Small fix to find_global_peaks_rough

[alckasoc]

Summary by CodeRabbit

• Refactor
  • Improved the peak finding algorithm to enhance accuracy in identifying peak points in data tensors.
  • Adjusted the threshold handling to better filter out values below the specified limit, ensuring more reliable peak detection.

[coderabbitai]

Walkthrough

The recent changes focus on enhancing the peak detection process in the sleap_nn/inference/peak_finding.py file. The modifications involve a more intricate calculation for identifying peak values and indices along with refined handling of values under a certain threshold. These changes aim to improve the accuracy and robustness of peak detection while maintaining the function's original signature.

Changes

File Path	Change Summary
.../inference/peak_finding.py	Updated find_global_peaks_rough to refine peak detection logic, including handling of threshold values.

Poem

In the realm of tensors, peaks must be found,
With a rabbit's keen eye, no peak is left bound.
🐇 Through the code, it hops with grace,
Finding each max, in its proper place.

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Comparison is base (c590ae5) 99.78% compared to head (72d88a9) 99.78%.
Report is 3 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main      #28   +/-   ##
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  Coverage   99.78%   99.78%           
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  Files          19       19           
  Lines         949      952    +3     
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+ Hits          947      950    +3     
  Misses          2        2           

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[coderabbitai]

Review Status

Actionable comments generated: 1

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between c590ae5 and de9ef46.

Files selected for processing (1)

• sleap_nn/inference/peak_finding.py (1 hunks)

Additional comments: 1

sleap_nn/inference/peak_finding.py (1)

• 118-119: The code correctly replaces peak values below the threshold with NaN and sets the corresponding max_values to 0. This change aligns with the PR objectives and summary, which mention adjusting the handling of values below a specified threshold.

[coderabbitai]

Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between b774c5a and 1caef62.

Files selected for processing (1)

• sleap_nn/inference/peak_finding.py (1 hunks)

Additional comments: 2

sleap_nn/inference/peak_finding.py (2)

• 118-121: The change from replacing values below the threshold with NaN to 0 could have implications on downstream processing that relies on NaN to indicate invalid or absent data. Ensure that this change is communicated to all users of the function and that it does not negatively impact any dependent code.

• 113-115: The summary does not mention the change in the order of concatenation for peak indices (max_indices_x and amax_indices_y). This change in the order of peak points could affect how the peaks are interpreted downstream. It is important to verify that this change is intentional and that it does not introduce any issues with the interpretation of peak data.

[coderabbitai]

Review Status

Actionable comments generated: 3

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 1caef62 and 72d88a9.

Files selected for processing (1)

• sleap_nn/inference/peak_finding.py (1 hunks)

Additional comments: 1

sleap_nn/inference/peak_finding.py (1)

• 113-115: The peak_points tensor is constructed by concatenating max_indices_x and amax_indices_y, which are derived from separate maximum value calculations. This mixing of indices from different calculations is unusual and could be intentional for a specific reason, but it should be clarified to ensure it's not a mistake.