def pca_3d(delay=6):
fr_per_su = util.get_dataset()
S1_mm=np.hstack([np.mean(onesu[f'S1_{delay}'],axis=2) for onesu in fr_per_su])
S2_mm=np.hstack([np.mean(onesu[f'S2_{delay}'],axis=2) for onesu in fr_per_su])
scaler=StandardScaler()
delaybin=range(16,16+delay*4);
scaler.fit(np.vstack((S1_mm[delaybin,:],S2_mm[delaybin,:])))
norm_mm=scaler.transform(np.vstack((S1_mm,S2_mm)))
pca = PCA(n_components=20)
#TODO 3s delay compatbility
pcamat=norm_mm[list(delaybin)+list(np.array(delaybin)+56),:]
pca.fit(pcamat)
comp=pca.transform(norm_mm)
coeff = pca.components_
ratio = pca.explained_variance_ratio_
fig=plt.figure()
ax = fig.add_subplot(projection='3d')
h1=ax.plot(comp[8:45,0],comp[8:45,1],comp[8:45,2],'-r')
h2=ax.plot(comp[64:101,0],comp[64:101,1],comp[64:101,2],'-b')
interp=[np.interp(np.array([12,16,12+56,16+56,40,44,40+56,44+56])-0.5,
range(comp.shape[0]),
comp[:,c])
for c in range(3)]
hs=[ax.plot(interp[0][t],interp[1][t],interp[2][t],'o',c=[0.5,0.5,0.5]) for t in range(4)]
ht=[ax.plot(interp[0][t],interp[1][t],interp[2][t],'ko') for t in range(4,8)]
ax.set_zlabel('PC3')
ax.set_xlabel('PC1')
ax.set_ylabel('PC2')
plt.legend([h1[0],h2[0],hs[0][0],ht[0][0]], ['S1 trial','S2 trial','Sample','Test'])
def cd_distance(delay=6):
fr_per_su = util.get_dataset()
# fr_error=util.get_dataset(correct_error='error')
S1_mm = np.hstack(
[np.mean(onesu[f'S1_{delay}'], axis=2) for onesu in fr_per_su])
S2_mm = np.hstack(
[np.mean(onesu[f'S2_{delay}'], axis=2) for onesu in fr_per_su])
# S1_mm_err=np.hstack([np.mean(onesu[f'S1_{delay}'],axis=2) for onesu in fr_error])
# S2_mm_err=np.hstack([np.mean(onesu[f'S2_{delay}'],axis=2) for onesu in fr_error])
cdMat = (S1_mm - S2_mm)
cdDelayE = np.mean(cdMat[16:20, :], axis=0)
cdDelayE = cdDelayE / np.linalg.norm(cdDelayE)
cdDelayM = np.mean(cdMat[26:30, :], axis=0)
cdDelayM = cdDelayM / np.linalg.norm(cdDelayM)
cdDelayL = np.mean(cdMat[36:40, :], axis=0)
cdDelayL = cdDelayL / np.linalg.norm(cdDelayL)
(proj1E, proj1M, proj1L) = (S1_mm @ cdDelayE, S1_mm @ cdDelayM,
S1_mm @ cdDelayL)
(proj2E, proj2M, proj2L) = (S2_mm @ cdDelayE, S2_mm @ cdDelayM,
S2_mm @ cdDelayL)
# (proj1Eerr,proj1Merr,proj1Lerr)=(S1_mm_err @ cdDelayE, S1_mm_err @ cdDelayM,S1_mm_err @ cdDelayL)
# (proj2Eerr,proj2Merr,proj2Lerr)=(S2_mm_err @ cdDelayE, S2_mm_err @ cdDelayM,S2_mm_err @ cdDelayL)
# eculidian=[np.linalg.norm([proj1E[t]-proj2E[t],proj1M[t]-proj2M[t],proj1L[t]-proj2L[t]]) for t in range(len(proj1E))]
fig = plt.figure()
he = plt.plot(proj1E - proj2E, '-r')
hm = plt.plot(proj1M - proj2M, '-m')
hl = plt.plot(proj1L - proj2L, '-b')
# hee=plt.plot(proj1Eerr-proj2Eerr,'--r')
# hme=plt.plot(proj1Merr-proj2Merr,'--m')
# hle=plt.plot(proj1Lerr-proj2Lerr,'--b')
# hecu=plt.plot(eculidian,'-k')
ax = plt.gca()
ax.set_xticks([12.5, 32.5, 52.5])
ax.set_xticklabels([0, 5, 10])
ax.set_xlabel('Time (s)')
ax.set_ylabel('S1-S2 C.D. distance (a.u.)')
testmark = [40, 44] if delay == 6 else [28, 32]
[
plt.axvline(x, ls='--', c='k')
for x in np.array([12, 16] + testmark) + 0.5
]
plt.legend((he[0], hm[0], hl[0]), ('Early CD', 'Mid CD', 'Late CD'))
def ica_dist(delay=6):
fr_per_su = util.get_dataset()
S1_mm=np.hstack([np.mean(onesu[f'S1_{delay}'],axis=2) for onesu in fr_per_su])
S2_mm=np.hstack([np.mean(onesu[f'S2_{delay}'],axis=2) for onesu in fr_per_su])
scaler=StandardScaler()
delaybin=range(16,16+delay*4);
scaler.fit(np.vstack((S1_mm[delaybin,:],S2_mm[delaybin,:])))
norm_mm=scaler.transform(np.vstack((S1_mm,S2_mm)))
ica = FastICA(n_components=20)
#TODO 3s delay compatbility
pcamat=norm_mm[list(delaybin)+list(np.array(delaybin)+56),:]
ica.fit(pcamat)
comp=ica.transform(norm_mm)
coeff = ica.mixing_
icnum=5
hp=[None]*icnum
plt.figure()
for c in range(icnum):
hp[c]=plt.plot(np.abs(np.array(comp[:56,c])-np.array(comp[56:,c])))
# eculidian=[np.linalg.norm([proj1E[t]-proj2E[t],proj1M[t]-proj2M[t],proj1L[t]-proj2L[t]]) for t in range(len(proj1E))]
#
plt.legend([hp[c][0] for c in range(icnum)], [f'IC{c+1}' for c in range(icnum)])
ax=plt.gca()
ax.set_ylabel('S1-S2 PC space distance (a.u.)')
ax.set_xticks(np.array([12,32,53])-0.5)
ax.set_xticklabels([0,5,10])
ax.set_xlabel('Time (s)')
testmark=[40,44] if delay==6 else [28,32]
[plt.axvline(x,ls='--',c='k') for x in np.array([12,16]+testmark)-0.5]
plt.title(f'IC1-IC{icnum}')
fig=plt.figure()
ax = fig.add_subplot(projection='3d')
h1=ax.plot(comp[8:45,0],comp[8:45,1],comp[8:45,2],'-r')
h2=ax.plot(comp[64:101,0],comp[64:101,1],comp[64:101,2],'-b')
interp=[np.interp(np.array([12,16,12+56,16+56,40,44,40+56,44+56])-0.5,
range(comp.shape[0]),
comp[:,c])
for c in range(3)]
hs=[ax.plot(interp[0][t],interp[1][t],interp[2][t],'o',c=[0.5,0.5,0.5]) for t in range(4)]
ht=[ax.plot(interp[0][t],interp[1][t],interp[2][t],'ko') for t in range(4,8)]
ax.set_zlabel('IC3')
ax.set_xlabel('IC1')
ax.set_ylabel('IC2')
plt.legend([h1[0],h2[0],hs[0][0],ht[0][0]], ['S1 trial','S2 trial','Sample','Test'])
def cd_FWHM(delay=6):
fr_per_su = util.get_dataset()
S1_mm = np.hstack(
[np.mean(onesu[f'S1_{delay}'], axis=2) for onesu in fr_per_su])
S2_mm = np.hstack(
[np.mean(onesu[f'S2_{delay}'], axis=2) for onesu in fr_per_su])
cdMat = (S1_mm - S2_mm)
#TODO:traverse bin
widths = []
binws = [8, 6, 4, 3, 2, 1]
for binw in binws:
widthbin = []
# fig=plt.figure()
for onset in range(16, 40, binw):
cdBin = np.mean(cdMat[onset:onset + binw, :], axis=0)
cdBin = cdBin / np.linalg.norm(cdBin)
proj1Bin = S1_mm @ cdBin
proj2Bin = S2_mm @ cdBin
curve = proj1Bin - proj2Bin
# plt.plot(curve)
(peaks, prop) = find_peaks(curve, distance=56)
(w, _, _, _) = peak_widths(curve, peaks)
widthbin.append(w)
widths.append(widthbin)
ci = np.array([bootstrap.ci(d, np.mean, n_samples=500)
for d in widths]) * 0.25
plt.figure()
plt.fill_between(np.array(binws) * 0.25,
ci[:, 0],
ci[:, 1],
color="r",
alpha=0.2)
plt.plot(np.array(binws) * 0.25, [np.mean(x) * 0.25 for x in widths], '-r')
plt.plot([0, 3], [0, 3], ':k')
ax = plt.gca()
ax.set_xlabel('CD window (s)')
ax.set_ylabel('FWHM of CD projection (s)')
ax.set_xlim((0, 2))
ax.set_ylim((0, 4))
def pca_dist(delay=6):
fr_per_su = util.get_dataset()
S1_mm=np.hstack([np.mean(onesu[f'S1_{delay}'],axis=2) for onesu in fr_per_su])
S2_mm=np.hstack([np.mean(onesu[f'S2_{delay}'],axis=2) for onesu in fr_per_su])
scaler=StandardScaler()
delaybin=range(16,16+delay*4);
scaler.fit(np.vstack((S1_mm[delaybin,:],S2_mm[delaybin,:])))
norm_mm=scaler.transform(np.vstack((S1_mm,S2_mm)))
pca = PCA(n_components=20)
#TODO 3s delay compatbility
pcamat=norm_mm[list(delaybin)+list(np.array(delaybin)+56),:]
pca.fit(pcamat)
comp=pca.transform(norm_mm)
coeff = pca.components_
ratio = pca.explained_variance_ratio_
pcnum=8
hp=[None]*pcnum
plt.figure()
for c in range(pcnum):
hp[c]=plt.plot(np.abs(np.array(comp[:56,c])-np.array(comp[56:,c])))
#TODO: eculidian distance
# eculidian=[np.linalg.norm(
# [proj1E[t]-proj2E[t],proj1M[t]-proj2M[t],proj1L[t]-proj2L[t]]) for t in range(len(proj1E))]
plt.legend([hp[c][0] for c in range(pcnum)], [f'PC{c+1}, {ratio[c]*100:.1f}%' for c in range(pcnum)])
ax=plt.gca()
ax.set_ylabel('S1-S2 PC space distance (a.u.)')
ax.set_xticks(np.array([12,32,53])-0.5)
ax.set_xticklabels([0,5,10])
ax.set_xlabel('Time (s)')
testmark=[40,44] if delay==6 else [28,32]
[plt.axvline(x,ls='--',c='k') for x in np.array([12,16]+testmark)-0.5]
plt.title(f'PC1-PC{pcnum}, total {np.sum(ratio[:pcnum])*100:.1f}% delay variance')
def cd_heatmap(delay=6, bound=0.5):
fr_per_su = util.get_dataset()
S1_mm = np.hstack(
[np.mean(onesu[f'S1_{delay}'], axis=2) for onesu in fr_per_su])
S2_mm = np.hstack(
[np.mean(onesu[f'S2_{delay}'], axis=2) for onesu in fr_per_su])
cdMat = (S1_mm - S2_mm)
cdco = np.corrcoef(cdMat)
plt.figure()
im = plt.imshow(cdco,
cmap="jet",
aspect="equal",
vmin=-1,
vmax=1,
origin='lower')
testmark = [40, 44] if delay == 6 else [28, 32]
[
plt.axhline(x, ls='--', c='w')
for x in np.array([12, 16] + testmark) - 0.5
]
[
plt.axvline(x, ls='--', c='w')
for x in np.array([12, 16] + testmark) - 0.5
]
plt.colorbar(im)
plt.title('C.D. cross-temporal corr. coef.')
ax = plt.gca()
ax.set_xticks([12.5, 32.5, 52.5])
ax.set_xticklabels([0, 5, 10])
ax.set_yticks([12.5, 32.5, 52.5])
ax.set_yticklabels([0, 5, 10])
ax.set_xlabel('Time (s)')
ax.set_ylabel('Time (s)')
ax.set_xlim(7.5, 43.5)
ax.set_ylim(7.5, 43.5)
def cdT_projection(delay=6):
fr_per_su = util.get_dataset()
S1_mm = np.hstack(
[np.mean(onesu[f'S1_{delay}'], axis=2) for onesu in fr_per_su])
S2_mm = np.hstack(
[np.mean(onesu[f'S2_{delay}'], axis=2) for onesu in fr_per_su])
cdMat = (S1_mm - S2_mm)
cdDelayE = np.mean(cdMat[16:24, :], axis=0)
cdDelayE = cdDelayE / np.linalg.norm(cdDelayE)
cdDelayM = np.mean(cdMat[24:32, :], axis=0)
cdDelayM = cdDelayM / np.linalg.norm(cdDelayM)
cdDelayL = np.mean(cdMat[32:40, :], axis=0)
cdDelayL = cdDelayL / np.linalg.norm(cdDelayL)
(proj1E, proj1M, proj1L) = (S1_mm @ cdDelayE, S1_mm @ cdDelayM,
S1_mm @ cdDelayL)
(proj2E, proj2M, proj2L) = (S2_mm @ cdDelayE, S2_mm @ cdDelayM,
S2_mm @ cdDelayL)
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.plot(proj1E, proj1M, proj1L, '-r')
ax.plot(proj2E, proj2M, proj2L, '-b')
cdDelayE = np.mean(cdMat[16:24, :], axis=0)
cdDelayE = cdDelayE / np.linalg.norm(cdDelayE)
cdDelayL = np.mean(cdMat[32:40, :], axis=0)
cdDelayL = cdDelayL / np.linalg.norm(cdDelayL)
(proj1E, proj1L) = (S1_mm @ cdDelayE, S1_mm @ cdDelayL)
(proj2E, proj2L) = (S2_mm @ cdDelayE, S2_mm @ cdDelayL)
testmark = [40, 44] if delay == 6 else [28, 32]
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
h1 = ax.plot(proj1E, proj1L, range(56), '-r')
h2 = ax.plot(proj2E, proj2L, range(56), '-b')
hs = [
ax.plot(proj1E[t], proj1L[t], t, 'o', c=[0.5, 0.5, 0.5])
for t in [12, 16]
]
[
ax.plot(proj2E[t], proj2L[t], t, 'o', c=[0.5, 0.5, 0.5])
for t in [12, 16]
]
ht = [ax.plot(proj1E[t], proj1L[t], t, 'ko') for t in testmark]
[ax.plot(proj2E[t], proj2L[t], t, 'ko') for t in testmark]
ax.set_zticks([12.5, 32.5, 52.5])
ax.set_zticklabels([0, 5, 10])
ax.set_zlabel('Time (s)')
ax.set_xlabel('Early delay CD proj. (a.u.)')
ax.set_ylabel('Late delay CD proj. (a.u.)')
plt.legend([h1[0], h2[0], hs[0][0], ht[0][0]],
['S1 trial', 'S2 trial', 'Sample', 'Test'])
def cd_projection(delay=6):
fr_per_su = util.get_dataset()
S1_mm = np.hstack(
[np.mean(onesu[f'S1_{delay}'], axis=2) for onesu in fr_per_su])
S2_mm = np.hstack(
[np.mean(onesu[f'S2_{delay}'], axis=2) for onesu in fr_per_su])
cdMat = (S1_mm - S2_mm)
cdDelayE = np.mean(cdMat[16:20, :], axis=0)
cdDelayE = cdDelayE / np.linalg.norm(cdDelayE)
cdDelayM = np.mean(cdMat[26:30, :], axis=0)
cdDelayM = cdDelayM / np.linalg.norm(cdDelayM)
cdDelayL = np.mean(cdMat[36:40, :], axis=0)
cdDelayL = cdDelayL / np.linalg.norm(cdDelayL)
(proj1E, proj1M, proj1L) = (S1_mm @ cdDelayE, S1_mm @ cdDelayM,
S1_mm @ cdDelayL)
(proj2E, proj2M, proj2L) = (S2_mm @ cdDelayE, S2_mm @ cdDelayM,
S2_mm @ cdDelayL)
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.plot(proj1E, proj1M, proj1L, '-r')
ax.plot(proj2E, proj2M, proj2L, '-b')
cdDelayE = np.mean(cdMat[16:24, :], axis=0)
cdDelayE = cdDelayE / np.linalg.norm(cdDelayE)
cdDelayL = np.mean(cdMat[32:40, :], axis=0)
cdDelayL = cdDelayL / np.linalg.norm(cdDelayL)
(proj1E, proj1L) = (S1_mm @ cdDelayE, S1_mm @ cdDelayL)
(proj2E, proj2L) = (S2_mm @ cdDelayE, S2_mm @ cdDelayL)
testmark = [40, 44] if delay == 6 else [28, 32]
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
h1 = ax.plot(proj1E[8:45], proj1M[8:45], proj1L[8:45], '-r')
h2 = ax.plot(proj2E[8:45], proj2M[8:45], proj2L[8:45], '-b')
interp = [
np.interp(
np.array([12, 16] + testmark) - 0.5, range(oneproj.shape[0]),
oneproj)
for oneproj in [proj1E, proj1M, proj1L, proj2E, proj2M, proj2L]
]
hs = [
ax.plot(interp[0][t],
interp[1][t],
interp[2][t],
'o',
c=[0.5, 0.5, 0.5]) for t in [0, 1]
]
[
ax.plot(interp[3][t],
interp[4][t],
interp[5][t],
'o',
c=[0.5, 0.5, 0.5]) for t in [0, 1]
]
ht = [
ax.plot(interp[0][t], interp[1][t], interp[2][t], 'ko')
for t in [2, 3]
]
[ax.plot(interp[3][t], interp[4][t], interp[5][t], 'ko') for t in [2, 3]]
# ax.set_zticks([12.5, 32.5, 52.5])
# ax.set_zticklabels([0, 5, 10])
ax.set_zlabel('Late delay CD proj. (a.u.)')
ax.set_xlabel('Early delay CD proj. (a.u.)')
ax.set_ylabel('Mid delay CD proj. (a.u.)')
plt.legend([h1[0], h2[0], hs[0][0], ht[0][0]],
['S1 trial', 'S2 trial', 'Sample', 'Test'])