def sort(trialsfile, plots, unit=None, network='p', **kwargs):
# Load trials
data = utils.load(trialsfile)
if len(data) == 9:
trials, U, Z, A, P, M, perf, r_p, r_v = data
else:
trials, U, Z, Z_b, A, P, M, perf, r_p, r_v = data
if network == 'p':
print("Sorting policy network activity.")
r = r_p
else:
print("Sorting value network activity.")
r = r_v
# Number of units
N = r.shape[-1]
# Time
time = trials[0]['time']
Ntime = len(time)
# Aligned time
time_a = np.concatenate((-time[1:][::-1], time))
Ntime_a = len(time_a)
#=====================================================================================
# Preferred targets
#=====================================================================================
preferred_targets = get_preferred_targets(trials, perf, r)
#=====================================================================================
# No-wager trials
#=====================================================================================
def get_no_wager(func_t0):
trials_by_cond = {}
for n, trial in enumerate(trials):
if trial['wager']:
continue
if trial['coh'] == 0:
continue
if perf.choices[n] is None:
continue
cond = trial['left_right']
m_n = np.tile(M[:,n], (N, 1)).T
r_n = r[:,n]*m_n
t0 = func_t0(trial['epochs'], perf.t_choices[n])
# Storage
trials_by_cond.setdefault(cond, {'r': np.zeros((Ntime_a, N)),
'n': np.zeros((Ntime_a, N))})
# Before
n_b = r_n[:t0].shape[0]
trials_by_cond[cond]['r'][Ntime-1-n_b:Ntime-1] += r_n[:t0]
trials_by_cond[cond]['n'][Ntime-1-n_b:Ntime-1] += m_n[:t0]
# After
n_a = r_n[t0:].shape[0]
trials_by_cond[cond]['r'][Ntime-1:Ntime-1+n_a] += r_n[t0:]
trials_by_cond[cond]['n'][Ntime-1:Ntime-1+n_a] += m_n[t0:]
# Average
for cond in trials_by_cond:
trials_by_cond[cond] = utils.div(trials_by_cond[cond]['r'],
trials_by_cond[cond]['n'])
return trials_by_cond
noTs_stimulus = get_no_wager(lambda epochs, t_choice: epochs['stimulus'][0] - 1)
noTs_choice = get_no_wager(lambda epochs, t_choice: t_choice)
#=====================================================================================
# Wager trials, aligned to stimulus onset
#=====================================================================================
def get_wager(func_t0):
trials_by_cond = {}
trials_by_cond_sure = {}
for n, trial in enumerate(trials):
if not trial['wager']:
continue
if perf.choices[n] is None:
continue
if trial['coh'] == 0:
continue
cond = trial['left_right']
m_n = np.tile(M[:,n], (N, 1)).T
r_n = r[:,n]*m_n
t0 = func_t0(trial['epochs'], perf.t_choices[n])
if perf.choices[n] == 'S':
# Storage
trials_by_cond_sure.setdefault(cond, {'r': np.zeros((Ntime_a, N)),
'n': np.zeros((Ntime_a, N))})
# Before
n_b = r_n[:t0].shape[0]
trials_by_cond_sure[cond]['r'][Ntime-1-n_b:Ntime-1] += r_n[:t0]
trials_by_cond_sure[cond]['n'][Ntime-1-n_b:Ntime-1] += m_n[:t0]
# After
n_a = r_n[t0:].shape[0]
trials_by_cond_sure[cond]['r'][Ntime-1:Ntime-1+n_a] += r_n[t0:]
trials_by_cond_sure[cond]['n'][Ntime-1:Ntime-1+n_a] += m_n[t0:]
else:
# Storage
trials_by_cond.setdefault(cond, {'r': np.zeros((Ntime_a, N)),
'n': np.zeros((Ntime_a, N))})
# Before
n_b = r_n[:t0].shape[0]
trials_by_cond[cond]['r'][Ntime-1-n_b:Ntime-1] += r_n[:t0]
trials_by_cond[cond]['n'][Ntime-1-n_b:Ntime-1] += m_n[:t0]
# After
n_a = r_n[t0:].shape[0]
trials_by_cond[cond]['r'][Ntime-1:Ntime-1+n_a] += r_n[t0:]
trials_by_cond[cond]['n'][Ntime-1:Ntime-1+n_a] += m_n[t0:]
# Average
for cond in trials_by_cond:
trials_by_cond[cond] = utils.div(trials_by_cond[cond]['r'],
trials_by_cond[cond]['n'])
# Average
for cond in trials_by_cond_sure:
trials_by_cond_sure[cond] = utils.div(trials_by_cond_sure[cond]['r'],
trials_by_cond_sure[cond]['n'])
return trials_by_cond, trials_by_cond_sure
Ts_stimulus, Ts_stimulus_sure = get_wager(lambda epochs, t_choice: epochs['stimulus'][0] - 1)
Ts_sure, Ts_sure_sure = get_wager(lambda epochs, t_choice: epochs['sure'][0] - 1)
Ts_choice, Ts_choice_sure = get_wager(lambda epochs, t_choice: t_choice)
#=====================================================================================
# Plot
#=====================================================================================
lw = kwargs.get('lw', 1.25)
dashes = kwargs.get('dashes', [3, 1.5])
in_opp_colors = {-1: '0.6', +1: 'k'}
def plot_noTs(noTs, plot, unit, tmin, tmax):
w, = np.where((tmin <= time_a) & (time_a <= tmax))
t = time_a[w]
yall = [[1]]
for lr in noTs:
color = in_opp_colors[lr*preferred_targets[unit]]
y = noTs[lr][w,unit]
plot.plot(t, y, color=color, lw=lw)
yall.append(y)
plot.xlim(tmin, tmax)
plot.xticks([0, tmax])
plot.lim('y', yall, lower=0)
return yall
def plot_Ts(Ts, Ts_sure, plot, unit, tmin, tmax):
w, = np.where((tmin <= time_a) & (time_a <= tmax))
t = time_a[w]
yall = [[1]]
for lr in Ts:
color = in_opp_colors[lr*preferred_targets[unit]]
y = Ts[lr][w,unit]
plot.plot(t, y, color=color, lw=lw)
yall.append(y)
for lr in Ts_sure:
color = in_opp_colors[lr*preferred_targets[unit]]
y = Ts_sure[lr][w,unit]
plot.plot(t, y, color=color, lw=lw, linestyle='--', dashes=dashes)
yall.append(y)
plot.xlim(tmin, tmax)
plot.xticks([0, tmax])
plot.lim('y', yall, lower=0)
return yall
if unit is not None:
y = []
tmin = kwargs.get('noTs-stimulus-tmin', -100)
tmax = kwargs.get('noTs-stimulus-tmax', 700)
y += plot_noTs(noTs_stimulus, plots['noTs-stimulus'], unit, tmin, tmax)
tmin = kwargs.get('noTs-choice-tmin', -500)
tmax = kwargs.get('noTs-choice-tmax', 0)
y += plot_noTs(noTs_choice, plots['noTs-choice'], unit, tmin, tmax)
tmin = kwargs.get('Ts-stimulus-tmin', -100)
tmax = kwargs.get('Ts-stimulus-tmax', 700)
y += plot_Ts(Ts_stimulus, Ts_stimulus_sure, plots['Ts-stimulus'], unit, tmin, tmax)
tmin = kwargs.get('Ts-sure-tmin', -200)
tmax = kwargs.get('Ts-sure-tmax', 700)
y += plot_Ts(Ts_sure, Ts_sure_sure, plots['Ts-sure'], unit, tmin, tmax)
tmin = kwargs.get('Ts-choice-tmin', -500)
tmax = kwargs.get('Ts-choice-tmax', 0)
y += plot_Ts(Ts_choice, Ts_choice_sure, plots['Ts-choice'], unit, tmin, tmax)
return y
else:
name = plots
for unit in xrange(N):
w = utils.mm_to_inch(174)
r = 0.35
fig = Figure(w=w, r=r)
x0 = 0.09
y0 = 0.15
w = 0.13
h = 0.75
dx = 0.05
DX = 0.08
fig.add('noTs-stimulus', [x0, y0, w, h])
fig.add('noTs-choice', [fig[-1].right+dx, y0, w, h])
fig.add('Ts-stimulus', [fig[-1].right+DX, y0, w, h])
fig.add('Ts-sure', [fig[-1].right+dx, y0, w, h])
fig.add('Ts-choice', [fig[-1].right+dx, y0, w, h])
#-----------------------------------------------------------------------------
y = []
plot = fig['noTs-stimulus']
y += plot_noTs(noTs_stimulus, plot, unit, -100, 700)
plot.vline(0)
plot = fig['noTs-choice']
y += plot_noTs(noTs_choice, plot, unit, -500, 200)
plot.vline(0)
plot = fig['Ts-stimulus']
y += plot_Ts(Ts_stimulus, Ts_stimulus_sure, plot, unit, -100, 700)
plot.vline(0)
plot = fig['Ts-sure']
y += plot_Ts(Ts_sure, Ts_sure_sure, plot, unit, -200, 700)
plot.vline(0)
plot = fig['Ts-choice']
y += plot_Ts(Ts_choice, Ts_choice_sure, plot, unit, -500, 200)
plot.vline(0)
for plot in fig.plots.values():
plot.lim('y', y, lower=0)
#-----------------------------------------------------------------------------
fig.save(name+'_{}{:03d}'.format(network, unit))
fig.close()
# analysis/rdm
rdm_analysisfile = os.path.join(analysispath, 'rdm.py')
rdm_analysis = imp.load_source('rdm_analysis', rdm_analysisfile)
# models/rdm_fixed
rdm_fixed_modelfile = os.path.join(modelspath, 'rdm_fixed.py')
rdm_fixed_model = imp.load_source('rdm_fixed_model', rdm_fixed_modelfile)
rdm_fixed_behavior = os.path.join(trialspath, 'rdm_fixed',
'trials_behavior.pkl')
rdm_fixed_activity = os.path.join(trialspath, 'rdm_fixed',
'trials_activity.pkl')
#=========================================================================================
w = utils.mm_to_inch(174)
r = 0.5
fig = Figure(w=w, r=r)
x0 = 0.12
y0 = 0.15
w = 0.37
h = 0.8
dx = 1.3 * w
fig.add('on-stimulus', [x0, y0, w, h])
fig.add('on-choice', [x0 + dx, y0, w, h])
#=========================================================================================
kwargs = {
analysispath = os.path.join(parent, 'examples', 'analysis')
modelspath = os.path.join(parent, 'examples', 'models')
# analysis/rdm
rdm_analysisfile = os.path.join(analysispath, 'rdm.py')
rdm_analysis = imp.load_source('rdm_analysis', rdm_analysisfile)
# models/rdm_fixed
rdm_fixed_modelfile = os.path.join(modelspath, 'rdm_fixed.py')
rdm_fixed_model = imp.load_source('rdm_fixed_model', rdm_fixed_modelfile)
rdm_fixed_behavior = os.path.join(trialspath, 'rdm_fixed', 'trials_behavior.pkl')
rdm_fixed_activity = os.path.join(trialspath, 'rdm_fixed', 'trials_activity.pkl')
#=========================================================================================
w = utils.mm_to_inch(174)
r = 0.5
fig = Figure(w=w, r=r)
x0 = 0.12
y0 = 0.15
w = 0.37
h = 0.8
dx = 1.3*w
fig.add('on-stimulus', [x0, y0, w, h])
fig.add('on-choice', [x0+dx, y0, w, h])
#=========================================================================================
kwargs = {'on-stimulus-tmin': -200, 'on-stimulus-tmax': 600,
def sort(trialsfile, plots, unit=None, network='p', **kwargs):
# Load trials
data = utils.load(trialsfile)
if len(data) == 9:
trials, U, Z, A, P, M, perf, r_p, r_v = data
else:
trials, U, Z, Z_b, A, P, M, perf, r_p, r_v = data
if network == 'p':
print("Sorting policy network activity.")
r = r_p
else:
print("Sorting value network activity.")
r = r_v
# Number of units
N = r.shape[-1]
# Time
time = trials[0]['time']
Ntime = len(time)
# Aligned time
time_a = np.concatenate((-time[1:][::-1], time))
Ntime_a = len(time_a)
#=====================================================================================
# Preferred targets
#=====================================================================================
preferred_targets = get_preferred_targets(trials, perf, r)
#=====================================================================================
# No-wager trials
#=====================================================================================
def get_no_wager(func_t0):
trials_by_cond = {}
for n, trial in enumerate(trials):
if trial['wager']:
continue
if trial['coh'] == 0:
continue
if perf.choices[n] is None:
continue
cond = trial['left_right']
m_n = np.tile(M[:,n], (N, 1)).T
r_n = r[:,n]*m_n
t0 = func_t0(trial['epochs'], perf.t_choices[n])
# Storage
trials_by_cond.setdefault(cond, {'r': np.zeros((Ntime_a, N)),
'n': np.zeros((Ntime_a, N))})
# Before
n_b = r_n[:t0].shape[0]
trials_by_cond[cond]['r'][Ntime-1-n_b:Ntime-1] += r_n[:t0]
trials_by_cond[cond]['n'][Ntime-1-n_b:Ntime-1] += m_n[:t0]
# After
n_a = r_n[t0:].shape[0]
trials_by_cond[cond]['r'][Ntime-1:Ntime-1+n_a] += r_n[t0:]
trials_by_cond[cond]['n'][Ntime-1:Ntime-1+n_a] += m_n[t0:]
# Average
for cond in trials_by_cond:
trials_by_cond[cond] = utils.div(trials_by_cond[cond]['r'],
trials_by_cond[cond]['n'])
return trials_by_cond
noTs_stimulus = get_no_wager(lambda epochs, t_choice: epochs['stimulus'][0] - 1)
noTs_choice = get_no_wager(lambda epochs, t_choice: t_choice)
#=====================================================================================
# Wager trials, aligned to stimulus onset
#=====================================================================================
def get_wager(func_t0):
trials_by_cond = {}
trials_by_cond_sure = {}
for n, trial in enumerate(trials):
if not trial['wager']:
continue
if perf.choices[n] is None:
continue
if trial['coh'] == 0:
continue
cond = trial['left_right']
m_n = np.tile(M[:,n], (N, 1)).T
r_n = r[:,n]*m_n
t0 = func_t0(trial['epochs'], perf.t_choices[n])
if perf.choices[n] == 'S':
# Storage
trials_by_cond_sure.setdefault(cond, {'r': np.zeros((Ntime_a, N)),
'n': np.zeros((Ntime_a, N))})
# Before
n_b = r_n[:t0].shape[0]
trials_by_cond_sure[cond]['r'][Ntime-1-n_b:Ntime-1] += r_n[:t0]
trials_by_cond_sure[cond]['n'][Ntime-1-n_b:Ntime-1] += m_n[:t0]
# After
n_a = r_n[t0:].shape[0]
trials_by_cond_sure[cond]['r'][Ntime-1:Ntime-1+n_a] += r_n[t0:]
trials_by_cond_sure[cond]['n'][Ntime-1:Ntime-1+n_a] += m_n[t0:]
else:
# Storage
trials_by_cond.setdefault(cond, {'r': np.zeros((Ntime_a, N)),
'n': np.zeros((Ntime_a, N))})
# Before
n_b = r_n[:t0].shape[0]
trials_by_cond[cond]['r'][Ntime-1-n_b:Ntime-1] += r_n[:t0]
trials_by_cond[cond]['n'][Ntime-1-n_b:Ntime-1] += m_n[:t0]
# After
n_a = r_n[t0:].shape[0]
trials_by_cond[cond]['r'][Ntime-1:Ntime-1+n_a] += r_n[t0:]
trials_by_cond[cond]['n'][Ntime-1:Ntime-1+n_a] += m_n[t0:]
# Average
for cond in trials_by_cond:
trials_by_cond[cond] = utils.div(trials_by_cond[cond]['r'],
trials_by_cond[cond]['n'])
# Average
for cond in trials_by_cond_sure:
trials_by_cond_sure[cond] = utils.div(trials_by_cond_sure[cond]['r'],
trials_by_cond_sure[cond]['n'])
return trials_by_cond, trials_by_cond_sure
Ts_stimulus, Ts_stimulus_sure = get_wager(lambda epochs, t_choice: epochs['stimulus'][0] - 1)
Ts_sure, Ts_sure_sure = get_wager(lambda epochs, t_choice: epochs['sure'][0] - 1)
Ts_choice, Ts_choice_sure = get_wager(lambda epochs, t_choice: t_choice)
#=====================================================================================
# Plot
#=====================================================================================
lw = kwargs.get('lw', 1.25)
dashes = kwargs.get('dashes', [3, 1.5])
in_opp_colors = {-1: '0.6', +1: 'k'}
def plot_noTs(noTs, plot, unit, tmin, tmax):
w, = np.where((tmin <= time_a) & (time_a <= tmax))
t = time_a[w]
yall = [[1]]
for lr in noTs:
color = in_opp_colors[lr*preferred_targets[unit]]
y = noTs[lr][w,unit]
plot.plot(t, y, color=color, lw=lw)
yall.append(y)
plot.xlim(tmin, tmax)
plot.xticks([0, tmax])
plot.lim('y', yall, lower=0)
return yall
def plot_Ts(Ts, Ts_sure, plot, unit, tmin, tmax):
w, = np.where((tmin <= time_a) & (time_a <= tmax))
t = time_a[w]
yall = [[1]]
for lr in Ts:
color = in_opp_colors[lr*preferred_targets[unit]]
y = Ts[lr][w,unit]
plot.plot(t, y, color=color, lw=lw)
yall.append(y)
for lr in Ts_sure:
color = in_opp_colors[lr*preferred_targets[unit]]
y = Ts_sure[lr][w,unit]
plot.plot(t, y, color=color, lw=lw, linestyle='--', dashes=dashes)
yall.append(y)
plot.xlim(tmin, tmax)
plot.xticks([0, tmax])
plot.lim('y', yall, lower=0)
return yall
if unit is not None:
y = []
tmin = kwargs.get('noTs-stimulus-tmin', -100)
tmax = kwargs.get('noTs-stimulus-tmax', 700)
y += plot_noTs(noTs_stimulus, plots['noTs-stimulus'], unit, tmin, tmax)
tmin = kwargs.get('noTs-choice-tmin', -500)
tmax = kwargs.get('noTs-choice-tmax', 0)
y += plot_noTs(noTs_choice, plots['noTs-choice'], unit, tmin, tmax)
tmin = kwargs.get('Ts-stimulus-tmin', -100)
tmax = kwargs.get('Ts-stimulus-tmax', 700)
y += plot_Ts(Ts_stimulus, Ts_stimulus_sure, plots['Ts-stimulus'], unit, tmin, tmax)
tmin = kwargs.get('Ts-sure-tmin', -200)
tmax = kwargs.get('Ts-sure-tmax', 700)
y += plot_Ts(Ts_sure, Ts_sure_sure, plots['Ts-sure'], unit, tmin, tmax)
tmin = kwargs.get('Ts-choice-tmin', -500)
tmax = kwargs.get('Ts-choice-tmax', 0)
y += plot_Ts(Ts_choice, Ts_choice_sure, plots['Ts-choice'], unit, tmin, tmax)
return y
else:
name = plots
for unit in xrange(N):
w = utils.mm_to_inch(174)
r = 0.35
fig = Figure(w=w, r=r)
x0 = 0.09
y0 = 0.15
w = 0.13
h = 0.75
dx = 0.05
DX = 0.08
fig.add('noTs-stimulus', [x0, y0, w, h])
fig.add('noTs-choice', [fig[-1].right+dx, y0, w, h])
fig.add('Ts-stimulus', [fig[-1].right+DX, y0, w, h])
fig.add('Ts-sure', [fig[-1].right+dx, y0, w, h])
fig.add('Ts-choice', [fig[-1].right+dx, y0, w, h])
#-----------------------------------------------------------------------------
y = []
plot = fig['noTs-stimulus']
y += plot_noTs(noTs_stimulus, plot, unit, -100, 700)
plot.vline(0)
plot = fig['noTs-choice']
y += plot_noTs(noTs_choice, plot, unit, -500, 200)
plot.vline(0)
plot = fig['Ts-stimulus']
y += plot_Ts(Ts_stimulus, Ts_stimulus_sure, plot, unit, -100, 700)
plot.vline(0)
plot = fig['Ts-sure']
y += plot_Ts(Ts_sure, Ts_sure_sure, plot, unit, -200, 700)
plot.vline(0)
plot = fig['Ts-choice']
y += plot_Ts(Ts_choice, Ts_choice_sure, plot, unit, -500, 200)
plot.vline(0)
for plot in fig.plots.values():
plot.lim('y', y, lower=0)
#-----------------------------------------------------------------------------
fig.save(name+'_{}{:03d}'.format(network, unit))
fig.close()
analysisfile = os.path.join(analysispath, 'postdecisionwager.py')
analysis = imp.load_source('postdecisionwager_analysis', analysisfile)
# model
modelfile = os.path.join(modelspath, 'postdecisionwager.py')
model = imp.load_source('model', modelfile)
trialsfile_b = os.path.join(trialspath, 'postdecisionwager',
'trials_behavior.pkl')
trialsfile_a = os.path.join(trialspath, 'postdecisionwager',
'trials_activity.pkl')
#=========================================================================================
# Figure
#=========================================================================================
w = utils.mm_to_inch(114)
r = 0.9
thickness = 0.4
axislabelsize = 6
fig = Figure(w=w,
r=r,
thickness=thickness,
ticksize=3,
ticklabelsize=5,
axislabelsize=axislabelsize,
labelpadx=3,
labelpady=4.5)
x0 = 0.16
y0 = 0.67
DX = 0.1
# analysis
analysisfile = os.path.join(analysispath, 'postdecisionwager.py')
analysis = imp.load_source('postdecisionwager_analysis', analysisfile)
# model
modelfile = os.path.join(modelspath, 'postdecisionwager.py')
model = imp.load_source('model', modelfile)
trialsfile_b = os.path.join(trialspath, 'postdecisionwager', 'trials_behavior.pkl')
trialsfile_a = os.path.join(trialspath, 'postdecisionwager', 'trials_activity.pkl')
#=========================================================================================
# Figure
#=========================================================================================
w = utils.mm_to_inch(114)
r = 0.9
thickness = 0.4
axislabelsize = 6
fig = Figure(w=w, r=r, thickness=thickness, ticksize=3, ticklabelsize=5,
axislabelsize=axislabelsize, labelpadx=3, labelpady=4.5)
x0 = 0.16
y0 = 0.67
DX = 0.1
w_task = 0.82
h_task = 0.3
dy0 = 0.09
dy = 0.12
