pl.title('Avg. output tuning curve')
dxx = np.pi/100.
xx = np.arange(0, np.pi, dxx)
for tr in range(trial_no):
zz = tc[4, tr, :]
zz_mean = np.zeros(len(xx))
zz_std = np.zeros(len(xx))
for ii, xi in enumerate(xx):
ids = np.where( (po_init > xi) * (po_init < xi+dxx) == True)
zz_mean[ii] = np.mean(zz[ids])
zz_std[ii] = np.std(zz[ids])
p, ft, tw, sc = OS_functions.vonMises(xx, zz_mean)
print(tw)
ax.plot(xx, zz_mean, 'k-', lw=2, alpha = al[tr], label=str(np.round(tw,1))+r'$^\circ$')
ax.fill_between(xx, zz_mean - zz_std, zz_mean + zz_std, color='k', alpha=.1)
inp_mean = np.mean(zz_mean)
ax.plot(xx, inp_mean*(1 + m*np.cos(2*(np.pi/2-xx))), 'g-', lw=2, alpha = al[tr])#, label='Inp.')
pl.legend(frameon=False, title='output TW', prop={'size':12.5})
ax.set_xlabel('Input PO (deg)')
ax.set_xticks([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
ax.set_xticklabels([0, 45, 90, 135, 180])
def sim_results(sim_folder):
ti = time.time()
results = {}
### reading data
os.chdir(res_path + sim_folder)
fl = open("info", "rb")
info = cPickle.load(fl)
fl.close()
stim_range = info["stim_range"]
trial_no = info["trial_no"]
N = info["N"]
simtime = info["simtime"]
t_trans = info["t_trans"]
trial_no = info["trial_no"]
n_smpl = info["n_smpl"]
contrast = info["contrast"]
### spike data and tuning curves
print("### spikes ###")
id_no = 1
tc_trans, tc = [], []
for st in enumerate(stim_range):
print("#### stim: ", str(st[0]))
t0 = time.time()
tc_st_trans, tc_st = [], []
for tr in range(trial_no):
print(" # trial: ", str(tr))
yyy = str(2 * N + id_no + tr)
n0 = "spikes-all-trans-st" + str(st[0]) + "-tr" + str(tr) + "-" + yyy + "-0.gdf"
z = np.loadtxt(n0)
fr_trans = np.array([len(np.where(z[:, 0] == n)[0]) / (t_trans / 1000) for n in range(1, N + 1)])
tc_st_trans.append(fr_trans)
yyy = str(2 * N + id_no + tr + trial_no)
n0 = "spikes-all-st" + str(st[0]) + "-tr" + str(tr) + "-" + yyy + "-0.gdf"
z = np.loadtxt(n0)
fr = np.array([len(np.where(z[:, 0] == n)[0]) / ((simtime - t_trans) / 1000) for n in range(1, N + 1)])
tc_st.append(fr)
t1 = time.time()
print("### %.2f s" % (t1 - t0))
tc.append(tc_st)
tc_trans.append(tc_st_trans)
tc = np.array(tc)
results["tc"] = tc
tc_trans = np.array(tc_trans)
results["tc_trans"] = tc_trans
### membrane potential data and tuning curves
print("### mem. pot. ###")
vm_tc_trans, vm_tc = [], []
vm_hist = {}
exc_excInp, exc_inhInp = [], []
inh_excInp, inh_inhInp = [], []
exc_excInp_std, exc_inhInp_std = [], []
inh_excInp_std, inh_inhInp_std = [], []
for st in enumerate(stim_range):
print("#### stim: ", str(st[0]))
vm_data_no = 2 * N + id_no + 2 * trial_no
### stationary
# exc
yyy = str(vm_data_no)
nex = "vm-exc-st" + str(st[0]) + "-" + yyy + "-0.dat"
zex = np.loadtxt(nex)
# inh
yyy = str(vm_data_no + 1)
nin = "vm-inh-st" + str(st[0]) + "-" + yyy + "-0.dat"
zin = np.loadtxt(nin)
vm_tc_st = []
vm_hist[st[0]] = []
for i in range(n_smpl):
zz = zex[i::n_smpl][:, 2]
vm_tc_st.append(np.mean(zz))
vm_hist[st[0]].append(np.histogram(zz, bins=100, normed=True))
for i in range(n_smpl):
zz = zin[i::n_smpl][:, 2]
vm_tc_st.append(np.mean(zz))
vm_hist[st[0]].append(np.histogram(zz, bins=100, normed=True))
vm_tc.append(vm_tc_st)
results["vm_tc"] = np.array(vm_tc)
results["vm_hist"] = vm_hist
### F0 and F2 components
tc_f0, tc_f1 = [], []
for ic, ct in enumerate(contrast):
tc_f0_tmp, tc_f1_tmp = [], []
# print ic, ct
for i in range(N):
f0, f1 = OS_functions._fft_(tc[:, ic, i])
tc_f0_tmp.append(f0)
tc_f1_tmp.append(f1)
tc_f0.append(tc_f0_tmp)
tc_f1.append(tc_f1_tmp)
tc_f0 = np.array(tc_f0)
tc_f1 = np.array(tc_f1)
results["tc_f0"] = tc_f0
results["tc_f1"] = tc_f1
### osi, vm fit and tw
TW_out_tot, VM_fit_tot, scs_fit_tot, err_fit_tot = [], [], [], []
PO_out_tot, OSI_out_tot = [], []
for ic, ct in enumerate(contrast):
TW_out, VM_fit, scs_fit, err_fit = [], [], [], []
PO_out, OSI_out = [], []
for i in range(N):
zz = tc[:, ic, i]
if i % 1000 == 0:
print(i)
osi_out, po_out = OS_functions._osv_(zz, stim_range)
OSI_out.append(osi_out)
PO_out.append(po_out / 2.0)
vm_fit = OS_functions.vonMises(stim_range, zz)
VM_fit.append(vm_fit[1])
scs_fit.append(vm_fit[3])
TW_out.append(vm_fit[2])
zer = zz - vm_fit[1]
zser = np.sqrt(np.sum(zer ** 2))
err_fit.append(100 * zser / np.sqrt(sum(zz ** 2)))
TW_out_tot.append(TW_out)
VM_fit_tot.append(VM_fit)
scs_fit_tot.append(scs_fit)
err_fit_tot.append(err_fit)
PO_out_tot.append(PO_out)
OSI_out_tot.append(OSI_out)
results["VM_fit"] = np.array(VM_fit_tot)
results["err_fit"] = np.array(err_fit_tot)
results["scs_fit"] = np.array(scs_fit_tot)
results["TW_out"] = np.array(TW_out_tot)
results["PO_out"] = np.array(PO_out_tot)
results["OSI_out"] = np.array(OSI_out_tot)
### writing the results
fl = open("results", "wb")
cPickle.dump(results, fl, 2)
fl.close()
tf = time.time()
print("### took : %.2f s" % (tf - ti))
os.chdir(code_path)
