Ufdjrw

I have two data files (FASTA) and each file represents one gene and the sequences are identified by species and local. I would like to concatenate these files into one as the example:

psbki.fas:
>E_oleracea_Docas_de_Belm 
AACCT
ycf1b.fas: 
>E_oleracea_Docas_de_B 
GGTTC

output:
>E_oleracea_Docas_de_Belm 
AACCTGGTTC

If you look to the names of the species in both files, they were written with some grammar issues, making then distinct from each other. Also, I have another problem: some species aren't in both files.

For solve those issues, I wrote the following code:

ids, sequences = parse_fasta(open('psbki.fas', 'r').read().split('n'))
ids2, sequences2 = parse_fasta(open('ycf1b.fas', 'r').read().split('n'))

for i, j, z, h in zip(ids, sequences, sequences2, ids2):
 if i != h:
 print(">"+i + "n"+j)
 else:
 print(">"+i + "n"+j+z)

The output for the two first sequences was ok. But for the other sequences, the code print only files from one file but they were in both files.
What is wrong with my code?
I'm a beginner in python

The output was:

>E_edulis_I1
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTGAATTGGTATTATTCTCATTATCAGCATAAATTATCACACGTCTGGCTCTTCTTGAACGAATTTCAATATCTTCTATCGGTTTTTCCTCATTTTCTTCCTCCTGTTCTTCCAGAAGATTGGTCAATTTATATGACCATCGAGAAACCTTTTTACTGATTTCTTCTATTCCAATAGATTCATTTCTAGTTGTTTTATCATTTGGATCAATTGTCATTATATCGAATACAAATTTCAAAGATTTTGCTTGACTTTCTGAATCCATTTTTCTTTGTTCTGCCAATAAAGAACAGTTTTTCAAACAAAAATTGGGTGTGAATTCAAAAGAAAATGAAGTTAAGGAATTACCGATATAATTCAAAAATGATTTACCACCACCAAGTGAATTCTTTTGATGTTCAAATTCTCTGAAATTATTAGGAAGTAGCTCATGGATCTTATTTATCCAAAGACTTTTTATGGAATCCTCCATATAAGGGAAAAAATCATTTATGATTGTACGTAAATCAAAATCTTTTATTGCTCCACGGCATGGTCCGCTCAATAAAGGATCATATGTTTTGGTCAAGCATTTTTGTTTATTCTCATGATTGCAAAATCTAGTCTTTTTTTCGAGCATATCTAGAGCAAGAAATCCCTTTTCTTTTTTTTCTTTTTCTAGAGCTTTTATTCGACTTATTAATTCATTGCTCAAGTTGTATTTTTTTTGTTCATTGGTAAAAACCCAAAAATTATACAGGTCTCCATGGGATAATTTTTT-GTCGTGTACAAAAACATTTTTCGTTCTATCATTTCC
>E_edulis_I2
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTGAATTGGTATTATTCTCATTATCAGCATAAATTATCACACGTCTGGCTCTTCTTGAACGAATTTCAATATCTTCTATCGGTTTTTCCTCAATTTCTTCCTCCTGTTCTTCCAGAAGATTGGTCAATTTATATGACCATCGAGAAACCTTTTTACTGATTTCTTCTATTCCAATAGATTCATTTCTAGTTGTTTTATCATTTGGATCAATTGTCATTATATCGAATACAAATTTCAAAGATTTTGCTTGACTTTCTGAATCCATTTTTCTTTGTTCTGCCAATAAAGAACAGTTTTTCAAACAAAAATTGGGTGTGAATTCAAAAGAAAATGAAGTTAAGGAATTACCGATATAATTCAAAAATGATTTACCACCACCAAGTGAATTCTTTTGATGTTCAAATTCTCTGAAATTATTAGGAAGTAGCTCATGGATCTTATTTATCCAAAGACTTTTTATGGAATCCTCCATATAAGGGAAAAAATCATTTATGATTGTACGTAAATCAAAATCTTTTATTGCTCCACGGCATGGTCCGCTCAATAAAGGATCATATGTTTTGGTCAAGCATTTTTGTTTATTCTCATGATTGCAAAATCTAGTCTTTTTTTCGAGCATATCTAGAGCAAGAAATCCCTTTTCTTTTTTTTCTTTTTCTAGAGCTTTTATTCGACTTATTAATTCATTGCTCAAGTTGTATTTTTTTTGTTCATTGGTAAAAACCCAAAAATTATACAGGTCTCCATGGGATAATTTTTTTGTCGTGTACAAAAACATTTTTCGTTCTATCATTTCC
>E_edulis_F7
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCTTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAA-G----ATCTTG
>E_edulis_R10
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTG
>E_edulis_R11
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGKGTATGTGGTAAAGTAAAAAATAASTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTG
>E_edulis_R12
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGWGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAA----ATCTTG
>E_edulis_IFES
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGARCAAAGACTTTATTAGGTTGCTTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAA-G----ATCTTG
>E_oleracea_Ilha_do_combu_1
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Ilha_do_combu_2
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Ilha_do_combu_3
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Ilha_do_combu_5
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Ilha_do_combu_10
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Mangal_2
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Mangal_3
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Docas_de_Belm
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Utinga
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Canto_de_Roa_1
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Canto_de_Roa_2
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_Canto_de_Roa_3
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG
>E_oleracea_IFES
AAATCGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAGATCTTATCTTG

In other words, I want to concatenate the genes that were in both files and not print and not concatenate the species that appears only in one file. I don't know how to fix the problem in which the species are written with some minimal mistakes.

EDIT 1:
I've changed the code, using the Levenshtein ratio to solve the writting error in some species names but the output is the same.

The new code was:

import Levenshtein as lev
Str1 = str(ids)
Str2 = str(ids2)
Ratio = lev.ratio(Str1.lower(),Str2.lower())

for i, j, z, h in zip(ids, sequences, sequences2, ids2):
 if lev.ratio(i,h) > 0.70 and i in h:
 print(">"+i + "n"+j+z)
 else:
 print(">"+i + "n"+j)

EDIT 2

 Input File1: gene 1
>E_edulis_I1
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTG
>E_edulis_I2
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTG
>E_edulis_F7
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCTTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTT-GGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAA-G----ATCTTG

Input File 2: gene 2
>E_edulis_I1
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTG
>E_ed_I2
AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTG

My desired output:
 >E_edulis_I1
 AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTGAAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTG
 >E_edulis_I2
 AAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTGAAATAGAAATTCTTGTATATTGAATAACCGCGGCGATGAATTTTGATCAACTTATTTCCTCGTTCTGACCTTACAGTGAGCAAAGACTTTATTAGGTTGCCTACAATACCTAATTATTCATATGACAAGAAATTTTTGATAACGAAGGAATCAAAATCTTATTCCAAAGAAATTCGTGAAAATGACTTTCTTTTCAAAAAACACTTCATTTTTTTTGGGGGTGTCATGTCAAAACAAAATAGTGTATGTGGTAAAGTAAAAAATAAGTAACCTATTCCCTTTTTCAAAAAAAAAAG----ATCTTG

P.S. In the second file, I have the same specie E_edulis_I2, with an incomplete name -> E_ed_I2. Iwant that the script recognize that and concatenate the sequence with the first one (file 1 = E_edulis_I2). The other problem is the E_edulis_F7 specie only appears in file 1 so I don't want thar specie in my output.

This was a bit more complicated than I expected. The problem is that, e.g., "E_edulis_I1" is a much closer match for "E_edulis_I2" than "E_ed_I" is. I think the best solution is to compare every pair of names between the two files and determine if they are similar enough to be called a match. Then, once you have this set of matching names and their similarity levels, you can go through it in order of most similar to least similar and add results to the combined FASTA file as you go. So, because there's an exact match for "E_edulis_I1" between the two files, that will be put into the combined FASTA first. Then when we get to the matching names "E_edulis_I1" and "E_edulis_I2", we'll see that we already used "E_edulis_I1", so this pair can't be a match.

This does still seem a bit brittle to me. You'll just have to be careful about what your similarity function and similarity threshold are. One thing you might add is to print out the names whenever a match is done without a similarity of 1. This way, you can quickly scan over these (hopefully there aren't too many) and determine if any names were matched that shouldn't have been.

Anyway, here's the code. It works at least on the examples you gave.

from pathlib import Path
from typing import Dict
import Levenshtein as lev

def fasta_to_dict(path: str) -> Dict[str, str]:
 """Read in a fasta file and return a dict mapping names to sequences."""
 fasta = Path(path).read_text().split("n")
 return fasta[i]: fasta[i + 1] for i in range(0, len(fasta) - 1, 2)

# put in the paths to your fasta files here
fasta1 = fasta_to_dict("f1.fasta")
fasta2 = fasta_to_dict("f2.fasta")

def get_similarity(name1: str, name2: str) -> float:
 """Return a number describing how similar the names are."""
 # this could be any string comparison function you want
 return lev.ratio(name1, name2)

similarity_threshold = 0.7 # must be at least this similar to be called a match

def get_correct_name(name1: str, name2: str) -> str:
 """Determine which of 2 close-match names is the correct one."""
 # this can also be whatever function makes sense in your application
 # I'm just using the longer name as the "correct" one
 return name1 if len(name1) > len(name2) else name2

possible_matches = []
for name1 in fasta1:
 for name2 in fasta2:
 similarity = get_similarity(name1, name2)
 if similarity > similarity_threshold:
 possible_matches.append((name1, name2, similarity))

# sort by most similar matches first
possible_matches = sorted(possible_matches, key=lambda match: -match[-1])

combined_fasta = 
used_names1 = set()
used_names2 = set()
for name1, name2, _ in possible_matches:
 if name1 in used_names1 or name2 in used_names2:
 continue
 correct_name = get_correct_name(name1, name2)
 combined_fasta[correct_name] = fasta1[name1] + fasta2[name2]
 used_names1.add(name1)
 used_names2.add(name2)

# put the path to your output fasta file here
with open("combined.fasta", "w") as f:
 for name, seq in combined_fasta.items():
 f.write(name + "n")
 f.write(seq + "n")