def test_img_to_relative_xyi(random_seed=None):
from skbeam.core.utils import img_to_relative_xyi
# make the RNG deterministic
if random_seed is not None:
np.random.seed(42)
# set the maximum image dims
maxx = 2000
maxy = 2000
# create a randomly sized image
nx = int(np.random.rand() * maxx)
ny = int(np.random.rand() * maxy)
# create a randomly located center
cx = np.random.rand() * nx
cy = np.random.rand() * ny
# generate the image
img = np.ones((nx, ny))
# generate options for the x center to test edge conditions
cx_lst = [0, cx, nx]
# generate options for the y center to test edge conditions
cy_lst = [0, cy, ny]
for cx, cy in zip(cx_lst, cy_lst):
# call the function
x, y, i = img_to_relative_xyi(img=img, cx=cx, cy=cy)
logger.debug('y {0}'.format(y))
logger.debug('sum(y) {0}'.format(sum(y)))
expected_total_y = sum(np.arange(ny, dtype=np.int64) - cy) * nx
logger.debug('expected_total_y {0}'.format(expected_total_y))
logger.debug('x {0}'.format(x))
logger.debug('sum(x) {0}'.format(sum(x)))
expected_total_x = sum(np.arange(nx, dtype=np.int64) - cx) * ny
logger.debug('expected_total_x {0}'.format(expected_total_x))
expected_total_intensity = nx * ny
try:
assert_almost_equal(sum(x), expected_total_x, decimal=0)
assert_almost_equal(sum(y), expected_total_y, decimal=0)
assert_equal(sum(i), expected_total_intensity)
except AssertionError as ae:
logger.error('img dims: ({0}, {1})'.format(nx, ny))
logger.error('img center: ({0}, {1})'.format(cx, cy))
logger.error('sum(returned_x): {0}'.format(sum(x)))
logger.error('expected_x: {0}'.format(expected_total_x))
logger.error('sum(returned_y): {0}'.format(sum(y)))
logger.error('expected_y: {0}'.format(expected_total_y))
logger.error('sum(returned_i): {0}'.format(sum(i)))
logger.error('expected_x: {0}'.format(expected_total_intensity))
six.reraise(AssertionError, ae, sys.exc_info()[2])
def test_img_to_relative_xyi(random_seed=None):
from skbeam.core.utils import img_to_relative_xyi
# make the RNG deterministic
if random_seed is not None:
np.random.seed(42)
# set the maximum image dims
maxx = 2000
maxy = 2000
# create a randomly sized image
nx = int(np.random.rand() * maxx)
ny = int(np.random.rand() * maxy)
# create a randomly located center
cx = np.random.rand() * nx
cy = np.random.rand() * ny
# generate the image
img = np.ones((nx, ny))
# generate options for the x center to test edge conditions
cx_lst = [0, cx, nx]
# generate options for the y center to test edge conditions
cy_lst = [0, cy, ny]
for cx, cy in zip(cx_lst, cy_lst):
# call the function
x, y, i = img_to_relative_xyi(img=img, cx=cx, cy=cy)
logger.debug('y {0}'.format(y))
logger.debug('sum(y) {0}'.format(sum(y)))
expected_total_y = sum(np.arange(ny, dtype=np.int64) - cy) * nx
logger.debug('expected_total_y {0}'.format(expected_total_y))
logger.debug('x {0}'.format(x))
logger.debug('sum(x) {0}'.format(sum(x)))
expected_total_x = sum(np.arange(nx, dtype=np.int64) - cx) * ny
logger.debug('expected_total_x {0}'.format(expected_total_x))
expected_total_intensity = nx * ny
try:
assert_almost_equal(sum(x), expected_total_x, decimal=0)
assert_almost_equal(sum(y), expected_total_y, decimal=0)
assert_equal(sum(i), expected_total_intensity)
except AssertionError as ae:
logger.error('img dims: ({0}, {1})'.format(nx, ny))
logger.error('img center: ({0}, {1})'.format(cx, cy))
logger.error('sum(returned_x): {0}'.format(sum(x)))
logger.error('expected_x: {0}'.format(expected_total_x))
logger.error('sum(returned_y): {0}'.format(sum(y)))
logger.error('expected_y: {0}'.format(expected_total_y))
logger.error('sum(returned_i): {0}'.format(sum(i)))
logger.error('expected_x: {0}'.format(expected_total_intensity))
six.reraise(AssertionError, ae, sys.exc_info()[2])
def _fail_img_to_relative_xyi_helper(input_dict):
core.img_to_relative_xyi(**input_dict)
def _fail_img_to_relative_xyi_helper(input_dict):
with pytest.raises(ValueError):
core.img_to_relative_xyi(**input_dict)
def _fail_img_to_relative_xyi_helper(input_dict):
core.img_to_relative_xyi(**input_dict)
