Scoring functions
*****************

Many scores are simply functions of an experimental map and a simulated one, such as CCC (cross correlation coefficient) and MI (mutual information).

.. doctest::
   :options: -ELLIPSIS, +NORMALIZE_WHITESPACE
  
   >>> from TEMPy.protein.structure_parser import PDBParser
   >>> from TEMPy.maps.map_parser import MapParser
   >>> from TEMPy.protein.scoring_functions import ScoringFunctions
   >>> from TEMPy.protein.structure_blurrer import StructureBlurrer
   >>>
   >>> model = PDBParser.read_PDB_file("1CS4", "../tests/test_data/1CS4.pdb")
   >>> exp_map = MapParser.readMRC("../tests/test_data/1CS4.mrc")
   >>> map_resolution = 8.0
   >>>
   >>> blurrer = StructureBlurrer(with_vc=True)  # use fast real-space blurring
   >>> sim_map = blurrer.gaussian_blur_real_space(
   ...     model,
   ...     map_resolution,
   ...     exp_map
   ... )
   >>>
   >>> scorer = ScoringFunctions()
   >>> ccc_score = scorer.CCC(exp_map, sim_map)
   >>> mi_score = scorer.MI(exp_map, sim_map)

The envelope score is an example of a model/map score. It calculates the fraction
of atoms which are within the boundary density.

.. doctest::
   :options: -ELLIPSIS, +NORMALIZE_WHITESPACE
  
   >>> from TEMPy.protein.structure_parser import PDBParser
   >>> from TEMPy.maps.map_parser import MapParser
   >>> from TEMPy.protein.scoring_functions import ScoringFunctions
   >>>
   >>> model = PDBParser.read_PDB_file("1CS4", "../tests/test_data/1CS4.pdb")
   >>> exp_map = MapParser.readMRC("../tests/test_data/1CS4.mrc")
   >>>
   >>> scorer = ScoringFunctions()
   >>> boundary = 0.1
   >>> envelope_score = scorer.envelope_score(exp_map, boundary, model)


A recent addition is a new faster SMOC score which uses dynamic programming to speed
up calculations.

.. doctest::
   :options: -ELLIPSIS, +NORMALIZE_WHITESPACE
  
   >>> from TEMPy.protein.structure_parser import PDBParser
   >>> from TEMPy.maps.map_parser import MapParser
   >>> from TEMPy.protein.scoring_functions import FastSMOC
   >>> from TEMPy.protein.structure_blurrer import StructureBlurrer
   >>>
   >>> model = PDBParser.read_PDB_file("1CS4", "../tests/test_data/1CS4.pdb")
   >>> exp_map = MapParser.readMRC("../tests/test_data/1CS4.mrc")
   >>> map_resolution = 8.0
   >>>
   >>> scorer = FastSMOC(model, exp_map, map_resolution)
   >>> window_size = 11
   >>> chain_a_scores = scorer.score_chain_contig('A', window_size)

To learn more about the different scores available, refer to the API reference: Link