def __init__(
self,
intervals_file,
fasta_file,
num_chr_fasta=False,
label_dtype=None,
auto_resize_len=None,
# max_seq_len=None,
# use_strand=False,
force_upper=True,
ignore_targets=False):
self.num_chr_fasta = num_chr_fasta
self.intervals_file = intervals_file
self.fasta_file = fasta_file
self.auto_resize_len = auto_resize_len
# self.use_strand = use_strand
self.force_upper = force_upper
# self.max_seq_len = max_seq_len
# if use_strand:
# # require a 6-column bed-file if strand is used
# bed_columns = 6
# else:
# bed_columns = 3
self.bed = BedDataset(self.intervals_file,
num_chr=self.num_chr_fasta,
bed_columns=3,
label_dtype=parse_dtype(label_dtype),
ignore_targets=ignore_targets)
self.fasta_extractors = None
def test_parse_type():
with pytest.raises(Exception):
parse_dtype('string')
with pytest.raises(Exception):
parse_dtype('np.float322')
assert parse_dtype('float') == float
assert parse_dtype(float) == float
assert parse_dtype("np.float32") == np.float32
def __init__(self,
alphabet=DNA,
neutral_alphabet='N',
neutral_value=0.25,
dtype=None,
alphabet_axis=1,
dummy_axis=None):
# make sure the alphabet axis and the dummy axis are valid:
if dummy_axis is not None:
if alphabet_axis == dummy_axis:
raise ValueError("dummy_axis can't be the same as dummy_axis")
if not (dummy_axis >= 0 and dummy_axis <= 2):
raise ValueError("dummy_axis can be either 0,1 or 2")
assert alphabet_axis >= 0 and (alphabet_axis < 2 or
(alphabet_axis <= 2
and dummy_axis is not None))
self.alphabet_axis = alphabet_axis
self.dummy_axis = dummy_axis
self.alphabet = parse_alphabet(alphabet)
self.dtype = parse_dtype(dtype)
self.neutral_alphabet = neutral_alphabet
self.neutral_value = neutral_value
# set the transform parameters correctly
if dummy_axis is not None and dummy_axis < 2:
# dummy axis is added somewhere in the middle, so the alphabet axis is at the end now
existing_alphabet_axis = 2
else:
# alphabet axis stayed the same
existing_alphabet_axis = 1
# check if no swapping needed
if existing_alphabet_axis == self.alphabet_axis:
self.alphabet_axis = None
# how to transform the input
self.transform = Compose([
OneHot(self.alphabet,
neutral_alphabet=self.neutral_alphabet,
neutral_value=self.neutral_value,
dtype=self.dtype), # one-hot-encode
DummyAxis(self.dummy_axis), # optionally inject the dummy axis
SwapAxes(existing_alphabet_axis,
self.alphabet_axis), # put the alphabet axis elsewhere
])