from typing import List
import numpy as np
from sparsestack import StackedSparseArray
from matchms.similarity.spectrum_similarity_functions import (
number_matching, number_matching_symmetric)
from matchms.typing import SpectrumType
from .BaseSimilarity import BaseSimilarity
[docs]class ParentMassMatch(BaseSimilarity):
"""Return True if spectrums match in parent mass (within tolerance), and False otherwise.
Example to calculate scores between 2 spectrums and iterate over the scores
.. testcode::
import numpy as np
from matchms import calculate_scores
from matchms import Spectrum
from matchms.similarity import ParentMassMatch
spectrum_1 = Spectrum(mz=np.array([]),
intensities=np.array([]),
metadata={"id": "1", "parent_mass": 100})
spectrum_2 = Spectrum(mz=np.array([]),
intensities=np.array([]),
metadata={"id": "2", "parent_mass": 110})
spectrum_3 = Spectrum(mz=np.array([]),
intensities=np.array([]),
metadata={"id": "3", "parent_mass": 103})
spectrum_4 = Spectrum(mz=np.array([]),
intensities=np.array([]),
metadata={"id": "4", "parent_mass": 111})
references = [spectrum_1, spectrum_2]
queries = [spectrum_3, spectrum_4]
similarity_score = ParentMassMatch(tolerance=5.0)
scores = calculate_scores(references, queries, similarity_score)
for (reference, query, score) in scores:
print(f"Parentmass match between {reference.get('id')} and {query.get('id')}" +
f" is {score}")
Should output
.. testoutput::
Parentmass match between 1 and 3 is [1.0]
Parentmass match between 2 and 4 is [1.0]
"""
# Set key characteristics as class attributes
is_commutative = True
# Set output data type, e.g. "float" or [("score", "float"), ("matches", "int")]
score_datatype = bool
[docs] def __init__(self, tolerance: float = 0.1):
"""
Parameters
----------
tolerance
Specify tolerance below which two masses are counted as match.
"""
self.tolerance = tolerance
[docs] def pair(self, reference: SpectrumType, query: SpectrumType) -> float:
"""Compare parent masses between reference and query spectrum.
Parameters
----------
reference
Single reference spectrum.
query
Single query spectrum.
"""
parentmass_ref = reference.get("parent_mass")
parentmass_query = query.get("parent_mass")
assert parentmass_ref is not None and parentmass_query is not None, "Missing parent mass."
score = abs(parentmass_ref - parentmass_query) <= self.tolerance
return np.asarray(score, dtype=self.score_datatype)
[docs] def matrix(self, references: List[SpectrumType], queries: List[SpectrumType],
array_type: str = "numpy",
is_symmetric: bool = False) -> np.ndarray:
"""Compare parent masses between all references and queries.
Parameters
----------
references
List/array of reference spectrums.
queries
List/array of Single query spectrums.
array_type
Specify the output array type. Can be "numpy" or "sparse".
Default is "numpy" and will return a numpy array. "sparse" will return a COO-sparse array.
is_symmetric
Set to True when *references* and *queries* are identical (as for instance for an all-vs-all
comparison). By using the fact that score[i,j] = score[j,i] the calculation will be about
2x faster.
"""
def collect_parentmasses(spectrums):
"""Collect parentmasses."""
parentmasses = []
for spectrum in spectrums:
parentmass = spectrum.get("parent_mass")
assert parentmass is not None, "Missing parent mass."
parentmasses.append(parentmass)
return np.asarray(parentmasses)
parentmasses_ref = collect_parentmasses(references)
parentmasses_query = collect_parentmasses(queries)
if is_symmetric: # assuming ref and query are identical
rows, cols, scores = number_matching_symmetric(parentmasses_ref,
self.tolerance)
else:
rows, cols, scores = number_matching(parentmasses_ref, parentmasses_query,
self.tolerance)
if array_type == "numpy":
scores_array = np.zeros((len(parentmasses_ref), len(parentmasses_query)))
scores_array[rows, cols] = scores.astype(self.score_datatype)
return scores_array
if array_type == "sparse":
scores_array = StackedSparseArray(len(parentmasses_ref), len(parentmasses_query))
scores_array.add_sparse_data(rows, cols, scores.astype(self.score_datatype), "")
return scores_array
return ValueError("`array_type` can only be 'numpy' or 'sparse'.")
