Saravanan666/pypi-org huggingface dataset:
Package: cutthelog
Cutthelogis a Python module and command-line tool to print an unseen tailing part of a log file.The documentation of the module is available in the code (import to readthedocs in progress). You can find the tool documentation below.On each execution, the tool saves an offset and value of the last line of a file in a cache. On the next reading of the same file, it jumps to that offset and checks the last line value. If the check passed the tool prints file content from the current position. If the check failed the tool prints the file from the beginning.Here is a demo:$ echo -e "one\ntwo\nthree" > example
$ cutthelog example
one
two
three
$ cutthelog example
$ echo -e "four\nfive\nsix" >> example
$ cutthelog example
four
five
six
$ cutthelog example
$ echo -e "seven\neight\nnine\nten\neleven\ntwelve" > example
$ cutthelog example
seven
eight
nine
ten
eleven
twelveThe tool requires only Python 2 or Python 3 interpreter of almost any version (by default using Python 3) and its standard libraries. All the code is located in the single file.By default, the cache is stored in the user’s home folder with the name.cutthelog. If the home folder is unavailable the tool creates the cache in the working directory. You can specify the cache path by the-c/--cache-fileoption.InstallationFrom PyPI:$ sudo pip install cutthelogor just download the tool file fromgithub, make it executable and move it to the proper bin folder e.g.$ chmod +x cutthelog.py; sudo mv cutthelog.py /usr/local/bin/cutthelogUsageIn most cases it’s enough to pass a logfile path:$ cutthelog /var/log/syslogYou can apply the tool to check the rate of some kinds of log messages:First, you should save the current log position to the cache by command$ sudo -u syslog cutthelog /var/log/syslog > /dev/nullThen you should create a cron job something likeMAILTO="admin@example.org"

*/10 * * * * syslog [ $(cutthelog /var/log/syslog | grep 'error' | wc -l) -gt 3 ] && echo "Too many errors"To process rotated logs more accurately you should add similar command as a pre-rotate script.To see all available options you should use the-h/--helpoption:$ cutthelog --help
Package: cuttlery
‘cuttlery’ is a package for determining the codon usage table of input
sequences. Instead of calculating an absolute codon usage frequency
for all observed genes as other packages do, cuttlery handles codon
usage frequency as a distribution for each codon. The cuttlery package
also contains tools for plotting codon usage information, calculating
if an unknown ORF is likely to belong to coding or noncoding sequence,
calculating nucleotide diversity of protein alignments and plotting
the results, as well as looking for synonymous and nonsynonymous
mutation heterogeneity in protein alignments.https://github.com/conchoecia/cuttlery
Package: cuttpy
CuttpyWrapper for theCuttlyURL shortener API.Installationpip install cuttpyorpython -m pip install cuttpyMake sure to generate an API key by signing up atcutt.lyand
by going to the edit account page,https://cutt.ly/edit.DocumentationThis READMEisthe documentation :smile:ExamplesBasic shortenerfromcuttpyimportCuttpy# define shortenershortener=Cuttpy("YOUR API KEY")# shorten URLresponse=shortener.shorten("https://www.google.com")# print shortened urlprint(response.shortened_url)Shortener with basic error handlingfromcuttpyimportCuttpy# define shortenershortener=Cuttpy("YOUR API KEY")# shorten URLresponse=shortener.shorten("https://www.google.com")try:# print shortened urlprint(response.shortened_url)# handle AttributeError because the attribute shortened_url does not return if there was an issue shortening the URLexceptAttributeError:print("An error occurred.")Shortener with advanced error handlingfromcuttpyimportCuttpy# define shortenershortener=Cuttpy("YOUR API KEY")# shorten URLresponse=shortener.shorten("https://www.google.com")# check response code and act accordingly# you can learn what codes there are later in the READMEifresponse.code==0:# a response code of 0 means there was a serverside error.# it is a good idea to also print the http code in this case.# print the builtin description for the errorprint(f"{response.description}\n{response.http}")elifnotresponse.code==7:# print the builtin description for the errorprint(response.description)elifresponse.code==7:# a response code of 7 means there was no error.# print shortened urlprint(response.shortened_url)ClassesCuttpy()The only class worth your time in this library.Methods:shorten(url)- Returns aCuttpyResponse()object with everything you need.CuttpyResponse()The return type of methodCuttpy().shorten()with various attributes.Attributes:Attributes that always returnThese attributes always return even if the API fails.NameDescriptionhttpThe HTTP status code returned by the API.codeThe code that the wrapper returns. View what they mean below.descriptionA hardcoded description for each wrapper code. View what they mean below.Wrapper Codes and descriptions


0 - Unknown serverside error

1 - URL has already been shortened

2 - Entered URL is not a URL

3 - Preferred URL name is already taken

4 - Invalid API key.

5 - URL did not pass the validation. Includes invalid characters

6 - URL provided is from a blocked domain

7 - URL has been shortened successfullyAttributes that return only if the URL was shortened.Title speaks for itself.NameDescriptionoriginal_urlThe original URL which was shortened by the API.shortened_urlThe shortened version of the original URL.ExceptionsThis API wrapper uses a system of error codes. View what they mean in the attributes section of theCuttpyResponse()class.
Package: cutty
An experimental Cookiecutter clone :construction:RequirementsPython 3.9+InstallationYou can installcuttyviapipfromPyPI:$pipinstallcuttyUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,cuttyis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: cut-video
介绍使用python来剪辑视频！借助moviepy这个库github repo:https://github.com/chunleili/cut_video安装你可以使用pip install cut_video来下载。（推荐，因为还会在你系统内安装同名的命令行工具）或者直接clone这个repo用法在当前文件夹下你需要提供mp4视频: full.mp4你需要在to_erase.json中输入需要剪辑去掉的部分例如[["0:22","1:14"],["1:25","1:30"],["2:13","2:25"]]代表我想要去掉0:22-1:14, 1:25-1:30 和2:13-2:25 这三个时间片段, 保留剩下的。结果在 final.mp4当中假如你使用pip的方式安装，只需要运行cut_video即可其他运行方式你还可以运行python脚本的方式来运行python cut_video.py或者如果你安装了jupyter，也可以运行ipython脚本cut_video.ipynb视频教程请见视频教程https://www.bilibili.com/video/BV1xG4y137WJ/
Package: cutword
jieba 不维护了，所以有了cutword。本项目充分借鉴了苏神的bytepiece的代码，在此表示感谢。https://github.com/bojone/bytepiececutword 是一个中文分词库，字典文件根据最新数据统计得到，词频更加合理。分词速度是jieba的两倍。
可通过 python -m cutword.comparewithjieba 进行测试。1、安装：pip install cutword2、使用：2.1分词功能fromcutwordimportCuttercutter=Cutter()res=cutter.cutword("你好，世界")print(res)本分词器提供两种词典库，一种是基本的词库，默认加载。一种是升级词库，升级词库总体长度会比基本词库更长一点。如需要加载升级词库，需要将 want_long_word 设为TruefromcutwordimportCuttercutter=Cutter()res=cutter.cutword("精诚所至，金石为开")print(res)# ['精诚', '所', '至', '，', '金石为开']cutter=Cutter(want_long_word=True)res=cutter.cutword("精诚所至，金石为开")print(res)# ['精诚所至', '，', '金石为开']2.2命名实体识别2.2.1 使用方法frompprintimportpprintfromcutwordimportNERner=NER()res=ner.predict("奈雪的茶，新茶饮赛道开创者，创立于2015年，推出“茶饮+软欧包”双品类模式。聚焦以茶为核心的现代生活方式，奈雪已形成“现制茶饮”、“奈雪茗茶”及“RTD瓶装茶”三大业务版块，成功打造“霸气玉油柑”、“鸭屎香宝藏茶”等多款行业经典产品。",return_words=False)pprint(res)'''[[NERItem(entity='奈雪的茶', begin=0, end=4, ner_type_en='COMMERCIAL', ner_type_zh='商业'),NERItem(entity='茶饮', begin=6, end=8, ner_type_en='MANUFACTURE', ner_type_zh='物品'),NERItem(entity='2015年', begin=17, end=22, ner_type_en='TIME', ner_type_zh='时间'),NERItem(entity='茶饮', begin=26, end=28, ner_type_en='MANUFACTURE', ner_type_zh='物品'),NERItem(entity='软欧包', begin=29, end=32, ner_type_en='MANUFACTURE', ner_type_zh='物品'),NERItem(entity='茶', begin=42, end=43, ner_type_en='FOOD', ner_type_zh='食品'),NERItem(entity='现代', begin=47, end=49, ner_type_en='TIME', ner_type_zh='时间'),NERItem(entity='奈雪', begin=54, end=56, ner_type_en='ORG', ner_type_zh='组织'),NERItem(entity='茶饮', begin=62, end=64, ner_type_en='MANUFACTURE', ner_type_zh='物品'),NERItem(entity='奈雪茗茶', begin=67, end=71, ner_type_en='COMMERCIAL', ner_type_zh='商业'),NERItem(entity='RTD瓶装茶', begin=74, end=80, ner_type_en='FOOD', ner_type_zh='食品'),NERItem(entity='玉油柑', begin=95, end=98, ner_type_en='FOOD', ner_type_zh='食品'),NERItem(entity='鸭屎香宝藏茶', begin=101, end=107, ner_type_en='FOOD', ner_type_zh='食品')]]'''2.2.2 支持的实体类型编号英文类型名中文类型名1FOOD食品2MATTER物质3MANUFACTURE物品4CREATION作品5ORG组织6PC计算机7PROPERTY属性8COMMERCIAL商业9INCIDENT事件10CREATURE生物11BASE基础12AFFAIR活动13TIME时间14LOC位置15PHYSIOLOGY组织器官16PERSON人名17TERMINOLOGY领域术语Star History
Package: cutyx
✨ Tool to organise your image gallery using machine learning. ✨💫CutyXTool to organise your image gallery using machine learning.To get an overview howCutyXcan be used, see the examples in thedocumentation.A simple usage is shown below:cutyxmatchfaces/path/to/some/image.jpgalbums/some-album
cutyxrunThis will register similar faces as inimage.jpgto be automatically sorted into thealbums/some-albumdirectory. The second command will do the actual sorting (copying or symlinking).The source for this project is available here.💿 InstallationTo install it with pip run:pip install cutyx.To install this package locally for development, clone it first usinggit clone https://github.com/leahevy/cutyxand run:pip install -e .[dev].For installation of the latest version you can install it with pip directly fromGitHubwith the command:pip install git+https://github.com/leahevy/cutyx.git.💡 Shell completionFor shell completion run one of the appropriate commands:cutyx --install-completion=bashcutyx --install-completion=zshcutyx --install-completion=fish⌨️ CommandsThe following commands are available:runProcess images anywhere in a directory hierarchy.process-imageProcess a single imagematchGroup of commands to register matching conditions for album directories.update-cacheGenerates or updates the cache for processed images.clear-cacheClears the local cache.📖 DocumentationThe documentation is available athttps://leahevy.github.io/cutyx.👥 ContributingWant to add a contribution toCutyX? Feel free to send apull request.See alsohere.🎓 LicenseCopyright (C) 2022 Leah LacknerThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program 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 General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, seehttp://www.gnu.org/licenses/.
Package: cuuats.datamodel
CUUATS Data Model DocumentationIntroductionCUUATS Data Model is a lightweight data access layer for ArcGIS. It provides
tools to build Python classes that can be used to interact with data stored in
geodatabase feature classes. CUUATS Data Model was originally developed to
perform quality control on field data and to transform raw measurements into
indexed values using scales.CompatibilityCUUATS Data Model is developed and tested with ArcGIS Desktop 10.2.2.CreditsCUUATS Data Model was developed by Matt Yoder for the Champaign Urbana
Urbanized Area Transportation Study (CUUATS). The many-to-many field was
developed by Edmond Lai.Changelog0.2.0 (2018-06-07)Added ManyToManyField.0.1.1 (2017-08-10)Added unicode type for coded domain values.0.1.0 (2017-06-20)Initial release
Package: cuvec
Unifying Python/C++/CUDA memory: Python buffered array ↔ C++11std::vector↔ CUDA managed memory.Table of contentsWhyNon objectivesInstallUsageExternal ProjectsContributingLicenceWhyData should be manipulated using the existing functionality and design paradigms of each programming language. Python code should be Pythonic. CUDA code should be… CUDActic? C code should be… er, Clean.However, in practice converting between data formats across languages can be a pain.Other libraries which expose functionality to convert/pass data formats between these different language spaces tend to be bloated, unnecessarily complex, and relatively unmaintainable. By comparison,cuvecuses the latest functionality of Python, C/C++11, and CUDA to keep its code (and yours) as succinct as possible. “Native” containers are exposed so your code follows the conventions of your language. Want something which works like anumpy.ndarray? Not a problem. Want to convert it to astd::vector? Or perhaps a rawfloat *to use in a CUDA kernel? Trivial.Less boilerplate code (fewer bugs, easier debugging, and faster prototyping)Fewer memory copies (faster execution)Lower memory usage (do more with less hardware)Non objectivesAnything to do with mathematical functionality. The aim is to expose functionality, not (re)create it.Even something as simple as setting element values is left to the user and/or pre-existing features - for example:Python:arr[:]= valueNumPy:arr.fill(value)CuPy:cupy.asarray(arr).fill(value)C++:std::fill(vec.begin(),vec.end(),value)C & CUDA:memset(vec.data(),value, sizeof(T) *vec.size())InstallRequirements:Python 3.7 or greater (e.g. viaAnaconda or Miniconda, or viapython3-dev)(optional)CUDA SDK/Toolkit(including drivers for an NVIDIA GPU)note that if the CUDA SDK/Toolkit is installedafterCuVec, then CuVec must be re-installed to enable CUDA supportpipinstallcuvecUsageSeethe usage documentationandquick examplesof how to upgrade a Python ↔ C++ ↔ CUDA interface.See alsoNumCu, a minimal stand-alone Python package built using CuVec.External ProjectsFor integration into Python, C++, CUDA, CMake, pybind11, and general SWIG projects, seethe external project documentation.
Full and explicit example modules using theCPython API,pybind11 API, andSWIGare also provided.ContributingSeeCONTRIBUTING.md.LicenceCopyright:Casper O. da Costa-LuisContributors
Package: cuvis
cuvis.pythoncuvis.python is the python wrapper for the Cuvis SDK written in C (available here).Website:https://www.cubert-hyperspectral.com/Source code:https://github.com/cubert-hyperspectral/Support:http://support.cubert-hyperspectral.com/This wrapper enables operating Cubert GmbH Hyperspectral Cameras, as well as,
analyzing data directly from the corporate data format(s) within python.
This wrapper provides an object-oriented full representation of the basic C SDK
capabilities and python return variable formats based on commonly used modules,
like dictionaries, datetime or numpy objects.For other supported program languages, please have a look at the
source code page.InstallationPrerequisitesFirst, you need to install the Cuvis C SDK fromhere.
The installation registers the installation path in the environment, which
the python wrapper is linked to.:warning:If the C SDK is reinstalled into another directory later on, the
linkage breaks and the python wrapper might stop working.Via pipIf you wish to use cuvis within another project, from within your
project environment, runpip install cuvisor addcuvisto your projectrequirements.txtorsetup.py.
We currently provide pre-compiled binaries for Python 3.9, 3.10, 3.11 and 3.12 for Windows 64-bit.Via repositoryIf you wish to download and use cuvis locally, clone the git repositorygitclonegit@github.com:cubert-hyperspectral/cuvis.python.gitand install the wrapper locally.For building the python bindings refer tocuvis.pyil.How to ...Getting startedWe provide an additional example repositoryhere,
covering some basic applications.Further, we provide a set of example measurements to explorehere.
These measurements are also used by the examples mentioned above.Getting involvedcuvis.hub welcomes your enthusiasm and expertise!With providing our SDK wrappers on GitHub, we aim for a community-driven open
source application development by a diverse group of contributors.
Cubert GmbH aims for creating an open, inclusive, and positive community.
Feel free to branch/fork this repository for later merge requests, open
issues or point us to your application specific projects.
Contact us, if you want your open source project to be included and shared
on this hub; either if you search for direct support, collaborators or any
other input or simply want your project being used by this community.
We ourselves try to expand the code base with further more specific
applications using our wrappers to provide starting points for research
projects, embedders or other users.Getting helpDirectly code related issues can be posted here on the GitHub page, other, more
general and application related issues should be directed to the
aforementioned Cubert GmbHsupport page.
Package: cuwais-common
AIWarSoc CommonThe common code and data shared by the other modules.Contains:A python repository defining the database tables and connection methods, as well as a config file parser and gamemode definitionsA default_config.yml file used by many other modules, which gives all of the available config options and their defaultsUsagePythonYou can add the python module usingpip install cuwais-commonFrom there you can use a database connection in the following way:withcuwais.database.create_session()asdb_session:user_scores=db_session.query(User,func.sum(Result.points_delta).label("total_score")).outerjoin(User.submissions)\.outerjoin(Submission.results)\.group_by(User.id)\.order_by("total_score")\.all()or get a gamemode and all of its data by usingGamemode.get(‘game-name’)ConfigThe default config file can be got usingwget default_config.yml https://raw.githubusercontent.com/AI-Wars-Soc/common/main/default_config.ymlContributingFirst ensure that you have read all of the Contributing rules within theserverrepository. If you do not set up your contribution environment correctly then you may end up missing a step.GamemodesThe most common way of contributing to this repository is to add a new gamemode. A gamemode describes a possible game that can be used in a competition, eg. chess.To add a new gamemode you must do the following:Add any libraries required by your gamemode tosetup.pyAdd a new class which implements the abstract classGamemodewithingamemodes.pyAllow your gamemode to be found by adding it to theget(gamemode_name: str)function in theGamemodeclass.Once you have done this, it will be possible for AIs to be written and battled for your new game. HOWEVER this does not mean that your game is fully implemented! You must also ensure that thesandboxdocker image contains all of the libraries that you have used in your gamemode, and if you want your game to be playable in the browser then you must also add rendering code toweb-app. Head to both of these repositories to find relevant contribution instructions.ConfigThe other way of contributing to this repository is by adding options to thedefault_config.ymlfile. This file is included in the build step of all relevant docker images, so the only tricky bit to remember when contributing here is that you must ensure that you merge your updates to thedefault_config.ymlfile here before you merge your updates to whatever needs the new config options. That way thedefault_config.ymlfile will be the new version when your new changes in the other modules are built.Along the same lines, if you change a default value then all of the modules that use that value must be rebuilt so that they are aware of the change.
Package: cv
cvCheck version of a Python module.Raises an error if the<module>.__version__:already present on PyPI or custom warehouse deployment;does not matchPEP 440;or does not match specified type: alpha/beta/rc/dev/release.All of this comes in handy during CI.And one more thing: No runtime dependencies!InstallationAvailable fromPyPI:pipinstallcvModule ExampleWith a <module> present on PyPI and<module>.pyin current directory:__version__='7.7.7'...Simply run:cv<module>If7.7.7version of <module> is on PyPI already you’ll get aVersionExistserror:__main__.VersionExists: Package "<module>" with version "7.7.7" already exists on PyPI.
Change the "<module>.__version__" to fix this error.Package ExamplePackages work in the same way as modules except__version__is defined in<module>/__init__.pyHelp$ cv --help
usage: cv [-h] [-w WAREHOUSE] [--alpha] [--beta] [--rc] [--dev] [--release]
          [--dry]
          module

Check version of a Python package or module.

positional arguments:
  module                the package/module with "__version__" defined

optional arguments:
  -h, --help            show this help message and exit
  -w WAREHOUSE, --warehouse WAREHOUSE
                        package index to use, default is
                        "https://pypi.org/pypi"
  --alpha               check that version is an alpha, e.g. 1.0.0a1
  --beta                check that version is a beta, e.g. 1.0.0b2
  --rc                  check that version is a release candidate, e.g.
                        1.0.0rc
  --dev                 check that version is in development, e.g. 1.0.0.dev3
  --release             check that version is a release without modifiers,
                        e.g. 1.0.0
  --dry                 make no request to PyPI
Package: cv2box
cv2box - cv toolbox# RECOMMEND - inclue cv2/numpy/tqdmpipinstallcv2box# clean install - no other dependenciespipinstallcv2box--no-dependencies# full install - referring requirements.txtpipinstallcv2box[full]# clone codes to your project without installgitclonegit@github.com:ykk648/cv2box.git--depth1# Examplefromcv2boximportCVImageimg_in='img path'# or img_in = cv2.imread('img path')img_bgr=CVImage(img_in).bgrCVImage(img_in).resize(256,256).show()A gather of tools or funcs frequently using in my work.Now including these functions:cv opsCVBbox, different bbox filter.CVCamera, loadmulticalcalibrate result and give any format of camera matrix.CVExcel, basic ops for insert data to excel file.CVFile, for multi-class file io.CVFolder, higher level folder op to do batch file operate.CVImage, a image class support multi format convert.CVQueue, queue-like high-level class which can be used for two different python projects communicate on same host machine.CVRotate, under dev.CVVideo, a video class support multi kinds of ops.cv gearsNow supports mode switch from multi-thread/multi-process/torch-process.CV threads base, fast construct your multithread AI program.CV video thread, give a video path to get a frame thread.CV multi video thread, give multical video path to get a multi frame thread.shell scriptsbatch uncompress script support different types(zip tar 7z etc.)utilsfps counterapschedulermath (cal distancesAI power (moved toAI_power)
Package: cv2-collage
Create a collage from images with OpenCVfromcv2_collageimportcreate_collagelst=["https://raw.githubusercontent.com/hansalemaos/screenshots/main/2022-12-27%2003_40_27-.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/2022-12-27%2003_49_27-.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/2022-12-27%2004_01_57-.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/2022-12-27%2004_02_09-.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/colorfind1.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/colorfind2.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/colorfind3.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/colorfind4.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/zoompics_0000006.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/zoompics_0000007.png","https://raw.githubusercontent.com/hansalemaos/screenshots/main/zoompics_0000008.png",]fromnumpy_choicesimportget_random_items_from_listcoldone=create_collage(lst=get_random_items_from_list(lst,16),width=1000,background=(0,0,0),save_path=r"F:\cv2mergepics\m\tetete.png",)