Project: s41467-024-51639-5 | Function: modification_base (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements the core anomaly detection algorithm that compares CTC losses between canonical and modified base models to determine nucleotide modification status, computing modification likelihood ratios and converting them to Ml-tags for single-molecule modification detection

**KEY TERMS:** {
  "can_lossvector": "canonical model CTC loss L_canonical",
  "mod_lossvector": "modification model CTC loss L_modified",
  "mls": "modification likelihood ratio l",
  "ml_tag": "modification tag Ml converted from likelihood",
  "scale": "threshold parameter for modification detection",
  "potention_m": "candidate modification positions",
  "basecall": "sequence with detected modifications"
}

**CONTEXT CODE:** #!/usr/bin/env python3
from taiyaki import (
    chunk_selection, ctc, flipflopfings, helpers, layers,
    mapped_signal_files, maths, signal_mapping)
from itertools import islice
import numpy as np
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from taiyaki.squiggle_match import squiggle_match_loss, embed_sequence
from taiyaki.cmdargs import (AutoBool, FileExists, NonNegative,
                             ParseToNamedTuple, Positive)





def modification_base(read,can_model, mod_model,alphabet_info,canonical="ACGT",
                        idx = [0,1,1,2,3],length = 100,scale=1,
                        right_len = 0, canonical_base = "C", mod_base = "m"):
    """Implements the core anomaly detection algorithm that compares CTC losses between canonical and modified base models to determine nucleotide modification status, computing modification likelihood ratios and converting them to Ml-tags for single-molecule modification detection"""
    raise NotImplementedError("Function implementation has been masked")


def extract_signal(pos,read,length = 50,right_len = 0, mapping = True):
    winlen = int(length/2)
    if mapping:
        map_location = read.get_reference_locations((read.Ref_to_signal[pos]-winlen,read.Ref_to_signal[pos]+winlen+right_len))
        seq = read.get_reference()[map_location[0]:map_location[1]]
    else:
        seq = read.get_reference()[pos-2:pos+3]
    signal = read.get_current()[read.Ref_to_signal[pos]-winlen:read.Ref_to_signal[pos]+winlen+right_len]
    return np.pad(signal,(0,length+right_len-signal.size),"edge"),seq

def get_parser():
    parser = argparse.ArgumentParser(
        description="Basecall reads using a taiyaki model",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        "input_hdf5", action=FileExists,
        help="The hdf5 to input")
    parser.add_argument(
        "inference_model", action=FileExists,
        help="The inference model")
    parser.add_argument(
        "can", type=str,
        help="the canonical label of the candidate base")
    parser.add_argument(
        "mod", type=str,
        help="the modification label of the candidate base")
    parser.add_argument(
        '--can_base_idx', default=[0,1,1,2,3], 
        help='The mapping from alphabet labels in the hdf5 files to canonical labels(default 0123 ATGC).'+
        'For example, for a hdf5 whose labels are ACm(5mC)h(5hmC)GT, the can_base_idx should be 01123.')
    parser.add_argument(
        "output", type=str,
        help="The fasta file to output")
    parser.add_argument(
        "--limit", type=Positive(int),default=None,
        help="Then number of output reads")
    parser.add_argument(
        "--length", type=int,default=100,
        help="signal length parameter for AD")
    parser.add_argument(
        "--type", type=str,default="dna",
        help="specify the output type: dna/rna")
    parser.add_argument(
        '--right_len', type=int,default=0,
        help='length for one base')
    parser.add_argument(
        '--scale', type=float,default=3)

    return parser

def main():
    args = get_parser().parse_args()
    # print(args)
    can_model = torch.load(args.can_model)
    mod_model = torch.load(args.mod_model)
    input = args.input_hdf5
    idx = [int(i) for i in list(args.can_base_idx)]
    with mapped_signal_files.MappedSignalReader(input) as msr:
        alphabet_info = msr.get_alphabet_information()
        read_data = list(islice(msr.reads(None), args.limit))

    print(len(read_data))
    fh = open(args.output,"w")
    ml = open(args.output.split(".fa")[0]+".ml","w")
    # read = read_data[0]
    for read in read_data:
        seq = modification_base(read, can_model, mod_model,alphabet_info,canonical = alphabet_info.alphabet,
        idx = idx,length = args.length*2, right_len = args.right_len,
        canonical_base=args.can, mod_base=args.mod,scale = args.scale)
        # print(seq)
        if seq !="":
            if args.type=="rna":
                fh.write("{}{}\n{}\n".format(
                ">", read.read_id[2:-1],
                seq[0][::-1]))
                ml.write("{}\t{}\n".format(
                read.read_id[2:-1],
                [int(ml) for ml in seq[1]]))
            else:
                fh.write("{}{}\n{}\n".format(
                ">", read.read_id[2:-1],
                seq[0]))
                ml.write("{}\t{}\n".format(
                read.read_id[2:-1],
                [int(ml) for ml in seq[1]]))
    fh.close()
    ml.close()

if __name__ == '__main__':
    main()


Complete the function implementation replacing "raise NotImplementedError".