def _reference_xc_map(dp, vectors, upsample_factor, center, calibration):
shifts = np.zeros_like(vectors, dtype=np.float64)
for i, vector in enumerate(vectors):
ref_disc = get_experimental_square(reference_dp, vector, square_size)
expt_disc = get_experimental_square(dp, vector, square_size)
shifts[i] = _conventional_xc(expt_disc, ref_disc, upsample_factor)
return (((vectors + shifts) - center) * calibration)
def _lg_map(dp, vectors, square_size, center, calibration):
shifts = np.zeros_like(vectors, dtype=np.float64)
for i, vector in enumerate(vectors):
expt_disc = get_experimental_square(dp, vector, square_size)
shifts[i] = _new_lg_idea(expt_disc)
return (((vectors + shifts) - center) * calibration)
def _com_experimental_square(z, vector, square_size):
"""Wrapper for get_experimental_square that makes the non-zero
elements symmetrical around the 'unsubpixeled' peak by zeroing a
'spare' row and column (top and left).
Parameters
----------
z : np.array
vector : np.array([x,y])
square_size : int (even)
Returns
-------
z_adpt : np.array
z, but with row and column zero set to 0
"""
# Copy to make sure we don't change the dp
z_adpt = np.copy(
get_experimental_square(z,
vector=vector,
square_size=square_size))
z_adpt[:, 0] = 0
z_adpt[0, :] = 0
return z_adpt
def _check_bad_square_map(dp, vectors, square_size):
bad_square = False
for i, vector in enumerate(vectors):
expt_disc = get_experimental_square(dp, vector, square_size)
bad_square = check_bad_square(expt_disc)
if bad_square:
return True
return False
def test_failure_for_non_even_errors_get_experimental_square(exp_disc):
square = get_experimental_square(exp_disc, [17, 19], 7)
def test_experimental_square_size(exp_disc):
square = get_experimental_square(exp_disc, [17, 19], 6)
assert square.shape[0] == int(6)
assert square.shape[1] == int(6)
def test_failure_for_non_even_errors_get_experimental_square(exp_disc):
with pytest.raises(ValueError,
match="'square_size' must be an even number"):
square = get_experimental_square(exp_disc, [17, 19], 7)
