def test_fitting():
psf = gaussian_psf(2.5, size=9)
model_orig = create_model()
imfit = Imfit(model_orig, psf=psf, quiet=False)
noise_level = 0.1
shape = (100, 100)
image = imfit.getModelImage(shape)
noise = image * noise_level
image += (np.random.random(shape) * noise)
mask = np.zeros_like(image, dtype='bool')
imfit.fit(image, noise, mask)
model_fitted = imfit.getModelDescription()
orig_params = get_model_param_array(model_orig)
fitted_params = get_model_param_array(model_fitted)
assert_allclose(orig_params, fitted_params, rtol=noise_level)
def test_fitting():
psf = gaussian_psf(2.5, size=9)
model_orig = create_model()
imfit = Imfit(model_orig, psf=psf, quiet=False)
noise_level = 0.1
shape = (100, 100)
image = imfit.getModelImage(shape)
noise = image * noise_level
image += (np.random.random(shape) * noise)
mask = np.zeros_like(image, dtype='bool')
imfit.fit(image, noise, mask)
model_fitted = imfit.getModelDescription()
orig_params = get_model_param_array(model_orig)
fitted_params = get_model_param_array(model_fitted)
assert_allclose(orig_params, fitted_params, rtol=noise_level)
logger.info('Creating original B-D models.')
norm_model = get_model(args.trueModel, with_default=True)
model_deriv = np.array(get_model(args.trueModelDeriv).getParams(), dtype=norm_model.dtype)
original_models = linear_model(norm_model, model_deriv, l_ssp)
logger.info('Original model at normalization window:\n%s\n' % str(norm_model))
imshape = (72,77)
ifsshape = (len(l_ssp),) + imshape
norm_y0 = norm_model.y0.value
norm_x0 = norm_model.x0.value
flagged = distance(imshape, norm_x0, norm_y0) > 32.0
noise = 0.05
flux_unit = 1e-16
logger.info('Creating original PSF (FWHM = %.2f ")' % args.truePsfFWHM)
original_PSF = gaussian_psf(args.truePsfFWHM, size=15)
logger.info('Creating model images.')
bulge_image = np.ma.empty(ifsshape)
disk_image = np.ma.empty(ifsshape)
for i, model in enumerate(original_models):
bulge_image[i], disk_image[i] = create_model_images(model, imshape, original_PSF)
bulge_image[:,flagged] = np.ma.masked
disk_image[:,flagged] = np.ma.masked
model_image = bulge_image + disk_image
#model_noise = model_image * noise
logger.info('Creating IFS.')
bulge_spectra = bulge_image * (flux_unit * bulge_flux[..., np.newaxis, np.newaxis])
disk_spectra = disk_image * (flux_unit * disk_flux[..., np.newaxis, np.newaxis])
full_spectra = bulge_spectra + disk_spectra
def setSynthPSF(self, FWHM=0.0, beta=None, size=15):
if beta is not None:
self.setPSF(moffat_psf(FWHM, beta, size=size))
else:
self.setPSF(gaussian_psf(FWHM, size=size))
