From d3f56e1382bfd574a7fc223e51138ba13b382d87 Mon Sep 17 00:00:00 2001 From: MaxRonce Date: Thu, 13 Jun 2024 15:10:01 +0200 Subject: [PATCH 01/16] add LICENSE --- LICENSE | 674 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 674 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. From 2e01b82701280ce75b57f3651c79768efc720857 Mon Sep 17 00:00:00 2001 From: MaxRonce Date: Thu, 13 Jun 2024 15:11:46 +0200 Subject: [PATCH 02/16] Add files via upload --- ressources/CC-BY-NC-SA-4.0 | 437 +++++++++++++++++++++++++++++++++++++ 1 file changed, 437 insertions(+) create mode 100644 ressources/CC-BY-NC-SA-4.0 diff --git a/ressources/CC-BY-NC-SA-4.0 b/ressources/CC-BY-NC-SA-4.0 new file mode 100644 index 0000000..d60efe4 --- /dev/null +++ b/ressources/CC-BY-NC-SA-4.0 @@ -0,0 +1,437 @@ +Attribution-NonCommercial-ShareAlike 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. Distribution of +Creative Commons public licenses does not create a lawyer-client or +other relationship. Creative Commons makes its licenses and related +information available on an "as-is" basis. 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From 5819911dda434c873e77d5f43143ae61dec98b37 Mon Sep 17 00:00:00 2001 From: mroncera Date: Mon, 13 May 2024 14:34:48 +0200 Subject: [PATCH 03/16] REFACTOR - move the optimisation tab to a separate module, add optimisation_tab.py --- src/view/gui.py | 3 ++- src/view/optimisation_tab.py | 41 ++++++++++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+), 1 deletion(-) create mode 100644 src/view/optimisation_tab.py diff --git a/src/view/gui.py b/src/view/gui.py index ac45bbf..8f4be61 100644 --- a/src/view/gui.py +++ b/src/view/gui.py @@ -32,6 +32,7 @@ from src.controler.controller import CalculationController, STRATEGY_MAP from src.model.visualisation.create_tree import create_tree, add_title_description +from view.optimisation_tab import OptimisationTab ureg: UnitRegistry = UnitRegistry() @@ -843,7 +844,7 @@ def init_ui(self): self.param_tab = QWidget() # QWidget that will contain the parameters layout as a tab self.strategy_tab = QWidget() # QWidget that will contain the strategy selection layout as a tab self.spice_tab = QWidget() # QWidget that will contain the spice layout as a tab - self.optimisation_tab = QWidget() # QWidget that will contain the optimisation layout as a tab + self.optimisation_tab = OptimisationTab() # QWidget that will contain the optimisation layout as a tab self.EMC_tab = QWidget() # QWidget that will contain the EMC layout as a tab self.params_layout = QVBoxLayout(self.param_tab) diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py new file mode 100644 index 0000000..696f608 --- /dev/null +++ b/src/view/optimisation_tab.py @@ -0,0 +1,41 @@ +from PyQt6.QtWidgets import QWidget, QVBoxLayout, QLabel, QPushButton, QMessageBox + + +class OptimisationTab(QWidget): + def __init__(self, parent=None): + super().__init__(parent) + self.setup_ui() + + def setup_ui(self): + self.layout = QVBoxLayout(self) + + self.label = QLabel("Welcome to the optimisation Tab !") + self.layout.addWidget(self.label) + + self.optimise_button = QPushButton("Optimise") + self.optimise_button.clicked.connect(self.on_optimise_clicked) + self.layout.addWidget(self.optimise_button) + + def on_optimise_clicked(self): + """Popup message box to confirm the optimisation.""" + + #show a message box inside a popup + msg = QMessageBox() + msg.setIcon(QMessageBox.Icon.Information) + msg.setText("Do you really want to start the optimisation process, it can take few minutes ?") + msg.setWindowTitle("Confirm") + msg.setStandardButtons(QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No) + msg.setDefaultButton(QMessageBox.StandardButton.No) + msg.buttonClicked.connect(self.on_message_box_clicked) + msg.exec() + + def on_message_box_clicked(self, button): + """Handle the message box button clicked event.""" + if button.text() == "&Yes": + print("Optimisation started !") + else: + print("Optimisation canceled !") + + + + From c2b254f03928bdb7420f31e46c345370d9d13a8a Mon Sep 17 00:00:00 2001 From: mroncera Date: Tue, 14 May 2024 10:54:18 +0200 Subject: [PATCH 04/16] Feature - add target on optimisation tab and add genetic optimisation --- src/controler/controller.py | 16 +- .../optimisation/genetic_optimisation.py | 151 ++++++++++++++++++ src/view/optimisation_tab.py | 144 +++++++++++++++-- 3 files changed, 299 insertions(+), 12 deletions(-) create mode 100644 src/model/optimisation/genetic_optimisation.py diff --git a/src/controler/controller.py b/src/controler/controller.py index 0229283..348ea7a 100644 --- a/src/controler/controller.py +++ b/src/controler/controller.py @@ -192,16 +192,30 @@ def __init__(self, params_dict=None, backups_count=3): input parameters of the engine. """ self.engine = CalculationEngine(backups_count=backups_count) + self.backup_count = backups_count self.is_data_ready = False self.params = None + self.STRATEGY_MAP = STRATEGY_MAP - for node_name, info in STRATEGY_MAP.items(): + for node_name, info in self.STRATEGY_MAP.items(): default_strategy = info["default"]() self.engine.add_or_update_node(node_name, default_strategy) if params_dict: self.update_parameters(params_dict) + def swap_strategy_map(self, strategy_map): + self.engine = CalculationEngine(backups_count=self.backup_count) + self.STRATEGY_MAP = strategy_map + + for node_name, info in self.STRATEGY_MAP.items(): + default_strategy = info["default"]() + self.engine.add_or_update_node(node_name, default_strategy) + + if self.params: + self.update_parameters(self.params) + + def delete_spice_nodes(self, spice_nodes : list): for node_name in spice_nodes: self.engine.delete_node(node_name) diff --git a/src/model/optimisation/genetic_optimisation.py b/src/model/optimisation/genetic_optimisation.py new file mode 100644 index 0000000..b8036fa --- /dev/null +++ b/src/model/optimisation/genetic_optimisation.py @@ -0,0 +1,151 @@ +import random + +import numpy as np +from PyQt6.QtCore import pyqtSignal, QThread +from deap import creator, base, tools, algorithms + +from model.optimisation.impedance_strategy_map import IMPEDANCE_STRATEGY_MAP +from src.controler.controller import CalculationController + +parameters_dict = { + # Initialize all required parameters + 'f_start': 1, + 'f_stop': 1000000, + 'nb_points_per_decade': 1000, + # Add other necessary parameters + 'mu_insulator': 1, + 'kapthon_thick': 30e-6, + 'insulator_thick': 10e-6, + 'diam_out_mandrel': 3.2e-3, + 'len_core': 20e-2, + 'diam_core': 3.2e-3, + 'mu_r': 100000, + 'rho_whire': 1.6, + 'coeff_expansion': 1, + 'stage_1_cutting_freq': 20000, + 'stage_2_cutting_freq': 20000, + 'gain_1_linear': 1, + 'gain_2_linear': 1, + 'mutual_inductance': 0.1, + 'feedback_resistance': 1000, + 'temperature': 300, + 'spice_resistance_test': 1000, + 'Para_A': 1, + 'Para_B': 1, + 'e_en': 1, + 'e_in': 1, + 'Alpha': 1, + "ray_spire": 5e-3, + "capa_tuning": 1e-12, + "capa_triwire": 10e-12 + } +class GeneticOptimisation(QThread): + update_signal = pyqtSignal(int, float, float) # generation, fitness average, best fitness + finished_signal = pyqtSignal(dict, float) # best parameters, resonance frequency + def __init__(self, population_size, generations, mutation_rate, parameters_dict, target_resonance_freq=2430): + super().__init__() + self.population_size = population_size + self.generations = generations + self.mutation_rate = mutation_rate + self.target_resonance_freq = target_resonance_freq + self.toolbox = None + + self.controller = CalculationController() + + # TODO : change this line + self.controller.swap_strategy_map(IMPEDANCE_STRATEGY_MAP) + + self.parameters_dict = parameters_dict + + self.init_creator() + + def update_target_resonance_freq(self, target_resonance_freq): + self.target_resonance_freq = target_resonance_freq + def determine_resonance_freq(self, freq_vector, impedance_vector): + index = impedance_vector.argmax() + return freq_vector[index] + + def evaluate(self, individual): + len_coil, diam_wire, nb_spire, capa_tuning, capa_triwire = individual + + params = parameters_dict.copy() + params.update({ + 'len_coil': len_coil, + 'diam_wire': diam_wire, + 'nb_spire': int(nb_spire), + 'capa_tuning': capa_tuning, + 'capa_triwire': capa_triwire + }) + + try: + self.controller.update_parameters(params) + results = self.controller.get_current_results() + impedance = results['impedance']["data"] + resonance_freq = self.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + target_resonance_freq = self.target_resonance_freq + return abs(resonance_freq - target_resonance_freq), + except Exception as e: + return 1e6, + + def init_creator(self): + creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) + creator.create("Individual", list, fitness=creator.FitnessMin) + + toolbox = base.Toolbox() + toolbox.register("attr_len_coil", random.uniform, 1e-3, 200e-3) + toolbox.register("attr_diam_wire", random.uniform, 10e-6, 300e-6) + toolbox.register("attr_nb_spire", random.uniform, 1000, 20000) + toolbox.register("attr_capa_tuning", random.uniform, 1e-12, 1000e-12) + toolbox.register("attr_capa_triwire", random.uniform, 10e-12, 1000e-12) + toolbox.register("individual", tools.initCycle, creator.Individual, + (toolbox.attr_len_coil, toolbox.attr_diam_wire, toolbox.attr_nb_spire, + toolbox.attr_capa_tuning, toolbox.attr_capa_triwire), + n=1) + toolbox.register("population", tools.initRepeat, list, toolbox.individual) + toolbox.register("evaluate", self.evaluate) + toolbox.register("mate", tools.cxTwoPoint) + toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.3) + toolbox.register("select", tools.selTournament, tournsize=5) + + self.toolbox = toolbox + + def run_optimisation(self): + population = self.toolbox.population(n=self.population_size) + hof = tools.HallOfFame(5) # Keep the top 5 individuals + stats = tools.Statistics(lambda ind: ind.fitness.values) + stats.register("min", min) + stats.register("avg", np.mean) + + for gen in range(self.generations): + population, logbook = algorithms.eaSimple(population, self.toolbox, cxpb=0.7, mutpb=self.mutation_rate, + ngen=1, stats=stats, halloffame=hof, verbose=False) + + avg_fitness = logbook.select("avg")[0] + best_fitness = logbook.select("min")[0][0] + print(best_fitness) + self.update_signal.emit(gen, avg_fitness, best_fitness) + + best_params = hof[0] + print("Best Parameters:", best_params) + print("Best Fitness:", self.evaluate(best_params)[0]) + + # Update parameters for the final evaluation + final_params = {'len_coil': best_params[0], 'diam_wire': best_params[1], 'nb_spire': int(best_params[2]), + 'capa_tuning': best_params[3], 'capa_triwire': best_params[4]} + self.controller.update_parameters(final_params) + final_results = self.controller.get_current_results() + impedance = final_results['impedance']["data"] + final_resonance_freq = self.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + print("Final Resonance Frequency:", final_resonance_freq) + print("Final parameters:", final_params) + self.finished_signal.emit(final_params, final_resonance_freq) + return final_resonance_freq, final_params + + def run(self): + self.run_optimisation() + + +if __name__ == "__main__": + + optimisation = GeneticOptimisation(100, 20, 0.3, parameters_dict, target_resonance_freq=24430) + optimisation.run_optimisation() \ No newline at end of file diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py index 696f608..ac0718b 100644 --- a/src/view/optimisation_tab.py +++ b/src/view/optimisation_tab.py @@ -1,4 +1,11 @@ -from PyQt6.QtWidgets import QWidget, QVBoxLayout, QLabel, QPushButton, QMessageBox +from PyQt6.QtWidgets import QWidget, QVBoxLayout, QLabel, QPushButton, QMessageBox, QProgressDialog, QGroupBox, \ + QComboBox, QLineEdit +from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.figure import Figure + +from src.model.optimisation.simulated_annealing import SimulatedAnnealing +from src.model.optimisation.genetic_optimisation import GeneticOptimisation, parameters_dict +from src.model.optimisation.particle_swarm_optimisation import ParticleSwarmOptimization class OptimisationTab(QWidget): @@ -6,28 +13,140 @@ def __init__(self, parent=None): super().__init__(parent) self.setup_ui() + self.optimisation = GeneticOptimisation(100, 20, 0.3, parameters_dict, target_resonance_freq=24430) + self.pso_optimisation = ParticleSwarmOptimization(parameters_dict) + self.sa_optimisation = SimulatedAnnealing(parameters_dict, target_resonance_freq=3255, n_iterations=1000, initial_temperature=1000, cooling_rate=0.99) + + self.optimisation.update_signal.connect(self.update_progress) + self.optimisation.finished_signal.connect(self.display_final_results) + + self.pso_optimisation.update_signal.connect(self.update_progress) + self.pso_optimisation.finished_signal.connect(self.display_final_results) + + self.sa_optimisation.update_signal.connect(self.update_progress) + self.sa_optimisation.finished_signal.connect(self.display_final_results) + def setup_ui(self): self.layout = QVBoxLayout(self) - self.label = QLabel("Welcome to the optimisation Tab !") + # Combobox for method selection + self.method_combobox = QComboBox() + self.method_combobox.addItem("Genetic Optimization") + self.method_combobox.addItem("Particle Swarm Optimization") + self.method_combobox.addItem("Simulated Annealing") + self.layout.addWidget(self.method_combobox) + + # Scroll area for target selection + self.target_group_box = QGroupBox("Target") + self.target_layout = QVBoxLayout() + + self.target_combobox = QComboBox() + self.target_combobox.addItem("Impedance") + self.target_layout.addWidget(self.target_combobox) + + self.target_value_input = QLineEdit() + self.target_value_input.setPlaceholderText("Enter target impedance value") + self.target_layout.addWidget(self.target_value_input) + + self.target_group_box.setLayout(self.target_layout) + self.layout.addWidget(self.target_group_box) + + # Add other controls + self.label = QLabel("Welcome to the optimisation Tab!") self.layout.addWidget(self.label) self.optimise_button = QPushButton("Optimise") self.optimise_button.clicked.connect(self.on_optimise_clicked) self.layout.addWidget(self.optimise_button) + self.figure = Figure() + self.canvas = FigureCanvas(self.figure) + self.layout.addWidget(self.canvas) + self.ax = self.figure.add_subplot(111) + self.ax.set_title('Average Fitness over Generations') + self.ax.grid() + self.ax.semilogy() + self.ax.set_xlabel('Generation') + self.ax.set_ylabel('Average Fitness') + + + def on_optimise_clicked(self): """Popup message box to confirm the optimisation.""" + try: + target_value = float(self.target_value_input.text()) + # Update target resonance frequency based on the selected method + if self.method_combobox.currentText() == "Genetic Optimization": + self.optimisation.update_target_resonance_freq(target_value) + elif self.method_combobox.currentText() == "Particle Swarm Optimization": + self.pso_optimisation.update_target_resonance_freq(target_value) + elif self.method_combobox.currentText() == "Simulated Annealing": + self.sa_optimisation.update_target_resonance_freq(target_value) + msg = QMessageBox() + msg.setIcon(QMessageBox.Icon.Information) + msg.setText("Do you really want to start the optimisation process, it can take few minutes ?") + msg.setWindowTitle("Confirm") + msg.setStandardButtons(QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No) + msg.setDefaultButton(QMessageBox.StandardButton.No) + msg.buttonClicked.connect(self.start_optimisation) + msg.exec() + except ValueError: + QMessageBox.warning(self, "Invalid Input", "Please enter a valid number for the target impedance.") - #show a message box inside a popup - msg = QMessageBox() - msg.setIcon(QMessageBox.Icon.Information) - msg.setText("Do you really want to start the optimisation process, it can take few minutes ?") - msg.setWindowTitle("Confirm") - msg.setStandardButtons(QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No) - msg.setDefaultButton(QMessageBox.StandardButton.No) - msg.buttonClicked.connect(self.on_message_box_clicked) - msg.exec() + + def start_optimisation(self, button): + if button.text() == "&Yes": + # Clear the plot + self.ax.clear() + self.ax.set_title('Average Fitness over Generations') + self.ax.grid() + self.ax.semilogy() + self.ax.set_xlabel('Generation') + self.ax.set_ylabel('Average Fitness') + + if self.method_combobox.currentText() == "Genetic Optimization": + total_iterations = self.optimisation.generations + elif self.method_combobox.currentText() == "Particle Swarm Optimization": + total_iterations = self.pso_optimisation.n_iterations + elif self.method_combobox.currentText() == "Simulated Annealing": + total_iterations = self.sa_optimisation.n_iterations + + self.progress_dialog = QProgressDialog("Optimizing...", "Abort", 0, total_iterations, self) # Update with correct total iterations + self.progress_dialog.setModal(True) + self.progress_dialog.show() + + if self.method_combobox.currentText() == "Genetic Optimization": + self.optimisation.start() + elif self.method_combobox.currentText() == "Particle Swarm Optimization": + self.pso_optimisation.start() + elif self.method_combobox.currentText() == "Simulated Annealing": + self.sa_optimisation.start() + else: + print("Optimization canceled!") + + def update_progress(self, generation, avg_fitness, best_fitness): + if self.progress_dialog: + self.progress_dialog.setValue(generation) + self.ax.plot(generation, best_fitness, 'ro-') # Plot each point as it comes in + self.canvas.draw() # Update the canvas + if self.method_combobox.currentText() == "Genetic Optimization": + total_generations = self.optimisation.generations + elif self.method_combobox.currentText() == "Particle Swarm Optimization": + total_generations = self.pso_optimisation.n_iterations + elif self.method_combobox.currentText() == "Simulated Annealing": + total_generations = self.sa_optimisation.n_iterations + + if generation == total_generations - 1: + self.progress_dialog.setValue(total_generations) + self.progress_dialog.close() + + def display_final_results(self, final_params, final_resonance_freq): + if self.progress_dialog: + self.progress_dialog.close() + message = (f"Optimization Completed!\n\n" + f"Final Resonance Frequency: {final_resonance_freq:.2f}\n" + f"Final Parameters:\n{final_params}") + QMessageBox.information(self, "Optimisation Results", message) def on_message_box_clicked(self, button): """Handle the message box button clicked event.""" @@ -39,3 +158,6 @@ def on_message_box_clicked(self, button): + + + From 80f2a0cdbd6a4c95679d390fbbfdc93f95568204 Mon Sep 17 00:00:00 2001 From: mroncera Date: Tue, 14 May 2024 13:53:40 +0200 Subject: [PATCH 05/16] FEAT - Add many files and differents optimisation methods --- .../optimisation/impedance_strategy_map.py | 45 +++++ .../particle_swarm_optimisation.py | 168 ++++++++++++++++++ src/model/optimisation/simulated_annealing.py | 128 +++++++++++++ src/view/optimisation_tab.py | 74 +++++++- 4 files changed, 406 insertions(+), 9 deletions(-) create mode 100644 src/model/optimisation/impedance_strategy_map.py create mode 100644 src/model/optimisation/particle_swarm_optimisation.py create mode 100644 src/model/optimisation/simulated_annealing.py diff --git a/src/model/optimisation/impedance_strategy_map.py b/src/model/optimisation/impedance_strategy_map.py new file mode 100644 index 0000000..1999a5a --- /dev/null +++ b/src/model/optimisation/impedance_strategy_map.py @@ -0,0 +1,45 @@ +from src.model.strategies.strategy_lib.Nz import AnalyticalNzStrategy +from src.model.strategies.strategy_lib.capacitance import AnalyticalCapacitanceStrategy +from src.model.strategies.strategy_lib.frequency import FrequencyVectorStrategy +from src.model.strategies.strategy_lib.impedance import AnalyticalImpedanceStrategy +from src.model.strategies.strategy_lib.inductance import AnalyticalInductanceStrategy +from src.model.strategies.strategy_lib.lambda_strategy import AnalyticalLambdaStrategy +from src.model.strategies.strategy_lib.mu_app import AnalyticalMu_appStrategy +from src.model.strategies.strategy_lib.resistance import AnalyticalResistanceStrategy, AnalyticalResistanceStrategyv2 + +IMPEDANCE_STRATEGY_MAP = { + "resistance": { + "default": AnalyticalResistanceStrategy, + "strategies": [AnalyticalResistanceStrategy, AnalyticalResistanceStrategyv2] + }, + "frequency_vector": { + "default": FrequencyVectorStrategy, + "strategies": [FrequencyVectorStrategy] + }, + "Nz": { + "default": AnalyticalNzStrategy, + "strategies": [AnalyticalNzStrategy] + }, + "mu_app": { + "default": AnalyticalMu_appStrategy, + "strategies": [AnalyticalMu_appStrategy] + }, + + "lambda_param": { + "default": AnalyticalLambdaStrategy, + "strategies": [AnalyticalLambdaStrategy] + }, + + "inductance": { + "default": AnalyticalInductanceStrategy, + "strategies": [AnalyticalInductanceStrategy] + }, + "capacitance": { + "default": AnalyticalCapacitanceStrategy, + "strategies": [AnalyticalCapacitanceStrategy] + }, + "impedance": { + "default": AnalyticalImpedanceStrategy, + "strategies": [AnalyticalImpedanceStrategy] + } +} \ No newline at end of file diff --git a/src/model/optimisation/particle_swarm_optimisation.py b/src/model/optimisation/particle_swarm_optimisation.py new file mode 100644 index 0000000..4b21e77 --- /dev/null +++ b/src/model/optimisation/particle_swarm_optimisation.py @@ -0,0 +1,168 @@ +import numpy as np +import random +from PyQt6.QtCore import QThread, pyqtSignal + +from src.model.optimisation.impedance_strategy_map import IMPEDANCE_STRATEGY_MAP +from src.controler.controller import CalculationController + +class ParticleSwarmOptimization(QThread): + update_signal = pyqtSignal(int, float, float) # iteration, avg_fitness, best_fitness + finished_signal = pyqtSignal(dict, float) # best_parameters, final_resonance_freq + + def __init__(self, parameters_dict, target_resonance_freq=3255, n_particles=10, n_iterations=10, bounds=None): + super().__init__() + self.parameters_dict = parameters_dict + self.target_resonance_freq = target_resonance_freq + self.n_particles = n_particles + self.n_iterations = n_iterations + self.bounds = [ + (1e-3, 200e-3), # len_coil + (10e-6, 300e-6), # diam_wire + (1000, 20000), # nb_spire + (1e-3, 10e-3), # diam_out_mandrel + (1e-2, 200e-2), # len_core + (1e-3, 100e-3), # diam_core + ] + self.controller = CalculationController() + + self.controller.swap_strategy_map(IMPEDANCE_STRATEGY_MAP) + + + self.w = 0.5 # Inertia weight + self.c1 = 0.5 # Cognitive constant + self.c2 = 0.9 # Social constant + + def clamp_positions(self, positions, bounds): + for i in range(len(bounds)): + positions[:, i] = np.clip(positions[:, i], bounds[i][0], bounds[i][1]) + + def reflect_positions(self, positions, bounds): + for i in range(len(bounds)): + lower_bound, upper_bound = bounds[i] + over_upper = positions[:, i] > upper_bound + under_lower = positions[:, i] < lower_bound + positions[over_upper, i] = 2 * upper_bound - positions[over_upper, i] + positions[under_lower, i] = 2 * lower_bound - positions[under_lower, i] + # Ensure they are within bounds after reflection + positions[:, i] = np.clip(positions[:, i], lower_bound, upper_bound) + + def run(self): + self.run_optimization() + + def determine_resonance_freq(self, freq_vector, impedance_vector): + index = impedance_vector.argmax() + return freq_vector[index] + + def evaluate(self, position): + params = self.parameters_dict.copy() + params.update(dict(zip(['len_coil', 'diam_wire', 'nb_spire', 'diam_out_mandrel', 'len_core', 'diam_core'], position))) + + try: + self.controller.update_parameters(params) + results = self.controller.get_current_results() + impedance = results['impedance']["data"] + resonance_freq = self.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + return abs(resonance_freq - self.target_resonance_freq), + except Exception as e: + return 1e6, + + def update_target_resonance_freq(self, target_resonance_freq): + self.target_resonance_freq = target_resonance_freq + + def run(self): + positions = np.array([np.random.uniform(low, high, self.n_particles) for low, high in self.bounds]).T + velocities = np.zeros_like(positions) + personal_best_positions = np.copy(positions) + personal_best_scores = np.array([self.evaluate(pos)[0] for pos in + positions]) # Make sure to take the first element of the tuple returned by evaluate + + global_best_position = personal_best_positions[np.argmin(personal_best_scores)] + global_best_score = np.min(personal_best_scores) + + for iteration in range(self.n_iterations): + # Emit the start of a new iteration with the current best global score and average score + avg_fitness = np.mean(personal_best_scores) + self.update_signal.emit(iteration, avg_fitness, global_best_score) + print("Iteration:", iteration, "Average fitness:", avg_fitness, "Best fitness:", global_best_score) + for i in range(self.n_particles): + # Update velocity + r1, r2 = np.random.random(), np.random.random() + velocities[i] = self.w * velocities[i] + self.c1 * r1 * ( + personal_best_positions[i] - positions[i]) + self.c2 * r2 * ( + global_best_position - positions[i]) + # Update position + positions[i] += velocities[i] + self.clamp_positions(positions, self.bounds) + # Evaluate new position + current_score = self.evaluate(positions[i])[0] + + # Update personal best + if current_score < personal_best_scores[i]: + personal_best_positions[i] = positions[i] + personal_bs = personal_best_scores[i] = current_score + + # Update global best + if current_score < global_best_score: + global_best_position = positions[i] + global_best_score = current_score + + # Emit finished signal with best parameters and final results + best_params = dict(zip(['len_coil', 'diam_wire', 'nb_spire', 'diam_out_mandrel', 'len_core', 'diam_core'], + global_best_position)) + + self.parameters_dict.update(best_params) + + self.controller.update_parameters(self.parameters_dict) + final_results = self.controller.get_current_results() + impedance = final_results['impedance']["data"] + final_resonance_freq = self.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + self.finished_signal.emit(best_params, final_resonance_freq) + + print("Best Parameters:", best_params) + print("Final Resonance Frequency:", final_resonance_freq) + + +# PSO initialization example: +if __name__ == "__main__": + parameters_dict = { + # Initialize all required parameters + 'f_start': 1, + 'f_stop': 1000000, + 'nb_points_per_decade': 1000, + # Add other necessary parameters + 'mu_insulator': 1, + 'kapthon_thick': 30e-6, + 'insulator_thick': 10e-6, + 'diam_out_mandrel': 3.2e-3, + 'len_core': 20e-2, + 'diam_core': 3.2e-3, + 'mu_r': 100000, + 'rho_whire': 1.6, + 'coeff_expansion': 1, + 'stage_1_cutting_freq': 20000, + 'stage_2_cutting_freq': 20000, + 'gain_1_linear': 1, + 'gain_2_linear': 1, + 'mutual_inductance': 0.1, + 'feedback_resistance': 1000, + 'temperature': 300, + 'spice_resistance_test': 1000, + 'Para_A': 1, + 'Para_B': 1, + 'e_en': 1, + 'e_in': 1, + 'Alpha': 1, + "ray_spire": 5e-3, + "capa_tuning": 1e-12, + "capa_triwire": 10e-12 + } + bounds = [ + (1e-3, 200e-3), # len_coil + (10e-6, 300e-6), # diam_wire + (1000, 20000), # nb_spire + (1e-3, 10e-3), # diam_out_mandrel + (1e-2, 200e-2), # len_core + (1e-3, 100e-3), # diam_core + ] + pso = ParticleSwarmOptimization(parameters_dict, bounds=bounds) + pso.run() diff --git a/src/model/optimisation/simulated_annealing.py b/src/model/optimisation/simulated_annealing.py new file mode 100644 index 0000000..4907024 --- /dev/null +++ b/src/model/optimisation/simulated_annealing.py @@ -0,0 +1,128 @@ +import random + +import numpy as np + +from src.controler.controller import CalculationController +from src.model.optimisation.genetic_optimisation import parameters_dict +from src.model.optimisation.impedance_strategy_map import IMPEDANCE_STRATEGY_MAP +from PyQt6.QtCore import pyqtSignal, QThread + +class SimulatedAnnealing(QThread): + update_signal = pyqtSignal(int, float, float) # iteration, avg_fitness, best_fitness + finished_signal = pyqtSignal(dict, float) # best_parameters, final_resonance_freq + + def __init__(self, parameters_dict, target_resonance_freq=3255, n_iterations=10, bounds=None, initial_temperature=1000, cooling_rate=0.003): + super().__init__() + self.parameters_dict = parameters_dict + self.target_resonance_freq = target_resonance_freq + self.n_iterations = n_iterations + self.initial_temperature = initial_temperature + self.cooling_rate = cooling_rate + self.bounds = [ + (1e-3, 200e-3), # len_coil + (10e-6, 300e-6), # diam_wire + (1000, 20000), # nb_spire + (1e-3, 10e-3), # diam_out_mandrel + (1e-2, 200e-2), # len_core + (1e-3, 100e-3), # diam_core + ] + #random init params with bounds + self.initial_paramater = [random.uniform(b[0], b[1]) for b in self.bounds] + + self.controller = CalculationController() + + self.controller.swap_strategy_map(IMPEDANCE_STRATEGY_MAP) + + def update_target_resonance_freq(self, target_resonance_freq): + self.target_resonance_freq = target_resonance_freq + + def set_initial_parameters(self, initial_parameters): + self.initial_paramater = initial_parameters + + def determine_resonance_freq(self, freq_vector, impedance_vector): + index = impedance_vector.argmax() + return freq_vector[index] + + def evaluate(self, position): + # Extract individual parameters from the position + len_coil, diam_wire, nb_spire, diam_out_mandrel, len_core, diam_core = position + + # Update parameter dictionary with the current position's values + params = parameters_dict.copy() + params.update({ + 'len_coil': len_coil, + 'diam_wire': diam_wire, + 'nb_spire': int(nb_spire), + 'diam_out_mandrel': diam_out_mandrel, + 'len_core': len_core, + 'diam_core': diam_core + }) + + try: + self.controller.update_parameters(params) + results = self.controller.get_current_results() + impedance = results['impedance']["data"] + resonance_freq = self.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + target_resonance_freq = self.target_resonance_freq + return abs(resonance_freq - target_resonance_freq) + except Exception as e: + return 1e6 + + def run(self): + print("Running Simulated Annealing, target resonance freq:", self.target_resonance_freq) + current_params = np.array(self.initial_paramater) + current_cost = self.evaluate(current_params) + best_cost = current_cost # Initialize the best cost + temp = self.initial_temperature + + for i in range(self.n_iterations): + next_params = current_params + np.random.uniform(-0.1, 0.1, size=current_params.shape) * (np.array([b[1] - b[0] for b in self.bounds]) * 0.05) + next_params = np.clip(next_params, [b[0] for b in self.bounds], [b[1] for b in self.bounds]) + next_cost = self.evaluate(next_params) + + if next_cost < current_cost or np.exp((current_cost - next_cost) / temp) > random.random(): + current_params = next_params + current_cost = next_cost + + if current_cost < best_cost: + best_cost = current_cost # Update the best cost if current cost is better + + temp *= self.cooling_rate + + if i % 100 == 0: + print(f"Iteration {i}: Best cost = {best_cost}, Params = {current_params}") + self.update_signal.emit(i, current_cost, best_cost) + + best_params = { + 'len_coil': current_params[0], + 'diam_wire': current_params[1], + 'nb_spire': int(current_params[2]), + 'diam_out_mandrel': current_params[3], + 'len_core': current_params[4], + 'diam_core': current_params[5] + } + + self.parameters_dict.update(best_params) + + # Update parameters for the final evaluation + self.controller.update_parameters(self.parameters_dict) + final_results = self.controller.get_current_results() + impedance = final_results['impedance']["data"] + final_resonance_freq = self.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + print("Final Resonance Frequency:", final_resonance_freq) + + self.finished_signal.emit(best_params, final_resonance_freq) + + +if __name__ == "__main__": + bounds = [ + (1e-3, 200e-3), # len_coil + (10e-6, 300e-6), # diam_wire + (1000, 20000), # nb_spire + (1e-3, 10e-3), # diam_out_mandrel + (1e-2, 200e-2), # len_core + (1e-3, 100e-3) # diam_core + ] + sa = SimulatedAnnealing(parameters_dict, target_resonance_freq=3255, n_iterations=1000, + initial_temperature=1000, cooling_rate=0.99) + sa.run() \ No newline at end of file diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py index ac0718b..f28c11f 100644 --- a/src/view/optimisation_tab.py +++ b/src/view/optimisation_tab.py @@ -26,6 +26,8 @@ def __init__(self, parent=None): self.sa_optimisation.update_signal.connect(self.update_progress) self.sa_optimisation.finished_signal.connect(self.display_final_results) + self.update_hyperparameters() + def setup_ui(self): self.layout = QVBoxLayout(self) @@ -34,6 +36,8 @@ def setup_ui(self): self.method_combobox.addItem("Genetic Optimization") self.method_combobox.addItem("Particle Swarm Optimization") self.method_combobox.addItem("Simulated Annealing") + self.method_combobox.currentIndexChanged.connect( + self.update_hyperparameters) # Connect the combobox change event self.layout.addWidget(self.method_combobox) # Scroll area for target selection @@ -51,6 +55,12 @@ def setup_ui(self): self.target_group_box.setLayout(self.target_layout) self.layout.addWidget(self.target_group_box) + # Add hyperparameters section + self.hyperparameters_group_box = QGroupBox("Hyperparameters") + self.hyperparameters_layout = QVBoxLayout() + self.hyperparameters_group_box.setLayout(self.hyperparameters_layout) + self.layout.addWidget(self.hyperparameters_group_box) + # Add other controls self.label = QLabel("Welcome to the optimisation Tab!") self.layout.addWidget(self.label) @@ -63,13 +73,39 @@ def setup_ui(self): self.canvas = FigureCanvas(self.figure) self.layout.addWidget(self.canvas) self.ax = self.figure.add_subplot(111) - self.ax.set_title('Average Fitness over Generations') + self.ax.set_title('Best Fitness over Generations') self.ax.grid() self.ax.semilogy() self.ax.set_xlabel('Generation') - self.ax.set_ylabel('Average Fitness') - - + self.ax.set_ylabel('Best Fitness') + + def update_hyperparameters(self): + # Clear current hyperparameters layout + for i in reversed(range(self.hyperparameters_layout.count())): + self.hyperparameters_layout.itemAt(i).widget().deleteLater() + + method = self.method_combobox.currentText() + if method == "Genetic Optimization": + self.add_hyperparameter_field("Number of Generations", "20") + self.add_hyperparameter_field("Population Size", "100") + self.add_hyperparameter_field("Mutation Rate", "0.3") + elif method == "Particle Swarm Optimization": + self.add_hyperparameter_field("w1", "0.5") + self.add_hyperparameter_field("c1", "1.0") + self.add_hyperparameter_field("c2", "1.0") + self.add_hyperparameter_field("Number of Particles", "50") + self.add_hyperparameter_field("Number of Iterations", "50") + elif method == "Simulated Annealing": + self.add_hyperparameter_field("Cooling Rate", "0.99") + self.add_hyperparameter_field("Initial Temperature", "5000") + self.add_hyperparameter_field("Number of Iterations", "2000") + + def add_hyperparameter_field(self, label_text, default_value): + label = QLabel(label_text) + line_edit = QLineEdit() + line_edit.setText(default_value) + self.hyperparameters_layout.addWidget(label) + self.hyperparameters_layout.addWidget(line_edit) def on_optimise_clicked(self): """Popup message box to confirm the optimisation.""" @@ -93,7 +129,6 @@ def on_optimise_clicked(self): except ValueError: QMessageBox.warning(self, "Invalid Input", "Please enter a valid number for the target impedance.") - def start_optimisation(self, button): if button.text() == "&Yes": # Clear the plot @@ -104,26 +139,47 @@ def start_optimisation(self, button): self.ax.set_xlabel('Generation') self.ax.set_ylabel('Average Fitness') + hyperparameters = self.get_hyperparameters() + if self.method_combobox.currentText() == "Genetic Optimization": + self.optimisation.generations = int(hyperparameters["Number of Generations"]) + self.optimisation.population_size = int(hyperparameters["Population Size"]) + self.optimisation.mutation_rate = float(hyperparameters["Mutation Rate"]) total_iterations = self.optimisation.generations elif self.method_combobox.currentText() == "Particle Swarm Optimization": + self.pso_optimisation.w1 = float(hyperparameters["w1"]) + self.pso_optimisation.c1 = float(hyperparameters["c1"]) + self.pso_optimisation.c2 = float(hyperparameters["c2"]) + self.pso_optimisation.n_particles = int(hyperparameters["Number of Particles"]) + self.pso_optimisation.n_iterations = int(hyperparameters["Number of Iterations"]) total_iterations = self.pso_optimisation.n_iterations elif self.method_combobox.currentText() == "Simulated Annealing": + self.sa_optimisation.cooling_rate = float(hyperparameters["Cooling Rate"]) + self.sa_optimisation.initial_temperature = float(hyperparameters["Initial Temperature"]) + self.sa_optimisation.n_iterations = int(hyperparameters["Number of Iterations"]) total_iterations = self.sa_optimisation.n_iterations - self.progress_dialog = QProgressDialog("Optimizing...", "Abort", 0, total_iterations, self) # Update with correct total iterations + self.progress_dialog = QProgressDialog("Optimizing...", "Abort", 0, total_iterations, self) self.progress_dialog.setModal(True) self.progress_dialog.show() if self.method_combobox.currentText() == "Genetic Optimization": - self.optimisation.start() + self.optimisation.start() # Ensure GeneticOptimisation runs in a thread elif self.method_combobox.currentText() == "Particle Swarm Optimization": - self.pso_optimisation.start() + self.pso_optimisation.start() # Ensure PSO runs in a thread elif self.method_combobox.currentText() == "Simulated Annealing": - self.sa_optimisation.start() + self.sa_optimisation.start() # Ensure Simulated Annealing runs in a thread else: print("Optimization canceled!") + def get_hyperparameters(self): + hyperparameters = {} + for i in range(self.hyperparameters_layout.count() // 2): + label = self.hyperparameters_layout.itemAt(2 * i).widget().text() + value = self.hyperparameters_layout.itemAt(2 * i + 1).widget().text() + hyperparameters[label] = value + return hyperparameters + def update_progress(self, generation, avg_fitness, best_fitness): if self.progress_dialog: self.progress_dialog.setValue(generation) From ecfd8680f82c24cc20852eebd3f471ceebd353f6 Mon Sep 17 00:00:00 2001 From: mroncera Date: Tue, 21 May 2024 09:41:16 +0200 Subject: [PATCH 06/16] minor fixes --- src/model/optimisation/impedance_strategy_map.py | 6 +++++- src/model/visualisation/create_tree.py | 4 ++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/src/model/optimisation/impedance_strategy_map.py b/src/model/optimisation/impedance_strategy_map.py index 1999a5a..c40bb3b 100644 --- a/src/model/optimisation/impedance_strategy_map.py +++ b/src/model/optimisation/impedance_strategy_map.py @@ -42,4 +42,8 @@ "default": AnalyticalImpedanceStrategy, "strategies": [AnalyticalImpedanceStrategy] } -} \ No newline at end of file +} + + + + diff --git a/src/model/visualisation/create_tree.py b/src/model/visualisation/create_tree.py index f232cc0..f246996 100644 --- a/src/model/visualisation/create_tree.py +++ b/src/model/visualisation/create_tree.py @@ -213,8 +213,8 @@ def add_nodes_edges_distance(graph, data, parent=None): "barnesHut": { "gravitationalConstant": -500000, "centralGravity": 10, - "springLength": 150, - "springConstant": 0.01, + "springLength": 50, + "springConstant": 0.05, "damping": 0.09, "avoidOverlap": 0.1 }, From 242b4f3c37e61b2321353da64838baff48942cde Mon Sep 17 00:00:00 2001 From: mroncera Date: Mon, 27 May 2024 10:45:30 +0200 Subject: [PATCH 07/16] temp --- src/view/optimisation_tab.py | 124 ++++++++++++++++++++++++++++++----- 1 file changed, 109 insertions(+), 15 deletions(-) diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py index f28c11f..96c88e0 100644 --- a/src/view/optimisation_tab.py +++ b/src/view/optimisation_tab.py @@ -1,5 +1,5 @@ from PyQt6.QtWidgets import QWidget, QVBoxLayout, QLabel, QPushButton, QMessageBox, QProgressDialog, QGroupBox, \ - QComboBox, QLineEdit + QComboBox, QLineEdit, QTableWidgetItem, QTableWidget from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.figure import Figure @@ -8,6 +8,8 @@ from src.model.optimisation.particle_swarm_optimisation import ParticleSwarmOptimization +from PyQt6.QtGui import QDoubleValidator, QColor + class OptimisationTab(QWidget): def __init__(self, parent=None): super().__init__(parent) @@ -36,8 +38,7 @@ def setup_ui(self): self.method_combobox.addItem("Genetic Optimization") self.method_combobox.addItem("Particle Swarm Optimization") self.method_combobox.addItem("Simulated Annealing") - self.method_combobox.currentIndexChanged.connect( - self.update_hyperparameters) # Connect the combobox change event + self.method_combobox.currentIndexChanged.connect(self.update_hyperparameters) # Connect the combobox change event self.layout.addWidget(self.method_combobox) # Scroll area for target selection @@ -46,12 +47,22 @@ def setup_ui(self): self.target_combobox = QComboBox() self.target_combobox.addItem("Impedance") + self.target_combobox.addItem("NEMI") + self.target_combobox.currentIndexChanged.connect(self.update_target) self.target_layout.addWidget(self.target_combobox) self.target_value_input = QLineEdit() self.target_value_input.setPlaceholderText("Enter target impedance value") self.target_layout.addWidget(self.target_value_input) + # NEMI table setup + self.nemi_table = QTableWidget(3, 1) + self.nemi_table.setHorizontalHeaderLabels(["Column 1"]) + self.nemi_table.setVerticalHeaderLabels(["Freq", "NEMI Value", "Weight"]) + self.nemi_table.setVisible(False) + self.nemi_table.itemChanged.connect(self.on_nemi_table_item_changed) + self.target_layout.addWidget(self.nemi_table) + self.target_group_box.setLayout(self.target_layout) self.layout.addWidget(self.target_group_box) @@ -107,17 +118,86 @@ def add_hyperparameter_field(self, label_text, default_value): self.hyperparameters_layout.addWidget(label) self.hyperparameters_layout.addWidget(line_edit) + def update_target(self): + if self.target_combobox.currentText() == "NEMI": + self.target_value_input.setVisible(False) + self.nemi_table.setVisible(True) + else: + self.target_value_input.setVisible(True) + self.nemi_table.setVisible(False) + + def on_nemi_table_item_changed(self, item): + self.validate_nemi_table() + + if item.column() == self.nemi_table.columnCount() - 1 and item.text(): + self.nemi_table.setColumnCount(self.nemi_table.columnCount() + 1) + self.nemi_table.setHorizontalHeaderItem(self.nemi_table.columnCount() - 1, + QTableWidgetItem(f"Column {self.nemi_table.columnCount()}")) + + empty_columns = 0 + for col in range(self.nemi_table.columnCount()): + is_empty = True + for row in range(self.nemi_table.rowCount()): + if self.nemi_table.item(row, col) and self.nemi_table.item(row, col).text(): + is_empty = False + break + if is_empty: + empty_columns += 1 + if empty_columns >= 2: + self.nemi_table.setColumnCount(self.nemi_table.columnCount() - 1) + self.nemi_table.setHorizontalHeaderItem(self.nemi_table.columnCount() - 1, + QTableWidgetItem(f"Column {self.nemi_table.columnCount()}")) + empty_columns -= 1 + + def validate_nemi_table(self): + weight_sum = 0 + is_valid = True + for row in range(self.nemi_table.rowCount()): + for col in range(self.nemi_table.columnCount()): + item = self.nemi_table.item(row, col) + if item: + try: + value = float(item.text()) + if row == 2: + weight_sum += value + item.setBackground(QColor(255, 255, 255)) + except ValueError: + item.setBackground(QColor(255, 0, 0)) + is_valid = False + + # Validate the sum of weights + if abs(weight_sum - 1) > 1e-6: # Allowing a small tolerance + for col in range(self.nemi_table.columnCount()): + item = self.nemi_table.item(2, col) + if item: + item.setBackground(QColor(255, 0, 0)) + is_valid = False + else: + for col in range(self.nemi_table.columnCount()): + item = self.nemi_table.item(2, col) + if item and item.background() == QColor(255, 0, 0): + item.setBackground(QColor(255, 255, 255)) + + return is_valid + def on_optimise_clicked(self): """Popup message box to confirm the optimisation.""" try: - target_value = float(self.target_value_input.text()) - # Update target resonance frequency based on the selected method - if self.method_combobox.currentText() == "Genetic Optimization": - self.optimisation.update_target_resonance_freq(target_value) - elif self.method_combobox.currentText() == "Particle Swarm Optimization": - self.pso_optimisation.update_target_resonance_freq(target_value) - elif self.method_combobox.currentText() == "Simulated Annealing": - self.sa_optimisation.update_target_resonance_freq(target_value) + if self.target_combobox.currentText() == "NEMI": + if not self.validate_nemi_table(): + QMessageBox.warning(self, "Invalid Input", "Please correct the invalid inputs in the NEMI table.") + return + target_value = self.get_nemi_table_values() + else: + target_value = float(self.target_value_input.text()) + # Update target resonance frequency based on the selected method + if self.method_combobox.currentText() == "Genetic Optimization": + self.optimisation.update_target_resonance_freq(target_value) + elif self.method_combobox.currentText() == "Particle Swarm Optimization": + self.pso_optimisation.update_target_resonance_freq(target_value) + elif self.method_combobox.currentText() == "Simulated Annealing": + self.sa_optimisation.update_target_resonance_freq(target_value) + msg = QMessageBox() msg.setIcon(QMessageBox.Icon.Information) msg.setText("Do you really want to start the optimisation process, it can take few minutes ?") @@ -129,6 +209,24 @@ def on_optimise_clicked(self): except ValueError: QMessageBox.warning(self, "Invalid Input", "Please enter a valid number for the target impedance.") + def get_nemi_table_values(self): + nemi_values = {"Freq": [], "NEMI Value": [], "Weight": []} + for row in range(self.nemi_table.rowCount()): + for col in range(self.nemi_table.columnCount()): + item = self.nemi_table.item(row, col) + if item and item.text(): + if row == 0: + nemi_values["Freq"].append(float(item.text())) + elif row == 1: + nemi_values["NEMI Value"].append(float(item.text())) + elif row == 2: + nemi_values["Weight"].append(float(item.text())) + else: + nemi_values["Freq"].append(0) + nemi_values["NEMI Value"].append(0) + nemi_values["Weight"].append(0) + return nemi_values + def start_optimisation(self, button): if button.text() == "&Yes": # Clear the plot @@ -213,7 +311,3 @@ def on_message_box_clicked(self, button): - - - - From e781231d22980a309422f307c4f467df7a293077 Mon Sep 17 00:00:00 2001 From: mroncera Date: Tue, 28 May 2024 16:02:58 +0200 Subject: [PATCH 08/16] FEAT - export and reload parameters working, still need to fix the units --- src/view/gui.py | 10 ++- src/view/optimisation_tab.py | 138 ++++++++++++++++++++++++++--------- 2 files changed, 112 insertions(+), 36 deletions(-) diff --git a/src/view/gui.py b/src/view/gui.py index 8f4be61..7bfe4c1 100644 --- a/src/view/gui.py +++ b/src/view/gui.py @@ -844,7 +844,7 @@ def init_ui(self): self.param_tab = QWidget() # QWidget that will contain the parameters layout as a tab self.strategy_tab = QWidget() # QWidget that will contain the strategy selection layout as a tab self.spice_tab = QWidget() # QWidget that will contain the spice layout as a tab - self.optimisation_tab = OptimisationTab() # QWidget that will contain the optimisation layout as a tab + self.optimisation_tab = OptimisationTab(self) # QWidget that will contain the optimisation layout as a tab self.EMC_tab = QWidget() # QWidget that will contain the EMC layout as a tab self.params_layout = QVBoxLayout(self.param_tab) @@ -1213,7 +1213,7 @@ def export_parameters_to_json(self): except Exception as e: self.display_error(f"Error exporting parameters: {e}") - def import_parameters_from_json(self): + def import_parameters_from_json(self, need_filename=True, path=None): """ Imports input parameters from a JSON file. The user is prompted to select a file to import the parameters from. @@ -1222,7 +1222,11 @@ def import_parameters_from_json(self): """ # Show an open file dialog to the user try: - fileName, _ = QFileDialog.getOpenFileName(self, "Import Parameters", "", "JSON Files (*.json)") + if need_filename: + fileName, _ = QFileDialog.getOpenFileName(self, "Import Parameters", "", "JSON Files (*.json)") + else: + if path is not None: + fileName = path if fileName: # If a file is selected, load the parameters from that file with open(fileName, 'r', encoding="utf-8") as json_file: diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py index 96c88e0..216aa2b 100644 --- a/src/view/optimisation_tab.py +++ b/src/view/optimisation_tab.py @@ -1,5 +1,7 @@ +import json + from PyQt6.QtWidgets import QWidget, QVBoxLayout, QLabel, QPushButton, QMessageBox, QProgressDialog, QGroupBox, \ - QComboBox, QLineEdit, QTableWidgetItem, QTableWidget + QComboBox, QLineEdit, QTableWidgetItem, QTableWidget, QDialog, QFileDialog from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.figure import Figure @@ -11,8 +13,9 @@ from PyQt6.QtGui import QDoubleValidator, QColor class OptimisationTab(QWidget): - def __init__(self, parent=None): + def __init__(self, gui ,parent=None): super().__init__(parent) + self.gui = gui self.setup_ui() self.optimisation = GeneticOptimisation(100, 20, 0.3, parameters_dict, target_resonance_freq=24430) @@ -152,31 +155,30 @@ def on_nemi_table_item_changed(self, item): def validate_nemi_table(self): weight_sum = 0 is_valid = True - for row in range(self.nemi_table.rowCount()): - for col in range(self.nemi_table.columnCount()): - item = self.nemi_table.item(row, col) - if item: - try: - value = float(item.text()) - if row == 2: - weight_sum += value - item.setBackground(QColor(255, 255, 255)) - except ValueError: - item.setBackground(QColor(255, 0, 0)) - is_valid = False - - # Validate the sum of weights - if abs(weight_sum - 1) > 1e-6: # Allowing a small tolerance - for col in range(self.nemi_table.columnCount()): - item = self.nemi_table.item(2, col) - if item: - item.setBackground(QColor(255, 0, 0)) - is_valid = False - else: - for col in range(self.nemi_table.columnCount()): - item = self.nemi_table.item(2, col) - if item and item.background() == QColor(255, 0, 0): - item.setBackground(QColor(255, 255, 255)) + self.nemi_table.blockSignals(True) # Block signals temporarily + try: + for row in range(self.nemi_table.rowCount()): + for col in range(self.nemi_table.columnCount()): + item = self.nemi_table.item(row, col) + if item: + try: + value = float(item.text()) + if row == 2: + weight_sum += value + item.setBackground(QColor(255, 255, 255)) # Set to white for valid input + except ValueError: + item.setBackground(QColor(255, 0, 0)) # Set to red for invalid input + is_valid = False + + if weight_sum != 1: + print("Weight sum is not equal to 1") + is_valid = False + for col in range(self.nemi_table.columnCount()): + item = self.nemi_table.item(2, col) + if item: + item.setBackground(QColor(255, 0, 0)) # Highlight the weight row if sum is not 1 + finally: + self.nemi_table.blockSignals(False) # Unblock signals return is_valid @@ -295,13 +297,83 @@ def update_progress(self, generation, avg_fitness, best_fitness): self.progress_dialog.close() def display_final_results(self, final_params, final_resonance_freq): - if self.progress_dialog: - self.progress_dialog.close() - message = (f"Optimization Completed!\n\n" - f"Final Resonance Frequency: {final_resonance_freq:.2f}\n" - f"Final Parameters:\n{final_params}") - QMessageBox.information(self, "Optimisation Results", message) + result_dialog = QDialog(self) + result_dialog.setWindowTitle("Optimisation Results") + + layout = QVBoxLayout(result_dialog) + + results_text = f"

Optimization Completed!

" + results_text += f"

Final Resonance Frequency: {final_resonance_freq:.2f}

" + results_text += "

Final Parameters:

    " + for param, value in final_params.items(): + formatted_value = "{:.2e}".format(value) + results_text += f"
  • {param}: {formatted_value}
    • " + results_text += "
" + + results_label = QLabel(results_text) + results_label.setWordWrap(True) + layout.addWidget(results_label) + + save_button = QPushButton("Save Results") + save_button.clicked.connect(lambda: self.save_results(final_params, final_resonance_freq)) + layout.addWidget(save_button) + + result_dialog.setLayout(layout) + result_dialog.exec() + + + def save_results(self, final_params, final_resonance_freq): + try : + path, _ = QFileDialog.getSaveFileName(self, "Export Optimisation data", "", "json Files (*.png)") + if not path: + return + + print("Saving results to:", path) + print(final_params) + + input_parameters_copy = self.gui.input_parameters.copy() + + print(input_parameters_copy) + + # Update the input parameters with the final results + for section, params in input_parameters_copy.items(): + if section == "SPICE_circuit" or section == "SPICE": + continue + for param, value in params.items(): + if str(param) in final_params.keys(): + input_parameters_copy[section][param]["default"] = final_params[param] + print("Updated parameter:", param, "with value:", final_params[param]) + + self.saved_path = path + + try : + # Retrieve the currelnly selected SPICE circuit from the combo box + selected_circuit = self.gui.spice_circuit_combo.currentText() + + # Add the selected SPICE circuit to the updated parameters + input_parameters_copy["SPICE_circuit"] = selected_circuit + except Exception as e: + print(f"Error adding SPICE circuit to updated parameters: {e}") + pass + + # Save the results to a file + with open(path, 'w') as file: + json.dump(input_parameters_copy, file, indent=4) + + self.reload_on_gui() + + except FileNotFoundError: + QMessageBox.warning(self, "Invalid File", "Please enter a valid file name.") + + except Exception as e: + QMessageBox.critical(self, "Error", f"An error occurred while saving the results: {e}") + + def reload_on_gui(self): + # Update the input parameters + self.gui.import_parameters_from_json(path=self.saved_path, need_filename=False) + self.gui.tabs.setCurrentIndex(0) + print("saving json") def on_message_box_clicked(self, button): """Handle the message box button clicked event.""" if button.text() == "&Yes": From 20560a3b456cb6370b82685c787a475c05b97d4b Mon Sep 17 00:00:00 2001 From: mroncera Date: Tue, 28 May 2024 16:12:52 +0200 Subject: [PATCH 09/16] FEAT - partially working reload after optimisation --- src/view/optimisation_tab.py | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py index 216aa2b..e7547ab 100644 --- a/src/view/optimisation_tab.py +++ b/src/view/optimisation_tab.py @@ -4,14 +4,31 @@ QComboBox, QLineEdit, QTableWidgetItem, QTableWidget, QDialog, QFileDialog from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.figure import Figure +from pint import UnitRegistry from src.model.optimisation.simulated_annealing import SimulatedAnnealing from src.model.optimisation.genetic_optimisation import GeneticOptimisation, parameters_dict from src.model.optimisation.particle_swarm_optimisation import ParticleSwarmOptimization - from PyQt6.QtGui import QDoubleValidator, QColor + +ureg: UnitRegistry = UnitRegistry() +def convert_unit(value, from_unit, to_unit): + """ + Converts the given value from one unit to another using Pint. + Args: + value (float): The value to convert. + from_unit (str): The unit of the input value. + to_unit (str): The target unit for the conversion. + Returns: + float: The converted value. + """ + if from_unit and to_unit: + return (value * ureg(from_unit)).to(ureg(to_unit)).magnitude + return value + + class OptimisationTab(QWidget): def __init__(self, gui ,parent=None): super().__init__(parent) @@ -341,6 +358,16 @@ def save_results(self, final_params, final_resonance_freq): continue for param, value in params.items(): if str(param) in final_params.keys(): + input_unit = input_parameters_copy[section][param]["input_unit"] + target_unit = input_parameters_copy[section][param]["target_unit"] + + converted_value = convert_unit(final_params[param], target_unit, input_unit) + + # only keep 3 decimal places + converted_value = round(converted_value, 3) + + final_params[param] = converted_value + input_parameters_copy[section][param]["default"] = final_params[param] print("Updated parameter:", param, "with value:", final_params[param]) From 69288ff213962073fb023997a3f29c9ad7c705b0 Mon Sep 17 00:00:00 2001 From: mroncera Date: Wed, 29 May 2024 10:17:41 +0200 Subject: [PATCH 10/16] FEAT - reload to parameters and reload to gui working --- src/view/gui.py | 7 +-- src/view/optimisation_tab.py | 85 +++++++++++++++++++++++++++++++++--- 2 files changed, 83 insertions(+), 9 deletions(-) diff --git a/src/view/gui.py b/src/view/gui.py index 7bfe4c1..1d74bf5 100644 --- a/src/view/gui.py +++ b/src/view/gui.py @@ -925,7 +925,7 @@ def init_menu(self): export_action.triggered.connect(self.export_parameters_to_json) import_action = file_menu.addAction('&Import Parameters') - import_action.triggered.connect(self.import_parameters_from_json) + import_action.triggered.connect(lambda _: self.import_parameters_from_json(need_filename=True)) import_specific_action = file_menu.addAction('&Import Flicker Params') import_specific_action.triggered.connect(self.import_flicker_data_from_json) @@ -1222,6 +1222,7 @@ def import_parameters_from_json(self, need_filename=True, path=None): """ # Show an open file dialog to the user try: + filename = None if need_filename: fileName, _ = QFileDialog.getOpenFileName(self, "Import Parameters", "", "JSON Files (*.json)") else: @@ -1459,10 +1460,10 @@ def display_error(self, error_message): :return: """ # Show an error message to the user - print(f"Calculation failed: {error_message}") + print(f"{error_message}") # use a QMessageBox for GUI error display - QMessageBox.critical(self, "An error occurred", f"Calculation failed: {error_message}") + QMessageBox.critical(self, "An error occurred", f"{error_message}") def update_plot(self, index): def plot_curve(data_with_meta, x_vector, linestyle='-', color=None): diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py index e7547ab..2ad04cb 100644 --- a/src/view/optimisation_tab.py +++ b/src/view/optimisation_tab.py @@ -132,6 +132,12 @@ def update_hyperparameters(self): self.add_hyperparameter_field("Number of Iterations", "2000") def add_hyperparameter_field(self, label_text, default_value): + """ + Add a hyperparameter field to the layout. + :param label_text: + :param default_value: + :return: + """ label = QLabel(label_text) line_edit = QLineEdit() line_edit.setText(default_value) @@ -139,6 +145,10 @@ def add_hyperparameter_field(self, label_text, default_value): self.hyperparameters_layout.addWidget(line_edit) def update_target(self): + """ + Update the target input based on the selected target. + :return: + """ if self.target_combobox.currentText() == "NEMI": self.target_value_input.setVisible(False) self.nemi_table.setVisible(True) @@ -147,6 +157,11 @@ def update_target(self): self.nemi_table.setVisible(False) def on_nemi_table_item_changed(self, item): + """ + Handle the NEMI table item changed event. + :param item: updated cell item + :return: + """ self.validate_nemi_table() if item.column() == self.nemi_table.columnCount() - 1 and item.text(): @@ -170,6 +185,12 @@ def on_nemi_table_item_changed(self, item): empty_columns -= 1 def validate_nemi_table(self): + """ + Table validator for NEMI values. + If the sum of the weights is not equal to 1, the weight row is highlighted in red. + If any of the values are invalid, the corresponding cell is highlighted in red. + :return: + """ weight_sum = 0 is_valid = True self.nemi_table.blockSignals(True) # Block signals temporarily @@ -229,6 +250,10 @@ def on_optimise_clicked(self): QMessageBox.warning(self, "Invalid Input", "Please enter a valid number for the target impedance.") def get_nemi_table_values(self): + """ + Get the values from the table. + :return: + """ nemi_values = {"Freq": [], "NEMI Value": [], "Weight": []} for row in range(self.nemi_table.rowCount()): for col in range(self.nemi_table.columnCount()): @@ -247,6 +272,11 @@ def get_nemi_table_values(self): return nemi_values def start_optimisation(self, button): + """ + Start the optimisation process, core function to handle the message box button clicked event. + :param button: + :return: + """ if button.text() == "&Yes": # Clear the plot self.ax.clear() @@ -290,6 +320,10 @@ def start_optimisation(self, button): print("Optimization canceled!") def get_hyperparameters(self): + """ + Get the hyperparameters from the input fields. + :return: + """ hyperparameters = {} for i in range(self.hyperparameters_layout.count() // 2): label = self.hyperparameters_layout.itemAt(2 * i).widget().text() @@ -298,6 +332,13 @@ def get_hyperparameters(self): return hyperparameters def update_progress(self, generation, avg_fitness, best_fitness): + """ + Update the progress dialog and plot the best fitness over generations. + :param generation: actual generation + :param avg_fitness: average fitness of the generation + :param best_fitness: best fitness of the generation + :return: + """ if self.progress_dialog: self.progress_dialog.setValue(generation) self.ax.plot(generation, best_fitness, 'ro-') # Plot each point as it comes in @@ -309,11 +350,20 @@ def update_progress(self, generation, avg_fitness, best_fitness): elif self.method_combobox.currentText() == "Simulated Annealing": total_generations = self.sa_optimisation.n_iterations - if generation == total_generations - 1: + if generation == total_generations: self.progress_dialog.setValue(total_generations) self.progress_dialog.close() def display_final_results(self, final_params, final_resonance_freq): + """ + Display the final results of the optimisation. + :param final_params: best parameters found + :param final_resonance_freq: + :return: + """ + + self.progress_dialog.close() + result_dialog = QDialog(self) result_dialog.setWindowTitle("Optimisation Results") @@ -327,21 +377,40 @@ def display_final_results(self, final_params, final_resonance_freq): results_text += f"
  • {param}: {formatted_value}
    • " results_text += "" + self.save_results(final_params, export_specific_path=False, reload_to_parameters=False) + results_label = QLabel(results_text) results_label.setWordWrap(True) layout.addWidget(results_label) save_button = QPushButton("Save Results") - save_button.clicked.connect(lambda: self.save_results(final_params, final_resonance_freq)) + save_button.clicked.connect(lambda: self.save_results(final_params, export_specific_path=True)) + layout.addWidget(save_button) + + save_button = QPushButton("Load to parameters") + save_button.clicked.connect(lambda: self.save_results(final_params, export_specific_path=False)) layout.addWidget(save_button) result_dialog.setLayout(layout) result_dialog.exec() - def save_results(self, final_params, final_resonance_freq): + def save_results(self, final_params, reload_to_parameters = False,export_specific_path = False): + """ + Used to save the results of the optimisation to a JSON file or reload the results to the input parameters. + Also create a temp.json file in the output folder to not lose the results. + :param final_params: best parameters found + :param reload_to_parameters: boolean to reload the results to the input parameters + :param export_specific_path: boolean to export the results to a specific path + :return: + """ try : - path, _ = QFileDialog.getSaveFileName(self, "Export Optimisation data", "", "json Files (*.png)") + if not export_specific_path: + # default path output/temp.json + path = "output/temp.json" + else : + path, _ = QFileDialog.getSaveFileName(self, "Export Optimisation data", "", "json Files (*.png)") + if not path: return @@ -386,8 +455,8 @@ def save_results(self, final_params, final_resonance_freq): # Save the results to a file with open(path, 'w') as file: json.dump(input_parameters_copy, file, indent=4) - - self.reload_on_gui() + if reload_to_parameters: + self.reload_on_gui() except FileNotFoundError: QMessageBox.warning(self, "Invalid File", "Please enter a valid file name.") @@ -397,6 +466,10 @@ def save_results(self, final_params, final_resonance_freq): def reload_on_gui(self): + """ + Reload the results to the input parameters. + :return: + """ # Update the input parameters self.gui.import_parameters_from_json(path=self.saved_path, need_filename=False) self.gui.tabs.setCurrentIndex(0) From 2c62750fc212e8e5b22a2cac5a9e261ba8c58760 Mon Sep 17 00:00:00 2001 From: mroncera Date: Wed, 29 May 2024 10:36:54 +0200 Subject: [PATCH 11/16] FIX - add a popup error message when switching to a bugged strategy --- src/controler/controller.py | 4 ++-- src/model/strategies/strategy_lib/impedance.py | 1 + src/view/gui.py | 6 +++++- 3 files changed, 8 insertions(+), 3 deletions(-) diff --git a/src/controler/controller.py b/src/controler/controller.py index 348ea7a..74422de 100644 --- a/src/controler/controller.py +++ b/src/controler/controller.py @@ -22,7 +22,7 @@ from src.model.strategies.strategy_lib.Nz import AnalyticalNzStrategy from src.model.strategies.strategy_lib.capacitance import AnalyticalCapacitanceStrategy from src.model.strategies.strategy_lib.frequency import FrequencyVectorStrategy -from src.model.strategies.strategy_lib.impedance import AnalyticalImpedanceStrategy +from src.model.strategies.strategy_lib.impedance import AnalyticalImpedanceStrategy, AnalyticalImpedanceStrategy_hsw from src.model.strategies.strategy_lib.inductance import AnalyticalInductanceStrategy from src.model.strategies.strategy_lib.lambda_strategy import AnalyticalLambdaStrategy from src.model.strategies.strategy_lib.mu_app import AnalyticalMu_appStrategy @@ -63,7 +63,7 @@ }, "impedance": { "default": AnalyticalImpedanceStrategy, - "strategies": [AnalyticalImpedanceStrategy] + "strategies": [AnalyticalImpedanceStrategy, AnalyticalImpedanceStrategy_hsw] }, "TF_ASIC_Stage_1": { "default": TF_ASIC_Stage_1_Strategy_linear, diff --git a/src/model/strategies/strategy_lib/impedance.py b/src/model/strategies/strategy_lib/impedance.py index 2c3a48b..fb02957 100644 --- a/src/model/strategies/strategy_lib/impedance.py +++ b/src/model/strategies/strategy_lib/impedance.py @@ -31,3 +31,4 @@ def calculate_impedance(self, R, L, C, f): @staticmethod def get_dependencies(): return ['resistance', 'inductance', 'capacitance', 'frequency_vector'] + diff --git a/src/view/gui.py b/src/view/gui.py index 1d74bf5..446e4a9 100644 --- a/src/view/gui.py +++ b/src/view/gui.py @@ -1719,7 +1719,11 @@ def update_node_strategy(self, node_name, strategy_class): print(f"Invalid input for parameter '{param}': '{text}'. Skipping calculation.") return - self.controller.set_node_strategy(node_name, strategy_class, params_dict) + try : + self.controller.set_node_strategy(node_name, strategy_class, params_dict) + except Exception as e: + self.display_error(f"Error updating strategy for {node_name}: {e}") + self.calculate() def save_results(self, index, button): From 94d1aa88ef449bd39c62adac1c3e449d1bf3fec0 Mon Sep 17 00:00:00 2001 From: mroncera Date: Thu, 13 Jun 2024 18:25:22 +0200 Subject: [PATCH 12/16] REFACTOR/DOC - delete contributing.md as all the documentation is now compilated and uploaded to readthedoc --- contributing.md | 320 ---------------------------------- docs/contributing.rst | 90 ++++++++-- readme.md | 13 +- src/controler/controller.py | 169 +----------------- src/controler/models/JUICE.py | 152 ++++++++++++++++ 5 files changed, 244 insertions(+), 500 deletions(-) delete mode 100644 contributing.md create mode 100644 src/controler/models/JUICE.py diff --git a/contributing.md b/contributing.md deleted file mode 100644 index 6363dee..0000000 --- a/contributing.md +++ /dev/null @@ -1,320 +0,0 @@ -# Table of Contents - -- [How to add a new calculation strategy](#How-to-add-a-new-calculation-strategy) - - [Preliminary Checks](#Preliminary-Checks) - - [Adding New Parameters](#Adding-New-Parameters) - - [Strategy Implementation](#Strategy-Implementation) - - [Method Specifications](#Method-Specifications) - - [Dependencies](#Dependencies) - - [Calculate method](#Calculate-method) -- [How to add a new parameter](#How-to-add-a-new-parameter) - - [Unit Conversion](#Unit-Conversion) -- [Linting](#Linting) -- [Git Guidelines](#Git-Guidelines) - - [Branching Strategy](#Branching-Strategy) - - [Commit Message Format](#Commit-Message-Format) - - [Types](#Types) - - [Best Practices for Pull Requests](#Best-Practices-for-Pull-Requests) -- [SPICE](#SPICE) - -# How to add a new calculation strategy - -## Preliminary Checks - -1. Review Existing Strategies: Inspect all the calculation strategies in the calculation_strategies folder to ensure your strategy hasn't already been implemented. -2. Review Parameters: Examine the parameters defined in `/data/default.json`. Determine if new parameters are needed for your strategy. - -## Adding New Parameters -3. Parameter Addition: If your strategy introduces new parameters, add them to the ``default.json`` file. Refer to the Adding a New Parameter section for detailed guidance.4. Check all the existing Nodes you can use. The list of existing nodes and their calculation strategy can be found in `src/controler/controller.py`. - -## Strategy Implementation -4. Utilize Existing Nodes: Explore the list of nodes in ``src/controller/controller.py``to understand the available calculation strategies. Avoid recalculating nodes. -```python -self.engine.add_or_update_node('frequency_vector', FrequencyVectorStrategy()) -self.engine.add_or_update_node('resistance', AnalyticalResistanceStrategy()) -self.engine.add_or_update_node('Nz', AnalyticalNzStrategy()) -... -``` -DO NOT recalculate the same node with a different strategy. Instead, apply your new strategy to an existing node. - - -5. If your calculation strategy is not implemented, create a new file in the `src/model/strategies/strategy_lib/` folder with the name of your calculation strategy. - -This file should: -- Inherit from the `CalculationStrategy` class. -- Implement the `calculate` method. -- Implement the `get_dependencies` method. - - -6. Node Association: Incorporate your newly created strategy into the controller by assigning it to a node. -```python - -self.engine.add_or_update_node('node_name', YourStrategy()) - -``` - -## Method Specifications - -### Dependencies -- The get_dependencies method must return a list of required parameters or nodes as strings, matching their names in the default.json file or controller. -### Calculate method -The `calculate` method must return the result of the calculation. The method takes two arguments: -- `dependencies`: A dictionary containing the values of all the nodes available in the engine. The keys are the names of the nodes, and the values are the results of the calculation. -- `parameters`: An instance of the `InputParameters` class containing the values of the parameters entered by the user. - -This method must return the result of the calculation. -The result can be on of the following: -- A single value (int, float, etc.) -- A Tensor (2D array) containing the result of the calculation for each frequency value. The first column must be the frequency values or (the x_axis for the plot), and the following columns must be the result of the calculation. - -ALL OUTPUTS MUST BE IN SI UNITS in linear scale (no dB, no log scale). - - -### Examples : -```python -class AnalyticalResistanceStrategy(CalculationStrategy): - - def calculate(self, dependencies: dict, parameters: InputParameters): - N = parameters.data['nb_spire'] - Rs = parameters.data['ray_spire'] - rho = parameters.data['rho_whire'] - return N * (2 * np.pi * Rs) * rho - - @staticmethod - def get_dependencies(): - return ['nb_spire', 'ray_spire', 'rho_whire'] -``` - -```python -class AnalyticalImpedanceStrategy(CalculationStrategy): - - def calculate(self, dependencies: dict, parameters: InputParameters): - R = dependencies['resistance'] - L = dependencies['inductance'] - C = dependencies['capacitance'] - - frequency_vector = dependencies['frequency_vector'] - - - vectorized_impedance = np.vectorize(self.calculate_impedance) - impedance_values = vectorized_impedance(R, L, C, frequency_vector) - frequency_impedance_tensor = np.column_stack((frequency_vector, impedance_values)) - return frequency_impedance_tensor - - def calculate_impedance(self, R, L, C, f): - impedance_num = (R ** 2) + (L * 2 * np.pi * f) ** 2 - impedance_den = (1 - L * C * (2 * np.pi * f) ** 2) ** 2 + (R * C * (2 * np.pi * f)) ** 2 - return np.sqrt(impedance_num / impedance_den) - - @staticmethod - def get_dependencies(): - return ['resistance', 'inductance', 'capacitance', 'frequency_vector'] - -``` - -# How to add a new parameter - -1. **Existing Parameters:** Verify if the new parameter exists within /data/default.json. -2. **Addition:** Introduce new parameters to `default.json` as needed, adhering to the specified format: -The file must be in the following format: - -```json -{ "section_name": - { - "param_name": { - "default": XX, - "min": XX, - "max": XX, - "description": "Short description, used for the user interface", - "input_unit": "", - "target_unit": "" - } - } -} - -``` - -Note that the `section_name` is the name of the section in the user interface. The `param_name` is the name of the parameter in the user interface. -Each section can have multiple parameters. - -These are the fields that must be filled in if your parameter is a number that require unit conversion: - -```json -"input_unit": "", -"target_unit": "" -``` -### Unit Conversion - - -For example, you don't want to input a lenght in mm and write 0.001 m in the input field. You can define the input unit as mm and the target unit as m. The user will input 1 mm and the system will convert it to 0.001 m. - -Utilize the ``pint`` library for unit conversion. Input and target units should be defined for parameters requiring conversion. -For comprehensive unit support and details, visit Pint's documentation. - -Example: -```json -"capa_triwire": { - "default": 150, - "min": 10, - "max": 1000, - "description": "Triwire capacitance in picofarads", - "input_unit": "picofarad", - "target_unit": "farad" - } -``` - -# Linting - -We use pylint to ensure compliance with PEP8 guidelines. Lint your code with: -```bash - pylint --rcfile pylintrc src/folder/file.py -``` - -Replace `src/folder/file.py` with the path to the file you want to lint. -Check the return of the command to see if there are any errors or warnings in the code. Adapt and correct the code according to the pylint output. - -You can read the [pylint documentation](https://pylint.pycqa.org/en/latest/) for more information on how to use pylint. - -# Git Guidelines - -To maintain the repository's integrity and streamline development processes, we adhere to a GitFlow-inspired workflow and specific naming conventions for branches and commit messages. Below is a comprehensive guide on how to contribute code to this project effectively. - -## Branching Strategy - -We use a branching strategy that categorizes work based on the nature of changes, ensuring that our repository remains organized and manageable. When starting work on a new feature, bug fix, or other tasks, you must create a new branch following these conventions: - -- **Feature Branches**: `engine/branch-name`, `UI/branch-name`, `controller/branch-name` -- **Refactoring**: `refactor/branch-name` - -**Important**: Direct commits to the `dev` branch are prohibited. Always create a new branch for your work, branching off from the latest `dev` branch. - -## Commit Message Format - -Commit messages should be clear, concise, and follow a formal structure to simplify the repository's history. Use the following format: - -``` -TYPE[TAG] - DESCRIPTION - -[optional body] - -[optional footer(s)] -``` - - -**Tags**: Include `#issue_id` if your work addresses a specific open issue. - -### Types - -- `FEAT`: Introduces a new feature. -- `FIX`: Fixes a bug. -- `CHORE`: Changes that don't affect the source or test files, like updating dependencies. -- `REFACTOR`: Code changes that neither fix a bug nor add a feature. -- `DOC`: Documentation updates. -- `QUAL`: General code quality improvements. -- `TEST`: Adds or updates tests. -- `PERF`: Performance improvements. -- `REVERT`: Reverts a previous commit. - -For more detailed examples and best practices on commit messages, refer to [this article](https://www.freecodecamp.org/news/how-to-write-better-git-commit-messages/). - -## Best Practices for Pull Requests - -- **Scope**: Keep your pull requests small and focused on a single feature or bug fix to facilitate the review process. -- **Adherence to Standards**: Ensure your contributions follow the project's coding standards and guidelines. -- **Stay Updated**: Regularly update your fork to keep it in sync with the main project. This helps in minimizing merge conflicts. -- **Linting**: Run the linter on your code before submitting a pull request to ensure compliance with PEP8 guidelines. -- **Documentation**: Update the documentation if your changes affect the project's functionality or require additional information. - -By following these guidelines, you contribute to the efficiency and clarity of the project, making it easier for others to review your contributions and maintain the project's health. - -# SPICE - -Simulation Program with Integrated Circuit Emphasis (SPICE) is a general-purpose analog electronic circuit simulator. It is a powerful tool for simulating and analyzing circuits, providing valuable insights into circuit behavior and performance. SPICE is widely used in the electronics industry for designing and testing circuits, making it an essential tool for electrical engineers and circuit designers. - -In PLASMAG, we integrate spice simulations to allow the user to create their own models and have more accurate results. - -## How to add a new SPICE model - -### 1. Prepare the JSON Configuration for the SPICE Module - -Each SPICE module is defined in a JSON file, which outlines the circuit characteristics along with associated parameters and calculation strategies. Here are the steps to prepare this file: - -1. **Module Key Creation:** -Add a new key in the JSON file (e.g., **spice_new_model**) that will act as a unique identifier for the new module. - -2. **Defining Module Attributes:** - - **name:** The name of the module, such as "New OP AMP Model". - - **description:** A detailed description of the module, used for the user interface. - - **image:** The filename of the image representing the circuit (must be pre-added to the relevant images directory). - -3. **Parameters:** - Define all necessary parameters for the module. Each parameter should include: - - **default, min, max:** Default, minimum, and maximum values. - - **description:** A description of the parameter for the user interface. - - **input_unit and target_unit:** Input and conversion units, if applicable. - -4. **Strategies:** - - Add references to specific calculation strategies used by this module in the `src/model/strategies/strategy_lib/` directory. - - Each strategy must have a **name** and a **file** pointing to the Python script implementing the strategy. - -#### Example JSON Section for a New SPICE Module - -```json -"spice_new_model" : { - "name" : "New OP AMP Model", - "description" : "An advanced test circuit for the spice simulator", - "image" : "new_op_amp_model.png", - "parameters" : { - "R1": { - "default": 500, - "min": 10, - "max": 10000, - "description": "Spice test resistance", - "input_unit": "ohm", - "target_unit": "ohm" - } - }, - "strategies" : { - "SPICE_impedance" : { - "name" : "SPICE Impedance", - "file" : "src/model/strategies/strategy_lib/SPICE.py" - } - } -} - -``` - -### 2. Implement Calculation Strategies - -For each strategy listed in the JSON configuration: - -- Create a Python class in the specified file within `src/model/strategies/strategy_lib/`. -- Each class should inherit from the **CalculationStrategy** class and implement necessary methods such as **calculate** and **get_dependencies**. -- The calculate method should handle the specific SPICE simulation logic based on input parameters and dependencies defined in the module. - - -### 3. Test and Validate - -Once the new module is configured and the strategies are implemented: - -- Test the module extensively to ensure that it works as expected with the SPICE simulator. You can try the script outside of PLASMAG -in the `if __name__ == "__main__"`, with mock data. -- Validate that all parameters and calculations produce accurate and reliable results. - -### 4. Update User Documentation and Interface - -- Add any necessary documentation to help users understand how to use the new SPICE module. - -### 5. Commit Changes - -Follow the project's Git guidelines to commit and push the new module to the repository: - -- Create a feature branch, e.g., feature/spice_new_model. -- Commit your changes with a clear message describing the addition of the new SPICE module. -- Open a pull request for review. - -### 5. MISC - -- Make sure to return the results in SI units for consistency. X-axis values should be in Hz, or seconds for time-domain simulations. -- If the X-axis is in the time domain, make sure to return the time vector as the first column of the tensor, with the following label : "Time" -- If you are returning the Gain and the Phase, make sure to return a linear Gain, and the phase in radians. The first column should be the frequency values, the second column the gain, and the third column the phase, with the following labels : "Frequency", "Gain", "Phase" diff --git a/docs/contributing.rst b/docs/contributing.rst index 47d2259..6335959 100644 --- a/docs/contributing.rst +++ b/docs/contributing.rst @@ -30,6 +30,21 @@ Contributing - `SPICE <#SPICE>`__ +Two cases are possible when you want to contribute to the project: +- You want to add a new calculation strategy and increment the actual model, or +you want to add a new strategy to a Node of the actual model. See `Add a New Strategy <#How-to-add-a-new-calculation-strategy>`__ for more information. + +- You want to start a brand new model, that differs highly from the actual model but keep using PLASMAG's architecture +see `New model <#New-model>`__ for more information. + + +Documentation +------------- + +DO NOT forget to update the documentation in ``PLASMAG/doccs/ for your module``. +Also recompile the documentation on readthedocs if you pushed your changes to Master. + + How to add a new calculation strategy ------------------------------------- @@ -38,7 +53,8 @@ Preliminary Checks 1. Review Existing Strategies: Inspect all the calculation strategies in the calculation_strategies folder to ensure your strategy hasn’t - already been implemented. + already been implemented. All the strategies are located in the + ``src/model/strategies/strategy_lib/`` folder. 2. Review Parameters: Examine the parameters defined in ``/data/default.json``. Determine if new parameters are needed for your strategy. @@ -55,19 +71,31 @@ Adding New Parameters Strategy Implementation ----------------------- -4. Utilize Existing Nodes: Explore the list of nodes in +4. Utilize Existing Nodes: Explore the list of nodes in the actual model STRATEGY_MAP used in ``src/controller/controller.py``\ to understand the available calculation strategies. Avoid recalculating nodes. +The strategy map can be found in the ``src/controler/models/JUICE.py`` file. .. code:: python - self.engine.add_or_update_node('frequency_vector', FrequencyVectorStrategy()) - self.engine.add_or_update_node('resistance', AnalyticalResistanceStrategy()) - self.engine.add_or_update_node('Nz', AnalyticalNzStrategy()) + STRATEGY_MAP = { + "resistance": { + "default": AnalyticalResistanceStrategy, + "strategies": [AnalyticalResistanceStrategy, AnalyticalResistanceStrategyv2] + }, + "frequency_vector": { + "default": FrequencyVectorStrategy, + "strategies": [FrequencyVectorStrategy] + }, + "Nz": { + "default": AnalyticalNzStrategy, + "strategies": [AnalyticalNzStrategy] + }, ... DO NOT recalculate the same node with a different strategy. Instead, -apply your new strategy to an existing node. +apply your new strategy to an existing node. Simply by adding it to the +"strategies" list of the node in the STRATEGY_MAP. Or replacing the "default" strategy by your new strategy. 5. If your calculation strategy is not implemented, create a new file in the ``src/model/strategies/strategy_lib/`` folder with the name of @@ -82,8 +110,12 @@ method. .. code:: python +from src.model.strategies.strategy_lib.YOUR_MODULE import YOUR_STRATEGY - self.engine.add_or_update_node('node_name', YourStrategy()) + "NODE_NAME": { + "default": YOUR_STRATEGY, + "strategies": [YOUR_STRATEGY] + }, Method Specifications --------------------- @@ -93,7 +125,8 @@ Dependencies - The get_dependencies method must return a list of required parameters or nodes as strings, matching their names in the default.json file or - controller. ### Calculate method The ``calculate`` method must return + controller. +- ``The ``calculate`` method must return the result of the calculation. The method takes two arguments: - ``dependencies``: A dictionary containing the values of all the nodes available in the engine. The keys are the names of the nodes, and the @@ -158,7 +191,7 @@ How to add a new parameter 1. **Existing Parameters:** Verify if the new parameter exists within /data/default.json. -2. **Addition:** Introduce new parameters to ``default.json`` as needed, +2. **Addition:** Introduce new parameters to ``default.json`` or ``your_config.json``as needed, adhering to the specified format: The file must be in the following format: @@ -329,11 +362,11 @@ How to add a new SPICE model 1. Prepare the JSON Configuration for the SPICE Module ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Each SPICE module is defined in a JSON file, which outlines the circuit +Each SPICE module is defined in a JSON file ``/data/SPICE.json``, which outlines the circuit characteristics along with associated parameters and calculation strategies. Here are the steps to prepare this file: -1. **Module Key Creation:** Add a new key in the JSON file (e.g., +1. **Module Key Creation:** Add a new key/circuit in the JSON file (e.g., **spice_new_model**) that will act as a unique identifier for the new module. @@ -343,7 +376,8 @@ strategies. Here are the steps to prepare this file: - **description:** A detailed description of the module, used for the user interface. - **image:** The filename of the image representing the circuit - (must be pre-added to the relevant images directory). + (must be pre-added to the relevant images directory) ``/ressources/``. + 3. **Parameters:** Define all necessary parameters for the module. Each parameter should include: @@ -354,6 +388,17 @@ strategies. Here are the steps to prepare this file: - **input_unit and target_unit:** Input and conversion units, if applicable. +.. code:: json + + "R1": { + "default": 1000, + "min": 1, + "max": 10000, + "description": "Spice resistance connected to pre amp", + "input_unit": "", + "target_unit": "" + } + 4. **Strategies:** - Add references to specific calculation strategies used by this @@ -361,6 +406,20 @@ strategies. Here are the steps to prepare this file: - Each strategy must have a **name** and a **file** pointing to the Python script implementing the strategy. +.. code:: json + + "strategies" : { + "SPICE_impedance" : { + "name" : "SPICE impedance", + "file" : "src/model/strategies/strategy_lib/SPICE.py" + }, + "SPICE_op_Amp_transcient" : { + "name" : "SPICE op Amp transcient", + "file" : "src/model/strategies/strategy_lib/SPICE.py" + }, + .... + + Example JSON Section for a New SPICE Module ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -442,3 +501,10 @@ the repository: linear Gain, and the phase in radians. The first column should be the frequency values, the second column the gain, and the third column the phase, with the following labels : “Frequency”, “Gain”, “Phase” + + + +New model +--------- + +TODO \ No newline at end of file diff --git a/readme.md b/readme.md index 1fcd9db..f832363 100644 --- a/readme.md +++ b/readme.md @@ -9,6 +9,7 @@ At its core, PLASMAG serves as a comprehensive tool for the parameters adjustmen Currently, PLASMAG is tailored to support search coil type instruments. However, its architecture is built with flexibility and extensibility in mind. This means PLASMAG is not only limited to current implementations but is also designed to easily accommodate the addition of new instruments and the integration of diverse calculation methods and models. +Online documentation can be found [here](https://plasmag.readthedocs.io/en/master/) ## Table of Contents @@ -100,14 +101,15 @@ python PLASMAG.py 3. Modularity and Flexibility: - Strategy Design Pattern: Implements a modular engine design using the strategy pattern, allowing dynamic changes in computation methods and easy adaptation to new requirements. -- Parallel Computation: Supports parallelization of computations for improved efficiency. +- Parallel Computation: Supports parallelization of computations for improved efficiency, to be implemented for futures improvements. - Graph Visualization Tools: Provides full graph visualization to monitor computation dependencies and processes. 4. Export and Import Capabilities: - Export Data: Allows exporting of any product or parameter for plotting or later re-importation. +- NEMI/CLTF Export: Exports the NEMI and Closed Loop Transfer Function (CLTF) graphs for easy design sharing. -5. Optimization Module (Work in Progress): +5. Optimization Module (WIP): - Genetic Optimization (DEAP): Utilizes evolutionary algorithms to find optimal parameter sets. - Particle Swarm Optimization: Implements swarm intelligence techniques to explore solution spaces. @@ -115,6 +117,9 @@ python PLASMAG.py - Mono-parameter and Multi-parameter Optimization: Supports optimization for single and multiple parameters. - Mono-criteria and Multi-criteria Optimization: Handles single and multiple criteria, combining them into a single framework using weighted polynomials. +**Available Targets:** +- Impedance (Resonance Frequency) +- NEMI (WIP) ## Availables Models @@ -135,6 +140,10 @@ increasingly difficult, leading to a slight deviation from expected behaviors. This discrepancy underlines a current limitation of the model, spotlighting the need for further refinement and development to enhance its accuracy. +The model (combination of all calculation strategy) is composed of 2 parts: +- ``data/JUICE.json`` : Contains the parameters of the JUICE search coil magnetometer, their values will be used for computations +- ```` + ## Documentation To generate project documentation: diff --git a/src/controler/controller.py b/src/controler/controller.py index 74422de..82fe5ef 100644 --- a/src/controler/controller.py +++ b/src/controler/controller.py @@ -6,174 +6,11 @@ import json import numpy as np - -from src.model.strategies.strategy_lib.Noise import PSD_R_cr, PSD_R_cr_filtered, PSD_R_Coil, PSD_R_Coil_filtered, \ - PSD_Flicker, PSD_e_en, PSD_e_en_filtered, PSD_e_in, PSD_e_in_filtered, PSD_Total, PSD_Total_filtered, \ - Display_all_PSD, NEMI, Display_all_PSD_filtered, NEMI_FIltered, NEMI_FIlteredv2, NEMI_FIlteredv3, PSD_R_cr_V2, \ - PSD_R_cr_filtered_V2, PSD_R_Coil_V2, PSD_R_Coil_filtered_V2, PSD_Flicker_V2, PSD_e_en_V2, PSD_e_en_filtered_V2, \ - PSD_e_in_V2, PSD_e_in_filtered_V2 -from src.model.strategies.strategy_lib.CLTF import CLTF_Strategy_Filtered, \ - CLTF_Strategy_Non_Filtered_legacy, Display_CLTF_OLTF -from src.model.strategies.strategy_lib.OLTF import OLTF_Strategy_Non_Filtered, OLTF_Strategy_Filtered -from src.model.strategies.strategy_lib.TF_ASIC import TF_ASIC_Stage_1_Strategy_linear, \ - TF_ASIC_Stage_2_Strategy_linear, TF_ASIC_Strategy_linear +from src.controler.models.JUICE import STRATEGY_MAP from src.model.input_parameters import InputParameters from src.model.engine import CalculationEngine -from src.model.strategies.strategy_lib.Nz import AnalyticalNzStrategy -from src.model.strategies.strategy_lib.capacitance import AnalyticalCapacitanceStrategy -from src.model.strategies.strategy_lib.frequency import FrequencyVectorStrategy -from src.model.strategies.strategy_lib.impedance import AnalyticalImpedanceStrategy, AnalyticalImpedanceStrategy_hsw -from src.model.strategies.strategy_lib.inductance import AnalyticalInductanceStrategy -from src.model.strategies.strategy_lib.lambda_strategy import AnalyticalLambdaStrategy -from src.model.strategies.strategy_lib.mu_app import AnalyticalMu_appStrategy -from src.model.strategies.strategy_lib.resistance import AnalyticalResistanceStrategy, AnalyticalResistanceStrategyv2 -from src.model.strategies.strategy_lib.SPICE import SPICE_test, SPICE_op_Amp_gain, SPICE_op_Amp_transcient, \ - SPICE_impedance - -STRATEGY_MAP = { - "resistance": { - "default": AnalyticalResistanceStrategy, - "strategies": [AnalyticalResistanceStrategy, AnalyticalResistanceStrategyv2] - }, - "frequency_vector": { - "default": FrequencyVectorStrategy, - "strategies": [FrequencyVectorStrategy] - }, - "Nz": { - "default": AnalyticalNzStrategy, - "strategies": [AnalyticalNzStrategy] - }, - "mu_app": { - "default": AnalyticalMu_appStrategy, - "strategies": [AnalyticalMu_appStrategy] - }, - - "lambda_param": { - "default": AnalyticalLambdaStrategy, - "strategies": [AnalyticalLambdaStrategy] - }, - - "inductance": { - "default": AnalyticalInductanceStrategy, - "strategies": [AnalyticalInductanceStrategy] - }, - "capacitance": { - "default": AnalyticalCapacitanceStrategy, - "strategies": [AnalyticalCapacitanceStrategy] - }, - "impedance": { - "default": AnalyticalImpedanceStrategy, - "strategies": [AnalyticalImpedanceStrategy, AnalyticalImpedanceStrategy_hsw] - }, - "TF_ASIC_Stage_1": { - "default": TF_ASIC_Stage_1_Strategy_linear, - "strategies": [TF_ASIC_Stage_1_Strategy_linear] - }, - "TF_ASIC_Stage_2": { - "default": TF_ASIC_Stage_2_Strategy_linear, - "strategies": [TF_ASIC_Stage_2_Strategy_linear] - }, - "TF_ASIC_linear": { - "default": TF_ASIC_Strategy_linear, - "strategies": [TF_ASIC_Strategy_linear] - }, - "OLTF_Non_filtered": { - "default": OLTF_Strategy_Non_Filtered, - "strategies": [OLTF_Strategy_Non_Filtered] - }, - "OLTF_Filtered": { - "default": OLTF_Strategy_Filtered, - "strategies": [OLTF_Strategy_Filtered] - }, - "CLTF_Non_filtered": { - "default": CLTF_Strategy_Non_Filtered_legacy, - "strategies": [CLTF_Strategy_Non_Filtered_legacy] - }, - "CLTF_Filtered": { - "default": CLTF_Strategy_Filtered, - "strategies": [CLTF_Strategy_Filtered] - }, - "Display_CLTF_OLTF": { - "default": Display_CLTF_OLTF, - "strategies": [Display_CLTF_OLTF] - }, - "PSD_R_cr": { - "default": PSD_R_cr, - "strategies": [PSD_R_cr, PSD_R_cr_V2] - }, - "PSD_R_cr_filtered": { - "default": PSD_R_cr_filtered, - "strategies": [PSD_R_cr_filtered,PSD_R_cr_filtered_V2] - }, - "PSD_R_Coil": { - "default": PSD_R_Coil, - "strategies": [PSD_R_Coil, PSD_R_Coil_V2] - }, - "PSD_R_Coil_filtered": { - "default": PSD_R_Coil_filtered, - "strategies": [PSD_R_Coil_filtered, PSD_R_Coil_filtered_V2] - }, - "PSD_Flicker": { - "default": PSD_Flicker, - "strategies": [PSD_Flicker, PSD_Flicker_V2] - }, - "PSD_e_en": { - "default": PSD_e_en, - "strategies": [PSD_e_en, PSD_e_en_V2] - }, - "PSD_e_en_filtered": { - "default": PSD_e_en_filtered, - "strategies": [PSD_e_en_filtered, PSD_e_en_filtered_V2] - }, - "PSD_e_in": { - "default": PSD_e_in, - "strategies": [PSD_e_in, PSD_e_in_V2] - }, - "PSD_e_in_filtered": { - "default": PSD_e_in_filtered, - "strategies": [PSD_e_in_filtered, PSD_e_in_filtered_V2] - }, - "PSD_Total": { - "default": PSD_Total, - "strategies": [PSD_Total] - }, - "PSD_Total_filtered": { - "default": PSD_Total_filtered, - "strategies": [PSD_Total_filtered] - }, - "Display_all_PSD": { - "default": Display_all_PSD, - "strategies": [Display_all_PSD] - }, - "Display_all_PSD_filtered": { - "default": Display_all_PSD_filtered, - "strategies": [Display_all_PSD_filtered] - }, - "NEMI": { - "default": NEMI, - "strategies": [NEMI] - }, - "NEMI_FIltered": { - "default": NEMI_FIltered, - "strategies": [NEMI_FIltered,NEMI_FIlteredv2, NEMI_FIlteredv3] - }, - # "SPICE_test": { - # "default": SPICE_test, - # "strategies": [SPICE_test] - # }, - # "SPICE_op_Amp_gain" : { - # "default": SPICE_op_Amp_gain, - # "strategies": [SPICE_op_Amp_gain] - # }, - # "SPICE_op_Amp_transcient": { - # "default": SPICE_op_Amp_transcient, - # "strategies": [SPICE_op_Amp_transcient] - # }, - # "SPICE_impedance" : { - # "default": SPICE_impedance, - # "strategies": [SPICE_impedance] - # } -} + + class CalculationController: """ The CalculationController class is responsible for managing the calculation engine and the input parameters diff --git a/src/controler/models/JUICE.py b/src/controler/models/JUICE.py new file mode 100644 index 0000000..7fd39be --- /dev/null +++ b/src/controler/models/JUICE.py @@ -0,0 +1,152 @@ +from src.model.strategies.strategy_lib.Noise import PSD_R_cr, PSD_R_cr_filtered, PSD_R_Coil, PSD_R_Coil_filtered, \ + PSD_Flicker, PSD_e_en, PSD_e_en_filtered, PSD_e_in, PSD_e_in_filtered, PSD_Total, PSD_Total_filtered, \ + Display_all_PSD, NEMI, Display_all_PSD_filtered, NEMI_FIltered, NEMI_FIlteredv2, NEMI_FIlteredv3, PSD_R_cr_V2, \ + PSD_R_cr_filtered_V2, PSD_R_Coil_V2, PSD_R_Coil_filtered_V2, PSD_Flicker_V2, PSD_e_en_V2, PSD_e_en_filtered_V2, \ + PSD_e_in_V2, PSD_e_in_filtered_V2 +from src.model.strategies.strategy_lib.CLTF import CLTF_Strategy_Filtered, \ + CLTF_Strategy_Non_Filtered_legacy, Display_CLTF_OLTF +from src.model.strategies.strategy_lib.OLTF import OLTF_Strategy_Non_Filtered, OLTF_Strategy_Filtered +from src.model.strategies.strategy_lib.TF_ASIC import TF_ASIC_Stage_1_Strategy_linear, \ + TF_ASIC_Stage_2_Strategy_linear, TF_ASIC_Strategy_linear + +from src.model.strategies.strategy_lib.Nz import AnalyticalNzStrategy +from src.model.strategies.strategy_lib.capacitance import AnalyticalCapacitanceStrategy +from src.model.strategies.strategy_lib.frequency import FrequencyVectorStrategy +from src.model.strategies.strategy_lib.impedance import AnalyticalImpedanceStrategy +from src.model.strategies.strategy_lib.inductance import AnalyticalInductanceStrategy +from src.model.strategies.strategy_lib.lambda_strategy import AnalyticalLambdaStrategy +from src.model.strategies.strategy_lib.mu_app import AnalyticalMu_appStrategy +from src.model.strategies.strategy_lib.resistance import AnalyticalResistanceStrategy, AnalyticalResistanceStrategyv2 +from src.model.strategies.strategy_lib.SPICE import SPICE_test, SPICE_op_Amp_gain, SPICE_op_Amp_transcient, \ + SPICE_impedance + + +STRATEGY_MAP = { + "resistance": { + "default": AnalyticalResistanceStrategy, + "strategies": [AnalyticalResistanceStrategy, AnalyticalResistanceStrategyv2] + }, + "frequency_vector": { + "default": FrequencyVectorStrategy, + "strategies": [FrequencyVectorStrategy] + }, + "Nz": { + "default": AnalyticalNzStrategy, + "strategies": [AnalyticalNzStrategy] + }, + "mu_app": { + "default": AnalyticalMu_appStrategy, + "strategies": [AnalyticalMu_appStrategy] + }, + + "lambda_param": { + "default": AnalyticalLambdaStrategy, + "strategies": [AnalyticalLambdaStrategy] + }, + + "inductance": { + "default": AnalyticalInductanceStrategy, + "strategies": [AnalyticalInductanceStrategy] + }, + "capacitance": { + "default": AnalyticalCapacitanceStrategy, + "strategies": [AnalyticalCapacitanceStrategy] + }, + "impedance": { + "default": AnalyticalImpedanceStrategy, + "strategies": [AnalyticalImpedanceStrategy] + }, + "TF_ASIC_Stage_1": { + "default": TF_ASIC_Stage_1_Strategy_linear, + "strategies": [TF_ASIC_Stage_1_Strategy_linear] + }, + "TF_ASIC_Stage_2": { + "default": TF_ASIC_Stage_2_Strategy_linear, + "strategies": [TF_ASIC_Stage_2_Strategy_linear] + }, + "TF_ASIC_linear": { + "default": TF_ASIC_Strategy_linear, + "strategies": [TF_ASIC_Strategy_linear] + }, + "OLTF_Non_filtered": { + "default": OLTF_Strategy_Non_Filtered, + "strategies": [OLTF_Strategy_Non_Filtered] + }, + "OLTF_Filtered": { + "default": OLTF_Strategy_Filtered, + "strategies": [OLTF_Strategy_Filtered] + }, + "CLTF_Non_filtered": { + "default": CLTF_Strategy_Non_Filtered_legacy, + "strategies": [CLTF_Strategy_Non_Filtered_legacy] + }, + "CLTF_Filtered": { + "default": CLTF_Strategy_Filtered, + "strategies": [CLTF_Strategy_Filtered] + }, + "Display_CLTF_OLTF": { + "default": Display_CLTF_OLTF, + "strategies": [Display_CLTF_OLTF] + }, + "PSD_R_cr": { + "default": PSD_R_cr, + "strategies": [PSD_R_cr, PSD_R_cr_V2] + }, + "PSD_R_cr_filtered": { + "default": PSD_R_cr_filtered, + "strategies": [PSD_R_cr_filtered,PSD_R_cr_filtered_V2] + }, + "PSD_R_Coil": { + "default": PSD_R_Coil, + "strategies": [PSD_R_Coil, PSD_R_Coil_V2] + }, + "PSD_R_Coil_filtered": { + "default": PSD_R_Coil_filtered, + "strategies": [PSD_R_Coil_filtered, PSD_R_Coil_filtered_V2] + }, + "PSD_Flicker": { + "default": PSD_Flicker, + "strategies": [PSD_Flicker, PSD_Flicker_V2] + }, + "PSD_e_en": { + "default": PSD_e_en, + "strategies": [PSD_e_en, PSD_e_en_V2] + }, + "PSD_e_en_filtered": { + "default": PSD_e_en_filtered, + "strategies": [PSD_e_en_filtered, PSD_e_en_filtered_V2] + }, + "PSD_e_in": { + "default": PSD_e_in, + "strategies": [PSD_e_in, PSD_e_in_V2] + }, + "PSD_e_in_filtered": { + "default": PSD_e_in_filtered, + "strategies": [PSD_e_in_filtered, PSD_e_in_filtered_V2] + }, + "PSD_Total": { + "default": PSD_Total, + "strategies": [PSD_Total] + }, + "PSD_Total_filtered": { + "default": PSD_Total_filtered, + "strategies": [PSD_Total_filtered] + }, + "Display_all_PSD": { + "default": Display_all_PSD, + "strategies": [Display_all_PSD] + }, + "Display_all_PSD_filtered": { + "default": Display_all_PSD_filtered, + "strategies": [Display_all_PSD_filtered] + }, + "NEMI": { + "default": NEMI, + "strategies": [NEMI] + }, + "NEMI_FIltered": { + "default": NEMI_FIltered, + "strategies": [NEMI_FIltered,NEMI_FIlteredv2, NEMI_FIlteredv3] + }, + +} \ No newline at end of file From 934080eab4d934d71bd13f19958cc9418b598206 Mon Sep 17 00:00:00 2001 From: mroncera Date: Thu, 30 May 2024 09:47:22 +0200 Subject: [PATCH 13/16] FIX - reload data to parameters wasn't working properly --- src/model/optimisation/cost_function.py | 108 +++++++++++++++ .../genetic_optimisation_refactor.py | 128 ++++++++++++++++++ src/view/gui.py | 19 ++- src/view/optimisation_tab.py | 17 ++- 4 files changed, 262 insertions(+), 10 deletions(-) create mode 100644 src/model/optimisation/cost_function.py create mode 100644 src/model/optimisation/genetic_optimisation_refactor.py diff --git a/src/model/optimisation/cost_function.py b/src/model/optimisation/cost_function.py new file mode 100644 index 0000000..b922bac --- /dev/null +++ b/src/model/optimisation/cost_function.py @@ -0,0 +1,108 @@ +from abc import ABC, abstractmethod + +import numpy as np + +from src.controler.controller import CalculationController + + +class BaseEvaluator(ABC): + def __init__(self, parameters_dict, target, strategy_map): + self.parameters_dict = parameters_dict + self.target = target + self.controller = CalculationController() + self.controller.swap_strategy_map(strategy_map) + + @abstractmethod + def evaluate(self, individual): + pass + + def update_target(self, target): + self.target = target + + def set_calculation_controller(self, controller): + self.controller = controller + + def set_strategy_map(self, strategy_map): + self.controller.swap_strategy_map(strategy_map) + + +class ResonanceFrequencyEvaluator(BaseEvaluator): + + def determine_resonance_freq(self, freq_vector, impedance_vector): + index = impedance_vector.argmax() + return freq_vector[index] + + + def evaluate(self, individual): + len_coil, diam_wire, nb_spire, capa_tuning, capa_triwire = individual + + params = self.parameters_dict.copy() + params.update({ + 'len_coil': len_coil, + 'diam_wire': diam_wire, + 'nb_spire': int(nb_spire), + 'capa_tuning': capa_tuning, + 'capa_triwire': capa_triwire + }) + + try: + self.controller.update_parameters(params) + results = self.controller.get_current_results() + impedance = results['impedance']["data"] + resonance_freq = self.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + return abs(resonance_freq - self.target), + except Exception as e: + return 1e6, + + +class NEMIEvaluator(BaseEvaluator): + def evaluate(self, individual): + len_coil, diam_wire, nb_spire, capa_tuning, capa_triwire = individual + + params = self.parameters_dict.copy() + params.update({ + 'len_coil': len_coil, + 'diam_wire': diam_wire, + 'nb_spire': int(nb_spire), + 'capa_tuning': capa_tuning, + 'capa_triwire': capa_triwire + }) + + try: + self.controller.update_parameters(params) + results = self.controller.get_current_results() + nemi_values = results['nemi']["data"] + distances = [(nemi_value - target_value)**2 for nemi_value, target_value in zip(nemi_values, self.target)] + return sum(distances), + except Exception as e: + return 1e6, + + +class ResonanceFrequencyEvaluator2(BaseEvaluator): + + def determine_resonance_freq(self, freq_vector, impedance_vector): + index = impedance_vector.argmax() + return freq_vector[index] + + def evaluate(self, individual): + len_coil, diam_wire, nb_spire, capa_tuning, capa_triwire = individual + + params = self.parameters_dict.copy() + params.update({ + 'len_coil': len_coil, + 'diam_wire': diam_wire, + 'nb_spire': int(nb_spire), + 'capa_tuning': capa_tuning, + 'capa_triwire': capa_triwire + }) + + try: + self.controller.update_parameters(params) + results = self.controller.get_current_results() + impedance = results['impedance']["data"] + resonance_freq = self.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + objective1 = abs(resonance_freq - self.target) + objective2 = np.sum(np.abs(impedance[:, 1])) # Example of a second objective + return objective1, objective2 + except Exception as e: + return 1e6, 1e6 diff --git a/src/model/optimisation/genetic_optimisation_refactor.py b/src/model/optimisation/genetic_optimisation_refactor.py new file mode 100644 index 0000000..d2409c5 --- /dev/null +++ b/src/model/optimisation/genetic_optimisation_refactor.py @@ -0,0 +1,128 @@ +import random +import numpy as np +from PyQt6.QtCore import pyqtSignal, QThread +from deap import creator, base, tools, algorithms + +from src.model.optimisation.cost_function import ResonanceFrequencyEvaluator, NEMIEvaluator +from src.model.optimisation.impedance_strategy_map import IMPEDANCE_STRATEGY_MAP +from src.controler.controller import CalculationController + + +class GeneticOptimisation(QThread): + update_signal = pyqtSignal(int, float, float) # generation, fitness average, best fitness + finished_signal = pyqtSignal(dict, float) # best parameters, resonance frequency + + def __init__(self, population_size, generations, mutation_rate, evaluator): + super().__init__() + self.population_size = population_size + self.generations = generations + self.mutation_rate = mutation_rate + self.evaluator = evaluator + self.toolbox = None + + self.init_creator() + + def init_creator(self): + creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) + creator.create("Individual", list, fitness=creator.FitnessMin) + + toolbox = base.Toolbox() + toolbox.register("attr_len_coil", random.uniform, 1e-3, 200e-3) + toolbox.register("attr_diam_wire", random.uniform, 10e-6, 300e-6) + toolbox.register("attr_nb_spire", random.uniform, 1000, 20000) + toolbox.register("attr_capa_tuning", random.uniform, 1e-12, 1000e-12) + toolbox.register("attr_capa_triwire", random.uniform, 10e-12, 1000e-12) + toolbox.register("individual", tools.initCycle, creator.Individual, + (toolbox.attr_len_coil, toolbox.attr_diam_wire, toolbox.attr_nb_spire, + toolbox.attr_capa_tuning, toolbox.attr_capa_triwire), + n=1) + toolbox.register("population", tools.initRepeat, list, toolbox.individual) + toolbox.register("evaluate", self.evaluator.evaluate) + toolbox.register("mate", tools.cxTwoPoint) + toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.3) + toolbox.register("select", tools.selTournament, tournsize=5) + + self.toolbox = toolbox + + def run_optimisation(self): + population = self.toolbox.population(n=self.population_size) + hof = tools.HallOfFame(5) # Keep the top 5 individuals + stats = tools.Statistics(lambda ind: ind.fitness.values) + stats.register("min", min) + stats.register("avg", np.mean) + + for gen in range(self.generations): + population, logbook = algorithms.eaSimple(population, self.toolbox, cxpb=0.7, mutpb=self.mutation_rate, + ngen=1, stats=stats, halloffame=hof, verbose=False) + + avg_fitness = logbook.select("avg")[0] + best_fitness = logbook.select("min")[0][0] + print(best_fitness) + self.update_signal.emit(gen, avg_fitness, best_fitness) + + best_params = hof[0] + print("Best Parameters:", best_params) + print("Best Fitness:", self.evaluator.evaluate(best_params)[0]) + + # Update parameters for the final evaluation + final_params = {'len_coil': best_params[0], 'diam_wire': best_params[1], 'nb_spire': int(best_params[2]), + 'capa_tuning': best_params[3], 'capa_triwire': best_params[4]} + self.evaluator.controller.update_parameters(final_params) + final_results = self.evaluator.controller.get_current_results() + impedance = final_results['impedance']["data"] + final_resonance_freq = self.evaluator.determine_resonance_freq(impedance[:, 0], impedance[:, 1]) + print("Final Resonance Frequency:", final_resonance_freq) + print("Final parameters:", final_params) + self.finished_signal.emit(final_params, final_resonance_freq) + return final_resonance_freq, final_params + + def run(self): + self.run_optimisation() + + + +parameters_dict = { + 'f_start': 1, + 'f_stop': 1000000, + 'nb_points_per_decade': 1000, + 'mu_insulator': 1, + 'kapthon_thick': 30e-6, + 'insulator_thick': 10e-6, + 'diam_out_mandrel': 3.2e-3, + 'len_core': 20e-2, + 'diam_core': 3.2e-3, + 'mu_r': 100000, + 'rho_whire': 1.6, + 'coeff_expansion': 1, + 'stage_1_cutting_freq': 20000, + 'stage_2_cutting_freq': 20000, + 'gain_1_linear': 1, + 'gain_2_linear': 1, + 'mutual_inductance': 0.1, + 'feedback_resistance': 1000, + 'temperature': 300, + 'spice_resistance_test': 1000, + 'Para_A': 1, + 'Para_B': 1, + 'e_en': 1, + 'e_in': 1, + 'Alpha': 1, + "ray_spire": 5e-3, + "capa_tuning": 1e-12, + "capa_triwire": 10e-12 +} + +if __name__ == "__main__": + # For resonance frequency optimization + resonance_evaluator = ResonanceFrequencyEvaluator(parameters_dict, target=2350, strategy_map=IMPEDANCE_STRATEGY_MAP) + optimisation = GeneticOptimisation(100, 20, 0.3, resonance_evaluator) + optimisation.run_optimisation() + + # resonance_evaluator.update_target(1350) + # optimisation.run_optimisation() + + # # For NEMI values optimization + # nemi_target = [1, 2, 3, 4, 5] # Example target values + # nemi_evaluator = NEMIEvaluator(parameters_dict, nemi_target, strategy_map=IMPEDANCE_STRATEGY_MAP) + # optimisation = GeneticOptimisation(100, 20, 0.3, nemi_evaluator) + # optimisation.run_optimisation() diff --git a/src/view/gui.py b/src/view/gui.py index 446e4a9..100b700 100644 --- a/src/view/gui.py +++ b/src/view/gui.py @@ -1213,14 +1213,14 @@ def export_parameters_to_json(self): except Exception as e: self.display_error(f"Error exporting parameters: {e}") - def import_parameters_from_json(self, need_filename=True, path=None): + def import_parameters_from_json(self, need_filename=True, path=None, data=None): """ Imports input parameters from a JSON file. The user is prompted to select a file to import the parameters from. The imported parameters are then used to update the UI. :return: """ - # Show an open file dialog to the user + try: filename = None if need_filename: @@ -1230,8 +1230,21 @@ def import_parameters_from_json(self, need_filename=True, path=None): fileName = path if fileName: # If a file is selected, load the parameters from that file + print(fileName) + # with os merge full path with file name + + path = os.path.dirname(os.path.abspath(__file__)) + # go back 2 times + path = os.path.dirname(path) + path = os.path.dirname(path) + full_path = os.path.join(path, fileName) + + with open(fileName, 'r', encoding="utf-8") as json_file: - self.input_parameters = json.load(json_file) + if data is None: + self.input_parameters = json.load(json_file) + else: + self.input_parameters = data self.reset_parameters(reload=False) # Update the UI with the imported parameters print(f"Parameters imported from {fileName}") except Exception as e: diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py index 2ad04cb..58b189b 100644 --- a/src/view/optimisation_tab.py +++ b/src/view/optimisation_tab.py @@ -366,6 +366,9 @@ def display_final_results(self, final_params, final_resonance_freq): result_dialog = QDialog(self) result_dialog.setWindowTitle("Optimisation Results") + # big size + result_dialog.resize(800, 600) + layout = QVBoxLayout(result_dialog) @@ -377,7 +380,6 @@ def display_final_results(self, final_params, final_resonance_freq): results_text += f"
  • {param}: {formatted_value}
    • " results_text += "" - self.save_results(final_params, export_specific_path=False, reload_to_parameters=False) results_label = QLabel(results_text) results_label.setWordWrap(True) @@ -388,7 +390,7 @@ def display_final_results(self, final_params, final_resonance_freq): layout.addWidget(save_button) save_button = QPushButton("Load to parameters") - save_button.clicked.connect(lambda: self.save_results(final_params, export_specific_path=False)) + save_button.clicked.connect(lambda: self.save_results(final_params, export_specific_path=False, reload_to_parameters=True)) layout.addWidget(save_button) result_dialog.setLayout(layout) @@ -455,8 +457,7 @@ def save_results(self, final_params, reload_to_parameters = False,export_specifi # Save the results to a file with open(path, 'w') as file: json.dump(input_parameters_copy, file, indent=4) - if reload_to_parameters: - self.reload_on_gui() + self.reload_on_gui(input_parameters_copy) except FileNotFoundError: QMessageBox.warning(self, "Invalid File", "Please enter a valid file name.") @@ -465,15 +466,17 @@ def save_results(self, final_params, reload_to_parameters = False,export_specifi QMessageBox.critical(self, "Error", f"An error occurred while saving the results: {e}") - def reload_on_gui(self): + def reload_on_gui(self, data): """ Reload the results to the input parameters. :return: """ # Update the input parameters - self.gui.import_parameters_from_json(path=self.saved_path, need_filename=False) + print("Updating input parameters from:", self.saved_path) + self.gui.import_parameters_from_json(path=self.saved_path, need_filename=False, data=data) + print("Called function import_parameters_from_json with path:", self.saved_path, "and need_filename:", False) self.gui.tabs.setCurrentIndex(0) - print("saving json") + def on_message_box_clicked(self, button): """Handle the message box button clicked event.""" if button.text() == "&Yes": From 57463c769e6a2bd5d6d19ca01291e555fa7df425 Mon Sep 17 00:00:00 2001 From: mroncera Date: Thu, 13 Jun 2024 17:15:13 +0200 Subject: [PATCH 14/16] REFACTOR - Flag the NEMI optimisation to "WIP", remove spice tab from gui --- src/view/gui.py | 2 +- src/view/optimisation_tab.py | 13 +++---------- 2 files changed, 4 insertions(+), 11 deletions(-) diff --git a/src/view/gui.py b/src/view/gui.py index 100b700..dbb08e7 100644 --- a/src/view/gui.py +++ b/src/view/gui.py @@ -870,7 +870,7 @@ def init_ui(self): self.tabs.addTab(self.param_tab, "Parameters") self.tabs.addTab(self.strategy_tab, "Strategy Selection") - self.tabs.addTab(self.spice_tab, "Spice Simulation") + #self.tabs.addTab(self.spice_tab, "Spice Simulation") self.tabs.addTab(self.optimisation_tab, "Optimisation") self.tabs.addTab(self.EMC_tab, "EMC") diff --git a/src/view/optimisation_tab.py b/src/view/optimisation_tab.py index 58b189b..fc26c91 100644 --- a/src/view/optimisation_tab.py +++ b/src/view/optimisation_tab.py @@ -67,7 +67,7 @@ def setup_ui(self): self.target_combobox = QComboBox() self.target_combobox.addItem("Impedance") - self.target_combobox.addItem("NEMI") + self.target_combobox.addItem("NEMI(WIP)") self.target_combobox.currentIndexChanged.connect(self.update_target) self.target_layout.addWidget(self.target_combobox) @@ -149,7 +149,7 @@ def update_target(self): Update the target input based on the selected target. :return: """ - if self.target_combobox.currentText() == "NEMI": + if self.target_combobox.currentText() == "NEMI(WIP)": self.target_value_input.setVisible(False) self.nemi_table.setVisible(True) else: @@ -208,13 +208,6 @@ def validate_nemi_table(self): item.setBackground(QColor(255, 0, 0)) # Set to red for invalid input is_valid = False - if weight_sum != 1: - print("Weight sum is not equal to 1") - is_valid = False - for col in range(self.nemi_table.columnCount()): - item = self.nemi_table.item(2, col) - if item: - item.setBackground(QColor(255, 0, 0)) # Highlight the weight row if sum is not 1 finally: self.nemi_table.blockSignals(False) # Unblock signals @@ -223,7 +216,7 @@ def validate_nemi_table(self): def on_optimise_clicked(self): """Popup message box to confirm the optimisation.""" try: - if self.target_combobox.currentText() == "NEMI": + if self.target_combobox.currentText() == "NEMI(WIP)": if not self.validate_nemi_table(): QMessageBox.warning(self, "Invalid Input", "Please correct the invalid inputs in the NEMI table.") return From fc1441d436da948b32c1cfd1da1d0beecd4134fb Mon Sep 17 00:00:00 2001 From: mroncera Date: Fri, 14 Jun 2024 12:03:50 +0200 Subject: [PATCH 15/16] FIX - minor path fixes --- environment.yml | 1 + src/model/optimisation/genetic_optimisation.py | 2 +- src/view/gui.py | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index 2609bde..2fac0b3 100644 --- a/environment.yml +++ b/environment.yml @@ -25,3 +25,4 @@ dependencies: - pyvis==0.3.2 - seaborn==0.13.2 - sphinx_rtd_theme==2.0.0 + - deap==1.4.1 diff --git a/src/model/optimisation/genetic_optimisation.py b/src/model/optimisation/genetic_optimisation.py index b8036fa..53e53eb 100644 --- a/src/model/optimisation/genetic_optimisation.py +++ b/src/model/optimisation/genetic_optimisation.py @@ -4,7 +4,7 @@ from PyQt6.QtCore import pyqtSignal, QThread from deap import creator, base, tools, algorithms -from model.optimisation.impedance_strategy_map import IMPEDANCE_STRATEGY_MAP +from src.model.optimisation.impedance_strategy_map import IMPEDANCE_STRATEGY_MAP from src.controler.controller import CalculationController parameters_dict = { diff --git a/src/view/gui.py b/src/view/gui.py index dbb08e7..3fb86f9 100644 --- a/src/view/gui.py +++ b/src/view/gui.py @@ -32,7 +32,7 @@ from src.controler.controller import CalculationController, STRATEGY_MAP from src.model.visualisation.create_tree import create_tree, add_title_description -from view.optimisation_tab import OptimisationTab +from src.view.optimisation_tab import OptimisationTab ureg: UnitRegistry = UnitRegistry() From 638cf86b77f391764631cf071d2b3af698ea2804 Mon Sep 17 00:00:00 2001 From: mroncera Date: Fri, 14 Jun 2024 12:05:59 +0200 Subject: [PATCH 16/16] FIX - env name changes --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 2fac0b3..af8df23 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: PLASMAG_22 +name: PLASMAG channels: - conda-forge - defaults