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EasyController.py

# Copyright 2009 by Tiago Antao <tiagoantao@gmail.com>.  All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license.  Please see the LICENSE file that should have been included
# as part of this package.



"""
This module allows to control GenePop through an easier interface.

This interface is less efficient than the standard GenePopControler

"""

from Controller import GenePopController
from Bio.PopGen import GenePop


class EasyController:
    def __init__(self, fname, genepop_dir = None):
        """Initializes the controller.
        
        genepop_dir is the directory where GenePop is.

        The binary should be called Genepop (capital G)
        
        """
        self._fname = fname
        self._controller = GenePopController(genepop_dir)

    def get_basic_info(self):
        f=open(self._fname)
        rec = GenePop.read(f)
        f.close()
        return rec.pop_list, rec.loci_list

    def test_hw_pop(self, pop_pos, test_type = "probability"):
        if test_type=="deficiency":
            hw_res = self._controller.test_pop_hz_deficiency(self._fname)
        elif test_type=="excess":
            hw_res = self._controller.test_pop_hz_excess(self._fname)
        else:
            loci_res, hw_res, fisher_full = self._controller.test_pop_hz_prob(self._fname, ".P")
        for i in range(pop_pos-1):
            hw_res.next()
        return hw_res.next()

    def test_hw_global(self, test_type = "deficiency", enum_test = True,
        dememorization = 10000, batches = 20, iterations = 5000):
        if test_type=="deficiency":
            pop_res, loc_res, all = self._controller.test_global_hz_deficiency(self._fname,
                enum_test, dememorization, batches, iterations)
        else:
            pop_res, loc_res, all = self._controller.test_global_hz_excess(self._fname,
                enum_test, dememorization, batches, iterations)
        return list(pop_res), list(loc_res), all

    def test_ld_all_pair(self, locus1, locus2,
        dememorization = 10000, batches = 20, iterations = 5000):
        all_ld = self._controller.test_ld(self._fname, dememorization, batches, iterations)[1]
        for ld_case in all_ld:
            (l1, l2), result = ld_case
            if (l1==locus1 and l2==locus2) or (l1==locus2 and l2==locus1):
                return result

    def estimate_nm(self):
        """ Estimate Nm. Just a simple bridge.
        """
        return self._controller.estimate_nm(self._fname)

    def get_heterozygosity_info(self, pop_pos, locus_name):
        """Returns the heterozygosity info for a certain locus on a population.

           Returns (Expected homozygotes, observed homozygotes,
                    Expected heterozygotes, observed heterozygotes)
        """
        geno_freqs = self._controller.calc_allele_genotype_freqs(self._fname)
        pop_iter, loc_iter = geno_freqs
        pops = list(pop_iter)
        return pops[pop_pos][1][locus_name][1]

    def get_genotype_count(self, pop_pos, locus_name):
        """Returns the genotype counts for a certain population and locus

        """
        geno_freqs = self._controller.calc_allele_genotype_freqs(self._fname)
        pop_iter, loc_iter = geno_freqs
        pop_iter = list(pop_iter)
        return pop_iter[pop_pos][1][locus_name][0]

    def get_fis(self, pop_pos, locus_name):
        """Returns the Fis for a certain population and locus

           Below CW means Cockerham and Weir and RH means Robertson and Hill. 

           Returns a pair:
           dictionary [allele] = (repetition count, frequency, Fis CW )
               with information for each allele
           a triple with total number of alleles, Fis CW, Fis RH


        """
        geno_freqs = self._controller.calc_allele_genotype_freqs(self._fname)
        pop_iter, loc_iter = geno_freqs
        pops = list(pop_iter)
        return pops[pop_pos][1][locus_name][2:]

    def get_alleles(self, pop_pos, locus_name):
        """Returns the alleles for a certain population and locus.

        """
        geno_freqs = self._controller.calc_allele_genotype_freqs(self._fname)
        pop_iter, loc_iter = geno_freqs
        pop_iter = list(pop_iter)
        return pop_iter[pop_pos][1][locus_name][2].keys()

    def get_alleles_all_pops(self, locus_name):
        """Returns the alleles for a certain population and locus.

        """
        geno_freqs = self._controller.calc_allele_genotype_freqs(self._fname)
        pop_iter, loc_iter = geno_freqs
        for locus_info in loc_iter:
            if locus_info[0] == locus_name:
                return locus_info[1]

    def get_allele_frequency(self, pop_pos, locus_name):
        geno_freqs = self._controller.calc_allele_genotype_freqs(self._fname)
        pop_iter, loc_iter = geno_freqs
        for locus_info in loc_iter:
            if locus_info[0] == locus_name:
                alleles =  locus_info[1]
                pop_name, freqs, total = locus_info[2][pop_pos]
                allele_freq = {}
                for i in range(len(alleles)):
                    allele_freq[alleles[i]] = freqs[i]
                return total, allele_freq

    def get_multilocus_f_stats(self):
        """ Returns the multilocus F stats

            Explain averaging.
            Returns Fis(CW), Fst, Fit
        """
        return self._controller.calc_fst_all(self._fname)[0]

    def get_f_stats(self, locus_name):
        """ Returns F stats for a locus

            Returns Fis(CW), Fst, Fit, Qintra, Qinter
        """
        loci_iter =  self._controller.calc_fst_all(self._fname)[1]
        for name, fis, fst, fit, qintra, qinter in loci_iter:
            if name == locus_name:
                return fis, fst, fit, qintra, qinter

    def get_avg_fis(self):
        return self._controller.calc_diversities_fis_with_identity(self._fname)[1]

    def get_avg_fst_pair(self):
        return self._controller.calc_fst_pair(self._fname)[1]

    def get_avg_fst_pair_locus(self, locus):
        iter = self._controller.calc_fst_pair(self._fname)[0]
        for locus_info in iter:
            if locus_info[0] == locus:
                return locus_info[1]

    def calc_ibd(self, is_diplo = True, stat="a", scale="Log", min_dist=0.00001):
        if is_diplo:
            return self._controller.calc_ibd_diplo(self._fname, stat, scale, min_dist)
        else:
            return self._controller.calc_ibd_haplo(self._fname, stat, scale, min_dist)

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