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import numpy as np
import torch
import matchms
from typing import Optional
from rdkit.Chem import AllChem as Chem
from flare.definitions import CHEM_ELEMS_SMALL
from massspecgym.data.transforms import MolTransform, SpecTransform, default_matchms_transforms
from massspecgym.data.transforms import SpecBinner
import dgllife.utils as chemutils
import re
class SpecBinnerLog(SpecTransform):
def __init__(
self,
max_mz: float = 1005,
bin_width: float = 1,
) -> None:
self.max_mz = max_mz
self.bin_width = bin_width
if not (max_mz / bin_width).is_integer():
raise ValueError("`max_mz` must be divisible by `bin_width`.")
def matchms_transforms(self, spec: matchms.Spectrum) -> matchms.Spectrum:
return default_matchms_transforms(spec, mz_to=self.max_mz, n_max_peaks=None)
def matchms_to_torch(self, spec: matchms.Spectrum) -> torch.Tensor:
"""
Bin the spectrum into a fixed number of bins.
"""
binned_spec = self._bin_mass_spectrum(
mzs=spec.peaks.mz,
intensities=spec.peaks.intensities,
max_mz=self.max_mz,
bin_width=self.bin_width,
)
return torch.from_numpy(binned_spec).to(dtype=torch.float32)
def _bin_mass_spectrum(
self, mzs, intensities, max_mz, bin_width
):
# Calculate the number of bins
num_bins = int(np.ceil(max_mz / bin_width))
# Calculate the bin indices for each mass
bin_indices = np.floor(mzs -1 / bin_width).astype(int)
# Filter out mzs that exceed max_mz
valid_indices = bin_indices[mzs <= max_mz]
valid_intensities = intensities[mzs <= max_mz]
# Clip bin indices to ensure they are within the valid range
valid_indices = np.clip(valid_indices, 0, num_bins - 1)
# Initialize an array to store the binned intensities
binned_intensities = np.zeros(num_bins)
# Use np.add.at to sum intensities in the appropriate bins
np.add.at(binned_intensities, valid_indices, valid_intensities)
binned_intensities = binned_intensities/np.max(binned_intensities) * 999
binned_intensities = np.log10(binned_intensities + 1) / 3
return binned_intensities
class SpecMzIntTokenizer(SpecTransform):
def __init__(self, max_mz, mz_mean_std=None, mask_precursor=None):
self.max_mz = max_mz
self.mz_mean_std = mz_mean_std
def matchms_transforms(self, spec: matchms.Spectrum):
return default_matchms_transforms(spec, mz_to=self.max_mz, n_max_peaks=None)
def matchms_to_torch(self, spec: matchms.Spectrum):
mzs = spec.peaks.mz
intensities = spec.peaks.intensities
spec = np.zeros((len(mzs), 2))
if self.mz_mean_std:
mz = (mzs-self.mz_mean_std['mz_mean'])/self.mz_mean_std['mz_std']
else:
mz = mzs/self.max_mz
spec[:, 0] = mz
spec[:, 1] = intensities
return torch.from_numpy(spec.astype(np.float32))
class SpecFormulaMzFeaturizer(SpecTransform):
''' Uses raw mz and intensities '''
def __init__(
self,
add_intensities: bool,
max_mz: float = 1005,
element_list: list = CHEM_ELEMS_SMALL,
formula_normalize_vector: Optional[np.array] = None,
mz_mean_std: dict[str, float] = None,
mask_precursor: bool = False,
) -> None:
self.max_mz = max_mz
self.elem_to_pos = {e: i for i, e in enumerate(element_list)}
if formula_normalize_vector is None:
formula_normalize_vector = np.ones(len(element_list))
self.formula_normalize_vector = formula_normalize_vector
self.CHEM_FORMULA_SIZE = "([A-Z][a-z]*)([0-9]*)"
self.mz_mean_std = mz_mean_std
self.add_intensities = add_intensities
self.mask_precursor = mask_precursor
def matchms_transforms(self, spec: matchms.Spectrum):
return spec
def matchms_to_torch(self, spec: matchms.Spectrum) -> torch.Tensor:
mzs = spec.peaks.mz
intensities = spec.peaks.intensities
formulas = spec.metadata['formulas'] # mz to formula dict
peak_idx = np.where(mzs <= self.max_mz)[0]
mzs = mzs[peak_idx]
intensities = intensities[peak_idx]
formulas = [formulas.get(mz, "NA") for mz in mzs[peak_idx]]
if self.mask_precursor:
try:
precursor_i = formulas.index(spec.metadata['precursor_formula'])
formulas[precursor_i] = 'NA'
except:
pass
formulas = self._featurize_formula(formulas)
formulas = formulas/self.formula_normalize_vector
if self.mz_mean_std:
mz = (mzs-self.mz_mean_std['mz_mean'])/self.mz_mean_std['mz_std']
else:
mz = mzs/self.max_mz
if self.add_intensities:
spec = np.concatenate((mz.reshape(-1,1), intensities.reshape(-1,1), formulas), axis=1)
else:
spec = np.concatenate((mz.reshape(-1,1), formulas), axis=1)
return torch.from_numpy(spec)
def _featurize_formula(self, formulas):
formula_vector = np.zeros((len(formulas), len(self.elem_to_pos)))
for i, f in enumerate(formulas):
if f == "NA":
# formula_vector[i] = np.zeros((1, len(self.elem_to_pos)))
formula_vector[i] = np.ones((1, len(self.elem_to_pos))) * -1
else:
for (e, ct) in re.findall(self.CHEM_FORMULA_SIZE, f):
ct = 1 if ct == "" else int(ct)
try:
formula_vector[i][self.elem_to_pos[e]]+=ct
except:
# print(f"Couldn't vectorize {f}, element {e} not supported")
continue
return formula_vector
class SpecFormulaFeaturizer(SpecTransform):
''' Uses processed mz and intensities, excludes mz values, keep peaks with formulas only'''
def __init__(
self,
add_intensities: bool,
max_mz: float = 1005,
element_list: list = CHEM_ELEMS_SMALL,
formula_normalize_vector: Optional[np.array] = None
) -> None:
self.max_mz = max_mz
self.elem_to_pos = {e: i for i, e in enumerate(element_list)}
self.add_intensities = add_intensities
if formula_normalize_vector is None:
formula_normalize_vector = np.ones(len(element_list))
self.formula_normalize_vector = formula_normalize_vector
self.CHEM_FORMULA_SIZE = "([A-Z][a-z]*)([0-9]*)"
def matchms_transforms(self, spec: matchms.Spectrum):
return spec
def matchms_to_torch(self, spec: matchms.Spectrum) -> torch.Tensor:
mzs = spec.peaks.mz
intensities = spec.peaks.intensities
formulas = spec.metadata['formulas'] # list of formulas
peak_idx = np.where(mzs <= self.max_mz)[0]
intensities = intensities[peak_idx]
formulas = formulas[peak_idx]
spec = self._featurize_formula(formulas)
spec = spec/self.formula_normalize_vector
if self.add_intensities:
spec = np.concatenate((spec, intensities.reshape(-1,1)), axis=1)
spec = spec.astype(np.float32)
return torch.from_numpy(spec)
def _featurize_formula(self, formulas):
formula_vector = np.zeros((len(formulas), len(self.elem_to_pos)))
for i, f in enumerate(formulas):
try:
for (e, ct) in re.findall(self.CHEM_FORMULA_SIZE, f):
ct = 1 if ct == "" else int(ct)
try:
formula_vector[i][self.elem_to_pos[e]]+=ct
except:
# print(f"Couldn't vectorize {f}, element {e} not supported")
continue
except:
print(f"Couldn't vectorize {f}, formula not supported")
continue
return formula_vector
class MolToGraph(MolTransform):
def __init__ (self, atom_feature: str = "full", bond_feature: str = "full", element_list: list = CHEM_ELEMS_SMALL):
self.atom_feature = atom_feature
self.bond_feature = bond_feature
self.node_featurizer = self._get_atom_featurizer(element_list=element_list)
self.edge_featurizer = self._get_bond_featurizer()
def from_smiles(self, mol:str):
mol = Chem.MolFromSmiles(mol)
g = chemutils.mol_to_bigraph(mol, node_featurizer=self.node_featurizer, edge_featurizer=self.edge_featurizer, add_self_loop = True,
num_virtual_nodes = 0, canonical_atom_order=False)
# atom_ids = [atom.GetIdx() for atom in mol.GetAtoms()] # added for visualization
# g.ndata['atom_id'] = torch.tensor(atom_ids, dtype=torch.long)
return g
def _get_atom_featurizer(self, element_list) -> dict:
feature_mode = self.atom_feature
atom_mass_fun = chemutils.ConcatFeaturizer(
[chemutils.atom_mass]
)
def atom_bond_type_one_hot(atom):
bs = atom.GetBonds()
bt = np.array([chemutils.bond_type_one_hot(b) for b in bs])
return [any(bt[:, i]) for i in range(bt.shape[1])]
def atom_type_one_hot(atom):
return chemutils.atom_type_one_hot(
atom, allowable_set = element_list, encode_unknown = True
)
if feature_mode == 'light':
atom_featurizer_funs = chemutils.ConcatFeaturizer([
chemutils.atom_mass,
atom_type_one_hot
])
elif feature_mode == 'full':
atom_featurizer_funs = chemutils.ConcatFeaturizer([
chemutils.atom_mass,
atom_type_one_hot,
atom_bond_type_one_hot,
chemutils.atom_degree_one_hot,
chemutils.atom_total_degree_one_hot,
chemutils.atom_explicit_valence_one_hot,
chemutils.atom_implicit_valence_one_hot,
chemutils.atom_hybridization_one_hot,
chemutils.atom_total_num_H_one_hot,
chemutils.atom_formal_charge_one_hot,
chemutils.atom_num_radical_electrons_one_hot,
chemutils.atom_is_aromatic_one_hot,
chemutils.atom_is_in_ring_one_hot,
chemutils.atom_chiral_tag_one_hot
])
elif feature_mode == 'medium':
atom_featurizer_funs = chemutils.ConcatFeaturizer([
chemutils.atom_mass,
atom_type_one_hot,
atom_bond_type_one_hot,
chemutils.atom_total_degree_one_hot,
chemutils.atom_total_num_H_one_hot,
chemutils.atom_is_aromatic_one_hot,
chemutils.atom_is_in_ring_one_hot,
])
return chemutils.BaseAtomFeaturizer(
{"h": atom_featurizer_funs,
"m": atom_mass_fun}
)
def _get_bond_featurizer(self, self_loop=True) -> dict:
feature_mode = self.bond_feature
if feature_mode == 'light':
return chemutils.BaseBondFeaturizer(
featurizer_funcs = {'e': chemutils.ConcatFeaturizer([
chemutils.bond_type_one_hot
])}, self_loop = self_loop
)
elif feature_mode == 'full':
return chemutils.CanonicalBondFeaturizer(
bond_data_field='e', self_loop = self_loop
)
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