def __init__(self,
datfile,
samplerate,
numchan,
dtype,
recording_channels=None,
frames_first=True,
geom=None,
offset=0,
gain=None):
RecordingExtractor.__init__(self)
self._datfile = Path(datfile)
self._frame_first = frames_first
self._dtype = str(dtype)
self._timeseries = read_binary(self._datfile, numchan, dtype,
frames_first, offset)
self._samplerate = float(samplerate)
self._gain = gain
self._geom = geom
if recording_channels is not None:
assert len(recording_channels) == self._timeseries.shape[0], \
'Provided recording channels have the wrong length'
self._channels = recording_channels
else:
self._channels = list(range(self._timeseries.shape[0]))
if geom is not None:
for m in range(self._timeseries.shape[0]):
self.set_channel_property(m, 'location', self._geom[m, :])
def __init__(self, npx_file, x_pitch=None, y_pitch=None):
RecordingExtractor.__init__(self)
self._npxfile = Path(npx_file)
numchan = 385
dtype = 'int16'
root = str(self._npxfile.stem).split('.')[0]
# find metafile in same folder
metafile = [
x for x in self._npxfile.parent.iterdir()
if 'meta' in str(x) and root in str(x) and 'ap' in str(x)
]
if len(metafile) == 0:
raise Exception(
"'meta' file for ap traces should be in the same folder.")
else:
metafile = metafile[0]
tot_chan, ap_chan, samplerate, locations = _parse_spikeglx_metafile(
metafile, x_pitch, y_pitch)
frames_first = True
self._timeseries = read_binary(self._npxfile,
tot_chan,
dtype,
frames_first,
offset=0)
self._samplerate = float(samplerate)
if ap_chan < tot_chan:
self._timeseries = self._timeseries[:ap_chan]
self._channels = list(range(self._timeseries.shape[0]))
if len(locations) > 0:
for m in range(self._timeseries.shape[0]):
self.set_channel_property(m, 'location', locations[m])
def __init__(self,
file_path,
sampling_frequency,
numchan,
dtype,
recording_channels=None,
time_axis=0,
geom=None,
offset=0,
gain=None):
RecordingExtractor.__init__(self)
self._datfile = Path(file_path)
self._time_axis = time_axis
self._dtype = str(dtype)
self._timeseries = read_binary(self._datfile, numchan, dtype,
time_axis, offset)
self._sampling_frequency = float(sampling_frequency)
self._gain = gain
self._geom = geom
if recording_channels is not None:
assert len(recording_channels) == self._timeseries.shape[0], \
'Provided recording channels have the wrong length'
self._channels = recording_channels
else:
self._channels = list(range(self._timeseries.shape[0]))
if geom is not None:
for m in range(self._timeseries.shape[0]):
self.set_channel_property(m, 'location', self._geom[m, :])
def __init__(self,
file_path,
sampling_frequency,
numchan,
dtype,
recording_channels=None,
time_axis=0,
geom=None,
offset=0,
gain=None,
is_filtered=None):
RecordingExtractor.__init__(self)
self._datfile = Path(file_path)
self._time_axis = time_axis
self._dtype = str(dtype)
self._sampling_frequency = float(sampling_frequency)
self._gain = gain
self._numchan = numchan
self._geom = geom
self._offset = offset
self._timeseries = read_binary(self._datfile, numchan, dtype,
time_axis, offset)
# keep track of filter status when dumping
if is_filtered is not None:
self.is_filtered = is_filtered
else:
self.is_filtered = False
if recording_channels is not None:
assert len(recording_channels) == self._timeseries.shape[0], \
'Provided recording channels have the wrong length'
self._channels = recording_channels
else:
self._channels = list(range(self._timeseries.shape[0]))
if geom is not None:
for idx, channel in enumerate(self._channels):
self.set_channel_property(channel, 'location',
self._geom[idx, :])
if 'numpy' in str(dtype):
dtype_str = str(dtype).replace("<class '", "").replace("'>", "")
# drop 'numpy
dtype_str = dtype_str.split('.')[1]
else:
dtype_str = str(dtype)
self._kwargs = {
'file_path': str(Path(file_path).absolute()),
'sampling_frequency': sampling_frequency,
'numchan': numchan,
'dtype': dtype_str,
'recording_channels': recording_channels,
'time_axis': time_axis,
'geom': geom,
'offset': offset,
'gain': gain,
'is_filtered': is_filtered
}
def __init__(self, file_path: PathType, sampling_frequency: float, numchan: int, dtype: DtypeType,
time_axis: int = 0, recording_channels: Optional[list] = None, geom: Optional[ArrayType] = None,
offset: Optional[float] = 0, gain: Optional[float] = None, gain_first: bool = True,
is_filtered: Optional[bool] = None):
RecordingExtractor.__init__(self)
self._datfile = Path(file_path)
self._time_axis = time_axis
self._dtype = str(dtype)
self._sampling_frequency = float(sampling_frequency)
self._gain = gain
self._numchan = numchan
self._geom = geom
self._offset = offset
self._gain_first = gain_first
self._timeseries = read_binary(self._datfile, numchan, dtype, time_axis, offset)
# keep track of filter status when dumping
if is_filtered is not None:
self.is_filtered = is_filtered
else:
self.is_filtered = False
if recording_channels is not None:
assert len(recording_channels) <= self._timeseries.shape[0], \
'Provided recording channels have the wrong length'
self._channels = recording_channels
else:
self._channels = list(range(self._timeseries.shape[0]))
if len(self._channels) == self._timeseries.shape[0]:
self._complete_channels = True
else:
assert max(self._channels) < self._timeseries.shape[0], "Channel ids exceed the number of " \
"available channels"
self._complete_channels = False
if geom is not None:
self.set_channel_locations(self._geom)
if 'numpy' in str(dtype):
dtype_str = str(dtype).replace("<class '", "").replace("'>", "")
# drop 'numpy
dtype_str = dtype_str.split('.')[1]
else:
dtype_str = str(dtype)
self._kwargs = {'file_path': str(Path(file_path).absolute()), 'sampling_frequency': sampling_frequency,
'numchan': numchan, 'dtype': dtype_str, 'recording_channels': recording_channels,
'time_axis': time_axis, 'geom': geom, 'offset': offset, 'gain': gain,
'is_filtered': is_filtered}
