How to use prophyle - 10 common examples

fastas_fn = node.fastapath.split("@")
                for fasta_fn in fastas_fn:
                    print(fasta_fn)

        else:
            children = node.get_children()

            for child in children:
                self.process_node(child)


if __name__ == "__main__":
    assert (len(sys.argv) == 2)
    newick_fn = sys.argv[1]

    ti = TreeIndex(tree_newick_fn=newick_fn, )
    ti.process_node(ti.tree.get_tree_root())

        hitmasks_dict = collections.defaultdict(lambda: bitarray(hitmask_empty))
        covmasks_dict = collections.defaultdict(lambda: bitarray(covmask_empty))

        hitmasks_dict = collections.defaultdict(lambda: bitarray(hitmask_empty))
        covmasks_dict = collections.defaultdict(lambda: bitarray(covmask_empty))

        covmasks_dict = collections.defaultdict(lambda: bitarray(covmask_empty))

def evaluate_single_assignment(self, nodename):
        """Evaluate a single assignment.

        Args:
            nodename (str): Name of the node for which we will compute characteristics.

        Returns:
            assignment (dict): Assignment dictionary.
        """

        #################################
        # A) Start with the current masks
        #################################
        hitmask = bitarray(self.hitmasks_dict[nodename])
        covmask = bitarray(self.covmasks_dict[nodename])

        node = self.tree_index.nodename_to_node[nodename]

        ##########################
        # B) Update from ancestors
        ##########################
        ancestors = self.tree_index.nodename_to_upnodenames[nodename] & self.hitmasks_dict.keys()
        for anc_nodename in ancestors:
            hitmask |= self.hitmasks_dict[anc_nodename]
            covmask |= self.covmasks_dict[anc_nodename]

        ##############################
        # C) Calculate characteristics
        ##############################
        hit = hitmask.count()
        cov = covmask.count()

def bitarray_block(alen, blen, pos):
        """Create a bitarray containing a block of one's.

        Args:
            alen (int): Array length.
            blen (int): Block length (within the array).
            pos (int): Position of the block (0-based).

        Return:
            bitarray (bitarray)
        """
        return bitarray(pos * "0" + blen * "1" + (alen - pos - blen) * "0")

def evaluate_single_assignment(self, nodename):
        """Evaluate a single assignment.

        Args:
            nodename (str): Name of the node for which we will compute characteristics.

        Returns:
            assignment (dict): Assignment dictionary.
        """

        #################################
        # A) Start with the current masks
        #################################
        hitmask = bitarray(self.hitmasks_dict[nodename])
        covmask = bitarray(self.covmasks_dict[nodename])

        node = self.tree_index.nodename_to_node[nodename]

        ##########################
        # B) Update from ancestors
        ##########################
        ancestors = self.tree_index.nodename_to_upnodenames[nodename] & self.hitmasks_dict.keys()
        for anc_nodename in ancestors:
            hitmask |= self.hitmasks_dict[anc_nodename]
            covmask |= self.covmasks_dict[anc_nodename]

        ##############################
        # C) Calculate characteristics
        ##############################
        hit = hitmask.count()

def evaluate_single_assignment(self, nodename):
        """Evaluate a single assignment.

        Args:
            nodename (str): Name of the node for which we will compute characteristics.

        Returns:
            assignment (dict): Assignment dictionary.
        """

        #################################
        # A) Start with the current masks
        #################################
        hitmask = bitarray(self.hitmasks_dict[nodename])
        covmask = bitarray(self.covmasks_dict[nodename])

        ##########################
        # B) Update from ancestors
        ##########################
        ancestors = self.tree_index.nodename_to_upnodenames[nodename] & self.hitmasks_dict.keys()
        for anc_nodename in ancestors:
            hitmask |= self.hitmasks_dict[anc_nodename]
            covmask |= self.covmasks_dict[anc_nodename]

        ##############################
        # C) Calculate characteristics
        ##############################
        hit = hitmask.count()
        cov = covmask.count()
        readlen = self.krakline_parser.readlen
        kcount = self.tree_index.nodename_to_kmercount[nodename]

class Rank(OrderedEnum):
    NO_RANK = 7
    SPECIES = 6
    GENUS = 5
    FAMILY = 4
    ORDER = 3
    CLASS = 2
    PHYLUM = 1
    KINGDOM = 0


str2rank = {
    'species': Rank.SPECIES,
    'genus': Rank.GENUS,
    'family': Rank.FAMILY,
    'order': Rank.ORDER,
    'class': Rank.CLASS,
    'phylum': Rank.PHYLUM,
    'kingdom': Rank.KINGDOM,
    'no rank': Rank.NO_RANK
}


def build_complete_tree(tree, log):
    """Build the taxonomic tree including internal nodes (for rank dependent evaluation)

    Args:
        tree (fileobject): file name of the reference tree, without internal nodes
        log (fileobject): log file
    """
    ncbi = NCBITaxa()

SPECIES = 6
    GENUS = 5
    FAMILY = 4
    ORDER = 3
    CLASS = 2
    PHYLUM = 1
    KINGDOM = 0


str2rank = {
    'species': Rank.SPECIES,
    'genus': Rank.GENUS,
    'family': Rank.FAMILY,
    'order': Rank.ORDER,
    'class': Rank.CLASS,
    'phylum': Rank.PHYLUM,
    'kingdom': Rank.KINGDOM,
    'no rank': Rank.NO_RANK
}


def build_complete_tree(tree, log):
    """Build the taxonomic tree including internal nodes (for rank dependent evaluation)

    Args:
        tree (fileobject): file name of the reference tree, without internal nodes
        log (fileobject): log file
    """
    ncbi = NCBITaxa()
    original_tree = Tree(tree, format=1)
    taxa = [n.taxid for n in original_tree.traverse('postorder')]
    built = False