def test_plv():
"""
Test PAC function: PLV.
1. Confirm consistency of output with example data
2. Confirm consistency of output with example data using firfls 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(plv(data, data, (13, 30), (80, 200)),
0.25114,
atol=10**-5)
assert np.allclose(plv(data, data, (13, 30), (80, 200), filterfn=firfls),
0.24715,
atol=10**-5)
# Test that the PLV function outputs close to 0 and 1 when expected
lo, hi = genPAC1()
assert plv(lo, hi, (4, 6), (90, 110)) > 0.99
lo, hi = genPAC0()
assert plv(lo, hi, (4, 6), (90, 110)) < 0.01
# Test that Filterfn = False works as expected
datalo = firf(data, (13, 30))
datahi = firf(data, (80, 200))
datahiamp = np.abs(hilbert(datahi))
datahiamplo = firf(datahiamp, (13, 30))
pha1 = np.angle(hilbert(datalo))
pha2 = np.angle(hilbert(datahiamplo))
pha1, pha2 = _trim_edges(pha1, pha2)
assert np.allclose(plv(pha1, pha2, (13, 30), (80, 200), filterfn=False),
plv(data, data, (13, 30), (80, 200)),
atol=10**-5)
def test_plv():
"""
Test PAC function: PLV.
1. Confirm consistency of output with example data
2. Confirm consistency of output with example data using firfls 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(
plv(data, data, (13, 30), (80, 200)), 0.25114, atol=10 ** -5)
assert np.allclose(
plv(data, data, (13, 30), (80, 200), filterfn=firfls), 0.24715, atol=10 ** -5)
# Test that the PLV function outputs close to 0 and 1 when expected
lo, hi = genPAC1()
assert plv(lo, hi, (4, 6), (90, 110)) > 0.99
lo, hi = genPAC0()
assert plv(lo, hi, (4, 6), (90, 110)) < 0.01
# Test that Filterfn = False works as expected
datalo = firf(data, (13, 30))
datahi = firf(data, (80, 200))
datahiamp = np.abs(hilbert(datahi))
datahiamplo = firf(datahiamp, (13, 30))
pha1 = np.angle(hilbert(datalo))
pha2 = np.angle(hilbert(datahiamplo))
pha1, pha2 = _trim_edges(pha1, pha2)
assert np.allclose(
plv(pha1, pha2, (13, 30), (80, 200), filterfn=False),
plv(data, data, (13, 30), (80, 200)), atol=10 ** -5)
def test_glm():
"""
Test PAC function: GLM
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(
glm(data, data, (13, 30), (80, 200)), 0.03191, atol=10 ** -5)
assert np.allclose(
glm(data, data, (13, 30), (80, 200), filterfn=butterf), 0.03476, atol=10 ** -5)
# Test that the GLM function outputs close to 0 and 1 when expected
lo, hi = genPAC1(glm_bias=True)
assert glm(lo, hi, (4, 6), (90, 110)) > 0.99
lo, hi = genPAC0()
assert glm(lo, hi, (4, 6), (90, 110)) < 0.01
# 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(
glm(pha, amp, (13, 30), (80, 200), filterfn=False),
glm(data, data, (13, 30), (80, 200)), atol=10 ** -5)
def test_ozkurt():
"""
Test PAC function: Ozkurt
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(
ozkurt(data, data, (13, 30), (80, 200)), 0.07548, atol=10 ** -5)
assert np.allclose(
ozkurt(data, data, (13, 30), (80, 200), filterfn=butterf), 0.07555, atol=10 ** -5)
# Test that the Ozkurt PAC 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))
weight = (np.sqrt(len(amp)) * np.sqrt(np.sum(amp ** 2))) / np.sum(amp)
assert ozkurt(lo, hi, (4, 6), (100, 400)) * weight > 0.99
lo, hi = genPAC0()
assert ozkurt(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(
ozkurt(pha, amp, (13, 30), (80, 200), filterfn=False),
ozkurt(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
# 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_firf():
"""
Confirm consistency in FIR filtering
"""
# Load data
data = np.load(os.path.dirname(pacpy.__file__) + '/tests/exampledata.npy')
assert np.allclose(np.sum(np.abs(firf(data, (13, 30)))),
6421935.78673,
atol=10**-5)
assert len(firf(data, (13, 30))) == len(data)
def test_firf():
"""
Confirm consistency in FIR filtering
"""
# Load data
data = np.load(os.path.dirname(pacpy.__file__) + "/tests/exampledata.npy")
assert np.allclose(np.sum(np.abs(firf(data, (13, 30)))), 5517466.5857, atol=10 ** -5)
def test_ozkurt():
"""
Test PAC function: Ozkurt
1. Confirm consistency of output with example data
2. Confirm PAC=1 when expected
3. Confirm PAC=0 when expected
"""
# Load data
data = np.load(os.path.dirname(pacpy.__file__) + '/tests/exampledata.npy')
assert np.allclose(
ozkurt(data, data, (13, 30), (80, 200)), 0.07658, atol=10 ** -5)
# Test that the Ozkurt PAC 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))
weight = (np.sqrt(len(amp)) * np.sqrt(np.sum(amp ** 2))) / np.sum(amp)
assert ozkurt(lo, hi, (4, 6), (100, 400)) * weight > 0.98
lo, hi = genPAC0()
assert ozkurt(lo, hi, (4, 6), (100, 400)) < 0.01
def test_ozkurt():
"""
Test PAC function: Ozkurt
1. Confirm consistency of output with example data
2. Confirm PAC=1 when expected
3. Confirm PAC=0 when expected
"""
# Load data
data = np.load(os.path.dirname(pacpy.__file__) + '/tests/exampledata.npy')
assert np.allclose(ozkurt(data, data, (13, 30), (80, 200)),
0.07658,
atol=10**-5)
# Test that the Ozkurt PAC 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))
weight = (np.sqrt(len(amp)) * np.sqrt(np.sum(amp**2))) / np.sum(amp)
assert ozkurt(lo, hi, (4, 6), (100, 400)) * weight > 0.98
lo, hi = genPAC0()
assert ozkurt(lo, hi, (4, 6), (100, 400)) < 0.01
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
def findpt(x, f_osc, Fs=1000., w=3, boundary=0):
"""
Calculate peaks and troughs over time series
Parameters
----------
x : array-like 1d
voltage time series
f_osc : (low, high), Hz
frequency range for narrowband signal of interest, used to find
zerocrossings of the oscillation
Fs : float
The sampling rate (default = 1000Hz)
w : float
Number of cycles for the filter order of the band-pass filter
boundary : int
distance from edge of recording that an extrema must be in order to be
accepted (in number of samples)
Returns
-------
Ps : array-like 1d
indices at which oscillatory peaks occur in the input signal x
Ts : array-like 1d
indices at which oscillatory troughs occur in the input signal x
"""
# Filter in narrow band
from pacpy.filt import firf
xn = firf(x, f_osc, Fs, w=w, rmvedge=False)
# Find zero crosses
def fzerofall(data):
pos = data > 0
return (pos[:-1] & ~pos[1:]).nonzero()[0]
def fzerorise(data):
pos = data < 0
return (pos[:-1] & ~pos[1:]).nonzero()[0]
zeroriseN = fzerorise(xn)
zerofallN = fzerofall(xn)
# Calculate # peaks and troughs
if zeroriseN[-1] > zerofallN[-1]:
P = len(zeroriseN) - 1
T = len(zerofallN)
else:
P = len(zeroriseN)
T = len(zerofallN) - 1
# Calculate peak samples
Ps = np.zeros(P, dtype=int)
for p in range(P):
# Calculate the sample range between the most recent zero rise
# and the next zero fall
mrzerorise = zeroriseN[p]
nfzerofall = zerofallN[zerofallN > mrzerorise][0]
Ps[p] = np.argmax(x[mrzerorise:nfzerofall]) + mrzerorise
# Calculate trough samples
Ts = np.zeros(T, dtype=int)
for tr in range(T):
# Calculate the sample range between the most recent zero fall
# and the next zero rise
mrzerofall = zerofallN[tr]
nfzerorise = zeroriseN[zeroriseN > mrzerofall][0]
Ts[tr] = np.argmin(x[mrzerofall:nfzerorise]) + mrzerofall
if boundary > 0:
Ps = _removeboundaryextrema(x, Ps, boundary)
Ts = _removeboundaryextrema(x, Ts, boundary)
return Ps, Ts
