def summary(self):
d = {
"a. sensors type": self.s_type,
"b. locations": reg_dict(self.locations, self.labels)
}
return formal_repr(self,
OrderedDict(sorted(d.items(), key=lambda t: t[0])))
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. vertices": self.vertices,
"b. triangles": self.triangles,
"c. vertex_normals": self.vertex_normals,
"d. triangle_normals": self.triangle_normals
}
return formal_repr(self, sort_dict(d))
def __repr__(self):
d = {"1. name": self.name,
"2. low": self.low,
"3. high": self.high,
"4. location": self.loc,
"5. scale": self.scale,
"6. distribution": self.distribution,
"7. shape": self.shape}
return formal_repr(self, sort_dict(d))
def __repr__(self):
d = {"01. Eigenvectors' number selection mode": self.eigen_vectors_number_selection,
"02. Eigenvectors' number": self.eigen_vectors_number_selection,
"03. Eigen values": self.eigen_values,
"04. Eigenvectors": self.eigen_vectors,
"05. Eigenvectors' number": self.eigen_vectors_number,
"06. Weighted eigenvector's sum flag": str(self.weighted_eigenvector_sum)
}
return formal_repr(self, d)
def __repr__(self):
d = {
"a. vertices": self.vertices,
"b. triangles": self.triangles,
"c. vertex_normals": self.vertex_normals,
"d. triangle_normals": self.triangle_normals
}
return formal_repr(self,
OrderedDict(sorted(d.items(), key=lambda t: t[0])))
def __repr__(self):
d = {
"01. Task": self.task,
"02. Main PSE object": self.pse_object,
"03. Number of computation loops": self.n_loops,
"04. Parameters": np.array(["%s" % l for l in self.params_names]),
}
return formal_repr(self, d)
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 = {"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 = {
"01. Sampling module": self.sampling_module,
"02. Sampler": self.sampler,
"03. Number of samples": self.n_samples,
"04. Number of output parameters": self.n_outputs,
"05. Samples' shape": self.shape,
}
return formal_repr(self, d) + "\n06. Distribution parameters: " + dict_str(self.params) + \
"\n07 Resulting statistics: " + dict_str(self.stats)
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 = {
"01. Number of regions": self.number_of_regions,
"02. x0": self.x0,
"03. Iext1": self.Iext1,
"04. a": self.a,
"05. b": self.b,
"06. x1eq_mode": self.x1eq_mode,
"07. K_unscaled": self.K_unscaled,
"08. K": self.K,
"09. E": self.E,
}
return formal_repr(self, d)
def __repr__(self):
d = {
"01. Sampling module": self.sampling_module,
"02. Sampler": self.sampler,
"03. Number of samples": self.n_samples,
"04. Number of output parameters": self.n_outputs,
"05. Samples' shape": self.shape,
"06. Random seed": self.random_seed,
}
return formal_repr(self, d) + \
"\n07. Distribution parameters: " + dict_str(self.params) + \
"\n08. Truncation limits: " + str([dict_str(d) for d in dicts_of_lists_to_lists_of_dicts(self.trunc_limits)]) + \
"\n08. Resulting statistics: " + dict_str(self.stats)
def __repr__(self):
d = {"1. name": self.name,
"2. type": self.type,
"3. number of regions": self.n_regions,
"4. number of active regions": self.n_active_regions,
"5. number of nonactive regions": self.n_nonactive_regions,
"6. number of observation signals": self.n_signals,
"7. number of time points": self.n_times,
"8. euler_method": self.euler_method,
"9. observation_expression": self.observation_expression,
"10. observation_model": self.observation_model,
"11. number of parameters": self.n_parameters,
"12. parameters": [p.__str__ for p in self.parameters.items()]}
return formal_repr(self, sort_dict(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 = {
"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 = {
"01. Excitability": self.x0_values,
"02. yc": self.yc,
"03. Iext1": self.Iext1,
"04. K": self.K,
"05. a": self.a,
"06. b": self.b,
"07. x0cr": self.x0cr,
"08. rx0": self.rx0,
"09. x1EQ": self.x1EQ,
"10. zEQ": self.zEQ,
"11. Ceq": self.Ceq,
"12. Epileptogenicity": self.e_values
}
return formal_repr(self, d)
def __repr__(self):
d = {
"01. integration_step": self.integration_step,
"02. simulated_period": self.simulated_period,
"03. scale_time": self.scale_time,
"04. integrator_type": self.integrator_type,
"05. noise_preconfig": self.noise_preconfig,
"06. noise_type": self.noise_type,
"07. noise_ntau": self.noise_ntau,
"08. noise_seed": self.noise_seed,
"09. noise_intensity": self.noise_intensity,
"10. monitors_preconfig": self.monitors_preconfig,
"11. monitor_type": self.monitor_type,
"12. monitor_sampling_period": self.monitor_sampling_period,
"13. monitor_expressions": self.monitor_expressions,
"14. variables_names": self.variables_names,
"15. initial_conditions": self.initial_conditions,
}
return formal_repr(self, 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. Number of regions": self.number_of_regions,
"02. x0_values": self.x0_values,
"03. e_values": self.e_values,
"04. K_unscaled": self.K_unscaled,
"05. K": self.K,
"06. yc": self.yc,
"07. Iext1": self.Iext1,
"08. Iext2": self.Iext2,
"09. K": self.K,
"10. a": self.a,
"11. b": self.b,
"12. d": self.d,
"13. s": self.s,
"14. slope": self.slope,
"15. gamma": self.gamma,
"16. zmode": self.zmode,
"07. x1eq_mode": self.x1eq_mode
}
return formal_repr(self, d)
def __repr__(self):
d = {
"01. Excitability": self.x0_values,
"02. Epileptor Model Excitability": self.x0,
"03. x1EQ": self.x1EQ,
"04. zEQ": self.zEQ,
"05. Ceq": self.Ceq,
"06. Epileptogenicity": self.e_values,
"07. yc": self.yc,
"08. Iext1": self.Iext1,
"09. Iext2": self.Iext2,
"10. K": self.K,
"11. a": self.a,
"12. b": self.b,
"13. d": self.d,
"14. s": self.s,
"15. slope": self.slope,
"16. gamma": self.gamma,
"17. zmode": self.zmode,
"18. Connectivity Matrix": self.connectivity_matrix
}
return formal_repr(self, d)
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])))
def __repr__(self):
d = {
"01. Name":
self.name,
"02. Type":
self.type,
"03. Number of regions":
self.number_of_regions,
"04. X0 disease indices":
self.x0_indices,
"05. X0 disease values":
self.x0_values,
"06. e_values disease indices":
self.e_indices,
"07. e_values disease indices":
self.e_values,
"08. Connectivity disease indices":
linear_index_to_coordinate_tuples(
self.w_indices,
(self.number_of_regions, self.number_of_regions)),
"09. Connectivity disease values":
self.w_values,
"10. Propagation indices":
self.propagation_indices,
}
if len(self.propagation_indices):
d.update({
"11. Propagation strengths of indices":
self.propagation_strengths[self.propagation_indices]
})
else:
d.update({
"11. Propagation strengths of indices":
self.propagation_strengths
})
# d.update({"11. Connectivity": str(self.connectivity)})
return formal_repr(self, d)
