def spk_contr_var(spk, contr, fn, tl, make = 1, mn=0):
if(make == 1):
vr = var(spk, axis = 1)*1000
vr = mean(vr,axis=1)
if(mn == 1):
spk_avg = mean(mean(spk, axis=1), axis=1)
contr = spk_avg * 1000
savetxt("txt/spk_contr_var_vr.txt", vr)
savetxt("txt/spk_contr_var_contr.txt", contr)
else:
vr = loadtxt("txt/spk_contr_var_vr.txt")
contr = loadtxt("txt/spk_contr_var_contr.txt")
bnr = 100
l = 10
vals, bins = bin_means(contr, vr, bnr)
run_avg = running_mean(vals, l)
print (run_avg)
d = len(run_avg)
print (d)
plt.clf()
plt.plot(contr, vr, 'go', alpha=.4)
plt.plot(bins[l/2:d+l/2], run_avg, lw = 8, color = "red")
# plt.ylim([0,300])
if (mn == 1):
myplot("mean spiking rate", "spike count variance", tl, fn, xlog=False, legend=False)
else:
myplot("inferred contrast", "spike count variance", tl, fn, xlog=False, legend=False)
def spk_contr_fano(spk, contr, fn, tl, make = 1, mn=0):
if(make == 1):
vr = var(spk, axis = 1)*1000
vr = mean(vr,axis=1)
spk_avg = mean(mean(spk, axis=1), axis=1)*1000
savetxt("txt/vr.txt", vr)
savetxt("txt/spk_avg.txt", spk_avg)
else:
vr = loadtxt("txt/vr.txt")
spk_avg = loadtxt("txt/spk_avg.txt")
fan = vr/spk_avg
bnr = 100
l = 10
vals, bins = bin_means(spk_avg, fan, bnr)
run_avg = running_mean(vals, l)
print (run_avg)
d = len(run_avg)
print (d)
plt.clf()
plt.plot(spk_avg, fan, 'go', alpha=.4)
plt.plot(bins[l/2:d+l/2], run_avg, lw = 8, color = "red") # was started from 1, first was buggy
# plt.ylim([0.7,1.1])
if (mn == 1):
myplot("mean spiking rate", "Fano-factor of spiketrains", tl, fn, xlog=False, legend=False)
else:
myplot("inferred contrast", "spike count variance", tl, fn, xlog=False, legend=False)
def do_it_matched_plot_fano(make=1):
if(make==1):
u_i = 1.0
u_p = 3.3
c_i = 19.6
c_p = 0.8
# u_arr = [1.95, 2.3, 2.9, 3.2, 3.5, 4]
ui_arr = arange(u_i, u_i*4, u_i*0.3)
up_arr = arange(u_p, u_p*2, u_p*0.1)
n = len(ui_arr)
pfano_arr = zeros(n)
ifano_arr = zeros(n)
pmean_arr = zeros(n)
imean_arr = zeros(n)
for i in range(0,n):
print (i)
pfano_arr[i], ifano_arr[i], pmean_arr[i], imean_arr[i] = do_it_matched_fano(ui_arr[i], up_arr[i], c_i, c_p)
save("txt/matched_scale_fano.npy", [pfano_arr, ifano_arr, pmean_arr, imean_arr])
else:
pfano_arr, ifano_arr, pmean_arr, imean_arr = load("txt/matched_scale_fano.npy")
tl = ""
fn = "images/matched_scale_fano.png"
plt.clf()
plt.plot(pmean_arr, pfano_arr, "b.", label = "Poisson", ms = 10)
[a,b] = polyfit(pmean_arr, pfano_arr, 1)
plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="blue")
plt.plot(imean_arr, ifano_arr, "r.", label = "Integration", ms = 10)
[a,b] = polyfit(imean_arr, ifano_arr, 1)
plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="red")
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
plt.xlim([25,65])
myplot(r"$Mean_{SC}$", r"$Fano_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
def do_it_matched_plot(make=1):
if(make==1):
# u_arr = [1.95, 2.3, 2.9, 3.2, 3.5, 4]
u_arr = arange(1.95, 4, 0.1)
n = len(u_arr)
pcoeff_arr = zeros(n)
icoeff_arr = zeros(n)
pvar_arr = zeros(n)
pmean_arr = zeros(n)
ivar_arr = zeros(n)
imean_arr = zeros(n)
for i in range(0,n):
print (i)
pcoeff_arr[i], icoeff_arr[i], pvar_arr[i], pmean_arr[i], ivar_arr[i], imean_arr[i] = do_it_matched(make=1, u_mean=u_arr[i])
save("txt/matched_scale.npy", [pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr])
else:
pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr = load("txt/matched_scale.npy")
tl = ""
fn = "images/matched_scale.png"
plt.clf()
plt.plot(pmean_arr, pcoeff_arr, "b.", label = "Poisson", ms = 10)
[a,b] = polyfit(pmean_arr, pcoeff_arr, 1)
plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="blue")
plt.plot(imean_arr, icoeff_arr, "r.", label = "Integration", ms = 10)
[a,b] = polyfit(imean_arr, icoeff_arr, 1)
plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="red")
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
plt.xlim([5,50])
myplot(r"$Mean_{SC}$", r"$\rho_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
def make_barplot():
#variance(lp,hp,li,hi) [ 0.03763326] [ 0.08509415] [ 0.20487687] [ 0.20641627]
#[0.20641627, 0.20487687,0.08509415, 0.03763326] (hi, li, hp, lp)
#mean(lp,hp,li,hi) [ 0.0377612] [ 0.02496299] [ 0.20685853] [ 0.27907393]
#[0.20685853, 0.27907393, 0.0377612, 0.02496299] (li, hi, lp, hp)
plt.clf()
tl = ""
name="tr2tr"
diff="variance"
fn = "images/%sdistrib_bar_%s.png"%(name,diff)
plt.xticks([0.4,0.8], ["I","P"])
barlist = plt.bar([0.3,0.4,0.7,0.8], [0.20641627, 0.20487687,0.08509415, 0.03763326], alpha = 0.7, width = 0.1, fill=False, lw=8)
barlist[0].set_color('b')
barlist[1].set_color('r')
barlist[2].set_color('b')
barlist[3].set_color('r')
myplot("", "", tl, fn, xlog=False, legend=False, xloc=False, fsax=50, fs = 50)
plt.clf()
tl = ""
name="tr2tr"
diff="mean"
fn = "images/%sdistrib_bar_%s.png"%(name,diff)
plt.xticks([0.4,0.8], ["I","P"])
barlist = plt.bar([0.3,0.4,0.7,0.8], [0.20685853, 0.27907393, 0.0377612, 0.02496299], alpha = 0.7, width = 0.1, fill=False, lw=8)
barlist[0].set_color('b')
barlist[1].set_color('r')
barlist[2].set_color('b')
barlist[3].set_color('r')
myplot("", "", tl, fn, xlog=False, legend=False, xloc=False, fsax=50, fs = 50)
return 0
def pairs_rates(rate_arr, fn, tl, b1, b2):
br1 = rate_arr[:,b1]
br2 = rate_arr[:,b2]
plt.clf()
plt.plot(br1, br2, "b.", ms = 10)
plt.xlim([0, 80])
plt.ylim([0, 90])
myplot("rate of neuron 1", "rate of neuron 2", tl, fn, xlog=False, legend=False)
def pairs_samples(samp_arr, fn, tl, b1, b2):
bs1 = samp_arr[:,b1]
bs2 = samp_arr[:,b2]
plt.clf()
plt.plot(bs1, bs2, "b.", ms = 10)
plt.xlim([-6, 9])
plt.ylim([-6, 9])
myplot("potential of neuron 1", "potential of neuron 2", tl, fn, xlog=False, legend=False)
def do_it_match_plotter3(make = 1):
imgNr = 3000
l = 50 # 500
k_p = 10.0
n_p = 1.1
ut_p = 1.9
k_i = 2.0
n_i = 1.1
ut_i = 1.9
u_mi = 2.2
u_mp = 2.2
u_ci = 1500.0
u_cp = .1
c1 = 1.5
c2 = 2.0
cs = 0.1
k1 = 0.0
k2 = 1.0
ks = 0.1
if(make == 1):
c_arr = pow(10, arange(c1, c2, cs))
cl = len(c_arr)
k_arr = pow(10, arange(k1, k2, ks))
kl = len(k_arr)
diff = zeros((cl, kl))
mnki = 0
mnci = 0
for ci in range(cl):
for ki in range(kl):
print(c_arr[ci], k_arr[ki])
pvar, pmean, ivar, imean = new_gpairs_match2(u_mi, u_mp, c_arr[ci], u_cp, 0.2, 0.7, l, imgNr, k_arr[k_i], n_i, ut_i, k_p, n_p, ut_p)[2:6]
print(pvar, pmean, ivar, imean)
vardiff = abs(pvar-ivar)/max(pvar,ivar)
meandiff = abs(pmean-imean)/max(pmean, imean)
print(vardiff, meandiff)
diff[ci][ki] = max(vardiff, meandiff) #0.5*vardiff + 0.5*meandiff
if (diff[ci][ki] < diff[mnci][mnki]):
mnci=ci
mnki=ki
print(c_arr[mnci], k_arr[mnki], diff[mnci][mnki])
save("txt/match_p_3.npy", diff)
else:
diff = load("txt/match_p_3.npy")
tl = ""
fn = "images/match_p_3.png"
plt.clf()
plt.imshow((diff.T)[::-1, :], interpolation="none", extent=[10**c1-10**(cs/2.0), 10**c2+10**(cs/2.0), 10**k1-10**(ks/2.0), 10**k2+10**(ks/2.0)], aspect = "auto")
plt.colorbar()
myplot(r"$c_i$", r"$k_i$", tl, fn, xlog=False, legend=False, fsax=50, xloc=False)
def do_it_match_plotter_input(make=1):
k_p = 4.2
n_p = 1.1
ut_p = 1.92
k_i = 10.0
n_i = 1.1
ut_i = 1.9
u_mi = 2.0
u_mp = 1.7
u_ci = 1.0
u_cp = 1.8
mp1 = 1.5
mp2 = 2.0
mps = 0.1
cp1 = 1.4
cp2 = 2.0
cps = 0.1
imgNr = 1500 # 3000
l = 50 # 500
if(make == 1):
umps = arange(mp1, mp2, mps)
umpl = len(umps)
ucps = arange(cp1, cp2, cps)
ucpl = len(ucps)
diff = zeros((umpl, ucpl))
mnumpi = 0
mnucpi = 0
for umpi in range(umpl):
for ucpi in range(ucpl):
print(umps[umpi], ucps[ucpi])
pvar, pmean, ivar, imean = new_gpairs_match2(u_mi, umps[umpi], u_ci, ucps[ucpi], 0.2, 0.7, l, imgNr, k_i, n_i, ut_i, k_p, n_p, ut_p)[2:6]
print(pvar, pmean, ivar, imean)
vardiff = abs(pvar-ivar)/max(pvar,ivar)
meandiff = abs(pmean-imean)/max(pmean, imean)
print(vardiff, meandiff)
diff[umpi][ucpi] = max(vardiff, meandiff) #0.5*vardiff + 0.5*meandiff
if (diff[umpi][ucpi] < diff[mnumpi][mnucpi]):
mnumpi=umpi
mnucpi=ucpi
print(umps[mnumpi], ucps[mnucpi], diff[mnumpi][mnucpi])
save("txt/match_p.npy", diff)
else:
diff = load("txt/match_p.npy")
tl = ""
fn = "images/match_p.png"
plt.clf()
plt.imshow((diff.T)[::-1, :], interpolation="none", extent=[mp1-mps/2.0, mp2+mps/2.0, cp1-cps/2.0, cp2+cps/2.0], aspect = "auto")
plt.colorbar()
myplot(r"$u_{mp}$", r"$u_{cp}$", tl, fn, xlog=False, legend=False, fsax=50, xloc=False)
def do_it_1b(make=1, u_mean = 2):
imgNr = 3000
l = 50 # 500
n = imgNr
ni = l*100
if(make == 1):
pspike_arr = zeros((n,2))
ispike_arr = zeros((n,2))
pspike_arri = zeros((n,2))
ispike_arri = zeros((n,2))
pspike_arr, ispike_arr = new_gpairs(u_mean, 0.3, l, imgNr)[6:8]
pspike_arri, ispike_arri = new_gpairs_small(u_mean, 0.3, ni)[6:8]
save("txt/spikecov%.2f.npy"%u_mean, [pspike_arr, ispike_arr, pspike_arri, ispike_arri])
else:
pspike_arr, ispike_arr, pspike_arri, ispike_arri = load("txt/spikecov%.2f.npy"%u_mean)
# trial to trial
tl = "" #"Spike count - Poisson"
fn = "images/tr2trspkp%.2f.png"%u_mean
plt.clf()
plt.plot(pspike_arr[:,0], pspike_arr[:,1], "b.", ms = 10)
[a,b] = polyfit(pspike_arr[:,0], pspike_arr[:,1], 1)
plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="black")
pspike_arrC = cov(pspike_arr, rowvar=False)
plt.annotate("r = %0.2f"%(pspike_arrC[0,1]/pspike_arrC[0,0]), xy=(0.05, 0.9), xycoords='axes fraction', fontsize=30)
pspike_arrm = pspike_arr.mean(axis=0)
plot_cov_ellipse(pspike_arrC, pspike_arrm, nstd=1.5, color='green', lw=4, zorder=4)
if(u_mean == 1.95):
plt.ylim([1,16])
plt.xlim([1,16])
else:
plt.ylim([10,45])
plt.xlim([10,45])
myplot(r"$n_1$", r"$n_2$", tl, fn, xlog=False, legend=False, fs=30, fsl=30, fsax=50)
tl = "" #"Spike count - Integration"
fn = "images/tr2trspki%.2f.png"%u_mean
plt.clf()
plt.plot(ispike_arr[:,0], ispike_arr[:,1], "b.", ms = 10)
[a,b] = polyfit(ispike_arr[:,0], ispike_arr[:,1], 1)
plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="black")
ispike_arrC = cov(ispike_arr, rowvar=False)
plt.annotate("r = %0.2f"%(ispike_arrC[0,1]/ispike_arrC[0,0]), xy=(0.05, 0.9), xycoords='axes fraction', fontsize=30)
ispike_arrm = ispike_arr.mean(axis=0)
plot_cov_ellipse(ispike_arrC, ispike_arrm, nstd=1.5, color='green', lw=4, zorder=4)
if(u_mean == 1.95):
plt.ylim([1,16])
plt.xlim([1,16])
else:
plt.ylim([10,45])
plt.xlim([10,45])
myplot(r"$n_1$", r"$n_2$", tl, fn, xlog=False, legend=False, fs=30, fsl=30, fsax=50)
def correlation_maximum_line(make = 1):
# surface for spike count mean and variance -- spkc corr. derivative
l = 50
imgNr = 30000 #30000
k = 10
ne = 1.4
ut = 0
corr_i = 0.15
corr_p = 0.85
ui_arr = arange(-1.5, 8, 2)
ui_cov = arange(1, 5, 1)
up_arr = arange(-1.5, 8, 2)
up_cov = arange(1, 5, 1)
n = len(ui_arr)
m = len(ui_cov)
if(make==1):
pcoeff_arr = zeros((n,m))
icoeff_arr = zeros((n,m))
pvar_arr = zeros((n,m))
pmean_arr = zeros((n,m))
ivar_arr = zeros((n,m))
imean_arr = zeros((n,m))
for i in range(0,n):
for j in range (0,m):
print (i, j)
pcoeff_arr[i,j], icoeff_arr[i,j], pvar_arr[i,j], pmean_arr[i,j], ivar_arr[i,j], imean_arr[i,j] = new_gpairs_match2(ui_arr[i], up_arr[i], ui_cov[j], up_cov[j], corr_i, corr_p, l, imgNr, k, ne, ut, k, ne, ut)[0:6]
print(pcoeff_arr[i,j], icoeff_arr[i,j])
save("txt/corrderiv4.npy", [pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr])
else:
pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr = load("txt/corrderiv4.npy")
# other one was made with 3 --
# return
mn = up_arr[argmax((100*pcoeff_arr).astype(int), 0)]
mn2 = up_arr[n-1-argmax((100*pcoeff_arr[::-1]).astype(int), 0)]
cv = up_cov
fn = "images/corrderiv_max.png"
tl = ""
plt.clf()
plt.plot(mn, cv, "w--", lw=5)
plt.plot(mn2, cv, "w--", lw=5)
plt.xlim([-1.5, 6])
extra = plt.text(5.8, 4.1, r"Mean$_{SC}$ (k)", fontsize=20)
plt.contourf(up_arr,up_cov,pmean_arr.T/10,250,cmap=plt.cm.jet)
plt.clim(0, 10)
plt.colorbar()
myplot(r"Mean$_{mb}$ (mV)", r"Var$_{mb}$ (mV$^2$)", tl, fn, fs=15, fsl=15, xlog=False, extra=(extra,), legend=False, fsax=20, xloc=False)
def pairs_spikec(spike_arr, fn, tl, b1, b2, l):
bs1 = spike_arr[:,b1].astype(float)
bs2 = spike_arr[:,b2].astype(float)
# sl = len(bs1)
# vals1, bins1 = bin_means(range(0,sl), bs1, l)
# vals2, bins2 = bin_means(range(0,sl), bs2, l)
# vals1 = vals1*sl/l
# vals2 = vals2*sl/l
# plt.clf()
# plt.plot(vals1, vals2, "o")
print(sum(bs1), sum(bs2))
sl = len(bs1)
delta = 0.23
vals1, bins1 = bin_means(range(0,sl), bs1, l)
vals2, bins2 = bin_means(range(0,sl), bs2, l)
vals1 = vals1*sl/l
vals2 = vals2*sl/l
xedges = arange(min(vals1), min(vals1) + int((max(vals1)-min(vals1))/delta)*delta + 2* delta, delta)-delta/2
yedges = arange(min(vals2), min(vals2)+ int((max(vals2)-min(vals2))/delta)*delta + 2 * delta, delta)-delta/2
h, xedges, yedges = histogram2d(vals1, vals2, bins = [xedges, yedges])
xmids = arange(min(vals1), min(vals1) + int((max(vals1)-min(vals1))/delta)*delta + delta, delta)
ymids = arange(min(vals2), min(vals2) + int((max(vals2)-min(vals2))/delta)*delta + delta, delta)
X, Y = meshgrid(xmids, ymids)
plt.clf()
plt.scatter(X, Y, s = h*20)
# plt.xlim([0, 24])
# plt.ylim([0, 28])
print(corrcoef(vals1, vals2))
# print(sum(bs1), sum(bs2))
# sl = len(bs1)
# vals1, bins1 = bin_means(range(0,sl), bs1, l)
# vals2, bins2 = bin_means(range(0,sl), bs2, l)
# vals1 = (vals1*sl/l).astype(int)
# vals2 = (vals2*sl/l).astype(int)
# xedges = arange(int(min(vals1)), int(max(vals1))+2)-0.5
# yedges = arange(int(min(vals2)), int(max(vals2))+2)-0.5
# h, xedges, yedges = histogram2d(vals1, vals2, bins = [xedges, yedges])
# xmids = arange(int(min(vals1)), int(max(vals1))+1)
# ymids = arange(int(min(vals2)), int(max(vals2))+1)
# X, Y = meshgrid(xmids, ymids)
# plt.clf()
# plt.scatter(X, Y, s = h*20)
# plt.xlim([0, 21])
# plt.ylim([0, 21])
# plt.xlim([int(min(vals1))-0.1, int(max(vals1))+0.1])
# plt.ylim([int(min(vals2))-0.1, int(max(vals2))+0.1])
myplot("spike count of neuron 1", "spike count of neuron 2", tl, fn, xlog=False, legend=False)
def do_it_2(make = 1):
if(make == 1):
new_g_pairs(make)
a3 = load("txt/t2t_potcorr3.npy")
a6 = load("txt/t2t_potcorr6.npy")
a9 = load("txt/t2t_potcorr9.npy")
tl = ""#"Trial-to-trial correlations"
fn = "images/tr2trpairs.png"
lw = 7
plt.plot(a3[0], a3[1], label = "Poisson, c=0.3", color="blue", linestyle="--", lw=lw);
plt.plot(a3[0], a3[2], label = "Integration, c=0.3", color="blue", linestyle="-", lw=lw);
plt.plot(a6[0], a6[1], label = "Poisson, c=0.6", color="red", linestyle="--", lw=lw);
plt.plot(a6[0], a6[2], label = "Integration, c=0.6", color="red", linestyle="-", lw=lw);
plt.plot(a9[0], a9[1], label = "Poisson, c=0.9", color="green", linestyle="--", lw=lw);
plt.plot(a9[0], a9[2], label = "Integration, c=0.9", color="green", linestyle="-", lw=lw);
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
myplot(r"$Mean(u)$", r"$\rho_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
def spk_contr_mean(spk, contr, fn, tl, make=1):
if(make == 1):
mn = mean(mean(spk, axis = 1)*1000,axis=1)
savetxt("txt/spk_contr_mean_mn.txt", mn)
savetxt("txt/spk_contr_mean_contr.txt", contr)
else:
mn = loadtxt("txt/spk_contr_mean_mn.txt")
contr = loadtxt("txt/spk_contr_mean_contr.txt")
bnr = 100
l = 10
vals, bins = bin_means(contr, mn, bnr)
run_avg = running_mean(vals, l)
print (run_avg)
d = len(run_avg)
print (d)
plt.clf()
plt.plot(contr, mn, 'go', alpha=.4)
plt.plot(bins[l/2:d+l/2], run_avg, lw = 8, color = "red")
myplot("inferred contrast", "mean spiking rate", tl, fn, xlog=False, legend=False)
def fig1b(make):
imgNr = 1500 # 3000
l = 50 # 500
# input ranges
u1 = 1.0
u2 = 10.0
ud = 1.0
c = 1.0
us = arange(u1, u2, ud)
ul = len(us)
pmeans = zeros(ul)
imeans = zeros(ul)
if(make == 1):
# nonlinearity parameters
k = 10.0
n = 1.1
ut = 1.9
for ui in range(ul):
print(ui)
pmeans[ui], haha, imeans[ui] = new_gpairs_match2(us[ui], us[ui], c, c, 0.2, 0.7, l, imgNr, k, n, ut, k, n, ut)[3:6]
save("txt/fig1b.npy", [pmeans, imeans])
else:
pmeans, imeans = load("txt/fig1b.npy")
tl = r"$Mean_{SC}$"
fn = "images/fig1b.png"
plt.clf()
plt.plot(us, pmeans, "b.", label = "Poisson", ms = 10)
# [a,b] = polyfit(pmean_arr, pfano_arr, 1)
# plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="blue")
plt.plot(us, imeans, "r.", label = "Integration", ms = 10)
# [a,b] = polyfit(imean_arr, ifano_arr, 1)
# plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="red")
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
myplot(r"$Mean_{u} (mV)$", r"$Mean_{SC} (sp/s)$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
def spk_contr_corr(spk, contr, fn, tl, make = 1, mn=0):
if(make==1):
imgNr = size(spk,0)
avg_cv = zeros(imgNr)
if(mn == 1):
spk_avg = mean(mean(spk, axis=1), axis=1)
contr = spk_avg * 1000
for i in range(0,imgNr):
cv = cov(spk[i], rowvar=0)
corr = nan_to_num(correlate_cov(cv))
a = abs(corr-diag(diag(corr)))
avg_cv[i] = mean(a)
savetxt("txt/spk_contr_corr_avg_cv.txt", avg_cv)
savetxt("txt/spk_contr_corr_contr_cv.txt", contr)
else:
avg_cv = loadtxt("txt/spk_contr_corr_avg_cv.txt")
contr = loadtxt("txt/spk_contr_corr_contr_cv.txt")
bnr = 100
l = 10
vals, bins = bin_means(contr, avg_cv, bnr)
run_avg = running_mean(vals, l)
d = len(run_avg)
plt.clf()
plt.plot(contr, avg_cv, 'go', alpha=.4)
plt.plot(bins[l/2:d+l/2], run_avg, lw = 8, color = "red")
# plt.ylim([0,0.035])
if(mn == 1):
myplot("mean spiking rate", "average correlations", tl, fn, xlog=False, legend=False)
else:
myplot("inferred contrast", "average correlations", tl, fn, xlog=False, legend=False)
features = np.random.random((n_features, n_data))
for i in range(10000):
weights = weights - d_weights * 0.001 * 0.1**(0.25 * np.log10(i + 1.0))
#print(weights)
#myplot(1, np.linspace(1,n_neurons,n_neurons), neurons, '-g', 2.0, None)
beta = MyNN.nn.predict(act_fn, np.asfortranarray(n_neurons_layer),
np.asfortranarray(weights),
np.asfortranarray(neurons),
np.asfortranarray(features))
if i == 0:
myplot("ML_init", np.linspace(1, n_data, n_data), beta, '-b', 2.0,
None)
print("ML Iteration: %6d\t\tLoss: %E" % (i, eval_loss(beta)))
#print("predicted")
d_beta = 2.0 * (beta - 1.0)
d_weights = MyNN.nn.get_sens(act_fn,
np.asfortranarray(n_neurons_layer),
np.asfortranarray(weights),
np.asfortranarray(neurons),
np.asfortranarray(features),
np.asfortranarray(d_beta))
d_weights = d_weights / np.max(np.abs(d_weights))
def correlation_derivative(make = 1):
# surface for spike count mean and variance -- spkc corr. derivative
l = 50
imgNr = 30000 #30000
k = 10
ne = 1.4
ut = 0
corr_i = 0.15
corr_p = 0.85
# not exactly these params, but almost
ui_arr = arange(-1.5, 8, 0.8)
ui_cov = arange(1, 5, 0.7)
up_arr = arange(-1.5, 8, 0.8)
up_cov = arange(1, 5, 0.7)
if(make==1):
n = len(ui_arr)
m = len(ui_cov)
pcoeff_arr = zeros((n,m))
icoeff_arr = zeros((n,m))
pvar_arr = zeros((n,m))
pmean_arr = zeros((n,m))
ivar_arr = zeros((n,m))
imean_arr = zeros((n,m))
for i in range(0,n):
for j in range (0,m):
print (i, j)
pcoeff_arr[i,j], icoeff_arr[i,j], pvar_arr[i,j], pmean_arr[i,j], ivar_arr[i,j], imean_arr[i,j] = new_gpairs_match2(ui_arr[i], up_arr[i], ui_cov[j], up_cov[j], corr_i, corr_p, l, imgNr, k, ne, ut, k, ne, ut)[0:6]
print(pcoeff_arr[i,j], icoeff_arr[i,j])
save("txt/corrderiv2.npy", [pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr])
else:
pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr = load("txt/corrderiv2.npy")
pcoeff_arr = pcoeff_arr.flatten()
pmean_arr = pmean_arr.flatten()
pvar_arr = pvar_arr.flatten()
m1 = 10
m2 = 25
v1 = 10
v2 = 50
from scipy.interpolate import griddata
from scipy.signal import convolve2d
meani = arange(m1,m2,1)
vari = arange(v1,v2,1)
coeffi = griddata((pmean_arr, pvar_arr), pcoeff_arr, (meani[None,:], vari[:,None]), method='linear')
#coeffi = convolve2d(coeffi, ones((3,3)), "same");
print(coeffi)
# make the derivative calculation here
coeffim = diff(coeffi,1,1)
meani = arange(m1+0.5, m2-0.5,1)
#vari = arange(v1+0.5, v2-0.5,1)
# print (coeffi.shape)
tl = ""
fn = "images/corrderiv_pmf.png"
plt.clf()
#plt.contour(meani,vari,coeffim,15,linewidths=0.5,colors='k')
M, V = meshgrid(meani,vari)
M = M.flatten()
V = V.flatten()
coeffim=coeffim.flatten()
F = V/M
fanos = arange(0.9,2.7, 0.1)
coeffis = griddata((M, F), coeffim, (meani[None,:], fanos[:,None]), method='linear')
#plt.contourf(meani,vari,coeffim,150,cmap=plt.cm.jet)
plt.contourf(meani/10,fanos,coeffis,150,cmap=plt.cm.jet)
#plt.imshow(coeffim,cmap=plt.cm.jet, interpolation = "bilinear")
plt.clim(-0.1, 0.1)
plt.colorbar()
extra = plt.text(2.6, 2.25, r"$\frac{d\rho_{SC}}{dMean_{SC}}$ (1/k)", fontsize=20)
#plt.scatter(pmean_arr,pvar_arr,marker='o',c='b',s=5)
plt.xlim([m1/10,m2/10])
plt.ylim([1.2,2.2])
myplot(r"Mean$_{SC}$ (k)", r"Fano$_{SC}$ (k/Hz)", tl, fn, xlog=False, extra = (extra,), legend=False, fsax=20)
def do_it_match_plotter(make=1, u_mean = 2):
imgNr = 1500 # 3000
l = 50 # 500
n = imgNr
if(make == 1):
pspike_arr = zeros((n,2))
ispike_arr = zeros((n,2))
k_p = 4.2
n_p = 1.1
ut_p = 1.92
k_i = 10.0
n_i = 1.1
ut_i = 1.9
u_mi = 2.0
u_mp = 1.7
u_ci = 1.0
u_cp = 1.8
u1 = 1.8
u2 = 2.1
us = 0.04
k1 = 4.1
k2 = 4.5
ks = 0.1
# for the same input mean and variance
# 5, 1.05, 1.4, 11, 2.0, 1.7 -- 0.27%
# 6, 1.1, 1.6, 11, 1.9, 1.7 -- 0.36%
# 3.5, 1.05, 0.7, 11, 2.1, 1.9 -- 0.2%, 0.13%
# 8.5, 1.05, 1.8, 11, 2.1, 1.9 -- 0.22%
utps = arange(u1, u2, us)
utpl = len(utps)
kps = arange(k1, k2, ks)
kpl = len(kps)
diff = zeros((kpl, utpl))
mnkpi = 0
mnutpi = 0
for utpi in range(utpl):
for kpi in range(kpl):
print(kps[kpi], utps[utpi])
pvar, pmean, ivar, imean = new_gpairs_match2(u_mi, u_mp, u_ci, u_cp, 0.2, 0.7, l, imgNr, k_i, n_i, ut_i, kps[kpi], n_p, utps[utpi])[2:6]
print(pvar, pmean, ivar, imean)
vardiff = abs(pvar-ivar)/max(pvar,ivar)
meandiff = abs(pmean-imean)/max(pmean, imean)
print(vardiff, meandiff)
diff[kpi][utpi] = max(vardiff, meandiff) #0.5*vardiff + 0.5*meandiff
if (diff[kpi][utpi] < diff[mnkpi][mnutpi]):
mnkpi=kpi
mnutpi=utpi
print(kps[mnkpi], utps[mnutpi], diff[mnkpi][mnutpi])
save("txt/match_p_sameinp.npy", diff)
else:
diff = load("txt/match_p_sameinp.npy")
tl = ""
fn = "images/match_p_sameinp.png"
plt.clf()
plt.imshow((diff.T)[::-1, :], interpolation="none", extent=[k1-ks/2.0, k2+ks/2.0, u1-us/2.0, u2+us/2.0], aspect = "auto")
plt.colorbar()
myplot(r"$kp$", r"$utp$", tl, fn, xlog=False, legend=False, fsax=50, xloc=False)
def do_it_0(make=1):
# fano-factors
# trial to trial
imgNr = 3000
l = 500
if(make == 1):
u_means = arange(0,10,0.5)
n = size(u_means)
pvar = zeros((n,1))
pmean = zeros((n,1))
ivar = zeros((n,1))
imean = zeros((n,1))
pvari = zeros((n,1))
pmeani = zeros((n,1))
ivari = zeros((n,1))
imeani = zeros((n,1))
for i in range(0,n):
pvar[i], pmean[i], ivar[i], imean[i] = new_gpairs(u_means[i], 0.3, l, imgNr)[2:6]
pvari[i], pmeani[i], ivari[i], imeani[i] = new_gpairs_small(u_means[i], 0.3, l*100)[2:6]
save("txt/tr2trfano.npy", [pvar, pmean, ivar, imean, pvari, pmeani, ivari, imeani])
else:
pvar, pmean, ivar, imean, pvari, pmeani, ivari, imeani = load("txt/tr2trfano.npy")
# rescaling to one second bins (fano factors stay, means rescaled)
t = l*0.02
ti = 0.02
pvar/=t
pmean/=t
ivar/=t
imean/=t
pvari/=ti
pmeani/=ti
ivari/=ti
imeani/=ti
# trial to trial
tl = "" #"Trial to trial Fano-factor"
fn = "images/tr2trfanop.png"
plt.ylim([0,1.3])
plt.xlim([10,100])
plt.plot(pmean, pvar/pmean, label="Poisson", linestyle="--", lw=10)
plt.plot(imean, ivar/imean, label="Integration", linestyle="-", lw=10)
# extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 15)
plt.xticks([20,60, 100])
myplot(r"$Mean(n)$", r"$Fano(n)$", tl, fn, xlog=False, legend=False, fs=30, fsl=30, fsax=50, xloc=False) #extra=(extra,),
# instantaneous
plt.clf()
plt.ylim([0,1.5])
plt.xlim([10,100])
tl = "" #"Instantaneous Fano-factor"
fn = "images/instafanop.png"
plt.plot(pmeani, pvari/pmeani, label="Poisson", linestyle="--", lw=10)
plt.plot(imeani, ivari/imeani, label="Integration", linestyle="-", lw=10)
# extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 15)
plt.xticks([20,60, 100])
myplot(r"$Mean(n)$", r"$Fano(n)$", tl, fn, xlog=False, legend=False, fs=30, fsl=30, fsax=50, xloc=False) #extra=(extra,),
plt.clf()
plt.ylim([0,30])
plt.xlim([0,20])
tl = "" #"Instantaneous Fano-factor"
fn = "images/instavarp.png"
plt.plot(pmeani, pvari, label="Poisson", linestyle="--", lw=10)
plt.plot(imeani, ivari, label="Integration", linestyle="-", lw=10)
# extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 15)
plt.xticks([5, 15])
myplot(r"$Mean(n)$", r"$Var(n)$", tl, fn, xlog=False, legend=False, fs=30, fsl=30, fsax=50, xloc=False) #extra=(extra,),
def match_to_fixed(make=1):
imgNr = 1000 # 3000
l = 50 # 500
# input ranges
u1 = 0.8
u2 = 1.2
ud = 0.05
c1 = 2
c2 = 2.5
cd = 0.1
aimed_mean = 30
aimed_var = 31
# m_p = 1.2
# v_p = 0.2
# m_i = 0.9
# v_i = 2.3
corr_i = 0.15
corr_p = 0.85
# Gergo case
#poisson 1.28, 0.31
#integration
# the case of 8, 8.5
# for poisson
#2.3 0.1
#8.13959375 7.7525
# for integration
#0.15 8.0
#6.78831875 9.1075
# 21%
# the case of 10, 10.5
# for poisson
#2.35 0.3
#10.62259375 9.9825
# for integration
#0.1 10.5
#9.41051875 12.0125
# 20%
# the case of 30, 31.5
# for poisson
#3.3 0.8
#31.9687644444 30.136
# for integration
#1.0 19.6
#31.1467831111 30.3
# saved with name_fit
# 2%
us = arange(u1, u2, ud)
ul = len(us)
cs = arange(c1, c2, cd)
cl = len(cs)
pvars = zeros((ul, cl))
pmeans = zeros((ul, cl))
ivars = zeros((ul, cl))
imeans = zeros((ul, cl))
if(make == 1):
# nonlinearity parameters
# k = 10.0
# n = 1.1
# ut = 1.9
k = 10
n = 1.4
ut = 0
for ui in range(ul):
for ci in range(cl):
print(ui, ci)
pvars[ui][ci], pmeans[ui][ci], ivars[ui][ci], imeans[ui][ci] = new_gpairs_match2(us[ui], us[ui], cs[ci], cs[ci], corr_i, corr_p, l, imgNr, k, n, ut, k, n, ut)[2:6]
save("txt/match_p_sameinp.npy", [pvars, pmeans, ivars, imeans])
else:
pvars, pmeans, ivars, imeans = load("txt/match_p_sameinp.npy")
a = argmin(abs(pvars - aimed_var)+abs(pmeans - aimed_mean))
i = int(a/cl)
j = a%cl
print ("poisson")
print (i,j)
print (us[i], cs[j])
print (pvars[i][j], pmeans[i][j])
a = argmin(abs(ivars - aimed_var)+abs(imeans - aimed_mean))
i = int(a/cl)
j = a%cl
print ("integration")
print (i,j)
print (us[i], cs[j])
print (ivars[i][j], imeans[i][j])
print ((pmeans.T)[::-1, :])
tl = ""
fn = "images/match_m_p.png"
plt.clf()
plt.imshow((pmeans.T)[::-1, :], interpolation="none", extent=[u1-ud/2.0, u2+ud/2.0, c1-cd/2.0, c2+cd/2.0], aspect = "auto")
plt.colorbar()
myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50, xloc=False)
tl = ""
fn = "images/match_v_p.png"
plt.clf()
plt.imshow((pvars.T)[::-1, :], interpolation="none", extent=[u1-ud/2.0, u2+ud/2.0, c1-cd/2.0, c2+cd/2.0], aspect = "auto")
plt.colorbar()
myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50, xloc=False)
tl = ""
fn = "images/match_m_i.png"
plt.clf()
plt.imshow((imeans.T)[::-1, :], interpolation="none", extent=[u1-ud/2.0, u2+ud/2.0, c1-cd/2.0, c2+cd/2.0], aspect = "auto")
plt.colorbar()
myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50, xloc=False)
tl = ""
fn = "images/match_v_i.png"
plt.clf()
plt.imshow((ivars.T)[::-1, :], interpolation="none", extent=[u1-ud/2.0, u2+ud/2.0, c1-cd/2.0, c2+cd/2.0], aspect = "auto")
plt.colorbar()
myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50, xloc=False)
def fig3b_o(make=1):
# orientation changes: variances stay, means increase then decresase. (in membrane potentials)
# the case of 30, 31.5
# for poisson
#3.3 0.8
#31.9687644444 30.136
# for integration
#1.0 19.6
#31.1467831111 30.3
# saved with name_fit
# 2%
times = 3
# m_p = 3.3
# v_p = 0.8
# m_i = 1.0
# v_i = 19.6
m_p = 1.2
v_p = 0.2
m_i = 0.9
v_i = 2.3
if(make==1):
l = 50
imgNr = 30000 #30000
k = 10
# ne = 1.1
# ut = 1.9
# corr_i = 0.15
# corr_p = 0.95
ne = 1.4
ut = 0
corr_i = 0.15
corr_p = 0.85
i_fano = v_i/m_i;
p_fano = v_p/m_p;
ui_arr = arange(m_i/2, 1.05*m_i, m_i/10) #make just a part of it, then at plotting symmetry
ui_cov = repeat([v_i],6)
up_arr = arange(m_p/2, 1.05*m_p, m_p/10)
up_cov = repeat([v_p],6)
n = len(ui_arr)
pcoeff_arr = zeros((n, times))
icoeff_arr = zeros((n, times))
pvar_arr = zeros((n, times))
pmean_arr = zeros((n, times))
ivar_arr = zeros((n, times))
imean_arr = zeros((n, times))
# o1 is for Gergo's params
# o2 for mine
for i in range(n):
print (i)
for j in range(times):
pcoeff_arr[i][j], icoeff_arr[i][j], pvar_arr[i][j], pmean_arr[i][j], ivar_arr[i][j], imean_arr[i][j] = new_gpairs_match2(ui_arr[i], up_arr[i], ui_cov[i], up_cov[i], corr_i, corr_p, l, imgNr, k, ne, ut, k, ne, ut)[0:6]
save("txt/fig3b_o1.npy", [pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr])
else:
pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr = load("txt/fig3b_o1.npy")
print(icoeff_arr)
print(pcoeff_arr)
orient = [-150, -130, -105, -75, -40 ,0, 40, 75, 105, 130, 150]
pmean_arr = concatenate([pmean_arr[:-1], pmean_arr[::-1]])
imean_arr = concatenate([imean_arr[:-1], imean_arr[::-1]])
pvar_arr = concatenate([pvar_arr[:-1], pvar_arr[::-1]])
ivar_arr = concatenate([ivar_arr[:-1], ivar_arr[::-1]])
pcoeff_arr = concatenate([pcoeff_arr[:-1], pcoeff_arr[::-1]])
icoeff_arr = concatenate([icoeff_arr[:-1], icoeff_arr[::-1]])
pfano_arr = divide(pvar_arr, pmean_arr)
ifano_arr = divide(ivar_arr, imean_arr)
tl = ""#r"$Mean_{SC}$"
fn = "images/fig3b_o1_m.png"
plt.clf()
plt.plot(orient, pmean_arr[:,0], "r.", label = "Poisson", ms = 10)
plt.plot(orient, imean_arr[:,0], "b.", label = "Integration", ms = 10)
plt.plot(orient, pmean_arr[:,1:], "r.", ms = 10)
plt.plot(orient, imean_arr[:,1:], "b.", ms = 10)
plt.xticks([-160, -80, 0, 80, 160])
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
myplot(r"$orientation (deg)$", r"$Mean_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
tl = ""#r"$Fano_{SC}$"
fn = "images/fig3b_o1_f.png"
plt.clf()
plt.plot(orient, pfano_arr[:,0], "r.", label = "Poisson", ms = 10)
plt.plot(orient, ifano_arr[:,0], "b.", label = "Integration", ms = 10)
plt.plot(orient, pfano_arr[:,1:], "r.", ms = 10)
plt.plot(orient, ifano_arr[:,1:], "b.", ms = 10)
plt.xticks([-160, -80, 0, 80, 160])
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
myplot(r"$orientation (deg)$", r"$Fano_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
tl = ""#r"$\rho_{SC}$"
fn = "images/fig3b_o1_c.png"
plt.clf()
plt.plot(orient, pcoeff_arr[:,0], "r.", label = "Poisson", ms = 10)
plt.plot(orient, icoeff_arr[:,0], "b.", label = "Integration", ms = 10)
plt.plot(orient, pcoeff_arr[:,1:], "r.", ms = 10)
plt.plot(orient, icoeff_arr[:,1:], "b.", ms = 10)
plt.xticks([-160, -80, 0, 80, 160])
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
myplot(r"$orientation (deg)$", r"$\rho_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
def new_pairs_spikec(contrasts, pcoeffs, icoeffs, fn, tl):
plt.plot(contrasts, pcoeffs, label = "Poisson");
plt.plot(contrasts, icoeffs, label = "Integration");
myplot("potential means", "Trial to trial correlations", tl, fn, xlog=False, legend=True)
def do_it_matched_plot2(make=1):
# what if we fit a plane through these points and plot that?
# surface for spike count mean and variance -- spkc corr.
if(make==1):
l = 50
imgNr = 30000 #30000
k = 10
ne = 1.1
ut = 1.9
corr_i = 0.15
corr_p = 0.95
#Mean(u_p) = 3.3, Var(u_p) = 0.8, Mean(u_i) = 1.0, Var(u_i) = 19.6
ui_arr = arange(1, 2.5, 0.3)
ui_cov = arange(17, 20, 0.3)
up_arr = arange(3.3, 3.55, 0.05)
up_cov = arange(0.3, 0.8, 0.05)
# ui_arr = arange(1.0, 1.1, 0.3)
# ui_cov = arange(19.6, 20, 4)
# up_arr = arange(3.3, 3.5, 0.9)
# up_cov = arange(0.8, 0.9, 0.2)
n = len(ui_arr)
m = len(ui_cov)
pcoeff_arr = zeros((n,m))
icoeff_arr = zeros((n,m))
pvar_arr = zeros((n,m))
pmean_arr = zeros((n,m))
ivar_arr = zeros((n,m))
imean_arr = zeros((n,m))
for i in range(0,n):
for j in range (0,m):
print (i, j)
pcoeff_arr[i,j], icoeff_arr[i,j], pvar_arr[i,j], pmean_arr[i,j], ivar_arr[i,j], imean_arr[i,j] = new_gpairs_match2(ui_arr[i], up_arr[i], ui_cov[j], up_cov[j], corr_i, corr_p, l, imgNr, k, ne, ut, k, ne, ut)[0:6]
print(pcoeff_arr[i,j], icoeff_arr[i,j])
save("txt/matched_scale2.npy", [pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr])
else:
pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr = load("txt/matched_scale2.npy")
# print(imean_arr)
# print(pmean_arr)
# print(ivar_arr)
# print(pvar_arr)
print(icoeff_arr)
print(pcoeff_arr)
pcoeff_arr = pcoeff_arr.flatten()
icoeff_arr = icoeff_arr.flatten()
pmean_arr = pmean_arr.flatten()
imean_arr = imean_arr.flatten()
pvar_arr = pvar_arr.flatten()
ivar_arr = ivar_arr.flatten()
# m1 = max(amin(pmean_arr), amin(imean_arr))
# m2 = min(amax(pmean_arr), amax(imean_arr))
# v1 = max(amin(pvar_arr), amin(ivar_arr))
# v2 = min(amax(pvar_arr), amax(ivar_arr))
# print(m1,m2,v1,v2)
m1 = 30
m2 = 33
v1 = 31
v2 = 35
from scipy.interpolate import griddata
meani = arange(m1,m2,0.3)
vari = arange(v1,v2,0.3)
# poisson
# print (shape(pmean_arr), shape(pvar_arr), shape(pcoeff_arr), shape(meani), shape(vari))
coeffi = griddata((pmean_arr, pvar_arr), pcoeff_arr, (meani[None,:], vari[:,None]), method='linear')
# print (coeffi.shape)
tl = r"Poisson: $\rho_{SC}$"
fn = "images/matched_scale2p.png"
plt.clf()
plt.contour(meani,vari,coeffi,15,linewidths=0.5,colors='k')
plt.contourf(meani,vari,coeffi,15,cmap=plt.cm.jet)
plt.clim(0.03, 0.125)
plt.colorbar()
plt.scatter(pmean_arr,pvar_arr,marker='o',c='b',s=5)
plt.xlim([m1,m2])
plt.ylim([v1,v2])
myplot(r"$Mean_{SC} (spk/s)$", r"$Var_{SC} (spk/s)^2$", tl, fn, xlog=False, legend=False, fsax=50)
# integration
coeffi = griddata((imean_arr, ivar_arr), icoeff_arr, (meani[None,:], vari[:,None]), method='linear')
# print (coeffi.shape)
tl = r"Integration: $\rho_{SC}$"
fn = "images/matched_scale2i.png"
plt.clf()
plt.contour(meani,vari,coeffi,15,linewidths=0.5,colors='k')
plt.contourf(meani,vari,coeffi,15,cmap=plt.cm.jet)
plt.clim(0.03, 0.125)
plt.colorbar()
plt.scatter(imean_arr,ivar_arr,marker='o',c='b',s=5)
plt.xlim([m1,m2])
plt.ylim([v1,v2])
myplot(r"$Mean_{SC} (spk/s)$", r"$Var_{SC} (spk/s)^2$", tl, fn, xlog=False, legend=False, fsax=50)
def do_it_1(u_mean = 2):
# ou-process potentials, rates and spike count correlations
dt = 0.001
tau = 0.02 # time constant of the ou process
samples = 300
corr = 0.3
# u_mean = 6
C = array([[1, corr],[corr,1]]) # covariance
m = u_mean * array([1.0, 1.0]) # center
k = 10 # 10 Hz
n = 1.1 # the exponent of nonlinearity
ut = 1.9 # the treshold potential
samps = evolve_ou(m, C, tau, dt, samples-1) # samples - 1 steps are needed in the process
rates = rate_from_pot(samps, k, ut, n)
#plot the samples
tl = "" #"Potentials with covariance ellipse"
fn = "images/poster_samples%.2f.png"%u_mean
plt.clf()
plt.plot(samps[:,0], samps[:,1], "b.", ms = 10)
plt.plot(samps[:,0], samps[:,1], lw = 1)
plt.ylim([-1,9])
plt.xlim([-1,9])
yl = [min(plt.ylim()[0], 1.9), max(plt.ylim()[1], 1.9)]
xl = [min(plt.xlim()[0], 1.9), max(plt.xlim()[1], 1.9)]
plt.plot([1.9,1.9], yl, lw=2, color="red") # the treshold potential lines
plt.plot(xl, [1.9,1.9], lw=2, color="red")
[a,b] = polyfit(samps[:,0], samps[:,1], 1)
plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="black")
sampsC = cov(samps, rowvar=False)
# plt.text(left, top, "r = %0.2f"%(sampsC[0,1]/sampsC[0,0]))
plt.annotate("r = %0.2f"%(sampsC[0,1]/sampsC[0,0]), xy=(0.05, 0.9), xycoords='axes fraction', fontsize=30)
sampsm = samps.mean(axis=0)
plot_cov_ellipse(sampsC, sampsm, nstd=1.5, color='green', lw=4)
myplot(r"$u_1$", r"$u_2$", tl, fn, xlog=False, legend=False, fs=30, fsl=30, fsax=50)
#plot time series
tl = "" #"Potentials with covariance ellipse"
fn = "images/poster_samplest%.2f.png"%u_mean
plt.clf()
plt.plot(samps[:,0], lw=5)
plt.plot(samps[:,1], lw=5) #arange(0, samples*dt*1000, dt*1000),
# plt.axes().set_aspect((10,20))
myplot(r"$t(ms)$", r"$u_2$", tl, fn, xlog=False, legend=False, fs=30, fsl=30, xloc=False, fsax=50)
#plot the rates
plt.clf()
tl = "" #"Spiking rates"
fn = "images/poster_rates%.2f.png"%u_mean
plt.plot(rates[:,0], rates[:,1], "b.", ms = 10)
plt.plot(rates[:,0], rates[:,1], lw = 1)
plt.ylim([0,80])
plt.xlim([0,80])
[a,b] = polyfit(rates[:,0], rates[:,1], 1)
plt.plot(plt.xlim(), a*array(plt.xlim())+b, lw=5, color="black")
# plt.ylim(0, plt.ylim()[1])
# plt.xlim(0, plt.xlim()[1])
ratesC = cov(rates, rowvar=False)
plt.annotate("r = %0.2f"%(ratesC[0,1]/ratesC[0,0]), xy=(0.05, 0.9), xycoords='axes fraction', fontsize=30)
ratesm = rates.mean(axis=0)
plot_cov_ellipse(ratesC, ratesm, nstd=1.5, color='green', lw=4)
myplot(r"$R_1$", r"$R_2$", tl, fn, xlog=False, legend=False, fs=30, fsl=30, fsax=50)
def do_it_4(make=1):
# heatmaps
if(make==1):
corr = 0.3
start = 0.3
stop = 10
step = 0.5
potmeans = arange(start, stop, step)
nrm = size(potmeans)
start = 1
stop = 11
step = 1
covs = arange(start, stop, step)
nrc = size(covs)
icoeffs = zeros((nrm, nrc))
pcoeffs = zeros((nrm, nrc))
ifanos = zeros((nrm, nrc))
pfanos = zeros((nrm, nrc))
icoeffsi = zeros((nrm, nrc))
pcoeffsi = zeros((nrm, nrc))
ifanosi = zeros((nrm, nrc))
pfanosi = zeros((nrm, nrc))
l = 500 # number of samples for each patch (trial) # 1000
imgNr = 5000 # 10000
for i in range(0, nrm):
for j in range(0,nrc):
pcoeffs[i,j], icoeffs[i,j], pv, pm, iv, im = new_gpairs(potmeans[i], corr, l, imgNr, cov=covs[j])[0:6]
pfanos[i,j], ifanos[i,j] = pv/pm, iv/im
pcoeffsi[i,j], icoeffsi[i,j], pv, pm, iv, im = new_gpairs_small(potmeans[i], corr, l*100, cov=covs[j])[0:6]
pfanosi[i,j], ifanosi[i,j] = pv/pm, iv/im
a = [potmeans, covs, pcoeffs, icoeffs, pfanos, ifanos, pcoeffsi, icoeffsi, pfanosi, ifanosi]
save("txt/heatmaps.npy", a)
else:
potmeans, covs, pcoeffs, icoeffs, pfanos, ifanos, pcoeffsi, icoeffsi, pfanosi, ifanosi = load("txt/heatmaps.npy")
step = potmeans[1] - potmeans[0]
p1, p2 = potmeans[0]-step/2.0, potmeans[-1]+step/2.0
step = covs[1] - covs[0]
c1, c2 = covs[0]-step/2.0, covs[-1]+step/2.0
# trial to trial
tl = "" #"Trial-to-trial correlations - Poisson"
fn = "images/heattr2trp.png"
plt.imshow(pcoeffs.T[ ::-1,:], interpolation='none', extent=[p1,p2,c1,c2])
# plt.clim(0,0.35)
plt.colorbar()
myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50)
tl = "" #"Trial-to-trial correlations - Integration"
fn = "images/heattr2tri.png"
plt.clf()
plt.imshow(icoeffs.T[ ::-1,:], interpolation='none', extent=[p1,p2,c1,c2])
# plt.clim(0,0.35)
plt.colorbar()
myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50)
# tl = "" #"Trial-to-trial Fano-factors - Poisson"
# fn = "images/heattr2trpf.png"
# plt.clf()
# plt.imshow(pfanos.T[ ::-1,:], interpolation='none', extent=[p1,p2,c1,c2])
# plt.clim(0,1.5)
# plt.colorbar()
# myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50)
#
# tl = "" #"Trial-to-trial Fano-factors - Integration"
# fn = "images/heattr2trif.png"
# plt.clf()
# plt.imshow(ifanos.T[ ::-1,:], interpolation='none', extent=[p1,p2,c1,c2])
# plt.clim(0,1.5)
# plt.colorbar()
# myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50)
## instantaneous
# tl = "Instantaneous correlations - Poisson"
# fn = "images/heatinstap.png"
# plt.clf()
# plt.imshow(pcoeffsi.T[ ::-1,:], interpolation='none', extent=[p1,p2,c1,c2])
# plt.clim(0,0.35)
# plt.colorbar()
# myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False)
#
# tl = "Instantaneous correlations - Integration"
# fn = "images/heatinstai.png"
# plt.clf()
# plt.imshow(icoeffsi.T[ ::-1,:], interpolation='none', extent=[p1,p2,c1,c2])
# plt.clim(0,0.35)
# plt.colorbar()
# myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False)
#
tl = ""
fn = "images/heatinstapf.png"
plt.clf()
plt.imshow(pfanosi.T[ ::-1,:], interpolation='none', extent=[p1,p2,c1,c2])
plt.clim(0,2)
plt.colorbar()
myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50)
tl = ""
fn = "images/heatinstaif.png"
plt.clf()
plt.imshow(ifanosi.T[ ::-1,:], interpolation='none', extent=[p1,p2,c1,c2])
plt.clim(0,2)
plt.colorbar()
myplot(r"$Mean(u)$", r"$Var(u)$", tl, fn, xlog=False, legend=False, fsax=50)
def fig3b_c(make=1):
# contrast changes: variances decrease, means increase. (in membrane potentials)
# the case of 30, 31.5
# for poisson
#3.3 0.8
#31.9687644444 30.136
# for integration
#1.0 19.6
#31.1467831111 30.3
# saved with name_fit
# 2%
# check out with these parameters; what rates are needed to make the fano-s parallel?
# maybe make a search around these parameters for a better match!
# try to balance the things out
#muRG=0.9;
#muP=1.2;
#cRG=.15;
#cP=.85;
#vRG=2.3;
#vP=.2;
#k=20; % ezt elosztja 50-nel
#m=1.4;
#threshold = 0;
# m_p = 3.3
# v_p = 0.8
# m_i = 1.0
# v_i = 19.6
m_p = 1.2
v_p = 0.2
m_i = 0.9
v_i = 2.3
if(make==1):
l = 50
imgNr = 30000 #30000
# k = 10
# ne = 1.1
# ut = 1.9
# corr_i = 0.15
# corr_p = 0.95
k = 10
ne = 1.4
ut = 0
corr_i = 0.15
corr_p = 0.85
# 1 is the other version, with fig3b_c
# maybe make it free to choose other variance-changing stuff
# 3 is the version with Gergo's params
ui_arr = arange(m_i/2, 1.05*m_i, m_i/10) #make just a part of it, then at plotting symmetry
ui_cov = arange(v_i, 1.42*v_i, v_i*0.08)[::-1]
up_arr = arange(m_p/2, 1.05*m_p, m_p/10)
up_cov = arange(v_p, 3.42*v_p, v_p*0.48)[::-1]
#3.4 for Poisson
#1.4 for Integration
times = 3
n = len(ui_arr)
pcoeff_arr = zeros((n,times))
icoeff_arr = zeros((n,times))
pvar_arr = zeros((n,times))
pmean_arr = zeros((n,times))
ivar_arr = zeros((n,times))
imean_arr = zeros((n,times))
for i in range(0,n):
print (i)
for j in range(times):
pcoeff_arr[i][j], icoeff_arr[i][j], pvar_arr[i][j], pmean_arr[i][j], ivar_arr[i][j], imean_arr[i][j] = new_gpairs_match2(ui_arr[i], up_arr[i], ui_cov[i], up_cov[i], corr_i, corr_p, l, imgNr, k, ne, ut, k, ne, ut)[0:6]
save("txt/fig3b_c3.npy", [pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr])
else:
pcoeff_arr, icoeff_arr, pvar_arr, pmean_arr, ivar_arr, imean_arr = load("txt/fig3b_c3.npy")
print(icoeff_arr)
print(pcoeff_arr)
contr = arange(0,1.05,0.2)
pfano_arr = divide(pvar_arr, pmean_arr)
ifano_arr = divide(ivar_arr, imean_arr)
tl = ""#r"$Mean_{SC}$"
fn = "images/fig3b_c2_m.png"
plt.clf()
plt.plot(contr, pmean_arr[:,0], "r.", label = "Poisson", ms = 10)
plt.plot(contr, imean_arr[:,0], "b.", label = "Integration", ms = 10)
plt.plot(contr, pmean_arr[:,1:], "r.", ms = 10)
plt.plot(contr, imean_arr[:,1:], "b.", ms = 10)
plt.xticks([0.2, 0.5, 0.8])
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
myplot(r"$contrast$", r"$Mean_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
tl = ""#r"$Fano_{SC}$"
fn = "images/fig3b_c2_f.png"
plt.clf()
plt.plot(contr, pfano_arr[:,0], "r.", label = "Poisson", ms = 10)
plt.plot(contr, ifano_arr[:,0], "b.", label = "Integration", ms = 10)
plt.plot(contr, pfano_arr[:,1:], "r.", ms = 10)
plt.plot(contr, ifano_arr[:,1:], "b.", ms = 10)
plt.xticks([0.2, 0.5, 0.8])
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
myplot(r"$contrast$", r"$Fano_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
tl = ""#r"$\rho_{SC}$"
fn = "images/fig3b_c2_c.png"
plt.clf()
plt.plot(contr, pcoeff_arr[:,0], "r.", label = "Poisson", ms = 10)
plt.plot(contr, icoeff_arr[:,0], "b.", label = "Integration", ms = 10)
plt.plot(contr, pcoeff_arr[:,1:], "r.", ms = 10)
plt.plot(contr, icoeff_arr[:,1:], "b.", ms = 10)
plt.xticks([0.2, 0.5, 0.8])
extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 30)
myplot(r"$contrast$", r"$\rho_{SC}$", tl, fn, xlog=False, legend=False, extra=(extra,), fsax=50, xloc=False)
while batch_start<=train_end:
batch_end = (batch_start - 1) + batch_size
if batch_end > train_end:
batch_end = train_end + 0
#weights = weights - d_weights * alpha * 0.1**(0.35*np.log10(i_epoch+1.0))
weights, m_adam, v_adam = OptimizationUpdate(weights,d_weights,m_adam,v_adam,alpha,i_epoch)
beta = nn.nn.nn_predict(network, act_fn_name, loss_fn_name, opt_name, weights, features, opt_params)
if i_epoch==0 and batch_start==1:
myplot("ML_init", np.linspace(1,n_data,n_data), beta, '-ob', 2.0, None)
d_beta = eval_loss_deriv(features, beta)
d_weights = nn.nn.nn_get_weights_sens(network, act_fn_name, loss_fn_name, opt_name, weights, features, batch_start, batch_end, d_beta, opt_params)
#print(np.linalg.norm(d_weights))
batch_start = batch_end + 1
#d_weights = d_weights / np.max(np.abs(d_weights))
print("ML Iteration: %6d\t\tLoss: %E"%(i_epoch,eval_loss(features, beta)))
t2 = time.time()
print("Time = %E"%(t2-t1))
def do_it_3(make = 1, mode = 0, diff = "variance"):
# correlations sampled from Gaussian
# how does it change with poisson and integartion process
# mode: 0 - trial-to-trial, 1 - instantaneous
# the first part is not general for variance/mean yet!!!!
name = "tr2tr"
if(mode == 1):
name = "insta"
if(make == 1):
stdd = 0.3
N = 250
u_mean = 2
u_meanh = 6
l = 150 #0
imgNr = 3000 # 0
cov = 1.0
icoeffs = zeros((N, 1))
pcoeffs = zeros((N, 1))
icoeffsh = zeros((N, 1))
pcoeffsh = zeros((N, 1))
correlations = normal(0, stdd, N)
for i in range(0,N):
if(mode == 0):
pcoeffs[i], icoeffs[i] = new_gpairs(u_mean, correlations[i], l, imgNr, cov)[0:2]
else:
pcoeffs[i], icoeffs[i] = new_gpairs_small(u_mean, correlations[i], l*10, cov)[0:2]
for i in range(0,N):
if(mode == 0):
pcoeffsh[i], icoeffsh[i] = new_gpairs(u_meanh, correlations[i], l, imgNr, cov)[0:2]
else:
pcoeffsh[i], icoeffsh[i] = new_gpairs_small(u_meanh, correlations[i], l*10, cov)[0:2]
save("txt/%sdistribc.npy"%name, [pcoeffs, icoeffs, pcoeffsh, icoeffsh, correlations])
else:
pcoeffs, icoeffs, pcoeffsh, icoeffsh, correlations = load("txt/%sdistrib_%s.npy"%(name,diff))
print (diff, std(pcoeffs), std(pcoeffsh), std(icoeffs), std(icoeffsh) )
#plot.tick_params(axis='both', which='major', labelsize=10)
tl = "" #"Trial-to-trial correlations distribution - Poisson"
if(mode == 1):
tl = "Instantaneous correlations distribution - Poisson"
fn = "images/%sdistrib_p1_%s.png"%(name,diff)
binwidth = 0.03
ticks = arange(-0.9,1.2,0.3)
alpha = 0.8
lw = 4
xmin = amin([pcoeffs, icoeffs, pcoeffsh, icoeffsh, correlations])
xmax = amax([pcoeffs, icoeffs, pcoeffsh, icoeffsh, correlations])
bins=arange(xmin, xmax + binwidth, binwidth)
plt.clf()
plt.xticks(ticks)
if(diff=="mean"):
plt.hist(pcoeffs, bins=bins, histtype="step", label="Low %s"%diff, color="blue", alpha=alpha, lw=lw)
plt.hist(pcoeffsh, bins=bins, histtype="step", label="High %s"%diff, color="red", alpha=alpha, lw=lw)
else:
plt.hist(pcoeffsh, bins=bins, histtype="step", label="High %s"%diff, color="blue", alpha=alpha, lw=lw)
plt.hist(pcoeffs, bins=bins, histtype="step", label="Low %s"%diff, color="red", alpha=alpha, lw=lw)
#yspan = plt.ylim()
# extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 15)
myplot(r"$\rho_{SC}$", "", tl, fn, xlog=False, legend=False, xloc=False, fsax=50) #, extra=(extra,)
fn = "images/%sdistrib_p_%s.png"%(name,diff)
binwidth = 0.1
# if(mode==1):
# binwidth = 0.05
alpha = 0.8
lw = 4
bins=arange(xmin, xmax + binwidth, binwidth)
plt.clf()
plt.xticks(ticks)
if(diff=="mean"):
plt.hist(pcoeffs, bins=bins, histtype="step", label="Low %s"%diff, color="blue", alpha=alpha, lw=lw)
plt.hist(pcoeffsh, bins=bins, histtype="step", label="High %s"%diff, color="red", alpha=alpha, lw=lw)
else:
plt.hist(pcoeffsh, bins=bins, histtype="step", label="High %s"%diff, color="blue", alpha=alpha, lw=lw)
plt.hist(pcoeffs, bins=bins, histtype="step", label="Low %s"%diff, color="red", alpha=alpha, lw=lw)
#yspan = plt.ylim()
# extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 15)
myplot(r"$\rho_{SC}$", "", tl, fn, xlog=False, legend=False, xloc=False, fsax=50) #, extra=(extra,)
tl = "" #"Trial-to-trial correlations distribution - Integration"
if(mode == 1):
tl = "Instantaneous correlations distribution - Integration"
fn = "images/%sdistrib_i_%s.png"%(name,diff)
plt.clf()
plt.xticks(ticks)
if(diff=="mean"):
plt.hist(icoeffs, bins=bins, histtype="step", label="Low %s"%diff, color="blue", alpha=alpha, lw=lw)
plt.hist(icoeffsh, bins=bins, histtype="step", label="High %s"%diff, color="red", alpha=alpha, lw=lw)
else:
plt.hist(icoeffsh, bins=bins, histtype="step", label="High %s"%diff, color="blue", alpha=alpha, lw=lw)
plt.hist(icoeffs, bins=bins, histtype="step", label="Low %s"%diff, color="red", alpha=alpha, lw=lw)
#plt.ylim(yspan)
#extra = plt.legend(bbox_to_anchor=(1, 0.5), loc='center left', ncol=1, fontsize = 15)
myplot(r"$\rho_{SC}$", "", tl, fn, xlog=False, legend=False, xloc=False, fsax=50) #, extra=(extra,))
tl = ""#"Trial-to-trial correlations distribution - Potentials"
if(mode == 1):
tl = "Instantaneous correlations distribution - Potentials"
fn = "images/%sdistrib_o_%s.png"%(name,diff)
plt.clf()
plt.xticks(ticks)
plt.hist(correlations, bins=bins, histtype="step", color="green", alpha=alpha, lw=lw)
#plt.ylim(yspan)
myplot(r"$\rho_{u}$", "", tl, fn, xlog=False, legend=False, xloc=False, fsax=50)
return 0
