Class EnsembleSelection

Constructor optionally takes a few parameters that determine the
behavior of the selection algorithm or that provide the input data.
The input data may be provided either as a dict or as a filename to
be parsed.
    * initial_seed: random seed used whenever the random sampling
        method is used. It may be set to None if non-reproducible
        results are desired.
    * max_exhaustive: the maximum number of combinations for a
        systmatic (exhaustive) search of the available combinations.
        When the number of combinations exceeds this number, random
        sampling is used instead.
    * n_random_comb: the number of iterations for random sampling.
    * tol: the tolerance used to determine if a ligand is docked
      "properly"
    * gscores: a dict of GlideScores to be passed on to the
        set_gscores method.
    * fname: a filename for a csv file to be passed to the read_csv
        method.
    * exp_dg: a dict of experimental DeltaGs to be passed on to the
        set_exp_dg method.
    * exp_dg_fname: a filename to be passed on to the read_exp method.
    * docking_failure_penalty: the assumed deviation between experimental
        DeltaG and GlideScore when the ligand fails to dock into the ensemble
        under consideration. Used by the rmsd_comb method.

Overrides: object.__init__

Calculate the RMS deviation between experimental DeltaG and the lowest GScore obtained for each ligand using a given combination 'comb' of receptors. 'comb' is a tuple of titles. NOTE: ligands that failed to dock into all of the receptors in 'comb' count as a deviation of 'docking_failure_penalty', as given to the constructor (10.0 kcal/mol by default).

Return the count of ligands that can be docked "properly" by at least one receptor in the given combination 'comb' of receptors. 'comb' is a tuple of titles.

Return an iterator that produces a random sample of n-element combinations from the list of receptors contained in self. The iterator will try to produce self.n_random_comb combinations, but it will skip duplicates so the actual number of combinations returned is likely to be smaller.

Return an iterator that produces n-element combinations from the list of receptors contained in self. If the number of combinations is less than self.max_exhaustive, a complete list of combinations is produced. If it is larger, a random sample is produced by deferring to the sample_combinations method.

Set the GlideScore matrix. 'gscores' must be a dict of dicts, where each value gscores[prot][lig] = gs is the GlideScore from docking lig into prot. Both lig and prot are titles.

Sets the following public attributes: * titles * N = len(titles)

Set the experimental data used for computing RMSDs. 'exp_dg' should be a dict where the key is a ligand title and the value is the DeltaG.

Import data from a CSV file, which is expected to contain an N*N matrix of GlideScores, plus the titles on the header and on the first column. Each column corresponds to a receptor and each row corresponds to a ligand. The set of ligand titles must be the same as the set of receptor titles. Ligands that failed to dock can be represented int the file either as a blank field or by the string "NA".

Read a file with the experimental data. The text file has two
whitespace-separated columns:
    1) title
    2) DeltaG