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)