def plot_levelvar(self, title: str = None, outdir: Path = None) -> None:
# label is always g1_v_g2, we want "attention" to be orange, "nonattend"
# to be black
if self.label in ["rest_v_task", "nopain_v_pain", "control_v_control_pre", "park_pre_v_parkinsons"]:
c1, c2 = "#000000", "#FD8208"
elif self.label in [
"allpain_v_nopain",
"allpain_v_duloxetine",
"high_v_low",
"control_pre_v_park_pre",
"control_v_parkinsons",
]:
c1, c2 = "#FD8208", "#000000"
else:
c1, c2 = "#EA00FF", "#FD8208"
df1 = pd.read_pickle(self.levelvar[0]).set_index("L")
df2 = pd.read_pickle(self.levelvar[1]).set_index("L")
L = np.array(df1.index)
_configure_sbn_style()
fig: plt.Figure
ax: plt.Axes
fig, ax = plt.subplots()
# plot curves for each group
for col in df1:
sbn.lineplot(x=L, y=df1[col], color=c1, alpha=0.05, ax=ax)
for col in df2:
sbn.lineplot(x=L, y=df2[col], color=c2, alpha=0.05, ax=ax)
# plot bootstrapped means and CIs for each group
boots = _percentile_boot(df1)
sbn.lineplot(x=L, y=boots["mean"], color=c1, label=self.subgroup1, ax=ax)
ax.fill_between(x=L, y1=boots["low"], y2=boots["high"], color=c1, alpha=0.3)
boots = _percentile_boot(df2)
sbn.lineplot(x=L, y=boots["mean"], color=c2, label=self.subgroup2, ax=ax)
ax.fill_between(x=L, y1=boots["low"], y2=boots["high"], color=c2, alpha=0.3)
# plot theoretically-expected curves
# sbn.lineplot(x=L, y=Poisson.level_variance(L=L), color="#08FD4F", label="Poisson", ax=ax)
sbn.lineplot(x=L, y=GOE.level_variance(L=L), color="#0066FF", label="GOE", ax=ax)
ax.legend().set_visible(True)
ax.set_title(f"{self.label}: Level Variance" if title is None else title)
ax.set_xlabel("L")
ax.set_ylabel("Σ²(L)", fontname="DejaVu Sans")
fig.set_size_inches(w=8, h=8)
if outdir is None:
plt.show()
plt.close()
else:
os.makedirs(outdir, exist_ok=True)
outfile = outdir / f"{self.levelvar[0].stem}_{self.label}.png"
plt.savefig(outfile, dpi=300)
plt.close()
print(f"Pooled levelvar plots saved to {relpath(outfile)}")
def plot_pred_rigidity(
args: Any,
dataset_name: str,
comparison: str,
unfold: List[int] = [5, 7, 9, 11, 13],
ensembles: bool = True,
silent: bool = False,
force: bool = False,
) -> None:
global ARGS
# ARGS.fullpre = True
for trim_idx in ["(1,-1)", "(1,-20)"]:
ARGS.trim = trim_idx
all_pairs = []
for normalize in [False]:
args.normalize = normalize
for degree in unfold:
ARGS.unfold["degree"] = degree
pairings = Pairings(args, dataset_name)
pairing = list(filter(lambda p: p.label == comparison, pairings.pairs))
if len(pairing) != 1:
raise ValueError("Too many pairings, something is wrong.")
all_pairs.append(pairing[0])
g1, _, g2 = all_pairs[0].label.split("_") # groupnames
fig: plt.Figure
fig, axes = plt.subplots(nrows=1, ncols=len(all_pairs), sharex=True)
for i, pair in enumerate(all_pairs):
ax: plt.Axes = axes.flat[i]
df1 = pd.read_pickle(pair.rigidity[0]).set_index("L")
df2 = pd.read_pickle(pair.rigidity[1]).set_index("L")
boots1 = _percentile_boot(df1)
boots2 = _percentile_boot(df2)
sbn.lineplot(x=df1.index, y=boots1["mean"], color="#FD8208", label=g1, ax=ax)
ax.fill_between(x=df1.index, y1=boots1["low"], y2=boots1["high"], color="#FD8208", alpha=0.3)
sbn.lineplot(x=df2.index, y=boots2["mean"], color="#000000", label=g2, ax=ax)
ax.fill_between(x=df2.index, y1=boots2["low"], y2=boots2["high"], color="#000000", alpha=0.3)
if ensembles:
L = df1.index
poisson = GDE.spectral_rigidity(L=L)
goe = GOE.spectral_rigidity(L=L)
sbn.lineplot(x=L, y=poisson, color="#08FD4F", label="Poisson", ax=ax)
sbn.lineplot(x=L, y=goe, color="#0066FF", label="GOE", ax=ax)
ax.legend().set_visible(False)
ax.set_title(f"Unfolding Degree {unfold[i]}")
ax.set_xlabel("")
ax.set_ylabel("")
axes.flat[-1].legend().set_visible(True)
fig.text(0.5, 0.04, "L", ha="center", va="center") # xlabel
fig.text(
0.03, 0.5, "∆₃(L)", ha="center", va="center", rotation="vertical", fontdict={"fontname": "DejaVu Sans"}
) # ylabel
fig.set_size_inches(w=12, h=3)
fig.subplots_adjust(top=0.83, bottom=0.14, left=0.085, right=0.955, hspace=0.2, wspace=0.2)
plt.suptitle(f"{dataset_name} {ARGS.trim} - Spectral Rigidity")
plt.show(block=False)
def plot_subject_levelvar(summaries: Dict[str, Dict[Observable, Path]],
args: Any,
outfile: Path = None) -> None:
sbn.set_context("paper")
sbn.set_style("ticks", {"ytick.left": False})
c1 = "#000000"
fig, axes = plt.subplots(ncols=6, nrows=4, sharex=True, sharey=True)
top = []
for i, (subj_id, summary) in enumerate(summaries.items()):
ax: Axes = axes.flat[i]
label = f"subj-{subj_id}"
levelvars = pd.read_pickle(summary["levelvar"]).set_index("L")
rename_columns(levelvars)
L = levelvars.index.to_numpy(dtype=float).ravel()
for col in levelvars:
sbn.lineplot(x=L,
y=levelvars[col],
color=c1,
alpha=1.0 / 8.0,
ax=ax)
# sbn.lineplot(x=L, y=levelvars[col], label=f"run-{col}", color=c1, alpha=1.0/8.0, ax=ax)
boots = _percentile_boot(levelvars, B=2000)
top.append(np.max(boots["mean"]))
# sbn.lineplot(x=L, y=boots["mean"], color=c1, label=label)
sbn.lineplot(x=L, y=boots["mean"], color=c1, ax=ax)
ax.fill_between(x=L,
y1=boots["low"],
y2=boots["high"],
color=c1,
alpha=0.3)
# plot theoretically-expected curves
sbn.lineplot(x=L,
y=Poisson.level_variance(L=L),
color="#08FD4F",
label="Poisson",
ax=ax)
sbn.lineplot(x=L,
y=GOE.level_variance(L=L),
color="#0066FF",
label="GOE",
ax=ax)
ax.set_ylabel("")
ax.set_title(label)
ax.legend(frameon=False, framealpha=0)
plt.setp(ax.get_legend().get_texts(), fontsize="6")
if i != 0:
handle, labels = ax.get_legend_handles_labels()
ax._remove_legend(handle)
ticks = [1, 5, 10]
ax.set_xticks(ticks)
ax.set_xticklabels(ticks, rotation=45, horizontalalignment="right")
plt.setp(axes, ylim=(0.0, np.percentile(top, 90))) # better use of space
fig.suptitle("Per-Subject Task Level Number Variances")
fig.set_size_inches(10, 6)
fig.subplots_adjust(left=0.13, bottom=0.15, wspace=0.1, hspace=0.35)
fig.text(0.5, 0.04, "L", ha="center", va="center") # xlabel
fig.text(0.05,
0.5,
"Σ²(L)",
ha="center",
va="center",
rotation="vertical",
fontname="DejaVu Sans") # ylabel
if outfile is None:
plt.show()
else:
fig.savefig(str(outfile.resolve()), dpi=300)
print(f"Saved levelvar plot to {str(outfile.relative_to(DATA_ROOT))}")
plt.close()