Saravanan666/pypi-org huggingface dataset:
Package: simx
SimX is a library for developing parallel, discrete-event simulations
in Python. SimX is written in C++ and Python.SimX provides the simulation developer with core functionality such as
- event queueing
- entity management
- domain parititioning
- synchronization and message passing.SimX also supports process oriented simulations. Parallel simulations are
implemented using distributed memory and message passing.SimX has been written to be flexible enough to suit a variety of
application domains that use discrete-event simulations for modeling,
and has been designed to allow applications to scale from single
processor workstations to high-performance clusters.The latest SimX sources can be downloaded from its github page athttps://github.com/sim-x/simxor checked out via anonymous git via:git clonehttps://github.com/sim-x/simx.gitSimX is free software; you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License v2.1 as published
by the Free Software Foundation.Copyright (C) 2012, 2013, 2014 Los Alamos National Security LLC. See
COPYRIGHT.txt for details.
Package: sinabs
Sinabs (Sinabs Is Not A Brain Simulator) is a python library for the development and implementation of Spiking Convolutional Neural Networks (SCNNs).
The library implements several layers that arespikingequivalents of CNN layers.
In addition it provides support to import CNN models implemented in torch conveniently to test theirspikingequivalent implementation.
This project is managed by SynSense (former aiCTX AG).InstallationFor the stable release on the main branch:pip install sinabsor (thanks to@Tobias-Fischer)conda install -c conda-forge sinabsFor the latest pre-release on the develop branch that passed the tests:pip install sinabs --preThe package has been tested on the following configurationsDocumentation and Exampleshttps://sinabs.readthedocs.io/Questions? Feedback?Please join us on the#sinabs Discord channel!LicenseSinabs is published under AGPL v3.0. See the LICENSE file for details.Contributing to SinabsCheckout thecontributingpage for more info.CitationIn case you find this software library useful for your work please consider citing it as follows:@software{sinabs,
author = {Sheik, Sadique and Lenz, Gregor  and Bauer, Felix and Kuepelioglu, Nogay },
doi = {10.5281/zenodo.8385545},
license = {AGPL-3.0},
title = {{SINABS: A simple Pytorch based SNN library specialised for Speck}},
url = {https://github.com/synsense/sinabs}
}
Package: sinabs-dynapcnn
sinabs-dynapcnnThissinabsplugin enables porting sinabs models to chips and dev-kits with DYNAP-CNN technology.DocumentationYou can find the latest documentation athttps://synsense.gitlab.io/sinabs-dynapcnn/Contact usIf you would like to report bugs or push any changes, you can do this on outgitlab repository.We encourage you to join ourSynSense Discord communityto get in touch with the maintainers and other users.
Package: sinacor-negs
sinacor_negs'sinacor_negs' é uma lib feita para facilitar a leitura de arquivos negs.txt da bolsa (B3/Sinacor). A lib foi feita utilizando pandas e retorna os dados em forma de DataFrames.Para mais informações sobre o formato do arquivo:https://clientes.b3.com.br/data/files/95/D2/6D/BC/0556F71034D833F7BFC9F9C2/Leiautes%20de%20Arquivos%20SINACOR%20-%20Ampliacao%20Codigo%20do%20Investidor_v2.pdfInstalaçãopip install sinacor_negsComo usarimportsinacor_negsassnegsnegs_txt_path='sample/negs.txt'negs=snegs.read_negs_txt(negs_txt_path)# negs.header -> header do negs# negs.trades -> trades do negs (todos os negócios).# negs.trailer -> trailer do negs.
Package: sinagot
SinagotSource Code:https://gitlab.com/YannBeauxis/sinagotSinagot is a Python lightweight workflow management framework usingRayas distributed computing engine.The key features are:Easy to use: Design workflow with simple Python classes and functions without external configuration files.Data exploration: Access to computed data directly with object attributes, including complex type as pandas DataFrame.Scalable: TheRayengine enable seamless scaling of workflows to external clusters.InstallationpipinstallsinagotGetting startedimportpandasaspdimportsinagotassg# Decorate functions to use them as workflow step@sg.stepdefmultiply(df:pd.DataFrame,factor:int)->pd.DataFrame:returndf*factor@sg.stepdefget_single_data(df:pd.DataFrame)->int:returnint(df.iloc[0,0])# Design a workflowclassTestWorkflow(sg.Workflow):raw_data:pd.DataFrame=sg.seed()# seed is input datafactor:int=sg.seed()multiplied_data:pd.DataFrame=multiply.step(raw_data,factor=factor)final_data:int=get_single_data.step(multiplied_data)# Create a workspace on top of workflow for storage policy of data producedclassTestWorkspace(sg.Workspace[TestWorkflow]):raw_data=sg.LocalStorage("raw_data/data-{workflow_id}.csv")factor=sg.LocalStorage("params/factor")multiplied_data=sg.LocalStorage("computed/multiplied_data-{workflow_id}.csv",write_kwargs={"index":False})# In this example final_data is not stored and computed on demand# Create a workspace with local storage folder root path parameterws=TestWorkspace("/path/to/local_storage")# Access to a single workflow with its IDwf=ws["001"]# Access to item data, computed automatically if it does not exist in storagedisplay(wf.multiplied_data)print(wf.final_data)In this example, the storage dataset is structured as follows :├── params/
│   └── factor
├── raw_data/
│   ├── data-{item_id}.csv
│   └── ...
└── computed/
    ├── step-1-{item_id}.csv
    └── ...And the workflow is :Development RoadmapSinagot is at an early development stage but ready to be tested on actual datasets for workflows prototyping.Features development roadmap will be prioritized depending on usage feedbacks, so feel free to post an issue if you have any requirement.
Package: sinai
sinaiThis is a library for developing monitoring applications that encourages rapid development and clean, pragmatic design.What your application monitors is up to you. It could be online data sources, the state of a business application, data integrity, anything you want.For a high-level overview, please readCreating a monitoring application with sinaiUsageTo develop a monitoring application with sinai, you install it and import it like any other Python library.See theinteractive tutorialsfor how to use it.Seea simple usage example.InstallThere are no required dependencies, but optional dependencies are required for the relevant features:requeststo use API sources and stores, and the Slack storepymongoto use MongoDB sources and storesboto3to use the CloudWatch storeTo install via pip:pipinstallsinaiTo install via setup tools:$gitclonegit@github.com:SinAI-monitoring/sinai.git
$cdsinai
$pythonsetup.pyinstallContributingTo develop sinai itself, use the following commands to create a development environment. These assume you havepipenvinstalled.$gitclonegit@github.com:SinAI-monitoring/sinai.git
$cdsinai
$pipenvinstall--dev
$pipenvshell
$pre-commitinstallCheck the pre-commit hooks are working with:$pre-commitrun-aRun the unit tests with:$pytest
Package: sinaraml
SinaraML Framework is a framework for creating and operating MLOps platformsFrom the first sight of a Data Scientist with Sinara to receiving the first Docker image with a model accessible via REST, it will take 15 minutes only.SinaraML Framework provides Sinara Server, Sinara Storage, Sinara Lib, Sinara Step Template, Sinara Tools to the Data Scientist.SinaraML Serveris a Jupyter Server, with all the necessary libraries for working with data and training models. SinaraML provides three Basic Servers for different purposes — classic ML, computer vision (CV) and natural language processing (NLP).SinaraML Storageis a long-term storage where ML pipeline stored input and output entities. Depending on infrastructure Sinara Storage can be implemented based on S3, HDFS protocols or local disk.SinaraML Spark(pandas_api) is a effective and uniform way to work with big data sets using Apache Spark. Pandas_api gives Apache Spark's effectivenes and familiar pandas datasets functions.SinaraML Libis a compact library that contains everything you need to create ML pipelines, for data preparation and versioning, model versioning and serving.SinaraML Step Templateis a component template for creating a SinaraML Step — an ML pipeline step. The ML pipeline consists of several steps. Each step based on this template.SinaraML CLIis a number of CLI tools for creating, deleting, stopping and starting a Sinara Server, creating docker images from BentoServices created by ML pipelines, ML pipelines management, visualization etc.SinaraML Basicis a preconfigured personal MLOps platform working on desktop, remote virtual machine which can be located on-prem or on clouds like Google Collab/DataProc + Google Objecs, Azure DataBricks + Azure Blobs, Yandex DataSphere/DataProc + S3 etc.SinaraML Customizable Infrais a way to customize orchestration of Sinara Server, Sinara Storage and Sinara Spark for integration with your infrastructure (Git, Docker repos, authentication and authorization methods including Active Directory).SinaraML Customizable Dev Processis a way to configure Sinara Template and Sinara Step for your development process.SinaraML Examplesis a library of ready to use configurable ML pipelines can be customized for your needs.SinaraML Bookallows you to dive deeply into the development of ML products with SinaraML examples.To start you off, go toGetting started pageto trySinaraML TutorialsInstallationTo install SinaraML CLI into your environment, run:pip install sinaramlReload shell or reboot your machine after installation to enable CLI commandsQuick StartCommands start with the keyword sinara (similar to git, docker, kubectl)If a command call is made without a mandatory parameter, help is displayed on the available parameters and methods of calling the command, for example:sinara server createsinara server startOr, for a remote VM platform:sinara server create --platform remote_vmsinara server startTo remove a server, run:sinara server remove
Package: sinaraml-cli
DescriptionThis a SinaraML CLI library which contains main module for launching CLI commands. It also needs plugin packages to work.CLI InstallationLinux / WSL:sudo pip install sinaraml_cliWindows:pip install sinaraml_cliPlugins installationLinux / WSL:sudo pip install sinaraml_cli_host
sudo pip install sinaraml_cli_jupyterWindows:pip install sinaraml_cli_host
pip install sinaraml_cli_jupyterCLI Quick StartCommands start with the keyword sinara (similar to git, docker, kubectl)If a command call is made without a mandatory parameter, help is displayed on the available parameters and methods of calling the command, for example:~$ sinara
usage: sinara [-h] {server,model} ...

options:
  -h, --help            show this help message and exit

subject:
  {server,model,pipeline}
                        subject to use [server, model, pipeline]
    server              server subject
    model               model subject
    pipeline            pipeline subject~$ sinara server
usage: sinara server [-h] {create,start} ...

options:
  -h, --help      show this help message and exit

action:
  {create,start}  Action to do with subject [create, start, stop, etc]
    create        create action
    start         start action
Package: sinaraml-cli-host
DescriptionThis the SinaraML CLI library plugin. It enables you to use server and model CLI commands. Needs sinara_cli package to workCLI InstallationLinux / WSL:sudo pip install sinaraml_cli_hostWindows:pip install sinaraml_cli_hostCLI Quick StartCommands start with the keyword sinara (similar to git, docker, kubectl)If a command call is made without a mandatory parameter, help is displayed on the available parameters and methods of calling the command, for example:~$ sinara
usage: sinara [-h] {server,model} ...

options:
  -h, --help            show this help message and exit

subject:
  {server,model,pipeline}
                        subject to use [server, model, pipeline]
    server              server subject
    model               model subject
    pipeline            pipeline subject~$ sinara server
usage: sinara server [-h] {create,start} ...

options:
  -h, --help      show this help message and exit

action:
  {create,start}  Action to do with subject [create, start, stop, etc]
    create        create action
    start         start action
Package: sinaraml_jupyter
SinaraML Framework is a framework for creating and operating MLOps platformsFrom the first sight of a Data Scientist with Sinara to receiving the first Docker image with a model accessible via REST, it will take 15 minutes only.SinaraML Framework provides Sinara Server, Sinara Storage, Sinara Lib, Sinara Step Template, Sinara Tools to the Data Scientist.SinaraML Serveris a Jupyter Server, with all the necessary libraries for working with data and training models. SinaraML provides three Basic Servers for different purposes — classic ML, computer vision (CV) and natural language processing (NLP).SinaraML Storageis a long-term storage where ML pipeline stored input and output entities. Depending on infrastructure Sinara Storage can be implemented based on S3, HDFS protocols or local disk.SinaraML Spark(pandas_api) is a effective and uniform way to work with big data sets using Apache Spark. Pandas_api gives Apache Spark's effectivenes and familiar pandas datasets functions.SinaraML Libis a compact library that contains everything you need to create ML pipelines, for data preparation and versioning, model versioning and serving.SinaraML Step Templateis a component template for creating a SinaraML Step — an ML pipeline step. The ML pipeline consists of several steps. Each step based on this template.SinaraML CLIis a number of CLI tools for creating, deleting, stopping and starting a Sinara Server, creating docker images from BentoServices created by ML pipelines, ML pipelines management, visualization etc.SinaraML Basicis a preconfigured personal MLOps platform working on desktop, remote virtual machine which can be located on-prem or on clouds like Google Collab/DataProc + Google Objecs, Azure DataBricks + Azure Blobs, Yandex DataSphere/DataProc + S3 etc.SinaraML Customizable Infrais a way to customize orchestration of Sinara Server, Sinara Storage and Sinara Spark for integration with your infrastructure (Git, Docker repos, authentication and authorization methods including Active Directory).SinaraML Customizable Dev Processis a way to configure Sinara Template and Sinara Step for your development process.SinaraML Examplesis a library of ready to use configurable ML pipelines can be customized for your needs.SinaraML Bookallows you to dive deeply into the development of ML products with SinaraML examples.To start you off, go toGetting started pageto trySinaraML TutorialsInstallationTo install SinaraML Jupyter CLI into your Sinara server environment, run:pip install sinaraml_jupyterReload shell or reboot your machine after installation to enable CLI commandsQuick StartCommands start with the keyword sinara (similar to git, docker, kubectl)If a command call is made without a mandatory parameter, help is displayed on the available parameters and methods of calling the command, for example:sinara pipeline create
Package: sina-shorturl
该库主要用于生成新浪短链接,完美支持`http://sina.lt`所有的短链接后缀,包括:sina.itt.cn(默认)dwz.cn(仅供部分知名网站使用)qq.cn.hn(仅供社交网站使用)tb.cn.hn(仅供购物网站使用)jd.cn.hn(仅供购物网站使用)tinyurl.comqr.netgoo.gl(仅供海外用户使用)is.gd(仅供海外用户使用)j.mp(仅供海外用户使用)bit.ly(仅供海外用户使用)其使用方法如下from sina_shorturl import generate
url = "The given website"
data = generate(url)我们只需要从`sina_shorturl`中导入generate函数,然后将给定的网站url作为参数传入即可。默认情况下为`t.cn`,如果想生成其他的后缀,可以指定`suffix`参数。例如data = generate(url,suffix="sina.it")我们只需要在suffix参数中填上上述支持的短链接后缀即可。
返回的结果可能如下{
 'short_url': 'http://t.cn/xxxx',
 'title': '关于这个站点的简单描述'
}
Package: sinaspider
准备工作安装 postgresql 数据库:brewinstallpostgresql
brewservicesstartpostgresql创建数据库.createdbyour_database_name配置信息fromsinaspiderimport_config# 写入配置信息config(account_id='your accout id'# 你的微博账号database_name='your_database_name'# 微博和用户信息将保存在该数据库write_xmp=True# 是否将微博信息写入图片(可选, 需安装Exiftool))# 读取配置信息config()>>>ConfigObj({'database_name':'sina_test','write_xmp':'True','account_id':'6619193364'})设置cookieimportkeyringcookie='...your cookie get from www.m.weibo.cn ...'# 需要m.weibo.cn网页的cookiekeyring.set_password('sinaspider','cookie',cookie)Quick Start将关注者放入配置列表中:owner=Owner()forfollowinginowner.following():UserConfig(following)读取配置列表中的用户:>>>foruser_configinUserConfig.yield_config_user():>>>pring(user_config)>>>break# 打开所有配置选项>>>user_config.toggle_all()FetchWeibo:TrueFetchRetweet:TrueDownloadMedia:TrueFetchfollowing:True# 获取所有微博>>>user_config.fetch_weibo()FetchingRetweet:TrueMediaSaving:~/Downloads/sinaspiderUpdateConfig:True每个用户提供如下配置选项:weibo_fetch: 是否下载微博weibo_since: 只获取该日期后的微博(默认为1970-01-01, 即获取所有微博)retweet_fetch: 是否下载转发微博media_download: 是否下载图片和视频微博保存与下载User获取用户信息>>>fromsinaspiderimportUser>>>uid=6619193364# 填写 用户id>>>user=User(uid)可通过user.weibos获取微博页面, 其具体参数参加get_weibo_pages# 获取第3页到第10页的所有微博, 并将文件保存在`path/to/download`weibos=user.weibos(retweet=True,star_page=3,end_page=10,download_dir='path/to/download')# 返回下一条微博next(weibos)OwnerfromsinaspiderimportOwnerfrompathlibimportPathowner=Owner()#获取自己的资料owner.info# 获取自己的关注信息myfollow=owner.following()# 获取自己的微博myweibo=owner.weibos(download_dir='path/to/dir')# 获取收藏页面>>>mycollection=owner.collections(download_dir='path/to/dir)>>>next(mycollection)
Package: sinaweibopy
Sina Weibo OAuth 2 API Python SDKThis software is a 100% pure Python interface to Sina Weibo API which provides OAuth2.0 authentication and API calling wrapper.You can find docs and source code at:http://michaelliao.github.com/sinaweibopy/Developed and mantained by Michael Liao. Please feel free to report bugs and your suggestions at:https://github.com/michaelliao/sinaweibopyFor more information, please refer to the wiki:https://github.com/michaelliao/sinaweibopy/wiki/OAuth2-HOWTO
Package: sinaweibopy3
Sina Weibo OAuth 2 API Python SDKThis software is a 100% pure Python interface to Sina Weibo API which provides OAuth2.0 authentication and API calling wrapper.You can find docs and source code at:http://michaelliao.github.com/sinaweibopy/Developed and mantained by Michael Liao. Please feel free to report bugs and your suggestions at:https://github.com/michaelliao/sinaweibopyFor more information, please refer to the wiki:https://github.com/michaelliao/sinaweibopy/wiki/OAuth2-HOWTO
Package: sinaweibopy-ng
Sina Weibo OAuth 2 API Python SDKThis software is a 100% pure Python interface to Sina Weibo API which provides OAuth2.0 authentication and API calling wrapper.You can find docs and source code at:http://michaelliao.github.com/sinaweibopy/Developed and mantained by Michael Liao. Please feel free to report bugs and your suggestions at:https://github.com/michaelliao/sinaweibopyFor more information, please refer to the wiki:https://github.com/michaelliao/sinaweibopy/wiki/OAuth2-HOWTO
Package: sincei
sincei: A user-friendly toolkit for QC, counting, clustering and plotting of single-cell (epi)genomics data.Featuressincei provides a flexible, easy-to-use command-line interface to work with single-cell data directly from BAM files. It can:Aggregate signal in bins, genes or any feature of interest from single-cells.Perform read-level and count-level quality control.Perform dimentionality reduction and clustering of all kinds of single-cell data (open chromatin, histone marks, methylation, gene expression etc..).Create coverage files (bigwigs) for visualization.Full DocumentationInstallationsincei is a command line toolkit based on python3, and can be installed usingconda.Create a new conda environment and install sincei stable release from github using:conda create -n sincei -c anaconda python=3.8
conda activate sincei
(sincei): pip install --editable=git+https://github.com/vivekbhr/sincei.git@master#egg=sinceiFor the development version, try:(sincei): pip install --editable=git+https://github.com/vivekbhr/sincei.git@develop#egg=sinceiUsageGet the tool list withsincei --helpEach tool begins with the prefix sc<tool_name>, such as:$ scBulkCoverage -b file1.bam -g groupinfo.txt -o coverageQuestions and discussionsTo ask  a question related to sincei or start a new discussion, please use ourgithub discussion forum.
Package: sincnet-tensorflow
SincNet in TensorflowAn Implementation ofSincNetusing Tenorflow 2.x.Models are converted from original torch networks.The main implementation of thesinc_convlayer is non-optimal. Instead of using loops in the call section, we used matrix multiplication and a few programming tricks that allow the hardware to run more efficiently (25 times faster).SincNetSincNet is a neural architecture for processingraw audio samples. It is a novel Convolutional Neural Network (CNN) that encourages the first convolutional layer to discover moremeaningful filters. SincNet is based on parametrized sinc functions, which implement band-pass filters.ArxivInstall$pipinstallsincnet-tensorflowUsageDemoTraining on a dummy database to check for error-free executionA layer for Keras Functionalimporttensorflowastffromtensorflow.keras.layersimportDense,Conv1Dfromtensorflow.keras.layersimportLeakyReLU,BatchNormalization,Flatten,MaxPooling1D,Inputfromsincnet_tensorflowimportSincConv1D,LayerNormout_dim=50#number of classessinc_layer=SincConv1D(N_filt=64,Filt_dim=129,fs=16000,stride=16,padding="SAME")inputs=Input((32000,1))x=sinc_layer(inputs)x=LayerNorm()(x)x=LeakyReLU(alpha=0.2)(x)x=MaxPooling1D(pool_size=2)(x)x=Conv1D(64,3,strides=1,padding='valid')(x)x=BatchNormalization(momentum=0.05)(x)x=LeakyReLU(alpha=0.2)(x)x=MaxPooling1D(pool_size=2)(x)x=Conv1D(64,3,strides=1,padding='valid')(x)x=BatchNormalization(momentum=0.05)(x)x=LeakyReLU(alpha=0.2)(x)x=MaxPooling1D(pool_size=2)(x)x=Conv1D(128,3,strides=1,padding='valid')(x)x=BatchNormalization(momentum=0.05)(x)x=LeakyReLU(alpha=0.2)(x)x=MaxPooling1D(pool_size=2)(x)x=Conv1D(128,3,strides=1,padding='valid')(x)x=BatchNormalization(momentum=0.05)(x)x=LeakyReLU(alpha=0.2)(x)x=MaxPooling1D(pool_size=2)(x)x=Flatten()(x)x=Dense(256)(x)x=BatchNormalization(momentum=0.05,epsilon=1e-5)(x)x=LeakyReLU(alpha=0.2)(x)x=Dense(256)(x)x=BatchNormalization(momentum=0.05,epsilon=1e-5)(x)x=LeakyReLU(alpha=0.2)(x)prediction=Dense(out_dim,activation='softmax')(x)model=tf.keras.models.Model(inputs=inputs,outputs=prediction)model.summary()References@inproceedings{ravanelli2018speaker,title={Speaker recognition from raw waveform with sincnet},author={Ravanelli, Mirco and Bengio, Yoshua},booktitle={2018 IEEE Spoken Language Technology Workshop (SLT)},pages={1021--1028},year={2018},organization={IEEE}}@misc{SincNet,title={SincNet},author={Mirco Ravanelli (mravanelli)},year={2018},url={https://github.com/mravanelli/SincNet},publisher={Github},}
Package: sindri
Sindri Python SDKThis SDK is an alpha (testing) release and may change at any time.Build StatusDescriptionThe Sindri Python SDK.
Contains SDK code for Sindri APIs. Please seesindri.appfor more details.Documentation and UsageDocumentation for this repo is automatically generated from the Python docstrings usinglazydocsand published tosindri.app/docs/reference/sdk/along with the rest of the documentation for Sindri.Licensesindri-pythonis licensed under aMIT Licenseand is copyrightSindri Labs, Inc..
Package: sindri-labs
Sindri Python SDKThis SDK is an alpha (testing) release and may change at any time.Build StatusDescriptionThe Sindri Python SDK.
Contains SDK code for Sindri APIs. Please seesindri.appfor more details.Documentation and UsageDocumentation for this repo is automatically generated from the Python docstrings usinglazydocsand published tosindri.app/docs/reference/sdk/along with the rest of the documentation for Sindri.Licensesindri-pythonis licensed under aMIT Licenseand is copyrightSindri Labs, Inc..
Package: sine.event
Class EventManager:provide event sending and listening.
use a hashable key to identify an event.deff(data):printdatamanager=EventManager()manager.start()# start listenmanager.addListener(key='evnet_key',listener=f)manager.sendEvent(key='evnet_key',data={msg:'hello'})# other methodsmanager.removeListener(key='evnet_key',f)manager.stop()# stop listenmanager.clear()# clear eventChange Logv0.0.2, 2018-11-8change to run with python3