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)