def get_max_loc_channel(params):
nodes, edges = get_nodes_and_edges(params)
max_loc_channel = 0
for key in edges.keys():
if len(edges[key]) > max_loc_channel:
max_loc_channel = len(edges[key])
return max_loc_channel
def plot_probe(ax, params, channel_ids=None):
nb_channels = params.getint('data', 'N_e')
probe = params.probe
nodes, edges = get_nodes_and_edges(params)
positions = []
for i in list(probe['channel_groups'][1]['geometry'].keys()):
positions.append(probe['channel_groups'][1]['geometry'][i])
positions = np.array(positions)
dx = np.median(np.diff(np.unique(
positions[:, 0]))) # horizontal inter-electrode distance
dy = np.median(np.diff(np.unique(
positions[:, 1]))) # vertical inter-electrode distance
if channel_ids is None:
channel_ids = np.arange(0, nb_channels)
patches = []
kwargs = dict(
radius=(min(dx, dy) / 2.0),
color='tab:gray',
alpha=0.5,
)
for channel_id in channel_ids:
xy = positions[nodes[channel_id]]
patch = mpatches.Circle(xy, **kwargs)
patches.append(patch)
collection = mcollections.PatchCollection(patches, match_original=True)
ax.set_aspect('equal')
ax.add_collection(collection)
return
def write_templates(path, params, extension):
max_loc_channel = get_max_loc_channel(params)
templates = io.load_data(params, 'templates', extension)
N_tm = templates.shape[1]//2
nodes, edges = get_nodes_and_edges(params)
if sparse_export:
n_channels_max = 0
for t in xrange(N_tm):
data = numpy.sum(numpy.sum(templates[:, t].toarray().reshape(N_e, N_t), 1) != 0)
if data > n_channels_max:
n_channels_max = data
else:
n_channels_max = N_e
if export_all:
to_write_sparse = numpy.zeros((N_tm + N_e, N_t, n_channels_max), dtype=numpy.float32)
mapping_sparse = -1 * numpy.ones((N_tm + N_e, n_channels_max), dtype=numpy.int32)
else:
to_write_sparse = numpy.zeros((N_tm, N_t, n_channels_max), dtype=numpy.float32)
mapping_sparse = -1 * numpy.ones((N_tm, n_channels_max), dtype=numpy.int32)
for t in xrange(N_tm):
tmp = templates[:, t].toarray().reshape(N_e, N_t).T
x, y = tmp.nonzero()
nb_loc = len(numpy.unique(y))
if sparse_export:
all_positions = numpy.zeros(y.max()+1, dtype=numpy.int32)
all_positions[numpy.unique(y)] = numpy.arange(nb_loc, dtype=numpy.int32)
pos = all_positions[y]
to_write_sparse[t, x, pos] = tmp[x, y]
mapping_sparse[t, numpy.arange(nb_loc)] = numpy.unique(y)
else:
pos = y
to_write_sparse[t, x, pos] = tmp[x, y]
if export_all:
garbage = io.load_data(params, 'garbage', extension)
for t in xrange(N_tm, N_tm + N_e):
elec = t - N_tm
spikes = garbage['gspikes'].pop('elec_%d' %elec).astype(numpy.uint64)
spikes = numpy.random.permutation(spikes)[:100]
mapping_sparse[t, 0] = t - N_tm
waveform = io.get_stas(params, times_i=spikes, labels_i=np.ones(len(spikes)), src=elec, neighs=[elec], nodes=nodes, mean_mode=True)
nb_loc = 1
if sparse_export:
to_write_sparse[t, :, 0] = waveform
else:
to_write_sparse[t, :, elec] = waveform
numpy.save(os.path.join(output_path, 'templates'), to_write_sparse)
if sparse_export:
numpy.save(os.path.join(output_path, 'template_ind'), mapping_sparse)
return N_tm
def get_max_loc_channel(params, extension):
if test_if_support(params, extension):
supports = io.load_data(params, 'supports', extension)
max_loc_channel = numpy.sum(supports, 1).max()
else:
nodes, edges = get_nodes_and_edges(params)
max_loc_channel = 0
for key in list(edges.keys()):
if len(edges[key]) > max_loc_channel:
max_loc_channel = len(edges[key])
return max_loc_channel
def get_neighbors(params, chan=None):
N_total = params.getint('data', 'N_total')
nodes, edges = get_nodes_and_edges(params, validating=True)
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
if chan is None:
# Select all the channels.
chans = inv_nodes[nodes]
else:
# Select only the neighboring channels of the best channel.
chans = inv_nodes[edges[nodes[chan]]]
return nodes, chans
def load_snippets(time_step_ids, params):
nb_channels = params.getint('data', 'N_e')
nb_time_steps = params.getint('detection', 'N_t')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
spatial_whitening = load_data(
params, 'spatial_whitening') if do_spatial_whitening else None
temporal_whitening = load_data(
params, 'temporal_whitening') if do_temporal_whitening else None
data_file = params.get_data_file()
chunk_size = nb_time_steps
nodes, edges = get_nodes_and_edges(params)
data_file.open()
_ = data_file.analyze(chunk_size) # i.e. count chunks in sources
snippets = []
for time_step_id in time_step_ids:
t_start = time_step_id - int(nb_time_steps - 1) // 2
idx = data_file.get_idx(t_start, chunk_size)
padding = (0, nb_time_steps - 1)
data, t_offset = data_file.get_data(idx,
chunk_size,
padding=padding,
nodes=nodes)
data = data[(t_start - t_offset) %
chunk_size:(t_start - t_offset) % chunk_size +
nb_time_steps, :]
if do_spatial_whitening:
data = np.dot(data, spatial_whitening)
if do_temporal_whitening:
data = sp.ndimage.filters.convolve1d(data,
temporal_whitening,
axis=0,
mode='constant')
snippets.append(data)
nb_snippets = len(snippets)
snippets = np.array(snippets)
snippets = np.reshape(snippets, (nb_snippets, nb_time_steps, nb_channels))
data_file.close()
return snippets
def plot_template(ax,
template,
params,
color='black',
vmin=None,
vmax=None,
label=None,
limits='auto'):
nb_channels = params.getint('data', 'N_e')
nb_time_steps = params.getint('detection', 'N_t')
probe = params.probe
nodes, edges = get_nodes_and_edges(params)
positions = []
for i in list(probe['channel_groups'][1]['geometry'].keys()):
positions.append(probe['channel_groups'][1]['geometry'][i])
positions = np.array(positions)
vmin = np.abs(np.min(template)) if vmin is None else vmin
vmax = np.abs(np.max(template)) if vmax is None else vmax
dx = np.median(np.diff(np.unique(
positions[:, 0]))) # horizontal inter-electrode distance
dy = np.median(np.diff(np.unique(
positions[:, 1]))) # vertical inter-electrode distance
x_scaling = 0.8 * dx / 1.0
y_scaling = 0.8 * dy / np.abs(vmax - vmin)
ax.set_aspect('equal')
for channel_id in range(0, nb_channels):
if np.any(template[:, channel_id] != 0.0):
x_c, y_c = positions[nodes[channel_id]]
x = x_scaling * np.linspace(-0.5, +0.5, num=nb_time_steps) + x_c
y = y_scaling * template[:, channel_id] + y_c
plot_kwargs = {
'color': color,
'label': label,
}
ax.plot(x, y, **plot_kwargs)
label = None # i.e. label first plot only
set_limits(ax, limits, positions)
return
def plot_snippets(ax,
snippets,
params,
color='black',
vmin=None,
vmax=None,
limits='auto'):
nb_channels = params.getint('data', 'N_e')
nb_time_steps = params.getint('detection', 'N_t')
probe = params.probe
nodes, edges = get_nodes_and_edges(params)
positions = []
for i in list(probe['channel_groups'][1]['geometry'].keys()):
positions.append(probe['channel_groups'][1]['geometry'][i])
positions = np.array(positions)
vmin = min(np.min(snippets), 0.0) if vmin is None else vmin
vmax = max(np.max(snippets), 0.0) if vmax is None else vmax
dx = np.median(np.diff(np.unique(
positions[:, 0]))) # horizontal inter-electrode distance
dy = np.median(np.diff(np.unique(
positions[:, 1]))) # vertical inter-electrode distance
x_scaling = 0.8 * dx / 1.0
y_scaling = 0.8 * dy / np.abs(vmax - vmin)
ax.set_aspect('equal')
for channel_id in range(0, nb_channels):
x_c, y_c = positions[nodes[channel_id]]
x = x_scaling * np.linspace(-0.5, +0.5, num=nb_time_steps) + x_c
for snippet in snippets:
y = y_scaling * snippet[:, channel_id] + y_c
ax.plot(x, y, color=color)
set_limits(ax, limits, positions)
return
def main(params, nb_cpu, nb_gpu, use_gpu):
# Part 1: Whitening
numpy.random.seed(420)
#params = detect_memory(params)
logger = init_logging(params.logfile)
logger = logging.getLogger('circus.whitening')
#################################################################
data_file = params.data_file
N_e = params.getint('data', 'N_e')
hdf5_compress = params.getboolean('data', 'hdf5_compress')
N_total = params.nb_channels
N_t = params.getint('detection', 'N_t')
dist_peaks = params.getint('detection', 'dist_peaks')
template_shift = params.getint('detection', 'template_shift')
file_out_suff = params.get('data', 'file_out_suff')
spike_thresh = params.getfloat('detection', 'spike_thresh')
spike_width = params.getfloat('detection', 'spike_width')
matched_filter = params.getboolean('detection', 'matched-filter')
matched_thresh = params.getfloat('detection', 'matched_thresh')
sign_peaks = params.get('detection', 'peaks')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
chunk_size = detect_memory(params, whitening=True)
plot_path = os.path.join(params.get('data', 'file_out_suff'), 'plots')
nodes, edges = get_nodes_and_edges(params)
safety_time = params.getint('whitening', 'safety_time')
safety_space = params.getboolean('whitening', 'safety_space')
nb_temp_white = min(max(20, comm.size), N_e)
max_silence_1 = int(20 * params.rate // comm.size)
max_silence_2 = 5000
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
jitter_range = params.getint('detection', 'jitter_range')
template_shift_2 = template_shift + jitter_range
use_hanning = params.getboolean('detection', 'hanning')
data_file.open()
#################################################################
if use_hanning:
hanning_filter = numpy.hanning(N_t)
if comm.rank == 0:
print_and_log(
["Analyzing data to get whitening matrices and thresholds..."],
'default', logger)
if use_gpu:
import cudamat as cmt
## Need to properly handle multi GPU per MPI nodes?
if nb_gpu > nb_cpu:
gpu_id = int(comm.rank // nb_cpu)
else:
gpu_id = 0
cmt.cuda_set_device(gpu_id)
cmt.init()
cmt.cuda_sync_threads()
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
if nb_chunks < comm.size:
res = io.data_stats(params, show=False)
chunk_size = int(res * params.rate // comm.size)
if comm.rank == 0:
print_and_log(
["Too much cores, automatically resizing the data chunks"],
'debug', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
# I guess this is more relevant, to take signals from all over the recordings
all_chunks = numpy.random.permutation(
numpy.arange(nb_chunks, dtype=numpy.int32))
all_electrodes = numpy.random.permutation(N_e)
for gidx in [all_chunks[comm.rank]]:
#print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
#print "Node", comm.rank, "computes the median absolute deviations in a random chunk"
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in xrange(N_e):
u = numpy.median(local_chunk[:, i], 0)
thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'w',
libver='earliest')
io.write_datasets(bfile, ['thresholds'],
{'thresholds': thresholds},
compression=hdf5_compress)
bfile.close()
comm.Barrier()
thresholds = io.load_data(params, 'thresholds')
#print "Extracting the peaks..."
local_peaktimes = numpy.zeros(0, dtype=numpy.uint32)
for i in xrange(N_e):
peaktimes = scipy.signal.find_peaks(numpy.abs(local_chunk[:, i]),
height=thresholds[i],
width=spike_width,
wlen=N_t)[0]
local_peaktimes = numpy.concatenate((local_peaktimes, peaktimes))
local_peaktimes = numpy.unique(local_peaktimes)
#print "Removing the useless borders..."
local_borders = (template_shift, local_shape - template_shift)
idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes <
local_borders[1])
local_peaktimes = numpy.compress(idx, local_peaktimes)
if len(local_peaktimes) > 0:
diff_times = local_peaktimes[-1] - local_peaktimes[0]
all_times = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool)
min_times = numpy.maximum(
local_peaktimes - local_peaktimes[0] - safety_time, 0)
max_times = numpy.minimum(
local_peaktimes - local_peaktimes[0] + safety_time + 1,
diff_times)
argmax_peak = numpy.random.permutation(
numpy.arange(len(local_peaktimes)))
all_idx = numpy.take(local_peaktimes, argmax_peak)
#print "Selection of the peaks with spatio-temporal masks..."
for idx, peak in zip(argmax_peak, all_idx):
elec = numpy.argmax(numpy.abs(local_chunk[peak]))
indices = numpy.take(inv_nodes, edges[nodes[elec]])
if safety_space:
all_times[indices, min_times[idx]:max_times[idx]] = True
else:
all_times[elec, min_times[idx]:max_times[idx]] = True
else:
all_times = numpy.zeros((N_e, len(local_chunk)), dtype=numpy.bool)
if do_temporal_whitening:
local_res_temp = []
for elec in all_electrodes[numpy.arange(comm.rank, nb_temp_white,
comm.size)]:
res = numpy.zeros((0, N_t), dtype=numpy.float32)
scount = 0
indices = numpy.take(inv_nodes, edges[nodes[elec]])
all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0),
0)
esubset = numpy.where(all_times_elec == False)[0]
bound = len(esubset) - N_t
while (scount < bound) and (len(res) < max_silence_2):
myslice = esubset[scount:scount + N_t]
if numpy.all((myslice - esubset[scount]) == numpy.arange(N_t)):
scount += N_t
res = numpy.vstack((res, local_chunk[myslice, elec]))
else:
scount += 1
if len(res) > 5:
local_res_temp += [numpy.cov(res.T)]
nb_elecs = numpy.array([len(local_res_temp)], dtype=numpy.float32)
local_res_temp = numpy.array(local_res_temp, dtype=numpy.float32)
if len(local_res_temp) == 0:
local_res_temp = numpy.zeros(0, dtype=numpy.float32)
else:
local_res_temp = numpy.sum(local_res_temp, 0)
all_res_temp = gather_array(local_res_temp.ravel(), comm, 0, 1)
all_elecs = gather_array(nb_elecs, comm, 0, 1)
if do_spatial_whitening:
local_res_spac = numpy.zeros((N_e, N_e), dtype=numpy.float32)
local_silences = []
for elec in numpy.arange(comm.rank, N_e, comm.size):
indices = numpy.take(inv_nodes, edges[nodes[elec]])
all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0),
0)
esubset = numpy.where(all_times_elec == False)[0]
local_data = local_chunk[esubset][:, indices]
local_whitening = get_whitening_matrix(local_data).astype(
numpy.float32)
pos = numpy.where(elec == indices)[0]
local_res_spac[elec, indices] = local_whitening[pos]
local_silences += [len(esubset)]
all_res_spac = gather_array(local_res_spac.ravel(), comm, 0, 1)
all_silences = gather_array(
numpy.array(local_silences, dtype=numpy.int32), comm, 0, 1,
'uint32')
if comm.rank == 0:
to_write = {}
if do_temporal_whitening:
try:
nb_silences = numpy.sum(all_elecs > 0)
all_res_temp = all_res_temp.reshape((nb_silences, N_t**2))
except Exception:
print_and_log([
"No silent periods detected: something wrong with the parameters?"
], 'error', logger)
all_res_temp = numpy.sum(all_res_temp, 0)
all_res_temp = all_res_temp.reshape(
(N_t, N_t)) / numpy.sum(all_elecs)
temporal_whitening = get_whitening_matrix(
all_res_temp.astype(numpy.double),
fudge=1e-3)[template_shift].astype(numpy.float32)
temporal_whitening /= temporal_whitening.sum()
to_write['temporal'] = temporal_whitening
have_nans = numpy.sum(numpy.isnan(temporal_whitening))
if have_nans > 0:
temporal_whitening = numpy.zeros(N_t, dtype=numpy.float32)
temporal_whitening[N_t // 2] = 1
to_write['temporal'] = temporal_whitening
print_and_log(
["Disabling temporal whitening because of NaNs found"],
'info', logger)
if do_spatial_whitening:
all_res_spac = all_res_spac.reshape(comm.size, N_e, N_e)
spatial_whitening = numpy.sum(all_res_spac, 0)
to_write['spatial'] = spatial_whitening
print_and_log([
"Found %gs without spikes for whitening matrices..." %
(numpy.mean(all_silences) / params.rate)
], 'default', logger)
have_nans = numpy.sum(numpy.isnan(spatial_whitening))
if have_nans > 0:
spatial_whitening = numpy.eye(spatial_whitening.shape[0],
dtype=numpy.float32)
to_write['spatial'] = spatial_whitening
print_and_log(
["Disabling spatial whitening because of NaNs found"],
'info', logger)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
io.write_datasets(bfile,
to_write.keys(),
to_write,
compression=hdf5_compress)
bfile.close()
comm.Barrier()
if do_spatial_whitening or do_temporal_whitening:
if comm.rank == 0:
print_and_log(
["Because of whitening, need to recompute the thresholds..."],
'default', logger)
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if use_gpu:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening,
copy_on_host=False)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
for gidx in [all_chunks[comm.rank]]:
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in xrange(N_e):
u = numpy.median(local_chunk[:, i], 0)
thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u),
0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
bfile.pop('thresholds')
io.write_datasets(bfile, ['thresholds'],
{'thresholds': thresholds},
compression=hdf5_compress)
bfile.close()
comm.Barrier()
#if comm.rank == 0:
#if not os.path.exists(plot_path):
# os.makedirs(plot_path)
#N_elec = min(int(numpy.sqrt(data_file.N_e)), 5)
#plot.view_fit(filename, t_start=0, t_stop=1, fit_on=False, square=True,
# n_elec=N_elec, save=[plot_path, 'electrodes'])
# Part 2: Basis
numpy.random.seed(422)
#################################################################
file_out = params.get('data', 'file_out')
alignment = params.getboolean('detection', 'alignment')
smoothing = params.getboolean('detection', 'smoothing')
isolation = params.getboolean('detection', 'isolation')
over_factor = params.getint('detection', 'oversampling_factor')
spike_thresh = params.getfloat('detection', 'spike_thresh')
nodes, edges = get_nodes_and_edges(params)
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
if matched_filter:
chunk_size = detect_memory(params, whitening=True)
else:
chunk_size = detect_memory(params)
safety_time = params.getint('whitening', 'safety_time')
max_elts_elec = params.getint('whitening', 'max_elts')
output_dim = params.getfloat('whitening', 'output_dim')
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
smoothing_factor = params.getfloat(
'detection', 'smoothing_factor') * (1. / spike_thresh)**2
if sign_peaks == 'both':
max_elts_elec *= 2
nb_elts = int(
params.getfloat('whitening', 'nb_elts') * N_e * max_elts_elec)
ignore_dead_times = params.getboolean('triggers', 'ignore_times')
if ignore_dead_times:
all_dead_times = get_dead_times(params)
data_file.open()
#################################################################
if comm.rank == 0:
print_and_log(["Searching spikes to construct the PCA basis..."],
'default', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
if nb_chunks < comm.size:
res = io.data_stats(params, show=False)
chunk_size = int(res * params.rate // comm.size)
if comm.rank == 0:
print_and_log(
["Too much cores, automatically resizing the data chunks"],
'debug', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
groups = {}
for i in xrange(N_e):
groups[i] = 0
# I guess this is more relevant, to take signals from all over the recordings
all_chunks = numpy.random.permutation(
numpy.arange(nb_chunks, dtype=numpy.int32))
max_elts_elec //= comm.size
nb_elts //= comm.size
elt_count_pos = 0
elt_count_neg = 0
if sign_peaks in ['positive', 'both']:
elts_pos = numpy.zeros((N_t, nb_elts), dtype=numpy.float32)
if sign_peaks in ['negative', 'both']:
elts_neg = numpy.zeros((N_t, nb_elts), dtype=numpy.float32)
chunks_to_load = all_chunks[comm.rank::comm.size]
thresholds = io.load_data(params, 'thresholds')
mads = io.load_data(params, 'mads')
if alignment:
cdata = numpy.linspace(-jitter_range, jitter_range,
int(over_factor * 2 * jitter_range))
xdata = numpy.arange(-template_shift_2, template_shift_2 + 1)
xoff = len(cdata) / 2.
if isolation:
yoff = numpy.array(range(0, N_t // 4) + range(3 * N_t // 4, N_t))
to_explore = xrange(comm.rank, nb_chunks, comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for gcount, gidx in enumerate(to_explore):
gidx = all_chunks[gidx]
if ((elt_count_pos + elt_count_neg) < nb_elts):
#print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
#print "Extracting the peaks..."
all_peaktimes = numpy.zeros(0, dtype=numpy.uint32)
all_extremas = numpy.zeros(0, dtype=numpy.uint32)
for i in xrange(N_e):
if sign_peaks == 'negative':
peaktimes = scipy.signal.find_peaks(-local_chunk[:, i],
height=thresholds[i],
width=spike_width,
distance=dist_peaks,
wlen=N_t)[0]
elif sign_peaks == 'positive':
peaktimes = scipy.signal.find_peaks(local_chunk[:, i],
height=thresholds[i],
width=spike_width,
wlen=N_t)[0]
elif sign_peaks == 'both':
peaktimes = scipy.signal.find_peaks(numpy.abs(
local_chunk[:, i]),
height=thresholds[i],
width=spike_width,
wlen=N_t)[0]
all_peaktimes = numpy.concatenate((all_peaktimes, peaktimes))
all_extremas = numpy.concatenate(
(all_extremas,
i * numpy.ones(len(peaktimes), dtype=numpy.uint32)))
#print "Removing the useless borders..."
if alignment:
local_borders = (template_shift_2,
local_shape - template_shift_2)
else:
local_borders = (template_shift, local_shape - template_shift)
idx = (all_peaktimes >= local_borders[0]) & (all_peaktimes <
local_borders[1])
all_peaktimes = numpy.compress(idx, all_peaktimes)
all_extremas = numpy.compress(idx, all_extremas)
local_peaktimes = numpy.unique(all_peaktimes)
if ignore_dead_times:
dead_indices = numpy.searchsorted(
all_dead_times, [t_offset, t_offset + local_shape])
if dead_indices[0] != dead_indices[1]:
is_included = numpy.in1d(
local_peaktimes + t_offset,
all_dead_times[dead_indices[0]:dead_indices[1]])
local_peaktimes = local_peaktimes[~is_included]
local_peaktimes = numpy.sort(local_peaktimes)
if len(local_peaktimes) > 0:
diff_times = local_peaktimes[-1] - local_peaktimes[0]
all_times = numpy.zeros((N_e, diff_times + 1),
dtype=numpy.bool)
min_times = numpy.maximum(
local_peaktimes - local_peaktimes[0] - safety_time, 0)
max_times = numpy.minimum(
local_peaktimes - local_peaktimes[0] + safety_time + 1,
diff_times)
n_times = len(local_peaktimes)
argmax_peak = numpy.random.permutation(numpy.arange(n_times))
all_idx = numpy.take(local_peaktimes, argmax_peak)
#print "Selection of the peaks with spatio-temporal masks..."
for midx, peak in zip(argmax_peak, all_idx):
if (elt_count_neg + elt_count_pos) == nb_elts:
break
if sign_peaks == 'negative':
elec = numpy.argmin(local_chunk[peak])
negative_peak = True
elif sign_peaks == 'positive':
elec = numpy.argmax(local_chunk[peak])
negative_peak = False
elif sign_peaks == 'both':
if N_e == 1:
if local_chunk[peak] < 0:
negative_peak = True
elif local_chunk[peak] > 0:
negative_peak = False
elec = 0
else:
if numpy.abs(numpy.max(
local_chunk[peak])) > numpy.abs(
numpy.min(local_chunk[peak])):
elec = numpy.argmax(local_chunk[peak])
negative_peak = False
else:
elec = numpy.argmin(local_chunk[peak])
negative_peak = True
indices = numpy.take(inv_nodes, edges[nodes[elec]])
myslice = all_times[indices,
min_times[midx]:max_times[midx]]
is_local_extrema = elec in all_extremas[all_peaktimes ==
peak]
if is_local_extrema and not myslice.any():
upper_bounds = max_elts_elec
if groups[elec] < upper_bounds:
if not alignment:
sub_mat = local_chunk[peak -
template_shift:peak +
template_shift + 1, elec]
elif alignment:
ydata = local_chunk[peak -
template_shift_2:peak +
template_shift_2 + 1, elec]
if smoothing:
factor = smoothing_factor * xdata.size
f = scipy.interpolate.UnivariateSpline(
xdata, ydata, s=factor, k=3)
else:
f = scipy.interpolate.UnivariateSpline(
xdata, ydata, k=3, s=0)
if negative_peak:
rmin = (numpy.argmin(f(cdata)) -
xoff) / over_factor
else:
rmin = (numpy.argmax(f(cdata)) -
xoff) / over_factor
ddata = numpy.linspace(rmin - template_shift,
rmin + template_shift,
N_t)
sub_mat = f(ddata).astype(numpy.float32)
if alignment:
if negative_peak:
if numpy.min(sub_mat) >= -thresholds[elec]:
to_accept = False
else:
if numpy.max(sub_mat) <= thresholds[elec]:
to_accept = False
if isolation:
to_accept = numpy.all(
numpy.max(numpy.abs(sub_mat[yoff])) <=
thresholds[elec])
else:
to_accept = True
if to_accept:
if negative_peak:
elts_neg[:, elt_count_neg] = sub_mat
else:
elts_pos[:, elt_count_pos] = sub_mat
if negative_peak:
elt_count_neg += 1
else:
elt_count_pos += 1
groups[elec] += 1
all_times[indices,
min_times[midx]:max_times[midx]] = True
sys.stderr.flush()
if isolation:
print_and_log([
"Node %d has collected %d isolated waveforms" %
(comm.rank, elt_count_pos + elt_count_neg)
], 'debug', logger)
else:
print_and_log([
"Node %d has collected %d waveforms" %
(comm.rank, elt_count_pos + elt_count_neg)
], 'debug', logger)
if sign_peaks in ['negative', 'both']:
gdata_neg = gather_array(elts_neg[:, :elt_count_neg].T, comm, 0, 1)
if sign_peaks in ['positive', 'both']:
gdata_pos = gather_array(elts_pos[:, :elt_count_pos].T, comm, 0, 1)
nb_waveforms = 0
if comm.rank == 0:
#DO PCA on elts and store the basis obtained.
if sign_peaks in ['negative', 'both']:
nb_waveforms += gdata_neg.shape[0]
if sign_peaks in ['positive', 'both']:
nb_waveforms += gdata_pos.shape[0]
nb_waveforms = all_gather_array(
numpy.array([nb_waveforms], dtype=numpy.float32), comm, 0)[0]
if comm.rank == 0:
if isolation:
print_and_log([
"Found %d isolated waveforms over %d requested" %
(nb_waveforms, int(nb_elts * comm.size))
], 'default', logger)
else:
print_and_log([
"Found %d waveforms over %d requested" %
(nb_waveforms, int(nb_elts * comm.size))
], 'default', logger)
if nb_waveforms == 0:
print_and_log(
['No waveforms found! Are the data properly loaded??'],
'error', logger)
if nb_waveforms == 0:
sys.exit(0)
if comm.rank == 0:
res = {}
pca = None
pca_pos = None
pca_neg = None
if sign_peaks in ['negative', 'both']:
if len(gdata_neg) > 0:
pca = PCA(output_dim)
if use_hanning:
pca.fit(gdata_neg * hanning_filter)
else:
pca.fit(gdata_neg)
res['proj'] = pca.components_.T.astype(numpy.float32)
pca_neg = numpy.sum(pca.explained_variance_ratio_)
else:
res['proj'] = numpy.identity(int(output_dim),
dtype=numpy.float32)
res['rec'] = res['proj'].T
res['waveform'] = numpy.median(gdata_neg, 0)
idx = numpy.random.permutation(numpy.arange(
gdata_neg.shape[0]))[:1000]
res['waveforms'] = gdata_neg[idx, :]
if sign_peaks in ['positive', 'both']:
if len(gdata_pos) > 0:
pca = PCA(output_dim)
if use_hanning:
pca.fit(gdata_pos * hanning_filter)
else:
pca.fit(gdata_pos)
res['proj_pos'] = pca.components_.T.astype(numpy.float32)
pca_pos = numpy.sum(pca.explained_variance_ratio_)
else:
res['proj_pos'] = numpy.identity(int(output_dim),
dtype=numpy.float32)
res['rec_pos'] = res['proj_pos'].T
res['waveform_pos'] = numpy.median(gdata_pos, 0)
idx = numpy.random.permutation(numpy.arange(
gdata_pos.shape[0]))[:1000]
res['waveforms_pos'] = gdata_pos[idx, :]
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
io.write_datasets(bfile, res.keys(), res, compression=hdf5_compress)
if sign_peaks == 'positive':
print_and_log([
"A basis with %s dimensions has been built" %
res['proj_pos'].shape[1]
], 'info', logger)
elif sign_peaks == 'negative':
print_and_log([
"A basis with %s dimensions has been built" %
res['proj'].shape[1]
], 'info', logger)
elif sign_peaks == 'both':
print_and_log([
"Two basis with %s dimensions has been built" %
res['proj'].shape[1]
], 'debug', logger)
if pca_pos is not None:
print_and_log([
"The percentage of variance explained is %s for positive spikes"
% pca_pos
], 'debug', logger)
if pca_neg is not None:
print_and_log([
"The percentage of variance explained is %s for negative spikes"
% pca_neg
], 'debug', logger)
bfile.close()
comm.Barrier()
if matched_filter:
if comm.rank == 0:
print_and_log([
"Because of matched filters, need to recompute the thresholds..."
], 'default', logger)
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if use_gpu:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening,
copy_on_host=False)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
if sign_peaks in ['negative', 'both']:
waveform_neg = io.load_data(params, 'waveform')[::-1]
waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) *
len(waveform_neg))
if sign_peaks in ['positive', 'both']:
waveform_pos = io.load_data(params, 'waveform-pos')[::-1]
waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) *
len(waveform_pos))
for gidx in [all_chunks[comm.rank]]:
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
local_chunk /= thresholds
if sign_peaks in ['negative', 'both']:
tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk,
waveform_neg,
axis=0,
mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in xrange(N_e):
u = numpy.median(tmp_chunk[:, i], 0)
thresholds[i] = numpy.median(
numpy.abs(tmp_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
io.write_datasets(bfile, ['matched_thresholds'],
{'matched_thresholds': thresholds},
compression=hdf5_compress)
bfile.close()
comm.Barrier()
if sign_peaks in ['positive', 'both']:
tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk,
waveform_pos,
axis=0,
mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in xrange(N_e):
u = numpy.median(tmp_chunk[:, i], 0)
thresholds[i] = numpy.median(
numpy.abs(tmp_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
io.write_datasets(bfile, ['matched_thresholds_pos'],
{'matched_thresholds_pos': thresholds},
compression=hdf5_compress)
bfile.close()
comm.Barrier()
data_file.close()
def write_pcs(path, params, extension, N_tm, mode=0):
spikes = numpy.load(os.path.join(output_path, 'spike_times.npy'))
labels = numpy.load(os.path.join(output_path, 'spike_templates.npy'))
max_loc_channel = get_max_loc_channel(params)
nb_features = params.getint('whitening', 'output_dim')
sign_peaks = params.get('detection', 'peaks')
nodes, edges = get_nodes_and_edges(params)
N_total = params.getint('data', 'N_total')
if export_all:
nb_templates = N_tm + N_e
else:
nb_templates = N_tm
pc_features_ind = numpy.zeros((nb_templates, max_loc_channel), dtype=numpy.int32)
clusters = io.load_data(params, 'clusters', extension)
best_elec = clusters['electrodes']
if export_all:
best_elec = numpy.concatenate((best_elec, numpy.arange(N_e)))
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
for count, elec in enumerate(best_elec):
nb_loc = len(edges[nodes[elec]])
pc_features_ind[count, numpy.arange(nb_loc)] = inv_nodes[edges[nodes[elec]]]
if sign_peaks in ['negative', 'both']:
basis_proj, basis_rec = io.load_data(params, 'basis')
elif sign_peaks in ['positive']:
basis_proj, basis_rec = io.load_data(params, 'basis-pos')
to_process = numpy.arange(comm.rank, nb_templates, comm.size)
all_offsets = numpy.zeros(nb_templates, dtype=numpy.int32)
for target in xrange(nb_templates):
if mode == 0:
all_offsets[target] = len(numpy.where(labels == target)[0])
elif mode == 1:
all_offsets[target] = min(500, len(numpy.where(labels == target)[0]))
all_paddings = numpy.concatenate(([0] , numpy.cumsum(all_offsets)))
total_pcs = numpy.sum(all_offsets)
pc_file = os.path.join(output_path, 'pc_features.npy')
pc_file_ids = os.path.join(output_path, 'pc_feature_spike_ids.npy')
from numpy.lib.format import open_memmap
if comm.rank == 0:
pc_features = open_memmap(pc_file, shape=(total_pcs, nb_features, max_loc_channel), dtype=numpy.float32, mode='w+')
if mode == 1:
pc_ids = open_memmap(pc_file_ids, shape=(total_pcs, ), dtype=numpy.int32, mode='w+')
comm.Barrier()
pc_features = open_memmap(pc_file, mode='r+')
if mode == 1:
pc_ids = open_memmap(pc_file_ids, mode='r+')
to_explore = xrange(comm.rank, nb_templates, comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
all_idx = numpy.zeros(0, dtype=numpy.int32)
for gcount, target in enumerate(to_explore):
count = all_paddings[target]
if mode == 1:
idx = numpy.random.permutation(numpy.where(labels == target)[0])[:500]
pc_ids[count:count+len(idx)] = idx
elif mode == 0:
idx = numpy.where(labels == target)[0]
elec = best_elec[target]
indices = inv_nodes[edges[nodes[elec]]]
labels_i = target*numpy.ones(len(idx))
times_i = numpy.take(spikes, idx).astype(numpy.int64)
sub_data = io.get_stas(params, times_i, labels_i, elec, neighs=indices, nodes=nodes, auto_align=False)
pcs = numpy.dot(sub_data, basis_proj)
pcs = numpy.swapaxes(pcs, 1,2)
if mode == 0:
pc_features[idx, :, :len(indices)] = pcs
elif mode == 1:
pc_features[count:count+len(idx), :, :len(indices)] = pcs
comm.Barrier()
if comm.rank == 0:
numpy.save(os.path.join(output_path, 'pc_feature_ind'), pc_features_ind.astype(numpy.uint32)) #n_templates, n_loc_chan
def main(params, nb_cpu, nb_gpu, use_gpu, extension):
logger = init_logging(params.logfile)
logger = logging.getLogger('circus.converting')
data_file = params.data_file
file_out_suff = params.get('data', 'file_out_suff')
probe = params.probe
output_path = params.get('data', 'file_out_suff') + extension + '.GUI'
N_e = params.getint('data', 'N_e')
N_t = params.getint('detection', 'N_t')
erase_all = params.getboolean('converting', 'erase_all')
export_pcs = params.get('converting', 'export_pcs')
export_all = params.getboolean('converting', 'export_all')
sparse_export = params.getboolean('converting', 'sparse_export')
if export_all and not params.getboolean('fitting', 'collect_all'):
if comm.rank == 0:
print_and_log(['Export unfitted spikes only if [fitting] collect_all is True'], 'error', logger)
sys.exit(0)
def generate_mapping(probe):
p = {}
positions = []
nodes = []
for key in probe['channel_groups'].keys():
p.update(probe['channel_groups'][key]['geometry'])
nodes += probe['channel_groups'][key]['channels']
positions += [p[channel] for channel in probe['channel_groups'][key]['channels']]
idx = numpy.argsort(nodes)
positions = numpy.array(positions)[idx]
return positions
def get_max_loc_channel(params):
nodes, edges = get_nodes_and_edges(params)
max_loc_channel = 0
for key in edges.keys():
if len(edges[key]) > max_loc_channel:
max_loc_channel = len(edges[key])
return max_loc_channel
def write_results(path, params, extension):
result = io.get_results(params, extension)
spikes = numpy.zeros(0, dtype=numpy.uint64)
clusters = numpy.zeros(0, dtype=numpy.uint32)
amplitudes = numpy.zeros(0, dtype=numpy.double)
N_tm = len(result['spiketimes'])
for key in result['spiketimes'].keys():
temp_id = int(key.split('_')[-1])
data = result['spiketimes'].pop(key).astype(numpy.uint64)
spikes = numpy.concatenate((spikes, data))
data = result['amplitudes'].pop(key).astype(numpy.double)
amplitudes = numpy.concatenate((amplitudes, data[:, 0]))
clusters = numpy.concatenate((clusters, temp_id*numpy.ones(len(data), dtype=numpy.uint32)))
if export_all:
print_and_log(["Last %d templates are unfitted spikes on all electrodes" %N_e], 'info', logger)
garbage = io.load_data(params, 'garbage', extension)
for key in garbage['gspikes'].keys():
elec_id = int(key.split('_')[-1])
data = garbage['gspikes'].pop(key).astype(numpy.uint64)
spikes = numpy.concatenate((spikes, data))
amplitudes = numpy.concatenate((amplitudes, numpy.zeros(len(data))))
clusters = numpy.concatenate((clusters, (elec_id + N_tm)*numpy.ones(len(data), dtype=numpy.uint32)))
idx = numpy.argsort(spikes)
numpy.save(os.path.join(output_path, 'spike_templates'), clusters[idx])
numpy.save(os.path.join(output_path, 'spike_times'), spikes[idx])
numpy.save(os.path.join(output_path, 'amplitudes'), amplitudes[idx])
return
def write_templates(path, params, extension):
max_loc_channel = get_max_loc_channel(params)
templates = io.load_data(params, 'templates', extension)
N_tm = templates.shape[1]//2
if sparse_export:
n_channels_max = 0
for t in xrange(N_tm):
data = numpy.sum(numpy.sum(templates[:, t].toarray().reshape(N_e, N_t), 1) != 0)
if data > n_channels_max:
n_channels_max = data
else:
n_channels_max = N_e
if export_all:
to_write_sparse = numpy.zeros((N_tm + N_e, N_t, n_channels_max), dtype=numpy.float32)
mapping_sparse = -1 * numpy.ones((N_tm + N_e, n_channels_max), dtype=numpy.int32)
else:
to_write_sparse = numpy.zeros((N_tm, N_t, n_channels_max), dtype=numpy.float32)
mapping_sparse = -1 * numpy.ones((N_tm, n_channels_max), dtype=numpy.int32)
for t in xrange(N_tm):
tmp = templates[:, t].toarray().reshape(N_e, N_t).T
x, y = tmp.nonzero()
nb_loc = len(numpy.unique(y))
if sparse_export:
all_positions = numpy.zeros(y.max()+1, dtype=numpy.int32)
all_positions[numpy.unique(y)] = numpy.arange(nb_loc, dtype=numpy.int32)
pos = all_positions[y]
to_write_sparse[t, x, pos] = tmp[x, y]
mapping_sparse[t, numpy.arange(nb_loc)] = numpy.unique(y)
else:
pos = y
to_write_sparse[t, x, pos] = tmp[x, y]
if export_all:
for t in xrange(N_tm, N_tm + N_e):
mapping_sparse[t, 0] = t - N_tm
numpy.save(os.path.join(output_path, 'templates'), to_write_sparse)
if sparse_export:
numpy.save(os.path.join(output_path, 'template_ind'), mapping_sparse)
return N_tm
def write_pcs(path, params, extension, N_tm, mode=0):
spikes = numpy.load(os.path.join(output_path, 'spike_times.npy'))
labels = numpy.load(os.path.join(output_path, 'spike_templates.npy'))
max_loc_channel = get_max_loc_channel(params)
nb_features = params.getint('whitening', 'output_dim')
sign_peaks = params.get('detection', 'peaks')
nodes, edges = get_nodes_and_edges(params)
N_total = params.getint('data', 'N_total')
if export_all:
nb_templates = N_tm + N_e
else:
nb_templates = N_tm
pc_features_ind = numpy.zeros((nb_templates, max_loc_channel), dtype=numpy.int32)
clusters = io.load_data(params, 'clusters', extension)
best_elec = clusters['electrodes']
if export_all:
best_elec = numpy.concatenate((best_elec, numpy.arange(N_e)))
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
for count, elec in enumerate(best_elec):
nb_loc = len(edges[nodes[elec]])
pc_features_ind[count, numpy.arange(nb_loc)] = inv_nodes[edges[nodes[elec]]]
if sign_peaks in ['negative', 'both']:
basis_proj, basis_rec = io.load_data(params, 'basis')
elif sign_peaks in ['positive']:
basis_proj, basis_rec = io.load_data(params, 'basis-pos')
to_process = numpy.arange(comm.rank, nb_templates, comm.size)
all_offsets = numpy.zeros(nb_templates, dtype=numpy.int32)
for target in xrange(nb_templates):
if mode == 0:
all_offsets[target] = len(numpy.where(labels == target)[0])
elif mode == 1:
all_offsets[target] = min(500, len(numpy.where(labels == target)[0]))
all_paddings = numpy.concatenate(([0] , numpy.cumsum(all_offsets)))
total_pcs = numpy.sum(all_offsets)
pc_file = os.path.join(output_path, 'pc_features.npy')
pc_file_ids = os.path.join(output_path, 'pc_feature_spike_ids.npy')
from numpy.lib.format import open_memmap
if comm.rank == 0:
pc_features = open_memmap(pc_file, shape=(total_pcs, nb_features, max_loc_channel), dtype=numpy.float32, mode='w+')
if mode == 1:
pc_ids = open_memmap(pc_file_ids, shape=(total_pcs, ), dtype=numpy.int32, mode='w+')
comm.Barrier()
pc_features = open_memmap(pc_file, mode='r+')
if mode == 1:
pc_ids = open_memmap(pc_file_ids, mode='r+')
to_explore = xrange(comm.rank, nb_templates, comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
all_idx = numpy.zeros(0, dtype=numpy.int32)
for gcount, target in enumerate(to_explore):
count = all_paddings[target]
if mode == 1:
idx = numpy.random.permutation(numpy.where(labels == target)[0])[:500]
pc_ids[count:count+len(idx)] = idx
elif mode == 0:
idx = numpy.where(labels == target)[0]
elec = best_elec[target]
indices = inv_nodes[edges[nodes[elec]]]
labels_i = target*numpy.ones(len(idx))
times_i = numpy.take(spikes, idx).astype(numpy.int64)
sub_data = io.get_stas(params, times_i, labels_i, elec, neighs=indices, nodes=nodes, auto_align=False)
pcs = numpy.dot(sub_data, basis_proj)
pcs = numpy.swapaxes(pcs, 1,2)
if mode == 0:
pc_features[idx, :, :len(indices)] = pcs
elif mode == 1:
pc_features[count:count+len(idx), :, :len(indices)] = pcs
comm.Barrier()
if comm.rank == 0:
numpy.save(os.path.join(output_path, 'pc_feature_ind'), pc_features_ind.astype(numpy.uint32)) #n_templates, n_loc_chan
do_export = True
if comm.rank == 0:
if os.path.exists(output_path):
if not erase_all:
do_export = query_yes_no(Fore.WHITE + "Export already made! Do you want to erase everything?", default=None)
if do_export:
if os.path.exists(os.path.abspath('.phy')):
shutil.rmtree(os.path.abspath('.phy'))
shutil.rmtree(output_path)
if do_export == True:
comm.bcast(numpy.array([1], dtype=numpy.int32), root=0)
elif do_export == False:
comm.bcast(numpy.array([0], dtype=numpy.int32), root=0)
else:
do_export = bool(comm.bcast(numpy.array([0], dtype=numpy.int32), root=0))
comm.Barrier()
if do_export:
if comm.rank == 0:
os.makedirs(output_path)
print_and_log(["Exporting data for the phy GUI with %d CPUs..." %nb_cpu], 'info', logger)
if params.getboolean('whitening', 'spatial'):
whitening_mat = io.load_data(params, 'spatial_whitening').astype(numpy.double)
numpy.save(os.path.join(output_path, 'whitening_mat'), whitening_mat)
numpy.save(os.path.join(output_path, 'whitening_mat_inv'), numpy.linalg.inv(whitening_mat))
else:
numpy.save(os.path.join(output_path, 'whitening_mat'), numpy.eye(N_e))
numpy.save(os.path.join(output_path, 'channel_positions'), generate_mapping(probe).astype(numpy.double))
nodes, edges = get_nodes_and_edges(params)
numpy.save(os.path.join(output_path, 'channel_map'), nodes.astype(numpy.int32))
write_results(output_path, params, extension)
N_tm = write_templates(output_path, params, extension)
apply_patch_for_similarities(params, extension)
template_file = h5py.File(file_out_suff + '.templates%s.hdf5' %extension, 'r', libver='earliest')
similarities = template_file.get('maxoverlap')[:]
template_file.close()
norm = N_e*N_t
if export_all:
to_write = numpy.zeros((N_tm + N_e, N_tm + N_e), dtype=numpy.single)
to_write[:N_tm, :N_tm] = (similarities[:N_tm, :N_tm]/norm).astype(numpy.single)
else:
to_write = (similarities[:N_tm, :N_tm]/norm).astype(numpy.single)
numpy.save(os.path.join(output_path, 'similar_templates'), to_write)
comm.bcast(numpy.array([N_tm], dtype=numpy.int32), root=0)
else:
N_tm = int(comm.bcast(numpy.array([0], dtype=numpy.int32), root=0))
comm.Barrier()
make_pcs = 2
if comm.rank == 0:
if export_pcs == 'prompt':
key = ''
while key not in ['a', 's', 'n']:
print(Fore.WHITE + "Do you want SpyKING CIRCUS to export PCs? (a)ll / (s)ome / (n)o")
key = raw_input('')
else:
key = export_pcs
if key == 'a':
make_pcs = 0
comm.bcast(numpy.array([0], dtype=numpy.int32), root=0)
elif key == 's':
make_pcs = 1
comm.bcast(numpy.array([1], dtype=numpy.int32), root=0)
elif key == 'n':
comm.bcast(numpy.array([2], dtype=numpy.int32), root=0)
if os.path.exists(os.path.join(output_path, 'pc_features.npy')):
os.remove(os.path.join(output_path, 'pc_features.npy'))
if os.path.exists(os.path.join(output_path, 'pc_feature_ind.npy')):
os.remove(os.path.join(output_path, 'pc_feature_ind.npy'))
else:
make_pcs = comm.bcast(numpy.array([0], dtype=numpy.int32), root=0)
make_pcs = make_pcs[0]
comm.Barrier()
if make_pcs < 2:
write_pcs(output_path, params, extension, N_tm, make_pcs)
def main(params, nb_cpu, nb_gpu, use_gpu):
logger = init_logging(params.logfile)
logger = logging.getLogger('circus.filtering')
#################################################################
do_filter = params.getboolean('filtering', 'filter')
filter_done = params.getboolean('noedits', 'filter_done')
artefacts_done = params.getboolean('noedits', 'artefacts_done')
median_done = params.getboolean('noedits', 'median_done')
clean_artefact = params.getboolean('triggers', 'clean_artefact')
remove_median = params.getboolean('filtering', 'remove_median')
nodes, edges = get_nodes_and_edges(params)
#################################################################
def filter_file(data_file_in, data_file_out, do_filtering,
do_remove_median):
try:
cut_off = params.getfloat('filtering', 'cut_off')
cut_off = [cut_off, 0.95 * (params.rate / 2.)]
except Exception:
cut_off = params.get('filtering', 'cut_off')
cut_off = cut_off.split(',')
try:
cut_off[0] = float(cut_off[0])
except Exception:
if comm.rank == 0:
print_and_log(
['First value of cut off must be a valid number'],
'error', logger)
sys.exit(0)
cut_off[1] = cut_off[1].replace(' ', '')
if cut_off[1] == 'auto':
cut_off[1] = 0.95 * (params.rate / 2.)
else:
try:
cut_off[1] = float(cut_off[1])
except Exception:
if comm.rank == 0:
print_and_log([
'Second value of cut off must either auto, or a valid a number'
], 'error', logger)
sys.exit(0)
chunk_size = params.getint('data', 'chunk_size')
nb_chunks, _ = data_file_in.analyze(chunk_size)
b, a = signal.butter(3, np.array(cut_off) / (params.rate / 2.), 'pass')
all_chunks = numpy.arange(nb_chunks, dtype=numpy.int64)
to_process = all_chunks[comm.rank::comm.size]
loc_nb_chunks = len(to_process)
N_total = params.nb_channels
if comm.rank == 0:
to_write = []
if do_filtering:
to_write += [
"Filtering the signal with a Butterworth filter in (%g, %g) Hz"
% (cut_off[0], cut_off[1])
]
if do_remove_median:
to_write += [
"Median over all channels is substracted to each channels"
]
print_and_log(to_write, 'default', logger)
to_explore = xrange(comm.rank, nb_chunks, comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for count, gidx in enumerate(to_explore):
local_chunk, t_offset = data_file_in.get_data(gidx, chunk_size)
if do_filtering:
for i in nodes:
try:
local_chunk[:, i] = signal.filtfilt(
b, a, local_chunk[:, i])
except Exception:
pass
local_chunk[:, i] -= numpy.median(local_chunk[:, i])
if do_remove_median:
if not numpy.all(nodes == numpy.arange(N_total)):
global_median = numpy.median(
numpy.take(local_chunk, nodes, axis=1), 1)
else:
global_median = numpy.median(local_chunk, 1)
for i in nodes:
local_chunk[:, i] -= global_median
if data_file_in != data_file_out and data_file_in.is_first_chunk(
gidx, nb_chunks):
if data_file_in.is_stream:
g_offset = t_offset - numpy.sum(
data_file_in.
_times[:data_file_in.
_get_streams_index_by_time(t_offset) + 1])
else:
g_offset = t_offset - data_file_in.t_start
else:
g_offset = t_offset
data_file_out.set_data(g_offset, local_chunk)
comm.Barrier()
def compute_artefacts(data_file):
chunk_size = params.getint('data', 'chunk_size')
trig_in_ms = params.getboolean('triggers', 'trig_in_ms')
artefacts = numpy.loadtxt(params.get('triggers', 'trig_file'))
windows = numpy.loadtxt(params.get('triggers', 'trig_windows'))
make_plots = params.get('triggers', 'make_plots')
plot_path = os.path.join(params.get('data', 'data_file_noext'),
'plots')
if len(windows.shape) == 1:
windows = windows.reshape(1, 2)
if len(artefacts.shape) == 1:
artefacts = artefacts.reshape(1, 2)
if trig_in_ms:
if comm.rank == 0:
print_and_log(['Artefact times are read in ms'], 'debug',
logger)
artefacts[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
windows[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
else:
if comm.rank == 0:
print_and_log(['Artefact times are read in timesteps'],
'debug', logger)
artefacts = artefacts.astype(numpy.int64)
windows = windows.astype(numpy.int64)
nb_stimuli = len(numpy.unique(artefacts[:, 0]))
mytest = nb_stimuli == len(windows)
if not mytest:
if comm.rank == 0:
print_and_log(['Error in the trigger files'], 'error', logger)
sys.exit(0)
all_labels = artefacts[:, 0].astype(numpy.int32)
all_times = artefacts[:, 1].astype(numpy.int32)
mask = (all_times >= 0) & (all_times + numpy.max(windows[:, 1]) <
data_file.t_stop)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
local_labels = numpy.unique(all_labels)[comm.rank::comm.size]
if comm.rank == 0:
to_write = [
"Computing averaged artefacts from %d stimuli" % (nb_stimuli)
]
print_and_log(to_write, 'default', logger)
if not os.path.exists(plot_path):
os.makedirs(plot_path)
local_labels = get_tqdm_progressbar(local_labels)
comm.Barrier()
# First we need to get the average artefacts
art_dict = {}
for count, artefact in enumerate(local_labels):
indices = numpy.where(all_labels == artefact)[0].astype(
numpy.int32)
tmp = numpy.where(windows[:, 0] == artefact)[0]
tau = numpy.int64(windows[tmp, 1])
pspikes = all_times[indices]
times = numpy.sort(numpy.random.permutation(pspikes)[:500])
if len(numpy.where(numpy.diff(times) < tau)[0]) > 0:
if comm.rank == 0:
print_and_log([
'Stimulation times for artefact %d are too close!' %
artefact
], 'error', logger)
sys.exit(0)
art_dict[artefact] = get_artefact(params, times, tau, nodes)
if make_plots not in ['None', '']:
save = [plot_path, '%d.%s' % (artefact, make_plots)]
plot.view_artefact(art_dict[artefact], save=save)
return art_dict
def remove_artefacts(data_file, art_dict):
chunk_size = params.getint('data', 'chunk_size')
trig_in_ms = params.getboolean('triggers', 'trig_in_ms')
artefacts = numpy.loadtxt(params.get('triggers',
'trig_file')).astype(numpy.int64)
windows = numpy.loadtxt(params.get('triggers',
'trig_windows')).astype(numpy.int64)
make_plots = params.get('triggers', 'make_plots')
plot_path = os.path.join(params.get('data', 'data_file_noext'),
'plots')
if len(windows.shape) == 1:
windows = windows.reshape(1, 2)
if len(artefacts.shape) == 1:
artefacts = artefacts.reshape(1, 2)
if trig_in_ms:
if comm.rank == 0:
print_and_log(['Artefact times are read in ms'], 'debug',
logger)
artefacts[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
windows[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
else:
if comm.rank == 0:
print_and_log(['Artefact times are read in timesteps'],
'debug', logger)
artefacts = artefacts.astype(numpy.int64)
windows = windows.astype(numpy.int64)
nb_stimuli = len(numpy.unique(artefacts[:, 0]))
mytest = nb_stimuli == len(windows)
if not mytest:
if comm.rank == 0:
print_and_log(['Error in the trigger files'], 'error', logger)
sys.exit(0)
all_labels = artefacts[:, 0].astype(numpy.int32)
all_times = artefacts[:, 1].astype(numpy.int32)
local_labels = numpy.unique(all_labels)[comm.rank::comm.size]
mask = numpy.in1d(all_labels, local_labels)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
mask = (all_times >= 0) & (all_times < data_file.t_stop)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
if comm.rank == 0:
to_write = ["Removing artefacts from %d stimuli" % (nb_stimuli)]
print_and_log(to_write, 'default', logger)
all_times = get_tqdm_progressbar(all_times)
comm.Barrier()
for count, time in enumerate(all_times):
label = all_labels[count]
tmp = numpy.where(windows[:, 0] == label)[0][0]
tau = numpy.int64(windows[tmp, 1])
if (data_file.t_stop - time) < tau:
tau = max_offset - time
local_chunk = data_file.get_snippet(time, tau)
for idx, i in enumerate(nodes):
local_chunk[:, i] -= art_dict[label][idx, :tau]
data_file.set_data(time, local_chunk)
comm.Barrier()
if comm.rank == 0:
print_and_log(['Initializing the filtering step...'], 'debug', logger)
if params.getboolean('data', 'overwrite'):
if comm.rank == 0:
print_and_log(['Reading the input file...'], 'debug', logger)
data_file_in = params.get_data_file()
data_file_out = data_file_in
else:
if comm.rank == 0:
print_and_log(
['Overwrite is set to False, so creating a new datafile...'],
'debug', logger)
if comm.rank == 0:
print_and_log(['Reading the input file...'], 'debug', logger)
if os.path.exists(params.get('data', 'data_file_no_overwrite')):
has_been_created = True
else:
has_been_created = False
if not has_been_created and (filter_done or median_done
or artefacts_done):
if comm.rank == 0:
print_and_log([
'The filtering is done but file not present. See no_edits section'
], 'error', logger)
sys.exit(0)
if not has_been_created:
data_file_in = params.get_data_file(
source=True, has_been_created=has_been_created)
else:
data_file_in = params.get_data_file(
source=False, has_been_created=has_been_created)
if comm.rank == 0:
print_and_log(
['Reading the output file and allocating ressources...'],
'debug', logger)
description = data_file_in.get_description()
description['data_dtype'] = 'float32'
description['dtype_offset'] = 0
description['data_offset'] = 0
data_file_out = params.get_data_file(is_empty=not has_been_created,
params=description)
if not has_been_created:
data_file_out.allocate(shape=data_file_in.shape)
comm.Barrier()
if clean_artefact:
if not (os.path.exists(params.get('triggers', 'trig_file'))
and os.path.exists(params.get('triggers', 'trig_windows'))):
if comm.rank == 0:
print_and_log(
['trig_file or trig_windows file can not be found'],
'error', logger)
sys.exit(0)
to_write = []
if do_filter and filter_done:
do_filter = False
to_write += ["Filtering has already been done"]
if remove_median and median_done:
remove_median = False
to_write += [
"Median over all channels has already been substracted to each channels"
]
if comm.rank == 0 and len(to_write) > 0:
print_and_log(to_write, 'info', logger)
if params.getboolean('data', 'overwrite'):
data_file_in.open(mode='r+')
else:
data_file_in.open()
data_file_out.open(mode='r+')
if do_filter or remove_median:
filter_file(data_file_in, data_file_out, do_filter, remove_median)
if comm.rank == 0:
if do_filter:
params.write('noedits', 'filter_done', 'True')
if remove_median:
params.write('noedits', 'median_done', 'True')
if clean_artefact and artefacts_done:
clean_artefact = False
if comm.rank == 0:
print_and_log(['Artefacts have already been removed'], 'debug',
logger)
if clean_artefact:
art_dict = compute_artefacts(data_file_in)
remove_artefacts(data_file_out, art_dict)
if comm.rank == 0:
if clean_artefact:
params.write('noedits', 'artefacts_done', 'True')
data_file_in.close()
if not params.getboolean('data', 'overwrite'):
data_file_out.close()
comm.Barrier()
def main(params, nb_cpu, nb_gpu, use_gpu, file_name, benchmark, sim_same_elec):
"""
Useful tool to create synthetic datasets for benchmarking.
Arguments
---------
benchmark : {'fitting', 'clustering', 'synchrony', 'pca-validation', 'smart-search', 'drifts'}
"""
if sim_same_elec is None:
sim_same_elec = 0.8
logger = init_logging(params.logfile)
logger = logging.getLogger('circus.benchmarking')
numpy.random.seed(265)
file_name = os.path.abspath(file_name)
data_path = os.path.dirname(file_name)
data_suff, ext = os.path.splitext(os.path.basename(file_name))
file_out, ext = os.path.splitext(file_name)
if ext == '':
ext = '.dat'
file_name += ext
if ext != '.dat':
if comm.rank == 0:
print_and_log(['Benchmarking produces raw files: select a .dat extension'], 'error', logger)
sys.exit(0)
if benchmark not in ['fitting', 'clustering', 'synchrony', 'smart-search', 'drifts']:
if comm.rank == 0:
print_and_log(['Benchmark need to be in [fitting, clustering, synchrony, smart-search, drifts]'], 'error', logger)
sys.exit(0)
# The extension `.p` or `.pkl` or `.pickle` seems more appropriate than `.pic`.
# see: http://stackoverflow.com/questions/4530111/python-saving-objects-and-using-pickle-extension-of-filename
# see: https://wiki.python.org/moin/UsingPickle
def write_benchmark(filename, benchmark, cells, rates, amplitudes, sampling, probe, trends=None):
"""Save benchmark parameters in a file to remember them."""
import cPickle
to_write = {'benchmark' : benchmark}
to_write['cells'] = cells
to_write['rates'] = rates
to_write['probe'] = probe
to_write['amplitudes'] = amplitudes
to_write['sampling'] = sampling
if benchmark == 'drifts':
to_write['drifts'] = trends
cPickle.dump(to_write, open(filename + '.pic', 'w'))
# Retrieve some key parameters.
templates = io.load_data(params, 'templates')
N_tm = templates.shape[1] // 2
trends = None
# Normalize some variables.
if benchmark == 'fitting':
nb_insert = 25
n_cells = numpy.random.random_integers(0, N_tm - 1, nb_insert)
rate = nb_insert * [10]
amplitude = numpy.linspace(0.5, 5, nb_insert)
if benchmark == 'clustering':
n_point = 5
n_cells = numpy.random.random_integers(0, N_tm - 1, n_point ** 2)
x, y = numpy.mgrid[0:n_point, 0:n_point]
rate = numpy.linspace(0.5, 20, n_point)[x.flatten()]
amplitude = numpy.linspace(0.5, 5, n_point)[y.flatten()]
if benchmark == 'synchrony':
nb_insert = 5
corrcoef = 0.2
n_cells = nb_insert * [numpy.random.random_integers(0, N_tm - 1, 1)[0]]
rate = 10. / corrcoef
amplitude = 2
if benchmark == 'pca-validation':
nb_insert = 10
n_cells = numpy.random.random_integers(0, N_tm - 1, nb_insert)
rate_min = 0.5
rate_max = 20.0
rate = rate_min + (rate_max - rate_min) * numpy.random.random_sample(nb_insert)
amplitude_min = 0.5
amplitude_max = 5.0
amplitude = amplitude_min + (amplitude_max - amplitude_min) * numpy.random.random_sample(nb_insert)
if benchmark == 'smart-search':
nb_insert = 10
n_cells = nb_insert*[numpy.random.random_integers(0, templates.shape[1]//2-1, 1)[0]]
rate = 1 + 5*numpy.arange(nb_insert)
amplitude = 2
if benchmark == 'drifts':
n_point = 5
n_cells = numpy.random.random_integers(0, templates.shape[1]//2-1, n_point**2)
x, y = numpy.mgrid[0:n_point,0:n_point]
rate = 5*numpy.ones(n_point)[x.flatten()]
amplitude = numpy.linspace(0.5, 5, n_point)[y.flatten()]
trends = numpy.random.randn(n_point**2)
# Delete the output directory tree if this output directory exists.
if comm.rank == 0:
if os.path.exists(file_out):
shutil.rmtree(file_out)
# Check and normalize some variables.
if n_cells is None:
n_cells = 1
cells = [numpy.random.permutation(numpy.arange(n_cells))[0]]
elif not numpy.iterable(n_cells):
cells = [n_cells]
n_cells = 1
else:
cells = n_cells
n_cells = len(cells)
if numpy.iterable(rate):
assert len(rate) == len(cells), "Should have the same number of rates and cells"
else:
rate = [rate] * len(cells)
if numpy.iterable(amplitude):
assert len(amplitude) == len(cells), "Should have the same number of amplitudes and cells"
else:
amplitude = [amplitude] * len(cells)
# Retrieve some additional key parameters.
#params = detect_memory(params)
data_file = params.get_data_file(source=True)
N_e = params.getint('data', 'N_e')
N_total = params.nb_channels
hdf5_compress = params.getboolean('data', 'hdf5_compress')
nodes, edges = get_nodes_and_edges(params)
N_t = params.getint('detection', 'N_t')
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
N_tm_init = templates.shape[1]//2
thresholds = io.load_data(params, 'thresholds')
limits = io.load_data(params, 'limits')
best_elecs = io.load_data(params, 'electrodes')
norms = io.load_data(params, 'norm-templates')
# Create output directory if it does not exist.
if comm.rank == 0:
if not os.path.exists(file_out):
os.makedirs(file_out)
# Save benchmark parameters in a file to remember them.
if comm.rank == 0:
write_benchmark(file_out, benchmark, cells, rate, amplitude,
params.rate, params.get('data', 'mapping'), trends)
# Synchronize all the threads/processes.
comm.Barrier()
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
# Retrieve some additional key parameters.
chunk_size = params.getint('data', 'chunk_size')
scalings = []
params.set('data', 'data_file', file_name)
data_file_out = params.get_data_file(is_empty=True)
data_file_out.allocate(shape=data_file.shape)
# Synchronize all the threads/processes.
comm.Barrier()
# For each wanted synthesized cell insert a generated template in the set of
# existing template.
for gcount, cell_id in enumerate(cells):
best_elec = best_elecs[cell_id]
indices = inv_nodes[edges[nodes[best_elec]]]
count = 0
new_indices = []
all_elecs = numpy.random.permutation(numpy.arange(N_e))
reference = templates[:, cell_id].toarray().reshape(N_e, N_t)
# Initialize the similarity (i.e. default value).
similarity = 1.0
# Find the first eligible template for the wanted synthesized cell.
while len(new_indices) != len(indices) or (similarity > sim_same_elec):
similarity = 0
if count == len(all_elecs):
if comm.rank == 0:
print_and_log(["No electrode to move template %d (max similarity is %g)" %(cell_id, similarity)], 'error', logger)
sys.exit(0)
else:
# Get the next shuffled electrode.
n_elec = all_elecs[count]
if benchmark not in ['synchrony', 'smart-search']:
# Process if the shuffled electrode and the nearest electrode
# to the synthesized cell are not identical.
local_test = n_elec != best_elec
else:
# Process if the shuffled electrode and the nearest electrode
# to the synthesized cell are identical.
local_test = n_elec == best_elec
if local_test:
# Shuffle the neighboring electrodes whithout modifying
# the nearest electrode to the synthesized cell.
new_indices = inv_nodes[edges[nodes[n_elec]]]
idx = numpy.where(new_indices != best_elec)[0]
new_indices[idx] = numpy.random.permutation(new_indices[idx])
if len(new_indices) == len(indices):
# Shuffle the templates on the neighboring electrodes.
new_temp = numpy.zeros(reference.shape,
dtype=numpy.float32)
new_temp[new_indices, :] = reference[indices, :]
# Compute the scaling factor which normalize the
# shuffled template.
gmin = new_temp.min()
data = numpy.where(new_temp == gmin)
scaling = -thresholds[data[0][0]]/gmin
for i in xrange(templates.shape[1]//2):
match = templates[:, i].toarray().reshape(N_e, N_t)
d = numpy.corrcoef(match.flatten(),
scaling * new_temp.flatten())[0, 1]
if d > similarity:
similarity = d
else:
new_indices = []
# Go to the next shuffled electrode.
count += 1
#if comm.rank == 0:
# print "Template", cell_id, "is shuffled from electrode", best_elec, "to", n_elec, "(max similarity is %g)" %similarity
N_tm = templates.shape[1]//2
to_insert = numpy.zeros(reference.shape, dtype=numpy.float32)
to_insert[new_indices] = scaling*amplitude[gcount]*templates[:, cell_id].toarray().reshape(N_e, N_t)[indices]
to_insert2 = numpy.zeros(reference.shape, dtype=numpy.float32)
to_insert2[new_indices] = scaling*amplitude[gcount]*templates[:, cell_id + N_tm].toarray().reshape(N_e, N_t)[indices]
## Insert the selected template.
# Retrieve the number of existing templates in the dataset.
N_tm = templates.shape[1]//2
# Generate the template of the synthesized cell from the selected
# template, the target amplitude and the rescaling (i.e. threshold of
# the target electrode).
to_insert = numpy.zeros(reference.shape, dtype=numpy.float32)
to_insert[new_indices] = scaling * amplitude[gcount] * templates[:, cell_id].toarray().reshape(N_e, N_t)[indices]
to_insert = to_insert.flatten()
to_insert2 = numpy.zeros(reference.shape, dtype=numpy.float32)
to_insert2[new_indices] = scaling * amplitude[gcount] * templates[:, cell_id + N_tm].toarray().reshape(N_e, N_t)[indices]
to_insert2 = to_insert2.flatten()
# Compute the norm of the generated template.
mynorm = numpy.sqrt(numpy.sum(to_insert ** 2) / (N_e * N_t))
mynorm2 = numpy.sqrt(numpy.sum(to_insert2 ** 2) / (N_e * N_t))
# Insert the limits of the generated template.
limits = numpy.vstack((limits, limits[cell_id]))
# Insert the best electrode of the generated template.
best_elecs = numpy.concatenate((best_elecs, [n_elec]))
# Insert the norm of the generated template (i.e. central component and
# orthogonal component).
norms = numpy.insert(norms, N_tm, mynorm)
norms = numpy.insert(norms, 2 * N_tm + 1, mynorm2)
# Insert the scaling of the generated template.
scalings += [scaling]
# Retrieve the data about the existing templates.
templates = templates.tocoo()
xdata = templates.row
ydata = templates.col
zdata = templates.data
# Shift by one the orthogonal components of the existing templates.
idx = numpy.where(ydata >= N_tm)[0]
ydata[idx] += 1
# Insert the central component of the selected template.
dx = to_insert.nonzero()[0].astype(numpy.int32)
xdata = numpy.concatenate((xdata, dx))
ydata = numpy.concatenate((ydata, N_tm * numpy.ones(len(dx), dtype=numpy.int32)))
zdata = numpy.concatenate((zdata, to_insert[dx]))
# Insert the orthogonal component of the selected template.
dx = to_insert2.nonzero()[0].astype(numpy.int32)
xdata = numpy.concatenate((xdata, dx))
ydata = numpy.concatenate((ydata, (2 * N_tm + 1) * numpy.ones(len(dx), dtype=numpy.int32)))
zdata = numpy.concatenate((zdata, to_insert2[dx]))
# Recontruct the matrix of templates.
templates = scipy.sparse.csc_matrix((zdata, (xdata, ydata)), shape=(N_e * N_t, 2 * (N_tm + 1)))
# Remove all the expired data.
if benchmark == 'pca-validation':
# Remove all the expired data.
N_tm_init = 0
N_tm = templates.shape[1] / 2
limits = limits[N_tm - nb_insert:, :]
best_elecs = best_elecs[N_tm - nb_insert:]
norms = numpy.concatenate((norms[N_tm-nb_insert:N_tm], norms[2*N_tm-nb_insert:2*N_tm]))
scalings = scalings
templates = templates.tocoo()
xdata = templates.row
ydata = templates.col
zdata = templates.data
idx_cen = numpy.logical_and(N_tm - nb_insert <= ydata, ydata < N_tm)
idx_cen = numpy.where(idx_cen)[0]
idx_ort = numpy.logical_and(2 * N_tm - nb_insert <= ydata, ydata < 2 * N_tm)
idx_ort = numpy.where(idx_ort)[0]
ydata[idx_cen] = ydata[idx_cen] - (N_tm - nb_insert)
ydata[idx_ort] = ydata[idx_ort] - 2 * (N_tm - nb_insert)
idx = numpy.concatenate((idx_cen, idx_ort))
xdata = xdata[idx]
ydata = ydata[idx]
zdata = zdata[idx]
templates = scipy.sparse.csc_matrix((zdata, (xdata, ydata)), shape=(N_e * N_t, 2 * nb_insert))
# Retrieve the information about the organisation of the chunks of data.
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
# Display informations about the generated benchmark.
if comm.rank == 0:
print_and_log(["Generating benchmark data [%s] with %d cells" %(benchmark, n_cells)], 'info', logger)
purge(file_out, '.data')
template_shift = params.getint('detection', 'template_shift')
all_chunks = numpy.arange(nb_chunks)
to_process = all_chunks[numpy.arange(comm.rank, nb_chunks, comm.size)]
loc_nb_chunks = len(to_process)
numpy.random.seed(comm.rank)
to_explore = xrange(comm.rank, nb_chunks, comm.size)
# Initialize the progress bar about the generation of the benchmark.
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
# Open the file for collective I/O.
#g = myfile.Open(comm, file_name, MPI.MODE_RDWR)
#g.Set_view(data_offset, data_mpi, data_mpi)
data_file_out.open(mode='r+')
# Open the thread/process' files to collect the results.
spiketimes_filename = os.path.join(file_out, data_suff + '.spiketimes-%d.data' %comm.rank)
spiketimes_file = open(spiketimes_filename, 'wb')
amplitude_filename = os.path.join(file_out, data_suff + '.amplitudes-%d.data' %comm.rank)
amplitudes_file = open(amplitude_filename, 'wb')
templates_filename = os.path.join(file_out, data_suff + '.templates-%d.data' %comm.rank)
templates_file = open(templates_filename, 'wb')
real_amps_filename = os.path.join(file_out, data_suff + '.real_amps-%d.data' %comm.rank)
real_amps_file = open(real_amps_filename, 'wb')
voltages_filename = os.path.join(file_out, data_suff + '.voltages-%d.data' %comm.rank)
voltages_file = open(voltages_filename, 'wb')
# For each chunk of data associate to the current thread/process generate
# the new chunk of data (i.e. with considering the added synthesized cells).
for count, gidx in enumerate(to_explore):
#if (last_chunk_len > 0) and (gidx == (nb_chunks - 1)):
# chunk_len = last_chunk_len
# chunk_size = last_chunk_len // N_total
result = {'spiketimes' : [], 'amplitudes' : [],
'templates' : [], 'real_amps' : [],
'voltages' : []}
offset = gidx * chunk_size
local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes)
if benchmark == 'pca-validation':
# Clear the current data chunk.
local_chunk = numpy.zeros(local_chunk.shape, dtype=local_chunk.dtype)
# Handle whitening if necessary.
if do_spatial_whitening:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(local_chunk,
temporal_whitening,
axis=0,
mode='constant')
if benchmark is 'synchrony':
# Generate some spike indices (i.e. times) at the given rate for
# 'synchrony' mode. Each synthesized cell will use a subset of this
# spike times.
mips = numpy.random.rand(chunk_size) < rate[0] / float(params.rate)
# For each synthesized cell generate its spike indices (i.e.times) and
# add them to the dataset.
for idx in xrange(len(cells)):
if benchmark is 'synchrony':
# Choose a subset of the spike indices generated before. The
# size of this subset is parameterized by the target correlation
# coefficients.
sidx = numpy.where(mips == True)[0]
spikes = numpy.zeros(chunk_size, dtype=numpy.bool)
spikes[sidx[numpy.random.rand(len(sidx)) < corrcoef]] = True
else:
# Generate some spike indices at the given rate.
spikes = numpy.random.rand(chunk_size) < rate[idx] / float(params.rate)
if benchmark == 'drifts':
amplitudes = numpy.ones(len(spikes)) + trends[idx]*((spikes + offset)/(5*60*float(params.rate)))
else:
amplitudes = numpy.ones(len(spikes))
# Padding with `False` to avoid the insertion of partial spikes at
# the edges of the signal.
spikes[:N_t] = False
spikes[-N_t:] = False
# Find the indices of the spike samples.
spikes = numpy.where(spikes == True)[0]
n_template = N_tm_init + idx
loc_template = templates[:, n_template].toarray().reshape(N_e, N_t)
first_flat = loc_template.T.flatten()
norm_flat = numpy.sum(first_flat ** 2)
# For each index (i.e. spike sample location) add the spike to the
# chunk of data.
refractory = int(5 * 1e-3 * params.rate)
t_last = - refractory
for scount, spike in enumerate(spikes):
if (spike - t_last) > refractory:
local_chunk[spike-template_shift:spike+template_shift+1, :] += amplitudes[scount]*loc_template.T
amp = numpy.dot(local_chunk[spike-template_shift:spike+template_shift+1, :].flatten(), first_flat)
amp /= norm_flat
result['real_amps'] += [amp]
result['spiketimes'] += [spike + offset]
result['amplitudes'] += [(amplitudes[scount], 0)]
result['templates'] += [n_template]
result['voltages'] += [local_chunk[spike, best_elecs[idx]]]
t_last = spike
# Write the results into the thread/process' files.
spikes_to_write = numpy.array(result['spiketimes'], dtype=numpy.uint32)
amplitudes_to_write = numpy.array(result['amplitudes'], dtype=numpy.float32)
templates_to_write = numpy.array(result['templates'], dtype=numpy.int32)
real_amps_to_write = numpy.array(result['real_amps'], dtype=numpy.float32)
voltages_to_write = numpy.array(result['voltages'], dtype=numpy.float32)
spiketimes_file.write(spikes_to_write.tostring())
amplitudes_file.write(amplitudes_to_write.tostring())
templates_file.write(templates_to_write.tostring())
real_amps_file.write(real_amps_to_write.tostring())
voltages_file.write(voltages_to_write.tostring())
#print count, 'spikes inserted...'
#new_chunk = numpy.zeros((chunk_size, N_total), dtype=numpy.float32)
#new_chunk[:, nodes] = local_chunk
# Overwrite the new chunk of data using explicit offset.
#new_chunk = new_chunk.flatten()
#g.Write_at(gidx * chunk_len, new_chunk)
data_file_out.set_data(offset, local_chunk)
# Update the progress bar about the generation of the benchmark.
# Close the thread/process' files.
spiketimes_file.flush()
os.fsync(spiketimes_file.fileno())
spiketimes_file.close()
amplitudes_file.flush()
os.fsync(amplitudes_file.fileno())
amplitudes_file.close()
templates_file.flush()
os.fsync(templates_file.fileno())
templates_file.close()
real_amps_file.flush()
os.fsync(real_amps_file.fileno())
real_amps_file.close()
voltages_file.flush()
os.fsync(voltages_file.fileno())
voltages_file.close()
# Close the file for collective I/O.
data_file_out.close()
data_file.close()
# Synchronize all the threads/processes.
comm.Barrier()
## Eventually, perform all the administrative tasks.
## (i.e. files and folders management).
file_params = file_out + '.params'
if comm.rank == 0:
# Create `injected` directory if it does not exist
result_path = os.path.join(file_out, 'injected')
if not os.path.exists(result_path):
os.makedirs(result_path)
# Copy initial configuration file from `<dataset1>.params` to `<dataset2>.params`.
shutil.copy2(params.get('data', 'data_file_noext') + '.params', file_params)
new_params = CircusParser(file_name)
# Copy initial basis file from `<dataset1>/<dataset1>.basis.hdf5` to
# `<dataset2>/injected/<dataset2>.basis.hdf5.
shutil.copy2(params.get('data', 'file_out') + '.basis.hdf5',
os.path.join(result_path, data_suff + '.basis.hdf5'))
# Save templates into `<dataset>/<dataset>.templates.hdf5`.
mydata = h5py.File(os.path.join(file_out, data_suff + '.templates.hdf5'), 'w')
templates = templates.tocoo()
if hdf5_compress:
mydata.create_dataset('temp_x', data=templates.row, compression='gzip')
mydata.create_dataset('temp_y', data=templates.col, compression='gzip')
mydata.create_dataset('temp_data', data=templates.data, compression='gzip')
else:
mydata.create_dataset('temp_x', data=templates.row)
mydata.create_dataset('temp_y', data=templates.col)
mydata.create_dataset('temp_data', data=templates.data)
mydata.create_dataset('temp_shape', data=numpy.array([N_e, N_t, templates.shape[1]],
dtype=numpy.int32))
mydata.create_dataset('limits', data=limits)
mydata.create_dataset('norms', data=norms)
mydata.close()
# Save electrodes into `<dataset>/<dataset>.clusters.hdf5`.
mydata = h5py.File(os.path.join(file_out, data_suff + '.clusters.hdf5'), 'w')
mydata.create_dataset('electrodes', data=best_elecs)
mydata.close()
comm.Barrier()
if comm.rank == 0:
# Gather data from all threads/processes.
f_next, extension = os.path.splitext(file_name)
file_out_bis = os.path.join(f_next, os.path.basename(f_next))
#new_params.set('data', 'file_out', file_out_bis) # Output file without suffix
#new_params.set('data', 'file_out_suff', file_out_bis + params.get('data', 'suffix'))
new_params.get_data_file()
io.collect_data(comm.size, new_params, erase=True, with_real_amps=True, with_voltages=True, benchmark=True)
# Change some flags in the configuration file.
new_params.write('whitening', 'temporal', 'False') # Disable temporal filtering
new_params.write('whitening', 'spatial', 'False') # Disable spatial filtering
new_params.write('data', 'data_dtype', 'float32') # Set type of the data to float32
new_params.write('data', 'dtype_offset', 'auto') # Set padding for data to auto
# Move results from `<dataset>/<dataset>.result.hdf5` to
# `<dataset>/injected/<dataset>.result.hdf5`.
shutil.move(os.path.join(file_out, data_suff + '.result.hdf5'), os.path.join(result_path, data_suff + '.result.hdf5'))
# Save scalings into `<dataset>/injected/<dataset>.scalings.npy`.
numpy.save(os.path.join(result_path, data_suff + '.scalings'), scalings)
file_name_noext, ext = os.path.splitext(file_name)
# Copy basis from `<dataset>/injected/<dataset>.basis.hdf5` to
# `<dataset>/<dataset>.basis.hdf5`.
shutil.copy2(os.path.join(result_path, data_suff + '.basis.hdf5'),
os.path.join(file_out, data_suff + '.basis.hdf5'))
if benchmark not in ['fitting', 'synchrony']:
# Copy templates from `<dataset>/<dataset>.templates.hdf5` to
# `<dataset>/injected/<dataset>.templates.hdf5`
shutil.move(os.path.join(file_out, data_suff + '.templates.hdf5'),
os.path.join(result_path, data_suff + '.templates.hdf5'))
def main(params, nb_cpu, nb_gpu, use_gpu):
#################################################################
# params = detect_memory(params)
_ = init_logging(params.logfile)
SHARED_MEMORY = get_shared_memory_flag(params)
logger = logging.getLogger('circus.fitting')
data_file = params.data_file
n_e = params.getint('data', 'N_e')
n_total = params.nb_channels
n_t = params.getint('detection', 'N_t')
template_shift = params.getint('detection', 'template_shift')
# file_out = params.get('data', 'file_out')
file_out_suff = params.get('data', 'file_out_suff')
sign_peaks = params.get('detection', 'peaks')
matched_filter = params.getboolean('detection', 'matched-filter')
# spike_thresh = params.getfloat('detection', 'spike_thresh')
ratio_thresh = params.getfloat('fitting', 'ratio_thresh')
two_components = params.getboolean('fitting', 'two_components')
# spike_width = params.getfloat('detection', 'spike_width')
# dist_peaks = params.getint('detection', 'dist_peaks')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
templates_normalization = params.getboolean('clustering', 'templates_normalization') # TODO test, switch, test!
chunk_size = detect_memory(params, fitting=True)
gpu_only = params.getboolean('fitting', 'gpu_only')
nodes, edges = get_nodes_and_edges(params)
tmp_limits = params.get('fitting', 'amp_limits').replace('(', '').replace(')', '').split(',')
tmp_limits = [float(v) for v in tmp_limits]
amp_auto = params.getboolean('fitting', 'amp_auto')
auto_nb_chances = params.getboolean('fitting', 'auto_nb_chances')
if auto_nb_chances:
nb_chances = io.load_data(params, 'nb_chances')
max_nb_chances = params.getint('fitting', 'max_nb_chances')
percent_nb_chances = params.getfloat('fitting', 'percent_nb_chances')
total_nb_chances = max(1, numpy.nanpercentile(nb_chances, percent_nb_chances))
total_nb_chances = min(total_nb_chances, max_nb_chances)
if comm.rank == 0:
print_and_log(['nb_chances set automatically to %g' %total_nb_chances], 'debug', logger)
else:
total_nb_chances = params.getfloat('fitting', 'nb_chances')
max_chunk = params.getfloat('fitting', 'max_chunk')
# noise_thr = params.getfloat('clustering', 'noise_thr')
collect_all = params.getboolean('fitting', 'collect_all')
min_second_component = params.getfloat('fitting', 'min_second_component')
debug = params.getboolean('fitting', 'debug')
ignore_dead_times = params.getboolean('triggers', 'ignore_times')
inv_nodes = numpy.zeros(n_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
data_file.open()
#################################################################
if use_gpu:
import cudamat as cmt
# # Need to properly handle multi GPU per MPI nodes?
if nb_gpu > nb_cpu:
gpu_id = int(comm.rank // nb_cpu)
else:
gpu_id = 0
cmt.cuda_set_device(gpu_id)
cmt.init()
cmt.cuda_sync_threads()
if SHARED_MEMORY:
templates, _ = io.load_data_memshared(params, 'templates', normalize=templates_normalization, transpose=True)
N_tm, x = templates.shape
else:
templates = io.load_data(params, 'templates')
x, N_tm = templates.shape
temp_2_shift = 2 * template_shift
temp_3_shift = 3 * template_shift
full_gpu = use_gpu and gpu_only
n_tm = N_tm // 2
n_scalar = n_e * n_t
temp_window = numpy.arange(-template_shift, template_shift + 1)
size_window = n_e * (2 * template_shift + 1)
if not amp_auto:
amp_limits = numpy.zeros((n_tm, 2))
amp_limits[:, 0] = tmp_limits[0]
amp_limits[:, 1] = tmp_limits[1]
else:
amp_limits = io.load_data(params, 'limits')
norm_templates = io.load_data(params, 'norm-templates')
if not templates_normalization:
norm_templates_2 = (norm_templates ** 2.0) * n_scalar
if not SHARED_MEMORY:
# Normalize templates (if necessary).
if templates_normalization:
for idx in range(templates.shape[1]):
myslice = numpy.arange(templates.indptr[idx], templates.indptr[idx+1])
templates.data[myslice] /= norm_templates[idx]
# Transpose templates.
templates = templates.T
waveform_neg = numpy.empty(0) # default assignment (for PyCharm code inspection)
matched_thresholds_neg = None # default assignment (for PyCharm code inspection)
waveform_pos = numpy.empty(0) # default assignment (for PyCharm code inspection)
matched_thresholds_pos = None # default assignment (for PyCharm code inspection)
if matched_filter:
if sign_peaks in ['negative', 'both']:
waveform_neg = io.load_data(params, 'waveform')[::-1]
waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg))
matched_thresholds_neg = io.load_data(params, 'matched-thresholds')
if sign_peaks in ['positive', 'both']:
waveform_pos = io.load_data(params, 'waveform-pos')[::-1]
waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos))
matched_thresholds_pos = io.load_data(params, 'matched-thresholds-pos')
if ignore_dead_times:
all_dead_times = get_dead_times(params)
else:
all_dead_times = None # default assignment (for PyCharm code inspection)
thresholds = io.get_accurate_thresholds(params, ratio_thresh)
neighbors = {}
if collect_all:
for i in range(0, n_tm):
tmp = templates[i, :].toarray().reshape(n_e, n_t)
if templates_normalization:
tmp = tmp * norm_templates[i]
neighbors[i] = numpy.where(numpy.sum(tmp, axis=1) != 0.0)[0]
if use_gpu:
templates = cmt.SparseCUDAMatrix(templates, copy_on_host=False)
info_string = ''
if comm.rank == 0:
if use_gpu:
info_string = "using %d GPUs" % comm.size
else:
info_string = "using %d CPUs" % comm.size
comm.Barrier()
c_overlap = io.get_overlaps(params, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu)
over_shape = c_overlap.get('over_shape')[:]
n_over = int(numpy.sqrt(over_shape[0]))
s_over = over_shape[1]
# # If the number of overlaps is different from templates, we need to recompute them.
if n_over != N_tm:
if comm.rank == 0:
print_and_log(['Templates have been modified, recomputing the overlaps...'], 'default', logger)
c_overlap = io.get_overlaps(params, erase=True, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu)
over_shape = c_overlap.get('over_shape')[:]
n_over = int(numpy.sqrt(over_shape[0]))
s_over = over_shape[1]
if SHARED_MEMORY:
c_overs, _ = io.load_data_memshared(params, 'overlaps')
else:
c_overs = io.load_data(params, 'overlaps')
comm.Barrier()
if n_tm == 0:
if comm.rank == 0:
print_and_log(["No templates present. Redo clustering?"], 'default', logger)
sys.exit(0)
if comm.rank == 0:
print_and_log(["Here comes the SpyKING CIRCUS %s and %d templates..." % (info_string, n_tm)], 'default', logger)
purge(file_out_suff, '.data')
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
else:
spatial_whitening = None # default assignment (for PyCharm code inspection)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
else:
temporal_whitening = None # default assignment (for PyCharm code inspection)
if full_gpu:
try:
# If memory on the GPU is large enough, we load the overlaps onto it
for i in range(n_over):
c_overs[i] = cmt.SparseCUDAMatrix(c_overs[i], copy_on_host=False)
except Exception:
if comm.rank == 0:
print_and_log(["Not enough memory on GPUs: GPUs are used for projection only"], 'info', logger)
for i in range(n_over):
if i in c_overs:
del c_overs[i]
full_gpu = False
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
processed_chunks = int(min(nb_chunks, max_chunk))
comm.Barrier()
spiketimes_file = open(file_out_suff + '.spiketimes-%d.data' % comm.rank, 'wb')
comm.Barrier()
amplitudes_file = open(file_out_suff + '.amplitudes-%d.data' % comm.rank, 'wb')
comm.Barrier()
templates_file = open(file_out_suff + '.templates-%d.data' % comm.rank, 'wb')
comm.Barrier()
if collect_all:
garbage_times_file = open(file_out_suff + '.gspiketimes-%d.data' % comm.rank, 'wb')
comm.Barrier()
garbage_temp_file = open(file_out_suff + '.gtemplates-%d.data' % comm.rank, 'wb')
comm.Barrier()
else:
garbage_times_file = None # default assignment (for PyCharm code inspection)
garbage_temp_file = None # default assignment (for PyCharm code inspection)
if debug:
# Open debug files.
chunk_nbs_debug_file = open(file_out_suff + '.chunk_nbs_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
iteration_nbs_debug_file = open(file_out_suff + '.iteration_nbs_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
peak_nbs_debug_file = open(file_out_suff + '.peak_nbs_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
peak_local_time_steps_debug_file = open(
file_out_suff + '.peak_local_time_steps_debug_%d.data' % comm.rank, mode='wb'
)
comm.Barrier()
peak_time_steps_debug_file = open(file_out_suff + '.peak_time_steps_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
peak_scalar_products_debug_file = open(
file_out_suff + '.peak_scalar_products_debug_%d.data' % comm.rank, mode='wb'
)
comm.Barrier()
peak_solved_flags_debug_file = open(file_out_suff + '.peak_solved_flags_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
template_nbs_debug_file = open(file_out_suff + '.template_nbs_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
success_flags_debug_file = open(file_out_suff + '.success_flags_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
else:
chunk_nbs_debug_file = None # default assignment (for PyCharm code inspection)
iteration_nbs_debug_file = None # default assignment (for PyCharm code inspection)
peak_nbs_debug_file = None # default assignment (for PyCharm code inspection)
peak_local_time_steps_debug_file = None # default assignment (for PyCharm code inspection)
peak_time_steps_debug_file = None # default assignment (for PyCharm code inspection)
peak_scalar_products_debug_file = None # default assignment (for PyCharm code inspection)
peak_solved_flags_debug_file = None # default assignment (for PyCharm code inspection)
template_nbs_debug_file = None # default assignment (for PyCharm code inspection)
success_flags_debug_file = None # default assignment (for PyCharm code inspection)
if use_gpu and do_spatial_whitening:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False)
last_chunk_size = 0
slice_indices = numpy.zeros(0, dtype=numpy.int32)
to_explore = range(comm.rank, processed_chunks, comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(params, to_explore)
for gcount, gidx in enumerate(to_explore):
# print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
# # We need to deal with the borders by taking chunks of size [0, chunck_size + template_shift].
is_first = data_file.is_first_chunk(gidx, nb_chunks)
is_last = data_file.is_last_chunk(gidx, nb_chunks)
if not (is_first and is_last):
if is_last:
padding = (-temp_3_shift, 0)
elif is_first:
padding = (0, temp_3_shift)
else:
padding = (-temp_3_shift, temp_3_shift)
else:
padding = (0, 0)
result = {
'spiketimes': [],
'amplitudes': [],
'templates': [],
}
result_debug = {
'chunk_nbs': [],
'iteration_nbs': [],
'peak_nbs': [],
'peak_local_time_steps': [],
'peak_time_steps': [],
'peak_scalar_products': [],
'peak_solved_flags': [],
'template_nbs': [],
'success_flags': [],
}
local_chunk, t_offset = data_file.get_data(gidx, chunk_size, padding, nodes=nodes)
len_chunk = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(local_chunk, temporal_whitening, axis=0, mode='constant')
# Extracting peaks.
all_found_spikes = {}
if collect_all:
for i in range(n_e):
all_found_spikes[i] = []
local_peaktimes = [numpy.empty(0, dtype=numpy.uint32)]
if matched_filter:
if sign_peaks in ['positive', 'both']:
filter_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_pos, axis=0, mode='constant')
for i in range(n_e):
peaktimes = scipy.signal.find_peaks(filter_chunk[:, i], height=matched_thresholds_pos[i])[0]
local_peaktimes.append(peaktimes)
if collect_all:
all_found_spikes[i] += peaktimes.tolist()
if sign_peaks in ['negative', 'both']:
filter_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_neg, axis=0, mode='constant')
for i in range(n_e):
peaktimes = scipy.signal.find_peaks(filter_chunk[:, i], height=matched_thresholds_neg[i])[0]
local_peaktimes.append(peaktimes)
if collect_all:
all_found_spikes[i] += peaktimes.tolist()
local_peaktimes = numpy.concatenate(local_peaktimes)
else:
for i in range(n_e):
if sign_peaks == 'negative':
peaktimes = scipy.signal.find_peaks(-local_chunk[:, i], height=thresholds[i])[0]
elif sign_peaks == 'positive':
peaktimes = scipy.signal.find_peaks(local_chunk[:, i], height=thresholds[i])[0]
elif sign_peaks == 'both':
peaktimes = scipy.signal.find_peaks(numpy.abs(local_chunk[:, i]), height=thresholds[i])[0]
else:
raise ValueError("Unexpected value %s" % sign_peaks)
local_peaktimes.append(peaktimes)
if collect_all:
all_found_spikes[i] += peaktimes.tolist()
local_peaktimes = numpy.concatenate(local_peaktimes)
local_peaktimes = numpy.unique(local_peaktimes)
g_offset = t_offset + padding[0]
if ignore_dead_times:
dead_indices = numpy.searchsorted(all_dead_times, [t_offset, t_offset + chunk_size])
if dead_indices[0] != dead_indices[1]:
is_included = numpy.in1d(local_peaktimes + g_offset, all_dead_times[dead_indices[0]:dead_indices[1]])
local_peaktimes = local_peaktimes[~is_included]
local_peaktimes = numpy.sort(local_peaktimes)
else:
dead_indices = None # default assignment (for PyCharm code inspection)
# print "Removing the useless borders..."
local_borders = (template_shift, len_chunk - template_shift)
idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1])
local_peaktimes = numpy.compress(idx, local_peaktimes)
if collect_all:
for i in range(n_e):
all_found_spikes[i] = numpy.array(all_found_spikes[i], dtype=numpy.uint32)
if ignore_dead_times:
if dead_indices[0] != dead_indices[1]:
is_included = numpy.in1d(
all_found_spikes[i] + g_offset, all_dead_times[dead_indices[0]:dead_indices[1]]
)
all_found_spikes[i] = all_found_spikes[i][~is_included]
all_found_spikes[i] = numpy.sort(all_found_spikes[i])
idx = (all_found_spikes[i] >= local_borders[0]) & (all_found_spikes[i] < local_borders[1])
all_found_spikes[i] = numpy.compress(idx, all_found_spikes[i])
nb_local_peak_times = len(local_peaktimes)
if full_gpu:
# all_indices = cmt.CUDAMatrix(all_indices)
# tmp_gpu = cmt.CUDAMatrix(local_peaktimes.reshape((1, nb_local_peak_times)), copy_on_host=False)
_ = cmt.CUDAMatrix(local_peaktimes.reshape((1, nb_local_peak_times)), copy_on_host=False)
if nb_local_peak_times > 0:
# print "Computing the b (should full_gpu by putting all chunks on GPU if possible?)..."
if collect_all:
c_local_chunk = local_chunk.copy()
else:
c_local_chunk = None # default assignment (for PyCharm code inspection)
sub_mat = local_chunk[local_peaktimes[:, None] + temp_window]
sub_mat = sub_mat.transpose(2, 1, 0).reshape(size_window, nb_local_peak_times)
del local_chunk
if use_gpu:
sub_mat = cmt.CUDAMatrix(sub_mat, copy_on_host=False)
b = cmt.sparse_dot(templates, sub_mat)
else:
b = templates.dot(sub_mat)
del sub_mat
local_restriction = (t_offset, t_offset + chunk_size)
all_spikes = local_peaktimes + g_offset
# Because for GPU, slicing by columns is more efficient, we need to transpose b
# b = b.transpose()
if use_gpu and not full_gpu:
b = b.asarray()
failure = numpy.zeros(nb_local_peak_times, dtype=numpy.int32)
if full_gpu:
mask = numpy.zeros((2 * n_tm, nb_local_peak_times), dtype=numpy.float32)
mask[:n_tm, :] = 1
# data = cmt.empty(mask.shape)
_ = cmt.empty(mask.shape)
patch_gpu = b.shape[1] == 1
else:
patch_gpu = None
if collect_all:
c_all_times = numpy.zeros((len_chunk, n_e), dtype=numpy.bool)
c_min_times = numpy.maximum(numpy.arange(len_chunk) - template_shift, 0)
c_max_times = numpy.minimum(numpy.arange(len_chunk) + template_shift + 1, len_chunk)
for i in range(n_e):
c_all_times[all_found_spikes[i], i] = True
else:
c_all_times = None # default assignment (for PyCharm code inspection)
c_min_times = None # default assignment (for PyCharm code inspection)
c_max_times = None # default assignment (for PyCharm code inspection)
iteration_nb = 0
local_max = 0
numerous_argmax = False
nb_argmax = n_tm
best_indices = numpy.zeros(0, dtype=numpy.int32)
data = b[:n_tm, :]
flatten_data = data.ravel()
while numpy.mean(failure) < total_nb_chances:
# Is there a way to update sub_b * mask at the same time?
if full_gpu:
b_array = b.asarray()
else:
b_array = None
if numerous_argmax:
if len(best_indices) == 0:
best_indices = largest_indices(flatten_data, nb_argmax)
best_template_index, peak_index = numpy.unravel_index(best_indices[0], data.shape)
else:
best_template_index, peak_index = numpy.unravel_index(data.argmax(), data.shape)
peak_scalar_product = data[best_template_index, peak_index]
best_template2_index = best_template_index + n_tm
if templates_normalization:
if full_gpu:
best_amp = b_array[best_template_index, peak_index] / n_scalar
best_amp2 = b_array[best_template2_index, peak_index] / n_scalar
else:
best_amp = b[best_template_index, peak_index] / n_scalar
if two_components:
best_amp2 = b[best_template2_index, peak_index] / n_scalar
else:
best_amp2 = 0.0
best_amp_n = best_amp / norm_templates[best_template_index]
best_amp2_n = best_amp2 / norm_templates[best_template2_index]
else:
if full_gpu:
best_amp = b_array[best_template_index, peak_index]
best_amp = best_amp / norm_templates_2[best_template_index]
# TODO is `best_amp` value correct?
best_amp2 = b_array[best_template2_index, peak_index]
best_amp2 = best_amp2 / norm_templates_2[best_template2_index]
# TODO is `best_amp2` value correct?
else:
best_amp = b[best_template_index, peak_index]
best_amp = best_amp / norm_templates_2[best_template_index]
# TODO is `best_amp` value correct?
if two_components:
best_amp2 = b[best_template2_index, peak_index]
best_amp2 = best_amp2 / norm_templates_2[best_template2_index]
# TODO is `best_amp2` value correct?
else:
best_amp2 = 0.0
best_amp_n = best_amp
best_amp2_n = best_amp2
# Verify amplitude constraint.
a_min, a_max = amp_limits[best_template_index, :]
if (a_min <= best_amp_n) & (best_amp_n <= a_max):
# Keep the matching.
peak_time_step = local_peaktimes[peak_index]
peak_data = (local_peaktimes - peak_time_step).astype(np.int32)
is_neighbor = np.where(np.abs(peak_data) <= temp_2_shift)[0]
idx_neighbor = peak_data[is_neighbor] + temp_2_shift
nb_neighbors = len(is_neighbor)
indices = np.zeros((s_over, nb_neighbors), dtype=np.int32)
indices[idx_neighbor, np.arange(nb_neighbors)] = 1
if full_gpu:
indices = cmt.CUDAMatrix(indices, copy_on_host=False)
if patch_gpu:
b_lines = b.get_col_slice(0, b.shape[0])
else:
b_lines = b.get_col_slice(is_neighbor[0], is_neighbor[-1]+1)
tmp1 = cmt.sparse_dot(c_overs[best_template_index], indices, mult=-best_amp)
tmp2 = cmt.sparse_dot(c_overs[best_template2_index], indices, mult=-best_amp2)
b_lines.add(tmp1.add(tmp2))
del tmp1, tmp2
else:
tmp1 = c_overs[best_template_index].multiply(-best_amp)
if numpy.abs(best_amp2) > min_second_component:
tmp1 += c_overs[best_template2_index].multiply(-best_amp2)
b[:, is_neighbor] += tmp1.dot(indices)
numerous_argmax = False
# Add matching to the result.
t_spike = all_spikes[peak_index]
if (t_spike >= local_restriction[0]) and (t_spike < local_restriction[1]):
result['spiketimes'] += [t_spike]
result['amplitudes'] += [(best_amp_n, best_amp2_n)]
result['templates'] += [best_template_index]
# Mark current matching as tried.
b[best_template_index, peak_index] = -numpy.inf
# Save debug data.
if debug:
result_debug['chunk_nbs'] += [gidx]
result_debug['iteration_nbs'] += [iteration_nb]
result_debug['peak_nbs'] += [peak_index]
result_debug['peak_local_time_steps'] += [local_peaktimes[peak_index]]
result_debug['peak_time_steps'] += [all_spikes[peak_index]]
result_debug['peak_scalar_products'] += [peak_scalar_product]
result_debug['peak_solved_flags'] += [b[best_template_index, peak_index]]
result_debug['template_nbs'] += [best_template_index]
result_debug['success_flags'] += [True]
else:
# Reject the matching.
numerous_argmax = True
# Update failure counter of the peak.
failure[peak_index] += 1
# If the maximal number of failures is reached then mark peak as solved (i.e. not fitted).
if failure[peak_index] >= total_nb_chances:
# Mark all the matching associated to the current peak as tried.
b[:, peak_index] = -numpy.inf
index = numpy.arange(n_tm) * nb_local_peak_times + peak_index
else:
# Mark current matching as tried.
b[best_template_index, peak_index] = -numpy.inf
index = best_template_index * nb_local_peak_times + peak_index
if numerous_argmax:
best_indices = best_indices[~numpy.in1d(best_indices, index)]
# Save debug data.
if debug:
result_debug['chunk_nbs'] += [gidx]
result_debug['iteration_nbs'] += [iteration_nb]
result_debug['peak_nbs'] += [peak_index]
result_debug['peak_local_time_steps'] += [local_peaktimes[peak_index]]
result_debug['peak_time_steps'] += [all_spikes[peak_index]]
result_debug['peak_scalar_products'] += [peak_scalar_product]
result_debug['peak_solved_flags'] += [b[best_template_index, peak_index]]
result_debug['template_nbs'] += [best_template_index]
result_debug['success_flags'] += [False]
iteration_nb += 1
spikes_to_write = numpy.array(result['spiketimes'], dtype=numpy.uint32)
amplitudes_to_write = numpy.array(result['amplitudes'], dtype=numpy.float32)
templates_to_write = numpy.array(result['templates'], dtype=numpy.uint32)
spiketimes_file.write(spikes_to_write.tostring())
amplitudes_file.write(amplitudes_to_write.tostring())
templates_file.write(templates_to_write.tostring())
if collect_all:
for temp, spike in zip(templates_to_write, spikes_to_write - g_offset):
c_all_times[c_min_times[spike]:c_max_times[spike], neighbors[temp]] = False
gspikes = numpy.where(numpy.sum(c_all_times, 1) > 0)[0]
c_all_times = numpy.take(c_all_times, gspikes, axis=0)
c_local_chunk = numpy.take(c_local_chunk, gspikes, axis=0) * c_all_times
if sign_peaks == 'negative':
bestlecs = numpy.argmin(c_local_chunk, 1)
if matched_filter:
threshs = -matched_thresholds_neg[bestlecs]
else:
threshs = -thresholds[bestlecs]
idx = numpy.where(numpy.min(c_local_chunk, 1) < threshs)[0]
elif sign_peaks == 'positive':
bestlecs = numpy.argmax(c_local_chunk, 1)
if matched_filter:
threshs = matched_thresholds_pos[bestlecs]
else:
threshs = thresholds[bestlecs]
idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0]
elif sign_peaks == 'both':
c_local_chunk = numpy.abs(c_local_chunk)
bestlecs = numpy.argmax(c_local_chunk, 1)
if matched_filter:
threshs = numpy.minimum(matched_thresholds_neg[bestlecs], matched_thresholds_pos[bestlecs])
else:
threshs = thresholds[bestlecs]
idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0]
else:
raise ValueError("Unexpected value %s" % sign_peaks)
gspikes = numpy.take(gspikes, idx)
bestlecs = numpy.take(bestlecs, idx)
gspikes_to_write = numpy.array(gspikes + g_offset, dtype=numpy.uint32)
gtemplates_to_write = numpy.array(bestlecs, dtype=numpy.uint32)
garbage_times_file.write(gspikes_to_write.tostring())
garbage_temp_file.write(gtemplates_to_write.tostring())
if debug:
# Write debug data to debug files.
for field_label, field_dtype, field_file in [
('chunk_nbs', numpy.uint32, chunk_nbs_debug_file),
('iteration_nbs', numpy.uint32, iteration_nbs_debug_file),
('peak_nbs', numpy.uint32, peak_nbs_debug_file),
('peak_local_time_steps', numpy.uint32, peak_local_time_steps_debug_file),
('peak_time_steps', numpy.uint32, peak_time_steps_debug_file),
('peak_scalar_products', numpy.float32, peak_scalar_products_debug_file),
('peak_solved_flags', numpy.float32, peak_solved_flags_debug_file),
('template_nbs', numpy.uint32, template_nbs_debug_file),
('success_flags', numpy.bool, success_flags_debug_file),
]:
field_to_write = numpy.array(result_debug[field_label], dtype=field_dtype)
field_file.write(field_to_write.tostring())
if full_gpu:
del b, data
sys.stderr.flush()
spiketimes_file.flush()
os.fsync(spiketimes_file.fileno())
spiketimes_file.close()
amplitudes_file.flush()
os.fsync(amplitudes_file.fileno())
amplitudes_file.close()
templates_file.flush()
os.fsync(templates_file.fileno())
templates_file.close()
if collect_all:
garbage_temp_file.flush()
os.fsync(garbage_temp_file.fileno())
garbage_temp_file.close()
garbage_times_file.flush()
os.fsync(garbage_times_file.fileno())
garbage_times_file.close()
if debug:
# Close debug files.
for field_file in [
chunk_nbs_debug_file,
iteration_nbs_debug_file,
peak_nbs_debug_file,
peak_local_time_steps_debug_file,
peak_time_steps_debug_file,
peak_scalar_products_debug_file,
peak_solved_flags_debug_file,
template_nbs_debug_file,
success_flags_debug_file,
]:
field_file.flush()
os.fsync(field_file.fileno())
field_file.close()
comm.Barrier()
if comm.rank == 0:
io.collect_data(comm.size, params, erase=True)
data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu):
#################################################################
# params = detect_memory(params)
_ = init_logging(params.logfile)
SHARED_MEMORY = get_shared_memory_flag(params)
logger = logging.getLogger('circus.fitting')
data_file = params.data_file
n_e = params.getint('data', 'N_e')
n_total = params.nb_channels
n_t = params.getint('detection', 'N_t')
template_shift = params.getint('detection', 'template_shift')
# file_out = params.get('data', 'file_out')
file_out_suff = params.get('data', 'file_out_suff')
sign_peaks = params.get('detection', 'peaks')
matched_filter = params.getboolean('detection', 'matched-filter')
# spike_thresh = params.getfloat('detection', 'spike_thresh')
ratio_thresh = params.getfloat('fitting', 'ratio_thresh')
two_components = params.getboolean('fitting', 'two_components')
sparse_threshold = params.getfloat('fitting', 'sparse_thresh')
# spike_width = params.getfloat('detection', 'spike_width')
# dist_peaks = params.getint('detection', 'dist_peaks')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
templates_normalization = params.getboolean('clustering', 'templates_normalization') # TODO test, switch, test!
chunk_size = detect_memory(params, fitting=True)
gpu_only = params.getboolean('fitting', 'gpu_only')
nodes, edges = get_nodes_and_edges(params)
tmp_limits = params.get('fitting', 'amp_limits').replace('(', '').replace(')', '').split(',')
tmp_limits = [float(v) for v in tmp_limits]
amp_auto = params.getboolean('fitting', 'amp_auto')
max_chunk = params.getfloat('fitting', 'max_chunk')
# noise_thr = params.getfloat('clustering', 'noise_thr')
collect_all = params.getboolean('fitting', 'collect_all')
min_second_component = params.getfloat('fitting', 'min_second_component')
debug = params.getboolean('fitting', 'debug')
ignore_dead_times = params.getboolean('triggers', 'ignore_times')
inv_nodes = numpy.zeros(n_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
data_file.open()
supports = io.load_data(params, 'supports')
low_channels_thr = params.getint('detection', 'low_channels_thr')
median_channels = numpy.median(numpy.sum(supports, 1))
fixed_amplitudes = params.getboolean('clustering', 'fixed_amplitudes')
weird_thresh = params.get('detection', 'weird_thresh')
if weird_thresh != '':
ignore_artefacts = True
weird_thresh = io.load_data(params, 'weird-thresholds')
else:
ignore_artefacts = False
if not fixed_amplitudes:
nb_amp_bins = params.getint('clustering', 'nb_amp_bins')
splits = np.linspace(0, params.data_file.duration, nb_amp_bins)
interpolated_times = np.zeros(len(splits) - 1, dtype=numpy.float32)
for count in range(0, len(splits) - 1):
interpolated_times[count] = (splits[count] + splits[count + 1])/2
interpolated_times = numpy.concatenate(([0], interpolated_times, [params.data_file.duration]))
nb_amp_times = len(splits) + 1
mse_error = params.getboolean('fitting', 'mse_error')
if mse_error:
stds = io.load_data(params, 'stds')
stds_norm = numpy.linalg.norm(stds)
# if median_channels < low_channels_thr:
# normalization = False
# if comm.rank == 0:
# print_and_log(['Templates defined on few channels (%g), turning off normalization' %median_channels], 'debug', logger)
#################################################################
if SHARED_MEMORY:
templates, mpi_memory_1 = io.load_data_memshared(params, 'templates', normalize=templates_normalization, transpose=True, sparse_threshold=sparse_threshold)
N_tm, x = templates.shape
is_sparse = not isinstance(templates, numpy.ndarray)
else:
templates = io.load_data(params, 'templates')
x, N_tm = templates.shape
if N_tm > 0:
sparsity = templates.nnz / (x * N_tm)
is_sparse = sparsity < sparse_threshold
else:
is_sparse = True
if not is_sparse:
if comm.rank == 0:
print_and_log(['Templates sparsity is low (%g): densified to speedup the algorithm' %sparsity], 'debug', logger)
templates = templates.toarray()
temp_2_shift = 2 * template_shift
temp_3_shift = 3 * template_shift
n_tm = N_tm // 2
n_scalar = n_e * n_t
temp_window = numpy.arange(-template_shift, template_shift + 1)
size_window = n_e * (2 * template_shift + 1)
if not amp_auto:
amp_limits = numpy.zeros((n_tm, 2))
amp_limits[:, 0] = tmp_limits[0]
amp_limits[:, 1] = tmp_limits[1]
else:
amp_limits = io.load_data(params, 'limits')
norm_templates = io.load_data(params, 'norm-templates')
sub_norm_templates = n_scalar * norm_templates[:n_tm]
if not templates_normalization:
norm_templates_2 = (norm_templates ** 2.0) * n_scalar
sub_norm_templates_2 = norm_templates_2[:n_tm]
if not SHARED_MEMORY:
# Normalize templates (if necessary).
if templates_normalization:
if is_sparse:
for idx in range(templates.shape[1]):
myslice = numpy.arange(templates.indptr[idx], templates.indptr[idx+1])
templates.data[myslice] /= norm_templates[idx]
else:
for idx in range(templates.shape[1]):
templates[:, idx] /= norm_templates[idx]
# Transpose templates.
templates = templates.T
maxoverlap = io.load_data(params, 'maxoverlap')/n_scalar
similar = np.where(maxoverlap > 0.5)
idx = similar[0] < similar[1]
similar = similar[0][idx], similar[1][idx]
nb_mixtures = len(similar[0])
waveform_neg = numpy.empty(0) # default assignment (for PyCharm code inspection)
matched_thresholds_neg = None # default assignment (for PyCharm code inspection)
waveform_pos = numpy.empty(0) # default assignment (for PyCharm code inspection)
matched_thresholds_pos = None # default assignment (for PyCharm code inspection)
if matched_filter:
if sign_peaks in ['negative', 'both']:
waveform_neg = io.load_data(params, 'waveform')[::-1]
waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg))
matched_thresholds_neg = io.load_data(params, 'matched-thresholds')
if sign_peaks in ['positive', 'both']:
waveform_pos = io.load_data(params, 'waveform-pos')[::-1]
waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos))
matched_thresholds_pos = io.load_data(params, 'matched-thresholds-pos')
if ignore_dead_times:
if SHARED_MEMORY:
all_dead_times, mpi_memory_3 = get_dead_times(params)
else:
all_dead_times = get_dead_times(params)
else:
all_dead_times = None # default assignment (for PyCharm code inspection)
thresholds = io.get_accurate_thresholds(params, ratio_thresh)
neighbors = {}
if collect_all:
is_sparse = not isinstance(templates, numpy.ndarray)
for i in range(0, n_tm):
if is_sparse:
tmp = templates[i, :].toarray().reshape(n_e, n_t)
else:
tmp = templates[i].reshape(n_e, n_t)
if templates_normalization:
tmp = tmp * norm_templates[i]
neighbors[i] = numpy.where(numpy.sum(tmp, axis=1) != 0.0)[0]
#N_tm, x = templates.shape
#sparsity_factor = templates.nnz / (N_tm * x)
#if sparsity_factor > sparse_threshold:
# if comm.rank == 0:
# print_and_log(['Templates are not sparse enough, we densify them for'], 'default', logger)
# templates = templates.toarray()
info_string = ''
if comm.rank == 0:
info_string = "using %d CPUs" % comm.size
comm.Barrier()
c_overlap = io.get_overlaps(params, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu)
over_shape = c_overlap.get('over_shape')[:]
n_over = int(numpy.sqrt(over_shape[0]))
s_over = over_shape[1]
s_center = s_over // 2
# # If the number of overlaps is different from templates, we need to recompute them.
if n_over != N_tm:
if comm.rank == 0:
print_and_log(['Templates have been modified, recomputing the overlaps...'], 'default', logger)
c_overlap = io.get_overlaps(params, erase=True, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu)
over_shape = c_overlap.get('over_shape')[:]
n_over = int(numpy.sqrt(over_shape[0]))
s_over = over_shape[1]
if SHARED_MEMORY:
c_overs, mpi_memory_2 = io.load_data_memshared(params, 'overlaps')
else:
c_overs = io.load_data(params, 'overlaps')
comm.Barrier()
if n_tm == 0:
if comm.rank == 0:
print_and_log(["No templates present. Redo clustering?"], 'default', logger)
sys.exit(0)
if comm.rank == 0:
print_and_log(["Here comes the SpyKING CIRCUS %s and %d templates..." % (info_string, n_tm)], 'default', logger)
purge(file_out_suff, '.data')
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
else:
spatial_whitening = None # default assignment (for PyCharm code inspection)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
else:
temporal_whitening = None # default assignment (for PyCharm code inspection)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
processed_chunks = int(min(nb_chunks, max_chunk))
comm.Barrier()
spiketimes_file = open(file_out_suff + '.spiketimes-%d.data' % comm.rank, 'wb')
comm.Barrier()
amplitudes_file = open(file_out_suff + '.amplitudes-%d.data' % comm.rank, 'wb')
comm.Barrier()
templates_file = open(file_out_suff + '.templates-%d.data' % comm.rank, 'wb')
comm.Barrier()
if ignore_artefacts:
comm.Barrier()
arte_spiketimes_file = open(file_out_suff + '.times-%d.sata' % comm.rank, 'wb')
comm.Barrier()
arte_electrodes_file = open(file_out_suff + '.elec-%d.sata' % comm.rank, 'wb')
comm.Barrier()
arte_amplitudes_file = open(file_out_suff + '.amp-%d.sata' % comm.rank, 'wb')
comm.Barrier()
if mse_error:
mse_file = open(file_out_suff + '.mses-%d.data' % comm.rank, 'wb')
comm.Barrier()
if collect_all:
garbage_times_file = open(file_out_suff + '.gspiketimes-%d.data' % comm.rank, 'wb')
comm.Barrier()
garbage_temp_file = open(file_out_suff + '.gtemplates-%d.data' % comm.rank, 'wb')
comm.Barrier()
else:
garbage_times_file = None # default assignment (for PyCharm code inspection)
garbage_temp_file = None # default assignment (for PyCharm code inspection)
if debug:
# Open debug files.
chunk_nbs_debug_file = open(file_out_suff + '.chunk_nbs_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
iteration_nbs_debug_file = open(file_out_suff + '.iteration_nbs_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
peak_nbs_debug_file = open(file_out_suff + '.peak_nbs_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
peak_local_time_steps_debug_file = open(
file_out_suff + '.peak_local_time_steps_debug_%d.data' % comm.rank, mode='wb'
)
comm.Barrier()
peak_time_steps_debug_file = open(file_out_suff + '.peak_time_steps_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
peak_scalar_products_debug_file = open(
file_out_suff + '.peak_scalar_products_debug_%d.data' % comm.rank, mode='wb'
)
comm.Barrier()
peak_solved_flags_debug_file = open(file_out_suff + '.peak_solved_flags_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
template_nbs_debug_file = open(file_out_suff + '.template_nbs_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
success_flags_debug_file = open(file_out_suff + '.success_flags_debug_%d.data' % comm.rank, mode='wb')
comm.Barrier()
else:
chunk_nbs_debug_file = None # default assignment (for PyCharm code inspection)
iteration_nbs_debug_file = None # default assignment (for PyCharm code inspection)
peak_nbs_debug_file = None # default assignment (for PyCharm code inspection)
peak_local_time_steps_debug_file = None # default assignment (for PyCharm code inspection)
peak_time_steps_debug_file = None # default assignment (for PyCharm code inspection)
peak_scalar_products_debug_file = None # default assignment (for PyCharm code inspection)
peak_solved_flags_debug_file = None # default assignment (for PyCharm code inspection)
template_nbs_debug_file = None # default assignment (for PyCharm code inspection)
success_flags_debug_file = None # default assignment (for PyCharm code inspection)
last_chunk_size = 0
slice_indices = numpy.zeros(0, dtype=numpy.int32)
to_explore = list(range(comm.rank, processed_chunks, comm.size))
if comm.rank == 0:
to_explore = get_tqdm_progressbar(params, to_explore)
if fixed_amplitudes:
min_scalar_products = amp_limits[:,0][:, numpy.newaxis]
max_scalar_products = amp_limits[:,1][:, numpy.newaxis]
if templates_normalization:
min_sps = min_scalar_products * sub_norm_templates[:, numpy.newaxis]
max_sps = max_scalar_products * sub_norm_templates[:, numpy.newaxis]
else:
min_sps = min_scalar_products * sub_norm_templates_2[:, numpy.newaxis]
max_sps = max_scalar_products * sub_norm_templates_2[:, numpy.newaxis]
for gcount, gidx in enumerate(to_explore):
# print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
# # We need to deal with the borders by taking chunks of size [0, chunck_size + template_shift].
is_first = data_file.is_first_chunk(gidx, nb_chunks)
is_last = data_file.is_last_chunk(gidx, nb_chunks)
if not (is_first and is_last):
if is_last:
padding = (-temp_3_shift, 0)
elif is_first:
padding = (0, temp_3_shift)
else:
padding = (-temp_3_shift, temp_3_shift)
else:
padding = (0, 0)
result = {
'spiketimes': [],
'amplitudes': [],
'templates': [],
}
if mse_error:
mse_fit = {
'spiketimes': [],
'amplitudes': [],
'templates': [],
}
result_debug = {
'chunk_nbs': [],
'iteration_nbs': [],
'peak_nbs': [],
'peak_local_time_steps': [],
'peak_time_steps': [],
'peak_scalar_products': [],
'peak_solved_flags': [],
'template_nbs': [],
'success_flags': [],
}
local_chunk, t_offset = data_file.get_data(gidx, chunk_size, padding, nodes=nodes)
len_chunk = len(local_chunk)
if is_last:
my_chunk_size = last_chunk_size
else:
my_chunk_size = chunk_size
if do_spatial_whitening:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(local_chunk, temporal_whitening, axis=0, mode='constant')
# Extracting peaks.
all_found_spikes = {}
if collect_all:
for i in range(n_e):
all_found_spikes[i] = []
local_peaktimes = [numpy.empty(0, dtype=numpy.uint32)]
if ignore_artefacts:
artefacts_peaktimes = [numpy.zeros(0, dtype=numpy.uint32)]
artefacts_elecs = [numpy.zeros(0, dtype=numpy.uint32)]
artefacts_amps = [numpy.zeros(0, dtype=numpy.float32)]
if matched_filter:
if sign_peaks in ['positive', 'both']:
filter_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_pos, axis=0, mode='constant')
for i in range(n_e):
peaktimes = scipy.signal.find_peaks(filter_chunk[:, i], height=matched_thresholds_pos[i])[0]
if ignore_artefacts:
artetimes = scipy.signal.find_peaks(numpy.abs(filter_chunk[:, i]), height=weird_thresh[i])[0]
to_keep = numpy.logical_not(numpy.in1d(peaktimes, artetimes))
peaktimes = peaktimes[to_keep]
artefacts_peaktimes.append(artetimes)
artefacts_elecs.append(i*numpy.ones(len(artetimes), dtype='uint32'))
artefacts_amps.append(local_chunk[artetimes, i])
local_peaktimes.append(peaktimes)
if collect_all:
all_found_spikes[i] += peaktimes.tolist()
if sign_peaks in ['negative', 'both']:
filter_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_neg, axis=0, mode='constant')
for i in range(n_e):
peaktimes = scipy.signal.find_peaks(filter_chunk[:, i], height=matched_thresholds_neg[i])[0]
if ignore_artefacts:
artetimes = scipy.signal.find_peaks(numpy.abs(filter_chunk[:, i]), height=weird_thresh[i])[0]
to_keep = numpy.logical_not(numpy.in1d(peaktimes, artetimes))
peaktimes = peaktimes[to_keep]
artefacts_peaktimes.append(artetimes)
artefacts_elecs.append(i*numpy.ones(len(artetimes), dtype='uint32'))
artefacts_amps.append(local_chunk[artetimes, i])
local_peaktimes.append(peaktimes)
if collect_all:
all_found_spikes[i] += peaktimes.tolist()
local_peaktimes = numpy.concatenate(local_peaktimes)
if ignore_artefacts:
artefacts_peaktimes = numpy.concatenate(artefacts_peaktimes)
artefacts_elecs = numpy.concatenate(artefacts_elecs)
artefacts_amps = numpy.concatenate(artefacts_amps)
else:
for i in range(n_e):
if sign_peaks == 'negative':
peaktimes = scipy.signal.find_peaks(-local_chunk[:, i], height=thresholds[i])[0]
elif sign_peaks == 'positive':
peaktimes = scipy.signal.find_peaks(local_chunk[:, i], height=thresholds[i])[0]
elif sign_peaks == 'both':
peaktimes = scipy.signal.find_peaks(numpy.abs(local_chunk[:, i]), height=thresholds[i])[0]
else:
raise ValueError("Unexpected value %s" % sign_peaks)
if ignore_artefacts:
artetimes = scipy.signal.find_peaks(numpy.abs(local_chunk[:, i]), height=weird_thresh[i])[0]
to_keep = numpy.logical_not(numpy.in1d(peaktimes, artetimes))
peaktimes = peaktimes[to_keep]
artefacts_peaktimes.append(artetimes)
artefacts_elecs.append(i*numpy.ones(len(artetimes), dtype='uint32'))
artefacts_amps.append(local_chunk[artetimes, i])
local_peaktimes.append(peaktimes)
if collect_all:
all_found_spikes[i] += peaktimes.tolist()
local_peaktimes = numpy.concatenate(local_peaktimes)
if ignore_artefacts:
artefacts_peaktimes = numpy.concatenate(artefacts_peaktimes)
artefacts_elecs = numpy.concatenate(artefacts_elecs)
artefacts_amps = numpy.concatenate(artefacts_amps)
local_peaktimes = numpy.unique(local_peaktimes)
g_offset = t_offset + padding[0]
if ignore_dead_times:
dead_indices = numpy.searchsorted(all_dead_times, [t_offset, t_offset + my_chunk_size])
if dead_indices[0] != dead_indices[1]:
is_included = numpy.in1d(local_peaktimes + g_offset, all_dead_times[dead_indices[0]:dead_indices[1]])
local_peaktimes = local_peaktimes[~is_included]
if ignore_artefacts:
is_included = numpy.in1d(artefacts_peaktimes + g_offset, all_dead_times[dead_indices[0]:dead_indices[1]])
artefacts_peaktimes = artefacts_peaktimes[~is_included]
artefacts_elecs = artefacts_elecs[~is_included]
artefacts_amps = artefacts_amps[~is_included]
local_peaktimes = numpy.sort(local_peaktimes)
else:
dead_indices = None # default assignment (for PyCharm code inspection)
# print "Removing the useless borders..."
local_borders = (template_shift, len_chunk - template_shift)
idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1])
local_peaktimes = numpy.compress(idx, local_peaktimes)
if ignore_artefacts:
artefacts_peaktimes = artefacts_peaktimes + g_offset
idx = (artefacts_peaktimes >= t_offset) & (artefacts_peaktimes < t_offset + my_chunk_size)
artefacts_peaktimes = numpy.compress(idx, artefacts_peaktimes)
artefacts_elecs = numpy.compress(idx, artefacts_elecs)
artefacts_amps = numpy.compress(idx, artefacts_amps)
if collect_all:
for i in range(n_e):
all_found_spikes[i] = numpy.array(all_found_spikes[i], dtype=numpy.uint32)
if ignore_dead_times:
if dead_indices[0] != dead_indices[1]:
is_included = numpy.in1d(
all_found_spikes[i] + g_offset, all_dead_times[dead_indices[0]:dead_indices[1]]
)
all_found_spikes[i] = all_found_spikes[i][~is_included]
all_found_spikes[i] = numpy.sort(all_found_spikes[i])
idx = (all_found_spikes[i] >= local_borders[0]) & (all_found_spikes[i] < local_borders[1])
all_found_spikes[i] = numpy.compress(idx, all_found_spikes[i])
nb_local_peak_times = len(local_peaktimes)
if nb_local_peak_times > 0:
# print "Computing the b (should full_gpu by putting all chunks on GPU if possible?)..."
if collect_all or mse_error:
c_local_chunk = local_chunk.copy()
else:
c_local_chunk = None # default assignment (for PyCharm code inspection)
sub_mat = local_chunk[local_peaktimes[:, None] + temp_window]
sub_mat = sub_mat.transpose(2, 1, 0).reshape(size_window, nb_local_peak_times)
del local_chunk
b = templates.dot(sub_mat)
del sub_mat
local_restriction = (t_offset, t_offset + my_chunk_size)
all_spikes = local_peaktimes + g_offset
if collect_all:
c_all_times = numpy.zeros((len_chunk, n_e), dtype=numpy.bool)
c_min_times = numpy.maximum(numpy.arange(len_chunk) - template_shift, 0)
c_max_times = numpy.minimum(numpy.arange(len_chunk) + template_shift + 1, len_chunk)
for i in range(n_e):
c_all_times[all_found_spikes[i], i] = True
else:
c_all_times = None # default assignment (for PyCharm code inspection)
c_min_times = None # default assignment (for PyCharm code inspection)
c_max_times = None # default assignment (for PyCharm code inspection)
iteration_nb = 0
data = b[:n_tm, :]
if not fixed_amplitudes:
amp_index = numpy.searchsorted(splits, local_restriction[0], 'right')
scaling = 1/(splits[amp_index] - splits[amp_index - 1])
min_scalar_products = amp_limits[:, amp_index, 0] + (amp_limits[:, amp_index, 0] - amp_limits[:, amp_index+1, 0])*scaling
max_scalar_products = amp_limits[:, amp_index, 1] + (amp_limits[:, amp_index, 1] - amp_limits[:, amp_index+1, 0])*scaling
min_scalar_products = min_scalar_products[:, numpy.newaxis]
max_scalar_products = max_scalar_products[:, numpy.newaxis]
if templates_normalization:
min_sps = min_scalar_products * sub_norm_templates[:, numpy.newaxis]
max_sps = max_scalar_products * sub_norm_templates[:, numpy.newaxis]
else:
min_sps = min_scalar_products * sub_norm_templates_2[:, numpy.newaxis]
max_sps = max_scalar_products * sub_norm_templates_2[:, numpy.newaxis]
while True:
is_valid = (data > min_sps)*(data < max_sps)
valid_indices = numpy.where(is_valid)
if len(valid_indices[0]) == 0:
break
best_amplitude_idx = data[is_valid].argmax()
best_template_index, peak_index = valid_indices[0][best_amplitude_idx], valid_indices[1][best_amplitude_idx]
peak_scalar_product = data[is_valid][best_amplitude_idx]
best_template2_index = best_template_index + n_tm
if templates_normalization:
best_amp = b[best_template_index, peak_index] / n_scalar
best_amp_n = best_amp / norm_templates[best_template_index]
if two_components:
best_amp2 = b[best_template2_index, peak_index] / n_scalar
best_amp2_n = best_amp2 / norm_templates[best_template2_index]
else:
best_amp2 = 0
best_amp2_n = 0
else:
best_amp = b[best_template_index, peak_index] / norm_templates_2[best_template_index]
best_amp_n = best_amp
if two_components:
best_amp2 = b[best_template2_index, peak_index] / norm_templates_2[best_template2_index]
best_amp2_n = best_amp2
else:
best_amp2 = 0
best_amp2_n = 0
peak_time_step = local_peaktimes[peak_index]
peak_data = (local_peaktimes - peak_time_step).astype(np.int32)
is_neighbor = np.abs(peak_data) <= temp_2_shift
idx_neighbor = peak_data[is_neighbor] + temp_2_shift
tmp1 = c_overs[best_template_index].multiply(-best_amp)
if numpy.abs(best_amp2_n) > min_second_component:
tmp1 += c_overs[best_template2_index].multiply(-best_amp2)
to_add = tmp1.toarray()[:, idx_neighbor]
b[:, is_neighbor] += to_add
b[best_template_index, peak_index] = -numpy.inf
# Add matching to the result.
t_spike = all_spikes[peak_index]
if (t_spike >= local_restriction[0]) and (t_spike < local_restriction[1]):
result['spiketimes'] += [t_spike]
result['amplitudes'] += [(best_amp_n, best_amp2_n)]
result['templates'] += [best_template_index]
elif mse_error:
mse_fit['spiketimes'] += [t_spike]
mse_fit['amplitudes'] += [(best_amp_n, best_amp2_n)]
mse_fit['templates'] += [best_template_index]
# Save debug data.
if debug:
result_debug['chunk_nbs'] += [gidx]
result_debug['iteration_nbs'] += [iteration_nb]
result_debug['peak_nbs'] += [peak_index]
result_debug['peak_local_time_steps'] += [local_peaktimes[peak_index]]
result_debug['peak_time_steps'] += [all_spikes[peak_index]]
result_debug['peak_scalar_products'] += [peak_scalar_product]
result_debug['peak_solved_flags'] += [b[best_template_index, peak_index]]
result_debug['template_nbs'] += [best_template_index]
result_debug['success_flags'] += [True]
iteration_nb += 1
spikes_to_write = numpy.array(result['spiketimes'], dtype=numpy.uint32)
amplitudes_to_write = numpy.array(result['amplitudes'], dtype=numpy.float32)
templates_to_write = numpy.array(result['templates'], dtype=numpy.uint32)
spiketimes_file.write(spikes_to_write.tostring())
amplitudes_file.write(amplitudes_to_write.tostring())
templates_file.write(templates_to_write.tostring())
if ignore_artefacts:
arte_spiketimes_file.write(artefacts_peaktimes.astype(numpy.uint32).tostring())
arte_electrodes_file.write(artefacts_elecs.tostring())
arte_amplitudes_file.write(artefacts_amps.tostring())
if mse_error:
curve = numpy.zeros((len_chunk, n_e), dtype=numpy.float32)
for spike, temp_id, amplitude in zip(result['spiketimes'], result['templates'], result['amplitudes']):
spike = spike - t_offset - padding[0]
if is_sparse:
tmp1 = templates[temp_id].toarray().reshape(n_e, n_t)
tmp2 = templates[temp_id + n_tm].toarray().reshape(n_e, n_t)
else:
tmp1 = templates[temp_id].reshape(n_e, n_t)
tmp2 = templates[temp_id + n_tm].reshape(n_e, n_t)
curve[spike - template_shift:spike + template_shift + 1, :] += (amplitude[0] * tmp1 + amplitude[1] * tmp2).T
for spike, temp_id, amplitude in zip(mse_fit['spiketimes'], mse_fit['templates'], mse_fit['amplitudes']):
spike = spike - t_offset + padding[0]
if is_sparse:
tmp1 = templates[temp_id].toarray().reshape(n_e, n_t)
tmp2 = templates[temp_id + n_tm].toarray().reshape(n_e, n_t)
else:
tmp1 = templates[temp_id].reshape(n_e, n_t)
tmp2 = templates[temp_id + n_tm].reshape(n_e, n_t)
try:
curve[int(spike) - template_shift:int(spike) + template_shift + 1, :] += (amplitude[0] * tmp1 + amplitude[1] * tmp2).T
except Exception:
pass
mse = numpy.linalg.norm((curve - c_local_chunk)[-padding[0]:-padding[1]])
nb_points = len(curve) - (padding[1] - padding[0])
mse_ratio = mse/(numpy.sqrt(nb_points)*stds_norm)
mse_to_write = numpy.array([g_offset, mse_ratio], dtype=numpy.float32)
mse_file.write(mse_to_write.tostring())
if collect_all:
for temp, spike in zip(templates_to_write, spikes_to_write - g_offset):
c_all_times[c_min_times[spike]:c_max_times[spike], neighbors[temp]] = False
gspikes = numpy.where(numpy.sum(c_all_times, 1) > 0)[0]
c_all_times = numpy.take(c_all_times, gspikes, axis=0)
c_local_chunk = numpy.take(c_local_chunk, gspikes, axis=0) * c_all_times
if sign_peaks == 'negative':
bestlecs = numpy.argmin(c_local_chunk, 1)
if matched_filter:
threshs = -matched_thresholds_neg[bestlecs]
else:
threshs = -thresholds[bestlecs]
idx = numpy.where(numpy.min(c_local_chunk, 1) < threshs)[0]
elif sign_peaks == 'positive':
bestlecs = numpy.argmax(c_local_chunk, 1)
if matched_filter:
threshs = matched_thresholds_pos[bestlecs]
else:
threshs = thresholds[bestlecs]
idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0]
elif sign_peaks == 'both':
c_local_chunk = numpy.abs(c_local_chunk)
bestlecs = numpy.argmax(c_local_chunk, 1)
if matched_filter:
threshs = numpy.minimum(matched_thresholds_neg[bestlecs], matched_thresholds_pos[bestlecs])
else:
threshs = thresholds[bestlecs]
idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0]
else:
raise ValueError("Unexpected value %s" % sign_peaks)
gspikes = numpy.take(gspikes, idx)
bestlecs = numpy.take(bestlecs, idx)
gspikes_to_write = numpy.array(gspikes + g_offset, dtype=numpy.uint32)
gtemplates_to_write = numpy.array(bestlecs, dtype=numpy.uint32)
garbage_times_file.write(gspikes_to_write.tostring())
garbage_temp_file.write(gtemplates_to_write.tostring())
if debug:
# Write debug data to debug files.
for field_label, field_dtype, field_file in [
('chunk_nbs', numpy.uint32, chunk_nbs_debug_file),
('iteration_nbs', numpy.uint32, iteration_nbs_debug_file),
('peak_nbs', numpy.uint32, peak_nbs_debug_file),
('peak_local_time_steps', numpy.uint32, peak_local_time_steps_debug_file),
('peak_time_steps', numpy.uint32, peak_time_steps_debug_file),
('peak_scalar_products', numpy.float32, peak_scalar_products_debug_file),
('peak_solved_flags', numpy.float32, peak_solved_flags_debug_file),
('template_nbs', numpy.uint32, template_nbs_debug_file),
('success_flags', numpy.bool, success_flags_debug_file),
]:
field_to_write = numpy.array(result_debug[field_label], dtype=field_dtype)
field_file.write(field_to_write.tostring())
sys.stderr.flush()
spiketimes_file.flush()
os.fsync(spiketimes_file.fileno())
spiketimes_file.close()
amplitudes_file.flush()
os.fsync(amplitudes_file.fileno())
amplitudes_file.close()
templates_file.flush()
os.fsync(templates_file.fileno())
templates_file.close()
if collect_all:
garbage_temp_file.flush()
os.fsync(garbage_temp_file.fileno())
garbage_temp_file.close()
garbage_times_file.flush()
os.fsync(garbage_times_file.fileno())
garbage_times_file.close()
if mse_error:
mse_file.flush()
os.fsync(mse_file.fileno())
mse_file.close()
if ignore_artefacts:
arte_spiketimes_file.flush()
os.fsync(arte_spiketimes_file.fileno())
arte_spiketimes_file.close()
arte_electrodes_file.flush()
os.fsync(arte_electrodes_file.fileno())
arte_electrodes_file.close()
arte_amplitudes_file.flush()
os.fsync(arte_amplitudes_file.fileno())
arte_amplitudes_file.close()
if debug:
# Close debug files.
for field_file in [
chunk_nbs_debug_file,
iteration_nbs_debug_file,
peak_nbs_debug_file,
peak_local_time_steps_debug_file,
peak_time_steps_debug_file,
peak_scalar_products_debug_file,
peak_solved_flags_debug_file,
template_nbs_debug_file,
success_flags_debug_file,
]:
field_file.flush()
os.fsync(field_file.fileno())
field_file.close()
comm.Barrier()
if SHARED_MEMORY:
for memory in mpi_memory_1 + mpi_memory_2:
memory.Free()
if ignore_dead_times:
mpi_memory_3.Free()
if comm.rank == 0:
io.collect_data(comm.size, params, erase=True)
if ignore_artefacts:
io.collect_artefacts(comm.size, params, erase=True)
data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu):
logger = init_logging(params.logfile)
logger = logging.getLogger('circus.filtering')
#################################################################
do_filter = params.getboolean('filtering', 'filter')
filter_done = check_if_done(params, 'filter_done', logger)
artefacts_done = check_if_done(params, 'artefacts_done', logger)
median_done = check_if_done(params, 'median_done', logger)
ground_done = check_if_done(params, 'ground_done', logger)
file_out_suff = params.get('data', 'file_out_suff')
clean_artefact = params.getboolean('triggers', 'clean_artefact')
remove_median = params.getboolean('filtering', 'remove_median')
sat_value = params.get('filtering', 'sat_value')
sat_threshold = params.get('filtering', 'sat_threshold')
if sat_value != '':
flag_saturation = True
sat_value = float(sat_value)
else:
flag_saturation = False
common_ground = params.common_ground
remove_ground = len(common_ground) > 0
nodes, edges = get_nodes_and_edges(params)
N_total = params.nb_channels
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
#################################################################
def filter_file(data_file_in, data_file_out, do_filtering, do_remove_median, do_remove_ground):
"""
Performs a high-pass and low-pass Butterworth filter on the data file.
Parameters
----------
data_file_in :
data_file_out :
do_filtering : bool
do_remove_median : bool
do_remove_median : bool
"""
try:
cut_off = params.getfloat('filtering', 'cut_off', check=False)
cut_off = [cut_off, 0.95 * (params.rate / 2.0)] # Nyquist
except Exception:
cut_off = params.get('filtering', 'cut_off', check=False)
cut_off = cut_off.split(',')
try:
cut_off[0] = float(cut_off[0])
except Exception:
if comm.rank == 0:
print_and_log(['First value of cut off must be a valid number'], 'error', logger)
sys.exit(0)
cut_off[1] = cut_off[1].replace(' ', '')
if cut_off[1] == 'auto':
cut_off[1] = 0.95 * (params.rate / 2.0)
else:
try:
cut_off[1] = float(cut_off[1])
except Exception:
if comm.rank == 0:
print_and_log(['Second value of cut off must either auto, or a valid a number'], 'error', logger)
sys.exit(0)
chunk_size = detect_memory(params, filtering=True)
butter_order = params.getint('filtering', 'butter_order')
nb_chunks, _ = data_file_in.analyze(chunk_size)
b, a = signal.butter(butter_order, np.array(cut_off)/(params.rate/2.), 'pass')
all_chunks = numpy.arange(nb_chunks, dtype=numpy.int64)
to_process = all_chunks[comm.rank::comm.size]
loc_nb_chunks = len(to_process)
N_total = params.nb_channels
nb_shanks = len(params.probe['channel_groups'])
if nb_shanks > 1:
shank_channels = {}
for i in list(params.probe['channel_groups'].keys()):
shank_channels[i] = numpy.array(params.probe['channel_groups'][i]['channels'], dtype=numpy.int32)
else:
channel_group = list(params.probe['channel_groups'].keys())[0]
process_all_channels = numpy.all(nodes == numpy.arange(N_total))
duration = int(0.1*params.rate)
if comm.rank == 0:
to_write = []
if do_filtering:
to_write += ["Filtering with a Butterworth filter (order %d) in [%g, %g] Hz" % (butter_order, cut_off[0], cut_off[1])]
if do_remove_median:
to_write += ["Median over all channels is subtracted to each channels"]
if do_remove_ground:
to_write += ["Channels %s are used as reference channels in respective shanks" % common_ground]
print_and_log(to_write, 'default', logger)
to_explore = list(range(comm.rank, nb_chunks, comm.size))
if comm.rank == 0:
to_explore = get_tqdm_progressbar(params, to_explore)
if data_file_in == data_file_out:
data_file_in.open(mode='r+')
else:
data_file_in.open(mode='r')
data_file_out.open(mode='r+')
if flag_saturation:
comm.Barrier()
saturation_times = open(file_out_suff + '.times-%d.data' % comm.rank, 'wb')
saturation_channels = open(file_out_suff + '.channels-%d.data' % comm.rank, 'wb')
saturation_values = open(file_out_suff + '.values-%d.data' % comm.rank, 'wb')
if data_file_in.data_dtype in ['float32', numpy.float32, 'float64', numpy.float64]:
max_value = numpy.finfo(data_file_in.data_dtype).max
else:
max_value = numpy.iinfo(data_file_in.data_dtype).max - data_file_in.dtype_offset
saturation = sat_value * max_value
for count, gidx in enumerate(to_explore):
is_first = data_file_in.is_first_chunk(gidx, nb_chunks)
is_last = data_file_in.is_last_chunk(gidx, nb_chunks)
if not (is_first and is_last):
if is_first:
padding = (0, duration)
elif is_last:
padding = (-duration, 0)
else:
padding = (-duration, duration)
else:
padding = (0, 0)
local_chunk, t_offset = data_file_in.get_data(gidx, chunk_size, padding)
len_chunk = len(local_chunk)
if flag_saturation:
if sat_threshold == 'negative':
indices = numpy.where(local_chunk <= saturation * data_file_in.gain)
elif sat_threshold == 'positive':
indices = numpy.where(local_chunk >= saturation * data_file_in.gain)
elif sat_threshold == 'both':
indices = numpy.where(numpy.abs(local_chunk) >= saturation * data_file_in.gain)
if not process_all_channels:
to_keep = numpy.in1d(indices[1], nodes)
channels = inv_nodes[indices[1][to_keep]]
times = indices[0][to_keep]
else:
channels = indices[1]
times = indices[0]
to_keep = (times >= padding[0]) & (times < (len_chunk-numpy.abs(padding[1])))
sub_times = times[to_keep]
sub_channels = channels[to_keep]
saturation_times.write((sub_times + t_offset).astype(numpy.uint32).tostring())
saturation_channels.write(sub_channels.astype(numpy.uint32).tostring())
saturation_values.write(local_chunk[sub_times, sub_channels].tostring())
if do_filtering:
if not process_all_channels:
local_chunk[:, nodes] = signal.filtfilt(b, a, local_chunk[:, nodes], axis=0)
local_chunk[:, nodes] -= numpy.median(local_chunk[:, nodes], 0)
else:
local_chunk = signal.filtfilt(b, a, local_chunk, axis=0)
local_chunk -= numpy.median(local_chunk, 0)
if flag_saturation:
local_chunk[times, channels] = 0
local_chunk = local_chunk[numpy.abs(padding[0]):len_chunk-numpy.abs(padding[1])]
if do_remove_median:
if nb_shanks == 1:
if not process_all_channels:
global_median = numpy.median(numpy.take(local_chunk, nodes, axis=1), 1)
else:
global_median = numpy.median(local_chunk, 1)
local_chunk -= global_median[:, numpy.newaxis]
else:
for i in list(params.probe['channel_groups'].keys()):
global_median = numpy.median(numpy.take(local_chunk, shank_channels[i], axis=1), 1)
local_chunk[:, shank_channels[i]] -= global_median[:, numpy.newaxis]
if do_remove_ground:
if nb_shanks == 1:
ground = local_chunk[:, common_ground[channel_group]]
local_chunk -= ground[:, numpy.newaxis]
else:
for i in list(params.probe['channel_groups'].keys()):
ground = local_chunk[:, common_ground[i]]
local_chunk[:, shank_channels[i]] -= ground[:, numpy.newaxis]
if data_file_in != data_file_out:
if data_file_in.is_stream:
g_offset = t_offset
else:
g_offset = t_offset - data_file_in.t_start
else:
g_offset = t_offset
data_file_out.set_data(g_offset, local_chunk)
sys.stderr.flush()
comm.Barrier()
if flag_saturation:
saturation_times.flush()
os.fsync(saturation_times.fileno())
saturation_times.close()
saturation_values.flush()
os.fsync(saturation_values.fileno())
saturation_values.close()
saturation_channels.flush()
os.fsync(saturation_channels.fileno())
saturation_channels.close()
# We need to gather the results into a single file
if comm.rank == 0:
collect_saturation(comm.size, params, erase=True)
sys.stderr.flush()
comm.Barrier()
def compute_artefacts(data_file):
"""
Compute artefact locations based on the [triggers] section of the params file.
Parameters
----------
data_file :
Return
------
dict
A dictionary with the location of the artefacts
"""
trig_in_ms = params.getboolean('triggers', 'trig_in_ms')
artefacts = numpy.loadtxt(params.get('triggers', 'trig_file'), comments=['#', '//'])
windows = numpy.loadtxt(params.get('triggers', 'trig_windows'), comments=['#', '//'])
make_plots = params.get('triggers', 'make_plots')
plot_path = os.path.join(params.get('data', 'file_out_suff'), 'plots')
if len(windows.shape) == 1:
windows = windows.reshape(1, 2)
if len(artefacts.shape) == 1:
artefacts = artefacts.reshape(1, 2)
if trig_in_ms:
if comm.rank == 0:
print_and_log(['Artefact times are read in ms'], 'debug', logger)
artefacts[:, 1] *= numpy.int64(data_file.sampling_rate*1e-3)
windows[:, 1] *= numpy.int64(data_file.sampling_rate*1e-3)
else:
if comm.rank == 0:
print_and_log(['Artefact times are read in timesteps'], 'debug', logger)
artefacts = artefacts.astype(numpy.int64)
windows = windows.astype(numpy.int64)
nb_stimuli = len(numpy.unique(artefacts[:, 0]))
mytest = numpy.all(numpy.in1d(numpy.unique(artefacts[:, 0]), numpy.unique(windows[:, 0])))
if not mytest:
if comm.rank == 0:
print_and_log(['Error in the trigger file: not all artefacts are defined'], 'error', logger)
sys.exit(0)
all_labels = artefacts[:, 0]
all_times = artefacts[:, 1]
mask = (all_times >= 0) & (all_times + numpy.max(windows[:, 1]) < data_file.t_stop)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
local_labels = numpy.unique(all_labels)[comm.rank::comm.size]
if comm.rank == 0:
to_write = ["Computing averaged artefacts from %d stimuli" % nb_stimuli]
print_and_log(to_write, 'default', logger)
if not os.path.exists(plot_path):
os.makedirs(plot_path)
local_labels = get_tqdm_progressbar(params, local_labels)
comm.Barrier()
# First we need to get the average artefacts
art_dict = {}
for count, artefact in enumerate(local_labels):
indices = numpy.where(all_labels == artefact)[0].astype(numpy.uint32)
tmp = numpy.where(windows[:, 0] == artefact)[0]
tau = numpy.int64(windows[tmp, 1])
pspikes = all_times[indices]
times = numpy.sort(numpy.random.permutation(pspikes)[:500])
if len(numpy.where(numpy.diff(times) < tau)[0]) > 0:
if comm.rank == 0:
print_and_log(['Stimulation times for artefact %d are too close!' % artefact], 'error', logger)
sys.exit(0)
art_dict[artefact] = get_artefact(params, times, tau, nodes)
if make_plots not in ['None', '']:
save = [plot_path, '%d.%s' % (artefact, make_plots)]
plot.view_artefact(art_dict[artefact], save=save)
sys.stderr.flush()
return art_dict
def remove_artefacts(data_file, art_dict):
"""
Remove artefact times based on the [triggers] section of the params file.
Parameters
----------
data_file :
art_dict : dict
a dictionary with the artefact times.
"""
trig_in_ms = params.getboolean('triggers', 'trig_in_ms')
artefacts = numpy.loadtxt(params.get('triggers', 'trig_file'), comments=['#', '//'])
windows = numpy.loadtxt(params.get('triggers', 'trig_windows'), comments=['#', '//'])
make_plots = params.get('triggers', 'make_plots')
plot_path = os.path.join(params.get('data', 'file_out_suff'), 'plots')
if len(windows.shape) == 1:
windows = windows.reshape(1, 2)
if len(artefacts.shape) == 1:
artefacts = artefacts.reshape(1, 2)
if trig_in_ms:
if comm.rank == 0:
print_and_log(['Artefact times are read in ms'], 'debug', logger)
artefacts[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
windows[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
else:
if comm.rank == 0:
print_and_log(['Artefact times are read in timesteps'], 'debug', logger)
artefacts = artefacts.astype(numpy.int64)
windows = windows.astype(numpy.int64)
nb_stimuli = len(numpy.unique(artefacts[:, 0]))
mytest = numpy.all(numpy.in1d(numpy.unique(artefacts[:, 0]), numpy.unique(windows[:, 0])))
if not mytest:
if comm.rank == 0:
print_and_log(['Error in the trigger files: not all artefacts are defined'], 'error', logger)
sys.exit(0)
all_labels = artefacts[:, 0]
all_times = artefacts[:, 1]
local_labels = numpy.unique(all_labels)[comm.rank::comm.size]
mask = numpy.in1d(all_labels, local_labels)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
mask = (all_times >= 0) & (all_times < data_file.t_stop)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
if comm.rank == 0:
to_write = ["Removing artefacts from %d stimuli" % nb_stimuli]
print_and_log(to_write, 'default', logger)
all_times = get_tqdm_progressbar(params, all_times)
comm.Barrier()
for count, time in enumerate(all_times):
label = all_labels[count]
tmp = numpy.where(windows[:, 0] == label)[0][0]
tau = numpy.int64(windows[tmp, 1])
if (data_file.t_stop - time) < tau:
tau = max_offset - time
local_chunk = data_file.get_snippet(time, tau)
for idx, i in enumerate(nodes):
local_chunk[:, i] -= art_dict[label][idx, :tau]
data_file.set_data(time, local_chunk)
comm.Barrier()
sys.stderr.flush()
if comm.rank == 0:
print_and_log(['Initializing the filtering step...'], 'debug', logger)
if params.getboolean('data', 'overwrite'):
if comm.rank == 0:
print_and_log(['Reading the input file...'], 'debug', logger)
data_file_in = params.get_data_file()
data_file_out = data_file_in
else:
if comm.rank == 0:
print_and_log(['Overwrite is set to False, so creating a new datafile...'], 'debug', logger)
if comm.rank == 0:
print_and_log(['Reading the input file...'], 'debug', logger)
if os.path.exists(params.get('data', 'data_file_no_overwrite')):
has_been_created = True
else:
has_been_created = False
if not has_been_created and (filter_done or median_done or artefacts_done):
if comm.rank == 0:
print_and_log(['The filtering is done but file not present. See no_edits section'], 'error', logger)
sys.exit(0)
if not has_been_created:
data_file_in = params.get_data_file(source=True, has_been_created=has_been_created)
else:
data_file_in = params.get_data_file(source=False, has_been_created=has_been_created)
if comm.rank == 0:
print_and_log(['Reading the output file and allocating ressources...'], 'debug', logger)
description = data_file_in.get_description()
description['data_dtype'] = 'float32'
description['dtype_offset'] = 0
description['data_offset'] = 0
comm.Barrier()
data_file_out = params.get_data_file(is_empty=not has_been_created, params=description)
if comm.rank == 0:
print_and_log(['Allocating space for filtered files...'], 'debug', logger)
if not has_been_created:
data_file_out.allocate(shape=data_file_in.shape)
comm.Barrier()
if clean_artefact:
if not (os.path.exists(params.get('triggers', 'trig_file')) and os.path.exists(params.get('triggers', 'trig_windows'))):
if comm.rank == 0:
print_and_log(['trig_file or trig_windows file can not be found'], 'error', logger)
sys.exit(0)
to_write = []
if do_filter and filter_done:
do_filter = False
to_write += ["Filtering has already been done"]
if remove_median and median_done:
remove_median = False
to_write += ["Median over all channels has already been removed"]
if remove_ground and ground_done:
remove_ground = False
to_write += ["Common ground %s has alread been subtracted" % common_ground]
if comm.rank == 0 and len(to_write) > 0:
print_and_log(to_write, 'info', logger)
if params.getboolean('data', 'overwrite'):
data_file_in.open(mode='r+')
else:
data_file_in.open(mode='r')
data_file_out.open(mode='r+')
if do_filter or remove_median or remove_ground:
if comm.rank == 0:
if do_filter:
params.write('noedits', 'filter_done', 'Started')
if remove_median:
params.write('noedits', 'median_done', 'Started')
if remove_ground:
params.write('noedits', 'ground_done', 'Started')
filter_file(data_file_in, data_file_out, do_filter, remove_median, remove_ground)
if comm.rank == 0:
if do_filter:
params.write('noedits', 'filter_done', 'True')
if remove_median:
params.write('noedits', 'median_done', 'True')
if remove_ground:
params.write('noedits', 'ground_done', 'True')
if clean_artefact and artefacts_done:
clean_artefact = False
if comm.rank == 0:
print_and_log(['Artefacts have already been removed'], 'debug', logger)
if clean_artefact:
art_dict = compute_artefacts(data_file_in)
if comm.rank == 0:
params.write('noedits', 'artefacts_done', 'Started')
remove_artefacts(data_file_out, art_dict)
if comm.rank == 0:
if clean_artefact:
params.write('noedits', 'artefacts_done', 'True')
data_file_in.close()
if not params.getboolean('data', 'overwrite'):
data_file_out.close()
comm.Barrier()
def extract_extra_spikes_(params):
"""Detect spikes from the extracellular traces"""
data_file = params.data_file
data_file.open()
dist_peaks = params.getint('detection', 'dist_peaks')
spike_thresh = params.getfloat('detection', 'spike_thresh')
template_shift = params.getint('detection', 'template_shift')
alignment = params.getboolean('detection', 'alignment')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
safety_time = params.getint('whitening', 'safety_time')
safety_space = params.getboolean('clustering', 'safety_space')
chunk_size = params.getint('data', 'chunk_size')
jitter_range = params.getint('detection', 'jitter_range')
template_shift_2 = template_shift + jitter_range
# chunk_size = params.getint('whitening', 'chunk_size')
N_total = params.nb_channels
file_out_suff = params.get('data', 'file_out_suff')
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
# mpi_file = MPI.File()
# mpi_input = mpi_file.Open(comm, data_filename, MPI.MODE_RDONLY)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
nodes, _ = get_nodes_and_edges(params)
N_elec = params.getint('data', 'N_e')
extra_medians, extra_mads = extract_extra_thresholds(params)
if comm.rank == 0:
# Save medians and median absolute deviations to BEER file.
path = "{}.beer.hdf5".format(file_out_suff)
beer_file = h5py.File(path, 'a', libver='earliest')
# # Save medians.
extra_medians_key = "extra_medians"
if extra_medians_key in list(beer_file.keys()):
beer_file.pop(extra_medians_key)
beer_file.create_dataset(extra_medians_key, data=extra_medians)
# # Save median absolute deviations.
extra_mads_key = "extra_mads"
if extra_mads_key in list(beer_file.keys()):
beer_file.pop(extra_mads_key)
beer_file.create_dataset(extra_mads_key, data=extra_mads)
beer_file.close()
def extract_chunk_spikes(gidx, extra_thresh, valley=True):
"""Detect spikes from a chunk of the extracellular traces"""
loc_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes)
loc_shape = len(loc_chunk)
# Whiten signal.
if do_spatial_whitening:
loc_chunk = numpy.dot(loc_chunk, spatial_whitening)
if do_temporal_whitening:
loc_chunk = scipy.ndimage.filters.convolve1d(loc_chunk,
temporal_whitening,
axis=0,
mode='constant')
# TODO uncomment or remove temporary zone.
# # For each electrode, center traces by removing the medians.
# extra_medians = numpy.median(loc_chunk, axis=0)
# loc_chunk = loc_chunk - extra_medians
# TODO end temporary zone
# Pre-allocation for results.
peak_times = N_elec * [None]
peak_channels = N_elec * [None]
# For each electrode.
for e in range(N_elec):
# Extract the peaks of the current chunk.
threshold = extra_thresh * extra_mads[e]
if valley is True:
peak_times[e] = scipy.signal.find_peaks(-local_chunk[:, e],
height=threshold,
distance=dist_peaks)[0]
else:
peak_times[e] = scipy.signal.find_peaks(local_chunk[:, e],
height=threshold,
distance=dist_peaks)[0]
peak_channels[e] = e * numpy.ones(peak_times[e].size,
dtype='int64')
peak_values = loc_chunk[peak_times[e], e]
if valley:
peak_indices = numpy.where(-10.0 * threshold <= peak_values)[0]
else:
peak_indices = numpy.where(peak_values <= +10.0 * threshold)[0]
peak_times[e] = peak_times[e][peak_indices]
peak_channels[e] = peak_channels[e][peak_indices]
peak_times = numpy.concatenate(peak_times)
peak_channels = numpy.concatenate(peak_channels)
# Remove the useless borders.
if alignment:
loc_borders = (2 * template_shift, loc_shape - 2 * template_shift)
else:
loc_borders = (template_shift, loc_shape - template_shift)
peak_flags = (loc_borders[0] <= peak_times) & (peak_times <
loc_borders[1])
peak_times = numpy.compress(peak_flags, peak_times)
peak_channels = numpy.compress(peak_flags, peak_channels)
# Filter unique peak times.
loc_peak_times = numpy.unique(peak_times)
# TODO remove debug zone.
# if gidx < 1:
# numpy.save("tmp/loc_peak_times_{}_{}_.npy".format(gidx, int(extra_thresh)), loc_peak_times)
# TODO end debug zone.
n_times = len(loc_peak_times)
loc_peak_flags = numpy.zeros(n_times, dtype='bool')
loc_peak_elecs = numpy.zeros(n_times, dtype='int64')
loc_peak_values = numpy.zeros(n_times, dtype='float32')
if 0 < len(loc_peak_times):
diff_times = loc_peak_times[-1] - loc_peak_times[0]
all_times = numpy.zeros((N_elec, diff_times + 1), dtype='bool')
min_times = numpy.maximum(
loc_peak_times - loc_peak_times[0] - safety_time, 0)
max_times = numpy.minimum(
loc_peak_times - loc_peak_times[0] + safety_time + 1,
diff_times)
# Shuffle peaks.
# TODO clean temporary zone.
# argmax_peak = numpy.random.permutation(numpy.arange(n_times))
if valley:
for i, loc_peak_time in enumerate(loc_peak_times):
loc_peak_values[i] = numpy.amin(
loc_chunk[loc_peak_time, :])
argmax_peak = numpy.argsort(loc_peak_values)
else:
for i, loc_peak_time in enumerate(loc_peak_times):
loc_peak_values[i] = numpy.amax(
loc_chunk[loc_peak_time, :])
argmax_peak = numpy.argsort(loc_peak_values)
argmes_peak = argmax_peak[::-1]
# TODO end temporary zone.
all_indices = loc_peak_times[argmax_peak]
# Select peaks with spatio-temporal masks.
for peak_index, peak_time in zip(argmax_peak, all_indices):
# Select electrode showing lowest amplitude.
if valley:
elec = numpy.argmin(loc_chunk[peak_time, :])
else:
elec = numpy.argmax(loc_chunk[peak_time, :])
_, neighs = get_neighbors(params, chan=elec)
if safety_space:
mslice = all_times[
neighs, min_times[peak_index]:max_times[peak_index]]
else:
mslice = all_times[
elec, min_times[peak_index]:max_times[peak_index]]
is_local_min = (elec in peak_channels[peak_times == peak_time])
if is_local_min and not mslice.any():
loc_peak_flags[peak_index] = True
loc_peak_elecs[peak_index] = elec
if valley:
loc_peak_values[peak_index] = -loc_chunk[peak_time,
elec]
else:
loc_peak_values[peak_index] = loc_chunk[peak_time,
elec]
if safety_space:
all_times[
neighs,
min_times[peak_index]:max_times[peak_index]] = True
# all_times[elec, min_times[peak_index]:max_times[peak_index]] = True
else:
all_times[
elec,
min_times[peak_index]:max_times[peak_index]] = True
loc_peak_times = numpy.compress(loc_peak_flags, loc_peak_times)
loc_peak_elecs = numpy.compress(loc_peak_flags, loc_peak_elecs)
loc_peak_values = numpy.compress(loc_peak_flags, loc_peak_values)
# TODO remove debug zone.
# if gidx < 1:
# numpy.save("tmp/loc_peak_times_{}_{}.npy".format(gidx, int(extra_thresh)), loc_peak_times)
# numpy.save("tmp/loc_peak_elecs_{}_{}.npy".format(gidx, int(extra_thresh)), loc_peak_elecs)
# numpy.save("tmp/loc_peak_values_{}_{}.npy".format(gidx, int(extra_thresh)), loc_peak_values)
# numpy.save("tmp/loc_chunk_{}_{}.npy".format(gidx, int(extra_thresh)), loc_chunk)
# TODO end debug zone.
return loc_peak_times + t_offset, loc_peak_elecs, loc_peak_values
comm.Barrier()
# Distribute chunks over CPUs.
all_chunks = numpy.arange(nb_chunks)
loc_all_chunks = all_chunks[comm.rank::comm.size]
loc_nb_chunks = len(loc_all_chunks)
if comm.rank == 0:
print_and_log(["Collecting extracellular spikes..."],
level='default',
logger=logger)
to_explore = list(range(comm.rank, nb_chunks, comm.size))
if comm.rank == 0:
to_explore = get_tqdm_progressbar(params, to_explore)
extra_valley = True
# TODO remove test zone (i.e. plots of extracellular spike times).
# plot_extracted_extra_spikes(loc_all_chunks, data_len, mpi_input, data_dtype,
# chunk_len, chunk_size, N_total, nodes,
# extra_medians, extra_mads, k, params, safety_space,
# safety_time)
# sys.exit(1)
# TODO end test zone.
# Pre-allocation for results.
times = len(loc_all_chunks) * [None]
channels = len(loc_all_chunks) * [None]
values = len(loc_all_chunks) * [None]
data_file.open()
# For each chunk attributed to the current CPU.
for (count, gidx) in enumerate(to_explore):
gidx = all_chunks[gidx]
time, channel, value = extract_chunk_spikes(gidx,
spike_thresh,
valley=extra_valley)
times[count] = time
channels[count] = channel
values[count] = value
sys.stderr.flush()
# Concatenate times, channels and values.
times = numpy.hstack(times)
channels = numpy.hstack(channels)
values = numpy.hstack(values)
data_file.close()
comm.Barrier()
# Gather times, channels and values.
times = gather_array(times.astype(numpy.int64), comm, 0, dtype='int64')
channels = gather_array(channels.astype(numpy.int64),
comm,
0,
dtype='int64')
values = gather_array(values.astype(numpy.float32),
comm,
0,
dtype='float32')
if comm.rank == 0:
# Sort times, channels and values according to time.
idx = numpy.argsort(times)
times = times[idx]
channels = channels[idx]
values = values[idx]
msg = [
"Total number of extracellular spikes extracted: {}".format(
channels.size),
]
msg2 = [
"Number of extracellular spikes extracted on channel {}: {}".
format(i, channels[channels == i].size)
for i in numpy.arange(N_elec)
]
print_and_log(msg, level='info', logger=logger)
print_and_log(msg2, level='debug', logger=logger)
path = "{}.beer.hdf5".format(file_out_suff)
beer_file = h5py.File(path, 'a', libver='earliest')
group_name = "extra_spiketimes"
if group_name in list(beer_file.keys()):
beer_file.pop(group_name)
beer_file.create_group(group_name)
for i in numpy.arange(0, N_elec):
mask = (channels == i)
triggers = times[mask]
beer_file.create_dataset("{}/elec_{}".format(group_name, i),
data=triggers)
group_name = "extra_spike_values"
if group_name in list(beer_file.keys()):
beer_file.pop(group_name)
beer_file.create_group(group_name)
for i in numpy.arange(0, N_elec):
mask = (channels == i)
data = values[mask]
beer_file.create_dataset("{}/elec_{}".format(group_name, i),
data=data)
beer_file.close()
comm.Barrier()
return
def main(params, nb_cpu, nb_gpu, use_gpu):
numpy.random.seed(426236)
#params = detect_memory(params)
parallel_hdf5 = get_parallel_hdf5_flag(params)
logger = init_logging(params.logfile)
logger = logging.getLogger('circus.extracting')
#################################################################
data_file = params.data_file
N_e = params.getint('data', 'N_e')
N_t = params.getint('detecton', 'N_t')
N_total = params.nb_channels
template_shift = params.getint('detection', 'template_shift')
chunk_size = params.getint('data', 'chunk_size')
file_out = params.get('data', 'file_out')
file_out_suff = params.get('data', 'file_out_suff')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
nodes, edges = get_nodes_and_edges(params)
safety_time = params.getint('extracting', 'safety_time')
max_elts_temp = params.getint('extracting', 'max_elts')
output_dim = params.getfloat('extracting', 'output_dim')
noise_thr = params.getfloat('extracting', 'noise_thr')
hdf5_compress = params.getboolean('data', 'hdf5_compress')
blosc_compress = params.getboolean('data', 'blosc_compress')
tmp_limits = params.get('fitting',
'amp_limits').replace('(',
'').replace(')',
'').split(',')
amp_limits = map(float, tmp_limits)
elt_count = 0
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
#################################################################
if comm.rank == 0:
print_and_log(["Extracting templates from already found clusters..."],
'default', logger)
thresholds = io.load_data(params, 'thresholds')
basis_proj, basis_rec = io.load_data(params, 'basis')
clusters, spiketimes, N_clusters = io.load_data(params, 'spike-cluster')
inv_clusters = numpy.zeros(clusters.max() + 1, dtype=numpy.int32)
inv_clusters[numpy.unique(clusters)] = numpy.argsort(
numpy.unique(clusters))
if use_gpu:
import cudamat as cmt
## Need to properly handle multi GPU per MPI nodes?
if nb_gpu > nb_cpu:
gpu_id = int(comm.rank // nb_cpu)
else:
gpu_id = 0
cmt.cuda_set_device(gpu_id)
cmt.init()
cmt.cuda_sync_threads()
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
if use_gpu and do_spatial_whitening:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening,
copy_on_host=False)
result = {}
for i in xrange(N_clusters):
result['data_tmp_' + str(i)] = numpy.zeros(
(0, N_e * basis_proj.shape[1]), dtype=numpy.float32)
result['times_' + str(i)] = numpy.zeros(0, dtype=numpy.int32)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
# I guess this is more relevant, to take signals from all over the recordings
all_chunks = numpy.random.permutation(numpy.arange(nb_chunks))
nb_templates = numpy.sum(
comm.rank == numpy.mod(numpy.arange(N_clusters), comm.size))
nb_elts = max_elts_temp * nb_templates
to_explore = all_chunks
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for gidx in all_chunks:
if (elt_count < nb_elts):
#print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
#print "Extracting the peaks..."
idx = numpy.where((spiketimes >= gidx * chunk_size)
& (spiketimes < (gidx + 1) * chunk_size))[0]
local_offset = t_offset
local_peaktimes = spiketimes[idx] - local_offset
#print "Removing the useless borders..."
local_borders = (template_shift, chunk_size - template_shift)
idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes <
local_borders[1])
local_peaktimes = local_peaktimes[idx]
local_clusters = inv_clusters[clusters[idx]]
if len(local_peaktimes) > 0:
all_times = numpy.zeros(
(N_e, local_peaktimes[-1] - local_peaktimes[0] + 1),
dtype=numpy.bool)
min_times = numpy.maximum(
local_peaktimes - local_peaktimes[0] - safety_time, 0)
max_times = numpy.minimum(
local_peaktimes - local_peaktimes[0] + safety_time + 1,
local_peaktimes[-1] - local_peaktimes[0])
n_times = len(local_peaktimes)
argmax_peak = numpy.random.permutation(numpy.arange(n_times))
clusters_id = local_clusters[argmax_peak]
local_peaktimes = local_peaktimes[argmax_peak]
#print "Selection of the peaks with spatio-temporal masks..."
for idx in xrange(len(local_peaktimes)):
if elt_count == nb_elts:
break
temp = clusters_id[idx]
if numpy.mod(temp, comm.size) == comm.rank:
elec = numpy.argmin(local_chunk[local_peaktimes[idx]])
indices = inv_nodes[edges[nodes[elec]]]
myslice = all_times[indices,
min_times[idx]:max_times[idx]]
peak = local_peaktimes[idx]
if not myslice.any():
if (len(result['data_tmp_' + str(temp)]) <
max_elts_temp):
elt_count += 1
sub_mat = local_chunk[peak -
template_shift:peak +
template_shift + 1, :]
sub_mat = numpy.dot(basis_rec, sub_mat)
nx, ny = sub_mat.shape
sub_mat = sub_mat.reshape((1, nx * ny))
result['data_tmp_' + str(temp)] = numpy.vstack(
(result['data_tmp_' + str(temp)], sub_mat))
to_add = numpy.array([peak + local_offset],
dtype=numpy.int32)
result['times_' +
str(temp)] = numpy.concatenate(
(result['times_' + str(temp)],
to_add))
all_times[indices,
min_times[idx]:max_times[idx]] = True
total_nb_elts = 0
for temp in xrange(N_clusters):
total_nb_elts += len(result['data_tmp_' + str(temp)])
gdata = gather_array(numpy.array([total_nb_elts], dtype=numpy.float32),
comm, 0)
if comm.rank == 0:
print_and_log([
"Found %d spikes over %d requested" %
(int(numpy.sum(gdata)), int(nb_elts))
], 'default', logger)
#print "Spikes extracted in", time.time() - t_start, "s"
comm.Barrier()
local_nb_clusters = 0
for temp in xrange(comm.rank, N_clusters, comm.size):
if len(result['data_tmp_' + str(temp)]) > 0:
local_nb_clusters += 1
#print total_nb_clusters, "found in", time.time() - t_start, "s"
gdata3 = gather_array(
numpy.array([local_nb_clusters], dtype=numpy.float32), comm, 0)
comm.Barrier()
if comm.rank == 0:
print_and_log(["Extracting the templates..."], 'default', logger)
total_nb_clusters = int(
comm.bcast(numpy.array([int(numpy.sum(gdata3))], dtype=numpy.int32),
root=0)[0])
offsets = numpy.zeros(comm.size, dtype=numpy.int32)
for i in xrange(comm.size - 1):
offsets[i + 1] = comm.bcast(numpy.array([local_nb_clusters],
dtype=numpy.int32),
root=i)
if parallel_hdf5:
node_pad = numpy.sum(offsets[:comm.rank + 1])
hfile = h5py.File(file_out_suff + '.templates.hdf5',
'w',
driver='mpio',
comm=comm,
libver='earliest')
norms = hfile.create_dataset('norms',
shape=(2 * total_nb_clusters, ),
dtype=numpy.float32,
chunks=True)
electrodes = hfile.create_dataset('electrodes',
shape=(total_nb_clusters, ),
dtype=numpy.int32,
chunks=True)
amps_lims = hfile.create_dataset('limits',
shape=(total_nb_clusters, 2),
dtype=numpy.float32,
chunks=True)
g_count = node_pad
g_offset = total_nb_clusters
else:
node_pad = 0
hfile = h5py.File(file_out_suff + '.templates-%d.hdf5' % comm.rank,
'w',
libver='earliest')
electrodes = hfile.create_dataset('electrodes',
shape=(local_nb_clusters, ),
dtype=numpy.int32,
chunks=True)
norms = hfile.create_dataset('norms',
shape=(2 * local_nb_clusters, ),
dtype=numpy.float32,
chunks=True)
amps_lims = hfile.create_dataset('limits',
shape=(local_nb_clusters, 2),
dtype=numpy.float32,
chunks=True)
g_count = 0
g_offset = local_nb_clusters
cfile = h5py.File(file_out_suff + '.clusters-%d.hdf5' % comm.rank,
'w',
libver='earliest')
count_templates = node_pad
temp_x = numpy.zeros(0, dtype=numpy.int32)
temp_y = numpy.zeros(0, dtype=numpy.int32)
temp_data = numpy.zeros(0, dtype=numpy.float32)
to_explore = xrange(comm.rank, N_clusters, comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for temp in to_explore:
n_data = len(result['data_tmp_' + str(temp)])
if n_data > 0:
data = result['data_tmp_' + str(temp)].reshape(
n_data, basis_proj.shape[1], N_e)
first_component = numpy.median(data, axis=0)
tmp_templates = numpy.dot(first_component.T, basis_rec)
electrodes[g_count] = indices[tmpidx[0][0]]
indices = inv_nodes[edges[nodes[electrodes[-1]]]]
templates = numpy.zeros((N_e, N_t), dtype=numpy.float32)
if shift > 0:
templates[indices, shift:] = tmp_templates[:, :-shift]
elif shift < 0:
templates[indices, :shift] = tmp_templates[:, -shift:]
else:
templates[indices, :] = tmp_templates
templates = templates.flatten()
dx = templates.nonzero()[0].astype(numpy.int32)
temp_x = numpy.concatenate((temp_x, dx))
temp_y = numpy.concatenate(
(temp_y,
count_templates * numpy.ones(len(dx), dtype=numpy.int32)))
temp_data = numpy.concatenate((temp_data, templates[dx]))
norms[g_count] = numpy.sqrt(
numpy.sum(templates.flatten()**2) / (N_e * N_t))
x, y, z = data.shape
data_flat = data.reshape(x, y * z)
first_flat = first_component.reshape(y * z, 1)
amplitudes = numpy.dot(data_flat, first_flat)
amplitudes /= numpy.sum(first_flat**2)
for i in xrange(x):
data_flat[i, :] -= amplitudes[i] * first_flat[:, 0]
variations = 10 * numpy.median(
numpy.abs(amplitudes - numpy.median(amplitudes)))
physical_limit = noise_thr * (
-thresholds[indices[tmpidx[0][0]]]) / tmp_templates.min()
amp_min = max(physical_limit,
numpy.median(amplitudes) - variations)
amp_max = min(amp_limits[1], numpy.median(amplitudes) + variations)
amps_lims[g_count] = [amp_min, amp_max]
if len(data_flat) > 1:
pca = PCA(1)
res_pca = pca.fit_transform(data_flat.astype(numpy.double))
second_component = pca.components_.T.astype(
numpy.float32).reshape(y, z)
else:
second_component = data_flat.reshape(y, z) / numpy.sum(
data_flat**2)
tmp_templates = numpy.dot(second_component.T, basis_rec)
offset = total_nb_clusters + count_templates
sub_templates = numpy.zeros((N_e, N_t), dtype=numpy.float32)
if shift > 0:
sub_templates[indices, shift:] = tmp_templates[:, :-shift]
elif shift < 0:
sub_templates[indices, :shift] = tmp_templates[:, -shift:]
else:
sub_templates[indices, :] = tmp_templates
sub_templates = sub_templates.flatten()
dx = sub_templates.nonzero()[0].astype(numpy.int32)
temp_x = numpy.concatenate((temp_x, dx))
temp_y = numpy.concatenate(
(temp_y, offset * numpy.ones(len(dx), dtype=numpy.int32)))
temp_data = numpy.concatenate((temp_data, sub_templates[dx]))
norms[g_count + g_offset] = numpy.sqrt(
numpy.sum(sub_templates.flatten()**2) / (N_e * N_t))
count_templates += 1
g_count += 1
io.write_datasets(cfile,
to_write,
result,
ielec,
compress=hdf5_compress)
#At the end we should have a templates variable to store.
cfile.close()
del result, templates, amps_lims
comm.Barrier()
#We need to gather the sparse arrays
temp_x = gather_array(temp_x, comm, dtype='int32', compress=blosc_compress)
temp_y = gather_array(temp_y, comm, dtype='int32', compress=blosc_compress)
temp_data = gather_array(temp_data, comm, compress=blosc_compress)
if parallel_hdf5:
if comm.rank == 0:
rs = [
h5py.File(file_out_suff + '.clusters-%d.hdf5' % i,
'r',
libver='earliest') for i in xrange(comm.size)
]
cfile = h5py.File(file_out_suff + '.clusters.hdf5',
'w',
libver='earliest')
io.write_datasets(cfile, ['electrodes'],
{'electrodes': electrodes[:]},
compress=hdf5_compress)
for i in xrange(comm.size):
for j in range(i, N_e, comm.size):
io.write_datasets(cfile,
to_write,
rs[i],
j,
compress=hdf5_compress)
rs[i].close()
os.remove(file_out_suff + '.clusters-%d.hdf5' % i)
cfile.close()
hfile.close()
else:
hfile.close()
if comm.rank == 0:
ts = [
h5py.File(file_out_suff + '.templates-%d.hdf5' % i,
'r',
libver='earliest') for i in xrange(comm.size)
]
rs = [
h5py.File(file_out_suff + '.clusters-%d.hdf5' % i,
'r',
libver='earliest') for i in xrange(comm.size)
]
result = {}
hfile = h5py.File(file_out_suff + '.templates.hdf5',
'w',
libver='earliest')
cfile = h5py.File(file_out_suff + '.clusters.hdf5',
'w',
libver='earliest')
electrodes = hfile.create_dataset('electrodes',
shape=(total_nb_clusters, ),
dtype=numpy.int32,
chunks=True)
norms = hfile.create_dataset('norms',
shape=(2 * total_nb_clusters, ),
dtype=numpy.float32,
chunks=True)
amplitudes = hfile.create_dataset('limits',
shape=(total_nb_clusters, 2),
dtype=numpy.float32,
chunks=True)
count = 0
for i in xrange(comm.size):
loc_temp = ts[i].get('templates')
middle = loc_temp.shape[2] // 2
norms[count:count + middle] = loc_norms[:middle]
norms[n_clusters + count:n_clusters + count +
middle] = loc_norms[middle:]
electrodes[count:count + middle] = ts[i].get('electrodes')
amplitudes[count:count + middle] = ts[i].get('limits')
count += middle
for j in range(i, N_e, comm.size):
io.write_datasets(cfile,
to_write,
rs[i],
j,
compress=hdf5_compress)
ts[i].close()
rs[i].close()
os.remove(file_out_suff + '.templates-%d.hdf5' % i)
os.remove(file_out_suff + '.clusters-%d.hdf5' % i)
io.write_datasets(cfile, ['electrodes'],
{'electrodes': electrodes[:]},
compress=hdf5_compress)
hfile.close()
cfile.close()
if comm.rank == 0:
hfile = h5py.File(file_out_suff + '.templates.hdf5',
'r+',
libver='earliest')
hfile.create_dataset('temp_x', data=temp_x)
hfile.create_dataset('temp_y', data=temp_y)
hfile.create_dataset('temp_data', data=temp_data)
hfile.create_dataset('temp_shape',
data=numpy.array(
[N_e, N_t, 2 * total_nb_clusters],
dtype=numpy.int32))
hfile.close()
comm.Barrier()
if comm.rank == 0:
print_and_log(["Merging similar templates..."], 'default', logger)
merged1 = algo.merging_cc(params, parallel_hdf5)
comm.Barrier()
if remove_mixture:
if comm.rank == 0:
print_and_log(["Removing mixtures..."], 'default', logger)
merged2 = algo.delete_mixtures(params, parallel_hdf5)
else:
merged2 = [0, 0]
if comm.rank == 0:
print_and_log([
"Number of global merges : %d" % merged1[1],
"Number of mixtures removed : %d" % merged2[1]
], 'info', logger)
comm.Barrier()
io.get_overlaps(params, erase=True, parallel_hdf5=parallel_hdf5)
data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu):
# Part 1: Whitening
numpy.random.seed(420)
# params = detect_memory(params)
_ = init_logging(params.logfile)
logger = logging.getLogger('circus.whitening')
#################################################################
data_file = params.data_file
N_e = params.getint('data', 'N_e')
hdf5_compress = params.getboolean('data', 'hdf5_compress')
N_total = params.nb_channels
N_t = params.getint('detection', 'N_t')
dist_peaks = params.getint('detection', 'dist_peaks')
template_shift = params.getint('detection', 'template_shift')
file_out_suff = params.get('data', 'file_out_suff')
spike_thresh = params.getfloat('detection', 'spike_thresh')
spike_width = params.getfloat('detection', 'spike_width')
matched_filter = params.getboolean('detection', 'matched-filter')
matched_thresh = params.getfloat('detection', 'matched_thresh')
fudge = params.getfloat('whitening', 'fudge')
sign_peaks = params.get('detection', 'peaks')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
ignore_spikes = params.getboolean('whitening', 'ignore_spikes')
chunk_size = detect_memory(params, whitening=True)
plot_path = os.path.join(params.get('data', 'file_out_suff'), 'plots')
nodes, edges = get_nodes_and_edges(params)
safety_time = params.getint('whitening', 'safety_time')
safety_space = params.getboolean('whitening', 'safety_space')
sort_waveforms = params.getboolean('whitening', 'sort_waveforms')
nb_temp_white = min(max(20, comm.size), N_e)
max_silence_1 = int(20 * params.rate // comm.size)
max_silence_2 = 5000
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
jitter_range = params.getint('detection', 'jitter_range')
template_shift_2 = template_shift + jitter_range
use_hanning = params.getboolean('detection', 'hanning')
rejection_threshold = params.getfloat('detection', 'rejection_threshold')
noise_window = params.getint('detection', 'noise_time')
data_file.open()
#################################################################
if use_hanning:
hanning_filter = numpy.hanning(N_t)
if comm.rank == 0:
print_and_log(
["Analyzing data to get whitening matrices and thresholds..."],
'default', logger)
nodes_indices = {}
for elec in numpy.arange(N_e):
nodes_indices[elec] = inv_nodes[edges[nodes[elec]]]
if use_gpu:
import cudamat as cmt
# # Need to properly handle multi GPU per MPI nodes?
if nb_gpu > nb_cpu:
gpu_id = int(comm.rank // nb_cpu)
else:
gpu_id = 0
cmt.cuda_set_device(gpu_id)
cmt.init()
cmt.cuda_sync_threads()
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
if nb_chunks < comm.size:
res = io.data_stats(params, show=False)
chunk_size = int(res * params.rate // comm.size)
if comm.rank == 0:
print_and_log(
["Too much cores, automatically resizing the data chunks"],
'debug', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
# I guess this is more relevant, to take signals from all over the recordings.
if nb_chunks > comm.size:
all_chunks = numpy.random.permutation(
numpy.arange(nb_chunks - 1, dtype=numpy.int32))
else:
all_chunks = numpy.random.permutation(
numpy.arange(nb_chunks, dtype=numpy.int32))
all_electrodes = numpy.random.permutation(N_e)
numpy.random.seed(comm.rank)
for gidx in [all_chunks[comm.rank]]:
# print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
# print "Node", comm.rank, "computes the median absolute deviations in a random chunk"
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in range(N_e):
u = numpy.median(local_chunk[:, i], 0)
thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'w',
libver='earliest')
io.write_datasets(bfile, ['thresholds'],
{'thresholds': thresholds},
compression=hdf5_compress)
bfile.close()
comm.Barrier()
thresholds = io.load_data(params, 'thresholds')
local_borders = (template_shift, local_shape - template_shift)
found_peaktimes = []
if ignore_spikes:
# Extracting the peaks.
local_peaktimes = [np.empty(0, dtype=numpy.uint32)]
for i in range(N_e):
peaktimes = scipy.signal.find_peaks(numpy.abs(local_chunk[:,
i]),
height=thresholds[i],
width=spike_width,
wlen=N_t)[0]
peaktimes = peaktimes.astype(numpy.uint32)
# print "Removing the useless borders..."
idx = (peaktimes >= local_borders[0]) & (peaktimes <
local_borders[1])
peaktimes = numpy.compress(idx, peaktimes)
found_peaktimes.append(peaktimes)
else:
for i in range(N_e):
found_peaktimes.append(numpy.zeros(0, dtype=numpy.uint32))
all_peaktimes = numpy.concatenate(found_peaktimes)
local_peaktimes = numpy.unique(all_peaktimes)
if len(local_peaktimes) > 0:
diff_times = local_peaktimes[-1] - local_peaktimes[0]
all_times = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool)
padded_peaks = (local_peaktimes - local_peaktimes[0]).astype(
numpy.int32)
min_times = numpy.maximum(padded_peaks - safety_time, 0)
max_times = numpy.minimum(padded_peaks + safety_time + 1,
diff_times + 1)
test_extremas = numpy.zeros((N_e, diff_times + 1),
dtype=numpy.bool)
for i in range(N_e):
test_extremas[i,
found_peaktimes[i] - local_peaktimes[0]] = True
argmax_peak = numpy.random.permutation(
numpy.arange(len(local_peaktimes)))
all_idx = numpy.take(local_peaktimes, argmax_peak)
# print "Selection of the peaks with spatio-temporal masks..."
for idx, peak in zip(argmax_peak, all_idx):
all_elecs = numpy.where(test_extremas[:, peak -
local_peaktimes[0]])[0]
data = local_chunk[peak, all_elecs]
elec = all_elecs[numpy.argmax(numpy.abs(data))]
indices = nodes_indices[elec]
if safety_space:
all_times[indices, min_times[idx]:max_times[idx]] = True
else:
all_times[elec, min_times[idx]:max_times[idx]] = True
else:
all_times = numpy.zeros((N_e, len(local_chunk)), dtype=numpy.bool)
if do_temporal_whitening:
local_res_temp = []
for elec in all_electrodes[numpy.arange(comm.rank, nb_temp_white,
comm.size)]:
res = numpy.zeros((0, N_t), dtype=numpy.float32)
scount = 0
indices = nodes_indices[elec]
all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0),
0)
esubset = numpy.where(all_times_elec == False)[0]
bound = len(esubset) - N_t
while (scount < bound) and (len(res) < max_silence_2):
myslice = esubset[scount:scount + N_t]
if numpy.all((myslice - esubset[scount]) == numpy.arange(N_t)):
scount += N_t
res = numpy.vstack((res, local_chunk[myslice, elec]))
else:
scount += 1
if len(res) > 5:
local_res_temp += [numpy.cov(res.T)]
nb_elecs = numpy.array([len(local_res_temp)], dtype=numpy.float32)
local_res_temp = numpy.array(local_res_temp, dtype=numpy.float32)
if len(local_res_temp) == 0:
local_res_temp = numpy.zeros(0, dtype=numpy.float32)
else:
local_res_temp = numpy.sum(local_res_temp, 0)
all_res_temp = gather_array(local_res_temp.ravel(), comm, 0, 1)
all_elecs = gather_array(nb_elecs, comm, 0, 1)
if do_spatial_whitening:
local_res_spac = numpy.zeros((N_e, N_e), dtype=numpy.float32)
local_silences = []
for elec in numpy.arange(comm.rank, N_e, comm.size):
indices = nodes_indices[elec]
all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0),
0)
esubset = numpy.where(all_times_elec == False)[0]
local_data = local_chunk[esubset][:, indices]
local_whitening = get_whitening_matrix(
local_data, fudge=fudge).astype(numpy.float32)
pos = numpy.where(elec == indices)[0]
local_res_spac[elec, indices] = local_whitening[pos]
local_silences += [len(esubset)]
all_res_spac = gather_array(local_res_spac.ravel(), comm, 0, 1)
all_silences = gather_array(
numpy.array(local_silences, dtype=numpy.int32), comm, 0, 1,
'uint32')
if comm.rank == 0:
to_write = {}
if do_temporal_whitening:
try:
nb_silences = numpy.sum(all_elecs > 0)
all_res_temp = all_res_temp.reshape((nb_silences, N_t**2))
except Exception:
print_and_log([
"No silent periods detected: something wrong with the parameters?"
], 'error', logger)
all_res_temp = numpy.sum(all_res_temp, 0)
all_res_temp = all_res_temp.reshape(
(N_t, N_t)) / numpy.sum(all_elecs)
temporal_whitening = get_whitening_matrix(
all_res_temp.astype(numpy.double),
fudge=1e-3)[template_shift].astype(numpy.float32)
temporal_whitening /= temporal_whitening.sum()
to_write['temporal'] = temporal_whitening
have_nans = numpy.sum(numpy.isnan(temporal_whitening))
if have_nans > 0:
temporal_whitening = numpy.zeros(N_t, dtype=numpy.float32)
temporal_whitening[N_t // 2] = 1
to_write['temporal'] = temporal_whitening
print_and_log(
["Disabling temporal whitening because of NaNs found"],
'info', logger)
if do_spatial_whitening:
all_res_spac = all_res_spac.reshape(comm.size, N_e, N_e)
spatial_whitening = numpy.sum(all_res_spac, 0)
to_write['spatial'] = spatial_whitening
if ignore_spikes:
print_and_log([
"Found %gs without spikes to compute the whitening matrix..."
% (numpy.mean(all_silences) / params.rate)
], 'default', logger)
else:
print_and_log([
"Found %gs to compute the whitening matrix..." %
(numpy.mean(all_silences) / params.rate)
], 'default', logger)
have_nans = numpy.sum(numpy.isnan(spatial_whitening))
if have_nans > 0:
spatial_whitening = numpy.eye(spatial_whitening.shape[0],
dtype=numpy.float32)
to_write['spatial'] = spatial_whitening
print_and_log(
["Disabling spatial whitening because of NaNs found"],
'info', logger)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
io.write_datasets(bfile,
list(to_write.keys()),
to_write,
compression=hdf5_compress)
bfile.close()
comm.Barrier()
if do_spatial_whitening or do_temporal_whitening:
if comm.rank == 0:
print_and_log(
["Because of whitening, need to recompute the thresholds..."],
'default', logger)
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if use_gpu:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening,
copy_on_host=False)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
for gidx in [all_chunks[comm.rank]]:
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in range(N_e):
u = numpy.median(local_chunk[:, i], 0)
thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u),
0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
bfile.pop('thresholds')
io.write_datasets(bfile, ['thresholds'],
{'thresholds': thresholds},
compression=hdf5_compress)
bfile.close()
comm.Barrier()
# if comm.rank == 0:
# if not os.path.exists(plot_path):
# os.makedirs(plot_path)
# N_elec = min(int(numpy.sqrt(data_file.N_e)), 5)
# plot.view_fit(filename, t_start=0, t_stop=1, fit_on=False, square=True,
# n_elec=N_elec, save=[plot_path, 'electrodes'])
# Part 2: Basis
numpy.random.seed(422)
SHARED_MEMORY = get_shared_memory_flag(params)
#################################################################
file_out = params.get('data', 'file_out')
alignment = params.getboolean('detection', 'alignment')
over_factor = params.getint('detection', 'oversampling_factor')
nb_jitter = params.getint('detection', 'nb_jitter')
spike_thresh = params.getfloat('detection', 'spike_thresh')
nodes, edges = get_nodes_and_edges(params)
_, positions = get_nodes_and_positions(params)
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
use_barycenter = params.getboolean('detection', 'use_barycenter')
if matched_filter:
chunk_size = detect_memory(params, whitening=True)
else:
chunk_size = detect_memory(params)
safety_time = params.getint('whitening', 'safety_time')
max_elts_elec = params.getint('whitening', 'max_elts')
output_dim = params.getfloat('whitening', 'output_dim')
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
smoothing_factor = params.getfloat('detection', 'smoothing_factor')
if sign_peaks == 'both':
max_elts_elec *= 2
nb_elts = int(
params.getfloat('whitening', 'nb_elts') * N_e * max_elts_elec)
weird_thresh = params.get('detection', 'weird_thresh')
if weird_thresh != '':
ignore_artefacts = True
weird_thresh = io.load_data(params, 'weird-thresholds')
else:
ignore_artefacts = False
ignore_dead_times = params.getboolean('triggers', 'ignore_times')
if ignore_dead_times:
if SHARED_MEMORY:
all_dead_times, mpi_memory_3 = get_dead_times(params)
else:
all_dead_times = get_dead_times(params)
data_file.open()
#################################################################
if comm.rank == 0:
print_and_log(["Searching spikes to construct the PCA basis..."],
'default', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
if nb_chunks < comm.size:
res = io.data_stats(params, show=False)
chunk_size = int(res * params.rate // comm.size)
if comm.rank == 0:
print_and_log(
["Too much cores, automatically resizing the data chunks"],
'debug', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
groups = {}
for i in range(N_e):
groups[i] = 0
# I guess this is more relevant, to take signals from all over the recordings
all_chunks = numpy.random.permutation(
numpy.arange(nb_chunks, dtype=numpy.int32))
max_elts_elec //= comm.size
nb_elts //= comm.size
elt_count_pos = 0
elt_count_neg = 0
if sign_peaks in ['positive', 'both']:
times_pos = numpy.zeros(nb_elts, dtype=numpy.int32)
electrodes_pos = numpy.zeros(nb_elts, dtype=numpy.int32)
extremum_pos = numpy.zeros(nb_elts, dtype=numpy.float32)
elts_pos = numpy.zeros((N_t, nb_elts), dtype=numpy.float32)
if sign_peaks in ['negative', 'both']:
times_neg = numpy.zeros(nb_elts, dtype=numpy.int32)
electrodes_neg = numpy.zeros(nb_elts, dtype=numpy.int32)
extremum_neg = numpy.zeros(nb_elts, dtype=numpy.float32)
elts_neg = numpy.zeros((N_t, nb_elts), dtype=numpy.float32)
thresholds = io.load_data(params, 'thresholds')
mads = io.load_data(params, 'mads')
stds = io.load_data(params, 'stds')
if alignment:
cdata = numpy.linspace(-jitter_range, +jitter_range, nb_jitter)
xdata = numpy.arange(-template_shift_2, template_shift_2 + 1)
xoff = len(cdata) / 2.0
snippet_duration = template_shift_2
m_size = 2 * template_shift_2 + 1
align_factor = m_size
local_factors = align_factor * ((smoothing_factor * mads)**2)
else:
snippet_duration = template_shift
xdata = numpy.arange(-template_shift, template_shift + 1)
if rejection_threshold > 0:
reject_noise = True
noise_levels = stds * (2 * noise_window + 1)
else:
reject_noise = False
to_explore = all_chunks[comm.rank::comm.size]
upper_bounds = max_elts_elec
if comm.rank == 0:
to_explore = get_tqdm_progressbar(params, to_explore)
for gcount, gidx in enumerate(to_explore):
if (elt_count_pos + elt_count_neg) < nb_elts:
# print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
local_borders = (snippet_duration, local_shape - snippet_duration)
if ignore_dead_times:
dead_indices = numpy.searchsorted(
all_dead_times, [t_offset, t_offset + local_shape])
# Extracting the peaks.
all_peaktimes = [numpy.empty(0, dtype=numpy.uint32)]
found_peaktimes = []
found_peak_amplitudes = []
for i in range(N_e):
height = thresholds[i]
if sign_peaks == 'negative':
peaktimes = scipy.signal.find_peaks(-local_chunk[:, i],
height=height,
distance=dist_peaks)[0]
elif sign_peaks == 'positive':
peaktimes = scipy.signal.find_peaks(local_chunk[:, i],
height=height,
distance=dist_peaks)[0]
elif sign_peaks == 'both':
peaktimes = scipy.signal.find_peaks(numpy.abs(
local_chunk[:, i]),
height=height,
distance=dist_peaks)[0]
else:
peaktimes = numpy.empty(0, dtype=numpy.uint32)
if ignore_artefacts:
artetimes = scipy.signal.find_peaks(
numpy.abs(local_chunk[:,
i]), height=weird_thresh[i])[0]
to_keep = numpy.logical_not(
numpy.in1d(peaktimes, artetimes))
peaktimes = peaktimes[to_keep]
idx = (peaktimes >= local_borders[0]) & (peaktimes <
local_borders[1])
peaktimes = peaktimes[idx]
if ignore_dead_times:
if dead_indices[0] != dead_indices[1]:
is_included = numpy.in1d(
peaktimes + t_offset,
all_dead_times[dead_indices[0]:dead_indices[1]])
peaktimes = peaktimes[~is_included]
peaktimes = peaktimes.astype(numpy.uint32)
found_peaktimes.append(peaktimes)
peak_amplitudes = local_chunk[peaktimes, i]
found_peak_amplitudes.append(peak_amplitudes)
all_peaktimes = numpy.concatenate(
found_peaktimes) # i.e. concatenate once for efficiency
all_peak_amplitudes = numpy.concatenate(found_peak_amplitudes)
local_peaktimes, local_indices = numpy.unique(all_peaktimes,
return_inverse=True)
if len(local_peaktimes) > 0:
diff_times = (local_peaktimes[-1] - local_peaktimes[0]) + 1
all_times = numpy.zeros((N_e, diff_times), dtype=numpy.bool)
padded_peaks = (local_peaktimes - local_peaktimes[0]).astype(
numpy.int32)
min_times = numpy.maximum(padded_peaks - safety_time, 0)
max_times = numpy.minimum(padded_peaks + safety_time + 1,
diff_times + 1)
test_extremas = numpy.zeros((N_e, diff_times + 1),
dtype=numpy.bool)
for i in range(N_e):
test_extremas[i, found_peaktimes[i] -
local_peaktimes[0]] = True
# Consider the peaks by decreasing extremum.
if sort_waveforms:
order = numpy.argsort(-np.abs(all_peak_amplitudes))
all_idx = numpy.take(all_peaktimes, order)
argmax_peak = local_indices[order]
else:
n_times = len(all_peaktimes)
shuffling = numpy.random.permutation(numpy.arange(n_times))
all_idx = numpy.take(all_peaktimes, shuffling)
argmax_peak = local_indices[shuffling]
# print "Selection of the peaks with spatio-temporal masks..."
for midx, peak in zip(argmax_peak, all_idx):
if (elt_count_neg + elt_count_pos) == nb_elts:
break
all_elecs = numpy.where(
test_extremas[:, peak - local_peaktimes[0]])[0]
data = local_chunk[peak, all_elecs]
#target_area = test_extremas[:, min_times[midx]:max_times[midx]].sum(1)
#all_elecs = numpy.where(target_area)[0]
#data = local_chunk[peak, all_elecs]
if sign_peaks == 'negative':
if N_e > 1:
if use_barycenter:
weighed_position = data[:, numpy.
newaxis] * positions[
all_elecs]
barycenter = weighed_position.sum(
0) / data.sum()
elec = numpy.argmin(
numpy.linalg.norm(barycenter -
positions[all_elecs],
axis=1))
else:
elec = numpy.argmin(data)
else:
elec = 0
negative_peak = True
elif sign_peaks == 'positive':
if N_e > 1:
if use_barycenter:
weighed_position = data[:, numpy.
newaxis] * positions[
all_elecs]
barycenter = weighed_position.sum(
0) / data.sum()
elec = numpy.argmax(
numpy.linalg.norm(barycenter -
positions[all_elecs],
axis=1))
else:
elec = numpy.argmax(data)
else:
elec = 0
negative_peak = False
elif sign_peaks == 'both':
if N_e == 1:
if data < 0:
negative_peak = True
elif data > 0:
negative_peak = False
elec = 0
else:
if numpy.abs(numpy.max(data)) > numpy.abs(
numpy.min(data)):
elec = numpy.argmax(data)
negative_peak = False
else:
elec = numpy.argmin(data)
negative_peak = True
elec = all_elecs[elec]
if groups[elec] < upper_bounds:
indices = nodes_indices[elec]
myslice = all_times[indices,
min_times[midx]:max_times[midx]]
if not myslice.any():
sub_mat = local_chunk[peak -
snippet_duration:peak +
snippet_duration + 1, elec]
if reject_noise:
slice_window = sub_mat[
snippet_duration -
noise_window:snippet_duration +
noise_window + 1]
value = numpy.linalg.norm(
slice_window) / noise_levels[elec]
is_noise = value < rejection_threshold
else:
is_noise = False
if not is_noise:
extrema = sub_mat[snippet_duration]
if alignment:
smoothed = True
try:
f = scipy.interpolate.UnivariateSpline(
xdata,
sub_mat,
s=local_factors[elec],
k=3)
except Exception:
smoothed = False
f = scipy.interpolate.UnivariateSpline(
xdata, sub_mat, k=3, s=0)
if negative_peak:
rmin = (numpy.argmin(f(cdata)) -
xoff) / over_factor
else:
rmin = (numpy.argmax(f(cdata)) -
xoff) / over_factor
ddata = numpy.linspace(
rmin - template_shift,
rmin + template_shift, N_t)
if smoothed:
f = scipy.interpolate.UnivariateSpline(
xdata,
sub_mat,
s=local_factors[elec],
k=3)
else:
f = scipy.interpolate.UnivariateSpline(
xdata, sub_mat, s=0, k=3)
sub_mat = f(ddata).astype(numpy.float32)
if negative_peak:
times_neg[elt_count_neg] = peak + t_offset
electrodes_neg[elt_count_neg] = elec
extremum_neg[elt_count_neg] = extrema
elts_neg[:, elt_count_neg] = sub_mat
elt_count_neg += 1
else:
times_pos[elt_count_pos] = peak + t_offset
electrodes_pos[elt_count_pos] = elec
extremum_pos[elt_count_pos] = extrema
elts_pos[:, elt_count_pos] = sub_mat
elt_count_pos += 1
groups[elec] += 1
all_times[
indices,
min_times[midx]:max_times[midx]] = True
test_extremas[elec, peak -
local_peaktimes[0]] = False
sys.stderr.flush()
print_and_log([
"Node %d has collected %d waveforms" %
(comm.rank, elt_count_pos + elt_count_neg)
], 'debug', logger)
if sign_peaks in ['negative', 'both']:
times_neg = gather_array(times_neg[:elt_count_neg],
comm,
0,
1,
dtype='int32')
electrodes_neg = gather_array(electrodes_neg[:elt_count_neg],
comm,
0,
1,
dtype='int32')
extremum_neg = gather_array(extremum_neg[:elt_count_neg], comm, 0, 1)
gdata_neg = gather_array(elts_neg[:, :elt_count_neg].T, comm, 0, 1)
if sign_peaks in ['positive', 'both']:
times_pos = gather_array(times_pos[:elt_count_pos],
comm,
0,
1,
dtype='int32')
electrodes_pos = gather_array(electrodes_pos[:elt_count_pos],
comm,
0,
1,
dtype='int32')
extremum_pos = gather_array(extremum_pos[:elt_count_pos], comm, 0, 1)
gdata_pos = gather_array(elts_pos[:, :elt_count_pos].T, comm, 0, 1)
nb_waveforms = 0
if comm.rank == 0:
# DO PCA on elts and store the basis obtained.
if sign_peaks in ['negative', 'both']:
nb_waveforms += gdata_neg.shape[0]
if sign_peaks in ['positive', 'both']:
nb_waveforms += gdata_pos.shape[0]
nb_waveforms = all_gather_array(
numpy.array([nb_waveforms], dtype=numpy.float32), comm, 0)[0]
if comm.rank == 0:
print_and_log([
"Found %d waveforms over %d requested" %
(nb_waveforms, int(nb_elts * comm.size))
], 'default', logger)
if nb_waveforms == 0:
print_and_log(
['No waveforms found! Are the data properly loaded??'],
'error', logger)
if nb_waveforms == 0:
sys.exit(0)
if comm.rank == 0:
res = {}
pca = None
pca_pos = None
pca_neg = None
warning_n_t = False
if sign_peaks in ['negative', 'both']:
res['times'] = times_neg
res['electrodes'] = electrodes_neg
res['extremum'] = extremum_neg
if len(gdata_neg) > 0:
pca = PCA(output_dim)
if use_hanning:
pca.fit(gdata_neg * hanning_filter)
else:
pca.fit(gdata_neg)
res['proj'] = pca.components_.T.astype(numpy.float32)
pca_neg = numpy.sum(pca.explained_variance_ratio_)
else:
res['proj'] = numpy.identity(int(output_dim),
dtype=numpy.float32)
res['rec'] = res['proj'].T
res['waveform'] = numpy.median(gdata_neg, 0)
# dispersion = numpy.std(gdata_neg, 0) / numpy.median(stds)
# ratio = numpy.sum(dispersion > 1.1) / float(len(dispersion))
# if ratio < 0.25:
# print_and_log(["Time window N_t in [detection] seems too large!"], 'info', logger)
# warning_n_t = True
# elif ratio == 1:
# print_and_log(["Time window N_t in [detection] seems too small!"], 'info', logger)
# warning_n_t = True
idx = numpy.random.permutation(numpy.arange(
gdata_neg.shape[0]))[:2500]
res['waveforms'] = gdata_neg[idx, :]
if sign_peaks in ['positive', 'both']:
res['times_pos'] = times_pos
res['electrodes_pos'] = electrodes_pos
res['extremum_pos'] = extremum_pos
if len(gdata_pos) > 0:
pca = PCA(output_dim)
if use_hanning:
pca.fit(gdata_pos * hanning_filter)
else:
pca.fit(gdata_pos)
res['proj_pos'] = pca.components_.T.astype(numpy.float32)
pca_pos = numpy.sum(pca.explained_variance_ratio_)
else:
res['proj_pos'] = numpy.identity(int(output_dim),
dtype=numpy.float32)
res['rec_pos'] = res['proj_pos'].T
res['waveform_pos'] = numpy.median(gdata_pos, 0)
# dispersion = numpy.std(gdata_pos, 0) / numpy.median(stds)
# ratio = numpy.sum(dispersion > 1.1) / float(len(dispersion))
# if ratio < 0.25 and not warning_n_t:
# print_and_log(["Time window N_t in [detection] seems too large!"], 'info', logger)
# elif ratio == 1 and not warning_n_t:
# print_and_log(["Time window N_t in [detection] seems too small!"], 'info', logger)
idx = numpy.random.permutation(numpy.arange(
gdata_pos.shape[0]))[:2500]
res['waveforms_pos'] = gdata_pos[idx, :]
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
io.write_datasets(bfile,
list(res.keys()),
res,
compression=hdf5_compress)
if sign_peaks == 'positive':
print_and_log([
"A basis with %s dimensions has been built" %
res['proj_pos'].shape[1]
], 'info', logger)
elif sign_peaks == 'negative':
print_and_log([
"A basis with %s dimensions has been built" %
res['proj'].shape[1]
], 'info', logger)
elif sign_peaks == 'both':
print_and_log([
"Two basis with %s dimensions has been built" %
res['proj'].shape[1]
], 'debug', logger)
if pca_pos is not None:
print_and_log([
"The percentage of variance explained is %s for positive spikes"
% pca_pos
], 'debug', logger)
if pca_neg is not None:
print_and_log([
"The percentage of variance explained is %s for negative spikes"
% pca_neg
], 'debug', logger)
bfile.close()
comm.Barrier()
if matched_filter:
if comm.rank == 0:
print_and_log([
"Because of matched filters, need to recompute the thresholds..."
], 'default', logger)
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if use_gpu:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening,
copy_on_host=False)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
if sign_peaks in ['negative', 'both']:
waveform_neg = io.load_data(params, 'waveform')[::-1]
waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) *
len(waveform_neg))
if sign_peaks in ['positive', 'both']:
waveform_pos = io.load_data(params, 'waveform-pos')[::-1]
waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) *
len(waveform_pos))
for gidx in [all_chunks[comm.rank]]:
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
local_chunk /= thresholds
if sign_peaks in ['negative', 'both']:
tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk,
waveform_neg,
axis=0,
mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in range(N_e):
u = numpy.median(tmp_chunk[:, i], 0)
thresholds[i] = numpy.median(
numpy.abs(tmp_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
io.write_datasets(bfile, ['matched_thresholds'],
{'matched_thresholds': thresholds},
compression=hdf5_compress)
bfile.close()
comm.Barrier()
if sign_peaks in ['positive', 'both']:
tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk,
waveform_pos,
axis=0,
mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in range(N_e):
u = numpy.median(tmp_chunk[:, i], 0)
thresholds[i] = numpy.median(
numpy.abs(tmp_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='earliest')
io.write_datasets(bfile, ['matched_thresholds_pos'],
{'matched_thresholds_pos': thresholds},
compression=hdf5_compress)
bfile.close()
comm.Barrier()
data_file.close()
if SHARED_MEMORY and ignore_dead_times:
mpi_memory_3.Free()
def main(params, nb_cpu, nb_gpu, use_gpu):
logger = init_logging(params.logfile)
logger = logging.getLogger('circus.filtering')
#################################################################
do_filter = params.getboolean('filtering', 'filter')
filter_done = params.getboolean('noedits', 'filter_done')
artefacts_done = params.getboolean('noedits', 'artefacts_done')
median_done = params.getboolean('noedits', 'median_done')
clean_artefact = params.getboolean('triggers', 'clean_artefact')
remove_median = params.getboolean('filtering', 'remove_median')
nodes, edges = get_nodes_and_edges(params)
#################################################################
def filter_file(data_file_in, data_file_out, do_filtering,
do_remove_median):
try:
cut_off = params.getfloat('filtering', 'cut_off')
cut_off = [cut_off, 0.95 * (params.rate / 2.)]
except Exception:
cut_off = params.get('filtering', 'cut_off')
cut_off = cut_off.split(',')
try:
cut_off[0] = float(cut_off[0])
except Exception:
if comm.rank == 0:
print_and_log(
['First value of cut off must be a valid number'],
'error', logger)
sys.exit(1)
cut_off[1] = cut_off[1].replace(' ', '')
if cut_off[1] == 'auto':
cut_off[1] = 0.95 * (params.rate / 2.)
else:
try:
cut_off[1] = float(cut_off[1])
except Exception:
if comm.rank == 0:
print_and_log([
'Second value of cut off must either auto, or a valid a number'
], 'error', logger)
sys.exit(1)
chunk_size = params.getint('data', 'chunk_size')
nb_chunks, _ = data_file_in.analyze(chunk_size)
do_butter = False # mmyros
do_lowess = False # otherwise wavelet if both lowess and butter are false
if do_butter:
b, a = signal.butter(3,
np.array(cut_off) / (params.rate / 2.),
'pass')
all_chunks = numpy.arange(nb_chunks, dtype=numpy.int64)
to_process = all_chunks[comm.rank::comm.size]
loc_nb_chunks = len(to_process)
N_total = params.nb_channels
if comm.rank == 0:
to_write = []
if do_filtering:
to_write += [
"Filtering the signal with a Butterworth filter in (%g, %g) Hz"
% (cut_off[0], cut_off[1])
]
if do_remove_median:
to_write += [
"Median over all channels is substracted to each channels"
]
print_and_log(to_write, 'default', logger)
to_explore = xrange(comm.rank, nb_chunks, comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for count, gidx in enumerate(to_explore):
local_chunk, t_offset = data_file_in.get_data(gidx, chunk_size)
if do_filtering:
for i in nodes:
#try:
if do_butter:
local_chunk[:, i] = signal.filtfilt(
b, a, local_chunk[:, i])
elif do_lowess:
local_chunk[:, i] = lowess(local_chunk[:, i])
else:
local_chunk[:, i] = WMLDR(local_chunk[:, i])
local_chunk[:, i] -= numpy.median(local_chunk[:, i])
if do_remove_median:
if not numpy.all(nodes == numpy.arange(N_total)):
global_median = numpy.median(
numpy.take(local_chunk, nodes, axis=1), 1)
else:
global_median = numpy.median(local_chunk, 1)
for i in nodes:
local_chunk[:, i] -= global_median
if data_file_in != data_file_out and data_file_in.is_first_chunk(
gidx, nb_chunks):
if data_file_in.is_stream:
g_offset = t_offset - numpy.sum(
data_file_in.
_times[:data_file_in.
_get_streams_index_by_time(t_offset) + 1])
else:
g_offset = t_offset - data_file_in.t_start
else:
g_offset = t_offset
data_file_out.set_data(g_offset, local_chunk)
comm.Barrier()
def lowess(data, frac=0.01):
''' Local regression (Lowess) filter for spike extraction
dependency: pip install cylowess
'''
#import statsmodels.api as sm
import cylowess
data = np.atleast_2d(data)
nt = data.shape[1]
# filter data in place, iterate over channels in rows:
nchans = len(data)
for chani in range(nchans):
# lowess using vanilla statsmodels
#fit=sm.nonparametric.lowess(data[chani],range(len(data[chani])))[:,1]
# cython implementation
delta = 67.61 #deltafrac*len(data[chani])
fit = cylowess.lowess(np.asarray(data[chani], dtype='float'),
np.asarray(range(len(data[chani])),
dtype='float'),
frac=frac,
it=0,
delta=delta)[:, 1]
data[chani] = data[chani] - fit
return data
def WMLDR(data, wname="db4", maxlevel=6, mode='sym'):
""" Function by Martin Spacek from https://github.com/spyke/spyke
Perform wavelet multi-level decomposition and reconstruction (WMLDR) on multichannel
data. See Wiltschko2008. Default to Daubechies(4) wavelet. Modifies data in-place, at
least for now. The effective cutoff frequency is:
fc = (sampfreq / 2) / 2**maxlevel (Wiltschko2008)
For sampfreq of 25 kHz and maxlevel of 6, the effective cutoff frequency is 195 Hz.
For sampfreq of 30 kHz and maxlevel of 6, the effective cutoff frequency is 234 Hz.
TODO: for now, this only returns highpass data. In the future, this probably should
return both low and highpass data (and not modify it in-place). The Discussion in
Wiltschko2008 suggests that this approach cannot be used to extract the LFP, but
I don't see why you can't simply subtract the highpass data from the raw data to get the
lowpass data.
Signal extension modes (from PyWavelets docs):
PyWavelets provides several methods of signal extrapolation that can be used to minimize
edge effects. PyWavelet's default is 'sym':
zpd - zero-padding - signal is extended by adding zero samples:
... 0 0 | x1 x2 ... xn | 0 0 ...
cpd - constant-padding - border values are replicated:
... x1 x1 | x1 x2 ... xn | xn xn ...
sym - symmetric-padding - signal is extended by mirroring samples:
... x2 x1 | x1 x2 ... xn | xn xn-1 ...
ppd - periodic-padding - signal is treated as a periodic one:
... xn-1 xn | x1 x2 ... xn | x1 x2 ...
sp1 - smooth-padding - signal is extended according to the first derivatives calculated on
the edges (straight line)
DWT performed for these extension modes is slightly redundant, but ensures perfect
reconstruction. To receive the smallest possible number of coefficients, computations can
be performed with the periodization mode:
per - periodization - is like periodic-padding but gives the smallest possible number of
decomposition coefficients. IDWT must be performed with the same mode.
"""
import pywt
data = np.atleast_2d(data)
nt = data.shape[1]
# reconstructed signals always seem to have an even number of points. If the number of
# input data points is odd, trim last data point from reconstructed signal:
isodd = nt % 2
# filter data in place, iterate over channels in rows:
nchans = len(data)
for chani in range(nchans):
# decompose the signal:
cs = pywt.wavedec(data[chani], wname, mode=mode, level=maxlevel)
# destroy the appropriate approximation coefficients to get highpass data:
cs[0] = None
# reconstruct the signal:
recsignal = pywt.waverec(cs, wname, mode=mode)
ntrec = len(recsignal)
data[chani] = recsignal[:ntrec - isodd]
return data
def compute_artefacts(data_file):
chunk_size = params.getint('data', 'chunk_size')
trig_in_ms = params.getboolean('triggers', 'trig_in_ms')
artefacts = numpy.loadtxt(params.get('triggers', 'trig_file'))
windows = numpy.loadtxt(params.get('triggers', 'trig_windows'))
make_plots = params.get('triggers', 'make_plots')
plot_path = os.path.join(params.get('data', 'data_file_noext'),
'plots')
if len(windows.shape) == 1:
windows = windows.reshape(1, 2)
if len(artefacts.shape) == 1:
artefacts = artefacts.reshape(1, 2)
if trig_in_ms:
if comm.rank == 0:
print_and_log(['Artefact times are read in ms'], 'debug',
logger)
artefacts[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
windows[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
else:
if comm.rank == 0:
print_and_log(['Artefact times are read in timesteps'],
'debug', logger)
artefacts = artefacts.astype(numpy.int64)
windows = windows.astype(numpy.int64)
nb_stimuli = len(numpy.unique(artefacts[:, 0]))
mytest = nb_stimuli == len(windows)
if not mytest:
if comm.rank == 0:
print_and_log(['Error in the trigger files'], 'error', logger)
sys.exit(1)
all_labels = artefacts[:, 0].astype(numpy.int32)
all_times = artefacts[:, 1].astype(numpy.int32)
mask = (all_times >= 0) & (all_times + numpy.max(windows[:, 1]) <
data_file.t_stop)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
local_labels = numpy.unique(all_labels)[comm.rank::comm.size]
if comm.rank == 0:
to_write = [
"Computing averaged artefacts from %d stimuli" % (nb_stimuli)
]
print_and_log(to_write, 'default', logger)
if not os.path.exists(plot_path):
os.makedirs(plot_path)
local_labels = get_tqdm_progressbar(local_labels)
comm.Barrier()
# First we need to get the average artefacts
art_dict = {}
for count, artefact in enumerate(local_labels):
indices = numpy.where(all_labels == artefact)[0].astype(
numpy.int32)
tmp = numpy.where(windows[:, 0] == artefact)[0]
tau = numpy.int64(windows[tmp, 1])
pspikes = all_times[indices]
times = numpy.sort(numpy.random.permutation(pspikes)[:500])
if len(numpy.where(numpy.diff(times) < tau)[0]) > 0:
if comm.rank == 0:
print_and_log([
'Stimulation times for artefact %d are too close!' %
artefact
], 'error', logger)
sys.exit(1)
art_dict[artefact] = get_artefact(params, times, tau, nodes)
if make_plots not in ['None', '']:
save = [plot_path, '%d.%s' % (artefact, make_plots)]
plot.view_artefact(art_dict[artefact], save=save)
return art_dict
def remove_artefacts(data_file, art_dict):
chunk_size = params.getint('data', 'chunk_size')
trig_in_ms = params.getboolean('triggers', 'trig_in_ms')
artefacts = numpy.loadtxt(params.get('triggers',
'trig_file')).astype(numpy.int64)
windows = numpy.loadtxt(params.get('triggers',
'trig_windows')).astype(numpy.int64)
make_plots = params.get('triggers', 'make_plots')
plot_path = os.path.join(params.get('data', 'data_file_noext'),
'plots')
if len(windows.shape) == 1:
windows = windows.reshape(1, 2)
if len(artefacts.shape) == 1:
artefacts = artefacts.reshape(1, 2)
if trig_in_ms:
if comm.rank == 0:
print_and_log(['Artefact times are read in ms'], 'debug',
logger)
artefacts[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
windows[:, 1] *= numpy.int64(data_file.sampling_rate * 1e-3)
else:
if comm.rank == 0:
print_and_log(['Artefact times are read in timesteps'],
'debug', logger)
artefacts = artefacts.astype(numpy.int64)
windows = windows.astype(numpy.int64)
nb_stimuli = len(numpy.unique(artefacts[:, 0]))
mytest = nb_stimuli == len(windows)
if not mytest:
if comm.rank == 0:
print_and_log(['Error in the trigger files'], 'error', logger)
sys.exit(1)
all_labels = artefacts[:, 0].astype(numpy.int32)
all_times = artefacts[:, 1].astype(numpy.int32)
local_labels = numpy.unique(all_labels)[comm.rank::comm.size]
mask = numpy.in1d(all_labels, local_labels)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
mask = (all_times >= 0) & (all_times < data_file.t_stop)
all_times = numpy.compress(mask, all_times)
all_labels = numpy.compress(mask, all_labels)
if comm.rank == 0:
to_write = ["Removing artefacts from %d stimuli" % (nb_stimuli)]
print_and_log(to_write, 'default', logger)
all_times = get_tqdm_progressbar(all_times)
comm.Barrier()
for count, time in enumerate(all_times):
label = all_labels[count]
tmp = numpy.where(windows[:, 0] == label)[0][0]
tau = numpy.int64(windows[tmp, 1])
if (data_file.t_stop - time) < tau:
tau = max_offset - time
local_chunk = data_file.get_snippet(time, tau)
for idx, i in enumerate(nodes):
local_chunk[:, i] -= art_dict[label][idx, :tau]
data_file.set_data(time, local_chunk)
comm.Barrier()
if comm.rank == 0:
print_and_log(['Initializing the filtering step...'], 'debug', logger)
if params.getboolean('data', 'overwrite'):
if comm.rank == 0:
print_and_log(['Reading the input file...'], 'debug', logger)
data_file_in = params.get_data_file()
data_file_out = data_file_in
else:
if comm.rank == 0:
print_and_log(
['Overwrite is set to False, so creating a new datafile...'],
'debug', logger)
if comm.rank == 0:
print_and_log(['Reading the input file...'], 'debug', logger)
data_file_in = params.get_data_file(source=True,
has_been_created=False)
if comm.rank == 0:
print_and_log(
['Reading the output file and allocating ressources...'],
'debug', logger)
description = data_file_in.get_description()
description['data_dtype'] = 'float32'
description['dtype_offset'] = 0
description['data_offset'] = 0
data_file_out = params.get_data_file(is_empty=True, params=description)
data_file_out.allocate(shape=data_file_in.shape)
#<<<<<<< HEAD
data_file_in._params = tmp_params
if data_file_in.is_stream:
for source in data_file_in._sources:
source._params = tmp_params
#=======
#>>>>>>> upstream/master
if clean_artefact:
if not (os.path.exists(params.get('triggers', 'trig_file'))
and os.path.exists(params.get('triggers', 'trig_windows'))):
if comm.rank == 0:
print_and_log(
['trig_file or trig_windows file can not be found'],
'error', logger)
sys.exit(1)
to_write = []
if do_filter and filter_done:
do_filter = False
to_write += ["Filtering has already been done"]
if remove_median and median_done:
remove_median = False
to_write += [
"Median over all channels has already been substracted to each channels"
]
if comm.rank == 0:
print_and_log(to_write, 'debug', logger)
if params.getboolean('data', 'overwrite'):
data_file_in.open(mode='r+')
else:
data_file_in.open()
data_file_out.open(mode='r+')
if do_filter or remove_median:
filter_file(data_file_in, data_file_out, do_filter, remove_median)
if comm.rank == 0:
if do_filter:
params.write('noedits', 'filter_done', 'True')
if remove_median:
params.write('noedits', 'median_done', 'True')
if clean_artefact and artefacts_done:
clean_artefact = False
if comm.rank == 0:
print_and_log(['Artefacts have already been removed'], 'debug',
logger)
if clean_artefact:
art_dict = compute_artefacts(data_file_in)
remove_artefacts(data_file_out, art_dict)
if comm.rank == 0:
if clean_artefact:
params.write('noedits', 'artefacts_done', 'True')
data_file_in.close()
if not params.getboolean('data', 'overwrite'):
data_file_out.close()
comm.Barrier()
def extract_extra_thresholds(params):
"""Compute the mean and the standard deviation for each extracellular channel"""
data_file = params.data_file
data_file.open()
chunk_size = params.getint('data', 'chunk_size')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
N_total = params.nb_channels
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
# mpi_file = MPI.File()
# mpi_input = mpi_file.Open(comm, data_filename, MPI.MODE_RDONLY)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
nodes, _ = get_nodes_and_edges(params)
N_elec = nodes.size
def weighted_mean(weights, values):
"""Compute a weighted mean for the given values"""
norm_weights = [
float(weight) / float(sum(weights)) for weight in weights
]
weighted_values = [
norm_weight * value
for (norm_weight, value) in zip(norm_weights, values)
]
weighted_mean = sum(weighted_values)
return weighted_mean
def extract_median(chunk_size, gidx):
"""Extract the medians from a chunk of extracellular traces"""
loc_chunk, _ = data_file.get_data(gidx, chunk_size, nodes=nodes)
# Whiten signal.
if do_spatial_whitening:
loc_chunk = numpy.dot(loc_chunk, spatial_whitening)
if do_temporal_whitening:
loc_chunk = scipy.ndimage.filters.convolve1d(loc_chunk,
temporal_whitening,
axis=0,
mode='constant')
median = numpy.median(loc_chunk, axis=0)
return median
def extract_median_absolute_deviation(chunk_size, gidx, median):
"""Extract the median absolute deviations from a chunk of extracellular traces"""
loc_chunk, _ = data_file.get_data(gidx, chunk_size, nodes=nodes)
# Whiten signal.
if do_spatial_whitening:
loc_chunk = numpy.dot(loc_chunk, spatial_whitening)
if do_temporal_whitening:
loc_chunk = scipy.ndimage.filters.convolve1d(loc_chunk,
temporal_whitening,
axis=0,
mode='constant')
mad = numpy.median(numpy.abs(loc_chunk - median), axis=0)
return mad
# Distribute chunks over the CPUs.
all_chunks = numpy.arange(nb_chunks)
loc_all_chunks = all_chunks[comm.rank::comm.size]
loc_nb_chunks = len(loc_all_chunks)
loc_nbs_chunks = comm.gather(loc_nb_chunks, root=0)
if comm.rank == 0:
print_and_log(["Computing extracellular medians..."],
level='default',
logger=logger)
to_explore = list(range(comm.rank, nb_chunks, comm.size))
if comm.rank == 0:
to_explore = get_tqdm_progressbar(params, to_explore)
medians = numpy.zeros((N_elec, loc_nb_chunks), dtype=numpy.float32)
# For each chunk attributed to the current CPU.
for count, gidx in enumerate(to_explore):
gidx = all_chunks[gidx]
medians[:, count] = extract_median(chunk_size, gidx)
sys.stderr.flush()
median = numpy.mean(medians, axis=1)
comm.Barrier()
medians = comm.gather(median, root=0)
if comm.rank == 0:
median = weighted_mean(loc_nbs_chunks, medians)
# Broadcast medians to each CPU.
median = comm.bcast(median, root=0)
comm.Barrier()
if comm.rank == 0:
print_and_log(["Computing extracellular thresholds..."],
level='default',
logger=logger)
to_explore = list(range(comm.rank, nb_chunks, comm.size))
if comm.rank == 0:
to_explore = get_tqdm_progressbar(params, to_explore)
mads = numpy.zeros((N_elec, loc_nb_chunks), dtype=numpy.float32)
# For each chunk attributed to the current CPU.
for count, gidx in enumerate(to_explore):
gidx = all_chunks[gidx]
mads[:, count] = extract_median_absolute_deviation(
chunk_size, gidx, median)
sys.stderr.flush()
mad = numpy.mean(mads, axis=1)
comm.Barrier()
mads = comm.gather(mad, root=0)
if comm.rank == 0:
mad = weighted_mean(loc_nbs_chunks, mads)
# Broadcast median absolute deviation to each CPU.
mad = comm.bcast(mad, root=0)
comm.Barrier()
data_file.close()
return median, mad
def main(params, nb_cpu, nb_gpu, use_gpu):
#################################################################
# params = detect_memory(params)
_ = init_logging(params.logfile)
SHARED_MEMORY = get_shared_memory_flag(params)
logger = logging.getLogger('circus.fitting')
data_file = params.data_file
N_e = params.getint('data', 'N_e')
N_total = params.nb_channels
N_t = params.getint('detection', 'N_t')
template_shift = params.getint('detection', 'template_shift')
file_out = params.get('data', 'file_out')
file_out_suff = params.get('data', 'file_out_suff')
sign_peaks = params.get('detection', 'peaks')
dist_peaks = params.getint('detection', 'dist_peaks')
matched_filter = params.getboolean('detection', 'matched-filter')
spike_thresh = params.getfloat('detection', 'spike_thresh')
spike_width = params.getfloat('detection', 'spike_width')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
chunk_size = detect_memory(params)
gpu_only = params.getboolean('fitting', 'gpu_only')
nodes, edges = get_nodes_and_edges(params)
tmp_limits = params.get('fitting',
'amp_limits').replace('(',
'').replace(')',
'').split(',')
tmp_limits = map(float, tmp_limits)
amp_auto = params.getboolean('fitting', 'amp_auto')
nb_chances = params.getint('fitting', 'nb_chances')
max_chunk = params.getfloat('fitting', 'max_chunk')
noise_thr = params.getfloat('clustering', 'noise_thr')
collect_all = params.getboolean('fitting', 'collect_all')
ignore_dead_times = params.getboolean('triggers', 'ignore_times')
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
data_file.open()
#################################################################
if use_gpu:
import cudamat as cmt
# # Need to properly handle multi GPU per MPI nodes?
if nb_gpu > nb_cpu:
gpu_id = int(comm.rank // nb_cpu)
else:
gpu_id = 0
cmt.cuda_set_device(gpu_id)
cmt.init()
cmt.cuda_sync_threads()
if matched_filter:
if sign_peaks in ['negative', 'both']:
waveform_neg = io.load_data(params, 'waveform')[::-1]
waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) *
len(waveform_neg))
matched_tresholds_neg = io.load_data(params, 'matched-thresholds')
if sign_peaks in ['positive', 'both']:
waveform_pos = io.load_data(params, 'waveform-pos')[::-1]
waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) *
len(waveform_pos))
matched_tresholds_pos = io.load_data(params,
'matched-thresholds-pos')
if ignore_dead_times:
all_dead_times = get_dead_times(params)
thresholds = io.load_data(params, 'thresholds')
comm.Barrier()
if comm.rank == 0:
print_and_log(["Extracting MUA activity..."], 'default', logger)
purge(file_out_suff, '.data')
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
else:
spatial_whitening = None # default assignment (PyCharm code inspection)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
else:
temporal_whitening = None # default assignment (PyCharm code inspection)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
processed_chunks = int(min(nb_chunks, max_chunk))
comm.Barrier()
spiketimes_file = open(file_out_suff + '.mua-%d.data' % comm.rank, 'wb')
comm.Barrier()
electrodes_file = open(file_out_suff + '.elec-%d.data' % comm.rank, 'wb')
comm.Barrier()
amplitudes_file = open(file_out_suff + '.amp-%d.data' % comm.rank, 'wb')
comm.Barrier()
if use_gpu and do_spatial_whitening:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening,
copy_on_host=False)
to_explore = range(comm.rank, processed_chunks, comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for gcount, gidx in enumerate(to_explore):
# print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
# # We need to deal with the borders by taking chunks of size [0, chunck_size + template_shift].
is_first = data_file.is_first_chunk(gidx, nb_chunks)
is_last = data_file.is_last_chunk(gidx, nb_chunks)
if is_last:
padding = (-dist_peaks, 0)
elif is_first:
padding = (0, dist_peaks)
else:
padding = (-dist_peaks, dist_peaks)
result = {'spiketimes': [], 'amplitudes': [], 'templates': []}
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
padding,
nodes=nodes)
len_chunk = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(local_chunk,
temporal_whitening,
axis=0,
mode='constant')
# print "Extracting the peaks..."
local_peaktimes = [numpy.zeros(0, dtype=numpy.uint32)]
local_elecs = [numpy.zeros(0, dtype=numpy.uint32)]
local_amps = [numpy.zeros(0, dtype=numpy.float32)]
if matched_filter:
if sign_peaks in ['positive', 'both']:
filter_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, waveform_pos, axis=0, mode='constant')
for i in range(N_e):
peaktimes = scipy.signal.find_peaks(
filter_chunk[:, i],
height=matched_tresholds_pos[i],
width=spike_width,
distance=dist_peaks,
wlen=N_t)[0]
local_peaktimes.append(peaktimes)
local_elecs.append(
i * numpy.ones(len(peaktimes), dtype='uint32'))
local_amps.append(filter_chunk[peaktimes, i])
if sign_peaks in ['negative', 'both']:
filter_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, waveform_neg, axis=0, mode='constant')
for i in range(N_e):
peaktimes = scipy.signal.find_peaks(
filter_chunk[:, i],
height=matched_tresholds_neg[i],
width=spike_width,
distance=dist_peaks,
wlen=N_t)[0]
local_peaktimes.append(peaktimes)
local_elecs.append(
i * numpy.ones(len(peaktimes), dtype='uint32'))
local_amps.append(filter_chunk[peaktimes, i])
else:
for i in range(N_e):
if sign_peaks == 'negative':
peaktimes = scipy.signal.find_peaks(-local_chunk[:, i],
height=thresholds[i],
width=spike_width,
distance=dist_peaks,
wlen=N_t)[0]
elif sign_peaks == 'positive':
peaktimes = scipy.signal.find_peaks(local_chunk[:, i],
height=thresholds[i],
width=spike_width,
distance=dist_peaks,
wlen=N_t)[0]
elif sign_peaks == 'both':
peaktimes = scipy.signal.find_peaks(numpy.abs(
local_chunk[:, i]),
height=thresholds[i],
width=spike_width,
distance=dist_peaks,
wlen=N_t)[0]
local_peaktimes.append(peaktimes)
local_elecs.append(i *
numpy.ones(len(peaktimes), dtype='uint32'))
local_amps.append(local_chunk[peaktimes, i])
local_peaktimes = numpy.concatenate(local_peaktimes)
local_elecs = numpy.concatenate(local_elecs)
local_amps = numpy.concatenate(local_amps)
g_offset = t_offset + padding[0]
if ignore_dead_times:
dead_indices = numpy.searchsorted(
all_dead_times, [t_offset, t_offset + chunk_size])
if dead_indices[0] != dead_indices[1]:
is_included = numpy.in1d(
local_peaktimes + g_offset,
all_dead_times[dead_indices[0]:dead_indices[1]])
local_peaktimes = local_peaktimes[~is_included]
local_elecs = local_elecs[~is_included]
local_amps = local_amps[~is_included]
# print "Removing the useless borders..."
local_borders = (dist_peaks, len_chunk - dist_peaks)
idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes <
local_borders[1])
local_peaktimes = numpy.compress(idx, local_peaktimes) + g_offset
local_elecs = numpy.compress(idx, local_elecs)
local_amps = numpy.compress(idx, local_amps)
spiketimes_file.write(local_peaktimes.astype(numpy.uint32).tostring())
electrodes_file.write(local_elecs.tostring())
amplitudes_file.write(local_amps.tostring())
sys.stderr.flush()
spiketimes_file.flush()
os.fsync(spiketimes_file.fileno())
spiketimes_file.close()
electrodes_file.flush()
os.fsync(electrodes_file.fileno())
electrodes_file.close()
amplitudes_file.flush()
os.fsync(amplitudes_file.fileno())
amplitudes_file.close()
comm.Barrier()
if comm.rank == 0:
io.collect_mua(comm.size, params, erase=True)
data_file.close()
def delete_mixtures(params, nb_cpu, nb_gpu, use_gpu):
templates = load_data(params, 'templates')
data_file = params.data_file
N_e = params.getint('data', 'N_e')
N_total = params.nb_channels
N_t = params.getint('detection', 'N_t')
template_shift = params.getint('detection', 'template_shift')
cc_merge = params.getfloat('clustering', 'cc_merge')
x, N_tm = templates.shape
nb_temp = N_tm//2
merged = [nb_temp, 0]
mixtures = []
to_remove = []
overlap = get_overlaps(params, extension='-mixtures', erase=True, normalize=False, maxoverlap=False, verbose=False, half=True, use_gpu=use_gpu, nb_cpu=nb_cpu, nb_gpu=nb_gpu)
filename = params.get('data', 'file_out_suff') + '.overlap-mixtures.hdf5'
result = []
norm_templates = load_data(params, 'norm-templates')
templates = load_data(params, 'templates')
result = load_data(params, 'clusters')
best_elec = load_data(params, 'electrodes')
limits = load_data(params, 'limits')
nodes, edges = get_nodes_and_edges(params)
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
distances = numpy.zeros((nb_temp, nb_temp), dtype=numpy.float32)
over_x = overlap.get('over_x')[:]
over_y = overlap.get('over_y')[:]
over_data = overlap.get('over_data')[:]
over_shape = overlap.get('over_shape')[:]
overlap.close()
overlap = scipy.sparse.csr_matrix((over_data, (over_x, over_y)), shape=over_shape)
for i in xrange(nb_temp-1):
distances[i, i+1:] = numpy.argmax(overlap[i*nb_temp+i+1:(i+1)*nb_temp].toarray(), 1)
distances[i+1:, i] = distances[i, i+1:]
all_temp = numpy.arange(comm.rank, nb_temp, comm.size)
overlap_0 = overlap[:, N_t].toarray().reshape(nb_temp, nb_temp)
sorted_temp = numpy.argsort(norm_templates[:nb_temp])[::-1][comm.rank::comm.size]
M = numpy.zeros((2, 2), dtype=numpy.float32)
V = numpy.zeros((2, 1), dtype=numpy.float32)
to_explore = xrange(comm.rank, len(sorted_temp), comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for count, k in enumerate(to_explore):
k = sorted_temp[k]
electrodes = numpy.take(inv_nodes, edges[nodes[best_elec[k]]])
overlap_k = overlap[k*nb_temp:(k+1)*nb_temp].tolil()
is_in_area = numpy.in1d(best_elec, electrodes)
all_idx = numpy.arange(len(best_elec))[is_in_area]
been_found = False
for i in all_idx:
if not been_found:
overlap_i = overlap[i*nb_temp:(i+1)*nb_temp].tolil()
M[0, 0] = overlap_0[i, i]
V[0, 0] = overlap_k[i, distances[k, i]]
for j in all_idx[i+1:]:
M[1, 1] = overlap_0[j, j]
M[1, 0] = overlap_i[j, distances[k, i] - distances[k, j]]
M[0, 1] = M[1, 0]
V[1, 0] = overlap_k[j, distances[k, j]]
try:
[a1, a2] = numpy.dot(scipy.linalg.inv(M), V)
except Exception:
[a1, a2] = [0, 0]
a1_lim = limits[i]
a2_lim = limits[j]
is_a1 = (a1_lim[0] <= a1) and (a1 <= a1_lim[1])
is_a2 = (a2_lim[0] <= a2) and (a2 <= a2_lim[1])
if is_a1 and is_a2:
new_template = (a1*templates[:, i].toarray() + a2*templates[:, j].toarray()).ravel()
similarity = numpy.corrcoef(templates[:, k].toarray().ravel(), new_template)[0, 1]
if similarity > cc_merge:
if k not in mixtures:
mixtures += [k]
been_found = True
break
#print "Template", k, 'is sum of (%d, %g) and (%d,%g)' %(i, a1, j, a2)
#print mixtures
to_remove = numpy.unique(numpy.array(mixtures, dtype=numpy.int32))
to_remove = all_gather_array(to_remove, comm, 0, dtype='int32')
if len(to_remove) > 0:
slice_templates(params, to_remove)
slice_clusters(params, result, to_remove=to_remove)
comm.Barrier()
if comm.rank == 0:
os.remove(filename)
return [nb_temp, len(to_remove)]
def main(params, nb_cpu, nb_gpu, use_gpu):
# Part 1: Whitening
numpy.random.seed(420)
logger = init_logging(params.logfile)
logger = logging.getLogger('circus.whitening')
#################################################################
data_file = params.data_file
data_file.open()
N_e = params.getint('data', 'N_e')
N_total = params.nb_channels
N_t = params.getint('detection', 'N_t')
dist_peaks = params.getint('detection', 'dist_peaks')
template_shift = params.getint('detection', 'template_shift')
file_out_suff = params.get('data', 'file_out_suff')
file_out = params.get('data', 'file_out')
spike_thresh = params.getfloat('detection', 'spike_thresh')
matched_filter = params.getboolean('detection', 'matched-filter')
matched_thresh = params.getfloat('detection', 'matched_thresh')
sign_peaks = params.get('detection', 'peaks')
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
chunk_size = params.getint('whitening', 'chunk_size')
plot_path = os.path.join(params.get('data', 'data_file_noext'), 'plots')
nodes, edges = get_nodes_and_edges(params)
safety_time = params.getint('whitening', 'safety_time')
nb_temp_white = min(max(20, comm.size), N_e)
max_silence_1 = int(20 * params.rate // comm.size)
max_silence_2 = 5000
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
#################################################################
if comm.rank == 0:
print_and_log(
["Analyzing data to get whitening matrices and thresholds..."],
'default', logger)
if use_gpu:
import cudamat as cmt
## Need to properly handle multi GPU per MPI nodes?
if nb_gpu > nb_cpu:
gpu_id = int(comm.rank // nb_cpu)
else:
gpu_id = 0
cmt.cuda_set_device(gpu_id)
cmt.init()
cmt.cuda_sync_threads()
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
if nb_chunks < comm.size:
res = io.data_stats(params, show=False)
chunk_size = int(res * params.rate // comm.size)
if comm.rank == 0:
print_and_log(
["Too much cores, automatically resizing the data chunks"],
'debug', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
# I guess this is more relevant, to take signals from all over the recordings
all_chunks = numpy.random.permutation(
numpy.arange(nb_chunks, dtype=numpy.int32))
all_electrodes = numpy.random.permutation(N_e)
for gidx in [all_chunks[comm.rank]]:
#print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
#print "Node", comm.rank, "computes the median absolute deviations in a random chunk"
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in xrange(N_e):
u = numpy.median(local_chunk[:, i], 0)
thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'w',
libver='latest')
io.write_datasets(bfile, ['thresholds'],
{'thresholds': thresholds})
bfile.close()
comm.Barrier()
thresholds = io.load_data(params, 'thresholds')
#print "Extracting the peaks..."
local_peaktimes = numpy.zeros(0, dtype=numpy.int32)
for i in xrange(N_e):
peaktimes = algo.detect_peaks(numpy.abs(local_chunk[:, i]),
thresholds[i],
valley=False,
mpd=dist_peaks)
local_peaktimes = numpy.concatenate((local_peaktimes, peaktimes))
local_peaktimes = numpy.unique(local_peaktimes)
#print "Removing the useless borders..."
local_borders = (template_shift, local_shape - template_shift)
idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes <
local_borders[1])
local_peaktimes = numpy.compress(idx, local_peaktimes)
if len(local_peaktimes) > 0:
diff_times = local_peaktimes[-1] - local_peaktimes[0]
all_times = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool)
min_times = numpy.maximum(
local_peaktimes - local_peaktimes[0] - safety_time, 0)
max_times = numpy.minimum(
local_peaktimes - local_peaktimes[0] + safety_time + 1,
diff_times)
argmax_peak = numpy.random.permutation(
numpy.arange(len(local_peaktimes)))
all_idx = numpy.take(local_peaktimes, argmax_peak)
#print "Selection of the peaks with spatio-temporal masks..."
for idx, peak in zip(argmax_peak, all_idx):
elec = numpy.argmax(numpy.abs(local_chunk[peak]))
indices = numpy.take(inv_nodes, edges[nodes[elec]])
all_times[indices, min_times[idx]:max_times[idx]] = True
else:
all_times = numpy.zeros((N_e, len(local_chunk)), dtype=numpy.bool)
all_times_Ne = numpy.any(all_times, 0)
subset = numpy.where(all_times_Ne == False)[0]
all_silences = []
if do_spatial_whitening:
local_silences = numpy.take(local_chunk, subset,
axis=0)[:max_silence_1]
all_silences = gather_array(local_silences, comm, 0, 1)
local_res = []
if do_temporal_whitening:
for elec in all_electrodes[numpy.arange(comm.rank, nb_temp_white,
comm.size)]:
res = numpy.zeros((0, N_t), dtype=numpy.float32)
scount = 0
indices = numpy.take(inv_nodes, edges[nodes[elec]])
all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0),
0)
esubset = numpy.where(all_times_elec == False)[0]
bound = len(esubset) - N_t
while (scount < bound) and (len(res) < max_silence_2):
myslice = esubset[scount:scount + N_t]
if numpy.all((myslice - esubset[scount]) == numpy.arange(N_t)):
scount += N_t
res = numpy.vstack((res, local_chunk[myslice, elec]))
else:
scount += 1
if len(res) > 5:
local_res += [numpy.cov(res.T)]
nb_elecs = numpy.array([len(local_res)], dtype=numpy.float32)
local_res = numpy.array(local_res, dtype=numpy.float32)
if len(local_res) == 0:
local_res = numpy.zeros(0, dtype=numpy.float32)
else:
local_res = numpy.sum(local_res, 0)
all_res = gather_array(local_res.ravel(), comm, 0, 1)
all_elecs = gather_array(nb_elecs, comm, 0, 1)
if comm.rank == 0:
to_write = {}
if do_temporal_whitening:
try:
nb_silences = numpy.sum(all_elecs > 0)
all_res = all_res.reshape((nb_silences, N_t**2))
except Exception:
print_and_log([
"No silent periods detected: something wrong with the parameters?"
], 'error', logger)
all_res = numpy.sum(all_res, 0)
all_res = all_res.reshape((N_t, N_t)) / numpy.sum(all_elecs)
temporal_whitening = get_whitening_matrix(
all_res.astype(numpy.double),
fudge=1e-3)[template_shift].astype(numpy.float32)
temporal_whitening /= temporal_whitening.sum()
to_write['temporal'] = temporal_whitening
have_nans = numpy.sum(numpy.isnan(temporal_whitening))
if have_nans > 0:
temporal_whitening = numpy.zeros(N_t, dtype=numpy.float32)
temporal_whitening[N_t // 2] = 1
to_write['temporal'] = temporal_whitening
print_and_log(
["Disabling temporal whitening because of NaNs found"],
'info', logger)
if do_spatial_whitening:
if len(all_silences) / params.rate == 0:
print_and_log([
"No silent periods detected: something wrong with the parameters?"
], 'error', logger)
spatial_whitening = get_whitening_matrix(
all_silences.astype(numpy.double)).astype(numpy.float32)
to_write['spatial'] = spatial_whitening
print_and_log([
"Found %gs without spikes for whitening matrices..." %
(len(all_silences) / params.rate)
], 'default', logger)
have_nans = numpy.sum(numpy.isnan(spatial_whitening))
if have_nans > 0:
spatial_whitening = numpy.eye(spatial_whitening.shape[0],
dtype=numpy.float32)
to_write['spatial'] = spatial_whitening
print_and_log(
["Disabling spatial whitening because of NaNs found"],
'info', logger)
bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='latest')
io.write_datasets(bfile, to_write.keys(), to_write)
bfile.close()
del all_silences
comm.Barrier()
if do_spatial_whitening or do_temporal_whitening:
if comm.rank == 0:
print_and_log(
["Because of whitening, need to recompute the thresholds..."],
'default', logger)
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if use_gpu:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening,
copy_on_host=False)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
for gidx in [all_chunks[comm.rank]]:
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in xrange(N_e):
u = numpy.median(local_chunk[:, i], 0)
thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u),
0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out_suff + '.basis.hdf5',
'r+',
libver='latest')
bfile.pop('thresholds')
io.write_datasets(bfile, ['thresholds'],
{'thresholds': thresholds})
bfile.close()
comm.Barrier()
#if comm.rank == 0:
#if not os.path.exists(plot_path):
# os.makedirs(plot_path)
#N_elec = min(int(numpy.sqrt(data_file.N_e)), 5)
#plot.view_fit(filename, t_start=0, t_stop=1, fit_on=False, square=True,
# n_elec=N_elec, save=[plot_path, 'electrodes'])
# Part 2: Basis
numpy.random.seed(422)
#################################################################
file_out = params.get('data', 'file_out')
alignment = params.getboolean('detection', 'alignment')
spike_thresh = params.getfloat('detection', 'spike_thresh')
nodes, edges = get_nodes_and_edges(params)
do_temporal_whitening = params.getboolean('whitening', 'temporal')
do_spatial_whitening = params.getboolean('whitening', 'spatial')
chunk_size = params.getint('data', 'chunk_size')
safety_time = params.getint('whitening', 'safety_time')
max_elts_elec = params.getint('whitening', 'max_elts')
nb_elts = int(
params.getfloat('whitening', 'nb_elts') * N_e * max_elts_elec)
output_dim = params.getfloat('whitening', 'output_dim')
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
if sign_peaks == 'both':
max_elts_elec *= 2
#################################################################
if comm.rank == 0:
print_and_log(["Searching spikes to construct the PCA basis..."],
'default', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
if nb_chunks < comm.size:
res = io.data_stats(params, show=False)
chunk_size = int(res * params.rate // comm.size)
if comm.rank == 0:
print_and_log(
["Too much cores, automatically resizing the data chunks"],
'debug', logger)
nb_chunks, last_chunk_len = data_file.analyze(chunk_size)
groups = {}
for i in xrange(N_e):
groups[i] = 0
# I guess this is more relevant, to take signals from all over the recordings
all_chunks = numpy.random.permutation(
numpy.arange(nb_chunks, dtype=numpy.int32))
max_elts_elec //= comm.size
nb_elts //= comm.size
elt_count_pos = 0
elt_count_neg = 0
if sign_peaks in ['positive', 'both']:
elts_pos = numpy.zeros((N_t, nb_elts), dtype=numpy.float32)
if sign_peaks in ['negative', 'both']:
elts_neg = numpy.zeros((N_t, nb_elts), dtype=numpy.float32)
chunks_to_load = all_chunks[comm.rank::comm.size]
thresholds = io.load_data(params, 'thresholds')
if comm.rank == 0:
pbar = get_progressbar(nb_elts)
if alignment:
cdata = numpy.linspace(-template_shift, template_shift, 5 * N_t)
xdata = numpy.arange(-2 * template_shift, 2 * template_shift + 1)
for gcount, gidx in enumerate(chunks_to_load):
if ((elt_count_pos + elt_count_neg) < nb_elts):
#print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..."
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
#print "Extracting the peaks..."
all_peaktimes = numpy.zeros(0, dtype=numpy.int32)
all_extremas = numpy.zeros(0, dtype=numpy.int32)
for i in xrange(N_e):
if sign_peaks == 'negative':
peaktimes = algo.detect_peaks(local_chunk[:, i],
thresholds[i],
valley=True,
mpd=dist_peaks)
elif sign_peaks == 'positive':
peaktimes = algo.detect_peaks(local_chunk[:, i],
thresholds[i],
valley=False,
mpd=dist_peaks)
elif sign_peaks == 'both':
peaktimes = algo.detect_peaks(numpy.abs(local_chunk[:, i]),
thresholds[i],
valley=False,
mpd=dist_peaks)
all_peaktimes = numpy.concatenate((all_peaktimes, peaktimes))
all_extremas = numpy.concatenate(
(all_extremas,
i * numpy.ones(len(peaktimes), dtype=numpy.int32)))
#print "Removing the useless borders..."
if alignment:
local_borders = (2 * template_shift,
local_shape - 2 * template_shift)
else:
local_borders = (template_shift, local_shape - template_shift)
idx = (all_peaktimes >= local_borders[0]) & (all_peaktimes <
local_borders[1])
all_peaktimes = numpy.compress(idx, all_peaktimes)
all_extremas = numpy.compress(idx, all_extremas)
local_peaktimes = numpy.unique(all_peaktimes)
if len(local_peaktimes) > 0:
diff_times = local_peaktimes[-1] - local_peaktimes[0]
all_times = numpy.zeros((N_e, diff_times + 1),
dtype=numpy.bool)
min_times = numpy.maximum(
local_peaktimes - local_peaktimes[0] - safety_time, 0)
max_times = numpy.minimum(
local_peaktimes - local_peaktimes[0] + safety_time + 1,
diff_times)
n_times = len(local_peaktimes)
argmax_peak = numpy.random.permutation(numpy.arange(n_times))
all_idx = numpy.take(local_peaktimes, argmax_peak)
#print "Selection of the peaks with spatio-temporal masks..."
for midx, peak in zip(argmax_peak, all_idx):
if (elt_count_neg + elt_count_pos) == nb_elts:
break
if sign_peaks == 'negative':
elec = numpy.argmin(local_chunk[peak])
negative_peak = True
elif sign_peaks == 'positive':
elec = numpy.argmax(local_chunk[peak])
negative_peak = False
elif sign_peaks == 'both':
if numpy.abs(numpy.max(local_chunk[peak])) > numpy.abs(
numpy.min(local_chunk[peak])):
elec = numpy.argmax(local_chunk[peak])
negative_peak = False
else:
elec = numpy.argmin(local_chunk[peak])
negative_peak = True
indices = numpy.take(inv_nodes, edges[nodes[elec]])
myslice = all_times[indices,
min_times[midx]:max_times[midx]]
is_local_extrema = elec in all_extremas[all_peaktimes ==
peak]
if is_local_extrema and not myslice.any():
upper_bounds = max_elts_elec
if groups[elec] < upper_bounds:
if negative_peak:
elts_neg[:, elt_count_neg] = local_chunk[
peak - template_shift:peak +
template_shift + 1, elec]
else:
elts_pos[:, elt_count_pos] = local_chunk[
peak - template_shift:peak +
template_shift + 1, elec]
if alignment:
ydata = local_chunk[peak -
2 * template_shift:peak +
2 * template_shift + 1,
elec]
f = scipy.interpolate.UnivariateSpline(xdata,
ydata,
s=0)
if negative_peak:
rmin = (numpy.argmin(f(cdata)) -
len(cdata) / 2.) / 5.
else:
rmin = (numpy.argmax(f(cdata)) -
len(cdata) / 2.) / 5.
ddata = numpy.linspace(rmin - template_shift,
rmin + template_shift,
N_t)
if negative_peak:
elts_neg[:,
elt_count_neg] = f(ddata).astype(
numpy.float32)
else:
elts_pos[:,
elt_count_pos] = f(ddata).astype(
numpy.float32)
if negative_peak:
elt_count_neg += 1
else:
elt_count_pos += 1
groups[elec] += 1
all_times[indices,
min_times[midx]:max_times[midx]] = True
if comm.rank == 0:
pbar.update(elt_count_pos + elt_count_neg)
if comm.rank == 0:
if (elt_count_pos + elt_count_neg <
(gcount + 1) * max_elts_elec // len(chunks_to_load)):
pbar.update(
(gcount + 1) * max_elts_elec // len(chunks_to_load))
if comm.rank == 0:
pbar.finish()
print_and_log([
"Node %d has collected %d waveforms" %
(comm.rank, elt_count_pos + elt_count_neg)
], 'debug', logger)
if sign_peaks in ['negative', 'both']:
gdata_neg = gather_array(elts_neg[:, :elt_count_neg].T, comm, 0, 1)
if sign_peaks in ['positive', 'both']:
gdata_pos = gather_array(elts_pos[:, :elt_count_pos].T, comm, 0, 1)
if comm.rank == 0:
#DO PCA on elts and store the basis obtained.
nb_waveforms = 0
if sign_peaks in ['negative', 'both']:
nb_waveforms += gdata_neg.shape[0]
if sign_peaks in ['positive', 'both']:
nb_waveforms += gdata_pos.shape[0]
print_and_log([
"Found %d waveforms over %d requested" %
(nb_waveforms, int(nb_elts * comm.size))
], 'default', logger)
pca = PCA(output_dim, copy=False)
res = {}
if sign_peaks in ['negative', 'both']:
if len(gdata_neg) > 0:
res_pca = pca.fit_transform(gdata_neg.astype(
numpy.double)).astype(numpy.float32)
res['proj'] = pca.components_.T.astype(numpy.float32)
else:
res['proj'] = numpy.identity(N_t, dtype=numpy.float32)
res['rec'] = res['proj'].T
res['waveform'] = numpy.median(gdata_neg, 0)
idx = numpy.random.permutation(numpy.arange(
gdata_neg.shape[0]))[:1000]
res['waveforms'] = gdata_neg[idx, :]
if sign_peaks in ['positive', 'both']:
if len(gdata_pos) > 0:
res_pca = pca.fit_transform(gdata_pos.astype(
numpy.double)).astype(numpy.float32)
res['proj_pos'] = pca.components_.T.astype(numpy.float32)
else:
res['proj_pos'] = numpy.identity(N_t, dtype=numpy.float32)
res['rec_pos'] = res['proj_pos'].T
res['waveform_pos'] = numpy.median(gdata_pos, 0)
idx = numpy.random.permutation(numpy.arange(
gdata_pos.shape[0]))[:1000]
res['waveforms_pos'] = gdata_pos[idx, :]
bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='latest')
io.write_datasets(bfile, res.keys(), res)
if sign_peaks == 'positive':
print_and_log([
"A basis with %s dimensions has been built" %
res['proj_pos'].shape[1]
], 'info', logger)
elif sign_peaks == 'negative':
print_and_log([
"A basis with %s dimensions has been built" %
res['proj'].shape[1]
], 'info', logger)
elif sign_peaks == 'both':
print_and_log([
"Two basis with %s dimensions has been built" %
res['proj'].shape[1]
], 'info', logger)
bfile.close()
comm.Barrier()
if matched_filter:
if comm.rank == 0:
print_and_log([
"Because of matched filters, need to recompute the thresholds..."
], 'default', logger)
if do_spatial_whitening:
spatial_whitening = io.load_data(params, 'spatial_whitening')
if use_gpu:
spatial_whitening = cmt.CUDAMatrix(spatial_whitening,
copy_on_host=False)
if do_temporal_whitening:
temporal_whitening = io.load_data(params, 'temporal_whitening')
if sign_peaks in ['negative', 'both']:
waveform_neg = io.load_data(params, 'waveform')
waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) *
len(waveform_neg))
if sign_peaks in ['positive', 'both']:
waveform_pos = io.load_data(params, 'waveform-pos')
waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) *
len(waveform_pos))
for gidx in [all_chunks[comm.rank]]:
local_chunk, t_offset = data_file.get_data(gidx,
chunk_size,
nodes=nodes)
local_shape = len(local_chunk)
if do_spatial_whitening:
if use_gpu:
local_chunk = cmt.CUDAMatrix(local_chunk,
copy_on_host=False)
local_chunk = local_chunk.dot(spatial_whitening).asarray()
else:
local_chunk = numpy.dot(local_chunk, spatial_whitening)
if do_temporal_whitening:
local_chunk = scipy.ndimage.filters.convolve1d(
local_chunk, temporal_whitening, axis=0, mode='constant')
if sign_peaks in ['negative', 'both']:
tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk,
waveform_neg,
axis=0,
mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in xrange(N_e):
u = numpy.median(tmp_chunk[:, i], 0)
thresholds[i] = numpy.median(
numpy.abs(tmp_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out + '.basis.hdf5',
'r+',
libver='latest')
io.write_datasets(bfile, ['matched_thresholds'],
{'matched_thresholds': thresholds})
bfile.close()
comm.Barrier()
if sign_peaks in ['positive', 'both']:
tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk,
waveform_pos,
axis=0,
mode='constant')
thresholds = numpy.zeros(N_e, dtype=numpy.float32)
for i in xrange(N_e):
u = numpy.median(tmp_chunk[:, i], 0)
thresholds[i] = numpy.median(
numpy.abs(tmp_chunk[:, i] - u), 0)
gdata = gather_array(thresholds, comm)
if comm.rank == 0:
gdata = gdata.reshape((comm.size, N_e))
thresholds = numpy.mean(gdata, 0)
bfile = h5py.File(file_out + '.basis.hdf5',
'r+',
libver='latest')
io.write_datasets(bfile, ['matched_thresholds_pos'],
{'matched_thresholds_pos': thresholds})
bfile.close()
comm.Barrier()
data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu, extension):
_ = init_logging(params.logfile)
logger = logging.getLogger('circus.converting')
data_file = params.data_file
file_out_suff = params.get('data', 'file_out_suff')
probe = params.probe
output_path = params.get('data', 'file_out_suff') + extension + '.GUI'
N_e = params.getint('data', 'N_e')
prelabelling = params.getboolean('converting', 'prelabelling')
N_t = params.getint('detection', 'N_t')
erase_all = params.getboolean('converting', 'erase_all')
export_pcs = params.get('converting', 'export_pcs')
export_all = params.getboolean('converting', 'export_all')
sparse_export = params.getboolean('converting', 'sparse_export')
rpv_threshold = params.getfloat('converting', 'rpv_threshold')
if export_all and not params.getboolean('fitting', 'collect_all'):
if comm.rank == 0:
print_and_log([
'Export unfitted spikes only if [fitting] collect_all is True'
], 'error', logger)
sys.exit(0)
def generate_mapping(probe):
p = {}
positions = []
nodes = []
shanks = []
for key in list(probe['channel_groups'].keys()):
p.update(probe['channel_groups'][key]['geometry'])
nodes += probe['channel_groups'][key]['channels']
positions += [
p[channel]
for channel in probe['channel_groups'][key]['channels']
]
shanks += [key] * len(probe['channel_groups'][key]['channels'])
positions = numpy.array(positions)
shanks = numpy.array(shanks)
return positions, shanks
def get_max_loc_channel(params, extension):
if test_if_support(params, extension):
supports = io.load_data(params, 'supports', extension)
max_loc_channel = numpy.sum(supports, 1).max()
else:
nodes, edges = get_nodes_and_edges(params)
max_loc_channel = 0
for key in list(edges.keys()):
if len(edges[key]) > max_loc_channel:
max_loc_channel = len(edges[key])
return max_loc_channel
def write_results(path, params, extension):
result = io.get_results(params, extension)
spikes = [numpy.zeros(0, dtype=numpy.uint64)]
clusters = [numpy.zeros(0, dtype=numpy.uint32)]
amplitudes = [numpy.zeros(0, dtype=numpy.double)]
N_tm = len(result['spiketimes'])
has_purity = test_if_purity(params, extension)
rpvs = []
if prelabelling:
labels = []
norms = io.load_data(params, 'norm-templates', extension)
norms = norms[:len(norms) // 2]
if has_purity:
purity = io.load_data(params, 'purity', extension)
for key in list(result['spiketimes'].keys()):
temp_id = int(key.split('_')[-1])
myspikes = result['spiketimes'].pop(key).astype(numpy.uint64)
spikes.append(myspikes)
myamplitudes = result['amplitudes'].pop(key).astype(numpy.double)
amplitudes.append(myamplitudes[:, 0])
clusters.append(temp_id *
numpy.ones(len(myamplitudes), dtype=numpy.uint32))
rpv = get_rpv(myspikes, params.data_file.sampling_rate)
rpvs += [[temp_id, rpv]]
if prelabelling:
if has_purity:
if rpv <= rpv_threshold:
if purity[temp_id] > 0.75:
labels += [[temp_id, 'good']]
else:
if purity[temp_id] > 0.75:
labels += [[temp_id, 'mua']]
else:
labels += [[temp_id, 'noise']]
else:
median_amp = numpy.median(myamplitudes[:, 0])
std_amp = numpy.std(myamplitudes[:, 0])
if rpv <= rpv_threshold and numpy.abs(median_amp -
1) < 0.25:
labels += [[temp_id, 'good']]
else:
if median_amp < 0.5:
labels += [[temp_id, 'mua']]
elif norms[temp_id] < 0.1:
labels += [[temp_id, 'noise']]
if export_all:
print_and_log([
"Last %d templates are unfitted spikes on all electrodes" % N_e
], 'info', logger)
garbage = io.load_data(params, 'garbage', extension)
for key in list(garbage['gspikes'].keys()):
elec_id = int(key.split('_')[-1])
data = garbage['gspikes'].pop(key).astype(numpy.uint64)
spikes.append(data)
amplitudes.append(numpy.ones(len(data)))
clusters.append((elec_id + N_tm) *
numpy.ones(len(data), dtype=numpy.uint32))
if prelabelling:
f = open(os.path.join(output_path, 'cluster_group.tsv'), 'w')
f.write('cluster_id\tgroup\n')
for l in labels:
f.write('%s\t%s\n' % (l[0], l[1]))
f.close()
# f = open(os.path.join(output_path, 'cluster_rpv.tsv'), 'w')
# f.write('cluster_id\trpv\n')
# for l in rpvs:
# f.write('%s\t%s\n' % (l[0], l[1]))
# f.close()
spikes = numpy.concatenate(spikes).astype(numpy.uint64)
amplitudes = numpy.concatenate(amplitudes).astype(numpy.double)
clusters = numpy.concatenate(clusters).astype(numpy.uint32)
idx = numpy.argsort(spikes)
numpy.save(os.path.join(output_path, 'spike_templates'), clusters[idx])
numpy.save(os.path.join(output_path, 'spike_times'), spikes[idx])
numpy.save(os.path.join(output_path, 'amplitudes'), amplitudes[idx])
return
def write_templates(path, params, extension):
max_loc_channel = get_max_loc_channel(params, extension)
templates = io.load_data(params, 'templates', extension)
N_tm = templates.shape[1] // 2
nodes, edges = get_nodes_and_edges(params)
if sparse_export:
n_channels_max = 0
for t in range(N_tm):
data = numpy.sum(
numpy.sum(templates[:, t].toarray().reshape(N_e, N_t), 1)
!= 0)
if data > n_channels_max:
n_channels_max = data
else:
n_channels_max = N_e
if export_all:
to_write_sparse = numpy.zeros((N_tm + N_e, N_t, n_channels_max),
dtype=numpy.float32)
mapping_sparse = -1 * numpy.ones(
(N_tm + N_e, n_channels_max), dtype=numpy.int32)
else:
to_write_sparse = numpy.zeros((N_tm, N_t, n_channels_max),
dtype=numpy.float32)
mapping_sparse = -1 * numpy.ones(
(N_tm, n_channels_max), dtype=numpy.int32)
has_purity = test_if_purity(params, extension)
if has_purity:
purity = io.load_data(params, 'purity', extension)
f = open(os.path.join(output_path, 'cluster_purity.tsv'), 'w')
f.write('cluster_id\tpurity\n')
for i in range(N_tm):
f.write('%d\t%g\n' % (i, purity[i]))
f.close()
for t in range(N_tm):
tmp = templates[:, t].toarray().reshape(N_e, N_t).T
x, y = tmp.nonzero()
nb_loc = len(numpy.unique(y))
if sparse_export:
all_positions = numpy.zeros(y.max() + 1, dtype=numpy.int32)
all_positions[numpy.unique(y)] = numpy.arange(
nb_loc, dtype=numpy.int32)
pos = all_positions[y]
to_write_sparse[t, x, pos] = tmp[x, y]
mapping_sparse[t, numpy.arange(nb_loc)] = numpy.unique(y)
else:
pos = y
to_write_sparse[t, x, pos] = tmp[x, y]
if export_all:
garbage = io.load_data(params, 'garbage', extension)
for t in range(N_tm, N_tm + N_e):
elec = t - N_tm
spikes = garbage['gspikes'].pop('elec_%d' % elec).astype(
numpy.int64)
spikes = numpy.random.permutation(spikes)[:100]
mapping_sparse[t, 0] = t - N_tm
waveform = io.get_stas(params,
times_i=spikes,
labels_i=np.ones(len(spikes)),
src=elec,
neighs=[elec],
nodes=nodes,
mean_mode=True)
nb_loc = 1
if sparse_export:
to_write_sparse[t, :, 0] = waveform
else:
to_write_sparse[t, :, elec] = waveform
numpy.save(os.path.join(output_path, 'templates'), to_write_sparse)
if sparse_export:
numpy.save(os.path.join(output_path, 'template_ind'),
mapping_sparse)
return N_tm
def write_pcs(path, params, extension, N_tm, mode=0):
spikes = numpy.load(os.path.join(output_path, 'spike_times.npy'))
labels = numpy.load(os.path.join(output_path, 'spike_templates.npy'))
max_loc_channel = get_max_loc_channel(params, extension)
nb_features = params.getint('whitening', 'output_dim')
sign_peaks = params.get('detection', 'peaks')
nodes, edges = get_nodes_and_edges(params)
N_total = params.getint('data', 'N_total')
has_support = test_if_support(params, extension)
if has_support:
supports = io.load_data(params, 'supports', extension)
else:
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.arange(len(nodes))
if export_all:
nb_templates = N_tm + N_e
else:
nb_templates = N_tm
pc_features_ind = numpy.zeros((nb_templates, max_loc_channel),
dtype=numpy.int32)
best_elec = io.load_data(params, 'electrodes', extension)
if export_all:
best_elec = numpy.concatenate((best_elec, numpy.arange(N_e)))
if has_support:
for count, support in enumerate(supports):
nb_loc = numpy.sum(support)
pc_features_ind[count, numpy.arange(nb_loc)] = numpy.where(
support == True)[0]
else:
for count, elec in enumerate(best_elec):
nb_loc = len(edges[nodes[elec]])
pc_features_ind[count, numpy.arange(nb_loc)] = inv_nodes[edges[
nodes[elec]]]
if sign_peaks in ['negative', 'both']:
basis_proj, basis_rec = io.load_data(params, 'basis')
elif sign_peaks in ['positive']:
basis_proj, basis_rec = io.load_data(params, 'basis-pos')
to_process = numpy.arange(comm.rank, nb_templates, comm.size)
all_offsets = numpy.zeros(nb_templates, dtype=numpy.int32)
for target in range(nb_templates):
if mode == 0:
all_offsets[target] = len(numpy.where(labels == target)[0])
elif mode == 1:
all_offsets[target] = min(
500, len(numpy.where(labels == target)[0]))
all_paddings = numpy.concatenate(([0], numpy.cumsum(all_offsets)))
total_pcs = numpy.sum(all_offsets)
pc_file = os.path.join(output_path, 'pc_features.npy')
pc_file_ids = os.path.join(output_path, 'pc_feature_spike_ids.npy')
from numpy.lib.format import open_memmap
if comm.rank == 0:
pc_features = open_memmap(pc_file,
shape=(total_pcs, nb_features,
max_loc_channel),
dtype=numpy.float32,
mode='w+')
if mode == 1:
pc_ids = open_memmap(pc_file_ids,
shape=(total_pcs, ),
dtype=numpy.int32,
mode='w+')
comm.Barrier()
pc_features = open_memmap(pc_file, mode='r+')
if mode == 1:
pc_ids = open_memmap(pc_file_ids, mode='r+')
to_explore = list(range(comm.rank, nb_templates, comm.size))
if comm.rank == 0:
to_explore = get_tqdm_progressbar(params, to_explore)
all_idx = numpy.zeros(0, dtype=numpy.int32)
for gcount, target in enumerate(to_explore):
count = all_paddings[target]
if mode == 1:
idx = numpy.random.permutation(
numpy.where(labels == target)[0])[:500]
pc_ids[count:count + len(idx)] = idx
elif mode == 0:
idx = numpy.where(labels == target)[0]
elec = best_elec[target]
if has_support:
if target >= len(supports):
indices = [target - N_tm]
else:
indices = numpy.where(supports[target])[0]
else:
indices = inv_nodes[edges[nodes[elec]]]
labels_i = target * numpy.ones(len(idx))
times_i = numpy.take(spikes, idx).astype(numpy.int64)
sub_data = io.get_stas(params,
times_i,
labels_i,
elec,
neighs=indices,
nodes=nodes,
auto_align=False)
pcs = numpy.dot(sub_data, basis_proj)
pcs = numpy.swapaxes(pcs, 1, 2)
if mode == 0:
pc_features[idx, :, :len(indices)] = pcs
elif mode == 1:
pc_features[count:count + len(idx), :, :len(indices)] = pcs
comm.Barrier()
if comm.rank == 0:
numpy.save(os.path.join(output_path, 'pc_feature_ind'),
pc_features_ind.astype(
numpy.uint32)) # n_templates, n_loc_chan
do_export = True
if comm.rank == 0:
if os.path.exists(output_path):
if not erase_all:
do_export = query_yes_no(
Fore.WHITE +
"Export already made! Do you want to erase everything?",
default=None)
if do_export:
if os.path.exists(os.path.abspath('.phy')):
shutil.rmtree(os.path.abspath('.phy'))
shutil.rmtree(output_path)
if do_export:
comm.bcast(numpy.array([1], dtype=numpy.int32), root=0)
else:
comm.bcast(numpy.array([0], dtype=numpy.int32), root=0)
else:
do_export = bool(
comm.bcast(numpy.array([0], dtype=numpy.int32), root=0))
comm.Barrier()
if do_export:
#apply_patch_for_similarities(params, extension)
if comm.rank == 0:
os.makedirs(output_path)
print_and_log(
["Exporting data for the phy GUI with %d CPUs..." % nb_cpu],
'info', logger)
if params.getboolean('whitening', 'spatial'):
whitening_mat = io.load_data(
params, 'spatial_whitening').astype(numpy.double)
numpy.save(os.path.join(output_path, 'whitening_mat'),
whitening_mat)
numpy.save(os.path.join(output_path, 'whitening_mat_inv'),
numpy.linalg.inv(whitening_mat))
else:
numpy.save(os.path.join(output_path, 'whitening_mat'),
numpy.eye(N_e))
positions, shanks = generate_mapping(probe)
numpy.save(os.path.join(output_path, 'channel_positions'),
positions.astype(numpy.double))
numpy.save(os.path.join(output_path, 'channel_shanks'),
shanks.astype(numpy.double))
nodes, edges = get_nodes_and_edges(params)
numpy.save(os.path.join(output_path, 'channel_map'),
nodes.astype(numpy.int32))
write_results(output_path, params, extension)
N_tm = write_templates(output_path, params, extension)
template_file = h5py.File(file_out_suff +
'.templates%s.hdf5' % extension,
'r',
libver='earliest')
similarities = template_file.get('maxoverlap')[:]
template_file.close()
norm = N_e * N_t
if export_all:
to_write = numpy.zeros((N_tm + N_e, N_tm + N_e),
dtype=numpy.single)
to_write[:N_tm, :N_tm] = (similarities[:N_tm, :N_tm] /
norm).astype(numpy.single)
else:
to_write = (similarities[:N_tm, :N_tm] / norm).astype(
numpy.single)
numpy.save(os.path.join(output_path, 'similar_templates'),
to_write)
comm.bcast(numpy.array([N_tm], dtype=numpy.int32), root=0)
else:
N_tm = int(comm.bcast(numpy.array([0], dtype=numpy.int32), root=0))
comm.Barrier()
make_pcs = 2
if comm.rank == 0:
if export_pcs == 'prompt':
key = ''
while key not in ['a', 's', 'n']:
print((
Fore.WHITE +
"Do you want SpyKING CIRCUS to export PCs? (a)ll / (s)ome / (n)o"
))
key = input('')
else:
key = export_pcs
if key == 'a':
make_pcs = 0
comm.bcast(numpy.array([0], dtype=numpy.int32), root=0)
elif key == 's':
make_pcs = 1
comm.bcast(numpy.array([1], dtype=numpy.int32), root=0)
elif key == 'n':
comm.bcast(numpy.array([2], dtype=numpy.int32), root=0)
if os.path.exists(os.path.join(output_path,
'pc_features.npy')):
os.remove(os.path.join(output_path, 'pc_features.npy'))
if os.path.exists(
os.path.join(output_path, 'pc_feature_ind.npy')):
os.remove(os.path.join(output_path, 'pc_feature_ind.npy'))
else:
make_pcs = comm.bcast(numpy.array([0], dtype=numpy.int32), root=0)
make_pcs = make_pcs[0]
comm.Barrier()
if make_pcs < 2:
write_pcs(output_path, params, extension, N_tm, make_pcs)
supported_by_phy = ['raw_binary', 'mcs_raw_binary', 'mda']
file_format = data_file.description
gui_params = {}
if file_format in supported_by_phy:
if not params.getboolean('data', 'overwrite'):
gui_params['dat_path'] = r"%s" % params.get(
'data', 'data_file_no_overwrite')
else:
if params.get('data', 'stream_mode') == 'multi-files':
data_file = params.get_data_file(source=True,
has_been_created=False)
gui_params['dat_path'] = "["
for f in data_file.get_file_names():
gui_params['dat_path'] += 'r"%s", ' % f
gui_params['dat_path'] += "]"
else:
gui_params['dat_path'] = 'r"%s"' % params.get(
'data', 'data_file')
else:
gui_params['dat_path'] = 'giverandomname.dat'
gui_params['n_channels_dat'] = params.nb_channels
gui_params['n_features_per_channel'] = 5
gui_params['dtype'] = data_file.data_dtype
if 'data_offset' in list(data_file.params.keys()):
gui_params['offset'] = data_file.data_offset
gui_params['sample_rate'] = params.rate
gui_params['dir_path'] = output_path
gui_params['hp_filtered'] = True
f = open(os.path.join(output_path, 'params.py'), 'w')
for key, value in list(gui_params.items()):
if key in ['dir_path', 'dtype']:
f.write('%s = r"%s"\n' % (key, value))
else:
f.write("%s = %s\n" % (key, value))
f.close()
def delete_mixtures(params, nb_cpu, nb_gpu, use_gpu):
data_file = params.data_file
N_e = params.getint('data', 'N_e')
N_total = params.nb_channels
N_t = params.getint('detection', 'N_t')
template_shift = params.getint('detection', 'template_shift')
cc_merge = params.getfloat('clustering', 'cc_mixtures')
mixtures = []
to_remove = []
filename = params.get('data', 'file_out_suff') + '.overlap-mixtures.hdf5'
norm_templates = load_data(params, 'norm-templates')
best_elec = load_data(params, 'electrodes')
limits = load_data(params, 'limits')
nodes, edges = get_nodes_and_edges(params)
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
overlap = get_overlaps(params,
extension='-mixtures',
erase=True,
normalize=False,
maxoverlap=False,
verbose=False,
half=True,
use_gpu=use_gpu,
nb_cpu=nb_cpu,
nb_gpu=nb_gpu)
overlap.close()
SHARED_MEMORY = get_shared_memory_flag(params)
if SHARED_MEMORY:
c_overs = load_data_memshared(params,
'overlaps',
extension='-mixtures',
use_gpu=use_gpu,
nb_cpu=nb_cpu,
nb_gpu=nb_gpu)
else:
c_overs = load_data(params, 'overlaps', extension='-mixtures')
if SHARED_MEMORY:
templates = load_data_memshared(params, 'templates', normalize=False)
else:
templates = load_data(params, 'templates')
x, N_tm = templates.shape
nb_temp = int(N_tm // 2)
merged = [nb_temp, 0]
overlap_0 = numpy.zeros(nb_temp, dtype=numpy.float32)
distances = numpy.zeros((nb_temp, nb_temp), dtype=numpy.int32)
for i in xrange(nb_temp - 1):
data = c_overs[i].toarray()
distances[i, i + 1:] = numpy.argmax(data[i + 1:, :], 1)
distances[i + 1:, i] = distances[i, i + 1:]
overlap_0[i] = data[i, N_t]
all_temp = numpy.arange(comm.rank, nb_temp, comm.size)
sorted_temp = numpy.argsort(
norm_templates[:nb_temp])[::-1][comm.rank::comm.size]
M = numpy.zeros((2, 2), dtype=numpy.float32)
V = numpy.zeros((2, 1), dtype=numpy.float32)
to_explore = xrange(comm.rank, len(sorted_temp), comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for count, k in enumerate(to_explore):
k = sorted_temp[k]
electrodes = numpy.take(inv_nodes, edges[nodes[best_elec[k]]])
overlap_k = c_overs[k]
is_in_area = numpy.in1d(best_elec, electrodes)
all_idx = numpy.arange(len(best_elec))[is_in_area]
been_found = False
t_k = None
for i in all_idx:
t_i = None
if not been_found:
overlap_i = c_overs[i]
M[0, 0] = overlap_0[i]
V[0, 0] = overlap_k[i, distances[k, i]]
for j in all_idx[i + 1:]:
t_j = None
M[1, 1] = overlap_0[j]
M[1, 0] = overlap_i[j, distances[k, i] - distances[k, j]]
M[0, 1] = M[1, 0]
V[1, 0] = overlap_k[j, distances[k, j]]
try:
[a1, a2] = numpy.dot(scipy.linalg.inv(M), V)
except Exception:
[a1, a2] = [0, 0]
a1_lim = limits[i]
a2_lim = limits[j]
is_a1 = (a1_lim[0] <= a1) and (a1 <= a1_lim[1])
is_a2 = (a2_lim[0] <= a2) and (a2 <= a2_lim[1])
if is_a1 and is_a2:
if t_k is None:
t_k = templates[:, k].toarray().ravel()
if t_i is None:
t_i = templates[:, i].toarray().ravel()
if t_j is None:
t_j = templates[:, j].toarray().ravel()
new_template = (a1 * t_i + a2 * t_j)
similarity = numpy.corrcoef(t_k, new_template)[0, 1]
local_overlap = numpy.corrcoef(t_i, t_j)[0, 1]
if similarity > cc_merge and local_overlap < cc_merge:
if k not in mixtures:
mixtures += [k]
been_found = True
#print "Template", k, 'is sum of (%d, %g) and (%d,%g)' %(i, a1, j, a2)
break
sys.stderr.flush()
#print mixtures
to_remove = numpy.unique(numpy.array(mixtures, dtype=numpy.int32))
to_remove = all_gather_array(to_remove, comm, 0, dtype='int32')
if len(to_remove) > 0 and comm.rank == 0:
result = load_data(params, 'clusters')
slice_templates(params, to_remove)
slice_clusters(params, result, to_remove=to_remove)
comm.Barrier()
del c_overs
if comm.rank == 0:
os.remove(filename)
return [nb_temp, len(to_remove)]
electrode = clusters_data['electrodes'][args.template_id]
local_cluster = clusters_data['local_clusters'][args.template_id]
assert electrode.shape == local_cluster.shape, (electrode.shape,
local_cluster.shape)
times = clusters_data['times'][electrode]
clusters = clusters_data['clusters'][electrode]
assert times.shape == clusters.shape, (times.shape, clusters.shape)
selection = (clusters == local_cluster)
times = times[selection]
clusters = clusters[selection]
if times.size > nb_snippets:
indices = np.random.choice(times.size, size=nb_snippets)
indices = np.sort(indices)
times = times[indices]
clusters = clusters[indices]
nodes, _ = get_nodes_and_edges(params)
inv_nodes = np.zeros(nb_electrodes, dtype=np.int)
inv_nodes[nodes] = np.arange(len(nodes))
indices = inv_nodes[nodes]
snippets = get_stas(params, times, clusters, electrode, indices, nodes=nodes)
snippets = np.transpose(snippets, axes=(0, 2, 1))
assert snippets.shape == (nb_snippets, nb_time_steps, nb_channels), \
(snippets.shape, nb_snippets, nb_time_steps, nb_channels)
# Load template.
template = load_template(args.template_id, params, extension='')
assert template.shape == (nb_time_steps, nb_channels)
# Compute the scalar products.
snippets_ = np.reshape(snippets, (nb_snippets, nb_channels * nb_time_steps))
template_ = np.reshape(template, (nb_channels * nb_time_steps, 1))
