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