def processSTA(expName='GNS', spikeFile='GaussianNatScene.spk', dnew={}):
'''
Load spikes from spikeFile
Extract last parameters used from expName in parameters.txt
Update any parameters according to dnew (in case I want to change something)
do whatever processing I feel is necessary
'''
# load basic dictionary with experimental data and all cells
print('Loading cells and variables')
d, cells = divideSpikesPerCondition(*preProcess(expName, spikeFile))
d.update(dnew)
# compute PSTHs
psths = ba.processNested(ba.psth, 0, cells, d['fixationLength'], d['trialsN'], returnFlag=1)
psthsX = ba.psth(cells[0][0][0], d['fixationLength'], d['trialsN'], returnFlag=2)
# load Gaussian noise wave to be able to correlate
print('Loading noise')
noise = loadNoise(d, (2))
# create stimulus object
stim = st.stimulus(noise, d=d)
# upsample stim to about 5ms time bins
stim = stim.upsamplestim(int(stim.deltaT/.005))
# compute STA for each condition
print('Computing stas')
stas = ba.processNested(ft.getsta, 2, stim.tsa, stim.stim, cells, 100, returnFlag = 1)
# compute one x axis for all stas
print('Computing stas X axis')
stasX = ft.getsta(stim.tsa, stim.stim, cells[0][0][0], 100, returnFlag=2)
# since in GNS filter sign is irrelevant, flip them to have negative peaks (off like filters)
stas = ba.processNested(flipSTA, 0, stas)
# since on/off filters are somewhat arbitrary with these stimulus, force them all to be of the same type (peak being off)
#return d, cells, psths, psthsX
return d, stim, cells, psths, psthsX, stas, stasX, stim
def getsta(*argv, **kwargv):
#_pdb.set_trace()
a1=ft.getsta(*argv, **kwargv)
b1=flipSTA(a1)
return b1
