def test_center():
data = np.random.rand(1000) * 2 * np.pi
try:
assert_allclose(pycircstat.mean(pycircstat.center(data)),
0, rtol=1e-3, atol=1e-3)
except:
assert_allclose(pycircstat.mean(pycircstat.center(data)),
2 * np.pi, rtol=1e-3, atol=1e-3)
def test_center():
data = np.random.rand(1000) * 2 * np.pi
try:
assert_allclose(pycircstat.mean(pycircstat.center(data)),
0, rtol=1e-3, atol=1e-3)
except:
assert_allclose(pycircstat.mean(pycircstat.center(data)),
2 * np.pi, rtol=1e-3, atol=1e-3)
def corr_circular_(alpha1, alpha2):
axis = None
from pycircstat import center
from scipy.stats import norm
alpha1_bar, alpha2_bar = center(alpha1, alpha2, axis=axis)
num = np.sum(np.sin(alpha1_bar) * np.sin(alpha2_bar), axis=axis)
den = np.sqrt(
np.sum(np.sin(alpha1_bar)**2, axis=axis) *
np.sum(np.sin(alpha2_bar)**2, axis=axis))
rho = num / den
# pvalue
l20 = np.mean(np.sin(alpha1 - alpha1_bar)**2)
l02 = np.mean(np.sin(alpha2 - alpha2_bar)**2)
l22 = np.mean(
(np.sin(alpha1 - alpha1_bar)**2) * (np.sin(alpha2 - alpha2_bar)**2))
ts = np.sqrt((float(len(alpha1)) * l20 * l02) / l22) * rho
pval = 2.0 * (1.0 - norm.cdf(np.abs(ts)))
return rho, pval
def center(x):
x['phase'] = circ.center(x.phase)
return x
