Saravanan666/pypi-org huggingface dataset:
Package: ddddocrfix
带带弟弟OCR通用验证码识别SDK免费开源版当前版本为1.4.81.4.3更新内容本次升级的主要原因为，dddd_trainer的开源进行适配，使dddd_trainer训练出的模型可以直接无缝导入到ddddocr里面来使用支持使用ddddocr调用dddd_trainer训练后的自定义模型dddd_trainer训练后会在models目录里导出charsets.json和onnx模型如下所示，import_onnx_path为onnx所在地址，charsets_path为onnx所在地址importddddocrocr=ddddocr.DdddOcr(det=False,ocr=False,import_onnx_path="myproject_0.984375_139_13000_2022-02-26-15-34-13.onnx",charsets_path="charsets.json")withopen('888e28774f815b01e871d474e5c84ff2.jpg','rb')asf:image_bytes=f.read()res=ocr.classification(image_bytes)print(res)捐赠 （如果项目有帮助到您，可以选择捐赠一些费用用于ddddocr的后续版本维护，本项目长期维护）赞助合作商赞助合作商推荐理由YesCaptcha谷歌reCaptcha验证码 / hCaptcha验证码 / funCaptcha验证码商业级识别接口点我直达VIP41.4.0版本更新内容本次更新新增了两种滑块识别算法，算法非深度神经网络实现，仅使用opencv和PIL完成。算法1小滑块为单独的png图片，背景是透明图，如下图然后背景为带小滑块坑位的，如下图det=ddddocr.DdddOcr(det=False,ocr=False)withopen('target.png','rb')asf:target_bytes=f.read()withopen('background.png','rb')asf:background_bytes=f.read()res=det.slide_match(target_bytes,background_bytes)print(res)提示：如果小图无过多背景部分，则可以添加simple_target参数， 通常为jpg或者bmp格式的图片slide=ddddocr.DdddOcr(det=False,ocr=False)withopen('target.jpg','rb')asf:target_bytes=f.read()withopen('background.jpg','rb')asf:background_bytes=f.read()res=slide.slide_match(target_bytes,background_bytes,simple_target=True)print(res)算法2一张图为带坑位的原图，如下图一张图为原图，如下图slide=ddddocr.DdddOcr(det=False,ocr=False)withopen('bg.jpg','rb')asf:target_bytes=f.read()withopen('fullpage.jpg','rb')asf:background_bytes=f.read()img=cv2.imread("bg.jpg")res=slide.slide_comparison(target_bytes,background_bytes)print(res)更新内容2添加全局ocr关闭参数，初始化时传入dddd = ddddocr.DdddOcr(ocr=False)则为关闭ocr功能，如果det = True，则会自动关闭ocr1.3.1版本更新内容想必很多做验证码的新手，一定头疼碰到点选类型的图像，做样本费时费力，神经网络不会写，训练设备太昂贵，模型效果又不好。市场上常见的点选类验证码图片如下图所示那么今天，他来了，ddddocr带着重磅更新大摇大摆的走来了。简介ddddocr是由sml2h3开发的专为验证码厂商进行对自家新版本验证码难易强度进行验证的一个python库，其由作者与kerlomz共同合作完成，通过大批量生成随机数据后进行深度网络训练，本身并非针对任何一家验证码厂商而制作，本库使用效果完全靠玄学，可能可以识别，可能不能识别。ddddocr奉行着开箱即用、最简依赖的理念，尽量减少用户的配置和使用成本，希望给每一位测试者带来舒适的体验项目地址：点我传送更新说明本次更新其实分为两部分，其中有一部分是在1.2.0版本就已经更新了，但是在这里还是有必要提一下的。第一部分 OCR识别部分在1.2.0开始，ddddocr的识别部分进行了一次beta更新，主要更新在于网络结构主体的升级，其训练数据并没有发生过多的改变，所以理论上在识别结果上，原先可能识别效果的很好的图形在1.2.0上有一小部分概率会有一定程度的下降，也有可能原本识别不好的图形在1.2.0之后效果却变得特别好。
测试代码：importddddocrocr=ddddocr.DdddOcr()withopen("test.jpg",'rb')asf:image=f.read()res=ocr.classification(image)print(res)通过在初始化ddddocr的时候使用beta参数即可快速切换新模型importddddocrocr=ddddocr.DdddOcr(beta=True)withopen("test.jpg",'rb')asf:image=f.read()res=ocr.classification(image)print(res)OCR部分应该已经有很多人做了测试，在这里就放一部分网友的测试图片。等等更多图片等你测试哟~第二部分 目标检测部分在本次1.3.0的更新中，目标检测部分隆重登场！
目标检测部分同样也是由大量随机合成数据训练而成，对于现在已有的点选验证码图片或者未知的验证码图片都有可能具备一定的识别能力，适用于文字点选和图标点选。
简单来说，对于点选类的验证码，可以快速的检测出图片上的文字或者图标。importddddocrimportcv2det=ddddocr.DdddOcr(det=True)withopen("test.jpg",'rb')asf:image=f.read()poses=det.detection(image)print(poses)im=cv2.imread("test.jpg")forboxinposes:x1,y1,x2,y2=boxim=cv2.rectangle(im,(x1,y1),(x2,y2),color=(0,0,255),thickness=2)cv2.imwrite("result.jpg",im)举些例子：以上只是目前我能找到的点选验证码图片，做了一个简单的测试。安装环境支持python <= 3.9Windows/Linux/Macos..暂时不支持Macbook M1(X)，M1(X)用户需要自己编译onnxruntime才可以使用安装命令pip install ddddocr以上命令将自动安装符合自己电脑环境的最新ddddocr拓展 一键部署ddddocr api，支持docker部署githubgitee爬虫框架推荐feapdercrawlab交流群 （加我好友拉你进群）Star 历史
Package: ddddsr
ddddsrA python library for end-to-end image super-resolution.InstallpipinstallddddsrFeaturesEnd-to-end super resolution in one package (currently supportwaifu2xmodels).(experimental) OCR for better text super resolution quality.Usageimportddddsr# read input.jpg and write to output.png# default is 2x upscaling on CPU using waifu2x cunet.ddddsr.SR()('input.jpg','output.png')API reference:sr=ddddsr.SR(# models: currently supports ['waifu2x_art', 'waifu2x_photo']model:str='waifu2x_art',# scalescale:Optional[float]=None,# output size (omitted if scale is set, if both scale and size are None, use scale = 2)size:Optional[Union[int,List[int],Tuple[int]]]=None,# denoise level: range in [-1, 3], -1 means no denoising.denoise_level:int=2,# whether to use gpuuse_gpu:bool=False,# if use gpu, set the device iddevice_id:int=0,# (experimental) use OCR for better text qualityocr_text:bool=False,# (experimental) OCR related configurationsocr_font_size:int=28,ocr_font_color:Union[Tuple[int],List[int]]=(0,0,0),ocr_background_color:Union[Tuple[int],List[int]]=(255,255,255),ocr_font_ttf:Optional[str]=None,# path for ttf font file# verboseverbose:bool=False,)sr(# image, support array of [H, W, 3] or [H, W, 4], or the file path.image:Union[np.ndarray,str],# output_path, output file path, if is None, will return the ndarray.output_path:str=None,# slide window size, -1 means no slide window.window:int=256,)ReferencesThe originalwaifu2x.ONNX models are fromwaifu2x-onnx.OCR models are fromppocr-onnxandPaddleOCR.
Package: dddd-utils
带带弟弟小工具1.功能爬虫基类爬虫基类2（selenium）mailftp 下载；删除retry 重试器，只支持在类中使用mysql 操作类使用pipinstall-ihttps://pypi.org/simple/dddd-utils打包python3-mbuild

python3-mtwineupload--repositorytestpypidist/*

python3-mtwineupload--repositorypypidist/*
Package: dddentity
dddentityDDD entity.UsagefromdddentityimportEntity,refUserId=strclassUser(Entity[UserId]):def__init__(self,id:UserId,name:str)->None:self.id=idself.name=namedef_ref_(self)->UserId:returnself.idassertUser("0001","Gilgamesh")==User("0001","Bilgamesh")assertref(User("0002","Enkidu"))=="0002"CaveatCurrentlyeq=Falsemust be set when using with dataclasses
to prevent to generate__eq__()method by dataclass.@dataclass(eq=False)classUser(Entity[UserId]):...APIModuledddentityabstract classEntity[T]Entity class.
Classes implementing this class must implement abstract method_ref_().This class is hashable, satisfying the following conditions:entity1 == entity2iffentity1._ref_() == entity2._ref_()hash(entity) == hash(entity._ref_())where entity, entity1, entity2 are entities.type parameterTType of the identifier of the entity, which must be hashable.abstract method_ref_() -> TShall return the identifier of the entity.functionref(entity: Entity[T]) -> TReturnentity._ref_().
Package: ddd-es
[![pipeline status](https://git.fenrys.io/fenrys/back/python_ddd/badges/master/pipeline.svg)](https://git.fenrys.io/fenrys/back/python_ddd/commits/master)
[![coverage report](https://git.fenrys.io/fenrys/back/python_ddd/badges/master/coverage.svg)](https://git.fenrys.io/fenrys/back/python_ddd/commits/master)
# Description
Python project aimed to implement DDD and event sourcing
Package: dddg
dddgMachine Learning Ducky Labeling and dddg game solver.Tldr how to usepipinstalldddgUsage: dddg [OPTIONS] URLdddghttps://cdn.ionite.io/img/Rjj91N.jpg051113104792593676910146IntroductionDuck Duck Duck Goose (dddg) is a game whose objective is to
find all "schools" of ducks. A valid school is defined where
each duck can either have the same or different attributes.Each card has 4 featuresColor, Number, Hat, and AccessoryA valid flight3 cards where each feature is either all same or all differentRequirementstorchtorchvisionopencv-pythonrequestswebcolorsModelDescription WIP
Package: dddmisc-core
DDDMisc is a set of libraries that provide basic solutions
for implementing domain-driven design methods
in the Python programming language.The development of libraries is in alpha version.
The public API is not guaranteed to be backward
compatible between minor versions of packages.DocumentationEnglish (Coming soon...)RussianLibrariesdddmisc-core- this package provides the core interfaces and
types for dddmisc packages family;dddmisc-domain- this package provides implementation domain's objects classes;dddmisc-messagebus- this package provides the implementation messagebus;dddmisc-handlers-collection- this package provides implementation a collection of command’s and event’s handlers;dddmisc-uow- This package provides the implementation pattern 'unit of work'.InstallationYou can only use the packages you need.
All packages installdddmisc-coreas its dependency.Installdddmics-domainpipinstalldddmisc-domainThis package installpydanticas its dependency.Installdddmisc-messagebuspipinstalldddmisc-messagebusInstalldddmisc-handlers-collectionpipinstalldddmisc-handlers-collectionThis package installtenacityas its dependency.Installdddmisc-uowpipinstalldddmisc-uow
Package: dddmisc-domain
DDDMisc is a set of libraries that provide basic solutions
for implementing domain-driven design methods
in the Python programming language.The development of libraries is in alpha version.
The public API is not guaranteed to be backward
compatible between minor versions of packages.DocumentationEnglish (Coming soon...)RussianLibrariesdddmisc-core- this package provides the core interfaces and
types for dddmisc packages family;dddmisc-domain- this package provides implementation domain's objects classes;dddmisc-messagebus- this package provides the implementation messagebus;dddmisc-handlers-collection- this package provides implementation a collection of command’s and event’s handlers;dddmisc-uow- This package provides the implementation pattern 'unit of work'.InstallationYou can only use the packages you need.
All packages installdddmisc-coreas its dependency.Installdddmics-domainpipinstalldddmisc-domainThis package installpydanticas its dependency.Installdddmisc-messagebuspipinstalldddmisc-messagebusInstalldddmisc-handlers-collectionpipinstalldddmisc-handlers-collectionThis package installtenacityas its dependency.Installdddmisc-uowpipinstalldddmisc-uow
Package: dddmisc-handlers-collection
DDDMisc is a set of libraries that provide basic solutions
for implementing domain-driven design methods
in the Python programming language.The development of libraries is in alpha version.
The public API is not guaranteed to be backward
compatible between minor versions of packages.DocumentationEnglish (Coming soon...)RussianLibrariesdddmisc-core- this package provides the core interfaces and
types for dddmisc packages family;dddmisc-domain- this package provides implementation domain's objects classes;dddmisc-messagebus- this package provides the implementation messagebus;dddmisc-handlers-collection- this package provides implementation a collection of command’s and event’s handlers;dddmisc-uow- This package provides the implementation pattern 'unit of work'.InstallationYou can only use the packages you need.
All packages installdddmisc-coreas its dependency.Installdddmics-domainpipinstalldddmisc-domainThis package installpydanticas its dependency.Installdddmisc-messagebuspipinstalldddmisc-messagebusInstalldddmisc-handlers-collectionpipinstalldddmisc-handlers-collectionThis package installtenacityas its dependency.Installdddmisc-uowpipinstalldddmisc-uow
Package: dddmisc-messagebus
DDDMisc is a set of libraries that provide basic solutions
for implementing domain-driven design methods
in the Python programming language.The development of libraries is in alpha version.
The public API is not guaranteed to be backward
compatible between minor versions of packages.DocumentationEnglish (Coming soon...)RussianLibrariesdddmisc-core- this package provides the core interfaces and
types for dddmisc packages family;dddmisc-domain- this package provides implementation domain's objects classes;dddmisc-messagebus- this package provides the implementation messagebus;dddmisc-handlers-collection- this package provides implementation a collection of command’s and event’s handlers;dddmisc-uow- This package provides the implementation pattern 'unit of work'.InstallationYou can only use the packages you need.
All packages installdddmisc-coreas its dependency.Installdddmics-domainpipinstalldddmisc-domainThis package installpydanticas its dependency.Installdddmisc-messagebuspipinstalldddmisc-messagebusInstalldddmisc-handlers-collectionpipinstalldddmisc-handlers-collectionThis package installtenacityas its dependency.Installdddmisc-uowpipinstalldddmisc-uow
Package: dddmisc-unit-of-work
DDDMisc is a set of libraries that provide basic solutions
for implementing domain-driven design methods
in the Python programming language.The development of libraries is in alpha version.
The public API is not guaranteed to be backward
compatible between minor versions of packages.DocumentationEnglish (Coming soon...)RussianLibrariesdddmisc-core- this package provides the core interfaces and
types for dddmisc packages family;dddmisc-domain- this package provides implementation domain's objects classes;dddmisc-messagebus- this package provides the implementation messagebus;dddmisc-handlers-collection- this package provides implementation a collection of command’s and event’s handlers;dddmisc-uow- This package provides the implementation pattern 'unit of work'.InstallationYou can only use the packages you need.
All packages installdddmisc-coreas its dependency.Installdddmics-domainpipinstalldddmisc-domainThis package installpydanticas its dependency.Installdddmisc-messagebuspipinstalldddmisc-messagebusInstalldddmisc-handlers-collectionpipinstalldddmisc-handlers-collectionThis package installtenacityas its dependency.Installdddmisc-uowpipinstalldddmisc-uow
Package: ddd-nginx
DDD Nginx FrameworkInstallationFrom source code:python setup.py installFrom pypi:pip install ddd-baseUsagefrom ddd_nginx.nginx import Nginx
from ddd_nginx.map import Map, MapKeyParis, MapDefinition
from ddd_nginx.server import Server
from ddd_nginx.location import Location, ReverseProxyStrategy
from ddd_nginx.upstream import Upstream


nginx = Nginx(host="oneapi.cc")
nginx.namespace = "api"

a_map = Map(MapDefinition(key="$http_apikey", value="$api_client_name"))
a_map.append(MapKeyParis("7B5zIqmRGXmrJTFmKa99vcit", "client_one"))
a_map.append(MapKeyParis("QzVV6y1EmQFbbxOfRCwyJs35", "client_two"))
a_map.append(MapKeyParis("mGcjH8Fv6U9y3BVF9H3Ypb9T", "client_six"))

a_upstream = Upstream(name="warehouse_inventory")
a_upstream.append("10.0.0.1:80")
a_upstream.append("10.0.0.2:80")
a_upstream.append("10.0.0.3:80")

b_upstream = Upstream(name="warehouse_pricing")
b_upstream.append("10.0.0.1:80")
b_upstream.append("10.0.0.2:80")
b_upstream.append("10.0.0.3:80")

a_location = Location(
    name="/api/warehouse/inventory",
    proxy=ReverseProxyStrategy('rewrite', '^ /_warehouse last')
)
a_location.set_var("$upstream", "warehouse_inventory")
b_location = Location(
    name="/api/warehouse/pricing",
    proxy=ReverseProxyStrategy('rewrite', '^ /_warehouse last')
)
b_location.set_var("$upstream", "warehouse_pricing")
c_location = Location(
    name="= /_warehouse",
    proxy=ReverseProxyStrategy('proxy_pass', 'http://$upstream$request_uri'),
    scope="internal"
)
c_location.set_var("$api_name", "Warehouse")

a_server = Server(name=nginx.namespace)
a_server.set_var("$api_name", "-")

nginx.append(a_map)
nginx.append(a_upstream)
nginx.append(b_upstream)
nginx.append(a_location)
nginx.append(b_location)
nginx.append(c_location)
nginx.append(a_server)

root_dir = "./dumps_dir"
nginx.dumps(root_dir)LicenseThis software is licensed under theMIT licenseSeeLicense fileChangelog0.1.7Https by default0.1.6Add magic dump0.1.2Template exposed0.1.1Add server to support tls or not0.1.0Provide all in one configure file0.0.8Include template files0.0.7Include jinja2 templates0.0.6Add json error page configuration file0.0.5Add coverage0.0.4Clear dependency0.0.3Dumps nginx to files0.0.2Render conf with template0.0.1Initial public release
Package: ddd-subplots
Python package making it easier to handle mixed 3d and 2d subplots.How do I install this package?As usual, just download it using pip:pipinstallddd_subplotsUsage Example3D subplotsTo get a set of 3d subplots just importsubplots:fromddd_subplotsimportsubplotsfig,axes=subplots(1,3,figsize=(15,5))Rotating 3D scatter plotsThe library also offers a method to render 3D scatter plots. Here’s a complete example:fromddd_subplotsimportsubplots,rotateimportnumpyasnpfromsklearnimportdatasetsfromsklearn.decompositionimportPCAdefwrite_frame(X_reduced,y):colors=np.array(["red","green","blue"])[y]fig,axes=subplots(1,3,figsize=(15,5))foraxisinaxes.flatten():axis.scatter(*X_reduced.T,depthshade=False,c=colors,marker='o',s=20)fig.tight_layout()returnfig,axesX,y=datasets.load_iris(return_X_y=True)X_reduced=PCA(n_components=3).fit_transform(X)rotate(write_frame,X_reduced,"test_animation.gif",y,duration=10,verbose=True)Output:
Package: ddecoapiparser
ddecoapiparserThis Package contains the code to request data via the DD-ECO-API and parse the returned JSON to pd.DataFrameExtra Information:Limited Documentation of the API is found here: https://github.com/DigitaleDeltaOrg/dd-eco-api-specs
Implementation of the API is found here: https://ddecoapi.aquadesk.nl/index.html
Syntax for Filtering the API request is found here: https://github.com/DigitaleDeltaOrg/dd-eco-api/blob/main/filtering.mdArguments of an API request query:query_url (str): Select the desired url of the API to get the correct data.
query_filter (str, optional): Filtering within API. Defaults to None.
skip_properties (str, optional): Properties to skip in response. Defaults to None.
api_key (str, optional): API key for identification as company, required for some url's . Defaults to None.
Package: ddeint
ddeintScipy-based delay differential equation (DDE) solver. See the docstrings and examples for more infos.Examplesfrompylabimportcos,linspace,subplotsfromddeintimportddeintdefmodel(Y,t):return-Y(t-3*cos(Y(t))**2)defvalues_before_zero(t):return1tt=linspace(0,30,2000)yy=ddeint(model,values_before_zero,tt)fig,ax=subplots(1,figsize=(4,4))ax.plot(tt,yy)ax.figure.savefig("variable_delay.jpeg")frompylabimportarray,linspace,subplotsfromddeintimportddeintdefmodel(Y,t,d):x,y=Y(t)xd,yd=Y(t-d)returnarray([0.5*x*(1-yd),-0.5*y*(1-xd)])g=lambdat:array([1,2])tt=linspace(2,30,20000)fig,ax=subplots(1,figsize=(4,4))fordin[0,0.2]:print("Computing for d=%.02f"%d)yy=ddeint(model,g,tt,fargs=(d,))# WE PLOT X AGAINST Yax.plot(yy[:,0],yy[:,1],lw=2,label="delay =%.01f"%d)ax.figure.savefig("lotka.jpeg")LicencePublic domain. Everyone is welcome to contribute !##Installationddeint can be installed by unzipping the source code in one directory and using this command: ::(sudo) python setup.py installYou can also install it directly from the Python Package Index with this command: ::(sudo) pip install ddeint
Package: ddenv
Docker Dev EnvironmentDdenv is a CLI tool to help with using docker as development environment.
It currently supports the following project management tools:yarnInstallationDdenv requires Python 3.10+pip install ddenvUsage$ ddenv --help
Usage: ddenv [OPTIONS] [COMMAND]...

  Run the command in a Docker container with all the dependencies and
  source code

Arguments:
  [COMMAND]...  The command to run

Options:
  -m, --manager TEXT              The project manager your project uses
                                  [required]
  -v, --version TEXT              The version of the project
                                  manager/runtime
  -p, --port TEXT                 Ports to forward. Example: -p 5000:5000
  --install-completion [bash|zsh|fish|powershell|pwsh]
                                  Install completion for the specified
                                  shell.
  --show-completion [bash|zsh|fish|powershell|pwsh]
                                  Show completion for the specified shell,
                                  to copy it or customize the installation.
  --help                          Show this message and exit.Example:ddenv -m yarn yarn dev
Package: ddestiny-django-pagination
About the forkThis project is a fork of apparently dead “django-pagination” project
originally written by ‘Eric Florenzano’. It is maintained by the Linaro
Validation/Infrastructure team. Latest releases can be found on launchpad and
pypi.We are in the process of deciding how to properly host and manage the project.
Feel free to hop to #linaro on irc.freenode.net and ask either ‘zyga’ or
‘mwhudson’ about this.About top-level package name changeThe top level package name was changed frompaginationtolinaro-django-pagination. I did this to ensure that existing users of the
original package could easily transition, on their own terms. Since both
packages are co-installable you are free to check things out and see if the new
pagination code works well for you or not.Also, since the implementation of the actual tags was rewritten I would not
feel entirely confident that the new package is a drop-in replacement.ContributorsWe would like to welcome any and all contributors. Please use the pull request
feature on github.How to use linaro-django-paginationSee our official documentation for more details.http://packages.python.org/linaro-django-pagination/
Package: ddeutil
Data Utility Packages:CoreTable of Contents::FeaturesBase Utility FunctionsUtility FunctionsTheCore Utilitypackage implements the utility functions and objects
that was created on sub-package namespace,ddeutil, design for independent
installation. I make this package able to extend with any sub-extension with this
namespace. This namespace able to scale out the coding with folder
structure design. You can add any extension features and import byimport ddeutil.{extension}.This package provide the Base Utility functions and objects for any data package
or data application.Install from PyPI:pipinstallddeutilFeaturesBase Utility Functionscore.base
    - cache
    - checker
    - convert
    - elements
    - hash
    - merge
    - sorting
    - splitterUtility Functionscore
    - decorator
    - dtutils
    - randomlyLicenseThis project was licensed under the terms of theMIT license.
Package: ddeutil-io
Data Utility Package:IOTable of Contents:InstallationFeaturesConfigRegisterThisUtility IOObject was created forloadthe config data from any file
format types like.yaml,.json, or.toml, and manage retention and version
of this config file lifecycle.Installationpipinstallddeutil-ioFeaturesConfigTheConfig Objectis the file system handler object.frompathlibimportPathfromddeutil.io.configimportConfFileconfig:ConfFile=ConfFile(path=Path(),compress="gzip")RegisterTheRegister Objectis the metadata generator object for the config data.
If you passing name and configs to this object, it will find the config name
in any stage storage and generate its metadata to you.fromddeutil.io.registerimportRegisterfromddeutil.io.modelsimportParamsregistry:Register=Register(name='examples:conn_data_local_file',config=Params.model_validate({"stages":{"raw":{"format":"{naming:%s}.{timestamp:%Y%m%d_%H%M%S}"},},}),)LicenseThis project was licensed under the terms of theMIT license.
Package: ddex
A Python DDEX implementation.Documentation:https://ddex.corletti.xyzSource Code:https://github.com/anthonycorletti/ddexA Python DDEX implementation.Some tasty features are:XML Support: Create and validate DDEX XML documents.JSON: Transform DDEX documents to JSON documents and vice versa.Python Objects: Create and validate DDEX documents with python objects and vice versa.RequirementsPython 3.8+InstallationpipinstallddexA Quick Examplefromddeximportxml_to_ddexddex=xml_to_ddex(path="Audio.xml")ddex.assert_valid()What is DDEX?DDEX is a standard for the exchange of data between parties. Data specifically for digitial content supply chains. Think of it as the metadata standard for describing how digital content is distributed around to the services that supply the world with movies, music, and multimedia.To learn more about DDEX, visit the following links:DDEX Website:https://ddex.netDDEX Knowledge Base:https://kb.ddex.netDDEX XML Content Library:http://service.ddex.net/xml/So what can I do with this library?Install it and use it in a server-side api for validating and creating DDEX documents and schemas, or for a batch processing service that's transforming data from one  multimedia metadata content standard to another. This library should support all DDEX content standards that python can parse.How can I help?Please open anissueon GitHub. Pull requests are also very welcome. Checkout ourcontributing guideto get started.