def __repr__(self):
d = {
"a. sensors type": self.s_type,
"b. labels": reg_dict(self.labels),
"c. locations": reg_dict(self.locations, self.labels),
"d. orientations": reg_dict(self.orientations, self.labels)
}
return formal_repr(self, sort_dict(d))
def __repr__(self):
d = {
"a. sensors type": self.s_type,
"b. labels": reg_dict(self.labels),
"c. locations": reg_dict(self.locations, self.labels),
"d. orientations": reg_dict(self.orientations, self.labels)
}
return formal_repr(self,
OrderedDict(sorted(d.items(), key=lambda t: t[0])))
def summary(self):
d = {
"a. centers": reg_dict(self.centers, self.region_labels),
# "c. normalized weights": self.normalized_weights,
# "d. tract_lengths": reg_dict(self.tract_lengths, self.region_labels),
"b. areas": reg_dict(self.areas, self.region_labels)
}
return formal_repr(self,
OrderedDict(sorted(d.items(), key=lambda t: t[0])))
def __repr__(self):
d = {"1. name": self.name,
"2. connectivity": self.connectivity,
"3. RM": reg_dict(self.region_mapping, self.connectivity.region_labels),
"4. VM": reg_dict(self.volume_mapping, self.connectivity.region_labels),
"5. surface": self.cortical_surface,
"6. T1": self.t1_background,
"7. SEEG": self.sensorsSEEG,
"8. EEG": self.sensorsEEG,
"9. MEG": self.sensorsMEG }
return formal_repr(self, sort_dict(d))
def __repr__(self):
d = {
"1. name": self.name,
"2. connectivity": self.connectivity,
"5. surface": self.cortical_surface,
"3. RM": reg_dict(self.region_mapping,
self.connectivity.region_labels),
"4. VM": reg_dict(self.volume_mapping,
self.connectivity.region_labels),
"6. T1": self.t1_background,
"7. SEEG": self.sensorsSEEG,
"8. EEG": self.sensorsEEG,
"9. MEG": self.sensorsMEG
}
return formal_repr(self,
OrderedDict(sorted(d.items(), key=lambda t: t[0])))
def __repr__(self):
d = {"f. normalized weights": reg_dict(self.normalized_weights, self.region_labels),
"g. weights": reg_dict(self.weights, self.region_labels),
"h. tract_lengths": reg_dict(self.tract_lengths, self.region_labels),
"a. region_labels": reg_dict(self.region_labels),
"b. centers": reg_dict(self.centers, self.region_labels),
"c. hemispheres": reg_dict(self.hemispheres, self.region_labels),
"d. orientations": reg_dict(self.orientations, self.region_labels),
"e. areas": reg_dict(self.areas, self.region_labels)}
return formal_repr(self, sort_dict(d))
def __repr__(self):
d = {
"f. normalized weights":
reg_dict(self.normalized_weights, self.region_labels),
"g. weights":
reg_dict(self.weights, self.region_labels),
"h. tract_lengths":
reg_dict(self.tract_lengths, self.region_labels),
"a. region_labels":
reg_dict(self.region_labels),
"b. centers":
reg_dict(self.centers, self.region_labels),
"c. hemispheres":
reg_dict(self.hemispheres, self.region_labels),
"d. orientations":
reg_dict(self.orientations, self.region_labels),
"e. areas":
reg_dict(self.areas, self.region_labels)
}
return formal_repr(self,
OrderedDict(sorted(d.items(), key=lambda t: t[0])))
def __repr__(self):
d = {
"01.name": self.name,
"02.K": vector2scalar(self.K),
"03.Iext1": vector2scalar(self.Iext1),
"04.seizure indices": self.seizure_indices,
"05. no of seizure nodes": self.n_seizure_nodes,
"06. x0": reg_dict(self.x0, sort='descend'),
"07. E": reg_dict(self.E, sort='descend'),
"08. PSlsa": reg_dict(self.lsa_ps, sort='descend'),
"09. x1EQ": reg_dict(self.x1EQ, sort='descend'),
"10. zEQ": reg_dict(self.zEQ, sort='ascend'),
"11. Ceq": reg_dict(self.Ceq, sort='descend'),
"12. weights for seizure nodes": self.weights_for_seizure_nodes,
"13. x1EQcr": vector2scalar(self.x1EQcr),
"14. x1LIN": vector2scalar(self.x1LIN),
"15. x1SQ": vector2scalar(self.x1SQ),
"16. x0cr": vector2scalar(self.x0cr),
"17. rx0": vector2scalar(self.rx0),
"18. x1eq_mode": self.x1eq_mode
}
return formal_repr(self,
OrderedDict(sorted(d.items(), key=lambda t: t[0])))
