Tables.jl
DifferentialEquations.jl
Tables.jl | DifferentialEquations.jl | |
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3 | 6 | |
291 | 2,756 | |
1.4% | 0.7% | |
4.6 | 7.2 | |
24 days ago | 25 days ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
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Tables.jl
- Julia or Python for analysis on Arrow datasets
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Beacon Biosignals raises $27M to scale EEG neurobiomarker discovery
Good questions!
> How exactly does Julia fit into your software architecture?
In a variety of ways:
- We have a bunch of external/internal Julia packages; Julia's package manager is really great at facilitating the development of "tooling ecosystems" comprised of lightweight libraries that compose well together. For example, we use Legolas.jl [1] in conjunction with a well-curated Arrow-in-S3 lake to help teams define lightweight, self-serviceable schemas for Arrow tables in a manner that integrates well with the wider Tables.jl ecosystem [2], interactive analysis workflows, and our own ETL/ELT-ish patterns.
- Julia powers some interesting services within Beacon's Platform. For example, one of our Julia services provides dynamic streaming DSP (multiplexing, filtering, statistics) for biosignal data, atop which we build other applications/pipelines for both product development and internal analysis work.
- We use Julia for exploratory distributed computing on K8s [3], which is awesome because Julia has a lot of potential in the distributed computing landscape (IMO [4]).
> Is your product a cloud offering and/or does it have a client side application?
We work with our clients to do neurobiomarker discovery, clinical trial design, deploy our analysis pipelines into clinical trials, and a few other interesting things :) One of the critical differentiators of Beacon is that we can precisely target and harness key EEG features to a degree that isn't possible without the kind of algorithms/tools we've developed.
> what do you even mean by data architecture for science-first teams
I want to do a blog post on this at some point, but a core value for us - across all of our processes, tooling, and data interactions - is self-serviceability and composability. IMO, the two are inextricably linked. Our goal is to empower each Beaconeer to perform analyses in an afternoon atop terabytes of data that would take them months in a lab atop gigabytes of data.
To achieve this, we treat large-scale data curation/manipulation as an activity that we're all empowered to participate in and contribute to, as opposed to an environment where separate data engineering teams have to administrate siloed systems. Tools like K8s/Julia/Arrow are key enablers here, by surfacing capabilities to domain experts that let them to iterate fast without needing to "throw problems over the wall" to other teams/systems.
It's not a perfect match, and it's a bit abstract, but I remember reading this post about "data meshes" [5] a while back and thinking "Hey, that's similar to what we're chasing after!"
[1] https://github.com/beacon-biosignals/Legolas.jl
[2] https://github.com/JuliaData/Tables.jl
[3] https://github.com/beacon-biosignals/K8sClusterManagers.jl
[4] https://news.ycombinator.com/item?id=24842084
[5] https://martinfowler.com/articles/data-mesh-principles.html
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Hello everyone! I’m new to Julia, and I’m trying to pass a JuliaDB table to another function. Does anyone know how I can do so? The documentation for examples and everything surrounding JuliaDB seems so little in comparison to other languages.
As you progress you'll likely learn to be a bit more relaxed about types - there's a Table Interface that JuliaDB implements along with many other data sources.But this should get you going.
DifferentialEquations.jl
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Startups are building with the Julia Programming Language
This lists some of its unique abilities:
https://docs.sciml.ai/DiffEqDocs/stable/
The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me.
[1] https://lwn.net/Articles/834571/
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From Common Lisp to Julia
https://github.com/SciML/DifferentialEquations.jl/issues/786. As you could see from the tweet, it's now at 0.1 seconds. That has been within one year.
Also, if you take a look at a tutorial, say the tutorial video from 2018,
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When is julia getting proper precompilation?
It's not faith, and it's not all from Julia itself. https://github.com/SciML/DifferentialEquations.jl/issues/785 should reduce compile times of what OP mentioned for example.
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Julia 1.7 has been released
Let's even put raw numbers to it. DifferentialEquations.jl usage has seen compile times drop from 22 seconds to 3 seconds over the last few months.
https://github.com/SciML/DifferentialEquations.jl/issues/786
- Suggest me a Good library for scientific computing in Julia with good support for multi-core CPUs and GPUs.
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DifferentialEquations compilation issue in Julia 1.6
https://github.com/SciML/DifferentialEquations.jl/issues/737 double posted, with the answer here. Please don't do that.
What are some alternatives?
Pluto.jl - 🎈 Simple reactive notebooks for Julia
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
DataFrames.jl - In-memory tabular data in Julia
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Tumble.jl - lazy predictive modeling for julia
Gridap.jl - Grid-based approximation of partial differential equations in Julia
julia - The Julia Programming Language
ApproxFun.jl - Julia package for function approximation
JSONTables.jl - JSON3.jl + Tables.jl
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
RequiredInterfaces.jl - A small package for providing the minimal required method surface of a Julia API
FFTW.jl - Julia bindings to the FFTW library for fast Fourier transforms