hollypacey
commented
1 year ago - clean out bad recordings:
- if for (stim = sucr; conc = 100) the bins are all 0
- then for every sensillum tested with that stimulus combination (i.e., each matching BeeID / sensillum)
- remove the data
- save to a new 'cleaned' dataset atm so original isn't lost.
- rebin a current dataset:
- determine the current binning
- what is the new binning wanted?
- check if doable - ie a multiple of prev. bins
- rebin and save to a new 'rebinned' dataset so original isn't lost.
- try to automate dataset characteristics more and set metadata/timeseries
- also can we note the time duration and bin sizes?
- use time values rather than time indices (redundant as accessible as list indices already?)
- make data read in more complex.
- add class functions to access and print properties of the dataset.
- filtering
- functions to inform you about the metadata e.g. what types of sugar are available the dataset
- functions to filter out a new dataset based on specific characteristics e.g. keep all the data from fructose and put it into a new dataset. [and then you could have a few different subsets of the data to compare in plots or input to clustering algs.]