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[New Check] Entire column of Timeseries is NaN #229
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da3908f
wip
CodyCBakerPhD 50a5b54
added and debugged check
CodyCBakerPhD 2bf4289
Merge branch 'dev' into check_timeseries_row_is_nan
CodyCBakerPhD 54fc7d0
Merge branch 'dev' into check_timeseries_row_is_nan
CodyCBakerPhD 72a4564
add early data access return
60d280c
debug
CodyCBakerPhD 194f16a
Merge branch 'dev' into check_timeseries_row_is_nan
CodyCBakerPhD 5a99410
Merge branch 'dev' into check_timeseries_row_is_nan
CodyCBakerPhD 23d2e6a
Merge branch 'dev' into check_timeseries_row_is_nan
CodyCBakerPhD 7377c26
Merge branch 'dev' into check_timeseries_row_is_nan
bendichter 6d2c54c
Merge branch 'dev' into check_timeseries_row_is_nan
CodyCBakerPhD 847f0d3
use clever slicing from tables PR
CodyCBakerPhD fb842e0
Merge branch 'dev' into check_timeseries_row_is_nan
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what if there are 1000s of columns?
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3 SpikeGLX probes would hit that amount... Not that I've seen anyone do more than 2 currently, but we can think towards the future.
Could be faster if we did the
np.isnancalculation vectorized over the entire extracted array of[subselected_rows, all_cols]...Same case could be made for the
check_table_cols_nanfor DynamicTables (could be thousands of columns there that we iterate over), but I get that the TimeSeries data is likely to be much larger.If you're suggesting to subselect over columns as well then here, I'd just point out that could ultimately reduce the accuracy if there are only a small number of
channels/ROIsthat had nothing in them but NaN at every time point - the likelihood of those offending columns being selected may not be very high, so I'd actually prefer limiting thenelemsof the row data selection to examine fewer data values per row of each column...Now, that's aside from the other idea I have for pulling data chunk-wise whenever possible, and fully utilizing each chunks data (described below).