <= b represented by raw numpy matrix A and array b.notesThe constraints 0 <= y <= 1 will be added for all variables in the system returned. Send me a note if you'd like for this to be optional.installpip install sherali_adamsTesting> nosetestsRan 5 tests in 0.423s

OKExamplesRun 1 round of SA on a system with 2 variables.A = np.matrix([1,1],[1,1])b = np.matrix([1,1])(AA,bb) = run_SA(1,2,A,b)Find the original monomial corresponding to new variables. Here we find it for y_3 in the result above after 1 round of SA on a system with 2 variables.(0 based indexing)monomial = invert(2,2,1)monomial == [0,1]Thus y_3 = y_{0,1} which delinearizes to x_0x_1Supports dynamic programming/memoized mode and brute force. To use memoized:run_SA(k = 2,n = 5,A = A,b = b,memoize = True)
Package: sherlibs
sherlibs LibraryCore library ofThe Ultroid, a python based telegram userbot.Installationpip3install-Upy-AyraDocumentationUsageCreate folders namedplugins,addons,assistantandresources.Add your plugins in thepluginsfolder and others accordingly.Create a.envfile with following mandatory Environment VariablesAPI_ID
API_HASH
SESSION
MONGO_URICheck.env.samplefor more details.Runpython3 -m Ayrato start the bot.Creating pluginsTo work everywhere@ultroid_cmd(pattern="start")asyncdef_(e):awaite.eor("Ayra Started!")To work only in groups@ultroid_cmd(pattern="start",groups_only=True,)asyncdef_(e):awaiteor(e,"Ayra Started.")Assistant Plugins 👇@asst_cmd("start")asyncdef_(e):awaite.reply("Ayra Started.")See more working plugins onthe offical repository!Made with 💕 byKynan.LicenseUltroid is licensed underGNU Affero General Public Licensev3 or later.CreditsLonamiforTelethon
Package: sherlockbikepy
sherlockbikepyThis Python library implements thesherlock.bikeAPI. It has been reverse
engineered from theAndroid app.InstallationpipinstallsherlockbikepyCode snippetfromsherlockbikepyimportSherlock# Loginsh=Sherlock('user@example.com','supaSecretPassw0rd')# Get devicessh.devices# > [<sherlockbikepy.device.SherlockDevice at 0x7fa840410400>]# Primary device (ie. the first one)sh.primary_device# > <sherlockbikepy.device.SherlockDevice at 0x7fa840421b38># Device statesh.primary_device.state# > 'ON'# Device location (only available if currently armed ie. "ON")sh.primary_device.location# >  Position(latitude=20.6297038,# >           longitude=2.8123654,# >           last_update=datetime.datetime(2018, 9, 22, 15, 55, 42))
Package: sherlock-lib
A fork of theSherlock ProjectList of social networksThis library was created to use the Sherlock Project in your own personal scripts.Installation#installingusingpip$pipinstallsherlock-libUsagedefsearch_target(username,timeout=60,sfw=False)Username: Target's name on social mediaTimeout: Maximum search time on each siteSafe for Work: Remove NSFW(Not Safe for Work) sitesThis function returns a dictionary with the name of the sites and the link to the target's profile on the sites.Examplefromsherlock_libimportsearch_targetusername="john_doe"result=search_target(username,sfw=True)fork,vinresult.items():print(f"{k}:{v}")ContributingWe would love to have you help us with the development of Sherlock. Each and every contribution is greatly valued!Here are some things we would appreciate your help on:Addition of new site support Â¹Bringing back site support ofsites that have been removedin the past due to false positives[1] Please look at the Wiki entry onadding new sitesto understand the issues.LicenseMIT Â© Sherlock ProjectOriginal Creator -Siddharth DushanthaLib Creator -Paulo Ricardo Mesquita
Package: sherlockml-boltzmannclean
Fill missing values in a pandas DataFrame using a Restricted Boltzmann Machine.Provides a class implementing the scikit-learn transformer interface for creating and training a Restricted Boltzmann Machine. This can then be sampled from to fill in missing values in training data or new data of the same format. Utility functions for applying the transformations to a pandas DataFrame are provided, with the option to treat columns as either continuous numerical or categorical features.Installationpipinstallsherlockml-boltzmanncleanUsageTo fill in missing values from a DataFrame with the minimum of fuss, a cleaning function is provided.importboltzmanncleanmy_clean_dataframe=boltzmannclean.clean(dataframe=my_dataframe,numerical_columns=['Height','Weight'],categorical_columns=['Colour','Shape'],tune_rbm=True# tune RBM hyperparameters for my data)To create and use the underlying scikit-learn transformer.my_rbm=boltzmannclean.RestrictedBoltzmannMachine(n_hidden=100,learn_rate=0.01,batchsize=10,dropout_fraction=0.5,max_epochs=1,adagrad=True)my_rbm.fit_transform(a_numpy_array)Here the default RBM hyperparameters are those listed above, and the numpy array operated on is expected to be composed entirely of numbers in the range [0,1] or np.nan/None. The hyperparameters are:n_hidden: the size of the hidden layerlearn_rate: learning rate for stochastic gradient descentbatchsize: batchsize for stochastic gradient descentdropout_fraction: fraction of hidden nodes to be dropped out on each backward pass during trainingmax_epochs: maximum number of passes over the training dataadagrad: whether to use the Adagrad update rules for stochastic gradient descentExampleimportboltzmanncleanimportnumpyasnpimportpandasaspdfromsklearnimportdatasetsiris=datasets.load_iris()df_iris=pd.DataFrame(iris.data,columns=iris.feature_names)df_iris['target']=pd.Series(iris.target,dtype=str)df_iris.head()_sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target05.13.51.40.2014.93.01.40.2024.73.21.30.2034.63.11.50.2045.03.61.40.20Add some noise:noise=[(0,1),(2,0),(0,4)]fornoisyinnoise:df_iris.iloc[noisy]=Nonedf_iris.head()_sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target05.1NaN1.40.2None14.93.01.40.202NaN3.21.30.2034.63.11.50.2045.03.61.40.20Clean the DataFrame:df_iris_cleaned=boltzmannclean.clean(dataframe=df_iris,numerical_columns=['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)'],categorical_columns=['target'],tune_rbm=True)df_iris_cleaned.round(1).head()_sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target05.13.31.40.2014.93.01.40.2026.33.21.30.2034.63.11.50.2045.03.61.40.20The larger and more correlated the dataset is, the better the imputed values will be.
Package: sherlockml-dataclean
Jupyter notebook extension and python library for interactive cleaning of pandas DataFrames with a selection of techniques, from simple replacements of missing values to imputation with a Restricted Boltzmann Machine.Installationpipinstallsherlockml-datacleanjupyternbextensionenabledataclean--py--sys-prefixUsageUse your Jupyter notebook as normal. When a pandas DataFrame is present in your python kernel you should see a new notification on the Data Cleaner icon in your toolbar. DataFrames with names beginning with an underscore will be ignored.Data Cleaner toolbar icon.Clicking on the icon will open a floating window containing a summary of the DataFrames in your kernel. Clicking on the name of one of these DataFrames will show some of the Data Cleaner controls and some summary statistics on the DataFrame columns.Data Cleaner window.Clicking on the name of one of these columns will show data cleaning tools specific to that column, with a histogram or bar chart showing the distribution of these values. As you create a step the effect that this will have on the data distribution is shown as a preview.Creating a data cleaning step on a column.You can also choose to fill in missing and mistyped values in your DataFrame with a Restricted Boltzmann Machine. This uses the sherlockml-boltzmannclean package.Creating a Restricted Boltzmann Machine cleaning step.Once you create your steps they are added to a processing pipeline which can be viewed in the “Pipeline” widget.A data cleaning pipeline.These steps can be modified or deleted using these controls, and when ready the pipeline can be executed on the dataframe or output to code. Executing your pipeline will create a new DataFrame with the suffix “_cleaned” in your kernel, while exporting will create a new code cell in your notebook defining a python function which will carry out the pipeline cleaning steps.An exported pipeline.CaveatsDuplicated or non string column names are not supported.For DataFrames over 1000 rows, a sample of 1000 rows will be used for previewing and creating your processing pipeline, with the whole DataFrame only operated on when the pipeline is executed.
Package: sherlock.py
Sherlock.pyis transpiler that translate python to shell script language.한국어로 보기ContentsMotivationInstallUsageLibraryTodoLicenseMotivation< Ditective who program with Shell script >Shell script is well known script language which is used in most of unix-like OS. Shell script lanuage garuntee to run in most unix-like OS. So many software developers are using this language when they program install script or configuration script. Because this language is really old, There are severalproblems. and it is hard to maintain program which is writted by Shell script language.Install$pipinstallsherlock.pySherlock.py supports python version over 2.6 or 3.3. and support linux and macOS. If there is error in install please write issue.Usageusage: sherlock [-h] [-o output] [-c] [-v] [--version] [file | command]

Python to bash trans-compiler.

positional arguments:
  [file | command]  program read from script file

optional arguments:
  -h, --help        show this help message and exit
  -o output         output file path
  -c, --command     program passed in as string
  -v, --verbose     program run in verbose mode
  --version         show program's version number and exitFollowing is basic usage.$sherlocktarget.pyUsing this command, target.py file is translated into shell sciprt. after, it is automatically run withsh. We can find out bug from the running result.$sherlocktarget.py-ooutput.shUsing-oflag, you can save sherlock result in file. In this case script isn’t run automatically.$sherlock-c"echo 'Hello World.'"Using-cflag, input command is immediately translated to shell script language and executed.If you want to details. Please checksample codesLibrarysherlock.cmd packageYou can use general shell command importing from sherlock.cmd. When you import package, it check user already installed command and create wrap function which return stdout output.fromsherlock.cmdimportlsls('-al')pipe functionIn shell script, you can combine command with|operator. To do same thing in python, Sherlock.py support pipe function.fromsherlock.cmdimportwcpipe(print('./test'),wc('-c'))Todoimport syntaxcommandpackagedocumentationsupport windows batch and powershell scriptmore test…LicenseMIT © 2017LuavisIcon designed bycyoh, Sherlock Holmes graphic by Matthew Davis from the Noun Project
Package: sherlogger
sherlogUsageDefault loggerBy default, logger will record everything in the directory
where you calling itfromsherloggerimportloggerlogger.info("Service is up...")To change logs directory call.set_streams()methodfromsherloggerimportloggerfromsherloggerimportFileSystemHandlerlogger.set_streams(# [<path_to_dirs>], <Handler>["~/home/ubuntu/..."],FileSystemHandler)logger.info("THIS IS LOG MESSAGE")Telegram loggerSetup.ini fileCreate plugins.ini file. Format of file and required
data is located in plugins.ini.exampleLogger sends your message to telegram in asynchronous manner.telegram_loggerwill send messages in separated thread.Custom loggerImportget_logger& handlersfrompathlibimportPathfromsherloggerimportget_logger,TelegramHandler,FileSystemHandlerABS_PATH=Path().resolve()logger=get_logger(__name__)logger.basic_config(handlers=[TelegramHandler,FileSystemHandler],plugins_ini_path=f"{ABS_PATH}/plugins.ini",level="INFO")logger.set_streams(["./logs/"],FileSystemHandler)logger.info("This is a message")
Package: sherlok
Sherlokis a flexible and powerful open source, distributed, real-time text-mining engine.pipinstall--upgradesherlokfromsherlokimportSherloks=Sherlok('neuroner')# returns a list of tuples (begin, end, text, annotation_type, attributes{})prints.annotate('layer 4 neuron')[(0,14,'layer 4 neuron',u'Neuron',{}),(8,14,'neuron',u'Neuron',{}),(8,14,'neuron',u'NeuronTrigger',{}),(0,7,'layer 4',u'Layer',{u'ontologyId':u'HBP_LAYER:0000004'})]# filtering and finding the text backtxt='parvalbumin-positive fast-spiking basket cells, somatostatin-positive regular-spiking bipolar and multipolar cells, and cholecystokinin-positive irregular-spiking bipolar and multipolar cells'morphology=s.annotate(txt,u'Morphology')for(start,end,text,_type,properties)inmorphology:printtext,properties[u'ontologyId']basketHBP_MORPHOLOGY:0000019bipolarHBP_MORPHOLOGY:0000006multipolarHBP_MORPHOLOGY:0000035bipolarHBP_MORPHOLOGY:0000006multipolarHBP_MORPHOLOGY:0000035
Package: sherok
SherokA new epic library for making python simpler.Here is the documentation -https://github.com/SreehariTheProgrammer/SherokDocumentation/blob/main/README.mdCHANGELOG0.0.1Features:First 16 Commands0.0.2Features:Updated Readme0.0.18Features:Updated CHANGELOG1.0Features:No Changes, Made the ReadMe Beautiful to see1.5Features:No Changes, Made the ReadMe Beautiful to see again bruh.1.8Features:Cannot do anything to the README. So, just making it like old.2.0Features:Trying to fix installation problems.