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 __init__(self, d, startT, endT, cells):
'''
inputs:
-------
startT: float, defines the start of the time window
endT: float, defines the end of the time window
cells: nested lists of ndarrays with the spikes for all conditions and all cells
'''
self.startT = startT
self.endT = endT
self.cells = ba.processNested(limitSpikes, 0, cells, d['fixationLength'], startT, endT)
def processInformation(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, cells and the seq of Home and Targets
print('Loading cells and variables')
d, cells, blockSeq, blockStartT, blockEndT = preProcess(expName, spikeFile)
d.update(dnew)
# Define a TW for computing basic response properties
TW_startT = .05
TW_endT = .20
binsN = 4
eventStartT = _np.array(blockStartT) + TW_startT
eventEndT = _np.array(blockStartT) + TW_endT
binLength = (TW_endT - TW_startT)/binsN
# compute Latency and spike count
print('Processing Latency for all cells')
latency = ba.processNested(rt.getLatency, 0, cells, eventStartT, eventEndT, noSpikeSymbol = - binLength/2)
print('Processing Spike Count for all cells')
spkCnt = ba.processNested(rt.getSpkCnt, 0, cells, eventStartT, eventEndT)
# convert latency into discrete symbols (for each cell)
latency = ba.processNested(lambda x: _np.floor(x/binLength), 0, latency)
# combine latency and spkCnt into a unique symbol
spkCntLat = ba.processNested(lambda x, y: list(zip(x, y)), 0, latency, spkCnt)
# process information
print('Processing Information')
latencyInfo = ba.processNested(info.mi, 0, latency, blockSeq)
spkCntInfo = ba.processNested(info.mi, 0, spkCnt, blockSeq)
spkCntLatInfo = [info.mi(spkCntLat[i], blockSeq) for i in range(len(cells))]
# as a control, process information with a shuffled version of blockSeq
shuffled = blockSeq.copy()
_np.random.shuffle(shuffled)
latencyInfoSh = ba.processNested(info.mi, 0, latency, shuffled)
spkCntInfoSh = ba.processNested(info.mi, 0, spkCnt, shuffled)
spkCntLatInfoSh = [info.mi(spkCntLat[i], shuffled) for i in range(len(cells))]
# make a nice plot
print('Plotting information')
_plt.close('Information')
_plt.figure('Information')
_plt.plot(latencyInfo, 'ro', label = 'Latency')
_plt.plot(spkCntInfo, 'bo', label = 'Spike Count')
_plt.plot(spkCntLatInfo, 'ko', label = 'Lat and SpkCnt')
_plt.plot(latencyInfoSh, 'r.')
_plt.plot(spkCntInfoSh, 'b.')
_plt.plot(spkCntLatInfoSh, 'k.')
_plt.ylabel('Information (Bits)')
_plt.xlabel('Image ID')
_plt.title('Mutual Information')
_plt.legend()
_plt.xlim(-.5, len(cells)+.5)
_plt.show()
return d, cells, latency, spkCnt, blockSeq, latencyInfo, spkCntInfo, spkCntLatInfo
