tuning/debugging KDE output
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 30/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- jupyter-notebook
- Domain
- data-visualization
Research direction
Start at the spatial_detection_score feature from issue #27 and compare its smoothed KDE outputs with the existing histogram results. Determine which differences are expected, then define an easy-to-understand visualization that shows them; done means the source of the discrepancies is documented and the comparison is usable.
Written by the indexing model from the issue text.
Description
great new feature #27 gives smoothed outputs to spatial_detection_score and the results differ in some ways from the histogram. figure out the source of this difference and accommodate any real differences in an easy-to-understand visualization
- Dominant language
- Jupyter Notebook
- Stars
- 7
- Forks
- 8
- PR merge metrics
- No merged PRs in 30d
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
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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AllenInstitute/spatial_compare#14 · 5 comments ·
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label composition in relation to detectionMay be free again @kandarpRJ claimed this 693 days ago, and no pull request is open. Open
AllenInstitute/spatial_compare#11 · 2 comments · 1 assignee ·
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