Add support for IBMA estimators with t-statistic images in Reports

[JulioAPeraza]

Closes None.

Changes proposed in this pull request:

• Add support for IBMA estimators with t-statistic images in Reports

Summary by Sourcery

Add support for IBMA estimators with t-statistic images in report generation and update the report generation logic to handle different types of input maps. Extend smoke tests to cover the new functionality.

New Features:

• Add support for IBMA estimators with t-statistic images in report generation.

Enhancements:

• Update report generation logic to handle different types of input maps (z_maps, t_maps, beta_maps) for IBMA estimators.

Tests:

• Extend smoke tests to include report generation with t-statistic images for IBMA estimators.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request adds support for IBMA estimators with t-statistic images in the report generation process. The changes include updating the test cases to handle t maps and modifying the report generation logic to accommodate t maps in addition to z maps and beta maps.

File-Level Changes

Files	Changes
nimare/tests/test_reports.py nimare/reports/base.py	Enhanced the IBMA report generation to support t-statistic images by updating the test cases and the report generation logic.

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[sourcery-ai]

Hey @JulioAPeraza - I've reviewed your changes and they look great!

Here's what I looked at during the review

• 🟡 General issues: 2 issues found
• 🟢 Security: all looks good
• 🟡 Testing: 1 issue found
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

---

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[sourcery-ai]

suggestion: Add a comment explaining the different approaches for aggressive and non-aggressive masking

The reasoning behind using different methods for correlation calculation based on the masking approach is not immediately clear. A brief comment would improve code understanding.

Suggested change

	if self.results.estimator.aggressive_mask:
	voxel_mask = self.results.estimator.inputs_["aggressive_mask"]
	corr = np.corrcoef(
	self.results.estimator.inputs_["z_maps"][:, voxel_mask],
	self.results.estimator.inputs_[key_maps][:, voxel_mask],
	rowvar=True,
	# Calculate correlation based on masking approach
	if self.results.estimator.aggressive_mask:
	voxel_mask = self.results.estimator.inputs_["aggressive_mask"]
	corr = np.corrcoef(
	self.results.estimator.inputs_[key_maps][:, voxel_mask],
	rowvar=True,
	)
	else:
	corr = np.corrcoef(self.results.estimator.inputs_[key_maps], rowvar=True)

[sourcery-ai]

suggestion (testing): Consider adding edge case tests for invalid or missing t-statistic images

While the current tests cover the basic functionality, it would be beneficial to add tests for edge cases such as invalid t-statistic images or missing images. This will ensure the robustness of the new feature.

def test_invalid_tstat_images():
    with pytest.raises(ValueError):
        IBMAWorkflow(invalid_image_path)

def test_missing_tstat_images():
    with pytest.raises(FileNotFoundError):
        IBMAWorkflow(non_existent_image_path)

[codecov]

Codecov Report

Attention: Patch coverage is 83.33333% with 1 line in your changes missing coverage. Please review.

Project coverage is 88.24%. Comparing base (5b6e337) to head (db8e0fe).
Report is 9 commits behind head on main.

Files with missing lines	Patch %	Lines
nimare/reports/base.py	83.33%	1 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #896   +/-   ##
=======================================
  Coverage   88.23%   88.24%           
=======================================
  Files          48       48           
  Lines        6386     6389    +3     
=======================================
+ Hits         5635     5638    +3     
  Misses        751      751           

☔ View full report in Codecov by Sentry.
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[jdkent]

LGTM!