Iterates on available markers.

        Returns:
            Genotypes instances.
347745
def iter_genotypes(self):
        
        # Parsing each column of the dataframe
        for variant in self.df.columns:
            genotypes = self.df.loc[:, variant].values
            info = self.map_info.loc[variant, :]

            yield Genotypes(
                Variant(info.name, info.chrom, info.pos, [info.a1, info.a2]),
                genotypes,
                reference=info.a2,
                coded=info.a1,
                multiallelic=False,
            )
1248823
def load_alleles_dict():
    
    alleles = load_alleles()
    result = {}
    for allele in alleles:
        for name in {allele.name}.union(allele.synonyms):
            result[name] = allele
    return result
1253246
def iter_points(x):
    
    if isinstance(x, (list, tuple)):
        if len(x):
            if isinstance(x[0], (list, tuple)):
                out = []
                for y in x:
                    out += iter_points(y)
                return out
            else:
                return [x]
        else:
            return []
    else:
        raise ValueError('List/tuple type expected. Got {!r}.'.format(x))
357562
def called_alts_from_genotype(self):
        ''''''
        if 'GT' not in self.FORMAT:
            return None

        genotype_indexes = set([int(x) for x in self.FORMAT['GT'].split('/')])
        alts = set()

        for i in genotype_indexes:
            if i == 0:
                alts.add(self.REF)
            else:
                alts.add(self.ALT[i-1])

        return alts
561373
def load_genotypes(self):
        

        if DataParser.compressed_pedigree:
            self.genotype_file = gzip.open("%s.gz" % self.tped_file, 'rb')
        else:
            self.genotype_file = open(self.tped_file)
        self.filter_missing()
705148
public ISeq<Genotype<G>> getGenotypes() {
		return _population.stream()
			.map(Phenotype::getGenotype)
			.collect(ISeq.toISeq());
	}
1312076
def gene_ids(self):
        
        return self.ensembl.gene_ids_at_locus(
            self.contig, self.start, self.end)
1327429
def get_variants_in_region(self, chrom, start, end):
        """"""
        if self.chrom is not None and chrom == self.chrom:
            # We are going to search for 'NA' since the chromosome was set
            chrom = "NA"

        iterator = self._bgen.iter_variants_in_region(
            CHROM_STR_DECODE.get(chrom, chrom), start, end,
        )
        for info, dosage in iterator:
            yield Genotypes(
                Variant(
                    info.name, CHROM_STR_ENCODE.get(info.chrom, info.chrom),
                    info.pos, [info.a1, info.a2],
                ),
                dosage,
                reference=info.a1,
                coded=info.a2,
                multiallelic=True,
            )
415279
def featuretypes(self):
        
        c = self.conn.cursor()
        c.execute(
            '''
            SELECT DISTINCT featuretype from features
            ''')
        for i, in c:
            yield i
749916
def load_alleles():
    
    result = []
    path = local_path()
    etree = xml.etree.ElementTree.parse(path)
    for allele in etree.iterfind("MhcAlleleName"):
        name_element = allele.find("DisplayedRestriction")
        mhc_class_element = allele.find("Class")
        # need at least a name and an HLA class
        if name_element is None or mhc_class_element is None:
            continue
        name = name_element.text

        synonyms = set([])
        for synonym_element in allele.iterfind("Synonyms"):
            for synonym in synonym_element.text.split(","):
                synonyms.add(synonym.strip())
        mhc_class = mhc_class_element.text
        organism_element = allele.find("Organsim")
        if organism_element is None:
            organism = None
        else:
            organism = organism_element.text

        locus_element = allele.find("Locus")

        if locus_element is None:
            locus = None
        else:
            locus = locus_element.text

        allele_object = Allele(
            name=name,
            mhc_class=mhc_class,
            locus=locus,
            organism=organism,
            synonyms=synonyms)
        result.append(allele_object)
    return result
664120
def marker_tags(self, iid):
        """"""
        tags = self._markers[iid]["tags"]
        for tag in tags:
            yield tag
364065
def iter_variants(self):
        
        for variant in self.repository.iter_variants(self.resource):
            yield Variant(variant, context=self.context, parent=self)
615804
def iter_markers(self):
        
        marker_finder = _MarkerFinder.from_stream(self._stream)
        start = 0
        marker_code = None
        while marker_code != JPEG_MARKER_CODE.EOI:
            marker_code, segment_offset = marker_finder.next(start)
            marker = _MarkerFactory(
                marker_code, self._stream, segment_offset
            )
            yield marker
            start = segment_offset + marker.segment_length
83840
def complement_alleles(self):
        
        self.alleles = self._encode_alleles(
            [complement_alleles(i) for i in self.alleles]
        )
281637
def iter_variants(self):
        """"""
        if not self.has_index:
            raise NotImplementedError("Not implemented when IMPUTE2 file is "
                                      "not indexed (see genipe)")

        for name, row in self._impute2_index.iterrows():
            # Seeking to the right place in the file
            f = self._impute2_file
            f.seek(int(row.seek))
            chrom, name, pos, a1, a2 = f.read(1024).split(" ")[:5]
            pos = int(pos)

            yield Variant(name, CHROM_STR_ENCODE.get(chrom, chrom), pos,
                          [a1, a2])
1083947
def sequencetyper(self):
        """"""
        logging.info('Performing sequence typing')
        for sample in self.runmetadata.samples:
            if sample.general.bestassemblyfile != 'NA':
                if type(sample[self.analysistype].allelenames) == list:
                    # Initialise variables
                    header = 0
                    genome = sample.name
                    # Initialise self.bestmatch[genome] with an int that will eventually be replaced by the # of matches
                    self.bestmatch[genome] = defaultdict(int)
                    if sample[self.analysistype].profile != 'NA':
                        # Create the profiledata variable to avoid writing self.profiledata[self.analysistype]
                        profiledata = sample[self.analysistype].profiledata
                        # Calculate the number of allele matches between each sequence type and the results
                        best_seqtype = dict()
                        for sequencetype in sample[self.analysistype].profiledata:
                            # Initialise a counter
                            count = 0
                            # Iterate through each gene for the sequence type
                            for gene, refallele in sample[self.analysistype].profiledata[sequencetype].items():
                                # Use the gene to extract the calculated allele
                                allele = self.matchdict[genome][gene]
                                # Increment the count on a match
                                if refallele == allele:
                                    count += 1
                            # Add the sequence type to the set of sequence types with the number of matches as the key
                            try:
                                best_seqtype[count].add(sequencetype)
                            except KeyError:
                                best_seqtype[count] = set()
                                best_seqtype[count].add(sequencetype)
                        # Find the highest number of matches from the dictionary
                        best = sorted(best_seqtype.items(), key=operator.itemgetter(0), reverse=True)[0][1]
                        # Deal with multiple allele matches
                        for gene in sample[self.analysistype].allelenames:
                            # Clear the appropriate count and lists
                            multiallele = list()
                            multipercent = list()
                            # Go through the alleles in plusdict
                            for allele in self.plusdict[genome][gene]:
                                percentid = list(self.plusdict[genome][gene][allele].keys())[0]
                                # "N" alleles screw up the allele splitter function
                                if allele not in ['N', 'NA']:
                                    # Append as appropriate - alleleNumber is treated as an integer for proper sorting
                                    multiallele.append(int(allele))
                                    multipercent.append(percentid)
                                # If the allele is "N"
                                else:
                                    # Append "N" and a percent identity of 0
                                    multiallele.append("N")
                                    multipercent.append(0)
                            # Populate self.bestdict with genome, gene, alleles joined with a space (this was made like
                            # this because allele is a list generated by the .iteritems() above
                            try:
                                self.bestdict[genome][gene][" ".join(str(allele)
                                                                     for allele in sorted(multiallele))] = \
                                    multipercent[0]
                            except IndexError:
                                self.bestdict[genome][gene]['NA'] = 0
                            # Find the profile with the most alleles in common with the query genome
                            for sequencetype in best:
                                # The number of genes in the analysis
                                header = len(profiledata[sequencetype])
                                # refallele is the allele number of the sequence type
                                refallele = profiledata[sequencetype][gene]
                                # If there are multiple allele matches for a gene in the reference profile e.g. 10 692
                                if len(refallele.split(" ")) > 1:
                                    # Map the split (on a space) alleles as integers - if they are treated as integers,
                                    # the alleles will sort properly
                                    intrefallele = map(int, refallele.split(" "))
                                    # Create a string of the joined, sorted alleles
                                    sortedrefallele = " ".join(str(allele) for allele in sorted(intrefallele))
                                else:
                                    # Use the reference allele as the sortedRefAllele
                                    sortedrefallele = refallele
                                for allele, percentid in self.bestdict[genome][gene].items():
                                    # If the allele in the query genome matches the allele in the reference profile, add
                                    # the result to the bestmatch dictionary. Genes with multiple alleles were sorted
                                    # the same, strings with multiple alleles will match: 10 692 will never be 692 10
                                    if allele == sortedrefallele and float(percentid) == 100.00:
                                        # Increment the number of matches to each profile
                                        self.bestmatch[genome][sequencetype] += 1
                                    # Special handling of BACT000060 and BACT000065 genes for E. coli and BACT000014
                                    # for Listeria. When the reference profile has an allele of 'N', and the query
                                    # allele doesn't, set the allele to 'N', and count it as a match
                                    elif sortedrefallele == 'N' and allele != 'N':
                                        # Increment the number of matches to each profile
                                        self.bestmatch[genome][sequencetype] += 1
                                    # Consider cases with multiple allele matches
                                    elif len(allele.split(' ')) > 1:
                                        # Also increment the number of matches if one of the alleles matches the
                                        # reference allele e.g. 41 16665 will match either 41 or 16665
                                        if sortedrefallele != 'N' and allele != 'N':
                                            match = False
                                            for sub_allele in allele.split(' '):
                                                if sub_allele == refallele:
                                                    match = True
                                            if match:
                                                # Increment the number of matches to each profile
                                                self.bestmatch[genome][sequencetype] += 1
                                    elif allele == sortedrefallele and sortedrefallele == 'N':
                                        # Increment the number of matches to each profile
                                        self.bestmatch[genome][sequencetype] += 1
                        # Get the best number of matches
                        # From: https://stackoverflow.com/questions/613183/sort-a-python-dictionary-by-value
                        try:
                            sortedmatches = sorted(self.bestmatch[genome].items(), key=operator.itemgetter(1),
                                                   reverse=True)[0][1]
                        # If there are no matches, set :sortedmatches to zero
                        except IndexError:
                            sortedmatches = 0
                        # Otherwise, the query profile matches the reference profile
                        if int(sortedmatches) == header:
                            # Iterate through best match
                            for sequencetype, matches in self.bestmatch[genome].items():
                                if matches == sortedmatches:
                                    for gene in profiledata[sequencetype]:
                                        # Populate resultProfile with the genome, best match to profile, # of matches
                                        # to the profile, gene, query allele(s), reference allele(s), and % identity
                                        self.resultprofile[genome][sequencetype][sortedmatches][gene][
                                            list(self.bestdict[genome][gene]
                                                 .keys())[0]] = str(list(self.bestdict[genome][gene].values())[0])
                                    sample[self.analysistype].sequencetype = sequencetype
                                    sample[self.analysistype].matchestosequencetype = matches
                        # If there are fewer matches than the total number of genes in the typing scheme
                        elif 0 < int(sortedmatches) < header:
                            mismatches = []
                            # Iterate through the sequence types and the number of matches in bestDict for each genome
                            for sequencetype, matches in self.bestmatch[genome].items():
                                # If the number of matches for a profile matches the best number of matches
                                if matches == sortedmatches:
                                    # Iterate through the gene in the analysis
                                    for gene in profiledata[sequencetype]:
                                        # Get the reference allele as above
                                        refallele = profiledata[sequencetype][gene]
                                        # As above get the reference allele split and ordered as necessary
                                        if len(refallele.split(" ")) > 1:
                                            intrefallele = map(int, refallele.split(" "))
                                            sortedrefallele = " ".join(str(allele) for allele in sorted(intrefallele))
                                        else:
                                            sortedrefallele = refallele
                                        # Populate self.mlstseqtype with the genome, best match to profile, # of matches
                                        # to the profile, gene, query allele(s), reference allele(s), and % identity
                                        if self.analysistype == 'mlst':
                                            self.resultprofile[genome][sequencetype][sortedmatches][gene][
                                                list(self.bestdict[genome][gene]
                                                     .keys())[0]] = str(list(self.bestdict[genome][gene].values())[0])
                                        else:
                                            self.resultprofile[genome][sequencetype][sortedmatches][gene][
                                                list(self.bestdict[genome][gene].keys())[0]] \
                                                = str(list(self.bestdict[genome][gene].values())[0])
                                            #
                                            if sortedrefallele != list(self.bestdict[sample.name][gene].keys())[0]:
                                                mismatches.append(
                                                    ({gene: ('{} ({})'.format(list(self.bestdict[sample.name][gene]
                                                                                   .keys())[0], sortedrefallele))}))
                                        sample[self.analysistype].mismatchestosequencetype = mismatches
                                        sample[self.analysistype].sequencetype = sequencetype
                                        sample[self.analysistype].matchestosequencetype = matches
                        elif sortedmatches == 0:
                            for gene in sample[self.analysistype].allelenames:
                                # Populate the results profile with negative values for sequence type and sorted matches
                                self.resultprofile[genome]['NA'][sortedmatches][gene]['NA'] = 0
                            # Add the new profile to the profile file (if the option is enabled)
                            sample[self.analysistype].sequencetype = 'NA'
                            sample[self.analysistype].matchestosequencetype = 'NA'
                            sample[self.analysistype].mismatchestosequencetype = 'NA'
                        else:
                            sample[self.analysistype].matchestosequencetype = 'NA'
                            sample[self.analysistype].mismatchestosequencetype = 'NA'
                            sample[self.analysistype].sequencetype = 'NA'
                else:
                    sample[self.analysistype].matchestosequencetype = 'NA'
                    sample[self.analysistype].mismatchestosequencetype = 'NA'
                    sample[self.analysistype].sequencetype = 'NA'

            else:
                sample[self.analysistype].matchestosequencetype = 'NA'
                sample[self.analysistype].mismatchestosequencetype = 'NA'
                sample[self.analysistype].sequencetype = 'NA'
            # Clear out the reverse_profiledata attribute - frozen sets can not be .json encoded
            try:
                delattr(sample[self.analysistype], 'reverse_profiledata')
            except AttributeError:
                pass
778840
def flip_alleles(genotypes):
    """"""
    warnings.warn("deprecated: use 'Genotypes.flip_coded'", DeprecationWarning)
    genotypes.reference, genotypes.coded = (genotypes.coded,
                                            genotypes.reference)
    genotypes.genotypes = 2 - genotypes.genotypes
    return genotypes