Saravanan666/pypi-org huggingface dataset:
Package: ts2py
ts2pyPython-interoperability for Typescript-Interfaces.
Transpiles TypeScript-Interface-definitions to Python TypedDictsWhat is thisThis repo is born as a fork ofts2pythonwritten by Eckhart Arnoldarnold@badw.de, Bavarian Academy of Sciences and Humanities. The reason behind this fork was that I needed asimplerway to quickly convertTypeScriptinterfaces to PythonTypedDict. Once I started looking into the initial code I decided to change (and remove) a lot of stuff.
I also managed to split the code in multiple files and addedTyperto create a beautiful CLI. The initial project should be much more powerful and should also havesupport for run-time type-checking of JSON-data.PurposeNowadaysa lotof applications are created with TypeScript. To enable a much better way of developing when dealing with Python backend it could be useful to quickly convertTypescript-Interfacesinto PythonTypedDicts. In order to enable structural validation on the Python-side, ts2py transpiles the typescript-interface definitions to Python-data structure definitions.Installationts2pycan be installed from the command line with the command:pip install ts2pyts2py requires the parsing-expression-grammar-frameworkDHParserwhich will automatically be installed as a dependency by thepip-command. ts2py requires at least Python Version 3.10 to run. However, the Python-code it produces is backwards compatible down to Python 3.6 thanks to thetyping extensions.UsageIn order to generate TypedDict-classes from Typescript-Interfaces, runts2pyon the Typescript-Interface definitions:ts2py interfaces.tsThis generates a.pyfile in same directory as the source file that contains the TypedDict-classes and can simpy be imported in Python-Code:frominterfacesimport*License and Source Codets2pyis open source software under theApache 2.0 LicenseThe complete source-code of ts2py can be downloaded from theits git-repository.
Package: ts3ekkomanage
This is the management module for ts3ekko. It is responsible mainly for spawning new ts3ekkoclient containers and
despawning old, unwanted ts3ekkoclient containers.related projectshttps://gitlab.com/networkjanitor/vagrant-ts3ekko-deployhttps://gitlab.com/networkjanitor/ts3ekkoutilhttps://gitlab.com/networkjanitor/ts3ekkoclient
Package: ts3ekkosingle
This is the client module for ts3ekko. It is responsible for all interactions between the application and the user. It starts the ts3 client and connects to the TS3ClientQuery after initialization.For documentation, have a look into the./docs/directory.related projectshttps://gitlab.com/networkjanitor/vagrant-ts3ekko-deployhttps://gitlab.com/networkjanitor/ts3ekkoutilhttps://gitlab.com/networkjanitor/ts3ekkomanage
Package: ts3l
TabularS3LA PyTorch-based library for self- and semi-supervised learning tabular models.
Currently,VIME,SubTabandSCARFare available.To DOLaunch nn.Module and Dataset of VIME, SubTab, and SCARFVIMESubTabSCARFLaunch pytorch lightning modules of VIME, SubTab, and SCARFVIMESubTabSCARFFinish README.mdInstallationWe provide a Python package ts3l of TabularS3L for users who want to use semi- and self-supervised learning tabular models.pipinstallts3lHow to use?# Assume we have X_train and y_train. And assume that we also have X_unlabeled for self-supervised learningfromts3l.modelsimportSCARFfromts3l.utils.scarf_utilsimportSCARFDatasetfromts3l.utils.scarf_utilsimportNTXentLossimporttorchimporttorch.nnasnnimporttorch.optimasoptimfromtorch.utils.dataimportRandomSampler,WeightedRandomSampler,Dataset,DataLoaderemb_dim=128encoder_depth=4head_depth=2corruption_rate=6dropout_rate=0.15batch_size=128model=SCARF(input_dim=X_train.shape[1],emb_dim=emb_dim,encoder_depth=encoder_depth,head_depth=head_depth,dropout_rate=dropout_rate,out_dim=2)pretraining_loss=NTXentLoss()optimizer=optim.Adam(model.parameters(),lr=0.001)train_ds=SCARFDataset(X_train.append(X_unlabeled),corruption_len=int(corruption_rate*X_train.shape[1]))model.do_pretraining()# Now, model.forward conducts self-superivsed learning.train_dl=DataLoader(train_ds,batch_size=batch_size,shuffle=False,sampler=RandomSampler(train_ds),num_workers=4,drop_last=True)forepochinrange(2):fori,datainenumerate(train_dl,0):optimizer.zero_grad()x,x_corrupted=dataemb_anchor,emb_corrupted=model(x,x_corrupted)loss=pretraining_loss(emb_anchor,emb_corrupted)loss.backward()optimizer.step()train_ds=SCARFDataset(X_train,y_train.values)criterion=nn.CrossEntropyLoss()optimizer=optim.Adam(model.parameters(),lr=0.001)model.do_finetunning()# Now, model.forward conducts (semi-)superivsed learning.train_dl=DataLoader(train_ds,batch_size=batch_size,shuffle=False,sampler=WeightedRandomSampler(train_ds.weights,num_samples=len(train_ds)),num_workers=4,drop_last=True)forepochinrange(2):fori,datainenumerate(train_dl,0):optimizer.zero_grad()x,y=datay_hat=model(x)loss=criterion(y_hat,y)loss.backward()optimizer.step()
Package: tsad
# Time Series Anomaly detection based on Deep LearningTSADis a Python module created for solving Anomaly Detection problems with time series data. The module is based on deep learning techniques.The main meaning of this module are:Forecast a multivariate Time Series (TS) one point ahead (Also works for univariate TS)Compute residuals between forecast and true valuesApply analysis of residuals (default is Hoteling Statics)Plot and return anomaliesThis module allows forecast multi-step ahead both multivariate and univariate time series also.As forecasting algorithms were implemented or will be implemented:A simple one-layer LSTM network (LSTM)A two-layer LSTM network (DeepLSTM)A bi-directional LSTM network (BLSTM)LSTM encoder-decoder (EncDec-AD)LSTM autoencoder (LSTM-AE);Convolutional LSTM network (ConvLSTM)Convolutional Bi-directional LSTM network (CBLSTM)Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED)Actually, the possibility of the module allows you to use any own forecasting algorithm, computer of residuals, or evaluator of residuals.#### Documentationhttps://tsad.readthedocs.io/
Package: tsa-lth
Python package for Time Series Analysis course at LTH.Written by Filipp Lernbo.InstallationI assume you have python 3.8 or later installed on your computer.1. Create virtual environmentInstall virtualenv if not already installed. This is done by typingpip install virtualenvin your terminal.Open terminal and navigate to your time-series directory. Typepython -m venv tsa_venvto create the virtual environment2. Activate the virtual environmentIn the newly created directory, there is a bin-folder. Among other things, this contains an activation script:<tsa-folder>/bin/activate. Typesource <path-to-activate-file>to activate your virtual environment.Tip! You can create an alias for this step. If you're on mac, you can open the file~/.bash_profileand add a line:alias tsa="source <path-to-activate-file>". Next time you open terminal, you only need to write "tsa" to activate the virtual environment.3. Install this packageNavigate to the root of this project and typepip install -r requirements.txtfollowed bypip install --editable ..Now, when you're in this virtual environment you can always import files from this repo, where ever you are on the computer.ContributingRead theCONTRIBUTING.mdfile before pushing code.
Package: tsampling
tsamplingThompson Sampling Multi-Armed Bandit for PythonA package that implements the Thompson Sampling approach for a Multi-Armed Bandit.
The purpose of this project is to help people simply create and maintain Thompson Sampling experiments that have Bernoulli and Poisson distributions.UsageSetting up the experiment:The following method will instantiate the experiment with the default priors.fromtsampling.bernoulliimportBernoulliExperimentexperiment=BernoulliExperiment(arms=2)You can also set your custom priors by using the Priors module:fromtsampling.bernoulliimportBernoulliExperimentfromtsampling.priorsimportBetaPriorpr=BetaPrior()pr.add_one(mean=0.5,variance=0.2,effective_size=10,label="option1")pr.add_one(mean=0.6,variance=0.3,effective_size=30,label="option2")experiment=BernoulliExperiment(priors=pr)Performing an action:You can randomly choos which arm to "pull" in the multi-armed bandit:experiment.choose_arm()Updating reward:You can update the different arms information by adding reward information:rewards=[{"label":"option1","reward":1},{"label":"option2","reward":0}]experiment.add_rewards(rewards)Plotting Posterior Distribution:You can plot the posterior distributionexperiment.plot_posterior()InstallationPippip install tsamplingLicenseFree software: MIT licenseCreditsThis package was created with Cookiecutter_ and theaudreyr/cookiecutter-pypackage_ project template._Cookiecutter:https://github.com/audreyr/cookiecutteraudreyr/cookiecutter-pypackage:https://github.com/audreyr/cookiecutter-pypackage
Package: ts-analysis
Time Series AnalysisThis is a MixIn framework to:read data (from files, stdin etc.)extract one or more event time seriesrun analysis to identify points of interest on the timelinetranslate the found events in extracted time series back to
(presumably time) markers in original datawrite finalized output in some formatMixIns for a pipeline are:InputAnalysis MethodOutputLoggingApproachesBelow is a list of currently implemented approaches.Kendall CorrelationKendall rank correlation coefficient (aka Kendall's τ coefficient) performs
pairwise identification of positive or negative correlation in two
timeseries.Time MotifsTime motifs are repeating patterns which indicate an underlying common cause.  Details on these approaches can befound here.
Package: tsapiness
tsapinesstsapiness (pronounced "zappiness") aims to make the TSAPI easy to access via Python.Currently, the project provides a python representation of the TSAPI so that the data can be manipulated accordingly.
More details on TSAPI can be found here.https://www.tsapi.net/These are early days for the project, in this early version you can:convert an SPSS sav file to tsapi formatconvert a triple-s file to tsapi formatread tsapi from a webserverFuture development intends to:convert other survey platform APIs to the tsapi formatprovide tools for converting flat tsapi structures that occur when importing from sav or triple-s into the more native hierarchical tsapi structureprovide full documentation on how to use tsapiness effectively.A simple implementation is below...import tsapiness as ts
import json

# Create tsapi object from TSAPI demo server

SERVER = 'https://tsapi-demo.azurewebsites.net'
conn = ts.connector_tsapi.Connection(server=SERVER)
surveys = ts.connector_tsapi.Surveys(connection=conn)
survey_id = surveys[0]['id']
survey_from_api = ts.connector_tsapi.Survey(survey_id=survey_id, connection=conn)

# create tsapi from triple s file:

sss_file = 'data/example.sss'
asc_file = 'data/example.asc'

conn = ts.connector_sss.Connection(sss_file=sss_file, asc_file=asc_file)
survey_from_sss = ts.connector_sss.Survey(connection=conn)

# create tsapi from sav file:
sav_file = 'data/Pew Global Attitudes Spring 2014.sav'
conn = ts.connector_sav.Connection(sav_file=sav_file)
# in this file the variable PSRAID holds the respid, 
# and Q165 the date of interview

survey_from_sav = ts.connector_sav.Survey(connection=conn, 
                                          id_var='PSRAID', 
                                          date_var='Q165')

# save back to json

survey_to_export = survey_from_sav 
# survey_to_export = survey_from_sss
# survey_to_export = survey_from_api

with open('data/metadata.json', 'w', encoding='utf8') as f:
    json.dump(survey_to_export.metadata.to_tsapi(),
              f,
              indent=4,
              ensure_ascii=False)
with open('data/data.json', 'w', encoding='utf8') as f:
    json.dump([interview.to_tsapi()
               for interview in survey_to_export.interviews],
              f,
              indent=4,
              ensure_ascii=False)
Package: ts-app
Time Series AppA dashboard application to help learn a little about, and applyTime Seriesanalysis&forecastingconcepts.You can create a sample, or upload a file, and interactively fit a time series model on it.The dashboard is built withDash, and the time series models are fitted usingStatsmodels.You cantry it out here, courtesy ofRender.NOTE:Free-hosted apps onRendermight take a while to load since they are shut down when not in use.InstallationThe easiest way to get the app is fromPyPI:pipinstallts-appBasic UsageThe commandts_applaunches the app:$ts_app-h
usage:ts_app[-h][-pPORT][--hostHOST][--no-browser]AsimpledashboardapplicationtolearntimeseriesbasicsandinteractivelyfitARIMAmodels.

optionalarguments:-h,--helpshowthishelpmessageandexit-pPORT,--portPORTTheTCPportonwhichtolisten(default:8000).--hostHOSTAhost-nameorIPaddress(default:'localhost').--no-browserAvoidopenningabrowsertaborwindow.You can also start the app from an interactive session:>>>importts_app>>>ts_app.run_app()Afterwards, pressCTRL+Cto stop the server.
Package: tsaug
tsaugtsaugis a Python package for time series augmentation. It offers a set of
augmentation methods for time series, as well as a simple API to connect
multiple augmenters into a pipeline.Seehttps://tsaug.readthedocs.iocomplete documentation.InstallationPrerequisites: Python 3.5 or later.It is recommended to install the most recentstablerelease of tsaug from PyPI.pipinstalltsaugAlternatively, you could install from source code. This will give you thelatest, but unstable, version of tsaug.gitclonehttps://github.com/arundo/tsaug.gitcdtsaug/
gitcheckoutdevelop
pipinstall./ExamplesA first-time user may start with two examples:Augment a batch of multivariate time seriesAugment a 2-channel audio sequenceExamples of every individual augmenter can be foundhereFor full references of implemented augmentation methods, please refer toReferences.ContributingPull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.Please make sure to update tests as appropriate.Please seeContributingfor more details.Licensetsaugis licensed under the Apache License 2.0. See theLICENSEfile for details.
Package: tsaw
TSAW Torrent Server API Wrapper.More information about the api athttp://ion-torrent-sdk.readthedocs.io/en/latest/Installationpip install tsawUsage exampleGet the 10 last results for plugin with id 42>>> from tsaw import TorrentServerApi
>>> ts = TorrentServerApi("http://myserver/rundb/api/v1/", "ION_TS_USERNAME", "ION_TS_PASSWORD")
>>> pluginresults = ts.pluginresult(params={"format": "json", "limit": 10, "order_by": "-id", "plugin": "42"})
Package: tsbb15-labs
Support functions for the labs in the TSBB15 courseInstallationSetupMake sure you have python installed.
Additionally we recommend using a package manager such asminiconda.Easiest installationRun the command:pip install tsbb15-labsNote that you do NOT need to clone this repo for this install.InstallIf you want to change any code (bugfixes etc). You may want to install the package in editable mode, so that changes can be easily observed.
This can be done with the following commands:git clone git@github.com:Parskatt/tsbb15_labs.git
cd tsbb15_labs
pip install -e .Some known issuestiff errorsIf you are using conda on windows you might get an error:tempfile.tif: Cannot read TIFF header.If thats the case, uninstall libtiff (conda uninstall libtiff)
and reinstall it. (this will also require you to reinstall pillow)
Package: tsBNgen
DescriptiontsBNgen is a Python package to generate time series data based on an arbitrary Bayesian Network Structures.CitationFor the correct citation please visithttps://github.com/manitadayon/tsBNgenFeaturesIt handles discrete nodes, continous nodes and hybrid (Mixture of discrete and continuous) network.It uses multinomila distribution for the discrete nodes and Gaussian distribution for the continuous nodes.It handles arbitrary Bayesian network structure.It supports arbitrary loopback values.The code can be modified easily to handle arbitrary static and temporal structures.InstructionTo run this code either clone this repo or use the package distribution in PyPI using the following commands:pipinstalltsBNgenThen Run through the set of examples inTime_Series_Generation_Examples.ipynbFor more information on how to use the package please visit the following:Original paperDocumentation in PDF available in the github repository.LicenseThis software is released under the MIT liecense.
Package: tsboost
ContextTSBoost is a framework for time series forecasting.It mixes classical statistics practices with non linear optimisation techniques of current Machine Learning.Requirements32-bit Python is not supported. Please install 64-bit version.TSBoost uses gradient boosting optimisation provided byXGBoost&LightGBM, both have C++ source code and need a compiler.ForWindowsusers,VC runtimeis needed ifVisual Studio(2015 or newer) is not installed.ForLinuxusers,glibc>= 2.14 is requiredsudo apt-get install build-essential      # Ubuntu/Debiansudo yum groupinstall ‘Development Tools’ # CentOS/RHELFormacOSusers, install OpenMP librairybrew install libompInstallationAfter installing the compiler, install fromPyPIUsingpippip install tsboostQuick StartYou can get started with a jupyter notebook tutorial :TSBoot quick startHistory0.1.0 (2019-06-10)First release on PyPI.
Package: tsbot
TSBotAsynchronous framework to buildTeamSpeak 3 Server Querybots✅ FeaturesUses modern PythonasyncandawaitsyntaxSecure connection through SSHEase of use query buildingBuilt-in and configurable ratelimiter if no access towhitelist.txtQuery caching✏️ Examplesfrom__future__importannotationsimportasynciofromtsbotimportTSBot,context,events,querybot=TSBot(username="USERNAME",password="PASSWORD",address="ADDRESS",)@bot.command("hello")asyncdefhello_world(bot:TSBot,ctx:context.TSCtx):awaitbot.respond(ctx,f"Hello{ctx['invokername']}!")@bot.on("cliententerview")asyncdefpoke_on_enter(bot:TSBot,event:events.TSEvent):poke_query=query("clientpoke").params(clid=event.ctx["clid"],msg="Welcome to the server!")awaitbot.send(poke_query)asyncio.run(bot.run())Check out📁examplesfor more📦 InstallationPython 3.10 or higher is requiredInstalling with pip:# Linux/macOSpython3-mpipinstalltsbot# Windowspy-3-mpipinstalltsbot
Package: tsbs
tsbs_rgThis package computes the baseline for a time seriesInputCompute baseline for a time series dataArguments:Positional Arg:data (array): time series data (reverse chronological)Keyword Arg:crit (float): critical multiplier value (default=None)perc (float): critical baselining percentage (default=None)false_positive_threshold (boolean): whether to use any false_positive_threshold (default=None)is_low (boolean): whether anomalies for the lower thresholds are to be detected   (default=False)usageBaseline(data, crit, perc, false_positive_threshold=None, is_low=True).baseline()
Package: tscat
Time Series CataloguesA library which stores, loads and filters time-series-events and associates them catalogues and
dynamic catalogues (filter-based).Free software: GNU General Public License v3Documentation:https://tscat.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.0 (2021-07-01)First release on PyPI.
Package: tscat-gui
Time series catalogue - GUITime-series catalogue - graphical user interface libraryFree software: GNU General Public License v3Documentation:https://tscat-gui.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-11-04)First release on PyPI.
Package: tsc-auto
安装pip install tsc-auto功能1: ta介绍可以自动检测 torch 和 tensorflow 版本, 并自动设置环境变量相应的 cuda, 同时自动选择占用显存最低的显卡运行.可以实现自动后台运行运行并记录和查看日志/中止程序.需要在 anaconda 环境下使用.在程序终止时可以自动发送微信通知.使用方式查看帮助: ta --help自动选择最低显存占用显卡跑 torch 的 cnn.py 程序: ta -p -a cnn.py手动选择编号0和1的显卡跑 tensorflow 的 cnn.py 程序: ta -t -d 0,1 -a cnn.py发送通知步骤（借助pushplus实现)微信登录:https://pushplus.plus/login.html激活微信公众号, 接收文章推送, 关闭消息免打扰获取token:https://pushplus.plus/push1.html测试: ta -x 你的token -o echo 成功注意: 运行机需要联网; 不能使用ta自带的后台运行方式(可以手动nohup); 频率太高可能封号/封ipta --benchmark 可测试显卡性能ta --wait -c=0.1 -m=0.8 -t=10 -g=1,2 python test.py 可等待显卡1,2满足空闲条件(至少剩余10%gpu使用率+至少剩余80%显存+持续10秒钟)后再执行后面的命令ta --showp 可显示当前用户的所有运行程序(去重复)命令ta --show=120 显示当前系统资源信息,120代表显示一行最大长度cuda的位置默认在用户主目录 (可以使用 ln -s 软链接)...~/cuda/8.0...~/cuda/11.3...下载处理好的cuda链接:https://pan.baidu.com/s/1tXzED_8GZjJzm_SFzMTbKA提取码: 3edj如果自行安装 cuda 和 cudnn, 需要处理的方式cd ~/cudamv /usr/local/cuda-11.0/ 11.0cp cudnn-v8.2.0.53/include/cudnn.h 11.0/include/cp cudnn-v8.2.0.53/lib64/libcudnn* 11.0/lib64chmod a+r 11.0/include/cudnn.h 11.0/lib64/libcudnn*功能2: tkill介绍用于限制linux系统的cpu/gpu资源使用, 例如可以针对以下内容进行限制:{'s':10,# 多少秒检测一次'cpu_core_u':2147483647,# 一个用户-最多CPU占用(百分比,如100表示占满1个超线程)'gpu_card_u':2,# 一个用户-最多显卡(张)'gpu_mem_u':24,# 一个用户-最大显存(GB)'gpu_card':2,# 单进程-最多显卡(张)'gpu_mem':21000,# 单进程-最大显存(MB)'gpu_day':15,# 单进程-最长显卡占用时间(天)'cpu_day':15,# 单进程-最长cpu占用时间(天)'cpu_day_core_limit':80,# CPU占用百分比超过此值的进程才会使 cpu_day 配置生效'ignore_u':{'999',},# 忽略的用户, 默认会包含 /etc/passwd 中路径不含有 /home/ 的用户'include_u':{'example_user',},# 不可忽略的用户, 优先级高于 ignore_u# 针对每个特殊配置设置用户，没写的默认使用上述设置，越靠list后面的优先级越高会覆盖前面一样的用户配置'conf':[{# 一组配置和对应的用户'gpu_mem':24000,'conf_u':{'tsc'},# 使用这组配置的用户},],# 针对每个用户的额外配置, 没写的默认使用上述设置, 优先级最高'user':{'example_user':{'gpu_mem_u':41,'gpu_card_u':3,},},}退出某用户在显卡中的进程, 防止程序结束而显存没有释放: tkill --knp username使用方式查看帮助: tkill -h首先, 运行 tkill -c 'kill.config' -t 再终止程序(ctrl+c), 用于生成默认配置文件然后, 修改配置文件的内容最后, 运行 tkill -c 'kill.config' 开启限制程序功能3: ressh介绍用于在断网后自动重新连接ssh,也可以在一行命令行中直接输入密码登录使用方式查看帮助: ressh -h登录: ressh user@ip -p port登录后自动输入密码并自动进入tmux: ressh user@ip -p port --tmux --password=你的密码功能4: tp介绍服务器代理使用方式服务器和客户端都需要安装包: pip install tsc-auto本地打开http代理开放到端口 7890, 可借助clash登录服务器: ressh ssh参数 --tp登录后使用代理: tp curl google.com
Package: ts-cc-extractor
TS-CC ExtractorWith this pure-python utility you can extractEIA-608 captionsfromVideo Transport Stream(*.ts)file
and convert them toSubRip(*.srt)orVideo Text Tracks(*.vtt)subtitles.Installationpip install ts-cc-extractorUsageusage: ts-cc-extractor -i PATH -o PATH [-f {SRT,VTT}] [-v] [-h]

required arguments:
  -i PATH        Path to *.ts file
  -o PATH        Output subtitles file

optional arguments:
  -f {SRT,VTT}   Subtitles format (default: SRT)
  -v, --version  show program's version number and exit
  -h, --help     show this help message and exitAPI examplefromts_cc_extractorimportextract_subtitleswithopen('video1.ts')asf:subs_text=extract_subtitles(f,fmt='VTT')print(subs_text)WEBVTT

00:01.705 --> 00:03.974 align:left position:10% line:5% size:80%
THAT'S JUST THE WAY IT GOES.
I DONE SEEN IT TOO MANY TIMES.

00:03.974 --> 00:05.609 align:left position:30% line:89% size:60%
HEY, Y'ALL READY?

...CLI example$ ts-cc-extractor -i video2.ts -o subs.srt -f SRT
$ cat subs.srt1
00:00:01,967 --> 00:00:05,633                       
>> HARRY AND MEGHAN WERE IN THE
MAJOR GENERAL'S OFFICE

2
00:00:05,633 --> 00:00:10,533       
OVERLOOKING THE PAGEANTLY OF THE
HORSE GUARDS PARADE AND THE

3
00:00:10,533 --> 00:00:10,800  
TROOPING.

...LicenseBSD
Package: tsc-cfg
介绍Cfg优势：可以构建出支持代码跳转的配置文件，同时还能保持类似yaml的层次结构，也可以模仿一般的json进行赋值/遍历/列表操作GlobalInfo优势：建立了一个redis到本地的映射，可以通过redis的key来访问本地的变量，实时读写安装正常安装：pip install tsc-cfg如果依赖包出现重大变化可以尝试：pip install tsc-cfg[recommended]
Package: tsccm
TSCCMTSCCM(Tenable.SC CLI Manager)by LimberDuck is a CLI tool which enables you to perform certain actions on Tenable.SC by (C) Tenable, Inc. viaTenable.SC API.Main featuresInitial version ofTSCCMlets you get Tenable.SC's:audit file listcredential listgroup listpolicy listrole listactive scan listscan result listserver infostatuslicensed IPsversionusers listHow toInstallpip install tsccmRuntsccmMetaChange logSeeCHANGELOG.LicenceMIT:LICENSE.AuthorsDamian KrawczykcreatedTSCCM(Tenable.SC CLI Manager)by LimberDuck.
Package: tscleaner
Time Series CleanerPython Boilerplate contains all the boilerplate you need to create a Python package.Free software: MIT licenseDocumentation:https://tscleaner.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-06-07)First release on PyPI.