Back on new functions for motif quorums. We jump function 7 in order to explain “simpler” ones, 8 and 9. Both functions use generators. We’ve already seen here generators, which are functions that use the yield statement to generate iterators. The generator is very similar to a function but instead of returning a value, it yields one and waits for another call to resume. In function 8, a generator is used to return the motif sequence that is used as a key in the defaultdict. Notice the scope of the generator that is coded inside a function.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

def get_quorums_08(seqs, mlen):
    """
    add seq id_no to a set
    use enumerate to create seq_no
    use an explicit generator to create the motifs
    """
    def motif_gen(seq):
        for n in range(len(seq)-mlen):
            yield seq[n:n+mlen]

    quorum = defaultdict(set)
    for id_no, seq in enumerate(seqs):
        for motif in motif_gen(seq):
            quorum[motif].add(id_no)

    return quorum

In function 9 a very similar structure is used but in this cases instead of a “pure” generator it uses generator expressions which a very similar to list comprehensions but with parentheses instead of square brackets. Generator expressions are generators that can be written in one line and have identical behaviour as generators coded in the “regular” inception. In the function below the generator expressions provide the iterator for the loop with motif as index. Very simple and elegant.

1
2
3
4
5
6
7
8
9
10
11
12

def get_quorums_09(seqs, mlen):
    """
    add seq id_no to a set
    use enumerate to create seq_no
    use a generator expression to create the motifs
    """
    quorum = defaultdict(set)
    for id_no, seq in enumerate(seqs):
        for motif in (seq[n:n+mlen] for n in range(len(seq)-mlen)):
            quorum[motif].add(id_no)

    return quorum

In the next post we will go back to the statistical module and soon we will see the remainder 5 functions sent by Mike.