MNT,DOC clean up notebooks and improve FIR notebook

[mvdoc]

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[Copilot]

Pull Request Overview

This PR focuses on cleaning up notebooks and improving the FIR notebook by introducing a new helper function for run‐based z-scoring and updating test cases and notebook examples to use it. Key changes include:

• Adding the new function zscore_runs to voxelwise_tutorials/utils.py to simplify z-scoring operations.
• Updating tests and notebooks to replace manual splitting and concatenation with zscore_runs.
• Refining markdown text and correcting terminology (e.g., “mamma” to “mammal”) for consistency.

Reviewed Changes

Copilot reviewed 10 out of 11 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
voxelwise_tutorials/utils.py	Introduced zscore_runs to standardize run-level z-scoring.
voxelwise_tutorials/tests/test_utils.py	Added tests for the new zscore_runs function.
voxelwise_tutorials/tests/test_delays_toy.py	Unchanged functionality; tests updated to accommodate new routines.
voxelwise_tutorials/delays_toy.py	No code changes; context for FIR simulation.
All Notebooks in tutorials/shortclips/	Replaced manual zscore operations with calls to zscore_runs and updated text for clarity.

Files not reviewed (1)

• tutorials/Makefile: Language not supported

Comments suppressed due to low confidence (1)

voxelwise_tutorials/utils.py:101

• [nitpick] Preserving the original data type by casting the z-scored data back to the input type may lead to loss of precision, especially when the input is an integer array. Consider keeping the result as a float to retain the statistical properties of the z-score computation.

orig_dtype = data.dtype