import functions as func
import numpy as np
np.set_printoptions(precision=3)
mat = func.read_matrix("./matrix.txt")
A = mat[:, :-1]
b = mat[:, -1]
print(f"Matrix A:\n{A}\nb column:\n {b}")
print(f"Gaussian elimination: {func.gaussian_elim(A, b)}")
print(f"Jacobi iterative method: {func.jacobi_method(A, b)}")
print(f"Gauss-Seidel iterative method: {func.gauss_seidel_method(A, b)}")
print(f"Square root method: {func.square_root_method(A, b)}")
print(f"Determinant: {func.determinant(A)}")
print(f"Inversed matrix(Square root method): \n{func.square_invert(A)}")
message.append('experiment_name = ' + experiment_name)
message.append('working_dir = ' + working_dir)
message.append('read_two = ' + read_two)
message.append('log_path = ' + log_path)
message.append('output_dir = ' + output_dir)
message.append('append_target_directory = ' + str(append_target_directory))
message.append('num_threads = ' + str(num_threads))
write_to_log(start_time, log_path, '\n'.join(message))
message = []
# MAIN FUNCTIONS
## Set up
start_time = time.time()
write_to_log(start_time, log_path, "Start set up")
group_barcode_matrix = read_matrix(csv_matrix)
indices_list = create_indices_list(indices_path)
create_target_directory(output_dir, append_target_directory)
desired_barcodes = read_matrix(desired_barcodes)
if groups:
desired_barcodes, group_barcode_matrix = convert_groups_to_barcodes(
desired_barcodes, group_barcode_matrix)
file_dictionary = create_fastq_files(output_dir, indices_list,
group_barcode_matrix)
write_to_log(start_time, log_path, "Finished set up")
start_time = time.time()
write_to_log(start_time, log_path, "Beginning creation of coord_dic")
coord_barcode_matrix, line_count, error_indices_count = \
create_coordinates_barcodes_dictionary (read_one, group_barcode_matrix, desired_barcodes, indices_list)
percentage_error = round((error_indices_count / (line_count / 4)) * 100, 2)