def setUp(self):
L = 8
n = 32
C = 1
SNR = 1
pixel_size = 5
voltage = 200
defocus_min = 1.5e4
defocus_max = 2.5e4
defocus_ct = 7
Cs = 2.0
alpha = 0.1
filters = [
RadialCTFFilter(pixel_size, voltage, defocus=d, Cs=2.0, alpha=0.1)
for d in np.linspace(defocus_min, defocus_max, defocus_ct)
]
# Since FFBBasis2D doesn't yet implement dtype, we'll set this to double to match its built in types.
sim = Simulation(n=n, C=C, filters=filters, dtype='double')
vols = np.load(os.path.join(DATA_DIR, 'clean70SRibosome_vol.npy'))
vols = vols[..., np.newaxis]
vols = downsample(vols, (L * np.ones(3, dtype=int)))
sim.vols = vols
self.basis = FFBBasis2D((L, L))
# use new methods to generate random rotations and clean images
sim.rots = qrand_rots(n, seed=0)
self.imgs_clean = vol2img(vols[..., 0], sim.rots)
self.h_idx = np.array([filters.index(f) for f in sim.filters])
self.filters = filters
self.h_ctf_fb = [filt.fb_mat(self.basis) for filt in self.filters]
self.imgs_ctf_clean = sim.eval_filters(self.imgs_clean)
sim.cache(self.imgs_ctf_clean)
power_clean = anorm(self.imgs_ctf_clean)**2 / np.size(
self.imgs_ctf_clean)
self.noise_var = power_clean / SNR
self.imgs_ctf_noise = self.imgs_ctf_clean + np.sqrt(
self.noise_var) * randn(L, L, n, seed=0)
self.cov2d = RotCov2D(self.basis)
self.coeff_clean = self.basis.evaluate_t(self.imgs_clean)
self.coeff = self.basis.evaluate_t(self.imgs_ctf_noise)
logger.info(
'Generate random distributed rotation angles and obtain corresponding 2D clean images.'
)
rots = qrand_rots(num_imgs, seed=0)
imgs_clean = vol2img(sim.vols[..., 0], rots)
# Assign the CTF information and index for each image
h_idx = np.array([filters.index(f) for f in sim.filters])
# Evaluate CTF in the 8X8 FB basis
h_ctf_fb = [filt.fb_mat(ffbbasis) for filt in filters]
# Apply the CTF to the clean images.
logger.info('Apply CTF filters to clean images.')
imgs_ctf_clean = Image(sim.eval_filters(imgs_clean))
sim.cache(imgs_ctf_clean)
# imgs_ctf_clean is an Image object. Convert to numpy array for subsequent statements
imgs_ctf_clean = imgs_ctf_clean.asnumpy()
# Apply the noise at the desired singal-noise ratio to the filtered clean images
logger.info('Apply noise filters to clean images.')
power_clean = anorm(imgs_ctf_clean)**2 / np.size(imgs_ctf_clean)
noise_var = power_clean / sn_ratio
imgs_noise = imgs_ctf_clean + np.sqrt(noise_var) * randn(
img_size, img_size, num_imgs, seed=0)
# Expand the images, both clean and noisy, in the Fourier-Bessel basis. This
# can be done exactly (that is, up to numerical precision) using the
# `basis.expand` function, but for our purposes, an approximation will do.
# Since the basis is close to orthonormal, we may approximate the exact