# - to properly evaluate the loss, load rvcFits, mask the trials
rvcCurr = hf.get_rvc_fits(data_loc,
expInd,
cellNum,
rvcName=rvcBase,
rvcMod=rvcMod)
stimOr = np.vstack(expData['sfm']['exp']['trial']['ori'])
mask = np.isnan(np.sum(stimOr, 0))
# sum over all stim components...if there are any nans in that trial, we know
# - now compute SFMGiveBof!
# ----
modRespWght = mod_resp.SFMGiveBof(modFits[1],
expData,
normType=normTypes[1],
lossType=lossType,
expInd=expInd,
cellNum=cellNum,
rvcFits=rvcCurr,
excType=excType,
maskIn=~mask)
# ----
modResps = [
mod_resp.SFMGiveBof(fit,
expData,
normType=norm,
lossType=lossType,
expInd=expInd,
cellNum=cellNum,
rvcFits=rvcCurr,
excType=excType,
maskIn=~mask) for fit, norm in zip(modFits, normTypes)
def setModel(cellNum, stopThresh, lr, lossType = 1, fitType = 1, subset_frac = 1, initFromCurr = 1, holdOutCondition = None):
# Given just a cell number, will fit the Robbe-inspired V1 model to the data
#
# stopThresh is the value (in NLL) at which we stop the fitting (i.e. if the difference in NLL between two full steps is < stopThresh, stop the fitting
#
# LR is learning rate
#
# lossType
# 1 - loss := square(sqrt(resp) - sqrt(pred))
# 2 - loss := poissonProb(spikes | modelRate)
# 3 - loss := modPoiss model (a la Goris, 2014)
#
# fitType - what is the model formulation?
# 1 := flat normalization
# 2 := gaussian-weighted normalization responses
# 3 := gaussian-weighted c50/norm "constant"
#
# holdOutCondition - [d, c, sf] or None
# which condition should we hold out from the dataset
########
# Load cell
########
#loc_data = '/Users/paulgerald/work/sfDiversity/sfDiv-OriModel/sfDiv-python/Analysis/Structures/'; # personal mac
loc_data = '/home/pl1465/SF_diversity/Analysis/Structures/'; # Prince cluster
# fitType
if fitType == 1:
fL_suffix1 = '_flat';
elif fitType == 2:
fL_suffix1 = '_wght';
elif fitType == 3:
fL_suffix1 = '_c50';
# lossType
if lossType == 1:
fL_suffix2 = '_sqrt.npy';
elif lossType == 2:
fL_suffix2 = '_poiss.npy';
elif lossType == 3:
fL_suffix2 = '_modPoiss.npy';
dataList = hf.np_smart_load(str(loc_data + 'dataList.npy'));
dataNames = dataList['unitName'];
print('loading data structure...');
S = hf.np_smart_load(str(loc_data + dataNames[cellNum-1] + '_sfm.npy')); # why -1? 0 indexing...
print('...finished loading');
trial_inf = S['sfm']['exp']['trial'];
prefOrEst = mode(trial_inf['ori'][1]).mode;
trialsToCheck = trial_inf['con'][0] == 0.01;
prefSfEst = mode(trial_inf['sf'][0][trialsToCheck==True]).mode;
########
# 00 = preferred spatial frequency (cycles per degree)
# 01 = derivative order in space
# 02 = normalization constant (log10 basis)
# 03 = response exponent
# 04 = response scalar
# 05 = early additive noise
# 06 = late additive noise
# 07 = variance of response gain - only used if lossType = 3
# if fitType == 2
# 08 = mean of (log)gaussian for normalization weights
# 09 = std of (log)gaussian for normalization weights
# if fitType == 3
# 08 = the offset of the c50 tuning curve which is bounded between [v_sigOffset, 1] || [0, 1]
# 09 = standard deviation of the gaussian to the left of the peak || >0.1
# 10 = "" to the right "" || >0.1
# 11 = peak of offset curve
curr_params = [];
initFromCurr = 0; # override initFromCurr so that we just go with default parameters
if np.any(np.isnan(curr_params)): # if there are nans, we need to ignore...
curr_params = [];
initFromCurr = 0;
pref_sf = float(prefSfEst) if initFromCurr==0 else curr_params[0];
dOrdSp = np.random.uniform(1, 3) if initFromCurr==0 else curr_params[1];
normConst = -0.8 if initFromCurr==0 else curr_params[2]; # why -0.8? Talked with Tony, he suggests starting with lower sigma rather than higher/non-saturating one
#normConst = np.random.uniform(-1, 0) if initFromCurr==0 else curr_params[2];
respExp = np.random.uniform(1, 3) if initFromCurr==0 else curr_params[3];
respScalar = np.random.uniform(10, 1000) if initFromCurr==0 else curr_params[4];
noiseEarly = np.random.uniform(0.001, 0.1) if initFromCurr==0 else curr_params[5];
noiseLate = np.random.uniform(0.1, 1) if initFromCurr==0 else curr_params[6];
varGain = np.random.uniform(0.1, 1) if initFromCurr==0 else curr_params[7];
if fitType == 1:
inhAsym = 0;
if fitType == 2:
normMean = np.random.uniform(-1, 1) if initFromCurr==0 else curr_params[8];
normStd = np.random.uniform(0.1, 1) if initFromCurr==0 else curr_params[9];
if fitType == 3:
sigOffset = np.random.uniform(0, 0.05) if initFromCurr==0 else curr_params[8];
stdLeft = np.random.uniform(1, 5) if initFromCurr==0 else curr_params[9];
stdRight = np.random.uniform(1, 5) if initFromCurr==0 else curr_params[10];
sigPeak = float(prefSfEst) if initFromCurr==0 else curr_params[11];
print('Initial parameters:\n\tsf: ' + str(pref_sf) + '\n\td.ord: ' + str(dOrdSp) + '\n\tnormConst: ' + str(normConst));
print('\n\trespExp ' + str(respExp) + '\n\trespScalar ' + str(respScalar));
#########
# Now get all the data we need
#########
# stimulus information
# vstack to turn into array (not array of arrays!)
stimOr = np.vstack(trial_inf['ori']);
#purge of NaNs...
mask = np.isnan(np.sum(stimOr, 0)); # sum over all stim components...if there are any nans in that trial, we know
objWeight = np.ones((stimOr.shape[1]));
# and get rid of orientation tuning curve trials
oriBlockIDs = np.hstack((np.arange(131, 155+1, 2), np.arange(132, 136+1, 2))); # +1 to include endpoint like Matlab
oriInds = np.empty((0,));
for iB in oriBlockIDs:
indCond = np.where(trial_inf['blockID'] == iB);
if len(indCond[0]) > 0:
oriInds = np.append(oriInds, indCond);
# get rid of CRF trials, too? Not yet...
conBlockIDs = np.arange(138, 156+1, 2);
conInds = np.empty((0,));
for iB in conBlockIDs:
indCond = np.where(trial_inf['blockID'] == iB);
if len(indCond[0]) > 0:
conInds = np.append(conInds, indCond);
objWeight[conInds.astype(np.int64)] = 1; # for now, yes it's a "magic number"
mask[oriInds.astype(np.int64)] = True; # as in, don't include those trials either!
# hold out a condition if we have specified, and adjust the mask accordingly
if holdOutCondition is not None:
# dispInd: [1, 5]...conInd: [1, 2]...sfInd: [1, 11]
# first, get all of the conditions... - blockIDs by condition known from Robbe code
dispInd = holdOutCondition[0];
conInd = holdOutCondition[1];
sfInd = holdOutCondition[2];
StimBlockIDs = np.arange(((dispInd-1)*(13*2)+1)+(conInd-1), ((dispInd)*(13*2)-5)+(conInd-1)+1, 2); # +1 to include the last block ID
currBlockID = StimBlockIDs[sfInd-1];
holdOutTr = np.where(trial_inf['blockID'] == currBlockID)[0];
mask[holdOutTr.astype(np.int64)] = True; # as in, don't include those trials either!
# Set up model here - get the parameters and parameter bounds
if fitType == 1:
param_list = (pref_sf, dOrdSp, normConst, respExp, respScalar, noiseEarly, noiseLate, varGain, inhAsym);
elif fitType == 2:
param_list = (pref_sf, dOrdSp, normConst, respExp, respScalar, noiseEarly, noiseLate, varGain, normMean, normStd);
elif fitType == 3:
param_list = (pref_sf, dOrdSp, normConst, respExp, respScalar, noiseEarly, noiseLate, varGain, sigOffset, stdLeft, stdRight, sigPeak);
all_bounds = hf.getConstraints(fitType);
# now set up the optimization
obj = lambda params: mod_resp.SFMGiveBof(params, structureSFM=S, normType=fitType, lossType=lossType, maskIn=~mask)[0];
tomin = opt.minimize(obj, param_list, bounds=all_bounds);
opt_params = tomin['x'];
NLL = tomin['fun'];
if holdOutCondition is not None:
holdoutNLL, _, = mod_resp.SFMGiveBof(opt_params, structureSFM=S, normType=fitType, lossType=lossType, trialSubset=holdOutTr);
else:
holdoutNLL = [];
return NLL, opt_params, holdoutNLL;
expData = np.load(str(data_loc + dL['unitName'][cellNum - 1] +
'_sfm.npy')).item()
expResp = expData
modFit = fitList[cellNum - 1]['params']
#
descrExpFit = descrExpFits[cellNum - 1]['params']
# nFam x nCon x nDescrParams
descrModFit = descrModFits[cellNum - 1]['params']
# nFam x nCon x nDescrParams
if len(normTypeArr
) == 3: # i.e. we've passed in gs_mean, gs_std, then replace...
modFit[-2] = normTypeArr[1]
modFit[-1] = normTypeArr[2]
ignore, modResp, normTypeArr = mod_resp.SFMGiveBof(modFit, expData,
normTypeArr)
norm_type = normTypeArr[0]
if norm_type == 1:
gs_mean = normTypeArr[1]
# guaranteed to exist after call to .SFMGiveBof, if norm_type == 1
gs_std = normTypeArr[2]
# guaranteed to exist ...
#modRespAll = mod_resp.SFMGiveBof(modParamsCurr, expData, normTypeArr)[1]; # NOTE: We're taking [1] (i.e. second) output of SFMGiveBof
oriModResp, conModResp, sfmixModResp, allSfMix = organize_modResp(
modResp, expData['sfm']['exp']['trial'])
oriExpResp, conExpResp, sfmixExpResp, allSfMixExp = organize_modResp(expData['sfm']['exp']['trial']['spikeCount'], \
expData['sfm']['exp']['trial'])
#pdb.set_trace();
# allSfMix is (nFam, nCon, nCond, nReps) where nCond is 11, # of SF centers and nReps is usually 10
modLow = np.nanmin(allSfMix, axis=3)
cellType = 'V1'; expData = np.load(str(data_loc + cellName + '_sfm.npy'), encoding='latin1').item(); expInd = hf.get_exp_ind(data_loc, cellName)[0]; # #### Load model fits modFit_fl = fitList_fl[cellNum-1]['params']; # modFit_wg = fitList_wg[cellNum-1]['params']; # modFits = [modFit_fl, modFit_wg]; normTypes = [1, 2]; # flat, then weighted # ### Organize data # #### determine contrasts, center spatial frequency, dispersions modResps = [mod_resp.SFMGiveBof(fit, expData, normType=norm, lossType=lossType, expInd=expInd) for fit, norm in zip(modFits, normTypes)]; modResps = [x[1] for x in modResps]; # 1st return output (x[0]) is NLL (don't care about that here) gs_mean = modFit_wg[8]; gs_std = modFit_wg[9]; # now organize the responses orgs = [hf.organize_resp(mr, expData, expInd) for mr in modResps]; oriModResps = [org[0] for org in orgs]; # only non-empty if expInd = 1 conModResps = [org[1] for org in orgs]; # only non-empty if expInd = 1 sfmixModResps = [org[2] for org in orgs]; allSfMixs = [org[3] for org in orgs]; modLows = [np.nanmin(resp, axis=3) for resp in allSfMixs]; modHighs = [np.nanmax(resp, axis=3) for resp in allSfMixs]; modAvgs = [np.nanmean(resp, axis=3) for resp in allSfMixs]; modSponRates = [fit[6] for fit in modFits];
# #### Load model fits modFit_fl = fitList_fl[cellNum-1]['params']; # modFit_wg = fitList_wg[cellNum-1]['params']; # modFits = [modFit_fl, modFit_wg]; normTypes = [1, 2]; # flat, then weighted # ### Organize data # #### determine contrasts, center spatial frequency, dispersions # SFMGiveBof returns spike counts per trial, NOT rates -- we will correct in hf.organize_resp call below # - to properly evaluate the loss, load rvcFits, mask the trials rvcCurr = hf.get_rvc_fits(data_loc, expInd, cellNum, rvcName=rvcBase, rvcMod=rvcMod); stimOr = np.vstack(expData['sfm']['exp']['trial']['ori']); mask = np.isnan(np.sum(stimOr, 0)); # sum over all stim components...if there are any nans in that trial, we know # - now compute SFMGiveBof! modResps = [mod_resp.SFMGiveBof(fit, expData, normType=norm, lossType=lossType, expInd=expInd, cellNum=cellNum, rvcFits=rvcCurr, excType=excType, maskIn=~mask, compute_varExpl=1, lgnFrontEnd=lgnFrontEnd) for fit, norm in zip(modFits, normTypes)]; # unpack the model fits! varExplSF_flat = modResps[0][3]; varExplSF = modResps[1][3]; varExplCon_flat = modResps[0][4]; varExplCon = modResps[1][4]; lossByCond_flat = modResps[0][2]; lossByCond = modResps[1][2]; # We only care about weighted... modResps = [x[1] for x in modResps]; # 1st return output (x[0]) is NLL (don't care about that here) #lossByCond = [x[2] for x in modResps]; # if we want both... gs_mean = modFit_wg[8]; gs_std = modFit_wg[9]; # now organize the responses orgs = [hf.organize_resp(mr, expData, expInd, respsAsRate=False) for mr in modResps]; oriModResps = [org[0] for org in orgs]; # only non-empty if expInd = 1
for c in range(nCells):
curr = hf.np_smart_load(loc_data + dL_mr['unitName'][c] + '_sfm.npy')
if 'respWght' in curr['sfm']['mod']['recovery'] and 'respFlat' in curr[
'sfm']['mod']['recovery'] and overwriteMR == 0:
print('\talready generated these model recovery responses; skipping')
continue
recov = curr['sfm']['mod']['recovery']
expInd = hf.exp_name_to_ind(dL_mr['expType'][c])
types = ['Wght', 'Flat']
paramStrs = ['params%s' % x for x in types]
respStrs = ['resp%s' % x for x in types]
normTypes = [2, 1]
for (paramStr, respStr, norm) in zip(paramStrs, respStrs, normTypes):
currResp = mod_resp.SFMGiveBof(recov[paramStr],
curr,
normType=norm,
lossType=lossType,
expInd=expInd)[1]
# 0th return is NLL
curr['sfm']['mod']['recovery'][respStr] = np.random.poisson(currResp)
# simulate from poisson model - this makes integer spike counts and introduces some variability
# now save it!
np.save(loc_data + dL_mr['unitName'][c] + '_sfm.npy', curr)
###########
# 3. fit model
# now, run model_responses while specifying the correct dataList/fitList
###########
print('\n\n\n********YOU HAVE MADE IT TO FITTING STAGE*********\n\n')
encoding='latin1').item()
# #### Load descriptive model fits, comp. model fits
descrFitName = hf.descrFit_name(descr_fit_type)
modParams = np.load(str(dataPath + fitListName), encoding='latin1').item()
modParamsCurr = modParams[which_cell - 1]['params']
# ### Organize data
# #### determine contrasts, center spatial frequency, dispersions
data = cellStruct['sfm']['exp']['trial']
ignore, modRespAll = mod_resp.SFMGiveBof(modParamsCurr,
cellStruct,
normType=norm_type,
lossType=lossType,
expInd=expInd)
print('norm type %02d' % (norm_type))
if norm_type == 2:
gs_mean = modParamsCurr[1]
# guaranteed to exist after call to .SFMGiveBof, if norm_type == 2
gs_std = modParamsCurr[2]
# guaranteed to exist ...
resp, stimVals, val_con_by_disp, validByStimVal, modResp = hf.tabulate_responses(
cellStruct, expInd, modRespAll)
blankMean, blankStd, _ = hf.blankResp(cellStruct)
modBlankMean = modParamsCurr[6]
# late additive noise is the baseline of the model
# all responses on log ordinate (y axis) should be baseline subtracted
def fit_descr(cell_num,
data_loc,
n_repeats=4,
fromModelSim=0,
fitLossType=1,
baseStr=None,
normType=None,
lossType=None):
nFam = 5
nCon = 2
nParam = 5
# get base descrFit name (including loss str)
if fitLossType == 1:
floss_str = '_lsq'
elif fitLossType == 2:
floss_str = '_sqrt'
elif fitLossType == 3:
floss_str = '_poiss'
descrFitBase = 'descrFits%s' % floss_str
# load cell information
dataList = hfunc.np_smart_load(data_loc + 'dataList.npy')
if fromModelSim:
# get model fit name
fL_name = baseStr
# normType
if normType == 1:
fL_suffix1 = '_flat'
elif normType == 2:
fL_suffix1 = '_wght'
elif normType == 3:
fL_suffix1 = '_c50'
# lossType
if lossType == 1:
fL_suffix2 = '_sqrt.npy'
elif lossType == 2:
fL_suffix2 = '_poiss.npy'
elif lossType == 3:
fL_suffix2 = '_modPoiss.npy'
elif lossType == 4:
fL_suffix2 = '_chiSq.npy'
fitListName = str(fL_name + fL_suffix1 + fL_suffix2)
dfModelName = '%s_%s' % (descrFitBase, fitListName)
if os.path.isfile(data_loc + dfModelName):
descrFits = hfunc.np_smart_load(data_loc + dfModelName)
else:
descrFits = dict()
else:
dfModelName = '%s.npy' % descrFitBase
if os.path.isfile(data_loc + dfModelName):
descrFits = hfunc.np_smart_load(data_loc + dfModelName)
else:
descrFits = dict()
data = hfunc.np_smart_load(data_loc + dataList['unitName'][cell_num - 1] +
'_sfm.npy')
if fromModelSim: # then we'll 'sneak' in the model responses in the place of the real data
modFits = hfunc.np_smart_load(data_loc + fitListName)
modFit = modFits[cell_num - 1]['params']
a, modResp = mod_resp.SFMGiveBof(modFit,
data,
normType=normType,
lossType=lossType)
# spike count must be integers! Simply round
data['sfm']['exp']['trial']['spikeCount'] = np.round(
modResp * data['sfm']['exp']['trial']['duration'])
if cell_num - 1 in descrFits:
bestNLL = descrFits[cell_num - 1]['NLL']
currParams = descrFits[cell_num - 1]['params']
else: # set values to NaN...
bestNLL = np.ones((nFam, nCon)) * np.nan
currParams = np.ones((nFam, nCon, nParam)) * np.nan
print('Doing the work, now')
for family in range(nFam):
for con in range(nCon):
print('.')
# set initial parameters - a range from which we will pick!
base_rate = data['sfm']['exp']['sponRateMean']
if base_rate <= 3:
range_baseline = (0, 3)
else:
range_baseline = (0.5 * base_rate, 1.5 * base_rate)
max_resp = np.amax(data['sfm']['exp']['sfRateMean'][family][con])
range_amp = (0.5 * max_resp, 1.5)
theSfCents = data['sfm']['exp']['sf'][family][con]
max_sf_index = np.argmax(
data['sfm']['exp']['sfRateMean'][family][con])
# what sf index gives peak response?
mu_init = theSfCents[max_sf_index]
if max_sf_index == 0: # i.e. smallest SF center gives max response...
range_mu = (mu_init / 2, theSfCents[max_sf_index + 3])
elif max_sf_index + 1 == len(
theSfCents): # i.e. highest SF center is max
range_mu = (theSfCents[max_sf_index - 3], mu_init)
else:
range_mu = (theSfCents[max_sf_index - 1],
theSfCents[max_sf_index + 1])
# go +-1 indices from center
log_bw_lo = 0.75
# 0.75 octave bandwidth...
log_bw_hi = 2
# 2 octave bandwidth...
denom_lo = hfunc.bw_log_to_lin(log_bw_lo, mu_init)[0]
# get linear bandwidth
denom_hi = hfunc.bw_log_to_lin(log_bw_hi, mu_init)[0]
# get lin. bw (cpd)
range_denom = (denom_lo, denom_hi)
# don't want 0 in sigma
# set bounds for parameters
min_bw = 1 / 4
max_bw = 10
# ranges in octave bandwidth
bound_baseline = (0, max_resp)
bound_range = (0, 1.5 * max_resp)
bound_mu = (0.01, 10)
bound_sig = (np.maximum(0.1,
min_bw / (2 * np.sqrt(2 * np.log(2)))),
max_bw / (2 * np.sqrt(2 * np.log(2))))
# Gaussian at half-height
all_bounds = (bound_baseline, bound_range, bound_mu, bound_sig,
bound_sig)
for n_try in range(n_repeats):
# pick initial params
init_base = hfunc.random_in_range(range_baseline)
init_amp = hfunc.random_in_range(range_amp)
init_mu = hfunc.random_in_range(range_mu)
init_sig_left = hfunc.random_in_range(range_denom)
init_sig_right = hfunc.random_in_range(range_denom)
init_params = [
init_base, init_amp, init_mu, init_sig_left, init_sig_right
]
# choose optimization method
if np.mod(n_try, 2) == 0:
methodStr = 'L-BFGS-B'
else:
methodStr = 'TNC'
obj = lambda params: descr_loss(params, data, family, con)
wax = opt.minimize(obj,
init_params,
method=methodStr,
bounds=all_bounds)
# compare
NLL = wax['fun']
params = wax['x']
if np.isnan(
bestNLL[family,
con]) or NLL < bestNLL[family, con] or invalid(
currParams[family, con, :], all_bounds):
bestNLL[family, con] = NLL
currParams[family, con, :] = params
# update stuff - load again in case some other run has saved/made changes
if os.path.isfile(data_loc + dfModelName):
print('reloading descrFitsModel...')
descrFits = hfunc.np_smart_load(data_loc + dfModelName)
if cell_num - 1 not in descrFits:
descrFits[cell_num - 1] = dict()
descrFits[cell_num - 1]['NLL'] = bestNLL
descrFits[cell_num - 1]['params'] = currParams
np.save(data_loc + dfModelName, descrFits)
print('saving for cell ' + str(cell_num))
# #### Load data
expData = np.load(str(data_loc + dL['unitName'][cellNum - 1] +
'_sfm.npy')).item()
expResp = expData
modFit = fitList[cellNum - 1]['params']
#
descrExpFit = descrExpFits[cellNum - 1]['params']
# nFam x nCon x nDescrParams
descrModFit = descrModFits[cellNum - 1]['params']
# nFam x nCon x nDescrParams
norm_type = fitType
ignore, modResp = mod_resp.SFMGiveBof(modFit,
expData,
normType=norm_type,
lossType=lossType)
if norm_type == 2:
gs_mean = modFit[8]
gs_std = modFit[9]
oriModResp, conModResp, sfmixModResp, allSfMix = organize_modResp(
modResp, expData['sfm']['exp']['trial'])
oriExpResp, conExpResp, sfmixExpResp, allSfMixExp = organize_modResp(expData['sfm']['exp']['trial']['spikeCount'], \
expData['sfm']['exp']['trial'])
#pdb.set_trace();
# allSfMix is (nFam, nCon, nCond, nReps) where nCond is 11, # of SF centers and nReps is usually 10
modLow = np.nanmin(allSfMix, axis=3)
modHigh = np.nanmax(allSfMix, axis=3)
modAvg = np.nanmean(allSfMix, axis=3)
modSponRate = modFit[6]
# #### Load descriptive model fits, comp. model fits
descrFits = np.load(str(dataPath + 'descrFits.npy'), encoding='latin1').item()
descrFits = descrFits[which_cell - 1]['params']
# just get this cell
modParams = np.load(str(dataPath + fitListName), encoding='latin1').item()
modParamsCurr = modParams[which_cell - 1]['params']
# ### Organize data
# #### determine contrasts, center spatial frequency, dispersions
data = cellStruct['sfm']['exp']['trial']
modRespAll = model_responses.SFMGiveBof(modParamsCurr, cellStruct)[1]
resp, stimVals, val_con_by_disp, validByStimVal, modResp = helper_fcns.tabulate_responses(
cellStruct, modRespAll)
blankMean, blankStd, _ = helper_fcns.blankResp(cellStruct)
# all responses on log ordinate (y axis) should be baseline subtracted
all_disps = stimVals[0]
all_cons = stimVals[1]
all_sfs = stimVals[2]
nCons = len(all_cons)
nSfs = len(all_sfs)
nDisps = len(all_disps)
# #### Unpack responses
modFit_fl = fitList_fl[cellNum - 1]['params']
#
modFit_wg = fitList_wg[cellNum - 1]['params']
#
modFits = [modFit_fl, modFit_wg]
normTypes = [1, 2]
# flat, then weighted
descrExpFit = descrExpFits[cellNum - 1]['params']
# nFam x nCon x nDescrParams
descrModFit = descrModFits[cellNum - 1]['params']
# nFam x nCon x nDescrParams
modResps = [
mod_resp.SFMGiveBof(fit, expData, normType=norm, lossType=lossType)
for fit, norm in zip(modFits, normTypes)
]
modResps = [x[1] for x in modResps]
# 1st return output is NLL (don't care about that here)
gs_mean = modFit_wg[8]
gs_std = modFit_wg[9]
# now organize the responses
orgs = [
organize_modResp(mr, expData['sfm']['exp']['trial']) for mr in modResps
]
oriModResps = [org[0] for org in orgs]
conModResps = [org[1] for org in orgs]
sfmixModResps = [org[2] for org in orgs]
allSfMixs = [org[3] for org in orgs]
# now organize the measured responses in the same way
inhAsym = normParams
# descrFit, if exists
if descrFits is not None:
descrParams = descrFits[cellNum - 1]['params']
else:
descrParams = None
###########
# Organize data
###########
# #### determine contrasts, center spatial frequency, dispersions
modResp = mod_resp.SFMGiveBof(modFit,
expData,
normType=fitType,
lossType=lossType,
expInd=expInd)[1]
# now organize the responses
orgs = hf.organize_resp(modResp, expData, expInd)
oriModResp = orgs[0]
# only non-empty if expInd = 1
conModResp = orgs[1]
# only non-empty if expInd = 1
sfmixModResp = orgs[2]
allSfMix = orgs[3]
modLow = np.nanmin(allSfMix, axis=3)
modHigh = np.nanmax(allSfMix, axis=3)
modAvg = np.nanmean(allSfMix, axis=3)
modSponRate = modFit[6]
expData,
expInd,
overwriteSpikes=spikes,
respsAsRates=rates,
modsAsRate=rates)
if fitList is None:
resps = resps_data
# otherwise, we'll still keep resps_data for reference
elif fitList is not None: # OVERWRITE the data with the model spikes!
if use_mod_resp == 1:
curr_fit = fitList[which_cell - 1]['params']
modResp = mod_resp.SFMGiveBof(curr_fit,
S,
normType=fitType,
lossType=lossType,
expInd=expInd,
cellNum=which_cell,
excType=excType)[1]
if f1f0_rat < 1: # then subtract baseline..
modResp = modResp - baseline * hf.get_exp_params(expInd).stimDur
# now organize the responses
resps, stimVals, val_con_by_disp, _, _ = hf.tabulate_responses(
expData,
expInd,
overwriteSpikes=modResp,
respsAsRates=False,
modsAsRate=False)
elif use_mod_resp == 2: # then pytorch model!
resp_str = hf_sf.get_resp_str(respMeasure)
curr_fit = fitList[which_cell - 1][resp_str]['params']
# TEMP HACK
modParamsCurr[2] = modParamsCurr[2] / 1.5
modParamsCurr[4] = modParamsCurr[4] * 10
if len(normTypeArr
) == 3: # i.e. we've passed in gs_mean, gs_std, then replace...
modParamsCurr[-2] = normTypeArr[1]
modParamsCurr[-1] = normTypeArr[2]
# ### Organize data
# #### determine contrasts, center spatial frequency, dispersions
data = cellStruct['sfm']['exp']['trial']
ignore, modRespAll, normTypeArr = model_responses.SFMGiveBof(
modParamsCurr, cellStruct, normTypeArr)
norm_type = normTypeArr[0]
if norm_type == 1:
gs_mean = normTypeArr[1]
# guaranteed to exist after call to .SFMGiveBof, if norm_type == 1
gs_std = normTypeArr[2]
# guaranteed to exist ...
#modRespAll = model_responses.SFMGiveBof(modParamsCurr, cellStruct, normTypeArr)[1]; # NOTE: We're taking [1] (i.e. second) output of SFMGiveBof
resp, stimVals, val_con_by_disp, validByStimVal, modResp = helper_fcns.tabulate_responses(
cellStruct, modRespAll)
blankMean, blankStd, _ = helper_fcns.blankResp(cellStruct)
modBlankMean = modParamsCurr[6]
# late additive noise is the baseline of the model
# all responses on log ordinate (y axis) should be baseline subtracted
all_disps = stimVals[0]
def plot_save_superposition(which_cell, expDir, use_mod_resp=0, fitType=2, excType=1, useHPCfit=1, conType=None, lgnFrontEnd=None, force_full=1, f1_expCutoff=2, to_save=1):
if use_mod_resp == 2:
rvcAdj = -1; # this means vec corrected F1, not phase adjustment F1...
_applyLGNtoNorm = 0; # don't apply the LGN front-end to the gain control weights
recenter_norm = 1;
newMethod = 1; # yes, use the "new" method for mrpt (not that new anymore, as of 21.03)
lossType = 1; # sqrt
_sigmoidSigma = 5;
basePath = os.getcwd() + '/'
if 'pl1465' in basePath or useHPCfit:
loc_str = 'HPC';
else:
loc_str = '';
rvcName = 'rvcFits%s_220531' % loc_str if expDir=='LGN/' else 'rvcFits%s_220609' % loc_str
rvcFits = None; # pre-define this as None; will be overwritten if available/needed
if expDir == 'altExp/': # we don't adjust responses there...
rvcName = None;
dFits_base = 'descrFits%s_220609' % loc_str if expDir=='LGN/' else 'descrFits%s_220631' % loc_str
if use_mod_resp == 1:
rvcName = None; # Use NONE if getting model responses, only
if excType == 1:
fitBase = 'fitList_200417';
elif excType == 2:
fitBase = 'fitList_200507';
lossType = 1; # sqrt
fitList_nm = hf.fitList_name(fitBase, fitType, lossType=lossType);
elif use_mod_resp == 2:
rvcName = None; # Use NONE if getting model responses, only
if excType == 1:
fitBase = 'fitList%s_210308_dG' % loc_str
if recenter_norm:
#fitBase = 'fitList%s_pyt_210312_dG' % loc_str
fitBase = 'fitList%s_pyt_210331_dG' % loc_str
elif excType == 2:
fitBase = 'fitList%s_pyt_210310' % loc_str
if recenter_norm:
#fitBase = 'fitList%s_pyt_210312' % loc_str
fitBase = 'fitList%s_pyt_210331' % loc_str
fitList_nm = hf.fitList_name(fitBase, fitType, lossType=lossType, lgnType=lgnFrontEnd, lgnConType=conType, vecCorrected=-rvcAdj);
# ^^^ EDIT rvc/descrFits/fitList names here;
############
# Before any plotting, fix plotting paramaters
############
plt.style.use('https://raw.githubusercontent.com/paul-levy/SF_diversity/master/paul_plt_style.mplstyle');
from matplotlib import rcParams
rcParams['font.size'] = 20;
rcParams['pdf.fonttype'] = 42 # should be 42, but there are kerning issues
rcParams['ps.fonttype'] = 42 # should be 42, but there are kerning issues
rcParams['lines.linewidth'] = 2.5;
rcParams['axes.linewidth'] = 1.5;
rcParams['lines.markersize'] = 8; # this is in style sheet, just being explicit
rcParams['lines.markeredgewidth'] = 0; # no edge, since weird tings happen then
rcParams['xtick.major.size'] = 15
rcParams['xtick.minor.size'] = 5; # no minor ticks
rcParams['ytick.major.size'] = 15
rcParams['ytick.minor.size'] = 0; # no minor ticks
rcParams['xtick.major.width'] = 2
rcParams['xtick.minor.width'] = 2;
rcParams['ytick.major.width'] = 2
rcParams['ytick.minor.width'] = 0
rcParams['font.style'] = 'oblique';
rcParams['font.size'] = 20;
############
# load everything
############
dataListNm = hf.get_datalist(expDir, force_full=force_full);
descrFits_f0 = None;
dLoss_num = 2; # see hf.descrFit_name/descrMod_name/etc for details
if expDir == 'LGN/':
rvcMod = 0;
dMod_num = 1;
rvcDir = 1;
vecF1 = -1;
else:
rvcMod = 1; # i.e. Naka-rushton (1)
dMod_num = 3; # d-dog-s
rvcDir = None; # None if we're doing vec-corrected
if expDir == 'altExp/':
vecF1 = 0;
else:
vecF1 = 1;
dFits_mod = hf.descrMod_name(dMod_num)
descrFits_name = hf.descrFit_name(lossType=dLoss_num, descrBase=dFits_base, modelName=dFits_mod, phAdj=1 if vecF1==-1 else None);
## now, let it run
dataPath = basePath + expDir + 'structures/'
save_loc = basePath + expDir + 'figures/'
save_locSuper = save_loc + 'superposition_220713/'
if use_mod_resp == 1:
save_locSuper = save_locSuper + '%s/' % fitBase
dataList = hf.np_smart_load(dataPath + dataListNm);
print('Trying to load descrFits at: %s' % (dataPath + descrFits_name));
descrFits = hf.np_smart_load(dataPath + descrFits_name);
if use_mod_resp == 1 or use_mod_resp == 2:
fitList = hf.np_smart_load(dataPath + fitList_nm);
else:
fitList = None;
if not os.path.exists(save_locSuper):
os.makedirs(save_locSuper)
cells = np.arange(1, 1+len(dataList['unitName']))
zr_rm = lambda x: x[x>0];
# more flexible - only get values where x AND z are greater than some value "gt" (e.g. 0, 1, 0.4, ...)
zr_rm_pair = lambda x, z, gt: [x[np.logical_and(x>gt, z>gt)], z[np.logical_and(x>gt, z>gt)]];
# zr_rm_pair = lambda x, z: [x[np.logical_and(x>0, z>0)], z[np.logical_and(x>0, z>0)]] if np.logical_and(x!=[], z!=[])==True else [], [];
# here, we'll save measures we are going use for analysis purpose - e.g. supperssion index, c50
curr_suppr = dict();
############
### Establish the plot, load cell-specific measures
############
nRows, nCols = 6, 2;
cellName = dataList['unitName'][which_cell-1];
expInd = hf.get_exp_ind(dataPath, cellName)[0]
S = hf.np_smart_load(dataPath + cellName + '_sfm.npy')
expData = S['sfm']['exp']['trial'];
# 0th, let's load the basic tuning characterizations AND the descriptive fit
try:
dfit_curr = descrFits[which_cell-1]['params'][0,-1,:]; # single grating, highest contrast
except:
dfit_curr = None;
# - then the basics
try:
basic_names, basic_order = dataList['basicProgName'][which_cell-1], dataList['basicProgOrder']
basics = hf.get_basic_tunings(basic_names, basic_order);
except:
try:
# we've already put the basics in the data structure... (i.e. post-sorting 2021 data)
basic_names = ['','','','',''];
basic_order = ['rf', 'sf', 'tf', 'rvc', 'ori']; # order doesn't matter if they are already loaded
basics = hf.get_basic_tunings(basic_names, basic_order, preProc=S, reducedSave=True)
except:
basics = None;
### TEMPORARY: save the "basics" in curr_suppr; should live on its own, though; TODO
curr_suppr['basics'] = basics;
try:
oriBW, oriCV = basics['ori']['bw'], basics['ori']['cv'];
except:
oriBW, oriCV = np.nan, np.nan;
try:
tfBW = basics['tf']['tfBW_oct'];
except:
tfBW = np.nan;
try:
suprMod = basics['rfsize']['suprInd_model'];
except:
suprMod = np.nan;
try:
suprDat = basics['rfsize']['suprInd_data'];
except:
suprDat = np.nan;
try:
cellType = dataList['unitType'][which_cell-1];
except:
# TODO: note, this is dangerous; thus far, only V1 cells don't have 'unitType' field in dataList, so we can safely do this
cellType = 'V1';
############
### compute f1f0 ratio, and load the corresponding F0 or F1 responses
############
f1f0_rat = hf.compute_f1f0(expData, which_cell, expInd, dataPath, descrFitName_f0=descrFits_f0)[0];
curr_suppr['f1f0'] = f1f0_rat;
respMeasure = 1 if f1f0_rat > 1 else 0;
if vecF1 == 1:
# get the correct, adjusted F1 response
if expInd > f1_expCutoff and respMeasure == 1:
respOverwrite = hf.adjust_f1_byTrial(expData, expInd);
else:
respOverwrite = None;
if (respMeasure == 1 or expDir == 'LGN/') and expDir != 'altExp/' : # i.e. if we're looking at a simple cell, then let's get F1
if vecF1 == 1:
spikes_byComp = respOverwrite
# then, sum up the valid components per stimulus component
allCons = np.vstack(expData['con']).transpose();
blanks = np.where(allCons==0);
spikes_byComp[blanks] = 0; # just set it to 0 if that component was blank during the trial
else:
if rvcName is not None:
try:
rvcFits = hf.get_rvc_fits(dataPath, expInd, which_cell, rvcName=rvcName, rvcMod=rvcMod, direc=rvcDir, vecF1=vecF1);
except:
rvcFits = None;
else:
rvcFits = None
spikes_byComp = hf.get_spikes(expData, get_f0=0, rvcFits=rvcFits, expInd=expInd);
spikes = np.array([np.sum(x) for x in spikes_byComp]);
rates = True if vecF1 == 0 else False; # when we get the spikes from rvcFits, they've already been converted into rates (in hf.get_all_fft)
baseline = None; # f1 has no "DC", yadig?
else: # otherwise, if it's complex, just get F0
respMeasure = 0;
spikes = hf.get_spikes(expData, get_f0=1, rvcFits=None, expInd=expInd);
rates = False; # get_spikes without rvcFits is directly from spikeCount, which is counts, not rates!
baseline = hf.blankResp(expData, expInd)[0]; # we'll plot the spontaneous rate
# why mult by stimDur? well, spikes are not rates but baseline is, so we convert baseline to count (i.e. not rate, too)
spikes = spikes - baseline*hf.get_exp_params(expInd).stimDur;
#print('###\nGetting spikes (data): rates? %d\n###' % rates);
_, _, _, respAll = hf.organize_resp(spikes, expData, expInd, respsAsRate=rates); # only using respAll to get variance measures
resps_data, stimVals, val_con_by_disp, _, _ = hf.tabulate_responses(expData, expInd, overwriteSpikes=spikes, respsAsRates=rates, modsAsRate=rates);
if fitList is None:
resps = resps_data; # otherwise, we'll still keep resps_data for reference
elif fitList is not None: # OVERWRITE the data with the model spikes!
if use_mod_resp == 1:
curr_fit = fitList[which_cell-1]['params'];
modResp = mod_resp.SFMGiveBof(curr_fit, S, normType=fitType, lossType=lossType, expInd=expInd, cellNum=which_cell, excType=excType)[1];
if f1f0_rat < 1: # then subtract baseline..
modResp = modResp - baseline*hf.get_exp_params(expInd).stimDur;
# now organize the responses
resps, stimVals, val_con_by_disp, _, _ = hf.tabulate_responses(expData, expInd, overwriteSpikes=modResp, respsAsRates=False, modsAsRate=False);
elif use_mod_resp == 2: # then pytorch model!
resp_str = hf_sf.get_resp_str(respMeasure)
curr_fit = fitList[which_cell-1][resp_str]['params'];
model = mrpt.sfNormMod(curr_fit, expInd=expInd, excType=excType, normType=fitType, lossType=lossType, lgnFrontEnd=lgnFrontEnd, newMethod=newMethod, lgnConType=conType, applyLGNtoNorm=_applyLGNtoNorm)
### get the vec-corrected responses, if applicable
if expInd > f1_expCutoff and respMeasure == 1:
respOverwrite = hf.adjust_f1_byTrial(expData, expInd);
else:
respOverwrite = None;
dw = mrpt.dataWrapper(expData, respMeasure=respMeasure, expInd=expInd, respOverwrite=respOverwrite); # respOverwrite defined above (None if DC or if expInd=-1)
modResp = model.forward(dw.trInf, respMeasure=respMeasure, sigmoidSigma=_sigmoidSigma, recenter_norm=recenter_norm).detach().numpy();
if respMeasure == 1: # make sure the blank components have a zero response (we'll do the same with the measured responses)
blanks = np.where(dw.trInf['con']==0);
modResp[blanks] = 0;
# next, sum up across components
modResp = np.sum(modResp, axis=1);
# finally, make sure this fills out a vector of all responses (just have nan for non-modelled trials)
nTrialsFull = len(expData['num']);
modResp_full = np.nan * np.zeros((nTrialsFull, ));
modResp_full[dw.trInf['num']] = modResp;
if respMeasure == 0: # if DC, then subtract baseline..., as determined from data (why not model? we aren't yet calc. response to no stim, though it can be done)
modResp_full = modResp_full - baseline*hf.get_exp_params(expInd).stimDur;
# TODO: This is a work around for which measures are in rates vs. counts (DC vs F1, model vs data...)
stimDur = hf.get_exp_params(expInd).stimDur;
asRates = False;
#divFactor = stimDur if asRates == 0 else 1;
#modResp_full = np.divide(modResp_full, divFactor);
# now organize the responses
resps, stimVals, val_con_by_disp, _, _ = hf.tabulate_responses(expData, expInd, overwriteSpikes=modResp_full, respsAsRates=asRates, modsAsRate=asRates);
predResps = resps[2];
respMean = resps[0]; # equivalent to resps[0];
respStd = np.nanstd(respAll, -1); # take std of all responses for a given condition
# compute SEM, too
findNaN = np.isnan(respAll);
nonNaN = np.sum(findNaN == False, axis=-1);
respSem = np.nanstd(respAll, -1) / np.sqrt(nonNaN);
############
### first, fit a smooth function to the overall pred V measured responses
### --- from this, we can measure how each example superposition deviates from a central tendency
### --- i.e. the residual relative to the "standard" input:output relationship
############
all_resps = respMean[1:, :, :].flatten() # all disp>0
all_preds = predResps[1:, :, :].flatten() # all disp>0
# a model which allows negative fits
# myFit = lambda x, t0, t1, t2: t0 + t1*x + t2*x*x;
# non_nan = np.where(~np.isnan(all_preds)); # cannot fit negative values with naka-rushton...
# fitz, _ = opt.curve_fit(myFit, all_preds[non_nan], all_resps[non_nan], p0=[-5, 10, 5], maxfev=5000)
# naka rushton
myFit = lambda x, g, expon, c50: hf.naka_rushton(x, [0, g, expon, c50])
non_neg = np.where(all_preds>0) # cannot fit negative values with naka-rushton...
try:
if use_mod_resp == 1: # the reference will ALWAYS be the data -- redo the above analysis for data
predResps_data = resps_data[2];
respMean_data = resps_data[0];
all_resps_data = respMean_data[1:, :, :].flatten() # all disp>0
all_preds_data = predResps_data[1:, :, :].flatten() # all disp>0
non_neg_data = np.where(all_preds_data>0) # cannot fit negative values with naka-rushton...
fitz, _ = opt.curve_fit(myFit, all_preds_data[non_neg_data], all_resps_data[non_neg_data], p0=[100, 2, 25], maxfev=5000)
else:
fitz, _ = opt.curve_fit(myFit, all_preds[non_neg], all_resps[non_neg], p0=[100, 2, 25], maxfev=5000)
rel_c50 = np.divide(fitz[-1], np.max(all_preds[non_neg]));
except:
fitz = None;
rel_c50 = -99;
############
### organize stimulus information
############
all_disps = stimVals[0];
all_cons = stimVals[1];
all_sfs = stimVals[2];
nCons = len(all_cons);
nSfs = len(all_sfs);
nDisps = len(all_disps);
maxResp = np.maximum(np.nanmax(respMean), np.nanmax(predResps));
# by disp
clrs_d = cm.viridis(np.linspace(0,0.75,nDisps-1));
lbls_d = ['disp: %s' % str(x) for x in range(nDisps)];
# by sf
val_sfs = hf.get_valid_sfs(S, disp=1, con=val_con_by_disp[1][0], expInd=expInd) # pick
clrs_sf = cm.viridis(np.linspace(0,.75,len(val_sfs)));
lbls_sf = ['sf: %.2f' % all_sfs[x] for x in val_sfs];
# by con
val_con = all_cons;
clrs_con = cm.viridis(np.linspace(0,.75,len(val_con)));
lbls_con = ['con: %.2f' % x for x in val_con];
############
### create the figure
############
fSuper, ax = plt.subplots(nRows, nCols, figsize=(10*nCols, 8*nRows))
sns.despine(fig=fSuper, offset=10)
allMix = [];
allSum = [];
### plot reference tuning [row 1 (i.e. 2nd row)]
## on the right, SF tuning (high contrast)
sfRef = hf.nan_rm(respMean[0, :, -1]); # high contrast tuning
ax[1, 1].plot(all_sfs, sfRef, 'k-', marker='o', label='ref. tuning (d0, high con)', clip_on=False)
ax[1, 1].set_xscale('log')
ax[1, 1].set_xlim((0.1, 10));
ax[1, 1].set_xlabel('sf (c/deg)')
ax[1, 1].set_ylabel('response (spikes/s)')
ax[1, 1].set_ylim((-5, 1.1*np.nanmax(sfRef)));
ax[1, 1].legend(fontsize='x-small');
#####
## then on the left, RVC (peak SF)
#####
sfPeak = np.argmax(sfRef); # stupid/simple, but just get the rvc for the max response
v_cons_single = val_con_by_disp[0]
rvcRef = hf.nan_rm(respMean[0, sfPeak, v_cons_single]);
# now, if possible, let's also plot the RVC fit
if rvcFits is not None:
rvcFits = hf.get_rvc_fits(dataPath, expInd, which_cell, rvcName=rvcName, rvcMod=rvcMod);
rel_rvc = rvcFits[0]['params'][sfPeak]; # we get 0 dispersion, peak SF
plt_cons = np.geomspace(all_cons[0], all_cons[-1], 50);
c50, pk = hf.get_c50(rvcMod, rel_rvc), rvcFits[0]['conGain'][sfPeak];
c50_emp, c50_eval = hf.c50_empirical(rvcMod, rel_rvc); # determine c50 by optimization, numerical approx.
if rvcMod == 0:
rvc_mod = hf.get_rvc_model();
rvcmodResp = rvc_mod(*rel_rvc, plt_cons);
else: # i.e. mod=1 or mod=2
rvcmodResp = hf.naka_rushton(plt_cons, rel_rvc);
if baseline is not None:
rvcmodResp = rvcmodResp - baseline;
ax[1, 0].plot(plt_cons, rvcmodResp, 'k--', label='rvc fit (c50=%.2f, gain=%0f)' %(c50, pk))
# and save it
curr_suppr['c50'] = c50; curr_suppr['conGain'] = pk;
curr_suppr['c50_emp'] = c50_emp; curr_suppr['c50_emp_eval'] = c50_eval
else:
curr_suppr['c50'] = np.nan; curr_suppr['conGain'] = np.nan;
curr_suppr['c50_emp'] = np.nan; curr_suppr['c50_emp_eval'] = np.nan;
ax[1, 0].plot(all_cons[v_cons_single], rvcRef, 'k-', marker='o', label='ref. tuning (d0, peak SF)', clip_on=False)
# ax[1, 0].set_xscale('log')
ax[1, 0].set_xlabel('contrast (%)');
ax[1, 0].set_ylabel('response (spikes/s)')
ax[1, 0].set_ylim((-5, 1.1*np.nanmax(rvcRef)));
ax[1, 0].legend(fontsize='x-small');
# plot the fitted model on each axis
pred_plt = np.linspace(0, np.nanmax(all_preds), 100);
if fitz is not None:
ax[0, 0].plot(pred_plt, myFit(pred_plt, *fitz), 'r--', label='fit')
ax[0, 1].plot(pred_plt, myFit(pred_plt, *fitz), 'r--', label='fit')
for d in range(nDisps):
if d == 0: # we don't care about single gratings!
dispRats = [];
continue;
v_cons = np.array(val_con_by_disp[d]);
n_v_cons = len(v_cons);
# plot split out by each contrast [0,1]
for c in reversed(range(n_v_cons)):
v_sfs = hf.get_valid_sfs(S, d, v_cons[c], expInd)
for s in v_sfs:
mixResp = respMean[d, s, v_cons[c]];
allMix.append(mixResp);
sumResp = predResps[d, s, v_cons[c]];
allSum.append(sumResp);
# print('condition: d(%d), c(%d), sf(%d):: pred(%.2f)|real(%.2f)' % (d, v_cons[c], s, sumResp, mixResp))
# PLOT in by-disp panel
if c == 0 and s == v_sfs[0]:
ax[0, 0].plot(sumResp, mixResp, 'o', color=clrs_d[d-1], label=lbls_d[d], clip_on=False)
else:
ax[0, 0].plot(sumResp, mixResp, 'o', color=clrs_d[d-1], clip_on=False)
# PLOT in by-sf panel
sfInd = np.where(np.array(v_sfs) == s)[0][0]; # will only be one entry, so just "unpack"
try:
if d == 1 and c == 0:
ax[0, 1].plot(sumResp, mixResp, 'o', color=clrs_sf[sfInd], label=lbls_sf[sfInd], clip_on=False);
else:
ax[0, 1].plot(sumResp, mixResp, 'o', color=clrs_sf[sfInd], clip_on=False);
except:
pass;
#pdb.set_trace();
# plot baseline, if f0...
# if baseline is not None:
# [ax[0, i].axhline(baseline, linestyle='--', color='k', label='spon. rate') for i in range(2)];
# plot averaged across all cons/sfs (i.e. average for the whole dispersion) [1,0]
mixDisp = respMean[d, :, :].flatten();
sumDisp = predResps[d, :, :].flatten();
mixDisp, sumDisp = zr_rm_pair(mixDisp, sumDisp, 0.5);
curr_rats = np.divide(mixDisp, sumDisp)
curr_mn = geomean(curr_rats); curr_std = np.std(np.log10(curr_rats));
# curr_rat = geomean(np.divide(mixDisp, sumDisp));
ax[2, 0].bar(d, curr_mn, yerr=curr_std, color=clrs_d[d-1]);
ax[2, 0].set_yscale('log')
ax[2, 0].set_ylim(0.1, 10);
# ax[2, 0].yaxis.set_ticks(minorticks)
dispRats.append(curr_mn);
# ax[2, 0].bar(d, np.mean(np.divide(mixDisp, sumDisp)), color=clrs_d[d-1]);
# also, let's plot the (signed) error relative to the fit
if fitz is not None:
errs = mixDisp - myFit(sumDisp, *fitz);
ax[3, 0].bar(d, np.mean(errs), yerr=np.std(errs), color=clrs_d[d-1])
# -- and normalized by the prediction output response
errs_norm = np.divide(mixDisp - myFit(sumDisp, *fitz), myFit(sumDisp, *fitz));
ax[4, 0].bar(d, np.mean(errs_norm), yerr=np.std(errs_norm), color=clrs_d[d-1])
# and set some labels/lines, as needed
if d == 1:
ax[2, 0].set_xlabel('dispersion');
ax[2, 0].set_ylabel('suppression ratio (linear)')
ax[2, 0].axhline(1, ls='--', color='k')
ax[3, 0].set_xlabel('dispersion');
ax[3, 0].set_ylabel('mean (signed) error')
ax[3, 0].axhline(0, ls='--', color='k')
ax[4, 0].set_xlabel('dispersion');
ax[4, 0].set_ylabel('mean (signed) error -- as frac. of fit prediction')
ax[4, 0].axhline(0, ls='--', color='k')
curr_suppr['supr_disp'] = dispRats;
### plot averaged across all cons/disps
sfInds = []; sfRats = []; sfRatStd = [];
sfErrs = []; sfErrsStd = []; sfErrsInd = []; sfErrsIndStd = []; sfErrsRat = []; sfErrsRatStd = [];
curr_errNormFactor = [];
for s in range(len(val_sfs)):
try: # not all sfs will have legitmate values;
# only get mixtures (i.e. ignore single gratings)
mixSf = respMean[1:, val_sfs[s], :].flatten();
sumSf = predResps[1:, val_sfs[s], :].flatten();
mixSf, sumSf = zr_rm_pair(mixSf, sumSf, 0.5);
rats_curr = np.divide(mixSf, sumSf);
sfInds.append(s); sfRats.append(geomean(rats_curr)); sfRatStd.append(np.std(np.log10(rats_curr)));
if fitz is not None:
#curr_NR = myFit(sumSf, *fitz); # unvarnished
curr_NR = np.maximum(myFit(sumSf, *fitz), 0.5); # thresholded at 0.5...
curr_err = mixSf - curr_NR;
sfErrs.append(np.mean(curr_err));
sfErrsStd.append(np.std(curr_err))
curr_errNorm = np.divide(mixSf - curr_NR, mixSf + curr_NR);
sfErrsInd.append(np.mean(curr_errNorm));
sfErrsIndStd.append(np.std(curr_errNorm))
curr_errRat = np.divide(mixSf, curr_NR);
sfErrsRat.append(np.mean(curr_errRat));
sfErrsRatStd.append(np.std(curr_errRat));
curr_normFactors = np.array(curr_NR)
curr_errNormFactor.append(geomean(curr_normFactors[curr_normFactors>0]));
else:
sfErrs.append([]);
sfErrsStd.append([]);
sfErrsInd.append([]);
sfErrsIndStd.append([]);
sfErrsRat.append([]);
sfErrsRatStd.append([]);
curr_errNormFactor.append([]);
except:
pass
# get the offset/scale of the ratio so that we can plot a rescaled/flipped version of
# the high con/single grat tuning for reference...does the suppression match the response?
offset, scale = np.nanmax(sfRats), np.nanmax(sfRats) - np.nanmin(sfRats);
sfRef = hf.nan_rm(respMean[0, val_sfs, -1]); # high contrast tuning
sfRefShift = offset - scale * (sfRef/np.nanmax(sfRef))
ax[2,1].scatter(all_sfs[val_sfs][sfInds], sfRats, color=clrs_sf[sfInds], clip_on=False)
ax[2,1].errorbar(all_sfs[val_sfs][sfInds], sfRats, sfRatStd, color='k', linestyle='-', clip_on=False, label='suppression tuning')
# ax[2,1].plot(all_sfs[val_sfs][sfInds], sfRats, 'k-', clip_on=False, label='suppression tuning')
ax[2,1].plot(all_sfs[val_sfs], sfRefShift, 'k--', label='ref. tuning', clip_on=False)
ax[2,1].axhline(1, ls='--', color='k')
ax[2,1].set_xlabel('sf (cpd)')
ax[2,1].set_xscale('log')
ax[2,1].set_xlim((0.1, 10));
#ax[2,1].set_xlim((np.min(all_sfs), np.max(all_sfs)));
ax[2,1].set_ylabel('suppression ratio');
ax[2,1].set_yscale('log')
#ax[2,1].yaxis.set_ticks(minorticks)
ax[2,1].set_ylim(0.1, 10);
ax[2,1].legend(fontsize='x-small');
curr_suppr['supr_sf'] = sfRats;
### residuals from fit of suppression
if fitz is not None:
# mean signed error: and labels/plots for the error as f'n of SF
ax[3,1].axhline(0, ls='--', color='k')
ax[3,1].set_xlabel('sf (cpd)')
ax[3,1].set_xscale('log')
ax[3,1].set_xlim((0.1, 10));
#ax[3,1].set_xlim((np.min(all_sfs), np.max(all_sfs)));
ax[3,1].set_ylabel('mean (signed) error');
ax[3,1].errorbar(all_sfs[val_sfs][sfInds], sfErrs, sfErrsStd, color='k', marker='o', linestyle='-', clip_on=False)
# -- and normalized by the prediction output response + output respeonse
val_errs = np.logical_and(~np.isnan(sfErrsRat), np.logical_and(np.array(sfErrsIndStd)>0, np.array(sfErrsIndStd) < 2));
norm_subset = np.array(sfErrsInd)[val_errs];
normStd_subset = np.array(sfErrsIndStd)[val_errs];
ax[4,1].axhline(0, ls='--', color='k')
ax[4,1].set_xlabel('sf (cpd)')
ax[4,1].set_xscale('log')
ax[4,1].set_xlim((0.1, 10));
#ax[4,1].set_xlim((np.min(all_sfs), np.max(all_sfs)));
ax[4,1].set_ylim((-1, 1));
ax[4,1].set_ylabel('error index');
ax[4,1].errorbar(all_sfs[val_sfs][sfInds][val_errs], norm_subset, normStd_subset, color='k', marker='o', linestyle='-', clip_on=False)
# -- AND simply the ratio between the mixture response and the mean expected mix response (i.e. Naka-Rushton)
# --- equivalent to the suppression ratio, but relative to the NR fit rather than perfect linear summation
val_errs = np.logical_and(~np.isnan(sfErrsRat), np.logical_and(np.array(sfErrsRatStd)>0, np.array(sfErrsRatStd) < 2));
rat_subset = np.array(sfErrsRat)[val_errs];
ratStd_subset = np.array(sfErrsRatStd)[val_errs];
#ratStd_subset = (1/np.log(2))*np.divide(np.array(sfErrsRatStd)[val_errs], rat_subset);
ax[5,1].scatter(all_sfs[val_sfs][sfInds][val_errs], rat_subset, color=clrs_sf[sfInds][val_errs], clip_on=False)
ax[5,1].errorbar(all_sfs[val_sfs][sfInds][val_errs], rat_subset, ratStd_subset, color='k', linestyle='-', clip_on=False, label='suppression tuning')
ax[5,1].axhline(1, ls='--', color='k')
ax[5,1].set_xlabel('sf (cpd)')
ax[5,1].set_xscale('log')
ax[5,1].set_xlim((0.1, 10));
ax[5,1].set_ylabel('suppression ratio (wrt NR)');
ax[5,1].set_yscale('log', basey=2)
# ax[2,1].yaxis.set_ticks(minorticks)
ax[5,1].set_ylim(np.power(2.0, -2), np.power(2.0, 2));
ax[5,1].legend(fontsize='x-small');
# - compute the variance - and put that value on the plot
errsRatVar = np.var(np.log2(sfErrsRat)[val_errs]);
curr_suppr['sfRat_VAR'] = errsRatVar;
ax[5,1].text(0.1, 2, 'var=%.2f' % errsRatVar);
# compute the unsigned "area under curve" for the sfErrsInd, and normalize by the octave span of SF values considered
val_errs = np.logical_and(~np.isnan(sfErrsRat), np.logical_and(np.array(sfErrsIndStd)>0, np.array(sfErrsIndStd) < 2));
val_x = all_sfs[val_sfs][sfInds][val_errs];
ind_var = np.var(np.array(sfErrsInd)[val_errs]);
curr_suppr['sfErrsInd_VAR'] = ind_var;
# - and put that value on the plot
ax[4,1].text(0.1, -0.25, 'var=%.3f' % ind_var);
else:
curr_suppr['sfErrsInd_VAR'] = np.nan
curr_suppr['sfRat_VAR'] = np.nan
#########
### NOW, let's evaluate the derivative of the SF tuning curve and get the correlation with the errors
#########
mod_sfs = np.geomspace(all_sfs[0], all_sfs[-1], 1000);
mod_resp = hf.get_descrResp(dfit_curr, mod_sfs, DoGmodel=dMod_num);
deriv = np.divide(np.diff(mod_resp), np.diff(np.log10(mod_sfs)))
deriv_norm = np.divide(deriv, np.maximum(np.nanmax(deriv), np.abs(np.nanmin(deriv)))); # make the maximum response 1 (or -1)
# - then, what indices to evaluate for comparing with sfErr?
errSfs = all_sfs[val_sfs][sfInds];
mod_inds = [np.argmin(np.square(mod_sfs-x)) for x in errSfs];
deriv_norm_eval = deriv_norm[mod_inds];
# -- plot on [1, 1] (i.e. where the data is)
ax[1,1].plot(mod_sfs, mod_resp, 'k--', label='fit (g)')
ax[1,1].legend();
# Duplicate "twin" the axis to create a second y-axis
ax2 = ax[1,1].twinx();
ax2.set_xscale('log'); # have to re-inforce log-scale?
ax2.set_ylim([-1, 1]); # since the g' is normalized
# make a plot with different y-axis using second axis object
ax2.plot(mod_sfs[1:], deriv_norm, '--', color="red", label='g\'');
ax2.set_ylabel("deriv. (normalized)",color="red")
ax2.legend();
sns.despine(ax=ax2, offset=10, right=False);
# -- and let's plot rescaled and shifted version in [2,1]
offset, scale = np.nanmax(sfRats), np.nanmax(sfRats) - np.nanmin(sfRats);
derivShift = offset - scale * (deriv_norm/np.nanmax(deriv_norm));
ax[2,1].plot(mod_sfs[1:], derivShift, 'r--', label='deriv(ref. tuning)', clip_on=False)
ax[2,1].legend(fontsize='x-small');
# - then, normalize the sfErrs/sfErrsInd and compute the correlation coefficient
if fitz is not None:
norm_sfErr = np.divide(sfErrs, np.nanmax(np.abs(sfErrs)));
norm_sfErrInd = np.divide(sfErrsInd, np.nanmax(np.abs(sfErrsInd))); # remember, sfErrsInd is normalized per condition; this is overall
non_nan = np.logical_and(~np.isnan(norm_sfErr), ~np.isnan(deriv_norm_eval))
corr_nsf, corr_nsfN = np.corrcoef(deriv_norm_eval[non_nan], norm_sfErr[non_nan])[0,1], np.corrcoef(deriv_norm_eval[non_nan], norm_sfErrInd[non_nan])[0,1]
curr_suppr['corr_derivWithErr'] = corr_nsf;
curr_suppr['corr_derivWithErrsInd'] = corr_nsfN;
ax[3,1].text(0.1, 0.25*np.nanmax(sfErrs), 'corr w/g\' = %.2f' % corr_nsf)
ax[4,1].text(0.1, 0.25, 'corr w/g\' = %.2f' % corr_nsfN)
else:
curr_suppr['corr_derivWithErr'] = np.nan;
curr_suppr['corr_derivWithErrsInd'] = np.nan;
# make a polynomial fit
try:
hmm = np.polyfit(allSum, allMix, deg=1) # returns [a, b] in ax + b
except:
hmm = [np.nan];
curr_suppr['supr_index'] = hmm[0];
for j in range(1):
for jj in range(nCols):
ax[j, jj].axis('square')
ax[j, jj].set_xlabel('prediction: sum(components) (imp/s)');
ax[j, jj].set_ylabel('mixture response (imp/s)');
ax[j, jj].plot([0, 1*maxResp], [0, 1*maxResp], 'k--')
ax[j, jj].set_xlim((-5, maxResp));
ax[j, jj].set_ylim((-5, 1.1*maxResp));
ax[j, jj].set_title('Suppression index: %.2f|%.2f' % (hmm[0], rel_c50))
ax[j, jj].legend(fontsize='x-small');
fSuper.suptitle('Superposition: %s #%d [%s; f1f0 %.2f; szSupr[dt/md] %.2f/%.2f; oriBW|CV %.2f|%.2f; tfBW %.2f]' % (cellType, which_cell, cellName, f1f0_rat, suprDat, suprMod, oriBW, oriCV, tfBW))
if fitList is None:
save_name = 'cell_%03d.pdf' % which_cell
else:
save_name = 'cell_%03d_mod%s.pdf' % (which_cell, hf.fitType_suffix(fitType))
pdfSv = pltSave.PdfPages(str(save_locSuper + save_name));
pdfSv.savefig(fSuper)
pdfSv.close();
#########
### Finally, add this "superposition" to the newest
#########
if to_save:
if fitList is None:
from datetime import datetime
suffix = datetime.today().strftime('%y%m%d')
super_name = 'superposition_analysis_%s.npy' % suffix;
else:
super_name = 'superposition_analysis_mod%s.npy' % hf.fitType_suffix(fitType);
pause_tm = 5*np.random.rand();
print('sleeping for %d secs (#%d)' % (pause_tm, which_cell));
time.sleep(pause_tm);
if os.path.exists(dataPath + super_name):
suppr_all = hf.np_smart_load(dataPath + super_name);
else:
suppr_all = dict();
suppr_all[which_cell-1] = curr_suppr;
np.save(dataPath + super_name, suppr_all);
return curr_suppr;
