if respVar == 1:
respVar = respSem
else:
respVar = respStd
if diffPlot: # otherwise, nothing to do
### Now, recenter data relative to flat normalization model
allAvgs = [respMean, modAvgs[1], modAvgs[0]]
# why? weighted is 1, flat is 0
respsRecenter = [x - allAvgs[2] for x in allAvgs]
# recentered
respMean = respsRecenter[0]
modAvgs = [respsRecenter[2], respsRecenter[1]]
blankMean, blankStd, _ = hf.blankResp(expData, expInd)
all_disps = stimVals[0]
all_cons = stimVals[1]
all_sfs = stimVals[2]
nCons = len(all_cons)
nSfs = len(all_sfs)
nDisps = len(all_disps)
# ### Plots
# set up model plot info
# i.e. flat model is red, weighted model is green
modColors = ['r', 'g']
modLabels = ['flat', 'wght']
def fit_all_CRF(cell_num,
data_loc,
each_c50,
loss_type,
n_iter=1,
each_expn=0,
each_base=0,
each_gain=1):
''' Given cell#, data loc, load the data. Other inputs:
each_c50/expn/base/gain : separate c50/expn/base/gain for each condition?
n_iter : how many iterations to fit?
'''
print(str(n_iter) + ' fit attempts')
np = numpy
conDig = 3
# round contrast to the thousandth
n_params = 5
# 4 for NR, 1 for varGain
if each_c50 == 1:
fit_key = 'fits_each_rpt'
else:
fit_key = 'fits_rpt'
if loss_type == 1:
loss_str = '-lsq'
if loss_type == 2:
loss_str = '-sqrt'
if loss_type == 3:
loss_str = '-poiss'
if loss_type == 4:
loss_str = '-poissMod'
fits_name = 'crfFitsCom' + loss_str + '.npy'
dataList = hf.np_smart_load(str(data_loc + 'dataList.npy'))
if os.path.isfile(data_loc + fits_name):
crfFits = hf.np_smart_load(str(data_loc + fits_name))
else:
crfFits = dict()
# load cell information
cellStruct = hf.np_smart_load(
str(data_loc + dataList['unitName'][cell_num - 1] + '_sfm.npy'))
data = cellStruct['sfm']['exp']['trial']
all_cons = np.unique(np.round(data['total_con'], conDig))
all_cons = all_cons[~np.isnan(all_cons)]
all_sfs = np.unique(data['cent_sf'])
all_sfs = all_sfs[~np.isnan(all_sfs)]
all_disps = np.unique(data['num_comps'])
all_disps = all_disps[all_disps > 0]
# ignore zero...
nCons = len(all_cons)
nSfs = len(all_sfs)
nDisps = len(all_disps)
nk_ru = dict()
all_data = dict()
# for use in fitting SF functions...
_, _, blankResps = hf.blankResp(cellStruct)
blankCons = np.zeros_like(blankResps)
for d in range(nDisps):
valid_disp = data['num_comps'] == all_disps[d]
cons = []
resps = []
nk_ru[d] = dict()
v_sfs = []
# keep track of valid sfs
all_data[d] = dict()
for sf in range(nSfs):
valid_sf = data['cent_sf'] == all_sfs[sf]
valid_tr = valid_disp & valid_sf
if np.all(np.unique(valid_tr) ==
False): # did we not find any trials?
continue
v_sfs.append(sf)
nk_ru[d][sf] = dict()
# create dictionary here; thus, only valid sfs have valid keys
# for unpacking loss/parameters later...
nk_ru[d][sf]['params'] = np.nan * np.zeros((n_params, 1))
nk_ru[d][sf]['loss'] = np.nan
resps.append(np.hstack((blankResps, data['spikeCount'][valid_tr])))
cons.append(np.hstack((blankCons, data['total_con'][valid_tr])))
# save data for later use
all_data[d]['resps'] = resps
all_data[d]['cons'] = cons
all_data[d]['valid_sfs'] = v_sfs
maxResp = np.max(np.max(resps))
n_v_sfs = len(v_sfs)
each_list = (each_base, each_gain, each_expn, each_c50)
n_per_param = [1 if i == 0 else n_v_sfs for i in each_list]
'''
if each_c50 == 1:
n_c50s = n_v_sfs; # separate for each SF...
else:
n_c50s = 1;
'''
init_base = 0.1
#bounds_base = (0, 0);
bounds_base = (0.1, maxResp)
init_gain = np.max(resps) - np.min(resps)
bounds_gain = (0, 10 * maxResp)
init_expn = 2
bounds_expn = (0.5, 10)
init_c50 = 0.1
#geomean(all_cons);
bounds_c50 = (0.01, 10 * max(all_cons))
# contrast values are b/t [0, 1]
init_varGain = 1
bounds_varGain = (0.01, None)
base_inits = np.repeat(init_base, n_per_param[0])
# default is only one baseline per SF
base_constr = [
tuple(x) for x in np.broadcast_to(bounds_base, (n_per_param[0], 2))
]
gain_inits = np.repeat(init_gain, n_per_param[1])
# gain is always separate for each SF
gain_constr = [
tuple(x) for x in np.broadcast_to(bounds_gain, (n_per_param[1], 2))
]
expn_inits = np.repeat(init_expn, n_per_param[2])
# exponent can be either, like baseline
expn_constr = [
tuple(x) for x in np.broadcast_to(bounds_expn, (n_per_param[2], 2))
]
c50_inits = np.repeat(init_c50, n_per_param[3])
# repeat n_v_sfs times if c50 separate for each SF; otherwise, 1
c50_constr = [
tuple(x) for x in np.broadcast_to(bounds_c50, (n_per_param[3], 2))
]
init_params = np.hstack(
(c50_inits, expn_inits, gain_inits, base_inits, init_varGain))
boundsAll = np.vstack((c50_constr, expn_constr, gain_constr,
base_constr, bounds_varGain))
boundsAll = [tuple(x) for x in boundsAll]
# turn the (inner) arrays into tuples...
c50_ind = 0
expn_ind = n_per_param[3]
# the number of c50s...
gain_ind = expn_ind + n_per_param[2]
# the number of exponents
base_ind = gain_ind + n_per_param[1]
# always n_v_sfs gain parameters
varGain_ind = base_ind + n_per_param[0]
obj = lambda params: hf.fit_CRF(cons, resps, params[c50_ind:c50_ind+n_per_param[3]], params[expn_ind:expn_ind+n_per_param[2]], params[gain_ind:gain_ind+n_per_param[1]], \
params[base_ind:base_ind+n_per_param[0]], params[varGain_ind], loss_type)
opts = opt.minimize(obj, init_params, bounds=boundsAll)
curr_params = opts['x']
curr_loss = opts['fun']
for iter in range(n_iter - 1): # now, extra iterations if chosen...
init_params = np.hstack(
(hf.random_in_range(bounds_c50,
n_c50s), hf.random_in_range(bounds_expn),
hf.random_in_range(bounds_gain,
n_v_sfs), hf.random_in_range(bounds_base),
hf.random_in_range((bounds_varGain[0], 1))))
# choose optimization method
if np.mod(iter, 2) == 0:
methodStr = 'L-BFGS-B'
else:
methodStr = 'TNC'
opt_iter = opt.minimize(obj,
init_params,
bounds=boundsAll,
method=methodStr)
if opt_iter['fun'] < curr_loss:
print('improve.')
curr_loss = opt_iter['fun']
curr_params = opt_iter['x']
# now unpack...
for sf_in in range(n_v_sfs):
param_ind = [0 if i == 1 else sf_in for i in n_per_param]
nk_ru[d][v_sfs[sf_in]]['params'][0] = curr_params[base_ind +
param_ind[0]]
nk_ru[d][v_sfs[sf_in]]['params'][1] = curr_params[gain_ind +
param_ind[1]]
nk_ru[d][v_sfs[sf_in]]['params'][2] = curr_params[expn_ind +
param_ind[2]]
nk_ru[d][v_sfs[sf_in]]['params'][3] = curr_params[c50_ind +
param_ind[3]]
# params (to match naka_rushton) are: baseline, gain, expon, c50
nk_ru[d][v_sfs[sf_in]]['params'][4] = curr_params[varGain_ind]
nk_ru[d][v_sfs[sf_in]]['loss'] = curr_loss
# update stuff - load again in case some other run has saved/made changes
if os.path.isfile(data_loc + fits_name):
print('reloading CRF Fits...')
crfFits = hf.np_smart_load(str(data_loc + fits_name))
if cell_num - 1 not in crfFits:
crfFits[cell_num - 1] = dict()
crfFits[cell_num - 1][fit_key] = nk_ru
crfFits[cell_num - 1]['data'] = all_data
crfFits[cell_num - 1]['blankResps'] = blankResps
np.save(data_loc + fits_name, crfFits)
print('saving for cell ' + str(cell_num))
return nk_ru
rvcFits,
rvcMod=-1,
descrFitName_f0=fLname,
baseline_sub=False,
force_dc=force_dc,
force_f1=force_f1,
return_measure=True,
vecF1=vecF1)
# 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)
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
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
respMean = resp[0]
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]; respStd = resp[1]; blankMean, blankStd, _ = hf.blankResp(expData); all_disps = stimVals[0]; all_cons = stimVals[1]; all_sfs = stimVals[2]; nCons = len(all_cons); nSfs = len(all_sfs); nDisps = len(all_disps); # ### Plots # set up model plot info # i.e. flat model is red, weighted model is green modColors = ['r', 'g'] modLabels = ['flat', 'wght']
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
respMean = resp[0]
respStd = resp[1]
predMean = resp[2]
def fit_descr(cell_num, data_loc, n_repeats = 4, loss_type = 1):
nParam = 5;
if loss_type == 1:
loss_str = '_lsq.npy';
elif loss_type == 2:
loss_str = '_sqrt.npy';
elif loss_type == 3:
loss_str = '_poiss.npy';
# load cell information
dataList = hfunc.np_smart_load(data_loc + 'dataList.npy');
if os.path.isfile(data_loc + 'descrFits' + loss_str):
descrFits = hfunc.np_smart_load(data_loc + 'descrFits' + loss_str);
else:
descrFits = dict();
data = hfunc.np_smart_load(data_loc + dataList['unitName'][cell_num-1] + '_sfm.npy');
print('Doing the work, now');
to_unpack = hfunc.tabulate_responses(data);
[respMean, respVar, predMean, predVar] = to_unpack[0];
[all_disps, all_cons, all_sfs] = to_unpack[1];
val_con_by_disp = to_unpack[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'];
else: # set values to NaN...
bestNLL = np.ones((nDisps, nCons)) * np.nan;
currParams = np.ones((nDisps, nCons, nParam)) * np.nan;
for family in range(nDisps):
for con in range(nCons):
if con not in val_con_by_disp[family]:
continue;
print('.');
# set initial parameters - a range from which we will pick!
base_rate = hfunc.blankResp(data)[0];
if base_rate <= 3:
range_baseline = (0, 3);
else:
range_baseline = (0.5 * base_rate, 1.5 * base_rate);
valid_sf_inds = ~np.isnan(respMean[family, :, con]);
max_resp = np.amax(respMean[family, valid_sf_inds, con]);
range_amp = (0.5 * max_resp, 1.5);
theSfCents = all_sfs[valid_sf_inds];
max_sf_index = np.argmax(respMean[family, valid_sf_inds, 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-2], 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, loss_type);
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 + 'descrFits' + loss_str):
print('reloading descrFits...');
descrFits = hfunc.np_smart_load(data_loc + 'descrFits' + loss_str);
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 + 'descrFits' + loss_str, descrFits);
print('saving for cell ' + str(cell_num));
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:
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;
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
# 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)
