def add_noise(image, kwargs_band, background_noise=True, poisson_noise=True):
"""
:param image: 2d numpy array of a simlulated image without noise
:param kwargs_band: keyword arguments containing the noise estimates
:return: noisy image
"""
single_band = SingleBand(**kwargs_band)
noise = single_band.noise_for_model(model=image,
background_noise=background_noise,
poisson_noise=poisson_noise)
return image + noise
class TestData(object):
def setup(self):
self.ccd_gain = 4.
pixel_scale = 0.13
self.read_noise = 10.
self.kwargs_instrument = {
'read_noise': self.read_noise,
'pixel_scale': pixel_scale,
'ccd_gain': self.ccd_gain
}
exposure_time = 100
sky_brightness = 20.
self.magnitude_zero_point = 21.
num_exposures = 2
seeing = 0.9
kwargs_observations = {
'exposure_time': exposure_time,
'sky_brightness': sky_brightness,
'magnitude_zero_point': self.magnitude_zero_point,
'num_exposures': num_exposures,
'seeing': seeing,
'psf_type': 'GAUSSIAN'
}
self.kwargs_data = util.merge_dicts(self.kwargs_instrument,
kwargs_observations)
self.data_adu = SingleBand(data_count_unit='ADU', **self.kwargs_data)
self.data_e_ = SingleBand(data_count_unit='e-', **self.kwargs_data)
def test_sky_brightness(self):
sky_adu = self.data_adu.sky_brightness
sky_e_ = self.data_e_.sky_brightness
assert sky_e_ == sky_adu * self.ccd_gain
npt.assert_almost_equal(sky_adu, 0.627971607877395, decimal=6)
def test_background_noise(self):
bkg_adu = self.data_adu.background_noise
bkg_e_ = self.data_e_.background_noise
assert bkg_adu == bkg_e_ / self.ccd_gain
self.data_adu._background_noise = 1
bkg = self.data_adu.background_noise
assert bkg == 1
def test_flux_noise(self):
flux_iid = 50.
flux_adu = flux_iid / self.ccd_gain
noise_adu = self.data_adu.flux_noise(flux_adu)
noise_e_ = self.data_e_.flux_noise(flux_iid)
assert noise_e_ == 100. / 200.
assert noise_e_ == noise_adu * self.ccd_gain
def test_noise_for_model(self):
model_adu = np.ones((10, 10))
model_e_ = model_adu * self.ccd_gain
noise_adu = self.data_adu.noise_for_model(model_adu,
background_noise=True,
poisson_noise=True,
seed=42)
noise_adu_2 = self.data_adu.noise_for_model(model_adu,
background_noise=True,
poisson_noise=True,
seed=42)
npt.assert_almost_equal(noise_adu, noise_adu_2, decimal=10)
noise_e_ = self.data_e_.noise_for_model(model_e_,
background_noise=True,
poisson_noise=True,
seed=42)
npt.assert_almost_equal(noise_adu,
noise_e_ / self.ccd_gain,
decimal=10)
noise_e_ = self.data_e_.noise_for_model(model_e_,
background_noise=True,
poisson_noise=True,
seed=None)
def test_estimate_noise(self):
image_adu = np.ones((10, 10))
image_e_ = image_adu * self.ccd_gain
noise_adu = self.data_adu.estimate_noise(image_adu)
noise_e_ = self.data_e_.estimate_noise(image_e_)
npt.assert_almost_equal(noise_e_, noise_adu * self.ccd_gain)
def test_magnitude2cps(self):
mag_0 = self.data_adu.magnitude2cps(
magnitude=self.magnitude_zero_point)
npt.assert_almost_equal(mag_0, 1. / self.ccd_gain, decimal=10)
mag_0_e_ = self.data_e_.magnitude2cps(
magnitude=self.magnitude_zero_point)
npt.assert_almost_equal(mag_0_e_, 1, decimal=10)
mag_0 = self.data_adu.magnitude2cps(
magnitude=self.magnitude_zero_point + 1)
npt.assert_almost_equal(mag_0, 0.0995267926383743, decimal=10)
mag_0 = self.data_adu.magnitude2cps(
magnitude=self.magnitude_zero_point - 1)
npt.assert_almost_equal(mag_0, 0.627971607877395, decimal=10)
def test_flux_iid(self):
flux_iid_adu = self.data_adu.flux_iid(flux_per_second=1)
flux_iid_e = self.data_e_.flux_iid(flux_per_second=1)
npt.assert_almost_equal(flux_iid_e,
flux_iid_adu / self.ccd_gain,
decimal=6)
flux_adu = 10
flux_e_ = flux_adu * self.ccd_gain
noise_e_ = self.data_e_.flux_noise(flux_e_)
noise_adu = self.data_adu.flux_noise(flux_adu)
npt.assert_almost_equal(noise_e_ / self.ccd_gain, noise_adu, decimal=8)
def test_psf_type(self):
assert self.data_adu._psf_type == 'GAUSSIAN'
kwargs_observations = {
'exposure_time': 1,
'sky_brightness': 1,
'magnitude_zero_point': self.magnitude_zero_point,
'num_exposures': 1,
'seeing': 1,
'psf_type': 'PIXEL'
}
kwargs_data = util.merge_dicts(self.kwargs_instrument,
kwargs_observations)
data_pixel = SingleBand(data_count_unit='ADU', **kwargs_data)
assert data_pixel._psf_type == 'PIXEL'
kwargs_observations = {
'exposure_time': 1,
'sky_brightness': 1,
'magnitude_zero_point': self.magnitude_zero_point,
'num_exposures': 1,
'seeing': 1,
'psf_type': 'NONE'
}
kwargs_data = util.merge_dicts(self.kwargs_instrument,
kwargs_observations)
data_pixel = SingleBand(data_count_unit='ADU', **kwargs_data)
assert data_pixel._psf_type == 'NONE'