import numpy as np
#import matplotlib.pylab as plt
import json
import os


#####################################
  
def save_obj(obj, name ):
    with open('dat/'+ name + '.txt', 'w') as f:
        json.dump(obj, f)
        
def load_obj(name ):
    with open('dat/' + name + '.txt', 'r') as f:
        return json.load(f)
        
def load_params(name ):
    script_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir))
    with open(os.path.join(script_dir,'params/' + name + '.txt'), 'r') as f:
        return json.load(f)
  
np.random.seed()  

print('===================================')
print('START Evolution sim')
print('===================================')

#####################################
###### Parameters
#####################################
store_name = 'dat_totalSim_evo_s6c'
store_osi = 'dat_totalSim_OSI_s6c'

m_par = load_params('model_params')

timeStep = m_par['timeStep']    
stimLen = m_par['stimLen']
deltaStim = m_par['deltaStim']
NrE_L3 = m_par['NrE_L3']
NrE_L4 = m_par['NrE_L4']
NrI_L3 = m_par['NrI_L3']
Nr4to3 = m_par['Nr4to3']
tau_e = m_par['tau_e']
tau_i = m_par['tau_i']
beta_v = m_par['beta_v']
beta_inh = m_par['beta_inh']
theta_v = m_par['theta_v']
vis_amp = m_par['vis_amp']
depr_value = m_par['depr_value']
nr_Ori = m_par['nr_Ori']
NrE_L4_tot =nr_Ori*NrE_L4
bck_current = m_par['bck_current']
layerfactor = m_par['layerfactor']

    
## Plasticity
p_par = load_params('plast_params')

wmax_taro = p_par['wmax_taro']
wmin_taro =  p_par['wmin_taro']
Theta_eeMax_H =  p_par['Theta_eeMax_H']-1
Theta_eeMax_L =  p_par['Theta_eeMax_L']-1
l_rate_ee =  p_par['l_rate_ee']
wmin_ei =  p_par['wmin_ei']
deltath =  p_par['deltath']
theta_ei_H =  p_par['theta_ei_H']
lrate_ei =  p_par['lrate_ei']
wmax_ie =  p_par['wmax_ie']
wmin_ie =  p_par['wmin_ie']
lrate_ie_Max =  p_par['lrate_ie_Max']
ie_target =  p_par['ie_target']
tau_avg =  p_par['tau_avg']
I3avg_init = p_par['I3_avg_ini']
wee_ini = 0#p_par['wee_ini'] 
wee_L4to3_ini = p_par['wee_L4to3_ini'] 
wie_ini = p_par['wie_ini'] 
w_ie_4to3_ini = p_par['w_ie_4to3_ini']
wei_ini = p_par['wei_ini'] 


tmax1 = 50000
tmax2 = 0 
tmax = tmax1 + tmax2
deprtime = tmax1/100 + 10

storeDT = timeStep
stimWindow = int(deltaStim/timeStep) 
nr_per_Ori = int(NrE_L3/nr_Ori)


CL_mod = 1
IL_mod = 1
I3_avg = I3avg_init*np.ones(NrI_L3)

activity_shift = 1 + .1*np.random.randn(NrE_L3) #.15*np.random.randn(NrE_L3)  #different firing rates
corr_act = np.outer(activity_shift,np.array(np.ones(NrE_L4_tot)))

backge = .1#1#0
backgi = .1#1
unos = (np.diag(np.ones(nr_Ori)) + backge*(np.ones((nr_Ori,nr_Ori))-np.diag(np.ones(nr_Ori)))) 
unos[0,-1] = backge
unos[-1,0] = backge
unos=np.expand_dims(unos,axis=1)
spec_Ori = np.repeat(np.repeat(unos,nr_per_Ori,axis=0),NrE_L4,axis=1)
spec_Ori = spec_Ori + np.random.rand(nr_Ori*nr_per_Ori,NrE_L4,nr_Ori)*.5

if NrI_L3>1:
    unos = backgi*(np.ones((NrI_L3,nr_Ori)))
    for ii in np.arange(NrI_L3):
        unos[ii,int(np.floor(np.random.rand()*nr_Ori))] = 1
#    print(unos)
    unos=np.expand_dims(unos,axis=1)
    spec_Ori_inh = np.repeat(unos,NrE_L4,axis=1)
else:
    spec_Ori_inh = np.ones((NrI_L3,NrE_L4,nr_Ori))



#*** Other
NrIp = 2       # total nr of eyes

#####################################
###### Variables
#####################################

#################################################################################
# LAYER 3
#################################################################################
Ev_L3 = np.zeros(NrE_L3)          # vector of excitatory activities
Iv_L3 = np.zeros(NrI_L3)          # vector of inhibitory activities

w_ie_4to3 = w_ie_4to3_ini*spec_Ori_inh#*np.ones((NrI_L3,NrE_L4,nr_Ori))     # #ffw weight matrix   
w_ie_4to3[w_ie_4to3<wmin_ie] = wmin_ie 
w_ie_4to3 =np.reshape(w_ie_4to3,(NrI_L3,nr_Ori*NrE_L4),order='F')
w_ei_L3 = wei_ini*np.ones((NrE_L3,NrI_L3))     # ffw weight matrix
w_ie_L3 = wie_ini*np.ones((NrI_L3,NrE_L3)) 
w_ii_L3 = np.zeros((NrI_L3,NrI_L3))

L3_connections = np.zeros((NrE_L3,NrE_L4_tot))

subfam = int(NrE_L4/5)

for ii in range(0,int(.1*NrE_L3)):
    arr = []
    distr = [5,5,5,10,25]
    distr = np.array(distr)*Nr4to3/50
    sp = len(distr)
    for _ in range(nr_Ori):
        for jj in range(sp):
            a = (np.random.permutation(np.arange(subfam))<distr[jj])*1
            arr = arr+a.tolist()
    L3_connections[ii,:] = np.array(arr)
for ii in range(int(.1*NrE_L3),int(.2*NrE_L3)):
    arr = []
    distr = [5,10,10,10,15]
    distr = np.array(distr)*Nr4to3/50
    sp = len(distr)
    for _ in range(nr_Ori):
        for jj in range(sp):
            a = (np.random.permutation(np.arange(subfam))<distr[jj])*1
            arr = arr+a.tolist()
    L3_connections[ii,:] = np.array(arr)
for ii in range(int(.2*NrE_L3),int(.4*NrE_L3)):
    arr = []
    distr = [10,10,10,10,10]
    distr = np.array(distr)*Nr4to3/50
    sp = len(distr)
    for _ in range(nr_Ori):
        for jj in range(sp):
            a = (np.random.permutation(np.arange(subfam))<distr[jj])*1
            arr = arr+a.tolist()
    L3_connections[ii,:] = np.array(arr)
for ii in range(int(.4*NrE_L3),int(.6*NrE_L3)):
    arr = []
    distr = [10,15,10,10,5]
    distr = np.array(distr)*Nr4to3/50
    sp = len(distr)
    for _ in range(nr_Ori):
        for jj in range(sp):
            a = (np.random.permutation(np.arange(subfam))<distr[jj])*1
            arr = arr+a.tolist()
    L3_connections[ii,:] = np.array(arr)
for ii in range(int(.6*NrE_L3),int(.8*NrE_L3)):
    arr = []
    distr = [10,15,15,5,5]
    distr = np.array(distr)*Nr4to3/50
    sp = len(distr)
    for _ in range(nr_Ori):
        for jj in range(sp):
            a = (np.random.permutation(np.arange(subfam))<distr[jj])*1
            arr = arr+a.tolist()
    L3_connections[ii,:] = np.array(arr)
for ii in range(int(.8*NrE_L3),int(1*NrE_L3)):
    arr = []
    distr = [25,10,5,5,5]
    distr = np.array(distr)*Nr4to3/50
    sp = len(distr)
    for _ in range(nr_Ori):
        for jj in range(sp):
            a = (np.random.permutation(np.arange(subfam))<distr[jj])*1
            arr = arr+a.tolist()
    L3_connections[ii,:] = np.array(arr)

np.random.shuffle(L3_connections)
    
L3_connections_inh = np.zeros((NrI_L3,NrE_L4_tot))# np.append(np.ones((NrI_L3,Nr4to3)),np.zeros((NrI_L3,NrE_L4-Nr4to3)),axis=1)
for ii in range(NrI_L3):
    L3_connections_inh[ii,:] =  np.tile(np.append(np.append(np.ones((1,int(.6*Nr4to3))),np.zeros((1,NrE_L4-Nr4to3))
                            ,axis=1),np.ones((1,int(.4*Nr4to3))),axis=1),(1,nr_Ori))

w_ee_4to3 = wee_L4to3_ini*spec_Ori#np.ones((NrE_L3,NrE_L4))
w_ee_4to3[w_ee_4to3<wmin_taro] = wmin_taro#np.ones((NrE_L3,NrE_L4))
w_ee_4to3 = np.reshape(w_ee_4to3,(NrE_L3,nr_Ori*NrE_L4),order='F')
w_taroee_4to3 = w_ee_4to3
w_ee_L3 = np.zeros((NrE_L3,NrE_L3))

#################################################################################


#################################################################################
# LAYER 4
#################################################################################
Ev_L4 = np.zeros(NrE_L4_tot)          # vector of excitatory activities
L4_connections = []
meancontra = 30
stdvcontra = 35
for _ in range(nr_Ori):
    q = meancontra +np.random.randn(NrE_L4)*stdvcontra
    q[q<0] = 100*np.random.rand(len(q[q<0])) #0
    q[q>100] = 100
    q = (np.sort(q)).tolist()
    L4_connections.append(q)
q = np.reshape(L4_connections,[NrE_L4_tot])
L4_connections = .01*np.transpose(np.array([q,100-q]))


#################################################################################

storeV = np.zeros((15,int(tmax/storeDT)))    # storage of data
weightV = np.zeros((15,int(tmax/storeDT)))    # storage of data
weightV2 = np.zeros((15,int(tmax/storeDT)))    # storage of data
weightV3 = np.zeros((15,int(tmax/storeDT)))    # storage of data


#####################################
###### functions
#####################################

def f_IEvol_WilCow2(y,ipt,exc,iiw,ipw,ip_corr,iew,tauy,ori,bckg):
    tot_inputs = np.dot(ipw*ip_corr,ipt) 
    y = y + (timeStep/tauy)*(-y + gainF_relu( tot_inputs+np.dot(iew,exc)-np.dot(iiw,y)+bckg,beta_inh ) )
    return y
    
def f_EEvol_WilCow2(y,ipt,inh,eew,ipw,ip_corr,eiw,tauy,ori,bckg):
    tot_inputs = np.dot(ipw*ip_corr,ipt) 
    y = y + (timeStep/tauy)*(-y + gainF_relu( tot_inputs+np.dot(eew,y)
                            -np.dot(eiw,inh)+bckg,beta_v ) )
    return y
    
def gainF_relu(x,slopef):
    out = slopef*(x-theta_v)
    out = out*(x>theta_v)    
    return out
     
def f_e_to_i_plasticity(x,y,yavg,weight): 
    
    theta_bcm = yavg**2/ie_target
    
    aux = np.outer(y-theta_bcm,2*(x>0))*(np.outer(y-theta_bcm,2)<0)+np.outer(y-theta_bcm,x*(x>3.2))*(np.outer(y-theta_bcm,2)>0)
    dw = np.multiply(np.outer(y,np.ones(np.shape(x))),aux)
    
    weight = weight + timeStep*lrate_ie_Max*dw 
    
    weight[weight>wmax_ie] = wmax_ie
    weight[weight<wmin_ie] = wmin_ie       
  
    return weight

def f_i_to_e_plasticity(x,y,weight,activityshift): 
    pre_o = np.ones(np.shape(x))    
    dw = np.outer(y>theta_ei_H*activityshift-deltath,pre_o)*np.outer(y-theta_ei_H*activityshift,x) 
         
    weight = weight + timeStep*lrate_ei*dw 
    weight[weight<wmin_ei] = wmin_ei   
    return weight

def f_ee_plasticity(pre=0,post=0,whebb=0,recurrent='no'):
    
    dw = ((np.outer(post,pre)>Theta_eeMax_L)*(np.outer(post,pre)-Theta_eeMax_H)
            *(np.outer(post,pre)-Theta_eeMax_L))
            
    dw = np.tanh(dw/0.0001)        
        
    whebb = whebb + timeStep*l_rate_ee*dw  
    
    whebb[whebb>wmax_taro] = wmax_taro        
    whebb[whebb<wmin_taro] = wmin_taro
    
    if recurrent == 'yes':
        if np.size(whebb,axis=0) == np.size(whebb,axis=1):
            whebb = whebb - np.diag(np.diag(whebb))
        else:
            print('Problem with recurrent connections: no square matrix!')
  
    return whebb   
    
#% Calculate OSI
def f_OSI(rates):
    nr_Ori = np.size(rates,axis=1)
    rt_var = np.sort(rates,axis=1)
    epsilon = 1e-10
    tot_rt = np.divide(1,np.sum(rates,axis=1)+epsilon)
    ori = np.arange(0,2*np.pi,2*np.pi/nr_Ori)
    R = np.dot((np.tile(np.expand_dims(tot_rt,axis=1),(1,nr_Ori))*rt_var),np.exp(1j*ori))
    return np.abs(R)
    
    
def f_calc_odi(w_ee_L3b,w_ee_L4to3b,w_ei_L3b,w_ie_L4to3b,w_ie_L3_effb,w_ii_L3b,bckg):
    tttime= deltaStim#nr_Ori*deltaStim
    respv = np.zeros((NrE_L3,2))    # storage of data
    respv2 = np.zeros((NrE_L4_tot,2))    # storage of data
    storV = np.zeros((NrE_L3,int(tttime/timeStep),nr_Ori))    # storage of data
    storVI = np.zeros((NrI_L3,int(tttime/timeStep),nr_Ori))    # storage of data
    storV4 = np.zeros((NrE_L4_tot,int(tttime/timeStep),nr_Ori))    # storage of data
    
    for ippp in range(2):
        if ippp == 1:
            eye = 'ipsi_closed'
        else:
            eye = 'contra_closed'
            
        if eye == 'ipsi_closed':
            a = 0
            b = 1
        else:
            a = 1
            b = 0
            
        for ori in np.arange(nr_Ori):
            EvL4 = np.zeros(NrE_L4_tot)          # vector of excitatory activities
            EvL3 = np.zeros(NrE_L3)          # vector of excitatory activities
            IvL3 = np.zeros(NrI_L3)          # vector of inhibitory activities
            for tt in np.arange(0,deltaStim,timeStep):
                if tt%stimLen == 0:
                    Iptv =  np.zeros(NrE_L4_tot)
                    if tt%deltaStim == 0:
                        Ori = ori
                        aux_IL = np.zeros((nr_Ori,NrE_L4))
                        aux_IL[Ori,:] = vis_amp
                        aux_IL = np.reshape(aux_IL,(NrE_L4_tot))
                        aux_CL = np.zeros((nr_Ori,NrE_L4))
                        aux_CL[Ori,:] = vis_amp
                        aux_CL = np.reshape(aux_CL,(NrE_L4_tot))
                        Visual_Ipt_gauss_ipsi = L4_connections[:,0]*aux_IL
                        Visual_Ipt_gauss_contra = L4_connections[:,1]*aux_CL          
                        Visual_Ipt_gauss = a*Visual_Ipt_gauss_ipsi + b*Visual_Ipt_gauss_contra
                        Iptv = Visual_Ipt_gauss + Iptv
                    Iptv = 1*Iptv*(Iptv>0)
                    
                
                                
                EvL4 = beta_v*(Iptv) 
                IvL3 = f_IEvol_WilCow2(IvL3,layerfactor*EvL4,EvL3,w_ii_L3b,w_ie_L4to3b,L3_connections_inh,
                               w_ie_L3_effb,tau_i,ori,0)
                                
                EvL3 = f_EEvol_WilCow2(EvL3,layerfactor*EvL4,IvL3,w_ee_L3b,
                                          w_ee_L4to3b,L3_connections*corr_act,w_ei_L3b,tau_e,ori,0)  
                                          
                storV[:,int(tt/timeStep),ori] = EvL3
                storVI[:,int(tt/timeStep),ori] = IvL3
                storV4[:,int(tt/timeStep),ori] = EvL4
        
        maxTime = np.amax(storV,axis=1)
        maxOri = np.argmax(maxTime,axis=1)
        maxTime4 = np.amax(storV4,axis=1)
        maxOri4 = np.argmax(maxTime4,axis=1)
        
        for xx in range(np.size(storV,axis=0)):
            respv[xx,ippp]= maxTime[xx,maxOri[xx]] - storV[xx,-1,maxOri[xx]]
        for xx in range(np.size(storV4,axis=0)):
            respv2[xx,ippp]= maxTime4[xx,maxOri4[xx]] - storV4[xx,-1,maxOri4[xx]]
            
    return respv,respv2
    
def f_calc_osi(w_ee_L3b,w_ee_L4to3b,w_ei_L3b,w_ie_L4to3b,w_ie_L3_effb,w_ii_L3b,nrOri,bckgr):
    tttime= deltaStim
    storV = np.zeros((NrE_L3,int(tttime/timeStep)))    # storage of data
    storVI = np.zeros((NrI_L3,int(tttime/timeStep)))    # storage of data
    storV4 = np.zeros((NrE_L4_tot,int(tttime/timeStep)))    # storage of data
    
    store_Osi_E = np.zeros((NrE_L3,nrOri))
    store_Osi_I = np.zeros((NrI_L3,nrOri))
    
    for ori in np.arange(nrOri):
        EvL4 = np.zeros(NrE_L4_tot)          # vector of excitatory activities
        EvL3 = np.zeros(NrE_L3)          # vector of excitatory activities
        IvL3 = np.zeros(NrI_L3)          # vector of inhibitory activities
        for tt in np.arange(0,deltaStim,timeStep):
            if tt%stimLen == 0:
                Iptv =  np.zeros(NrE_L4_tot)
                if tt%deltaStim == 0:
                    Ori = ori
                    aux_IL = np.zeros((nr_Ori,NrE_L4))
                    aux_IL[Ori,:] = vis_amp#+bckgr
                    aux_IL = np.reshape(aux_IL,(NrE_L4_tot))
                    aux_CL = np.zeros((nr_Ori,NrE_L4))
                    aux_CL[Ori,:] = vis_amp#+bckgr
                    aux_CL = np.reshape(aux_CL,(NrE_L4_tot))
                    Visual_Ipt_gauss_ipsi = L4_connections[:,0]*aux_IL
                    Visual_Ipt_gauss_contra = L4_connections[:,1]*aux_CL          
                    Visual_Ipt_gauss = Visual_Ipt_gauss_ipsi + Visual_Ipt_gauss_contra
                    Iptv = Visual_Ipt_gauss + Iptv 
                Iptv = 1*Iptv*(Iptv>0)
                            
            EvL4 = beta_v*(Iptv) 
            IvL3 = f_IEvol_WilCow2(IvL3,layerfactor*EvL4,EvL3,w_ii_L3b,w_ie_L4to3b,L3_connections_inh,
                           w_ie_L3_effb,tau_i,ori,0)
            
            EvL3 = f_EEvol_WilCow2(EvL3,layerfactor*EvL4,IvL3,w_ee_L3b,
                                      w_ee_L4to3b,L3_connections*corr_act,w_ei_L3b,tau_e,ori,0)  
                                      
            storV[:,int(tt/timeStep)] = EvL3
            storVI[:,int(tt/timeStep)] = IvL3
            storV4[:,int(tt/timeStep)] = EvL4
            
        store_Osi_E[:,ori] = np.amax(storV,axis=1)
        store_Osi_I[:,ori] = np.amax(storVI,axis=1)
        
    return store_Osi_E,store_Osi_I    
    
            
   
######################################
#----- Run -----#
######################################
Ori = int(np.floor(np.random.rand(1)*nr_Ori)[0])
w_ie_L3_eff = w_ie_L3


strV3 = np.zeros((NrE_L3,stimWindow))    # storage of data
strV4 = np.zeros((NrE_L4_tot,stimWindow))    # storage of data
strI3 = np.zeros((NrI_L3,stimWindow))

OSI_evolution_E = np.zeros(20)    
OSI_evolution_I = np.zeros(20)   
osi_index = 0     
bckg = bck_current

rvv_d0, rvv2 = f_calc_odi(w_ee_L3,w_ee_4to3,w_ei_L3,w_ie_4to3,w_ie_L3_eff,w_ii_L3,bck_current)
for tt in np.arange(0,int(tmax),timeStep):
    if tt%stimLen == 0:
            Iptv =  np.zeros(NrE_L4_tot)
            if tt%deltaStim == 0:
                Ori = int(np.floor(np.random.rand(1)*nr_Ori)[0])
                Ori_vect = np.zeros((nr_Ori,NrE_L4))
                Ori_vect[Ori,:] = 1
                Ori_vect = np.reshape(Ori_vect,(NrE_L4_tot))
                Visual_Ipt_gauss_ipsi = L4_connections[:,0]*Ori_vect*IL_mod*vis_amp 
                Visual_Ipt_gauss_contra = L4_connections[:,1]*Ori_vect*CL_mod*vis_amp      
                Visual_Ipt_gauss = Visual_Ipt_gauss_ipsi + Visual_Ipt_gauss_contra
                Iptv = Visual_Ipt_gauss + Iptv +bckg*Ori_vect
            Iptv = 1*Iptv*(Iptv>0)
            
            
    strV3[:,int((tt%deltaStim)/timeStep)] = Ev_L3
    strV4[:,int((tt%deltaStim)/timeStep)] = Ev_L4
    strI3[:,int((tt%deltaStim)/timeStep)] = Iv_L3
    
    I3_avg = I3_avg + (timeStep/tau_avg)*(Iv_L3-I3_avg)
    
    w_ie_L3_eff = w_ie_L3 
    
    #####################################################################################################   
    # LAYER 4
    #####################################################################################################    
     
    Ev_L4 = beta_v*(Iptv)           
    
    #####################################################################################################   
    # LAYER 3
    #####################################################################################################        
            
    Iv_L3 = f_IEvol_WilCow2(Iv_L3,layerfactor*Ev_L4,Ev_L3,w_ii_L3,w_ie_4to3,
                            L3_connections_inh,w_ie_L3_eff,tau_i,Ori,0)
    Ev_L3 = f_EEvol_WilCow2(Ev_L3,layerfactor*Ev_L4,Iv_L3,w_ee_L3,
                           w_ee_4to3,L3_connections*corr_act,w_ei_L3,tau_e,Ori,0) 
                           
    
    w_ie_4to3 = f_e_to_i_plasticity(Ev_L4,Iv_L3,I3_avg,w_ie_4to3)
    w_ie_L3 = f_e_to_i_plasticity(Ev_L3,Iv_L3,I3_avg,w_ie_L3) 
    
#    w_ee_L3 = f_ee_plasticity(pre=Ev_L3,post=Ev_L3,whebb=w_ee_L3,recurrent='no')
    w_ee_4to3 = f_ee_plasticity(pre=Ev_L4,post=Ev_L3,whebb=w_ee_4to3,recurrent='no')
                    
    w_ei_L3 = f_i_to_e_plasticity(Iv_L3,Ev_L3,w_ei_L3,activity_shift)  
    
    #####################################################################################################   
    #####################################################################################################   
    
      ##################################
      # Display the progress
      ###################################
    if np.abs(tt-tmax*0.05*np.round(20*tt/tmax)) < 0.5*timeStep:
        print('> '+str(int(np.round(100*tt/tmax)))+'%') 
    
    # store data
    nrpoints = .05
    if np.abs(tt-timeStep-tmax*nrpoints*np.round((1/nrpoints)*tt/tmax)) < 0.5*timeStep:
        osi_E,osi_I = f_calc_osi(w_ee_L3,w_ee_4to3,w_ei_L3,w_ie_4to3,w_ie_L3_eff,w_ii_L3,nr_Ori,bck_current)
        osi_E = f_OSI(osi_E)
        osi_I = f_OSI(osi_I)
        OSI_evolution_E[osi_index] = np.mean(osi_E)
        OSI_evolution_I[osi_index] = np.mean(osi_I)
        osi_index = osi_index + 1


rvv_d3, rvv2 = f_calc_odi(w_ee_L3,w_ee_4to3,w_ei_L3,w_ie_4to3,w_ie_L3_eff,w_ii_L3,bck_current)

odi_v = {
        'OSI_evolution_E' : OSI_evolution_E.tolist(),        # 
        'OSI_evolution_I' : OSI_evolution_I.tolist()     # 
        }

save_obj(odi_v, store_osi )

w_v = {
        'w_ee_L3' : w_ee_L3.tolist(),        # 
        'w_ei_L3' : w_ei_L3.tolist(),     # 
        'w_ie_L3' : w_ie_L3.tolist(),     # 
        'w_ii_L3' : w_ii_L3.tolist(),        # 
        'w_ee_4to3' : w_ee_4to3.tolist(),     # 
        'w_ie_4to3' : w_ie_4to3.tolist(),        # 
        'activity_shift' : activity_shift.tolist(),     # 
        'L3_connections' : L3_connections.tolist(),        # 
        'L3_connections_inh' : L3_connections_inh.tolist(),        # 
        'spec_Ori' : spec_Ori.tolist(),     # 
        'spec_Ori_inh' : spec_Ori_inh.tolist(),        # 
        'I3_avg' : I3_avg.tolist(),     # 
        'L4_connections' : L4_connections.tolist(),        # 
        }

save_obj(w_v, store_name )
