def test_affine_field_rd(realdata):
afff = packing.affine_field(realdata[1],
realdata[100],
rdcutoff / 1.5 * 2.5,
d2min_scale=rdcutoff / 1.5)
assert abs(abs(afff.hstrain.mean()) - 0.01) < 0.005 * 11
assert afff.d2min.mean() < 0.005
def test_affine_field_fd(fakedata):
ftr0, ftr1 = fakedata
afff = packing.affine_field(ftr0, ftr1, cutoff=2.5)
assert np.allclose((ftr1.set_index("particle").x - afff.set_index("particle").x).dropna(), 0)
_check_scalar(afff.hstrain, -0.1)
_check_scalar(afff.vstrain, 0)
_check_scalar(afff.xdil, 1)
_check_scalar(afff.ydil, 1)
_check_scalar(afff.d2min, 0)
def test_affine_field_perturbed(fakedata):
ftr0, ftr1 = fakedata
pid, ftr1p = _perturb_ftr(ftr0)
afff = packing.affine_field(ftr1p, ftr0, cutoff=2.5)
assert np.isclose(afff.hstrain.median(), 0)
assert np.isclose(afff.d2min.median(), 0)
pd2min = afff.d2min[afff.particle == pid]
# Upper bound on D2min is set by
# (N * (0.1**2)) / N, where N is number of neighbors
assert np.isclose(pd2min, 0.1 ** 2, rtol=1e-2, atol=1e-3)
def test_affine_field_perturbed(fakedata):
ftr0, ftr1 = fakedata
pid, ftr1p = _perturb_ftr(ftr0)
afff = packing.affine_field(ftr1p, ftr0, cutoff=2.5)
assert np.isclose(afff.hstrain.median(), 0)
assert np.isclose(afff.d2min.median(), 0)
pd2min = afff.d2min[afff.particle == pid]
# Upper bound on D2min is set by
# (N * (0.1**2)) / N, where N is number of neighbors
assert np.isclose(pd2min, 0.1**2, rtol=1e-2, atol=1e-3)
def test_affine_field_fd(fakedata):
ftr0, ftr1 = fakedata
afff = packing.affine_field(ftr0, ftr1, cutoff=2.5)
assert np.allclose((ftr1.set_index('particle').x \
- afff.set_index('particle').x).dropna(), 0)
_check_scalar(afff.hstrain, -0.1)
_check_scalar(afff.vstrain, 0)
_check_scalar(afff.xdil, 1)
_check_scalar(afff.ydil, 1)
_check_scalar(afff.d2min, 0)
def test_affine_field_rd(realdata):
afff = packing.affine_field(realdata[1], realdata[100], rdcutoff / 1.5 * 2.5, d2min_scale=rdcutoff / 1.5)
assert abs(abs(afff.hstrain.mean()) - 0.01) < 0.005
assert afff.d2min.mean() < 0.002
