matchms.similarity.IntersectMz module

class matchms.similarity.IntersectMz.IntersectMz(scaling: float = 1.0)[source]

Bases: BaseSimilarity

Example score for illustrating how to build custom spectra similarity score.

IntersectMz will count all exact matches of peaks and divide it by all unique peaks found in both spectrums.

Example of how matchms similarity functions can be used:

import numpy as np
from matchms import Spectrum
from matchms.similarity import IntersectMz

spectrum_1 = Spectrum(mz=np.array([100, 150, 200.]),
                      intensities=np.array([0.7, 0.2, 0.1]))
spectrum_2 = Spectrum(mz=np.array([100, 140, 190.]),
                      intensities=np.array([0.4, 0.2, 0.1]))

# Construct a similarity function
similarity_measure = IntersectMz(scaling=1.0)

score = similarity_measure.pair(spectrum_1, spectrum_2)

print(f"IntersectMz score is {score:.2f}")

Should output

IntersectMz score is 0.20
__init__(scaling: float = 1.0)[source]

Constructor. Here, function parameters are defined.

Parameters:

scaling – Scale scores to maximum possible score being ‘scaling’.

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

Optional: Provide optimized method to calculate an np.array of similarity scores for given reference and query spectrums. If no method is added here, the following naive implementation (i.e. a double for-loop) is used.

Parameters:

  • references – List of reference objects

  • queries – List of query objects

  • 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[source]

This will calculate the similarity score between two spectra.

score_datatype

alias of float64

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.