From 65a8de7bed3c6aa482323647a6554f8322483dc3 Mon Sep 17 00:00:00 2001
From: Zizhe Wang <zizhe.wang@tu-dresden.de>
Date: Sat, 8 Jun 2024 09:28:27 +0200
Subject: [PATCH] clean old scripts
---
src/OptimizationxOneScript/README.md | 8 -
.../SimpleHeatingSystem.mo | 40 ---
.../optimize_one_script.py | 279 ------------------
3 files changed, 327 deletions(-)
delete mode 100644 src/OptimizationxOneScript/README.md
delete mode 100644 src/OptimizationxOneScript/SimpleHeatingSystem.mo
delete mode 100644 src/OptimizationxOneScript/optimize_one_script.py
diff --git a/src/OptimizationxOneScript/README.md b/src/OptimizationxOneScript/README.md
deleted file mode 100644
index 8673250..0000000
--- a/src/OptimizationxOneScript/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-This is the one-script solution.
-
-This one-script Python file has been developed first, because of better separate of concern and more readable code structure this script has been further developed into the following four scripts:
-
-* config.py
-* optimization_libraries.py
-* optimize_main.py
-* parallel_computing.py
\ No newline at end of file
diff --git a/src/OptimizationxOneScript/SimpleHeatingSystem.mo b/src/OptimizationxOneScript/SimpleHeatingSystem.mo
deleted file mode 100644
index 61f055b..0000000
--- a/src/OptimizationxOneScript/SimpleHeatingSystem.mo
+++ /dev/null
@@ -1,40 +0,0 @@
-# (c) Zizhe Wang
-# https://zizhe.wang
-
-model SimpleHeatingSystem
- parameter Real Q_max = 5000 "Maximum heating power in watts";
- parameter Real T_set = 293.15 "Setpoint temperature in Kelvin";
- parameter Real efficiency = 0.9 "Heater efficiency";
- parameter Real hysteresis = 0.5 "Hysteresis for temperature control";
- parameter Real T_comfort = 293.15 "Comfortable room temperature in Kelvin";
-
- Real Q_heat "Heating power";
- Real T_room(start = 278.15) "Room temperature";
- Real cost(start=0) "Cost of heating";
- Real energy(start=0) "Energy consumed";
- Real comfort(start=0) "Thermal comfort";
- Boolean heater_on(start=false) "Heater status";
-
- constant Real c_p = 1005 "Specific heat capacity of air (J/(kg·K))";
- constant Real m_air = 50 "Mass of air in the room (kg)";
- Real accumulated_discomfort(start=0) "Accumulated thermal discomfort";
-
-equation
- // Room temperature dynamics
- der(T_room) = (Q_heat * efficiency - (T_room - 273.15)) / (m_air * c_p);
-
- // Heater control logic with hysteresis
- heater_on = if T_room < T_set - hysteresis then true else if T_room > T_set + hysteresis then false else pre(heater_on);
- Q_heat = if heater_on then Q_max else 0;
-
- // Calculations for energy and cost
- der(energy) = Q_heat * efficiency;
- der(cost) = 0.1 * Q_heat * efficiency;
-
- // Calculations for accumulated discomfort (thermal discomfort over time)
- der(accumulated_discomfort) = abs(T_room - T_comfort);
-
- // Comfort calculation related to energy consumption
- der(comfort) = if heater_on then efficiency * Q_heat / (accumulated_discomfort + 1) else 0;
-
-end SimpleHeatingSystem;
diff --git a/src/OptimizationxOneScript/optimize_one_script.py b/src/OptimizationxOneScript/optimize_one_script.py
deleted file mode 100644
index 55c324c..0000000
--- a/src/OptimizationxOneScript/optimize_one_script.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# (c) Zizhe Wang
-# https://zizhe.wang
-
-######################################
-# #
-# OPTIMIZATION USING ONLY ONE SCRIPT #
-# #
-######################################
-
-import os
-import tempfile
-import shutil
-import numpy as np
-from time import sleep
-from OMPython import OMCSessionZMQ
-from pymoo.algorithms.moo.nsga2 import NSGA2
-from pymoo.algorithms.moo.nsga3 import NSGA3
-from pymoo.algorithms.soo.nonconvex.cmaes import CMAES
-from pymoo.optimize import minimize
-from pymoo.core.problem import Problem
-from scipy.optimize import differential_evolution, minimize as scipy_minimize
-from joblib import Parallel, delayed # parallel processing
-import matplotlib.pyplot as plt
-
-"""
-============================== GLOBAL SETTINGS ==============================
-"""
-# ====================================================
-# !!! Be sure to only edit your setups in global settings!!!
-# !!! Don't modify the code outside of it !!!
-# ====================================================
-
-# Simulation time and model path
-SIMULATION_STOP_TIME = 2000 # in seconds
-MODEL_NAME = "SimpleHeatingSystem"
-MODEL_FILE = f"{MODEL_NAME}.mo"
-
-current_directory = os.getcwd()
-model_path = os.path.join(current_directory, MODEL_FILE)
-
-# Parameters and result variables
-PARAMETERS = ["Q_max", "T_set"]
-RESULTS = ["energy", "cost", "comfort"]
-
-# Parameter bounds
-PARAM_BOUNDS = {
- "Q_max": (1000, 5000),
- "T_set": (280, 310),
-}
-
-# Optimization settings
-POP_SIZE = 5 # Population size for NSGA2
-N_GEN = 5 # Number of generations
-
-# Algorithm selection
-# Options: 'pymoo.NSGA2', 'pymoo.NSGA3', 'pymoo.CMAES', 'scipy.de', 'scipy.minimize'
-OPTIMIZATION_LIBRARY = 'pymoo'
-ALGORITHM_NAME = 'NSGA2'
-
-def initialize_algorithm(): # Factory Function for Algorithm Initialization
- if OPTIMIZATION_LIBRARY == 'pymoo':
- if ALGORITHM_NAME == 'NSGA2':
- return NSGA2(pop_size=POP_SIZE)
- elif ALGORITHM_NAME == 'NSGA3':
- return NSGA3(pop_size=POP_SIZE)
- elif ALGORITHM_NAME == 'CMAES':
- return CMAES(pop_size=POP_SIZE)
- else:
- raise ValueError(f"Unsupported algorithm: {ALGORITHM_NAME}")
- elif OPTIMIZATION_LIBRARY == 'scipy':
- if ALGORITHM_NAME == 'de':
- return differential_evolution
- elif ALGORITHM_NAME == 'minimize':
- return scipy_minimize
- else:
- raise ValueError(f"Unsupported algorithm: {ALGORITHM_NAME}")
- else:
- raise ValueError(f"Unsupported optimization library: {OPTIMIZATION_LIBRARY}")
-
-# Parallel processing
-N_JOBS = -1 # Options: '-1', '1', 'n', 'None'
- # ====================================================================
- # -1 = use all available CPU cores
- # 1 = disables parallel processing and runs the tasks sequentially
- # n = specifies the exact number of CPU cores to use
- # None = will run the tasks sequentially, equivalent to '1'
- # ====================================================================
-"""
-=========================== END OF GLOBAL SETTINGS ===========================
-"""
-
-"""
-============================== Pre-compilation Block==============================
-"""
-# """
-# Pre-compile the Modelica model to ensure it can be loaded and built correctly.
-# This helps to catch any errors early and verify the model setup.
-# Uncomment to use this block if you need
-# """
-# # Pre-compile the model
-# def precompile_model():
-# try:
-# omc = OMCSessionZMQ()
-
-# load_model_result = omc.sendExpression(f"loadFile(\"{model_path}\")")
-# print(f"Load model result: {load_model_result}")
-
-# build_model_result = omc.sendExpression(f"buildModel({MODEL_NAME}.mo)")
-# print(f"Build model result: {build_model_result}")
-
-# if not build_model_result:
-# raise RuntimeError(f"Failed to build model: {MODEL_NAME}.mo")
-
-# return True
-# except Exception as e:
-# print(f"Error during model pre-compilation: {e}")
-# return False
-
-# # Pre-compile the model before running optimization
-# if not precompile_model():
-# raise RuntimeError("Model pre-compilation failed. Aborting optimization.")
-
-"""
-=========================== End of Pre-compilation Block ===========================
-"""
-
-temp_dirs = [] # List to store paths of temporary directories
-
-# Define the optimization function
-def optimization_function(param_values):
- """
- Function to optimize the Modelica model given specific parameter values.
- This function runs the simulation and retrieves the results.
- """
- temp_dir = tempfile.mkdtemp() # Create a unique temporary directory for each worker
- temp_dirs.append(temp_dir) # Store the path for later cleanup
- try:
- omc = OMCSessionZMQ() # Create a new OpenModelica session
- temp_dir = temp_dir.replace('\\', '/')
- omc.sendExpression(f'cd("{temp_dir}")')
-
- # Copy model file to temporary directory
- temp_model_path = os.path.join(temp_dir, MODEL_FILE).replace('\\', '/')
- shutil.copy(model_path, temp_model_path)
-
- load_temp_model_result = omc.sendExpression(f"loadFile(\"{temp_model_path}\")")
- print(f"Load model result in worker: {load_temp_model_result}")
- if load_temp_model_result is not True:
- messages = omc.sendExpression("getErrorString()")
- print(f"OpenModelica error messages: {messages}")
- raise RuntimeError(f"Failed to load model: {MODEL_NAME}")
-
- # Build the model in the worker session
- build_model_result = omc.sendExpression(f"buildModel({MODEL_NAME})")
- print(f"Build model result in worker: {build_model_result}")
- if not (isinstance(build_model_result, tuple) and build_model_result[0]):
- messages = omc.sendExpression("getErrorString()")
- print(f"OpenModelica error messages: {messages}")
- raise RuntimeError(f"Failed to build model: {MODEL_NAME}")
-
- # Check if model files are created
- expected_files = [
- os.path.join(temp_dir, f"{MODEL_NAME}.exe").replace('\\', '/'),
- os.path.join(temp_dir, f"{MODEL_NAME}_init.xml").replace('\\', '/')
- ]
- for file in expected_files:
- if not os.path.isfile(file):
- raise RuntimeError(f"Expected file not found: {file}")
-
- # Set model parameters
- rounded_param_values = {param: round(value, 2) for param, value in param_values.items()} # two decimal places
- for param, value in rounded_param_values.items():
- set_param_result = omc.sendExpression(f"setParameterValue({MODEL_NAME}, {param}, {value})")
- if not set_param_result:
- raise RuntimeError(f"Failed to set model parameter: {param} = {value}")
- print(f"Parameters set: {rounded_param_values}")
-
- # Simulate the model
- simulate_result = omc.sendExpression(f"simulate({MODEL_NAME}, stopTime={SIMULATION_STOP_TIME})")
- print(f"Simulate result: {simulate_result}")
-
- # Retrieve simulation results
- result_values = {}
- for result in RESULTS:
- value_command = f"val({result}, {SIMULATION_STOP_TIME})"
- print(f"Executing command: {value_command}")
- value = omc.sendExpression(value_command)
- print(f"Value for {result} at {SIMULATION_STOP_TIME}: {value}")
- # Round the result to 2 decimal places
- result_values[result] = round(value, 2)
- if value is None:
- raise ValueError(f"Simulation result returned None for {result}")
- if not isinstance(value, (float, int)):
- raise ValueError(f"Simulation result for {result} is not in expected format (float or int)")
-
- print(f"Simulation results: {result_values}")
- return list(result_values.values())
-
- except Exception as e:
- print(f"Error during simulation: {e}")
- return [np.nan] * len(RESULTS)
-
-# Define the optimization problem class for 'pymoo'
-class OptimizationProblem(Problem):
- def __init__(self):
- self.param_names = list(PARAM_BOUNDS.keys())
- n_var = len(self.param_names)
- xl = np.array([PARAM_BOUNDS[param][0] for param in self.param_names])
- xu = np.array([PARAM_BOUNDS[param][1] for param in self.param_names])
-
- # Debugging statements to check dimensions and bounds
- print(f"Number of variables: {n_var}")
- print(f"Lower bounds: {xl}")
- print(f"Upper bounds: {xu}")
-
- super().__init__(n_var=n_var, n_obj=len(RESULTS), n_constr=0, xl=xl, xu=xu)
-
- def _evaluate(self, X, out, *args, **kwargs):
- param_values_list = [dict(zip(self.param_names, x)) for x in X]
- results = Parallel(n_jobs=N_JOBS)(delayed(optimization_function)(param_values) for param_values in param_values_list)
- out["F"] = np.array(results)
-
-# Define the objective function for 'SciPy' directly
-def scipy_objective_function(x):
- param_values = dict(zip(PARAMETERS, x))
- result_values = optimization_function(param_values)
- return [result_values[RESULTS.index(res)] for res in RESULTS]
-
-# Initialize the optimization algorithm
-algorithm = initialize_algorithm()
-
-# Define the global optimization problem and algorithm
-problem = OptimizationProblem()
-
-if OPTIMIZATION_LIBRARY == 'pymoo':
- res = minimize(problem, algorithm, ("n_gen", N_GEN), verbose=True)
-
-elif OPTIMIZATION_LIBRARY == 'scipy':
- bounds = [(PARAM_BOUNDS[param][0], PARAM_BOUNDS[param][1]) for param in PARAMETERS]
- if ALGORITHM_NAME == 'de':
- result = algorithm(scipy_objective_function, bounds=bounds, strategy='best1bin', maxiter=N_GEN, popsize=POP_SIZE)
- elif ALGORITHM_NAME == 'minimize':
- x0 = np.mean(bounds, axis=1) # Initial guess: midpoint of bounds
- result = algorithm(scipy_objective_function, x0=x0, bounds=bounds, method='L-BFGS-B', options={'maxiter': N_GEN})
-
- # Extract results for plotting (assuming DE algorithm results for now)
- res = {
- 'F': np.array([scipy_objective_function(ind) for ind in result.x])
- }
-
-# Cleanup temporary directories
-for temp_dir in temp_dirs:
- try:
- shutil.rmtree(temp_dir)
- except PermissionError:
- print(f"PermissionError: Retrying to remove {temp_dir}")
- sleep(2)
- try:
- shutil.rmtree(temp_dir)
- except PermissionError:
- print(f"Failed to remove {temp_dir} after multiple attempts")
-
-# Print the results
-print("Optimization Results:")
-for i, result in enumerate(res.F):
- print(f"Solution {i}: ", end="")
- for name, value in zip(RESULTS, result):
- print(f"{name.capitalize()} = {value:.2f}", end=", ")
- print()
-
-# Plot the results for Energy Consumption vs Comfort
-plt.figure(figsize=(8, 6))
-plt.scatter(res.F[:, 0], res.F[:, 2])
-plt.xlabel('Energy Consumption')
-plt.ylabel('Comfort')
-plt.title('Pareto Front of Energy Consumption vs Comfort')
-plt.grid(True)
-plt.tight_layout()
-plt.show()
\ No newline at end of file
--
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