def do(action, args, p):
"""
Manage tasks.
"""
print("ACTION*: " + str(action))
print("ARGS*: " + str(args))
#-------------------------------------------------------------------------------------
# Trials
#-------------------------------------------------------------------------------------
if action == 'trials':
run_trials(p, args)
#-------------------------------------------------------------------------------------
# Psychometric function
#-------------------------------------------------------------------------------------
elif action == 'psychometric':
threshold = False
if 'threshold' in args:
threshold = True
fig = Figure()
plot = fig.add()
#---------------------------------------------------------------------------------
# Plot
#---------------------------------------------------------------------------------
trialsfile = get_trialsfile(p)
psychometric_function(trialsfile, plot, threshold=threshold)
plot.xlabel(r'Percent coherence toward $T_\text{in}$')
plot.ylabel(r'Percent $T_\text{in}$')
#---------------------------------------------------------------------------------
fig.save(path=p['figspath'], name=p['name'] + '_' + action)
fig.close()
#-------------------------------------------------------------------------------------
# Sort
#-------------------------------------------------------------------------------------
elif action == 'sort_stim_onset':
sort_trials_stim_onset(get_trialsfile(p), get_sortedfile_stim_onset(p))
elif action == 'sort_response':
sort_trials_response(get_trialsfile(p), get_sortedfile_response(p))
#-------------------------------------------------------------------------------------
# Plot single-unit activity aligned to stimulus onset
#-------------------------------------------------------------------------------------
elif action == 'units_stim_onset':
from glob import glob
# Remove existing files
filenames = glob(join(p['figspath'], 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)
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
fig = Figure()
plot = fig.add()
#-----------------------------------------------------------------------------
# Plot
#-----------------------------------------------------------------------------
plot_unit(i, sortedfile, plot)
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)
#-----------------------------------------------------------------------------
fig.save(path=p['figspath'],
name=p['name'] + '_stim_onset_unit{:03d}'.format(i))
fig.close()
#-------------------------------------------------------------------------------------
# Plot single-unit activity aligned to response
#-------------------------------------------------------------------------------------
elif action == 'units_response':
from glob import glob
# Remove existing files
filenames = glob(join(p['figspath'], p['name'] + '_response_unit*'))
for filename in filenames:
os.remove(filename)
print("Removed {}".format(filename))
# Load sorted trials
sortedfile = get_sortedfile_response(p)
with open(sortedfile) as f:
t, sorted_trials = pickle.load(f)
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
fig = Figure()
plot = fig.add()
#-----------------------------------------------------------------------------
# Plot
#-----------------------------------------------------------------------------
plot_unit(i, sortedfile, plot)
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)
#-----------------------------------------------------------------------------
fig.save(path=p['figspath'],
name=p['name'] + '_response_unit_{:03d}'.format(i))
fig.close()
#-------------------------------------------------------------------------------------
# Selectivity
#-------------------------------------------------------------------------------------
elif action == 'selectivity':
# Model
m = p['model']
trialsfile = get_trialsfile(p)
dprime = get_choice_selectivity(trialsfile)
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))
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,
rnn.z[i],
color=Figure.colors(colors[int(i / NoutSplit)]))
Nexc = int(N * 0.8)
plot.plot(rnn.t / tau,
rnn.r[:Nexc].mean(axis=0),
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
for i, clr in enumerate(colors):
fig = Figure(w=2, h=1)
plot = fig.add(None, 'none')
plot.plot(rnn.t[::DT], rnn.u[i][::DT], color=Figure.colors(clr), lw=1)
plot.ylim(0, 1.5)
fig.save(path=figspath, name='fig1a_input{}'.format(i+1))
fig.close()
# Outputs
for i, clr in enumerate(colors):
fig = Figure(w=2, h=1)
plot = fig.add(None, 'none')
plot.plot(rnn.t[::DT], rnn.z[i][::DT], color=Figure.colors(clr), lw=1)
plot.ylim(0, 1.5)
import numpy as np
from pycog.figtools import Figure
w = 174 / 25.4
r = 0.6
h = r * w
fig = Figure(w=w, h=h)
w = 0.4
h = 0.18
x0 = 0.07
y0 = 0.72
dx = 1.23 * w
dy = 1.25 * h
plots = {
'SL-f': fig.add([x0, y0, w, h]),
'SL-s': fig.add([x0, y0 - dy, w, h]),
'SL-e': fig.add([x0, y0 - 2 * dy, w, h], 'none'),
'SL-o': fig.add([x0, y0 - 3 * dy, w, h]),
'RL-f': fig.add([x0 + dx, y0, w, h]),
'RL-s': fig.add([x0 + dx, y0 - dy, w, h]),
'RL-e': fig.add([x0 + dx, y0 - 2 * dy, w, h], 'none'),
'RL-o': fig.add([x0 + dx, y0 - 3 * dy, w, h], 'none')
}
x0 = 0.01
x1 = x0 + dx
y0 = 0.95
plotlabels = {'A': (x0, y0), 'B': (x1, y0)}
fig.plotlabels(plotlabels, fontsize=12.5)
def do(action, args, p):
"""
Manage tasks.
"""
print("ACTION*: " + str(action))
print("ARGS*: " + str(args))
#-------------------------------------------------------------------------------------
# Trials
#-------------------------------------------------------------------------------------
if action == 'trials':
run_trials(p, args)
#-------------------------------------------------------------------------------------
# Psychometric function
#-------------------------------------------------------------------------------------
elif action == 'psychometric':
fig = Figure()
plot = fig.add()
#---------------------------------------------------------------------------------
# Plot
#---------------------------------------------------------------------------------
trialsfile = get_trialsfile(p)
psychometric_function(trialsfile, plot)
plot.xlabel('$f_1$ (Hz)')
plot.ylabel('$f_2$ (Hz)')
#---------------------------------------------------------------------------------
fig.save(path=p['figspath'], name=p['name'] + '_' + action)
fig.close()
#-------------------------------------------------------------------------------------
# Sort
#-------------------------------------------------------------------------------------
elif action == 'sort':
sort_trials(get_trialsfile(p), get_sortedfile(p))
#-------------------------------------------------------------------------------------
# Plot single-unit activity aligned to stimulus onset
#-------------------------------------------------------------------------------------
elif action == 'units':
from glob import glob
# Remove existing files
print("[ {}.do ]".format(THIS))
filenames = glob('{}_unit*'.format(join(p['figspath'], p['name'])))
for filename in filenames:
os.remove(filename)
print(" Removed {}".format(filename))
# Load sorted trials
sortedfile = get_sortedfile(p)
with open(sortedfile) as f:
t, sorted_trials = pickle.load(f)
# Colormap
cmap = mpl.cm.jet
norm = mpl.colors.Normalize(vmin=p['model'].fmin, vmax=p['model'].fmax)
smap = mpl.cm.ScalarMappable(norm, cmap)
for i in xrange(p['model'].N):
# Check if the unit does anything
active = False
for condition_averaged in sorted_trials.values():
if is_active(condition_averaged[i]):
active = True
break
if not active:
continue
fig = Figure()
plot = fig.add()
#-----------------------------------------------------------------------------
# Plot
#-----------------------------------------------------------------------------
plot_unit(i, sortedfile, plot, smap)
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)
#-----------------------------------------------------------------------------
fig.save(path=p['figspath'],
name=p['name'] + '_unit{:03d}'.format(i))
fig.close()
#-------------------------------------------------------------------------------------
else:
print("[ {}.do ] Unrecognized action '{}'.".format(THIS, action))
def do(action, args, p):
print("ACTION*: " + str(action))
print("ARGS*: " + str(args))
#-------------------------------------------------------------------------------------
# Trials
#-------------------------------------------------------------------------------------
if action == 'trials':
run_trials(p, args)
#-------------------------------------------------------------------------------------
# Psychometric function
#-------------------------------------------------------------------------------------
elif action == 'psychometric':
#---------------------------------------------------------------------------------
# Figure setup
#---------------------------------------------------------------------------------
w = 6.5
h = 3
fig = Figure(w=w,
h=h,
axislabelsize=7.5,
labelpadx=6,
labelpady=7.5,
thickness=0.6,
ticksize=3,
ticklabelsize=6.5,
ticklabelpad=2)
w = 0.39
h = 0.7
L = 0.09
R = L + w + 0.1
y = 0.2
plots = {'m': fig.add([L, y, w, h]), 'c': fig.add([R, y, w, h])}
#---------------------------------------------------------------------------------
# Labels
#---------------------------------------------------------------------------------
plot = plots['m']
plot.xlabel('Motion coherence (\%)')
plot.ylabel('Choice to right (\%)')
plot = plots['c']
plot.xlabel('Color coherence (\%)')
plot.ylabel('Choice to green (\%)')
#---------------------------------------------------------------------------------
# Plot
#---------------------------------------------------------------------------------
trialsfile = get_trialsfile(p)
psychometric_function(trialsfile, plots)
# Legend
prop = {
'prop': {
'size': 7
},
'handlelength': 1.2,
'handletextpad': 1.1,
'labelspacing': 0.5
}
plots['m'].legend(bbox_to_anchor=(0.41, 1), **prop)
#---------------------------------------------------------------------------------
fig.save(path=p['figspath'], name=p['name'] + '_' + action)
fig.close()
#-------------------------------------------------------------------------------------
# Sort
#-------------------------------------------------------------------------------------
elif action == 'sort':
trialsfile = get_trialsfile(p)
sortedfile = get_sortedfile(p)
sort_trials(trialsfile, sortedfile)
#-------------------------------------------------------------------------------------
# Plot single-unit activity
#-------------------------------------------------------------------------------------
elif action == 'units':
from glob import glob
# Remove existing files
print("[ {}.do ]".format(THIS))
filenames = glob('{}_unit*'.format(join(p['figspath'], p['name'])))
for filename in filenames:
os.remove(filename)
print(" Removed {}".format(filename))
trialsfile = get_trialsfile(p)
sortedfile = get_sortedfile(p)
units = get_active_units(trialsfile)
for unit in units:
#-----------------------------------------------------------------------------
# Figure setup
#-----------------------------------------------------------------------------
w = 2.5
h = 6
fig = Figure(w=w,
h=h,
axislabelsize=7.5,
labelpadx=6,
labelpady=7.5,
thickness=0.6,
ticksize=3,
ticklabelsize=6.5,
ticklabelpad=2)
w = 0.55
x0 = 0.3
h = 0.17
dy = h + 0.06
y0 = 0.77
y1 = y0 - dy
y2 = y1 - dy
y3 = y2 - dy
plots = {
'choice': fig.add([x0, y0, w, h]),
'motion_choice': fig.add([x0, y1, w, h]),
'colour_choice': fig.add([x0, y2, w, h]),
'context_choice': fig.add([x0, y3, w, h])
}
#-----------------------------------------------------------------------------
# Plot
#-----------------------------------------------------------------------------
plot_unit(unit, sortedfile, plots, sortby_fontsize=7)
plots['context_choice'].xlabel('Time (ms)')
#-----------------------------------------------------------------------------
fig.save(path=p['figspath'],
name=p['name'] + '_unit{:03d}'.format(unit))
fig.close()
print("[ {}.do ] {} units processed.".format(THIS, len(units)))
#-------------------------------------------------------------------------------------
# Regress
#-------------------------------------------------------------------------------------
elif action == 'regress':
trialsfile = get_trialsfile(p)
sortedfile = get_sortedfile(p)
betafile = get_betafile(p)
regress(trialsfile, sortedfile, betafile)
#-------------------------------------------------------------------------------------
elif action == 'inactivation':
# load Weight value info
import cPickle as pickle
n_ne = 120
try:
w_inact_bin = int(args[1])
except:
w_inact_bin = 10
try:
w_inact_dep = int(args[2])
except:
w_inact_dep = int(n_ne / w_inact_bin)
fn = 'C:\\Users\\sasak\\OneDrive\\workspace\\pycog\\examples\\work\\data\\mante\\mante_init.pkl'
with open(fn, 'rb') as f:
data = pickle.load(f)
Wrec_init = data['best']['params'][1]
fn = 'C:\\Users\\sasak\\OneDrive\\workspace\\pycog\\examples\\work\\data\\mante\\mante.pkl'
with open(fn, 'rb') as f:
data = pickle.load(f)
Wrec_last = data['best']['params'][1]
Wrec_diff = Wrec_init - Wrec_last
Wee_diff = Wrec_diff[:n_ne, :n_ne]
run_trials_inact(p, None, args) # full condition
# sort descending order
Neu_rec_diff = np.mean(np.abs(Wee_diff), axis=1)
sorted_id = np.argsort(Neu_rec_diff)[::-1]
for i in xrange(w_inact_dep):
id_inact = sorted_id[:(i + 1) * w_inact_bin]
run_trials_inact(p, id_inact, args)
# sort ascending order
Neu_rec_diff = np.mean(np.abs(Wee_diff), axis=1)
sorted_id = np.argsort(Neu_rec_diff)
for i in xrange(w_inact_dep):
id_inact = sorted_id[:(i + 1) * w_inact_bin]
run_trials_inact(p, id_inact, args)
# shuffled order
rng = np.random.RandomState(p['seed'])
#rng = np.random.RandomState(data['params']['seed'])
sorted_id = range(n_ne)
rng.shuffle(sorted_id)
for i in xrange(w_inact_dep):
id_inact = sorted_id[:(i + 1) * w_inact_bin]
run_trials_inact(p, id_inact, args)
#-------------------------------------------------------------------------------------
else:
print("[ {}.do ] Unrecognized action.".format(THIS))
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 plot_trial(trial_info, trial, figspath, name):
U, Z, A, R, M, init, states_0, perf = trial_info
U = U[:,0,:]
Z = Z[:,0,:]
A = A[:,0,:]
R = R[:,0]
M = M[:,0]
t = int(np.sum(M))
w = 0.65
h = 0.18
x = 0.17
dy = h + 0.05
y0 = 0.08
y1 = y0 + dy
y2 = y1 + dy
y3 = y2 + dy
fig = Figure(h=6)
plots = {'observables': fig.add([x, y3, w, h]),
'policy': fig.add([x, y2, w, h]),
'actions': fig.add([x, y1, w, h]),
'rewards': fig.add([x, y0, w, h])}
time = trial['time']
dt = time[1] - time[0]
act_time = time[:t]
obs_time = time[:t-1] + dt
reward_time = act_time + dt
xlim = (0, max(time))
#-------------------------------------------------------------------------------------
# Observables
#-------------------------------------------------------------------------------------
plot = plots['observables']
plot.plot(obs_time, U[:t-1,0], 'o', ms=5, mew=0, mfc=Figure.colors('blue'))
plot.plot(obs_time, U[:t-1,0], lw=1.25, color=Figure.colors('blue'), label='Keydown')
plot.plot(obs_time, U[:t-1,1], 'o', ms=5, mew=0, mfc=Figure.colors('orange'))
plot.plot(obs_time, U[:t-1,1], lw=1.25, color=Figure.colors('orange'), label=r'$f_\text{pos}$')
plot.plot(obs_time, U[:t-1,2], 'o', ms=5, mew=0, mfc=Figure.colors('purple'))
plot.plot(obs_time, U[:t-1,2], lw=1.25, color=Figure.colors('purple'), label=r'$f_\text{neg}$')
plot.xlim(*xlim)
plot.ylim(0, 1)
plot.ylabel('Observables')
if trial['gt_lt'] == '>':
f1, f2 = trial['fpair']
else:
f2, f1 = trial['fpair']
plot.text_upper_right(str((f1, f2)))
#coh = trial['left_right']*trial['coh']
#if coh < 0:
# color = Figure.colors('orange')
#elif coh > 0:
# color = Figure.colors('purple')
#else:
# color = Figure.colors('k')
#plot.text_upper_right('Coh = {:.1f}\%'.format(coh), color=color)
props = {'prop': {'size': 7}, 'handlelength': 1.2,
'handletextpad': 1.2, 'labelspacing': 0.8}
plot.legend(bbox_to_anchor=(1.2, 0.8), **props)
#-------------------------------------------------------------------------------------
# Policy
#-------------------------------------------------------------------------------------
plot = plots['policy']
plot.plot(act_time, Z[:t,0], 'o', ms=5, mew=0, mfc=Figure.colors('blue'))
plot.plot(act_time, Z[:t,0], lw=1.25, color=Figure.colors('blue'),
label='Keydown')
plot.plot(act_time, Z[:t,1], 'o', ms=5, mew=0, mfc=Figure.colors('orange'))
plot.plot(act_time, Z[:t,1], lw=1.25, color=Figure.colors('orange'),
label='$f_1 > f_2$')
plot.plot(act_time, Z[:t,2], 'o', ms=5, mew=0, mfc=Figure.colors('purple'))
plot.plot(act_time, Z[:t,2], lw=1.25, color=Figure.colors('purple'),
label='$f_1 < f_2$')
plot.xlim(*xlim)
plot.ylim(0, 1)
plot.ylabel('Action probabilities')
props = {'prop': {'size': 7}, 'handlelength': 1.2,
'handletextpad': 1.2, 'labelspacing': 0.8}
plot.legend(bbox_to_anchor=(1.27, 0.8), **props)
#-------------------------------------------------------------------------------------
# Actions
#-------------------------------------------------------------------------------------
plot = plots['actions']
actions = [np.argmax(a) for a in A[:t]]
plot.plot(act_time, actions, 'o', ms=5, mew=0, mfc=Figure.colors('red'))
plot.plot(act_time, actions, lw=1.25, color=Figure.colors('red'))
plot.xlim(*xlim)
plot.ylim(0, 2)
plot.yticks([0, 1, 2])
plot.yticklabels(['Keydown', '$f_1 > f_2$', '$f_1 < f_2$'])
plot.ylabel('Action')
#-------------------------------------------------------------------------------------
# Rewards
#-------------------------------------------------------------------------------------
plot = plots['rewards']
plot.plot(reward_time, R[:t], 'o', ms=5, mew=0, mfc=Figure.colors('red'))
plot.plot(reward_time, R[:t], lw=1.25, color=Figure.colors('red'))
plot.xlim(*xlim)
plot.ylim(R_TERMINATE, R_CORRECT)
plot.xlabel('Time (ms)')
plot.ylabel('Reward')
#-------------------------------------------------------------------------------------
fig.save(path=figspath, name=name)
fig.close()
def do(action, args, p):
"""
Manage tasks.
"""
print("ACTION*: " + str(action))
print("ARGS*: " + str(args))
#-------------------------------------------------------------------------------------
# Trials
#-------------------------------------------------------------------------------------
if action == 'trials':
run_trials(p, args)
#-------------------------------------------------------------------------------------
# Psychometric function
#-------------------------------------------------------------------------------------
elif action == 'psychometric':
fig = Figure()
plot = fig.add()
#---------------------------------------------------------------------------------
# Plot
#---------------------------------------------------------------------------------
trialsfile = get_trialsfile(p)
psychometric_function(trialsfile, plot)
plot.xlabel(r'Rate (events/sec)')
plot.ylabel(r'Percent high')
#---------------------------------------------------------------------------------
fig.save(path=p['figspath'], name=p['name'] + '_' + action)
fig.close()
#-------------------------------------------------------------------------------------
# Sort
#-------------------------------------------------------------------------------------
elif action == 'sort':
trialsfile = get_trialsfile(p)
sortedfile = get_sortedfile(p)
sort_trials(trialsfile, sortedfile)
#-------------------------------------------------------------------------------------
# Plot single-unit activity
#-------------------------------------------------------------------------------------
elif action == 'units':
from glob import glob
# Remove existing files
print("[ {}.do ]".format(THIS))
filenames = glob('{}_unit*'.format(join(p['figspath'], p['name'])))
for filename in filenames:
os.remove(filename)
print(" Removed {}".format(filename))
# Load sorted trials
sortedfile = get_sortedfile(p)
with open(sortedfile) as f:
t, sorted_trials = pickle.load(f)
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
fig = Figure()
plot = fig.add()
#---------------------------------------------------------------------------------
# Plot
#---------------------------------------------------------------------------------
plot_unit(i, sortedfile, plot)
plot.xlabel('Time (ms)')
plot.ylabel('Firing rate (a.u.)')
#---------------------------------------------------------------------------------
fig.save(path=p['figspath'],
name=p['name'] + '_unit{:03d}'.format(i))
fig.close()
#-------------------------------------------------------------------------------------
else:
print("[ {}.do ] Unrecognized action.".format(THIS))
betafile = join(base, 'examples', 'work', 'data', 'mante', 'mante_beta.pkl')
units = [16, 17, 64, 74]
#=========================================================================================
# Figure setup
#=========================================================================================
w = 7.5
h = 6.7
r = w / h
fig = Figure(w=w,
h=h,
axislabelsize=6.5,
labelpadx=5,
labelpady=5,
thickness=0.6,
ticksize=3,
ticklabelsize=6,
ticklabelpad=2,
format=paper.format)
#-----------------------------------------------------------------------------------------
# Psychometric functions
#-----------------------------------------------------------------------------------------
w_psy = 0.16
dx_psy = w_psy + 0.07
x_psy = 0.08
h_psy = 0.15
dy_psy = h_psy + 0.07
def do(action, args, p):
print("ACTION*: " + str(action))
print("ARGS*: " + str(args))
#-------------------------------------------------------------------------------------
# Trials
#-------------------------------------------------------------------------------------
if action == 'trials':
run_trials(p, args)
#-------------------------------------------------------------------------------------
# Psychometric function
#-------------------------------------------------------------------------------------
elif action == 'psychometric':
#---------------------------------------------------------------------------------
# Figure setup
#---------------------------------------------------------------------------------
w = 6.5
h = 3
fig = Figure(w=w,
h=h,
axislabelsize=7.5,
labelpadx=6,
labelpady=7.5,
thickness=0.6,
ticksize=3,
ticklabelsize=6.5,
ticklabelpad=2)
w = 0.39
h = 0.7
L = 0.09
R = L + w + 0.1
y = 0.2
plots = {'m': fig.add([L, y, w, h]), 'c': fig.add([R, y, w, h])}
#---------------------------------------------------------------------------------
# Labels
#---------------------------------------------------------------------------------
plot = plots['m']
plot.xlabel('Motion coherence (\%)')
plot.ylabel('Choice to right (\%)')
plot = plots['c']
plot.xlabel('Color coherence (\%)')
plot.ylabel('Choice to green (\%)')
#---------------------------------------------------------------------------------
# Plot
#---------------------------------------------------------------------------------
trialsfile = get_trialsfile(p)
psychometric_function(trialsfile, plots)
# Legend
prop = {
'prop': {
'size': 7
},
'handlelength': 1.2,
'handletextpad': 1.1,
'labelspacing': 0.5
}
plots['m'].legend(bbox_to_anchor=(0.41, 1), **prop)
#---------------------------------------------------------------------------------
fig.save(path=p['figspath'], name=p['name'] + '_' + action)
fig.close()
#-------------------------------------------------------------------------------------
# Sort
#-------------------------------------------------------------------------------------
elif action == 'sort':
trialsfile = get_trialsfile(p)
sortedfile = get_sortedfile(p)
sort_trials(trialsfile, sortedfile)
#-------------------------------------------------------------------------------------
# Plot single-unit activity
#-------------------------------------------------------------------------------------
elif action == 'units':
from glob import glob
# Remove existing files
print("[ {}.do ]".format(THIS))
filenames = glob('{}_unit*'.format(join(p['figspath'], p['name'])))
for filename in filenames:
os.remove(filename)
print(" Removed {}".format(filename))
trialsfile = get_trialsfile(p)
sortedfile = get_sortedfile(p)
units = get_active_units(trialsfile)
for unit in units:
#-----------------------------------------------------------------------------
# Figure setup
#-----------------------------------------------------------------------------
w = 2.5
h = 6
fig = Figure(w=w,
h=h,
axislabelsize=7.5,
labelpadx=6,
labelpady=7.5,
thickness=0.6,
ticksize=3,
ticklabelsize=6.5,
ticklabelpad=2)
w = 0.55
x0 = 0.3
h = 0.17
dy = h + 0.06
y0 = 0.77
y1 = y0 - dy
y2 = y1 - dy
y3 = y2 - dy
plots = {
'choice': fig.add([x0, y0, w, h]),
'motion_choice': fig.add([x0, y1, w, h]),
'colour_choice': fig.add([x0, y2, w, h]),
'context_choice': fig.add([x0, y3, w, h])
}
#-----------------------------------------------------------------------------
# Plot
#-----------------------------------------------------------------------------
plot_unit(unit, sortedfile, plots, sortby_fontsize=7)
plots['context_choice'].xlabel('Time (ms)')
#-----------------------------------------------------------------------------
fig.save(path=p['figspath'],
name=p['name'] + '_unit{:03d}'.format(unit))
fig.close()
print("[ {}.do ] {} units processed.".format(THIS, len(units)))
#-------------------------------------------------------------------------------------
# Regress
#-------------------------------------------------------------------------------------
elif action == 'regress':
trialsfile = get_trialsfile(p)
sortedfile = get_sortedfile(p)
betafile = get_betafile(p)
regress(trialsfile, sortedfile, betafile)
#-------------------------------------------------------------------------------------
else:
print("[ {}.do ] Unrecognized action.".format(THIS))
('rdm_fixed', '3C: Decision-making (Dale, fixed)'),
('mante', '4: Context-dependent int.'),
('mante_areas', '5B: Context-dependent int. (areas)'),
('multisensory', '6: Multisensory int.'),
('romo', '7: Parametric working memory'), ('lee', '8: Lee')]
labels = list('ABCDEFGHIJ')
#=========================================================================================
# Figure setup
#=========================================================================================
fig = Figure(w=3.8,
h=6.75,
axislabelsize=6.5,
labelpadx=4,
labelpady=3,
thickness=0.6,
ticksize=3,
ticklabelsize=6,
ticklabelpad=2,
format=paper.format)
ncols = 2
nrows = 5
w = 0.36
dx = w + 0.12
x0 = 0.12
xall = x0 + dx * np.arange(ncols)
h = 0.13
y0 = 0.82
}
# Color map
cmap = mpl.cm.jet
norm = mpl.colors.Normalize(vmin=m.fmin, vmax=m.fmax)
smap = mpl.cm.ScalarMappable(norm, cmap) #To plot frequencies as different colors
#=========================================================================================
# Figure setup
#=========================================================================================
w = 6.3 #width, height, and the ratio
h = 4
r = w/h
fig = Figure(w=w, h=h, axislabelsize=7, labelpadx=5, labelpady=5.5, #initiate figure
thickness=0.6, ticksize=3, ticklabelsize=6, ticklabelpad=2,
format=paper.format)
#-----------------------------------------------------------------------------------------
# Inputs
#-----------------------------------------------------------------------------------------
w = 0.25
dx = w + 0.04
x0 = 0.09
h = 0.165
dy = h + 0.045
y0 = 0.78
y1 = y0 - dy