def add_spikes(
fd,
channel_group_id=None,
clustering='main',
time_samples=None,
time_fractional=0,
recording=0,
cluster=0,
cluster_original=0,
features_masks=None,
features=None,
masks=None,
waveforms_raw=None,
waveforms_filtered=None,
):
"""fd is returned by `open_files`: it is a dict {type: tb_file_handle}."""
if channel_group_id is None:
channel_group_id = '0'
kwik = fd.get('kwik', None)
kwx = fd.get('kwx', None)
# The KWIK needs to be there.
assert kwik is not None
# The channel group id containing the new cluster group must be specified.
assert channel_group_id is not None
spikes = kwik.root.channel_groups.__getattr__(channel_group_id).spikes
time_samples = ensure_vector(time_samples)
nspikes = len(time_samples)
ds_features_masks = kwx.root.channel_groups.__getattr__(
channel_group_id).features_masks
ds_waveforms_raw = kwx.root.channel_groups.__getattr__(
channel_group_id).waveforms_raw
ds_waveforms_filtered = kwx.root.channel_groups.__getattr__(
channel_group_id).waveforms_filtered
nfeatures = ds_features_masks.shape[1]
if features_masks is None:
# Default features and masks
if features is None:
features = np.zeros((nspikes, nfeatures), dtype=np.float32)
if masks is None:
masks = np.zeros((features.shape[0], nfeatures), dtype=np.float32)
# Ensure features and masks have the right number of dimensions.
# features.shape is (1, nfeatures)
# masks.shape is however (nchannels,)
if features.ndim == 1:
features = np.expand_dims(features, axis=0)
if masks.ndim == 1:
masks = np.expand_dims(masks, axis=0)
# masks.shape is now (1,nchannels,)
# Tile the masks if needed: same mask value on each channel.
if masks.shape[1] < features.shape[1]:
nfeatures_per_channel = features.shape[1] // masks.shape[1]
masks = np.repeat(masks, nfeatures_per_channel, axis=1)
# masks.shape is (1, nfeatures) - what we want
# Concatenate features and masks
features_masks = np.dstack((features, masks))
time_fractional = ensure_vector(time_fractional, size=nspikes)
recording = ensure_vector(recording, size=nspikes)
cluster = ensure_vector(cluster, size=nspikes)
cluster_original = ensure_vector(cluster_original, size=nspikes)
if waveforms_raw is None:
waveforms_raw = empty_row(ds_waveforms_raw, nrows=nspikes)
if waveforms_raw.ndim < 3:
waveforms_raw = np.expand_dims(waveforms_raw, axis=0)
if waveforms_filtered is None:
waveforms_filtered = empty_row(ds_waveforms_filtered, nrows=nspikes)
if waveforms_filtered.ndim < 3:
waveforms_filtered = np.expand_dims(waveforms_filtered, axis=0)
# Make sure we add the correct number of rows to every object.
assert len(time_samples) == nspikes
assert len(time_fractional) == nspikes
assert len(recording) == nspikes
assert len(cluster) == nspikes
assert len(cluster_original) == nspikes
assert features_masks.shape[0] == nspikes
assert waveforms_raw.shape[0] == nspikes
assert waveforms_filtered.shape[0] == nspikes
# WARNING: need to normalize the waveforms before converting them to
# int16. They need to be in [-1,1].
# _normalize_inplace(waveforms_raw)
# _normalize_inplace(waveforms_filtered)
spikes.time_samples.append(time_samples)
spikes.time_fractional.append(time_fractional)
spikes.recording.append(recording)
spikes.clusters.main.append(cluster)
spikes.clusters.original.append(cluster_original)
ds_features_masks.append(features_masks)
# HACK: disable normalization for the time being, to try to
# retrieve old output of SD1
# ds_waveforms_raw.append(convert_dtype(waveforms_raw, np.int16))
# ds_waveforms_filtered.append(convert_dtype(waveforms_filtered, np.int16))
ds_waveforms_raw.append(waveforms_raw.astype(np.int16))
ds_waveforms_filtered.append(waveforms_filtered.astype(np.int16))
def add_spikes(fd, channel_group_id=None,
time_samples=None, time_fractional=0,
recording=0, cluster=0, cluster_original=0,
features_masks=None, features=None, masks=None,
waveforms_raw=None, waveforms_filtered=None,
fill_empty=True):
"""fd is returned by `open_files`: it is a dict {type: tb_file_handle}."""
if channel_group_id is None:
channel_group_id = '0'
kwik = fd.get('kwik', None)
kwx = fd.get('kwx', None)
# The KWIK needs to be there.
assert kwik is not None
# The channel group id containing the new cluster group must be specified.
assert channel_group_id is not None
spikes = kwik.root.channel_groups.__getattr__(channel_group_id).spikes
time_samples = ensure_vector(time_samples)
nspikes = len(time_samples)
ds_features_masks = kwx.root.channel_groups.__getattr__(channel_group_id).features_masks
ds_waveforms_raw = kwx.root.channel_groups.__getattr__(channel_group_id).waveforms_raw
ds_waveforms_filtered = kwx.root.channel_groups.__getattr__(channel_group_id).waveforms_filtered
nfeatures = ds_features_masks.shape[1]
if features_masks is None:
# Deal with masks only if they're supported in the file.
if ds_features_masks.ndim == 2:
features_masks = features
# Otherwise, deal with masks.
# Fill features masks only if features OR masks is specified.
elif not(features is None and masks is None):
# Unless features and masks are both None,
# make sure features AND masks are filled.
if features is None:
features = np.zeros((nspikes, nfeatures), dtype=np.float32)
if masks is None:
masks = np.zeros((features.shape[0], nfeatures), dtype=np.float32)
# Ensure both have 2 dimensions.
if features.ndim == 1:
features = np.expand_dims(features, axis=0)
if masks.ndim == 1:
masks = np.expand_dims(masks, axis=0)
# Tile the masks if needed: same mask value on each channel.
if masks.shape[1] < features.shape[1]:
nfeatures_per_channel = features.shape[1] // masks.shape[1]
masks = np.repeat(masks, nfeatures_per_channel, axis=1)
# Concatenate features and masks.
features_masks = np.dstack((features, masks))
time_fractional = ensure_vector(time_fractional, size=nspikes)
recording = ensure_vector(recording, size=nspikes)
cluster = ensure_vector(cluster, size=nspikes)
cluster_original = ensure_vector(cluster_original, size=nspikes)
if fill_empty and features_masks is None:
features_masks = empty_row(ds_features_masks, nrows=nspikes)
if features_masks is not None and features_masks.ndim < ds_features_masks.ndim:
features_masks = np.expand_dims(features_masks, axis=0)
if fill_empty and waveforms_raw is None:
waveforms_raw = empty_row(ds_waveforms_raw, nrows=nspikes)
if waveforms_raw is not None and waveforms_raw.ndim < 3:
waveforms_raw = np.expand_dims(waveforms_raw, axis=0)
if fill_empty and waveforms_filtered is None:
waveforms_filtered = empty_row(ds_waveforms_filtered, nrows=nspikes)
if waveforms_filtered is not None and waveforms_filtered.ndim < 3:
waveforms_filtered = np.expand_dims(waveforms_filtered, axis=0)
# Make sure we add the correct number of rows to every object.
assert len(time_samples) == nspikes
assert len(time_fractional) == nspikes
assert len(recording) == nspikes
assert len(cluster) == nspikes
assert len(cluster_original) == nspikes
if features_masks is not None:
assert features_masks.shape[0] == nspikes
if waveforms_raw is not None:
assert waveforms_raw.shape[0] == nspikes
if waveforms_filtered is not None:
assert waveforms_filtered.shape[0] == nspikes
spikes.time_samples.append(time_samples)
spikes.time_fractional.append(time_fractional)
spikes.recording.append(recording)
spikes.clusters.main.append(cluster)
spikes.clusters.original.append(cluster_original)
if features_masks is not None:
ds_features_masks.append(features_masks)
if waveforms_raw is not None:
ds_waveforms_raw.append(waveforms_raw.astype(np.int16))
if waveforms_filtered is not None:
ds_waveforms_filtered.append(waveforms_filtered.astype(np.int16))
def add_spikes(fd, channel_group_id=None, clustering='main',
time_samples=None, time_fractional=0,
recording=0, cluster=0, cluster_original=0,
features_masks=None, features=None, masks=None,
waveforms_raw=None, waveforms_filtered=None,):
"""fd is returned by `open_files`: it is a dict {type: tb_file_handle}."""
if channel_group_id is None:
channel_group_id = '0'
kwik = fd.get('kwik', None)
kwx = fd.get('kwx', None)
# The KWIK needs to be there.
assert kwik is not None
# The channel group id containing the new cluster group must be specified.
assert channel_group_id is not None
spikes = kwik.root.channel_groups.__getattr__(channel_group_id).spikes
time_samples = ensure_vector(time_samples)
nspikes = len(time_samples)
ds_features_masks = kwx.root.channel_groups.__getattr__(channel_group_id).features_masks
ds_waveforms_raw = kwx.root.channel_groups.__getattr__(channel_group_id).waveforms_raw
ds_waveforms_filtered = kwx.root.channel_groups.__getattr__(channel_group_id).waveforms_filtered
nfeatures = ds_features_masks.shape[1]
if features_masks is None:
# Default features and masks
if features is None:
features = np.zeros((nspikes, nfeatures), dtype=np.float32)
if masks is None:
masks = np.zeros((features.shape[0], nfeatures), dtype=np.float32)
# Ensure features and masks have the right number of dimensions.
# features.shape is (1, nfeatures)
# masks.shape is however (nchannels,)
if features.ndim == 1:
features = np.expand_dims(features, axis=0)
if masks.ndim == 1:
masks = np.expand_dims(masks, axis=0)
# masks.shape is now (1,nchannels,)
# Tile the masks if needed: same mask value on each channel.
if masks.shape[1] < features.shape[1]:
nfeatures_per_channel = features.shape[1] // masks.shape[1]
masks = np.repeat(masks, nfeatures_per_channel, axis = 1)
# masks.shape is (1, nfeatures) - what we want
# Concatenate features and masks
features_masks = np.dstack((features, masks))
time_fractional = ensure_vector(time_fractional, size=nspikes)
recording = ensure_vector(recording, size=nspikes)
cluster = ensure_vector(cluster, size=nspikes)
cluster_original = ensure_vector(cluster_original, size=nspikes)
if waveforms_raw is None:
waveforms_raw = empty_row(ds_waveforms_raw, nrows=nspikes)
if waveforms_raw.ndim < 3:
waveforms_raw = np.expand_dims(waveforms_raw, axis=0)
if waveforms_filtered is None:
waveforms_filtered = empty_row(ds_waveforms_filtered, nrows=nspikes)
if waveforms_filtered.ndim < 3:
waveforms_filtered = np.expand_dims(waveforms_filtered, axis=0)
# Make sure we add the correct number of rows to every object.
assert len(time_samples) == nspikes
assert len(time_fractional) == nspikes
assert len(recording) == nspikes
assert len(cluster) == nspikes
assert len(cluster_original) == nspikes
assert features_masks.shape[0] == nspikes
assert waveforms_raw.shape[0] == nspikes
assert waveforms_filtered.shape[0] == nspikes
# WARNING: need to normalize the waveforms before converting them to
# int16. They need to be in [-1,1].
# _normalize_inplace(waveforms_raw)
# _normalize_inplace(waveforms_filtered)
spikes.time_samples.append(time_samples)
spikes.time_fractional.append(time_fractional)
spikes.recording.append(recording)
spikes.clusters.main.append(cluster)
spikes.clusters.original.append(cluster_original)
ds_features_masks.append(features_masks)
# HACK: disable normalization for the time being, to try to
# retrieve old output of SD1
# ds_waveforms_raw.append(convert_dtype(waveforms_raw, np.int16))
# ds_waveforms_filtered.append(convert_dtype(waveforms_filtered, np.int16))
ds_waveforms_raw.append(waveforms_raw.astype(np.int16))
ds_waveforms_filtered.append(waveforms_filtered.astype(np.int16))
