def read_features(filename_fet, nchannels, fetdim, freq):
"""Read a .fet file and return the normalize features array,
as well as the spiketimes."""
features = load_text(filename_fet, np.int32, skiprows=1)
features = np.array(features, dtype=np.float32)
# HACK: There are either 1 or 5 dimensions more than fetdim*nchannels
# we can't be sure so we first try 1, if it does not work we try 5.
for nextradim in [1, 5]:
try:
features = features.reshape((-1,
fetdim * nchannels + nextradim))
# if the features array could be reshape, directly break the loop
break
except ValueError:
features = None
if features is None:
raise ValueError("""The number of columns in the feature matrix
is not fetdim (%d) x nchannels (%d) + 1 or 5.""" %
(fetdim, nchannels))
# get the spiketimes
spiketimes = features[:,-1].copy()
spiketimes *= (1. / freq)
# count the number of extra features
nextrafet = features.shape[1] - nchannels * fetdim
# normalize normal features while keeping symmetry
features[:,:-nextrafet] = normalize(features[:,:-nextrafet],
symmetric=True)
# normalize extra features without keeping symmetry
features[:,-nextrafet:] = normalize(features[:,-nextrafet:],
symmetric=False)
return features, spiketimes
def read_masks(filename_mask, fetdim):
full_masks = load_text(filename_mask, np.float32, skiprows=1)
masks = full_masks[:,:-1:fetdim]
return masks, full_masks
def read_clusters(filename_clu):
clusters = load_text(filename_clu, np.int32)
clusters = clusters[1:]
return clusters
