Source code for utils.Metadata

import csv
import os



class Metadata:
    def __init__(self):
        self.data = {}



    def set_field(self, key, value):
        self.data[key] = value




    def add_nested_field(self, parent_key, key, value):
        if parent_key not in self.data:
            self.data[parent_key] = {}
        self.data[parent_key][key] = value




    def add_repeating_block(self, block_name, block_data):
        if block_name not in self.data:
            self.data[block_name] = []
        self.data[block_name].append(block_data)




    def generate_next_model_name(self, base_name, csv_filename="metadata.csv"):
        if not os.path.isfile(csv_filename):
            return f"{base_name}_1"

        with open(csv_filename, mode='r') as file:
            reader = csv.reader(file)
            rows = list(reader)
            if len(rows) <= 1:
                return f"{base_name}_1"
            last_model_name = rows[-1][0]
            last_model_number = int(last_model_name.split('_')[-1])
            next_model_number = last_model_number + 1
            return f"{base_name}_{next_model_number}"

        




    def save_to_csv(self, filename="metadata.csv"):
        file_exists = os.path.isfile(filename)
        
        with open(filename, mode='a', newline='') as file:  # Open the file in append mode
            writer = csv.writer(file)
            
            if not file_exists:
                # Write headers if the file does not already exist
                headers = []
                for key in self.data:
                    if isinstance(self.data[key], dict):
                        for subkey in self.data[key]:
                            headers.append(f"{key} ({subkey})")
                    elif isinstance(self.data[key], list):
                        for i, item in enumerate(self.data[key]):
                            for subkey in item:
                                headers.append(f"{key} {i+1} ({subkey})")
                    else:
                        headers.append(key)
                writer.writerow(headers)
            
            # Prepare row data
            row = []
            for key in self.data:
                if isinstance(self.data[key], dict):
                    for subkey in self.data[key]:
                        row.append(self.data[key][subkey])
                elif isinstance(self.data[key], list):
                    for item in self.data[key]:
                        for subkey in item:
                            row.append(item[subkey])
                else:
                    row.append(self.data[key])
            
            writer.writerow(row)



'''
# Usage example:
metadata = DynamicMetadata()
metadata.set_field("Model Name", "PINN_Model")
metadata.set_field("Inlet Points File", "inlet_points.csv")
metadata.set_field("Airfoil Points File", "airfoil_points.csv")
metadata.set_field("Fluid Field Points File", "fluid_field_points.csv")
metadata.add_nested_field("Inlet Boundary Conditions", "u", 1.0)
metadata.add_nested_field("Inlet Boundary Conditions", "v", 0.0)
metadata.add_nested_field("Inlet Boundary Conditions", "p", 101325)
metadata.add_nested_field("Airfoil Boundary Conditions", "u", 0.0)
metadata.add_nested_field("Airfoil Boundary Conditions", "v", 0.0)
metadata.add_nested_field("Airfoil Boundary Conditions", "p", 101325)
metadata.set_field("Execution Device", "cuda:0")
metadata.set_field("Model Structure", "2 hidden layers, 20 neurons each")
metadata.add_repeating_block("Training Parameters", {"optimizer": "Adam", "iterations": 1000, "batch_size": 32, "epochs": 10})
metadata.add_repeating_block("Training Parameters", {"optimizer": "SGD", "iterations": 2000, "batch_size": 64, "epochs": 20})

metadata.save_to_csv("metadata.csv")
'''