As mentioned in #93 (specifically this comment), both PREP and PyPREP don't flag bad-by-dropout channels as bad or unusable on the first pass before average referencing. The problem with this is that after average referencing, the dropout regions in any bad-by-dropout channels likely aren't going to be completely flat anymore, so they're unable to be detected unless the bad-by-correlation detector manages to flag them.
To fix this, it seems like initial bad-by-dropout channels should be added to the initial noisy channels state here unless matlab_strict is True:
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# Initialize channels to permanently flag as bad during referencing |
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noisy = { |
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"bad_by_nan": noisy_detector.bad_by_nan, |
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"bad_by_flat": noisy_detector.bad_by_flat, |
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"bad_by_deviation": [], |
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"bad_by_hf_noise": [], |
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"bad_by_correlation": [], |
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"bad_by_SNR": [], |
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"bad_by_dropout": [], |
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"bad_by_ransac": [], |
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"bad_all": [], |
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} |
What do you think?
As mentioned in #93 (specifically this comment), both PREP and PyPREP don't flag bad-by-dropout channels as bad or unusable on the first pass before average referencing. The problem with this is that after average referencing, the dropout regions in any bad-by-dropout channels likely aren't going to be completely flat anymore, so they're unable to be detected unless the bad-by-correlation detector manages to flag them.
To fix this, it seems like initial bad-by-dropout channels should be added to the initial noisy channels state here unless
matlab_strictis True:pyprep/pyprep/reference.py
Lines 219 to 230 in 3625b26
What do you think?