#!/usr/bin/env python3
"""
RNA 3D Structure Prediction - Template-Based Approach
Uses structural templates from PDB database for similar sequences
"""
import pandas as pd
import numpy as np
from pathlib import Path
from collections import defaultdict

# Simple template database (mock - in production would use real PDB structures)
TEMPLATE_DB = {
    # Short sequences with known structures (simplified)
    'GGGG': np.array([[0, 0, 0], [0.3, 0, 0], [0.6, 0.1, 0], [0.9, 0, 0]]),
    'CCCC': np.array([[0, 0, 0], [0.3, -0.1, 0], [0.6, 0, 0], [0.9, 0.1, 0]]),
    'AAAA': np.array([[0, 0, 0], [0.3, 0.1, 0.1], [0.6, 0, 0.1], [0.9, -0.1, 0]]),
    'UUUU': np.array([[0, 0, 0], [0.3, 0, -0.1], [0.6, 0.1, -0.1], [0.9, 0, 0]]),
}

def find_best_template(sequence, template_db=TEMPLATE_DB):
    """Find best matching template for a sequence"""
    # Simple k-mer matching
    best_match = None
    best_score = 0
    
    for template_seq, template_coords in template_db.items():
        # Count matching k-mers
        score = sum(1 for i in range(len(sequence) - len(template_seq) + 1)
                   if sequence[i:i+len(template_seq)] == template_seq)
        if score > best_score:
            best_score = score
            best_match = template_coords
    
    return best_match

def build_structure_from_template(sequence, template):
    """Build 3D structure using template as guide"""
    seq_len = len(sequence)
    
    if template is None or len(template) == 0:
        # Fallback: helical structure
        coords = np.zeros((seq_len, 3))
        for i in range(seq_len):
            angle = i * 2 * np.pi / 10  # 10 residues per turn
            radius = 1.0
            coords[i] = [radius * np.cos(angle), radius * np.sin(angle), i * 0.3]
        return coords
    
    # Scale template to match sequence length
    template_len = len(template)
    coords = np.zeros((seq_len, 3))
    
    for i in range(seq_len):
        # Interpolate from template
        template_idx = int(i * template_len / seq_len)
        template_idx = min(template_idx, template_len - 1)
        coords[i] = template[template_idx].copy()
        
        # Add small random perturbation
        coords[i] += np.random.randn(3) * 0.05
    
    # Scale to realistic RNA dimensions
    coords *= 3.0  # Angstroms per residue
    
    return coords

def generate_ensemble(sequence, num_structures=5):
    """Generate ensemble of 5 structures"""
    template = find_best_template(sequence)
    structures = []
    
    for i in range(num_structures):
        # Set different random seed for each structure
        np.random.seed(hash(sequence + str(i)) % 2**32)
        coords = build_structure_from_template(sequence, template)
        
        # Add conformational diversity
        if i > 0:
            # Add rotation
            angle = i * np.pi / 4
            rotation = np.array([
                [np.cos(angle), -np.sin(angle), 0],
                [np.sin(angle), np.cos(angle), 0],
                [0, 0, 1]
            ])
            coords = coords @ rotation.T
            
            # Add small deformation
            coords += np.random.randn(*coords.shape) * 0.1 * i
        
        # Center
        coords -= coords.mean(axis=0)
        structures.append(coords)
    
    return structures

def create_submission(test_df):
    """Create submission DataFrame"""
    data = []
    
    for idx, row in test_df.iterrows():
        target_id = row['target_id']
        sequence = row['sequence']
        
        # Generate 5 structures
        structures = generate_ensemble(sequence, num_structures=5)
        
        # Create rows for each residue
        for res_idx, residue in enumerate(sequence):
            row_data = [
                f"{target_id}_{res_idx+1}",  # ID
                residue,                      # resname
                res_idx + 1                   # resid (1-indexed)
            ]
            
            # Add x,y,z for each of 5 structures
            for struct_idx in range(5):
                for coord_idx in range(3):
                    row_data.append(structures[struct_idx][res_idx, coord_idx])
            
            data.append(row_data)
        
        if (idx + 1) % 5 == 0:
            print(f"Processed {idx + 1}/{len(test_df)} sequences")
    
    # Create DataFrame
    columns = ['ID', 'resname', 'resid']
    for i in range(1, 6):
        columns += [f'x_{i}', f'y_{i}', f'z_{i}']
    
    return pd.DataFrame(data, columns=columns)

def main():
    # Load test data
    test_path = Path("/kaggle/input/stanford-rna-3d-folding-2/test_sequences.csv")
    test_df = pd.read_csv(test_path)
    print(f"Loaded {len(test_df)} test sequences")
    
    # Generate submission
    submission_df = create_submission(test_df)
    
    # Save
    output_path = "submission.csv"
    submission_df.to_csv(output_path, index=False)
    print(f"\nSubmission saved to {output_path}")
    print(f"Shape: {submission_df.shape}")

if __name__ == "__main__":
    main()