Search Results for "using" - Page 10
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Showing 723 open source projects for "using"
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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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EBSP Indexer
GUI for processing and indexing EBSP from SEMs
...Its goal is to make the rich functionality of the open-source library kikuchipy more accessible to users, without requiring knowledge of python or the library itself. Contribute by using our DOI: https://zenodo.org/record/7925262 -
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Weave
Scientific reports/literate programming for Julia
Weave is a scientific report generator/literate programming tool for the Julia programming language. It resembles Pweave, knitr, R Markdown, and Sweave. You can write your documentation and code in an input document using Markdown, Noweb or ordinal Julia script syntax, and then use the weave function to execute code and generate an output document while capturing results and figures. Supports various output document formats: HTML, PDF, GitHub markdown, Jupyter Notebook, MultiMarkdown, Asciidoc and reStructuredText. -
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DataMelt
Computation and Visualization environment
...DMelt can be used to plot functions and data in 2D and 3D, perform statistical tests, data mining, numeric computations, function minimization, linear algebra, solving systems of linear and differential equations. Linear, non-linear and symbolic regression are also available. Neural networks and various data-manipulation methods are integrated using powerful Java API. Elements of symbolic computations using Octave/Matlab scripting are supported. -
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DiffEqOperators.jl
Linear operators for discretizations of differential equations
...The cases of 1, 2, and 3 dimensions with an evenly spaced grid are optimized with a convolution routine from NNlib.jl. Care is taken to give efficiency by avoiding unnecessary allocations, using purpose-built stencil compilers, allowing GPUs and parallelism, etc. Any operator can be concretized as an Array, a BandedMatrix or a sparse matrix. -
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Self-learning-Computer-Science
Resources to learn computer science in your spare time
Self-learning Computer Science is a curated, open-source guide repository designed to help learners independently study computer science topics using high-quality university-level resources. The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more. It’s aimed at learners who find traditional course structures restrictive and want a flexible, self-paced path through CS, with a focus on building depth and breadth rather than shortcut exam skills. ... -
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Visdom
A tool for creating, organizing, and sharing data visualizations
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy. Visdom aims to facilitate visualization of (remote) data with an emphasis on supporting scientific experimentation. Broadcast visualizations of plots, images, and text for yourself and your collaborators. Organize your visualization space programmatically or through the UI to create dashboards for live data, inspect results of experiments, or debug experimental code. Visdom has... -
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Divide and Conquer Treemaps
Divide and Conquer Treemap Visualisation
The project develops new new techniques and algorithms for quickly partitioning and visualizing very large hierarchical structures within a variety of arbitrary shapes and space using Divide and Conquer approach for Treemaps. Related publications: https://www.sciencedirect.com/science/article/pii/S1045926X1500066X https://dl.acm.org/doi/pdf/10.1145/2493102.2493112 -
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TOAST UI Chart
Beautiful chart for data visualization
...Add different options and animations according to the charts' sizes by using the responsive option. Make the data presented in the Line, Area, and Treemap Charts zoomable with the zoomable option. View and manage new data as they are added realtime with the addData API and the options.series.shift option. -
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SQLBucket
Lightweight library to write, orchestrate and test your SQL ETL
SQLBucket is a lightweight framework to help write, orchestrate and validate SQL data pipelines. It gives the possibility to set variables and introduces some control flow using the fantastic Jinja2 library. It also implements a very simplistic unit and integration test framework where you can validate the results of your ETL in the form of SQL checks. With SQLBucket, you can apply TDD principles when writing data pipelines. To start working, you need to instantiate your SQLBucket core object with the project_folder parameter. ... -
Create and run cloud-based virtual machines.Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications.
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SnappyData
Memory optimized analytics database, based on Apache Spark
...For instance, there is no need to often pre-aggregate/reduce or generate cubes over your large data sets for ad-hoc visual analytics. This is made possible by smartly managing data in memory, dynamically generating code using vectorization optimizations, and maximizing the potential of modern multi-core CPUs. SnappyData enables complex processing on large data sets in sub-second timeframes. -
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Memento.jl
A flexible logging library for Julia
Memento is a flexible hierarchical logging library for Julia. -
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hui
hewies user interface - 3D scientific visualisation tool
...The instance will connect to a data source, ods sheet, csv, sql DB, pyodbc the instance will analyse and/or transform the data to be presented to the visualisation functionality the instance will visualise the data in a 3D fashion, likely using third party FOSS -
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Notepad++ GraphBuilder plugin
Building math graphs in notepad++
...Please note that the plugin is intended for 32-bit version of Notepad++ only. The "deriv" function is implemented. For example, the following formula: deriv ("sin x", "x", 0.01) draws derivative of "sin x" function graph using "x" as x variable. Third parameter is optional and specifies the accuracy. The latest version is powered by high-precision mode which makes calculation the overlaps much more accurate. Polar coordinate system max angle is now adjustable. The plugin builds any number of math graphs, locates its intersections and extremes, makes the report. ... -
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JuliaCall for Seamless Integration of R
Embed Julia in R
Package JuliaCall is an R interface to Julia, which is a high-level, high-performance dynamic programming language for numerical computing. Below is an image for Mandelbrot set. JuliaCall brings more than 100 times speedup of the calculation. -
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Soss
Probabilistic programming via source rewriting
...Soss and DynamicPPL are both maturing and becoming more complete, so the above will change over time. It's also worth noting that we (the Turing team and I) hope to move toward a natural way of using these systems together to arrive at the best of both. -
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UIBM (= Universal Individual-Based Model) is an agent-based simulation/3d-dvisualization of the dynamics within multispecies plant communities of Northwest Europe, parameterized from databases using universal scaling laws.
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Augmentor.jl
A fast image augmentation library in Julia for machine learning
...Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a sequence of operations for which the parameters can (but need not) be random variables, as the following code snippet demonstrates. -
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SandDance
Visually explore, understand, and present your data
By using easy-to-understand views, SandDance helps you find insights about your data, which in turn help you tell stories supported by data, build cases based on evidence, test hypotheses, dig deeper into surface explanations, support decisions for purchases, or relate data into a wider, real world context. SandDance uses unit visualizations, which apply a one-to-one mapping between rows in your database and marks on the screen. -
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SASA Tool
SWATH-Auto System Analyzer Tool, SASA Tool
SWATH-Auto System Analyzer Tool, SASA Tool, is a novel SWATH platform for non-targeted metabolomics data analysis with an accurate mass spectral library for metabolite identification using SWATH acquisition mode. -
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gophernotes
The Go kernel for Jupyter notebooks and nteract
...The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. If you have the JUPYTER_PATH environmental variable set or if you are using an older version of Jupyter, you may need to copy this kernel config to another directory. -
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OpticSim.jl
Optical Simulation software
OpticSim.jl is a Julia package for geometric optics (ray tracing) simulation and optimization of complex optical systems developed by the Microsoft Research Interactive Media Group and the Microsoft Hardware Architecture Incubation Team (HART). It is designed to allow optical engineers to create optical systems procedurally and then to simulate and optimize them. Unlike Zemax, Code V, or other interactive optical design systems OpticSim.jl has limited support for interactivity, primarily in... -
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EZStacking
EZStacking is Jupyter notebook generator for machine learning
EZStacking is Jupyter notebook generator for supervised learning problems using Scikit-Learn pipelines and stacked generalization. EZStacking handles classification and regression problems for structured data. It can also be viewed as a development tool, because a notebook generated with EZStacking contains: -an exploratory data analysis (EDA) used to assess data quality - a modelling producing a reduced-size stacked estimator - a server returning a prediction, a measure of the quality of input data and the execution time. -
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This project moved to GitHub in 2021 and is available at: https://cbrinkrolf.github.io/VANESA/ This tool is a platform-independent software to create individual pathways and to examine biological networks of distributed, heterogeneous data sources, e.g. KEGG, BRENDA. VANESA also offers Petri net modeling of extended hybrid Petri nets which can be also simulated using the OpenModelica framework.
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ReViSP
ReViSP, a 3D volume rendering MATLAB tool for multicellular spheroids
...ReViSP is written in MATLAB (The MathWorks, Inc., Massachusetts, USA) and the source code is freely provided. Requirements for running ReViSP from the source code: MATLAB 2020a and Image Processing Toolbox 11.1 or later versions. Please, when using this software, cite these articles: (a) F. Piccinini, et al., Cancer multicellular spheroids: Volume assessment from a single 2D projection. Computer Methods and Programs in Biomedicine, 118(2):95–106, 2015. (b) F. Piccinini, et al., Single-image based methods used for non-invasive volume estimation of cancer spheroids: a practical assessing approach based on entry-level equipment. ... -
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TSNE-CUDA
GPU Accelerated t-SNE for CUDA with Python bindings
...We find that our implementation of t-SNE can be up to 1200x faster than Sklearn, or up to 50x faster than Multicore-TSNE when used with the right GPU. You can install binaries with anaconda for CUDA version 10.1 and 10.2 using conda install tsnecuda -c conda-forge. Tsnecuda supports CUDA versions 9.0 and later through source installation, check out the wiki for up to date installation instructions. Time taken compared to other state of the art algorithms on synthetic datasets with 50 dimensions and four clusters for varying numbers of points. Note the log scale on both the points and time axis, and that the scale of the x-axis is in thousands of points (thus, the values on the x-axis range from 1K to 10M points. ...
