def test_sigmoid():
"""Test sigmoidal fitting and generation
"""
n_pts = 1000
x = np.random.randn(n_pts)
p0 = (0., 1., 0., 1.)
y = ea.sigmoid(x, *p0)
assert_true(np.all(np.logical_and(y <= 1, y >= 0)))
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
y += np.random.rand(n_pts) * 0.01
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=0.1, rtol=0.1)
def test_sigmoid():
"""Test sigmoidal fitting and generation."""
n_pts = 1000
x = np.random.randn(n_pts)
p0 = (0., 1., 0., 1.)
y = ea.sigmoid(x, *p0)
assert_true(np.all(np.logical_and(y <= 1, y >= 0)))
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
p = ea.fit_sigmoid(x, y, (0, 1, None, None), ('upper', 'lower'))
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
y += np.random.rand(n_pts) * 0.01
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=0.1, rtol=0.1)
def test_sigmoid():
"""Test sigmoidal fitting and generation."""
n_pts = 1000
x = np.random.RandomState(0).randn(n_pts)
p0 = (0., 1., 0., 1.)
y = ea.sigmoid(x, *p0)
assert np.all(np.logical_and(y <= 1, y >= 0))
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
with pytest.warns(None): # scipy convergence
p = ea.fit_sigmoid(x, y, (0, 1, None, None), ('upper', 'lower'))
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
y += np.random.rand(n_pts) * 0.01
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=0.1, rtol=0.1)
def test_sigmoid():
"""Test sigmoidal fitting and generation."""
n_pts = 1000
x = np.random.RandomState(0).randn(n_pts)
p0 = (0., 1., 0., 1.)
y = ea.sigmoid(x, *p0)
assert np.all(np.logical_and(y <= 1, y >= 0))
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
with pytest.warns(None): # scipy convergence
p = ea.fit_sigmoid(x, y, (0, 1, None, None), ('upper', 'lower'))
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
y += np.random.rand(n_pts) * 0.01
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=0.1, rtol=0.1)