import os

import os

current_directory = os.getcwd()
print("Current Working Directory:", current_directory)

# Replace 'blosum62.txt' with the actual filename
filename = 'blosum62.txt'
file_path = os.path.join(current_directory, filename)

# Get the directory of the file
directory_path = os.path.dirname(file_path)

print("Directory of the file:", directory_path)

def build_scoring_matrix(matrix_file):
    with open(matrix_file, 'r') as file:
        lines = file.readlines()

    header = None
    scoring_matrix = {}

    for line in lines:
        if line.startswith('#'):
            continue  # Skip comment lines

        if not header:
            # Parse header line
            header = line.split()
        else:
            # Parse data lines
            elements = line.split()
            amino_acid = elements[0]

            try:
                scores = [int(score) if score != '*' else 0 for score in elements[1:]]
            except ValueError as e:
                print(f"Error processing line: {line.strip()}. Skipping this line.")
                continue

            scoring_matrix[amino_acid] = dict(zip(header, scores))

    return scoring_matrix


def perform_local_alignment(sequence, reference_sequence, scoring_matrix, gap_penalty):
    m, n = len(sequence), len(reference_sequence)

    # Check if all amino acids in sequences are present in the scoring matrix
    for amino_acid in set(sequence + reference_sequence):
        if amino_acid not in scoring_matrix:
            print(f"Error: Amino acid '{amino_acid}' not found in the scoring matrix.")
            return "", "", 0

    # Initialize the scoring matrix
    dp_matrix = [[0] * (n + 1) for _ in range(m + 1)]

    # Traceback matrix to record the direction of the alignment
    traceback_matrix = [[''] * (n + 1) for _ in range(m + 1)]

    # Fill in the scoring matrix
    max_score = 0
    max_i, max_j = 0, 0

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            match = dp_matrix[i-1][j-1] + scoring_matrix[sequence[i-1]][reference_sequence[j-1]]
            delete = dp_matrix[i-1][j] + gap_penalty
            insert = dp_matrix[i][j-1] + gap_penalty
            dp_matrix[i][j] = max(0, match, delete, insert)

            # Update traceback matrix
            if dp_matrix[i][j] == 0:
                traceback_matrix[i][j] = ''
            elif dp_matrix[i][j] == match:
                traceback_matrix[i][j] = 'diagonal'
            elif dp_matrix[i][j] == delete:
                traceback_matrix[i][j] = 'up'
            elif dp_matrix[i][j] == insert:
                traceback_matrix[i][j] = 'left'

            # Update maximum score and position
            if dp_matrix[i][j] > max_score:
                max_score = dp_matrix[i][j]
                max_i, max_j = i, j

    # Traceback to find the alignment
    aligned_sequence = []
    aligned_reference = []

    i, j = max_i, max_j

    while i > 0 and j > 0 and dp_matrix[i][j] > 0:
        if traceback_matrix[i][j] == 'diagonal':
            aligned_sequence.append(sequence[i-1])
            aligned_reference.append(reference_sequence[j-1])
            i -= 1
            j -= 1
        elif traceback_matrix[i][j] == 'up':
            aligned_sequence.append(sequence[i-1])
            aligned_reference.append('-')
            i -= 1
        elif traceback_matrix[i][j] == 'left':
            aligned_sequence.append('-')
            aligned_reference.append(reference_sequence[j-1])
            j -= 1

    aligned_sequence.reverse()
    aligned_reference.reverse()

    return "".join(aligned_sequence), "".join(aligned_reference), max_score


def main():
    # Paths to file and just 
    sequence = "SPEAR"
    reference_sequence = "SPHERE"

    # Specify the path to the Blosum62 matrix file





    blosum62_file = "/home/user/blosum62.txt"

    # Set gap penalty
    gap_penalty = -10

    # Build the scoring matrix
    scoring_matrix = build_scoring_matrix(blosum62_file)

    # Perform local alignment
    aligned_seq, aligned_ref, score = perform_local_alignment(sequence, reference_sequence, scoring_matrix, gap_penalty)

    # Print the result
    print(f"Sequence: {aligned_seq}")
    print(f"Reference: {aligned_ref}")
    print(f"Score: {score}")


if __name__ == "__main__":
    main()

#This is how to define the function of local alignnment read fasta file and output the fasta filr

#Defining the function for reading fasta fle
def read_fasta(file_path):
    with open(file_path, 'r') as file:
        lines = file.readlines()

    header = lines[0].strip()
    sequence = ''.join(line.strip() for line in lines[1:])

    return header, sequence

#Defining function how tpo write fasta file
def write_fasta(file_path, header, sequence):
    with open(file_path, 'w') as file:
        file.write(f">{header}\n")
        file.write(sequence + "\n")

#Defining the scoring matrix
def build_scoring_matrix(matrix_file):
    with open(matrix_file, 'r') as file:
        lines = file.readlines()

    header = None
    scoring_matrix = {}

    for line in lines:
        if line.startswith('#'):
            continue  # Skip comment lines

        if not header:
            # Parse header line
            header = line.split()
        else:
            # Parse data lines
            elements = line.split()
            amino_acid = elements[0]

            try:
                scores = [int(score) if score != '*' else 0 for score in elements[1:]]
            except ValueError as e:
                print(f"Error processing line: {line.strip()}. Skipping this line.")
                continue

            scoring_matrix[amino_acid] = dict(zip(header, scores))

    return scoring_matrix

#Defining to perform local alignmnet
def perform_local_alignment(sequence, reference_sequence, scoring_matrix, gap_penalty):
    m, n = len(sequence), len(reference_sequence)

    # Check if all amino acids in sequences are present in the scoring matrix
    for amino_acid in set(sequence + reference_sequence):
        if amino_acid not in scoring_matrix:
            print(f"Error: Amino acid '{amino_acid}' not found in the scoring matrix.")
            return "", "", 0

    # Initialize the scoring matrix
    dp_matrix = [[0] * (n + 1) for _ in range(m + 1)]

    # Traceback matrix to record the direction of the alignment
    traceback_matrix = [[''] * (n + 1) for _ in range(m + 1)]

    # Fill in the scoring matrix
    max_score = 0
    max_i, max_j = 0, 0

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            match = dp_matrix[i-1][j-1] + scoring_matrix[sequence[i-1]][reference_sequence[j-1]]
            delete = dp_matrix[i-1][j] + gap_penalty
            insert = dp_matrix[i][j-1] + gap_penalty
            dp_matrix[i][j] = max(0, match, delete, insert)

            # Update traceback matrix
            if dp_matrix[i][j] == 0:
                traceback_matrix[i][j] = ''
            elif dp_matrix[i][j] == match:
                traceback_matrix[i][j] = 'diagonal'
            elif dp_matrix[i][j] == delete:
                traceback_matrix[i][j] = 'up'
            elif dp_matrix[i][j] == insert:
                traceback_matrix[i][j] = 'left'

            # Update maximum score and position
            if dp_matrix[i][j] > max_score:
                max_score = dp_matrix[i][j]
                max_i, max_j = i, j

    # Traceback to find the alignment
    aligned_sequence = []
    aligned_reference = []

    i, j = max_i, max_j

    while i > 0 and j > 0 and dp_matrix[i][j] > 0:
        if traceback_matrix[i][j] == 'diagonal':
            aligned_sequence.append(sequence[i-1])
            aligned_reference.append(reference_sequence[j-1])
            i -= 1
            j -= 1
        elif traceback_matrix[i][j] == 'up':
            aligned_sequence.append(sequence[i-1])
            aligned_reference.append('-')
            i -= 1
        elif traceback_matrix[i][j] == 'left':
            aligned_sequence.append('-')
            aligned_reference.append(reference_sequence[j-1])
            j -= 1

    aligned_sequence.reverse()
    aligned_reference.reverse()

    return "".join(aligned_sequence), "".join(aligned_reference), max_score


def main():
    #Paths to file
    scoring_matrix_file = "/home/user/blosum62.txt"
    sequence_file = "/home/user/.fasta"
    reference_sequence_file = "/home/user/.fasta"
    output_file = "/home/user/.fasta"

    # Set gap penalty
    gap_penalty = -10

    # Build the scoring matrix
    scoring_matrix = build_scoring_matrix(scoring_matrix_file)

    # Read sequences from files
    _, sequence = read_fasta(sequence_file)
    _, reference_sequence = read_fasta(reference)