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957 lines (787 loc) · 39.3 KB
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import numpy as np
import logging
import pytest
from brainiak.isc import (isc, isfc, bootstrap_isc, permutation_isc,
squareform_isfc, timeshift_isc,
phaseshift_isc)
from scipy.spatial.distance import squareform
logger = logging.getLogger(__name__)
# Create simple simulated data with high intersubject correlation
def simulated_timeseries(n_subjects, n_TRs, n_voxels=30,
noise=1, data_type='array',
random_state=None):
prng = np.random.RandomState(random_state)
if n_voxels:
signal = prng.randn(n_TRs, n_voxels)
prng = np.random.RandomState(prng.randint(0, 2**32 - 1))
data = [signal + prng.randn(n_TRs, n_voxels) * noise
for subject in np.arange(n_subjects)]
elif not n_voxels:
signal = prng.randn(n_TRs)
prng = np.random.RandomState(prng.randint(0, 2**32 - 1))
data = [signal + prng.randn(n_TRs) * noise
for subject in np.arange(n_subjects)]
if data_type == 'array':
if n_voxels:
data = np.dstack(data)
elif not n_voxels:
data = np.column_stack(data)
return data
# Create 3 voxel simulated data with correlated time series
def correlated_timeseries(n_subjects, n_TRs, noise=0,
random_state=None):
prng = np.random.RandomState(random_state)
signal = prng.randn(n_TRs)
correlated = True
while correlated:
uncorrelated = np.random.randn(n_TRs,
n_subjects)[:, np.newaxis, :]
unc_max = np.amax(squareform(np.corrcoef(
uncorrelated[:, 0, :].T), checks=False))
unc_mean = np.mean(squareform(np.corrcoef(
uncorrelated[:, 0, :].T), checks=False))
if unc_max < .25 and np.abs(unc_mean) < .001:
correlated = False
data = np.repeat(np.column_stack((signal, signal))[..., np.newaxis],
n_subjects, axis=2)
data = np.concatenate((data, uncorrelated), axis=1)
data = data + np.random.randn(n_TRs, 3, n_subjects) * noise
return data
# Compute ISCs using different input types
# List of subjects with one voxel/ROI
def test_isc_input():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
random_state = 42
logger.info("Testing ISC inputs")
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=None, data_type='list',
random_state=random_state)
iscs_list = isc(data, pairwise=False, summary_statistic=None)
# Array of subjects with one voxel/ROI
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=None, data_type='array',
random_state=random_state)
iscs_array = isc(data, pairwise=False, summary_statistic=None)
# Check they're the same
assert np.array_equal(iscs_list, iscs_array)
# List of subjects with multiple voxels/ROIs
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='list',
random_state=random_state)
iscs_list = isc(data, pairwise=False, summary_statistic=None)
# Array of subjects with multiple voxels/ROIs
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
iscs_array = isc(data, pairwise=False, summary_statistic=None)
# Check they're the same
assert np.array_equal(iscs_list, iscs_array)
logger.info("Finished testing ISC inputs")
# Check pairwise and leave-one-out, and summary statistics for ISC
def test_isc_options():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
random_state = 42
logger.info("Testing ISC options")
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
iscs_loo = isc(data, pairwise=False, summary_statistic=None)
assert iscs_loo.shape == (n_subjects, n_voxels)
# Just two subjects
iscs_loo = isc(data[..., :2], pairwise=False, summary_statistic=None)
assert iscs_loo.shape == (n_voxels,)
iscs_pw = isc(data, pairwise=True, summary_statistic=None)
assert iscs_pw.shape == (n_subjects*(n_subjects-1)/2, n_voxels)
# Check summary statistics
isc_mean = isc(data, pairwise=False, summary_statistic='mean')
assert isc_mean.shape == (n_voxels,)
isc_median = isc(data, pairwise=False, summary_statistic='median')
assert isc_median.shape == (n_voxels,)
with pytest.raises(ValueError):
isc(data, pairwise=False, summary_statistic='min')
logger.info("Finished testing ISC options")
# Make sure ISC recovers correlations of 1 and less than 1
def test_isc_output():
logger.info("Testing ISC outputs")
data = correlated_timeseries(20, 60, noise=0,
random_state=42)
iscs = isc(data, pairwise=False)
assert np.allclose(iscs[:, :2], 1., rtol=1e-05)
assert np.all(iscs[:, -1] < 1.)
iscs = isc(data, pairwise=True)
assert np.allclose(iscs[:, :2], 1., rtol=1e-05)
assert np.all(iscs[:, -1] < 1.)
logger.info("Finished testing ISC outputs")
# Check for proper handling of NaNs in ISC
def test_isc_nans():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
random_state = 42
logger.info("Testing ISC options")
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
# Inject NaNs into data
data[0, 0, 0] = np.nan
# Don't tolerate NaNs, should lose zeroeth voxel
iscs_loo = isc(data, pairwise=False, tolerate_nans=False)
assert np.sum(np.isnan(iscs_loo)) == n_subjects
# Tolerate all NaNs, only subject with NaNs yields NaN
iscs_loo = isc(data, pairwise=False, tolerate_nans=True)
assert np.sum(np.isnan(iscs_loo)) == 1
# Pairwise approach shouldn't care
iscs_pw_T = isc(data, pairwise=True, tolerate_nans=True)
iscs_pw_F = isc(data, pairwise=True, tolerate_nans=False)
assert np.allclose(iscs_pw_T, iscs_pw_F, equal_nan=True)
assert (np.sum(np.isnan(iscs_pw_T)) ==
np.sum(np.isnan(iscs_pw_F)) ==
n_subjects - 1)
# Set proportion of nans to reject (70% and 90% non-NaN)
data[0, 0, :] = np.nan
data[0, 1, :n_subjects - int(n_subjects * .7)] = np.nan
data[0, 2, :n_subjects - int(n_subjects * .9)] = np.nan
iscs_loo_T = isc(data, pairwise=False, tolerate_nans=True)
iscs_loo_F = isc(data, pairwise=False, tolerate_nans=False)
iscs_loo_95 = isc(data, pairwise=False, tolerate_nans=.95)
iscs_loo_90 = isc(data, pairwise=False, tolerate_nans=.90)
iscs_loo_80 = isc(data, pairwise=False, tolerate_nans=.8)
iscs_loo_70 = isc(data, pairwise=False, tolerate_nans=.7)
iscs_loo_60 = isc(data, pairwise=False, tolerate_nans=.6)
assert (np.sum(np.isnan(iscs_loo_F)) ==
np.sum(np.isnan(iscs_loo_95)) == 60)
assert (np.sum(np.isnan(iscs_loo_80)) ==
np.sum(np.isnan(iscs_loo_90)) == 42)
assert (np.sum(np.isnan(iscs_loo_T)) ==
np.sum(np.isnan(iscs_loo_60)) ==
np.sum(np.isnan(iscs_loo_70)) == 28)
assert np.array_equal(np.sum(np.isnan(iscs_loo_F), axis=0),
np.sum(np.isnan(iscs_loo_95), axis=0))
assert np.array_equal(np.sum(np.isnan(iscs_loo_80), axis=0),
np.sum(np.isnan(iscs_loo_90), axis=0))
assert np.all((np.array_equal(
np.sum(np.isnan(iscs_loo_T), axis=0),
np.sum(np.isnan(iscs_loo_60), axis=0)),
np.array_equal(
np.sum(np.isnan(iscs_loo_T), axis=0),
np.sum(np.isnan(iscs_loo_70), axis=0)),
np.array_equal(
np.sum(np.isnan(iscs_loo_60), axis=0),
np.sum(np.isnan(iscs_loo_70), axis=0))))
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
# Make sure voxel with NaNs across all subjects is always removed
data[0, 0, :] = np.nan
iscs_loo_T = isc(data, pairwise=False, tolerate_nans=True)
iscs_loo_F = isc(data, pairwise=False, tolerate_nans=False)
assert np.allclose(iscs_loo_T, iscs_loo_F, equal_nan=True)
assert (np.sum(np.isnan(iscs_loo_T)) ==
np.sum(np.isnan(iscs_loo_F)) ==
n_subjects)
iscs_pw_T = isc(data, pairwise=True, tolerate_nans=True)
iscs_pw_F = isc(data, pairwise=True, tolerate_nans=False)
assert np.allclose(iscs_pw_T, iscs_pw_F, equal_nan=True)
assert (np.sum(np.isnan(iscs_pw_T)) ==
np.sum(np.isnan(iscs_pw_F)) ==
n_subjects * (n_subjects - 1) / 2)
# Test one-sample bootstrap test
def test_bootstrap_isc():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
random_state = 42
n_bootstraps = 10
logger.info("Testing bootstrap hypothesis test")
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
iscs = isc(data, pairwise=False, summary_statistic=None)
observed, ci, p, distribution = bootstrap_isc(iscs, pairwise=False,
summary_statistic='median',
n_bootstraps=n_bootstraps,
ci_percentile=95)
assert distribution.shape == (n_bootstraps, n_voxels)
# Test one-sample bootstrap test with pairwise approach
n_bootstraps = 10
iscs = isc(data, pairwise=True, summary_statistic=None)
observed, ci, p, distribution = bootstrap_isc(iscs, pairwise=True,
summary_statistic='median',
n_bootstraps=n_bootstraps,
ci_percentile=95)
assert distribution.shape == (n_bootstraps, n_voxels)
# Check random seeds
iscs = isc(data, pairwise=False, summary_statistic=None)
distributions = []
for random_state in [42, 42, None]:
observed, ci, p, distribution = bootstrap_isc(
iscs, pairwise=False,
summary_statistic='median',
n_bootstraps=n_bootstraps,
ci_percentile=95,
random_state=random_state)
distributions.append(distribution)
assert np.array_equal(distributions[0], distributions[1])
assert not np.array_equal(distributions[1], distributions[2])
# Check output p-values
data = correlated_timeseries(20, 60, noise=.5,
random_state=42)
iscs = isc(data, pairwise=False)
observed, ci, p, distribution = bootstrap_isc(iscs, pairwise=False)
assert np.all(iscs[:, :2] > .5)
assert np.all(iscs[:, -1] < .5)
assert p[0] < .05 and p[1] < .05
assert p[2] > .01
print(p)
iscs = isc(data, pairwise=True)
observed, ci, p, distribution = bootstrap_isc(iscs, pairwise=True)
assert np.all(iscs[:, :2] > .5)
assert np.all(iscs[:, -1] < .5)
assert p[0] < .05 and p[1] < .05
assert p[2] > .01
# Check that ISC computation and bootstrap observed are same
iscs = isc(data, pairwise=False)
observed, ci, p, distribution = bootstrap_isc(iscs, pairwise=False,
summary_statistic='median')
assert np.array_equal(observed, isc(data, pairwise=False,
summary_statistic='median'))
# Check that ISC computation and bootstrap observed are same
iscs = isc(data, pairwise=True)
observed, ci, p, distribution = bootstrap_isc(iscs, pairwise=True,
summary_statistic='median')
assert np.array_equal(observed, isc(data, pairwise=True,
summary_statistic='median'))
logger.info("Finished testing bootstrap hypothesis test")
# Test permutation test with group assignments
def test_permutation_isc():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
random_state = 42
group_assignment = [1] * 10 + [2] * 10
logger.info("Testing permutation test")
# Create dataset with two groups in pairwise approach
data = np.dstack((simulated_timeseries(10, n_TRs, n_voxels=n_voxels,
noise=1, data_type='array',
random_state=3),
simulated_timeseries(10, n_TRs, n_voxels=n_voxels,
noise=5, data_type='array',
random_state=4)))
iscs = isc(data, pairwise=True, summary_statistic=None)
observed, p, distribution = permutation_isc(
iscs,
group_assignment=group_assignment,
pairwise=True,
summary_statistic='mean',
n_permutations=200)
# Create data with two groups in leave-one-out approach
data_1 = simulated_timeseries(10, n_TRs, n_voxels=n_voxels,
noise=1, data_type='array',
random_state=3)
data_2 = simulated_timeseries(10, n_TRs, n_voxels=n_voxels,
noise=10, data_type='array',
random_state=4)
iscs = np.vstack((isc(data_1, pairwise=False, summary_statistic=None),
isc(data_2, pairwise=False, summary_statistic=None)))
observed, p, distribution = permutation_isc(
iscs,
group_assignment=group_assignment,
pairwise=False,
summary_statistic='mean',
n_permutations=200)
# One-sample leave-one-out permutation test
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
iscs = isc(data, pairwise=False, summary_statistic=None)
observed, p, distribution = permutation_isc(iscs,
pairwise=False,
summary_statistic='median',
n_permutations=200)
# One-sample pairwise permutation test
iscs = isc(data, pairwise=True, summary_statistic=None)
observed, p, distribution = permutation_isc(iscs,
pairwise=True,
summary_statistic='median',
n_permutations=200)
# Small one-sample pairwise exact test
data = simulated_timeseries(12, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
iscs = isc(data, pairwise=False, summary_statistic=None)
observed, p, distribution = permutation_isc(iscs, pairwise=False,
summary_statistic='median',
n_permutations=10000)
# Small two-sample pairwise exact test (and unequal groups)
data = np.dstack((simulated_timeseries(3, n_TRs, n_voxels=n_voxels,
noise=1, data_type='array',
random_state=3),
simulated_timeseries(4, n_TRs, n_voxels=n_voxels,
noise=50, data_type='array',
random_state=4)))
iscs = isc(data, pairwise=True, summary_statistic=None)
group_assignment = [1, 1, 1, 2, 2, 2, 2]
observed, p, distribution = permutation_isc(
iscs,
group_assignment=group_assignment,
pairwise=True,
summary_statistic='mean',
n_permutations=10000)
# Small two-sample leave-one-out exact test (and unequal groups)
data_1 = simulated_timeseries(3, n_TRs, n_voxels=n_voxels,
noise=1, data_type='array',
random_state=3)
data_2 = simulated_timeseries(4, n_TRs, n_voxels=n_voxels,
noise=50, data_type='array',
random_state=4)
iscs = np.vstack((isc(data_1, pairwise=False, summary_statistic=None),
isc(data_2, pairwise=False, summary_statistic=None)))
group_assignment = [1, 1, 1, 2, 2, 2, 2]
observed, p, distribution = permutation_isc(
iscs,
group_assignment=group_assignment,
pairwise=False,
summary_statistic='mean',
n_permutations=10000)
# Check output p-values
data = correlated_timeseries(20, 60, noise=.5,
random_state=42)
iscs = isc(data, pairwise=False)
observed, p, distribution = permutation_isc(iscs, pairwise=False)
assert np.all(iscs[:, :2] > .5)
assert np.all(iscs[:, -1] < .5)
assert p[0] < .05 and p[1] < .05
assert p[2] > .01
iscs = isc(data, pairwise=True)
observed, p, distribution = permutation_isc(iscs, pairwise=True)
assert np.all(iscs[:, :2] > .5)
assert np.all(iscs[:, -1] < .5)
assert p[0] < .05 and p[1] < .05
assert p[2] > .01
# Check that ISC computation and permutation observed are same
iscs = isc(data, pairwise=False)
observed, p, distribution = permutation_isc(iscs, pairwise=False,
summary_statistic='median')
assert np.allclose(observed, isc(data, pairwise=False,
summary_statistic='median'),
rtol=1e-03)
# Check that ISC computation and permuation observed are same
iscs = isc(data, pairwise=True)
observed, p, distribution = permutation_isc(iscs, pairwise=True,
summary_statistic='mean')
assert np.allclose(observed, isc(data, pairwise=True,
summary_statistic='mean'),
rtol=1e-03)
logger.info("Finished testing permutaton test")
def test_timeshift_isc():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
logger.info("Testing circular time-shift")
# Circular time-shift on one sample, leave-one-out
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array')
observed, p, distribution = timeshift_isc(data, pairwise=False,
summary_statistic='median',
n_shifts=200)
# Circular time-shift on one sample, pairwise
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array')
observed, p, distribution = timeshift_isc(data, pairwise=True,
summary_statistic='median',
n_shifts=200)
# Circular time-shift on one sample, leave-one-out
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array')
observed, p, distribution = timeshift_isc(data, pairwise=False,
summary_statistic='mean',
n_shifts=200)
# Check output p-values
data = correlated_timeseries(20, 60, noise=.5,
random_state=42)
iscs = isc(data, pairwise=False)
observed, p, distribution = timeshift_isc(data, pairwise=False)
assert np.all(iscs[:, :2] > .5)
assert np.all(iscs[:, -1] < .5)
assert p[0] < .05 and p[1] < .05
assert p[2] > .01
iscs = isc(data, pairwise=True)
observed, p, distribution = timeshift_isc(data, pairwise=True)
assert np.all(iscs[:, :2] > .5)
assert np.all(iscs[:, -1] < .5)
assert p[0] < .05 and p[1] < .05
assert p[2] > .01
# Check that ISC computation and permutation observed are same
iscs = isc(data, pairwise=False)
observed, p, distribution = timeshift_isc(data, pairwise=False,
summary_statistic='median')
assert np.allclose(observed, isc(data, pairwise=False,
summary_statistic='median'),
rtol=1e-03)
# Check that ISC computation and permuation observed are same
iscs = isc(data, pairwise=True)
observed, p, distribution = timeshift_isc(data, pairwise=True,
summary_statistic='mean')
assert np.allclose(observed, isc(data, pairwise=True,
summary_statistic='mean'),
rtol=1e-03)
logger.info("Finished testing circular time-shift")
# Phase randomization test
def test_phaseshift_isc():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
logger.info("Testing phase randomization")
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array')
observed, p, distribution = phaseshift_isc(data, pairwise=True,
summary_statistic='median',
n_shifts=200)
# Phase randomization one-sample test, leave-one-out
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array')
observed, p, distribution = phaseshift_isc(data, pairwise=False,
summary_statistic='mean',
n_shifts=200)
# Check output p-values
data = correlated_timeseries(20, 60, noise=.5,
random_state=42)
iscs = isc(data, pairwise=False)
observed, p, distribution = phaseshift_isc(data, pairwise=False)
assert np.all(iscs[:, :2] > .5)
assert np.all(iscs[:, -1] < .5)
assert p[0] < .05 and p[1] < .05
assert p[2] > .01
iscs = isc(data, pairwise=True)
observed, p, distribution = phaseshift_isc(data, pairwise=True)
assert np.all(iscs[:, :2] > .5)
assert np.all(iscs[:, -1] < .5)
assert p[0] < .05 and p[1] < .05
assert p[2] > .01
# Check that ISC computation and permutation observed are same
iscs = isc(data, pairwise=False)
observed, p, distribution = phaseshift_isc(data, pairwise=False,
summary_statistic='median')
assert np.allclose(observed, isc(data, pairwise=False,
summary_statistic='median'),
rtol=1e-03)
# Check that ISC computation and permuation observed are same
iscs = isc(data, pairwise=True)
observed, p, distribution = phaseshift_isc(data, pairwise=True,
summary_statistic='mean')
assert np.allclose(observed, isc(data, pairwise=True,
summary_statistic='mean'),
rtol=1e-03)
logger.info("Finished testing phase randomization")
# Test ISFC
def test_isfc_options():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
logger.info("Testing ISFC options")
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array')
isfcs, iscs = isfc(data, pairwise=False, summary_statistic=None)
assert isfcs.shape == (n_subjects, n_voxels * (n_voxels - 1) / 2)
assert iscs.shape == (n_subjects, n_voxels)
# Without vectorized upper triangle
isfcs = isfc(data, pairwise=False, summary_statistic=None,
vectorize_isfcs=False)
assert isfcs.shape == (n_subjects, n_voxels, n_voxels)
# Just two subjects
isfcs, iscs = isfc(data[..., :2], pairwise=False, summary_statistic=None)
assert isfcs.shape == (n_voxels * (n_voxels - 1) / 2,)
assert iscs.shape == (n_voxels,)
isfcs = isfc(data[..., :2], pairwise=False, summary_statistic=None,
vectorize_isfcs=False)
assert isfcs.shape == (n_voxels, n_voxels)
# ISFC with pairwise approach
isfcs, iscs = isfc(data, pairwise=True, summary_statistic=None)
assert isfcs.shape == (n_subjects * (n_subjects - 1) / 2,
n_voxels * (n_voxels - 1) / 2)
assert iscs.shape == (n_subjects * (n_subjects - 1) / 2,
n_voxels)
isfcs = isfc(data, pairwise=True, summary_statistic=None,
vectorize_isfcs=False)
assert isfcs.shape == (n_subjects * (n_subjects - 1) / 2,
n_voxels, n_voxels)
# ISFC with summary statistics
isfcs, iscs = isfc(data, pairwise=True, summary_statistic='mean')
isfcs, iscs = isfc(data, pairwise=True, summary_statistic='median')
# Check output p-values
data = correlated_timeseries(20, 60, noise=.5,
random_state=42)
isfcs = isfc(data, pairwise=False, vectorize_isfcs=False)
assert np.all(isfcs[:, 0, 1] > .5) and np.all(isfcs[:, 1, 0] > .5)
assert np.all(isfcs[:, :2, 2] < .5) and np.all(isfcs[:, 2, :2] < .5)
isfcs = isfc(data, pairwise=True, vectorize_isfcs=False)
assert np.all(isfcs[:, 0, 1] > .5) and np.all(isfcs[:, 1, 0] > .5)
assert np.all(isfcs[:, :2, 2] < .5) and np.all(isfcs[:, 2, :2] < .5)
# Check that ISC and ISFC diagonal are identical
iscs = isc(data, pairwise=False)
isfcs = isfc(data, pairwise=False, vectorize_isfcs=False)
for s in np.arange(len(iscs)):
assert np.allclose(isfcs[s, ...].diagonal(), iscs[s, :], rtol=1e-03)
isfcs, iscs_v = isfc(data, pairwise=False)
assert np.allclose(iscs, iscs_v, rtol=1e-03)
# Check that ISC and ISFC diagonal are identical (pairwise)
iscs = isc(data, pairwise=True)
isfcs = isfc(data, pairwise=True, vectorize_isfcs=False)
for s in np.arange(len(iscs)):
assert np.allclose(isfcs[s, ...].diagonal(), iscs[s, :], rtol=1e-03)
isfcs, iscs_v = isfc(data, pairwise=True)
assert np.allclose(iscs, iscs_v, rtol=1e-03)
# Generate 'targets' data and use for ISFC
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array')
n_targets = 15
targets_data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_targets,
data_type='array')
isfcs = isfc(data, targets=targets_data, pairwise=False,
vectorize_isfcs=False)
assert isfcs.shape == (n_subjects, n_voxels, n_targets)
# Ensure 'square' output enforced
isfcs = isfc(data, targets=targets_data, pairwise=False,
vectorize_isfcs=True)
assert isfcs.shape == (n_subjects, n_voxels, n_targets)
# Check list input for targets
targets_data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_targets,
data_type='list')
isfcs = isfc(data, targets=targets_data, pairwise=False,
vectorize_isfcs=False)
assert isfcs.shape == (n_subjects, n_voxels, n_targets)
# Check that mismatching subjects / TRs breaks targets
targets_data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_targets,
data_type='array')
with pytest.raises(ValueError):
isfcs = isfc(data, targets=targets_data[..., :-1],
pairwise=False, vectorize_isfcs=False)
assert isfcs.shape == (n_subjects, n_voxels, n_targets)
with pytest.raises(ValueError):
isfcs = isfc(data, targets=targets_data[:-1, ...],
pairwise=False, vectorize_isfcs=False)
# Check targets for only 2 subjects
isfcs = isfc(data[..., :2], targets=targets_data[..., :2],
pairwise=False, summary_statistic=None)
assert isfcs.shape == (2, n_voxels, n_targets)
isfcs = isfc(data[..., :2], targets=targets_data[..., :2],
pairwise=True, summary_statistic=None)
assert isfcs.shape == (2, n_voxels, n_targets)
# Check that supplying targets enforces leave-one-out
isfcs_pw = isfc(data, targets=targets_data, pairwise=True,
vectorize_isfcs=False, tolerate_nans=False)
assert isfcs_pw.shape == (n_subjects, n_voxels, n_targets)
logger.info("Finished testing ISFC options")
# Check for proper ha
10BC8
ndling of NaNs in ISFC
def test_isfc_nans():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
random_state = 42
logger.info("Testing ISC options")
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
# Inject NaNs into data
data[0, 0, 0] = np.nan
# Don't tolerate NaNs, should lose zeroeth voxel
isfcs_loo = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=False)
assert np.sum(np.isnan(isfcs_loo)) == n_subjects * (n_voxels * 2 - 1)
# With vectorized ISFCs
isfcs_loo, iscs_loo = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=False)
assert np.sum(np.isnan(isfcs_loo)) == n_subjects * (n_voxels - 1)
# Tolerate all NaNs, only subject with NaNs yields NaN
isfcs_loo = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=True)
assert np.sum(np.isnan(isfcs_loo)) == n_voxels * 2 - 1
isfcs_loo, iscs_loo = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=True)
assert np.sum(np.isnan(isfcs_loo)) == n_voxels - 1
# Pairwise approach shouldn't care
isfcs_pw_T = isfc(data, pairwise=True, vectorize_isfcs=False,
tolerate_nans=True)
isfcs_pw_F = isfc(data, pairwise=True, vectorize_isfcs=False,
tolerate_nans=False)
assert np.allclose(isfcs_pw_T, isfcs_pw_F, equal_nan=True)
assert (np.sum(np.isnan(isfcs_pw_T)) ==
np.sum(np.isnan(isfcs_pw_F)) ==
(n_voxels * 2 - 1) * (n_subjects - 1))
isfcs_pw_T, iscs_pw_T = isfc(data, pairwise=True, vectorize_isfcs=True,
tolerate_nans=True)
isfcs_pw_F, iscs_pw_T = isfc(data, pairwise=True, vectorize_isfcs=True,
tolerate_nans=False)
assert np.allclose(isfcs_pw_T, isfcs_pw_F, equal_nan=True)
assert (np.sum(np.isnan(isfcs_pw_T)) ==
np.sum(np.isnan(isfcs_pw_F)) ==
(n_voxels - 1) * (n_subjects - 1))
# Set proportion of nans to reject (70% and 90% non-NaN)
data[0, 0, :] = np.nan
data[0, 1, :n_subjects - int(n_subjects * .7)] = np.nan
data[0, 2, :n_subjects - int(n_subjects * .9)] = np.nan
isfcs_loo_T = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=True)
isfcs_loo_F = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=False)
isfcs_loo_95 = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=.95)
isfcs_loo_90 = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=.90)
isfcs_loo_80 = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=.8)
isfcs_loo_70 = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=.7)
isfcs_loo_60 = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=.6)
assert (np.sum(np.isnan(isfcs_loo_F)) ==
np.sum(np.isnan(isfcs_loo_95)) == 3420)
assert (np.sum(np.isnan(isfcs_loo_80)) ==
np.sum(np.isnan(isfcs_loo_90)) == 2430)
assert (np.sum(np.isnan(isfcs_loo_T)) ==
np.sum(np.isnan(isfcs_loo_60)) ==
np.sum(np.isnan(isfcs_loo_70)) == 1632)
assert np.array_equal(np.sum(np.isnan(isfcs_loo_F), axis=0),
np.sum(np.isnan(isfcs_loo_95), axis=0))
assert np.array_equal(np.sum(np.isnan(isfcs_loo_80), axis=0),
np.sum(np.isnan(isfcs_loo_90), axis=0))
assert np.all((np.array_equal(
np.sum(np.isnan(isfcs_loo_T), axis=0),
np.sum(np.isnan(isfcs_loo_60), axis=0)),
np.array_equal(
np.sum(np.isnan(isfcs_loo_T), axis=0),
np.sum(np.isnan(isfcs_loo_70), axis=0)),
np.array_equal(
np.sum(np.isnan(isfcs_loo_60), axis=0),
np.sum(np.isnan(isfcs_loo_70), axis=0))))
isfcs_loo_T, _ = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=True)
isfcs_loo_F, _ = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=False)
isfcs_loo_95, _ = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=.95)
isfcs_loo_90, _ = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=.90)
isfcs_loo_80, _ = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=.8)
isfcs_loo_70, _ = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=.7)
isfcs_loo_60, _ = isfc(data, pairwise=False, vectorize_isfcs=True,
tolerate_nans=.6)
assert (np.sum(np.isnan(isfcs_loo_F)) ==
np.sum(np.isnan(isfcs_loo_95)) == 1680)
assert (np.sum(np.isnan(isfcs_loo_80)) ==
np.sum(np.isnan(isfcs_loo_90)) == 1194)
assert (np.sum(np.isnan(isfcs_loo_T)) ==
np.sum(np.isnan(isfcs_loo_60)) ==
np.sum(np.isnan(isfcs_loo_70)) == 802)
assert np.array_equal(np.sum(np.isnan(isfcs_loo_F), axis=0),
np.sum(np.isnan(isfcs_loo_95), axis=0))
assert np.array_equal(np.sum(np.isnan(isfcs_loo_80), axis=0),
np.sum(np.isnan(isfcs_loo_90), axis=0))
assert np.all((np.array_equal(
np.sum(np.isnan(isfcs_loo_T), axis=0),
np.sum(np.isnan(isfcs_loo_60), axis=0)),
np.array_equal(
np.sum(np.isnan(isfcs_loo_T), axis=0),
np.sum(np.isnan(isfcs_loo_70), axis=0)),
np.array_equal(
np.sum(np.isnan(isfcs_loo_60), axis=0),
np.sum(np.isnan(isfcs_loo_70), axis=0))))
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
# Make sure voxel with NaNs across all subjects is always removed
data[0, 0, :] = np.nan
isfcs_loo_T = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=True)
isfcs_loo_F = isfc(data, pairwise=False, vectorize_isfcs=False,
tolerate_nans=False)
assert np.allclose(isfcs_loo_T, isfcs_loo_F, equal_nan=True)
assert (np.sum(np.isnan(isfcs_loo_T)) ==
np.sum(np.isnan(isfcs_loo_F)) ==
1180)
isfcs_pw_T = isfc(data, pairwise=True, vectorize_isfcs=False,
tolerate_nans=True)
isfcs_pw_F = isfc(data, pairwise=True, vectorize_isfcs=False,
tolerate_nans=False)
assert np.allclose(isfcs_pw_T, isfcs_pw_F, equal_nan=True)
assert (np.sum(np.isnan(isfcs_pw_T)) ==
np.sum(np.isnan(isfcs_pw_T)) ==
11210)
# Check for NaN-handling in targets
n_targets = 15
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
targets_data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_targets,
data_type='array')
# Inject NaNs into targets_data
targets_data[0, 0, 0] = np.nan
# Don't tolerate NaNs, should lose zeroeth voxel
isfcs_loo = isfc(data, targets=targets_data, pairwise=False,
vectorize_isfcs=False, tolerate_nans=False)
assert np.sum(np.isnan(isfcs_loo)) == (n_subjects - 1) * (n_targets * 2)
# Single NaN in targets will get averaged out with tolerate
isfcs_loo = isfc(data, targets=targets_data, pairwise=False,
vectorize_isfcs=False, tolerate_nans=True)
assert np.sum(np.isnan(isfcs_loo)) == 0
def test_squareform_isfc():
# Set parameters for toy time series data
n_subjects = 20
n_TRs = 60
n_voxels = 30
random_state = 42
logger.info("Testing ISC options")
data = simulated_timeseries(n_subjects, n_TRs,
n_voxels=n_voxels, data_type='array',
random_state=random_state)
# Generate square redundant ISFCs
isfcs_r = isfc(data, vectorize_isfcs=False)
assert isfcs_r.shape == (n_subjects, n_voxels, n_voxels)
# Squareform these into condensed ISFCs and ISCs
isfcs_c, iscs_c = squareform_isfc(isfcs_r)
assert isfcs_c.shape == (n_subjects, n_voxels * (n_voxels - 1) / 2)
assert iscs_c.shape == (n_subjects, n_voxels)
# Go back the other way and check it's the same
isfcs_new = squareform_isfc(isfcs_c, iscs_c)
assert np.array_equal(isfcs_r, isfcs_new)
# Check against ISC function
assert np.allclose(isc(data), iscs_c, rtol=1e-03)
# Check for two subjects
isfcs_r = isfc(data[..., :2], vectorize_isfcs=False)
assert isfcs_r.shape == (n_voxels, n_voxels)
isfcs_c, iscs_c = squareform_isfc(isfcs_r)
assert isfcs_c.shape == (n_voxels * (n_voxels - 1) / 2,)
assert iscs_c.shape == (n_voxels,)
assert np.array_equal(isfcs_r, squareform_isfc(isfcs_c, iscs_c))
if __name__ == '__main__':
test_isc_input()
test_isc_options()
test_isc_output()
test_isc_nans()
test_bootstrap_isc()
test_permutation_isc()
test_timeshift_isc()
test_phaseshift_isc()
test_isfc_options()
test_isfc_nans()
test_squareform_isfc()
logger.info("Finished all ISC tests")