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)