def run():
(fname, datum, value) = ('92AV3C.lan', (99, 99, 99), 2057.0)
image = spy.open_image(fname)
print('\n' + '-' * 72)
print('Running LinearTransform tests on SpyFile object.')
print('-' * 72)
test = LinearTransformTest(image, datum, value)
test.run()
data = image.load()
print('\n' + '-' * 72)
print('Running LinearTransform tests on ImageArray object.')
print('-' * 72)
test = LinearTransformTest(data, datum, value)
test.run()
image.scale_factor = 10000.0
print('\n' + '-' * 72)
print('Running LinearTransform tests on SpyFile object with scale factor.')
print('-' * 72)
test = LinearTransformTest(image, datum, value / 10000.0)
test.run()
if __name__ == '__main__':
from spectral.tests.run import parse_args, reset_stats, print_summary
parse_args()
reset_stats()
run()
print_summary()
'''Test spatial averaging near border with shifted window.'''
from spectral.algorithms.spatial import map_window
f = lambda X, ij: np.mean(X.reshape((-1, X.shape[-1])), axis=0)
X = self.data
y = map_window(f, X, (3, 5), (100, None), (100, None), border='shift')
t = np.mean(X[-3:, -5:].reshape((-1, X.shape[-1])), axis=0)
assert_allclose(y[-1, -1], t)
def test_map_window_stepped(self):
'''Test spatial averaging with non-unity row/column step sizes.'''
from spectral.algorithms.spatial import map_window
f = lambda X, ij: np.mean(X.reshape((-1, X.shape[-1])), axis=0)
X = self.data
y = map_window(f, X, (3, 5), (30, 60, 3), (70, 100, 4), border='shift')
t = np.mean(X[32:35, 72:77].reshape((-1, X.shape[-1])), axis=0)
assert_allclose(y[1, 1], t)
def run():
print('\n' + '-' * 72)
print('Running spatial tests.')
print('-' * 72)
for T in [SpatialWindowTest]:
T().run()
if __name__ == '__main__':
from spectral.tests.run import parse_args, reset_stats, print_summary
parse_args()
reset_stats()
run()
print_summary()
