def test_ndvar_binning():
"Test NDVar.bin()"
x = np.arange(10)
time = UTS(-0.1, 0.1, 10)
x_dst = x.reshape((5, 2)).mean(1)
time_dst = np.arange(0., 0.9, 0.2)
# 1-d
ndvar = NDVar(x, (time,))
b = ndvar.bin(0.2)
assert_array_equal(b.x, x_dst, "Binned data")
assert_array_equal(b.time.x, time_dst, "Bin times")
b = ndvar.sub(time=(0, 0.8)).bin(0.4)
eq_(b.shape, (2,))
# 2-d
ndvar = NDVar(np.vstack((x, x, x)), ('case', time))
b = ndvar.bin(0.2)
assert_array_equal(b.x, np.vstack((x_dst, x_dst, x_dst)), "Binned data")
assert_array_equal(b.time.x, time_dst, "Bin times")
# time:
x = np.ones((5, 70))
ndvar = NDVar(x, ('case', UTS(0.45000000000000007, 0.005, 70)))
binned_ndvar = ndvar.bin(0.05)
assert_array_equal(binned_ndvar.x, 1.)
eq_(binned_ndvar.shape, (5, 7))
def test_dim_uts():
"Test UTS Dimension"
uts = UTS(-0.1, 0.005, 301)
# make sure indexing rounds correctly for floats
for i, s in enumerate(np.arange(0, 1.4, 0.05)):
idx = uts.dimindex((-0.1 + s, s))
assert_equal(idx.start, 10 * i)
assert_equal(idx.stop, 20 + 10 * i)
# intersection
uts1 = UTS(-0.1, 0.01, 50)
uts2 = UTS(0, 0.01, 20)
intersection = uts1.intersect(uts2)
assert_equal(intersection, uts2)
idx = uts1.dimindex((0, 0.2))
assert_equal(uts1[idx], uts2)
def run_as_ndanova(y, x, ds):
yt = ds.eval(y).x[:, None]
y2 = np.concatenate((yt, yt * 2), 1)
ndvar = NDVar(y2, ('case', UTS(0, 0.1, 2)))
res = testnd.anova(ndvar, x, ds=ds)
f1 = [fmap.x[0] for fmap in res.f]
f2 = [fmap.x[1] for fmap in res.f]
for f1_, f2_ in zip(f1, f2):
assert f1_ == f2_
return f1
def test_dim_uts():
"Test UTS Dimension"
uts = UTS(-0.1, 0.005, 301)
# make sure indexing rounds correctly for floats
for i, s in enumerate(np.arange(0, 1.4, 0.05)):
idx = uts.dimindex((-0.1 + s, s))
eq_(idx.start, 10 * i)
eq_(idx.stop, 20 + 10 * i)
# intersection
uts1 = UTS(-0.1, 0.01, 50)
uts2 = UTS(0, 0.01, 20)
intersection = uts1.intersect(uts2)
eq_(intersection, uts2)
idx = uts1.dimindex((0, 0.2))
eq_(uts1[idx], uts2)
import numpy as np
from eelbrain import *
# the time dimension object
from eelbrain._data_obj import UTS
T = UTS(-.2, .01, 100)
# create simulated data:
# 4 conditions, 15 subjects
y = np.random.normal(0, .5, (60, len(T)))
# add an interaction effect
y[:15, 20:60] += np.hanning(40) * 1
# add a main effect
y[:30, 50:80] += np.hanning(30) * 1
Y = NDVar(y, dims=('case', T), name='Y')
A = Factor(['a0', 'a1'], rep=30, name='A')
B = Factor(['b0', 'b1'], rep=15, tile=2, name='B')
# fixed effects model
# (increase the number of samples for a more accurate result)
res = testnd.anova(Y, A * B, samples=100)
plot.UTSClusters(res, title="Fixed Effects Model")
# random effects model:
subject = Factor(range(15), tile=4, random=True, name='subject')
res = testnd.anova(Y, A * B * subject, samples=100, match=subject)
plot.UTSClusters(res, title="Random Effects Model")
# plot Y
import numpy as np
from eelbrain import *
# dimension objects
from eelbrain._data_obj import UTS, Sensor
"""
Create simulated data with shape
(2 conditions * 15 subjects, 5 sensors, len(T) time points)
"""
# create the time dimension
time = UTS(-.2, .01, 100)
# random data
x = np.random.normal(0, 1, (30, 5, len(time)))
# add an effect to the random data
x[15:,:3,20:40] += np.hanning(20) * 2
# create the sensor dimension from 5 sensor locations in 3d space
sensor = Sensor([[0,0,0],[1,0,0],[0,-1,0],[-1,0,0],[0,1,0]],
sysname='testnet', proj2d=None)
# combine all these into the NDVar. Plotting defaults are stored in the info
# dict:
info = {'vmax': 2.5, 'meas': 'B', 'cmap': 'xpolar', 'unit': 'pT'}
Y = NDVar(x, dims=('case', sensor, time), name='Y', info=info)
# To describe the cases ('case' dimension), create a condition and a subject Factor
def test_clusterdist():
"Test _ClusterDist class"
shape = (10, 6, 6, 4)
locs = [[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]]
x = np.random.normal(0, 1, shape)
sensor = Sensor(locs, ['0', '1', '2', '3'])
sensor.set_connectivity(connect_dist=1.1)
dims = ('case', UTS(-0.1, 0.1, 6), Ordered('dim2', range(6),
'unit'), sensor)
y = NDVar(x, dims)
# test connecting sensors
logger.info("TEST: connecting sensors")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3, :2] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(y, 0, 1.5)
print repr(cdist)
cdist.add_original(pmap)
print repr(cdist)
assert_equal(cdist.n_clusters, 1)
assert_array_equal(cdist._original_cluster_map == cdist._cids[0],
cdist._crop(bin_map).swapaxes(0, cdist._nad_ax))
assert_equal(cdist.parameter_map.dims, y.dims[1:])
# test connecting many sensors
logger.info("TEST: connecting sensors")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(y, 0, 1.5)
cdist.add_original(pmap)
assert_equal(cdist.n_clusters, 1)
assert_array_equal(cdist._original_cluster_map == cdist._cids[0],
cdist._crop(bin_map).swapaxes(0, cdist._nad_ax))
# test keeping sensors separate
logger.info("TEST: keeping sensors separate")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3, 0] = True
bin_map[:3, :3, 2] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(y, 1, 1.5)
cdist.add_original(pmap)
assert_equal(cdist.n_clusters, 2)
# criteria
ds = datasets.get_uts(True)
res = testnd.ttest_rel('utsnd',
'A',
match='rm',
ds=ds,
samples=0,
pmin=0.05)
assert_less(res.clusters['duration'].min(), 0.01)
eq_(res.clusters['n_sensors'].min(), 1)
res = testnd.ttest_rel('utsnd',
'A',
match='rm',
ds=ds,
samples=0,
pmin=0.05,
mintime=0.02,
minsensor=2)
assert_greater_equal(res.clusters['duration'].min(), 0.02)
eq_(res.clusters['n_sensors'].min(), 2)
# TFCE
logger.info("TEST: TFCE")
sensor = Sensor(locs, ['0', '1', '2', '3'])
sensor.set_connectivity(connect_dist=1.1)
dims = ('case', UTS(-0.1, 0.1,
4), sensor, Ordered('dim2', range(10), 'unit'))
y = NDVar(np.random.normal(0, 1, (10, 4, 4, 10)), dims)
cdist = _ClusterDist(y, 3, None)
cdist.add_original(y.x[0])
cdist.finalize()
assert_equal(cdist.dist.shape, (3, ))
# I/O
string = pickle.dumps(cdist, pickle.HIGHEST_PROTOCOL)
cdist_ = pickle.loads(string)
assert_equal(repr(cdist_), repr(cdist))
# find peaks
x = np.array([[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 6, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 7, 5, 5, 0, 0],
[0, 0, 0, 0, 5, 4, 4, 4, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 4, 0, 0],
[0, 0, 0, 0, 7, 0, 0, 3, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]])
tgt = np.equal(x, 7)
peaks = cdist._find_peaks(x)
logging.debug(' detected: \n%s' % (peaks.astype(int)))
logging.debug(' target: \n%s' % (tgt.astype(int)))
assert_array_equal(peaks, tgt)
mps = False, True
thresholds = (None, 'tfce')
for mp, threshold in product(mps, thresholds):
logger.info("TEST: multiprocessing=%r, threshold=%r" %
(mp, threshold))
_testnd.multiprocessing = mp
# test keeping dimension
cdist = _ClusterDist(y, 5, threshold, dist_dim='sensor')
print repr(cdist)
cdist.add_original(y.x[0])
print repr(cdist)
assert_equal(cdist.dist.shape, (5, 4))
# test keeping time bins
cdist = _ClusterDist(y, 5, threshold, dist_tstep=0.2)
cdist.add_original(y.x[0])
assert_equal(cdist.dist.shape, (5, 2))
assert_raises(ValueError,
_ClusterDist,
y,
5,
threshold,
dist_tstep=0.3)
# test keeping dimension and time bins
cdist = _ClusterDist(y,
5,
threshold,
dist_dim='sensor',
dist_tstep=0.2)
cdist.add_original(y.x[0])
assert_equal(cdist.dist.shape, (5, 4, 2))
# test keeping 2 dimensions and time bins
cdist = _ClusterDist(y,
5,
threshold,
dist_dim=('sensor', 'dim2'),
dist_tstep=0.2)
cdist.add_original(y.x[0])
assert_equal(cdist.dist.shape, (5, 4, 2, 10))
def test_clusterdist():
"Test _ClusterDist class"
shape = (10, 6, 6, 4)
locs = [[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]]
x = np.random.normal(0, 1, shape)
dims = ('case', UTS(-0.1, 0.1, 6), Ordered('dim2', range(6), 'unit'),
Sensor(locs, ['0', '1', '2', '3'], connect_dist=1.1))
Y = NDVar(x, dims)
# test connecting sensors
logger.info("TEST: connecting sensors")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3, :2] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(Y, 0, 1.5)
print repr(cdist)
cdist.add_original(pmap)
print repr(cdist)
assert_equal(cdist.n_clusters, 1)
assert_array_equal(cdist._original_cluster_map == cdist._cids[0],
cdist._crop(bin_map).swapaxes(0, cdist._nad_ax))
assert_equal(cdist.parameter_map.dims, Y.dims[1:])
# test connecting many sensors
logger.info("TEST: connecting sensors")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(Y, 0, 1.5)
cdist.add_original(pmap)
assert_equal(cdist.n_clusters, 1)
assert_array_equal(cdist._original_cluster_map == cdist._cids[0],
cdist._crop(bin_map).swapaxes(0, cdist._nad_ax))
# test keeping sensors separate
logger.info("TEST: keeping sensors separate")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3, 0] = True
bin_map[:3, :3, 2] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(Y, 1, 1.5)
cdist.add_original(pmap)
assert_equal(cdist.n_clusters, 2)
# TFCE
logger.info("TEST: TFCE")
dims = ('case', UTS(-0.1, 0.1,
4), Sensor(locs, ['0', '1', '2', '3'],
connect_dist=1.1),
Ordered('dim2', range(10), 'unit'))
Y = NDVar(np.random.normal(0, 1, (10, 4, 4, 10)), dims)
cdist = _ClusterDist(Y, 3, None)
cdist.add_original(Y.x[0])
for i in xrange(1, 4):
cdist.add_perm(Y.x[i])
assert_equal(cdist.dist.shape, (3, ))
# I/O
string = pickle.dumps(cdist, pickle.HIGHEST_PROTOCOL)
cdist_ = pickle.loads(string)
assert_equal(repr(cdist_), repr(cdist))
# find peaks
x = np.array([[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 6, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 7, 5, 5, 0, 0],
[0, 0, 0, 0, 5, 4, 4, 4, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 4, 0, 0],
[0, 0, 0, 0, 7, 0, 0, 3, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]])
tgt = np.equal(x, 7)
peaks = cdist._find_peaks(x)
logging.debug(' detected: \n%s' % (peaks.astype(int)))
logging.debug(' target: \n%s' % (tgt.astype(int)))
assert_array_equal(peaks, tgt)
mps = False, True
thresholds = (None, 'tfce')
for mp, threshold in product(mps, thresholds):
logger.info("TEST: multiprocessing=%r, threshold=%r" %
(mp, threshold))
_testnd.multiprocessing = mp
# test keeping dimension
cdist = _ClusterDist(Y, 5, threshold, dist_dim='sensor')
print repr(cdist)
cdist.add_original(Y.x[0])
print repr(cdist)
for i in xrange(1, 6):
cdist.add_perm(Y.x[i])
print repr(cdist)
assert_equal(cdist.dist.shape, (5, 4))
# test keeping time bins
cdist = _ClusterDist(Y, 5, threshold, dist_tstep=0.2)
cdist.add_original(Y.x[0])
for i in xrange(1, 6):
cdist.add_perm(Y.x[i])
assert_equal(cdist.dist.shape, (5, 2))
assert_raises(ValueError,
_ClusterDist,
Y,
5,
threshold,
dist_tstep=0.3)
# test keeping dimension and time bins
cdist = _ClusterDist(Y,
5,
threshold,
dist_dim='sensor',
dist_tstep=0.2)
cdist.add_original(Y.x[0])
for i in xrange(1, 6):
cdist.add_perm(Y.x[i])
assert_equal(cdist.dist.shape, (5, 4, 2))
# test keeping 2 dimensions and time bins
cdist = _ClusterDist(Y,
5,
threshold,
dist_dim=('sensor', 'dim2'),
dist_tstep=0.2)
cdist.add_original(Y.x[0])
for i in xrange(1, 6):
cdist.add_perm(Y.x[i])
assert_equal(cdist.dist.shape, (5, 4, 2, 10))
def test_clusterdist():
"Test _ClusterDist class"
shape = (10, 6, 6, 4)
locs = [[0, 0, 0],
[1, 0, 0],
[1, 1, 0],
[0, 1, 0]]
x = np.random.normal(0, 1, shape)
sensor = Sensor(locs, ['0', '1', '2', '3'])
sensor.set_connectivity(connect_dist=1.1)
dims = ('case', UTS(-0.1, 0.1, 6), Ordered('dim2', list(range(6)), 'unit'),
sensor)
y = NDVar(x, dims)
# test connecting sensors
logging.info("TEST: connecting sensors")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3, :2] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(y, 0, 1.5)
print(repr(cdist))
cdist.add_original(pmap)
print(repr(cdist))
assert_equal(cdist.n_clusters, 1)
assert_array_equal(cdist._original_cluster_map == cdist._cids[0],
cdist._crop(bin_map).swapaxes(0, cdist._nad_ax))
assert_equal(cdist.parameter_map.dims, y.dims[1:])
# test connecting many sensors
logging.info("TEST: connecting sensors")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(y, 0, 1.5)
cdist.add_original(pmap)
assert_equal(cdist.n_clusters, 1)
assert_array_equal(cdist._original_cluster_map == cdist._cids[0],
cdist._crop(bin_map).swapaxes(0, cdist._nad_ax))
# test keeping sensors separate
logging.info("TEST: keeping sensors separate")
bin_map = np.zeros(shape[1:], dtype=np.bool8)
bin_map[:3, :3, 0] = True
bin_map[:3, :3, 2] = True
pmap = np.random.normal(0, 1, shape[1:])
np.clip(pmap, -1, 1, pmap)
pmap[bin_map] = 2
cdist = _ClusterDist(y, 1, 1.5)
cdist.add_original(pmap)
assert_equal(cdist.n_clusters, 2)
# criteria
ds = datasets.get_uts(True)
res = testnd.ttest_rel('utsnd', 'A', match='rm', ds=ds, samples=0, pmin=0.05)
assert_less(res.clusters['duration'].min(), 0.01)
eq_(res.clusters['n_sensors'].min(), 1)
res = testnd.ttest_rel('utsnd', 'A', match='rm', ds=ds, samples=0, pmin=0.05,
mintime=0.02, minsensor=2)
assert_greater_equal(res.clusters['duration'].min(), 0.02)
eq_(res.clusters['n_sensors'].min(), 2)
# 1d
res1d = testnd.ttest_rel('utsnd.sub(time=0.1)', 'A', match='rm', ds=ds,
samples=0, pmin=0.05)
assert_dataobj_equal(res1d.p_uncorrected, res.p_uncorrected.sub(time=0.1))
# TFCE
logging.info("TEST: TFCE")
sensor = Sensor(locs, ['0', '1', '2', '3'])
sensor.set_connectivity(connect_dist=1.1)
dims = ('case', UTS(-0.1, 0.1, 4), sensor,
Ordered('dim2', list(range(10)), 'unit'))
y = NDVar(np.random.normal(0, 1, (10, 4, 4, 10)), dims)
cdist = _ClusterDist(y, 3, None)
cdist.add_original(y.x[0])
cdist.finalize()
assert_equal(cdist.dist.shape, (3,))
# I/O
string = pickle.dumps(cdist, pickle.HIGHEST_PROTOCOL)
cdist_ = pickle.loads(string)
assert_equal(repr(cdist_), repr(cdist))
# find peaks
x = np.array([[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 6, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 7, 5, 5, 0, 0],
[0, 0, 0, 0, 5, 4, 4, 4, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 4, 0, 0],
[0, 0, 0, 0, 7, 0, 0, 3, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]])
tgt = np.equal(x, 7)
peaks = cdist._find_peaks(x)
logging.debug(' detected: \n%s' % (peaks.astype(int)))
logging.debug(' target: \n%s' % (tgt.astype(int)))
assert_array_equal(peaks, tgt)