Project: s41467-025-60789-z | Function: matchUpto1 (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 sequence alignment algorithm allowing up to 1 mismatch between target and query sequences. Searches for target sequence in two halves using string matching, validates alignments by calculating total mismatches, and returns position of best match within mismatch tolerance.

**KEY TERMS:** {
  "target": "target sequence from CRISPR guide RNA",
  "seq": "NGS read sequence",
  "mismatch": "sequence alignment score",
  "half_len": "sequence partitioning for alignment",
  "alignedSeq": "aligned region for comparison"
}

**CONTEXT CODE:** import time

from pathlib import Path


import pandas as pd
import numpy as np
import Levenshtein as ed


write_to_tsv = lambda df, output : df.to_csv(output,header=False,index=False,sep='\t')
toCharArray = lambda seq : np.array([seq]).astype('S').view('S1')

trans=str.maketrans("ATGC", "TACG")
revertedSeq = lambda seq : seq.translate(trans)[::-1]

def mismatch(seq1,seq2):
    n = min(len(seq1),len(seq2))
    m = max(len(seq1),len(seq2))
    mismatch = np.sum(toCharArray(seq1[:n])!=toCharArray(seq2[:n]))
    return mismatch + m-n

def matchUpto1(target, seq):
    # MASKED: Performs sequence alignment with up to 1 mismatch tolerance using half-length subsequence matching and position validation implementation
    raise NotImplementedError("This function is masked for testing purposes.")



def countPerLocation2(df_target_regions, s_seq, len_rgen):
    zero = lambda nt : {'targetRegion':nt*len(s_seq), 'n_seq':0}
    df_reads = df_target_regions[['targetRegion','n_seq']].append([zero('A'),zero('C'),zero('G'),zero('T')], ignore_index=True)

    df_sseq = pd.DataFrame(np.array([s_seq]).astype('S%d'%len_rgen).view('int8'))
    df = pd.DataFrame(df_reads.targetRegion.astype('S%d'%len_rgen).values.view('int8').reshape(-1,len_rgen))
    df.index = df_reads.index
    #df2 : idx nc n_seq
    df2 = df.stack().reset_index(level=1).merge(df_reads[['n_seq']],left_index=True,right_index=True)
    df2.columns = ['idx', 'nc', 'n_seq']
    df3 = pd.pivot_table(df2, values='n_seq', index=['idx'], columns=['nc'], aggfunc=np.sum, fill_value=0)
    df_subst = df3.unstack().drop(list(zip(*[df_sseq[0],df_sseq.index])), errors='ignore').groupby(level=1).sum()
    df_count = pd.concat([df3,df_subst],axis=1)
    return df_count

def countPerLocation(df_reads, is_rev_match, s_seq, rgen_cridx_beg, rgen_cridx_end, len_rgen):
    a = rgen_cridx_beg
    b = rgen_cridx_end
    df = df_reads.copy()
    df['targetRegion'] = df_reads.seq.str.slice(a,b)    
    return countPerLocation2(df, s_seq, len_rgen)


def run_maund(args, logger):
    # MASKED: Comprehensive base editing analysis pipeline including FASTQ processing, sequence alignment, mutation analysis, and statistical reporting implementation
    raise NotImplementedError("This function is masked for testing purposes.")




Complete the function implementation replacing "raise NotImplementedError".