def get_signal(fast5_fn, read_id, scale=True):
""" Get raw signal from read.
"""
with get_fast5_file(fast5_fn, 'r') as fast5_fp:
raw_sig = fast5_fp.get_read(read_id).get_raw_data()
if scale:
med, mad = mh.med_mad(raw_sig)
raw_sig = (raw_sig - med) / mad
return raw_sig
def get_signal(read, scale=True):
"""Get raw signal from read."""
try:
raw_sig = read.get_raw_data()
except IOError:
raise mh.MegaError("Error extracting raw data. Ensure VBZ plugin is " +
"installed (if applicable).")
if scale:
med, mad = mh.med_mad(raw_sig)
raw_sig = ((raw_sig - med) / mad).astype(np.float32)
return raw_sig
def extract_signal_info(self, fast5_fn, read_id, extract_dacs=False):
read = fast5_io.get_read(fast5_fn, read_id)
seq_summ_info = mh.extract_seq_summary_info(read)
dacs = scale_params = raw_sig = None
if extract_dacs:
# if not processing signal mappings, don't save dacs
dacs = fast5_io.get_signal(read, scale=False)
# scale parameters and trimming computed by guppy
if not self.model_type == PYGUPPY_NAME:
scale_params = mh.med_mad(dacs)
raw_sig = (dacs - scale_params[0]) / scale_params[1]
if self.model_type == TAI_NAME:
if raw_sig is None:
raw_sig = fast5_io.get_signal(read, scale=True)
sig_data = SIGNAL_DATA(
raw_signal=raw_sig, dacs=dacs, scale_params=scale_params,
raw_len=raw_sig.shape[0], fast5_fn=fast5_fn, read_id=read_id,
stride=self.stride)
return sig_data, seq_summ_info
elif self.model_type == FAST5_NAME:
bc_mod_post = fast5_io.get_posteriors(read)
if extract_dacs:
trim_start, trim_len = fast5_io.get_signal_trim_coordiates(
read)
dacs = dacs[trim_start:trim_start + trim_len]
sig_data = SIGNAL_DATA(
raw_len=bc_mod_post.shape[0] * self.stride, dacs=dacs,
fast5_fn=fast5_fn, read_id=read_id, stride=self.stride,
posteriors=bc_mod_post)
return sig_data, seq_summ_info
elif self.model_type == PYGUPPY_NAME:
if dacs is None:
dacs = fast5_io.get_signal(read, scale=False)
sig_data = SIGNAL_DATA(
dacs=dacs, raw_len=dacs.shape[0], fast5_fn=fast5_fn,
read_id=read_id, stride=self.stride,
channel_info=read.get_channel_info())
return sig_data, seq_summ_info
raise mh.MegaError('Invalid model type')