stillyslalom
Joined 14 years ago
Karma
32
Submissions
11
Member since
Sep 17
Recent submissions
Comment · 2 years ago
Comment · 3 years ago
Take a look at DataFramesMeta for nicer manipulation of Julia's dataframes. Your example would look like
julia> df = DataFrame(y = rand(10^6), filter=randn(10^6));
julia> @transform!(df, :x = cumsum(:y .* :filter))
1000000×3 DataFrame
Row │ y filter x
│ Float64 Float64 Float64
─────────┼────────────────────────────────────
1 │ 0.0726663 1.7213 0.125081
2 │ 0.183898 -0.392131 0.0529686
3 │ 0.150274 1.08083 0.21539
⋮ │ ⋮ ⋮ ⋮
[1] https://juliadata.github.io/DataFramesMeta.jl/dev/#Chaining-...
Comment · 5 years ago
For long-tail adverse events? It's easy to engineer power distribution systems for the average case, but unless we accept occasional brownouts as a cost of doing business, we need to build robustness into our generation fleet. Let's say we get another Krakatoa-scale eruption that reduces insolation (and thus drops solar & wind output while increasing heating demand). Some amount of firm generation (hydro, gas peaker, nuclear) is insurance against correlated failure in the global climate system.
Comment · 5 years ago
iPads can run out of battery or get smashed in turbulence or fall prey to bugs/malware; old-fashioned steam gauges cannot. The FAA's regulations are written in blood - the aviation fatality rate has fallen sharply over the years [0] thanks to a combination of technological advancement and regulation. Aircraft builders are still free to do pretty much whatever they want within the 'experimental' category, provided they mount a placard informing occupants that the aircraft is experimental.
[0] https://aviation-safety.net/graphics/infographics/Fatal-Acci...
Comment · 5 years ago
You may want to look at [DaemonMode.jl](https://github.com/dmolina/DaemonMode.jl), which spins up a persistent Julia process in the background so you don't have to pay the TTFP penalty more than once, even when shell-scripting.
Comment · 6 years ago
Comparing OpenBLAS and MKL with `peakflops` in Julia, there's definitely an advantage for MKL:
julia> using LinearAlgebra
julia> BLAS.vendor()
:openblas64
julia> BLAS.set_num_threads(1)
julia> peakflops()
3.9023447970402664e10
julia> using LinearAlgebra
julia> BLAS.vendor()
:mkl
julia> BLAS.set_num_threads(1)
julia> peakflops()
4.8113846984735275e10
Comment · 8 years ago
The current market doesn't price the externalities of carbon emissions. Without accurate pricing, market forces will stall some distance away from the optimal level of emissions, and we'll be worse off. Governments are the only entities powerful enough to solve such a widely-distributed coordination problem, and properly-functioning markets are contingent on properly-functioning governments.
Comment · 8 years ago
This presentation is more than five years old, and some of the details have changed. See https://julialang.org/learning/ for more up-to-date introductory material.
Comment · 11 years ago
Generalized nullables exist in Base already (http://docs.julialang.org/en/release-0.4/manual/types/#nulla...), unless you had something else in mind.
Comment · 11 years ago
I've seen a decent amount of chatter over the past year on the Julia mailing list from people integrating Julia into production. It's still in the beta stage, though, so risk-averse corporate users are generally being advised to hold tight until 1.0 is released. This (somewhat outdated) thread should give you some idea of where things are headed: https://groups.google.com/d/topic/julia-users/2mR24E0EtIg/di...
Comment · 13 years ago
Unfortunately, the series is no longer available on Netflix due to contract expiration.
I don't think you'd actually want to include each of those packages in a standard distro: does the average user really need to programmatically send emails or deal with Voronoi tessellations? Probably not, but I still think there's value in a batteries-included approach, especially when working with students.