def init():
files_para = '../data/ForNorData_MSTd/'
files_r = '../data/AllData_MSTd/AllData/'
both_azimuth_files = os.listdir(files_para)
r_files = os.listdir(files_r)
VEST = list(range(0, 360 + 45, 45)) # H: vest azimuth
VIS = list(range(0, 360 + 45, 45)) # H: vis azimuth
# H_hat: vest azimuth load, vis azimuth load, vest elevation all zero, vis elevation all zero,
# H: vest azimuth in [0,360,45] range, vis azimuth in [0,360,45] range,
# vest elevation all zero, vis elevation all zero,
global vest_a, vis_a, vest_e, vis_e, n1, n2, n3, e, c_vest, c_vis, n0, \
S_vest_m, S_vis_m,RawR_trial,RawR
vest_a = [] # vest azimuth in H_hat
vis_a = [] # vis azimuth in H_hat
RawR = [
] # firing curve, mean of neuron responses under different stimulus conditions, 就是我们最后要fit的y值（平均数）
RawR_trial = [
] # neuron responses under different stimulus conditions, 就是我们最后要fit的y值（实际trial的y值）
RawR_cam = []
RawR_ves = []
index = []
# vest_resp = [] # 不需要用到，只是在算Rmax的时候用了
# vis_resp = []
collection = CoherenceSelect.CoherenceSelect()
for f in both_azimuth_files:
if f[:-18] in collection:
temp_file = format_matfile(files_para + f, 'ForNorData')
vest_a.append(temp_file['vest_Direc'])
vis_a.append(temp_file['vis_Direc'])
index.append(files_r + f[:-15])
temp_file = format_matfile(
files_r + 'SmoothData_' + f[:-15] + '.mat',
'CueConflictDataSmooth')
RawR_trial.append(temp_file['resp_trial_conflict'])
# RawR.append(temp_file['resp_conflict_200'])
RawR_cam.append(temp_file['resp_vis'])
RawR_ves.append(temp_file['resp_ves'])
SIZE = len(RawR_cam)
vest_resp = RawR_ves
vis_resp = RawR_cam
vest_a = [i + 360 if i < 0 else i for i in np.ravel(vest_a)]
vis_a = [i + 360 if i < 0 else i for i in np.ravel(vis_a)]
vest_e = np.zeros(len(vest_a))
vis_e = np.zeros(len(vis_a))
vest_stim_ele = 0
vis_stim_ele = 0
n1 = n2 = n3 = e = c_vest = c_vis = 1
n0 = 2
init_vect = np.array([
np.random.rand(SIZE) + 9.5,
np.random.rand(SIZE) + 0.5,
np.random.rand(SIZE) - 0.53,
np.random.rand(SIZE) + 0.55,
np.zeros((SIZE, ))
]) # 参数初始值
global Rmax_m
Rmax_m = []
# Rmax_m = np.zeros((200, 9, 9))
S_vest_m_cam = np.zeros((SIZE, 9, 9))
S_vis_m_cam = np.zeros((SIZE, 9, 9))
S_vest_m_ves = np.zeros((SIZE, 9, 9))
S_vis_m_ves = np.zeros((SIZE, 9, 9))
c_vest = 0
c_vis = 1
for k in range(len(VEST)):
for j in range(len(VIS)):
vest_stim_az = VEST[k]
vis_stim_az = VIS[j]
# Rmax = np.maximum(np.ravel(np.array(vest_resp)[:, k]), np.ravel(np.array(vis_resp)[:, :, j]))
# Rmax_m[:, k, j] = Rmax
S_vest = spherical_sinusoid(vest_stim_az, vest_a, vest_stim_ele,
vest_e, c_vest, n0)
S_vest_m_cam[:, k, j] = S_vest
S_vis = spherical_sinusoid(vis_stim_az, vis_a, vis_stim_ele, vis_e,
c_vis, n0)
S_vis_m_cam[:, k, j] = S_vis
Rmax1 = []
Rmax2 = []
for k in range(SIZE):
Rmax1.append([])
for i in range(len(VIS)):
Max = np.array(vis_resp)[k, 0, i]
for j in range(len(VEST)):
Max = max(Max, np.array(vest_resp)[k, j, 0])
Rmax1[k].append(Max)
print(np.array(Rmax1).shape)
for k in range(SIZE):
Rmax2.append([])
for i in range(len(VIS)):
Max = np.array(vest_resp)[k, i, 0]
for j in range(len(VEST)):
Max = max(Max, np.array(vis_resp)[k, 0, j])
Rmax2[k].append(Max)
print(np.array(Rmax2).shape)
for k in range(len(Rmax1)):
Rmax_m.append([])
Rmax_m[k].append(Rmax1[k])
Rmax_m[k].append(Rmax2[k])
print(np.array(Rmax_m).shape)
c_vest = 1
c_vis = 0
for k in range(len(VEST)):
for j in range(len(VIS)):
vest_stim_az = VEST[k]
vis_stim_az = VIS[j]
# Rmax = np.maximum(np.ravel(np.array(vest_resp)[:, k]), np.ravel(np.array(vis_resp)[:, :, j]))
# Rmax_m[:, k, j] = Rmax
S_vest = spherical_sinusoid(vest_stim_az, vest_a, vest_stim_ele,
vest_e, c_vest, n0)
S_vest_m_ves[:, k, j] = S_vest
S_vis = spherical_sinusoid(vis_stim_az, vis_a, vis_stim_ele, vis_e,
c_vis, n0)
S_vis_m_ves[:, k, j] = S_vis
c_vest = 1
c_vis = 1
#select_good
global selected_neurons
selected_neurons = list(range(SIZE))
S_vest_m = []
for i in range(len(selected_neurons)):
S_vest_m.append([])
S_vis_m = []
for i in range(len(selected_neurons)):
S_vis_m.append([])
# S_vest_m_cam = S_vest_m_cam[selected_neurons]
# S_vest_m_cam=S_vest_m_cam.tolist()
# S_vest_m_ves = S_vest_m_ves[selected_neurons]
# S_vest_m_ves=S_vest_m_ves.tolist()
for i in range(len(selected_neurons)):
S_vest_m[i].append(S_vest_m_cam[i][0])
u = []
for j in range(len(S_vest_m_ves[i])):
u.append(S_vest_m_ves[i][j][0])
S_vest_m[i].append(u)
S_vest_m = np.array(S_vest_m)
# S_vis_m_cam= S_vis_m_cam[selected_neurons]
# S_vis_m_ves= S_vis_m_ves[selected_neurons]
# S_vis_m_cam=S_vis_m_cam.tolist()
# S_vis_m_ves=S_vis_m_ves.tolist()
for i in range(len(selected_neurons)):
S_vis_m[i].append(S_vis_m_cam[i][0])
u = []
for j in range(len(S_vis_m_ves[i])):
u.append(S_vis_m_ves[i][j][0])
S_vis_m[i].append(u)
S_vis_m = np.array(S_vis_m)
# init_vect = init_vect[:, selected_neurons]
# vis_a = np.array(vis_a)[selected_neurons].tolist()
# vest_a = np.array(vest_a)[selected_neurons].tolist()
for i in range(len(selected_neurons)):
RawR.append([])
# RawR_cam=(np.array(RawR_cam)[selected_neurons]).tolist()
# RawR_ves=(np.array(RawR_ves)[selected_neurons]).tolist()
for i in range(len(selected_neurons)):
RawR[i].append(RawR_cam[i][0])
u = []
for j in range(len(RawR_ves[i])):
u.append(RawR_ves[i][j][0])
RawR[i].append(u)
RawR = np.array(RawR)
print(RawR.shape)
# RawR_trial = np.array(RawR_trial)[selected_neurons]
corr_list = [(np.triu(np.corrcoef([i.ravel()
for i in RawR_trial[n]])).sum() -
RawR_trial[n].shape[0]) / comb(RawR_trial[n].shape[0], 2)
for n in range(SIZE)]
selected_neurons = [
i for i in range(SIZE) if corr_list[i] > 0.5
] # select good neurons: correlation between trials over 0.4
S_vest_m = S_vest_m[selected_neurons]
S_vis_m = S_vis_m[selected_neurons]
Rmax_m = np.array(Rmax_m)[selected_neurons]
Rmax_m = Rmax_m.tolist()
init_vect = init_vect[:, selected_neurons]
vis_a = np.array(vis_a)[selected_neurons].tolist()
vest_a = np.array(vest_a)[selected_neurons].tolist()
RawR = np.array(RawR)[selected_neurons]
RawR_trial = np.array(RawR_trial)[selected_neurons]
S_vest_m = S_vest_m
S_vis_m = S_vis_m
# optimize()
start_time = time.time()
global errors
errors = []
print('init_vect:', np.array(init_vect).shape)
res = minimize(optimize_f, np.ravel(init_vect), method='SLSQP',
bounds=((0, None),) * len(selected_neurons) + ((0, None),) * len(selected_neurons) \
+ ((None, None),) * len(selected_neurons) + ((None, None),) * len(selected_neurons) + (
(None, None),) * len(selected_neurons),
options={'maxiter': 10000})
data_write('../resultConsiderCoherence/参数4_nobound_unimodal.xls',
(np.array(res.x).reshape(5, len(selected_neurons)).tolist()))
print('Error:', res.fun)
# R_square = 1 - res.fun/np.ravel((RawR - np.array(len(selected_neurons)*[np.mean(RawR,axis=0)]))**2).sum()
R_square = 1 - res.fun * 10000000 / np.ravel(
(RawR - np.array(len(selected_neurons) * [np.mean(RawR, axis=0)]))**
2).sum()
print('R_square:', R_square)
print("--- %s seconds ---" % (time.time() - start_time))
rs = calculate_R(res.x)
files_para = '../data/ForNorData_MSTd/'
files_r = '../data/AllData_MSTd/AllData/'
both_azimuth_files = os.listdir(files_para)
r_files = os.listdir(files_r)
VEST = list(range(0, 360 + 45, 45)) # H: vest azimuth
VIS = list(range(0, 360 + 45, 45)) # H: vis azimuth
global vest_a, vis_a, vest_e, vis_e, n1, n2, n3, e, c_vest, c_vis, n0, \
S_vest_m, S_vis_m,RawR_trial,RawR
RawR = [
] # firing curve, mean of neuron responses under different stimulus conditions, 就是我们最后要fit的y值（平均数）
RawR_trial = [
] # neuron responses under different stimulus conditions, 就是我们最后要fit的y值（实际trial的y值）
collection = CoherenceSelect.CoherenceSelect()
for f in both_azimuth_files:
if f[:-18] in collection:
temp_file = format_matfile(files_r + 'SmoothData_' + f[:-15] + '.mat',
'CueConflictDataSmooth')
RawR_trial.append(temp_file['resp_trial_conflict'])
RawR.append(temp_file['resp_conflict'])
SIZE = len(RawR)
corr_list = [(np.triu(np.corrcoef([i.ravel() for i in RawR_trial[n]])).sum() -
RawR_trial[n].shape[0]) / comb(RawR_trial[n].shape[0], 2)
for n in range(SIZE)]
global selected_neurons
selected_neurons = [i for i in range(SIZE) if corr_list[i] > 0.4]
RawR = np.array(RawR)[selected_neurons]
RawR_trial = np.array(RawR_trial)[selected_neurons]
print(np.array(RawR).shape)
def init():
files_para = '../data/ForNorData_MSTd/'
files_r = '../data/AllData_MSTd/AllData/'
both_azimuth_files = os.listdir(files_para)
r_files = os.listdir(files_r)
VEST = list(range(0, 360 + 45, 45)) # H: vest azimuth
VIS = list(range(0, 360 + 45, 45)) # H: vis azimuth
# H_hat: vest azimuth load, vis azimuth load, vest elevation all zero, vis elevation all zero,
# H: vest azimuth in [0,360,45] range, vis azimuth in [0,360,45] range,
# vest elevation all zero, vis elevation all zero,
global vest_a, vis_a, vest_e, vis_e, n1, n2, n3, e, c_vest, c_vis, n0, \
S_vest_m, S_vis_m,RawR_trial,RawR
vest_a = [] # vest azimuth in H_hat
vis_a = [] # vis azimuth in H_hat
RawR = [
] # firing curve, mean of neuron responses under different stimulus conditions, 就是我们最后要fit的y值（平均数）
RawR_trial = [
] # neuron responses under different stimulus conditions, 就是我们最后要fit的y值（实际trial的y值）
vest_resp = [] # 不需要用到，只是在算Rmax的时候用了
vis_resp = []
collection = CoherenceSelect.CoherenceSelect()
for f in both_azimuth_files:
if f[:-18] in collection:
temp_file = format_matfile(files_para + f, 'ForNorData')
vest_a.append(temp_file['vest_Direc'])
vis_a.append(temp_file['vis_Direc'])
temp_file = format_matfile(
files_r + 'SmoothData_' + f[:-15] + '.mat',
'CueConflictDataSmooth')
RawR_trial.append(temp_file['resp_trial_conflict'])
RawR.append(temp_file['resp_conflict'])
vest_resp.append(temp_file['resp_ves'])
vis_resp.append(temp_file['resp_vis'])
SIZE = len(vis_resp)
# temp_file = format_matfile(files_r + f[:-15] + '.mat', 'CueConflictData')
# RawR_trial.append(temp_file['resp_trial_conflict_200'])
# RawR.append(temp_file['resp_conflict_200'])
vest_a = [i + 360 if i < 0 else i for i in np.ravel(vest_a)]
vis_a = [i + 360 if i < 0 else i for i in np.ravel(vis_a)]
vest_e = np.zeros(len(vest_a))
vis_e = np.zeros(len(vis_a))
vest_stim_ele = 0
vis_stim_ele = 0
n1 = n2 = n3 = e = c_vest = c_vis = 1
n0 = 2
init_vect = np.array([
np.random.rand(SIZE) + 9.5,
np.random.rand(SIZE) + 0.5,
np.random.rand(SIZE) - 0.53,
np.random.rand(SIZE) + 0.55,
np.random.rand(SIZE)
]) # 参数初始值
global Rmax_m
Rmax_m = np.zeros((SIZE, 9, 9))
S_vest_m = np.zeros((SIZE, 9, 9))
S_vis_m = np.zeros((SIZE, 9, 9))
for k in range(len(VEST)):
for j in range(len(VIS)):
vest_stim_az = VEST[k]
vis_stim_az = VIS[j]
Rmax = np.maximum(np.ravel(np.array(vest_resp)[:, k]),
np.ravel(np.array(vis_resp)[:, :, j]))
Rmax_m[:, k, j] = Rmax
S_vest = spherical_sinusoid(vest_stim_az, vest_a, vest_stim_ele,
vest_e, c_vest, n0)
S_vest_m[:, k, j] = S_vest
S_vis = spherical_sinusoid(vis_stim_az, vis_a, vis_stim_ele, vis_e,
c_vis, n0)
S_vis_m[:, k, j] = S_vis
corr_list = [(np.triu(np.corrcoef([i.ravel()
for i in RawR_trial[n]])).sum() -
RawR_trial[n].shape[0]) / comb(RawR_trial[n].shape[0], 2)
for n in range(SIZE)]
global selected_neurons
selected_neurons = [
i for i in range(SIZE) if corr_list[i] > 0.5
] # select good neurons: correlation between trials over 0.4
# global selected_neurons
# selected_neurons=list(range(200))
S_vest_m = S_vest_m[selected_neurons]
S_vis_m = S_vis_m[selected_neurons]
Rmax_m = Rmax_m[selected_neurons]
init_vect = init_vect[:, selected_neurons]
vis_a = np.array(vis_a)[selected_neurons].tolist()
vest_a = np.array(vest_a)[selected_neurons].tolist()
RawR = np.array(RawR)[selected_neurons]
RawR_trial = np.array(RawR_trial)[selected_neurons]
S_vest_m = S_vest_m
S_vis_m = S_vis_m
current_vect = np.array(read())
path = '../resultConsiderCoherence/all/'
for i in os.listdir(path):
path_file = os.path.join(path, i)
if os.path.isfile(path_file):
os.remove(path_file)
rs = calculate_R(current_vect)
for i in range(len(selected_neurons)):
path = '../resultConsiderCoherence/all/' + str(i) + '.xls'
data_write(path, rs[i])
def init():
files_para = '../data/ForNorData_MSTd/'
files_r = '../data/AllData_MSTd/AllData/'
both_azimuth_files = os.listdir(files_para)
r_files = os.listdir(files_r)
VEST = list(range(0, 360 + 45, 45)) # H: vest azimuth
VIS = list(range(0, 360 + 45, 45)) # H: vis azimuth
global vest_a, vis_a, vest_e, vis_e, n1, n2, n3, e, c_vest, c_vis, n0, \
S_vest_m, S_vis_m,RawR_trial,RawR
vest_a = [] # vest azimuth in H_hat
vis_a = [] # vis azimuth in H_hat
RawR = [
] # firing curve, mean of neuron responses under different stimulus conditions, 就是我们最后要fit的y值（平均数）
RawR_trial = [
] # neuron responses under different stimulus conditions, 就是我们最后要fit的y值（实际trial的y值）
vest_resp = [] # 不需要用到，只是在算Rmax的时候用了
vis_resp = []
collection = CoherenceSelect.CoherenceSelect()
for f in both_azimuth_files:
if f[:-18] in collection:
temp_file = format_matfile(files_para + f, 'ForNorData')
vest_a.append(temp_file['vest_Direc'])
vis_a.append(temp_file['vis_Direc'])
temp_file = format_matfile(
files_r + 'SmoothData_' + f[:-15] + '.mat',
'CueConflictDataSmooth')
RawR_trial.append(temp_file['resp_trial_conflict'])
vis_resp.append(temp_file['resp_vis'])
vest_resp.append(temp_file['resp_ves'])
SIZE = len(vest_a)
vest_a = [i + 360 if i < 0 else i for i in np.ravel(vest_a)]
vis_a = [i + 360 if i < 0 else i for i in np.ravel(vis_a)]
vest_e = np.zeros(len(vest_a))
vis_e = np.zeros(len(vis_a))
vest_stim_ele = 0
vis_stim_ele = 0
n1 = n2 = n3 = e = c_vis = 1
c_vest = 0
n0 = 2
global Rmax_m
Rmax_m = []
S_vest_m = np.zeros((SIZE, 9, 9))
S_vis_m = np.zeros((SIZE, 9, 9))
for k in range(len(VEST)):
for j in range(len(VIS)):
vest_stim_az = VEST[k]
vis_stim_az = VIS[j]
S_vest = spherical_sinusoid(vest_stim_az, vest_a, vest_stim_ele,
vest_e, c_vest, n0)
# print(np.array(S_vest).shape)
S_vest_m[:, k, j] = S_vest
S_vis = spherical_sinusoid(vis_stim_az, vis_a, vis_stim_ele, vis_e,
c_vis, n0)
S_vis_m[:, k, j] = S_vis
# S_vis_m_temp=[]
# S_vest_m_temp=[]
# for i in range(200):
# S_vest_m_temp.append([])
# S_vis_m_temp.append([])
# S_vis_m_temp[i].append(S_vis_m[i][0])
# S_vest_m_temp[i].append(S_vest_m[i][0])
# print(np.array(S_vis_m_temp).shape)
# print(np.array(S_vest_m_temp).shape)
Rmax1 = []
# Rmax2 = []
for k in range(SIZE):
Rmax1.append([])
for i in range(len(VIS)):
Max = np.array(vis_resp)[k, 0, i]
for j in range(len(VEST)):
Max = max(Max, np.array(vest_resp)[k, j, 0])
Rmax1[k].append(Max)
# print(np.array(Rmax1).shape)
# for k in range(200):
# Rmax2.append([])
# for i in range(len(VIS)):
# Max = np.array(vest_resp)[k, i, 0]
# for j in range(len(VEST)):
# Max = max(Max, np.array(vis_resp)[k, 0, j])
# Rmax2[k].append(Max)
# print(np.array(Rmax2).shape)
for k in range(len(Rmax1)):
Rmax_m.append([])
Rmax_m[k].append(Rmax1[k])
# Rmax_m[k].append(Rmax2[k])
# print(np.array(Rmax_m).shape)
#select_good
# corr_list = [(np.triu(np.corrcoef([i.ravel() for i in RawR_trial[n]])).sum() - RawR_trial[n].shape[0]) / comb(
# RawR_trial[n].shape[0], 2) for n in range(200)]
# global selected_neurons
# selected_neurons = [i for i in range(200) if
# corr_list[i] > 0.4] # select good neurons: correlation between trials over 0.4
global selected_neurons
selected_neurons = list(range(SIZE))
# S_vest_m=S_vest_m[selected_neurons]
# S_vis_m = S_vis_m[selected_neurons]
# # print("S_vest_m:",S_vest_m.shape)
# # print("S_vis_m:", S_vest_m.shape)
# # init_vect = init_vect[:, selected_neurons]
# vis_a = np.array(vis_a)[selected_neurons].tolist()
# vest_a = np.array(vest_a)[selected_neurons].tolist()
vest_resp
S_vest_m_temp = []
for i in range(len(selected_neurons)):
S_vest_m_temp.append([])
corr_list = [(np.triu(np.corrcoef([i.ravel()
for i in RawR_trial[n]])).sum() -
RawR_trial[n].shape[0]) / comb(RawR_trial[n].shape[0], 2)
for n in range(SIZE)]
selected_neurons = [
i for i in range(SIZE) if corr_list[i] > 0.5
] # select good neurons: correlation between trials over 0.4
S_vest_m = S_vest_m[selected_neurons]
S_vis_m = S_vis_m[selected_neurons]
Rmax_m = np.array(Rmax_m)[selected_neurons]
Rmax_m = Rmax_m.tolist()
# init_vect = init_vect[:, selected_neurons]
vis_a = np.array(vis_a)[selected_neurons].tolist()
vest_a = np.array(vest_a)[selected_neurons].tolist()
# RawR = np.array(RawR)[selected_neurons]
RawR_trial = np.array(RawR_trial)[selected_neurons]
S_vest_m = S_vest_m
S_vis_m = S_vis_m
path = '../resultConsiderCoherence/vis/'
for i in os.listdir(path):
path_file = os.path.join(path, i)
if os.path.isfile(path_file):
os.remove(path_file)
current_vect = np.array(read())
#计算c1=c1!=0的rs
rs = calculate_R(current_vect)
for i in range(len(selected_neurons)):
path = '../resultConsiderCoherence/vis/' + str(i) + '.xls'
data_write(path, rs[i][0])
def init():
files_para = '../data/ForNorData_MSTd/'
files_r = '../data/AllData_MSTd/AllData/'
# files_r = 'C:/Users/76774/Desktop/my_RSA/Monkey/data/OriginalData_MST'
both_azimuth_files = os.listdir(files_para)
r_files = os.listdir(files_r)
VEST = list(range(0, 360 + 45, 45)) # H: vest azimuth
VIS = list(range(0, 360 + 45, 45)) # H: vis azimuth
# H_hat: vest azimuth load, vis azimuth load, vest elevation all zero, vis elevation all zero,
# H: vest azimuth in [0,360,45] range, vis azimuth in [0,360,45] range,
# vest elevation all zero, vis elevation all zero,
global vest_a, vis_a, vest_e, vis_e, n1, n2, n3, e, c_vest, c_vis, n0, \
S_vest_m, S_vis_m,RawR_trial,RawR
vest_a = [] # vest azimuth in H_hat
vis_a = [] # vis azimuth in H_hat
RawR = [
] # firing curve, mean of neuron responses under different stimulus conditions, 就是我们最后要fit的y值（平均数）
RawR_trial = [
] # neuron responses under different stimulus conditions, 就是我们最后要fit的y值（实际trial的y值）
vest_resp = [] # 不需要用到，只是在算Rmax的时候用了
vis_resp = []
index = []
collection = CoherenceSelect.CoherenceSelect()
# print(collection)
Name = []
for f in both_azimuth_files:
print(f[:-15])
if f[:-18] in collection:
temp_file = format_matfile(files_para + f, 'ForNorData')
vest_a.append(temp_file['vest_Direc'])
vis_a.append(temp_file['vis_Direc'])
index.append(files_r + f[:-15])
temp_file = format_matfile(
files_r + 'SmoothData_' + f[:-15] + '.mat',
'CueConflictDataSmooth')
# print(files_r +'SmoothData_'+ f[:-15] + '.mat')
RawR_trial.append(temp_file['resp_trial_conflict'])
RawR.append(temp_file['resp_conflict'])
vest_resp.append(temp_file['resp_ves'])
vis_resp.append(temp_file['resp_vis'])
Name.append(f[:-18])
SIZE = len(vis_resp)
vest_a = [i + 360 if i < 0 else i for i in np.ravel(vest_a)]
vis_a = [i + 360 if i < 0 else i for i in np.ravel(vis_a)]
vest_e = np.zeros(len(vest_a))
vis_e = np.zeros(len(vis_a))
vest_stim_ele = 0
vis_stim_ele = 0
n1 = n2 = n3 = e = c_vest = c_vis = 1
n0 = 2
global Rmax_m
Rmax_m = np.zeros((SIZE, 9, 9))
S_vest_m = np.zeros((SIZE, 9, 9))
S_vis_m = np.zeros((SIZE, 9, 9))
for k in range(len(VEST)):
for j in range(len(VIS)):
vest_stim_az = VEST[k]
vis_stim_az = VIS[j]
Rmax = np.maximum(np.ravel(np.array(vest_resp)[:, k]),
np.ravel(np.array(vis_resp)[:, :, j]))
Rmax_m[:, k, j] = Rmax
S_vest = spherical_sinusoid(vest_stim_az, vest_a, vest_stim_ele,
vest_e, c_vest, n0)
S_vest_m[:, k, j] = S_vest
S_vis = spherical_sinusoid(vis_stim_az, vis_a, vis_stim_ele, vis_e,
c_vis, n0)
S_vis_m[:, k, j] = S_vis
global selected_neurons
corr_list = [(np.triu(np.corrcoef([i.ravel()
for i in RawR_trial[n]])).sum() -
RawR_trial[n].shape[0]) / comb(RawR_trial[n].shape[0], 2)
for n in range(SIZE)]
selected_neurons = [
i for i in range(SIZE) if corr_list[i] > 0.5
] # select good neurons: correlation between trials over 0.4
Name = np.array(Name)[selected_neurons].tolist()
f = open('../resultConsiderCoherence/neuronsNumber.txt', 'w')
for i, name in enumerate(Name):
if i != len(Name) - 1:
line = name + '\t' + str(i) + '\n'
else:
line = name + '\t' + str(i)
print(line)
f.write(line)
f.close()