matchms.similarity.PrecursorMzMatch module

class matchms.similarity.PrecursorMzMatch.PrecursorMzMatch(tolerance: float = 0.1, tolerance_type: str = 'Dalton')

Bases: BaseSimilarity

Return True if spectrums match in precursor m/z (within tolerance), and False otherwise. The match within tolerance can be calculated based on an absolute m/z difference (tolerance_type=”Dalton”) or based on a relative difference in ppm (tolerance_type=”ppm”).

Example to calculate scores between 2 pairs of spectrums and iterate over the scores

import numpy as np
from matchms import calculate_scores
from matchms import Spectrum
from matchms.similarity import PrecursorMzMatch

spectrum_1 = Spectrum(mz=np.array([]),
                      intensities=np.array([]),
                      metadata={"id": "1", "precursor_mz": 100})
spectrum_2 = Spectrum(mz=np.array([]),
                      intensities=np.array([]),
                      metadata={"id": "2", "precursor_mz": 110})
spectrum_3 = Spectrum(mz=np.array([]),
                      intensities=np.array([]),
                      metadata={"id": "3", "precursor_mz": 103})
spectrum_4 = Spectrum(mz=np.array([]),
                      intensities=np.array([]),
                      metadata={"id": "4", "precursor_mz": 111})
references = [spectrum_1, spectrum_2]
queries = [spectrum_3, spectrum_4]

similarity_score = PrecursorMzMatch(tolerance=5.0, tolerance_type="Dalton")
scores = calculate_scores(references, queries, similarity_score)

for (reference, query, score) in scores:
    print(f"Precursor m/z match between {reference.get('id')} and {query.get('id')}" +
          f" is {score}")

Should output

Precursor m/z match between 1 and 3 is [1.0]
Precursor m/z match between 2 and 4 is [1.0]

__init__(tolerance: float = 0.1, tolerance_type: str = 'Dalton')

Parameters:

• tolerance – Specify tolerance below which two m/z are counted as match.

• tolerance_type – Chose between fixed tolerance in Dalton (=”Dalton”) or a relative difference in ppm (=”ppm”).

keep_score(score)

In the .matrix method scores will be collected in a sparse way. Overwrite this method here if values other than False or 0 should not be stored in the final collection.

matrix(references: List[Spectrum], queries: List[Spectrum], array_type: str = 'numpy', is_symmetric: bool = False) → 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.

pair(reference: Spectrum, query: Spectrum) → float

Compare precursor m/z between reference and query spectrum.

Parameters:

• reference – Single reference spectrum.

• query – Single query spectrum.

score_datatype

alias of bool

sparse_array(references: List[Spectrum], queries: List[Spectrum], idx_row, idx_col, is_symmetric: bool = False)

Optional: Provide optimized method to calculate an sparse matrix of similarity scores.

Compute similarity scores for pairs of reference and query spectrums as given by the indices idx_row (references) and idx_col (queries). If no method is added here, the following naive implementation (i.e. a for-loop) is used.

Parameters:

• references – List of reference objects

• queries – List of query objects

• idx_row – List/array of row indices

• idx_col – List/array of column indices

• 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.

to_dict() → dict

Return a dictionary representation of a similarity function.