dysin = dy * math.sin(angle)
center = np.array(center)
return [
center + np.array([-dxcos + dysin, -dxsin - dycos]),
center + np.array([dxcos + dysin, dxsin - dycos]),
center + np.array([dxcos - dysin, dxsin + dycos]),
center + np.array([-dxcos - dysin, -dxsin + dycos]),
]
@given(
length_a=h_int(min_value=1, max_value=100),
width_a=h_int(min_value=1, max_value=100),
center_ax=h_float(min_value=-100, max_value=100),
center_ay=h_float(min_value=-100, max_value=100),
length_b=h_int(min_value=1, max_value=100),
width_b=h_int(min_value=1, max_value=100),
center_bx=h_float(min_value=-100, max_value=100),
center_by=h_float(min_value=-100, max_value=100),
)
def test_vs_shapely_zero_angle(length_a, width_a, center_ax, center_ay,
length_b, width_b, center_bx, center_by):
rectangle_a = to_coords((center_ax, center_ay), length_a, width_a, 0)
rectangle_b = to_coords((center_bx, center_by), length_b, width_b, 0)
rectangle_a_shapely = Polygon(rectangle_a + [rectangle_a[0]])
rectangle_b_shapely = Polygon(rectangle_b + [rectangle_b[0]])
assert math.isclose(
def test_fix_center(position, width):
new_position = fix_center(position, IMAGE_WIDTH, width=width)
if width < IMAGE_WIDTH:
assert math.ceil(width / 2) <= new_position < IMAGE_WIDTH - math.floor(width / 2) + 1
if width > IMAGE_WIDTH:
assert new_position == math.ceil(IMAGE_WIDTH / 2)
@given(
x_min=h_int(min_value=0, max_value=int(IMAGE_WIDTH * 3 / 4) - 1),
y_min=h_int(min_value=0, max_value=int(IMAGE_HEIGHT * 5 / 6) - 1),
x_max=h_int(min_value=int(IMAGE_WIDTH * 3 / 4), max_value=IMAGE_WIDTH - 1),
y_max=h_int(min_value=int(IMAGE_HEIGHT * 5 / 6), max_value=IMAGE_HEIGHT - 1),
resize_coeff=h_float(min_value=1, max_value=10),
)
def test_resize(x_min, y_min, x_max, y_max, resize_coeff):
x_min_1, y_min_1, x_max_1, y_max_1 = resize(
x_min, y_min, x_max, y_max, image_height=IMAGE_HEIGHT, image_width=IMAGE_WIDTH, resize_coeff=resize_coeff
)
assert 0 <= x_min_1 < x_max_1 < IMAGE_WIDTH
assert 0 <= y_min_1 < y_max_1 < IMAGE_HEIGHT
new_width = min(math.floor(resize_coeff * (x_max - x_min)), IMAGE_WIDTH)
if new_width < IMAGE_WIDTH:
assert x_max_1 - x_min_1 == new_width
else:
assert x_min_1 == 0
from hypothesis.strategies import characters as h_char
from hypothesis.strategies import floats as h_float
from help_functions.utils.tabular_utils import (
CyclicEncoder,
LabelEncoderUnseen,
GeneralEncoder,
)
MIN_VALUE = -11
MAX_VALUE = 17
ARRAY_SHAPE = 3
@given(
x=h_arrays(dtype=float, shape=ARRAY_SHAPE, elements=h_float(-MIN_VALUE, MAX_VALUE))
)
def test_cyclic_day_hours(x):
amplitude = MAX_VALUE - MIN_VALUE
encoder = CyclicEncoder(amplitude)
transformed = encoder.fit_transform(x)
assert transformed.shape[1] == 2
encoder2 = CyclicEncoder(amplitude)
encoder2.fit(x)
transformed2 = encoder2.transform(x)
assert transformed2.shape[1] == 2
from iglovikov_helper_functions.utils.tabular_utils import (
CyclicEncoder,
GeneralEncoder,
LabelEncoderUnseen,
MinMaxScaler,
)
MIN_VALUE = -11
MAX_VALUE = 17
ARRAY_SHAPE = 3
@given(x=h_arrays(dtype=float,
shape=ARRAY_SHAPE,
elements=h_float(-MIN_VALUE, MAX_VALUE)))
def test_cyclic_day_hours(x):
amplitude = MAX_VALUE - MIN_VALUE
encoder = CyclicEncoder(amplitude)
transformed = encoder.fit_transform(x)
assert transformed.shape == (len(x), 2)
encoder2 = CyclicEncoder(amplitude)
encoder2.fit(x)
transformed2 = encoder2.transform(x)
assert transformed.shape == (len(x), 2)