def setup():
# Special setup fixture to create a data set with 2 shanks.
# No need for teardown as the 'mockdata' folder will be cleared at the
# end of the tests anyway.
# Create mock directory if needed.
dir = TEST_FOLDER
if not os.path.exists(dir):
os.mkdir(dir)
# Create mock data.
waveforms = create_waveforms(nspikes, nsamples, nchannels)
features = create_features(nspikes, nchannels, fetdim, duration, freq)
clusters = create_clusters(nspikes, nclusters)
cluster_colors = create_cluster_colors(nclusters)
masks = create_masks(nspikes, nchannels, fetdim)
xml = create_xml(nchannels, nsamples, fetdim)
probe = create_probe(nchannels)
# Create mock files.
save_binary(os.path.join(dir, 'test.spk.2'), waveforms)
save_text(os.path.join(dir, 'test.fet.2'), features,
header=nchannels * fetdim + 1)
save_text(os.path.join(dir, 'test.aclu.2'), clusters, header=nclusters)
save_text(os.path.join(dir, 'test.clu.2'), clusters, header=nclusters)
save_text(os.path.join(dir, 'test.fmask.2'), masks, header=nclusters,
fmt='%.6f')
save_text(os.path.join(dir, 'test.xml'), xml)
save_text(os.path.join(dir, 'test.probe'), probe)
def setup():
# Create mock directory if needed.
dir = TEST_FOLDER
# Create mock data.
waveforms = create_waveforms(nspikes, nsamples, nchannels)
features = create_features(nspikes, nchannels, fetdim, duration, freq)
clusters = create_clusters(nspikes, nclusters)
cluster_colors = create_cluster_colors(nclusters)
cluster_groups = create_cluster_groups(nclusters)
cluster_info = pd.DataFrame(
{'color': cluster_colors,
'group': cluster_groups},
dtype=np.int32,
index=np.unique(clusters))
group_colors = create_group_colors(ngroups)
group_names = create_group_names(ngroups)
group_info = pd.DataFrame(
{'color': group_colors,
'name': group_names},
index=np.arange(ngroups))
# channel_colors = create_channel_colors(nchannels)
# channel_groups = create_channel_groups(nchannels)
# channel_names = create_channel_names(nchannels)
# channel_info = pd.DataFrame(
# {'color': channel_colors,
# 'group': channel_groups},
# dtype=np.int32,
# index=np.unique(channels))
#
# channel_group_colors = create_channel_group_colors(ngroups)
# channel_group_names = create_channel_group_names(ngroups)
# channel_group_info = pd.DataFrame(
# {'color': channel_group_colors,
# 'name': channel_group_names},
# index=np.arange(ngroups))
masks = create_masks(nspikes, nchannels, fetdim)
xml = create_xml(nchannels, nsamples, fetdim)
probe = create_probe(nchannels)
# Create mock files.
save_binary(os.path.join(dir, 'test.spk.1'), waveforms)
save_text(os.path.join(dir, 'test.fet.1'), features,
header=nchannels * fetdim + 1)
save_text(os.path.join(dir, 'test.aclu.1'), clusters, header=nclusters)
# save_cluster_info(os.path.join(dir, 'test.acluinfo.1'), cluster_info)
# save_group_info(os.path.join(dir, 'test.groupinfo.1'), group_info)
save_text(os.path.join(dir, 'test.clu.1'), clusters, header=nclusters)
save_text(os.path.join(dir, 'test.fmask.1'), masks, header=nclusters,
fmt='%.6f')
save_text(os.path.join(dir, 'test.xml'), xml)
save_text(os.path.join(dir, 'test.probe'), probe)
def setup():
# Create mock directory if needed.
dir = TEST_FOLDER
if not os.path.exists(dir):
os.mkdir(dir)
dat = create_trace(nsamples, nchannels)
# Create mock DAT file.
save_binary(os.path.join(dir, 'test.dat'), dat)
def setup():
# Create mock directory if needed.
dir = TEST_FOLDER
if not os.path.exists(dir):
os.mkdir(dir)
# Create 2 DAT files with nsamples and 2*nsamples samples, respectively.
# The generated KWD file must have 3*nsamples samples.
dat1 = create_trace(nsamples, nchannels)
dat2 = create_trace(nsamples * 2, nchannels)
# Create mock DAT file.
save_binary(os.path.join(dir, 'test1.dat'), dat1)
save_binary(os.path.join(dir, 'test2.dat'), dat2)
def setup():
# Create mock directory if needed.
dir = TEST_FOLDER
# Create mock data.
waveforms = create_waveforms(nspikes, nsamples, nchannels)
features = create_features(nspikes, nchannels, fetdim, duration, freq)
clusters = create_clusters(nspikes, nclusters)
cluster_colors = create_cluster_colors(nclusters)
cluster_groups = create_cluster_groups(nclusters)
cluster_info = pd.DataFrame(
{
'color': cluster_colors,
'group': cluster_groups
},
dtype=np.int32,
index=np.unique(clusters))
group_colors = create_group_colors(ngroups)
group_names = create_group_names(ngroups)
group_info = pd.DataFrame({
'color': group_colors,
'name': group_names
},
index=np.arange(ngroups))
masks = create_masks(nspikes, nchannels, fetdim)
xml = create_xml(nchannels, nsamples, fetdim)
probe = create_probe(nchannels)
# Create mock files.
save_binary(os.path.join(dir, 'test.spk.1'), waveforms)
save_text(os.path.join(dir, 'test.fet.1'),
features,
header=nchannels * fetdim + 1)
save_text(os.path.join(dir, 'test.aclu.1'), clusters, header=nclusters)
save_text(os.path.join(dir, 'test.clu.1'), clusters, header=nclusters)
save_text(os.path.join(dir, 'test.fmask.1'),
masks,
header=nclusters,
fmt='%.6f')
save_text(os.path.join(dir, 'test.xml'), xml)
save_text(os.path.join(dir, 'test.probe'), probe)
def test_memmap_numpy():
folder = tempfile.gettempdir()
filename = os.path.join(folder, 'memmapb')
dtype = np.int16
freq = 20000.
duration = 10.
nchannels = 32
nsamples = int(freq * duration)
x = np.random.randint(size=(nsamples, nchannels), low=0,
high=1000).astype(dtype)
save_binary(filename, x)
m = read_dat(filename, nchannels=nchannels, dtype=dtype)
slices = (slice(1000, 10000, 4), slice(2, 30, 3))
assert m.shape == x.shape
np.testing.assert_equal(x[slices], m[slices])
def test_memmap_numpy():
folder = tempfile.gettempdir()
filename = os.path.join(folder, 'memmapb')
dtype = np.int16
freq = 20000.
duration = 10.
nchannels = 32
nsamples = int(freq * duration)
x = np.random.randint(size=(nsamples, nchannels),
low=0, high=1000).astype(dtype)
save_binary(filename, x)
m = read_dat(filename, nchannels=nchannels, dtype=dtype)
slices = (slice(1000, 10000, 4), slice(2, 30, 3))
assert m.shape == x.shape
np.testing.assert_equal(x[slices], m[slices])
"""Generate a DAT file with random raw data.""" from kwiklib.dataio import save_binary from kwiklib.dataio.tests.mock_data import create_trace nsamples = 20000. * 60 nchannels = 32 filename = "myexperiment.dat" dat = create_trace(nsamples, nchannels) save_binary(filename, dat)
