forked from pierreberthet/bg_dopa_nest
/
main.py
executable file
·902 lines (804 loc) · 37.1 KB
/
main.py
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import sys
import os
import nest
import BasalGanglia
import Reward
import json
import simulation_parameters
import utils
import numpy as np
import time
import tempfile
import mynest
import mynest_light
import pprint as pp
os.environ['MPLCONFIGDIR'] = tempfile.mkdtemp()
import matplotlib
matplotlib.use('Agg')
import pylab as pl
print 'INIT'
# Load BCPNN synapse and bias-iaf neuron module
if (not 'bcpnn_dopamine_synapse' in nest.Models()):
nest.sr('(/cfs/milner/scratch/b/berthet/modules/bcpnndopa_module/share/ml_module/sli) addpath') #t/tully/sequences/share/nest/sli
nest.Install('/cfs/milner/scratch/b/berthet/modules/bcpnndopa_module/lib/nest/ml_module') #t/tully/sequences/lib/nest/pt_module
# nest.Install('ml_module')
try:
from mpi4py import MPI
USE_MPI = True
comm = MPI.COMM_WORLD
pc_id, n_proc = comm.rank, comm.size
print "USE_MPI:", USE_MPI, 'pc_id, n_proc:', pc_id, n_proc
except:
USE_MPI = False
pc_id, n_proc, comm = 0, 1, None
print "MPI not used"
nest.ResetKernel()
nest.ResetKernel()
#USE_MPI = False
#pc_id, n_proc, comm = 0, 1, None
#print "MPI not used"
#nest.SetKernelStatus({'local_num_threads':12})
# load bcpnn synapse module and iaf neuron with bias
#if (not 'bcpnn_synapse' in nest.Models('synapses')):
# nest.Install('pt_module')
nest.SetKernelStatus({"overwrite_files": True})
#nest.SetKernelStatus({"total_num_virtual_procs":20})
#N_vp = nest.GetKernelStatus(['total_num_virtual_procs'])[0]
################# RANDOM SEEDING
#msd = 44777333
#N_vp = comm.size
#pyrngs = [np.random.RandomState(s) for s in range(msd, msd+N_vp)]
#nest.SetKernelStatus({'grng_seed': msd+N_vp})
#nest.SetKernelStatus({'rng_seeds':range(msd+N_vp+1, msd+2*N_vp+1 )})
#np.random.seed(np.random.randint(0,10000))
def save_spike_trains(params, iteration, stim_list, gid_list):
n_units = len(stim_list)
fn_base = params['input_st_fn_mpn']
for i_ in xrange(n_units):
if len(stim_list[i_]) > 0:
fn = fn_base + '%d_%d.dat' % (iteration, gid_list[i_] - 1)
np.savetxt(fn, stim_list[i_])
def remove_files_from_folder(folder):
print 'Removing all files from folder:', folder
path = os.path.abspath(folder)
cmd = 'rm %s/*' % path
print cmd
os.system(cmd)
if __name__ == '__main__':
print 'number of arguments ', len (sys.argv)
if len(sys.argv) != 4: # program name + number of args
sys.exit('Usage: %s' %sys.argv[0])
args = map(float, sys.argv[1:])
counter = int(args[1])
# param_fn = sys.argv[1]
# if os.path.isdir(param_fn): # go to the path containing the json object storing old parameters
# param_fn += '/Parameters/simulation_parameters.json' # hard coded subpath in ParameterContainer
# assert os.path.exists(param_fn), 'ERROR: Can not find %s - please give an existing parameter filename or folder name to re-run a simulation' % (param_fn)
# f = file(param_fn, 'r')
# print 'Loading parameters from', param_fn
# params = json.load(f)
GP = simulation_parameters.global_parameters(args)
if comm != None:
comm.barrier()
if pc_id==0:
GP.write_parameters_to_file() # write_parameters_to_file MUST be called before every simulation
if comm != None:
comm.barrier()
params = GP.params
nest.SetKernelStatus({"resolution":params['dt'] })
t0 = time.time()
# #####################################
# TEMP STORAGE
# #####################################
#weights
staten2D1 = np.empty(( params['n_recordings'], params['n_actions'] ))
staten2D2 = np.empty(( params['n_recordings'], params['n_actions']))
stateb2D1 = np.empty(( params['n_recordings'], params['n_actions'] ))
stateb2D2 = np.empty(( params['n_recordings'], params['n_actions']))
state2D1s = np.empty(( params['n_recordings'], params['n_states'] ))
state2D2s = np.empty(( params['n_recordings'], params['n_states'] ))
br_w0 = np.empty(( params['n_recordings'], params['n_actions']))
br_w1 = np.empty(( params['n_recordings'], params['n_actions']))
br_w2 = np.empty(( params['n_recordings'], params['n_actions']))
rp_w = np.zeros(( params['n_recordings'], params['n_states'] * params['n_actions']))
rp_bias = np.zeros(( params['n_recordings'], params['n_states'] * params['n_actions']))
rp_pi = np.zeros(( params['n_recordings'], params['n_states'] * params['n_actions']))
rp_pij = np.zeros(( params['n_recordings'], params['n_states'] * params['n_actions']))
list_d1 = [ staten2D1, [],[],[],[],[],[],[], stateb2D1 ]
list_d2 = [ staten2D2, [],[],[],[],[],[],[], stateb2D2 ]
list_br = [ [],[],[],[], br_w0, br_w1, br_w2 ]
list_rp = [ rp_w , rp_pi,rp_pij, rp_bias]
if not params['light_record']:
# e_ij_c
state_D1_eijc = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
state_D2_eijc = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
# e_ij
state_D1_eij = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
state_D2_eij = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
# p_i
state_D1_pi = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
state_D2_pi = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
# p_j
state_D1_pj = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
state_D2_pj = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
# p_ij
state_D1_pij = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
state_D2_pij = [[0. for i_action in range(params['n_actions'])] for j in range(params['n_iterations'])]
# e_ij_c
state_D1_eijc = np.empty(params['n_actions'])
state_D2_eijc = np. empty(params['n_actions'])
# e_ij
state_D1_eij = np.empty(params['n_actions'])
state_D2_eij = np.empty(params['n_actions'])
# p_i
state_D1_pi = np.empty(params['n_actions'])
state_D2_pi = np.empty(params['n_actions'])
# p_j
state_D1_pj = np.empty(params['n_actions'])
state_D2_pj = np.empty(params['n_actions'])
# p_ij
state_D1_pij = np.empty(params['n_actions'])
state_D2_pij = np.empty(params['n_actions'])
action2D1 = np.empty(params['n_states'])
action2D2 = np.empty(params['n_states'])
stdaction2D1 = np.empty(params['n_states'])
stdaction2D2 = np.empty(params['n_states'])
br_pi = np.empty(params['n_actions'])
br_pj = np.empty(params['n_actions'])
br_pij = np.empty(params['n_actions'])
br_eij = np.empty(params['n_actions'])
rp_pj = np.zeros(params['n_states'] * params['n_actions'])
list_d1 = [ staten2D1, action2D1, state_D1_pi, state_D1_pj, state_D1_pij, state_D1_eij, state_D1_eijc, stdaction2D1, state2D1s]
list_d2 = [ staten2D2, action2D2, state_D2_pi, state_D2_pj, state_D2_pij, state_D2_eij, state_D2_eijc, stdaction2D2, state2D2s]
list_br = [br_eij, br_pi, br_pj, br_pij, br_w0, br_w1, br_w2]
#list_rp = [rp_w, rp_pi, rp_pj, rp_pij]
list_rp = [rp_w, [], rp_pj, []]
#kf = filtered k
dopa_kf = np.array([])
#k = gain_dopa * m
dopa_k = np.array([])
# m
dopa_m = np.array([])
# n
dopa_n = np.array([])
BG = BasalGanglia.BasalGanglia(params, comm)
R = Reward.Reward(params)
actions = np.empty(params['n_iterations'])
states = np.empty(params['n_iterations'])
rewards = np.empty(params['n_iterations'])
block = 0
BG.set_noise()
BG.baseline_reward()
#BG.set_gain(0.)
BG.set_init()
if comm != None:
comm.barrier()
if params['light_record']:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest_light.Simulate(params['t_init'], params['resolution'], params['n_actions'], params['n_states'], BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp,comm)
else:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest.Simulate(params['t_init'], params['resolution'], params['n_actions'], params['n_states'], BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp,comm)
#BG.baseline_reward()
#BG.set_gain(params['gain'])
##BG.set_gain(1.)
BG.set_noise()
BG.stop_efference()
##### ################### ###############
##### ################### LOOP ###############
##### ################### ###############
for iteration in xrange(params['n_iterations']):
print 'ITERATION', iteration
state = iteration % params['n_states']
if params['trigger1'] and block==params['block_trigger1']:
BG.trigger_reduce_pop_dopa(params['value_trigg_dopa_death'])
#state = (iteration-1) % params['n_states']
#block = int ((iteration-1) / params['block_len'])
#BG.trigger_habit(True)
#BG.trigger_change_dopa_zero(params['value_trigg_bias'])
#params['baseline_poisson_rew_rate']= params['new_value']
params['trigger1'] = False
if params['trigger2'] and block==params['block_trigger2']:
BG.trigger_habit(False)
params['trigger2'] = False
# state = utils.communicate_state(comm, params['n_states'])
BG.set_state(state)
BG.baseline_reward()
# BG.set_kappa_OFF()
if comm != None:
comm.barrier()
if params['light_record']:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest_light.Simulate(params['t_selection'], params['resolution'], params['n_actions'], params['n_states'], BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
else:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest.Simulate(params['t_selection'], params['resolution'], params['n_actions'], params['n_states'], BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
if comm != None:
comm.barrier()
states[iteration] = state
if iteration==0:
actions[iteration] = utils.communicate_action(comm, params['n_actions'])
BG.t_current += params['t_iteration']
else:
actions[iteration] = BG.get_action() # BG returns the selected action
BG.set_efference_copy(actions[iteration])
#BG.stop_state()
##BG.set_gain(0.)
if comm != None:
comm.barrier()
BG.set_rp(states[iteration], actions[iteration], 0., 0.)
###BG.set_striosomes( states[iteration], actions[iteration], params['weight_efference_rp'])
if params['light_record']:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest_light.Simulate(params['t_efference'], params['resolution'], params['n_actions'], params['n_states'], BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
else:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest.Simulate(params['t_efference'],
params['resolution'], params['n_actions'], params['n_states'],
BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp. comm)
if comm != None:
comm.barrier()
if params['delay']:
BG.stop_efference()
BG.set_rest()
BG.set_gain(1.)
if params['light_record']:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest_light.Simulate(params['t_delay'], params['resolution'], params['n_actions'], params['n_states'], BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
else:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest.Simulate(params['t_delay'],
params['resolution'], params['n_actions'], params['n_states'],
BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
if comm != None:
comm.barrier()
#if comm != None:
# comm.barrier()
rew = utils.communicate_reward(comm, R , state, actions[iteration], block )
rewards[iteration] = rew
# if params['delay']:
# BG.set_striosomes( states[iteration], actions[iteration], 0.)
print 'REWARD =', rew
# BG.set_gain(0.)
if rew == 1:
# BG.set_kappa_ON(params['rpe'], states[iteration], actions[iteration])
BG.set_reward(rew)
else:
# BG.set_kappa_ON(-params['rpe'], states[iteration], actions[iteration])
BG.no_reward()
#BG.set_reward(rew)
BG.set_rp(states[iteration], actions[iteration], 1. , 1.)
# BG.stop_efference()
if comm != None:
comm.barrier()
if params['light_record']:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest_light.Simulate(params['t_reward'], params['resolution'], params['n_actions'], params['n_states'], BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
else:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest.Simulate(params['t_reward'], params['resolution'], params['n_actions'], params['n_states'], BG,dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
if comm != None:
comm.barrier()
###BG.set_rp(states[iteration], actions[iteration], 0., 0. )
#BG.set_gain(params['gain'])
if not(params['delay']):
BG.set_gain(1.)
# BG.set_kappa_OFF()
BG.set_rest()
if comm != None:
comm.barrier()
if params['light_record']:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest_light.Simulate(params['t_rest'], params['resolution'], params['n_actions'], params['n_states'], BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
else:
dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp = mynest.Simulate(params['t_rest'], params['resolution'], params['n_actions'], params['n_states'],
BG, dopa_kf, dopa_k, dopa_m, dopa_n, list_d1, list_d2, list_br, list_rp, comm)
if comm != None:
comm.barrier()
block = int (iteration / params['block_len'])
### ########################################
# END of SIMULATION LOOP
### ########################################
print 'NEST simulation done for thead ', pc_id, ' process ', counter
if pc_id == 0:
np.savetxt(params['weights_d1_multi_fn']+'_'+str(counter) , list_d1[0])
np.savetxt(params['weights_d2_multi_fn']+'_'+str(counter) , list_d2[0])
np.savetxt(params['weights_rp_multi_fn']+'_'+str(counter) , list_rp[0])
np.savetxt(params['rewards_multi_fn']+'_'+str(counter) , rewards)
if params['record_spikes']:
if counter == 0:
if pc_id == 0:
binsize = params['binsize_histo_raster']
np.savetxt(params['actions_taken_fn'], actions)
np.savetxt(params['states_fn'], states)
np.savetxt(params['rewards_fn'], rewards)
exc_sptimes = nest.GetStatus(BG.recorder_d1[0])[0]['events']['times']
for i_proc in xrange(1,n_proc ):
exc_sptimes = np.r_[exc_sptimes, comm.recv(source=i_proc, tag=0 )]
else:
comm.send(nest.GetStatus(BG.recorder_d1[0])[0]['events']['times'],dest=0, tag=0)
if comm != None:
comm.barrier()
if pc_id == 0:
exc_spids = nest.GetStatus(BG.recorder_d1[0])[0]['events']['senders']
for i_proc in xrange(1, n_proc):
exc_spids = np.r_[exc_spids, comm.recv(source=i_proc, tag=1)]
pl.figure(33)
pl.subplot(211)
pl.scatter(exc_sptimes, exc_spids,s=1.)
pl.xlim([0,params['t_sim']])
bins=np.arange(0, params['t_sim']+1, binsize)
c_exc,b = np.histogram(exc_sptimes,bins=bins)
rate_exc = c_exc*(1000./binsize)*(1./params['num_msn_d1'])
pl.subplot(212)
pl.plot(b[0:-1],rate_exc)
pl.xlim([0,params['t_sim']])
pl.title('firing rate of STR D1 action 0')
pl.savefig('fig3_firingrate.pdf')
else:
comm.send(nest.GetStatus(BG.recorder_d1[0])[0]['events']['senders'],dest=0, tag=1)
if comm != None:
comm.barrier()
# if comm != None:
# comm.barrier()
##################
if pc_id == 0:
exc_sptimes = nest.GetStatus(BG.recorder_rew)[0]['events']['times']
for i_proc in xrange(1,n_proc ):
exc_sptimes = np.r_[exc_sptimes, comm.recv(source=i_proc, tag=2)]
else:
comm.send(nest.GetStatus(BG.recorder_rew)[0]['events']['times'],dest=0, tag=2)
if comm != None:
comm.barrier()
if pc_id == 0:
exc_spids = nest.GetStatus(BG.recorder_rew)[0]['events']['senders']
for i_proc in xrange(1, n_proc):
exc_spids = np.r_[exc_spids, comm.recv(source=i_proc, tag = 3)]
pl.figure(34)
pl.subplot(211)
pl.scatter(exc_sptimes, exc_spids,s=1.)
pl.xlim([0,params['t_sim']])
bins=np.arange(0, params['t_sim']+1, binsize)
c_exc,b = np.histogram(exc_sptimes,bins=bins)
rate_exc = c_exc*(1000./binsize)*(1./params['num_rew_neurons'])
pl.subplot(212)
pl.plot(b[0:-1],rate_exc)
pl.xlim([0,params['t_sim']])
pl.title('firing rate of dopa neurons')
pl.savefig('fig7_dopafiringrate.pdf')
else:
comm.send(nest.GetStatus(BG.recorder_rew)[0]['events']['senders'],dest=0, tag=3)
if comm != None:
comm.barrier()
##########################
if pc_id == 0:
exc_sptimes = nest.GetStatus(BG.recorder_states[0])[0]['events']['times']
for i_proc in xrange(1,n_proc ):
exc_sptimes = np.r_[exc_sptimes, comm.recv(source=i_proc, tag=4)]
else:
comm.send(nest.GetStatus(BG.recorder_states[0])[0]['events']['times'],dest=0, tag=4)
if comm != None:
comm.barrier()
if pc_id == 0:
exc_spids = nest.GetStatus(BG.recorder_states[0])[0]['events']['senders']
for i_proc in xrange(1, n_proc):
exc_spids = np.r_[exc_spids, comm.recv(source=i_proc, tag = 5)]
pl.figure(35)
pl.subplot(211)
pl.scatter(exc_sptimes, exc_spids,s=1.)
pl.xlim([0,params['t_sim']])
bins=np.arange(0, params['t_sim']+1, binsize)
c_exc,b = np.histogram(exc_sptimes,bins=bins)
rate_exc = c_exc*(1000./binsize)*(1./params['num_neuron_states'])
pl.subplot(212)
pl.plot(b[0:-1],rate_exc)
pl.xlim([0,params['t_sim']])
pl.title('firing rate of states neurons')
pl.savefig('fig10_states_firingrate.pdf')
else:
comm.send(nest.GetStatus(BG.recorder_states[0])[0]['events']['senders'],dest=0, tag=5)
if comm != None:
comm.barrier()
#######################
if pc_id == 0:
exc_sptimes = nest.GetStatus(BG.recorder_gpi[0])[0]['events']['times']
for i_proc in xrange(1,n_proc ):
exc_sptimes = np.r_[exc_sptimes, comm.recv(source=i_proc, tag=8)]
else:
comm.send(nest.GetStatus(BG.recorder_gpi[0])[0]['events']['times'],dest=0, tag=8)
if comm != None:
comm.barrier()
if pc_id == 0:
exc_spids = nest.GetStatus(BG.recorder_gpi[0])[0]['events']['senders']
for i_proc in xrange(1, n_proc):
exc_spids = np.r_[exc_spids, comm.recv(source=i_proc, tag = 9)]
pl.figure(315)
pl.subplot(211)
pl.scatter(exc_sptimes, exc_spids,s=1.)
pl.xlim([0,params['t_sim']])
bins=np.arange(0, params['t_sim']+1, binsize)
c_exc,b = np.histogram(exc_sptimes,bins=bins)
rate_exc = c_exc*(1000./binsize)*(1./params['num_gpi'])
pl.subplot(212)
pl.plot(b[0:-1],rate_exc)
pl.xlim([0,params['t_sim']])
pl.title('firing rate of GPi neurons')
pl.savefig('fig12_gpi_firingrate.pdf')
else:
comm.send(nest.GetStatus(BG.recorder_gpi[0])[0]['events']['senders'],dest=0, tag=9)
if comm != None:
comm.barrier()
#######################
if pc_id == 0:
exc_sptimes = nest.GetStatus(BG.recorder_brainstem[0])[0]['events']['times']
for i_proc in xrange(1,n_proc ):
exc_sptimes = np.r_[exc_sptimes, comm.recv(source=i_proc, tag=12)]
else:
comm.send(nest.GetStatus(BG.recorder_brainstem[0])[0]['events']['times'],dest=0, tag=12)
if comm != None:
comm.barrier()
if pc_id == 0:
exc_spids = nest.GetStatus(BG.recorder_brainstem[0])[0]['events']['senders']
for i_proc in xrange(1, n_proc):
exc_spids = np.r_[exc_spids, comm.recv(source=i_proc, tag = 13)]
pl.figure(31525)
pl.subplot(211)
#pl.scatter(exc_sptimes, exc_spids,s=1.)
pl.plot(exc_sptimes)
pl.plot(exc_spids)
pl.xlim([0,params['t_sim']])
bins=np.arange(0, params['t_sim']+1, binsize)
c_exc,b = np.histogram(exc_sptimes,bins=bins)
rate_exc = c_exc*(1000./binsize)*(1./params['num_brainstem_neurons'])
pl.subplot(212)
pl.plot(b[0:-1],rate_exc)
pl.xlim([0,params['t_sim']])
pl.title('firing rate of brainstem neurons')
pl.savefig('fig13_brainstem_firingrate.pdf')
else:
comm.send(nest.GetStatus(BG.recorder_gpi[0])[0]['events']['senders'],dest=0, tag=13)
if comm != None:
comm.barrier()
#######################
if pc_id == 0:
exc_sptimes = nest.GetStatus(BG.recorder_d2[0])[0]['events']['times']
for i_proc in xrange(1,n_proc ):
exc_sptimes = np.r_[exc_sptimes, comm.recv(source=i_proc, tag=6)]
else:
comm.send(nest.GetStatus(BG.recorder_d2[0])[0]['events']['times'],dest=0, tag=6)
if comm != None:
comm.barrier()
if pc_id == 0:
exc_spids = nest.GetStatus(BG.recorder_d2[0])[0]['events']['senders']
for i_proc in xrange(1, n_proc):
exc_spids = np.r_[exc_spids, comm.recv(source=i_proc, tag = 7)]
pl.figure(36)
pl.subplot(211)
pl.scatter(exc_sptimes, exc_spids,s=1.)
pl.xlim([0,params['t_sim']])
bins=np.arange(0, params['t_sim']+1, binsize)
c_exc,b = np.histogram(exc_sptimes,bins=bins)
rate_exc = c_exc*(1000./binsize)*(1./params['num_msn_d2'])
pl.subplot(212)
pl.plot(b[0:-1],rate_exc)
pl.xlim([0,params['t_sim']])
pl.title('firing rate of D2 neurons')
pl.savefig('fig11_D2firingrate.pdf')
else:
comm.send(nest.GetStatus(BG.recorder_d2[0])[0]['events']['senders'],dest=0, tag=7)
if comm != None:
comm.barrier()
#######################
if pc_id == 0:
exc_sptimes = nest.GetStatus(BG.recorder_rp[0])[0]['events']['times']
for i_proc in xrange(1,n_proc ):
exc_sptimes = np.r_[exc_sptimes, comm.recv(source=i_proc, tag=10)]
else:
comm.send(nest.GetStatus(BG.recorder_rp[0])[0]['events']['times'],dest=0, tag=10)
if comm != None:
comm.barrier()
if pc_id == 0:
exc_spids = nest.GetStatus(BG.recorder_rp[0])[0]['events']['senders']
for i_proc in xrange(1, n_proc):
exc_spids = np.r_[exc_spids, comm.recv(source=i_proc, tag = 11)]
pl.figure(316)
pl.subplot(211)
pl.scatter(exc_sptimes, exc_spids,s=1.)
pl.xlim([0,params['t_sim']])
bins=np.arange(0, params['t_sim']+1, binsize)
c_exc,b = np.histogram(exc_sptimes,bins=bins)
rate_exc = c_exc*(1000./binsize)*(1./params['num_rp_neurons'])
pl.subplot(212)
pl.plot(b[0:-1],rate_exc)
pl.xlim([0,params['t_sim']])
pl.title('firing rate of striosomes neurons')
pl.savefig('fig14_rp_firingrate.pdf')
else:
comm.send(nest.GetStatus(BG.recorder_rp[0])[0]['events']['senders'],dest=0, tag=11)
if comm != None:
comm.barrier()
#CC.get_weights(, BG) implement in BG or utils
print 'Im process ', pc_id, 'VTmod is ', nest.GetStatus(BG.vt_dopa)
# print 'weight simu ', weights_sim
#if pc_id ==3:
#print 'bias_d1', list_d1[8]
#print 'bias_d2', list_d2[8]
#print 'bias_rp', list_rp[3]
# pl.figure(301)
# pl.subplot(211)
# pl.title('D1')
# pl.plot(list_d1[8])
# pl.ylabel(r'$b_{j}$')
# pl.subplot(212)
# pl.title('D2')
# pl.plot(list_d2[8])
# pl.ylabel(r'$b_{j}$')
# pl.xlabel('trials')
# pl.suptitle('Computed biases from state '+str(params['recorded'])+' to all actions')
# pl.savefig('fig301_bias_allactions.pdf')
# pl.figure(302)
# pl.title('RP bias')
# pl.plot(list_rp[3])
# pl.ylabel(r'$b_{j}$')
# pl.xlabel('trials')
# pl.savefig('fig302_bias_rp.pdf')
if pc_id ==1:
t1 = time.time() - t0
print 'Time: %.2f [sec] %.2f [min]' % (t1, t1 / 60.)
print 'Isnan', np.isnan(np.sum(list_d1[0]))
print 'LIST d1' , list_d1
print 'D1[1]', list_d1[1]
print 'learning completed'
pl.figure(1)
pl.subplot(211)
pl.title('D1')
pl.plot(list_d1[0])
pl.ylabel(r'$w_{0j}$')
pl.subplot(212)
pl.title('D2')
pl.plot(list_d2[0])
pl.ylabel(r'$w_{0j}$')
pl.xlabel('trials')
pl.suptitle('Computed weights from state '+str(params['recorded'])+' to all actions')
pl.savefig('fig1_allactions.pdf')
if not params['light_record']:
pl.figure(2)
pl.subplot(211)
pl.plot(list_d1[1])
pl.title('D1')
pl.ylabel(r'$w_{0j}$')
pl.subplot(212)
pl.plot(list_d2[1])
pl.ylabel(r'$w_{0j}$')
pl.title('D2')
pl.xlabel('trials')
pl.suptitle('Computed weights from all states to action 0')
pl.savefig('fig2_allstates.pdf')
if not params['light_record']:
if pc_id == 2:
pl.figure(3)
pl.subplot(221)
pl.title('D1')
pl.plot(list_d1[5], label='e_ij' )
pl.ylabel(r'$e_{trace}$')
pl.legend()
pl.subplot(222)
pl.title('D2')
pl.plot(list_d2[5], label='e_ij' )
pl.subplot(223)
pl.title('D1')
pl.plot(list_d1[6], label='e_ijc' )
pl.xlabel('trials')
pl.ylabel(r'$e_{trace}$')
pl.legend()
pl.subplot(224)
pl.title('D2')
pl.plot(list_d2[6], label='e_ijc' )
pl.xlabel('trials')
pl.legend()
pl.suptitle('Computed e traces from state 0 to all actions')
pl.savefig('fig5_etraces.pdf')
pl.figure(4)
pl.subplot(221)
pl.title('D1')
pl.plot(list_d1[2], label='p_i' )
pl.plot(list_d1[3], label='p_j' )
pl.ylabel(r'$p_{trace}$')
pl.legend()
pl.subplot(222)
pl.title('D2')
pl.plot(list_d2[2], label='p_i' )
pl.plot(list_d2[3], label='p_j' )
pl.subplot(223)
pl.title('D1')
pl.plot(list_d1[4], label='p_ij' )
pl.ylabel(r'$p_{trace}$')
pl.xlabel('trials')
pl.legend()
pl.subplot(224)
pl.title('D2')
pl.plot(list_d2[4], label='p_ij' )
pl.xlabel('trials')
pl.legend()
pl.suptitle('Computed p traces from state 0 to all states')
pl.savefig('fig4_ptraces.pdf')
if pc_id == 2:
pl.figure(5)
pl.plot(dopa_kf, label='kf')
#pl.plot(dopa_k, label='k')
pl.plot(dopa_m, label = 'm')
pl.plot(dopa_n, label = 'n')
pl.title('dopamine RPE dynamics' )
pl.xlabel('time in '+ str(params['resolution'])+ 'ms')
pl.legend()
pl.ylabel('variable value')
pl.savefig('fig6_dopa.pdf')
pl.figure(1005)
#pl.plot(dopa_kf, label='kf')
#pl.plot(dopa_k, label='k')
pl.plot(dopa_m, label = 'm')
pl.plot(dopa_n, label = 'n')
pl.title('dopamine RPE dynamics' )
pl.xlabel('time in '+ str(params['resolution'])+ 'ms')
pl.legend()
pl.ylabel('variable value')
pl.savefig('fig60_dopa.pdf')
if not params['light_record']:
pl.figure(88)
pl.plot(list_d1[7], label='w std D1')
pl.plot(list_d2[7], label='w std D2')
pl.title('standard deviation incoming weights to action 0')
pl.xlabel('trials')
pl.legend()
pl.savefig('fig88_stdw.pdf')
pl.figure(89)
pl.subplot(211)
#pl.plot(list_br[0], label='eij')
pl.plot(list_br[1], label='pi')
pl.plot(list_br[2], label='pj')
pl.plot(list_br[3], label='pij')
pl.subplot(212)
pl.plot(list_br[4], label='w0')
pl.plot(list_br[5], label='w1')
pl.plot(list_br[6], label='w2')
pl.title('Brainstem traces + weights from state 0')
pl.xlabel('trials')
pl.legend()
pl.savefig('fig89_brainstem.pdf')
else:
pl.figure(89)
pl.plot(list_br[4], label='w0')
pl.plot(list_br[5], label='w1')
pl.plot(list_br[6], label='w2')
pl.title('Brainstem weights from state 0')
pl.xlabel('trials')
pl.legend()
pl.savefig('fig89_brainstem.pdf')
# if pc_id == 0:
# for i_proc in xrange(1,n_proc ):
# dopa_n += comm.recv(source=i_proc, tag=6)
# dopa_m = dopa_n + params['params_dopa_bcpnn']['b']
# dopa_k = dopa_m * params['params_dopa_bcpnn']['gain_dopa']
# dopa_kf = np.power(dopa_k,params['params_dopa_bcpnn']['k_pow'])
# pl.figure(8)
# pl.plot(dopa_k, label='k')
# pl.plot(dopa_kf, label='kf')
# pl.plot(dopa_m, label = 'm')
# pl.plot(dopa_n, label = 'n')
# pl.title('dopa parameters dynamics' )
# pl.xlabel('trials')
# pl.legend()
# pl.ylabel('parameter value')
# pl.savefig('fig8_dopa_mpi.pdf')
# pl.figure(8878)
# pl.plot(dopa_m, label = 'm')
# pl.plot(dopa_n, label = 'n')
# pl.title('dopa parameters dynamics' )
# pl.xlabel('trials')
# pl.legend()
# pl.ylabel('parameter value')
# pl.savefig('fig889_dopa_mpi.pdf')
# else:
# comm.send(dopa_n, dest=0, tag=6)
# if pc_id != 0:
# pl.figure(1300+pc_id)
# pl.plot(dopa_k, label='k')
# pl.plot(dopa_kf, label='kf')
# pl.plot(dopa_m, label = 'm')
# pl.plot(dopa_n, label = 'n')
# pl.title('dopa parameters dynamics for process '+ str(pc_id) )
# pl.xlabel('trials')
# pl.legend()
# pl.ylabel('parameter value')
# pl.savefig('fig'+str(1300+pc_id)+'_dopa.pdf')
if not params['light_record']:
nd1= len(nest.GetConnections(BG.states[0], BG.strD1[BG.who]))
nd2= len(nest.GetConnections(BG.states[0], BG.strD2[BG.who]))
sample = int(params['t_sim'] / params['resolution'])
#a1 = np.empty((nd1,sample))
a1 = np.zeros((nd1,len(list_d1[8][0])/nd1))
b1 = np.zeros((nd1,len(list_d1[8][1])/nd1))
c1 = np.zeros((nd1,len(list_d1[8][2])/nd1))
#b1 = np.empty((nd1,sample))
#c1 = np.empty((nd1,sample))
a2 = np.zeros((nd2,len(list_d2[8][0])/nd2))
b2 = np.zeros((nd2,len(list_d2[8][1])/nd2))
c2 = np.zeros((nd2,len(list_d2[8][2])/nd2))
#a2 = np.empty((nd2,sample))
#b2 = np.empty((nd2,sample))
#c2 = np.empty((nd2,sample))
print 'sizetest a1 ', len(a1), 'nd1 ', nd1, 'sample ', sample
print 'sizetest listd1 ', len(list_d1), len(list_d1[8]), len(list_d1[8][0])
for i in xrange(nd1):
for j in xrange(len(list_d1[8][0])/nd1 -nd1):
a1[i,j]=list_d1[8][0][1+i+nd1*j]
for i in xrange(nd1):
for j in xrange(len(list_d1[8][1])/nd1 -nd1):
b1[i,j]=list_d1[8][1][1+i+nd1*j]
for i in xrange(nd1):
for j in xrange(len(list_d1[8][2])/nd1 -nd1):
c1[i,j]=list_d1[8][2][1+i+nd1*j]
pl.figure(887)
pl.plot(np.transpose(a1))
pl.title('state 0 to D1['+str(BG.who)+']')
pl.savefig('fig'+str(810+pc_id)+'_singleW_a1.pdf')
pl.figure(888)
pl.plot(np.transpose(b1))
pl.title('state 1 to D1['+str(BG.who)+']')
pl.savefig('fig'+str(850+pc_id)+'_singleW_b1.pdf')
pl.figure(889)
pl.plot(np.transpose(c1))
pl.title('state 2 to D1['+str(BG.who)+']')
pl.savefig('fig'+str(880+pc_id)+'_singleW_c1.pdf')
for i in xrange(nd2):
for j in xrange(len(list_d2[8][0])/nd2 -nd2):
a2[i,j]=list_d2[8][0][1+i+nd2*j]
for i in xrange(nd2):
for j in xrange(len(list_d2[8][1])/nd2 -nd2):
b2[i,j]=list_d2[8][1][1+i+nd2*j]
for i in xrange(nd2):
for j in xrange(len(list_d2[8][2])/nd2 -nd2):
c2[i,j]=list_d2[8][2][1+i+nd2*j]
pl.figure(897)
pl.plot(np.transpose(a2))
pl.title('state 0 to D2['+str(BG.who)+']')
pl.savefig('fig'+str(810+pc_id)+'_singleW_a2.pdf')
pl.figure(898)
pl.plot(np.transpose(b2))
pl.title('state 1 to D2['+str(BG.who)+']')
pl.savefig('fig'+str(850+pc_id)+'_singleW_b2.pdf')
pl.figure(899)
pl.plot(np.transpose(c2))
pl.title('state 2 to D2['+str(BG.who)+']')
pl.savefig('fig'+str(880+pc_id)+'_singleW_c2.pdf')
print 'DATA management completed for thread ', pc_id, ' process ', counter
if pc_id == 1:
# print 'list_rp_check_0'
# pp.pprint(list_rp[0])
# print 'list_rp_check_1'
# pp.pprint(list_rp[1])
# print 'list_rp_check_2'
# pp.pprint(list_rp[2])
# print 'list_rp_check_3'
# pp.pprint(list_rp[3])
pl.figure(9001)
pl.plot(list_rp[0], label='Wij')
pl.title('Weight RP/Rew')
pl.savefig('fig9001_meanWijRP.pdf')
# pl.figure(9033)
# pl.plot(list_rp[1], label='Pi')
# pl.title('Traces RP Pi')
# pl.legend()
# pl.savefig('fig_meantracesRPi.pdf')
# pl.figure(9034)
# pl.plot(list_rp[2], label='Pij')
# pl.title('Traces RP Pij')
# pl.legend()
# pl.savefig('fig_meantracesRPij.pdf')
# pl.figure(9035)
# pl.plot(list_rp[3], label='Pj')
# pl.title('Traces RP Pj')
# pl.legend()
# pl.savefig('fig_meantracesRPj.pdf')
if not params['light_record']:
pl.figure(9002)
pl.plot(list_rp[1], label='Pi')
pl.plot(list_rp[2], label='Pj')
pl.plot(list_rp[3], label='Pij')
pl.title('Traces RP/Rew')
pl.legend()
pl.savefig('fig9002_meantracesRP.pdf')
print 'States ', states
print 'Actions ', actions
print 'Rewards ', rewards
print 'Thread ', pc_id, ' from process ', counter, ' DONE'
#print 'RANDOM for pc_id ', pc_id , ' is ', np.random.randint(100)