Saravanan666/pypi-org huggingface dataset:
Package: simpute-py
What does it do?Have you ever had a time when your missing data was holding you back? Well then this package is for you!Our python package for simple data imputation will allow you to quickly and seamlessly impute any missing data (be numeric, categorical, date/time or boolean values) using any large datasets.All you have to do is follow these simple 4 steps:Import the package and the data you wish you imputeSelect the function and method for imputation (this will depend on the data type - read the usage section below for more details)Hit runSave your newly imputed datasetOur package will help simplify all your imputation needs so your data is ready when you need it!Contributors & MaintainersLisa SequeiraRenee KwonFujie SunKen WangInstallation$pipinstallsimpute_pyUsage and ExamplesWe have four main functions dealing with each data type:Num_imputer: This function fills in the empty values of a numeric column with values derived from your selected imputation method. Your options for method includeknn(autogenerated values based on KNN),mean,medianandmode.Cat_imputer: This function fills in the empty values of a categorical column with values derived based on most frequent (mode) category.Bol_imputer: This function fills in the empty values of a boolean column with values derived using most frequent (mode) boolean value.Date_imputer: This function fills in empty values of a date column with median point of the range of dates in that column.To get started first install our imputation functions:fromsimpute_py.bol_imputerimportbol_imputer#For imputing on boolean columnsfromsimpute_py.cat_imputerimportcat_imputer#For imputing on categorical columnsfromsimpute_py.date_imputerimportdate_imputer#For imputing on date columnsfromsimpute_py.num_imputerimportnum_imputer#For imputing on numerical columnsTo run to the function, simply enter the following:importpandasaspd#Load test data from home directorytest_df=pd.read_csv('tests/tesla_deaths_mini.csv')#Test functionstest_df=bol_imputer(test_df,"Driver")test_df=cat_imputer(test_df,"Country")test_df=date_imputer(test_df,"Date")test_df=num_imputer(test_df,"Deaths")print(test_df)Place in the Python EcosystemCurrently, there are many other ways you can impute a dataset, using various functions build within Python, but this packages it neatly into one place and simplifies the process. We do have other packages you can use such asAutoImputeandMIDASpy. However our package aims to provide functionality not provided in either package and for more general audience use.ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensesimpute_pywas created by Lisa Sequeira, Renee Kwon, Fujie Sun, and Ken Wang. It is licensed under the terms of the MIT license.Creditssimpute_pywas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: simpy
SimPy is a process-based discrete-event simulation framework based on standard
Python. Processes in SimPy are defined by Pythongeneratorfunctions and
can, for example, be used to model active components like customers, vehicles or
agents.  SimPy also provides various types of shared resources to model
limited capacity congestion points (like servers, checkout counters and
tunnels).Simulations can be performed “as fast as possible”, in real time (wall clock
time) or by manually stepping through the events.Though it is theoretically possible to do continuous simulations with SimPy, it
has no features that help you with that. Also, SimPy is not really required for
simulations with a fixed step size and where your processes don’t interact with
each other or with shared resources.Thedocumentationcontains atutorial,several guidesexplaining key
concepts, a number ofexamplesand theAPI reference.SimPy is released under the MIT License. Simulation model developers are
encouraged to share their SimPy modeling techniques with the SimPy community.
Please post a message to theSimPy mailing list.There is an introductory talk that explains SimPy’s concepts and provides some
examples:watch the videoorget the slides.A Simple ExampleOne of SimPy’s main goals is to be easy to use. Here is an example for a simple
SimPy simulation: aclockprocess that prints the current simulation time at
each step:>>>importsimpy>>>>>>defclock(env,name,tick):...whileTrue:...print(name,env.now)...yieldenv.timeout(tick)...>>>env=simpy.Environment()>>>env.process(clock(env,'fast',0.5))<Process(clock)objectat0x...>>>>env.process(clock(env,'slow',1))<Process(clock)objectat0x...>>>>env.run(until=2)fast0slow0fast0.5slow1fast1.0fast1.5InstallationSimPy requires Python >= 3.8, both CPython and PyPy3 are known to work.You can install SimPy easily viapip:$python-mpipinstallsimpyYou can also download and install SimPy manually:$cdwhere/you/put/simpy/$python-mbuild$python-mpipinstalldist/simpy-*.whlTo run SimPy’s test suite on your installation, execute:$python-mtoxGetting startedIf you’ve never used SimPy before, theSimPy tutorialis a good starting
point for you. You can also try out some of theExamplesshipped with
SimPy.Documentation and HelpYou can finda tutorial,examples,topical guidesand anAPI
reference, as well as some information aboutSimPy and its historyin
ouronline documentation. For more help, contact theSimPy mailing
list. SimPy users are pretty helpful. You can, of course, also dig through
thesource code.If you find any bugs, please post them on ourissue tracker.Enjoy simulation programming in SimPy!Ports and comparable librariesRe-implementations of SimPy and libraries similar to SimPy are available in the
following languages:C#:SimSharp(written by Andreas Beham)Julia:ConcurrentSimR:Simmer
Package: simpy-agents
simpy-agents extendsSimPyby
adding the ‘agent’ field to events, allowing the user to track which agent is
associated with each event.# Create an environment and schedule a Timeoutenv=Environment()env.timeout(10,agent='Test Agent')# The 'agent' field is passed to the event and stored in the queueenv._queue>>>[(10,1,0,<Timeout(10)objectat0x10f913bd0>,'Test Agent')]# The Environment class now has a 'scheduled_agents' propertyenv.scheduled_agents>>>['Test Agent']Installationsimpy-agents is installable via pip:pip installsimpy-agents.All new functionality is optional and the original SimPy API is maintained. It
is recommended to remove any other installations SimPy from the environment to
ensure the intended version is imported.
Package: simpyder
Simpyder - Simple Python SpiderSimpyder - 轻量级协程Python爬虫特点轻量级：下载便利，依赖较少，使用简单。协程：单线程，通过协程实现并发。可定制：简单配置，适应各种爬取场合。快速开始下载#使用pip3pip3installsimpyder--user# 更新包pip3installsimpyder--upgrade编码用户只需要定义三个函数，实现三个模块：链接获取我们需要一个定义一个异步生成器，用于产生链接。asyncdefgen_url():foreach_idinrange(100):yield"https://www.biliob.com/api/video/{}".format(each_id)链接解析我们需要定义一个解析链接的函数。其中第一个参数是Response对象，也就是上述函数对应URL的访问结果。该函数需要返回一个对象，作为处理结果。注意，与普通函数不同，这是一个协程函数。需要在前面加上async。代表该函数是异步的。asyncdefparse(response):returnresponse.xpath('//meta[@name="title"]/@content')[0]数据导出上面函数的处理结果将在这个函数中统一被导出。下列例子为直接在控制台中打印导出结果。保存需要IO操作，因此这个函数可能运行较慢，因此也需要是异步的。我们在前面添加async关键词asyncdefsave(item):print(item)然后将这些模块组成一个Spider首先导入爬虫对象:importAsynSpiderfromsimpyder.spiders你可以这样组装Spiderspider=AsyncSpider()spider.gen_url=gen_urlspider.parse=parsespider.save=save接着就可以开始爬虫任务s.run()你也可以通过构造函数进行一些配置spider=AsyncSpider(name="TEST")示例程序fromsimpyder.spidersimportAsynSpider# new一个异步爬虫s=AsynSpider()# 定义链接生成的生成器，这里是爬取800次百度首页的爬虫defg():count=0whilecount<800:count+=1yield"https://www.baidu.com"# 绑定生成器s.gen_url=g# 定义用于解析的异步函数，这里不进行任何操作，返回一段文本asyncdefp(res):return"parsed item"# 绑定解析器s.parse=p# 定义用于存储的异步函数，这里不进行任何操作，但是返回2，表示解析出2个对象asyncdefs(item):return2# 绑定存储器s.save=s# 运行s.run()理论速率运行上述代码，可以得到单进程、并发数：64、仅进行计数操作的下载速率：[2020-09-02 23:42:48,097][CRITICAL] @ Simpyder: user_agent: Simpyder ver.0.1.9
[2020-09-02 23:42:48,169][CRITICAL] @ Simpyder: concurrency: 64
[2020-09-02 23:42:48,244][CRITICAL] @ Simpyder: interval: 0
[2020-09-02 23:42:48,313][INFO] @ Simpyder: 已经爬取0个链接(0/min)，共产生0个对象(0/min) 
[2020-09-02 23:42:48,319][INFO] @ Simpyder: Start Crawler: 0
[2020-09-02 23:42:53,325][INFO] @ Simpyder: 已经爬取361个链接(4332/min)，共产生658个对象(7896/min) 
[2020-09-02 23:42:58,304][INFO] @ Simpyder: 已经爬取792个链接(5280/min)，共产生1540个对象(10266/min) 
[2020-09-02 23:43:03,304][INFO] @ Simpyder: 已经爬取1024个链接(4388/min)，共产生2048个对象(8777/min) 
[2020-09-02 23:43:05,007][CRITICAL] @ Simpyder: Simpyder任务执行完毕
[2020-09-02 23:43:05,008][CRITICAL] @ Simpyder: 累计消耗时间：0:00:16.695013
[2020-09-02 23:43:05,008][CRITICAL] @ Simpyder: 累计爬取链接：1024
[2020-09-02 23:43:05,009][CRITICAL] @ Simpyder: 累计生成对象：2048该项目由@Jannchie维护你可以通过邮箱jannchie@gmail.com进行联系
Package: simpy-helpers
Simpy HelpersThesimpy_helperspackage was written to make building simulations and collecting statistics about simulations using theSimpyframework simpler.simpy_helpersprovides 4 main classes:EntityResourceSourceStatsThese building blocks allow you to build complex simulations quickly, while hiding much of the necessary orchestration of simpy components hidden from the user.Entity, Resource and Source areabstract classes. Read the API documentation to learn which methods are required for building a simulation.Why Not Just Use Simpy Directly?Simpy is not that simple to learn and use...Simpy Helpers hides much of this complexity from end users, so they can focus on building simulations instead of orchestrating simpy.Simpy does not collect statistics for you...Simpy Helpers provides aStatsclass which collects relevant statistics about your simulation automatically e.g. utilization of resourcesAPI DocumentationSee theUsing_Simpy_Helpers_Package.ipynbnotebook for more detail, and a walkthrough of a simple M/M/K simulation using helper classes.For info on a simulation that involves a container, seeContainer_Resource_Example.ipynb
Package: simpymenu
Auto generate a text menu with box border from a list of items.Different box styles with option for no box.Auto generating numbers for list of itemsOptional header for menuOptional sub-header for menuOptional pairing of callables to menu itemsAuto formatting with the headers/ sub-headers/ menu itemsIf no optional callables are paired, it will return the number selectedThis version requires Python 3 or later.
Tested on macOS High Seirra, Ubuntu 16.04, Windows 10 Creator edition.Please refer to attached README.rst or Github page for usage example.
Package: simpyple
A bunch of utilities and convenience methods for [simpy](http://simpy.readthedocs.org/). The name “simpyple” stands for “simpy made simple” or something like that, you get the idea.Elements:
* ScheduleBuild status: [![Build Status](https://travis-ci.org/thinkingbox/simpyple.png?branch=master)](https://travis-ci.org/thinkingbox/simpyple)
Package: simpysql
SimpySql一个数据库orm, 目前支持mysql,mongodbmysql文档,mongo文档安装pip install simpysql初始化你需要在你的项目根目录下创建一个.env文件，内容如下:[default]#数据库配置名称(对应model.__database__)DB_TYPE=mysql#数据库类型 mysql 或者 mongoDB_HOST=127.0.0.1#数据库IPDB_PORT=3306#端口DB_NAME=test_db1#库名DB_USER=root#账号DB_PASSWORD=123456#密码DB_CHARSET=utf8mb4#数据库编码LOG_DIR=/home/logs/python/#开启日志， 日志路径: /home/logs/python/[test_db2]#其他的库DB_TYPE=mysqlDB_HOST=127.0.0.1DB_PORT=3306DB_NAME=test_db2DB_USER=rootDB_PASSWORD=123456DB_CHARSET=utf8mb4#LOG_DIR=/home/logs/python/                     #关闭日志创建表model创建数据库model 并继承simpysql.DBModel:#!/usr/bin/python# -*- coding: UTF-8 -*-fromsimpysql.DBModelimportDBModelclassModelDemo(DBModel):__basepath__='/home/project/'# .env 文件路径#__database__ = 'default'                   # 库选择， 没有该属性，则默认default库__tablename__='lh_test'# table name__create_time__='create_time'# 自动添加创建时间字段create_time(精确到秒)， 设置为None或者删除该属性，则不自动添加__update_time__='update_time'# 自动更新时间字段update_time(精确到秒)， 设置为None或者删除该属性，则不自动更新columns=[# table columns'id','name','token_name','status','create_time','update_time',]# 可以通过该方法设置自动添加时间字段的格式# def fresh_timestamp(self):#     return datetime.datetime.now().strftime("%Y%m%d")操作实例ModelDemo.where('id',4).select('id','name').take(5).get()其他欢迎志同道合的朋友一起参与本项目开发SQLServer、PostgreSQL开发, 联系方式:490573621@qq.com
Package: sim-racing-tools
sim-racing-toolsA collection of python tools to make sim-racing more fun. All a work in-progress based on things I've
been playing around with. Feel free to contribute with any ideas or suggestions and let me know if you find any
bugs.InstallationPre-requisitesA Windows computer (I plan to support Linux in future)TheGitversion control system needs to be installedpython3needs to be installedEitherContent ManagerorQuickBMSneeds to be downloaded and you will need to know the folder it is in. We will need to use one of these to unpack data.acd files.If using QuickBMS the.acd extractor pluginfor QuickBMS needs to be downloaded and placed inside the folder containingquickbms.exeA willingness to hack around with a bit of python programmingOptional first step:I would recommend creating apython virtual environmentto install the tools into. This will allow you to use the tools in an isolated environment without installing
anything system-wide.Creating a virtual environmentTo setup a virtual environment first decide where you would like this to live, change into that directory andopen a command-prompt within it. Inside the command prompt run the command:py -m venv sim-racing-tool-venvThat will have created a folder calledsim-racing-tool-venvinside the directory you are in.Entering the virtual environmentThis newly created folder contains a self-contained python environment that you can enter at anytime
by opening a command prompt inside thesim-racing-tool-venvfolder and running:.\Scripts\activate.batthis command prompt window will now be running in the virtual environment and anything you install will only
be installed into it - rather than on the whole systemExiting the virtual environmentYou can exit a virtual environment at any time by typingdeactivateinto the consoleDownloading and installing sim-racing-toolsTo download and install first make sure that you have entered the virtual environment (if you set one up) and
run the following command inside it:py -m pip install git+https://github.com/zephyrj/sim-racing-tools@mainThe tools will now be installed and available from a inside your virtual environment.Things you can doClone existing AC carTransfer an engine made in Automation into a car in ACCheck that an engine created in Automation matches some criteria
Package: simranjeet-co6-101703548
This is a simple package to use topsis(technique for Order of preference by Similarity to Ideal Solution)
approach to select best among many things based on different attributes, such as selecting the best
Machine Learning algorithms based on correlation,R-square,root mean square error and accuracy.
You can use this simple package to get ranks on basis of topsis approach, ypu simply need to pass
NumPy array after pre-processing your data along with weights between 0 and 1 and all weights should
sum to 1 to get correct results and you need to pass impacts as well in form of '+' or '-'
where '+' indicates that you need to maximise value of that particular column and '-' indicates that
you need to minimise value of that particular column.
After passing all these parameters you will get your rank row.
Package: simrank-cmp
SimRank-CMPThis is an implementation of SimRank for comparing small, undirected graphs. Notably, it can be used for augmenting pairwise similarities calculated using just the node labels alone, with structural similarity information from the edges.The algorithm used is exact and non-iterative, and is based on a matrix formulation of SimRank. The main complexity comes from calculating the full eigendecomposition of the adjacency matrices and then doing matrix multiplication. As such, it is not meant to be used on graphs much larger than a few thousand nodes. A derivation can be foundhere.For larger graphs and where 1) you don't need exact results, or 2) you don't need pairwise similarities, only similarities for specific node-pairs, there are alternative algorithms.Installation and UsageInstall withpip install simrank_cmp.Usage is straightforward:from simrank_cmp import compute_similarities

updated_similarities = compute_similarities(f_adj, g_adj, initial_similarities, decay=0.8)initial_similaritiesshould be the pairwise node similarities calculated using some other metric (such as Jaccard). It is important for there to be some signal here in this matrix; SimRank will propagate this information across the graphs.ExamplesInexamples/similarity_propagation.py, we visualize the propagation of similarity information across the graph from a single node (the center one in this picture). Darker means more similar.Inexamples/match_robustness.py, we are trying to match nodes from two identical graphs. Only 10% of the nodes are labeled, resulting in a ~10% baseline if you were to match nodes at random. The rest of the other nodes are indistinguishable.By propagating the similarity information from the 10% across the two graphs, we are able to achieve a perfect 100% match of all nodes. We then slowly remove edges from one of the graphs and see that, as expected, accuracy drops until we hit the baseline.Inexamples/symmetry.py, we use the algorithm to detect symmetries in the graph. Nodes that have the same color can be swapped without affecting the structure of the graph. This can be useful, for example, during constrained optimization, where symmetry points to redundant solutions that can be pruned.
Package: simrpc
SimRpcWhy SimRpcall of the popular rpc framework today, is fast and stable.but it is kind of not good enough for you to easily start.
you have to accept all the rpc's definition,learn their structure, and you have to adapt your code to them.so if you dont like them,and you havn't have other alternatives. may beSimRpcis suit for youIntructionSimRpc is an rpc tools for you to easily transfer your code to rpc service(both client-side and server-side),built on top of zeromq and msgpack.zeromqassures the message transfer extremely fast and stablemsgpackenables the message blazing smallUsageYour Original Codeclassa:defminus(self,x,y):print("x+y=",x-y)returnx-ydefadd(x,y):returnx+yClient Side1) start an client instancefromsimrpc.clientimportSimRpcClientrpc_client=SimRpcClient()or maybe you want to start a specific addressfromsimrpc.clientimportSimRpcClientrpc_client=SimRpcClient(server_address="tcp://localhost:6666")theserver_addressis a zeromq address.for more information you can refer tozeromq.2) add @rpc_client.task decorator to your code# response_only=True means you only want the resultclassa:@rpc_client.task(response_only=True)defminus(self,x,y):print("x+y=",x-y)returnx-y# func=True means this is a funcion,not a class service@rpc_client.task(response_only=True,func=True)defadd(x,y):returnx+y3) Use these functions as usuala().minus(4,5,rpc_client=True)you must point it out this a rpc client(as use rpc_client=True prameter), otherwise will use local codeServer Side1) start an server instancefromserverimportSimRpcServerserver=SimRpcServer()like client,you can also give specific addresses to server.parametervalueinstructiondevice_front_address"tcp://127.0.0.1:5559"address that receive message. used for client to connect.device_backend_address"tcp://127.0.0.1:5560"address that send message to service worker.worker_address"tcp://127.0.0.1:5560"address that worker connect to. usually the same with device_backend_address.for advanced infomation,you can refer to zeromqmore ways of address(like ipc) are supported, you can refer to zeromq2) register your service to serverinitial your class service as usualservice=a()server.register(service)or you let server to initial your service(just pass your init arguments to server.)settings={"a":{"kwargs":{"name":"test"}}}service=a()server.register(service)# or you can have this class init with server# server.register_with_init(cls_list=[a], settings=settings)3) start your rpc server brokerserver.start_broker()# if you also want to start worker in process ,you could use server.run(),this will start a few workers.# server.run()with this broker,you can easily expand your service from both client-side and server-side4) start more workers# the same as above but without server.start_broker()server.run()For details,you can refer to examplesDependenceZeromqMsgpackCodinglike this project, star itany suggestion is welcomethis project is under MIT lisensemore docs are writing.
Package: simsad-cjp
Modèle de projection du soutien à l'autonomie du QuébecCe package contient le modèle de projection du soutien à l'autonomie du Québec produit par laChaire de recherche Jacques-Parizeau en politiques économiques. Le simulateur est disponible surpip. Ladocumentationdu modèle devrait être consultée concernant l'installation et l'utilisation du modèle.
Package: simsalabim
SIMSALABIM: Simple Interface for MS ApplicationsInstallationWithpippip install simsalabimFrom sourcegit clone https://github.com/MatthewThe/simsalabim.git
cd simsalabim
pip install .Usageapl to mzML conversionThesimsalabim.convertmodule automatically detects the output format based on the file extension, e.g. to convert to mzML name the output filespectra.mzML.python -m simsalabim.split_apl <mq_andromeda_folder> --output_dir <output_dir>
python -m simsalabim.convert <apl_file> --output_fn <mzml_file>
Package: simsam
Simsam: Simplex Sampling MethodsThis small package implements methods for sampling from a unit simplex,
a problem that often crops up in a data analysis context.UsageThere is only a single sampling strategy that results in uniform samples
from the unit simplex:fromsimsamimportkraemer_sampling# Sample 1,000 points from the 10-dimensional unit simplex.dim=10N=1000samples=kraemer_sampling(dim,N)For comparison purposes, there is also a naive sampling procedure, which
doesnotresult in uniform samples.fromsimsamimportnaive_sampling# Sample 1,000 points from the 10-dimensional unit simplex. Notice that# the samples will be biased.dim=10N=1000samples=naive_sampling(dim,N)
Package: sim-sensors
sim_sensorsThis is an educational python package that holds classes intended to simulate sensors. The sensors are those you would find in a typical IoT lab or home project.The objects created can be placed in seperate threads and spun up to spit out values. You can use the created values to demonstrate data visualisation and analysis skills. Or store them in a database and practice sql operations.Please check the code for additional comments that describe how the various components work.ContributingWant to contribute? Check out ourcontributor guidelines!
Package: simserver
Index plain text documents and query the index for semantically related documents.Simserver uses transactions internally to provide a robust and scalable similarity server.InstallationSimserver builds on thegensimframework for
topic modelling.The simple way to installsimserveris with:sudo easy_install -U simserverOr, if you have instead downloaded and unzipped thesource tar.gzpackage,
you’ll need to run:python setup.py test
sudo python setup.py installThis version has been tested under Python 2.5 and 2.7, but should run on any 2.5 <= Python < 3.0.DocumentationSeehttp://radimrehurek.com/gensim/simserver.html. More coming soon.LicensingSimserver is released under theGNU Affero GPL license v3.This means you may use simserver freely in your application (even commercial application!),
but youmust then open-source your application as well, under an AGPL-compatible license.The AGPL license makes sure that this applies even when you make your application
available only remotely (such as through the web).TL;DR:simserver is open-source, but you have to contact me for any proprietary use.History0.1.4:performance improvements to shardingchange to threading model – removed restriction on per-thread session accessbug fix in index optmize()0.1.3:changed behaviour for very few training documents: instead of latent semantic analysis, use simpler log-entropy modelfixed bug with leaking SQLite file descriptorsCopyright (c) 2011 Radim Rehurek