fix AHP_slow_time and min_AHP_values

[AurelienJaquier]

Description

min_AHP_values and AHP_slow_time are a little bit special because they are computed during other features computation (min_AHP_indices and AHP_depth_abs_slow). So when we call them, the feature function does nothing, except calling the other feature with the dependency graph. The problem was that the 'empty' feature function still returns -1, and that can be caught afterwards, and replace the feature values with None (depending on the order of the features passed to efel).

This PR just replaces these -1 with 1s. The feature is no longer replaced by None. In the case where the feature cannot be computed but still returns 1, this does not cause a problem, because when pyfeatures.cppfeature_access.get_cpp_feature will not find the feature, it will just replace it with None, as expected.

Checklist:

• Unit tests are added to cover the changes (skip if not applicable).
• The changes are mentioned in the documentation (skip if not applicable).
• CHANGELOG file is updated (skip if not applicable).

[AurelienJaquier]

replace empty list features with None, following what is described here: Issue #321

[AurelienJaquier]

This will turn empty lists into None

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 54.28571% with 16 lines in your changes are missing coverage. Please review.

Project coverage is 91.78%. Comparing base (2cddd26) to head (e1f5966).
Report is 17 commits behind head on master.

Files	Patch %	Lines
efel/cppcore/SpikeEvent.cpp	44.44%	5 Missing and 5 partials ⚠️
efel/cppcore/SpikeShape.cpp	50.00%	3 Missing and 3 partials ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #394      +/-   ##
==========================================
+ Coverage   91.73%   91.78%   +0.05%     
==========================================
  Files          36       37       +1     
  Lines        6278     7096     +818     
  Branches     2033     2276     +243     
==========================================
+ Hits         5759     6513     +754     
- Misses        266      304      +38     
- Partials      253      279      +26     

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[ilkilic]

wouldn't be clearer to call the dependent feature by using getFeatures and return their returning values instead?

[AurelienJaquier]

How would we do that concretely?

[ilkilic]

would something like this make sense? (for min_AHP_values)

const auto& intFeatures = getFeatures(IntFeatureData, {"min_AHP_indices"});
auto retval = intFeatures.at("min_AHP_indices").size();
if (retval <= 0) {
    return -1;
} else {
    return retval;
}

[AurelienJaquier]

Ok I see, it makes sense. I'll implement it, thanks

[AurelienJaquier]

@ilkilic done in latest commit!

[ilkilic]

Fantastic! Thank you!