def plot_rigidity(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.rigidity[0]).set_index("L")
df2 = pd.read_pickle(self.rigidity[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.spectral_rigidity(L=L), color="#08FD4F", label="Poisson", ax=ax)
sbn.lineplot(x=L, y=GOE.spectral_rigidity(L=L), color="#0066FF", label="GOE", ax=ax)
ax.legend().set_visible(True)
ax.set_title(f"{self.label}: Rigidity" if title is None else title)
ax.set_xlabel("L")
ax.set_ylabel("∆₃(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.rigidity[0].stem}_{self.label}.png"
fig.savefig(outfile, dpi=300)
plt.close()
print(f"Pooled rigidity plots saved to {relpath(outfile)}")
def _brody_fit(
unfolded: ndarray,
method: str = "spacing",
title: str = "Brody distribution fit",
mode: PlotMode = "block",
outfile: Path = None,
save_dpi: int = None,
ensembles: List[str] = ["goe", "poisson"],
bins: int = 50,
kde: bool = True,
trim: float = 5.0,
trim_kde: bool = False,
kde_bw: Union[float, str] = "scott",
) -> PlotResult:
_configure_sbn_style()
fig, axes = plt.subplots(1, 2, sharex=True, sharey=True)
_spacings(
unfolded=unfolded,
bins=bins,
kde=kde,
trim=trim,
trim_kde=trim_kde,
kde_bw=kde_bw,
brody=True,
brody_fit=method,
title="Brody distribution fit: density",
mode="return",
ensembles=ensembles,
fig=fig,
axes=axes[0],
)
spacings = np.diff(unfolded)
s = spacings[spacings > 0]
res = brody_fit_evaluate(s, method=method)
x = res["spacings"]
ecdf, bcdf = res["ecdf"], res["brody_cdf"]
ax = axes.flat[1]
ax_e = ax.plot(x, ecdf)
plt.setp(ax_e, label="Empirical CDF")
ax_b = ax.plot(x, bcdf)
plt.setp(ax_b, label="Brody CDF", color="#9d0000", linestyle="--")
if "goe" in ensembles:
ax_goe = ax.plot(x, GOE.nnsd_cdf(spacings=x))
plt.setp(ax_goe, label="GOE", color="#FD8208")
if "poisson" in ensembles:
ax_poi = ax.plot(x, Poisson.nnsd_cdf(spacings=x))
plt.setp(ax_poi, label="Poisson", color="#08FD4F")
ax.legend().set_visible(True)
ax.set_title("Brody distribution fit: cumulative density")
return _handle_plot_mode(mode, fig, axes, outfile, save_dpi)
def _next_spacings(
unfolded: ndarray,
bins: int = 50,
kde: bool = True,
trim: float = 0.0,
trim_kde: bool = False,
brody: bool = False,
brody_fit: str = "spacing",
title: str = "next Nearest-Neigbors Spacing Distribution",
mode: PlotMode = "block",
outfile: Path = None,
ensembles: List[str] = ["goe", "poisson"],
fig: Figure = None,
axes: Axes = None,
) -> PlotResult:
"""Plots a histogram of the next Nearest-Neighbors Spacing Distribution
Parameters
----------
unfolded: ndarray
the unfolded eigenvalues
bins: int
the number of (equal-sized) bins to display and use for the histogram
kde: boolean
If False (default), do not display a kernel density estimate. If true, use
[statsmodels.nonparametric.kde.KDEUnivariate](https://www.statsmodels.org/stable/generated/statsmodels.nonparametric.kde.KDEUnivariate.html#statsmodels.nonparametric.kde.KDEUnivariate)
with arguments {kernel="gau", bw="scott", cut=0} to compute and display
the kde
trim: float
If True, only use spacings <= `trim` for computing the KDE and plotting.
Useful for when large spacings distort the histogram.
trim_kde: bool
If True, fit the KDE using only spacings <= `trim`. Otherwise, fit the
KDE using all available spacings.
brody: bool
If True, compute the best-fitting Brody distribution via MLE, and plot
that distribution.
brody_fit: "spacing" | "mle"
Method for parametric distribution fitting of the Brody distribution to
the data. If "spacing", use
[maximum spacing estimation](https://en.wikipedia.org/wiki/Maximum_spacing_estimation).
If "mle", use the maximum likelihood method. The default is "spacing",
as this may be preferable for the J-shape of the Brody distribution.
title: string
The plot title string
mode: "block" | "noblock" | "save" | "return"
If "block", call plot.plot() and display plot in a blocking fashion.
If "noblock", attempt to generate plot in nonblocking fashion.
If "save", save plot to pathlib Path specified in `outfile` argument
If "return", return (fig, axes), the matplotlib figure and axes object
for modification.
outfile: Path
If mode="save", save generated plot to Path specified in `outfile` argument.
Intermediate directories will be created if needed.
ensembles: List["poisson", "goe"]
Which ensembles to display the expected next-NNSD curves for.
fig: Figure
If provided with `axes`, configure plotting with the provided `fig`
object instead of creating a new figure. Useful for creating subplots.
axes: Axes
If provided with `fig`, plot to the provided `axes` object. Useful for
creating subplots.
Returns
-------
(fig, axes): (Figure, Axes)
The handles to the matplotlib objects, only if `mode` is "return".
"""
_configure_sbn_style()
fig, axes = _setup_plotting(fig, axes)
_spacings = np.sort((unfolded[2:] - unfolded[:-2]) / 2)
all_spacings = np.copy(_spacings)
if trim > 0.0:
_spacings = _spacings[_spacings <= trim]
# Generate expected distributions for classical ensembles
s_min, s_max = _spacings.min(), _spacings.max()
s = np.linspace(s_min, s_max, 10000)
axes = sbn.distplot(
_spacings,
norm_hist=True,
bins=bins, # doane
kde=False,
label="next NNSD",
axlabel="spacing (s_2)",
color="black",
ax=axes,
)
if kde is True:
if trim_kde:
_kde_plot(_spacings, s, axes)
else:
_kde_plot(all_spacings, s, axes)
if brody is True:
beta = fit_brody(_spacings)
brody_vals = brody_dist(s, beta=beta)
brod = axes.plot(s, brody_vals, label="Brody dist.")
plt.setp(brod, color="#9d0000", linestyle="--")
if "goe" in ensembles:
goe = GOE.nnnsd(spacings=s)
goe = axes.plot(s, goe, label="Gaussian Orthogonal")
plt.setp(goe, color="#FD8208")
if "poisson" in ensembles:
poisson = Poisson.nnnsd(spacings=s)
poisson = axes.plot(s, poisson, label="Poisson")
plt.setp(poisson, color="#08FD4F")
# adjusting the right bounds can be necessary when / if there are
# many large eigenvalue spacings
axes.set_ylim(top=2.0, bottom=0)
axes.set_xlim(left=0, right=2.5)
axes.set(title=title, ylabel="Density p(s)")
axes.legend().set_visible(True)
return _handle_plot_mode(mode, fig, axes, outfile)
def plot_nnsd(
self,
trim_args: str,
unfold_args: dict,
n_bins: int = 20,
max_spacing: float = 5.0,
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"
elif self.label in ["control_pre_v_parkinsons", "control_v_park_pre"]:
return # meaningless comparison
else:
c1, c2 = "#EA00FF", "#FD8208"
unfolded1 = []
for path in self.eigs1:
vals = np.load(path)
if trim_args in ["(1,:)", "", "(0,:)"]:
vals = vals[1:] # smallest eigenvalue is always spurious here
else:
low, high = eval(trim_args)
vals = vals[low:high]
unfolded1.append(np.sort(Eigenvalues(vals).unfold(**unfold_args).vals))
unfolded2 = []
for path in self.eigs2:
vals = np.load(path)
if trim_args in ["(1,:)", "", "(0,:)"]:
vals = vals[1:] # smallest eigenvalue is always spurious here
else:
low, high = eval(trim_args)
vals = vals[low:high]
unfolded2.append(np.sort(Eigenvalues(vals).unfold(**unfold_args).vals))
spacings1 = [np.diff(unfolded) for unfolded in unfolded1]
spacings2 = [np.diff(unfolded) for unfolded in unfolded2]
# trim largest histogram skewing spacing
spacings1 = [spacings[spacings < max_spacing] for spacings in spacings1]
spacings2 = [spacings[spacings < max_spacing] for spacings in spacings2]
mean_brody1 = np.round(float(pd.read_pickle(self.brody[0]).mean(axis=1)), 2)
mean_brody2 = np.round(float(pd.read_pickle(self.brody[1]).mean(axis=1)), 2)
_configure_sbn_style()
fig, axes = plt.subplots(ncols=2, sharex=True, sharey=True)
# plot curves for each group
for spacings in spacings1:
sbn.distplot(
spacings,
norm_hist=True,
bins=n_bins,
kde=True,
axlabel="spacing (s)",
color=c1,
kde_kws={"alpha": np.max([0.1, 1 / len(spacings1)])},
hist_kws={"alpha": np.max([0.1, 1 / len(spacings1)])},
ax=axes[0],
)
for spacings in spacings2:
sbn.distplot(
spacings,
norm_hist=True,
bins=n_bins,
kde=True,
axlabel="spacing (s)",
color=c2,
kde_kws={"alpha": np.max([0.1, 1 / len(spacings1)])},
hist_kws={"alpha": np.max([0.1, 1 / len(spacings2)])},
ax=axes[1],
)
# 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
s = np.linspace(0, max_spacing, 5000)
sbn.lineplot(x=s, y=Poisson.nnsd(spacings=s), color="#08FD4F", label="Poisson", ax=axes[0])
sbn.lineplot(x=s, y=Poisson.nnsd(spacings=s), color="#08FD4F", label="Poisson", ax=axes[1])
sbn.lineplot(x=s, y=GOE.nnsd(spacings=s), color="#0066FF", label="GOE", ax=axes[0])
sbn.lineplot(x=s, y=GOE.nnsd(spacings=s), color="#0066FF", label="GOE", ax=axes[1])
# ensure all plots have identical axes
axes[0].set_ylim(top=2.0)
axes[1].set_ylim(top=2.0)
# ax.legend().set_visible(True)
fig.suptitle(f"{self.label}: NNSD" if title is None else title)
for i, ax in enumerate(axes):
ax.set_xlabel("spacing (s)")
ax.set_ylabel("density p(s)", fontname="DejaVu Sans")
ax.set_title(
f"{self.subgroup1 if i == 0 else self.subgroup2} (<β> = {mean_brody1 if i == 0 else mean_brody2})"
)
if outdir is None:
plt.show()
plt.close()
else:
os.makedirs(outdir, exist_ok=True)
prefix = _prefix(trim_args, unfold_args)
outfile = outdir / f"{prefix}_{self.label}_nnsd.png"
fig.set_size_inches(10, 5)
plt.savefig(outfile, dpi=300)
plt.close()
print(f"Pooled nnsd plot saved to {relpath(outfile)}")
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()
def plot_subject_nnsd(args: Any,
n_bins: int = 20,
outfile: Path = None) -> None:
subjects = get_subjects_dict()
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)
pbar = tqdm(desc="NNSD", total=8 * len(subjects))
for i, (subj_id, subject) in enumerate(subjects.items()):
ax: Axes = axes.flat[i]
eigpaths = subject["runs"]
unfoldeds = []
for path in eigpaths:
vals = np.load(path)
if args.trim in ["(1,:)", "", "(0,:)"]:
vals = vals[1:] # smallest eigenvalue is always spurious here
else:
low, high = eval(args.trim)
vals = vals[low:high]
unfoldeds.append(
np.sort(Eigenvalues(vals).unfold(**args.unfold).vals))
all_spacings = [np.diff(unfolded) for unfolded in unfoldeds]
kde_gridsize = 1000
kdes = np.empty([len(all_spacings), kde_gridsize], dtype=float)
s = np.linspace(0, 3, kde_gridsize)
bins = np.linspace(0, 3, n_bins + 1)
for j, spacings in enumerate(all_spacings):
sbn.distplot(
spacings[(spacings > 0) & (spacings <= 3)],
norm_hist=True,
bins=bins,
kde=False,
color=c1,
hist_kws={
"alpha": 1.0 / 8,
"range": (0.0, 3.0)
},
ax=ax,
)
kde = _kde(spacings, grid=s)
kdes[j, :] = kde
pbar.update()
# sbn.lineplot(x=s, y=kde, color=c1, alpha=1.0 / 8, ax=ax)
# sbn.lineplot(x=s, y=kdes.mean(axis=0), color="#9d0000", label="Mean KDE", ax=ax)
sbn.lineplot(x=s,
y=kdes.mean(axis=0),
color=c1,
label="Mean KDE",
ax=ax)
sbn.lineplot(x=s,
y=Poisson.nnsd(spacings=s),
color="#08FD4F",
label="Poisson",
ax=ax)
sbn.lineplot(x=s,
y=GOE.nnsd(spacings=s),
color="#0066FF",
label="GOE",
ax=ax)
ax.set_title(f"subj-{subj_id}")
ax.set_ylabel("")
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 = [0, 1, 2, 3]
ax.set_xticks(ticks)
# ax.set_xticklabels(ticks, rotation=45, horizontalalignment="right")
ax.set_xticklabels(ticks)
pbar.close()
fig.suptitle("Per-Subject NNSD")
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, "spacing (s)", ha="center", va="center") # xlabel
fig.text(0.05,
0.5,
"density p(s)",
ha="center",
va="center",
rotation="vertical") # ylabel
plt.setp(axes, xlim=(0.0, 3.0), ylim=(0.0, 1.2)) # better use of space
handles, labels = axes.flat[-1].get_legend_handles_labels()
fig.legend(handles,
labels,
loc="center right",
frameon=False,
framealpha=0,
fontsize="8")
if outfile is None:
plt.show()
else:
fig.savefig(str(outfile.resolve()), dpi=300)
print(f"Saved NNSD plot to {str(outfile.relative_to(DATA_ROOT))}")
plt.close()