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diff --git a/connection/test/expo_connection.py b/connection/test/expo_connection.py
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@@ -1,525 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Sep  4 12:58:31 2020
-
-@author: shni2598
-"""
-
-import numpy as np
-#import brian2.numpy_ as np
-#import matplotlib.pyplot as plt
-import connection as cn
-import warnings
-from scipy import sparse
-#from brian2.only import *
-#import time
-#import mydata
-#import firing_rate_analysis
-#import os
-#import datetime
-#%%
-class get_ijwd:
-    
-    def __init__(self, *param_dict, **kwargs):
-        
-        self.Ne = 3969
-        self.Ni = 1024
-        self.width = 62
-        self.decay_p_ee = 8 # decay constant of e to e connection probability as distance increases
-        self.decay_p_ei = 10 # decay constant of e to i connection probability as distance increases
-        self.decay_p_ie = 20 # decay constant of i to e connection probability as distance increases
-        self.decay_p_ii = 20 # decay constant of i to i connection probability as distance increases
-        self.mean_SynNumIn_ee = 320     ; # p = 0.08
-        self.mean_SynNumIn_ei = 500     ; # p = 0.125
-        self.mean_SynNumIn_ie = 200     ; # p = 0.2
-        self.mean_SynNumIn_ii = 250     ; # p = 0.25
-        self.w_ee_mean = 4
-        self.w_ei_mean = 5
-        self.w_ie_mean = 20
-        self.w_ii_mean = 25
-        self.delay = 4
-        for dictionary in param_dict:
-            for key in dictionary:
-                setattr(self, key, dictionary[key])
-        for key in kwargs:
-            setattr(self, key, kwargs[key])
-        # if either param_dict or kwargs is not empty, reset some parameters which are dependent on other parameters    
-        if bool(param_dict) or bool(kwargs): self.change_dependent_para()
-    
-    def change_dependent_para(self):
-        pass
-    
-    def generate_ijw(self):
-        
-        self.e_lattice = cn.coordination.makelattice(int(round(np.sqrt(self.Ne))), self.width, [0,0])
-        self.i_lattice = cn.coordination.makelattice(int(round(np.sqrt(self.Ni))), self.width, [0,0])
-        
-        self.p_peak_ee = self.find_peak_probability(self.mean_SynNumIn_ee, self.decay_p_ee, self.e_lattice, self.width, position=[0,0])
-        self.p_peak_ei = self.find_peak_probability(self.mean_SynNumIn_ei, self.decay_p_ei, self.e_lattice, self.width, position=[0,0])
-        self.p_peak_ie = self.find_peak_probability(self.mean_SynNumIn_ie, self.decay_p_ie, self.i_lattice, self.width, position=[0,0])
-        self.p_peak_ii = self.find_peak_probability(self.mean_SynNumIn_ii, self.decay_p_ii, self.i_lattice, self.width, position=[0,0])
-        
-        self.i_ee, self.j_ee, _ = cn.connect_2lattice.expo_decay(self.e_lattice, self.e_lattice, np.arange(self.Ne), self.width, periodic_boundary=True, interarea_dist=0, \
-                                       peak_p=self.p_peak_ee, tau_d=self.decay_p_ee, src_equal_trg = True, self_cnt = False)
-        
-        self.i_ei, self.j_ei, _ = cn.connect_2lattice.expo_decay(self.e_lattice, self.i_lattice, np.arange(self.Ne), self.width, periodic_boundary=True, interarea_dist=0, \
-                                       peak_p=self.p_peak_ei, tau_d=self.decay_p_ei, src_equal_trg = False, self_cnt = False)
-        
-        self.i_ie, self.j_ie, _ = cn.connect_2lattice.expo_decay(self.i_lattice, self.e_lattice, np.arange(self.Ni), self.width, periodic_boundary=True, interarea_dist=0, \
-                                       peak_p=self.p_peak_ie, tau_d=self.decay_p_ie, src_equal_trg = False, self_cnt = False)
-        
-        self.i_ii, self.j_ii, _ = cn.connect_2lattice.expo_decay(self.i_lattice, self.i_lattice, np.arange(self.Ni), self.width, periodic_boundary=True, interarea_dist=0, \
-                                       peak_p=self.p_peak_ii, tau_d=self.decay_p_ii, src_equal_trg = True, self_cnt = False)
-
-        self.num_in_ee = self.find_indegree(self.i_ee, self.j_ee, self.Ne, self.Ne)
-        self.num_in_ei = self.find_indegree(self.i_ei, self.j_ei, self.Ne, self.Ni)
-        self.num_in_ie = self.find_indegree(self.i_ie, self.j_ie, self.Ni, self.Ne)
-        self.num_in_ii = self.find_indegree(self.i_ii, self.j_ii, self.Ni, self.Ni)
-        
-        self.J_ee = self.find_J(self.num_in_ee, self.w_ee_mean)
-        self.J_ei = self.find_J(self.num_in_ei, self.w_ei_mean)
-        self.J_ie = self.find_J(self.num_in_ie, self.w_ie_mean)
-        self.J_ii = self.find_J(self.num_in_ii, self.w_ii_mean)
-        
-        self.w_ee = self.find_synWeight(self.j_ee, self.J_ee, self.Ne, self.num_in_ee)
-        self.w_ei = self.find_synWeight(self.j_ei, self.J_ei, self.Ni, self.num_in_ei)
-        self.w_ie = self.find_synWeight(self.j_ie, self.J_ie, self.Ne, self.num_in_ie)
-        self.w_ii = self.find_synWeight(self.j_ii, self.J_ii, self.Ni, self.num_in_ii)
-    
-    def generate_d_rand(self):
-        
-        self.d_ee = np.random.uniform(0, self.delay, len(self.i_ee))
-        self.d_ie = np.random.uniform(0, self.delay, len(self.i_ie))
-        self.d_ei = np.random.uniform(0, self.delay, len(self.i_ei))
-        self.d_ii = np.random.uniform(0, self.delay, len(self.i_ii))
-
-    def find_peak_probability(self, mean_SynNum, tau_d, lattice, width, position=[0,0]):
-    
-        dist = cn.coordination.lattice_dist(lattice, width, position)
-        p_peak = mean_SynNum/(np.sum(np.exp(-dist/tau_d)))
-        if p_peak > 1:
-            warnings.warn(" Error: peak connection probability exceeds '1', adjust 'decay_p' or 'mean_SynSum' to reduce it below '1'" )
-            
-        return p_peak
-    
-    def find_indegree(self, i, j, N_src, N_trg):
-        
-        A = sparse.csc_matrix((np.ones(i.shape, dtype=int), (i, j)),shape=(N_src,N_trg))
-        num_in = A.sum(0)    
-        return num_in.A[0]
-
-    def find_J(self, num_in, J_mean):
-        J = J_mean*np.sum(num_in)/(np.sum(np.sqrt(num_in)))
-        return J 
-
-    def find_synWeight(self, j_cnt, J_weight, N_trg, num_indegree):
-    
-        w_cnt = np.zeros(j_cnt.shape)
-        for j in range(N_trg):
-            J_mean = J_weight/np.sqrt(num_indegree[j])
-            J_std = J_mean/4
-            w_tmp = np.random.randn(num_indegree[j])*J_std + J_mean
-            negative_w = np.any(w_tmp <= 0)
-            while negative_w:
-                #w_tmp = np.random.randn(num_indegree[j])*J_std + J_mean
-                w_tmp[w_tmp <= 0] = np.random.randn(np.sum(w_tmp <= 0))*J_std + J_mean
-                negative_w = np.any(w_tmp <= 0)
-                
-            w_cnt[j_cnt == j] = w_tmp
-                
-        return w_cnt
-#%%
-"""
-def find_peak_probability(mean_SynNum, tau_d, lattice, width, position=[0,0]):
-    
-    dist = cn.coordination.lattice_dist(lattice, width, position)
-    p_peak = mean_SynNum/(np.sum(np.exp(-dist/tau_d)))
-    if p_peak > 1:
-        warnings.warn(" Error: peak connection probability exceeds '1', adjust 'tau_d' or 'mean_SynSum' to reduce it below '1'" )
-    
-    return p_peak
-
-#%%
-e_lattice = cn.coordination.makelattice(63, 62, [0,0])
-i_lattice = cn.coordination.makelattice(32, 62, [0,0])
-#%%
-tau_d_ee = 8             ;tau_d_ei = 10             ;tau_d_ie = 20             ;tau_d_ii = 20             ;
-mean_SynNum_ee = 320     ;mean_SynNum_ei = 500      ;mean_SynNum_ie = 200      ;mean_SynNum_ii = 250      ;
-
-width = 62
-#%%
-
-p_peak_ee = find_peak_probability(mean_SynNum_ee, tau_d_ee, e_lattice, width, position=[0,0])
-p_peak_ei = find_peak_probability(mean_SynNum_ei, tau_d_ei, e_lattice, width, position=[0,0])
-p_peak_ie = find_peak_probability(mean_SynNum_ie, tau_d_ie, i_lattice, width, position=[0,0])
-p_peak_ii = find_peak_probability(mean_SynNum_ii, tau_d_ii, i_lattice, width, position=[0,0])
-#%%
-Ne = 3969;
-Ni = 1024;
-i_ee, j_ee, _ = cn.connect_2lattice.expo_decay(e_lattice, e_lattice, np.arange(Ne), 62, periodic_boundary=True, interarea_dist=0, \
-                               peak_p=p_peak_ee, tau_d=tau_d_ee, src_equal_trg = True, self_cnt = False)
-
-i_ei, j_ei, _ = cn.connect_2lattice.expo_decay(e_lattice, i_lattice, np.arange(Ne), 62, periodic_boundary=True, interarea_dist=0, \
-                               peak_p=p_peak_ei, tau_d=tau_d_ei, src_equal_trg = False, self_cnt = False)
-
-i_ie, j_ie, _ = cn.connect_2lattice.expo_decay(i_lattice, e_lattice, np.arange(Ni), 62, periodic_boundary=True, interarea_dist=0, \
-                               peak_p=p_peak_ie, tau_d=tau_d_ie, src_equal_trg = False, self_cnt = False)
-
-i_ii, j_ii, _ = cn.connect_2lattice.expo_decay(i_lattice, i_lattice, np.arange(Ni), 62, periodic_boundary=True, interarea_dist=0, \
-                               peak_p=p_peak_ii, tau_d=tau_d_ii, src_equal_trg = True, self_cnt = False)
-
-
-#%%
-def find_indegree(i, j, N_src, N_trg):
-    A = sparse.csc_matrix((np.ones(i.shape, dtype=int), (i, j)),shape=(N_src,N_trg))
-    num_in = A.sum(0)
-    return num_in.A[0]
-#%%
-num_in_ee = find_indegree(i_ee, j_ee, Ne, Ne)
-num_in_ei = find_indegree(i_ei, j_ei, Ne, Ni)
-num_in_ie = find_indegree(i_ie, j_ie, Ni, Ne)
-num_in_ii = find_indegree(i_ii, j_ii, Ni, Ni)
-
-
-#%%
-def find_J(num_in, J_mean):
-    J = J_mean*np.sum(num_in)/(np.sum(np.sqrt(num_in)))
-    return J 
-#%%
-w_ee_mean = 4
-w_ei_mean = 5
-w_ie_mean = 20
-w_ii_mean = 25
-
-J_ee = find_J(num_in_ee, w_ee_mean)
-J_ei = find_J(num_in_ei, w_ei_mean)
-J_ie = find_J(num_in_ie, w_ie_mean)
-J_ii = find_J(num_in_ii, w_ii_mean)
-
-#%%
-def find_synWeight(j_cnt, J_weight, N_trg, num_indegree):
-    
-    w_cnt = np.zeros(j_cnt.shape)
-    for j in range(N_trg):
-        J_mean = J_weight/np.sqrt(num_indegree[j])
-        J_std = J_mean/4
-        w_tmp = np.random.randn(num_indegree[j])*J_std + J_mean
-        negative_w = np.any(w_tmp <= 0)
-        while negative_w:
-            w_tmp = np.random.randn(num_indegree[j])*J_std + J_mean
-            w_tmp[w_tmp <= 0] = np.random.randn(np.sum(w_tmp <= 0))*J_std + J_mean
-            negative_w = np.any(w_tmp <= 0)
-            
-        w_cnt[j_cnt == j] = w_tmp
-    
-    return w_cnt
-#%%
-w_ee = find_synWeight(j_ee, J_ee, Ne, num_in_ee)
-w_ei = find_synWeight(j_ei, J_ei, Ni, num_in_ei)
-w_ie = find_synWeight(j_ie, J_ie, Ne, num_in_ie)
-w_ii = find_synWeight(j_ii, J_ii, Ni, num_in_ii)
-
-
-
-#%%
-plt.figure()
-plt.hist(num_in_ie)
-#%%
-#%%%%%%%%%%%%%%%%%%%%%%
-e_lattice = cn.coordination.makelattice(63, 62, [0,0])
-i_lattice = cn.coordination.makelattice(32, 62, [0,0])
-"""
-#%%
-
-"""
-ijwd = get_ijwd()
-ijwd.Ne = 77*77; ijwd.Ni = 39*39
-ijwd.width = 76
-ijwd.generate_ijw()
-ijwd.generate_d_rand()
-#%%
-
-chg_adapt_loca = [0, 0]
-chg_adapt_range = 6
-chg_adapt_neuron = cn.findnearbyneuron.findnearbyneuron(ijwd.e_lattice, chg_adapt_loca, chg_adapt_range, ijwd.width)
-
-
-
-#%%
-start_scope()
-
-neuronmodel_e = cn.model_neu_syn_AD.neuron_e_AD
-neuronmodel_i = cn.model_neu_syn_AD.neuron_i_AD
-
-synapse_e = cn.model_neu_syn_AD.synapse_e_AD
-synapse_i = cn.model_neu_syn_AD.synapse_i_AD
-
-group_e_1 =NeuronGroup(ijwd.Ne, model=neuronmodel_e,
-                     threshold='v>v_threshold', method='euler',
-                     reset='''v = v_reset
-                              g_k += delta_gk''', refractory='(t-lastspike)<t_ref')
-
-group_i_1 =NeuronGroup(ijwd.Ni, model=neuronmodel_i,
-                     threshold='v>v_threshold', method='euler',
-                     reset='v = v_reset', refractory='(t-lastspike)<t_ref')
-
-syn_ee_1 = Synapses(group_e_1, group_e_1, model=synapse_e, 
-                  on_pre='x_E_post += w')
-syn_ei_1 = Synapses(group_e_1, group_i_1, model=synapse_e, 
-                  on_pre='x_E_post += w')
-syn_ie_1 = Synapses(group_i_1, group_e_1, model=synapse_i, 
-                  on_pre='x_I_post += w')
-syn_ii_1 = Synapses(group_i_1, group_i_1, model=synapse_i, 
-                  on_pre='x_I_post += w')
-
-'''external input'''
-#stim_rate = psti.input_spkrate(maxrate = [800,800], sig=[6,6], position=[[0, 0],[31.5,31.5]])*Hz
-bkg_rate2e = 850*Hz
-bkg_rate2i = 1000*Hz
-extnl_e = PoissonGroup(ijwd.Ne, bkg_rate2e)
-extnl_i = PoissonGroup(ijwd.Ni, bkg_rate2i)
-
-#tau_x_re = 1*ms
-syn_extnl_e = Synapses(extnl_e, group_e_1, model=synapse_e, on_pre='x_E_extnl_post += w')
-syn_extnl_i = Synapses(extnl_i, group_i_1, model=synapse_e, on_pre='x_E_extnl_post += w')
-
-syn_extnl_e.connect('i==j')
-syn_extnl_i.connect('i==j')
-
-w_extnl_ = 1.5 # nS
-syn_extnl_e.w = w_extnl_*5.8*nS#*tau_s_de_*nS
-syn_extnl_i.w = w_extnl_*5.8*nS#*tau_s_de_*nS
-
-
-syn_ee_1.connect(i=ijwd.i_ee, j=ijwd.j_ee)
-syn_ei_1.connect(i=ijwd.i_ei, j=ijwd.j_ei)
-syn_ie_1.connect(i=ijwd.i_ie, j=ijwd.j_ie)
-syn_ii_1.connect(i=ijwd.i_ii, j=ijwd.j_ii)
-
-
-#tau_s_di_try = tau_s_di_
-syn_ee_1.w = ijwd.w_ee*nsiemens * 5.8 #tau_s_de_
-syn_ei_1.w = ijwd.w_ei*nsiemens * 5.8 #tau_s_de_ #5*nS
-syn_ii_1.w = ijwd.w_ii*nsiemens * 6. #tau_s_di_#25*nS
-syn_ie_1.w = ijwd.w_ie*nsiemens * 6. #tau_s_di_#
-#w_ext = 2*nS
-#syn_pois_e.w = w_ext
-#syn_pois_i.w = w_ext
-
-def set_delay(syn, delay_up):
-    #n = len(syn)
-    syn.delay = delay_up*ms
-    #syn.down.delay = (delay_up + 1)*ms
-    
-    return syn 
-
-#generate_d_dist()
-#generate_d_rand()
-#def generate_d_rand(delay,len_i_ee,len_i_ie,len_i_ei,len_i_ii):
-#        
-#    d_ee = np.random.uniform(0, delay, len_i_ee)    
-#    d_ie = np.random.uniform(0, delay, len_i_ie)
-#    d_ei = np.random.uniform(0, delay, len_i_ei)
-#    d_ii = np.random.uniform(0, delay, len_i_ii)
-#    
-#    return d_ee, d_ie, d_ei, d_ii
-
-#d_ee, d_ie, d_ei, d_ii = generate_d_rand(4,len(i_ee),len(i_ie),len(i_ei),len(i_ii))
-syn_ee_1 = set_delay(syn_ee_1, ijwd.d_ee)
-syn_ie_1 = set_delay(syn_ie_1, ijwd.d_ie)
-syn_ei_1 = set_delay(syn_ei_1, ijwd.d_ei)
-syn_ii_1 = set_delay(syn_ii_1, ijwd.d_ii)
-
-#syn_pois_e = set_delay(syn_pois_e)
-#syn_pois_i = set_delay(syn_pois_i)
-
-tau_s_de_ = 5.8; tau_s_di_ = 6.5
-delta_gk_ = 10
-
-
-group_e_1.tau_s_de = tau_s_de_*ms; 
-group_e_1.tau_s_di = tau_s_di_*ms
-group_e_1.tau_s_re = group_e_1.tau_s_ri = 1*ms
-
-group_e_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_e_1.tau_s_re_inter = 1*ms
-group_e_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_e_1.tau_s_re_extnl = 1*ms
-
-group_i_1.tau_s_de = tau_s_de_*ms
-group_i_1.tau_s_di = tau_s_di_*ms
-group_i_1.tau_s_re = group_i_1.tau_s_ri = 1*ms
-
-group_i_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_i_1.tau_s_re_inter = 1*ms
-group_i_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_i_1.tau_s_re_extnl = 1*ms
-
-#syn_pois_e.effect = 0
-#syn_pois_i.effect = 0
-#group_e_v2.v = np.random.random(Ne)*35*mV-85*mV
-#group_i_v2.v = np.random.random(Ni)*35*mV-85*mV
-#seed(1000)
-group_e_1.v = np.random.random(ijwd.Ne)*35*mV-85*mV
-group_i_1.v = np.random.random(ijwd.Ni)*35*mV-85*mV
-group_e_1.delta_gk = delta_gk_*nS
-group_e_1.tau_k = 80*ms
-#group_e_v2.v = np.random.random(Ne)*10*mV-60*mV
-#group_i_v2.v = np.random.random(Ni)*10*mV-60*mV
-group_e_1.I_extnl_crt = 0*nA #0.51*nA
-group_i_1.I_extnl_crt = 0*nA #0.60*nA
-
-#%%
-spk_e = SpikeMonitor(group_e_1, record = True)
-spk_i = SpikeMonitor(group_i_1, record = True)
-#lfp_moni = StateMonitor(group_LFP_record, ('lfp1','lfp2','lfp3'), record = True)
-#%%
-net = Network(collect())
-net.store('state1')
-#%%
-scale_ee = 1.; 
-scale_ei = 1.03
-scale_ie = 1.
-scale_ii = 0.9
-syn_ee_1.w = ijwd.w_ee*nsiemens * 5.8 * scale_ee#tau_s_de_
-syn_ei_1.w = ijwd.w_ei*nsiemens * 5.8 * scale_ei #tau_s_de_ #5*nS
-syn_ii_1.w = ijwd.w_ii*nsiemens * 6. * scale_ie#tau_s_di_#25*nS
-syn_ie_1.w = ijwd.w_ie*nsiemens * 6. * scale_ii#tau_s_di_#
-#%%
-#change_ie(4.4)
-#syn_ie_1.w = w_ie*usiemens
-
-print('ie_w: %fnsiemens' %(syn_ie_1.w[0]/nsiemens))
-#Ne = 63*63; Ni = 1000;
-C = 0.25*nF # capacitance
-g_l = 16.7*nS # leak capacitance
-v_l = -70*mV # leak voltage
-v_threshold = -50*mV
-v_reset = -60*mV
-v_rev_I = -80*mV
-v_rev_E = 0*mV
-v_k = -85*mV
-#tau_k = 80*ms# 80*ms
-#delta_gk = 10*nS #10*nS
-t_ref = 4*ms # refractory period
-new_delta_gk_ = 0
-#tau_s_de = 5*ms
-#tau_s_di = 3*ms
-#tau_s_re = 1*ms
-#tau_s_ri = 1*ms
-#tau_x_re = 1*ms
-tic = time.perf_counter()
-#seed(10)
-simu_time1 = 1000*ms#2000*ms
-simu_time2 = 2000*ms
-#simu_time2 = 2000*ms#8000*ms
-#simu_time3 = 1000*ms
-#simu_time_tot = 30000*ms
-#group_input.active = False
-extnl_e.rates = bkg_rate2e
-net.run(simu_time1, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e + stim_rate
-group_e_1.delta_gk[chg_adapt_neuron] = new_delta_gk_*nS
-net.run(simu_time2, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e
-#net.run(simu_time3, profile=False) #,namespace={'tau_k': 80*ms}
-#group_input.active = True
-#group_e_1.delta_gk[chg_adapt_neuron] = 2*nS; group_e_1.tau_k[chg_adapt_neuron] = 40*ms
-#net.run(simu_time2, report = 'text', profile=False) #,namespace={'tau_k': 80*ms}
-print('total time elapsed:',time.perf_counter() - tic)
-#%%
-spk_tstep_e = np.round(spk_e.t/(0.1*ms)).astype(int)
-spk_tstep_i = np.round(spk_i.t/(0.1*ms)).astype(int)
-now = datetime.datetime.now()
-loop_num = 0
-#param_1 = {'delta_gk':10,'tau_s_di':3,'tau_s_de':5,'ie_ratio':3.1725,'sti_onset':'10s'}
-'''
-param_new = {'delta_gk':delta_gk_,
-         'new_delta_gk':new_delta_gk_,
-         #'new_tau_k':40,
-         'tau_s_di':tau_s_di_,
-         'tau_s_de':tau_s_de_, 'ie_ratio':ie_ratio_,
-         'mean_J_ee': ijwd.mean_J_ee,
-         #'chg_adapt_range':6, 
-         'p_ee':p_ee,
-         'simutime':simu_time_tot/ms,
-         'chg_adapt_time': simu_time1/ms,
-         'chg_adapt_range': chg_adapt_range,
-         'chg_adapt_loca': chg_adapt_loca,}
-param = {**param, **param_new}
-'''
-param = {}
-#param_12 = {'scale_e_12':scale_e_12[i], 'scale_e_21':scale_e_21[j]}
-
-#data = {'param':param_12, 'a1':{'param':param_1,'e':{'i':spk_1.i[:],'t':spk_1_tstep}},
-#                            'a2':{'param':param_2,'e':{'i':spk_2.i[:],'t':spk_2_tstep}}}
-data = {'datetime':now.strftime("%Y-%m-%d %H:%M:%S"), 'dt':0.1, 'loop_num':loop_num, 'data_dir': os.getcwd(),
-        'a1':{'param':param,
-              #'LFP':{'lfp1':lfp_moni.lfp1/nA, 'lfp2':lfp_moni.lfp2/nA, 'lfp3':lfp_moni.lfp3/nA},
-              'ge':{'i':spk_e.i[:],'t':spk_tstep_e},
-              'gi':{'i':spk_i.i[:],'t':spk_tstep_i}}}
-
-
-data1 = mydata.mydata(data)
-#%%
-data1.a1.ge.get_spike_rate(start_time=500, end_time=3000,\
-                           sample_interval = 1, n_neuron = ijwd.Ne, window = 10, dt = 0.1, reshape_indiv_rate = True)
-data1.a1.gi.get_spike_rate(start_time=500, end_time=3000,\
-                           sample_interval = 1, n_neuron = ijwd.Ni, window = 10, dt = 0.1, reshape_indiv_rate = True)
-
-firing_rate_analysis.show_pattern(data1.a1.ge.spk_rate.spk_rate, data1.a1.gi.spk_rate.spk_rate, frames = 2500, start_time = 500, anititle='animation')
-
-#%%
-plt.figure()
-plt.plot(data1.a1.ge.t/10,data1.a1.ge.i,'|')
-#%%
-plt.figure()
-plt.imshow(data1.a1.ge.spk_rate.spk_rate[:,:,100])
-#%%
-plt.figure()
-plt.scatter( ijwd.e_lattice[:,0], ijwd.e_lattice[:,1])
-plt.figure()
-plt.scatter( ijwd.i_lattice[:,0], ijwd.i_lattice[:,1])
-#%%
-pre_neuron = 5500
-pre = ijwd.e_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ee[ijwd.i_ee==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 1000
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ie[ijwd.i_ie==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 1000
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.i_lattice[ijwd.j_ii[ijwd.i_ii==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-net.restore('state1')
-
-#%%
-ijwd_63 = get_ijwd()
-ijwd_63.Ne = 63*63; ijwd_63.Ni = 32*32
-ijwd_63.width = 62
-ijwd_63.generate_ijw()
-ijwd_63.generate_d_rand()
-#%%
-def plot_outgoing(i, j, neuron_src, lattice_src, lattice_trg):
-    #pre_neuron = 5500
-    pre = lattice_src[neuron_src]; post = lattice_trg[j[i==neuron_src]]
-    plt.figure()
-    plt.plot(post[:,0],post[:,1], '.')
-    plt.plot(pre[0], pre[1], 'or')
-#%%
-plot_outgoing(ijwd_63.i_ie, ijwd_63.j_ie, 0, ijwd_63.i_lattice, ijwd_63.e_lattice)   
-plot_outgoing(ijwd.i_ie, ijwd.j_ie, 0, ijwd.i_lattice, ijwd.e_lattice)   
-   
-#%%
-plot_outgoing(ijwd_63.i_ii, ijwd_63.j_ii, 0, ijwd_63.i_lattice, ijwd_63.i_lattice)   
-plot_outgoing(ijwd.i_ii, ijwd.j_ii, 0, ijwd.i_lattice, ijwd.i_lattice)   
-"""
-    
-
-
diff --git a/connection/test/gaus_connection.py b/connection/test/gaus_connection.py
deleted file mode 100644
index 7959cc0..0000000
--- a/connection/test/gaus_connection.py
+++ /dev/null
@@ -1,434 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Sep 28 12:53:42 2020
-
-@author: shni2598
-"""
-
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Sep  4 12:58:31 2020
-
-@author: shni2598
-"""
-
-import numpy as np
-#import brian2.numpy_ as np
-#import matplotlib.pyplot as plt
-import connection as cn
-import warnings
-from scipy import sparse
-#from brian2.only import *
-#import time
-#import mydata
-#import firing_rate_analysis
-#import os
-#import datetime
-#%%
-class get_ijwd:
-    
-    def __init__(self):
-        
-        self.Ne = 3969
-        self.Ni = 1024
-        self.width = 62
-        self.decay_p_ee = 8 # decay constant of e to e connection probability as distance increases
-        self.decay_p_ei = 10 # decay constant of e to i connection probability as distance increases
-        self.decay_p_ie = 20 # decay constant of i to e connection probability as distance increases
-        self.decay_p_ii = 20 # decay constant of i to i connection probability as distance increases
-
-        self.mean_SynNumIn_ee = 320     ; # p = 0.08
-        self.mean_SynNumIn_ei = 500     ; # p = 0.125
-        self.mean_SynNumIn_ie = 200     ; # p = 0.2
-        self.mean_SynNumIn_ii = 250     ; # p = 0.25
-        self.w_ee_mean = 4
-        self.w_ei_mean = 5
-        self.w_ie_mean = 20
-        self.w_ii_mean = 25
-        self.delay = 4
-        
-    def generate_ijw(self):
-        
-        self.e_lattice = cn.coordination.makelattice(int(round(np.sqrt(self.Ne))), self.width, [0,0])
-        self.i_lattice = cn.coordination.makelattice(int(round(np.sqrt(self.Ni))), self.width, [0,0])
-        
-        self.p_peak_ee = self.find_peak_probability(self.mean_SynNumIn_ee, self.decay_p_ee, self.e_lattice, self.width, position=[0,0])
-        self.p_peak_ei = self.find_peak_probability(self.mean_SynNumIn_ei, self.decay_p_ei, self.e_lattice, self.width, position=[0,0])
-        self.p_peak_ie = self.find_peak_probability(self.mean_SynNumIn_ie, self.decay_p_ie, self.i_lattice, self.width, position=[0,0])
-        self.p_peak_ii = self.find_peak_probability(self.mean_SynNumIn_ii, self.decay_p_ii, self.i_lattice, self.width, position=[0,0])
-        
-
-        self.i_ee, self.j_ee, _ = cn.connect_2lattice.gaussian_decay(self.e_lattice, self.e_lattice, np.arange(self.Ne), self.width, periodic_boundary=True, interarea_dist=0, \
-                                                                     peak_p=self.p_peak_ee, sig_d=self.decay_p_ee, src_equal_trg = True, self_cnt = False)
-        
-        self.i_ei, self.j_ei, _ = cn.connect_2lattice.gaussian_decay(self.e_lattice, self.i_lattice, np.arange(self.Ne), self.width, periodic_boundary=True, interarea_dist=0, \
-                                                                     peak_p=self.p_peak_ei, sig_d=self.decay_p_ei, src_equal_trg = False, self_cnt = False)
-        
-        self.i_ie, self.j_ie, _ = cn.connect_2lattice.gaussian_decay(self.i_lattice, self.e_lattice, np.arange(self.Ni), self.width, periodic_boundary=True, interarea_dist=0, \
-                                                                     peak_p=self.p_peak_ie, sig_d=self.decay_p_ie, src_equal_trg = False, self_cnt = False)
-        
-        self.i_ii, self.j_ii, _ = cn.connect_2lattice.gaussian_decay(self.i_lattice, self.i_lattice, np.arange(self.Ni), self.width, periodic_boundary=True, interarea_dist=0, \
-                                                                     peak_p=self.p_peak_ii, sig_d=self.decay_p_ii, src_equal_trg = True, self_cnt = False)
-
-        self.num_in_ee = self.find_indegree(self.i_ee, self.j_ee, self.Ne, self.Ne)
-        self.num_in_ei = self.find_indegree(self.i_ei, self.j_ei, self.Ne, self.Ni)
-        self.num_in_ie = self.find_indegree(self.i_ie, self.j_ie, self.Ni, self.Ne)
-        self.num_in_ii = self.find_indegree(self.i_ii, self.j_ii, self.Ni, self.Ni)
-        
-        self.J_ee = self.find_J(self.num_in_ee, self.w_ee_mean)
-        self.J_ei = self.find_J(self.num_in_ei, self.w_ei_mean)
-        self.J_ie = self.find_J(self.num_in_ie, self.w_ie_mean)
-        self.J_ii = self.find_J(self.num_in_ii, self.w_ii_mean)
-        
-        self.w_ee = self.find_synWeight(self.j_ee, self.J_ee, self.Ne, self.num_in_ee)
-        self.w_ei = self.find_synWeight(self.j_ei, self.J_ei, self.Ni, self.num_in_ei)
-        self.w_ie = self.find_synWeight(self.j_ie, self.J_ie, self.Ne, self.num_in_ie)
-        self.w_ii = self.find_synWeight(self.j_ii, self.J_ii, self.Ni, self.num_in_ii)
-    
-    def generate_d_rand(self):
-        
-        self.d_ee = np.random.uniform(0, self.delay, len(self.i_ee))
-        self.d_ie = np.random.uniform(0, self.delay, len(self.i_ie))
-        self.d_ei = np.random.uniform(0, self.delay, len(self.i_ei))
-        self.d_ii = np.random.uniform(0, self.delay, len(self.i_ii))
-
-    def find_peak_probability(self, mean_SynNum, tau_d, lattice, width, position=[0,0]):
-    
-        dist = cn.coordination.lattice_dist(lattice, width, position)
-        p_peak = mean_SynNum/(np.sum(np.exp(-0.5*(dist/tau_d)**2)))
-        if p_peak > 1:
-            warnings.warn(" Error: peak connection probability exceeds '1', adjust 'decay_p' or 'mean_SynSum' to reduce it below '1'" )
-            
-        return p_peak
-    
-    def find_indegree(self, i, j, N_src, N_trg):
-        
-        A = sparse.csc_matrix((np.ones(i.shape, dtype=int), (i, j)),shape=(N_src,N_trg))
-        num_in = A.sum(0)    
-        return num_in.A[0]
-
-    def find_J(self, num_in, J_mean):
-        J = J_mean*np.sum(num_in)/(np.sum(np.sqrt(num_in)))
-        return J 
-
-    def find_synWeight(self, j_cnt, J_weight, N_trg, num_indegree):
-    
-        w_cnt = np.zeros(j_cnt.shape)
-        for j in range(N_trg):
-            J_mean = J_weight/np.sqrt(num_indegree[j])
-            J_std = J_mean/4
-            w_tmp = np.random.randn(num_indegree[j])*J_std + J_mean
-            negative_w = np.any(w_tmp <= 0)
-            while negative_w:
-                #w_tmp = np.random.randn(num_indegree[j])*J_std + J_mean
-                w_tmp[w_tmp <= 0] = np.random.randn(np.sum(w_tmp <= 0))*J_std + J_mean
-                negative_w = np.any(w_tmp <= 0)
-                
-            w_cnt[j_cnt == j] = w_tmp
-                
-        return w_cnt
-#%%
-
-"""
-#%%
-plt.figure()
-plt.hist(num_in_ie)
-#%%%%%%%%%%%%%%%%%%%%%%
-e_lattice = cn.coordination.makelattice(63, 62, [0,0])
-i_lattice = cn.coordination.makelattice(32, 62, [0,0])
-"""
-#%%
-
-"""
-ijwd = get_ijwd()
-ijwd.Ne = 77*77; ijwd.Ni = 39*39
-ijwd.width = 76
-ijwd.generate_ijw()
-ijwd.generate_d_rand()
-#%%
-
-chg_adapt_loca = [0, 0]
-chg_adapt_range = 6
-chg_adapt_neuron = cn.findnearbyneuron.findnearbyneuron(ijwd.e_lattice, chg_adapt_loca, chg_adapt_range, ijwd.width)
-
-
-
-#%%
-start_scope()
-
-neuronmodel_e = cn.model_neu_syn_AD.neuron_e_AD
-neuronmodel_i = cn.model_neu_syn_AD.neuron_i_AD
-
-synapse_e = cn.model_neu_syn_AD.synapse_e_AD
-synapse_i = cn.model_neu_syn_AD.synapse_i_AD
-
-group_e_1 =NeuronGroup(ijwd.Ne, model=neuronmodel_e,
-                     threshold='v>v_threshold', method='euler',
-                     reset='''v = v_reset
-                              g_k += delta_gk''', refractory='(t-lastspike)<t_ref')
-
-group_i_1 =NeuronGroup(ijwd.Ni, model=neuronmodel_i,
-                     threshold='v>v_threshold', method='euler',
-                     reset='v = v_reset', refractory='(t-lastspike)<t_ref')
-
-syn_ee_1 = Synapses(group_e_1, group_e_1, model=synapse_e, 
-                  on_pre='x_E_post += w')
-syn_ei_1 = Synapses(group_e_1, group_i_1, model=synapse_e, 
-                  on_pre='x_E_post += w')
-syn_ie_1 = Synapses(group_i_1, group_e_1, model=synapse_i, 
-                  on_pre='x_I_post += w')
-syn_ii_1 = Synapses(group_i_1, group_i_1, model=synapse_i, 
-                  on_pre='x_I_post += w')
-
-'''external input'''
-#stim_rate = psti.input_spkrate(maxrate = [800,800], sig=[6,6], position=[[0, 0],[31.5,31.5]])*Hz
-bkg_rate2e = 850*Hz
-bkg_rate2i = 1000*Hz
-extnl_e = PoissonGroup(ijwd.Ne, bkg_rate2e)
-extnl_i = PoissonGroup(ijwd.Ni, bkg_rate2i)
-
-#tau_x_re = 1*ms
-syn_extnl_e = Synapses(extnl_e, group_e_1, model=synapse_e, on_pre='x_E_extnl_post += w')
-syn_extnl_i = Synapses(extnl_i, group_i_1, model=synapse_e, on_pre='x_E_extnl_post += w')
-
-syn_extnl_e.connect('i==j')
-syn_extnl_i.connect('i==j')
-
-w_extnl_ = 1.5 # nS
-syn_extnl_e.w = w_extnl_*5.8*nS#*tau_s_de_*nS
-syn_extnl_i.w = w_extnl_*5.8*nS#*tau_s_de_*nS
-
-
-syn_ee_1.connect(i=ijwd.i_ee, j=ijwd.j_ee)
-syn_ei_1.connect(i=ijwd.i_ei, j=ijwd.j_ei)
-syn_ie_1.connect(i=ijwd.i_ie, j=ijwd.j_ie)
-syn_ii_1.connect(i=ijwd.i_ii, j=ijwd.j_ii)
-
-
-#tau_s_di_try = tau_s_di_
-syn_ee_1.w = ijwd.w_ee*nsiemens * 5.8 #tau_s_de_
-syn_ei_1.w = ijwd.w_ei*nsiemens * 5.8 #tau_s_de_ #5*nS
-syn_ii_1.w = ijwd.w_ii*nsiemens * 6. #tau_s_di_#25*nS
-syn_ie_1.w = ijwd.w_ie*nsiemens * 6. #tau_s_di_#
-#w_ext = 2*nS
-#syn_pois_e.w = w_ext
-#syn_pois_i.w = w_ext
-
-def set_delay(syn, delay_up):
-    #n = len(syn)
-    syn.delay = delay_up*ms
-    #syn.down.delay = (delay_up + 1)*ms
-    
-    return syn 
-
-#generate_d_dist()
-#generate_d_rand()
-#def generate_d_rand(delay,len_i_ee,len_i_ie,len_i_ei,len_i_ii):
-#        
-#    d_ee = np.random.uniform(0, delay, len_i_ee)    
-#    d_ie = np.random.uniform(0, delay, len_i_ie)
-#    d_ei = np.random.uniform(0, delay, len_i_ei)
-#    d_ii = np.random.uniform(0, delay, len_i_ii)
-#    
-#    return d_ee, d_ie, d_ei, d_ii
-
-#d_ee, d_ie, d_ei, d_ii = generate_d_rand(4,len(i_ee),len(i_ie),len(i_ei),len(i_ii))
-syn_ee_1 = set_delay(syn_ee_1, ijwd.d_ee)
-syn_ie_1 = set_delay(syn_ie_1, ijwd.d_ie)
-syn_ei_1 = set_delay(syn_ei_1, ijwd.d_ei)
-syn_ii_1 = set_delay(syn_ii_1, ijwd.d_ii)
-
-#syn_pois_e = set_delay(syn_pois_e)
-#syn_pois_i = set_delay(syn_pois_i)
-
-tau_s_de_ = 5.8; tau_s_di_ = 6.5
-delta_gk_ = 10
-
-
-group_e_1.tau_s_de = tau_s_de_*ms; 
-group_e_1.tau_s_di = tau_s_di_*ms
-group_e_1.tau_s_re = group_e_1.tau_s_ri = 1*ms
-
-group_e_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_e_1.tau_s_re_inter = 1*ms
-group_e_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_e_1.tau_s_re_extnl = 1*ms
-
-group_i_1.tau_s_de = tau_s_de_*ms
-group_i_1.tau_s_di = tau_s_di_*ms
-group_i_1.tau_s_re = group_i_1.tau_s_ri = 1*ms
-
-group_i_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_i_1.tau_s_re_inter = 1*ms
-group_i_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_i_1.tau_s_re_extnl = 1*ms
-
-#syn_pois_e.effect = 0
-#syn_pois_i.effect = 0
-#group_e_v2.v = np.random.random(Ne)*35*mV-85*mV
-#group_i_v2.v = np.random.random(Ni)*35*mV-85*mV
-#seed(1000)
-group_e_1.v = np.random.random(ijwd.Ne)*35*mV-85*mV
-group_i_1.v = np.random.random(ijwd.Ni)*35*mV-85*mV
-group_e_1.delta_gk = delta_gk_*nS
-group_e_1.tau_k = 80*ms
-#group_e_v2.v = np.random.random(Ne)*10*mV-60*mV
-#group_i_v2.v = np.random.random(Ni)*10*mV-60*mV
-group_e_1.I_extnl_crt = 0*nA #0.51*nA
-group_i_1.I_extnl_crt = 0*nA #0.60*nA
-
-#%%
-spk_e = SpikeMonitor(group_e_1, record = True)
-spk_i = SpikeMonitor(group_i_1, record = True)
-#lfp_moni = StateMonitor(group_LFP_record, ('lfp1','lfp2','lfp3'), record = True)
-#%%
-net = Network(collect())
-net.store('state1')
-#%%
-scale_ee = 1.; 
-scale_ei = 1.03
-scale_ie = 1.
-scale_ii = 0.9
-syn_ee_1.w = ijwd.w_ee*nsiemens * 5.8 * scale_ee#tau_s_de_
-syn_ei_1.w = ijwd.w_ei*nsiemens * 5.8 * scale_ei #tau_s_de_ #5*nS
-syn_ii_1.w = ijwd.w_ii*nsiemens * 6. * scale_ie#tau_s_di_#25*nS
-syn_ie_1.w = ijwd.w_ie*nsiemens * 6. * scale_ii#tau_s_di_#
-#%%
-#change_ie(4.4)
-#syn_ie_1.w = w_ie*usiemens
-
-print('ie_w: %fnsiemens' %(syn_ie_1.w[0]/nsiemens))
-#Ne = 63*63; Ni = 1000;
-C = 0.25*nF # capacitance
-g_l = 16.7*nS # leak capacitance
-v_l = -70*mV # leak voltage
-v_threshold = -50*mV
-v_reset = -60*mV
-v_rev_I = -80*mV
-v_rev_E = 0*mV
-v_k = -85*mV
-#tau_k = 80*ms# 80*ms
-#delta_gk = 10*nS #10*nS
-t_ref = 4*ms # refractory period
-new_delta_gk_ = 0
-#tau_s_de = 5*ms
-#tau_s_di = 3*ms
-#tau_s_re = 1*ms
-#tau_s_ri = 1*ms
-#tau_x_re = 1*ms
-tic = time.perf_counter()
-#seed(10)
-simu_time1 = 1000*ms#2000*ms
-simu_time2 = 2000*ms
-#simu_time2 = 2000*ms#8000*ms
-#simu_time3 = 1000*ms
-#simu_time_tot = 30000*ms
-#group_input.active = False
-extnl_e.rates = bkg_rate2e
-net.run(simu_time1, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e + stim_rate
-group_e_1.delta_gk[chg_adapt_neuron] = new_delta_gk_*nS
-net.run(simu_time2, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e
-#net.run(simu_time3, profile=False) #,namespace={'tau_k': 80*ms}
-#group_input.active = True
-#group_e_1.delta_gk[chg_adapt_neuron] = 2*nS; group_e_1.tau_k[chg_adapt_neuron] = 40*ms
-#net.run(simu_time2, report = 'text', profile=False) #,namespace={'tau_k': 80*ms}
-print('total time elapsed:',time.perf_counter() - tic)
-#%%
-spk_tstep_e = np.round(spk_e.t/(0.1*ms)).astype(int)
-spk_tstep_i = np.round(spk_i.t/(0.1*ms)).astype(int)
-now = datetime.datetime.now()
-loop_num = 0
-#param_1 = {'delta_gk':10,'tau_s_di':3,'tau_s_de':5,'ie_ratio':3.1725,'sti_onset':'10s'}
-'''
-param_new = {'delta_gk':delta_gk_,
-         'new_delta_gk':new_delta_gk_,
-         #'new_tau_k':40,
-         'tau_s_di':tau_s_di_,
-         'tau_s_de':tau_s_de_, 'ie_ratio':ie_ratio_,
-         'mean_J_ee': ijwd.mean_J_ee,
-         #'chg_adapt_range':6, 
-         'p_ee':p_ee,
-         'simutime':simu_time_tot/ms,
-         'chg_adapt_time': simu_time1/ms,
-         'chg_adapt_range': chg_adapt_range,
-         'chg_adapt_loca': chg_adapt_loca,}
-param = {**param, **param_new}
-'''
-param = {}
-#param_12 = {'scale_e_12':scale_e_12[i], 'scale_e_21':scale_e_21[j]}
-
-#data = {'param':param_12, 'a1':{'param':param_1,'e':{'i':spk_1.i[:],'t':spk_1_tstep}},
-#                            'a2':{'param':param_2,'e':{'i':spk_2.i[:],'t':spk_2_tstep}}}
-data = {'datetime':now.strftime("%Y-%m-%d %H:%M:%S"), 'dt':0.1, 'loop_num':loop_num, 'data_dir': os.getcwd(),
-        'a1':{'param':param,
-              #'LFP':{'lfp1':lfp_moni.lfp1/nA, 'lfp2':lfp_moni.lfp2/nA, 'lfp3':lfp_moni.lfp3/nA},
-              'ge':{'i':spk_e.i[:],'t':spk_tstep_e},
-              'gi':{'i':spk_i.i[:],'t':spk_tstep_i}}}
-
-
-data1 = mydata.mydata(data)
-#%%
-data1.a1.ge.get_spike_rate(start_time=500, end_time=3000,\
-                           sample_interval = 1, n_neuron = ijwd.Ne, window = 10, dt = 0.1, reshape_indiv_rate = True)
-data1.a1.gi.get_spike_rate(start_time=500, end_time=3000,\
-                           sample_interval = 1, n_neuron = ijwd.Ni, window = 10, dt = 0.1, reshape_indiv_rate = True)
-
-firing_rate_analysis.show_pattern(data1.a1.ge.spk_rate.spk_rate, data1.a1.gi.spk_rate.spk_rate, frames = 2500, start_time = 500, anititle='animation')
-
-#%%
-plt.figure()
-plt.plot(data1.a1.ge.t/10,data1.a1.ge.i,'|')
-#%%
-plt.figure()
-plt.imshow(data1.a1.ge.spk_rate.spk_rate[:,:,100])
-#%%
-plt.figure()
-plt.scatter( ijwd.e_lattice[:,0], ijwd.e_lattice[:,1])
-plt.figure()
-plt.scatter( ijwd.i_lattice[:,0], ijwd.i_lattice[:,1])
-#%%
-pre_neuron = 5500
-pre = ijwd.e_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ee[ijwd.i_ee==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 1000
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ie[ijwd.i_ie==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 1000
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.i_lattice[ijwd.j_ii[ijwd.i_ii==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-net.restore('state1')
-
-#%%
-ijwd_63 = get_ijwd()
-ijwd_63.Ne = 63*63; ijwd_63.Ni = 32*32
-ijwd_63.width = 62
-ijwd_63.generate_ijw()
-ijwd_63.generate_d_rand()
-#%%
-def plot_outgoing(i, j, neuron_src, lattice_src, lattice_trg):
-    #pre_neuron = 5500
-    pre = lattice_src[neuron_src]; post = lattice_trg[j[i==neuron_src]]
-    plt.figure()
-    plt.plot(post[:,0],post[:,1], '.')
-    plt.plot(pre[0], pre[1], 'or')
-#%%
-plot_outgoing(ijwd_63.i_ie, ijwd_63.j_ie, 0, ijwd_63.i_lattice, ijwd_63.e_lattice)   
-plot_outgoing(ijwd.i_ie, ijwd.j_ie, 0, ijwd.i_lattice, ijwd.e_lattice)   
-   
-#%%
-plot_outgoing(ijwd_63.i_ii, ijwd_63.j_ii, 0, ijwd_63.i_lattice, ijwd_63.i_lattice)   
-plot_outgoing(ijwd.i_ii, ijwd.j_ii, 0, ijwd.i_lattice, ijwd.i_lattice)   
-"""
-    
-
-
diff --git a/connection/test/refractory/test_refractory.py b/connection/test/refractory/test_refractory.py
deleted file mode 100644
index 7260a25..0000000
--- a/connection/test/refractory/test_refractory.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Sep 16 12:47:50 2020
-
-@author: nishe
-"""
-
-import brian2.numpy_ as np
-import matplotlib.pyplot as plt
-import connection as cn
-#import warnings
-from scipy import sparse
-from brian2.only import *
-import time
-import mydata
-import firing_rate_analysis
-import os
-import datetime
-
-import expo_connection
-#%%
-prefs.codegen.target = 'numpy'
-
-#%%
-neuronmodel_e = cn.model_neu_syn_AD.neuron_e_AD
-#neuronmodel_i = cn.model_neu_syn_AD.neuron_i_AD
-
-synapse_e = cn.model_neu_syn_AD.synapse_e_AD
-#synapse_i = cn.model_neu_syn_AD.synapse_i_AD_SP
-#%%
-Ne = 1
-start_scope()
-
-group_e_1 =NeuronGroup(Ne, model=neuronmodel_e,
-                     threshold='v>v_threshold', method='euler',
-                     reset='''v = v_reset
-                              g_k += delta_gk''', refractory='(t-lastspike)<t_ref')
-
-#syn_ee_1 = Synapses(group_e_1, group_e_1, model=synapse_e, 
-#                  on_pre='''x_E_post += w
-#                            ''')
-
-#syn_ee_1 = Synapses(group_e_1, group_e_1, model=synapse_e, 
-#                  on_pre='''x_E_post += w*u*L
-#                            L -= u*L''')
-
-#indices = np.zeros(50)
-#times =np.arange(5, 51, 5)*ms
-#inpt = SpikeGeneratorGroup(1, indices, times)
-#%%
-
-#syn_ee_1.connect(i=[0], j=[0])
-
-#tau_s_di_try = tau_s_di_
-#syn_ee_1.w = 4 * 5.8 * nS #tau_s_de_
-#u_base = 0.3
-#syn_ee_1.u = u_base; syn_ee_1.L = 1
-#syn_ei_1.u = u_base; syn_ee_1.L = 1
-#syn_ii_1.u = u_base; syn_ee_1.L = 1
-#syn_ie_1.u = u_base; syn_ee_1.L = 1
-#tau_f = 1500*ms; tau_d = 200*ms
-
-#def set_delay(syn, delay_up):
-#    #n = len(syn)
-#    syn.delay = delay_up*ms
-#    #syn.down.delay = (delay_up + 1)*ms
-#    
-#    return syn 
-
-#generate_d_dist()
-#generate_d_rand()
-#def generate_d_rand(delay,len_i_ee,len_i_ie,len_i_ei,len_i_ii):
-#        
-#    d_ee = np.random.uniform(0, delay, len_i_ee)    
-#    d_ie = np.random.uniform(0, delay, len_i_ie)
-#    d_ei = np.random.uniform(0, delay, len_i_ei)
-#    d_ii = np.random.uniform(0, delay, len_i_ii)
-#    
-#    return d_ee, d_ie, d_ei, d_ii
-
-#d_ee, d_ie, d_ei, d_ii = generate_d_rand(4,len(i_ee),len(i_ie),len(i_ei),len(i_ii))
-#syn_ee_1 = set_delay(syn_ee_1, ijwd.d_ee)
-#syn_ie_1 = set_delay(syn_ie_1, ijwd.d_ie)
-#syn_ei_1 = set_delay(syn_ei_1, ijwd.d_ei)
-#syn_ii_1 = set_delay(syn_ii_1, ijwd.d_ii)
-
-#syn_pois_e = set_delay(syn_pois_e)
-#syn_pois_i = set_delay(syn_pois_i)
-
-tau_s_de_ = 5.8; tau_s_di_ = 6.5
-delta_gk_ = 10
-
-
-group_e_1.tau_s_de = tau_s_de_*ms; 
-group_e_1.tau_s_di = tau_s_di_*ms
-group_e_1.tau_s_re = group_e_1.tau_s_ri = 1*ms
-
-group_e_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_e_1.tau_s_re_inter = 1*ms
-group_e_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_e_1.tau_s_re_extnl = 1*ms
-
-#group_i_1.tau_s_de = tau_s_de_*ms
-#group_i_1.tau_s_di = tau_s_di_*ms
-#group_i_1.tau_s_re = group_i_1.tau_s_ri = 1*ms
-#
-#group_i_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-#group_i_1.tau_s_re_inter = 1*ms
-#group_i_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-#group_i_1.tau_s_re_extnl = 1*ms
-
-#syn_pois_e.effect = 0
-#syn_pois_i.effect = 0
-#group_e_v2.v = np.random.random(Ne)*35*mV-85*mV
-#group_i_v2.v = np.random.random(Ni)*35*mV-85*mV
-#seed(1000)
-group_e_1.v = -70*mV#np.random.random(ijwd.Ne)*35*mV-85*mV
-#group_i_1.v = np.random.random(ijwd.Ni)*35*mV-85*mV
-delta_gk_ = 10
-group_e_1.delta_gk = delta_gk_*nS
-group_e_1.tau_k = 80*ms
-#group_e_v2.v = np.random.random(Ne)*10*mV-60*mV
-#group_i_v2.v = np.random.random(Ni)*10*mV-60*mV
-group_e_1.I_extnl_crt = 0.7*nA #0.51*nA
-#group_i_1.I_extnl_crt = 0*nA #0.60*nA
-
-#%%
-spk_e = SpikeMonitor(group_e_1, record = True)
-s_moni = StateMonitor(group_e_1, ('v','I_exi'), record = True)
-#%%
-net = Network(collect())
-net.store('state1')
-#%%
-#change_ie(4.4)
-#syn_ie_1.w = w_ie*usiemens
-
-#print('ie_w: %fnsiemens' %(syn_ie_1.w[0]/nsiemens))
-#Ne = 63*63; Ni = 1000;
-C = 0.25*nF # capacitance
-g_l = 16.7*nS # leak capacitance
-v_l = -70*mV # leak voltage
-v_threshold = -50*mV
-v_reset = -70*mV
-v_rev_I = -80*mV
-v_rev_E = 0*mV
-v_k = -85*mV
-#tau_k = 80*ms# 80*ms
-#delta_gk = 10*nS #10*nS
-t_ref = 4*ms # refractory period
-delta_gk_ = 0
-group_e_1.delta_gk = delta_gk_*nS
-
-tic = time.perf_counter()
-#seed(10)
-simu_time1 = 500*ms#2000*ms
-#simu_time2 = 2000*ms
-#simu_time2 = 2000*ms#8000*ms
-#simu_time3 = 1000*ms
-#simu_time_tot = 30000*ms
-#group_input.active = False
-#extnl_e.rates = bkg_rate2e
-net.run(simu_time1, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e + stim_rate
-#group_e_1.delta_gk[chg_adapt_neuron] = new_delta_gk_*nS
-#net.run(simu_time2, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e
-#net.run(simu_time3, profile=False) #,namespace={'tau_k': 80*ms}
-#group_input.active = True
-#group_e_1.delta_gk[chg_adapt_neuron] = 2*nS; group_e_1.tau_k[chg_adapt_neuron] = 40*ms
-#net.run(simu_time2, report = 'text', profile=False) #,namespace={'tau_k': 80*ms}
-print('total time elapsed:',time.perf_counter() - tic)
-#%%
-plt.figure()
-plt.plot(s_moni.t/ms, s_moni.v[0]/mV)
-#%%
-plt.figure()
-plt.plot(spk_e.t/ms, spk_e.i, '|')
-#%%
-spk_e.i.shape
-
-#%%
-net.restore('state1')
-
-
diff --git a/connection/test/test_expo.py b/connection/test/test_expo.py
deleted file mode 100644
index a2fb905..0000000
--- a/connection/test/test_expo.py
+++ /dev/null
@@ -1,322 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Sep 14 18:26:34 2020
-
-@author: nishe
-"""
-#import numpy as np
-import brian2.numpy_ as np
-import matplotlib.pyplot as plt
-import connection as cn
-import warnings
-from scipy import sparse
-from brian2.only import *
-import time
-import mydata
-import firing_rate_analysis
-import os
-import datetime
-
-import expo_connection
-#%%
-ijwd = expo_connection.get_ijwd()
-#ijwd.Ne = 77*77; ijwd.Ni = 39*39
-#ijwd.width = 76
-ijwd.w_ee_mean *= 2; ijwd.w_ei_mean *= 2; ijwd.w_ie_mean *= 2; ijwd.w_ii_mean *= 2; 
-
-ijwd.generate_ijw()
-ijwd.generate_d_rand()
-#%%
-
-chg_adapt_loca = [0, 0]
-chg_adapt_range = 6
-chg_adapt_neuron = cn.findnearbyneuron.findnearbyneuron(ijwd.e_lattice, chg_adapt_loca, chg_adapt_range, ijwd.width)
-
-
-
-#%%
-start_scope()
-
-neuronmodel_e = cn.model_neu_syn_AD.neuron_e_AD
-neuronmodel_i = cn.model_neu_syn_AD.neuron_i_AD
-
-synapse_e = cn.model_neu_syn_AD.synapse_e_AD_SP
-synapse_i = cn.model_neu_syn_AD.synapse_i_AD_SP
-
-group_e_1 =NeuronGroup(ijwd.Ne, model=neuronmodel_e,
-                     threshold='v>v_threshold', method='euler',
-                     reset='''v = v_reset
-                              g_k += delta_gk''', refractory='(t-lastspike)<t_ref')
-
-group_i_1 =NeuronGroup(ijwd.Ni, model=neuronmodel_i,
-                     threshold='v>v_threshold', method='euler',
-                     reset='v = v_reset', refractory='(t-lastspike)<t_ref')
-
-syn_ee_1 = Synapses(group_e_1, group_e_1, model=synapse_e, 
-                  on_pre='''x_E_post += w*u*L
-                            L -= u*L''')
-syn_ei_1 = Synapses(group_e_1, group_i_1, model=synapse_e, 
-                  on_pre='''x_E_post += w*u*L
-                            L -= u*L''')
-syn_ie_1 = Synapses(group_i_1, group_e_1, model=synapse_i, 
-                  on_pre='''x_I_post += w*u*L
-                            L -= u*L''')
-syn_ii_1 = Synapses(group_i_1, group_i_1, model=synapse_i, 
-                  on_pre='''x_I_post += w*u*L
-                            L -= u*L''')
-
-'''external input'''
-#stim_rate = psti.input_spkrate(maxrate = [800,800], sig=[6,6], position=[[0, 0],[31.5,31.5]])*Hz
-bkg_rate2e = 850*Hz
-bkg_rate2i = 1000*Hz
-extnl_e = PoissonGroup(ijwd.Ne, bkg_rate2e)
-extnl_i = PoissonGroup(ijwd.Ni, bkg_rate2i)
-
-#tau_x_re = 1*ms
-synapse_e_extnl = cn.model_neu_syn_AD.synapse_e_AD
-syn_extnl_e = Synapses(extnl_e, group_e_1, model=synapse_e_extnl, on_pre='x_E_extnl_post += w')
-syn_extnl_i = Synapses(extnl_i, group_i_1, model=synapse_e_extnl, on_pre='x_E_extnl_post += w')
-
-syn_extnl_e.connect('i==j')
-syn_extnl_i.connect('i==j')
-
-w_extnl_ = 1.5 # nS
-syn_extnl_e.w = w_extnl_*5.8*nS#*tau_s_de_*nS
-syn_extnl_i.w = w_extnl_*5.8*nS#*tau_s_de_*nS
-
-
-syn_ee_1.connect(i=ijwd.i_ee, j=ijwd.j_ee)
-syn_ei_1.connect(i=ijwd.i_ei, j=ijwd.j_ei)
-syn_ie_1.connect(i=ijwd.i_ie, j=ijwd.j_ie)
-syn_ii_1.connect(i=ijwd.i_ii, j=ijwd.j_ii)
-
-
-#tau_s_di_try = tau_s_di_
-syn_ee_1.w = ijwd.w_ee*nsiemens * 5.8 #tau_s_de_
-syn_ei_1.w = ijwd.w_ei*nsiemens * 5.8 #tau_s_de_ #5*nS
-syn_ii_1.w = ijwd.w_ii*nsiemens * 6. #tau_s_di_#25*nS
-syn_ie_1.w = ijwd.w_ie*nsiemens * 6. #tau_s_di_#
-#w_ext = 2*nS
-#syn_pois_e.w = w_ext
-#syn_pois_i.w = w_ext
-u_base = 0.3
-syn_ee_1.u = u_base; syn_ee_1.L = 1
-syn_ei_1.u = u_base; syn_ee_1.L = 1
-syn_ii_1.u = u_base; syn_ee_1.L = 1
-syn_ie_1.u = u_base; syn_ee_1.L = 1
-tau_f = 1500*ms; tau_d = 200*ms
-
-def set_delay(syn, delay_up):
-    #n = len(syn)
-    syn.delay = delay_up*ms
-    #syn.down.delay = (delay_up + 1)*ms
-    
-    return syn 
-
-#generate_d_dist()
-#generate_d_rand()
-#def generate_d_rand(delay,len_i_ee,len_i_ie,len_i_ei,len_i_ii):
-#        
-#    d_ee = np.random.uniform(0, delay, len_i_ee)    
-#    d_ie = np.random.uniform(0, delay, len_i_ie)
-#    d_ei = np.random.uniform(0, delay, len_i_ei)
-#    d_ii = np.random.uniform(0, delay, len_i_ii)
-#    
-#    return d_ee, d_ie, d_ei, d_ii
-
-#d_ee, d_ie, d_ei, d_ii = generate_d_rand(4,len(i_ee),len(i_ie),len(i_ei),len(i_ii))
-syn_ee_1 = set_delay(syn_ee_1, ijwd.d_ee)
-syn_ie_1 = set_delay(syn_ie_1, ijwd.d_ie)
-syn_ei_1 = set_delay(syn_ei_1, ijwd.d_ei)
-syn_ii_1 = set_delay(syn_ii_1, ijwd.d_ii)
-
-#syn_pois_e = set_delay(syn_pois_e)
-#syn_pois_i = set_delay(syn_pois_i)
-
-tau_s_de_ = 5.8; tau_s_di_ = 6.5
-delta_gk_ = 10
-
-
-group_e_1.tau_s_de = tau_s_de_*ms; 
-group_e_1.tau_s_di = tau_s_di_*ms
-group_e_1.tau_s_re = group_e_1.tau_s_ri = 1*ms
-
-group_e_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_e_1.tau_s_re_inter = 1*ms
-group_e_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_e_1.tau_s_re_extnl = 1*ms
-
-group_i_1.tau_s_de = tau_s_de_*ms
-group_i_1.tau_s_di = tau_s_di_*ms
-group_i_1.tau_s_re = group_i_1.tau_s_ri = 1*ms
-
-group_i_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_i_1.tau_s_re_inter = 1*ms
-group_i_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_i_1.tau_s_re_extnl = 1*ms
-
-#syn_pois_e.effect = 0
-#syn_pois_i.effect = 0
-#group_e_v2.v = np.random.random(Ne)*35*mV-85*mV
-#group_i_v2.v = np.random.random(Ni)*35*mV-85*mV
-#seed(1000)
-group_e_1.v = np.random.random(ijwd.Ne)*35*mV-85*mV
-group_i_1.v = np.random.random(ijwd.Ni)*35*mV-85*mV
-group_e_1.delta_gk = delta_gk_*nS
-group_e_1.tau_k = 80*ms
-#group_e_v2.v = np.random.random(Ne)*10*mV-60*mV
-#group_i_v2.v = np.random.random(Ni)*10*mV-60*mV
-group_e_1.I_extnl_crt = 0*nA #0.51*nA
-group_i_1.I_extnl_crt = 0*nA #0.60*nA
-
-#%%
-spk_e = SpikeMonitor(group_e_1, record = True)
-spk_i = SpikeMonitor(group_i_1, record = True)
-#lfp_moni = StateMonitor(group_LFP_record, ('lfp1','lfp2','lfp3'), record = True)
-#%%
-net = Network(collect())
-net.store('state1')
-#%%
-scale_ee = 1.1; 
-scale_ei = 1.
-scale_ie = 1.
-scale_ii = 1.
-syn_ee_1.w = ijwd.w_ee*nsiemens * 5.8 * scale_ee#tau_s_de_
-syn_ei_1.w = ijwd.w_ei*nsiemens * 5.8 * scale_ei #tau_s_de_ #5*nS
-syn_ie_1.w = ijwd.w_ii*nsiemens * 6. * scale_ie#tau_s_di_#25*nS
-syn_ii_1.w = ijwd.w_ie*nsiemens * 6. * scale_ii#tau_s_di_#
-
-tau_d = 100*ms
-#%%
-#change_ie(4.4)
-#syn_ie_1.w = w_ie*usiemens
-
-print('ie_w: %fnsiemens' %(syn_ie_1.w[0]/nsiemens))
-#Ne = 63*63; Ni = 1000;
-C = 0.25*nF # capacitance
-g_l = 16.7*nS # leak capacitance
-v_l = -70*mV # leak voltage
-v_threshold = -50*mV
-v_reset = -60*mV
-v_rev_I = -80*mV
-v_rev_E = 0*mV
-v_k = -85*mV
-#tau_k = 80*ms# 80*ms
-#delta_gk = 10*nS #10*nS
-t_ref = 4*ms # refractory period
-new_delta_gk_ = 0
-#tau_s_de = 5*ms
-#tau_s_di = 3*ms
-#tau_s_re = 1*ms
-#tau_s_ri = 1*ms
-#tau_x_re = 1*ms
-tic = time.perf_counter()
-#seed(10)
-simu_time1 = 1000*ms#2000*ms
-simu_time2 = 2000*ms
-#simu_time2 = 2000*ms#8000*ms
-#simu_time3 = 1000*ms
-#simu_time_tot = 30000*ms
-#group_input.active = False
-extnl_e.rates = bkg_rate2e
-net.run(simu_time1, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e + stim_rate
-group_e_1.delta_gk[chg_adapt_neuron] = new_delta_gk_*nS
-net.run(simu_time2, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e
-#net.run(simu_time3, profile=False) #,namespace={'tau_k': 80*ms}
-#group_input.active = True
-#group_e_1.delta_gk[chg_adapt_neuron] = 2*nS; group_e_1.tau_k[chg_adapt_neuron] = 40*ms
-#net.run(simu_time2, report = 'text', profile=False) #,namespace={'tau_k': 80*ms}
-print('total time elapsed:',time.perf_counter() - tic)
-#%%
-spk_tstep_e = np.round(spk_e.t/(0.1*ms)).astype(int)
-spk_tstep_i = np.round(spk_i.t/(0.1*ms)).astype(int)
-now = datetime.datetime.now()
-loop_num = 0
-#param_1 = {'delta_gk':10,'tau_s_di':3,'tau_s_de':5,'ie_ratio':3.1725,'sti_onset':'10s'}
-'''
-param_new = {'delta_gk':delta_gk_,
-         'new_delta_gk':new_delta_gk_,
-         #'new_tau_k':40,
-         'tau_s_di':tau_s_di_,
-         'tau_s_de':tau_s_de_, 'ie_ratio':ie_ratio_,
-         'mean_J_ee': ijwd.mean_J_ee,
-         #'chg_adapt_range':6, 
-         'p_ee':p_ee,
-         'simutime':simu_time_tot/ms,
-         'chg_adapt_time': simu_time1/ms,
-         'chg_adapt_range': chg_adapt_range,
-         'chg_adapt_loca': chg_adapt_loca,}
-param = {**param, **param_new}
-'''
-param = {}
-#param_12 = {'scale_e_12':scale_e_12[i], 'scale_e_21':scale_e_21[j]}
-
-#data = {'param':param_12, 'a1':{'param':param_1,'e':{'i':spk_1.i[:],'t':spk_1_tstep}},
-#                            'a2':{'param':param_2,'e':{'i':spk_2.i[:],'t':spk_2_tstep}}}
-data = {'datetime':now.strftime("%Y-%m-%d %H:%M:%S"), 'dt':0.1, 'loop_num':loop_num, 'data_dir': os.getcwd(),
-        'a1':{'param':param,
-              #'LFP':{'lfp1':lfp_moni.lfp1/nA, 'lfp2':lfp_moni.lfp2/nA, 'lfp3':lfp_moni.lfp3/nA},
-              'ge':{'i':spk_e.i[:],'t':spk_tstep_e},
-              'gi':{'i':spk_i.i[:],'t':spk_tstep_i}}}
-
-
-data1 = mydata.mydata(data)
-#%%
-data1.a1.ge.get_spike_rate(start_time=500, end_time=3000,\
-                           sample_interval = 1, n_neuron = ijwd.Ne, window = 10, dt = 0.1, reshape_indiv_rate = True)
-data1.a1.gi.get_spike_rate(start_time=500, end_time=3000,\
-                           sample_interval = 1, n_neuron = ijwd.Ni, window = 10, dt = 0.1, reshape_indiv_rate = True)
-
-firing_rate_analysis.show_pattern(data1.a1.ge.spk_rate.spk_rate, data1.a1.gi.spk_rate.spk_rate, frames = 2500, start_time = 500, anititle='animation')
-
-#%%
-plt.figure()
-plt.plot(data1.a1.ge.t/10,data1.a1.ge.i,'|')
-#%%
-plt.figure()
-plt.imshow(data1.a1.ge.spk_rate.spk_rate[:,:,100])
-#%%
-plt.figure()
-plt.scatter( ijwd.e_lattice[:,0], ijwd.e_lattice[:,1])
-plt.figure()
-plt.scatter( ijwd.i_lattice[:,0], ijwd.i_lattice[:,1])
-#%%
-pre_neuron = 5500
-pre = ijwd.e_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ee[ijwd.i_ee==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 1000
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ie[ijwd.i_ie==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 1000
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.i_lattice[ijwd.j_ii[ijwd.i_ii==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-net.restore('state1')
-
-#%%
-ijwd_63 = expo_connection.get_ijwd()
-ijwd_63.Ne = 63*63; ijwd_63.Ni = 32*32
-ijwd_63.width = 62
-ijwd_63.generate_ijw()
-ijwd_63.generate_d_rand()
-#%%
-def plot_outgoing(i, j, neuron_src, lattice_src, lattice_trg):
-    #pre_neuron = 5500
-    pre = lattice_src[neuron_src]; post = lattice_trg[j[i==neuron_src]]
-    plt.figure()
-    plt.plot(post[:,0],post[:,1], '.')
-    plt.plot(pre[0], pre[1], 'or')
-#%%
-plot_outgoing(ijwd_63.i_ie, ijwd_63.j_ie, 0, ijwd_63.i_lattice, ijwd_63.e_lattice)   
-plot_outgoing(ijwd.i_ie, ijwd.j_ie, 0, ijwd.i_lattice, ijwd.e_lattice)   
-   
-#%%
-plot_outgoing(ijwd_63.i_ii, ijwd_63.j_ii, 0, ijwd_63.i_lattice, ijwd_63.i_lattice)   
-plot_outgoing(ijwd.i_ii, ijwd.j_ii, 0, ijwd.i_lattice, ijwd.i_lattice)   
diff --git a/connection/test/test_gaus.py b/connection/test/test_gaus.py
deleted file mode 100644
index aedd97c..0000000
--- a/connection/test/test_gaus.py
+++ /dev/null
@@ -1,371 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Sep 28 13:20:00 2020
-
-@author: shni2598
-"""
-
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Sep 14 18:26:34 2020
-
-@author: nishe
-"""
-#import numpy as np
-import brian2.numpy_ as np
-import matplotlib.pyplot as plt
-import connection as cn
-import warnings
-from scipy import sparse
-from brian2.only import *
-import time
-import mydata
-import firing_rate_analysis
-import os
-import datetime
-
-import gaus_connection
-#%%
-ijwd = gaus_connection.get_ijwd()
-ijwd.decay_p_ee = 10 # decay constant of e to e connection probability as distance increases
-ijwd.decay_p_ei = 10 # decay constant of e to i connection probability as distance increases
-ijwd.decay_p_ie = 10 # decay constant of i to e connection probability as distance increases
-ijwd.decay_p_ii = 10 # decay constant of i to i connection probability as distance increases
-
-ijwd.mean_SynNumIn_ie = 140     ; # p = 0.2
-ijwd.mean_SynNumIn_ii = 140     ; # p = 0.25
-#ijwd.Ne = 77*77; ijwd.Ni = 39*39
-#ijwd.width = 76
-#ijwd.w_ee_mean *= 2; ijwd.w_ei_mean *= 2; ijwd.w_ie_mean *= 2; ijwd.w_ii_mean *= 2; 
-
-ijwd.generate_ijw()
-ijwd.generate_d_rand()
-#%%
-pre_neuron = 0
-pre = ijwd.e_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ee[ijwd.i_ee==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-
-pre_neuron = 0
-pre = ijwd.e_lattice[pre_neuron]; post = ijwd.i_lattice[ijwd.j_ei[ijwd.i_ei==pre_neuron]]
-#plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 0
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ie[ijwd.i_ie==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 0
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.i_lattice[ijwd.j_ii[ijwd.i_ii==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-
-chg_adapt_loca = [0, 0]
-chg_adapt_range = 6
-chg_adapt_neuron = cn.findnearbyneuron.findnearbyneuron(ijwd.e_lattice, chg_adapt_loca, chg_adapt_range, ijwd.width)
-
-
-
-#%%
-start_scope()
-
-neuronmodel_e = cn.model_neu_syn_AD.neuron_e_AD
-neuronmodel_i = cn.model_neu_syn_AD.neuron_i_AD
-
-synapse_e = cn.model_neu_syn_AD.synapse_e_AD
-synapse_i = cn.model_neu_syn_AD.synapse_i_AD
-
-group_e_1 =NeuronGroup(ijwd.Ne, model=neuronmodel_e,
-                     threshold='v>v_threshold', method='euler',
-                     reset='''v = v_reset
-                              g_k += delta_gk''', refractory='(t-lastspike)<t_ref')
-
-group_i_1 =NeuronGroup(ijwd.Ni, model=neuronmodel_i,
-                     threshold='v>v_threshold', method='euler',
-                     reset='v = v_reset', refractory='(t-lastspike)<t_ref')
-
-#syn_ee_1 = Synapses(group_e_1, group_e_1, model=synapse_e, 
-#                  on_pre='''x_E_post += w*u*L
-#                            L -= u*L''')
-#syn_ei_1 = Synapses(group_e_1, group_i_1, mosyn_extnl_edel=synapse_e, 
-#                  on_pre='''x_E_post += w*u*L
-#                            L -= u*L''')
-#syn_ie_1 = Synapses(group_i_1, group_e_1, model=synapse_i, 
-#                  on_pre='''x_I_post += w*u*L
-#                            L -= u*L''')
-#syn_ii_1 = Synapses(group_i_1, group_i_1, model=synapse_i, 
-#                  on_pre='''x_I_post += w*u*L
-#                            L -= u*L''')
-
-syn_ee_1 = Synapses(group_e_1, group_e_1, model=synapse_e, 
-                  on_pre='''x_E_post += w''')
-syn_ei_1 = Synapses(group_e_1, group_i_1, model=synapse_e, 
-                  on_pre='''x_E_post += w''')
-syn_ie_1 = Synapses(group_i_1, group_e_1, model=synapse_i, 
-                  on_pre='''x_I_post += w''')
-syn_ii_1 = Synapses(group_i_1, group_i_1, model=synapse_i, 
-                  on_pre='''x_I_post += w''')
-#%%
-'''external input'''
-#stim_rate = psti.input_spkrate(maxrate = [800,800], sig=[6,6], position=[[0, 0],[31.5,31.5]])*Hz
-bkg_rate2e = 850*Hz
-bkg_rate2i = 1000*Hz
-extnl_e = PoissonGroup(ijwd.Ne, bkg_rate2e)
-extnl_i = PoissonGroup(ijwd.Ni, bkg_rate2i)
-
-#tau_x_re = 1*ms
-synapse_e_extnl = cn.model_neu_syn_AD.synapse_e_AD
-syn_extnl_e = Synapses(extnl_e, group_e_1, model=synapse_e_extnl, on_pre='x_E_extnl_post += w')
-syn_extnl_i = Synapses(extnl_i, group_i_1, model=synapse_e_extnl, on_pre='x_E_extnl_post += w')
-#%%
-syn_extnl_e.connect('i==j')
-syn_extnl_i.connect('i==j')
-
-w_extnl_ = 1.5 # nS
-syn_extnl_e.w = w_extnl_*5.8*nS#*tau_s_de_*nS
-syn_extnl_i.w = w_extnl_*5.8*nS#*tau_s_de_*nS
-#%%
-
-syn_ee_1.connect(i=ijwd.i_ee, j=ijwd.j_ee)
-syn_ei_1.connect(i=ijwd.i_ei, j=ijwd.j_ei)
-syn_ie_1.connect(i=ijwd.i_ie, j=ijwd.j_ie)
-syn_ii_1.connect(i=ijwd.i_ii, j=ijwd.j_ii)
-
-
-#tau_s_di_try = tau_s_di_
-syn_ee_1.w = ijwd.w_ee*nsiemens * 5.8 #tau_s_de_
-syn_ei_1.w = ijwd.w_ei*nsiemens * 5.8 #tau_s_de_ #5*nS
-syn_ii_1.w = ijwd.w_ii*nsiemens * 6. #tau_s_di_#25*nS
-syn_ie_1.w = ijwd.w_ie*nsiemens * 6. #tau_s_di_#
-#w_ext = 2*nS
-#syn_pois_e.w = w_ext
-#syn_pois_i.w = w_ext
-#u_base = 0.3
-#syn_ee_1.u = u_base; syn_ee_1.L = 1
-#syn_ei_1.u = u_base; syn_ee_1.L = 1
-#syn_ii_1.u = u_base; syn_ee_1.L = 1
-#syn_ie_1.u = u_base; syn_ee_1.L = 1
-#tau_f = 1500*ms; tau_d = 200*ms
-
-def set_delay(syn, delay_up):
-    #n = len(syn)
-    syn.delay = delay_up*ms
-    #syn.down.delay = (delay_up + 1)*ms
-    
-    return syn 
-
-#generate_d_dist()
-#generate_d_rand()
-#def generate_d_rand(delay,len_i_ee,len_i_ie,len_i_ei,len_i_ii):
-#        
-#    d_ee = np.random.uniform(0, delay, len_i_ee)    
-#    d_ie = np.random.uniform(0, delay, len_i_ie)
-#    d_ei = np.random.uniform(0, delay, len_i_ei)
-#    d_ii = np.random.uniform(0, delay, len_i_ii)
-#    
-#    return d_ee, d_ie, d_ei, d_ii
-
-#d_ee, d_ie, d_ei, d_ii = generate_d_rand(4,len(i_ee),len(i_ie),len(i_ei),len(i_ii))
-syn_ee_1 = set_delay(syn_ee_1, ijwd.d_ee)
-syn_ie_1 = set_delay(syn_ie_1, ijwd.d_ie)
-syn_ei_1 = set_delay(syn_ei_1, ijwd.d_ei)
-syn_ii_1 = set_delay(syn_ii_1, ijwd.d_ii)
-
-#syn_pois_e = set_delay(syn_pois_e)
-#syn_pois_i = set_delay(syn_pois_i)
-
-tau_s_de_ = 5.8; tau_s_di_ = 6.5
-delta_gk_ = 10
-
-
-group_e_1.tau_s_de = tau_s_de_*ms; 
-group_e_1.tau_s_di = tau_s_di_*ms
-group_e_1.tau_s_re = group_e_1.tau_s_ri = 1*ms
-
-group_e_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_e_1.tau_s_re_inter = 1*ms
-group_e_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_e_1.tau_s_re_extnl = 1*ms
-
-group_i_1.tau_s_de = tau_s_de_*ms
-group_i_1.tau_s_di = tau_s_di_*ms
-group_i_1.tau_s_re = group_i_1.tau_s_ri = 1*ms
-
-group_i_1.tau_s_de_inter = tau_s_de_*ms #5.0*ms; 
-group_i_1.tau_s_re_inter = 1*ms
-group_i_1.tau_s_de_extnl = tau_s_de_*ms #5.0*ms
-group_i_1.tau_s_re_extnl = 1*ms
-
-#syn_pois_e.effect = 0
-#syn_pois_i.effect = 0
-#group_e_v2.v = np.random.random(Ne)*35*mV-85*mV
-#group_i_v2.v = np.random.random(Ni)*35*mV-85*mV
-#seed(1000)
-group_e_1.v = np.random.random(ijwd.Ne)*35*mV-85*mV
-group_i_1.v = np.random.random(ijwd.Ni)*35*mV-85*mV
-group_e_1.delta_gk = delta_gk_*nS
-group_e_1.tau_k = 80*ms
-#group_e_v2.v = np.random.random(Ne)*10*mV-60*mV
-#group_i_v2.v = np.random.random(Ni)*10*mV-60*mV
-group_e_1.I_extnl_crt = 0*nA #0.51*nA
-group_i_1.I_extnl_crt = 0*nA #0.60*nA
-
-#%%
-spk_e = SpikeMonitor(group_e_1, record = True)
-spk_i = SpikeMonitor(group_i_1, record = True)
-#lfp_moni = StateMonitor(group_LFP_record, ('lfp1','lfp2','lfp3'), record = True)
-#%%
-net = Network(collect())
-net.store('state1')
-#%%
-scale_ee = 1.3; 
-scale_ei = 1.
-scale_ie = 1.
-scale_ii = 0.99
-syn_ee_1.w = ijwd.w_ee*nsiemens * 5.8 * scale_ee#tau_s_de_
-syn_ei_1.w = ijwd.w_ei*nsiemens * 5.8 * scale_ei #tau_s_de_ #5*nS
-syn_ie_1.w = ijwd.w_ie*nsiemens * 6. * scale_ie#tau_s_di_#25*nS
-syn_ii_1.w = ijwd.w_ii*nsiemens * 6. * scale_ii#tau_s_di_#
-
-#tau_d = 100*ms
-#%%
-#change_ie(4.4)
-#syn_ie_1.w = w_ie*usiemens
-
-print('ie_w: %fnsiemens' %(syn_ie_1.w[0]/nsiemens))
-#Ne = 63*63; Ni = 1000;
-C = 0.25*nF # capacitance
-g_l = 16.7*nS # leak capacitance500
-v_l = -70*mV # leak voltage
-v_threshold = -50*mV
-v_reset = -60*mV
-v_rev_I = -80*mV
-v_rev_E = 0*mV
-v_k = -85*mV
-#tau_k = 80*ms# 80*ms
-#delta_gk = 10*nS #10*nS
-t_ref = 4*ms # refractory period
-new_delta_gk_ = 0
-#tau_s_de = 5*ms
-#tau_s_di = 3*ms
-#tau_s_re = 1*ms
-#tau_s_ri = 1*ms
-#tau_x_re = 1*ms
-tic = time.perf_counter()
-#seed(10)
-simu_time1 = 2000*ms#2000*ms
-simu_time2 = 2000*ms
-#simu_time2 = 2000*ms#8000*ms
-#simu_time3 = 1000*ms
-#simu_time_tot = 30000*ms
-#group_input.active = False
-extnl_e.rates = bkg_rate2e
-net.run(simu_time1, profile=False) #,namespace={'tau_k': 80*ms}
-#extnl_e.rates = bkg_rate2e + stim_rate
-
-group_e_1.delta_gk[chg_adapt_neuron] = new_delta_gk_*nS
-net.run(simu_time2, profile=False) #,namespace={'tau_k': 80*ms}
-
-#extnl_e.rates = bkg_rate2e
-#net.run(simu_time3, profile=False) #,namespace={'tau_k': 80*ms}
-#group_input.active = True
-#group_e_1.delta_gk[chg_adapt_neuron] = 2*nS; group_e_1.tau_k[chg_adapt_neuron] = 40*ms
-#net.run(simu_time2, report = 'text', profile=False) #,namespace={'tau_k': 80*ms}
-print('total time elapsed:',time.perf_counter() - tic)
-#%%
-spk_tstep_e = np.round(spk_e.t/(0.1*ms)).astype(int)
-spk_tstep_i = np.round(spk_i.t/(0.1*ms)).astype(int)
-now = datetime.datetime.now()
-loop_num = 0
-#param_1 = {'delta_gk':10,'tau_s_di':3,'tau_s_de':5,'ie_ratio':3.1725,'sti_onset':'10s'}
-'''
-param_new = {'delta_gk':delta_gk_,
-         'new_delta_gk':new_delta_gk_,
-         #'new_tau_k':40,
-         'tau_s_di':tau_s_di_,
-         'tau_s_de':tau_s_de_, 'ie_ratio':ie_ratio_,
-         'mean_J_ee': ijwd.mean_J_ee,
-         #'chg_adapt_range':6, 
-         'p_ee':p_ee,
-         'simutime':simu_time_tot/ms,
-         'chg_adapt_time': simu_time1/ms,
-         'chg_adapt_range': chg_adapt_range,
-         'chg_adapt_loca': chg_adapt_loca,}
-param = {**param, **param_new}
-'''
-param = {}
-#param_12 = {'scale_e_12':scale_e_12[i], 'scale_e_21':scale_e_21[j]}
-
-#data = {'param':param_12, 'a1':{'param':param_1,'e':{'i':spk_1.i[:],'t':spk_1_tstep}},
-#                            'a2':{'param':param_2,'e':{'i':spk_2.i[:],'t':spk_2_tstep}}}
-data = {'datetime':now.strftime("%Y-%m-%d %H:%M:%S"), 'dt':0.1, 'loop_num':loop_num, 'data_dir': os.getcwd(),
-        'a1':{'param':param,
-              #'LFP':{'lfp1':lfp_moni.lfp1/nA, 'lfp2':lfp_moni.lfp2/nA, 'lfp3':lfp_moni.lfp3/nA},
-              'ge':{'i':spk_e.i[:],'t':spk_tstep_e},
-              'gi':{'i':spk_i.i[:],'t':spk_tstep_i}}}
-
-
-data1 = mydata.mydata(data)
-#%%
-start_time = 1000; end_time = 4000
-frames = end_time - start_time
-data1.a1.ge.get_spike_rate(start_time=start_time, end_time=end_time,\
-                           sample_interval = 1, n_neuron = ijwd.Ne, window = 10, dt = 0.1, reshape_indiv_rate = True)
-data1.a1.gi.get_spike_rate(start_time=start_time, end_time=end_time,\
-                           sample_interval = 1, n_neuron = ijwd.Ni, window = 10, dt = 0.1, reshape_indiv_rate = True)
-
-firing_rate_analysis.show_pattern(data1.a1.ge.spk_rate.spk_rate, data1.a1.gi.spk_rate.spk_rate, frames = frames, start_time = start_time, anititle='animation')
-
-#%%
-plt.figure()
-plt.plot(data1.a1.ge.t/10,data1.a1.ge.i,'|')
-#%%
-plt.figure()
-plt.imshow(data1.a1.ge.spk_rate.spk_rate[:,:,100])
-#%%
-net.restore('state1')
-
-#%%
-plt.figure()
-plt.scatter( ijwd.e_lattice[:,0], ijwd.e_lattice[:,1])
-plt.figure()
-plt.scatter( ijwd.i_lattice[:,0], ijwd.i_lattice[:,1])
-#%%
-pre_neuron = 5500
-pre = ijwd.e_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ee[ijwd.i_ee==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 1000
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.e_lattice[ijwd.j_ie[ijwd.i_ie==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-#%%
-pre_neuron = 1000
-pre = ijwd.i_lattice[pre_neuron]; post = ijwd.i_lattice[ijwd.j_ii[ijwd.i_ii==pre_neuron]]
-plt.figure()
-plt.plot(post[:,0],post[:,1], '.')
-
-#%%
-ijwd_63 = expo_connection.get_ijwd()
-ijwd_63.Ne = 63*63; ijwd_63.Ni = 32*32
-ijwd_63.width = 62
-ijwd_63.generate_ijw()
-ijwd_63.generate_d_rand()
-#%%
-def plot_outgoing(i, j, neuron_src, lattice_src, lattice_trg):
-    #pre_neuron = 5500
-    pre = lattice_src[neuron_src]; post = lattice_trg[j[i==neuron_src]]
-    plt.figure()
-    plt.plot(post[:,0],post[:,1], '.')
-    plt.plot(pre[0], pre[1], 'or')
-#%%
-plot_outgoing(ijwd_63.i_ie, ijwd_63.j_ie, 0, ijwd_63.i_lattice, ijwd_63.e_lattice)   
-plot_outgoing(ijwd.i_ie, ijwd.j_ie, 0, ijwd.i_lattice, ijwd.e_lattice)   
-   
-#%%
-plot_outgoing(ijwd_63.i_ii, ijwd_63.j_ii, 0, ijwd_63.i_lattice, ijwd_63.i_lattice)   
-plot_outgoing(ijwd.i_ii, ijwd.j_ii, 0, ijwd.i_lattice, ijwd.i_lattice)