Saravanan666/pypi-org huggingface dataset:
Package: conkits
Conkits (Console Kits)👋作用👋该库旨在封装常见 ANSI 控制码，使其使用的使用更加清晰和方便(大概)。ANSI 控制码包含了标准的前景色和背景色，256色，字体样式的设置，光标的移动和控制和终端擦除显示的功能。除此之外，还包含两个有趣的类(Dynamic 和 Choice)，可以实现在终端逐字打印字符(就像 gal 游戏里面逐字文本显示一样)和更加友好的选项选择。✨编写 conkits 的动机✨在写这个库的时候，我还是一个刚入门两个月不到的 python 新手。因为喜欢研究终端文本的显示效果，再加上我当时并不知道已经有很多类似封装 ANSI 代码的第三方库(而且还有像 rich 这种非常 nb 的库)，也不知道在 pypi 上搜索类似的库，导致我经常在代码中写了大量的 ANSI 控制代码😓。逐渐的，我产生了一种想法，那就是写一个包含常用 ANSI 代码的 python 库，然后上传到 pypi，让不知道 ANSI 代码的人也可以轻松的设置终端文本的显示样式。(当时我竟天真的认为类似的库几乎没有😃)然后有某天我接触到colorama这个库，才知道已经有人封装了 ANSI 代码😢。但是当时的我不太认可 colorama 封装的方式，认为写Fore.GREEN比写\033[32m要麻烦很多，何必在费力的去多敲这么多字符呢?不过这也暂时打消了我写一个库的想法，但是经过我一段时间的使用，发现 colorama 似乎没有封装控光标移动的 ANSI 代码🤔？这又突然激起了我写一个库的想法，于是，抱着扩展 colorama 库的想法并参考了 colorama 的部分代码，我决定写一个自己的第三方库，于是就有了 conkits。然而当我写到一半才发现，已经有太多封装 ANSI 代码的库了，比如 blessing，colored，库名中包含 ansi, term, color 等等的各种库。而且大多代码写得都很好😢😢😢。但是已经付出了这么多精力，总得有个结果吧，因为简单的封装大多数的库都有了，所以我决定再加一些特别的功能。就这样经过中途不断的增改和调试，一个多月后，我终于上传了conkits 0.2.1，一个算是稳定能用的版本，之后就在也没更新过了。就这样过了好几个月，回头再看这个库，发现很多地方的想法认知都有不少错误和不足，略感羞愧，于是我就从 pypi 中删除了 conkits。然后最近，之前被我介绍过 conkits 的某网友跟我说，他的某个参赛代码中用到了我的库，但是我已经把库删除了😧，所以我在稍微改了一些代码后，又上传了 conkits 然后就是现在的版本 0.2.2。🎁用法🎁查看 conkits_helpconkits_help是一个在终端交互式介绍用法的函数。灵感来源于 python 的help函数。当然这可能会不太方便，但是当时的我还不会写 Markdown，所以想了这么个歪招😭。但是介绍真的很详细，写 conkits_help 花了我好几个星期，把我都快写吐了。你可以在命令行通过以下命令运行 conkits_help:python-mconkits或者在 python 中通过以下代码运行 conkits_help:fromconkitsimportconkits_helpconkits_help()运行后大概会看到如下界面:conkits_help 运行截图使用 Fore, Back, Style 设置文本颜色和样式由于我当初设计上的问题，在定义的关于前/背景色和样式 ANSI 代码中，我保留了对应的 ANSI 整数值和 ANSI 代码字符串，因此，如果要真正的设置上样式，需要在对应属性的后面加上小写的s。比如下面的代码:fromconkitsimportFore# GREEN 代表对应的 ANSI 码，为整数值，GREENs 则是可以被终端识别的 ANSI 控制码字符串print(Fore.GREEN,repr(Fore.GREENs))print(Fore.YELLOWs+'Hello'+Fore.GREENs+'World'+Fore.RESETs)运行后的输出为:设置文本样式示例 1除此之外，还有 Back, Style 两个类分别用于设置背景色和文本样式，用法也基本同上，具体包含的属性可以查看源代码，文件为conkits/ansi.py。很简单，不用怕看不懂😀。使用 Cursor 类控制光标移动控制光标移动需要使用Cursor类，在该类中有一些方法带后缀_s，这表明该方法会返回一个可以输出到终端的 ANSI 控制码字符串，而不带_s后缀的则直接输出到终端。比如下面的例子:fromconkitsimportCursorprint('你好',end='')Cursor.move_left(2)print('吗')# 查看实际实际输出的 ANSI 控制码print(repr(Cursor.move_left_s(2)))Cursor 示例上述代码中，我们先输出你好二字，并且不换行，然后让光标向左移动两个单元格，之后再输出的吗将会覆盖好。实际上，上述代码中的Cursor.move_left(2)相当于print(Cursor.move_left(2), flush=True, end='')。其余方法可以查看源代码，文件为*conkits/ansi.py。下面的内容可以在我的 Github 主页上查看使用 Conio 类清除屏幕清除行进行非缓冲输入进行非阻塞性输入进行可打断的 sleep使用 Colors256 设置前/背景色使用 DynamicPrint 动态输出字符使用 Choice 创建选项
Package: conll-iterator
CONTENTS OF THIS FILEIntroductionSetupGetting startedExamplesINTRODUCTIONA simple iterator that reads conll and conllu files (https://universaldependencies.org/format.html) without keeping them in memory. It can iterate over words, sentences, or documents.SETUPpip install git+https://github.com/nicolaCirillo/conll_iterator.gitGETTING STARTEDfrom conll_iterator import ConllIterator
from tqdm import tqdm


sentences = ConllIterator('sample/sample_corpus.conllu', 
                         fields=['form', 'upos'], 
                         mode='sentences', 
                         join_char='/')

for s in tqdm(sentences):
    # do somethingEXAMPLESTraining word2vecfrom gensim.models import Word2Vec
from tqdm import tqdm
from conll_iterator import ConllIterator

sentences = ConllIterator('sample/sample_corpus.conllu', fields=['lemma', 'upos'], mode='sentences', join_char='/')
w2v_parameters = {'vector_size': 25, 'window': 5, 'min_count': 1, 'sg': 1, 'epochs': 15}
model = Word2Vec(tqdm(sentences), workers=5, **w2v_parameters)
model.save('sample_w2v')
word_vectors = model.wv
similar = list(zip(*word_vectors.most_similar('Pecorino/PROPN')[:10]))[0]
print("Most similar words to Pecorino/PROPN:")
print(similar)Keyword extraction via tf-idffrom itertools import chain
from collections import Counter

docs = ConllIterator('sample/sample_corpus.conllu', fields=['lemma', 'upos'], lower=['lemma'], mode='documents')
doc_tf = list()
df = Counter()
allowed_pos = ['NOUN', 'PROPN','VERB', 'ADJ']
for d in docs:
    tokens = list(chain(*d))
    tokens = [t[0] for t in tokens if t[1] in allowed_pos]
    tf = Counter(tokens)
    df.update(set(tokens))
    doc_tf.append(tf)

doc_keywords = list()
for d in doc_tf:
    doc_tfidf = [(w, d[w]/df[w]) for w in d]
    doc_tfidf = sorted(doc_tfidf, key=lambda x:x[1], reverse=True)
    doc_keywords.append(list(zip(*doc_tfidf[:10]))[0])

for i, k in enumerate(doc_keywords[:20]):
    print('keywords of doc {}:'.format(i+1), '; '.join(k))
Package: conll-transform
CONLL Transform - functions to manipulate CONLL dataThis package constains several functions to manipulate conll data:read_files: Read one or several conll files and return a dictionary of documents.read_file: Read a conll file and return dictionary of documents.write_file: Write a conll file.compute_mentions: Compute mentions from the raw last column of the conll file.compute_chains: Compute and return the chains from the conll data.sentpos2textpos: Transform mentions[SENT, START, STOP]to[TEXT_START, TEXT_STOP].textpos2sentpos: Transform mentions[TEXT_START, TEXT_STOP]to[SENT, START, STOP].write_chains: Convert a list of chains to a conll coreference column.replace_coref_col: Replace the last column oftar_docsby the last column ofsrc_docs.remove_singletons: Remove the singletons of the conll fileinfpath, and write the version without singleton in the conll fileoutfpath.filter_pos: Filter mentions that have POS in unwanted_pos, return a new mention list.check_no_duplicate_mentions: Return True if there is no duplicate mentions.merge_boundaries: Add the mentions ofboundary_docstocoref_docsif they don't already exist, as singletons.remove_col: Remove columns from all tokens in docs.write_mentions: Opposite forcompute_mentions().  Write the last column insent.compare_coref_cols: Build a conll file that merge the corefcols of several other files.to_corefcol: Write the conll fileoutfpathwith just the last column (coref) of the conll fileinfpath.get_conll_2012_key_pattern: Return a compiled pattern object to match conll2012 key format.merge_amalgams: Add amalgams in documents from where they have been removed.To use it, just import the function fromconll_transform, for example:fromconll_transformimportread_filesdocuments=read_files("myfile.conll","myfile2.conll")print(documents)The source can be found atGitHub.
Package: con-log
conlogA console logger / debugger for Python CLI programsCurrently conlog only support functions. support for classes will be added later.Installation$pipinstallconlogGetting Started# Create a Conlog from the constructor.conlog=Conlog.create("main")# Set the required logging levelconlog.level=Conlog.DEBUG# To use on the `main` moduleconsole=conlog.get_console()####  in module2.py@Conlog.fndefsay_hi(name):console=Conlog.get_console("main")console.debug("args {name=}")print(f"Hi{name}"How it works.console.debugsupportsexpr debugexpressions introduced in Python 3.8.
It dynamically rewrites the function in Python 3.8 and uses a beautiful way to support it in < 3.7See tests/ for samples.LicenseCopyright (c) 2019 Cswl Coldwindconlog is distributed under the termsMIT License <https://choosealicense.com/licenses/mit>_
Package: conmato
ConmatoA Command Line Interface (CLI) for Codeforces Management Tools that helps coaches to
manage Codeforces groups easier.FeaturesManaging members in a group (confirming or removing by Regex)Managing contests in a groupCrawling informations, standings, submissions in a contest to csv formatChecking plagiarism in a contestTo see more, check the followingdocsInstallationIt requirespython>=3.6. To install:python3-mpipinstallconmatoUsageYou can see document by passing--helpargument in each commandconmato--help

Usage:conmato[OPTIONS]COMMAND[ARGS]...

Options:--helpShowthismessageandexit.

Commands:configConfigcommand.contestContestcommands.getGetcommands.loginLogincommand.memberMembercommands.plagiarismPlagiarismcommands.conmatoget--help

Usage:conmatoget[OPTIONS]COMMAND[ARGS]...Getcommands.

Options:--helpShowthismessageandexit.

Commands:contestGetallstandingandsubmissioninacontest.memberGetmembersinagroup.pstandingsGetallstandingwithplagiarisminacontest.standingsGetstandinginacontest.submissionGetallsubmissioninacontest.usernameGetthisloggedusername.conmatogetstandings--help

Usage:conmatogetstandings[OPTIONS]Getstandinginacontest.

Options:
-g,--group-idTEXTGroupidinCodeforces.com.
-c,--contest-idTEXTContestidinCodeforces.com.[required]-f,--user-formatTEXTUserformat.
-cm,--commonFlagforgettingallparticipants(groupoutterincluded).

-o,--output-dirTEXTOutputdirectory.
--helpShowthismessageandexit.Almost all functions of Conmato require a manager account logging in before performing.
Usually, Conmato will ask on each action.
You can log in once and save the session for future uses by:conmatologinFor example, to get standings in a contest (e.x.http://codeforces.com/group/Ir5CI6f3FD/contest/269185/standings/groupmates/true),
you have to passgroup_id(e.x.Ir5CI6f3FD) andcontest_id(e.x.269185)conmatogetstandings-gIr5CI6f3FD-c269185For more detail, check thisdoc.Issues and ContributionThis project is in development, if you find any issues, please create an issuehere.If you are interested in contributing to this project, feel free to create a pull requesthere. We appreciate all your contributions.AcknowledgementThis project is sponsored and led by Prof. Do Phan Thuan, Hanoi University of Science and Technology.
Thank Thai Do (Dec1mo) and Tung Tran (tungtran309) for helping me to develop this project.
Package: conML
#  C O N S T R U C T I V I S T__ __#   _____ ____   ____   /  |/  // /#  / ___// __ \ / __ \ / /|_/ // /# / /__ / /_/ // / / // /  / // /___# \___/ \____//_/ /_//_/  /_//_____/#  M A C H I N E   L E A R N I N G
Package: conmo
ConmoDocumentation|PyPI PackageConmo is a framework developed in Python whose main objective is to facilitate the execution and comparison of different experiments mainly related to Anomaly Detection and Condition Monitoring problems.
These experiments consist of a series of concatenated stages forming a pipeline architecture, i.e. the output of one stage is the input of the next one.
This framework aims to provide a way to standarize machine learning experiments, thus being able to reconstruct result tables of scientific papers.RequirementsConmo works properly in Python versions 3.7 and 3.8. It has not yet been tested for operation with newer or deprecated versions.
To be able to start working with Conmo you need to have installed this list of libraries:NumpyPandasTensorflowScikit-LearnScipyRequestsPyarrowIf you want to make a contribution by modifying code and documentation you need to include these libraries as well:SphinxSphinx-rtd-themeIsortAutopep8InstallationThere are currently two ways to install Conmo:Package manager PipThe easiest way is to use the pip command so that it's installed together with all its dependencies.pipinstallconmoFrom source codeYou can also download this repository and then create a virtual environment to install the dependencies in.
We recommend this option if you plan to contribute to Conmo.gitclonehttps://github.com/MyM-Uniovi/conmo.gitcdconmoIn /scripts folder we provide a bash script to prepare a Conda environment ready for running Conmo:cdscripts
./install_conmo_conda.shIn case you are not using a Linux distribution and your OS is Windows 10/11 you can use Windows Subsytem for Linux (WSL) tool or create the virtual environment manually.
To check if the Conda enviroment is activated you should see a (conda_env_name) in your command line. If it is not activated, then you can activated it using:condaactivateconda_env_nameOverviewThe experiments in Conmo have a pipeline-based architecture. A pipeline consists of a chain of processes connected in such a way that the output of each element of the chain is the input of the next, thus creating a data flow. Each of these processes represents one of the typical generic steps in Machine Learning experiments. These steps are:DatasetsSplittersPreprocessesAlgorithmsMetricsIn "/examples" folder there are a small set of Conmo experiments with source code explained to try to help you understand how this framework works.AuthorsConmo has been developed byMetrology and Models Groupin University of Oviedo (Principality of Asturias, Spain).
However, as this is a collaborative project it is intended that anyone can include their own experiments, so please feel free to collaborate with us!Issues & BugsAs the project is currently in an early stage of development, it is easy for different bugs and issues to appear, so please, if you detect one post a ticket in the issues tab or contact the developers directly via email:mym.inv.uniovi@gmail.com
Package: connapi
connapiWrapper to Use all Public Apis with single call methodsFree software: MIT licenseDocumentation:https://connapi.readthedocs.io.APIS ImplementedAGIFY -(https://agify.io/)TODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2020-02-16)First release on PyPI.
Package: connattractor
connattractorLaboratory for Predictive Neuroimaging - University Hospital Essen, GermanyWebpage with manuscript and getting started guidepni-lab.github.io/connattractorHow to install the connattractor package?The package 'connattractor' will be soon available on PyPI.Afterwards the preferred way to install it will be:pip install connattractorFor now, the easiest way to try out the package is using docker, see below:How to re-run the analyses?Run the analyses with 1 click (in the cloud)Open in GitHub Codespaces (clickcodeon top right on the github page of the repo)Set up everything to run in 5 mins (locally on your computer)installdockerclone the repo and start your notebook in a docker containergitclonehttps://github.com/pni-lab/connattractor.gitcdconnattractor
dockerrun-it-v$PWD:/mounted/connattractor-p8080:8080-p8888:8888pnilab/connattractor:latestjupyternotebookcopy paste the last link in your browser to start the notebookBare-metal(developers)clone this repository with git as aboveset up a python environment (e.g with conda)install all requirements from requirements.txtstart developingHow to render the web-page locally?cddocs
myststart
Package: conn-check-configs
This is a set of Python modules and utilities for generatingconn-checkconfig YAML from other sources, such as Django settings modules.Supported sourcesDjango settings modulesUsageDjangoYou can export common settings from a Django application using theconn-check-djangocommand line
utility, which takes the following arguments:-m,--settings-module: the Python module for Django to import.-d,--database-name: the database schema name if not set asNAMEin the Django settings.--statsd-send: Optional string to send with statsd checks (defaults to a conn-check specific metric).--statsd-expect: Optional response string to expect from a statsd check.-f,output-file: Optionally output to a file rather thanSTDOUT.Followed by a path to the generated YAML file, for example:$ conn-check-django -m myapp.settings -f /tmp/myapp-conncheck.yaml
$ conn-check /tmp/myapp-conncheck.yamlExtending configuration generationYou may want to extend the generated checks with custom (or unsupported) settings,
this can be done by creating your own script and importing all the functions/variables
from the relevantconn_check_configssubmodule (e.g.django), and then extending
theEXTRA_CHECK_MAKERSlist with your own check making functions, which must take
2 arguments: the Django settings module and the options from the CLI (usually aargparse.Namespaceinstance).Building wheelsTo allow for easier/more portable distribution of this tool you can build
conn-check-configs and all it’s dependencies asPython wheels:make clean-wheels
make build-wheelsThebuild-wheelsmake target will build conn-check-configs and it’s base dependencies.By default all the wheels will be placed in./wheels.
Package: connect-4-cli
Connect 4 CLIThe classic 4-in-a-line game, also called connect 4, playable from the CLI or shell.DetailsThis is a simple implementation of the classic connect 4 game. It can be played directly from the CLI or shell.InstallationThe game is available as apippackage. Download it with the following command:pip install connect-4-cliPreferably, install invirtual environment.
In Windows, for example:Run the following commands from the CLI:py -m venv venvActivate the virtual environment with theactivatescript inside the./venv/Scripts/folder:Using command window:call venv/Scripts/activateOr usingpowershell:./venv/Scripts/activate.ps1pipinstall the package.How to playRun the following command (with activated virtual environment if installed there) to start the game:connect-4-cliPlay the game using keyboard keys, as prompted in the game.
Package: connectai
企联AI DevConnectAI定位为一个帮助公司更快地建立AI内部工具的平台产品背景由于各个公司的内部管理流程不同，会有很多无法标准化的长尾AI需求企业内部技术团队需要将1/3的时间用来开发维护这些内部AI工具如果能将低代码用于AI内部工具的开发，无疑将极大地提升企业技术团队的效率既然AI需求是无法标准化的，那么就标准化这些长尾AI需求的开发工具以低代码的方式提高内部AI工具开发效率
Package: connectapi-core
python-core-client (Python + ConnectAPI ❤)Client lib for communicating with the API backendMy purposeThis library is responsible for secure communication with the ConnectAPI backend,
this mean refreshing the access token and validating it.This library by it self can't communicate easily with the backend,
to communicate with a specific service you need to install the auto-generated library for that service,
you can get the information about this library and how to install it in the system dashboard under the service tab.This is a required library for every communication with the ConnectAPI backend
Package: connect-box
Python Client for interacting with the cable modem/router Compal CH7465LG which
is provided under different names by various ISP in Europe.UPC Connect Box (CH)Play Connect Box (PL)Irish Virgin Media Super Hub 3.0 (IE)Ziggo Connectbox (NL)Unitymedia Connect Box (DE)This module is not official, developed, supported or endorsed by UPC,
Unitymedia or Compal.There is an interface with is providing details about various states like the
DHCP lease table for Ethernet and Wifi.<?xml version="1.0" encoding="UTF-8"?><LanUserTable><Ethernet><clientinfo><interface>Ethernet2</interface><IPv4Addr>192.168.0.160/24</IPv4Addr><index>0</index><interfaceid>2</interfaceid><hostname>GW-B072BF27A983</hostname><MACAddr>B0:72:BF:27:A9:83</MACAddr><method>1</method><leaseTime>00:00:34:53</leaseTime>...connect_boxis handling the retrieval of the data and the parsing. The
primary use case is theHome Assistantupc_connectdevice tracker but one could use it in other projects as well.InstallationThe module is available from thePython Package Index.$pip3installconnect_boxOn a Fedora-based system or on a CentOS/RHEL 8 machine which has EPEL enabled.$sudodnf-yinstallpython3-connect-boxFor Nix or NixOS ispre-packed moduleavailable. The lastest release is usually present in theunstablechannel.$nix-env-iAnixos.python3Packages.connect-boxUsageThe fileexample.pycontains an example about how to use this module.DevelopmentFor development is recommended to use avenv.$python-mvenv.$sourcebin/activate$pythonsetup.pydevelopLicenseconnect_boxis licensed under MIT, for more details check LICENSE.
Package: connect-cli
CloudBlue Connect Command Line InterfaceIntroductionThe CloudBlue Connect Command Line Interface (CLI) is an extensible unified tool to perform various automation scenarios. With just one tool, you can control multiple Connect modules from the command line and automate them through scripts.Since it is extensible, users can write their own plugins to extend its functionalities.InstallPrerequisitesconnect-clidepends onGit,Cairo,PangoandGDK-PixBuf.Please refers to the platform-specific instructions on how to install these dependecies:LinuxMac OSWindowsUsing PIPTo useconnect-cliyou need a system with python 3.8 or later installed.$pipinstall--upgradeconnect-cliUsing DockerTo use the Docker image ofconnect-cli:$dockerrun-it-v$HOME/.ccli:/home/connect/.cclicloudblueconnect/connect-cliccliPlease refer to theconnect-clidocker image documentationfor more information.Using Homebrew on Mac OSTo installconnect-cliwith homebrew run:$brewupdate$brewtapcloudblue/connect$brewinstallcloudblue/connect/connect-cliUsing the installer on WindowsAn installer package is available for Windows 10 or newer.
You can download its zip file from theGithub Releasespage.UsageGeneralLocalesProductsCustomersReportsTranslationsProjectsStreamsDevelopmentWe useisortlibrary to order and format our imports, and we check it usingflake8-isortlibrary (automatically onflake8run).For convenience you may runpoetry run isort .to order imports.Run testsconnect-cliusespoetryfor dependencies management and packaging.To run theconnect-clitests suite run:$ pip install poetry
$ poetry install
$ poetry run pytestLicenseconnect-cliis released under theApache License Version 2.0.
Package: connect-client
This is the officialConnectpython client.Sign upIf you don’t already have a Connect account, sign up athttps://getconnect.io- it’s free!InstallThe library can be installed by pippip install connect-clientDocumentationAll of the documentation can be found athttp://docs.getconnect.ioContributingWe love open source, so we are happy to accept pull requests and will endeavour to fix the issues and include the enhancements that the community raise.TestsTests are run via py.test and tox.Changelog0.1.0Initial version
Package: connect-eaas-core
Connect Extension as a Service Core LibraryIntroductionConnect EaaS Coreis a library that contains the base building blocks to develop extensions for the CloudBlue Connect platform.InstallConnect EaaS Corerequires python 3.8 or later.Connect EaaS Corecan be installed frompypi.orgusing pip:$ pip install connect-eaas-coreDocumentationConnect EaaS Coredocumentationis hosted on theRead the Docsservice.LicenseConnect EaaS Coreis released under theApache License Version 2.0.