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ME-ICA / mapca

A Python implementation of the moving average principal components analysis methods from GIFT
GNU General Public License v2.0
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Return variance explained for all components #50

Closed eurunuela closed 2 years ago

eurunuela commented 2 years ago

This PR aims to give the necessary data to build a variance explained graph for the case when all possible components are kept.

codecov-commenter commented 2 years ago

Codecov Report

Merging #50 (019e0d5) into main (cf42a93) will increase coverage by 0.03%. The diff coverage is 100.00%.

Impacted file tree graph

@@            Coverage Diff             @@
##             main      #50      +/-   ##
==========================================
+ Coverage   90.26%   90.30%   +0.03%     
==========================================
  Files           3        3              
  Lines         298      299       +1     
==========================================
+ Hits          269      270       +1     
  Misses         29       29
Impacted Files Coverage Δ
mapca/mapca.py 86.95% <100.00%> (+0.07%) :arrow_up:

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eurunuela commented 2 years ago

@tsalo could you have a look at this PR? #839 in tedana needs this PR to work.