def model_fit(model, ds, bold_transient=10000, fc=True, fcd=False):
result = {}
if fc:
result["fc_scores"] = [
func.matrix_correlation(
func.fc(model.BOLD.BOLD[:,
model.BOLD.t_BOLD > bold_transient]),
fc) for i, fc in enumerate(ds.FCs)
]
result["mean_fc_score"] = np.mean(result["fc_scores"])
if fcd:
fcd_sim = func.fcd(model.BOLD.BOLD[:,
model.BOLD.t_BOLD > bold_transient])
# if the FCD dataset is already computed, use it
if hasattr(ds, "FCDs"):
fcd_scores = [
func.matrix_kolmogorov(
fcd_sim,
fcd_emp,
) for fcd_emp in ds.FCDs
]
else:
fcd_scores = [
func.ts_kolmogorov(
model.BOLD.BOLD[:, model.BOLD.t_BOLD > bold_transient],
bold) for bold in ds.BOLDs
]
fcd_meanScore = np.mean(fcd_scores)
result["fcd"] = fcd_scores
result["mean_fcd"] = fcd_meanScore
return result
def plot_outputs(model,
ds=None,
activity_xlim=None,
bold_transient=10000,
spectrum_windowsize=1,
plot_fcd=None):
# check if BOLD signal is long enough for FCD
FCD_THRESHOLD = 60 # seconds
# plot_fcd = False
if "BOLD" in model.outputs and plot_fcd is None:
if len(model.BOLD.BOLD.T
) > FCD_THRESHOLD / 2: # div by 2 because of bold sampling rate
plot_fcd = True
nrows = 2
if plot_fcd:
nrows += 1
fig, axs = plt.subplots(nrows, 3, figsize=(12, nrows * 3), dpi=150)
if "t" in model.outputs:
axs[0, 0].set_ylabel("Activity")
axs[0, 0].set_xlabel("Time [s]")
axs[0, 0].plot(model.outputs.t / 1000,
model.output.T,
alpha=0.8,
lw=0.5)
axs[0, 0].plot(model.outputs.t / 1000,
np.mean(model.output, axis=0),
c="r",
alpha=0.8,
lw=1.5,
label="average",
zorder=3)
axs[0, 0].plot(
model.outputs.t / 1000,
np.mean(model.output[1::2, :], axis=0),
c="k",
alpha=0.8,
lw=1,
label="L average",
)
axs[0, 0].plot(
model.outputs.t / 1000,
np.mean(model.output[::2, :], axis=0),
c="k",
alpha=0.8,
lw=1,
label="R average",
)
axs[0, 0].set_xlim(activity_xlim)
axs[0, 1].set_ylabel("Node")
axs[0, 1].set_xlabel("Time [s]")
# plt.imshow(rates_exc*1000, aspect='auto', extent=[0, params['duration'], N, 0], clim=(0, 10))
axs[0, 1].imshow(
model.output,
aspect="auto",
extent=[0, model.t[-1] / 1000, model.params.N, 0],
clim=(0, 10),
)
# output frequency spectrum
axs[0, 2].set_ylabel("Power")
axs[0, 2].set_xlabel("Frequency [Hz]")
for o in model.output:
frs, pwrs = getPowerSpectrum(
o, dt=model.params.dt, spectrum_windowsize=spectrum_windowsize)
axs[0, 2].plot(frs, pwrs, alpha=0.8, lw=0.5)
frs, pwrs = getMeanPowerSpectrum(
model.output,
dt=model.params.dt,
spectrum_windowsize=spectrum_windowsize)
axs[0, 2].plot(frs, pwrs, lw=3, c="springgreen")
## frequency spectrum annotations
peaks = scipy.signal.find_peaks_cwt(pwrs, np.arange(2, 3))
for p in peaks:
axs[0, 2].scatter(frs[p], pwrs[p], c="springgreen", zorder=20)
# p = np.argmax(Pxxs)
axs[0, 2].annotate(s=" {0:.1f} Hz".format(frs[p]),
xy=(frs[p], pwrs[p]),
fontsize=10)
if "BOLD" in model.outputs:
# BOLD plotting ----------
axs[1, 0].set_ylabel("BOLD")
axs[1, 0].set_xlabel("Time [s]")
t_bold = model.outputs.BOLD.t_BOLD[
model.outputs.BOLD.t_BOLD > bold_transient] / 1000
bold = model.outputs.BOLD.BOLD[:, model.outputs.BOLD.
t_BOLD > bold_transient]
axs[1, 0].plot(t_bold, bold.T, lw=1.5, alpha=0.8)
axs[1, 1].set_title("FC", fontsize=12)
if ds is not None:
fc_fit = model_fit(model, ds, bold_transient,
fc=True)["mean_fc_score"]
axs[1, 1].set_title(f"FC (corr: {fc_fit:0.2f})", fontsize=12)
axs[1, 1].imshow(func.fc(bold), origin="upper")
axs[1, 1].set_ylabel("Node")
axs[1, 1].set_xlabel("Node")
axs[1, 2].set_title("FC corr over time", fontsize=12)
axs[1, 2].plot(
np.arange(4, bold.shape[1] * 2, step=2),
np.array([[
func.matrix_correlation(func.fc(bold[:, :t]), fc)
for t in range(2, bold.shape[1])
] for fc in ds.FCs]).T,
)
axs[1, 2].set_ylabel("FC fit")
axs[1, 2].set_xlabel("Simulation time [s]")
# FCD plotting ------------
if plot_fcd:
# plot image of fcd
axs[2, 0].set_title("FCD", fontsize=12)
axs[2, 0].set_ylabel("$n_{window}$")
axs[2, 0].set_xlabel("$n_{window}$")
axs[2, 0].imshow(func.fcd(bold), origin="upper")
# plot distribution in fcd
fcd_fit = model_fit(model, ds, bold_transient,
fcd=True)["mean_fcd"]
axs[2, 1].set_title(f"FCD distance {fcd_fit:0.2f}", fontsize=12)
axs[2, 1].set_ylabel("P")
axs[2, 1].set_xlabel("triu(FCD)")
m1 = func.fcd(bold)
triu_m1_vals = m1[np.triu_indices(m1.shape[0], k=1)]
axs[2, 1].hist(triu_m1_vals,
density=True,
color="springgreen",
zorder=10,
alpha=0.6)
# plot fcd distributions of data
if hasattr(ds, "FCDs"):
for emp_fcd in ds.FCDs:
m1 = emp_fcd
triu_m1_vals = m1[np.triu_indices(m1.shape[0], k=1)]
axs[2, 1].hist(triu_m1_vals, density=True, alpha=0.5)
# temp bullshit
axs[2, 2].plot(model.outputs.rates_exc[0, :],
model.outputs.rates_inh[0, :],
lw=0.5)
axs[2, 2].set_xlabel("$r_{exc}$")
axs[2, 2].set_ylabel("$r_{inh}$")
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
def test_fcd(self):
rFCD = func.fcd(self.model.rates_exc, stepsize=100)