Saravanan666/pypi-org huggingface dataset:
Package: kcsd
This 2.0 version of kCSD method.Only supported for python 3.Relevant PapersPaper 1: “Kernel Current Source Density
Method”, J. Potworowski, W. Jakuczun, S. Łȩski, D. K. Wójcik; Neural
Comput 2012; 24 (2): 541–575, doi:https://doi.org/10.1162/NECO_a_00236Paper 2 : “What we can and what we cannot see with extracellular
multielectrodes”, C. Chintaluri, M. Bejtka, W.
Średniawa, M. Czerwiński, J. M. Dzik, J.
Jędrzejewska-Szmek, K. Kondrakiewicz, E. Kublik, D. K. Wójcik; PLoS
Computational Biology (2021), 17(5): e1008615, doi:https://doi.org/10.1371/journal.pcbi.1008615Paper 3 : “kCSD-python, reliable current source density estimation
with quality
control”, C. Chintaluri, M. Bejtka, W. Średniawa, M. Czerwiński, J. M. Dzik, J. Jędrzejewska-Szmek, D. K. Wójcik;
bioRxiv, doi:https://doi.org/10.1101/708511Paper 1 is the original paper with software code in Matlab.
Paper 2 is an improvement and development of the paper 1.
Paper 3 is a feature showcase and walk-through of the method and its applications.TutorialsThis library comes with three tutorials anddoes not require any installation.Basic tutorialAdvanced tutorialsKCSD tutorialMore information on the tutorials is provided hereTutorials!You can also save these tutorials on your desktop, for this you will
need to install jupyter-notebook. Do this bypipinstalljupyternotebookFiguresThis library includes all the necessary scripts to generate the figures for papers 2 and 3.Figures for Paper 2Figures for Paper 3Installationuser:~/$pipinstallkcsdMore InstallationsDocumentationAutogenerated documentation available from readthedocs:kCSD-python_ReferenceAlso included here are authors and their contributions, citation policy, contacts etc.,Earlier Stable versionsPlease see git tags for earlier versions. These are not available as packages unfortunately.v1.2 corresponds to the first time kCSD-python released as a python packagev1.0 corresponds to the version with the test cases written inside tests folderv1.1 corresponds to the elephant python library version - no tests hereHealth ReportContinuous Integration (Travis):Test Coverage :Documentation Status:LicenseModified BSD License
Package: kcsd-battleship-mp
This package provides a simple client and server for the Battleship game.
It tries to be roughly compatible with the Collaborative Software Design implementation.
Behind the scenes, it usesWebSocketfor communication between client and server.Client UsageThe entire client functionality is encapsulated bybattleship_mp.client.GameSession.
This allows to join a game, transmit a board of set ships, and exchange shots.
Note that the server doesnotperform any notable game state evaluation;
multiplayer peersmustexchange their game state (notably, their entire game board)
to locally check the game progress.
See the class’ description on how to use it.Set the environment variableBMP_SERVER_URLto the server’s websocket URL.
For example, if the server runs locally on port8765then useBMP_SERVER_URL="ws://localhost:8765"before starting the program.
Alternatively, this can also be set inside Python by usingos.environ["BMP_SERVER_URL"]="ws://localhost:8765".Server UsageThe server is self-contained and does not need to be embedded in a program.
It can be run directly from the command line, requiring a port and optionally
the addresses/hostnames to bind to.
For testing, it is suitable to bind tolocalhost:~$python3-mbattleship_mp.server8765localhostSee the--helpflag for options.InstallationYou can install the package directly viapip;
only Python >= 3.8 is required.~$python3-mpipinstallkcsd-battleship-mp
Package: kcshell
What is it:Simple Python3 based interactive assembly/disassembly shell for various
architectures powered byKeystone/Capstone.I simply got tired of usingmetasm_shellandnasm_shellto assemble and disassemble code.KeystoneandCapstoneare awesome and… I
like Python.How to install it:pip3installkcshellOR (assuming you have Keystone and Capstone build toolchains installed)gitclonehttps://github.com/fdiskyou/kcshellcdkcshellpythonsetup.pyinstallUsageBy default ‘kcshell’ starts in ‘assembler’ mode (x86 32 bits). You can
change modes with ‘setmode’, and you can also change the default
architecture for both the ‘assembler’ and ‘disassembler’ with ‘setarch’.$kcshell-=[kcshellv0.0.3]=-DefaultAssemblerarchitectureisx86(32bits)asm>lsmodesdisasm,asmasm>setmodedisasmDefaultDisassemblerarchitectureisx86(32bits)disasm>lsarchsx86,mips32,arm_t,x64,arm,x16,arm64,mips64disasm>setarchx64Disassemblerarchitectureisnowx64disasm>To assemble instructions just type the instructions in the command line.asm>jmpesp"\xff\xe4"asm>xoreax,eax"\x31\xc0"asm>setarchx64Assemblerarchitectureisnowx64asm>incrax"\x48\xff\xc0"asm>To go from opcodes to instructions just type them in the command line.disasm>\xff\xe40x00400000:jmpespdisasm>\x31\xc00x00400000:xoreax,eaxdisasm>setarchx64Disassemblerarchitectureisnowx64disasm>\x48\xff\xc00x00400000:incraxdisasm>For help just use ‘?’ or ‘help <command>’.asm>?Documentedcommands(typehelp<topic>):========================================EOFexithelplsarchslsmodesquitsetarchsetmodeasm>setmodedisasmDefaultDisassemblerarchitectureisx86(32bits)disasm>?Documentedcommands(typehelp<topic>):========================================EOFexithelplsarchslsmodesquitsetarchsetmodedisasm>To list all the supported architectures just go to the desired mode and
use ‘lsarchs’.asm>lsarchsmips64,sparc64,sparc,arm_t,x64,x16,arm64,hexagon,systemz,mips32,ppc64,x86,arm,ppc32asm>lsmodesasm,disasmasm>setmodedisasmDefaultDisassemblerarchitectureisx86(32bits)disasm>lsarchsmips64,x16,arm64,mips32,arm_t,x86,arm,x64disasm>TODORead input from filesSet a proper base address for 64 bits architectures
Package: kctzstyle-first-package
kctzstyle’s first package@Author: kctzstyle
@Twitter: @kctzstyle
@Reddit: u/kctzstyle
@GitHub:https://github.com/kctzstyle/@GitHub Repository:https://github.com/kctzstyle/first-pypi-package/:: Description ::
This is kctzstyle’s First Package Tutorial using GitHub Python Actions!
깃허브 파이썬 액션 튜토리얼입니다! 감사합니다!
Package: kc-utils
kc_utilsinstallpipinstall--upgradekc_utils-ihttps://pypi.org/simple/
pipinstall--upgradekc_utils['cache_redis']-ihttps://pypi.org/simple/usetimeget_time#:param num: 和unit配合使用计算时间#:param sf: %Y%m%d%H%M%S#:param unit: days = None, seconds = None, microseconds = None, milliseconds = None, minutes = None, hours = None, weeks = None#:return: %Y%m%d%H%M%S 格式化时间fromkc_utils.timeimportget_timeget_time()#'20220320'get_time(1)#'20220321'get_time(-1)#'20220319'get_time(-1,sf="%Y%m%d%H%M%S")#'20220319055451'get_time(-1,sf="%Y%m%d%H%M%S",unit="hours")#'20220320045504'format_time#:param log_date: 字符串日期#:param sf: %Y%m%d%H%M%S#:param new_sf: %Y%m%d%H%M%S#:return: 字符串日期fromkc_utils.timeimportget_time,format_timeformat_time(get_time())# '2022-03-20'cachepipinstall--upgradekc_utils['cache_redis']-ihttps://pypi.org/simple/redis#redis 装饰器#:param time: ttl## time==0 or redis_store=None, 则不走缓存;## time > 0, 则走缓存,缓存时间为time;## time = -1, 则走缓存，缓存时间为永久.## time = -2, 则不走缓存，并覆盖现有缓存#:param redis_key_prefix: redis key prefix#:param redis_store: redis_storefromkc_utils.cache.redis_helperimportredising,KcRedisdb_redis=KcRedis(host='x',port=6379,password="xxx").get_redis()@redising(time=10,redis_key_prefix="test",redis_store=db_redis)deffunc_xxx(*args,**kwargs):return"xxx"
Package: kcwidrp
This pipeline is the official DRP for the Keck Cosmic Web Imager at the W. M.
Keck Observatory. It is built on theKeckDRPFramework,
and is a port of the oldIDL pipeline.The pipeline has only been thoroughly tested on MacOS, Ubuntu, and CentOS, although it
should run smoothly on any unix-like system that runs Python. Unfortunately, Windows
is not currently supported.01-Apr-2021DocumentationDocumentation for this project is maintained with ReadTheDocs at the following link:https://kcwi-drp.readthedocs.ioTestsStatus of tests:https://travis-ci.com/github/Keck-DataReductionPipelines/KCWI_DRPDependenciesPlease see ‘Installing KCWIDRP’ in the documentation above for details about
dependencies and how to satisfy them.Developed by Luca Rizzi, Don Neill, Max Brodheim (04/2021)
Package: kd100
kd100I’m a small script that help you get express package information use kuaidi100.com api.Install$pipinstallkd100Usagekd100: normal use, type code in shell, return info.kd100-ccode:-cmeans code, specified express code.kd100-ccode-ofilename:-omeans output, specified output filename.kd100-ccode-ofilename-pcompany_name:-pmeans company, specified the express company name, useful when auto guess is error.Typekd100-hfor more help.Exampleyourname@computer:~$kd100Inputyourexpresscode:<Yourcode>Possiblecompany:zhongtong,kuayue,yuantong,zhaijisong,shengfengwuliuTryzhongtong...Donecode:<Yourcode>company:zhongtongischecked:0=================================================================time|content-----------------------------------------------------------------2015-08-1617:40:28|firstcontent-----------------------------------------------------------------2015-08-1617:02:32|somecontent-----------------------------------------------------------------...-----------------------------------------------------------------2015-08-1320:41:27|lastcontent=================================================================Changelogv0.0.6add fake Chrome UA to solve the random result problemv0.0.5express code should no long be numbers, to support company like APELAXv0.0.4remove third party module dependencies.add-poption to specified company.v0.0.2kuaidi100 webside change the rule, need special header referer when send queryv0.0.1first version, with base function.LICENSEMIT.
Package: kd163PhotoDownloader
# kd163PhotoDownloader本软件用于下载网易相册,使用python3和pyqt5开发，适用于linux系统和windows系统。可以下载任何人的公开的相册，私密相册暂不支持下载，也不支持登录个人帐号以下载自己的私密相册。# 安装- 安装python3和pip，[安装包地址](https://www.python.org/downloads/ 下载地址)- 使用pip安装kd163PhotoDownloader，命令** pip install kd163PhotoDownloader **- 点击桌面的快捷方式或开始菜单启动kd163PhotoDownloader# 使用说明![网易相册下载工具截图](/kd163PhotoDownloader/doc/screenshot-dfc607b6.png "截图")# 截图[去官网查看截图](https://github.com/bkdwei/kd163PhotoDownloader)![kd163PhotoDownloader_screenshot](/kd163PhotoDownloader/doc/kd163PhotoDownloader_screenshot.png "截图")# 依赖项- Python3- PyQt5- shortcutter# 提交bug和建议[提交bug和建议](https://github.com/bkdwei/kd163PhotoDownloader/issues)
Package: kdaHDFE
FixedEffectModel: A Python Package for Linear Model with High Dimensional Fixed Effects.FixedEffectModelis a Python Package designed and built byKuaishou DA ecology group. It provides solutions for linear model with high dimensional fixed effects,including support for calculation in variance (robust variance and multi-way cluster variance), fixed effects, and standard error of fixed effects. It also supports model with instrument variables.This version was forked version of the originalFixedEffectModel, which was altered to simplify it, add
unit tests, fix runtime errors, and expand where relevant. This extension is more designed around running hundreds of thousands if not millions of most the same regression bar a single variable for genetic analysis.InstallationInstall from pyPi viapipinstallkdaHDFEExampleUnlike the original FixedEffectModel youmustto use a formula akin to:'dependent variable ~ continuous variable|fixed_effect|clustersIf you want to cluster without FE you can just leave it blank as in BMI~Calories||PlaceOfBirthfromkdaHDFEimportHDFEimportpandasaspddf=pd.read_csv('yourdata.csv')# define modelformula='y~x+x2|id+firm|id+firm'result1=HDFE(df,formula=formula,robust=False,epsilon=1e-8,ps_def=True,max_iter=1e6)RequirementsPython 3.6+Pandas and its dependencies (Numpy, etc.)Scipy and its dependenciesstatsmodels and its dependenciesnetworkxCitationIf you use FixedEffectModel in your research, please consider citing the original author at:Kuaishou DA Ecology.FixedEffectModel: A Python Package for Linear Model with High Dimensional Fixed Effects.https://github.com/ksecology/FixedEffectModel,2020.Version XAs well as this update if you did us this kdaHDFE extension at:
Baker, S. E.kdaHDFE: A Python Package for Linear Model with High Dimensional Fixed Effects.https://github.com/sbaker-dev/kdaHDFE,2021.Version X.Reference[1] Simen Gaure(2019).  lfe: Linear Group Fixed Effects. R package. version:v2.8-5.1 URL:https://www.rdocumentation.org/packages/lfe/versions/2.8-5.1[2] A Colin Cameron and Douglas L Miller. A practitioner’s guide to cluster-robust inference. Journal of human resources, 50(2):317–372, 2015.[3] Simen Gaure. Ols with multiple high dimensional category variables. Computational Statistics & Data Analysis, 66:8–18, 2013.[4] Douglas L Miller, A Colin Cameron, and Jonah Gelbach. Robust inference with multi-way clustering. Technical report, Working Paper, 2009.[5] Jeffrey M Wooldridge. Econometric analysis of cross section and panel data. MIT press, 2010.
Package: kdap
Knowledge Data Analysis and Processing PlatformThis library contains a collection of utilities for efficiently processing and anlyzing the data of Wiki-based and QnA-based portals (eg., Wikipedia, Wikia, Stack Exchange, etc.). The function takes Knol-ML files as input. Most of the functions of this library are implemented in such a way that parallel processing can be achieved.The library is currently in the development phase, feel free to raise an issue. We welcome the community to contribute towards KDAP code base and ARK dataset.
Please check our webpage for more detailsGitHub PageTable of contentsRequirementsInstallSource codeTutorialHow to contributeKnol-ML data dumpREQUIREMENTSPython3requests==2.21.0internetarchive==1.8.5numpywikipediapsutil>=5.6.6mwparserfromhell==0.5.4nltk==3.4.5matplotlibbx-pythonpyunpackmwviewsThe list of dependencies is shown in./requirements.txt, however the installer takes care of installing them for you.INSTALLInstalling kdap is easily done using pip. Assuming it is installed, just run the following from the command-line:pip install kdapThis will also install the necessary dependencies.SOURCE CODEIf you like to clone from source, you can do it with very simple steps.
First, clone the repo:> git clone https://github.com/descentis/kdap.git
> cd kdapTUTORIALFor information on how to use KDAP, refer to the official documentation:http://kdap.readthedocs.ioSoon we will provide a video Tutorial, till then please watch our teaser on Youtube:https://youtu.be/-3boFWHB5oMHow to contributeFork the project and follow the instructions on how to get started withsource code. I will be updating the test cases on which you can test your code. Stay tuned.LicenseThis software is licensed under the BSD 3-Clause License.This project has received funding from the CSRI, Department of Science and Technology India via grant no. SR/CSRI/344/2016AuthorsAmit Arjun Verma - Author -descentisS.R.S Iyengar - Author -sudarshansudarshanAayush Sabharwal - Contributor -AayushSabharwalKnol-ML data dumpThe data dump can be foundhere
Package: kdAutoTeamviewer
# kdAutoTeamviewer
一个自动控制脚本，开机自动启动teamviewer，自动截图，并将截图发送到指定邮箱# 截图
![截图](/screenshot/screenshot.png)# 安装
- 安装python3和pip，[安装包地址](https://www.python.org/downloads/)
- 使用pip安装kdAutoTeamviewer，命令
> pip install kdAutoTeamviewer
- 你也可以使用[kdPythonInstaller(https://github.com/bkdwei/kdPythonInstaller/releases/download/v1.0.0/kdPythonInstaller-1.0.0.exe/) 来安装python和kdAutoTeamviewer# 使用方式
- 在主目录（比如C:Usersbkd）新建kdAutoTeamviewer.ini配置文件，并写入自己邮箱的用户名和密码，以及接收截图的邮箱。>[global]>username =abc@163.com>password = 123>receiver=edf@qq.com将teamviewer.exe的快捷方式teamviewer.exe.lnk放到主目录将桌面的启动脚本放到系统的启动目录(在开始-启动，比如C:UsersbkdAppDataRoamingMicrosoftWindowsStart MenuProgramsStartup），系统开机后，就会自动启动teamviewer，自动截图，并将截图发送到指定邮箱
Package: kdb
kdb is a small pluggable IRC Bot framework utilizing thecircuitsPython Application Framework.kdb was one of the first “test” applications written
in circuits and is still maintained and kept up-to-date
with new features and changes in circuits.You may use kdb as a framework to build your own projects that
use the IRC protocol. kdb itself comes with a suite of useful
plugins and features. If you’d like to see it in action, feel
free to get in touch with the developerJames Mills(prologic)
on the FreeNode IRC Network who resides in the #circuits channel.kdb was created by and is primarily maintained byJames MillsWebsiteIssues#circuitson theFreeNode IRC NetworkInstallation$ pip install kdbUsage$ kdb irc.freenode.netFor other options:$ kdb --helpInstallation and Usage on Dockerkdb is nowDockerready and can be run with:$ CID=$(docker run -d -p 8000:8000 -v $(pwd)/etc:/etc/kdb --name="kdb" prologic/kdb --config=/etc/kdb/kdb.ini)NoteBe sure to editetc/kdb.inior change the above line
to suit your needs mounting the configuration directory
from another path on your host.Pluginskdb has a full complementary set of plugins available.For a list of available plugins, see:https://bitbucket.org/prologic/kdb/src/tip/kdb/plugins/You may install the latestDevelopment Versionvia:$ pip install kdb==devChangeskdb 1.0.2 (2014-04-27)Fixed a bug in the new Web API.Fixed a bug withkdb.plugins.help.format_msg().Updated the Web Plugin UI to use jQuery Terminal.Added a JSON RPC Plugin.Updated Drone Plugin to listen toregisteredandnickevents.Fixed a typo in Greeting Pluginkdb 1.0.1 (2014-04-16)Fixed Web Plugin proper and make package nonzip_safe.Fixed templates path in Web Plugin.Fixed the Web Plugin so it works if installed as a package and run
separately.kdb 1.0.0 (2014-04-16)Fixed missinghtml2textandaspell-endependency.Updated requirements and DockerfileMoved example config intoetc/Print a treceback on plugin load failure.Useos.path.existsto check for--configfile.Fixed DockerfileAdded Remote Plugin PluginFixed sub-commands for Channels Plugin.Fixed state management.Set a filename to save RSS Feeds to for the RSS Plugin in the default
sample configuration file.Fixed sub-commands with the Channels Plugin.Fixed display ofRLISTcommand in RSS Plugin.Fixed counting usage statistics of commands.kdb 0.9.0 (2014-04-14)Ported to circuits 3.0
Package: kdbai-client
KDB.AI Client for PythonIntroductionFor full documentation and detailed examples, visitKDB.AI website.Installing kdbai-client usingpipEnsure you have a recent version of pip:pip install --upgrade pipThen install the latest version of kdbai-client with the following command:pip install kdbai-clientTo install a specific version of kdbai-client run the following command replacing <INSERT_VERSION> with a specific released semver version of the interfacepip install kdbai-client==<INSERT_VERSION>Warning:Python packages should typically be installed in a virtual environment.This can be done with the venv package from the standard library.
Package: kdbq_kernel
A Jupyter kernel for Kdb/qNo other dependencies beides Jupyter and Q. Easy setup.It is probably very rough around the edges, but seems to work. Please
report any issues you end up having.Other Existing KernelsIKdbQTodo[STRIKEOUT:Multiline support] Should be done nowImage support?Code cleanupImplementing code completion.Testing (I doubt k support currently works)[STRIKEOUT:Pip/Conda publication]InstallThis requires IPython 3. Note thatqmust be in your path and$QHOMEmust be correctly defined.pip install kdbq_kernel
python -m kdbq_kernel.installTo make sure it is installed:jupyter kernelspec listTo use it, run one of:jupyter notebook
# In the notebook interface, select Kdb/Q from the 'New' menu
jupyter qtconsole --kernel kdbq
jupyter console --kernel kdbqMore InfoMore information of Kdb and Q:Q
WikiReferenceFor more details on how this works, also see:Wrapper
KernelsPepect
replwrapThanksThis kernel is heavily/entirely based on theBash Kernel by
takluyver.
Package: kdb-vault-tools
KDB to Vault ToolsPackage that allow migrate from kdb to vault and vise versaExampleStart vault in development modedocker-composeupPrepare simple script or usekdb_2_vault.pyfromkdb_vault_toolsimportProcessordefcreate_kdb():vault_conf={"url":"http://localhost:1234","token":"myroot"}kdb_conf={"filename":"kdb_tmp/secrets-20190422.kdbx","password":"superSecret123",}processor=Processor(vault_settings=vault_conf,kdb_settings=kdb_conf)processor.sync_from_vault()processor.write_kdb()deffill_vault():vault_conf={"url":"http://localhost:1234","token":"myroot"}kdb_conf={"filename":"kdb_tmp/secrets-20190422.kdbx","password":"superSecret123",}processor=Processor(vault_settings=vault_conf,kdb_settings=kdb_conf)processor.sync_from_kdb()processor.write_vault(base_path="/sandbox/org/team/foo/")HistoryVersion 0.1.0 (2021-02-23) - Base ConceptCreditsLead Developer - Max Nikitenko (moaddib666@gmail.com)LicenseMIT
Package: kdbx-1pif-importer
Thekdbx-1pif-importercommand reads two files:.1piffile generated from exporting a Vault from 1Password; and.kdbxfile generated by creating a new database in KeePass.Pros:Imports most things.Will carry over OTP tokens.Supports multiple URLs.Cons:Doesn’t import Identities or Credit Cards because I didn’t need to import
those.Installationpipinstallkdbx-1pif-importerUsagekdbx-1pif-importer--dry-runpath/to/onepassword.1pifpath/to/tax-returns.kdbx# Once you've verified all the output looks sane-ish, try running:kdbx-1pif-importerpath/to/onepassword.1pifpath/to/tax-returns.kdbxHacking# Checkout a copy of the source code.gitclonegit@github.com:bheesham/kdbx-1pif-importer.gitcdkdbx-1pif-importer# Install the package locally.pipinstall-e.pipinstallmypypylintblacktwine# Run the various quality checks.mypykdbx_1pif_importer# Type checkpylintkdbx_1pif_importer# Lintblackkdbx_1pif_importer# Formatpython-munittestdiscovertests# Unit tests# Upload it to pypi.python3setup.pysdistpython3-mtwineuploaddist/*Note: I am no longer maintaining this. It worked once and that was all I
needed.Licensed under the GPLv3. See theCOPYING.gplv3file for more information.
Package: kdbxpasswordpwned
# kdbxpasswordpwnedCheck keepass passwords against https://haveibeenpwned.com/Passwords[![Build Status](https://travis-ci.org/fopina/kdbxpasswordpwned.svg?branch=master)](https://travis-ci.org/fopina/kdbxpasswordpwned)[![Coverage Status](https://coveralls.io/repos/github/fopina/kdbxpasswordpwned/badge.svg?branch=master)](https://coveralls.io/github/fopina/kdbxpasswordpwned?branch=master)[![Docker Version](https://images.microbadger.com/badges/version/fopina/kdbxpasswordpwned.svg)](https://microbadger.com/images/fopina/kdbxpasswordpwned)[![Docker Image](https://images.microbadger.com/badges/image/fopina/kdbxpasswordpwned.svg)](https://microbadger.com/images/fopina/kdbxpasswordpwned)[![PyPI Version](https://img.shields.io/pypi/v/kdbxpasswordpwned.svg)](https://pypi.python.org/pypi/kdbxpasswordpwned)[![PyPI Python Versions](https://img.shields.io/pypi/pyversions/kdbxpasswordpwned.svg)](https://pypi.python.org/pypi/kdbxpasswordpwned)### DisclosureEven if Troy Hunt's API does provide some sense of privacy (as we don't share the password nor even the full SHA1), always review the tools you use with your KeePass files (such as this script which is small and you can easily see the password is not sent anywhere except HIBP API). I have reviewed libkeepass code (0.3.0, pinned in requirements) which is also small, and, as PyPI does not allow replacing existing versions, it is safe.Also be sure to install tools you trust from places you trust or you might end up installing some shady version such as [this fork](https://github.com/fopina/kdbxpasswordpwned/compare/master...SlivTaMere:bea0f5c) which sends the full password (not the hash) to a different endpoint.### UsageInstall using `pip````bash$ pip install kdbxpasswordpwnedCollecting kdbxpasswordpwnedSuccessfully installed kdbxpasswordpwned-0.3```And use the CLI```bash$ kdbxpasswordpwned -husage: kdbxpasswordpwned [-h] [-k KEYFILE] [-u] [-p] kdbxpositional arguments:kdbx                  keepass fileoptional arguments:-h, --help            show this help message and exit-k KEYFILE, --keyfile KEYFILEKeyfile if needed-u, --show-user       show username for found entries-p, --show-password   show password for found entries (high shoulders?)``````bash$ kdbxpasswordpwned /path/to/test_assets/sample.kdbxPassword:Password for title1 seen 1151 times beforePassword for title2 seen 61164 times before```Or simply use the docker image```bash$ docker run --rm -ti \-v /path/to/test_assets/sample_with_key.kdbx:/tmp.kdbx:ro \-v /path/to/test_assets/sample.key:/tmp.key:ro \fopina/kdbxpasswordpwned -upk /tmp.key /tmp.kdbxPassword for title1 seen 1151 times before - testuser - testitPassword for title2 seen 61164 times before - None - blabla```