import re

def parse_txt_to_dict(file_path):
    result = {
        'metadata': {},
        'columns': [],
        'data': []
    }
    
    with open(file_path, 'r') as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            
            # Parse Metadata Header
            if line.startswith('#'):
                # Handle specific key:value pairs like "Project Name: Lavacchi..."
                if ':' in line:
                    content = line[1:].strip()
                    key, value = content.split(':', 1)
                    result['metadata'][key.strip()] = value.strip()
                # Handle parameter assignments like "Ring energy (GeV) = 2.4"
                elif '=' in line:
                    content = line[1:].strip()
                    key, value = content.split('=', 1)
                    result['metadata'][key.strip()] = value.strip()
                # Identify Column Headers (usually the last line starting with #)
                elif 'EnergyMot' in line:
                    # Strip leading # and split by tabs
                    result['columns'] = [col.strip() for col in line[1:].split('\t') if col.strip()]
                continue
            
            # Parse Tabular Data
            try:
                # Split by tabs and convert values to floats
                values = [float(val) for val in line.split('\t') if val.strip()]
                if values:
                    result['data'].append(values)
            except ValueError:
                # Skip lines that are not strictly numeric data
                continue
                
    return result

# Execution
file_data = parse_txt_to_dict('Pd10Cell3_003.txt')

# Example Access:
print(f"Project: {file_data['metadata']['Project Name']}") # Lavacchi20200450_0
print(f"Energy (eV): {file_data['metadata']['Monochromator Energy (eV)']}") # 23605.73
print(f"First Data Row: {file_data['data'][0]}")