def rnn_run(savefile, parameters, col, value):
rnn = RNN(savefile, parameters)
trial_args = {}
for j in range(int(0.8 * N)):
rnn.Wrec[j, col] = rnn.Wrec[j, col] - value
info1 = rnn.run(inputs=(generate_trial, trial_args), seed=200)
rnn_zs = np.zeros([Nout, len(rnn.z[0])])
for j in range(Nout):
rnn_zs[j, :] = rnn.z[j] / np.max(rnn.z[j])
return rnn_zs
from __future__ import division
import numpy as np
import imp
from pycog import tasktools
from pycog import RNN
from pycog.figtools import Figure
m = imp.load_source('model', 'romo.py')
rng = np.random.RandomState(1066)
rnn = RNN('romo_savefile.pkl', {'dt': 2})
trial_args = {'name': 'test', 'catch': False, 'fpair': (34, 26), 'gt_lt': '>'}
info = rnn.run(inputs=(m.generate_trial, trial_args), rng=rng)
fig = Figure()
plot = fig.add()
epochs = info['epochs']
f1_start, f1_end = epochs['f1']
f2_start, f2_end = epochs['f2']
t0 = f1_start
tmin = 0
tmax = f2_end
t = 1e-3*(rnn.t-t0)
#=========================================================================================
# Paths
#=========================================================================================
here = get_here(__file__)
base = get_parent(here)
figspath = join(here, 'figs')
modelfile = join(base, 'examples', 'models', 'rdm_dense.py')
savefile = join(base, 'examples', 'work', 'data', 'rdm_dense', 'rdm_dense.pkl')
#=========================================================================================
m = imp.load_source('model', modelfile)
rnn = RNN(savefile, {'dt': 0.5}, verbose=False)
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'coh': 16,
'in_out': 1
}
info = rnn.run(inputs=(trial_func, trial_args), seed=10)
colors = ['orange', 'purple']
DT = 15
# Inputs
def run_trials(p, args):
"""
Run trials.
"""
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
# Trials
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
# Condition
b = i % m.nconditions
k0, k1 = tasktools.unravel_index(b, (len(m.fpairs), len(m.gt_lts)))
fpair = m.fpairs[k0]
gt_lt = m.gt_lts[k1]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'fpair': fpair,
'gt_lt': gt_lt
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
if info['f1'] > info['f2']:
gt_lt = '>'
else:
gt_lt = '<'
s = ("Trial {:>{}}/{}: {:>2} {} {:>2}".format(
i + 1, w, ntrials, info['f1'], gt_lt, info['f2']))
sys.stdout.write(backspaces * '\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save'] / dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:, ::step],
'r': rnn.r[:, ::step],
'z': rnn.z[:, ::step],
'info': info
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename) * 1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(
THIS, filename, size))
# Psychometric function
psychometric_function(filename)
def run_trials(p, args):
"""
Run trials.
"""
# Model
m = p['model']
# ntrials: Number of trials for each condition
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
# Trials
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
b = i % m.nconditions
# All conditions
intensity = m.intensity_range[b]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'intensity': intensity,
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
s = ("Trial {:>{}}/{}: intentsity: {:>+3}".format(
i + 1, w, ntrials, info['intensity']))
sys.stdout.write(backspaces * '\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save'] / dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:, ::step],
'r': rnn.r[:, ::step],
'z': rnn.z[:, ::step],
'info': info
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename) * 1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(
THIS, filename, size))
n_validation = 100
#n_gradient = 1
#mode = 'continuous'
if __name__ == '__main__':
from pycog import RNN
from pycog.figtools import Figure
rng = np.random.RandomState(1234) # Added by Alfred
# savefile = 'examples/work/data/delay_react/delay_react.pkl'
# savefile = 'examples/work/data/run_57000_lr1em3_1_1000_50/delay_react.pkl'
savefile = 'examples/work/data/run_10000_lr1em3_1_1_100_10/delay_react.pkl'
# savefile = 'examples/work/data/run_52000_lr1em3_1_100_100/delay_react.pkl'
rnn = RNN(savefile, {'dt': 0.5, 'var_rec': 0.01**2})
trial_args = {}
info1 = rnn.run(inputs=(generate_trial, trial_args), seed=200)
Z0 = rnn.z
# signal_time
# delay = 500
# width = 20
# magnitude = 4
# Y = np.zeros((len(t), Nout)) # Output matrix
# for i in range(Nout):
# for tt in range(len(t)):
# Y[tt][i] = np.exp( -(tt - (signal_time + delay / Nout * (i + 1)))**2 / (2 * width**2)) * magnitude
train_bout=train_bout,
var_rec=var_rec,
generate_trial=generate_trial,
mode=mode,
n_validation=n_validation,
min_error=min_error)
model.train('savefile.pkl', seed=100, recover=False)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
from pycog import RNN
from pycog.figtools import Figure
rnn = RNN('savefile.pkl', {'dt': 0.5, 'var_rec': 0.01**2})
info = rnn.run(T=2 * period)
fig = Figure()
plot = fig.add()
plot.plot(rnn.t / tau, rnn.z[0], color=Figure.colors('blue'))
plot.xlim(rnn.t[0] / tau, rnn.t[-1] / tau)
plot.ylim(0, 2)
print rnn.t[0]
print rnn.t[-1]
plot.plot((rnn.t / tau)[:], (0.9 * np.power(rnn.t / (2 * period), 2))[:],
color=Figure.colors('orange'))
plot.xlabel(r'$t/\tau$')
from __future__ import division
import numpy as np
import imp
from pycog import tasktools
from pycog import RNN
from pycog.figtools import Figure
m = imp.load_source('model', 'romo.py')
rng = np.random.RandomState(1066)
rnn = RNN('romo_savefile.pkl', {'dt': 2})
trial_args = {'name': 'test', 'catch': False, 'fpair': (34, 26), 'gt_lt': '>'}
info = rnn.run(inputs=(m.generate_trial, trial_args), rng=rng)
fig = Figure()
plot = fig.add()
epochs = info['epochs']
f1_start, f1_end = epochs['f1']
f2_start, f2_end = epochs['f2']
t0 = f1_start
tmin = 0
tmax = f2_end
t = 1e-3 * (rnn.t - t0)
#=========================================================================================
# Paths
#=========================================================================================
here = get_here(__file__)
base = get_parent(here)
figspath = join(here, 'figs')
#-----------------------------------------------------------------------------------------
# Load RNNs to compare
#-----------------------------------------------------------------------------------------
datapath = join(base, 'examples', 'work', 'data')
savefile = join(datapath, 'mante', 'mante.pkl')
rnn1 = RNN(savefile, verbose=True)
savefile = join(datapath, 'mante_areas', 'mante_areas.pkl')
rnn2 = RNN(savefile, verbose=True)
# Load model
modelfile = join(base, 'examples', 'models', 'mante_areas.py')
m = imp.load_source('model', modelfile)
#=========================================================================================
# Figure setup
#=========================================================================================
w = 7.5
h = 3.8
r = w/h
if __name__ == '__main__':
from pycog import Model
model = Model(N=N, Nout=Nout, ei=ei, tau=tau, dt=dt,
train_brec=train_brec, train_bout=train_bout, var_rec=var_rec,
generate_trial=generate_trial,
mode=mode, n_validation=n_validation, min_error=min_error)
model.train('savefile.pkl', seed=100, recover=False)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
from pycog import RNN
from pycog.figtools import Figure
rnn = RNN('savefile.pkl', {'dt': 0.5, 'var_rec': 0.01**2})
info = rnn.run(T=20*period)
fig = Figure()
plot = fig.add()
plot.plot(rnn.t/tau, rnn.z[0], color=Figure.colors('blue'))
plot.xlim(rnn.t[0]/tau, rnn.t[-1]/tau)
plot.ylim(-1, 1)
plot.xlabel(r'$t/\tau$')
plot.ylabel('$\sin t$')
fig.save(path='.', name='sinewave')
desc = model
plots[model].text_upper_center(desc, dy=0.05, fontsize=6.5)
#=========================================================================================
# Plot performance
#=========================================================================================
clr_target = Figure.colors('red')
clr_actual = '0.2'
clr_seeds = '0.8'
for model, _ in models:
plot = plots[model]
try:
rnn = RNN(get_savefile(model), verbose=True)
except SystemExit:
continue
xall = []
ntrials = [int(costs[0]) for costs in rnn.costs_history]
ntrials = np.asarray(ntrials, dtype=int) / int(1e4)
performance = [costs[1][-1] for costs in rnn.costs_history]
# Because the network is run continuously, the first validation run is meaningless.
if 'lee' in model:
ntrials = ntrials[1:]
performance = performance[1:]
# Get target performance
plot.arrow(pos[0], pos[1], alen, 0, **arrowp)
plot.xlim(-r_screen, r_screen)
plot.ylim(-r_screen, r_screen)
#=========================================================================================
# PCA
#=========================================================================================
plot = plots['pca']
#-----------------------------------------------------------------------------------------
# PCA analysis
#-----------------------------------------------------------------------------------------
# RNN
rnn = RNN(savefile, {'dt': 0.5}, verbose=False)
# Run each sequence separately
rnn.p['mode'] = None
# Turn off noise
rnn.p['var_in'] = 0
rnn.p['var_rec'] = 0
dt_save = 2
trials = {}
for seq in xrange(1, 1+m.nseq):
print('Sequence #{}, noiseless'.format(seq))
# Trial
trial_func = m.generate_trial
plot = plots['R']
plot.text_upper_center('Pos. tuned during $f_1$, neg. during $f_2$', dy=0.1, fontsize=7)
plot = plots['<']
plot.xlabel('Time from $f_1$ onset (sec)')
plot.ylabel('Input (a.u.)')
plot = plots['sig']
plot.ylabel('Prop. sig. tuned units')
#=========================================================================================
# Sample inputs
#=========================================================================================
rng = np.random.RandomState(1066)
rnn = RNN(savefile, {'dt': 2}, verbose=False)
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'fpair': (34, 26),
'gt_lt': '>'
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Stimulus durations
epochs = info['epochs']
f1_start, f1_end = epochs['f1']
f2_start, f2_end = epochs['f2']
t0 = f1_start
def run_trials(p, args): #args are the number of trials
"""
Run trials.
"""
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
# Trials
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
# Condition
b = i % m.nconditions #iterate through all conditions
k0, k1 = tasktools.unravel_index(b, (len(m.pairs), 1))
pair = m.pairs[k0]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'pair': pair,
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
s = ("Trial {:>{}}/{}: {:>2} {:>2}"
.format(i+1, w, ntrials, info['f1'], info['f2']))
sys.stdout.write(backspaces*'\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save']/dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:,::step],
'r': rnn.r[:,::step],
'z': rnn.z[:,::step],
'info': info
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename)*1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(THIS, filename, size))
# Psychometric function
psychometric_function(filename)
def run_trials(p, args):
"""
Run trials.
"""
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions + 1
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
if 'ant_level' in p:
rnn.Wrec[np.where(rnn.Wrec<0)] *= (1 - p['ant_level'])
# Trials
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
b = i % (m.nconditions + 1)
if b == 0:
# Zero-coherence condition
coh = 0
in_out = rng.choice(m.in_outs)
else:
# All other conditions
k1, k2 = tasktools.unravel_index(b-1, (len(m.cohs), len(m.in_outs)))
coh = m.cohs[k1]
in_out = m.in_outs[k2]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'coh': coh,
'in_out': in_out
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
#if coh == 0:
# s = "Trial {:>{}}/{}: {:>3}".format(i+1, w, ntrials, info['coh'])
#else:
# s = ("Trial {:>{}}/{}: {:>+3}"
# .format(i+1, w, ntrials, info['in_out']*info['coh']))
# Update the user with the current progress
if (i/100) % 1 == 0:
print("We are {:.2f}% complete".format(100*i/ntrials))
sys.stdout.flush()
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save']/dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:,::step],
'r': rnn.r[:,::step],
'z': rnn.z[:,::step],
'info': info
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
dump(filename, trials)
size = os.path.getsize(filename)*1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(THIS, filename, size))
# Compute the psychometric function
psychometric_function(filename)
from pycog import Model
model = Model(N=N, Nin=Nin, Nout=Nout, ei=ei, Crec=Crec, Cout=Cout,
generate_trial=generate_trial,
n_validation=n_validation, performance=performance, terminate=terminate)
model.train('workingMemory_savefile.pkl', seed=100, recover=False)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
from pycog import RNN
from pycog.figtools import Figure
rng = np.random.RandomState(1066)
rnn = RNN('workingMemory_savefile.pkl', {'dt': 2})
trial_args = {'name': 'test', 'catch': False, 'pair': (15, 30)}
info = rnn.run(inputs=(generate_trial, trial_args), rng=rng)
fig = Figure()
plot = fig.add()
epochs = info['epochs']
f1_start, f1_end = epochs['f1']
f2_start, f2_end = epochs['f2']
t0 = f1_start
tmin = 0
tmax = f2_end
def run_trials(p, args):
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
# Condition
k = tasktools.unravel_index(i % m.nconditions,
(len(m.cohs), len(m.left_rights),
len(m.cohs), len(m.left_rights),
len(m.contexts)))
coh_m = m.cohs[k[0]]
left_right_m = m.left_rights[k[1]]
coh_c = m.cohs[k[2]]
left_right_c = m.left_rights[k[3]]
context = m.contexts[k[4]]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'coh_m': coh_m,
'left_right_m': left_right_m,
'coh_c': coh_c,
'left_right_c': left_right_c,
'context': context
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
s = ("Trial {:>{}}/{}: ({}) m{:>+3}, c{:>+3}"
.format(i+1, w, ntrials, info['context'],
info['left_right_m']*info['coh_m'],
info['left_right_c']*info['coh_c']))
sys.stdout.write(backspaces*'\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save']/dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:,::step],
'r': rnn.r[:,::step],
'z': rnn.z[:,::step],
'info': info,
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename)*1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(THIS, filename, size))
# Compute the psychometric function
psychometric_function(filename)
#=========================================================================================
# Test resting state
#=========================================================================================
elif action == 'restingstate':
import numpy as np
from pycog import RNN
from pycog.figtools import Figure
# Create RNN
if 'init' in args:
print("* Initial network.")
base, ext = os.path.splitext(savefile)
savefile_init = base + '_init' + ext
rnn = RNN(savefile_init, {'dt': dt}, verbose=True)
else:
rnn = RNN(savefile, {'dt': dt}, verbose=True)
rnn.run(3e3, seed=seed)
# Summary
mean = np.mean(rnn.z)
std = np.std(rnn.z)
print("Mean output: {:.6f}".format(mean))
print("Std. output: {:.6f}".format(std))
fig = Figure()
plot = fig.add()
colors = [Figure.colors('blue'), Figure.colors('orange')]
for i in xrange(rnn.z.shape[0]):
rdm.psychometric_function(dense_trialsfile, plot, ms=5)
# Dale, fixed
plot = plots['Cpsy']
rdm.psychometric_function(fixed_trialsfile, plot, ms=5)
#=========================================================================================
# Connection matrices
#=========================================================================================
for rnn, sortbyfile, s, dprimefile in zip([rnn_nodale, rnn_dense, rnn_fixed],
[sortby_nodale, sortby_dense, sortby_fixed],
['A', 'B', 'C'],
[dprime_nodale, dprime_dense, dprime_fixed]):
idx = np.loadtxt(sortbyfile, dtype=int)
RNN.plot_connection_matrix(plots[s+'in'], rnn.Win[idx,:],
smap_exc_in, smap_inh_in)
RNN.plot_connection_matrix(plots[s+'rec'], rnn.Wrec[idx,:][:,idx],
smap_exc_rec, smap_inh_rec)
RNN.plot_connection_matrix(plots[s+'out'], rnn.Wout[:,idx],
smap_exc_out, smap_inh_out)
dprime = np.loadtxt(dprimefile)
transitions = []
for i in xrange(1, len(dprime)):
if dprime[i-1] > 0 and dprime[i] < 0:
transitions.append(i)
plot = plots[s+'rec']
if s == 'A':
n = transitions[0]
plot.text(n-0.5, -1.53, '|', ha='center', va='center', fontsize=8)
#---------------------------------------------------------------------------
return trial
# Performance measure: two-alternative forced choice
performance = tasktools.performance_2afc
# Terminate training when psychometric performance exceeds 85%
def terminate(performance_history):
return np.mean(performance_history[-5:]) > 85
# Validation dataset size
n_validation = 100*(nconditions + 1)
#///////////////////////////////////////////////////////////////////////////////
if __name__ == '__main__':
# Train model
model = Model(Nin=Nin, N=N, Nout=Nout, ei=ei, Cout=Cout,
generate_trial=generate_trial,
performance=performance, terminate=terminate,
n_validation=n_validation)
model.train('savefile.pkl')
# Run the trained network with 16*3.2% = 51.2% coherence for choice 1
rnn = RNN('savefile.pkl', {'dt': 0.5})
trial_func = generate_trial
trial_args = {'name': 'test', 'catch': False, 'coh': 16, 'left_right': 1}
info = rnn.run(inputs=(trial_func, trial_args))
nodale_trialsfile = join(paper.scratchpath, 'rdm_nodale', 'trials',
'rdm_nodale_trials.pkl')
dense_trialsfile = join(paper.scratchpath, 'rdm_dense', 'trials',
'rdm_dense_trials.pkl')
fixed_trialsfile = join(paper.scratchpath, 'rdm_fixed', 'trials',
'rdm_fixed_trials.pkl')
#-----------------------------------------------------------------------------------------
# Load RNNs to compare
#-----------------------------------------------------------------------------------------
datapath = join(base, 'examples', 'work', 'data')
savefile = join(datapath, 'rdm_nodale', 'rdm_nodale.pkl')
rnn_nodale = RNN(savefile, verbose=True)
dprime_nodale = join(datapath, 'rdm_nodale', 'rdm_nodale_dprime.txt')
sortby_nodale = join(datapath, 'rdm_nodale', 'rdm_nodale_selectivity.txt')
savefile = join(datapath, 'rdm_dense', 'rdm_dense.pkl')
rnn_dense = RNN(savefile, verbose=True)
dprime_dense = join(datapath, 'rdm_dense', 'rdm_dense_dprime.txt')
sortby_dense = join(datapath, 'rdm_dense', 'rdm_dense_selectivity.txt')
savefile = join(datapath, 'rdm_fixed', 'rdm_fixed.pkl')
rnn_fixed = RNN(savefile, verbose=True)
dprime_fixed = join(datapath, 'rdm_fixed', 'rdm_fixed_dprime.txt')
sortby_fixed = join(datapath, 'rdm_fixed', 'rdm_fixed_selectivity.txt')
#=========================================================================================
# Figure setup
def do(action, args, p):
"""
Manage tasks.
"""
print("ACTION*: " + str(action))
print("ARGS*: " + str(args))
#-------------------------------------------------------------------------------------
# Trials
#-------------------------------------------------------------------------------------
if action == 'trials':
run_trials(p, args)
#-------------------------------------------------------------------------------------
# Sort
#-------------------------------------------------------------------------------------
elif action == 'sort_stim_onset':
sort_trials(get_trialsfile(p), get_sortedfile_stim_onset(p))
#-------------------------------------------------------------------------------------
# activate state
#-------------------------------------------------------------------------------------
# TODO plot multiple units in the same figure
# TODO replace units name with real neurons
elif action == 'activatestate':
# Model
m = p['model']
# Intensity
try:
intensity = float(args[0])
except:
intensity = 1
# Plot unit
try:
unit = int(args[1])
if unit == -1:
unit = None
except:
unit = None
# Create RNN
if 'init' in args:
print("* Initial network.")
base, ext = os.path.splitext(p['savefile'])
savefile_init = base + '_init' + ext
rnn = RNN(savefile_init, {'dt': p['dt']}, verbose=True)
else:
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=True)
trial_func = p['model'].generate_trial
trial_args = {
'name': 'test',
'catch': False,
'intensity': intensity,
}
info = rnn.run(inputs=(trial_func, trial_args), seed=p['seed'])
# Summary
mean = np.mean(rnn.z)
std = np.std(rnn.z)
print("Intensity: {:.6f}".format(intensity))
print("Mean output: {:.6f}".format(mean))
print("Std. output: {:.6f}".format(std))
# Figure setup
x = 0.12
y = 0.12
w = 0.80
h = 0.80
dashes = [3.5, 1.5]
t_forward = 1e-3 * np.array(info['epochs']['forward'])
t_stimulus = 1e-3 * np.array(info['epochs']['stimulus'])
t_reversal = 1e-3 * np.array(info['epochs']['reversal'])
fig = Figure(w=4,
h=3,
axislabelsize=7,
labelpadx=5,
labelpady=5,
thickness=0.6,
ticksize=3,
ticklabelsize=6,
ticklabelpad=2)
plots = {
'in': fig.add([x, y + 0.72 * h, w, 0.3 * h]),
'out': fig.add([x, y, w, 0.65 * h]),
}
plot = plots['in']
plot.ylabel('Input', labelpad=7, fontsize=6.5)
plot = plots['out']
plot.xlabel('Time (sec)', labelpad=6.5)
plot.ylabel('Output', labelpad=7, fontsize=6.5)
# -----------------------------------------------------------------------------------------
# Input
# -----------------------------------------------------------------------------------------
plot = plots['in']
plot.axis_off('bottom')
plot.plot(1e-3 * rnn.t, rnn.u[0], color=Figure.colors('red'), lw=0.5)
plot.lim('y', rnn.u[0])
plot.xlim(1e-3 * rnn.t[0], 1e-3 * rnn.t[-1])
# -----------------------------------------------------------------------------------------
# Output
# -----------------------------------------------------------------------------------------
plot = plots['out']
# Outputs
colors = [Figure.colors('orange'), Figure.colors('blue')]
if unit is None:
plot.plot(1e-3 * rnn.t,
rnn.z[0],
color=colors[0],
label='Forward module')
plot.plot(1e-3 * rnn.t,
rnn.z[1],
color=colors[1],
label='Reversal module')
plot.lim('y', np.ravel(rnn.z), lower=0)
else:
plot.plot(1e-3 * rnn.t,
rnn.r[unit],
color=colors[1],
label='unit ' + str(unit))
plot.lim('y', np.ravel(rnn.r[unit]))
plot.xlim(1e-3 * rnn.t[0], 1e-3 * rnn.t[-1])
# Legend
props = {'prop': {'size': 7}}
plot.legend(bbox_to_anchor=(1.1, 1.6), **props)
plot.vline(t_forward[-1],
color='0.2',
linestyle='--',
lw=1,
dashes=dashes)
plot.vline(t_reversal[0],
color='0.2',
linestyle='--',
lw=1,
dashes=dashes)
# Epochs
plot.text(np.mean(t_forward),
plot.get_ylim()[1],
'forward',
ha='center',
va='center',
fontsize=7)
plot.text(np.mean(t_stimulus),
plot.get_ylim()[1],
'stimulus',
ha='center',
va='center',
fontsize=7)
plot.text(np.mean(t_reversal),
plot.get_ylim()[1],
'reversal',
ha='center',
va='center',
fontsize=7)
if 'init' in args:
savename = p['name'] + '_' + action + '_init'
else:
savename = p['name'] + '_' + action
if unit is not None:
savename += '_unit_' + str(unit)
fig.save(path=p['figspath'], name=savename)
fig.close()
# -------------------------------------------------------------------------------------
# Plot single-unit activity aligned to stimulus onset
# -------------------------------------------------------------------------------------
elif action == 'units_stim_onset':
from glob import glob
try:
lower_bon = float(args[0])
except:
lower_bon = None
try:
higher_bon = float(args[1])
except:
higher_bon = None
# Remove existing files
unitpath = join(p['figspath'], 'units')
filenames = glob(join(unitpath, p['name'] + '_stim_onset_unit*'))
for filename in filenames:
os.remove(filename)
print("Removed {}".format(filename))
# Load sorted trials
sortedfile = get_sortedfile_stim_onset(p)
with open(sortedfile) as f:
t, sorted_trials = pickle.load(f)
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=True)
trial_func = p['model'].generate_trial
trial_args = {
'name': 'test',
'catch': False,
}
info = rnn.run(inputs=(trial_func, trial_args), seed=p['seed'])
t_stimulus = np.array(info['epochs']['stimulus'])
stimulus_d = t_stimulus[1] - t_stimulus[0]
for i in xrange(p['model'].N):
# Check if the unit does anything
# active = False
# for r in sorted_trials.values():
# if is_active(r[i]):
# active = True
# break
# if not active:
# continue
dashes = [3.5, 1.5]
fig = Figure()
plot = fig.add()
# -----------------------------------------------------------------------------
# Plot
# -----------------------------------------------------------------------------
plot_unit(i, sortedfile, plot, tmin=lower_bon, tmax=higher_bon)
plot.xlabel('Time (ms)')
plot.ylabel('Firing rate (a.u.)')
props = {
'prop': {
'size': 8
},
'handletextpad': 1.02,
'labelspacing': 0.6
}
plot.legend(bbox_to_anchor=(0.18, 1), **props)
plot.vline(0, color='0.2', linestyle='--', lw=1, dashes=dashes)
plot.vline(stimulus_d,
color='0.2',
linestyle='--',
lw=1,
dashes=dashes)
# Epochs
plot.text(-np.mean((0, stimulus_d)),
plot.get_ylim()[1],
'forward',
ha='center',
va='center',
fontsize=7)
plot.text(np.mean((0, stimulus_d)),
plot.get_ylim()[1],
'stimulus',
ha='center',
va='center',
fontsize=7)
plot.text(3 * np.mean((0, stimulus_d)),
plot.get_ylim()[1],
'reversal',
ha='center',
va='center',
fontsize=7)
# -----------------------------------------------------------------------------
fig.save(path=unitpath,
name=p['name'] + '_stim_onset_unit{:03d}'.format(i))
fig.close()
#-------------------------------------------------------------------------------------
# Selectivity
#-------------------------------------------------------------------------------------
elif action == 'selectivity':
try:
lower = float(args[0])
except:
lower = None
try:
higher = float(args[1])
except:
higher = None
# Model
m = p['model']
trialsfile = get_trialsfile(p)
dprime = get_choice_selectivity(trialsfile,
lower_bon=lower,
higher_bon=higher)
def get_first(x, p):
return x[:int(p * len(x))]
psig = 0.25
units = np.arange(len(dprime))
try:
idx = np.argsort(abs(dprime[m.EXC]))[::-1]
exc = get_first(units[m.EXC][idx], psig)
idx = np.argsort(abs(dprime[m.INH]))[::-1]
inh = get_first(units[m.INH][idx], psig)
idx = np.argsort(dprime[exc])[::-1]
units_exc = list(exc[idx])
idx = np.argsort(dprime[inh])[::-1]
units_inh = list(units[inh][idx])
units = units_exc + units_inh
dprime = dprime[units]
except AttributeError:
idx = np.argsort(abs(dprime))[::-1]
all = get_first(units[idx], psig)
idx = np.argsort(dprime[all])[::-1]
units = list(units[all][idx])
dprime = dprime[units]
# Save d'
filename = get_dprimefile(p)
np.savetxt(filename, dprime)
print("[ {}.do ] d\' saved to {}".format(THIS, filename))
# Save selectivity
filename = get_selectivityfile(p)
np.savetxt(filename, units, fmt='%d')
print("[ {}.do ] Choice selectivity saved to {}".format(
THIS, filename))
#-------------------------------------------------------------------------------------
else:
print("[ {}.do ] Unrecognized action.".format(THIS))
def run_trials(p, args):
"""
Run trials.
"""
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions + 1
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
# Trials
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
b = i % (m.nconditions + 1)
if b == 0:
# Zero-coherence condition
coh = 0
in_out = rng.choice(m.in_outs)
else:
# All other conditions
k1, k2 = tasktools.unravel_index(b-1, (len(m.cohs), len(m.in_outs)))
coh = m.cohs[k1]
in_out = m.in_outs[k2]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'coh': coh,
'in_out': in_out
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
if coh == 0:
s = "Trial {:>{}}/{}: {:>3}".format(i+1, w, ntrials, info['coh'])
else:
s = ("Trial {:>{}}/{}: {:>+3}"
.format(i+1, w, ntrials, info['in_out']*info['coh']))
sys.stdout.write(backspaces*'\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save']/dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:,::step],
'r': rnn.r[:,::step],
'z': rnn.z[:,::step],
'info': info
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename)*1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(THIS, filename, size))
# Compute the psychometric function
psychometric_function(filename)
def run_trials(p, args):
"""
Run trials.
"""
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
# Trials
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
b = i % m.nconditions
k1, k2 = tasktools.unravel_index(b, (len(m.modalities), len(m.freqs)))
modality = m.modalities[k1]
freq = m.freqs[k2]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'modality': modality,
'freq': freq
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
if info['modality'] == 'v':
s = "Trial {:>{}}/{}: v |{:>2}".format(i+1, w, ntrials, info['freq'])
elif info['modality'] == 'a':
s = "Trial {:>{}}/{}: a|{:>2}".format(i+1, w, ntrials, info['freq'])
else:
s = "Trial {:>{}}/{}: va|{:>2}".format(i+1, w, ntrials, info['freq'])
sys.stdout.write(backspaces*'\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save']/dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:,::step],
'r': rnn.r[:,::step],
'z': rnn.z[:,::step],
'info': info
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename)*1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(THIS, filename, size))
# Compute the psychometric function
psychometric_function(filename)
plot = plots['modality']
plot.text_upper_center('Modality selectivity', dy=0.1, fontsize=7)
plot = plots['mixed']
plot.text_upper_center('Mixed selectivity', dy=0.1, fontsize=7)
#=========================================================================================
# Sample inputs
#=========================================================================================
freq0 = int(np.ceil(m.boundary))
boundary_v = m.baseline_in + m.scale_v_p(m.boundary)
boundary_a = m.baseline_in + m.scale_a_p(m.boundary)
rng = np.random.RandomState(1215)
rnn = RNN(savefile, {'dt': 0.5}, verbose=False)
trials = []
for i in xrange(3):
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'modality': ['v', 'a', 'va'][i],
'freq': freq0,
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
dt = rnn.t[1] - rnn.t[0]
step = int(5/dt)
trial = {
't': rnn.t[::step],
def run_trials(p, args):
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
# Condition
k = tasktools.unravel_index(
i % m.nconditions, (len(m.cohs), len(m.left_rights), len(
m.cohs), len(m.left_rights), len(m.contexts)))
coh_m = m.cohs[k[0]]
left_right_m = m.left_rights[k[1]]
coh_c = m.cohs[k[2]]
left_right_c = m.left_rights[k[3]]
context = m.contexts[k[4]]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'coh_m': coh_m,
'left_right_m': left_right_m,
'coh_c': coh_c,
'left_right_c': left_right_c,
'context': context
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
s = ("Trial {:>{}}/{}: ({}) m{:>+3}, c{:>+3}".format(
i + 1, w, ntrials, info['context'],
info['left_right_m'] * info['coh_m'],
info['left_right_c'] * info['coh_c']))
sys.stdout.write(backspaces * '\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save'] / dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:, ::step],
'r': rnn.r[:, ::step],
'z': rnn.z[:, ::step],
'info': info,
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename) * 1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(
THIS, filename, size))
# Compute the psychometric function
psychometric_function(filename)
rnn_zs = np.zeros([Nout, len(rnn.z[0])])
for j in range(Nout):
rnn_zs[j, :] = rnn.z[j] / np.max(rnn.z[j])
return rnn_zs
if __name__ == '__main__':
from pycog import RNN
from pycog.figtools import Figure
rng = np.random.RandomState(1234) # Added by Alfred
savefile = 'examples/work/data/delay_react/delay_react.pkl'
# savefile = 'examples/work/data/run_10000_lr1em3_1_1_100_10/delay_react.pkl'
parameters = {'dt': 0.5, 'var_rec': 0.01**2}
rnn = RNN(savefile, parameters)
trial_args = {}
info = rnn.run(inputs=(generate_trial, trial_args), seed=200)
Z0 = rnn.z
rnn_zs0 = np.zeros([Nout, len(rnn.z[0])])
for j in range(Nout):
rnn_zs0[j, :] = rnn.z[j] / np.max(rnn.z[j])
n_values = 21
n_cols = int(0.2 * N)
shifts = np.zeros([n_cols, n_values, 2])
values = np.linspace(-0.2, 0.2, n_values)
for i in range(n_cols):
def run_trials(p, args):
"""
Run trials.
"""
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 100
ntrials *= m.nconditions + 1
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
# Trials
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
b = i % (m.nconditions + 1)
if b == 0:
# Zero-coherence condition
coh = 0
in_out = rng.choice(m.in_outs)
else:
# All other conditions
k1, k2 = tasktools.unravel_index(b - 1,
(len(m.cohs), len(m.in_outs)))
coh = m.cohs[k1]
in_out = m.in_outs[k2]
# Trial
trial_func = m.generate_trial
trial_args = {
'name': 'test',
'catch': False,
'coh': coh,
'in_out': in_out
}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
if coh == 0:
s = "Trial {:>{}}/{}: {:>3}".format(i + 1, w, ntrials,
info['coh'])
else:
s = ("Trial {:>{}}/{}: {:>+3}".format(
i + 1, w, ntrials, info['in_out'] * info['coh']))
sys.stdout.write(backspaces * '\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Save
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save'] / dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:, ::step],
'r': rnn.r[:, ::step],
'z': rnn.z[:, ::step],
'info': info
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename) * 1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(
THIS, filename, size))
# Compute the psychometric function
psychometric_function(filename)
#=========================================================================================
# Test resting state
#=========================================================================================
elif action == 'restingstate':
import numpy as np
from pycog import RNN
from pycog.figtools import Figure
# Create RNN
if 'init' in args:
print("* Initial network.")
base, ext = os.path.splitext(savefile)
savefile_init = base + '_init' + ext
rnn = RNN(savefile_init, {'dt': dt}, verbose=True)
else:
rnn = RNN(savefile, {'dt': dt}, verbose=True)
rnn.run(3e3, seed=seed)
# Summary
mean = np.mean(rnn.z)
std = np.std(rnn.z)
print("Mean output: {:.6f}".format(mean))
print("Std. output: {:.6f}".format(std))
fig = Figure()
plot = fig.add()
colors = [Figure.colors('blue'), Figure.colors('orange')]
for i in xrange(rnn.z.shape[0]):
trial['outputs'] = Y
return trial
min_error = 0.1
n_validation = 100
if __name__ == '__main__':
from pycog import RNN
from pycog.figtools import Figure
rnn = RNN('work/data/multi_sequence8mod/multi_sequence8mod.pkl', {
'dt': 0.5,
'var_rec': 0.01**2,
'var_in': np.array([0.003**2])
})
trial_args = {}
info = rnn.run(inputs=(generate_trial, trial_args), seed=7423)
fig = Figure()
plot = fig.add()
colors = [
'red', 'green', 'yellow', 'orange', 'purple', 'cyan', 'magenta', 'pink'
]
plot.plot(rnn.t / tau, rnn.u[0], color=Figure.colors('blue'))
for i in range(Nout):
plot.plot(rnn.t / tau,
def run_trials(p, args):
"""
Run trials.
"""
# Model
m = p['model']
# Number of trials
try:
ntrials = int(args[0])
except:
ntrials = 1
ntrials *= m.nseq
# RNN
rng = np.random.RandomState(p['seed'])
rnn = RNN(p['savefile'], {'dt': p['dt']}, verbose=False)
# Trials
w = len(str(ntrials))
trials = []
backspaces = 0
try:
for i in xrange(ntrials):
b = i % m.nseq
if b == 0:
if not trials:
seqs = range(m.nseq)
else:
seqs = rng.permutation(m.nseq)
# Sequence number
seq = seqs[b] + 1
# Trial
trial_func = m.generate_trial
trial_args = {'name': 'test', 'seq': seq}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)
# Display trial type
s = "Trial {:>{}}/{}: Sequence #{}".format(i+1, w, ntrials, info['seq'])
sys.stdout.write(backspaces*'\b' + s)
sys.stdout.flush()
backspaces = len(s)
# Add
dt = rnn.t[1] - rnn.t[0]
step = int(p['dt_save']/dt)
trial = {
't': rnn.t[::step],
'u': rnn.u[:,::step],
'r': rnn.r[:,::step],
'z': rnn.z[:,::step],
'info': info
}
trials.append(trial)
except KeyboardInterrupt:
pass
print("")
# Save all
filename = get_trialsfile(p)
with open(filename, 'wb') as f:
pickle.dump(trials, f, pickle.HIGHEST_PROTOCOL)
size = os.path.getsize(filename)*1e-9
print("[ {}.run_trials ] Trials saved to {} ({:.1f} GB)".format(THIS, filename, size))
