Search Results for "attribute"
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Showing 22 open source projects for "attribute"
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Iris Powered By Generali - Iris puts your customer in control of their identity.Iris Identity Protection API sends identity monitoring and alerts data into your existing digital environment – an ideal solution for businesses that are looking to offer their customers identity protection services without having to build a new product or app from scratch.
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Inventory and Order Management Software for Multichannel SellersWe are the most powerful inventory and order management platform for Amazon, Walmart, and multichannel product sellers. Centralize orders, product information, and fulfillment operations to run more efficiently, sell more products, and stay compliant with marketplace requirements so you can grow profitably.
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deepface
A Lightweight Face Recognition and Facial Attribute Analysis
DeepFace is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. -
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peewee
A small, expressive orm, which supports postgresql, mysql and sqlite
...It has few (but expressive) concepts, making it easy to learn and intuitive to use. Peewee will automatically infer the database table name from the name of the class. You can override the default name by specifying a table_name attribute in the inner “Meta” class (alongside the database attribute). To learn more about how Peewee generates table names, refer to the Table Names section. There are lots of field types suitable for storing various types of data. Peewee handles converting between pythonic values and those used by the database, so you can use Python types in your code without having to worry. ... -
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PyExcelerate
Accelerated Excel XLSX Writing Library for Python 2/3
Accelerated Excel XLSX writing library for Python. PyExcelerate is a Python for writing Excel-compatible XLSX spreadsheet files, with an emphasis on speed. -
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Orange Data Mining
Orange: Interactive data analysis
...Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight! ... -
The full-stack observability platform that protects your dataLayer, tags and conversion dataCode-Cube.io detects issues instantly, alerts you in real time and helps you resolve them fast. No manual QA. No unreliable data. Just data you can trust and act on.
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django-import-export
Django application and library for importing and exporting data
...Also, the report_skipped option controls whether skipped records appear in the import Result object, and if using the admin whether skipped records will show in the import preview page. Not all data can be easily extracted from an object/model attribute. In order to turn complicated data model into a (generally simpler) processed data structure on export, dehydrate_<fieldname> method should be defined. -
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Rio
WebApps in pure Python. No JavaScript, HTML and CSS needed
Rio is a Python framework designed to build web applications without the need for HTML, CSS, or JavaScript. Inspired by frameworks like Flutter and React, Rio offers a declarative interface and reusable components, enabling developers to create dynamic web apps entirely in Python. It streamlines the development process by managing both frontend and backend seamlessly. -
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django-rest-framework-gis
Geographic add-ons for Django REST Framework
...While precision and remove_duplicates are designed to reduce the byte size of the API response, they will also increase the processing time required to render the response. This will likely be negligible for small GeoJSON responses but may become an issue for large responses. The primary key of the model (usually the "id" attribute) is automatically used as the id field of each GeoJSON Feature Object. The GeoJSON specification allows a feature to contain a boundingbox of a feature. GeoFeatureModelSerializer allows two different ways to fill this property. The first is using the geo_field to calculate the bounding box of a feature. -
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pytype
A static type analyzer for Python code
pytype is a static type analyzer that checks and infers types for Python code without executing it, catching errors at “compile time” and generating actionable diagnostics. It grew alongside Python typing at Google and can understand both inline annotations and unannotated code via powerful inference. The tool consumes stub files (.pyi) for the standard library and third-party packages (from typeshed and its own built-ins), enabling accurate checks even in large, mixed-quality codebases.... -
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Amazon DynamoDB Encryption Client Python
Amazon DynamoDB Encryption Client for Python
...You usually will not need to interact with these low-level functions, but for certain advanced use cases it can be useful. If you do choose to use the item encryptor functions directly, you will need to provide a CryptoConfig for each call. By default, the helper clients use your attribute actions and cryptographic materials provider to build the CryptoConfig that is provided to the item encryptor. For some advanced use cases, you might want to provide a custom CryptoConfig for specific operations. -
Get full visibility and control over your tasks and projects with Wrike.Wrike offers world-class features that empower cross-functional, distributed, or growing teams take their projects from the initial request stage all the way to tracking work progress and reporting results.
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DreamCraft3D
Official implementation of DreamCraft3D
...Because 3D generation is hardware‐intensive, the repository likely also includes optimizations like quantization, pruning, or inference accelerations (e.g. using FlashMLA or DeepEP) to make the generation pipeline faster or more efficient. DreamCraft3D may also support style or attribute control (e.g. “make this object metallic,” “add textures”) via prompt conditioning or guides. -
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Large Concept Model
Language modeling in a sentence representation space
...It includes utilities to build concept vocabularies, map supervision signals to those vocabularies, and measure zero-shot or few-shot generalization. Probing tools help diagnose what the model knows—e.g., attribute recognition, relation understanding, or compositionality—so you can iterate on data and objectives. The design is modular, making it straightforward to swap backbones, change objectives, or integrate retrieval components. -
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Django friendly finite state machine
Django friendly finite state machine support
Django-fsm adds simple declarative state management for Django models. If you need parallel task execution, view, and background task code reuse over different flows - check my new project Django-view flow. Instead of adding a state field to a Django model and managing its values by hand, you use FSMField and mark model methods with the transition decorator. These methods could contain side effects of the state change. You may also take a look at the Django-fsm-admin project containing a... -
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Awesome Graph Classification
Graph embedding, classification and representation learning papers
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations. -
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StarGAN
Official PyTorch Implementation
StarGAN is an implementation of the Star Generative Adversarial Network, a model designed for multi-domain image-to-image translation using a single unified GAN architecture. Unlike earlier GAN approaches that required separate models for each domain pair, StarGAN enables flexible attribute transfer across multiple domains within one network, significantly improving efficiency and scalability. The repository includes full training and inference pipelines for tasks such as facial attribute manipulation and style transfer. It demonstrates adversarial training strategies, domain classification losses, and generator-discriminator coordination required for stable multi-domain translation. ... -
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Flask-GraphQL
Adds GraphQL support to your Flask application
Adds GraphQL support to your Flask application. This will add /graphql endpoint to your app and enable the GraphiQL IDE. If you are using the Schema type of Graphene library, be sure to use the graphql_schema attribute to pass as schema on the GraphQLView view. Otherwise, the GraphQLSchema from graphql-core is the way to go. The GraphQLSchema object that you want the view to execute when it gets a valid request. A value to pass as the context_value to graphql execute function. By default is set to dict with request object at key request. The root_value you want to provide to graphql execute. -
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Aglyph
Aglyph is a Dependency Injection framework for Python.
Aglyph is a Dependency Injection framework for Python, supporting type 2 (setter) and type 3 (constructor) injection. Aglyph runs on CPython (http://www.python.org/) 2.7 and 3.4+, and on recent versions of the PyPy (http://pypy.org/>),Jython (http://www.jython.org/), IronPython (http://ironpython.net/), and Stackless Python (http://www.stackless.com/) variants. Aglyph can assemble "prototype" components (a new instance is created every time), "singleton" components (the same instance... -
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auto_ml
Automated machine learning for analytics & production
...Load up some data (either a DataFrame, or a list of dictionaries, where each dictionary is a row of data). Make a column_descriptions dictionary that tells us which attribute name in each row represents the value we’re trying to predict. Pass all that into auto_ml, and see what happens! You can pass in your own function to perform feature engineering on the data. This will be called as the first step in the pipeline that auto_ml builds out. You will be passed the entire X dataset (not the y dataset), and are expected to return the entire X dataset. ... -
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...The first factor is validation of 3 different AD attributes. User will need to provide 3 different information which only he will know such as Home Telephone, Pin Code or any new AD attribute say PIN, Date of Birth, Date of Joining etc. The second factor is One Time Password (OTP). After successful validation of First Factor, a OTP is sent to the Email address of the user, may it be official or personal.
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radiustest
Radius client written in python
...The main idea is to have a client which could be easily used to test different Radius servers. Client supports: - Radius PAP authentication - Multi thread (sniffing separated from sending) - Several Attribute Value Pairs (AVP) supported (nas-ip-address, service-type, nas-port-type, calling-station-id, called-station-id) - We can add new AVP easily - Flooding mode (performance/stress testing) - Accounting messages Client uses scapy library to send/receive packets. It consists of two python classes: - RadiusExt: Radius class supporting AVP, authentication, packet manipulation (derived from Scapy Packet class) - RadiusAttr: Class for AVP (derived from Scapy Packet class) I use this client to: - test basic PAP authentication - send different AVP to test if Radius server is behaving correctly (RFC compliance) - display returned AVP - flood Radius server with multiple packets (performance testing) For examples please refer to readme.txt -
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pycallgraphTAexport is an extension of the python module pycallgraph. pycallgraphTAexport is a modified version of pycallgraph which adds the ability to export in the TA (tuple-attribute) format used by many software architecture analysis tools.
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This is a pure-Python XPath evaluator based on ElementTree. It supports a substantial fraction of the XPath 1.0 specification, but only the self, child, and attribute axes. The parser underlying the evaluator attempts to handle all of XPath 1.0.
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Framework for application development, consisting of a persistant object framework, object notification, central property/attribute management, reporting, automatic display generation etc.
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