# Curation of dataset import pandas as pd import os from tqdm import tqdm # Path to the input CSV and the folder containing XYZ files csv_path = 'formed.csv' xyz_folder = 'XYZ_FORMED' # Load the CSV file df = pd.read_csv(csv_path) # Select necessary columns in 'formed.csv' df = df[['name', 'gap']] # Function to clean and load XYZ file data def load_xyz(mol_name): file_path = os.path.join(xyz_folder, mol_name + '.xyz') if not os.path.exists(file_path): print(f"File not found: {file_path}") # Debugging line return None try: with open(file_path, 'r') as f: xyz_data = f.read() return clean_xyz(xyz_data) except Exception as e: print(f"Error reading {file_path}: {e}") # Debugging line return None # Function to clean XYZ data (removes unwanted meta information and formats correctly) def clean_xyz(xyz_str): # Remove the quotes if there are any around the whole string xyz_str = xyz_str.replace('"', '').strip() # Remove all quotes and strip whitespace # Split the string into lines xyz_lines = xyz_str.splitlines() # Remove the first two lines (e.g., "10", "./CONFAM_opt.xyz") if len(xyz_lines) >= 2: xyz_lines = xyz_lines[2:] # Remove the first two lines # Prepare cleaned data by keeping atom names and coordinates only cleaned_data = [] for line in xyz_lines: parts = line.split() if len(parts) == 4: # Ensure that there are exactly 4 parts (atom and coordinates) cleaned_data.append(" ".join(parts[0:4])) # Join atom symbol with coordinates # Join the cleaned data into a single string return "\n".join(cleaned_data) # Apply the cleaning function to the 'name' column and load the corresponding XYZ data tqdm.pandas() df['xyz'] = df['name'].progress_apply(load_xyz) # Check if xyz column has any data missing or empty print(f"Number of missing XYZ values: {df['xyz'].isnull().sum()}") # Save the cleaned dataset to a new CSV file df.to_csv('formed_xyz_combined.csv', index=False)