[Feature] filter signals with missing data

[danibene]

Description

As a step towards better handling of missing data (see #838), this PR aims to allow for filtering signals even if they contain NaNs.

Proposed Changes

1. I changed the signal_filter() function so that NaNs are interpolated before filtering and then added back after filtering.
2. I added test_signal_filter_with_missing() to test that the signal containing NaNs is filtered.

Checklist

• I have read the CONTRIBUTING file.
• My PR is targeted at the dev branch (and not towards the master branch).

[danibene]

I wonder, should we have a warning at this stage if missing samples are detected (or a certain proportion of missing samples)? Or only have warnings in specific feature extraction functions?

[codecov-commenter]

Codecov Report

Patch coverage: 86.66% and project coverage change: +0.06 🎉

Comparison is base (5116fb4) 53.88% compared to head (2713a37) 53.95%.

❗ Current head 2713a37 differs from pull request most recent head 9bfec57. Consider uploading reports for the commit 9bfec57 to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##              dev     #839      +/-   ##
==========================================
+ Coverage   53.88%   53.95%   +0.06%     
==========================================
  Files         295      295              
  Lines       13811    13820       +9     
==========================================
+ Hits         7442     7456      +14     
+ Misses       6369     6364       -5     

Impacted Files	Coverage Δ
neurokit2/signal/signal_filter.py	70.90% <86.66%> (+2.28%)	⬆️

... and 4 files with indirect coverage changes

☔ View full report in Codecov by Sentry.
📢 Do you have feedback about the report comment? Let us know in this issue.

[DominiqueMakowski]

I wonder, should we have a warning at this stage if missing samples are detected (or a certain proportion of missing samples)? Or only have warnings in specific feature extraction functions?

Mmh good question... any preference?

[danibene]

I wonder, should we have a warning at this stage if missing samples are detected (or a certain proportion of missing samples)? Or only have warnings in specific feature extraction functions?

Mmh good question... any preference?

Not really, I noticed that there is already a warning in ecg_clean():

NeuroKit/neurokit2/ecg/ecg_clean.py

Lines 89 to 96 in 1170887

	n_missing = np.sum(np.isnan(ecg_signal))
	if n_missing > 0:
	warn(
	"There are " + str(n_missing) + " missing data points in your signal."
	" Filling missing values by using the forward filling method.",
	category=NeuroKitWarning,
	)
	ecg_signal = _ecg_clean_missing(ecg_signal)

So I guess to avoid being repetitive we can leave it out for now.

[DominiqueMakowski]

Sounds good, feel free to merge