Saravanan666/pypi-org huggingface dataset:
Package: dc-cli-jawide
Docker compose software cliUsageinstall packagedcinstallpackage_nameuninstall packagedcuninstallpackage_nameupdate packagedcupdatepackage_nameTest Publishrm-rfdist&python-mbuild&&python-mtwineupload--repositorytestpypidist/*rd /S /Q dist & python -m build && python -m twine upload --repository testpypi dist/*pipuninstall-ydc-cli-jawide&&pipinstall--index-urlhttps://test.pypi.org/simple/--no-deps--no-cache-dir--upgradedc-cli-jawidePublishrm-rfdist&python-mbuild&&python-mtwineuploaddist/*rd /S /Q dist & python -m build && python -m twine upload dist/*pipuninstall-ydc-cli-jawide&&pipinstall--no-cache-dir--upgradedc-cli-jawideFuture修改install子命令使其只下载应用不启动应用，添加更多命令，例如stop、start、pause添加search子命令，提供应用搜索功能添加ps子命令，提供应用运行状态显示功能添加config子命令，用于配置全局变量优化错误信息的提示
Package: dcclog
dcclogdcclogis a simple wrapper around the python logging module that makes it easy to colorize and encrypt log messages.InstallationFrom pypi:$pipinstalldcclogwith built-in ciphers:$pipinstall'dcclog[cipher]'From github:$gitclonehttps://github.com/jamazi/dcclog.git$cddcclog$pipinstall'.[all]'How To use dcclogimportdcclogdcclog.default_config()logger=dcclog.getLogger(name=__name__)logger.error("error message.")logger.warning("warning message.")logger.info("info message.")logger.debug("debug message.")@dcclog.logdeflogged_function(x:int,y:int)->int:returnx+ylogged_function(4,6)or with RSA encryption:importdcclogfromdcclog.cipher.rsaimportRSAEncryptiondcclog.default_config(level=dcclog.INFO,filename=".logs/app.log",cipher=RSAEncryption("pubkey.pem"),)logger=dcclog.getLogger(name=__name__)logger.error("error message.")logger.warning("warning message.")logger.info("info message.")logger.debug("debug message.")@dcclog.logdeflogged_function(x:int,y:int)->int:returnx+ylogged_function(4,6)
Package: dc-consent
Cookie ConsentA Django app to add a cookie consent modal to your site.
This can then be used to check if the user has given consent to use cookies.To clear the cookie preferences; the user can simple navigate to:reverse('cookie_consent:revoke')In templates; it is also possible to check if the user has given consent like so:request.cookie_consent.allows('analytics') -> boolQuick startAdd 'cookie_consent' to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
...,
'cookie_consent',
]Include the cookie_consent URLconf in your project urls.py like this:path('cookies/', include('cookie_consent.urls')),Add the cookie_consent middleware to your MIDDLEWARE setting like this:MIDDLEWARE = [
...,
'cookie_consent.middleware.CookieConsentMiddleware',
]Use the 'cookie_consent' template tag in your base template like this:{% load cookies_tags %}
{% cookie_consent %}Check if the user has given consent to use cookies in your templates like this:{% consents_to 'analytics' %}
	<script>
		// Will only get rendered if the user has given consent to use analytics cookies.
	</script>
{% endconsents_to %}We require you to use two asset files. These are located in:static/cookie_consent/cookie-consent.css
static/cookie_consent/cookie-consent.js
Package: dc-converter
File converterFast converter using Python and Rust.BindingBinding between Rust and Python is done usingPyO3, a library like PyBind11 for C++.CheckUserguide.BuildCreate venv and build the Rust lib. Needs also Rust installation.https://www.rust-lang.org/tools/installsudoaptinstalllibopencv-devclanglibclang-dev

python3-mvenv.envsource.env/binactivate
pipinstall-rrequirements.txt# Test:maturindevelop-r# Publish:maturinpublish-ipython3.10python3.11
Package: dccutils
This library offers a serie of classes to abstract the most common features
available in Digital Content Creation (DCC) tools.It currently supports Blender, Unreal, Maya and Houdini.How to use itInstall the library:pipinstalldccutilsThen in your code (let’s say you are working in Blender):fromdccutilsimportBlenderContextdcc_software_manager.blender=BlenderContext()print(dcc_software_manager.get_cameras())ContributionsAll contributions are welcome as long as they respect theC4
contract.Code must follow the pep8 convention.You can use the pre-commit hook for Black (a python code formatter) before commiting :pipinstallpre-commitpre-commitinstallAbout authorsDCCUtils is written by CGWire, a company based in France. We help animation and VFX studios to collaborate better through efficient tooling. We already work with more than 70 studios around the world.Visitcg-wire.comfor more information.
Package: dcc-utils
Python library to decode the EU Covid-19 vaccine certificate, asspecified by the
EU.Setuppipinstalldcc-utilsMake surezbaris installed in your system * For Mac OS X, it can
be installed viabrew install zbar* Debian systems viaapt install libzbar0.Source* Fedora / Red Hatdnf install zbarUsageParse DCCThis library takes an image with a QR code or a raw repr. of a vaccine
certificate as the parameter and will show the certificate’s content.fromdcc_utilsimportdccdcc_from_img=dcc.from_image("/my/certificate/path")dcc_from_raw=dcc.from_raw("HC1:6BF...FTPQ3C3F")Then you can access topayloadandkidassertdcc_from_img.kid=="53FOjX/4aJs="assertdcc_from_img.payload["v"][0]["ci"]=="URN:UVCI:01:FR:W7V2BE46QSBJ#L"👉🏻payloadfollowsEU Digital COVID Certificates JSON Schema
Specificationfrom_imageandfrom_rawmethods may riseDCCParsingErrorfromdcc_utils.exceptionsimportDCCParsingErrorValidate DCC digital signaturesignature=b"""
-----BEGIN CERTIFICATE-----
MIIIAjCCBeqgAwIBAgIQAnq8g/T
-----END CERTIFICATE-----
"""assertmy_dcc.check_signature(signature)check_signaturemethod may riseDCCSignatureErrorfromdcc_utils.exceptionsimportDCCSignatureErrorEvaluate CertLogic business rulesWith dcc-utils you can evaluatebusiness
rulesagainst a DCCfromdcc_utilsimportrule,dccmy_dcc=dcc.from_image("/my/certificate/path")my_rule=rule.from_file("/my/rule.json")print(my_rule.description["en"])my_rule.evaluate_dcc(my_dcc)# True or Falseevaluate_dccaccepts extra variables as a second parameter,
e.g.validationClockimportdatetimeclock=datetime.datetime(2022,10,10,0,0,tzinfo=datetime.timezone.utc)my_rule.evaluate_dcc(my_dcc,{"validationClock":clock,},)you can also load rules from JSON (from_json), useful to evaluate
rules exposed on a servermy_rule=rule.from_json({...})from_fileandfrom_jsonmethod may riseDCCRuleErrorfromdcc_utils.exceptionsimportDCCRuleErrorDev setupInstall dependencies using pip:pip install -r requirements-dev.txtRun testsmake testEU Digital COVID Certificate SpecificationsWhat’s in a EU Digital COVID/Green Certificate? * Value Sets for
Digital Green Certificateshttps://ec.europa.eu/health/sites/default/files/ehealth/docs/digital-green-certificates_dt-specifications_en.pdf* JSON schema:https://github.com/ehn-dcc-development/ehn-dcc-schemaSample dataDigital Green Certificate Gateway (DGCG) samples for all participating
countries:https://github.com/eu-digital-green-certificates/dgc-testdataCreditsParts of this code are adapted fromvacdec
project.LicenseThis library is available under theMITlicense.
Package: dccutils-server
This library offers a fastAPI server for DCCs based on dccutils to abstract the most common features
available in Digital Content Creation (DCC) tools.It currently supports Blender, Unreal, Maya and Houdini.How to use itInstall the library:pipinstalldccutils_serverThen in your code:importdccutils_serverdccutils_server.server_start_threading()ContributionsAll contributions are welcome as long as they respect theC4
contract.Code must follow the pep8 convention.You can use the pre-commit hook for Black (a python code formatter) before commiting :pipinstallpre-commitpre-commitinstallAbout authorsDCCUtils is written by CGWire, a company based in France. We help animation and VFX studios to collaborate better through efficient tooling. We already work with more than 70 studios around the world.Visitcg-wire.comfor more information.
Package: dcdm
dcdm[C]ommendLine [D]ownload [M]anagerdcdm is a commend line Download MangerYou can download your file by giving arguments to the program
‍dcdm [OPTIONS] URLOPTIONSOptions:
  --number_of_threads INTEGER  No of Threads
  --name PATH                  Name of the file with extension
  --help                       Show this message and exit.
Package: dce-mh-selenium
## mh-seleniumSelenium stuff for DCE Matterhorn## Getting started1. Create & activate a virtualenv: `virtualenv venv && source venv/bin/activate`2. `pip install dce-mh-selenium`3. `mh_driver --help` for commands and `mh_driver [cmd] --help` for command options## Example processThe following sequence of commands shows an example of how to upload some mediato the Matterhorn inbox, create symlinks to populate the inbox selection menuin the **Upload Recording** UI, execute a number of uploads**Note**: The initial inbox upload/symlink commands will prompt for the sshlogin password. Obvs in an automated testing scenario you will need have eithera passphraseless ssh key or AWS instance profiles set up.#### 1. Upload the initial media to the inbox`mh_driver inbox put -H [admin ip] -f path/to/presenter.mp4`Use an s3 url for files > 1g:`mh_driver inbox put -H [admin ip] -f https://s3.amazonaws.com/my-bucket/presenter.mp4`#### 2. Create symlinksThis will create 10 inbox symlinks to each of the previously uploaded files,i.e., 10 identical presenter/presentation copies. Each copy will be named usingthe sequence integer: `presenter_1.mp4`, `presenter_2.mp4`, etc.`mh_driver inbox symlink -H [admin ip] -f presenter.mp4 -c 10``mh_driver inbox symlink -H [admin ip] -f presentation.mp4 -c 10`#### 3. Check inbox contents (optional)`mh_driver inbox list -H [admin ip]`#### 4. Run the selenium upload tasksThe `uname` and `passwd` values here correspond to the Matthorn admin interfacelogin.```for i in `seq 1 10`; do mh_driver upload -u [uname] -p [passwd] --inbox --presenter presenter_${i}.mp4 --presentation presentation_${i}.mp4 [admin base_url] ; done```This *should* result in 10 processing workflows. The flakiness of Selenium + MHmakes this not guaranteed.#### 5. (later) execute the trim tasksIn a happy world you'll have a set of 10 of workflows that process theirrespective copies of the uploaded media and then pause/hold at the `editor`operation waiting for a human to intervene. We don't need stinking humans; wehave Selenium!`mh_driver trim -u [uname] -p [passwd] [admin base_url]`## Troubleshooting### Known issues#### UnexpectedAlertPresentException: Alert Text: Could not resume Workflow: errorCheck that Matterhron nodes, particularly admin, are not in maintenance state.## Resources* python selenium docs: https://selenium-python.readthedocs.org/index.htmldce-mh-selenium change log==========================0.1.0 (2015-09-14)------------------Initial release!0.2.0 (2015-11-30)------------------Updated default inbox paths0.3.0 (2015-11-30)------------------Allow "upload" from s3. disallow upload from local for files > 1g.
Package: dcextensions
============================Extensions for dogpile.cache============================This library provides extensions for dogpile.cache. For the time being only one extension is provided, a memory backend that can be limited in size (number of items to cache)... image:: https://badge.fury.io/py/dcextensions.png:target: http://badge.fury.io/py/dcextensions.. image:: https://travis-ci.org/OnroerendErfgoed/dcextensions.svg?branch=master:target: https://travis-ci.org/OnroerendErfgoed/dcextensions.. image:: https://coveralls.io/repos/OnroerendErfgoed/dcextensions/badge.png?branch=master:target: https://coveralls.io/r/OnroerendErfgoed/dcextensions?branch=masterInstallation------------To install dcextensions, use pip.. code-block:: bashpip install dcextensionsUsage-----To use the memory backend.. code-block:: python.. code-block:: pythonfrom dogpile.cache import make_regionregion = make_region().configure('dogpile.cache.memory.limited',expiration_time = 3600,arguments = {'cache_size': 100,'cache_dict': my_dict})Both arguments are optional. When use my_dict must be an instance of OrderedDict.
Package: dcFBA
Dynamic Community FBAWelcome to Dynamic Community FBA (dcFBA): The Python package that makes modeling Microbial Communities dynamically a breeze!This project is licensed under the MIT License - please refer to theLICENSEfile for more details.AboutDynamic Community FBA (dcFBA) is a versatile tool designed for modeling microbial communities as single organisms using Genome Scale Metabolic Models (GSMMs). This package builds upon the solid foundation ofcbmpyand seamlessly integrates with SBML and COBRApy models. dcFBA empowers users with three distinct dynamic modeling methods:Dynamic Joint FBA- Incrementally updates the concentrations of biomass and metabolites within the combined stoichiometric matrix of the provided models.Dynamic Parallel FBA- Simultaneously updates the concentrations of biomass and metabolites while performing FBA on individual models.EndPointFBA- Duplicates the CommunityMatrix N times and performs FBA on the community's time-dependent stoichiometric matrix.For a comprehensive understanding of these methods and their underlying mathematics, please consult [^1].Whether you're exploring parasitic interactions or investigating costly cross-feeding behaviors in microbial communities, dcFBA offers an elegant and efficient solution.InstallationPrerequisitesBefore installing dcFBA, make sure you have the following prerequisites in place:Python >= 3.8, < 3.11cbmpycplexInstallation StepsTo install dcFBA, follow these simple steps:Install using pip:pipinstalldcFBAUsageFor basic usage examples and detailed documentation on bothcbmpyanddcFBA, please refer to their respective documentation pages.[^1]: [Cite paper.]
Package: dcflags
dcflagsPython package for initializing a dataclass with values from the command line and from environmental variables.Installdcflags is available through pippipinstalldcflagsUsage# main.pyimportdataclassesimportdcflags@dataclasses.dataclassclassConfig:output:strworkers:int=1verbose:bool=Falseif__name__=="__main__":cfg=dcflags.parse(Config)print(cfg)# help message
$ python main.py --help
usage: main.py [-h] [--output OUTPUT] [--workers WORKERS] [--verbose [VERBOSE]]

options:
  -h, --help           show this help message and exit
  --output OUTPUT      type: str, env: $OUTPUT
  --workers WORKERS    type: int, env: $WORKERS, default: 1
  --verbose [VERBOSE]  type: bool, env: $VERBOSE, default: False

# missing required argument
$ python main.py
usage: main.py [-h] [--output OUTPUT] [--workers WORKERS] [--verbose [VERBOSE]]
main.py: error: the following arguments are required: --output/$OUTPUT

# command line arguments
$ python main.py --output=file.txt
Config(output='file.txt', workers=1, verbose=False)

# env vars
$ OUTPUT=test.txt python main.py
Config(output='test.txt', workers=1, verbose=False)

# a bit of everything
$ OUTPUT=new.txt python main.py --verbose --workers=3
Config(output='new.txt', workers=3, verbose=True)
Package: dcggraph
Directed Cyclic Graph Utilities for Pythoninstallingpip install dcggraphdescriptionThis package currently used for doing predicated searches from a given
node in a directed graph that is allowed to be cyclic. Some points to clear
up confusion:directed: means an edge between 2 nodes can only be travelled in one
direction _(although there can be two edges going opposite directions between the same two nodes)predicated: means that even if a node is reachable, it still has to
pass a prediate test (i.e.predicate(node) == True) in order to be
returned.this means there are really 2 search filters in the graph:the node has to be reachable without any filteringthe node has to pass the predicate testcyclic: means that if you start from a node, and follow a bunch of
paths, you can end up at the same node. This makes things a lot harder
than if you were working with DAGs.cycles are trickyThe main trick used to deal with cycles is having the predicate return 3 states:PASS: means the node confidently passes the predicateFAIL: means the node confidently fails the predicateWAIT: means the node is unsure if it passes yet, and would like to defer giving a
confident answer until more nodes are seen (since starting point matters in cyclical graphs)Nodes that return theWAITstate from a predicate get put in theunresolvedlist, and are re-evaluated every time another node returnsPASSorFAIL.  After all reachable nodes have been evaluated, any remaining nodes in theunresolvedlist are demoted to theFAILstate, since they couldn't make up their mind, and there are no more chances for them to give a real answer.This algorithm seems to work well for the cases I use predicated searches in a graph. I'mpositivebetter algorithms exist than what is implemented here.APIThe main feature is predicated graph compression and search.
In addition to the snippet below, look at the tests for example usageimportrefromdcggraphimportDCGnode,DCGedge,DCGgraphfromdcggraph.searchimportPredicateResult,DCGsearchg=DCGgraph()n0=g.create_node("n0")n1=g.create_node("n1")n2=g.create_node("n2")n3=g.create_node("n3")n4=g.create_node("n4")g.create_edge(n0,n1)g.create_edge(n0,n2)g.create_edge(n1,n3)g.create_edge(n1,n4)defeven(node,visited,passed,failed):iffre.match("[a-z][02468]",node.name):returnPredicateResult.PASSelse:returnPredicateResult.FAILs1=DCGsearch(g,even)# reuse the search object to cache FAIL/PASS results between searchesassertEqual(len(s1.search(n0)),3)assertEqual(len(s1.search(n1)),1)assertEqual(len(s1.search(n2)),1)assertEqual(len(s1.search(n3)),0)assertEqual(len(s1.search(n4)),1)
Package: dcgoss
dcgossThis is a Python implementation of thedcgossshell script that provides a wrapper aroundgossfor executing tests within docker containers using docker-compose.Prerequisitespython3dockerdocker-composegossFor non-Linux systems, place the Linux goss binary on yourPATHor point to it with theGOSS_PATHenvironment variable.Installationpip3installdcgossUsageRun tests:dcgossrun<servicename>[<composepath>]Edit tests:dcgossedit<servicename>[<composepath>]
Package: dchartjs
dchartjsdchartjs is python package. it is used to draw charts in frontend Using DjangoInstallationUse the package managerpipto install dchartjs.pipinstalldchartjsBasic Usage In Djangoin Django views.pyfromdchartjs.basic_chartimportBchartdefDjangoChart(request):Chart=Bchart()Chart.chart_data(xdata=[0,2,5,10],ydata=[1,5,5,25])data=Chart.project_djangoview()returnrender(request,'chart.html',data)in chart.html<body>{{ chart_as }}</body>Basic Usage In No Frameworkif your not using any Framework then you can also plot directly into webfromdchartjs.basic_chartimportBchartChart=Bchart()Chart.chart_data(xdata=[0,2,5,10],ydata=[1,5,5,25])Chart.project_web()#it creat temp.html file then open chart in browserBasic Style dchartjsfromdchartjs.basic_chartimportBchartdefDjangoChart(request):Chart=Bchart()Chart.chart_style_conf(width=100,height=100)Chart.chart_data(xdata=[0,2,5,10],ydata=[1,5,5,25])data=Chart.project_djangoview()returnrender(request,'chart.html',data)
Package: dc-help
@[TOC]参考博文：docker部署规范-目录结构与辅助工具本规范根据多个项目实践总结，通过目录结构规范化，结合辅助工具，可以降低在多节点、新环境部署中的重复工作与沟通工作量，大幅提供部署效率。规范部署目录结构目录结构的规划基于下面几点考虑：一个主机上可以部署多套系统一个系统应该是”自洽“的，具有完整的逻辑性，系统涉及的所有文件是自包含的目录结果尽量简单，便于管理(不再执行dockcer-compose up --build命令)，本地的配置修改通过volumes文件夹或文件挂载的方式进行容器内文件覆盖完成目录结构如下所示:[产品]-deplopy:docker-compose.yml: 主文件conf： 配置文件，如nginx配置，mysql配置文件等web/nginx.conf: nginx的配置文件夹，例如路由配置，安全设置等。web/ca/:nginx的证书文件目录。mysql/:msql的配置文件等data: 运行过程中的数据，如数据库存储目录，程序的临时文件目录mysql/:msql的数据库文件目录mongo/:mongodb的数据库文件目录jar: 包含服务用的jar文件、或者其他格式的代码打包文件等log: 程序日志目标back: 程序备份目录辅助工具解决辅助工具参考：github代码 dc-help工具安装gitclonehttps://github.com/perfectstorm88/dc-helpcddc-help
pythonsetup.pyinstall在docker-compose.yml所在目录，执行dc-help -husage: dc-help COMMAND

docker-compose辅助工具,帮助管理镜像、版本文件

optional arguments:
  -h, --help           show this help message and exit

COMMAND:
  {image,file,daemon}
    image              管理docker-compose.yml中的镜像，打包、装载、清理、升级
    file               对文件夹进行压缩和解压缩,默认是conf、data、jar等文件夹
    daemon             daemon的状态、启动和停止其中dc-help image -husage: dc-help COMMAND image [-h] (--pack | --unpack | --clear | --upgrade)

optional arguments:
  -h, --help  show this help message and exit
  --pack      对镜像进行自动打包
  --unpack    对镜像进行自动装载
  --clear     对镜像文件进行清理
  --upgrade   对镜像文件进行自动装载，然后升级`功能介绍dc-help image --pack：对项目的镜像进行自动打包dc-help image --unpack：对项目的镜像进行自动装载dc-help image --clear：对项目的镜像进行清理dc-help image --upgrade：扫描备份目录，判断是否有镜像更新，进行自动装载，更新集群服务dc-help file --pack：对项目的配置和数据文件进行自动打包压缩dc-help file --unpack：对项目的配置和数据文件进行自动解压缩dc-help daemon --status：查看自动升级服务的状态，running 或 not runningdc-help daemon --start：启动自动升级服务dc-help daemon --stop：停止自动升级服务安装部署pip安装pip install dc-help源代码安装git clone https://github.com/perfectstorm88/dc-help.git
cd dc-help
python setup.py install离线安装(docker,推荐)如果部署在客户内网，离线建议采用docker方式，在容器内执行docker命令首先下载源代码，构建docker镜像：gitclonehttps://github.com/perfectstorm88/dc-help.gitcddc-help
dockerbuild-tdc-help.# 构建dockerdockersavedc-help>dc-help-docker-image.tar
gzipdc-help-docker-image.tar在内网主机，把dc-help-docker-image.tar.gz上传到部署主机,还原镜像：dockerload-idc-help-docker-image.tar配置alias,在~/.bashrc中增加alias：（通过 vim ~/.bashrc编辑后，执行 source ~/.bashrc后生效）aliasdc-help='docker run --rm -t -u root  -v $(which docker):/usr/bin/docker  -v $(which docker-compose):/usr/bin/docker-compose  -v /var/run/docker.sock:/var/run/docker.sock  -v /usr/lib64/libltdl.so.7:/usr/lib/x86_64-linux-gnu/libltdl.so.7 -v $(pwd):$(pwd)  -w $(pwd) dc-help dc-help'离线源码安装dc-help工具(centos7)● (安装setuptools（若没有的话需要安装，视centos的版本，有的环境不需要这一步）https://pypi.org/project/setuptools/36.5.0/下载setuptools-36.5.0.tar.gz
不能下过高版本，因为python2语法不支持tar-zxvfsetuptools-36.5.0.tar.gz#若外网下载zip格式，则 unzip setuptools-36.5.0.zipcdsetuptools-36.5.0
pythonsetup.pyinstall源代码安装步骤同上离线源码安装dc-help工具(centos8)centos8已经默认不自带python版本，需要先安装python2，参见VirtualBox CentOS8 挂载iso镜像源代码安装步骤同上参考How to get exact date for docker images?如何抽取镜像时间Declare default environment variables in file 通过文件声明默认环境变量COMPOSE_PROJECT_NAME如何从容器内部执行宿主机的docker命令
Package: dc-html-styler
dcStylerPackage for styling html-tables generated from Pandas DataFrames.Free software: MIT licenseDocumentation:https://dcstyler.readthedocs.io.FeaturesExpands the pandas.io.formats.style.Styler class with the following methods:dc_tabledc_headersdc_peakdc_signdc_row_dividerdc_col_dividerdc_heatmapdc_even_unevendc_add_titledc_make_col_intdc_renderHistory0.1.1 (2021-03-18)First release on PyPI.0.1.2 (2021-03-18)Small bugfix
Package: dciauth
python-dciauthDCI authentication module used by dci-control-server and python-dciclientThis section shows example programs written in python that illustrate how to work with Signature Version 2 in DCI. The algorithm used by dciauth is identical toSignature Version 4 in AWS.Authentication example:GETimportrequestsfromdciauth.v2.headersimportgenerate_headersheaders=generate_headers({"endpoint":"/api/v1/jobs"},{"access_key":"remoteci/client_id","secret_key":"secret"},)requests.get("http://api.distributed-ci.io/api/v1/jobs",headers=headers)POSTimportrequestsfromdciauth.v2.headersimportgenerate_headersdata={"name":"user 1"}headers=generate_headers({"method":"POST","endpoint":"http://api.distributed-ci.io/api/v1/users","data":data,},{"access_key":"remoteci/client_id","secret_key":"secret"},)requests.post("http://api.distributed-ci.io/api/v1/users",headers=headers,json=data)Validation examplefromflaskimportrequestfromdciauth.v2.headersimportparse_headersfromdciauth.v2.signatureimportis_validvalid,error_message=is_valid({"method":request.method,"endpoint":request.path,"data":request.data,"params":request.args.to_dict(flat=True),},{"secret_key":"secret"},parse_headers(request.headers),)ifnotvalid:raiseException("Authentication failed:%s"%error_message)Using POSTMANIf you are using POSTMAN to discover DCI API you can use the following parameters with the AWS Signature authorization header:GET https://api.distributed-ci.io/api/v1/identity
AccessKey=<DCI_CLIENT_ID>
SecretKey=<DCI_API_SECRET>
AWS Region="BHS3"
Service Name="api"LicenseApache 2.0Author InformationDistributed-CI Teamdistributed-ci@redhat.com
Package: dcicutils
Check out our full documentationhereThis repository contains various utility modules shared amongst several projects in the 4DN-DCIC. It is meant to be used internally by the DCIC team and externally as a Python API toFourfront, the 4DN data portal.pip installable as thedcicutilspackage with:pip install dcicutilsSeethis documentfor tips on getting started.Go herefor examples of some of the most useful functions.