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import numpy as np
# https://stackoverflow.com/questions/46091111/python-slice-array-at-different-position-on-every-row
def take_per_row(A, indx, num_elem):
"""
Matrix A, indx is a vector for each row which specifies
slice beginning for that row. Each has width num_elem.
"""
all_indx = indx[:,None] + np.arange(num_elem)
return A[np.arange(all_indx.shape[0])[:,None], all_indx]
def random_crop(seqs, labels, seq_crop_width, label_crop_width, coords):
"""
Takes sequences and corresponding counts labels. They should have the same
#examples. The widths would correspond to inputlen and outputlen respectively,
and any additional flanking width for jittering which should be the same
for seqs and labels. Each example is cropped starting at a random offset.
seq_crop_width - label_crop_width should be equal to seqs width - labels width,
essentially implying they should have the same flanking width.
"""
assert(seqs.shape[1]>=seq_crop_width)
assert(labels.shape[1]>=label_crop_width)
assert(seqs.shape[1] - seq_crop_width == labels.shape[1] - label_crop_width)
max_start = seqs.shape[1] - seq_crop_width # This should be the same for both input and output
starts = np.random.choice(range(max_start+1), size=seqs.shape[0], replace=True)
new_coords = coords.copy()
new_coords[:,1] = new_coords[:,1].astype(int) - (seqs.shape[1]//2) + starts
return take_per_row(seqs, starts, seq_crop_width), take_per_row(labels, starts, label_crop_width), new_coords
def random_rev_comp(seqs, labels, coords, frac=0.5):
"""
Data augmentation: applies reverse complement randomly to a fraction of
sequences and labels.
Assumes seqs are arranged in ACGT. Then ::-1 gives TGCA which is revcomp.
NOTE: Performs in-place modification.
"""
pos_to_rc = np.random.choice(range(seqs.shape[0]),
size=int(seqs.shape[0]*frac),
replace=False)
seqs[pos_to_rc] = seqs[pos_to_rc, ::-1, ::-1]
labels[pos_to_rc] = labels[pos_to_rc, ::-1]
coords[pos_to_rc,2] = "r"
return seqs, labels, coords
def crop_revcomp_augment(seqs, labels, coords, seq_crop_width, label_crop_width, add_revcomp, rc_frac=0.5, shuffle=False):
"""
seqs: B x IL x 4
labels: B x OL
Applies random crop to seqs and labels and reverse complements rc_frac.
"""
assert(seqs.shape[0]==labels.shape[0])
# this does not modify seqs and labels
#mod_seqs, mod_labels, mod_coords = random_crop(seqs, labels, seq_crop_width, label_crop_width, coords)
mod_seqs, mod_labels, mod_coords = seqs, labels, coords
# this modifies mod_seqs, mod_labels in-place
if add_revcomp:
mod_seqs, mod_labels, mod_coords = random_rev_comp(mod_seqs, mod_labels, mod_coords, frac=rc_frac)
if shuffle:
perm = np.random.permutation(mod_seqs.shape[0])
mod_seqs = mod_seqs[perm]
mod_labels = mod_labels[perm]
mod_coords = mod_coords[perm]
return mod_seqs, mod_labels, mod_coords
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