def _CreateChannelData(data_source, request, max_samples):
"""Returns a list of channel names and a dictionary of their values.
Args:
data_source: The DataSource for the waveform data.
request: A DataRequest proto instance.
max_samples: The maximum number of samples in one channel response.
Returns:
A dictionary of channel names mapped to the requested time slice of their
data and an ordered list of the channel names.
Raises:
ValueError: Too many feature keys provided (only handles raw features or
subtraction of two features).
"""
channel_indices = _GetChannelIndicesInChannelDataIdList(
request.channel_data_ids)
single_channel_data = data_source.GetChannelData(
channel_indices, request.chunk_start, request.chunk_duration_secs)
subsampling = utils.GetSubsamplingRate(
len(single_channel_data.values()[0]), max_samples)
req_channel_data = {}
channel_names = []
for channel_data_id in request.channel_data_ids:
if channel_data_id.HasField('bipolar_channel'):
primary_index = channel_data_id.bipolar_channel.index
primary_data = _FilterData(single_channel_data[str(primary_index)],
primary_index, data_source, request)
ref_index = channel_data_id.bipolar_channel.referential_index
ref_data = _FilterData(single_channel_data[str(ref_index)],
ref_index, data_source, request)
channel_data = [
reference - primary
for (primary, reference) in zip(primary_data, ref_data)
]
channel_name = '-'.join(
data_source.GetChannelName(index)
for index in [primary_index, ref_index])
elif channel_data_id.HasField('single_channel'):
index = channel_data_id.single_channel.index
channel_data = _FilterData(single_channel_data[str(index)], index,
data_source, request)
channel_name = data_source.GetChannelName(index)
else:
raise ValueError('Unfamiliary channel type %s' % channel_data_id)
req_channel_data[channel_name] = channel_data[::subsampling]
channel_names.append(channel_name)
return req_channel_data, channel_names
def _CreateChannelData(data_source,
channel_data_ids,
low_cut,
high_cut,
notch,
start=0,
duration=None,
max_samples=None):
"""Returns a list of channel names and a dictionary of their values.
Args:
data_source: The DataSource for the waveform data.
channel_data_ids: ChannelDataIds list.
low_cut: lower frequency to apply a band-pass filter.
high_cut: higher frequency to apply a band-pass filter.
notch: frequency to apply a notch filter.
start: start time to crop the data, relative to the start of the file (in
seconds). Defaults to the start of the file.
duration: duration to crop from the data, in seconds. If None, will get the
whole file data.
max_samples: The maximum number of samples in one channel response.
If None, there is no maximum limit.
Returns:
A dictionary of channel names mapped to the requested time slice of their
data and an ordered list of the channel names.
Raises:
ValueError: Too many feature keys provided (only handles raw features or
subtraction of two features).
"""
if duration is None:
duration = data_source.GetLength()
channel_indices = _GetChannelIndicesInChannelDataIdList(channel_data_ids)
single_channel_data = data_source.GetChannelData(channel_indices, start,
duration)
subsampling = 1 if max_samples is None else utils.GetSubsamplingRate(
len(list(single_channel_data.values())[0]), max_samples)
def _GetFilteredData(index):
"""Wrapper to call _FilterData function.
Args:
index: the index for the selected channel.
Returns:
Filtered data for the selected channel.
"""
return _FilterData(single_channel_data[str(index)], index, data_source,
low_cut, high_cut, notch)
req_channel_data = {}
channel_names = []
for channel_data_id in channel_data_ids:
if channel_data_id.HasField('bipolar_channel'):
primary_index = channel_data_id.bipolar_channel.index
primary_data = _GetFilteredData(primary_index)
ref_index = channel_data_id.bipolar_channel.referential_index
ref_data = _GetFilteredData(ref_index)
channel_data = [
reference - primary
for (primary, reference) in zip(primary_data, ref_data)
]
channel_name = '-'.join(
data_source.GetChannelName(index)
for index in [primary_index, ref_index])
elif channel_data_id.HasField('single_channel'):
index = channel_data_id.single_channel.index
channel_data = _GetFilteredData(index)
channel_name = data_source.GetChannelName(index)
else:
raise ValueError('Unfamiliary channel type %s' % channel_data_id)
req_channel_data[channel_name] = channel_data[::subsampling]
channel_names.append(channel_name)
return req_channel_data, channel_names