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])))