## reshape the model/exp responses by condition, group by dispersion
# we reshape the responses to combine within dispersion, i.e. (nDisp, nSf*nCon)
shapeByDisp = lambda resps: resps.reshape(
(resps.shape[0], resps.shape[1] * resps.shape[2]))
measured_resps = hf.organize_resp(
data['spikeCount'], cellStruct,
expInd)[2] # 3rd output is organized sfMix resp.
measured_byDisp = shapeByDisp(measured_resps)
nDisps = len(measured_byDisp)
## get the final filter tunings
omega = np.logspace(-2, 2, 1000)
# where are we evaluating?
# first, normalization
inhSfTuning = hf.getSuppressiveSFtuning(sfs=omega)
nInhChan = cellStruct['sfm']['mod']['normalization']['pref']['sf']
nTrials = inhSfTuning.shape[0]
if fitType == 2:
gs_mean, gs_std = [finalParams[normMu], finalParams[normStd]]
inhWeight = hf.genNormWeights(cellStruct, nInhChan, gs_mean, gs_std,
nTrials, expInd)
inhWeight = inhWeight[:, :, 0]
# genNormWeights gives us weights as nTr x nFilters x nFrames - we have only one "frame" here, and all are the same
# first, tuned norm:
sfNorm = np.sum(-.5 * (inhWeight * np.square(inhSfTuning)), 1)
sfNorm = sfNorm / np.amax(np.abs(sfNorm))
# update function to be used below
updateInhWeight = lambda mn, std: hf.genNormWeights(
cellStruct, nInhChan, mn, std, nTrials, expInd)[:, :, 0]
else:
sigLow = i[1]
sigHigh = i[-1]
sfRel = np.divide(omega, prefSf)
# - set the sigma appropriately, depending on what the stimulus SF is
sigma = np.multiply(sigLow, [1] * len(sfRel))
sigma[[x for x in range(len(sfRel)) if sfRel[x] > 1]] = sigHigh
# - now, compute the responses (automatically normalized, since max gaussian value is 1...)
s = [
np.exp(-np.divide(np.square(np.log(x)), 2 * np.square(y)))
for x, y in zip(sfRel, sigma)
]
sfExcCurr = s
sfExc.append(sfExcCurr)
inhSfTuning = hf.getSuppressiveSFtuning()
# Compute weights for suppressive signals
nInhChan = expData['sfm']['mod']['normalization']['pref']['sf']
nTrials = inhSfTuning.shape[0]
inhWeight = hf.genNormWeights(expData, nInhChan, gs_mean, gs_std, nTrials,
expInd)
inhWeight = inhWeight[:, :, 0]
# genNormWeights gives us weights as nTr x nFilters x nFrames - we have only one "frame" here, and all are the same
# first, tuned norm:
sfNormTune = np.sum(-.5 * (inhWeight * np.square(inhSfTuning)), 1)
sfNormTune = sfNormTune / np.amax(np.abs(sfNormTune))
# then, untuned norm:
inhAsym = 0
inhWeight = []
for iP in range(len(nInhChan)):
prefOri = 0
# just fixed value since no model param for this
aRatio = 1
# just fixed value since no model param for this
filtTemp = model_responses.oriFilt(imSizeDeg, pixSize, prefSf, prefOri, dOrder,
aRatio)
filt = (filtTemp - filtTemp[0, 0]) / np.amax(np.abs(filtTemp - filtTemp[0, 0]))
# get model details - exc/suppressive components
omega = np.logspace(-2, 2, 1000)
sfRel = omega / prefSf
s = np.power(omega, dOrder) * np.exp(-dOrder / 2 * np.square(sfRel))
sMax = np.power(prefSf, dOrder) * np.exp(-dOrder / 2)
sfExc = s / sMax
inhSfTuning = helper_fcns.getSuppressiveSFtuning()
nInhChan = cellStruct['sfm']['mod']['normalization']['pref']['sf']
if norm_type == 1:
nTrials = inhSfTuning.shape[0]
inhWeight = helper_fcns.genNormWeights(cellStruct, nInhChan, gs_mean,
gs_std, nTrials)
inhWeight = inhWeight[:, :, 0]
# genNormWeights gives us weights as nTr x nFilters x nFrames - we have only one "frame" here, and all are the same
else:
if modFit[8]: # i.e. if this parameter exists...
inhAsym = modFit[8]
else:
inhAsym = 0
inhWeight = []