from collections.abc import Sequence
from .Scores import Scores
from .similarity.BaseSimilarity import BaseSimilarity
from .typing import SpectrumType
[docs]
def calculate_scores(spectra_1: Sequence[SpectrumType], spectra_2: Sequence[SpectrumType],
similarity_function: BaseSimilarity) -> Scores:
"""Calculate the similarity between all reference objects versus all query objects.
Example to calculate scores between 2 spectra and iterate over the scores
.. testcode::
import numpy as np
from matchms import calculate_scores, Spectrum
from matchms.similarity import CosineGreedy
spectrum_1 = Spectrum(mz=np.array([100, 150, 200.]),
intensities=np.array([0.7, 0.2, 0.1]),
metadata={'id': 'spectrum1'})
spectrum_2 = Spectrum(mz=np.array([100, 140, 190.]),
intensities=np.array([0.4, 0.2, 0.1]),
metadata={'id': 'spectrum2'})
spectra = [spectrum_1, spectrum_2]
scores = calculate_scores(spectra, spectra, CosineGreedy())
for (reference, query, score) in scores:
print(f"Cosine score between {spectrum_1.get('id')} and {spectrum_2.get('id')}" +
f" is {score[0]:.2f} with {score[1]} matched peaks")
Should output
.. testoutput::
Cosine score between spectrum1 and spectrum1 is 1.00 with 3 matched peaks
Cosine score between spectrum1 and spectrum2 is 0.83 with 1 matched peaks
Cosine score between spectrum2 and spectrum1 is 0.83 with 1 matched peaks
Cosine score between spectrum2 and spectrum2 is 1.00 with 3 matched peaks
Parameters
----------
spectra_1
List of reference objects
spectra_2
List of query objects
similarity_function
Function which accepts a reference + query object and returns a score or tuple of scores
Returns
-------
~matchms.Scores.Scores
"""
return similarity_function.matrix(spectra_1, spectra_2)