import pytest
import numpy as np
from unittest import TestCase
from scipy.stats import circmean
from pingouin import read_dataset
from pingouin.circular import convert_angles, _checkangles
from pingouin.circular import (circ_axial, circ_corrcc, circ_corrcl, circ_mean,
circ_r, circ_rayleigh, circ_vtest)
np.random.seed(123)
a1 = [-1.2, 2.5, 3.1, -3.1, 0.2, -0.2] # -np.pi / pi
a2 = np.pi + np.array(a1) # 0 / 2 * np.pi
a3 = [150, 180, 32, 340, 54, 0, 360] # 0 / 360 deg
a4 = [22, 23, 0.5, 1.2, 0, 24] # Hours (0 - 24)
a5 = np.random.randint(0, 1440, (2, 4)) # Minutes, 2D array
a3_nan = np.array([150, 180, 32, 340, 54, np.nan, 0, 360])
a5_nan = a5.astype(float)
a5_nan[1, 2] = np.nan
class TestCircular(TestCase):
"""Test circular.py."""
def test_helper_angles(self):
"""Test helper circular functions."""
# Check angles
_checkangles(a1)
_checkangles(a2, axis=None)
with pytest.raises(ValueError):
_checkangles(a3)
with pytest.raises(ValueError):
_checkangles(a3, axis=None)
# Convert angles
np.testing.assert_array_almost_equal(a1, convert_angles(a1, low=-np.pi,
high=np.pi))
np.testing.assert_array_almost_equal(a2, convert_angles(a2, low=0,
high=2 * np.pi,
positive=True))
_checkangles(convert_angles(a2, low=0, high=2 * np.pi))
_checkangles(convert_angles(a3, low=0, high=360))
_checkangles(convert_angles(a3_nan, low=0, high=360))
_checkangles(convert_angles(a4, low=0, high=24))
_checkangles(convert_angles(a5, low=0, high=1440))
_checkangles(convert_angles(a5_nan, low=0, high=1440))
_checkangles(convert_angles(a5, low=0, high=1440, positive=True))
convert_angles(a1, low=-np.pi, high=np.pi)
convert_angles(a2, low=0, high=2 * np.pi)
convert_angles(a3)
convert_angles(a4, low=0, high=24)
convert_angles(a5, low=0, high=1440)
assert convert_angles(a5, low=0, high=1440, positive=True).min() >= 0
assert convert_angles(a5, low=0, high=1440).min() <= 0
# Compare with scipy.stats.circmean
def assert_circmean(x, low, high, axis=-1):
m1 = convert_angles(circmean(x, low=low, high=high, axis=axis,
nan_policy='omit'), low, high)
m2 = circ_mean(convert_angles(x, low, high), axis=axis)
assert (np.round(m1, 4) == np.round(m2, 4)).all()
assert_circmean(a1, low=-np.pi, high=np.pi)
assert_circmean(a2, low=0, high=2 * np.pi)
assert_circmean(a3, low=0, high=360)
assert_circmean(a3_nan, low=0, high=360)
assert_circmean(a4, low=0, high=24)
assert_circmean(a5, low=0, high=1440, axis=1)
assert_circmean(a5, low=0, high=1440, axis=0)
assert_circmean(a5, low=0, high=1440, axis=None)
assert_circmean(a5_nan, low=0, high=1440, axis=-1)
assert_circmean(a5_nan, low=0, high=1440, axis=0)
assert_circmean(a5_nan, low=0, high=1440, axis=None)
def test_circ_axial(self):
"""Test function circ_axial."""
df = read_dataset('circular')
angles = df['Orientation'].to_numpy()
angles = circ_axial(np.deg2rad(angles), 2)
assert np.allclose(np.round(angles, 4),
[0, 0.7854, 1.5708, 2.3562, 3.1416, 3.9270, 4.7124,
5.4978])
def test_circ_corrcc(self):
"""Test function circ_corrcc."""
x = [0.785, 1.570, 3.141, 3.839, 5.934]
y = [0.593, 1.291, 2.879, 3.892, 6.108]
r, pval = circ_corrcc(x, y)
# Compare with the CircStats MATLAB toolbox
assert round(r, 3) == 0.942
assert np.round(pval, 3) == 0.066
_, pval2 = circ_corrcc(x, y, tail='one-sided')
assert pval2 == pval / 2
# With correction for uniform marginals
circ_corrcc(x, y, correction_uniform=True)
def test_circ_corrcl(self):
"""Test function circ_corrcl."""
x = [0.785, 1.570, 3.141, 0.839, 5.934]
y = [1.593, 1.291, -0.248, -2.892, 0.102]
r, pval = circ_corrcl(x, y)
# Compare with the CircStats MATLAB toolbox
assert round(r, 3) == 0.109
assert np.round(pval, 3) == 0.971
_, pval2 = circ_corrcl(x, y, tail='one-sided')
assert pval2 == pval / 2
def test_circ_mean(self):
"""Test function circ_mean."""
x = [0.785, 1.570, 3.141, 0.839, 5.934]
x_nan = np.array([0.785, 1.570, 3.141, 0.839, 5.934, np.nan])
# Compare with the CircStats MATLAB toolbox
assert np.round(circ_mean(x), 3) == 1.013
assert np.round(circ_mean(x_nan), 3) == 1.013
# Binned data
np.random.seed(123)
nbins = 18 # Number of bins to divide the unit circle
angles_bins = np.linspace(-np.pi, np.pi, nbins)
# w represents the number of incidences per bins, or "weights".
w = np.random.randint(low=0, high=5, size=angles_bins.size)
assert round(circ_mean(angles_bins, w), 4) == -2.5355
def test_circ_r(self):
"""Test function circ_r."""
x = [0.785, 1.570, 3.141, 0.839, 5.934]
x_nan = np.array([0.785, 1.570, 3.141, 0.839, 5.934, np.nan])
# Compare with the CircStats MATLAB toolbox
assert np.round(circ_r(x), 3) == 0.497
assert np.round(circ_r(x_nan), 3) == 0.497
def test_circ_rayleigh(self):
"""Test function circ_rayleigh."""
x = [0.785, 1.570, 3.141, 0.839, 5.934]
z, pval = circ_rayleigh(x)
# Compare with the CircStats MATLAB toolbox
assert round(z, 3) == 1.236
assert round(pval, 4) == 0.3048
z, pval = circ_rayleigh(x, w=[.1, .2, .3, .4, .5], d=0.2)
assert round(z, 3) == 0.278
assert round(pval, 4) == 0.8070
def test_circ_vtest(self):
"""Test function circ_vtest."""
x = [0.785, 1.570, 3.141, 0.839, 5.934]
v, pval = circ_vtest(x, dir=1)
# Compare with the CircStats MATLAB toolbox
assert round(v, 3) == 2.486
assert round(pval, 4) == 0.0579
v, pval = circ_vtest(x, dir=0.5, w=[.1, .2, .3, .4, .5], d=0.2)
assert round(v, 3) == 0.637
assert round(pval, 4) == 0.2309
