def raw_chunkify(signal,
mapping_table,
chunk_len,
kmer_len,
normalisation,
downsample_factor,
interpolation,
mapping_attrs=None):
""" Generate labelled data chunks from raw signal and mapping table
"""
assert len(signal) >= chunk_len
assert normalisation in AVAILABLE_NORMALISATIONS
assert mapping_table_is_registered(signal, mapping_table)
ml = len(signal) // chunk_len
ub = ml * chunk_len
signal, mapping_table = trim_signal_and_mapping(signal, mapping_table, 0,
ub)
assert mapping_table_is_registered(signal, mapping_table)
new_inMat = signal.reshape((ml, chunk_len, 1))
if normalisation == "per-chunk":
chunk_medians = np.median(new_inMat, axis=1, keepdims=True)
chunk_mads = mad(new_inMat, axis=1, keepdims=True)
new_inMat = (new_inMat - chunk_medians) / chunk_mads
elif normalisation == "per-read":
new_inMat = (new_inMat - np.median(new_inMat)) / mad(new_inMat)
else:
assert normalisation == "none"
if interpolation:
block_midpoints = np.arange(0, ub, downsample_factor)
pos = interpolate_pos(mapping_table, mapping_attrs)(block_midpoints,
kmer_len)
sig_labels = interpolate_labels(mapping_table,
mapping_attrs)(block_midpoints,
kmer_len)
sig_labels[np.ediff1d(pos, to_begin=1) == 0] = 0
sig_labels = sig_labels.reshape((ml, -1))
else:
all_labels = labels_from_mapping_table(mapping_table['kmer'], kmer_len)
labels = all_labels[mapping_table['move'] > 0]
all_starts = mapping_table['start'][index_of_previous_non_zero(
mapping_table['move'])]
starts = all_starts[mapping_table['move'] > 0]
idx = np.zeros(ub, dtype=np.int)
idx[starts] = np.arange(len(labels)) + 1
idx = fill_zeros_with_prev(idx)
idx = idx.reshape((ml, chunk_len))[:, ::downsample_factor]
idx = np.apply_along_axis(replace_repeats_with_zero, 1, idx)
sig_labels = np.concatenate([[0], labels])[idx].astype('i4')
# Bad state isn't supported yet with raw models
sig_bad = np.zeros((ml, chunk_len), dtype=bool)
return new_inMat, sig_labels, sig_bad
def test_007_mad_keepdims(self):
x = np.zeros((5, 6, 7))
self.assertTrue(
np.allclose(maths.mad(x, axis=0, keepdims=True), np.zeros(
(1, 6, 7))))
self.assertTrue(
np.allclose(maths.mad(x, axis=1, keepdims=True), np.zeros(
(5, 1, 7))))
self.assertTrue(
np.allclose(maths.mad(x, axis=2, keepdims=True), np.zeros(
(5, 6, 1))))
def raw_worker(fast5_file_name, trim, open_pore_fraction, kmer_len, transducer, bad, min_prob,
alphabet=DEFAULT_ALPHABET, skip=5.0, trans=None):
""" Worker function for basecall_network.py for basecalling from raw data
This worker used the global variable `calc_post` which is set by
init_worker. `calc_post` is an unpickled compiled sloika model that
is used to calculate a posterior matrix over states
:param open_pore_fraction: maximum allowed fraction of signal length to
trim due to classification as open pore signal
:param trim: (int, int) events to remove from read beginning and end
:param kmer_len, min_prob, transducer, bad, trans, skip: see `decode_post`
:param fast5_file_name: filename for single-read fast5 file with raw data
"""
from sloika import batch, config
try:
with fast5.Reader(fast5_file_name) as f5:
signal = f5.get_read(raw=True)
sn = f5.filename_short
except Exception as e:
sys.stderr.write("Error getting raw data for file {}\n{!r}\n".format(fast5_file_name, e))
return None
signal = batch.trim_open_pore(signal, open_pore_fraction)
signal = util.trim_array(signal, *trim)
if signal.size == 0:
sys.stderr.write("Read too short in file {}\n".format(fast5_file_name))
return None
inMat = (signal - np.median(signal)) / mad(signal)
inMat = inMat[:, None, None].astype(config.sloika_dtype)
score, call = decode_post(calc_post(inMat), kmer_len, transducer, bad, min_prob, skip, trans, nbase=len(alphabet))
return sn, score, call, inMat.shape[0]
def trim_open_pore(signal,
max_op_fraction=0.3,
var_method='mad',
window_size=100):
"""Locate raw read in signal by thresholding local variance
:param signal: raw data containing a read
:param max_op_fraction: (float) Maximum expected fraction of signal that
consists of open pore. Higher values will find smaller reads at the
cost of slightly truncating longer reads.
:param var_method: ('std' | 'mad') method used to compute the local
variation. std: standard deviation, mad: Median Absolute Deviation
:param window_size: size of patches used to estimate local variance
"""
assert var_method in TRIM_OPEN_PORE_LOCAL_VAR_METHODS, "var_method not understood: {}".format(
var_method)
ml = len(signal) // window_size
ub = ml * window_size
if var_method == 'std':
local_var = signal[:ub].reshape((ml, window_size)).std(1)
if var_method == 'mad':
sig_chunks = signal[:ub].reshape((ml, window_size))
local_var = maths.mad(sig_chunks, axis=1)
probably_read = (local_var > np.percentile(local_var,
100 * max_op_fraction))
ix = np.arange(local_var.shape[0])[probably_read]
start = ix.min() * window_size
end = (ix.max() + 1) * window_size
return signal[start:end]
def raw_remap(ref, signal, min_prob, kmer_len, prior, slip):
""" Map raw signal to reference sequence using transducer model"""
from sloika import config # local import to avoid CUDA init in main thread
inMat = (signal - np.median(signal)) / mad(signal)
inMat = inMat[:, None, None].astype(config.sloika_dtype)
post = sloika.decode.prepare_post(batch.calc_post(inMat),
min_prob=min_prob,
drop_bad=False)
kmers = np.array(bio.seq_to_kmers(ref, kmer_len))
seq = [batch.kmer_to_state[k] + 1 for k in kmers]
prior0 = None if prior[0] is None else sloika.util.geometric_prior(
len(seq), prior[0])
prior1 = None if prior[1] is None else sloika.util.geometric_prior(
len(seq), prior[1], rev=True)
score, path = sloika.transducer.map_to_sequence(post,
seq,
slip=slip,
prior_initial=prior0,
prior_final=prior1,
log=False)
mapping_dtype = [
('start', '<i8'),
('length', '<i8'),
('seq_pos', '<i8'),
('move', '<i8'),
('kmer', 'S{}'.format(kmer_len)),
('good_emission', '?'),
]
mapping_table = np.zeros(post.shape[0], dtype=mapping_dtype)
stride = int(np.ceil(signal.shape[0] / float(post.shape[0])))
mapping_table['start'] = np.arange(
0, signal.shape[0], stride, dtype=np.int) - stride // 2
mapping_table['length'] = stride
mapping_table['seq_pos'] = path
mapping_table['move'] = np.ediff1d(path, to_begin=1)
mapping_table['kmer'] = kmers[path]
# We set 'good_emission' for compatability only
mapping_table['good_emission'] = True
_, mapping_table = trim_signal_and_mapping(signal, mapping_table, 0,
len(signal))
return (score, mapping_table, path, seq)
def get_read_stats(self):
"""Combines stats based on events with output of .summary, assumes a
one read file.
"""
data = deepcopy(self.summary())
read = self.get_read()
n_events = len(read)
q = np.percentile(read['mean'], [10, 50, 90])
data['range_current'] = q[2] - q[0]
data['median_current'] = q[1]
data['num_events'] = n_events
data['median_sd'] = np.median(read['stdv'])
data['median_dwell'] = np.median(read['length'])
data['sd_current'] = np.std(read['mean'])
data['mad_current'] = mad(read['mean'])
data['eps'] = data['num_events'] / data['strand_duration']
return data
