# Source code for matchms.calculate_scores

```
from .Scores import Scores
from .similarity.BaseSimilarity import BaseSimilarity
from .typing import QueriesType, ReferencesType
[docs]def calculate_scores(references: ReferencesType, queries: QueriesType,
similarity_function: BaseSimilarity,
is_symmetric: bool = False) -> Scores:
"""Calculate the similarity between all reference objects versus all query objects.
Example to calculate scores between 2 spectrums 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'})
spectrums = [spectrum_1, spectrum_2]
scores = calculate_scores(spectrums, spectrums, CosineGreedy())
for (reference, query, score) in scores:
print(f"Cosine score between {reference.get('id')} and {query.get('id')}" +
f" is {score['score']:.2f} with {score['matches']} 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
----------
references
List of reference objects
queries
List of query objects
similarity_function
Function which accepts a reference + query object and returns a score or tuple of scores
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. Default is False.
Returns
-------
~matchms.Scores.Scores
"""
return Scores(references=references, queries=queries,
similarity_function=similarity_function,
is_symmetric=is_symmetric).calculate()
```