def segment(input_data, n_samples, overlap=DEFAULT_SEGMENT_OVERLAP):
"""Break into segments length n_samples.
Overlap of 2.0 starts a new segment halfway into previous, overlap=1 is
no overlap. overlap should divide into n_samples. Probably should
consider a nicer definition such as in pyfusion 0
"""
from pyfusion.data.base import DataSet
from pyfusion.data.timeseries import TimeseriesData
if isinstance(input_data, DataSet):
output_dataset = DataSet()
for ii,data in enumerate(input_data):
try:
output_dataset.update(data.segment(n_samples))
except AttributeError:
pyfusion.logger.warning("Data filter 'segment' not applied to item in dataset")
return output_dataset
output_data = DataSet('segmented_%s, %d samples, %.3f overlap' %(datetime.now(), n_samples, overlap))
for el in arange(0,len(input_data.timebase), n_samples/overlap):
if input_data.signal.ndim == 1:
tmp_data = TimeseriesData(timebase=input_data.timebase[el:el+n_samples],
signal=input_data.signal[el:el+n_samples],
channels=input_data.channels, bypass_length_check=True)
else:
tmp_data = TimeseriesData(timebase=input_data.timebase[el:el+n_samples],
signal=input_data.signal[:,el:el+n_samples],
channels=input_data.channels, bypass_length_check=True)
tmp_data.meta = input_data.meta.copy()
tmp_data.history = input_data.history # bdb - may be redundant now meta is copied
output_data.add(tmp_data)
return output_data
def segment(input_data, n_samples, overlap=DEFAULT_SEGMENT_OVERLAP):
"""Break into segments length n_samples.
Overlap of 2.0 starts a new segment halfway into previous, overlap=1 is
no overlap. overlap should divide into n_samples. Probably should
consider a nicer definition such as in pyfusion 0
"""
from pyfusion.data.base import DataSet
from pyfusion.data.timeseries import TimeseriesData
if isinstance(input_data, DataSet):
output_dataset = DataSet()
for ii, data in enumerate(input_data):
try:
output_dataset.update(data.segment(n_samples))
except AttributeError:
pyfusion.logger.warning(
"Data filter 'segment' not applied to item in dataset")
return output_dataset
output_data = DataSet('segmented_%s, %d samples, %.3f overlap' %
(datetime.now(), n_samples, overlap))
for el in arange(0, len(input_data.timebase), n_samples / overlap):
if input_data.signal.ndim == 1:
tmp_data = TimeseriesData(
timebase=input_data.timebase[el:el + n_samples],
signal=input_data.signal[el:el + n_samples],
channels=input_data.channels,
bypass_length_check=True)
else:
tmp_data = TimeseriesData(
timebase=input_data.timebase[el:el + n_samples],
signal=input_data.signal[:, el:el + n_samples],
channels=input_data.channels,
bypass_length_check=True)
tmp_data.meta = input_data.meta.copy()
tmp_data.history = input_data.history # bdb - may be redundant now meta is copied
output_data.add(tmp_data)
return output_data
