def _gen_store_1(chunk_size):
_reset_store()
_free_cache()
t0 = default_timer()
# chunk_size = 10000
# print("chunks")
for i in range(n_spikes // chunk_size):
# print(i, end='\r')
a, b = i * chunk_size, (i + 1) * chunk_size
# Load a chunk from HDF5.
assert isinstance(arr, h5py.Dataset)
sub_arr = arr[a:b]
assert isinstance(sub_arr, np.ndarray)
sub_sc = sc[a:b]
sub_spikes = np.arange(a, b)
# Split the spikes.
sub_spc = _spikes_per_cluster(sub_spikes, sub_sc)
# Go through the clusters.
clusters = sorted(sub_spc.keys())
for cluster in clusters:
idx = _index_of(sub_spc[cluster], sub_spikes)
# Save part of the array to a binary file.
with open(_flat_file(cluster), 'ab') as f:
sub_arr[idx].tofile(f)
# print()
# ds = DiskStore(_store_path)
# # Next, put the flat binary files back to HDF5.
# # print("flat to HDF5")
# for cluster in range(n_clusters):
# # print(cluster, end='\r')
# data = np.fromfile(_flat_file(cluster),
# dtype=np.float32).reshape((-1, n_channels))
# ds.store(cluster, data=data)
print("time", default_timer() - t0)
# print()
# Test.
cluster = 0
# arr2 = ds.load(cluster, 'data')
arr2 = np.fromfile(_flat_file(cluster),
dtype=np.float32).reshape((-1, n_channels))
ac(arr[spc[cluster], :], arr2)
def _gen_store_1(chunk_size):
_reset_store()
_free_cache()
t0 = default_timer()
# chunk_size = 10000
# print("chunks")
for i in range(n_spikes // chunk_size):
# print(i, end='\r')
a, b = i * chunk_size, (i + 1) * chunk_size
# Load a chunk from HDF5.
assert isinstance(arr, h5py.Dataset)
sub_arr = arr[a:b]
assert isinstance(sub_arr, np.ndarray)
sub_sc = sc[a:b]
sub_spikes = np.arange(a, b)
# Split the spikes.
sub_spc = _spikes_per_cluster(sub_spikes, sub_sc)
# Go through the clusters.
clusters = sorted(sub_spc.keys())
for cluster in clusters:
idx = _index_of(sub_spc[cluster], sub_spikes)
# Save part of the array to a binary file.
with open(_flat_file(cluster), 'ab') as f:
sub_arr[idx].tofile(f)
# print()
# ds = DiskStore(_store_path)
# # Next, put the flat binary files back to HDF5.
# # print("flat to HDF5")
# for cluster in range(n_clusters):
# # print(cluster, end='\r')
# data = np.fromfile(_flat_file(cluster),
# dtype=np.float32).reshape((-1, n_channels))
# ds.store(cluster, data=data)
print("time", default_timer() - t0)
# print()
# Test.
cluster = 0
# arr2 = ds.load(cluster, 'data')
arr2 = np.fromfile(_flat_file(cluster), dtype=np.float32).reshape(
(-1, n_channels))
ac(arr[spc[cluster], :], arr2)
def _reset_store():
for path in (_store_path, '_flat'):
if op.exists(path):
shutil.rmtree(path)
os.mkdir(path)
_gen_spike_clusters()
_gen_arr()
f = open_h5('test', 'r')
sc = _load_spike_clusters()
arr = f.read('/test')
spikes = np.arange(n_spikes)
spc = _spikes_per_cluster(spikes, sc)
def _flat_file(cluster):
return op.join('_flat', str(cluster))
def _free_cache():
os.system('sync')
os.system('sudo sh -c "sync; echo 3 > /proc/sys/vm/drop_caches"')
# @profile
def _gen_store_1(chunk_size):
_reset_store()
_free_cache()
def _reset_store():
for path in (_store_path, '_flat'):
if op.exists(path):
shutil.rmtree(path)
os.mkdir(path)
_gen_spike_clusters()
_gen_arr()
f = open_h5('test', 'r')
sc = _load_spike_clusters()
arr = f.read('/test')
spikes = np.arange(n_spikes)
spc = _spikes_per_cluster(spikes, sc)
def _flat_file(cluster):
return op.join('_flat', str(cluster))
def _free_cache():
os.system('sync')
os.system('sudo sh -c "sync; echo 3 > /proc/sys/vm/drop_caches"')
# @profile
def _gen_store_1(chunk_size):
_reset_store()
_free_cache()
