Hacktoberfest 2026: the issues maintainers tagged for October, open and beginner-friendly. Browse Hacktoberfest issues

DREDge differences (parameters, online mode, math)

Open
  • Is something wrong?
#4,829 2 comments 1 reaction 0 assignees View on GitHub

Maintainers usually reply within 2 days

Nobody has claimed this yet.

Assessment

Difficulty
3/5
Estimated time
Half a day
Newbie friendliness
65/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Active
Tech stack
python
Domain
machine-learning

Research direction

Compare the online DREDge implementation with the cwindolf/dredge repository to identify parameter mismatches (win_scale_um, win_step_um) and missing raster transforms (np.log1p). Fix the time_horizon bug in cross-chunk matrix extraction and add missing weights to the AP version's cross-correlation computation. Run the online algorithm tests to verify changes.

Written by the indexing model from the issue text.

Description

enhancement motion correction

Hi all,

After @chrishalcrow and @alejoe91 released the DREDge online mode in SpikeInterface, I was quite excited to integrate it into dartsort, so I started looking into it more. I realized that there were a couple of details that I had not thought of when we discussed the implementation. Separately, I have been meaning to look into possible parameter mismatches between https://github.com/cwindolf/dredge and here for a while. I thought I'd gather up the findings here and break them into pieces so that we can discuss which ones you'd be happy to have changed, then I can open PRs to do so. Apologies for not thinking of these before / having bugs!

Online algorithm issues

There are two main differences between the implementation here and what I just wrote up in the dredge repo:

  • Weights. The AP version of dredge does some weighting of each bin when computing cross correlations, but this isn't done in the LFP version. I think it's important to include this, and I'm happy to add it here.
  • Cross-chunk time_horizon bug. I wasn't using time_horizon_s much in the LFP code, so I never noticed that it is applied incorrectly in the online algorithm. Right now, it exctracts a "main diagonal band" from the cross-chunk matrices, rather than grabbing elements on the bottom of the lower triangle / top of upper triangle as it should. The main diagonals are used within the same chunk, but the time horizon works differently in the cross-chunk terms!

There was also a math issue in my dredge code, a tiny thing involving one term missed in the prior/regularizer across the online algorithm's chunks. If I'm adding the above stuff, I think it would be cool to fix this smaller thing as well.

Parameters and raster processing

Compared to the dredge repo (D), here (SI) we have:

  • win_scale_um: D 300 (dredge parametrizes it as 600/2), SI does seem to have the same default, but across several files default values of 150, 400, and 300 all appear.
  • win_step_um: D has 400, in SI values of both 400 and 200 appear. I think 200 is the actually used value, which is a lot of windows!
  • Raster transform: in D, the raster has a np.log1p applied, which is not done in SI. I remember this being important in my initial development, but I don't have evidence at the moment.
    There are also some other differences which seem minor.

So, what would be the best moves that I could make for you guys? I think that the online algorithm stuff is important to fix before this gets much usage. For the AP stuff, I'd like to at least go to step 400 if that's acceptable. I feel a bit afraid to add the log1p, but I do think it is a good thing to have.

Dominant language
Python
Stars
858
Forks
281
Avg merge
2d 18h
Merged PRs (30d)
38

Getting set up

This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from SpikeInterface/spikeinterface

All issues in SpikeInterface/spikeinterface

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.