text: from __future__ import annotations
  import numpy
  from deprecated.sphinx import deprecated
  from matchms.similarity.BaseSimilarity import BaseSimilarity
  from matchms.typing import QueriesType
  from matchms.typing import ReferencesType
  
  
  class Scores:
      """Contains reference and query spectrums and the scores between them.
  
      The scores can be retrieved as a matrix with the :py:attr:`Scores.scores` attribute.
      The reference spectrum, query spectrum, score pairs can also be iterated over in query then reference order.
  
      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 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'})
          spectrum_3 = Spectrum(mz=np.array([110, 140, 195.]),
                                intensities=np.array([0.6, 0.2, 0.1]),
                                metadata={'id': 'spectrum3'})
          spectrum_4 = Spectrum(mz=np.array([100, 150, 200.]),
                                intensities=np.array([0.6, 0.1, 0.6]),
                                metadata={'id': 'spectrum4'})
          references = [spectrum_1, spectrum_2]
          queries = [spectrum_3, spectrum_4]
  
          similarity_measure = CosineGreedy()
          scores = calculate_scores(references, queries, similarity_measure)
  
          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 spectrum3 is 0.00 with 0 matched peaks
          Cosine score between spectrum1 and spectrum4 is 0.80 with 3 matched peaks
          Cosine score between spectrum2 and spectrum3 is 0.14 with 1 matched peaks
          Cosine score between spectrum2 and spectrum4 is 0.61 with 1 matched peaks
      """
      def __init__(self, references: ReferencesType, queries: QueriesType,
                   similarity_function: BaseSimilarity, is_symmetric: bool = False):
          """
  
          Parameters
          ----------
          references
              List of reference objects
          queries
              List of query objects
          similarity_function
              Expected input is an object based on :class:`~matchms.similarity.BaseSimilarity`. It is
              expected to provide a *.pair()* and *.matrix()* method for computing similarity scores between
              references and queries.
          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.
          """
          Scores._validate_input_arguments(references, queries, similarity_function)
  
          self.n_rows = len(references)
          self.n_cols = len(queries)
          self.references = numpy.asarray(references)
          self.queries = numpy.asarray(queries)
          self.similarity_function = similarity_function
          self.is_symmetric = is_symmetric
          self._scores = numpy.empty([self.n_rows, self.n_cols], dtype="object")
          self._index = 0
  
      def __iter__(self):
          return self
  
      def __next__(self):
          if self._index < self.scores.size:
              # pylint: disable=unbalanced-tuple-unpacking
              r, c = numpy.unravel_index(self._index, self._scores.shape)
              self._index += 1
              result = self._scores[r, c]
              if not isinstance(result, tuple):
                  result = (result,)
              return (self.references[r], self.queries[c]) + result
          self._index = 0
          raise StopIteration
  
      def __str__(self):
          return self._scores.__str__()
  
      @staticmethod
      def _validate_input_arguments(references, queries, similarity_function):
          assert isinstance(references, (list, tuple, numpy.ndarray)),\
              "Expected input argument 'references' to be list or tuple or numpy.ndarray."
  
          assert isinstance(queries, (list, tuple, numpy.ndarray)),\
              "Expected input argument 'queries' to be list or tuple or numpy.ndarray."
  
          assert isinstance(similarity_function, BaseSimilarity),\
              "Expected input argument 'similarity_function' to have BaseSimilarity as super-class."
  
      @deprecated(version='0.6.0', reason="Calculate scores via calculate_scores() function.")
      def calculate(self) -> Scores:
          """
          Calculate the similarity between all reference objects v all query objects using
          the most suitable available implementation of the given similarity_function.
          Advised method to calculate similarity scores is :meth:`~matchms.calculate_scores`.
          """
          if self.n_rows == self.n_cols == 1:
              self._scores[0, 0] = self.similarity_function.pair(self.references[0],
                                                                 self.queries[0])
          else:
              self._scores = self.similarity_function.matrix(self.references,
                                                             self.queries,
                                                             is_symmetric=self.is_symmetric)
          return self
  
      def scores_by_reference(self, reference: ReferencesType,
                              sort: bool = False) -> numpy.ndarray:
          """Return all scores for the given reference spectrum.
  
          Parameters
          ----------
          reference
              Single reference Spectrum.
          sort
              Set to True to obtain the scores in a sorted way (relying on the
              :meth:`~.BaseSimilarity.sort` function from the given similarity_function).
          """
          assert reference in self.references, "Given input not found in references."
          selected_idx = int(numpy.where(self.references == reference)[0])
          if sort:
              query_idx_sorted = self.similarity_function.sort(self._scores[selected_idx, :])
              return list(zip(self.queries[query_idx_sorted],
                              self._scores[selected_idx, query_idx_sorted].copy()))
          return list(zip(self.queries, self._scores[selected_idx, :].copy()))
  
      def scores_by_query(self, query: QueriesType, sort: bool = False) -> numpy.ndarray:
          """Return all scores for the given query spectrum.
  
          For example
  
          .. testcode::
  
              import numpy as np
              from matchms import calculate_scores, 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'})
              spectrum_3 = Spectrum(mz=np.array([110, 140, 195.]),
                                    intensities=np.array([0.6, 0.2, 0.1]),
                                    metadata={'id': 'spectrum3'})
              spectrum_4 = Spectrum(mz=np.array([100, 150, 200.]),
                                    intensities=np.array([0.6, 0.1, 0.6]),
                                    metadata={'id': 'spectrum4'})
              references = [spectrum_1, spectrum_2, spectrum_3]
              queries = [spectrum_2, spectrum_3, spectrum_4]
  
              scores = calculate_scores(references, queries, CosineGreedy())
              selected_scores = scores.scores_by_query(spectrum_4, sort=True)
              print([x[1]["score"].round(3) for x in selected_scores])
  
          Should output
  
          .. testoutput::
  
              [0.796, 0.613, 0.0]
  
          Parameters
          ----------
          query
              Single query Spectrum.
          sort
              Set to True to obtain the scores in a sorted way (relying on the
              :meth:`~.BaseSimilarity.sort` function from the given similarity_function).
  
          """
          assert query in self.queries, "Given input not found in queries."
          selected_idx = int(numpy.where(self.queries == query)[0])
          if sort:
              references_idx_sorted = self.similarity_function.sort(self._scores[:, selected_idx])
              return list(zip(self.references[references_idx_sorted],
                              self._scores[references_idx_sorted, selected_idx].copy()))
          return list(zip(self.references, self._scores[:, selected_idx].copy()))
  
      @property
      def scores(self) -> numpy.ndarray:
          """Scores as numpy array
  
          For example
  
          .. testcode::
  
              import numpy as np
              from matchms import calculate_scores, Scores, 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]))
              spectrums = [spectrum_1, spectrum_2]
  
              scores = calculate_scores(spectrums, spectrums, IntersectMz()).scores
  
              print(scores[0].dtype)
              print(scores.shape)
              print(scores)
  
          Should output
  
          .. testoutput::
  
               float64
               (2, 2)
               [[1.  0.2]
                [0.2 1. ]]
          """
          return self._scores.copy()
  