def updatefig(
frames, artists
): # ignore frames; artists will be to_update (i.e. the animated bits)
global step
if (step < nstepsTot):
step += stepsize
if step >= nstepsTot:
step = nstepsTot - 1
# update loss
artists[loss_start].set_data(step, fitDet['loss'][step])
# update model responses
resps_org = hf.organize_resp(fitDet['resp'][step], cellStruct, expInd)[2]
[
artists[disp_start + i].set_data(data, mod) for i, data, mod in zip(
range(nDisps), measured_byDisp, shapeByDisp(resps_org))
]
# get current params
params_curr = fitDet['params'][step]
# update filter properties
artists[filt_start].set_data(params_curr[prefSf], params_curr[dOrder])
# update non_linear properties
artists[nlin_start].set_data(np.power(10, params_curr[normConst]),
params_curr[respExp])
# update normalization properties
if len(params_curr) > 9:
artists[norm_start].set_data(np.exp(params_curr[normMu]),
params_curr[normStd])
# update text parameters
# update tuning curves
if fitType == 2:
normPrm = [params_curr[normMu], params_curr[normStd]]
else:
normPrm = [0, 0]
# dummy vars
excPrm = [params_curr[prefSf], params_curr[dOrder]]
updatePrm = [excPrm, normPrm]
[
artists[tune_start + i].set_data(omega, lam(prm))
for i, lam, prm in zip(range(nsfTuning), sfTuningUpdates, updatePrm)
]
# update resp non-linearity curve
artists[nlinc_start].set_data(nlinIn, nlinPlot(nlinIn,
params_curr[respExp]))
return artists
normType=norm,
lossType=lossType,
expInd=expInd,
cellNum=cellNum,
rvcFits=rvcCurr,
excType=excType,
maskIn=~mask) 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, respsAsRate=False) 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]
# more tabulation - stim vals, organize measured responses
_, stimVals, val_con_by_disp, validByStimVal, _ = hf.tabulate_responses(
# #### 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]; # more tabulation - stim vals, organize measured responses _, stimVals, val_con_by_disp, _, _ = hf.tabulate_responses(expData, expInd); if rvcAdj == 1: rvcFlag = '_f1'; rvcFits = hf.get_rvc_fits(data_loc, expInd, cellNum, rvcName=rvcBase);
# let's also get the baseline
force_baseline = True if force_dc else False
# plotting baseline will depend on F1/F0 designation
if force_baseline or (
f1f0rat < 1 and expDir != 'LGN/'
): # i.e. if we're in LGN, DON'T get baseline, even if f1f0 < 1 (shouldn't happen)
baseline_resp = hf.blankResp(trialInf,
expInd,
spikes=spikes_rate,
spksAsRate=True)[0]
else:
baseline_resp = int(0)
# now get the measured responses
_, _, respOrg, respAll = hf.organize_resp(spikes_rate,
trialInf,
expInd,
respsAsRate=True)
respMean = respOrg
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)
# pick which measure of response variance
if respVar == 1:
respVar = respSem
else:
respVar = respStd
dataTxt = 'data'
refClr = 'm'
refTxt = 'ref'
# #### Plots by dispersion
sfs_plot = np.logspace(np.log10(maskSf[0]), np.log10(maskSf[-1]), 100)
if ddogs_pred and descrMod == 3: # i.e. is d-DoG-S model
all_preds = hf.parker_hawken_all_stim(trialInf,
expInd,
descrParams,
comm_s_calc=comm_S_calc &
(joint > 0))
_, _, pred_org, pred_all = hf.organize_resp(all_preds,
trialInf,
expInd,
respsAsRate=True)
else:
pred_org = None
n_v_cons = len(maskCon)
fDisp, dispAx = plt.subplots(n_v_cons,
2,
figsize=(2 * 10, n_v_cons * 12),
sharey=False)
minResp = np.min(np.min(respMean[~np.isnan(respMean[:, :])]))
maxResp = np.max(np.max(respMean[~np.isnan(respMean[:, :])]))
if old_refprm:
ref_params = descrParams[-1] if joint > 0 else None
def fit_descr_DoG(cell_num,
data_loc=dataPath,
n_repeats=1000,
loss_type=3,
DoGmodel=1,
disp=0,
rvcName=rvcName,
dir=-1,
gain_reg=0,
fLname=dogName):
nParam = 4
# load cell information
dataList = hf.np_smart_load(data_loc + 'dataList.npy')
assert dataList != [], "data file not found!"
if loss_type == 1:
loss_str = '_poiss'
elif loss_type == 2:
loss_str = '_sqrt'
elif loss_type == 3:
loss_str = '_sach'
elif loss_type == 4:
loss_str = '_varExpl'
if DoGmodel == 1:
mod_str = '_sach'
elif DoGmodel == 2:
mod_str = '_tony'
fLname = str(data_loc + fLname + loss_str + mod_str + '.npy')
if os.path.isfile(fLname):
descrFits = hf.np_smart_load(fLname)
else:
descrFits = dict()
cellStruct = hf.np_smart_load(data_loc +
dataList['unitName'][cell_num - 1] +
'_sfm.npy')
data = cellStruct['sfm']['exp']['trial']
rvcNameFinal = hf.phase_fit_name(rvcName, dir)
rvcFits = hf.np_smart_load(data_loc + rvcNameFinal)
adjResps = rvcFits[cell_num - 1][disp]['adjMeans']
adjSem = rvcFits[cell_num - 1][disp]['adjSem']
if 'adjByTr' in rvcFits[cell_num - 1][disp]:
adjByTr = rvcFits[cell_num - 1][disp]['adjByTr']
if disp == 1:
adjResps = [np.sum(x, 1) if x else [] for x in adjResps]
if adjByTr:
adjByTr = [np.sum(x, 1) if x else [] for x in adjByTr]
adjResps = np.array(adjResps)
# indexing multiple SFs will work only if we convert to numpy array first
adjSem = np.array([np.array(x) for x in adjSem])
# make each inner list an array, and the whole thing an array
print('Doing the work, now')
# first, get the set of stimulus values:
resps, stimVals, valConByDisp, _, _ = hf.tabulate_responses(data,
expInd=expInd)
# LGN is expInd=3
all_disps = stimVals[0]
all_cons = stimVals[1]
all_sfs = stimVals[2]
nDisps = len(all_disps)
nCons = len(all_cons)
if cell_num - 1 in descrFits:
bestNLL = descrFits[cell_num - 1]['NLL']
currParams = descrFits[cell_num - 1]['params']
varExpl = descrFits[cell_num - 1]['varExpl']
prefSf = descrFits[cell_num - 1]['prefSf']
charFreq = descrFits[cell_num - 1]['charFreq']
else: # set values to NaN...
bestNLL = np.ones((nDisps, nCons)) * np.nan
currParams = np.ones((nDisps, nCons, nParam)) * np.nan
varExpl = np.ones((nDisps, nCons)) * np.nan
prefSf = np.ones((nDisps, nCons)) * np.nan
charFreq = np.ones((nDisps, nCons)) * np.nan
# set bounds
if DoGmodel == 1:
bound_gainCent = (1e-3, None)
bound_radiusCent = (1e-3, None)
bound_gainSurr = (1e-3, None)
bound_radiusSurr = (1e-3, None)
allBounds = (bound_gainCent, bound_radiusCent, bound_gainSurr,
bound_radiusSurr)
elif DoGmodel == 2:
bound_gainCent = (1e-3, None)
bound_gainFracSurr = (1e-2, 1)
bound_freqCent = (1e-3, None)
bound_freqFracSurr = (1e-2, 1)
allBounds = (bound_gainCent, bound_freqCent, bound_gainFracSurr,
bound_freqFracSurr)
for d in range(
1
): # should be nDisps - just setting to 1 for now (i.e. fitting single gratings and mixtures separately)
for con in range(nCons):
if con not in valConByDisp[disp]:
continue
valSfInds = hf.get_valid_sfs(data, disp, con, expInd)
valSfVals = all_sfs[valSfInds]
print('.')
# adjResponses (f1) in the rvcFits are separate by sf, values within contrast - so to get all responses for a given SF,
# access all sfs and get the specific contrast response
respConInd = np.where(np.asarray(valConByDisp[disp]) == con)[0]
pdb.set_trace()
### interlude...
spks = hf.get_spikes(data,
rvcFits=rvcFits[cell_num - 1],
expInd=expInd)
_, _, mnResp, alResp = hf.organize_resp(spks, data, expInd)
###
resps = flatten([x[respConInd] for x in adjResps[valSfInds]])
resps_sem = [x[respConInd] for x in adjSem[valSfInds]]
if isinstance(resps_sem[0],
np.ndarray): # i.e. if it's still array of arrays...
resps_sem = flatten(resps_sem)
#resps_sem = None;
maxResp = np.max(resps)
freqAtMaxResp = all_sfs[np.argmax(resps)]
for n_try in range(n_repeats):
# pick initial params
if DoGmodel == 1:
init_gainCent = hf.random_in_range(
(maxResp, 5 * maxResp))[0]
init_radiusCent = hf.random_in_range((0.05, 2))[0]
init_gainSurr = init_gainCent * hf.random_in_range(
(0.1, 0.8))[0]
init_radiusSurr = hf.random_in_range((0.5, 4))[0]
init_params = [
init_gainCent, init_radiusCent, init_gainSurr,
init_radiusSurr
]
elif DoGmodel == 2:
init_gainCent = maxResp * hf.random_in_range((0.9, 1.2))[0]
init_freqCent = np.maximum(
all_sfs[2],
freqAtMaxResp * hf.random_in_range((1.2, 1.5))[0])
# don't pick all_sfs[0] -- that's zero (we're avoiding that)
init_gainFracSurr = hf.random_in_range((0.7, 1))[0]
init_freqFracSurr = hf.random_in_range((.25, .35))[0]
init_params = [
init_gainCent, init_freqCent, init_gainFracSurr,
init_freqFracSurr
]
# choose optimization method
if np.mod(n_try, 2) == 0:
methodStr = 'L-BFGS-B'
else:
methodStr = 'TNC'
obj = lambda params: DoG_loss(params,
resps,
valSfVals,
resps_std=resps_sem,
loss_type=loss_type,
DoGmodel=DoGmodel,
dir=dir,
gain_reg=gain_reg)
wax = opt.minimize(obj,
init_params,
method=methodStr,
bounds=allBounds)
# compare
NLL = wax['fun']
params = wax['x']
if np.isnan(bestNLL[disp, con]) or NLL < bestNLL[disp, con]:
bestNLL[disp, con] = NLL
currParams[disp, con, :] = params
varExpl[disp,
con] = hf.var_explained(resps, params, valSfVals)
prefSf[disp, con] = hf.dog_prefSf(params, DoGmodel,
valSfVals)
charFreq[disp, con] = hf.dog_charFreq(params, DoGmodel)
# update stuff - load again in case some other run has saved/made changes
if os.path.isfile(fLname):
print('reloading descrFits...')
descrFits = hf.np_smart_load(fLname)
if cell_num - 1 not in descrFits:
descrFits[cell_num - 1] = dict()
descrFits[cell_num - 1]['NLL'] = bestNLL
descrFits[cell_num - 1]['params'] = currParams
descrFits[cell_num - 1]['varExpl'] = varExpl
descrFits[cell_num - 1]['prefSf'] = prefSf
descrFits[cell_num - 1]['charFreq'] = charFreq
descrFits[cell_num - 1]['gainRegFactor'] = gain_reg
np.save(fLname, descrFits)
print('saving for cell ' + str(cell_num))
# #### 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 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]; #orgs = [hf.organize_modResp(mr, expData) for mr in modResps]; sfmixModResps = [org[2] for org in orgs]; allSfMixs = [org[3] for org in orgs]; # now organize the measured responses in the same way _, _, sfmixExpResp, allSfMixExp = hf.organize_resp(expData['sfm']['exp']['trial']['spikeCount'], expData, expInd); 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]; # more tabulation resp, stimVals, val_con_by_disp, _, _ = hf.tabulate_responses(expData, expInd, modResps[0]); respMean = resp[0];
### set up
nstepsTot = len(fitDet['loss'])
nFrames = np.int(np.ceil(nstepsTot / stepsize))
## indices for accessing parameters
[prefSf, dOrder, normConst, respExp, normMu, normStd] = [0, 1, 2, 3, 8, 9]
finalParams = fitDet['params'][-1]
# i.e. parameters at end of optimization
## 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)
return_measure=True,
vecF1=vecF1)
# let's also get the baseline
if force_baseline or (
f1f0rat < 1 and expDir != 'LGN/'
): # i.e. if we're in LGN, DON'T get baseline, even if f1f0 < 1 (shouldn't happen)
baseline_resp = hf.blankResp(trialInf,
expInd,
spikes=spikes_rate,
spksAsRate=True)[0]
else:
baseline_resp = int(0)
# now get the measured responses
_, _, respOrg, respAll = hf.organize_resp(spikes_rate,
trialInf,
expInd,
respsAsRate=True)
nTrials = np.sum(~np.isnan(respAll[0, -1, -1]))
# single grating, highest SF/CON
halfway = np.floor(nTrials / 2).astype(int)
respMean = respOrg
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)
# pick which measure of response variance
if respVar == 1:
respVar = respSem
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]
# more tabulation - stim vals, organize measured responses
if modRecov == 1:
modParamGT, overwriteSpikes = hf.get_recovInfo(expData, fitType)
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,
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;
expInd=expInd)
spikes = np.array([np.sum(x) for x in spikes_byComp])
rates = True
# when we get the spikes from rvcFits, they've already been converted into rates (in hf.get_all_fft)
baseline_sfMix = None
# f1 has no "DC", yadig?
else: # otherwise, if it's complex, just get F0
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_sfMix = 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_sfMix * hf.get_exp_params(expInd).stimDur
_, _, respOrg, respAll = hf.organize_resp(spikes, expData, expInd)
resps, stimVals, val_con_by_disp, _, _ = hf.tabulate_responses(
expData, expInd, overwriteSpikes=spikes, respsAsRates=rates)
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)
# organize stimulus values
all_disps = stimVals[0]
