def test_mi_canolty():
"""
Test PAC function: Canolty MI
1. Confirm consistency of output with example data
"""
# Load data
data = np.load(os.path.dirname(pacpy.__file__) + '/tests/exampledata.npy')
np.random.seed(0)
assert np.allclose(
mi_canolty(data, data, (13, 30), (80, 200)), 22.08946, atol=10 ** -5)
def test_mi_canolty():
"""
Test PAC function: Canolty MI
1. Confirm consistency of output with example data
"""
# Load data
data = np.load(os.path.dirname(pacpy.__file__) + '/tests/exampledata.npy')
np.random.seed(0)
assert np.allclose(mi_canolty(data, data, (13, 30), (80, 200)),
22.08946,
atol=10**-5)
def test_mi_canolty():
"""
Test PAC function: Canolty MI
1. Confirm consistency of output with example data
2. Confirm consistency of output with example data using iir filter
3. Confirm PAC=1 when expected
4. Confirm PAC=0 when expected
"""
# Load data
data = np.load(os.path.dirname(pacpy.__file__) + '/tests/exampledata.npy')
assert np.allclose(
mi_canolty(data, data, (13, 30), (80, 200)), 1.10063, atol=10 ** -5)
assert np.allclose(mi_canolty(
data, data, (13, 30), (80, 200), filterfn=butterf), 1.14300, atol=10 ** -5)
# Test that the Canolty MI function outputs close to 0 and 1 when expected
lo, hi = genPAC1(phabias=.2, fhi=300)
hif = firf(hi, (100, 400))
amp = np.abs(hilbert(hif))
assert mi_canolty(lo, hi, (4, 6), (100, 400)) / np.mean(amp) > 0.99
lo, hi = genPAC0()
assert mi_canolty(lo, hi, (4, 6), (90, 110)) < 0.001
# Test that Filterfn = False works as expected
datalo = firf(data, (13,30))
datahi = firf(data, (80,200))
pha = np.angle(hilbert(datalo))
amp = np.abs(hilbert(datahi))
assert np.allclose(
mi_canolty(pha, amp, (13, 30), (80, 200), filterfn=False),
mi_canolty(data, data, (13, 30), (80, 200)), atol=10 ** -5)
def test_mi_canolty():
"""
Test PAC function: Canolty MI
1. Confirm consistency of output with example data
2. Confirm consistency of output with example data using iir filter
3. Confirm PAC=1 when expected
4. Confirm PAC=0 when expected
"""
# Load data
data = np.load(os.path.dirname(pacpy.__file__) + '/tests/exampledata.npy')
assert np.allclose(
mi_canolty(data, data, (13, 30), (80, 200)), 1.10063, atol=10 ** -5)
assert np.allclose(mi_canolty(
data, data, (13, 30), (80, 200), filterfn=butterf), 1.14300, atol=10 ** -5)
# Test that the Canolty MI function outputs close to 0 and 1 when expected
lo, hi = genPAC1(phabias=.2, fhi=300)
hif = firf(hi, (100, 400))
amp = np.abs(hilbert(hif))
assert mi_canolty(lo, hi, (4, 6), (100, 400)) / np.mean(amp) > 0.99
lo, hi = genPAC0()
assert mi_canolty(lo, hi, (4, 6), (90, 110)) < 0.001
