def test_namedtuple(self):
v = Point(1, 2)
tp_point = typeof(v)
self.assertEqual(tp_point, types.NamedUniTuple(types.intp, 2, Point))
v = Point(1, 2.0)
self.assertEqual(typeof(v),
types.NamedTuple([types.intp, types.float64], Point))
w = Rect(3, 4)
tp_rect = typeof(w)
self.assertEqual(tp_rect, types.NamedUniTuple(types.intp, 2, Rect))
self.assertNotEqual(tp_rect, tp_point)
self.assertNotEqual(tp_rect,
types.UniTuple(tp_rect.dtype, tp_rect.count))
def test_ndim_not_on_array(self):
FakeArray = collections.namedtuple('FakeArray', ['ndim'])
fa = FakeArray(ndim=2)
def impl(fa):
if fa.ndim == 2:
return fa.ndim
else:
object()
# check prune works for array ndim
self.assert_prune(impl, (types.Array(types.float64, 2, 'C'), ),
[False], np.zeros((2, 3)))
# check prune fails for something with `ndim` attr that is not array
FakeArrayType = types.NamedUniTuple(types.int64, 1, FakeArray)
self.assert_prune(impl, (FakeArrayType, ), [None],
fa,
flags=enable_pyobj_flags)
def test_namedtuples(self):
ty1 = types.NamedUniTuple(types.intp, 2, Point)
self.check_pickling(ty1)
ty2 = types.NamedTuple((types.intp, types.float64), Point)
self.check_pickling(ty2)
# Calculate the theoretical bounding box if the image was
# perfectly centered on the closest reference point
# Clip the bounding box to the extents of the image
left = viewed_img_center_x - half_canvas_width
right = viewed_img_center_x + half_canvas_width
return left, right
@jit(float64(float64, float64, float64), nopython=True, cache=True, nogil=True)
def clip(v, n, x):
return max(min(v, x), n)
@jit(nb_types.NamedUniTuple(float64, 4,
Box)(nb_types.NamedUniTuple(float64, 4, Box),
nb_types.Array(float64, 1, 'C'),
nb_types.Array(float64, 1, 'C'),
nb_types.UniTuple(int64, 2), float64,
float64),
nopython=True,
cache=True,
nogil=True)
def calc_view_extents(image_extents_box: Box, canvas_point, image_point,
canvas_size, dx, dy) -> Box:
left, right = _calc_extent_component(canvas_point[0], image_point[0],
canvas_size[0], dx)
left = clip(left, image_extents_box.left, image_extents_box.right)
right = clip(right, image_extents_box.left, image_extents_box.right)
bot, top = _calc_extent_component(canvas_point[1], image_point[1],
canvas_size[1], dy)
bot = clip(bot, image_extents_box.bottom, image_extents_box.top)
dx = abs((image_point[1, 0] - image_point[0, 0]) /
(canvas_size[0] * (canvas_point[1, 0] - canvas_point[0, 0]) / 2.))
dy = abs((image_point[1, 1] - image_point[0, 1]) /
(canvas_size[1] * (canvas_point[1, 1] - canvas_point[0, 1]) / 2.))
return dx, dy
@jit(nopython=True)
def clip(v, n, x):
return max(min(v, x), n)
@jit(nb_types.NamedUniTuple(float64, 4, box)(
nb_types.NamedUniTuple(float64, 4, box),
nb_types.Array(float64, 1, 'C'),
nb_types.Array(float64, 1, 'C'),
nb_types.UniTuple(int64, 2),
float64,
float64
),
nopython=True)
def calc_view_extents(image_extents_box, canvas_point, image_point, canvas_size, dx, dy):
l, r = _calc_extent_component(canvas_point[0], image_point[0], canvas_size[0], dx)
l = clip(l, image_extents_box.l, image_extents_box.r)
r = clip(r, image_extents_box.l, image_extents_box.r)
b, t = _calc_extent_component(canvas_point[1], image_point[1], canvas_size[1], dy)
b = clip(b, image_extents_box.b, image_extents_box.t)
t = clip(t, image_extents_box.b, image_extents_box.t)
if (r - l) < CANVAS_EXTENTS_EPSILON or (t - b) < CANVAS_EXTENTS_EPSILON:
# they are viewing essentially nothing or the image isn't in view
