def add_recording_in_kwd(kwd,
recording_id=0,
downsample_factor=None,
nchannels=None,
nsamples=None,
data=None):
if isinstance(kwd, string_types):
kwd = open_file(kwd, 'a')
to_close = True
else:
to_close = False
if data is not None:
nsamples, nchannels = data.shape
recording = kwd.createGroup('/recordings', str(recording_id))
recording._f_setAttr('downsample_factor', downsample_factor)
dataset = kwd.createEArray(recording,
'data',
tb.Int16Atom(), (0, nchannels),
expectedrows=nsamples)
# Add raw data.
if data is not None:
assert data.shape[1] == nchannels
data_int16 = convert_dtype(data, np.int16)
dataset.append(data_int16)
kwd.createGroup(recording, 'filter')
# TODO: filter
if to_close:
kwd.close()
return kwd
def add_recording_in_kwd(kwd, recording_id=0,
downsample_factor=None, nchannels=None,
nsamples=None, data=None):
if isinstance(kwd, string_types):
kwd = open_file(kwd, 'a')
to_close = True
else:
to_close = False
if data is not None:
nsamples, nchannels = data.shape
recording = kwd.createGroup('/recordings', str(recording_id))
recording._f_setAttr('downsample_factor', downsample_factor)
dataset = kwd.createEArray(recording, 'data',
tb.Int16Atom(),
(0, nchannels), expectedrows=nsamples)
# Add raw data.
if data is not None:
assert data.shape[1] == nchannels
data_int16 = convert_dtype(data, np.int16)
dataset.append(data_int16)
kwd.createGroup(recording, 'filter')
# TODO: filter
if to_close:
kwd.close()
return kwd
def add_recording_in_kwd(kwd,
recording_id=0,
downsample_factor=None,
nchannels=None,
nsamples=None,
data=None,
name=None,
sample_rate=None,
start_time=None,
start_sample=None,
bit_depth=None,
band_high=None,
band_low=None,
filter_name=''):
if isinstance(kwd, string_types):
kwd = open_file(kwd, 'a')
to_close = True
else:
to_close = False
if data is not None:
nsamples, nchannels = data.shape
try:
recording = kwd.createGroup('/recordings', str(recording_id))
except tb.NodeError:
if to_close:
kwd.close()
return kwd
recording._f_setAttr('downsample_factor', downsample_factor)
dataset = kwd.createEArray(recording,
'data',
tb.Int16Atom(), (0, nchannels),
expectedrows=nsamples)
# Add raw data.
if data is not None:
assert data.shape[1] == nchannels
data_int16 = convert_dtype(data, np.int16)
dataset.append(data_int16)
# Add filter info.
fil = kwd.createGroup(recording, 'filter')
fil._f_setAttr('name', filter_name)
# Copy recording info from kwik to kwd.
recording._f_setAttr('name', name)
recording._f_setAttr('start_time', start_time)
recording._f_setAttr('start_sample', start_sample)
recording._f_setAttr('sample_rate', sample_rate)
recording._f_setAttr('bit_depth', bit_depth)
recording._f_setAttr('band_high', band_high)
recording._f_setAttr('band_low', band_low)
if to_close:
kwd.close()
return kwd
def load(self, fname, run_idx, seq, load_idx_rows, load_idx_cols,
file_content, tmp_data, pos_idxs):
"""Load the data.
Args:
fname: The name of the file containing the data to be loaded.
run_idx: The run currently looked at (not the actual run number but
the index in the overall run list). This is needed to get
the corresponding preprocessing information.
seq: The sequence number to be loaded.
load_idx_rows: The data of which rows should be loaded only.
load_idx_cols: The data of which columns should be loaded only.
file_content: All metadata in corresponding to the data.
tmp_data: Array where the data is stored into.
pos_idxs: Which data parts should be loaded (shich columns and rows),
load and store positions are the same.
"""
self.pos_idxs = pos_idxs
# fill in the wildcard for the module position
fname = glob.glob(fname)[0]
# load data
with h5py.File(fname, "r") as f:
idx = (Ellipsis, load_idx_rows, load_idx_cols)
raw_data = f[self._path['data']][idx]
utils.check_data_type(raw_data)
# cfel stores the data as int16 whereas xfel stores it at uint16
# -> have one common type uint16 because ADC values also should be
# positive
utils.convert_dtype(raw_data, np.uint16)
print("raw_data.shape", raw_data.shape)
print("self._raw_shape", self._raw_shape)
self.get_seq_number(file_content[self._path['seq_number']])
self.get_frame_loss_indices()
self.fillup_frame_loss(tmp_data, raw_data)
def read_next_spike(self):
if self.spike >= self.klusters_data[self.shank]['nspikes']:
return {}
data = self.klusters_data[self.shank]
read = {}
read['cluster'] = data['aclu'][self.spike]
fet = data['fet'].next()
read['time'] = fet[-1]
read['fet'] = convert_dtype(fet, np.float32)
if 'spk' in data:
read['spk'] = data['spk'].next()
if 'uspk' in data:
read['uspk'] = data['uspk'].next()
if 'mask' in data:
read['mask'] = data['mask'].next()
# else:
# read['mask'] = np.ones_like(read['fet'])
self.spike += 1
return read
def read_next_spike(self):
if self.spike >= self.klusters_data[self.shank]['nspikes']:
return {}
data = self.klusters_data[self.shank]
read = {}
read['cluster'] = data['aclu'][self.spike]
fet = data['fet'].next()
read['time'] = fet[-1]
read['fet'] = convert_dtype(fet, np.float32)
if 'spk' in data:
read['spk'] = data['spk'].next()
if 'uspk' in data:
read['uspk'] = data['uspk'].next()
if 'mask' in data:
read['mask'] = data['mask'].next()
# else:
# read['mask'] = np.ones_like(read['fet'])
self.spike += 1
return read
