def test_normalizer(self):
from astroNN.nn.utilities.normalizer import Normalizer
from astroNN.config import MAGIC_NUMBER
import numpy as np
data = np.random.normal(0, 1, (100, 10))
magic_idx = (10, 5)
data[magic_idx] = MAGIC_NUMBER
# create a normalizer instance
normer = Normalizer(mode=1)
norm_data = normer.normalize(data)
# make sure normalizer preserve magic_number
self.assertEqual(norm_data[magic_idx], MAGIC_NUMBER)
# test demoralize
data_denorm = normer.denormalize(norm_data)
# make sure demoralizer preserve magic_number
self.assertEqual(data_denorm[magic_idx], MAGIC_NUMBER)
npt.assert_array_almost_equal(data_denorm, data)
errorous_norm = Normalizer(mode=-1234)
self.assertRaises(ValueError, errorous_norm.normalize, data)
# test mode='3s' can do identity transformation
s3_norm = Normalizer(mode='3s')
data = np.random.normal(0, 1, (100, 10))
npt.assert_array_almost_equal(s3_norm.denormalize(s3_norm.normalize(data)), data)
def test_normalizer(self):
from astroNN.nn.utilities.normalizer import Normalizer
from astroNN.config import MAGIC_NUMBER
import numpy as np
data = np.random.normal(0, 1, (100, 10))
magic_idx = (10, 5)
data[magic_idx] = MAGIC_NUMBER
# create a normalizer instance for mode 0
normer = Normalizer(mode=0)
norm_data = normer.normalize(data)
self.assertEqual(norm_data[magic_idx], MAGIC_NUMBER) # make sure normalizer preserve magic_number
# test demoralize
data_denorm = normer.denormalize(norm_data)
# make sure demoralizer preserve magic_number
self.assertEqual(data_denorm[magic_idx], MAGIC_NUMBER)
npt.assert_array_almost_equal(data_denorm, data)
npt.assert_array_almost_equal(norm_data, data)
# create a normalizer instance for mode 1
normer = Normalizer(mode=1)
norm_data = normer.normalize(data)
self.assertEqual(norm_data[magic_idx], MAGIC_NUMBER) # make sure normalizer preserve magic_number
# test demoralize
data_denorm = normer.denormalize(norm_data)
# make sure demoralizer preserve magic_number
self.assertEqual(data_denorm[magic_idx], MAGIC_NUMBER)
npt.assert_array_almost_equal(data_denorm, data)
# test mode='3s' can do identity transformation
s3_norm = Normalizer(mode='3s')
data = np.random.normal(0, 1, (100, 10))
npt.assert_array_almost_equal(s3_norm.denormalize(s3_norm.normalize(data)), data)
data_8bit = np.random.randint(0, 256, (100, 50, 50))
normer = Normalizer(mode=255)
norm_data_8bit = normer.normalize(data_8bit)
self.assertEqual(np.max(norm_data_8bit), 1.) # make sure max of normalized image is 1.
self.assertEqual(np.min(norm_data_8bit), 0.) # make sure max of normalized image is 0.
normer = Normalizer(mode={'input': 255, 'aux': 0})
norm_data_dict = normer.normalize({'input': data_8bit, 'aux': data})
self.assertEqual(np.max(norm_data_dict['input']), 1.) # make sure max of normalized image is 1.
self.assertEqual(np.min(norm_data_dict['input']), 0.) # make sure max of normalized image is 0.
npt.assert_array_almost_equal(norm_data_dict['aux'], data) # make sure aux data is not normalized in this case
errorous_norm = Normalizer(mode=-1234)
self.assertRaises(ValueError, errorous_norm.normalize, data)