Module SimilarityMaps

Calculates the atomic weights for the probe molecule 
based on a fingerprint function and a metric.

Parameters:
  refMol -- the reference molecule
  probeMol -- the probe molecule
  fpFunction -- the fingerprint function
  metric -- the similarity metric

Note:
  If fpFunction needs additional parameters, use a lambda construct

Calculates the atomic weights for the probe molecule based on 
a fingerprint function and the prediction function of a ML model.

Parameters:
  probeMol -- the probe molecule
  fpFunction -- the fingerprint function
  predictionFunction -- the prediction function of the ML model

Normalizes the weights,
such that the absolute maximum weight equals 1.0.

Parameters:
  weights -- the list with the atomic weights

Generates the similarity map for a molecule given the atomic weights.

Parameters:
  mol -- the molecule of interest
  colorMap -- the matplotlib color map scheme
  scale -- the scaling: scale < 0 -> the absolute maximum weight is used as maximum scale
                        scale = double -> this is the maximum scale
  size -- the size of the figure
  sigma -- the sigma for the Gaussians
  coordScale -- scaling factor for the coordinates
  step -- the step for calcAtomGaussian
  colors -- color of the contour lines
  contourLines -- if integer number N: N contour lines are drawn
                  if list(numbers): contour lines at these numbers are drawn
  alpha -- the alpha blending value for the contour lines
  kwargs -- additional arguments for drawing

Generates the similarity map for a given reference and probe molecule, 
fingerprint function and similarity metric.

Parameters:
  refMol -- the reference molecule
  probeMol -- the probe molecule
  fpFunction -- the fingerprint function
  metric -- the similarity metric.
  kwargs -- additional arguments for drawing

Generates the similarity map for a given ML model and probe molecule, 
and fingerprint function.

Parameters:
  probeMol -- the probe molecule
  fpFunction -- the fingerprint function
  predictionFunction -- the prediction function of the ML model
  kwargs -- additional arguments for drawing

Calculates the atom pairs fingerprint with the torsions of atomId removed.

Parameters:
  mol -- the molecule of interest
  atomId -- the atom to remove the pairs for (if -1, no pair is removed)
  fpType -- the type of AP fingerprint ('normal', 'hashed', 'bv')
  nBits -- the size of the bit vector (only for fpType='bv')
  minLength -- the minimum path length for an atom pair
  maxLength -- the maxmimum path length for an atom pair
  nBitsPerEntry -- the number of bits available for each pair

Calculates the topological torsion fingerprint with the pairs of atomId removed.

Parameters:
  mol -- the molecule of interest
  atomId -- the atom to remove the torsions for (if -1, no torsion is removed)
  fpType -- the type of TT fingerprint ('normal', 'hashed', 'bv')
  nBits -- the size of the bit vector (only for fpType='bv')
  minLength -- the minimum path length for an atom pair
  maxLength -- the maxmimum path length for an atom pair
  nBitsPerEntry -- the number of bits available for each torsion

Calculates the Morgan fingerprint with the environments of atomId removed.

Parameters:
  mol -- the molecule of interest
  radius -- the maximum radius
  fpType -- the type of Morgan fingerprint: 'count' or 'bv'
  atomId -- the atom to remove the environments for (if -1, no environments is removed)
  nBits -- the size of the bit vector (only for fpType = 'bv')
  useFeatures -- if false: ConnectivityMorgan, if true: FeatureMorgan

Calculates the RDKit fingerprint with the paths of atomId removed.

Parameters:
  mol -- the molecule of interest
  atomId -- the atom to remove the paths for (if -1, no path is removed)
  fpType -- the type of RDKit fingerprint: 'bv'
  nBits -- the size of the bit vector
  minPath -- minimum path length
  maxPath -- maximum path length
  nBitsPerHash -- number of to set per path