def test_build_one_hot(fake_geo_data):
dataroot, DIV_datas, Vx_datas, Vy_datas = fake_geo_data
p = GeoPickler(dataroot)
p.collect_all()
p.group_by_series()
data_dict = p.get_data_dict(0, 0)
DIV = (np.random.randn(*data_dict['A_DIV'].shape) * 20000)
data_dict['A_DIV'] = DIV
p.create_one_hot(data_dict, 1000)
one_hot = data_dict['A']
assert ([i in np.where(DIV > 1000) for i in np.where(one_hot[:, :, 0])])
assert ([
i in np.where(np.logical_and(DIV < 1000, DIV < -1000))
for i in np.where(one_hot[:, :, 1])
])
assert ([i in np.where(DIV < -1000) for i in np.where(one_hot[:, :, 2])])
def test_build_data_dict(fake_geo_data):
dataroot, DIV_datas, Vx_datas, Vy_datas = fake_geo_data
p = GeoPickler(dataroot)
p.collect_all()
p.group_by_series()
data_dict = p.get_data_dict(0, 0)
assert ((data_dict['A_DIV'] == DIV_datas[0]).all())
assert ((data_dict['A_Vx'] == Vx_datas[0]).all())
assert ((data_dict['A_Vy'] == Vy_datas[0]).all())
def test_resized(fake_geo_data):
dataroot, DIV_datas, Vx_datas, Vy_datas = fake_geo_data
p = GeoPickler(dataroot, row_height=18)
p.collect_all()
p.group_by_series()
data_dict = p.get_data_dict(0, 0)
p.create_one_hot(data_dict, 1000)
assert ((data_dict['A_DIV'].shape == (18, 36)))
assert ((data_dict['A_Vx'].shape == (18, 36)))
assert ((data_dict['A_Vy'].shape == (18, 36)))
assert ((data_dict['A'].shape == (18, 36, 3)))
def test_normalises_continuous_data(fake_geo_data):
dataroot, _, _, _ = fake_geo_data
p = GeoPickler(dataroot)
p.collect_all()
p.group_by_series()
data_dict = p.get_data_dict(0, 0)
p.normalise_continuous_data(data_dict)
assert (np.max(data_dict['A_DIV'].ravel()) == 1.0)
assert (np.min(data_dict['A_DIV'].ravel()) == -1.0)
assert (np.max(data_dict['A_Vx'].ravel()) == 1.0)
assert (np.min(data_dict['A_Vx'].ravel()) == -1.0)
assert (np.max(data_dict['A_Vy'].ravel()) == 1.0)
assert (np.min(data_dict['A_Vy'].ravel()) == -1.0)
def test_mask_params_stored_in_dict(fake_geo_data):
dataroot, DIV_datas, Vx_datas, Vy_datas = fake_geo_data
p = GeoPickler(dataroot)
p.collect_all()
p.group_by_series()
data_dict = p.get_data_dict(0, 0)
DIV = (np.random.randn(*data_dict['A_DIV'].shape) * 20000)
data_dict['A_DIV'] = DIV
p.create_one_hot(data_dict, 1000)
p.get_mask_loc(data_dict, 4, 6)
assert (data_dict['mask_size'] == 4)
assert (data_dict['min_pix_in_mask'] == 6)
def test_mask_location(fake_geo_data):
dataroot, DIV_datas, Vx_datas, Vy_datas = fake_geo_data
p = GeoPickler(dataroot)
p.collect_all()
p.group_by_series()
data_dict = p.get_data_dict(0, 0)
DIV = (np.random.randn(*data_dict['A_DIV'].shape) * 20000)
data_dict['A_DIV'] = DIV
p.create_one_hot(data_dict, 1000)
p.get_mask_loc(data_dict, 4, 6)
one_hot = data_dict['A']
mask_loc = data_dict['mask_locs']
assert (len(mask_loc) > 0)
for x in range(one_hot.shape[1] - 4):
for y in range(one_hot.shape[0] - 4):
sum1 = np.sum(one_hot[y:y + 4, x:x + 4, 0])
sum2 = np.sum(one_hot[y:y + 4, x:x + 4, 2])
if (y, x) in mask_loc:
assert (np.sum(one_hot[y:y + 4, x:x + 4, 0]) >= 6)
assert (np.sum(one_hot[y:y + 4, x:x + 4, 2]) >= 6)
else:
assert (np.sum(one_hot[y:y + 4, x:x + 4, 0]) < 6
or np.sum(one_hot[y:y + 4, x:x + 4, 2]) < 6)