def plot_tfr(
tfr,
time_cutoff,
vmin,
vmax,
tl,
cluster_correct=False,
threshold=0.05,
plot_colorbar=False,
ax=None,
cmap=None,
stat_cutoff=None,
):
from pymeg.contrast_tfr import get_tfr_stats
# colorbar:
from matplotlib.colors import LinearSegmentedColormap
if cmap is None:
cmap = LinearSegmentedColormap.from_list(
"custom", ["blue", "lightblue", "lightgrey", "yellow", "red"],
N=100)
if stat_cutoff is None:
stat_cutoff = time_cutoff
# data:
times, freqs, X = contrast_tfr.get_tfr(tfr, stat_cutoff)
mask = None
if cluster_correct:
hash = joblib.hash([times, freqs, X, threshold])
try:
_, _, cluster_p_values, _ = cluster_correct[hash]
sig = cluster_p_values.reshape((X.shape[1], X.shape[2]))
mask = sig < threshold
except KeyError:
s = get_tfr_stats(times, freqs, X, threshold)
_, _, cluster_p_values, _ = s[hash]
sig = cluster_p_values.reshape((X.shape[1], X.shape[2]))
mask = sig < threshold
cax = pmi(
plt.gca(),
np.nanmean(X, 0),
times,
yvals=freqs,
yscale="linear",
vmin=vmin,
vmax=vmax,
mask=mask,
mask_alpha=1,
mask_cmap=cmap,
cmap=cmap,
)
plt.xlim(time_cutoff)
plt.ylim([freqs.min() - 0.5, freqs.max() + 0.5])
ax.axvline(0, ls="--", lw=0.75, color="black")
if plot_colorbar:
plt.colorbar(cax, ticks=[vmin, 0, vmax])
return ax
def plot_epoch_pair(
tfr_data,
vmin=-25,
vmax=25,
cmap="RdBu_r",
gs=None,
stats=False,
threshold=0.05,
ylabel=None,
):
from matplotlib import gridspec
import pylab as plt
import joblib
if gs is None:
g = gridspec.GridSpec(1, 2)
else:
g = gridspec.GridSpecFromSubplotSpec(1,
2,
subplot_spec=gs,
wspace=0.01,
width_ratios=[1, 0.4])
times, freq, tfr = None, None, None
for epoch in ["stimulus", "response"]:
row = 0
if epoch == "stimulus":
col = 0
time_cutoff = (-0.35, 1.1)
xticks = [0, 0.25, 0.5, 0.75, 1]
yticks = [25, 50, 75, 100, 125]
xmarker = [0, 1]
else:
col = 1
time_cutoff = (-0.35, 0.1)
xticks = [0]
yticks = [1, 25, 50, 75, 100, 125]
xmarker = [0, 1]
plt.subplot(g[row, col])
tdata = tfr_data.query('epoch=="%s"' % (epoch))
if len(tdata) == 0:
plt.yticks([], [""])
plt.xticks([], [""])
continue
times, freqs, tfr = get_tfr(tdata, time_cutoff)
mask = None
if stats:
hash = joblib.hash([times, freqs, tfr, threshold])
try:
_, _, cluster_p_values, _ = stats[hash]
except KeyError:
s = get_tfr_stats(times, freqs, tfr, threshold)
_, _, cluster_p_values, _ = s[hash]
sig = cluster_p_values.reshape((tfr.shape[1], tfr.shape[2]))
mask = sig < threshold
_ = pmi(
plt.gca(),
np.nanmean(tfr, 0),
times,
yvals=freqs,
yscale="linear",
vmin=vmin,
vmax=vmax,
mask=mask,
mask_alpha=1,
mask_cmap=cmap,
cmap=cmap,
)
if (ylabel is not None) and (epoch == "stimulus"):
plt.ylabel(ylabel, labelpad=-2, fontdict={"fontsize": 4})
# for xmark in xmarker:
# plt.axvline(xmark, color='k', lw=1, zorder=-1, alpha=0.5)
plt.yticks(yticks, [""] * len(yticks))
plt.xticks(xticks, [""] * len(xticks))
plt.tick_params(direction="inout", length=2, zorder=100)
plt.xlim(time_cutoff)
plt.ylim([1, 147.5])
# plt.axhline(10, color='k', lw=1, alpha=0.5, linestyle='--')
# plt.axhline(25, color='k', lw=1, alpha=0.5, linestyle=':')
# plt.axhline(50, color='k', lw=1, alpha=0.5, linestyle=':')
plt.axvline(0, color="k", lw=1, zorder=5, alpha=0.5)
if epoch == "stimulus":
plt.axvline(1, color="k", lw=1, zorder=5, alpha=0.5)
return times, freqs, tfr
def plot_mosaic(
tfr_data,
vmin=-25,
vmax=25,
cmap="RdBu_r",
ncols=4,
epoch="stimulus",
stats=False,
threshold=0.05,
):
if epoch == "stimulus":
time_cutoff = (-0.5, 1.35)
xticks = [0, 0.25, 0.5, 0.75, 1]
xticklabels = ["0\nStim on", "", ".5", "", "1\nStim off"]
yticks = [25, 50, 75, 100, 125]
yticklabels = ["25", "", "75", "", "125"]
xmarker = [0, 1]
baseline = (-0.25, 0)
else:
time_cutoff = (-1, 0.5)
xticks = [-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5]
xticklabels = ["-1", "", "-0.5", "", "0\nResponse", "", "0.5"]
yticks = [1, 25, 50, 75, 100, 125]
yticklabels = ["1", "25", "", "75", "", "125"]
xmarker = [0, 1]
baseline = None
from matplotlib import gridspec
import pylab as plt
import seaborn as sns
contrast_tfr.set_jw_style()
sns.set_style("ticks")
nrows = (len(atlas_glasser.areas) // ncols) + 1
gs = gridspec.GridSpec(nrows, ncols)
gs.update(wspace=0.01, hspace=0.01)
for i, (name, area) in enumerate(atlas_glasser.areas.items()):
try:
column = np.mod(i, ncols)
row = i // ncols
plt.subplot(gs[row, column])
times, freqs, tfr = get_tfr(tfr_data.query('cluster=="%s"' % area),
time_cutoff)
# cax = plt.gca().pcolormesh(times, freqs, np.nanmean(
# tfr, 0), vmin=vmin, vmax=vmax, cmap=cmap, zorder=-2)
mask = None
if stats:
import joblib
hash = joblib.hash([times, freqs, tfr, threshold])
try:
_, _, cluster_p_values, _ = stats[hash]
except KeyError:
s = get_tfr_stats(times, freqs, tfr, threshold)
_, _, cluster_p_values, _ = s[hash]
sig = cluster_p_values.reshape((tfr.shape[1], tfr.shape[2]))
mask = sig < threshold
cax = pmi(
plt.gca(),
np.nanmean(tfr, 0),
times,
yvals=freqs,
yscale="linear",
vmin=vmin,
vmax=vmax,
mask=mask,
mask_alpha=1,
mask_cmap=cmap,
cmap=cmap,
)
# plt.grid(True, alpha=0.5)
for xmark in xmarker:
plt.axvline(xmark, color="k", lw=1, zorder=-1, alpha=0.5)
plt.yticks(yticks, [""] * len(yticks))
plt.xticks(xticks, [""] * len(xticks))
set_title(name, times, freqs, plt.gca())
plt.tick_params(direction="inout", length=2, zorder=100)
plt.xlim(time_cutoff)
plt.ylim([1, 147.5])
plt.axhline(10, color="k", lw=1, alpha=0.5, linestyle="--")
except ValueError as e:
print(name, area, e)
plt.subplot(gs[nrows - 2, 0])
sns.despine(left=True, bottom=True)
plt.subplot(gs[nrows - 1, 0])
pmi(
plt.gca(),
np.nanmean(tfr, 0) * 0,
times,
yvals=freqs,
yscale="linear",
vmin=vmin,
vmax=vmax,
mask=None,
mask_alpha=1,
mask_cmap=cmap,
cmap=cmap,
)
plt.xticks(xticks, xticklabels)
plt.yticks(yticks, yticklabels)
for xmark in xmarker:
plt.axvline(xmark, color="k", lw=1, zorder=-1, alpha=0.5)
if baseline is not None:
plt.fill_between(baseline,
y1=[1, 1],
y2=[150, 150],
color="k",
alpha=0.5)
plt.tick_params(direction="in", length=3)
plt.xlim(time_cutoff)
plt.ylim([1, 147.5])
plt.xlabel("time [s]")
plt.ylabel("Freq [Hz]")
sns.despine(ax=plt.gca())
def plot_tfr(tfr,
time_cutoff,
vmin,
vmax,
tl,
cluster_correct=False,
threshold=0.05,
plot_colorbar=False,
ax=None,
cmap=None,
stat_cutoff=None,
aspect=None,
cluster=None,
contrast_name=None,
time_lock=None):
from pymeg.contrast_tfr import get_tfr_stats
# colorbar:
from matplotlib.colors import LinearSegmentedColormap
if cmap is None:
cmap = LinearSegmentedColormap.from_list(
"custom", ["blue", "lightblue", "lightgrey", "yellow", "red"],
N=100)
if stat_cutoff is None:
stat_cutoff = time_cutoff
# data:
times, freqs, X = contrast_tfr.get_tfr(tfr, stat_cutoff)
#import ipdb; ipdb.set_trace()
### Save data to data source file
from conf_analysis.meg.figures import array_to_data_source_file
panel = 'A' if 'all' in contrast_name else 'B'
if not 'choice' in contrast_name:
fnr = 2
else:
fnr = 'S6'
panel = 'A'
array_to_data_source_file(
fnr, panel, cluster + str(time_lock), X, {
'dim_0_subjects': np.arange(1, 16),
'dim_1_frequencies': freqs,
'dim_2_time': times
})
mask = None
if cluster_correct:
hash = joblib.hash([times, freqs, X, threshold])
try:
_, _, cluster_p_values, _ = cluster_correct[hash]
sig = cluster_p_values.reshape((X.shape[1], X.shape[2]))
mask = sig < threshold
except KeyError:
s = get_tfr_stats(times, freqs, X, threshold)
_, _, cluster_p_values, _ = s[hash]
sig = cluster_p_values.reshape((X.shape[1], X.shape[2]))
mask = sig < threshold
earliest_sig = None
if mask is not None:
idt = np.where(np.any(mask, 0).ravel())[0]
idt = [
t for t in idt
if (time_cutoff[0] <= times[t]) and (times[t] <= time_cutoff[1])
]
if len(idt) > 0:
earliest_sig = times[idt[0]]
freqs_idx = freqs >= 4
Xb = np.nanmean(X, 0)[freqs_idx, :]
freqsb = freqs[freqs_idx]
cax = pmi(
plt.gca(),
Xb,
times,
yvals=freqsb,
yscale="linear",
vmin=vmin,
vmax=vmax,
mask=mask[freqs_idx, :],
mask_alpha=1,
mask_cmap=cmap,
cmap=cmap,
)
plt.gca().set_aspect(aspect)
plt.xlim(time_cutoff)
plt.ylim([freqs.min() - 0.5, freqs.max() + 0.5])
ax.axvline(0, ls="--", lw=0.75, color="black")
ax.axvline(1, ls="--", lw=0.75, color="black")
if plot_colorbar:
plt.colorbar(cax, ticks=[vmin, 0, vmax])
return ax, earliest_sig
