def var_explained(data, modParams, whichInd=None, DoGmodel=1, rvcModel=None, whichSfs = None, ref_params=None, ref_rc_val=None, dataAreResps=False):
''' given a set of responses and model parameters, compute the variance explained by the model (DoGsach)
--- whichInd is either the contrast index (if doing SF tuning)
or SF index (if doing RVCs)
'''
resp_dist = lambda x, y: np.sum(np.square(x-y))/np.maximum(len(x), len(y))
var_expl = lambda m, r, rr: 100 * (1 - resp_dist(m, r)/resp_dist(r, rr));
if dataAreResps:
obs_mean = data; # we've directly passed in the means of interest
else:
respsSummary, stims, allResps = tabulateResponses(data); # Need to fit on f1
f1 = respsSummary[1];
if rvcModel is None: # SF
all_sfs = stims[1];
obs_mean = f1['mean'][whichInd, :];
else:
all_cons = stims[0];
obs_mean = f1['mean'][:, whichInd];
if whichSfs is not None:
all_sfs = whichSfs; # maybe we've passed in the Sfs to use...
if rvcModel is None: # then we're doing vExp for SF tuning
pred_mean = get_descrResp(modParams, all_sfs, DoGmodel, ref_rc_val=ref_rc_val);
else: # then we've getting RVC responses!
pred_mean = get_rvcResp(modParams, cons, rvcMod)
obs_grand_mean = np.mean(obs_mean) * np.ones_like(obs_mean); # make sure it's the same shape as obs_mean
return var_expl(pred_mean, obs_mean, obs_grand_mean);
curr_con],
fmt='o',
clip_on=False,
color=dataClr)
# now, let's also plot the baseline, if complex cell
if baseline_resp > 0: #is not None: # i.e. complex cell
sfMixAx[plt_ind_row, plt_ind_col].axhline(baseline_resp,
color=dataClr,
linestyle='dashed')
# plot descrFit
prms_curr = descrParams[curr_disp, curr_con]
descrResp = hf.get_descrResp(prms_curr,
sfs_plot,
descrMod,
baseline=baseline_resp,
fracSig=fracSig)
sfMixAx[plt_ind_row, plt_ind_col].plot(sfs_plot, descrResp, color=modClr)
# plot prefSF, center of mass
#ctr = hf.sf_com(resps, sfVals);
pSf = hf.descr_prefSf(prms_curr, dog_model=descrMod, all_sfs=all_sfs)
sfMixAx[plt_ind_row, plt_ind_col].plot(pSf,
1,
linestyle='None',
marker='v',
color=modClr,
clip_on=False)
# plot at y=1
color=currClr,
fmt='o',
clip_on=False,
label=dataTxt)
# now, let's also plot the baseline, if complex cell
if baseline_resp > 0: # i.e. complex cell
dispAx[c_plt_ind, 0].axhline(baseline_resp,
color=currClr,
linestyle='dashed')
## plot descr fit
prms_curr = descrParams[c]
descrResp = hf.get_descrResp(prms_curr,
stim_sf=sfs_plot,
DoGmodel=descrMod,
baseline=baseline_resp,
fracSig=fracSig,
ref_params=ref_params)
dispAx[c_plt_ind, 0].plot(sfs_plot,
descrResp,
color=currClr,
label='descr. fit')
# --- and also ddogs prediction (perhaps...)
if pred_org is not None:
dispAx[c_plt_ind, 0].plot(sfVals,
baseline_resp + pred_org[v_sfs, c],
color=currClr,
linestyle='--',
clip_on=False,
label='pred')
np.square(respsCurr[d, v_sfs, v_cons[c]] -
respMean[d, v_sfs, v_cons[c]]))
elif descrLoss == 2:
rS = respsCurr[d, v_sfs, v_cons[c]]
rA = respMean[d, v_sfs, v_cons[c]]
data_loss += np.sum(
np.square(
np.sign(rS) * np.sqrt(np.abs(rS)) -
np.sign(rA) * np.sqrt(np.abs(rA))))
elif j == -1: # model
# plot descr fit [1]
descrResp = hf.get_descrResp(prms_curr,
sfs_plot,
descrMod,
baseline=baseline_resp,
fracSig=fracSig,
ref_params=ref_params,
ref_rc_val=ref_rc_val)
dispAx[d][row_i][col_i].plot(sfs_plot,
descrResp - to_sub,
color=col)
# set the nice things
dispAx[d][row_i][col_i].set_xlim(
(0.5 * min(all_sfs), 1.2 * max(all_sfs)))
dispAx[d][row_i][col_i].set_xscale('log')
if expDir == 'LGN/' or forceLog == 1: # we want double-log if it's the LGN!
dispAx[d][row_i][col_i].set_yscale('log')
#dispAx[d][row_i][col_i].set_ylim((minToPlot, 1.5*maxResp));
def dog_fit(resps, all_cons, all_sfs, DoGmodel, loss_type, n_repeats, joint=0, ref_varExpl=None, veThresh=-np.nan, fracSig=1, ftol=2.220446049250313e-09, jointMinCons=3):
''' Helper function for fitting descriptive funtions to SF responses
if joint=True, (and DoGmodel is 1 or 2, i.e. not flexGauss), then we fit assuming
a fixed ratio for the center-surround gains and [freq/radius]
- i.e. of the 4 DoG parameters, 2 are fit separately for each contrast, and 2 are fit
jointly across all contrasts!
- note that ref_varExpl (optional) will be of the same form that the output for varExpl will be
- note that jointMinCons is the minimum # of contrasts that must be included for a joint fit to be run (e.g. 2)
inputs: self-explanatory, except for resps, which should be "f1" from tabulateResponses
outputs: bestNLL, currParams, varExpl, prefSf, charFreq, [overallNLL, paramList; if joint=True]
'''
nCons = len(all_cons);
if DoGmodel == 0:
nParam = 5;
else:
nParam = 4;
# unpack responses
resps_mean = resps['mean'];
resps_sem = resps['sem'];
# next, let's compute some measures about the responses
max_resp = np.nanmax(resps_mean.flatten());
min_resp = np.nanmin(resps_mean.flatten());
############
### WARNING - we're subtracting min_resp-1 from all responses
############
#resps_mean = np.subtract(resps_mean, min_resp-1); # i.e. make the minimum response 1 spk/s...
# and set up initial arrays
bestNLL = np.ones((nCons, ), dtype=np.float32) * np.nan;
currParams = np.ones((nCons, nParam), dtype=np.float32) * np.nan;
varExpl = np.ones((nCons, ), dtype=np.float32) * np.nan;
prefSf = np.ones((nCons, ), dtype=np.float32) * np.nan;
charFreq = np.ones((nCons, ), dtype=np.float32) * np.nan;
if joint>0:
overallNLL = np.nan;
params = np.nan;
success = False;
else:
success = np.zeros((nCons, ), dtype=np.bool_);
### set bounds
if DoGmodel == 0:
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
if fracSig:
bound_sigFrac = (0.2, 2);
allBounds = (bound_baseline, bound_range, bound_mu, bound_sig, bound_sigFrac);
else:
allBounds = (bound_baseline, bound_range, bound_mu, bound_sig, bound_sig);
elif DoGmodel == 1: # SACH
bound_gainCent = (1, 3*max_resp);
bound_radiusCent= (1e-2, 1.5);
bound_gainSurr = (1e-2, 1); # multiplier on gainCent, thus the center must be weaker than the surround
bound_radiusSurr = (1, 10); # (1,10) # multiplier on radiusCent, thus the surr. radius must be larger than the center
if joint>0:
if joint == 1: # original joint (fixed gain and radius ratios across all contrasts)
bound_gainRatio = (1e-3, 1); # the surround gain will always be less than the center gain
bound_radiusRatio= (1, 10); # the surround radius will always be greater than the ctr r
# we'll add to allBounds later, reflecting joint gain/radius ratios common across all cons
allBounds = (bound_gainRatio, bound_radiusRatio);
elif joint == 2: # fixed surround radius for all contrasts
allBounds = (bound_radiusSurr, );
elif joint == 3: # fixed center AND surround radius for all contrasts
allBounds = (bound_radiusCent, bound_radiusSurr);
# In advance of the thesis/publishing the LGN data, we will replicate some of Sach's key results
# In particular, his thesis covers 4 joint models:
# -- volume ratio: center and surround radii are fixed, but gains can vary (already covered in joint == 3)
# -- center radius: fixed center radius across contrast (joint=4) AND fixed volume (i.e. make surround gain constant across contrast)
# -- surround radius: fixed surround radius across contrast (joint=5) AND fixed volume (i.e. make surround gain constant across contrast) // fixed not in proportion to center, but in absolute value
# -- center-surround: center and surround radii can vary, but ratio of gains is fixed (joint == 6)
# ---- NOTE: joints 3-5 have 2*nCons + 2 parms; joint==6 has 3*nCons + 1
elif joint == 4: # fixed center radius
allBounds = (bound_radiusCent, bound_gainSurr, ); # center radius AND bound_gainSurr are fixed across condition
elif joint == 5: # fixed surround radius (again, in absolute terms here, not relative, as is usually specified)
allBounds = (bound_gainSurr, bound_radiusSurr, ); # surround radius AND bound_gainSurr are fixed across condition
elif joint == 6: # fixed center:surround gain ratio
allBounds = (bound_gainSurr, ); # we can fix the ratio by allowing the center gain to vary and keeping the surround in fixed proportion
elif joint == 7 or joint == 8: # center radius determined by slope! we'll also fixed surround radius; if joint == 8, fixed surround gain instead of radius
bound_xc_slope = (-1, 1); # 220505 fits inbounded; 220519 fits bounded (-1,1)
bound_xc_inter = (None, None); #bound_radiusCent; # intercept - shouldn't start outside the bounds we choose for radiusCent
allBounds = (bound_xc_inter, bound_xc_slope, bound_radiusSurr, ) if joint == 7 else (bound_xc_slope, bound_xc_inter, bound_gainSurr, )
else:
allBounds = (bound_gainCent, bound_radiusCent, bound_gainSurr, bound_radiusSurr);
elif DoGmodel == 2:
bound_gainCent = (1e-3, None);
bound_freqCent = (1e-3, 2e1);
bound_gainFracSurr = (1e-3, 2); # surround gain always less than center gain NOTE: SHOULD BE (1e-3, 1)
bound_freqFracSurr = (5e-2, 1); # surround freq always less than ctr freq NOTE: SHOULD BE (1e-1, 1)
if joint>0:
if joint == 1: # original joint (fixed gain and radius ratios across all contrasts)
bound_gainRatio = (1e-3, 3);
bound_freqRatio = (1e-1, 1);
# we'll add to allBounds later, reflecting joint gain/radius ratios common across all cons
allBounds = (bound_gainRatio, bound_freqRatio);
elif joint == 2: # fixed surround radius for all contrasts
allBounds = (bound_freqFracSurr,);
elif joint == 3: # fixed center AND surround radius for all contrasts
allBounds = (bound_freqCent, bound_freqFracSurr);
elif joint==0:
bound_gainFracSurr = (1e-3, 1);
bound_freqFracSurr = (1e-1, 1);
allBounds = (bound_gainCent, bound_freqCent, bound_gainFracSurr, bound_freqFracSurr);
### organize responses -- and fit, if joint=0
allResps = []; allRespsSem = []; allSfs = []; valCons = []; start_incl = 0; incl_inds = [];
base_rate = np.min(resps_mean.flatten());
for con in range(nCons):
if all_cons[con] == 0: # skip 0 contrast...
continue;
else:
valCons.append(all_cons[con]);
valSfInds_curr = np.where(~np.isnan(resps_mean[con,:]))[0];
resps_curr = resps_mean[con, valSfInds_curr];
sem_curr = resps_sem[con, valSfInds_curr];
### prepare for the joint fitting, if that's what we've specified!
if joint>0:
if resps_curr.size == 0:
continue;
if ref_varExpl is None:
start_incl = 1; # hacky...
if start_incl == 0:
if ref_varExpl[con] < veThresh:
continue; # i.e. we're not adding this; yes we could move this up, but keep it here for now
else:
start_incl = 1; # now we're ready to start adding to our responses that we'll fit!
allResps.append(resps_curr);
allRespsSem.append(sem_curr);
allSfs.append(all_sfs[valSfInds_curr]);
incl_inds.append(con);
# and add to the bounds list!
if DoGmodel == 1:
if joint == 1: # add the center gain and center radius for each contrast
allBounds = (*allBounds, bound_gainCent, bound_radiusCent);
if joint == 2: # add the center and surr. gain and center radius for each contrast
allBounds = (*allBounds, bound_gainCent, bound_radiusCent, bound_gainSurr);
if joint == 3: # add the center and surround gain for each contrast
allBounds = (*allBounds, bound_gainCent, bound_gainSurr);
elif joint == 4: # fixed center radius, so add all other parameters
allBounds = (*allBounds, bound_gainCent, bound_radiusSurr);
elif joint == 5: # add the center and surr. gain and center radius for each contrast
allBounds = (*allBounds, bound_gainCent, bound_radiusCent);
elif joint == 6: # fixed center:surround gain ratio
allBounds = (*allBounds, bound_gainCent, bound_radiusCent, bound_radiusSurr);
elif joint == 7: # center radius det. by slope, surround radius fixed
allBounds = (*allBounds, bound_gainCent, bound_gainSurr);
elif joint == 8: # center radius det. by slope, surround gain fixed
allBounds = (*allBounds, bound_gainCent, bound_radiusSurr);
elif DoGmodel == 2:
if joint == 1: # add the center gain and center radius for each contrast
allBounds = (*allBounds, bound_gainCent, bound_freqCent);
if joint == 2: # add the center and surr. gain and center radius for each contrast
allBounds = (*allBounds, bound_gainCent, bound_freqCent, bound_gainFracSurr);
if joint == 3: # add the center and surround gain for each contrast
allBounds = (*allBounds, bound_gainCent, bound_gainFracSurr);
continue;
### otherwise, we're really going to fit here! [i.e. if joint is False]
# first, specify the objection function!
obj = lambda params: DoG_loss(params, resps_curr, all_sfs[valSfInds_curr], resps_std=sem_curr, loss_type=loss_type, DoGmodel=DoGmodel, joint=joint); # if we're here, then joint=0, but we'll still keep joint=joint
for n_try in range(n_repeats):
###########
### pick initial params
###########
init_params = dog_init_params(resps_curr, base_rate, all_sfs, valSfInds_curr, DoGmodel, fracSig=fracSig, bounds=allBounds)
# choose optimization method
if np.mod(n_try, 2) == 0:
methodStr = 'L-BFGS-B';
else:
methodStr = 'TNC';
try:
wax = opt.minimize(obj, init_params, method=methodStr, bounds=allBounds);
except:
continue; # the fit has failed (bound issue, for example); so, go back to top of loop, try again
# compare
NLL = wax['fun'];
params = wax['x'];
if np.isnan(bestNLL[con]) or NLL < bestNLL[con]:
bestNLL[con] = NLL;
currParams[con, :] = params;
curr_mod = get_descrResp(params, all_sfs[valSfInds_curr], DoGmodel);
# TODO: 22.05.10 --> previously ignored sf==0 case for varExpl
varExpl[con] = var_expl_direct(resps_curr, curr_mod);
prefSf[con] = dog_prefSf(params, dog_model=DoGmodel, all_sfs=all_sfs[all_sfs>0]); # do not include 0 c/deg SF condition
charFreq[con] = dog_charFreq(params, DoGmodel=DoGmodel);
success[con] = wax['success'];
if joint==0: # then we're DONE
return bestNLL, currParams, varExpl, prefSf, charFreq, None, None, success; # placeholding None for overallNLL, params [full list]
### NOW, we do the fitting if joint=True
if joint>0:
if len(allResps)<jointMinCons: # need at least jointMinCons contrasts!
return bestNLL, currParams, varExpl, prefSf, charFreq, overallNLL, params, success;
### now, we fit!
for n_try in range(n_repeats):
# first, estimate the joint parameters; then we'll add the per-contrast parameters after
# --- we'll estimate the joint parameters based on the high contrast response
ref_resps = allResps[-1];
ref_init = dog_init_params(ref_resps, base_rate, all_sfs, all_sfs, DoGmodel);
if joint == 1: # gain ratio (i.e. surround gain) [0] and shape ratio (i.e. surround radius) [1] are joint
allInitParams = [ref_init[2], ref_init[3]];
elif joint == 2: # surround radius [0] (as ratio) is joint
allInitParams = [ref_init[3]];
elif joint == 3: # center radius [0] and surround radius [1] ratio are joint
allInitParams = [ref_init[1], ref_init[3]];
elif joint == 4: # center radius, surr. gain fixed
allInitParams = [ref_init[1], ref_init[2]];
elif joint == 5: # surround gain AND radius [0] (as ratio in 2; fixed in 5) are joint
allInitParams = [ref_init[2], ref_init[3]];
elif joint == 6: # center:surround gain is fixed
allInitParams = [ref_init[2]];
elif joint == 7 or joint == 8: # center radius offset and slope fixed; surround radius fixed [7] or surr. gain fixed [8]
# the slope will be calculated on log contrast, and will start from the lowest contrast
# -- i.e. xc = np.power(10, init+slope*log10(con))
# to start, let's assume no slope, so the intercept should be equal to our xc guess
init_intercept, init_slope = random_in_range([-1.3, -0.6])[0], random_in_range([-0.1,0.2])[0]
#init_intercept, init_slope = np.log10(ref_init[1]), 0;
allInitParams = [init_intercept, init_slope, ref_init[3]] if joint == 7 else [init_intercept, init_slope, ref_init[2]];
# now, we cycle through all responses and add the per-contrast parameters
for resps_curr in allResps:
curr_init = dog_init_params(resps_curr, base_rate, all_sfs, all_sfs, DoGmodel);
if joint == 1:
allInitParams = [*allInitParams, curr_init[0], curr_init[1]];
elif joint == 2: # then we add center gain, center radius, surround gain (i.e. params 0:3
allInitParams = [*allInitParams, curr_init[0], curr_init[1], curr_init[2]];
elif joint == 3: # then we add center gain and surround gain (i.e. params 0, 2)
allInitParams = [*allInitParams, curr_init[0], curr_init[2]];
elif joint == 4: # then we add center gain, surround radius
allInitParams = [*allInitParams, curr_init[0], curr_init[3]];
elif joint == 5: # then we add center gain, center radius
allInitParams = [*allInitParams, curr_init[0], curr_init[1]];
elif joint == 6: # then we add center gain and both radii
allInitParams = [*allInitParams, curr_init[0], curr_init[1], curr_init[3]];
elif joint == 7: # then we add center and surround gains
allInitParams = [*allInitParams, curr_init[0], curr_init[2]];
elif joint == 8: # then we add center gain, surr. radius
allInitParams = [*allInitParams, curr_init[0], curr_init[3]];
methodStr = 'L-BFGS-B';
obj = lambda params: DoG_loss(params, allResps, allSfs, resps_std=allRespsSem, loss_type=loss_type, DoGmodel=DoGmodel, joint=joint, n_fits=len(allResps), conVals=valCons, ); # if joint, it's just one fit!
wax = opt.minimize(obj, allInitParams, method=methodStr, bounds=allBounds, options={'ftol': ftol});
# compare
NLL = wax['fun'];
params_curr = wax['x'];
if np.isnan(overallNLL) or NLL < overallNLL:
overallNLL = NLL;
params = params_curr;
success = wax['success'];
### Done with multi-start fits; now, unpack the fits to fill in the "true" parameters for each contrast
# --- first, get the global parameters
ref_rc_val = None;
if joint == 1:
gain_rat, shape_rat = params[0], params[1];
elif joint == 2:
surr_shape = params[0]; # radius or frequency, if Tony model
elif joint == 3:
center_shape, surr_shape = params[0], params[1]; # radius or frequency, if Tony model
elif joint == 4: # center radius, surr. gain fixed
center_shape, surr_gain = params[0], params[1];
elif joint == 5: # surr. gain, surr. radius fixed
surr_gain, surr_shape = params[0], params[1];
ref_rc_val = params[2]; # center radius for high contrast
elif joint == 6: # ctr:surr gain fixed
surr_gain = params[0];
elif joint == 7: # center gain det. from slope, surround radius fixed
xc_inter, xc_slope, surr_shape = params[0:3];
elif joint == 8: # center gain det. from slope, surround gain fixed
xc_inter, xc_slope, surr_gain = params[0:3];
for con in range(len(allResps)):
# --- then, go through each contrast and get the "local", i.e. per-contrast, parameters
if joint == 1: # center gain, center shape
center_gain = params[2+con*2];
center_shape = params[3+con*2]; # shape, as in radius/freq, depending on DoGmodel
curr_params = [center_gain, center_shape, gain_rat, shape_rat];
elif joint == 2: # center gain, center radus, surround gain
center_gain = params[1+con*3];
center_shape = params[2+con*3];
surr_gain = params[3+con*3];
curr_params = [center_gain, center_shape, surr_gain, surr_shape];
elif joint == 3: # center gain, surround gain
center_gain = params[2+con*2];
surr_gain = params[3+con*2];
curr_params = [center_gain, center_shape, surr_gain, surr_shape];
elif joint == 4: # center radius, surr. gain fixed for all contrasts
center_gain = params[2+con*2];
surr_shape = params[3+con*2];
curr_params = [center_gain, center_shape, surr_gain, surr_shape];
elif joint == 5: # surround gain, radius fixed for all contrasts
center_gain = params[2+con*2];
center_shape = params[3+con*2];
curr_params = [center_gain, center_shape, surr_gain, surr_shape];
elif joint == 6: # ctr:surr gain fixed for all contrasts
center_gain = params[1+con*3];
center_shape = params[2+con*3];
surr_shape = params[3+con*3];
curr_params = [center_gain, center_shape, surr_gain, surr_shape];
elif joint == 7 or joint == 8: # surr radius [7] or gain [8] fixed; need to determine center radius from slope
center_gain = params[3+con*2];
center_shape = get_xc_from_slope(params[0], params[1], all_cons[con]);
if joint == 7:
surr_gain = params[4+con*2];
elif joint == 8:
surr_shape = params[4+con*2];
curr_params = [center_gain, center_shape, surr_gain, surr_shape];
# -- then the responses, and overall contrast index
resps_curr = allResps[con];
sem_curr = allRespsSem[con];
# now, compute!
conInd = incl_inds[con];
bestNLL[conInd] = DoG_loss(curr_params, resps_curr, allSfs[con], resps_std=sem_curr, loss_type=loss_type, DoGmodel=DoGmodel, joint=0, ref_rc_val=ref_rc_val); # now it's NOT joint!
currParams[conInd, :] = curr_params;
curr_mod = get_descrResp(curr_params, allSfs[con], DoGmodel, ref_rc_val=ref_rc_val);
varExpl[conInd] = var_expl_direct(resps_curr, curr_mod);
prefSf[conInd] = dog_prefSf(curr_params, dog_model=DoGmodel, all_sfs=all_sfs[all_sfs>0], ref_rc_val=ref_rc_val);
charFreq[conInd] = dog_charFreq(curr_params, DoGmodel=DoGmodel);
# and NOW, we can return!
return bestNLL, currParams, varExpl, prefSf, charFreq, overallNLL, params, success;
linestyle='dashed',
label='spon. rate')
## plot model fit
dispAx[d][c_plt_ind, 0].plot(all_sfs[v_sfs],
modAvg[d, v_sfs, v_cons[c]],
alpha=0.7,
color=modClr,
clip_on=False,
label=modTxt)
dispAx[d][c_plt_ind, 0].axhline(modSponRate,
color=modClr,
linestyle='dashed')
if descrParams is not None:
prms_curr = descrParams[d, v_cons[c]]
descrResp = hf.get_descrResp(prms_curr, sfs_plot, descrMod)
dispAx[d][c_plt_ind, 0].plot(sfs_plot,
descrResp,
color=descrClr,
label='descr. fit')
### right side of plots
if d == 0:
## plot everything again on log-log coordinates...
# first data
dispAx[d][c_plt_ind, 1].errorbar(curr_sfs,
curr_resp,
respVar[d, v_sfs, v_cons[c]],
fmt='o',
color=dataClr,
clip_on=False,
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_ylim([-1, 1])
# since the g' is normalized
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;