Saravanan666/pypi-org huggingface dataset:
Package: ppma
📦 Paddle Model Analysis这是基于飞桨开发的工具包，以极简主义为特色，用于对分类任务模型进行快速分析 🚀🚀🚀本项目追求最短的代码、更小的冗余，用最小的学习成本让用户使用，以用户体验为根本，在这里你可以用最少三行代码来实现想要的功能。🎉🎉🎉 十分感谢 PaddlePaddle 公众号推荐本项目 -->点我查看具体详情目前所支持的功能有：ImageNet 精度测试测试图片 Top5 类别测试模型 Params、Throughput、FLOPsCAM (Class Activation Mapping)Coming Soon ...更新Update(2022-01-29)：规范 time 测试Update(2021-12-03)：更新 ImageNet 验证的normalize，统一参数规范Update(2021-12-03)：TTA(测试时数据增强)不符合PPMA用途，后续版本逐渐废弃，敬请期待下一个工具更早的：Update(2021-11-26)：增加 FLOPs 计算Update(2021-11-26)：更新 Params、Troughput 的计算Update(2021-09-29)：优化 ImageNet1k 验证，规范代码为PEP8安装pipinstallppma快速开始🔥🔥🔥 Note：推荐去 AI Studio 在线免费运行项目PPMA 快速指南ImageNet 精度测试当训练了新的模型后，或者复现了某个模型，我们需要在 ImageNet 数据集上验证性能，先准备数据集结构如下（可去ImageNet官网或者AI Studio下载）data/ILSVRC2012├─ILSVRC2012_val_00000001.JPEG├─ILSVRC2012_val_00000002.JPEG├─ILSVRC2012_val_00000003.JPEG├─...├─ILSVRC2012_val_00050000.JPEG└─val.txt# target准备好数据集后，运行以下代码importppmaimportpaddlemodel=paddle.vision.models.resnet50(pretrained=True)# 可以替换自己的模型data_path="data/ILSVRC2012"# ImageNet 数据路径ppma.imagenet.val(model,data_path,batch_size=128,img_size=224,crop_pct=0.875,normalize='default')测试图片 Top5 类别importppmaimportpaddleimg_path='test.jpg'# 图片路径model=paddle.vision.models.resnet50(pretrained=True)# 可以替换自己的模型ppma.imagenet.test_img(model,img_path,img_size=224,crop_pct=0.875,normalize='default')测试模型 Params、FLOPs、Throughputimportppmaimportpaddleres50=paddle.vision.models.resnet50()# 可以替换自己的模型# FLOPs、Params -- depend model and resolutionppma.modelstat.flops(model=res50,img_size=224,detail=True)# Thoughput -- depend model and resolutionppma.modelstat.throughput(model=res50,img_size=224)CAM (Class Activation Mapping)importpaddleimportmatplotlib.pyplotaspltfromppmaimportcamimg_path='img1.jpg'# 图片路径model=paddle.vision.models.resnet18(pretrained=True)# 模型定义target_layer=model.layer4[-1]# 提取模型某层的激活图cam_extractor=cam.GradCAMPlusPlus(model,target_layer)# 支持 GradCAM、XGradCAM、GradCAM++# 提取激活图activation_map=cam_extractor(img_path,label=None)plt.imshow(activation_map)plt.axis('off')plt.show()# 与原图融合cam_image=cam.overlay(img_path,activation_map)plt.imshow(cam_image)plt.axis('off')plt.show()设计的哲学目前有很多优秀的库拥有着简洁易用的体验，比如fastai、keras、scikit-learn，以及飞桨自家的PaddleHapi，这些都是对复杂的代码进行封装，大大降低了用户上手难度以及用户学习成本，同时高度的封装带来的是灵活性、自定义性的下降，如何去权衡这两个矛盾是一个需要思考的问题命名的艺术对函数的命名是一个需要推敲磨打的技术活，一方面函数命名尽量简单直观，做到用户自然记忆不需要查函数名，这就要求尽量命名短，单词意思尽量贴合使用场景。一方面又要避免与用户变量命名习惯冲突，比如"img"这个命名很常见，大多数用来表示一张图片，故命名函数时尽可能避开它，这能降低因为命名问题造成的error# 针对 ImageNet 数据集进行验证的函数# 当前方案ppma.imagenet.val(model,img_path)# 简洁优雅# 曾经方案ppma.ILSVRC2012.val(model,img_path)# ILSVRC2012 太长不方便记忆ppma.imagenet2012.validate(model,img_path)# 变量命名有些冗余，可以缩短而不影响理解...结构的设计高度封装以及灵活性是一对矛盾。比如具有高度封装的Keras，其灵活性和自定义性没有Pytorch那么好。在本项目（模型分析）上，因为我们只是针对具体任务进行分析，其自定义性需求并不是那么高，我们可以适当提高封装性，同时支持更多的默认参数，用户可以通过修改默认参数来完成特定需求，比如CAM增加自定义标签（默认是网络输出的标签）查看相应激活图。# 本项目设计参考 Box 思想，用户只需要准备需要的放入函数里一键运行即可##  model \#  img   － Box － Result#  ...   /效果预览
Package: ppmail
Tool to send messages via Sendgrid/SlackPyPI package:ppmailIf you spot any problems or have any improvement ideas then feel free to
open an issue and I will be glad to look into it for you.InstallationA recommended way of installing the tool is pip install.Once installed, a command line toolppmailshould be available in
your system’s PATH.pip installThe tool is available in PyPI and can be installed using pip:$ pip install --user ppmail
$ ppmail --helpConfigurationBy default, the tool reads its configuration from~/.config/ppmailfile (the location can be overridden by setting environment variableXDG_CONFIG_HOME). If the config file (or directory) does not exist
then it will be automatically created and populated with sample config
upon the next run.Usage - Help$ ppmail --help
usage: ppmail [--version] [--help] [--debug] [--verbose] [-S] [-f SENDER] -t
              RECIPIENTS [RECIPIENTS ...] [-c CC [CC ...]] [-s SUBJECT] [-H]
              [-F FONT_SIZE]

Tool to send messages via Sendgrid/Slack

optional arguments:
  --version             show program's version number and exit
  --help                show this help message and exit
  --debug               debugging mode
  --verbose             verbose logging mode
  -S, --slack           Use Slack instead of Sendgrid
  -f SENDER, --from SENDER
                        sender
  -t RECIPIENTS [RECIPIENTS ...], --to RECIPIENTS [RECIPIENTS ...]
                        recipient
  -c CC [CC ...], --cc CC [CC ...]
                        carbon copy recipient
  -s SUBJECT, --subject SUBJECT
                        subject
  -H, --code            send HTML formatted email/code block
  -F FONT_SIZE, --font-size FONT_SIZE
                        font size in px for HTML formatted email (use with -H)Usage - CLI$ echo 'The king is dead, long live the king!' \
  | ppmail -SHf 'Jon Snow' \
  -t 'arya.stark@winterfell.com' \
  -c 'sansa.stark@winterfell.com' \
  -s 'Re: secret message'Usage - Python modulefrom ppmail import Mailer

mailer = Mailer(slack=True)

status = mailer.send(
    sender='Jon Snow',
    recipients=['arya.stark@winterfell.com'],
    cc=['sansa.stark@winterfell.com'],
    subject='Re: secret message',
    message='The king is dead, long live the king!',
    code=True
)
Package: ppmd
# ppmd
Converts PowerPoint OOXML (PPTX) files to Markdown-based presentationsOperation:callpython3 main.py [path/to/file.pptx]Options:`–slidesep`- determines how individual slides will be separated. Five dashes (—–) by default.`–headingsfirst`/`–noheadings`- sets whether to assume the first text on each slide is a heading. Y by default.`–includenotes`/`–excludenotes`- sets whether to include slide notes in the output. Includes by default`–notesprefix`- sets the string used to prefix lines in the notes output - three > (>>>) by default
Package: ppmd-cffi
PPM(Prediction by partial matching) is a compression algorithm which has several variations of implementations.
PPMd is the implementation by Dmitry Shkarin. It is used in the RAR and by 7-Zip as one of several possible methods.ppmd-cffi is a python bindings with PPMd implementation by C language.
The C codes are derived from p7zip, portable 7-zip implementation.
ppmd-cffi supports PPMd var.H and PPMd var.I.PyPPMdis a brother project that use python buffer protocol for interface.
It supports PPMd var.H, and PPMd var. I on posix platforms(mac and linux).
It supports CPython and PyPy.ManualsYou can find a manual at thereadthedocsInstallationAs usual, you can install ppmd-cffi using python standard pip command.pip install ppmd-cffiCommandppmd-cffi provide small utility compress/decompress files.$ ppmd target.txt
$ ppmd -x target.txt.ppmdLicenseCopyright (C) 2020,2021 Hiroshi Miura7-Zip Copyright (C) 1999-2010 Igor PavlovLZMA SDK Copyright (C) 1999-2010 Igor PavlovThis library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
02110-1301  USA
Package: ppm-display
ppm-displayA simple tool that displays ppm/pgm files via OpenCV and saves the images in png format. The default maximum window size is 600x600.Usageppm-disp -i
ppm-disp -s <size> <file>...
ppm-disp <file>...Options-i  about the tool
-s  display images with a non-default maximum windows sizePossible Use Case- Create a batch file/shell script with the following line:
- (Windows) ppm-disp %*
- (Linux)   ppm-disp $*
- And now you can drag-and-drop ppm/pgm files into it and view the images instantly
- A batch file example is included in the Github repo
Package: pp-me
Pretty Print Me!Pretty print anything* to the terminalTable of ContentsPretty Print Me!Table of ContentsAboutCurrently supported formatsHow to InstallHow to RunAboutPython module to pretty print a variety of formats to the terminal.Currently supported formats.jsonHow to InstallHavePython 3.9.6 +installedpip install pp-meHow to Runpp-me -hto display all optionsGet silly with it
Package: ppmi-downloader
A downloader of PPMI metadata and imaging data files.Example usageimportppmi_downloaderppmi=ppmi_downloader.PPMIDownloader()# Download metadata filesppmi.download_metadata(['Demographics.csv','Age_at_visit.csv'])# Download 3D imaging metadatappmi.download_3D_T1_info()# Download imaging data (in DICOM format)ppmi.download_imaging_data([3001,3003,3011])# Download imaging data (in Nifti format)ppmi.download_imaging_data([3001,3003,3011],type='nifti')Scripts to use Selenium Gridppmi_scrapperprovides scripts for building and running
selenium webdriverbuild_seleniumBuild selenium grid singularity containerThis function is intended to be used as script
so arguments are passed by environment variables.PPMI_SINGULARITY_BUILD_CACHE: cache folder to store the built imagePPMI_SINGULARITY_SELENIUM_VERSION: version of selenium usedPPMI_SINGULARITY_BUILD_VERBOSE: enable verbose mode for the buildPPMI_SINGULARITY_BUILD_LOG: log file name to dump build's outputsUpon success, it exits with 0.
Upon failure, Client raises exceptions caught by the script wrapper
generated during the buildrun_seleniumRun selenium grid singularity containerThis function is intended to be used as script
so arguments are passed by environment variables.PPMI_SINGULARITY_SELENIUM_VERSION: version of selenium usedPPMI_SINGULARITY_RUN_CACHE: cache folder to find the built imagePPMI_SINGULARITY_RUN_VERBOSE: enable verbose mode for the runPPMI_SINGULARITY_RUN_LOG: log file name to dump run's outputs
Run the selenium grid singularity container by
creating and binding files required by the container
Communication is mapped on 4444 port.Upon success, it exits with 0.
Upon failure, Client raises exceptions caught by the script wrapper
generated during the run
Package: ppmongo
ppmongo-------Pretty-prints data from mongousage: ppmongo [-h] [-H HOST] [-P PORT] [-D DB] [-C COLLECTION][-f FIELDNAME [FIELDNAME ...]] [-j JSON] [--count] [--analyze][--flat][FIELD=VALUE [FIELD=VALUE ...]]Retrieve and pretty-print data from mongodbpositional arguments:FIELD=VALUE           A quick way to search by specific fieldsoptional arguments:-h, --help            show this help message and exit-H HOST, --host HOST  MongoDB host name-P PORT, --port PORT  MongoDB port-D DB, --database DB  database name-C COLLECTION, --collection COLLECTIONMongoDB collection name-f FIELDNAME [FIELDNAME ...], --field FIELDNAME [FIELDNAME ...]A list of fields that should be returned.-j JSON, --json JSON  A SON object that results must match--count               Count objects instead of displaying--analyze             Analyze documents structure--flat                Output flat tab-separated of field values instead ofpretty-printing objects. Requires a list of fields setwith -f options.
Package: ppnet
PPNetIntroductionWhat is PPNet?PPNet is designed to uses genome information and analysis of phylogenetic profiles with binary similarity and distance measures to derive large-scale bacterial association networks of a single species.InstallationPPNet has the following dependencies:prokkaroaryPython(>=version 3.7)Python modules:biopythonpyvisnumpyscipystatsmodelskneedpyaniInstall with the source codesDownload the source codes:gitclonehttps://github.com/liyangjie/PPNet.gitRename the main program and add the path to the environment variable:# Rename PPNet.py to PPNetmvPPNet/bin/ppnet.pyPPNet/bin/ppnet# Give the scripts executable permissionchmod+xPPNet/bin/*# Add the path to the environment variableechoexportPATH="/Path/to/PPNet/bin:$PATH">>~/.bashrcsource~/.bashrcInstall the Python dependencies:pipinstallbiopythonpyvisnumpyscipystatsmodelspyaniInstall the external dependances either from source or from your packaging system:prokkaroaryUsageppnet[Options]Options:
      [-h] show this help message and exit
      [-i1] [Required] The path of input genomes
      [-i2] [Required] The path of phenotype (e.g., pathogenic or non-pathogenic) of all strains
      [-o] The path of output (Default "./PPNet_output")
      [-x] The suffix of genomes data (Default "fasta")
      [-c] number of CPUs to use
      [-a] [Required] Select the algorithm for calculating the correlation coefficient [1-81], or set 0 to use all algorithm.
      [-pt] What percentage of interactions will be visualized (Default "1")AlgorithmSee Algorithm.docxExamplesppnet -i1 PATH/to/your/genomes/ -i2 group.csv -x fasta -c 4 -a 1InputThe genome file should be in fasta format and placed in the same path. The group.csvOutputPPNet_output/HQ_data/*:High quality genomes which with N50 > 10000；PPNet_output/NR_data/*:Non-redundant genome sets after deduplication；PPNet_output/Prokka_result/*:The result files of ProkkaPPNet_output/Gff_file/*:Include the GFF file extracted from the prokka_result folder with the input file for roaryPPNet_output/Roary_result/*:Result files generated by roaryPPNet_output/Roary_result/Statistical_test_result.csv:The result of Fisher's exact test for the distribution of each gene, by default, PPNet reports all genes with a adjusted p-value <0.05.PPNet_output/Roary_result/filted_phylogenetic_profile.csv:The phylogenetic profile of orthologs with significantly different distributions.PPNet_output/Roary_result/netwrok_result_method_x.csv:List the association coefficient calculated by algorithm x between each pair of genes.PPNet_output/Gene_net_x.html:A network plot inferred by algorithm x that can be opened with a browser(Google Chrome,Microsoft Edge etc.).By default, only first percent of interactions were visualized.LicensePPNet is free software under a GPLv3 license.
Package: ppnrespeakerdemo
Porcupine ReSpeaker DemoMade in Vancouver, Canada byPicovoiceThis package contains a commandline demo for controlling ReSpeaker 4-mic microphone array LEDs using Porcupine.PorcupinePorcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled
applications. It isusing deep neural networks trained in real-world environments.compact and computationally-efficient. It is perfect for IoT.cross-platform:Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RTRaspberry Pi, NVIDIA Jetson Nano, and BeagleBoneAndroid and iOSChrome, Safari, Firefox, and EdgeLinux (x86_64), macOS (x86_64, arm64), and Windows (x86_64)scalable. It can detect multiple always-listening voice commands with no added runtime footprint.self-service. Developers can train custom wake word models usingPicovoice Console.InstallationFollow the instructions onSeeed Studioto install and set up the microphone array.Then install the demo:sudo pip3 install ppnrespeakerdemoAccessKeyPorcupine requires a valid PicovoiceAccessKeyat initialization.AccessKeyacts as your credentials when using Porcupine SDKs.
You can get yourAccessKeyfor free. Make sure to keep yourAccessKeysecret.
Signup or Login toPicovoice Consoleto get yourAccessKey.UsageBelow are the colors associated with supported wake words for this demo:AlexaBumblebeeComputerHey GoogleHey SiriJarvisPicovoicePorcupineTerminatorRun the demo:porcupine_respeaker_demo --access_key ${ACCESS_KEY}Wait for the demo to initialize and print[Listening]in the terminal. Say:PicovoiceThe demo outputs:detected 'Picovoice'The lights are now set togreen. Say:AlexaThe lights are set toyellownow. Say:Terminatorto turn off the lights.
Package: ppocr-onnx
PPOCR-ONNX简介利用 onnxruntime 及 PaddleOCR 提供的模型, 对图片中的文字进行检测与识别.使用模型文字检测:ch_PP-OCRv3_det_infer方向分类:cls mobile v2文字识别:ch_PP-OCRv2_rec_infer参考PaddleOCR手把手教你使用ONNXRunTime部署PP-OCRch_PP-OCRv3_det_infer及ch_PP-OCRv2_rec_infer模型来自RapidAI/RapidOCR安装pipinstallppocr-onnx使用fromppocronnx.predict_systemimportTextSystemimportcv2text_sys=TextSystem()# 识别单行文本res=text_sys.ocr_single_line(cv2.imread('single_line_text.png'))print(res)# 批量识别单行文本res=text_sys.ocr_lines([cv2.imread('single_line_text.png')])print(res[0])# 检测并识别文本img=cv2.imread('test.png')res=text_sys.detect_and_ocr(img)forboxed_resultinres:print("{},{:.3f}".format(boxed_result.ocr_text,boxed_result.score))
Package: pponnxcr
pponnxcr - PaddlePaddle ONNXruntime OCROCR based on ONNX Runtime with PaddleOCR modelsRefractor by@hgjazhgjforppocr-onnxand implements:Updaterecmodel to v3Remove all unnecessary components e.g.draw-ocrAdd English, Japanese and TraditionalChinese language supportInstallpipinstallpponnxcrUsagefrompponnxcrimportTextSystemimportcv2ZHS=TextSystem('zhs')ZHT=TextSystem('zht')JA=TextSystem('ja')EN=TextSystem('en')img=cv2.imread('test.png')ZHS.ocr_single_line(img)ZHS.ocr_lines([img,...])ZHS.detect_and_ocr(img)LicenseGNU AGPLv3Which means that unless commercially licensed, any modification or use of this project in any way requires open sourceReferenceppocr-onnxRapidOCRPaddleOCRPaddle2ONNX
Package: ppopt
PPOPTPythonParametricOPtimizationToolbox (PPOPT) is a software platform for solving and manipulating
multiparametric programs in Python.InstallationCurrently, PPOPT requires Python 3.7 or higher and can be installed with the following commands.pipinstallppoptTo install PPOPT and install all optional solvers the following installation is recommended.pipinstallppopt[optional]In Python 3.11 and beyond there is currently an error with the quadprog package. An alternate version that fixed this error can be installed here.pipinstallgit+https://github.com/HKaras/quadprog/Completed FeaturesSolver interface for mpLPs and mpQPs with the following algorithmsSerial and Parallel Combinatorial AlgorithmSerial and Parallel Geometrical AlgorithmSerial and Parallel Graph based AlgorithmSolver interface for mpMILPs and mpMIQPs with the following algorithmsEnumeration based algorithmMultiparametric solution export to C++, JavaScript, and PythonPlotting utilitiesPresolver and Conditioning for Multiparametric ProgramsKey ApplicationsExplicit Model Predictive ControlMultilevel OptimizationIntegrated Design, Control, and SchedulingRobust OptimizationFor more information about Multiparametric programming and it's
applications,this paperis a good jumping point.Quick OverviewTo give a fast primer of what we are doing, we are solving multiparametric programming problems (fast) by writing
parallel algorithms efficiently. Here is a quick scaling analysis on a large multiparametric program with the
combinatorial algorithm.Here is a benchmark against the state of the art multiparametric programming solvers. All tests run on the Terra
Supercomputer at Texas A&M University. Matlab 2021b was used for solvers written in matlab and Python 3.8 was used for
PPOPT.CitationSince a lot of time and effort has gone into PPOPT's development, please cite the following publication if you are using
PPOPT for your own research.@incollection{kenefake2022ppopt,
  title={PPOPT-Multiparametric Solver for Explicit MPC},
  author={Kenefake, Dustin and Pistikopoulos, Efstratios N},
  booktitle={Computer Aided Chemical Engineering},
  volume={51},
  pages={1273--1278},
  year={2022},
  publisher={Elsevier}
}
Package: pp-out
视频运维平台实现对全网设备"全天候、全过程、全方位"集中监控
保证视频监控系统发挥最大效益简介视频运维平台适用于不同规模的视频监控系统的日常运维管理，系统对监控视频网络中前端、网络、存储、计算资源等全部设备运行信息进行采集，提供设备管理、设备及链路检测、视频质量诊断、网络拓扑可视化、巡检管理、告警提醒、工单管理、统计报表和绩效考核等日常运维管理应用功能，为视频监控系统发现故障、定位故障、跟踪处理情况及运维工作质量等提供全面支持。视频质量诊断对视频图像出现的雪花、滚屏、模糊、偏色、画面冻结、增益失衡、云台失控、视频信号丢失等常见摄像头故障、视频信号干扰、视频质量下降进行准确分析、判断和报警。基础监控底层采用zabbix，上层采用python对接zabbix jsonrpc webapi进行接口交互，基础监控功能如下：硬件监控通过SNMP、IPMI来进行路由器交换机的监控、服务器的温度等系统监控。CPU的负载内存使用率磁盘读写磁盘使用率磁盘inode服务监控。db：sqlserver、mysql、postgresql、oracle、redis、mongodb、Hadoop中间件：nginx、iis、tomcat、exchange、apache等网络监控dns解析可用性网络速度Web监控js相应时间服务响应日志监控自定义日志服务日志Patrol(巡警) 接口接口基于python语言3.x版本flask-restful框架开发。Auth 认证接口Login登录LoginOut登出Verify验证码Org 组织结构User API用户Organization组织Role角色Asset 资产接口
Package: pppack
PPPACKAuthor:Daniele TomatisDate:02/05/2017Piece-wise Polynomial Package for multivariate interpolation of real-valued scalar functions.This package offers representations of multi-dimensional functions using piece-wise polynomials. Python handles memory management, whereas external FORTRAN libraries perform all the numerics in order to ensure high computational performances with big amount of data.The main FORTRAN library is an enhanced version of pppack.f90, whose original version by J. Burkardt is available on www.netlib.org/pppack. External FORTRAN libraries are available in the folder src. To get the shared object to import in a python module just enter the pppack/lib folder and run…/bin/wrap.sh libnamewherelibnamecan be pppack, chebyshev_inter or divdif.The documentation is produced by Sphinx, so additional directives (as math) are used in the docstrings. Please see also the additional sphinx.extension-s used before making the doc.ContactPlease send bug reports, patches and other feedback to:Daniele . Tomatis ( at ) gmail . comContributions02/05/2017 - Esteban Szames, implementation of tests.
Package: pp-paths
pp_paths: A python script to pretty print directory structurespass the paths of files to be printed throughstdin. They will be printed as a tree using ascii art.Installationpip install pp_paths --userpp_paths [-c] [-b]-ccollapse directory sequences-bprint trees with a common base named '////'. The default is to print all the
trees formed separated with a '////'Examples> find b -type f|pp_paths
b/
├─a/
│ └─b/
│   └─c/
│     └─d/
│       ├─1
│       ├─2
│       └─3
├─s
├─b/
│ ├─w/
│ │ └─a
│ ├─q
│ └─x
├─r
└─q/
  ├─e
  └─y


> find b -type f|pp_paths -c
b/
├─a/b/c/d/
│ ├─1
│ ├─2
│ └─3
├─s
├─b/
│ ├─w/a
│ ├─q
│ └─x
├─r
└─q/
  ├─e
  └─y

> {find b -type d; find a}|pp_paths
b/
├─a/
│ └─b/
│   └─c/
│     └─d
├─b/
│ └─w
├─z
└─q

-*-*-
a/
└─b

user@machine /dev/pts/4 /home/user/src/pp_paths
> {find b -type d; find a}|pp_paths -b
////
├─b/
│ ├─a/
│ │ └─b/
│ │   └─c/
│ │     └─d
│ ├─b/
│ │ └─w
│ │
│ ├─z
│ └─q
└─a/
  └─bYou can easily integrate it with other tools which output list of files:> pacman -Ql tmux |awk '{print $2}'|pp_paths -c -b
/usr/
├─bin/tmux
└─share/
  ├─licenses/tmux/LICENSE
  └─man/man1/tmux.1.gz


> bsdtar tf ~/Downloads/emacs-for-clojure-book1.zip |pp_paths -c
emacs-for-clojure-book1/
├─.gitignore
├─README.md
├─customizations/
│ ├─editing.el
│ ├─elisp-editing.el
│ ├─misc.el
│ ├─navigation.el
│ ├─setup-clojure.el
│ ├─setup-js.el
│ ├─shell-integration.el
│ └─ui.el
│
...
Package: ppping
Descriptionping monitoring tool written in Python affecteddeadmanDemoEnvironmentppping works using python’s cursesList of environments actually checking the operationLinux distribution (Ubuntu, ArchLinux)Windows Subsystem for Linux (Ubuntu)UNIX (macOS)RequirementsPython 3.5 or latercURL (optional: used to acquire global IP)InstallFrom PyPi$pipinstall-UpppingStandalone (recommended)Download standalone script to a directory that is convenient for you (e.g. “~/.local/bin/” or “/usr/local/bin/”)$curl"https://raw.githubusercontent.com/johejo/ppping/master/standalone/ppping"-O$chmod+xpppingUsageSimple Usage$pppingfoo.combar.orgWW.XX.YY.ZZ...Futher help$ppping--helpConfig File Usage$ppping-c[CONFIG_FILE]This is a sample of configuration file.[Hosts]
google: www.google.com
google DNS: 8.8.8.8
GitHub: www.github.comDocker image$dockerrun--rm-itjohejo/pppingfoo.bar...When using the docker container, you can not read the config file by the method shown above.
It is necessary to mount the config file on the container. For example-vCONFIGFILE:/CONFIGFILELicenseMIT
Package: pppipam
PPPIPAM: Poor Person's Python IP Address ManagerPPPIPAMis a distribution package to provide a single IP address space manager for both IPv4 and IPv6 as a Python module for developers.InstallationPPPIPAMcan be installed usingpip. It requires Python 3.7.0+ to use.$ pip install pppipamUsage>>> from pppipam import AddressSpaceFeaturesSingle address space manager for both IPv4 and IPv6 networks and addresses.Strict or loose address space description (if strict, must add delegated networks first).Deleting IP objects can be done in cascade (e.g. removing a described network can remove all subnets and address).Data can be exported as adictcontaining all described IP instances and a nested network information according to address space's version.ConstraintsSource code must use only Python language and Python Standard Library.