Search Results for "graphs" - Page 5
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Showing 171 open source projects for "graphs"
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interactive-coding-challenges
120+ interactive Python coding interview challenges
...The repository emphasizes a learn-by-doing approach: you read a prompt, attempt a solution, and verify behavior with tests, often within notebooks or scripts. Problems span arrays, strings, stacks, queues, linked lists, trees, graphs, dynamic programming, and more, mirroring common interview themes. Many challenges include hints and reference solutions so you can compare approaches and learn idiomatic patterns. The structure encourages incremental improvement—start with a brute-force idea, then refine to optimal time and space complexity. It serves both as a self-study path and as a warm-up bank for interview prep or coding katas. -
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Piko stats
Piko solar inverter data communicator and manager
Piko solar inverter interface. Get online real time data and status. Get also history data. Database management of data. Exports, graphs, ... Manage data and stats using a SQLite or MySQL database. Third party intergration. See the Wiki home page for more informations. -
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CTOSC
Tools for processing solar cell characterization data (Rsh,ECV,QSSPC)
CTOSC stands for 'Characterization Toolbox for Solar Cells' and contains a few functionalities to process solar cell data: sheet resistance mapping, making ECV data graphs, QSSPC lifetime data graphs and showing/averaging PL/EL images. For ECV/Rsh/QSSPC data the program currently supports output files from the Sunlab Sherescan four-point probe (.txt), the WEP Wafer Profiler CVP21 (.csv) and Sinton WCT-120 (.txt). -
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Graph Nets library
Build Graph Nets in Tensorflow
Graph Nets, developed by Google DeepMind, is a Python library designed for constructing and training graph neural networks (GNNs) using TensorFlow and Sonnet. It provides a high-level, flexible framework for building neural architectures that operate directly on graph-structured data. A graph network takes graphs as inputs, consisting of edges, nodes, and global attributes, and produces updated graphs with modified feature representations at each level. This library implements the foundational ideas from DeepMind’s paper “Relational Inductive Biases, Deep Learning, and Graph Networks”, offering tools to explore relational reasoning and message-passing neural networks. ... -
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Miasm
Reverse engineering framework in Python
...Among them, the SSA/Out-of-SSA transformation, expression propagation and high-level operators can be joined to “lift” Miasm IR to a more human-readable language. We use graphviz to illustrate some graphs. Its layout does not always totally conform with a reverse engineering “ideal view”, so please be tolerant of those odd graphs. Miasm is not the first tool to implement this feature. But, well, as the tool already had everything needed to implement DSE, it was just a matter of time before these features landed in the main branch. -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
PyTorch-BigGraph (PBG) is a system for learning embeddings on massive graphs—think billions of nodes and edges—using partitioning and distributed training to keep memory and compute tractable. It shards entities into partitions and buckets edges so that each training pass only touches a small slice of parameters, which drastically reduces peak RAM and enables horizontal scaling across machines. PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions, negative sampling strategies, and typed entities, making it suitable for link prediction and retrieval. ... -
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TestRest
TestRest is a fully QA Management Tool
TestRest is Test Management offers test case authoring, reusable test cases, test execution and reporting. TestRest supports statistic and graph reports with simple modern UI interfaces. -
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King Phisher
Phishing Campaign Toolkit
King Phisher is an open source tool that can simulate real world phishing attacks. It has an easy-to-use, flexible architecture that allows for full control over both emails and server content. It is useful for running awareness campaigns and training, and can only be used for legal applications when the explicit permission of the targeted organization has been obtained. -
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TI2BioP allows mainly the calculation of topological indices (spectral moments) derived from inferred and artificial 2D structures of DNA, RNA and proteins being possible to carry out a structure-function correlation irrespective of sequence alignments. TI2BioP version 3.0 is a python platform with a graphical interface designed for Windows, Linux and Mac OS.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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distribution-is-all-you-need
The basic distribution probability Tutorial for Deep Learning Research
distribution-is-all-you-need is a Python-based probability tutorial aimed at deep learning researchers and students. It explains common discrete and continuous distributions through short scripts, formulas, descriptions, and plotted graphs. Covered topics include uniform, Bernoulli, binomial, categorical, multinomial, beta, Dirichlet, gamma, exponential, Gaussian, normal, chi-squared, and Student's t distributions. The material highlights relationships such as conjugate priors and special-case distributions. Examples connect probability functions with machine learning concepts including binary and multiclass cross-entropy. ... -
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A symbolic framework for C++, Python and Octave implementing automatic differentiation by source code transformation in forward and reverse modes on sparse matrix-valued computational graphs.
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Zabbix-in-Telegram
Zabbix Notifications with graphs in Telegram
Zabbix Notifications with graphs in Telegram. -
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plot.py
direct data plotting and evaluation
The Plot.py project tries to supply a measurement data visualization and treatment framework being easy to use while keeping the freedom for advanced users to execute additional data treatment algorithms. Plotting is done via gnuplot and the script used to produce the graphs can be exported for later use/changes. Many raw experimental data types (mostly of x-ray and neutron scattering experiments) are supported with more to be added on user request. The data treatment includes non-linear fitting, integration and differentiation, peak-finder and more. User python code can be executed in the integrated IPython console. -
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ComplexEventExtraction
Expression pattern collection of Chinese compound event extraction
...It defines patterns for causal, conditional, sequential, contrastive, and parallel event structures. The system uses explicit connective words and phrase combinations to split text into linked event pairs. Extracted results can support event graphs that represent how situations develop, conflict, or depend on one another. The repository catalogs hundreds of linguistic patterns and demonstrates them on a large Chinese news corpus. It also discusses several event representations, including clauses, token sequences, and syntactic phrases. The project is intended as a research reference for event extraction, knowledge modeling, forecasting, and language-resource development. -
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SG2Im
Code for "Image Generation from Scene Graphs", Johnson et al, CVPR 201
sg2im is a research codebase that learns to synthesize images from scene graphs—structured descriptions of objects and their relationships. Instead of conditioning on free-form text alone, it leverages graph structure to control layout and interactions, generating scenes that respect constraints like “person left of dog” or “cup on table.” The pipeline typically predicts object layouts (bounding boxes and masks) from the graph, then renders a realistic image conditioned on those layouts. ... -
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Python Taint
Static Analysis Tool for Detecting Security Vulnerabilities in Python
Static analysis of Python web applications based on theoretical foundations (Control flow graphs, fixed point, dataflow analysis) Detect command injection, SSRF, SQL injection, XSS, directory traveral etc. A lot of customization is possible. For functions from builtins or libraries, e.g. url_for or os.path.join, use the -m option to specify whether or not they return tainted values given tainted inputs, by default this file is used. -
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Scikit-plot
An intuitive library to add plotting functionality to scikit-learn
...That said, there are a number of visualizations that frequently pop up in machine learning. Scikit-plot is a humble attempt to provide aesthetically challenged programmers (such as myself) the opportunity to generate quick and beautiful graphs and plots with as little boilerplate as possible. -
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Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter... -
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Pytrainer is a tool to log all your sport excursion coming from GPS devices (with a focus on ForeRunnner 205, 305 and 405) or GPX (http://www.topografix.com) files. Pytrainer supports GPS track files and displays it in graphs, maps...
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BlockSparse
Efficient GPU kernels for block-sparse matrix multiplication
The blocksparse repository provides efficient GPU kernels (TensorFlow custom ops) for block-sparse matrix multiplication and convolution operations. The idea is to exploit block-level sparsity — i.e. treat matrices or weight tensors as composed of blocks, many of which may be zero or unused — to save compute and memory when sparsity patterns are structured. This is particularly useful in models like Sparse Transformers, where attention matrices or intermediate layers may adopt block-sparse... -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
...The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. ... -
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WOW-Auctions-API
Python classes to work with Blizzards API
Auction House API that consumes the official Blizzard's open API Help users to track and monitor AH prices from any World of Warcaft official Server Sends emails when the price of an object is below a treshold predefined by the user. Plot graphs of price evolution -
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HyperSQL is like a doxygen plus javadoc for SQL, hypermapping SQL views, packages, procedures, and functions to HTML source code listings and showing all code locations where these are used.
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Kaengu
kaengu: Framework to build C-Code Flow Graphs and to calculate Metrics
Kaengu is a framework to build Code Flow Graphs from C-Source Code and to calculate different metrics. Graphical view on Code is a strong tool to a.) understand software and b.) detect flaws. Additionally, Kaengu can calculate some interesting metrics, such as the newly developed F-Complexity as well as Graph energy and propositions for Code Refactoring. -
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Klonwerk
advanced variability management tool
Klonwerk is an advanced and easy-to-use tool for reactive variability management and large-scale software reuse. It allows you to automatically clone and modify directories of textural or binary files (e.g. code, architecture, documentation), based on conceptual feature/decision models.
