def test_noise_map(self):
noise_map = np.ones((self.numPix, self.numPix))
kwargs_noise = {
'image_data': np.zeros((self.numPix, self.numPix)),
'exposure_time': 1,
'background_rms': 1,
'noise_map': noise_map,
'verbose': True
}
noise = ImageNoise(**kwargs_noise)
noise_map_out = noise.C_D
npt.assert_almost_equal(noise_map_out, noise_map, decimal=8)
noise_map_out = noise.C_D_model(model=np.ones((self.numPix,
self.numPix)))
npt.assert_almost_equal(noise_map_out, noise_map, decimal=8)
kwargs_noise = {
'image_data': np.zeros((self.numPix, self.numPix)),
'exposure_time': 1,
'noise_map': noise_map,
'verbose': True
}
noise = ImageNoise(**kwargs_noise)
bkg = noise.background_rms
npt.assert_almost_equal(bkg, np.median(noise_map))
def __init__(self,
image_data,
exposure_time=None,
background_rms=None,
noise_map=None,
ra_at_xy_0=0,
dec_at_xy_0=0,
transform_pix2angle=None,
ra_shift=0,
dec_shift=0):
"""
:param image_data: 2d numpy array of the image data
:param exposure_time: int or array of size the data; exposure time
(common for all pixels or individually for each individual pixel)
:param background_rms: root-mean-square value of Gaussian background noise
:param noise_map: int or array of size the data; joint noise sqrt(variance) of each individual pixel.
:param transform_pix2angle: 2x2 matrix, mapping of pixel to coordinate
:param ra_at_xy_0: ra coordinate at pixel (0,0)
:param dec_at_xy_0: dec coordinate at pixel (0,0)
:param ra_shift: RA shift of pixel grid
:param dec_shift: DEC shift of pixel grid
"""
nx, ny = np.shape(image_data)
self._data = image_data
if transform_pix2angle is None:
transform_pix2angle = np.array([[1, 0], [0, 1]])
PixelGrid.__init__(self, nx, ny, transform_pix2angle,
ra_at_xy_0 + ra_shift, dec_at_xy_0 + dec_shift)
ImageNoise.__init__(self,
image_data,
exposure_time=exposure_time,
background_rms=background_rms,
noise_map=noise_map,
verbose=False)
def setup(self):
self.numPix = 10
kwargs_noise = {'image_data': np.zeros((self.numPix, self.numPix)), 'exposure_time': 1, 'background_rms': 1,
'noise_map': None, 'verbose': True}
self.Noise = ImageNoise(**kwargs_noise)
kwargs_noise = {'image_data': np.zeros((self.numPix, self.numPix)), 'exposure_time': np.ones((self.numPix, self.numPix)), 'background_rms': 1,
'noise_map': None, 'verbose': True}
noise = ImageNoise(**kwargs_noise)
def test_gradient_boost(self):
kwargs_noise = {
'image_data': np.zeros((self.numPix, self.numPix)),
'exposure_time': 1000000000000,
'background_rms': 1,
'noise_map': None,
'gradient_boost_factor': 1.,
'verbose': True
}
noise = ImageNoise(**kwargs_noise)
model = np.zeros((self.numPix, self.numPix))
CD_zero = noise.C_D_model(model)
model = np.ones((self.numPix, self.numPix))
CD_one = noise.C_D_model(model)
npt.assert_almost_equal(CD_zero, CD_one)
def test_raise(self):
kwargs_noise = {'image_data': np.zeros((10, 10)), 'exposure_time': None,
'background_rms': None, 'noise_map': None, 'verbose': True}
noise = ImageNoise(**kwargs_noise)
with self.assertRaises(ValueError):
out = noise.background_rms
with self.assertRaises(ValueError):
out = noise.exposure_map
def test_exposure_time(self):
kwargs_noise = {
'image_data': np.zeros((self.numPix, self.numPix)),
'exposure_time': 0.,
'background_rms': 1,
'noise_map': None,
'verbose': True
}
noise = ImageNoise(**kwargs_noise)
exp_map = noise.exposure_map
assert exp_map > 0