Spaces:
Running
Running
File size: 2,389 Bytes
7b7a7b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
""" magma_utils.py
Additional utility file to assist with fingerprinting.
"""
import os
from ast import literal_eval
import numpy as np
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem, DataStructs
import re
def extract_adduct_ion(adduct, default='+H'):
pattern = re.compile(r"\[M([+-][^\]]+)\]")
match = pattern.search(adduct)
if match:
return match.group(1)
return default
def get_magma_fingerprint(smile):
""" get_magma_fingerprint. """
mol = Chem.MolFromSmiles(smile, sanitize=False)
Chem.SanitizeMol(
mol,
sanitizeOps=Chem.SanitizeFlags.SANITIZE_ALL
^ Chem.SanitizeFlags.SANITIZE_KEKULIZE,
)
curr_fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=2048)
fingerprint = np.zeros((0,), dtype=np.uint8)
DataStructs.ConvertToNumpyArray(curr_fp, fingerprint)
return fingerprint
def get_magma_fingerprint_bits(smile):
""" get magma fingerprint bits """
fingerprint = get_magma_fingerprint(smile)
hot_indices = list(np.where(np.array(list(fingerprint)) == 1)[0])
return hot_indices
def read_magma_file(magma_frag_file):
"""Read in magma file"""
if (
magma_frag_file is not None
and os.path.exists(magma_frag_file)
and os.path.getsize(magma_frag_file) > 0
):
# correct for inconsistency by me in file parsing (sad)
sep = "\t"
spectra_df = pd.read_csv(magma_frag_file, index_col=0, sep=sep)
if (
"smiles" not in spectra_df.columns
or "chemical_formula" not in spectra_df.columns
):
pass
else:
spectra_df = _convert_str_to_list(spectra_df, "smiles")
spectra_df = _convert_str_to_list(spectra_df, "chemical_formula")
if "mass_to_charge" not in spectra_df.columns:
spectra_df["mass_to_charge"] = spectra_df["mz"]
return spectra_df
spectra_df = pd.DataFrame(
columns=[
"mass_to_charge",
"intensity",
"chemical_formula",
"smiles",
"molecule_peak",
]
)
return spectra_df
def _convert_str_to_list(df, column):
"""_convert_str_to_list"""
df.loc[:, column] = df.loc[:, column].apply(
lambda x: literal_eval(x) if x != "NAN" and not pd.isna(x) else []
)
return df
|