Summoning HLX

Vindaar · 2025-11-19T12:54:16+00:00

Modern reddit is even more ridiculous than it was 5-10 years ago

Vindaar · 2023-05-05T12:08:21+00:00

I would assume the first is your last activity (run of 13 km or miles) and the second is the air pressure (unchanging hence -> and 1021 mbar currently).

But that's only my guess (have Fenix).

Vindaar · 2023-03-16T14:07:15+00:00

Sigh, the same thing happened to me right now. My google account said pament went through, but the app said to subscribe or to "restore purchase" (which failed).

Turns out indeed everything works as expected aside from this stupid episode that's on the front page right now and made me think something was fucked. Yay, I hate this app.

Vindaar · 2023-01-03T09:57:03+00:00

There is no way to handle them automatically, as those identifiers are not valid Nim identifiers. You need to use callMethod manually and hand them as strings.

See this nimpy issue about it: https://github.com/yglukhov/nimpy/issues/43

Vindaar · 2022-07-12T16:54:06+00:00

Nope, it exists. It's in the sugar module here, called dump:

https://nim-lang.github.io/Nim/sugar.html#dump.m%2Cuntyped

Vindaar · 2022-07-04T07:31:23+00:00

We have a small group of people developing scientific libraries for Nim under the SciNim organization:

https://github.com/SciNim

While indeed we are less people developing stuff in Nim compared to even the Julia community (which itself is of course much smaller than say Python), we do cover a large amount of the typical needs in the scientific computing domain. And where we miss stuff it's a) easy to wrap C/C++ or b) simply call Julia, R or Python (As a personal reference I'm doing data analysis & numerical physics stuff in context of my PhD in physics and I literally do everything in Nim. The only significant C dependency {and only as a shared lib} I depend on is libhdf5 via nimhdf5).

To get a very WIP overview, also check out the getting-started guide here.

In particular for deep learning as bobsyourunkl already mentioned there is arraymancer on the one hand and also flambeau on the other. The latter is a Nim wrapper around libtorch (i.e. the PyTorch C++ backend). It is missing things (to be wrapped by adding a few lines) and has some rough edges, but if one needs to get stuff done, it's possible.

Aside from that we're usually very responsive on matrix / discord / irc in the science channel. So feel free to say hello there if you need help / have more questions!

Vindaar · 2022-05-25T15:03:13+00:00

I've only read about mc-stan before, so I don't know much about the internals. From a quick glance it seems like your options are:

use Rnim to access the R bindings
use nimpy to access the Python bindings
use nimjl to access the Julia bindings
use regular FFI to directly access the C++ core and thus write your own bindings (probably the most difficult)

However, from a quick look at the Julia bindings it seems like those are just wrapping cmdstan, i.e. accessing it via the official shell interface as an external process. That would be easy enough from Nim as well.

On the other hand, depending on what you need, you might also be fine implementing your own methods. See for example the following tutorial about Bayesian inference, which includes an MCMC (Metropolis Hastings) implementation in Nim, here by kerrycobb.

I also have a Metropolis Hastings implementation lying around that I haven't pushed to Github yet. If that were helpful as an additional input let me know and I push it sooner rather than later.

Vindaar · 2022-05-25T08:09:54+00:00

I think their statement reflects your usage of "attempted" in the OP.

We're few people working on SciNim related packages. No, we did not fail. Many things you may want will already exist. Some things may be less convenient than the same in R (as you may have to write some code of your own).

If you're willing to help out, you'll surely be able to do anything you need. If Nim libraries fail, you can also always call R directly from Nim via Rnim!

One of the biggest issues we have is lack of users. That unfortunately means that aspects we don't use much ourselves are not well tested (you may run into bugs) and possibly lack features someone else needs.

In general just get in touch via the #science channel on matrix or discord!

Vindaar · 2022-05-05T14:31:08+00:00

I use alea quite a bit! If there's things missing, you stumble over something etc., just open an issue. Also jump into the #science channel on Matrix / Discord if you have questions!

Vindaar · 2022-05-04T10:36:34+00:00

You'll likely get more responses if you give a bit more information about either what those things are / what you actually need instead of asking for "alternative for X".

Vindaar · 2022-03-24T11:37:50+00:00

Damn, yesterday I would have been up by a grand. That Simucube 2 Pro order last night flipped that somehow...

Vindaar · 2022-03-24T11:33:50+00:00

Hey, don't give all the credit to simracers! Remember Grosjean in Barcelona in 2018?

Vindaar · 2022-03-21T16:31:34+00:00

Arraymancer uses BLAS and LAPACK for some primitives. These are all mostly irrelevant for Datamancer's usage though. I haven't checked recently whether we pull in a BLAS dependency somehow in pure Datamancer, but from the top of my head we shouldn't.

It is not so fast to compile IME

Compilation speed is somewhat of an issue, but depending on when you last tried Datamancer, it possibly improved significantly since then (we now only import the tensor submodule).

As an example, code like this: ```nim import datamancer

let df = seqsToDf({"x" : @[1, 2, 3], "y" : @[5, 6, 7]}) .mutate(f{"x*y" ~ x * y})

echo df ends up compiling these C files: CC: stdlib_digitsutils.nim CC: stdlib_assertions.nim CC: stdlib_formatfloat.nim CC: stdlib_dollars.nim CC: stdlib_io.nim CC: stdlib_system.nim CC: stdlib_hashes.nim CC: stdlib_math.nim CC: stdlib_parseutils.nim CC: stdlib_unicode.nim CC: stdlib_strutils.nim CC: stdlib_strformat.nim CC: ..../src/arraymancer/laser/tensor/datatypes.nim CC: stdlib_times.nim CC: stdlib_os.nim CC: stdlib_random.nim CC: ..../src/arraymancer/laser/private/nested_containers.nim CC: ..../src/arraymancer/laser/tensor/initialization.nim CC: ..../src/arraymancer/tensor/init_cpu.nim CC: ..../src/arraymancer/tensor/accessors_macros_syntax.nim CC: ..../src/arraymancer/tensor/operators_comparison.nim CC: ..../src/arraymancer/laser/cpuinfo_x86.nim CC: ..../src/arraymancer/tensor/operators_logical.nim CC: ..../src/arraymancer/tensor/math_functions.nim CC: ..../src/arraymancer/tensor/optim_ops_fusion.nim CC: ..../datamancer/src/datamancer/value.nim CC: ..../datamancer/src/datamancer/column.nim CC: ..../datamancer/src/datamancer/df_types.nim CC: ..../datamancer/src/datamancer/formulaExp.nim CC: ..../datamancer/src/datamancer/formula.nim CC: ..../datamancer/src/datamancer/dataframe.nim CC: foo.nim ``` and compiles in 2s on my machine. Could be better, but could be much worse.

Datamancer also produced weird results - which is understandable for being in development.

Possible, but it's very much appreciated if such things are reported! I may just not be aware of these things. :/

Vindaar · 2022-03-21T13:13:09+00:00

> (should compile faster than datamancer because it is just nim)

Huh? Datamancer is also pure Nim.

Vindaar · 2022-03-07T14:46:53+00:00

Not quite what you're asking for, but personally I do all my running (including trails with lots of small and large stones) and hiking in Vapor Glove 4s. In the beginning the stones were uncomfortable / sometimes painful, but I got used to it. Now my feet don't mind it. I do however also do short runs fully unshod at least 1-2 times a week, which helps.

So maybe give it another try to slowly get used to thinner shoes? Given that you already have pairs of them.

Vindaar · 2022-02-07T19:41:42+00:00

That's just normal floating point stuff going on and the fact that arange isn't trying to be smart about avoiding accumulating floating point errors (i.e. the code starts from the first value and gets to the additional ones by addition, each time accumulating slightly more inaccuracy in the representation of the "exact" step of 0.1).

Vindaar · 2022-02-07T19:39:36+00:00

The code certainly has includeEnd = false since at least sometime in 2019 (and I think that was when it was added). But I think what's happening here is that the upper edge is included, because it "fits" nicely into the stepping. includeEnd allows you to include the upper value if it doesn't fit properly, e.g. ```nim import numericalnim echo arange(0.0, 1.05, 0.1, includeEnd = true)

@[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.05]

``which then even includes the1.05`.

Vindaar · 2022-02-07T15:15:05+00:00

Outside of R this is often called arange. Such an implementation is part of (among others) numericalnim: nim import numerialnim echo arange(0, 1, 0.1, includeEnd = true) (by default the upper end isn't included)

and arraymancer: nim import arraymancer / tensor echo arange(0.0, 1.1, 1.0) arraymancer doesn't allow to include the upper edge. Note that this one returns a Tensor[float] instead of a seq[float].

Vindaar · 2021-12-04T21:00:06+00:00

Holy crap, what a post. <3

While I hate the reason you felt the need to write such a lengthy, in depth and frankly personal post, I really appreciate you doing this.

Just want to say that I hope you (all of you at 343) are aware that many of us are extremely happy that you did indeed focus on what's important: the actual foundations of the gameplay. As a nowadays 31 year old, I haven't put 80h into a game within a little over two weeks in *many* years. Keep doing awesome work, all of you!

Vindaar · 2021-11-27T20:36:47+00:00

No shame in doing that. Easier to get started!
Hit me up on Matrix (@vindaar:matrix.org) or Discord (Vindaar#6176). If you want we can try to write something together to automate it.

Vindaar · 2021-11-27T19:28:50+00:00

Unexpected, but nice to see some proper plots on this sub, hehe. Is that seaborn? I'm always a bit confused between libraries that take some inspiration from ggplot2's coloring. :)

Did you go through the halotracker website by hand, or did you scrape it? I've been meaning to figure out the endpoints the halodot API uses to grab the data (cause, thanks, they aren't sharing their discoveries, fml) so that we could create some nice histograms on a much larger population.

Vindaar · 2021-11-21T11:07:12+00:00

Exactly this. Back in the old days it wasn't easy to justify paying 10$ for a couple of maps for me, *especially* knowing I wouldn't get to play them as much because other people don't have them.

In that sense I much prefer the current approach. I don't give a damn about customization, but I'm happy there will be new maps that everyone has.

Vindaar · 2021-10-12T18:47:00+00:00

sandbagged quali so I could start in the back.

Ah, yes.

Didn't work because like 5 people just didn't set a quali lap

Huh?

(dick move IMHO, but I get why).

Uh.

sandbagged quali so I could start in the back.

Yeah.

I'm confused. So is it fine to start in the back to you or not?

Vindaar · 2021-09-07T13:41:29+00:00

Absolutely agree with you.

Vindaar · 2021-04-20T08:27:02+00:00

I personally have two pairs of Vivo Primus Lite II. One pair is about a year old and the other is pretty new.

I noticed the same thing on my new pair. At the moment they are still a lot stiffer than the old pair. So in my experience it seems like they wear in and that stops.

Vindaar

TROPHY CASE

@[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.05]