MergedSpikeAnalysis

Module for merging datasets. Once data has been curated it may be beneficial to look at a series of animals altogether. To facilitate this the MergedSpikeAnalysis object can be used. This is done in similar fashion to other classes

import spikeanalysis as sa

# we start with SpikeAnalysis or CuratedSpikeAnalysis objects st1
# and st2
merged_data = sa.MergedSpikeAnalysis(spikeanalysis_list=[st1, st2], name_list=['animal1', 'animal2'])

# if we need to add an animal, st3 we can use
merged_data.add_analysis(analysis=st3, name='animal3')

# or we can use lists
merged_data.add_analysis(analysis=[st3,st4], name=['animal3', 'animal4'])

Once the data to merge is ready to be merged one can use the merge_data() function.

merged_data.merge_data()

After merging the datasets the standard SpikeAnalysis functions can be run. Under the hood each dataset will be run with the exact same conditions to ensure the time bins are balanced. At a fundamental level the data is set up as a series of matrices with (n_neurons, n_trialgroups, n_time_bins).

Since different animals each have different numbers of trial groups the functions after get_raw_psth() are run with the fill which will take animals missing a trial group and fill with fill. The default for this is np.nan.

merged_data.get_raw_psth(window=[-1, 2], time_bin_ms=1)
merged_data.zscore_data(time_bin_ms=10, bsl_window=[-1,-.1], z_window=[-1,2], fill=np.nan)

Finally, the merged data set can be return for use in the SpikePlotter class.

plotter = sa.SpikePlotter()
plotter.set_analysis(merged_data)

This works because the merged_data is a SpikeAnalysis object that has specific guardrails around methods which can no longer be accessed. Plotting can occur as would normally occur.