Saravanan666/pypi-org huggingface dataset:
Package: xadmin-croxlink2
All rights reserved.Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.Neither the name of Django Xadmin nor the names of its contributors may be used
to endorse or promote products derived from this software without
specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Download-URL:https://github.com/CROXLINK/django-xadmin/archive/django2.zipDescription: UNKNOWN
Keywords: admin,django,xadmin,bootstrap
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Framework :: Django
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: JavaScript
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Internet :: WWW/HTTP
Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Provides-Extra: Excel
Provides-Extra: Reversion
Package: xadmin-django
All rights reserved.Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.Neither the name of Django Xadmin nor the names of its contributors may be used
to endorse or promote products derived from this software without
specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Download-URL:http://github.com/liangdongchang/django-xadmin/master.zipDescription: Based on xadmin.Drop-in replacement of Django admin comes with lots of goodies, fully extensible with plugin support, pretty UI based on Twitter Bootstrap.
Keywords: admin,django,xadmin,bootstrap
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Framework :: Django
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: JavaScript
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Internet :: WWW/HTTP
Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Provides-Extra: Excel
Provides-Extra: Reversion
Package: xadmin-nimbus
A Python Extended for Django.Supports Python 2.7, 3.4 or 3.5+Free software: Apache License 2.0On thePython Package Index (PyPI)Python Package Index (PyPI):https://pypi.python.org/pypi/xadmin_nimbus/InstallEasiest installation is viapippip install xadmin_nimbusQuick UsageCopyright and LicenseCopyright (c) 2001-2017william.ren@live.cnAll rights reserved.This software is licensed under the Apache License, Version 2.0. The
Apache License is a well-established open source license, enabling
collaborative open source software development.See the “LICENSE” file for the full text of the license terms.
Package: xadmin-py3
Xadmin-py3使用 Xadmin 您只需定义您数据的字段等信息，即可即刻获得一个功能全面的管理系统。不仅如此，您还可以方便的扩展更多的定制功能和系统界面。在线演示待更新...功能更好的过滤器，日期范围，数量范围等。基于Bootstrap3，支持在多种屏幕上无缝浏览，并支持Bootstrap主题模板内置丰富的插件功能。包括数据导出、书签、图表、数据添加向导及图片相册等多种扩展功能插件开发简单，安装方便。截图待更新...入门安装通过PyPI安装，请运行：pip install xadmin-py3或从GitHub源安装：pip install git+git://github.com/ldsxp/xadmin-py3.git安装需要django>=1.9django-crispy-forms>=1.6.0 (For xadmin crispy forms)django-reversion([OPTION] For object history and reversion feature, please select right version by your django, seechangelog)django-formtools([OPTION] For wizward form)xlwt([OPTION] For export xls files)xlsxwriter([OPTION] For export xlsx files)文档Chinese更改日志在本地运行Democd demo_app
./manage.py migrate
./manage.py runserverhttp://127.0.0.1:8000在浏览器中打开http://127.0.0.1:8000，管理员用户密码为admin。
Package: xadnacos
xadnacosxadnacos是一个用来调用naccos的sdk, 基于KcangNacos进行修改参考:
*https://github.com/KcangYan/nacos-python-sdk.gitFree software: MIT license文档导入dkimageapp库:from xadnacos import xadnacos as nacos创建nacos连接对象:nacosServer = nacos.nacos(ip=nacosIp,port=nacosPort)将本地配置注入到nacos对象中即可获取远程配置:GlobalConfig={}
nacosServer.config(dataId="demo-python.json",group="dev",tenant="python",myConfig=GlobalConfig)注册nacos服务:serverHost = socket.gethostbyname(socket.gethostname())
metadata = {
    "tier": "backend",
    "projectid": "THID89782455-HJ45678963"
}
clusterName = myConfig.region

nacosServer.registerService(serviceIp=serverHost,servicePort=myConfig.port,serviceName="python-provider",
                            namespaceId="python",groupName="dev",clusterName=clusterName,metadata=metadata)开启监听配置线程和服务注册心跳进程的健康检查进程:nacosServer.healthyCheck()FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-02-04)First release on PyPI.
Package: x-ae-a-12
X-AE-A-12Command line application for displaying random facts from wikipedia on the console.
Available as a python package onPyPI:pip install x-ae-a-12Documentation available at:X-AE-A-12 docsTable of contentsBuilt WithFeaturesCode ExamplePrerequisitesInstallationTestsDeploymentContributionsBug / Feature RequestAuthorsLicenseBuilt WithPython 3.8- The programming language used.Poetry- The dependency manager used.Nox- The automation tool used.Pytest- The testing framework used.Flake8- The linting tool used.Sphinx- The documentation generator used.GitHub Actions- CI-CD tool used.FeaturesDisplay random facts from Wikipedia.Select Wikipedia language edition to be used.Code Exampledefmain(language:str)->None:"""The X-AE-A-12 Python project."""page=wikipedia.random_page(language=language)click.secho(page.title,fg="green")click.echo(textwrap.fill(page.extract))PrerequisitesWhat things you need to install the software and how to install thempython 3.8Linux:sudo apt-get install python3.8Windows:Download frompython.orgMac OS:brew install python3pipLinux and Mac OS:pip install -U pipWindows:python -m pip install -U pippoetrycurl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | pythonnoxpip install --user --upgrade noxInstallationClone this repository:git clone https://github.com/SpencerOfwiti/X-AE-A-12To set up virtual environment and install dependencies:poetry installTo run application (by default the English language edition is selected):poetry run x-ae-a-12To run Swahili language edition:poetry run x-ae-a-12 -l swTestsThis system uses pytest to run automated tests.To run automated tests:nox -s testsDeploymentTo deploy application on PyPI(Python Package Index):poetry buildpoetry publishContributionsTo contribute, follow these steps:Fork this repository.Create a branch:git checkout -b <branch_name>.Make your changes and commit them:git commit -m '<commit_message>'Push to the original branch:git push origin <project_name>/<location>Create the pull request.Alternatively see the GitHub documentation oncreating a pull request.Bug / Feature RequestIf you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issuehereby including your search query and the expected result.If you'd like to request a new function, feel free to do so by opening an issuehere. Please include sample queries and their corresponding results.AuthorsSpencer Ofwiti-Initial workSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: xai-explainer
InstallationAll stable versions can be installed fromPyPIby usingpipor your favorite package managerpip install xai-explainerDevelopmentRequirements:Python 3.9 or higherpoetry1.7makeConfigure poetry for vscodepoetryconfigvirtualenvs.in-projecttrueFor installing the development environment runmakeinstall-devRun scripts using poetrypoetryrunpythonscript_name.py
Package: xai-inference-engine
XAI Inference EngineThis package is a wrapper for CNN-based PyTorch models that is capable of performing XAI inferencing. It wraps a trained PyTorch CNN model and allows it to return the predictions, sorted prediction indices and saliency maps when provided with a preprocessed input. For the saliency maps the library uses theFM-G-CAMmethod. More types of saliency map generation methods will be added in the future. The package also provides a method to superimpose the saliency maps on the input image.Users can also use the package to create their own inference engine by extending theXAIInferenceEngineclass.Advanced Tutorials: Coming Soon...Github|PyPiRequirementsPython 3.8+PyTorch 2.0+InstallationExecute the following command in your terminal to install the package.pipinstallxai-inference-engineUsageFollow the example below to use the package. Copy and paste the code into a python script and run it. Make sure you have the requirements installed. 😊print("[INFO]: Testing XAIInferenceEngine...")print("[INFO]: Importing Libraries...")fromxai_inference_engineimportXAIInferenceEnginefromtorchvision.modelsimportresnet50,ResNet50_Weightsdevice=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")print("[INFO]: Device:{}".format(device))print("[INFO]: Loading Model...")# Modelmodel=resnet50(weights=ResNet50_Weights.IMAGENET1K_V2).to(device)weights=ResNet50_Weights.DEFAULTpreprocess=weights.transforms()# Model config# Set model to eval modemodel.eval()last_conv_layer=model.layer4[2].conv3class_count=5class_list=weights.meta["categories"]img_h=224print("[INFO]: Image Preprocessing...")# Image Preprocessingurl="https://raw.githubusercontent.com/utkuozbulak/pytorch-cnn-visualizations/master/input_images/cat_dog.png"r=requests.get(url,allow_redirects=True)open("dog-and-cat-cover.jpg","wb").write(r.content)img=Image.open("dog-and-cat-cover.jpg")img=img.resize((img_h,img_h),resample=Image.BICUBIC)img_tensor=preprocess(img).to(device)print("[INFO]: Creating XAIInferenceEngine...")xai_inferencer=XAIInferenceEngine(model=model,last_conv_layer=last_conv_layer,device=device,)print("[INFO]: Running XAIInferenceEngine.predict()...")preds,sorted_pred_indices,super_imp_img,saliency_maps=xai_inferencer.predict(img=img,img_tensor=img_tensor,)print("[INFO]: Saving Results to the root folder...")super_imp_img.save("super_imp_img.jpg")saliency_maps.save("saliency_maps.jpg")print("[INFO]: Displaying Results...")print("        Predictions:{}".format(preds.shape))print("        Sorted Prediction Indices:{}".format(sorted_pred_indices.cpu().numpy()[:10]))print("        Heatmaps shape:{}".format(saliency_maps))print("        Super Imposed Image:{}".format(super_imp_img))ResultsFollowing image shows comparison between the saliency maps generated by the FM-G-CAM method and the Grad-CAM method.AuthorRavidu Silva
Package: xai-kit
XAI (eXplainable Artificial Intelligence) LibraryOverviewThe XAI (eXplainable Artificial Intelligence) Library is a powerful toolkit designed to empower data scientists and machine learning practitioners in their journey to understand, interpret, and visualize complex machine learning models. In an era of advanced AI algorithms, transparency and interpretability are paramount. XAI addresses these needs by offering a suite of advanced visualizations tailored for regression analysis, curated datasets, pre-trained regression models, and comprehensive documentation.FeaturesAdvanced Visualizations: Explore a rich collection of visualizations in theXAI Regression Visualizations Moduledesigned to unravel the intricacies of your regression models.Datasets and Models: Access curated datasets and pre-trained regression models to streamline your regression analysis process.Model-Agnostic Explanations: Enjoy model-agnostic explanations for compatibility with a wide array of machine learning models.Interpretability for Diverse Data Types: XAI is built to cater to diverse data types, including tabular data, image data, and natural language processing (NLP) models.Continuous Improvement: Expect regular updates with additional features and support for various machine learning tasks.Getting StartedTo harness the power of the XAI Library, follow these steps:Install the Library: Begin by including the library in your Python environment.Explore Documentation: Refer to theGetting Started Guideto understand the library's objectives and scope.Usage Guide: Dive into theUsage Guidefor detailed instructions on using the library's features.ExamplesExplore practical examples demonstrating the library's capabilities in theExample Notebooksdirectory.DocumentationGetting Started Guide: Learn about the library's objectives and scope.Usage Guide: Detailed instructions on using the library's features.Index: The main page with information about the library and its creator.AuthorZeed Almelhem (GitHub)ContactFor inquiries and feedback, please feel free to contact the author:Email (z@zeed-almelhem.com)Personal WebsiteKaggleGitHubMediumInstagramLinkedInTwitterBlogProjectsContactLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: xai-metrics
XAI-metricsA package for analysis and evaluating metrics for machine learning models explainability.InstallationInstall from PyPI:pip install xai-metricsUsageExamples of usage:Perturbation based on permutation importancesfrom xai_metrics import examine_interpretation

X_train.columns = ['0','1','2','3']
X_test.columns = ['0','1','2','3']
xgb_model = xgb.XGBClassifier()
xgb_model.fit(X_train, y_train)
perm = PermutationImportance(xgb_model, random_state=1).fit(X_test, y_test)
perm_importances = perm.feature_importances_

examine_interpretation(xgb_model, X_test, y_test, perm_importances, epsilon=4, resolution=50, proportionality_mode=0)Perturbation based on local importancesfrom xai_metrics import examine_local_fidelity

examine_local_fidelity(xgb_model, X_test, y_test, epsilon=3)Gradual eliminationfrom xai_metrics import gradual_elimination

gradual_elimination(f_forest, f_X_test, f_y_test, f_shap)Seeherefor notebooks with full examples of usage.
Package: xaitk-saliency
XAITK - SaliencyThexaitk-saliencypackage is an open source, Explainable AI (XAI) framework
for visual saliency algorithm interfaces and implementations, built for
analytics and autonomy applications.Seeherefor a more formal introduction to the topic of XAI and visual saliency
explanations.This framework is a part of theExplainable AI Toolkit (XAITK).Supported AlgorithmsThexaitk-saliencypackage provides saliency algorithms for a wide range of image understanding
tasks, including image classification, image similarity, object detection, and reinforcement learning.
The current list of supported saliency algorithms can be foundhere.Target AudienceThis toolkit is intended to help data scientists and developers who want to
add visual saliency explanations to their workflow or product.
Functionality provided here is both directly accessible for targeted
experimentation, and throughStrategyandAdapterpatterns to allow for
modular integration into systems and applications.InstallationInstall the latest release via pip:pipinstallxaitk-saliencySome plugins may require additional dependencies in order to be utilized at
runtime.
Such details are describedhere.Seehere for more installation documentation.Getting StartedWe provide a number of examples based on Jupyter notebooks in the./examples/directory to show usage of thexaitk-saliencypackage in a number of
different contexts.Contributions are welcome!
See theCONTRIBUTING.mdfile for details.DocumentationDocumentation snapshots for releases as well as the latest master are hosted onReadTheDocs.The sphinx-based documentation may also be built locally for the most
up-to-date reference:# Install dependenciespoetryinstall# Navigate to the documentation root.cddocs# Build the docs.poetryrunmakehtml# Open in your favorite browser!firefox_build/html/index.htmlXAITK Saliency Demonstration ToolThisassociated projectprovides a local web-application that provides a demonstration of visual
saliency generation in a user-interface.
This provides an example of how visual saliency, as generated by this package,
can be utilized in a user-interface to facilitate model and results
exploration.
This tool uses thetrame framework.
Package: xaitk-saliency-demo
XAITK Saliency functionality demonstration via an interactive Web UI that can run locally, in jupyter or in the cloud.
A docker image is available for cloud deployment or local testing (kitware/trame:xaitk).
This application has been mainly tested on Linux but should work everywhere assuming all the dependencies manage to install on your system.LicenseThis application is provided under the BSD Open Source License.InstallingOn Linux,pipis enough to install this Python library.python3.9 -m venv .venv
source .venv/bin/activate
pip install -U pip xaitk-saliency-demoThen from that virtual-environment you can run the application like below#Executablexaitk-saliency-demo#Moduleapproachpython -m xaitk_saliency_demo.appWithin Jupyter you can do the followingfrom xaitk_saliency_demo.app.jupyter import create_app
xaitk = await create_app()
xaitk.guiDocker imageFor each commit to master the CI will push a newkitware/trame:xaitkimage to dockerhub.
Such image can be ran locally using the following command line.docker run -it --rm --gpus all -p 8080:80 kitware/trame:xaitk
Package: xaiz
XAI (eXplainable Artificial Intelligence) LibraryOverviewThe XAI (eXplainable Artificial Intelligence) Library is a powerful toolkit designed to empower data scientists and machine learning practitioners in their journey to understand, interpret, and visualize complex machine learning models. In an era of advanced AI algorithms, transparency and interpretability are paramount. XAI addresses these needs by offering a suite of advanced visualizations tailored for regression analysis, curated datasets, pre-trained regression models, and comprehensive documentation.FeaturesAdvanced Visualizations: Explore a rich collection of visualizations in theXAI Regression Visualizations Moduledesigned to unravel the intricacies of your regression models.Datasets and Models: Access curated datasets and pre-trained regression models to streamline your regression analysis process.Model-Agnostic Explanations: Enjoy model-agnostic explanations for compatibility with a wide array of machine learning models.Interpretability for Diverse Data Types: XAI is built to cater to diverse data types, including tabular data, image data, and natural language processing (NLP) models.Continuous Improvement: Expect regular updates with additional features and support for various machine learning tasks.Getting StartedTo harness the power of the XAI Library, follow these steps:Install the Library: Begin by including the library in your Python environment.Explore Documentation: Refer to theGetting Started Guideto understand the library's objectives and scope.Usage Guide: Dive into theUsage Guidefor detailed instructions on using the library's features.ExamplesExplore practical examples demonstrating the library's capabilities in theExample Notebooksdirectory.DocumentationGetting Started Guide: Learn about the library's objectives and scope.Usage Guide: Detailed instructions on using the library's features.Index: The main page with information about the library and its creator.AuthorZeed Almelhem (GitHub)ContactFor inquiries and feedback, please feel free to contact the author:Email (z@zeed-almelhem.com)Personal WebsiteKaggleGitHubMediumInstagramLinkedInTwitterBlogProjectsContactLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: xaj
.. warning::
This version ofXAJworks only withjaxandjaxlib0.3.
Butjaxlib0.3 is not available on PyPI.
To install this version ofXAJ, please first installjaxlibwith:.. code-block:: bash
    pip install jaxlib==0.3.25 -f https://storage.googleapis.com/jax-releases/jax_releases.htmlXAJOrdinary differential equation (ODE) integrator compatible withGoogle's JAX.XAJimplements the Runge-Kutta Dormand-Prince 4/5 pair (DP5) with
adaptive step control and dense output according to theNumerical Recipes.
It provides a fast and efficient way to solve non-stiff ODEs.
The programming interface is designed so it feels similar to the
derivative functions inJAX.Specifically,XAJprovides a single functionodeint().
Applying it to another functionrhs()and the initial conditions
returns the numerical solution as a callablens, which interpolates
the dense output.from xaj import odeint
from jax import numpy as np

rhs = lambda x, y: y
x0  = 0
y0  = 1

ns  = odeint(rhs, x0, y0, 1)

x   = np.linspace(0, 5)
y   = ns(x)The numerical integration happens in a "lazy" way, which is triggered
by the extreme values of the argument ofns.
Alternatively, it is possible to obtain the numerical solutions at the
full steps byxs = ns.xs
ys = ns.ysDemos on how to useXAJcan be found in thedemosdirectory.
Package: xako
Xako - Document sharing transport layerOverviewXako is document sharing transport layer system with a simple REST API.  It
allows application to send files to other peers.  Xako handles the job of
communicating with the peers and make the dispatch.Xako ensures data integrity at delivery/arrival of documents.See the documentation for more information.BackendsXako uses backends to send the documents fragments.  At this moment xako only
has a single backend: email.  This means you must provide each xako domain a
POP/SMTP pair to send and receive documents.See the email backend documentation.ChangelogSeries 0.x2018-11-20.  Release 0.7.0Initial release for Python 3.