def __repr__(self): d = {"01. Task": self.task, "02. Main PSE object": self.simulator, "03. Number of computation loops": self.n_loops, "04. Parameters": numpy.array(["%s" % l for l in self.params_names]), } return formal_repr(self, d)
def __repr__(self): d = { "1. name": self.name, "2. connectivity": self.connectivity, "3. cortical region mapping": reg_dict(self.cortical_region_mapping, self.connectivity.region_labels), "4. subcortical region mapping": reg_dict(self.subcortical_region_mapping, self.connectivity.region_labels), "5. VM": reg_dict(self.volume_mapping, self.connectivity.region_labels), "6. cortical surface": self.cortical_surface, "7. subcortical surface": self.cortical_surface, "8. T1": self.t1_background, "9. SEEG": self.sensorsSEEG, "10. EEG": self.sensorsEEG, "11. MEG": self.sensorsMEG } return formal_repr(self, sort_dict(d))
def __repr__(self): d = { "01. Name": self.name, "02. Type": self.type, "03. Number of regions": self.number_of_regions, "04. Excitability (x0) disease indices": self.x0_disease_indices, # x0_indices, "05. Excitability (x0) disease values": self.x0_disease_values, # x0_values, "06. Epileptogenicity (E) disease indices": self.e_disease_indices, # e_indices, "07. Epileptogenicity (E) disease values": self.e_disease_values, # e_values, "08. Connectivity (W) disease indices": self.w_indices, "09. Connectivity (W) disease values": self.w_values, "10. Propagation indices": self.lsa_propagation_indices, } if len(self.lsa_propagation_indices): d.update({ "11. Propagation strengths of indices": self.lsa_propagation_strengths[self.lsa_propagation_indices] }) else: d.update({ "11. Propagation strengths of indices": self.lsa_propagation_strengths }) # d.update({"11. Connectivity": str(self.connectivity)}) return formal_repr(self, sort_dict(d))
def __repr__(self): d = {"00. surface subtype": self.surface_subtype, "01. vertices": self.vertices, "02. triangles": self.triangles, "03. vertex_normals": self.vertex_normals, "04. triangle_normals": self.triangle_normals, "05. voxel to ras matrix": self.vox2ras} return formal_repr(self, sort_dict(d))
def __repr__(self): d = { "01. Sampling module": self.sampling_module, "02. Sampler": self.sampler, "03. Number of samples": self.n_samples, "04. Samples' shape": self.shape, } return formal_repr( self, d) + "\n05. Resulting statistics: " + dict_str(self.stats)
def __repr__(self): d = { "1. sensors' type": self.s_type, "2. number of sensors": self.number_of_sensors, "3. labels": reg_dict(self.labels), "4. locations": reg_dict(self.locations, self.labels), "5. gain_matrix": self.gain_matrix } return formal_repr(self, sort_dict(d))
def __repr__(self): d = {"01. model": self.model, "02. Number of regions": self.number_of_regions, "03. connectivity": self.connectivity, "04. coupling": self.coupling, "05. monitor": self.monitor, "06. initial_conditions": self.initial_conditions, "07. noise": self.noise } return formal_repr(self, d)
def __repr__(self): d = {"01. model": self.model, "02. Number of regions": self.number_of_regions, "03. x0_values": self.x0_values, "04. e_values": self.e_values, "05. K": self.K, "06. x1eq_mode": self.x1eq_mode, "07. connectivity": self.connectivity, "08. coupling": self.coupling, "09. monitor": self.monitor, "10. initial_conditions": self.initial_conditions, "11. noise": self.noise } return formal_repr(self, d)
def __repr__(self): d = { "01. integrator_type": self.integrator_type, "02. integration_step": self.integration_step, "03. simulation_length": self.simulation_length, "04. integrator_type": self.integrator_type, "05. noise_type": self.noise_type, "06. noise_ntau": self.noise_ntau, "07. noise_seed": self.noise_seed, "08. noise_intensity": self.noise_intensity, "09. monitor_type": self.monitor_type, "10. monitor_sampling_period": self.monitor_sampling_period, "11. monitor_vois": self.monitor_vois, } return formal_repr(self, d)
def __repr__(self): d = { "01. Method": self.method, "02. Second order calculation flag": self.calc_second_order, "03. Confidence level": self.conf_level, "05. Number of inputs": self.n_inputs, "06. Number of outputs": self.n_outputs, "07. Input names": self.input_names, "08. Output names": self.output_names, "09. Input bounds": self.input_bounds, "10. Problem": dict_str(self.problem), "11. Other parameters": dict_str(self.other_parameters), } return formal_repr(self, d)
def __repr__(self): d = { "01. model": self.model, "02. number_of_regions": self.number_of_regions, "03. Excitability": self.x0_values, "04. Epileptor Model Excitability": self.x0, "05. Epileptogenicity": self.e_values, "06. x1eq": self.x1eq, "07. zeq": self.zeq, "08. Ceq": self.Ceq, "09. connectivity": self.connectivity, "10. coupling": self.coupling, "11. monitor": self.monitor, "12. initial_conditions": self.initial_conditions, "13. noise": self.noise, } return formal_repr(self, d)
def __repr__(self): d = { "01. LSA method": self.lsa_method, "02. Eigenvectors' number selection mode": self.eigen_vectors_number_selection, "03. Eigenvectors' number": self.eigen_vectors_number_selection, "04. Eigen values": self.eigen_values, "05. Eigenvectors": self.eigen_vectors, "06. Eigenvectors' number": self.eigen_vectors_number, "07. Weighted eigenvector's sum flag": str(self.weighted_eigenvector_sum) } return formal_repr(self, d)
def __repr__(self, d=OrderedDict()): return formal_repr(self, self._repr())
def __repr__(self): d = {"01. Task": self.task, "02. Main PSE object": self.hypothesis, "03. Parameters": numpy.array(["%s" % l for l in self.params_names]), } return formal_repr(self, d)