def plot_p_ch_vs_ev(ev_cond,
p_ch,
style='pred',
ax: plt.Axes = None,
**kwargs) -> plt.Line2D:
"""
@param ev_cond: [condition] or [condition, frame]
@type ev_cond: torch.Tensor
@param p_ch: [condition, ch] or [condition, rt_frame, ch]
@type p_ch: torch.Tensor
@return:
"""
if ax is None:
ax = plt.gca()
if ev_cond.ndim != 1:
if ev_cond.ndim == 3:
ev_cond = npt.p2st(ev_cond)[0]
assert ev_cond.ndim == 2
ev_cond = ev_cond.mean(1)
if p_ch.ndim != 2:
assert p_ch.ndim == 3
p_ch = p_ch.sum(1)
kwargs = get_kw_plot(style, **kwargs)
h = ax.plot(*npys(ev_cond, npt.p2st(npt.sumto1(p_ch, -1))[1]), **kwargs)
plt2.box_off(ax=ax)
x_lim = ax.get_xlim()
plt2.detach_axis('x', amin=x_lim[0], amax=x_lim[1], ax=ax)
plt2.detach_axis('y', amin=0, amax=1, ax=ax)
ax.set_yticks([0, 0.5, 1])
ax.set_yticklabels(['0', '', '1'])
ax.set_xlabel('evidence')
ax.set_ylabel(r"$\mathrm{P}(z=1 \mid c)$")
return h
def beautify(ax):
xticks = np.arange(0, 1.2 + 0.1, 0.1)
plt.sca(ax)
plt.xlim(xmax=1.2 * 1.05)
plt2.detach_axis('x', 0, 1.2)
plt2.box_off()
def plot_params(self,
named_bounded_params: Sequence[Tuple[
str, BoundedParameter]] = None,
exclude: Iterable[str] = (),
cmap='coolwarm',
ax: plt.Axes = None) -> mpl.container.BarContainer:
if ax is None:
ax = plt.gca()
ax = plt.gca()
names, v, grad, lb, ub, requires_grad = self.get_named_bounded_params(
named_bounded_params, exclude=exclude)
max_grad = np.amax(np.abs(grad))
if max_grad == 0:
max_grad = 1.
v01 = (v - lb) / (ub - lb)
grad01 = (grad + max_grad) / (max_grad * 2)
n = len(v)
grad01[~requires_grad] = np.nan
# (np.amin(grad01) + np.amax(grad01)) / 2
# ax = plt.gca() # CHECKED
for i, (lb1, v1, ub1, g1,
r1) in enumerate(zip(lb, v, ub, grad, requires_grad)):
color = 'k' if r1 else 'gray'
plt.text(0, i, '%1.2g' % lb1, ha='left', va='center', color=color)
plt.text(1, i, '%1.2g' % ub1, ha='right', va='center', color=color)
plt.text(
0.5,
i,
'%1.2g %s' %
(v1, ('(e%1.0f)' % np.log10(np.abs(g1))) if r1 else '(fixed)'),
ha='center',
va='center',
color=color)
lut = 256
colors = plt.get_cmap(cmap, lut)(grad01)
for i, r in enumerate(requires_grad):
if not r:
colors[i, :3] = np.array([0.95, 0.95, 0.95])
h = ax.barh(np.arange(n), v01, left=0, color=colors)
ax.set_xlim(-0.025, 1)
ax.set_xticks([])
ax.set_yticks(np.arange(n))
names = [v.replace('_', '-') for v in names]
ax.set_yticklabels(names)
ax.set_ylim(n - 0.5, -0.5)
plt2.box_off(['top', 'right', 'bottom'])
plt2.detach_axis('x', amin=0, amax=1)
plt2.detach_axis('y', amin=0, amax=n - 1)
# plt.show() # CHECKED
return h
def beautify(row, col, axs):
plt.sca(axs[row, col])
plt.xlim(xmax=1.2 * 1.05)
plt2.detach_axis('x', 0, 1.2)
plt2.box_off()
beautify_ticks(axs[row, col],
add_ticklabel=(row == n_row - 1) and (col == 0))
if col > 0:
plt.ylabel('')
def plot_bars(gs1, bufdurs, losses1, add_xticklabel=True):
i_break = np.amax(np.nonzero(np.array(bufdurs) < 0.3)[0])
bax = brokenaxes(
subplot_spec=gs1,
xlims=((-1, i_break + 0.5), (i_break + 0.5,
len(bufdurs) - 0.5)),
ylims=((-3, 20), (20, 1250 / 5)),
height_ratios=(50 / 100, 500 / (1250 - 100)),
hspace=0.15,
wspace=0.075,
d=0.005,
)
bax.bar(np.arange(len(bufdurs)), losses1[i_subj, :], color='k')
ax11 = bax.axs[3] # type: plt.Axes
ax11.set_xticks([bufdurs.index(0.6), bufdurs.index(1.2)])
if i_subj == 0 and add_xticklabel:
ax11.set_xticklabels(['0.6', '1.2'])
else:
ax11.set_xticklabels([])
ax00 = bax.axs[0] # type: plt.Axes
ax00.set_yticks([500, 1000])
ax10 = bax.axs[2] # type: plt.Axes
ax10.set_yticks([0, 50])
plt.sca(ax10)
plt2.detach_axis('x', amin=-0.4, amax=i_break + 0.5)
for ax in [ax10, ax11]:
plt.sca(ax)
plt.axhline(0, linewidth=0.5, color='k', linestyle='--')
for sign in [-1, 1]:
plt.axhline(sign * thres_strong,
linewidth=0.5,
color='silver',
linestyle='--')
ax10.set_xticks([bufdurs.index(0.), bufdurs.index(0.2)])
if i_subj == 0:
if add_xticklabel:
ax10.set_xticklabels(['0', '0.2'])
else:
ax10.set_xticklabels([])
else:
ax10.set_yticklabels([])
ax10.set_xticklabels([])
ax00.set_yticklabels([])
return bax
def plot_bound(self,
t_all: Sequence[float] = None,
ax: plt.Axes = None,
**kwargs) -> plt.Line2D:
if ax is None:
ax = plt.gca()
if t_all is None:
t_all = self.t_all
kwargs = argsutil.kwdefault(kwargs, color='k', linestyle='-')
h = ax.plot(*npys(t_all, self.get_bound(t_all)), **kwargs)
ax.set_xlabel('time (s)')
ax.set_ylabel(r"$b(t)$")
y_lim = ax.get_ylim()
y_min = -y_lim[1] * 0.05
ax.set_ylim(ymin=y_min)
plt2.detach_axis('y', amin=0)
plt2.detach_axis('x', amin=0)
plt2.box_off()
return h
def plot_p_tnd(self,
t_all: Sequence[float] = None,
ax: plt.Axes = None,
**kwargs) -> Union[plt.Line2D, Sequence[plt.Line2D]]:
if t_all is None:
t_all = self.t_all
if ax is None:
ax = plt.gca()
p_tnd = self.get_p_tnd(t_all)
kwargs = argsutil.kwdefault(kwargs, color='k', linestyle='-')
h = ax.plot(*npys(t_all, p_tnd), **kwargs)
ax.set_xlabel('time (s)')
plt2.box_off(ax=ax)
# ax.set_ylim(ymin=0)
# ax.set_xlim(xmin=0)
plt2.detach_axis('y', amin=0, amax=1, ax=ax)
plt2.detach_axis('x', amin=0, ax=ax)
return h
def plot_rt_distrib(
n_cond_rt_ch: np.ndarray,
ev_cond_dim: np.ndarray,
abs_cond=True,
lump_wrong=True,
dt=consts.DT,
colors=None,
alpha=1.,
alpha_face=0.5,
smooth_sigma_sec=0.05,
to_normalize_max=False,
to_cumsum=False,
to_exclude_last_frame=True,
to_skip_zero_trials=False,
label='',
# to_exclude_bins_wo_trials=10,
kw_plot=(),
fig=None,
axs=None,
to_use_sameaxes=True,
):
"""
:param n_cond_rt_ch:
:param ev_cond_dim:
:param abs_cond:
:param lump_wrong:
:param dt:
:param gs:
:param colors:
:param alpha:
:param smooth_sigma_sec:
:param kw_plot:
:param axs:
:return: axs, p_cond01__rt_ch01, p_cond01__rt_ch01_sm, hs
"""
if colors is None:
colors = ['red', 'blue']
elif type(colors) is str:
colors = [colors] * 2
else:
assert len(colors) == 2
nt = n_cond_rt_ch.shape[1]
t_all = np.arange(nt) * dt + dt
out = np.meshgrid(np.unique(ev_cond_dim[:, 0]),
np.unique(ev_cond_dim[:, 1]),
np.arange(nt),
np.arange(2),
np.arange(2),
indexing='ij')
cond0, cond1, fr, ch0, ch1 = [v.flatten() for v in out]
from copy import deepcopy
n0 = deepcopy(n_cond_rt_ch)
if to_exclude_last_frame:
n0[:, -1, :] = 0.
n0 = n0.flatten()
def sign_cond(v):
v1 = np.sign(v)
v1[v == 0] = 1
return v1
if abs_cond:
ch0 = consts.ch_bool2sign(ch0)
ch1 = consts.ch_bool2sign(ch1)
# 1 = correct, -1 = wrong
ch0 = sign_cond(cond0) * ch0
ch1 = sign_cond(cond1) * ch1
cond0 = np.abs(cond0)
cond1 = np.abs(cond1)
ch0 = consts.ch_sign2bool(ch0).astype(np.int)
ch1 = consts.ch_sign2bool(ch1).astype(np.int)
else:
raise ValueError()
if lump_wrong:
# treat all choices as correct when cond == 0
ch00 = ch0 | (cond0 == 0)
ch10 = ch1 | (cond1 == 0)
ch0 = (ch00 & ch10)
ch1 = np.ones_like(ch00, dtype=np.int)
cond_dim = np.stack([cond0, cond1], -1)
conds = []
dcond_dim = []
for cond in cond_dim.T:
conds1, dcond1 = np.unique(cond, return_inverse=True)
conds.append(conds1)
dcond_dim.append(dcond1)
dcond_dim = np.stack(dcond_dim)
n_cond01_rt_ch01 = npg.aggregate(
[*dcond_dim, fr, ch0, ch1], n0, 'sum',
[*(np.amax(dcond_dim, 1) + 1), nt, consts.N_CH, consts.N_CH])
p_cond01__rt_ch01 = np2.nan2v(n_cond01_rt_ch01 / n_cond01_rt_ch01.sum(
(2, 3, 4), keepdims=True))
n_conds = p_cond01__rt_ch01.shape[:2]
if axs is None:
axs = plt2.GridAxes(
n_conds[1],
n_conds[0],
left=0.6,
right=0.3,
bottom=0.45,
top=0.74,
widths=[1],
heights=[1],
wspace=0.04,
hspace=0.04,
)
kw_label = {
'fontsize': 12,
}
pad = 8
axs[0, 0].set_title('strong\nmotion', pad=pad, **kw_label)
axs[0, -1].set_title('weak\nmotion', pad=pad, **kw_label)
axs[0, 0].set_ylabel('strong\ncolor', labelpad=pad, **kw_label)
axs[-1, 0].set_ylabel('weak\ncolor', labelpad=pad, **kw_label)
if smooth_sigma_sec > 0:
from scipy import signal, stats
sigma_fr = smooth_sigma_sec / dt
width = np.ceil(sigma_fr * 2.5).astype(np.int)
kernel = stats.norm.pdf(np.arange(-width, width + 1), 0, sigma_fr)
kernel = np2.vec_on(kernel, 2, 5)
p_cond01__rt_ch01_sm = signal.convolve(p_cond01__rt_ch01,
kernel,
mode='same')
else:
p_cond01__rt_ch01_sm = p_cond01__rt_ch01.copy()
if to_cumsum:
p_cond01__rt_ch01_sm = np.cumsum(p_cond01__rt_ch01_sm, axis=2)
if to_normalize_max:
p_cond01__rt_ch01_sm = np2.nan2v(
p_cond01__rt_ch01_sm /
np.amax(np.abs(p_cond01__rt_ch01_sm), (2, 3, 4), keepdims=True))
n_row = n_conds[1]
n_col = n_conds[0]
for dcond0 in range(n_conds[0]):
for dcond1 in range(n_conds[1]):
row = n_row - 1 - dcond1
col = n_col - 1 - dcond0
ax = axs[row, col] # type: plt.Axes
for ch0 in [0, 1]:
for ch1 in [0, 1]:
if lump_wrong and ch1 == 0:
continue
p1 = p_cond01__rt_ch01_sm[dcond0, dcond1, :, ch0, ch1]
kw = {
'linewidth': 1,
'color': colors[ch1],
'alpha': alpha,
'zorder': 1,
**dict(kw_plot)
}
y = p1 * consts.ch_bool2sign(ch0)
p_cond01__rt_ch01_sm[dcond0, dcond1, :, ch0, ch1] = y
if to_skip_zero_trials and np.sum(np.abs(y)) < 1e-2:
h = None
else:
h = ax.plot(
t_all,
y,
label=label if ch0 == 1 and ch1 == 1 else None,
**kw)
ax.fill_between(t_all,
0,
y,
ec='None',
fc=kw['color'],
alpha=alpha_face,
zorder=-1)
plt2.box_off(ax=ax)
ax.axhline(0, color='k', linewidth=0.5)
ax.set_yticklabels([])
if row < n_row - 1 or col > 0:
ax.set_xticklabels([])
ax.set_xticks([])
plt2.box_off(['bottom'], ax=ax)
else:
ax.set_xlabel('RT (s)')
# if col > 0:
ax.set_yticks([])
ax.set_yticklabels([])
plt2.box_off(['left'], ax=ax)
plt2.detach_axis('x', 0, 5, ax=ax)
if to_use_sameaxes:
plt2.sameaxes(axs)
axs[-1, 0].set_xlabel('RT (s)')
return axs, p_cond01__rt_ch01, p_cond01__rt_ch01_sm, h
def plot_p_ch_vs_ev(
ev_cond: Union[torch.Tensor, np.ndarray],
n_ch: Union[torch.Tensor, np.ndarray],
style='pred',
ax: plt.Axes = None,
dim_rel=0,
group_dcond_irr: Iterable[Iterable[int]] = None,
cmap: Union[str, Callable] = 'cool',
kw_plot=(),
) -> Iterable[plt.Line2D]:
"""
@param ev_cond: [condition, dim] or [condition, frame, dim, (mean, var)]
@type ev_cond: torch.Tensor
@param n_ch: [condition, ch] or [condition, rt_frame, ch]
@type n_ch: torch.Tensor
@return: hs[cond_irr][0] = Line2D, conds_irr
"""
if ax is None:
ax = plt.gca()
if ev_cond.ndim != 2:
assert ev_cond.ndim == 4
ev_cond = npt.p2st(ev_cond.mean(1))[0]
if n_ch.ndim != 2:
assert n_ch.ndim == 3
n_ch = n_ch.sum(1)
ev_cond = npy(ev_cond)
n_ch = npy(n_ch)
n_cond_all = n_ch.shape[0]
ch_rel = np.repeat(np.array(consts.CHS[dim_rel])[None, :], n_cond_all, 0)
n_ch = n_ch.reshape([-1])
ch_rel = ch_rel.reshape([-1])
dim_irr = consts.get_odim(dim_rel)
conds_rel, dcond_rel = np.unique(ev_cond[:, dim_rel], return_inverse=True)
conds_irr, dcond_irr = np.unique(np.abs(ev_cond[:, dim_irr]),
return_inverse=True)
if group_dcond_irr is not None:
conds_irr, dcond_irr = group_conds(conds_irr, dcond_irr,
group_dcond_irr)
n_conds = [len(conds_rel), len(conds_irr)]
n_ch_rel = npg.aggregate(
np.stack([
ch_rel,
np.repeat(dcond_irr[:, None], consts.N_CH_FLAT, 1).flatten(),
np.repeat(dcond_rel[:, None], consts.N_CH_FLAT, 1).flatten(),
]), n_ch, 'sum', [consts.N_CH, n_conds[1], n_conds[0]])
p_ch_rel = n_ch_rel[1] / n_ch_rel.sum(0)
hs = []
for dcond_irr1, p_ch1 in enumerate(p_ch_rel):
if type(cmap) is str:
color = plt.get_cmap(cmap, n_conds[1])(dcond_irr1)
else:
color = cmap(n_conds[1])(dcond_irr1)
kw1 = get_kw_plot(style, color=color, **dict(kw_plot))
h = ax.plot(conds_rel, p_ch1, **kw1)
hs.append(h)
plt2.box_off(ax=ax)
x_lim = ax.get_xlim()
plt2.detach_axis('x', amin=x_lim[0], amax=x_lim[1], ax=ax)
plt2.detach_axis('y', amin=0, amax=1, ax=ax)
ax.set_yticks([0, 0.5, 1])
ax.set_yticklabels(['0', '', '1'])
ax.set_xlabel('evidence')
ax.set_ylabel(r"$\mathrm{P}(z=1 \mid c)$")
return hs, conds_irr
def plot_coef_by_dur_vs_odif(coef,
se_coef,
normalize_bias=True,
coef_name='slope',
savefig=True,
difs=[[2, 1], [0]],
t_RDK_durs=np.arange(1, 11) * 0.12,
fig=None,
horizontal_panels=False):
"""
@param dat0:
@param parad:
@param use_data:
@param correct_only:
@param plot_slope: if False, plot bias
@param i_subj:
@param savefig:
@param fig:
@return:
"""
if fig is None:
if horizontal_panels:
fig = plt.figure(figsize=(7, 2))
else:
fig = plt.figure(figsize=(4, 3))
if horizontal_panels:
gs = plt.GridSpec(figure=fig,
nrows=1,
ncols=2,
hspace=0.3,
left=0.11,
right=0.98,
top=0.9,
bottom=0.15)
else:
gs = mpl.gridspec.GridSpec(figure=fig,
nrows=2,
ncols=1,
left=0.22,
right=0.98,
top=0.9,
bottom=0.15)
coef_names = ['bias', 'slope']
i_coef = coef_names.index(coef_name)
units = ['(logit)', '(logit/coh)']
if normalize_bias:
coef[0] = -coef[0] / coef[1]
se_coef[0] = -se_coef[0] / coef[1]
units[0] = '(coh)'
unit = units[i_coef]
y = coef[i_coef]
se = se_coef[i_coef]
if len(difs) == 1:
labels = ['all']
elif len(difs) == 2:
labels = ['easy', 'hard']
elif len(difs) == 3:
labels = ['easy', 'medium', 'hard']
else:
raise ValueError()
for dim, (slope1, se_slope1) in enumerate(zip(y, se)):
odim = consts.N_DIM - 1 - dim
if horizontal_panels:
plt.subplot(gs[0, dim])
else:
plt.subplot(gs[dim, 0])
# plt.plot(t_RDK_durs, slope[dim])
for odif, (slope2, se_slope2) in enumerate(zip(slope1, se_slope1)):
label = labels[odif] + ' ' + consts.DIM_NAMES_SHORT[odim]
plt.errorbar(t_RDK_durs,
slope2,
se_slope2,
marker='o',
label=label)
plt.ylabel(consts.DIM_NAMES_LONG[dim].lower() + ' ' + coef_name +
'\n' + unit)
plt.axis('auto')
plt.xlim(xmin=-0.02)
if coef_name == 'slope':
plt.ylim(ymin=-0.05)
plt2.box_off()
plt2.detach_axis('x')
if dim == 0 and not horizontal_panels:
plt.gca().set_xticklabels([])
plt.legend(handlelength=0.4, frameon=False, loc='upper left')
plt.xlabel('stimulus duration (s)')
return gs
def main_fit(
subjs=None,
skip_fit_if_absent=False,
bufdur=None,
bufdur_best=0.12,
bufdurs_sim=None,
bufdurs_fit=None,
loss_kind='NLL',
plot_kind='line_sim_fit',
fix_post=None,
seed_sims=(0, ),
err_kind='std',
base=10,
):
# if torch.cuda.is_available():
# torch.set_default_tensor_type(torch.cuda.FloatTensor)
torch.set_num_threads(1)
torch.set_default_dtype(torch.double)
dict_res_add = {}
parad = 'VD'
seed_sims = np.array(seed_sims)
if subjs is None:
subjs = consts.SUBJS_VD
if bufdur is not None:
bufdurs_sim = list({bufdur_best}.union({bufdur}))
bufdurs_fit = list({bufdur_best}.union({bufdur}))
else:
if bufdurs_sim is None:
bufdurs_sim = bufdurs0 #
if bufdurs_fit is None:
bufdurs_fit = bufdurs0
else:
if bufdurs_fit is None:
bufdurs_fit0 = [0., bufdur_best, 1.2]
if bufdurs_sim[0] in bufdurs_fit0:
bufdurs_fit = copy(bufdurs_fit0)
else:
bufdurs_fit = bufdurs_fit0[:2] + bufdurs_sim + [1.2]
n_dur_sim = len(bufdurs_sim)
n_dur_fit = len(bufdurs_fit)
# DEF: losses[seed, subj, simSerDurPar, fitDurs]
size_all = [len(seed_sims), len(subjs), n_dur_sim, n_dur_fit]
losses0 = np.zeros(size_all) + np.nan
ns = np.zeros(size_all) + np.nan
ks0 = np.zeros(size_all) + np.nan
for i_seed, seed_sim in enumerate(seed_sims):
for i_subj, subj in enumerate(subjs):
for i_fit, bufdur_fit in enumerate(bufdurs_fit):
for i_sim, bufdur_sim in enumerate(bufdurs_sim):
d, dict_fit_sim, dict_subdir_sim = get_fit_sim(
subj,
seed_sim,
bufdur_sim,
bufdur_fit,
parad=parad,
skip_fit_if_absent=skip_fit_if_absent,
fix_post=fix_post,
)
if d is not None:
losses0[i_seed, i_subj, i_sim,
i_fit] = d['loss_NLL_test']
ns[i_seed, i_subj, i_sim, i_fit] = d['loss_ndata_test']
ks0[i_seed, i_subj, i_sim, i_fit] = d['loss_nparam']
ks = copy(ks0)
if loss_kind == 'BIC':
losses = ks * np.log(ns) + 2 * losses0
# REF: Kass, Raftery 1995 https://doi.org/10.2307%2F2291091
# https://en.wikipedia.org/wiki/Bayesian_information_criterion#Gaussian_special_case
thres_strong = 10. / np.log(base)
elif loss_kind == 'NLL':
losses = copy(losses0)
thres_strong = np.log(100) / np.log(base)
else:
raise ValueError('Unsupported loss_kind: %s' % loss_kind)
#%%
losses = losses / np.log(base)
if plot_kind == 'loss_serpar':
n_row = len(subjs)
n_col = 1
axs = plt2.GridAxes(n_row, n_col, left=1, widths=2, bottom=0.75)
for i_subj, subj in enumerate(subjs):
plt.sca(axs[i_subj, 0])
loss_dur_sim_ser_fit = losses[:, i_subj, :, 0].mean(0)
loss_dur_sim_par_fit = losses[:, i_subj, :, -1].mean(0)
dloss_serpar = loss_dur_sim_par_fit - loss_dur_sim_ser_fit
plt.bar(bufdurs_sim, dloss_serpar, color='k')
plt.axhline(0, color='k', linestyle='-', linewidth=0.5)
if i_subj < len(subjs) - 1:
plt2.box_off(['top', 'right', 'bottom'])
plt.xticks([])
else:
plt2.box_off()
plt.xticks(np.arange(0, 1.4, 0.2), ['0\nser'] + [''] * 2 +
['0.6'] + [''] * 2 + ['1.2\npar'])
plt2.detach_axis('x', 0, 1.2)
vdfit.patch_chance_level()
plt.sca(axs[0, 0])
plt.title('Support for Serial\n'
'($\mathcal{L}_\mathrm{ser} - \mathcal{L}_\mathrm{par}$)')
plt.sca(axs[-1, 0])
plt.xlabel('true buffer duration (s)')
plt2.rowtitle(consts.SUBJS_VD, axs)
elif plot_kind == 'imshow_ser_buf_par':
n_row = len(subjs)
n_col = 1
axs = plt2.GridAxes(n_row,
n_col,
left=1,
widths=2,
heights=2,
bottom=0.75)
for i_subj, subj in enumerate(subjs):
plt.sca(axs[i_subj, 0])
dloss = losses[:, i_subj, :, :].mean(0)
dloss = dloss - np.diag(dloss)[:, None]
plt.imshow(dloss, cmap='bwr')
cmax = np.amax(np.abs(dloss))
plt.clim(-cmax, +cmax)
plt.sca(axs[0, 0])
plt.sca(axs[-1, 0])
plt.xlabel('true buffer duration (s)')
plt.ylabel('model buffer duration (s)')
plt2.rowtitle(consts.SUBJS_VD, axs)
elif plot_kind == 'line_sim_fit':
bufdur_bests = np.array([get_bufdur_best(subj) for subj in subjs])
i_bests = np.array([
list(bufdurs_fit).index(bufdur_best)
for bufdur_best in bufdur_bests
])
i_subjs = np.arange(len(subjs))
loss_sim_best_fit_best = losses[:, i_subjs, i_bests, i_bests]
loss_sim_best_fit_rest = (losses[:, i_subjs, i_bests, :] -
loss_sim_best_fit_best[:, :, None])
mean_loss_sim_best_fit_rest = np.mean(loss_sim_best_fit_rest, 0)
if err_kind == 'std':
err_loss_sim_best_fit_rest = np.std(loss_sim_best_fit_rest, 0)
elif err_kind == 'sem':
err_loss_sim_best_fit_rest = np2.sem(loss_sim_best_fit_rest, 0)
n_dur = len(bufdurs_fit)
loss_sim_rest_fit_best = np.swapaxes(
np.stack([
losses[:, i_subj, np.arange(n_dur), i_best] -
losses[:, i_subj,
np.arange(n_dur),
np.arange(n_dur)]
for i_subj, i_best in zip(i_subjs, i_bests)
]), 0, 1)
mean_loss_sim_rest_fit_best = np.mean(loss_sim_rest_fit_best, 0)
if err_kind == 'std':
err_loss_sim_rest_fit_best = np.std(loss_sim_rest_fit_best, 0)
elif err_kind == 'sem':
err_loss_sim_rest_fit_best = np2.sem(loss_sim_rest_fit_best, 0)
dict_res_add['err'] = err_kind
n_row = 2
n_col = len(subjs)
axs = plt2.GridAxes(n_row,
n_col,
left=1.15,
right=0.1,
widths=2,
heights=1.5,
wspace=0.35,
hspace=0.5,
top=0.25,
bottom=0.6)
for row, (m, s, xlabel, ylabel) in enumerate([
(mean_loss_sim_best_fit_rest, err_loss_sim_best_fit_rest,
'model buffer capacity (s)',
(r'$-\mathrm{log}_{%g}\mathrm{BF}$ given simulated' +
'\nbest duration data') % base),
(mean_loss_sim_rest_fit_best, err_loss_sim_rest_fit_best,
'simulated data buffer capacity (s)',
(r'$-\mathrm{log}_{%g}\mathrm{BF}$ of the' +
'\nbest duration model') % base)
]):
for i_subj, subj in enumerate(subjs):
ax = axs[row, i_subj]
plt.sca(ax)
gs1 = axs.gs[row * 2 + 1, i_subj * 2 + 1]
bax = vdfit.breakaxis(gs1)
bax.axs[1].errorbar(bufdurs0,
m[i_subj, :],
yerr=s[i_subj, :],
color='k',
marker='.',
linewidth=0.75,
elinewidth=0.5,
markersize=3)
m1 = copy(m[i_subj, :])
m1[3:] = np.nan
s1 = copy(s[i_subj, :])
s1[3:] = np.nan
bax.axs[0].errorbar(bufdurs0,
m1,
yerr=s1,
color='k',
marker='.',
linewidth=0.75,
elinewidth=0.5,
markersize=3)
ax1 = bax.axs[1] # type: plt.Axes
plt.sca(ax1)
vdfit.patch_chance_level(level=thres_strong, signs=[-1, 1])
plt.axhline(0, color='k', linestyle='--', linewidth=0.5)
vdfit.beautify_ticks(ax1, )
vdfit.beautify(ax1)
ax1.set_yticks([0, 20])
if i_subj > 0:
ax1.set_yticklabels([])
if row == 0:
ax1.set_xticklabels([])
ax1 = bax.axs[0] # type: plt.Axes
plt.sca(ax1)
ax1.set_yticks([40, 200])
if i_subj > 0:
ax1.set_yticklabels([])
plt.sca(ax)
plt2.box_off('all')
if row == 0:
plt.title(consts.SUBJS['VD'][i_subj])
if i_subj == 0:
plt.xlabel(xlabel, labelpad=8 if row == 0 else 20)
plt.ylabel(ylabel, labelpad=30)
plt2.sameaxes(bax.axs, xy='x')
elif plot_kind == 'bar_sim_fit':
i_best = bufdurs_fit.index(bufdur_best)
loss_sim_best_fit_best = losses[0, :, i_best, i_best]
loss_sim_best_fit_rest = np2.nan2v(losses[0, :, i_best, :] -
loss_sim_best_fit_best[:, None])
n_dur = len(bufdurs_fit)
loss_sim_rest_fit_best = np2.nan2v(
losses[0, :, np.arange(n_dur), i_best] -
losses[0, :, np.arange(n_dur),
np.arange(n_dur)]).T
n_row = 2
n_col = len(subjs)
axs = plt2.GridAxes(n_row,
n_col,
left=1,
widths=3,
right=0.25,
heights=1,
wspace=0.3,
hspace=0.6,
top=0.25,
bottom=0.6)
for i_subj, subj in enumerate(subjs):
def plot_bars(gs1, bufdurs, losses1, add_xticklabel=True):
i_break = np.amax(np.nonzero(np.array(bufdurs) < 0.3)[0])
bax = brokenaxes(
subplot_spec=gs1,
xlims=((-1, i_break + 0.5), (i_break + 0.5,
len(bufdurs) - 0.5)),
ylims=((-3, 20), (20, 1250 / 5)),
height_ratios=(50 / 100, 500 / (1250 - 100)),
hspace=0.15,
wspace=0.075,
d=0.005,
)
bax.bar(np.arange(len(bufdurs)), losses1[i_subj, :], color='k')
ax11 = bax.axs[3] # type: plt.Axes
ax11.set_xticks([bufdurs.index(0.6), bufdurs.index(1.2)])
if i_subj == 0 and add_xticklabel:
ax11.set_xticklabels(['0.6', '1.2'])
else:
ax11.set_xticklabels([])
ax00 = bax.axs[0] # type: plt.Axes
ax00.set_yticks([500, 1000])
ax10 = bax.axs[2] # type: plt.Axes
ax10.set_yticks([0, 50])
plt.sca(ax10)
plt2.detach_axis('x', amin=-0.4, amax=i_break + 0.5)
for ax in [ax10, ax11]:
plt.sca(ax)
plt.axhline(0, linewidth=0.5, color='k', linestyle='--')
for sign in [-1, 1]:
plt.axhline(sign * thres_strong,
linewidth=0.5,
color='silver',
linestyle='--')
ax10.set_xticks([bufdurs.index(0.), bufdurs.index(0.2)])
if i_subj == 0:
if add_xticklabel:
ax10.set_xticklabels(['0', '0.2'])
else:
ax10.set_xticklabels([])
else:
ax10.set_yticklabels([])
ax10.set_xticklabels([])
ax00.set_yticklabels([])
return bax
bax = plot_bars(axs.gs[1, i_subj * 2 + 1],
bufdurs_fit,
loss_sim_best_fit_rest,
add_xticklabel=False)
ax = axs[0, i_subj]
plt.sca(ax)
plt2.box_off('all')
plt.title(consts.SUBJS['VD'][consts.SUBJS['VD'].index(subj)])
if i_subj == 0:
ax.set_ylabel(
'misfit to simulated\nbest duration data\n'
r'($\Delta$BIC)',
labelpad=35)
ax.set_xlabel('model buffer duration (s)', labelpad=8)
plot_bars(axs.gs[3, i_subj * 2 + 1],
bufdurs_sim,
loss_sim_rest_fit_best,
add_xticklabel=True)
ax = axs[1, i_subj]
plt.sca(ax)
plt2.box_off('all')
if i_subj == 0:
ax.set_ylabel(
'misfit of\nbest duration model\n'
r'($\Delta$BIC)',
labelpad=35)
ax.set_xlabel('simulated data buffer duration (s)',
labelpad=20)
elif plot_kind == 'bar_ser_buf_par':
n_row = len(subjs)
n_col = 1
axs = plt2.GridAxes(n_row * n_dur_sim,
n_col,
left=1.25,
widths=1.5,
right=0.25,
heights=0.4,
hspace=[0.15] * (n_dur_sim - 1) + [0.5] + [0.15] *
(n_dur_sim - 1),
top=0.6,
bottom=0.5)
row = -1
for i_subj, subj in enumerate(subjs):
for i_sim in range(n_dur_sim):
row += 1
plt.sca(axs[row, 0])
loss1 = losses[:, i_subj, i_sim, :].mean(0)
dloss = loss1 - loss1[i_sim]
x = np.arange(n_dur_fit)
for x1, dloss1 in zip(x, dloss):
plt.bar(x1, dloss1, color='r' if dloss1 > 0 else 'b')
plt.axhline(0, color='k', linewidth=0.5)
vdfit.patch_chance_level(6.)
plt.ylim([-100, 100])
plt.yticks([-100, 0, 100], [''] * 2)
plt.ylabel('%g' % bufdurs_sim[i_sim],
rotation=0,
va='center',
ha='right')
if i_sim == 0:
plt.title(subj)
if i_sim < n_dur_sim - 1 or i_subj < len(subjs) - 1:
plt2.box_off(['top', 'bottom', 'right'])
plt.xticks([])
else:
plt2.box_off(['top', 'right'])
plt.sca(axs[-1, 0])
plt.xticks(np.arange(n_dur_sim), ['%g' % v for v in bufdurs_sim])
plt2.detach_axis('x', 0, n_dur_sim - 1)
plt.xlabel('model buffer duration (s)', fontsize=10)
c = axs[-1, 0].get_position().corners()
plt.figtext(x=0.15,
y=np.mean(c[:2, 1]),
fontsize=10,
s='true\nbuffer\nduration\n(s)',
rotation=0,
va='center',
ha='center')
plt.figtext(
x=(1.25 + 1.5 / 2) / (1.25 + 1.5 + 0.25),
y=0.98,
s=r'$\mathrm{BIC} - \mathrm{BIC}_\mathrm{true}$',
ha='center',
va='top',
fontsize=12,
)
if plot_kind != 'None':
dict_res = deepcopy(dict_fit_sim) # noqa
for k in ['sbj', 'prd']:
dict_res.pop(k)
dict_res = {**dict_res, 'los': loss_kind}
dict_res.update(dict_res_add)
for ext in ['.png', '.pdf']:
file = locfile.get_file_fig(plot_kind,
dict_res,
subdir=dict_subdir_sim,
ext=ext) # noqa
plt.savefig(file, dpi=300)
print('Saved to %s' % file)
#%%
print('--')
return losses
def plot_coefs_dur_odif(
n_cond_dur_ch: np.ndarray = None,
data: Data2DVD = None,
dif_irrs: Sequence[Union[Iterable[int], int]] = ((0, 1), (2, )),
ev_cond_dim: np.ndarray = None,
durs: np.ndarray = None,
style='data',
kw_plot=(),
jitter0=0.,
coefs_to_plot=(0, 1),
add_rowtitle=True,
dim_incl=(0, 1),
axs=None,
fig=None,
):
"""
:param n_cond_dur_ch:
:param data:
:param dif_irrs:
:param ev_cond_dim:
:param durs:
:param style:
:param kw_plot:
:param jitter0:
:param coefs_to_plot:
:param add_rowtitle:
:param dim_incl:
:param axs:
:param fig:
:return: coef, se_coef, axs, hs[coef, dim, dif]
"""
if ev_cond_dim is None:
ev_cond_dim = data.ev_cond_dim
if durs is None:
durs = npy(data.durs)
if n_cond_dur_ch is None:
n_cond_dur_ch = npy(data.get_data_by_cond('all')[1])
coef, se_coef = coefdur.get_coefs_mesh_from_histogram(
n_cond_dur_ch, ev_cond_dim=ev_cond_dim,
dif_irrs=dif_irrs)[:2] # type: (np.ndarray, np.ndarray)
coef_names = ['bias', 'slope']
n_coef = len(coefs_to_plot)
n_dim = len(dim_incl)
if axs is None:
if fig is None:
fig = plt.figure(figsize=[6, 1.5 * n_coef])
n_row = n_dim
n_col = n_coef
gs = plt.GridSpec(nrows=n_row,
ncols=n_col,
figure=fig,
left=0.25,
right=0.95,
bottom=0.15,
top=0.9)
axs = np.empty([n_row, n_col], dtype=np.object)
for row in range(n_row):
for col in range(n_col):
axs[row, col] = plt.subplot(gs[row, col])
n_dif = len(dif_irrs)
hs = np.empty(
[len(coefs_to_plot), len(dim_incl),
len(dif_irrs)], dtype=np.object)
for ii_coef, i_coef in enumerate(coefs_to_plot):
coef_name = coef_names[i_coef]
for i_dim, dim_rel in enumerate(dim_incl):
ax = axs[i_dim, ii_coef] # type: plt.Axes
plt.sca(ax)
cmap = consts.CMAP_DIM[dim_rel](n_dif)
for idif, dif_irr in enumerate(dif_irrs):
y = coef[i_coef, dim_rel, idif, :]
e = se_coef[i_coef, dim_rel, idif, :]
ddur = durs[1] - durs[0]
jitter = ddur * jitter0 * (idif - (n_dif - 1) / 2)
kw = consts.get_kw_plot(style,
color=cmap(idif),
for_err=True,
**dict(kw_plot))
if style.startswith('data'):
h = plt.errorbar(durs + jitter, y, yerr=e, **kw)[0]
else:
h = plt.plot(durs + jitter, y, **kw)
hs[ii_coef, i_dim, idif] = h
plt2.box_off()
max_dur = np.amax(npy(durs))
ax.set_xlim(-0.05 * max_dur, 1.05 * max_dur)
plt2.detach_axis('x', 0, max_dur)
if dim_rel == 0:
ax.set_xticklabels([])
ax.set_title(coef_name.lower())
elif i_coef == 0:
ax.set_xlabel('duration (s)')
if add_rowtitle:
plt2.rowtitle(consts.DIM_NAMES_LONG, axes=axs)
return coef, se_coef, axs, hs
def main(base=10.):
ds = load_comp(os.path.join(locfile_in.pth_root, file_model_comp))
ds['dcost'] = np.array(ds['dcost']) / np.log(base)
vmax = 160
m = np.empty(3)
e = np.empty(3)
axs = plot_bar_dloss_across_subjs(dlosses=np.array(ds['dcost']),
subj_parad_bis=ds['subj_parad_bi'],
vmax=vmax)
axs = plt2.GridAxes(nrows=1,
ncols=3,
heights=axs.heights,
widths=[2],
left=axs.left,
right=axs.right,
bottom=axs.bottom)
plot_bar_dloss_across_subjs(
dlosses=np.array(ds['dcost']),
subj_parad_bis=ds['subj_parad_bi'],
vmax=vmax,
axs=axs[:, [0]],
base=base,
)
plt.title('Data')
m[0] = np.mean(ds['dcost'])
e[0] = np2.sem(ds['dcost'])
print('--')
titles = ['Simulated\nSerial', 'Simulated\nParallel']
for i in range(2):
ds = load_comp(
os.path.join(locfile_in.pth_root, files_model_recovery[i]))
ds['dcost'] = np.array(ds['dcost']) / np.log(base)
plot_bar_dloss_across_subjs(
dlosses=ds['dcost'],
subj_parad_bis=ds['subj_parad_bi'],
vmax=vmax,
axs=axs[:, [i + 1]],
base=base,
)
plt.title(titles[i])
m[i + 1] = np.mean(ds['dcost'])
e[i + 1] = np2.sem(ds['dcost'])
plt.sca(axs[0, i + 1])
plt2.box_off(['left'])
plt.yticks([])
for ext in ['.pdf', '.png']:
file = locfile_out.get_file_fig('model_comp_recovery',
{'easiest_only': use_easiest_only},
ext=ext)
plt.savefig(file, dpi=300)
print('Saved to %s' % file)
# --- Print mean +- SEM to CSV
csv_file = locfile_out.get_file_csv('model_comp_recovery',
{'easiest_only': use_easiest_only})
d_csv = np2.dictlist2listdict({
'data': ['original', 'simulated serial', 'simulated parallel'],
'mean_dcost':
m,
'sem_dcost':
e,
})
with open(csv_file, 'w') as f:
writer = csv.DictWriter(f, fieldnames=d_csv[0].keys())
writer.writeheader()
for d in d_csv:
writer.writerow(d)
print('Wrote to %s' % csv_file)
# --- Mean NLL within each
axs = plt2.GridAxes(
1,
1,
left=0.85,
right=0.25,
heights=[1.5],
top=0.1,
bottom=0.9,
)
plt.sca(axs[0, 0])
plt.barh(np.arange(3), m, xerr=e, color='w', edgecolor='k')
plt.yticks(np.arange(3),
['data', 'simulated\nserial', 'simulated\nparallel'])
plt2.box_off(['top', 'right'])
vmax = np.amax(np.abs(m) + np.abs(e))
plt.xlim(np.array([-vmax, vmax]) * 1.1)
plt2.detach_axis(xy='y', amin=0, amax=2)
plt2.detach_axis(xy='x', amin=-vmax, amax=vmax)
axvline_dcost()
xticks_serial_vs_parallel(vmax, base)
for ext in ['.pdf', '.png']:
file = locfile_out.get_file_fig('model_comp_vs_recovery',
{'easiest_only': use_easiest_only},
ext=ext)
plt.savefig(file, dpi=300)
print('Saved to %s' % file)
print('--')
def plot_rt_vs_ev(
ev_cond,
n_cond__rt_ch: Union[torch.Tensor, np.ndarray],
style='pred',
pool='mean',
dt=consts.DT,
correct_only=True,
thres_n_trial=10,
color='k',
color_ch=('tab:red', 'tab:blue'),
ax: plt.Axes = None,
kw_plot=(),
) -> (Sequence[plt.Line2D], List[np.ndarray]):
"""
@param ev_cond: [condition]
@type ev_cond: torch.Tensor
@param n_cond__rt_ch: [condition, frame, ch]
@type n_cond__rt_ch: torch.Tensor
@return:
"""
if ax is None:
ax = plt.gca()
if ev_cond.ndim != 1:
if ev_cond.ndim == 3:
ev_cond = npt.p2st(ev_cond)[0]
assert ev_cond.ndim == 2
ev_cond = ev_cond.mean(1)
assert n_cond__rt_ch.ndim == 3
ev_cond = npy(ev_cond)
n_cond__rt_ch = npy(n_cond__rt_ch)
def plot_rt_given_cond_ch(ev_cond1, n_rt_given_cond_ch, **kw1):
# n_rt_given_cond_ch[cond, fr]
# p_rt_given_cond_ch[cond, fr]
p_rt_given_cond_ch = np2.sumto1(n_rt_given_cond_ch, 1)
nt = n_rt_given_cond_ch.shape[1]
t = np.arange(nt) * dt
# n_in_cond_ch[cond]
n_in_cond_ch = n_rt_given_cond_ch.sum(1)
if pool == 'mean':
rt_pooled = (t[None, :] * p_rt_given_cond_ch).sum(1)
elif pool == 'var':
raise NotImplementedError()
else:
raise ValueError()
if style.startswith('data'):
rt_pooled[n_in_cond_ch < thres_n_trial] = np.nan
kw = get_kw_plot(style, **kw1)
h = ax.plot(ev_cond1, rt_pooled, **kw)
return h, rt_pooled
if correct_only:
hs = []
rtss = []
n_cond__rt_ch1 = n_cond__rt_ch.copy() # type: np.ndarray
if style.startswith('data'):
cond0 = ev_cond == 0
n_cond__rt_ch1[cond0, :, :] = np.sum(n_cond__rt_ch1[cond0, :, :],
axis=-1,
keepdims=True)
# # -- Choose the ch with correct sign (or both chs if cond == 0)
for ch in range(consts.N_CH):
cond_sign = np.sign(ev_cond)
ch_sign = consts.ch_bool2sign(ch)
cond_accu = cond_sign != -ch_sign
h, rts = plot_rt_given_cond_ch(ev_cond[cond_accu],
n_cond__rt_ch1[cond_accu, :, ch],
color=color,
**dict(kw_plot))
hs.append(h)
rtss.append(rts)
# rts = np.stack(rtss)
rts = rtss
else:
hs = []
rtss = []
for ch in range(consts.N_CH):
# n_rt_given_cond_ch[cond, fr]
n_rt_given_cond_ch = n_cond__rt_ch[:, :, ch]
h, rts = plot_rt_given_cond_ch(ev_cond,
n_rt_given_cond_ch,
color=color_ch[ch])
hs.append(h)
rtss.append(rts)
rts = np.stack(rtss)
y_lim = ax.get_ylim()
x_lim = ax.get_xlim()
plt2.box_off()
plt2.detach_axis('x', ax=ax, amin=x_lim[0], amax=x_lim[1])
plt2.detach_axis('y', ax=ax, amin=y_lim[0], amax=y_lim[1])
ax.set_xlabel('evidence')
ax.set_ylabel(r"$\mathrm{E}[T^\mathrm{r} \mid c]~(\mathrm{s})$")
return hs, rts
def plot_bar_dloss_across_subjs(
dlosses,
elosses=None,
ix_datas=None,
subj_parad_bis: Iterable[Tuple[str, str, bool]] = None,
axs: Union[plt2.GridAxes, plt2.AxesArray] = None,
vmax=None,
add_scale=True,
base=10.,
):
"""
:param dlosses: [ix_data]
:param ix_datas:
:param axs:
:param subj_parad_bis: [('subj', 'parad', is_bimanual), ...]
:return: axs
"""
if subj_parad_bis is None:
subj_parad_bis = subj_parad_bis0
if vmax is None:
vmax = np.amax(np.abs(dlosses))
# order: eye S1-S3, hand by ID, paired uni-bimanual
subjs, parads, bis = zip(*subj_parad_bis)
subjs = np.array(
['ID0' + v[-1] if v[:2] == 'ID' and len(v) == 3 else v for v in subjs])
parads = np.array(parads)
bis = np.array(bis)
is_eye = parads == 'RT'
is_bin = parads == 'binary'
ix = np.arange(len(subjs))
def filt_sort(filt):
ind = [int(subj[1:]) for subj in subjs[filt]]
return ix[filt][np.argsort(ind)]
ix = np.concatenate([
filt_sort(is_eye & ~is_bin),
np.stack([
filt_sort(~is_eye & ~bis & ~is_bin),
filt_sort(~is_eye & bis & ~is_bin)
], -1).flatten('C'),
filt_sort(is_bin)
])
subjs = subjs[ix]
parads = parads[ix]
bis = bis[ix]
is_eye = is_eye[ix]
dlosses = dlosses[ix]
subj_parad_bis = subj_parad_bis[ix]
n_eye = int(np.sum(is_eye))
n_hand = int(np.sum(~is_eye))
y = np.empty([n_eye + n_hand])
y[is_eye] = 1.5 + np.arange(n_eye)
y[~is_eye] = n_eye - 1 + 1.5 + np.cumsum([1.5, 1.] * (n_hand // 2))
y_max = np.amax(y) + 1.5
if axs is None:
axs = plt2.GridAxes(nrows=1,
ncols=1,
heights=y_max * 0.2,
widths=2,
left=1.5,
right=0.25,
bottom=0.85)
ax = axs[0, 0]
plt.sca(ax)
m = dlosses
if elosses is None:
e = np.zeros_like(m)
else:
e = elosses
for y1, m1, e1, parad1, bi1 in zip(y, m, e, parads, bis):
plt.barh(y1,
m1,
xerr=e1,
color=colors_parad[(parad1, '%s' % bi1)],
edgecolor='None')
if add_scale:
dy = y[1] - y[0]
axvline_dcost()
x_lim = [-vmax * 1.2, vmax * 1.2]
for ix_big in range(len(y)):
if np.abs(m[ix_big]) > vmax:
for i_sign, sign in enumerate([1, -1]):
plt2.patch_wave(
y[ix_big],
x_lim[i_sign] * 1.01,
ax=ax,
color='w',
wave_margin=0.15,
wave_amplitude=sign * 0.025,
)
plt.xlim(x_lim)
xticks_serial_vs_parallel(vmax, base)
subj_parad_bi_str = get_subj_parad_bi_str(subj_parad_bis)
plt.yticks(y, subj_parad_bi_str)
plt2.detach_axis('y', y[0], y[-1])
plt2.detach_axis('x', -vmax, vmax)
plt.ylim([y_max - 1, 1.])
return axs
def plot_fit_combined(
data: Union[sim2d.Data2DRT, dict] = None,
pModel_cond_rt_chFlat=None, model=None,
pModel_dimRel_condDense_chFlat=None,
# --- in place of data:
pAll_cond_rt_chFlat=None,
evAll_cond_dim=None,
pTrain_cond_rt_chFlat=None,
evTrain_cond_dim=None,
pTest_cond_rt_chFlat=None,
evTest_cond_dim=None,
dt=None,
# --- optional
ev_dimRel_condDense_fr_dim_meanvar=None,
dt_model=None,
to_plot_internals=True,
to_plot_params=True,
to_plot_choice=True,
# to_group_irr=False,
group_dcond_irr=None,
to_combine_ch_irr_cond=True,
kw_plot_pred=(),
kw_plot_pred_ch=(),
kw_plot_data=(),
axs=None,
):
"""
:param data:
:param pModel_cond_rt_chFlat:
:param model:
:param pModel_dimRel_condDense_chFlat:
:param ev_dimRel_condDense_fr_dim_meanvar:
:param to_plot_internals:
:param to_plot_params:
:param to_group_irr:
:param to_combine_ch_irr_cond:
:param kw_plot_pred:
:param kw_plot_data:
:param axs:
:return:
"""
if data is None:
if pTrain_cond_rt_chFlat is None:
pTrain_cond_rt_chFlat = pAll_cond_rt_chFlat
if evTrain_cond_dim is None:
evTrain_cond_dim = evAll_cond_dim
if pTest_cond_rt_chFlat is None:
pTest_cond_rt_chFlat = pAll_cond_rt_chFlat
if evTest_cond_dim is None:
evTest_cond_dim = evAll_cond_dim
else:
_, pAll_cond_rt_chFlat, _, _, evAll_cond_dim = \
data.get_data_by_cond('all')
_, pTrain_cond_rt_chFlat, _, _, evTrain_cond_dim = data.get_data_by_cond(
'train_valid', mode_train='easiest')
_, pTest_cond_rt_chFlat, _, _, evTest_cond_dim = data.get_data_by_cond(
'test', mode_train='easiest')
dt = data.dt
hs = {}
if model is None:
assert not to_plot_internals
assert not to_plot_params
if dt_model is None:
if model is None:
dt_model = dt
else:
dt_model = model.dt
if axs is None:
if to_plot_params:
axs = plt2.GridAxes(3, 3)
else:
if to_plot_internals:
axs = plt2.GridAxes(3, 3)
else:
if to_plot_choice:
axs = plt2.GridAxes(2, 2)
else:
axs = plt2.GridAxes(1, 2) # TODO: beautify ratios
rts = []
hs['rt'] = []
for dim_rel in range(consts.N_DIM):
# --- data_pred may not have all conditions, so concatenate the rest
# of the conditions so that the color scale is correct. Then also
# concatenate p_rt_ch_data_pred1 with zeros so that nothing is
# plotted in the concatenated.
evTest_cond_dim1 = np.concatenate([
evTest_cond_dim, evAll_cond_dim
], axis=0)
pTest_cond_rt_chFlat1 = np.concatenate([
pTest_cond_rt_chFlat, np.zeros_like(pAll_cond_rt_chFlat)
], axis=0)
if ev_dimRel_condDense_fr_dim_meanvar is None:
evModel_cond_dim = evAll_cond_dim
else:
if ev_dimRel_condDense_fr_dim_meanvar.ndim == 5:
evModel_cond_dim = npy(ev_dimRel_condDense_fr_dim_meanvar[
dim_rel][:, 0, :, 0])
else:
assert ev_dimRel_condDense_fr_dim_meanvar.ndim == 4
evModel_cond_dim = npy(ev_dimRel_condDense_fr_dim_meanvar[
dim_rel][:, 0, :])
pModel_cond_rt_chFlat = npy(pModel_dimRel_condDense_chFlat[dim_rel])
if to_plot_choice:
# --- Plot choice
ax = axs[0, dim_rel]
plt.sca(ax)
if to_combine_ch_irr_cond:
ev_cond_model1, p_rt_ch_model1 = combine_irr_cond(
dim_rel, evModel_cond_dim, pModel_cond_rt_chFlat
)
sim2d.plot_p_ch_vs_ev(ev_cond_model1, p_rt_ch_model1,
dim_rel=dim_rel, style='pred',
group_dcond_irr=None,
kw_plot=kw_plot_pred_ch,
cmap=lambda n: lambda v: [0., 0., 0.],
)
else:
sim2d.plot_p_ch_vs_ev(evModel_cond_dim, pModel_cond_rt_chFlat,
dim_rel=dim_rel, style='pred',
group_dcond_irr=group_dcond_irr,
kw_plot=kw_plot_pred,
cmap=cmaps[dim_rel]
)
hs, conds_irr = sim2d.plot_p_ch_vs_ev(
evTest_cond_dim1, pTest_cond_rt_chFlat1,
dim_rel=dim_rel, style='data_pred',
group_dcond_irr=group_dcond_irr,
cmap=cmaps[dim_rel],
kw_plot=kw_plot_data,
)
hs1 = [h[0] for h in hs]
odim = 1 - dim_rel
odim_name = consts.DIM_NAMES_LONG[odim]
legend_odim(conds_irr, hs1, odim_name)
sim2d.plot_p_ch_vs_ev(evTrain_cond_dim, pTrain_cond_rt_chFlat,
dim_rel=dim_rel, style='data_fit',
group_dcond_irr=group_dcond_irr,
cmap=cmaps[dim_rel],
kw_plot=kw_plot_data
)
plt2.detach_axis('x', np.amin(evTrain_cond_dim[:, dim_rel]),
np.amax(evTrain_cond_dim[:, dim_rel]))
ax.set_xlabel('')
ax.set_xticklabels([])
if dim_rel != 0:
plt2.box_off(['left'])
plt.yticks([])
ax.set_ylabel('P(%s choice)' % consts.CH_NAMES[dim_rel][1])
# --- Plot RT
ax = axs[int(to_plot_choice) + 0, dim_rel]
plt.sca(ax)
hs1, rts1 = sim2d.plot_rt_vs_ev(
evModel_cond_dim,
pModel_cond_rt_chFlat,
dim_rel=dim_rel, style='pred',
group_dcond_irr=group_dcond_irr,
dt=dt_model,
kw_plot=kw_plot_pred,
cmap=cmaps[dim_rel]
)
hs['rt'].append(hs1)
rts.append(rts1)
sim2d.plot_rt_vs_ev(evTest_cond_dim1, pTest_cond_rt_chFlat1,
dim_rel=dim_rel, style='data_pred',
group_dcond_irr=group_dcond_irr,
dt=dt,
cmap=cmaps[dim_rel],
kw_plot=kw_plot_data
)
sim2d.plot_rt_vs_ev(evTrain_cond_dim, pTrain_cond_rt_chFlat,
dim_rel=dim_rel, style='data_fit',
group_dcond_irr=group_dcond_irr,
dt=dt,
cmap=cmaps[dim_rel],
kw_plot=kw_plot_data
)
plt2.detach_axis('x', np.amin(evTrain_cond_dim[:, dim_rel]),
np.amax(evTrain_cond_dim[:, dim_rel]))
if dim_rel != 0:
ax.set_ylabel('')
plt2.box_off(['left'])
plt.yticks([])
ax.set_xlabel(consts.DIM_NAMES_LONG[dim_rel].lower() + ' strength')
if dim_rel == 0:
ax.set_ylabel('RT (s)')
if to_plot_internals:
for ch1 in range(consts.N_CH):
ch0 = dim_rel
ax = axs[3 + ch1, dim_rel]
plt.sca(ax)
ch_flat = consts.ch_by_dim2ch_flat(np.array([ch0, ch1]))
model.tnds[ch_flat].plot_p_tnd()
ax.set_xlabel('')
ax.set_xticklabels([])
ax.set_yticks([0, 1])
if ch0 > 0:
ax.set_yticklabels([])
ax.set_ylabel(r"$\mathrm{P}(T^\mathrm{n} \mid"
" \mathbf{z}=[%d,%d])$"
% (ch0, ch1))
ax = axs[5, dim_rel]
plt.sca(ax)
if hasattr(model.dtb, 'dtb1ds'):
model.dtb.dtb1ds[dim_rel].plot_bound(color='k')
plt2.sameaxes(axs[-1, :consts.N_DIM], xy='y')
if to_plot_params:
ax = axs[0, -1]
plt.sca(ax)
model.plot_params()
return axs, rts, hs
def plot_rt_distrib_pred_data(
p_pred_cond_rt_ch,
n_cond_rt_ch, ev_cond_dim, dt_model, dt_data=None,
smooth_sigma_sec=0.1,
to_plot_scale=False,
to_cumsum=False,
to_normalize_max=True,
xlim=None,
colors=('magenta', 'cyan'),
kw_plot_pred=(),
kw_plot_data=(),
to_skip_zero_trials=False,
labels=None,
**kwargs
):
"""
:param n_cond_rt_ch: [cond, rt, ch] = n_tr(cond, rt, ch)
:param p_pred_cond_rt_ch: [model, cond, rt, ch] = P(rt, ch | cond, model)
:param ev_cond_dim:
:param dt_model:
:param dt_data:
:param smooth_sigma_sec:
:param to_plot_scale:
:param to_cumsum:
:param xlim:
:param kwargs:
:return:
"""
axs = None
ps = []
ps0 = []
hss = []
p_pred_cond_rt_ch = p_pred_cond_rt_ch / np.sum(
p_pred_cond_rt_ch, (-1, -2), keepdims=True)
n_preds1 = p_pred_cond_rt_ch * np.sum(
n_cond_rt_ch, (-1, -2))[None, :, None, None]
nt = p_pred_cond_rt_ch.shape[-2]
if dt_data is None:
dt_data = dt_model
if labels is None:
labels = [''] * (len(n_preds1) + 1)
for i_pred, n_pred in enumerate(n_preds1):
color = colors[i_pred]
axs, p0, p1, hs = sim2d.plot_rt_distrib(
n_pred, ev_cond_dim,
dt=dt_model,
axs=axs,
alpha=1.,
smooth_sigma_sec=smooth_sigma_sec,
to_skip_zero_trials=to_skip_zero_trials,
colors=color,
alpha_face=0,
to_normalize_max=to_normalize_max,
to_cumsum=to_cumsum,
to_use_sameaxes=False,
kw_plot={
'linewidth': 1.5,
**dict(kw_plot_pred),
},
label=labels[i_pred],
**kwargs,
)[:4]
ps.append(p1)
ps0.append(p0)
hss.append(hs)
axs, p0, p1, hs = sim2d.plot_rt_distrib(
n_cond_rt_ch, ev_cond_dim,
dt=dt_data,
axs=axs,
smooth_sigma_sec=smooth_sigma_sec,
colors='k',
alpha_face=0.,
to_normalize_max=to_normalize_max, # normalize across preds and data instead
to_cumsum=to_cumsum,
to_skip_zero_trials=to_skip_zero_trials,
kw_plot={
'linewidth': 0.5,
**dict(kw_plot_data),
},
label=labels[-1],
**kwargs,
)
ps.append(p1)
ps0.append(p0)
hss.append(hs)
ps = np.stack(ps)
ps0 = np.stack(ps0)
ps_flat = np.swapaxes(ps, 0, 2).reshape([ps.shape[1] * ps.shape[2], -1])
for ax in axs.flatten():
if xlim is None:
if to_cumsum:
xlim = [0.5, 4.5]
else:
xlim = [0.5, 4.5]
plt2.detach_axis('x', *xlim, ax=ax)
ax.set_xlim(xlim[0] - 0.1, xlim[1] + 0.1)
axs[-1, 0].set_xticks(xlim)
axs[-1, 0].set_xticklabels(['%g' % v for v in xlim])
from lib.pylabyk import numpytorch as npt
t = torch.arange(nt) * dt_model
mean_rts = []
for p1 in ps0:
p11 = npt.sumto1(torch.tensor(p1).sum([-1, -2])[0, 0, :])
mean_rts.append(npy((torch.tensor(t) * p11).sum()))
print('mean_rts:')
print(mean_rts)
print(mean_rts[1] - mean_rts[0])
conds = [np.unique(ev_cond_dim[:, i]) for i in [0, 1]]
p_preds = torch.tensor(n_preds1).reshape([
2, len(conds[0]), len(conds[1]), nt, 2, 2
]) + 1e-12
if to_plot_scale:
y = 0.8
axs[-1, -1].plot(mean_rts[:2], y + np.zeros(2), 'k-', linewidth=0.5)
x = np.mean(mean_rts[:2])
plt.text(x, y + 0.1,
'%1.0f ms' % (np.abs(mean_rts[1] - mean_rts[0]) * 1e3),
ha='center', va='bottom')
return axs, hss
def plot_coefs_dur_irrixn(
n_cond_dur_ch: np.ndarray = None,
data: Data2DVD = None,
ev_cond_dim: np.ndarray = None,
durs: np.ndarray = None,
style='data',
kw_plot=(),
jitter0=0.,
coefs_to_plot=(2, ),
axs=None,
fig=None,
):
if ev_cond_dim is None:
ev_cond_dim = data.ev_cond_dim
if durs is None:
durs = npy(data.durs)
if n_cond_dur_ch is None:
n_cond_dur_ch = npy(data.get_data_by_cond('all')[1])
coef, se_coef = coefdur.get_coefs_irr_ixn_from_histogram(
n_cond_dur_ch,
ev_cond_dim=ev_cond_dim)[:2] # type: (np.ndarray, np.ndarray)
coef_names = [
'bias', 'slope', 'rel x abs(irr)', 'rel x irr', 'abs(irr)', 'irr'
]
n_coef = len(coefs_to_plot)
if axs is None:
if fig is None:
fig = plt.figure(figsize=[6, 1.5 * n_coef])
n_row = consts.N_DIM
n_col = n_coef
gs = plt.GridSpec(nrows=n_row,
ncols=n_col,
figure=fig,
left=0.25,
right=0.95,
bottom=0.15,
top=0.9)
axs = np.empty([n_row, n_col], dtype=np.object)
for row in range(n_row):
for col in range(n_col):
axs[row, col] = plt.subplot(gs[row, col])
for ii_coef, i_coef in enumerate(coefs_to_plot):
coef_name = coef_names[i_coef]
for dim_rel in range(consts.N_DIM):
ax = axs[dim_rel, ii_coef] # type: plt.Axes
plt.sca(ax)
y = coef[i_coef, dim_rel, :]
e = se_coef[i_coef, dim_rel, :]
jitter = 0.
kw_plot = {'color': 'k', 'for_err': True, **dict(kw_plot)}
kw = consts.get_kw_plot(style, **kw_plot)
if style.startswith('data'):
plt.errorbar(durs + jitter, y, yerr=e, **kw)
else:
plt.plot(durs + jitter, y, **kw)
plt2.box_off()
max_dur = np.amax(npy(durs))
ax.set_xlim(-0.05 * max_dur, 1.05 * max_dur)
plt2.detach_axis('x', 0, max_dur) # , ax=ax)
if dim_rel == 0:
ax.set_xticklabels([])
ax.set_title(coef_name.lower())
elif i_coef == 0:
ax.set_xlabel('duration (s)')
plt2.rowtitle(consts.DIM_NAMES_LONG, axes=axs)
return coef, se_coef, axs
