def test_detect_input_type(self):
# Load some input: DbInput
dbi = DbInput.from_file(DB_SEQUENCES_FILE, {"index": 0})
# Run it through the preprocessor
datatype, obj = detect_input_type(dbi)
# Get the datatype from the type name lists
datatype2 = input_type_name(type(obj))
self.assertEqual(datatype, datatype2)
# Do the same with ChordInput
ci = ChordInput.from_file(CHORDS_FILE, options={"roman": True})
datatype, obj = detect_input_type(ci)
datatype2 = input_type_name(type(obj))
self.assertEqual(datatype, datatype2)
# Try some bulk input
bulk = DbBulkInput.from_file(DB_SEQUENCES_FILE)
datatype, obj = detect_input_type(bulk, allow_bulk=True)
datatype2 = input_type_name(type(obj))
self.assertEqual(datatype, datatype2)
# Try restricting the allowed type
datatype, obj = detect_input_type(ci, allowed=["chords"])
# And this one should get rejected
self.assertRaises(InputTypeError, detect_input_type, (ci,), {"allowed": "db"})
def test_detect_input_type(self):
# Load some input: DbInput
dbi = DbInput.from_file(DB_SEQUENCES_FILE, {'index': 0})
# Run it through the preprocessor
datatype, obj = detect_input_type(dbi)
# Get the datatype from the type name lists
datatype2 = input_type_name(type(obj))
self.assertEqual(datatype, datatype2)
# Do the same with ChordInput
ci = ChordInput.from_file(CHORDS_FILE, options={'roman': True})
datatype, obj = detect_input_type(ci)
datatype2 = input_type_name(type(obj))
self.assertEqual(datatype, datatype2)
# Try some bulk input
bulk = DbBulkInput.from_file(DB_SEQUENCES_FILE)
datatype, obj = detect_input_type(bulk, allow_bulk=True)
datatype2 = input_type_name(type(obj))
self.assertEqual(datatype, datatype2)
# Try restricting the allowed type
datatype, obj = detect_input_type(ci, allowed=['chords'])
# And this one should get rejected
self.assertRaises(InputTypeError, detect_input_type, (ci, ),
{'allowed': 'db'})
def test_from_file(self):
# Simply load a sequence index file
bulk = DbBulkInput.from_file(DB_SEQUENCES_FILE)
# We can get the sequences just but converting the iter to a list
seqs = list(bulk)
# There should be a non-zero number of sequences loaded
self.assertNotEqual(len(seqs), 0)
# Check the type of the first one
self.assertIsInstance(seqs[0], DbInput)
def test_from_file(self):
# Simply load a sequence index file
bulk = DbBulkInput.from_file(DB_SEQUENCES_FILE)
# We can get the sequences just but converting the iter to a list
seqs = list(bulk)
# There should be a non-zero number of sequences loaded
self.assertNotEqual(len(seqs), 0)
# Check the type of the first one
self.assertIsInstance(seqs[0], DbInput)
# Get the bulk input to supply names
name_getter = iter(input_data.get_identifiers())
num_inputs = len(input_data)
# Fill the progress record with names and mark as incomplete
completed_parses = dict([(name,False) \
for name in input_data.get_identifiers()])
if partitions > 1:
if options.sequence_partitions is not None:
# Split the inputs up into partitions on the basis of
# an even partitioning of chord sequences
# This can only be done with
if not isinstance(input_data, SegmentedMidiBulkInput):
logger.error("option --sequence-partitions is only "\
"valid with bulk midi input data")
return 1
chord_seqs = DbBulkInput.from_file(options.sequence_partitions)
# Partition the chord sequences: we only need indices
seq_indices = enumerate(partition(
[i for i in range(len(chord_seqs))], partitions))
seq_partitions = dict(
sum([[(index,part_num) for index in part] for
(part_num,part) in seq_indices], []) )
# Associate a partition num with each midi input
partition_numbers = [
seq_partitions[midi.sequence_index] for midi in input_data]
else:
# Prepare a list of partition numbers to append to model names
partition_numbers = sum([
[partnum for i in part] for (partnum,part) in \
enumerate(partition(range(num_inputs), partitions))], [])
else:
