def test_ts_kolmogorov(self):
func.ts_kolmogorov(
self.model.rates_exc[::20, :],
self.model.rates_exc,
stepsize=250,
windowsize=30,
)
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 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_scores = [
func.ts_kolmogorov(
model.BOLD.BOLD[:, model.BOLD.t_BOLD > bold_transient],
ds.BOLDs[i]) for i in range(len(ds.BOLDs))
]
fcd_meanScore = np.mean(fcd_scores)
result["fcd"] = fcd_scores
result["mean_fcd"] = fcd_meanScore
return result