def scarlet1_multi_initialize(images, peaks, psfs, variances, bands):
""" Initializes scarlet MultiComponentSource at locations input as
peaks in the (multi-band) input images.
Args:
images: Numpy array of multi-band image to run scarlet on
[Number of bands, height, width].
peaks: Array of x and y coordinates of centroids of objects in
the image [number of sources, 2].
bg_rms: Background RMS value of the images [Number of bands]
Returns
blend: scarlet.Blend object for the initialized sources
rejected_sources: list of sources (if any) that scarlet was
unable to initialize the image with.
"""
model_psf = scarlet.PSF(partial(scarlet.psf.gaussian, sigma=0.8),
shape=(None, 41, 41))
model_frame = scarlet.Frame(images.shape, psfs=model_psf, channels=bands)
observation = scarlet.Observation(images, psfs=scarlet.PSF(psfs),
weights=1./variances,
channels=bands).match(model_frame)
sources = []
for n, peak in enumerate(peaks):
result = scarlet.MultiComponentSource(model_frame, (peak[1], peak[0]),
observation, symmetric=True,
monotonic=True, thresh=1,
shifting=True)
for i in range(result.n_sources):
sed = result.components[i].sed
morph = result.components[i].morph
if np.all([s < 0 for s in sed]) or np.sum(morph) == 0:
raise ValueError("Incorrectly initialized")
sources.append(result)
blend = scarlet.Blend(sources, observation)
return blend, observation
def scarlet1_initialize(images, peaks, psfs, variances, bands):
""" Deblend input images with scarlet
Args:
images: Numpy array of multi-band image to run scarlet on
[Number of bands, height, width].
peaks: Array of x and y coordinates of centroids of objects in
the image [number of sources, 2].
bg_rms: Background RMS value of the images [Number of bands]
iters: Maximum number of iterations if scarlet doesn't converge
(Default: 200).
e_rel: Relative error for convergence (Default: 0.015)
Returns
blend: scarlet.Blend object for the initialized sources
rejected_sources: list of sources (if any) that scarlet was
unable to initialize the image with.
"""
model_psf = scarlet.PSF(partial(scarlet.psf.gaussian, sigma=0.8),
shape=(None, 41, 41))
model_frame = scarlet.Frame(images.shape, psfs=model_psf, channels=bands)
observation = scarlet.Observation(images, psfs=scarlet.PSF(psfs),
weights=1./variances,
channels=bands).match(model_frame)
sources = []
for n, peak in enumerate(peaks):
result = scarlet.ExtendedSource(model_frame, (peak[1], peak[0]),
observation, symmetric=True,
monotonic=True, thresh=1,
shifting=True)
sed = result.sed
morph = result.morph
if np.all([s < 0 for s in sed]) or np.sum(morph) == 0:
raise ValueError("Incorrectly initialized")
sources.append(result)
blend = scarlet.Blend(sources, observation)
return blend, observation
def test_render_loss(self):
# model frame with minimal PSF
shape0 = (3, 13, 13)
s0 = 0.9
model_psf = scarlet.PSF(partial(scarlet.psf.gaussian, sigma=s0), shape=shape0)
shape = (3, 43, 43)
channels = np.arange(shape[0])
model_frame = scarlet.Frame(shape, psfs=model_psf, channels=channels)
# insert point source manually into center for model
origin = (0, shape[1] // 2 - shape0[1] // 2, shape[2] // 2 - shape0[2] // 2)
bbox = scarlet.Box(shape0, origin=origin)
model = np.zeros(shape)
box = np.stack([model_psf.image[0] for c in range(shape[0])], axis=0)
bbox.insert_into(model, box)
# generate observation with wider PSFs
psf = scarlet.PSF(self.get_psfs(shape[1:], [2.1, 1.1, 3.5]))
images = np.ones(shape)
observation = scarlet.Observation(images, psfs=psf, channels=channels)
observation.match(model_frame)
model_ = observation.render(model)
assert_almost_equal(model_, psf.image)
# compute the expected loss
weights = 1
log_norm = (
np.prod(images.shape) / 2 * np.log(2 * np.pi)
+ np.sum(np.log(1 / weights)) / 2
)
true_loss = log_norm + np.sum(weights * (model_ - images) ** 2) / 2
# loss is negative logL
assert_almost_equal(observation.get_log_likelihood(model), -true_loss)
def define_model(images,weights,psf="startpsf.npy"):
""" Create model psf and obsevation
"""
start_psf = np.load(psf)
out = np.outer(np.ones(len(images)),start_psf)
# WARNING, using same arbitray psf for all now.
out.shape = (len(images),start_psf.shape[0],start_psf.shape[1])
psfs = scarlet.PSF(out)
model_psf = scarlet.PSF(partial(scarlet.psf.gaussian, sigma=.8),
shape=(None, 8, 8))
model_frame = scarlet.Frame(
images.shape,
psfs=model_psf)
observation = scarlet.Observation(
images,
weights=weights,
psfs=psfs).match(model_frame)
return model_frame, observation
def get_psfs(self, shape, sigmas):
shape_ = (None, *shape)
psfs = np.array([
scarlet.PSF(partial(scarlet.psf.gaussian, sigma=s), shape=shape_).image[0]
for s in sigmas
])
psfs /= psfs.sum(axis=(1, 2))[:, None, None]
return psfs
def test_threshold(self):
# Use a random seed in the test to prevent race conditions
np.random.seed(0)
noise = np.random.rand(21, 21) * 2 # noise background to eliminate
signal = np.zeros(noise.shape)
func = scarlet.psf.gaussian
signal[7:14, 7:14] = 10 * scarlet.PSF(
func, shape=(None, 21, 21)).image[0, 7:14, 7:14]
X = signal + noise
step = 0
constraint = scarlet.ThresholdConstraint()
X_ = constraint(X, step)
# regression test with thresh from reference version
thresh = 0.05704869232578929
mask = X < thresh
assert all(X_[mask] == 0)
assert_array_equal(X_[~mask], X[~mask])