def predict(self, sequences):
with tempfile.NamedTemporaryFile(suffix="<STR_LIT>", mode="<STR_LIT:w>") as input_fd:<EOL><INDENT>for (i, sequence) in enumerate(sequences):<EOL><INDENT>input_fd.write("<STR_LIT>" % i)<EOL>input_fd.write(sequence)<EOL>input_fd.write("<STR_LIT:\n>")<EOL><DEDENT>input_fd.flush()<EOL>try:<EOL><INDENT>output = subprocess.check_output(["<STR_LIT>", input_fd.name])<EOL><DEDENT>except subprocess.CalledProcessError as e:<EOL><INDENT>logging.error("<STR_LIT>" % (e, e.output))<EOL>raise<EOL><DEDENT><DEDENT>parsed = self.parse_netchop(output)<EOL>assert len(parsed) == len(sequences),"<STR_LIT>" % (<EOL>len(sequences), len(parsed))<EOL>assert [len(x) for x in parsed] == [len(x) for x in sequences]<EOL>return parsed<EOL>
Return netChop predictions for each position in each sequence.

Parameters
-----------
sequences : list of string
    Amino acid sequences to predict cleavage for

Returns
-----------
list of list of float

The i'th list corresponds to the i'th sequence. Each list gives
the cleavage probability for each position in the sequence.