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June 18, 2024 16:32
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Part II module selection heuristic
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| [Area.Mechanical] | |
| min = 4 | |
| required = [{core = 4}] | |
| core = """4A2 Computational Fluid Dynamics | |
| 4A3 Turbomachinery I | |
| 4A7 Aircraft Aerodynamics and Design | |
| 4A9 Molecular Thermodynamics | |
| 4A10 Flow Instability | |
| 4A12 Turbulence and Vortex Dynamics | |
| 4A13 Combustion and Engines | |
| 4B5 Quantum and Nano-technologies | |
| 4B13 Electronic Sensors and Instrumentation | |
| 4B19 Renewable Electrical Power | |
| 4C2 Designing with Composites | |
| 4C3 Advanced Functional Materials and Devices | |
| 4C4 Design Methods | |
| 4C5 Design Case Studies | |
| 4C6 Advanced Linear Vibrations | |
| 4C7 Random and Non-linear Vibrations | |
| 4C8 Vehicle Dynamics | |
| 4C9 Continuum Mechanics | |
| 4C11 Data-driven and Learning Based Methods in Mechanics and Materials | |
| 4D6 Dynamics in Civil Engineering | |
| 4D2 Advanced Structural Design | |
| 4F1 Control System Design | |
| 4G1 Mathematical Biology of the Cell | |
| 4G5 Materials and Molecules: Modelling, Simulation and Machine Learning | |
| 4G6 Cellular and Molecular Biomechanics | |
| 4I10 Nuclear Reactor Engineering | |
| 4I11 Advanced Fission and Fusion Systems | |
| 4I14 Biosensors and Bioelectronics | |
| 4M12 Partial Differential Equations and Variational Methods | |
| 4M16 Nuclear Power Engineering | |
| 4M17 Practical Optimization | |
| 4M19 Advanced Building Physics | |
| 4M22 Climate Change Mitigation | |
| 4M23 Electricity and Environment (TPE22) | |
| 4M24 Computational Statistics and Machine Learning""" | |
| [Area.Energy] | |
| min = 4 | |
| required = [{core = 4}] | |
| core = """4A2 Computational Fluid Dynamics | |
| 4A3 Turbomachinery | |
| 4A9 Molecular Thermodynamics | |
| 4A13 Combustion and Engines | |
| 4B19 Renewable Electric Power | |
| 4D13 Architectural Engineering | |
| 4I10 Nuclear Reactor Engineering | |
| 4I11 Advanced Fission and Fusion Systems | |
| 4M16 Nuclear Power Engineering | |
| 4M22 Climate Change Mitigation | |
| 4M23 Electricity and Environment""" | |
| [Area.Aero] | |
| min = 4 | |
| required = [{core = 4}, {core=3, companion = 2}] | |
| core = """4A2 Computational Fluid Dynamics | |
| 4A3 Turbomachinery I | |
| 4A4 Aircraft Stability and Control | |
| 4A7 Aircraft Aerodynamics and Design | |
| 4A9 Molecular Thermodynamics | |
| 4A10 Flow Instability | |
| 4A12 Turbulence and Vortex Dynamics | |
| 4A13 Combustion and Engines | |
| 4A15 Acoustics""" | |
| companion = """4B13 Electronic Sensors and Instrumentation | |
| 4B23 Optical Fibre Communication | |
| 4B24 Radio frequency Systems | |
| 4C2 Designing with Composites | |
| 4C4 Design Methods | |
| 4C5 Design Case Studies | |
| 4C6 Advanced Linear Vibrations | |
| 4C7 Random and Non-linear Vibrations | |
| 4C9 Continuum Mechanics | |
| 4F1 Control System Design | |
| 4F2 Robust and Non-linear Control | |
| 4F3 An Optimisation Based Approve to Control | |
| 4M24 Computational Statistics and Machine Learning""" | |
| [Area.Civil] | |
| min = 4 | |
| required = [{core = 4}] | |
| core = """4C11 Data-driven and Learning Based Methods in Mechanics and Materials | |
| 4D2 Advanced Structural Design | |
| 4D4 Construction Engineering | |
| 4D5 Foundation Engineering | |
| 4D6 Dynamics in Civil Engineering | |
| 4D7 Concrete and Prestressed Concrete | |
| 4D9 Offshore Geotechnical Engineering | |
| 4D10 Structural Steelwork | |
| 4D13 Architectural Engineering | |
| 4D16 Construction Management | |
| 4M19 Advanced Building Physics | |
| 4M22 Climate Change Mitigation | |
| 4M24 Computational Statistics and Machine Learning""" | |
| [Area.EEE] | |
| min = 4 | |
| required = [{core = 4}] | |
| core = """4B2 Power Micro Electronics | |
| 4B5 Quantum and Nano-technologies | |
| 4B11 Photonic Systems | |
| 4B13 Electronic Sensors and Instrumentation | |
| 4B19 Renewable Electrical Power | |
| 4B23 Optical Fibre Communication | |
| 4B24 Radio Frequency Systems | |
| 4B25 Embedded Systems for the Internet of Things | |
| 4B27 Internet of Everything | |
| 4C3 Advanced Functional Materials and Devices | |
| 4F5 Advanced Information Theory and Coding | |
| 4I14 Biosensors and Bioelectronics""" | |
| [Area.ICE] | |
| min = 4 | |
| required = [{core = 4}] | |
| core = """4B23 Optical Fibre Communication | |
| 4B25 Embedded Systems for the Internet of Things | |
| 4C11 Data-driven and Learning Based Methods in Mechanics and Materials | |
| 4F1 Control System Design | |
| 4F2 Robust and Non-linear Control | |
| 4F3 An Optimisation Based Approach to Control | |
| 4F5 Advanced Information Theory and Coding | |
| 4F8 Image Processing and Image Coding | |
| 4F10 Deep Learning and Structured data | |
| 4F12 Computer Vision | |
| 4F13 Probabilistic Machine Learning | |
| 4F14 Computer Systems | |
| 4G10 Brain Machine Interfaces | |
| 4M17 Practical Optimization | |
| 4M21 Software Engineering and Design | |
| 4M24 Computational Statistics and Machine Learning | |
| 4M26 Algorithms and Data Structures""" | |
| [Area.EIS] | |
| min = 6 | |
| required = [{core = 6}] | |
| core = """4B2 Power micro electronics | |
| 4B5 Quantum and Nano-technologies | |
| 4B11 Photonic systems | |
| 4B13 Electronic sensors and instrumentation | |
| 4B19 Renewable electrical power | |
| 4B23 Optical Fibre Communication | |
| 4B24 Radio Frequency Systems | |
| 4B25 Embedded Systems for the Internet of Things | |
| 4B27 Internet of Everything | |
| 4C3 Advanced Functional Materials and Devices | |
| 4F1 Control system design | |
| 4F2 Robust and non-linear control | |
| 4F3 An Optimisation Based Approve to Control | |
| 4F5 Advanced Information Theory and Coding | |
| 4F8 Image processing and image coding | |
| 4F10 Deep Learning and Structured data | |
| 4F12 Computer vision | |
| 4F13 Probabilistic Machine learning | |
| 4F14 Computer systems | |
| 4G10 Brain Machine Interfaces | |
| 4M12 Partial differential equations and variational methods | |
| 4M17 Practical optimization | |
| 4M21 Software Engineering and Design | |
| 4M26 Algorithms and data structures""" | |
| [Area.Control] | |
| min = 4 | |
| required = [{core = 4}] | |
| core = """4A4 Aircraft stability and control | |
| 4B11 Photonic systems | |
| 4B13 Electronic sensors and instrumentation | |
| 4B24 Radio Frequency Systems | |
| 4B25 Embedded Systems for the Internet of Things | |
| 4B27 Internet of Everything | |
| 4C6 Advanced linear vibrations | |
| 4C7 Random and non-linear vibrations | |
| 4F1 Control system design | |
| 4F2 Robust and non-linear control | |
| 4F3 An Optimisation Based Approve to Control | |
| 4F5 Advanced Information Theory and Coding | |
| 4F8 Image processing and image coding | |
| 4F10 Deep Learning and Structured data | |
| 4F12 Computer vision | |
| 4F13 Probabilistic Machine learning | |
| 4G10 Brain Machine Interfaces | |
| 4M21 Software Engineering and Design""" | |
| [Area.Bio] | |
| min = 4 | |
| required = [{core = 2}] | |
| core = """4G1 Mathematical Biology of the Cell | |
| 4G3 Computational Neuroscience | |
| 4G5 Materials and Molecules: Modelling, Simulation and Machine Learning | |
| 4G6 Cellular and Molecular Biomechanics | |
| 4G7 Control and Molecular Biomechanics | |
| 4G9 Biomedical Engineering | |
| 4G10 Brain Machine Interfaces | |
| 4I14 Biosensors and Bioelectronics""" | |
| companion = """4B13 Electronic Sensors and Instrumentation | |
| 4C4 Design Methods | |
| 4C5 Design Case Studies | |
| 4C9 Continuum Mechanics | |
| 4F8 Image Processing and Image Coding | |
| 4F12 Computer Vision | |
| 4F13 Probabilistic Machine Learning | |
| 4I8 Medical Physics""" | |
| [Modules] | |
| 4A2 = {assumed = ["3A1", "3A3"], useful = []} | |
| 4A3 = {assumed = ["3A1", "3A3"], useful = []} | |
| 4A4 = {assumed = [], useful = []} | |
| 4A7 = {assumed = ["3A1", "3A3"], useful = []} | |
| 4A9 = {assumed = [], useful = ["3A1", "3A5"]} | |
| 4A10 = {assumed = ["3A1"], useful = []} | |
| 4A12 = {assumed = ["3A1"], useful = ["3A3"]} | |
| 4A13 = {assumed = [], useful = ["3A5", "3A6"]} | |
| 4A15 = {assumed = [], useful = []} | |
| 4B2 = {assumed = [], useful = ["3B3", "3B5"]} | |
| 4B5 = {assumed = ["3B5"], useful = []} | |
| 4B11 = {assumed = [], useful = ["3B6"]} | |
| 4B13 = {assumed = ["3B1"], useful = []} | |
| 4B19 = {assumed = ["3B3", "3B4", "3B6"], useful = []} | |
| 4B23 = {assumed = [], useful = ["3F4", "3B6"]} | |
| 4B24 = {assumed = ["3B1"], useful = []} | |
| 4B25 = {assumed = [], useful = ["3B2"]} | |
| 4B27 = {assumed = [], useful = []} | |
| 4C2 = {assumed = [], useful = []} | |
| 4C3 = {assumed = [], useful = ["3B5"]} | |
| 4C4 = {assumed = [], useful = []} | |
| 4C5 = {assumed = [], useful = ["4C4"]} | |
| 4C6 = {assumed = ["3C6"], useful = []} | |
| 4C7 = {assumed = [], useful = ["3C6"]} | |
| 4C8 = {assumed = [], useful = ["3C5", "3C6"]} | |
| 4C9 = {assumed = ["3C7"], useful = ["3D7"]} | |
| 4C11 = {assumed = ["3C7"], useful = ["3D7"]} | |
| 4D2 = {assumed = ["3D3", "3D4"], useful = []} | |
| 4D4 = {assumed = [], useful = ["3D1", "3D2", "4D16"]} | |
| 4D5 = {assumed = ["3D2"], useful = []} | |
| 4D6 = {assumed = [], useful = ["3D2", "3D4", "3D7"]} | |
| 4D7 = {assumed = ["2P8", "3D3"], useful = []} | |
| 4D9 = {assumed = ["3D2"], useful = []} | |
| 4D10 = {assumed = ["3D4"], useful = ["3D3"]} | |
| 4D13 = {assumed = [], useful = ["3D3", "3D4", "3D8"]} | |
| 4D16 = {assumed = [], useful = []} | |
| 4E1 = {assumed = [], useful = []} | |
| 4E3 = {assumed = [], useful = []} | |
| 4E5 = {assumed = [], useful = []} | |
| 4E6 = {assumed = [], useful = []} | |
| 4E11 = {assumed = [], useful = []} | |
| 4E12 = {assumed = [], useful = []} | |
| 4F1 = {assumed = [], useful = ["3F1", "3F2"]} | |
| 4F2 = {assumed = ["3F2"], useful = []} | |
| 4F3 = {assumed = [], useful = ["3F1", "3F2"]} | |
| 4F5 = {assumed = ["3F7"], useful = ["3F1", "3F4"]} | |
| 4F8 = {assumed = ["3F1"], useful = ["3F3", "3F7"]} | |
| 4F10 = {assumed = [], useful = ["3F1", "3F3", "3F8"]} | |
| 4F12 = {assumed = [], useful = []} | |
| 4F13 = {assumed = [], useful = ["3F3"]} | |
| 4F14 = {assumed = [], useful = []} # Assumes Part I Digital circuits and computing | |
| 4G1 = {assumed = [], useful = []} | |
| 4G3 = {assumed = [], useful = ["3G2", "3G3"]} | |
| 4G5 = {assumed = [], useful = []} | |
| 4G6 = {assumed = [], useful = ["3C7"]} | |
| 4G7 = {assumed = [], useful = ["3G1", "3G2", "3G3", "3F0"]} | |
| 4G9 = {assumed = [], useful = []} | |
| 4G10 = {assumed = [], useful = ["3M1", "3G3", "3F2", "3F8"]} | |
| 4I1 = {assumed = [], useful = []} | |
| 4I8 = {assumed = [], useful = ["3G4"]} | |
| 4I10 = {assumed = ["4M16"], useful = []} | |
| 4I11 = {assumed = ["4M16"], useful = []} | |
| 4I14 = {assumed = [], useful = ["3G3"]} | |
| 4M1 = {assumed = [], useful = []} | |
| 4M3 = {assumed = [], useful = []} | |
| 4M12 = {assumed = [], useful = []} | |
| 4M16 = {assumed = [], useful = []} | |
| 4M17 = {assumed = ["3M1"], useful = []} | |
| 4M19 = {assumed = ["3D8"], useful = []} | |
| 4M21 = {assumed = [], useful = []} | |
| 4M22 = {assumed = [], useful = []} | |
| 4M23 = {assumed = [], useful = []} | |
| 4M24 = {assumed = ["3F3", "3F8", "3M1"], useful = []} | |
| 4M26 = {assumed = [], useful = []} |
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| import tomlkit as tk | |
| import pulp | |
| with open("data.toml") as f: | |
| data = tk.load(f) | |
| # Define the problem | |
| prob = pulp.LpProblem("Maximize_Specializations", pulp.LpMaximize) | |
| # Extract modules and specializations | |
| modules = set() | |
| specializations = {} | |
| for area, details in data["Area"].items(): | |
| core_modules = details["core"].split("\n") | |
| companion_modules = ( | |
| details.get("companion", "").split("\n") if "companion" in details else [] | |
| ) | |
| modules.update(core_modules) | |
| modules.update(companion_modules) | |
| specializations[area] = { | |
| "min": details["min"], | |
| "required": details["required"], | |
| "core": core_modules, | |
| "companion": companion_modules, | |
| } | |
| # Variables | |
| x = pulp.LpVariable.dicts("Module", modules, 0, 1, pulp.LpBinary) | |
| y = pulp.LpVariable.dicts("Specialization", specializations.keys(), 0, 1, pulp.LpBinary) | |
| # Variables for 3rd year modules | |
| prev_modules = set() | |
| for mod in modules: | |
| prev_modules.update(data["Modules"][mod.split(" ")[0]]["assumed"]) | |
| y_prev = pulp.LpVariable.dicts("PrevModule", prev_modules, 0, 1, pulp.LpBinary) | |
| # Objective function | |
| prob += ( | |
| pulp.lpSum([y[spec] for spec in specializations]), | |
| "Maximize the number of specializations", | |
| ) | |
| # Constraints | |
| for spec, details in specializations.items(): | |
| min_modules = details["min"] | |
| core_modules = details["core"] | |
| companion_modules = details["companion"] | |
| # Minimum total modules constraint | |
| prob += ( | |
| pulp.lpSum([x[mod] for mod in core_modules + companion_modules]) | |
| >= min_modules * y[spec], | |
| f"MinModules_{spec}", | |
| ) | |
| # Additional required constraints | |
| for req in details["required"]: | |
| core_req = req.get("core", 0) | |
| companion_req = req.get("companion", 0) | |
| if core_req > 0: | |
| prob += ( | |
| pulp.lpSum([x[mod] for mod in core_modules]) >= core_req * y[spec], | |
| f"CoreReq_{spec}_{core_req}", | |
| ) | |
| if companion_req > 0: | |
| prob += ( | |
| pulp.lpSum([x[mod] for mod in companion_modules]) | |
| >= companion_req * y[spec], | |
| f"CompanionReq_{spec}_{companion_req}", | |
| ) | |
| # Add the constraint to limit the total number of modules chosen to 8 | |
| prob += pulp.lpSum([x[mod] for mod in modules]) <= 8, "MaxModules" | |
| # Add constraints for assumed modules from the previous year | |
| for mod in modules: | |
| for assumed_mod in data["Modules"][mod.split(" ")[0]]["assumed"]: | |
| prob += y_prev[assumed_mod] >= x[mod], f"Assumed_{mod}_{assumed_mod}" | |
| # Add the constraint to select exactly 9 modules from the previous year | |
| prob += pulp.lpSum([y_prev[mod] for mod in prev_modules]) == 9, "PrevModules" | |
| # Solve the problem | |
| prob.solve() | |
| # Output the results | |
| print("Status:", pulp.LpStatus[prob.status]) | |
| for v in prob.variables(): | |
| print(v.name, "=", v.varValue) | |
| # Print the selected modules and specializations | |
| selected_modules = [mod for mod in modules if x[mod].varValue == 1] | |
| qualified_specializations = [spec for spec in specializations if y[spec].varValue == 1] | |
| selected_prev_modules = [mod for mod in prev_modules if y_prev[mod].varValue == 1] | |
| print("Selected Modules:", selected_modules) | |
| print("Qualified Specializations:", qualified_specializations) | |
| print("Selected Previous Year Modules:", selected_prev_modules) | |
| # Print how each specialization's requirements are met | |
| for spec in qualified_specializations: | |
| print(f"Area.{spec}") | |
| details = specializations[spec] | |
| min_modules = details["min"] | |
| core_modules = details["core"] | |
| companion_modules = details["companion"] | |
| # Check minimum total modules requirement | |
| selected_modules_count = sum( | |
| x[mod].varValue for mod in core_modules + companion_modules | |
| ) | |
| print( | |
| f" Minimum total modules ({min_modules}): {selected_modules_count} modules selected" | |
| ) | |
| # Check additional requirements | |
| for req in details["required"]: | |
| core_req = req.get("core", 0) | |
| companion_req = req.get("companion", 0) | |
| if core_req > 0: | |
| selected_core_modules = sum(x[mod].varValue for mod in core_modules) | |
| print( | |
| f" Core modules requirement ({core_req}): {selected_core_modules} core modules selected" | |
| ) | |
| if companion_req > 0: | |
| selected_companion_modules = sum( | |
| x[mod].varValue for mod in companion_modules | |
| ) | |
| print( | |
| f" Companion modules requirement ({companion_req}): {selected_companion_modules} companion modules selected" | |
| ) | |
| for mod in core_modules + companion_modules: | |
| if x[mod].varValue == 1: | |
| prerequisites = data["Modules"][mod.split(" ")[0]]["assumed"] | |
| print(f" - {mod}" + (f" {prerequisites}" if prerequisites else "")) | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| modules = list(data["Modules"].keys()) | |
| prev_modules = list(sorted(prev_modules)) | |
| # Initialize the matrix for the table | |
| matrix = np.zeros((len(modules), len(prev_modules))) | |
| # Populate the matrix based on the relationships | |
| for i, (mod_4, details_4) in enumerate(data["Modules"].items()): | |
| for j, mod_3 in enumerate(prev_modules): | |
| if mod_3 in details_4["assumed"]: | |
| matrix[i, j] = 2 # assumed relationship | |
| if mod_3 in details_4["useful"]: | |
| matrix[i, j] = 1 # useful relationship | |
| # Create the plot | |
| fig = plt.figure(1) | |
| ax = plt.subplot() | |
| # Define the colors for the matrix | |
| cmap = plt.cm.get_cmap("coolwarm", 3) | |
| cax = ax.matshow(matrix.T, cmap=cmap) | |
| ax = cax.axes | |
| # Set the axis labels | |
| ax.set_yticks(np.arange(len(prev_modules))) | |
| ax.set_xticks(np.arange(len(modules))) | |
| ax.set_yticklabels(prev_modules) | |
| ax.set_xticklabels([m.split(" ")[0] for m in modules], rotation=90) | |
| # Add a colorbar | |
| # cbar = fig.colorbar(cax, ticks=[0, 1, 2]) | |
| # cbar.ax.set_yticklabels(["No relation", "Assumed", "Useful"], rotation=90) | |
| # Display the plot | |
| # plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.2) | |
| # plt.tight_layout() | |
| plt.grid(True, linewidth=0.2) | |
| plt.show() |
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