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Showing 656 open source projects for "can="
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
...Libraries from Python, R, C/Fortran, C++, and Java can also be used. -
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Query.jl
Query almost anything in julia
Query is a package for querying julia data sources. It can filter, project, join and group data from any iterable data source, including all the sources supported in IterableTables.jl. One can for example query any of the following data sources: any array, DataFrames, DataStreams (including CSV, Feather, SQLite, ODBC), DataTables, IndexedTables, TimeSeries, Temporal, TypedTables and DifferentialEquations (any DESolution). -
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wavesurfer.js
Navigable waveform built on Web Audio and Canvas
wavesurfer.js is a customizable audio waveform visualization, built on top of Web Audio API and HTML5 Canvas. With wavesurfer.js you can create anything from an HTML5 audio player to a sophisticated DJ application. wavesurfer.js works only in modern browsers supporting Web Audio. It will fallback to Audio Element without graphics in other browsers (IE 11 and lower). You can also try wavesurfer.swf which is a Flash-based fallback. The audio will start playing as you press play. ... -
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Books.jl
Create books with Julia
In a nutshell, this package is meant to generate books (or reports or dashboards) with embedded Julia output. Via Pandoc, the package can live serve a website and build various outputs including a website and PDF. For many standard output types, such as DataFrames and plots, the package can run your code and will automatically handle proper embedding in the output documents, and also try to guess suitable captions and labels. Also, it is possible to work via the live server, which shows changes within seconds. -
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
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LinearOperators.jl
Linear Operators for Julia
Operators behave like matrices (with some exceptions - see below) but are defined by their effect when applied to a vector. They can be transposed, conjugated, or combined with other operators cheaply. The costly operation is deferred until multiplied with a vector. -
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Winston.jl
2D plotting for Julia
2D plotting for Julia. Winston offers an easy-to-use plot command to create figures without any fuss. After Winston is loaded by typing using Winston, the most basic plot can be created. -
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Calculus.jl
Calculus functions in Julia
The Calculus package provides tools for working with the basic calculus operations of differentiation and integration. You can use the Calculus package to produce approximate derivatives by several forms of finite differencing or to produce exact derivatives using symbolic differentiation. You can also compute definite integrals by different numerical methods. -
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Catalyst.jl
Chemical reaction network and systems biology interface
...Generated models can be used with solvers throughout the broader SciML ecosystem, including higher-level SciML packages (e.g. for sensitivity analysis, parameter estimation, machine learning applications, etc). -
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LLVM.jl
Julia wrapper for the LLVM C API
A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Kibana
Your window into the Elastic Stack
Kibana is a analytics and search dashboard for Elasticsearch that allows you to visualize Elasticsearch data and efficiently navigate the Elastic Stack. With Kibana you can visualize and shape your data simply and intuitively, share visualizations for greater collaboration, organize dashboards and visualizations, and so much more. -
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HCubature.jl
Pure-Julia multidimensional h-adaptive integration
The HCubature module is a pure-Julia implementation of multidimensional "h-adaptive" integration. then hcubature(f, a, b) computes the integral, adaptively subdividing the integration volume into smaller and smaller pieces until convergence is achieved to the desired tolerance (specified by optional rtol and atol keyword arguments. Because hcubature is written purely in Julia, the integrand f(x) can return any vector-like object (technically, any type supporting +, -, * real, and norm: a Banach space). You can integrate real, complex, and matrix-valued integrands, for example. Note that HCubature assumes that your function f(x) can be computed at arbitrary points in the integration domain. (This is the ideal way to do numerical integration.) ... -
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jlrs
Julia bindings for Rust
...Julia must be installed before jlrs can be used, jlrs is compatible with Julia 1.6 up to and including Julia 1.10. The JlrsCore package must also have been installed, if this is not the case it will automatically be added when jlrs is initialized by default. jlrs has not been tested with juliaup yet on Linux and macOS. -
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PythonCall & JuliaCall
Python and Julia in harmony
...Call Python code from Julia and Julia code from Python via a symmetric interface. Simple syntax, so the Python code looks like Python and the Julia code looks like Julia. Intuitive and flexible conversions between Julia and Python: anything can be converted, you are in control. Fast non-copying conversion of numeric arrays in either direction: modify Python arrays (e.g. bytes, array. array, numpy.ndarray) from Julia or Julia arrays from Python. Helpful wrappers: interpret Python sequences, dictionaries, arrays, dataframes and IO streams as their Julia counterparts, and vice versa. -
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Pluto.jl
Simple reactive notebooks for Julia plutojl.org
We are on a mission to make scientific computing more accessible and fun. Writing a notebook is not just about writing the final document, Pluto empowers the experiments and discoveries that are essential to getting there. -
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FileTrees.jl
Parallel file processing made easy
...FileTrees is a set of tools to lazy-load, process and save file trees. Built-in parallelism allows you to max out all threads and processes that Julia is running with. Files and subtrees in a file tree can have any value attached to them, you can map and reduce over these values, or combine them by merging or collapsing trees or subtrees. When computing lazy trees, these values are held in distributed memory and operated on in parallel. -
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FEniCS.jl
A scientific machine learning (SciML) wrapper for the FEniCS
...DifferentialEquations.jl ecosystem. Paraview can also be used to visualize various results just like in FEniCS. -
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DIY Split-Flap Display
DIY split-flap display
This is a work in progress split-flap display. Each module can flip between 40 unique characters: letters, numbers, and a few symbols. Multiple modules fit perfectly alongside each other to build bigger displays. The primary design goal was to make something that's possible to fabricate at home in small or single quantities and can be customized and built by an intermediate hobbyist at a reasonable price. -
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PhysicalConstants.jl
Collection of fundamental physical constants with uncertainties
PhysicalConstants.jl provides common physical constants. They are defined as instances of the new Constant type, which is a subtype of AbstractQuantity (from Unitful.jl package) and can also be turned into Measurement objects (from Measurements.jl package) at request. Constants are grouped into different submodules so that the user can choose different datasets as needed. Currently, 2014 and 2018 editions of CODATA recommended values of the fundamental physical constants are provided. -
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PGFPlotsX.jl
Plots in Julia using the PGFPlots LaTeX package
...It is similar in spirit to the package PGFPlots.jl but it tries to have a very close mapping to the PGFPlots API as well as minimize the number of dependencies. The fact that the syntax is similar to the TeX version means that examples from Stack Overflow and the PGFPlots manual can easily be incorporated in the Julia code. -
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ggrepel
epel overlapping text labels away from each other in your ggplot2
ggrepel is an R package that provides “smart” repulsion for text and label geoms in ggplot2. When placing text labels on a plot (e.g. labeling points), the labels can often overlap; ggrepel ensures labels don’t overlap (or overlap less) by repelling labels / pushing them away, adding connecting lines or nudges, etc. It improves the readability of plots, especially when many labels are present. Support for point and segment geoms (so labels can be connected by lines when moved). Supports both plotting of labels inside or outside plot area, with trimming/clipping etc. -
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nb-clean
Clean Jupyter notebooks of outputs, metadata, and empty cells
nb-clean cleans Jupyter notebooks of cell execution counts, metadata, outputs, and (optionally) empty cells, preparing them for committing to version control. It provides both a Git filter and pre-commit hook to automatically clean notebooks before they're staged, and can also be used with other version control systems, as a command line tool, and as a Python library. It can determine if a notebook is clean or not, which can be used as a check in your continuous integration pipelines. nb-clean can also be used as a pre-commit hook. You may prefer this to the Git filter if your project already uses the pre-commit framework. ... -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function spaces. ... -
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PowerSimulations.jl
Julia for optimization simulation and modeling of PowerSystems
...The modularity of PowerSimulations results from the structure of the simulations enabled by the package. Simulations define a set of problems that can be solved using numerical techniques. -
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G2
Interactive data-driven visualization grammar for statistical charts
G2 is a highly interactive data-driven visualization grammar for statistical charts. with a high level of usability and scalability. It provides a set of grammar, and takes users beyond a limited set of charts to an almost unlimited world of graphical forms. With G2, you can describe the visual appearance and interactive behavior of visualization just by one statement, and generate web-based views using Canvas or SVG. We have summarized a series of story design templates from lots of real data visualization cases so that users can use them directly. Any chart can be drawn flexibly based on graphical grammar to meet your unlimited creativity. ... -
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ExponentialUtilities.jl
Fast and differentiable implementations of matrix exponentials
...These methods are more numerically stable, generic (thus support a wider range of number types), and faster than the matrix exponentiation tools in Julia's Base. The tools are used by the exponential integrators in OrdinaryDiffEq. The package has no external dependencies, so it can also be used independently.
