def compare_gainControl(fitBase, normType, lossType, expInd, conSteps=11):
''' given a fitBase (str) with associated norm/loss indices, and experiment indices, get the RVC for all cells
'''
_, _, _, _, nCells, dir = hf.get_exp_params(expInd);
if expInd == 1:
dir = str(dir+'Structures/');
else:
dir = str(dir+'structures/');
dataList = hf.np_smart_load(str(dir + 'dataList.npy'));
fl_str = hf.fitList_name(fitBase, normType, lossType);
fitList = hf.np_smart_load(str(dir + fl_str));
rvcs = np.nan * np.zeros((nCells, conSteps));
for i in range(nCells):
print('cell %d' % i);
cellStr = hf.np_smart_load(str(dir + dataList['unitName'][i] + '_sfm.npy'));
currParams = fitList[i]['params'];
rvcs[i, :] = evaluate_RVC(cellStr, currParams, normType=normType, expInd=expInd, conSteps=conSteps);
if expInd == 1:
np.save(str('rvcV1_' + fl_str), rvcs);
elif expInd == 3:
np.save(str('rvcLGN_' + fl_str), rvcs);
return rvcs;
def compute_diffs(lossType, expInd=1, baseStr='holdoutFits', date='181121'):
_, nDisps, _, nCons, nCells, dataLoc = hf.get_exp_params(expInd);
if expInd == 1: # main V1 branch
dataLoc = dataLoc + 'Structures/';
else:
dataLoc = dataLoc + 'structures/';
# then the loss type
if lossType == 1:
lossSuf = '_sqrt.npy';
elif lossType == 2:
lossSuf = '_poiss.npy';
elif lossType == 3:
lossSuf = '_modPoiss.npy';
losses = np.nan * np.zeros((nCells, nDisps, nCons, nFits));
preds = np.nan * np.zeros((nCells, nDisps, nCons, nFits));
flat = np.load(dataLoc + '%s_%s_flat%s' % (baseStr, date, lossSuf), encoding='latin1').item();
weight = np.load(dataLoc + '%s_%s_wght%s' % (baseStr, date, lossSuf), encoding='latin1').item();
for i in range(nCells):
try:
losses[i, :, :, 0] = np.mean(flat[i]['NLL'], 2)
preds[i, :, :, 0] = np.mean(flat[i]['holdoutNLL'], 2)
losses[i, :, :, 1] = np.mean(weight[i]['NLL'], 2)
preds[i, :, :, 1] = np.mean(weight[i]['holdoutNLL'], 2)
except:
continue;
diffs_loss = losses[:, :, :, 0] - losses[:, :, :, 1];
diffs_pred = preds[:, :, :, 0] - preds[:, :, :, 1];
return diffs_loss, diffs_pred, losses, preds, flat, weight;
dispAx[d][i].set_title('D%02d - sf tuning %s' % (d, labels[i]))
dispAx[d][i].legend()
saveName = "/allCons_cell_%03d.pdf" % (cellNum)
full_save = os.path.dirname(str(save_loc + 'byDisp%s/' % rvcFlag))
if not os.path.exists(full_save):
os.makedirs(full_save)
pdfSv = pltSave.PdfPages(full_save + saveName)
for f in fDisp:
pdfSv.savefig(f)
plt.close(f)
pdfSv.close()
# #### Plot just sfMix contrasts
mixCons = hf.get_exp_params(expInd).nCons
minResp = np.min(np.min(np.min(respMean[~np.isnan(respMean)])))
maxResp = np.max(np.max(np.max(respMean[~np.isnan(respMean)])))
f, sfMixAx = plt.subplots(mixCons, nDisps, figsize=(25, 1.5 * 20))
sfs_plot = np.logspace(np.log10(all_sfs[0]), np.log10(all_sfs[-1]), 100)
for d in range(nDisps):
v_cons = np.array(val_con_by_disp[d])
n_v_cons = len(v_cons)
v_cons = v_cons[np.arange(np.maximum(0, n_v_cons - mixCons), n_v_cons)]
# max(1, .) for when there are fewer contrasts than 4
n_v_cons = len(v_cons)
for c in reversed(range(n_v_cons)):
### Load datalist, cell, and "onset list"
expName = hf.get_datalist(expDir)
onsetName = 'onset_transients%s.npy' % onsStr
# auto...
path = '%sstructures/' % expDir
dataList = hf.np_smart_load(path + expName)
expName = 'sfBB_core'
unitNm = dataList['unitName'][cellNum - 1]
cell = hf.np_smart_load('%s%s_sfBB.npy' % (path, unitNm))
expInfo = cell[expName]
byTrial = expInfo['trial']
# we're enforcing expInd = -1 for now...
expInd = -1
stimDur = hf.get_exp_params(expInd).stimDur
msTenthToS = 1e-4
# the spike times are in 1/10th ms, so multiply by 1e-4 to convert to S
nonBlank = np.where(np.logical_or(byTrial['maskOn'], byTrial['maskOn']))[0]
nTrials = len(nonBlank)
# print('%d non-blank trials considered' % nTrials);
allSpikes = np.hstack(expInfo['spikeTimes'][nonBlank] * msTenthToS)
maskInd, baseInd = hf_sf.get_mask_base_inds()
# what is the length (in mS) of one cycle (mask, base separately)
maskTf = np.unique(expInfo['trial']['tf'][maskInd, :])[0]
baseTf = np.unique(expInfo['trial']['tf'][baseInd, :])[0]
cycleDur_mask = 1e3 / maskTf # guaranteed only one TF value
cycleDur_base = 1e3 / baseTf # guaranteed only one TF value
def phase_by_cond(which_cell,
data,
expInd,
disp,
con,
sf,
sv_loc=save_loc,
dir=-1,
cycle_fold=2,
n_bins_fold=8,
dp=dataPath,
expName=expName,
date_suffix=''):
''' Given a cell and the disp/con/sf indices, plot the spike raster for each trial, a folded PSTH,
and finally the response phase - first relative to trial onset, and finally relative to the stimulus phase
dir = -1 or +1 (i.e. stimulus moving left or right?)
cycle_fold = over how many stimulus cycles to fold when creating folded psth?
n_bins_fold = how many bins per stimulus period when folding?
'''
dataList = hf.np_smart_load(str(dp + expName))
save_base = sv_loc + 'phasePlots_%s/' % date_suffix
val_trials, allDisps, allCons, allSfs = hf.get_valid_trials(
data, disp, con, sf, expInd)
if not np.any(
val_trials[0]
): # val_trials[0] will be the array of valid trial indices --> if it's empty, leave!
warnings.warn('this condition is not valid')
return
### SINGLE GRATINGS
if disp == 0:
# get the phase relative to the stimulus
ph_rel_stim, stim_ph, resp_ph, all_tf = hf.get_true_phase(
data, val_trials, expInd, dir)
# compute the fourier amplitudes
stimDur = hf.get_exp_params(expInd).stimDur
psth_val, _ = hf.make_psth(data['spikeTimes'][val_trials],
stimDur=stimDur)
_, rel_amp, full_fourier = hf.spike_fft(psth_val, all_tf, stimDur)
# now plot!
f, ax = plt.subplots(3, 2, figsize=(20, 30))
relSpikes = data['spikeTimes'][val_trials]
colors = cm.rainbow(np.linspace(0, 1, len(val_trials[0])))
#####
# plot spike raster - trial-by-trial
#####
# only works for SINGLE GRATINGS
# draw the beginning of each cycle for each trial
ax = plt.subplot(3, 1, 1)
for i in range(len(relSpikes)):
ax.scatter(relSpikes[i],
i * np.ones_like(relSpikes[i]),
s=45,
color=colors[i])
stimPh = stim_ph[i]
stimPeriod = np.divide(1.0, all_tf[i])
stimDur = hf.get_exp_params(expInd).stimDur
# i.e. at what point during the trial (in s) does the stimulus component first begin a cycle?
firstPh0 = hf.first_ph0(stimPh, all_tf[i])[1]
for j in range(len(all_tf[i])):
allPh0 = [
stimPeriod[j] * np.arange(-1, stimDur * all_tf[i][j]) +
firstPh0[j]
]
allPh90 = allPh0 + stimPeriod[j] / 4
allPh180 = allPh90 + stimPeriod[j] / 4
allPh270 = allPh180 + stimPeriod[j] / 4
ax.errorbar(allPh0[0],
i * np.ones_like(allPh0[0]),
0.25,
linestyle='none',
color='k',
linewidth=1)
ax.errorbar(allPh90[0],
i * np.ones_like(allPh0[0]),
0.05,
linestyle='none',
color='k',
linewidth=1)
ax.errorbar(allPh180[0],
i * np.ones_like(allPh0[0]),
0.05,
linestyle='none',
color='k',
linewidth=1)
ax.errorbar(allPh270[0],
i * np.ones_like(allPh0[0]),
0.05,
linestyle='none',
color='k',
linewidth=1)
ax.set_xlabel('time (s)')
ax.set_ylabel('repetition #')
ax.set_title('Spike rasters')
#####
# plot PSTH - per trial, but folded over N cycles
#####
# only works for SINGLE GRATINGS
ax = plt.subplot(3, 1, 2)
for i in range(len(relSpikes)):
_, bin_edges, psth_norm = hf.fold_psth(relSpikes[i],
all_tf[i],
stim_ph[i],
cycle_fold,
n_bins_fold,
dir=dir)
plt.plot(bin_edges[0:-1], i - 0.5 + psth_norm, color=colors[i])
stimPeriod = np.divide(1.0, all_tf[i])
for j in range(cycle_fold):
cycStart = plt.axvline(j * stimPeriod[0])
cycHalf = plt.axvline((j + 0.5) * stimPeriod[0],
linestyle='--')
ax.set_xlabel('time (s)')
ax.set_ylabel('spike count (normalized by trial)')
ax.set_title('PSTH folded')
ax.set_xlim(
[-stimPeriod[0] / 4.0, (cycle_fold + 0.25) * stimPeriod[0]])
ax.legend((cycStart, cycHalf), ('ph = 0', 'ph = 180'))
#####
# plot response phase - without accounting for stimulus phase
#####
ax = plt.subplot(3, 2, 5, projection='polar')
ax.scatter(np.radians(resp_ph),
rel_amp,
s=60,
color=colors,
clip_on=False)
ax.set_title('Stimulus-blind')
ax.set_ylim(auto='True')
polar_ylim = ax.get_ylim()
# now, compute the average amplitude/phase over all trials
[avg_r, avg_ph, _, _] = hf.polar_vec_mean([rel_amp], [ph_rel_stim])
avg_r = avg_r[0]
# just get it out of the array!
avg_ph = avg_ph[0]
# just get it out of the array!
#####
# plot response phase - relative to stimulus phase
#####
ax = plt.subplot(3, 2, 6, projection='polar')
ax.scatter(np.radians(ph_rel_stim),
rel_amp,
s=60,
color=colors,
clip_on=False)
ax.plot([0, np.radians(avg_ph)], [0, avg_r],
color='k',
linestyle='--',
clip_on=False)
ax.set_ylim(polar_ylim)
ax.set_title('Stimulus-accounted')
### MIXTURE STIMULI
elif disp > 0:
nComp = hf.get_exp_params(expInd).nStimComp
# a useful function for swapping inside/outside of nested list...
switch_inner_outer = lambda arr: [[x[i] for x in arr]
for i in range(len(arr[0]))]
### first, gather the isolated component responses from the mixture
val_trials, allDisp, allCons, allSfs = hf.get_valid_trials(
data, disp, con, sf, expInd)
# outer-most list (for ph_rel_stim, stim_phase, resp_phase) is trial/repetition, inner lists are by component
ph_rel_stim, stim_phase, resp_phase, all_tf = hf.get_true_phase(
data, val_trials, expInd, dir=dir)
# f1all is nComp lists, each with nReps/nTrials values
_, _, _, f1all, conByComp, sfByComp = hf.get_isolated_response(
data, val_trials)
# need to switch ph_rel_stim (and resp_phase) to be lists of phases by component (rather than list of phases by trial)
ph_rel_stim = switch_inner_outer(ph_rel_stim)
resp_phase = switch_inner_outer(resp_phase)
# compute vector means
r_comp, th_comp, _, _ = hf.polar_vec_mean(f1all, ph_rel_stim)
nrow = 1 + nComp
ncol = 2
f, ax = plt.subplots(nrow, ncol, figsize=(ncol * 10, nrow * 10))
# in-mixture response phase - NOT stimulus-aligned; just demonstrate for 1st component
colors = cm.rainbow(np.linspace(0, 1, len(val_trials[0])))
ax = plt.subplot(
nrow, 1, 1, projection='polar'
) # pretend only one column, i.e. take up whole top row
ax.scatter(np.radians(resp_phase[0]),
hf.flatten(f1all[0]),
s=45,
color=colors,
clip_on=False)
ax.set_ylim([0, 1.1 * np.max(hf.flatten(f1all[0]))])
ax.set_title('Stimulus-blind (compound; comp #1)')
for i in range(nComp):
# compute the isolated response
# Then, pick one of the components and get the response (when presented in isolation)
# phases/amplitudes and align relative to the stimulus phase
isolConInd = np.where(allCons == conByComp[i])[0][0]
# unwrap fully (will be only one value...)
isolSfInd = np.where(allSfs == sfByComp[i])[0][0]
val_trials_isol, _, _, _ = hf.get_valid_trials(data,
disp=0,
con=isolConInd,
sf=isolSfInd,
expInd=expInd)
ph_rel_stim_isol, stim_phase_isol, resp_phase_isol, all_tf_isol = hf.get_true_phase(
data, val_trials_isol, expInd, dir=dir)
stimDur = hf.get_exp_params(expInd).stimDur
psth_val, _ = hf.make_psth(data['spikeTimes'][val_trials_isol],
stimDur=stimDur)
_, rel_amp_isol, _ = hf.spike_fft(psth_val, all_tf_isol, stimDur)
# and compute vector mean
r_isol, th_isol, _, _ = hf.polar_vec_mean([rel_amp_isol],
[ph_rel_stim_isol])
# isolated response phase - relative to stimulus phase
colors = cm.rainbow(np.linspace(0, 1, len(val_trials[0])))
ax = plt.subplot(nrow, ncol, 2 * (i + 1) + 1, projection='polar')
ax.scatter(np.radians(ph_rel_stim_isol),
rel_amp_isol,
s=45,
color=colors,
clip_on=False)
ax.plot([0, np.radians(th_isol[0])], [0, r_isol[0]],
ls='--',
color='k')
ax.set_ylim([0, 1.1 * np.max(rel_amp_isol)])
ax.set_title('isolated (r, phi) = (%.2f, %.2f)' %
(r_isol[0], th_isol[0]))
# in-mixture response phase - relative to stimulus phase
colors = cm.rainbow(np.linspace(0, 1, len(val_trials[0])))
ax = plt.subplot(nrow, ncol, 2 * (i + 2), projection='polar')
ax.scatter(np.radians(ph_rel_stim[i]),
hf.flatten(f1all[i]),
s=45,
color=colors,
clip_on=False)
ax.plot([0, np.radians(th_comp[i])], [0, r_comp[i]],
ls='--',
color='k')
ax.set_ylim([0, 1.1 * np.max(rel_amp_isol)])
ax.set_title('compound (r, phi) = (%.2f, %.2f)' %
(r_comp[i], th_comp[i]))
## Common to all
f.subplots_adjust(wspace=0.2, hspace=0.25)
f1f0_ratio = hf.compute_f1f0(data,
which_cell,
expInd,
dp,
descrFitName_f0=descrFit_f0)[0]
try: # not always exists
cell_label = dataList['unitType'][which_cell - 1]
except: # always works
cell_label = dataList['unitArea'][which_cell - 1]
f.suptitle('%s (f1f0: %.2f) #%d: disp %d, con %.2f, sf %.2f' %
(cell_label, f1f0_ratio, which_cell, allDisps[disp],
allCons[con], allSfs[sf]))
saveName = "/cell_%03d_d%dsf%dcon%d_phase.pdf" % (which_cell, disp, sf,
con)
save_loc = save_base + "cell_%03d/" % which_cell
full_save = os.path.dirname(str(save_loc))
if not os.path.exists(full_save):
os.makedirs(full_save)
pdfSv = pltSave.PdfPages(full_save + saveName)
pdfSv.savefig(f)
plt.close(f)
pdfSv.close()
def plot_phase_advance(which_cell,
disp,
sv_loc=save_loc,
dir=-1,
dp=dataPath,
expName=expName,
phAdvStr=phAdvName,
rvcStr=rvcName,
date_suffix=''):
''' RVC, resp-X-phase, phase advance model split by SF within each cell/dispersion condition
1. response-versus-contrast; shows original and adjusted response
2. polar plot of response amplitude and phase with phase advance model fit
3. response amplitude (x) vs. phase (y) with the linear phase advance model fit
'''
# basics
dataList = hf.np_smart_load(str(dp + expName))
cellName = dataList['unitName'][which_cell - 1]
expInd = hf.get_exp_ind(dp, cellName)[0]
cellStruct = hf.np_smart_load(str(dp + cellName + '_sfm.npy'))
rvcFits = hf.np_smart_load(str(dp + hf.phase_fit_name(rvcStr, dir)))
rvcFits = rvcFits[which_cell - 1]
rvc_model = hf.get_rvc_model()
phAdvFits = hf.np_smart_load(str(dp + hf.phase_fit_name(phAdvStr, dir)))
phAdvFits = phAdvFits[which_cell - 1]
phAdv_model = hf.get_phAdv_model()
save_base = sv_loc + 'phasePlots_%s/' % date_suffix
# gather/compute everything we need
data = cellStruct['sfm']['exp']['trial']
_, stimVals, val_con_by_disp, validByStimVal, _ = hf.tabulate_responses(
data, expInd)
valDisp = validByStimVal[0]
valCon = validByStimVal[1]
valSf = validByStimVal[2]
allDisps = stimVals[0]
allCons = stimVals[1]
allSfs = stimVals[2]
con_inds = val_con_by_disp[disp]
# now get ready to plot
fPhaseAdv = []
# we will summarize for all spatial frequencies for a given cell!
for j in range(len(allSfs)):
# first, get the responses and phases that we need:
amps = []
phis = []
sf = j
for i in con_inds:
val_trials = np.where(valDisp[disp] & valCon[i] & valSf[sf])
# get the phase of the response relative to the stimulus (ph_rel_stim)
ph_rel_stim, stim_ph, resp_ph, all_tf = hf.get_true_phase(
data, val_trials, expInd, dir=dir)
phis.append(ph_rel_stim)
# get the relevant amplitudes (i.e. the amplitudes at the stimulus TF)
stimDur = hf.get_exp_params(expInd).stimDur
psth_val, _ = hf.make_psth(data['spikeTimes'][val_trials],
stimDur=stimDur)
_, rel_amp, _ = hf.spike_fft(psth_val, all_tf, stimDur=stimDur)
amps.append(rel_amp)
r, th, _, _ = hf.polar_vec_mean(amps, phis)
# mean amp/phase (outputs 1/2); std/var for amp/phase (outputs 3/4)
# get the models/fits that we need:
con_values = allCons[con_inds]
## phase advance
opt_params_phAdv = phAdvFits['params'][sf]
ph_adv = phAdvFits['phAdv'][sf]
## rvc
opt_params_rvc = rvcFits[disp]['params'][sf]
con_gain = rvcFits[disp]['conGain'][sf]
adj_means = rvcFits[disp]['adjMeans'][sf]
if disp == 1: # then sum adj_means (saved by component)
adj_means = [np.sum(x, 1) if x else [] for x in adj_means]
# (Above) remember that we have to project the amp/phase vector onto the "correct" phase for estimate of noiseless response
## now get ready to plot!
f, ax = plt.subplots(2, 2, figsize=(20, 10))
fPhaseAdv.append(f)
n_conds = len(r)
colors = cm.viridis(np.linspace(0, 0.95, n_conds))
#####
## 1. now for plotting: first, response amplitude (with linear contrast)
#####
plot_cons = np.linspace(0, 1, 100)
mod_fit = rvc_model(opt_params_rvc[0], opt_params_rvc[1],
opt_params_rvc[2], plot_cons)
ax = plt.subplot(2, 2, 1)
plot_amp = adj_means
plt_measured = ax.scatter(allCons[con_inds],
plot_amp,
s=100,
color=colors,
label='ph. corr')
plt_og = ax.plot(allCons[con_inds],
r,
linestyle='None',
marker='o',
markeredgecolor='k',
markerfacecolor='None',
alpha=0.5,
label='vec. mean')
plt_fit = ax.plot(plot_cons,
mod_fit,
linestyle='--',
color='k',
label='rvc fit')
ax.set_xlabel('contrast')
ax.set_ylabel('response (f1)')
ax.set_title('response versus contrast')
ax.legend(loc='upper left')
# also summarize the model fit on this plot
ymax = np.maximum(np.max(r), np.max(mod_fit))
plt.text(0.8,
0.30 * ymax,
'b: %.2f' % (opt_params_rvc[0]),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
plt.text(0.8,
0.20 * ymax,
'slope:%.2f' % (opt_params_rvc[1]),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
plt.text(0.8,
0.10 * ymax,
'c0: %.2f' % (opt_params_rvc[2]),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
plt.text(0.8,
0.0 * ymax,
'con gain: %.2f' % (con_gain),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
#####
## 3. then the fit/plot of phase as a function of ampltude
#####
plot_amps = np.linspace(0, np.max(r), 100)
mod_fit = phAdv_model(opt_params_phAdv[0], opt_params_phAdv[1],
plot_amps)
ax = plt.subplot(2, 1, 2)
plt_measured = ax.scatter(r,
th,
s=100,
color=colors,
clip_on=False,
label='vec. mean')
plt_fit = ax.plot(plot_amps,
mod_fit,
linestyle='--',
color='k',
clip_on=False,
label='phAdv model')
ax.set_xlabel('response amplitude')
if phAdv_set_ylim:
ax.set_ylim([0, 360])
ax.set_ylabel('response phase')
ax.set_title('phase advance with amplitude')
ax.legend(loc='upper left')
## and again, summarize the model fit on the plot
xmax = np.maximum(np.max(r), np.max(plot_amps))
ymin = np.minimum(np.min(th), np.min(mod_fit))
ymax = np.maximum(np.max(th), np.max(mod_fit))
yrange = ymax - ymin
if phAdv_set_ylim:
if mod_fit[-1] > 260: # then start from ymin and go dwn
start, sign = mod_fit[-1] - 30, -1
else:
start, sign = mod_fit[-1] + 30, 1
plt.text(0.9 * xmax,
start + 1 * 30 * sign,
'phi0: %.2f' % (opt_params_phAdv[0]),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
plt.text(0.9 * xmax,
start + 2 * 30 * sign,
'slope:%.2f' % (opt_params_phAdv[1]),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
plt.text(0.9 * xmax,
start + 3 * 30 * sign,
'phase advance: %.2f ms' % (ph_adv),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
else:
plt.text(0.8 * xmax,
ymin + 0.25 * yrange,
'phi0: %.2f' % (opt_params_phAdv[0]),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
plt.text(0.8 * xmax,
ymin + 0.15 * yrange,
'slope:%.2f' % (opt_params_phAdv[1]),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
plt.text(0.8 * xmax,
ymin + 0.05 * yrange,
'phase advance: %.2f ms' % (ph_adv),
fontsize=12,
horizontalalignment='center',
verticalalignment='center')
#center_phi = lambda ph1, ph2: np.arcsin(np.sin(np.deg2rad(ph1) - np.deg2rad(ph2)));
#####
## 2. now the polar plot of resp/phase together
#####
ax = plt.subplot(2, 2, 2, projection='polar')
th_center = np.rad2deg(np.radians(-90) + np.radians(th[np.argmax(r)]))
# "anchor" to the phase at the highest amplitude response
#data_centered = center_phi(th, th_center);
#model_centered = center_phi(mod_fit, th_center);
#ax.scatter(data_centered, r, s=50, color=colors);
#ax.plot(model_centered, plot_amps, linestyle='--', color='k');
data_centered = np.mod(th - th_center, 360)
model_centered = np.mod(mod_fit - th_center, 360)
ax.scatter(np.deg2rad(data_centered), r, s=50, color=colors)
ax.plot(np.deg2rad(model_centered),
plot_amps,
linestyle='--',
color='k')
ax.set_ylim(0, 1.25 * np.max(r))
ax.set_title('phase advance')
# overall title
f.subplots_adjust(wspace=0.2, hspace=0.25)
f1f0_ratio = hf.compute_f1f0(data,
which_cell,
expInd,
dp,
descrFitName_f0=descrFit_f0)[0]
try:
f.suptitle('%s (%.2f) #%d: disp %d, sf %.2f cpd' %
(dataList['unitType'][which_cell - 1], f1f0_ratio,
which_cell, allDisps[disp], allSfs[sf]))
except:
f.suptitle('%s (%.2f) #%d: disp %d, sf %.2f cpd' %
(dataList['unitArea'][which_cell - 1], f1f0_ratio,
which_cell, allDisps[disp], allSfs[sf]))
saveName = "/cell_%03d_d%d_phaseAdv.pdf" % (which_cell, disp)
save_loc = save_base + 'summary/'
full_save = os.path.dirname(str(save_loc))
if not os.path.exists(full_save):
os.makedirs(full_save)
pdfSv = pltSave.PdfPages(full_save + saveName)
for f in fPhaseAdv:
pdfSv.savefig(f)
plt.close(f)
pdfSv.close()
def makeStimulus(stimFamily, conLevel, sf_c, expInd, template):
# returns [Or, Tf, Co, Ph, Sf, trial_used]
# 1/23/16 - This function is used to make arbitrary stimuli for use with
# the Robbe V1 model. Rather than fitting the model responses at the actual
# experimental stimuli, we instead can simulate from the model at any
# arbitrary spatial frequency in order to determine peak SF
# response/bandwidth/etc
# If argument 'template' is given, then orientation, phase, and tf_center will be
# taken from the template, which will be actual stimuli from that cell
# Fixed parameters
warnings.warn(
'makeStimulus is not set to work for experiments besides expInd=1; take caution when making own stimulus for simulation'
)
exper = hf.get_exp_params(expInd)
num_families = exper.nFamilies
num_gratings = exper.nStimComp
spreadVec = numpy.logspace(math.log10(.125), math.log10(1.25),
num_families)
octSeries = numpy.linspace(1.5, -1.5, num_gratings)
# set contrast and spatial frequency
if conLevel == 1:
total_contrast = 1
elif conLevel == 2:
total_contrast = 1 / 3
elif conLevel >= 0 and conLevel <= 1:
total_contrast = conLevel
else:
#warning('Contrast should be given as 1 [full] or 2 [low/one-third]; setting contrast to 1 (full)');
total_contrast = 1
# default to that
spread = spreadVec[stimFamily - 1]
profTemp = norm.pdf(octSeries, 0, spread)
profile = profTemp / sum(profTemp)
if stimFamily == 1: # do this for consistency with actual experiment - for stimFamily 1, only one grating is non-zero; round gives us this
profile = round(profile)
Sf = numpy.power(2, octSeries + numpy.log2(sf_c))
# final spatial frequency
Co = numpy.dot(profile, total_contrast)
# final contrast
# The others
# get orientation - IN RADIANS
trial = template.get('sfm').get('exp').get('trial')
OriVal = mode(trial.get('ori')[0]).mode * numpy.pi / 180
# pick arbitrary grating, mode for this is cell's pref Ori for experiment
Or = numpy.matlib.repmat(OriVal, 1, num_gratings)[0]
# weird tuple [see below], just get the array we care about...
if template.get('trial_used') is not None: #use specified trial
trial_to_copy = template.get('trial_used')
else: # get random trial for phase, TF
# we'll draw from a random trial with the same stimulus family/contrast
valid_blockIDs = numpy.arange(
(stimFamily - 1) * (13 * 2) + 1 + (conLevel - 1),
((stimFamily) * (13 * 2) - 5) + (conLevel - 1), 2)
# above from Robbe's plotSfMix
num_blockIDs = len(valid_blockIDs)
# for phase and TF
valid_trials = trial.get('blockID') == valid_blockIDs[random.randint(
0, num_blockIDs - 1)] # pick a random block ID
valid_trials = numpy.where(valid_trials)[0]
# 0 is to get array...weird "tuple" return type...
trial_to_copy = valid_trials[random.randint(0,
len(valid_trials) - 1)]
# pick a random trial from within this
trial_used = trial_to_copy
# grab Tf and Phase [IN RADIANS] from each grating for the given trial
Tf = numpy.asarray([i[trial_to_copy] for i in trial.get('tf')])
Ph = numpy.asarray(
[i[trial_to_copy] * math.pi / 180 for i in trial.get('ph')])
return {
'Ori': Or,
'Tf': Tf,
'Con': Co,
'Ph': Ph,
'Sf': Sf,
'trial_used': trial_used
}
def measure_chiSq(lossType, expInd=1, date='181121'):
_, nDisps, _, nCons, nCells, dataLoc = hf.get_exp_params(expInd);
if expInd == 1: # main V1 branch
dataLoc = dataLoc + 'Structures/';
else:
dataLoc = dataLoc + 'structures/';
dataList = hf.np_smart_load(str(dataLoc + 'dataList.npy'));
# then the loss type
if lossType == 1:
lossSuf = '_sqrt.npy';
elif lossType == 2:
lossSuf = '_poiss.npy';
elif lossType == 3:
lossSuf = '_modPoiss.npy';
chi_wght = np.nan * np.zeros((nCells, ));
chi_flat = np.nan * np.zeros((nCells, ));
flat = hf.np_smart_load(dataLoc + 'fitList_%s_flat%s' % (date, lossSuf));
weight = hf.np_smart_load(dataLoc + 'fitList_%s_wght%s' % (date, lossSuf));
for i in range(nCells):
print('cell %d' % i);
S = hf.np_smart_load(str(dataLoc + dataList['unitName'][i] + '_sfm.npy'));
# first, the data
_, _, sfmixExpResp, allSfMixExp = hf.organize_modResp(S['sfm']['exp']['trial']['spikeCount'], S['sfm']['exp']['trial'])
exp_responses = [sfmixExpResp, np.nanvar(allSfMixExp, axis=3)];
## then, the model
# flat normalization
if i in flat:
flat_params = flat[i]['params'];
ignore, modResp = SFMGiveBof(flat_params, S, normType=1, lossType=lossType, expInd=expInd);
_, _, sfmixModResp, allSfMixMod = hf.organize_modResp(modResp, S['sfm']['exp']['trial'])
mod_responses = [sfmixModResp, np.nanvar(allSfMixMod)];
chi_flat[i] = hf.chiSq(exp_responses, mod_responses);
print('chi: %.1f' % chi_flat[i]);
# weighted normalization
if i in weight:
wght_params = weight[i]['params'];
ignore, modResp = SFMGiveBof(wght_params, S, normType=2, lossType=lossType, expInd=expInd);
_, _, sfmixModResp, allSfMixMod = hf.organize_modResp(modResp, S['sfm']['exp']['trial'])
mod_responses = [sfmixModResp, np.nanvar(allSfMixMod)];
chi_wght[i] = hf.chiSq(exp_responses, mod_responses);
print('\tchi: %.1f' % chi_wght[i]);
n_flat_params = len(flat[0]['params']);
n_wght_params = len(weight[0]['params']);
chiNorm_flat = np.divide(chi_flat, n_flat_params);
chiNorm_wght = np.divide(chi_flat, n_wght_params);
chiAnalysis = dict();
chiAnalysis['flat_norm'] = chiNorm_flat;
chiAnalysis['wght_norm'] = chiNorm_wght;
chiAnalysis['flat'] = chi_flat;
chiAnalysis['wght'] = chi_wght;
np.save('chiAnalysis', chiAnalysis);
return chiNorm_flat, chiNorm_wght, chi_flat, chi_wght;
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!
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;
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)
nonNaN = np.sum(findNaN == False, axis=-1)
respSem = np.nanstd(respAll, -1) / np.sqrt(nonNaN)
