def setUp(self):
self.sim = Simulation(n=1024,
filters=SourceFilter([
RadialCTFFilter(defocus=d)
for d in np.linspace(1.5e4, 2.5e4, 7)
],
n=1024))
def testRadialCTFFilterMultiplier(self):
filter = RadialCTFFilter(defocus=2.5e4) * RadialCTFFilter(defocus=2.5e4)
result = filter.evaluate_grid(8)
self.assertEqual(result.shape, (8, 8))
self.assertTrue(np.allclose(
result,
np.array([
[ 0.461755701877834, -0.995184514498978, 0.063120922443392, 0.833250206225063, 0.961464660252150, 0.833250206225063, 0.063120922443392, -0.995184514498978],
[-0.995184514498978, 0.626977423649552, 0.799934516166400, 0.004814348317439, -0.298096205735759, 0.004814348317439, 0.799934516166400, 0.626977423649552],
[ 0.063120922443392, 0.799934516166400, -0.573061561512667, -0.999286510416273, -0.963805291282899, -0.999286510416273, -0.573061561512667, 0.799934516166400],
[ 0.833250206225063, 0.004814348317439, -0.999286510416273, -0.633095739808868, -0.368890743119366, -0.633095739808868, -0.999286510416273, 0.004814348317439],
[ 0.961464660252150, -0.298096205735759, -0.963805291282899, -0.368890743119366, -0.070000000000000, -0.368890743119366, -0.963805291282899, -0.298096205735759],
[ 0.833250206225063, 0.004814348317439, -0.999286510416273, -0.633095739808868, -0.368890743119366, -0.633095739808868, -0.999286510416273, 0.004814348317439],
[ 0.063120922443392, 0.799934516166400, -0.573061561512667, -0.999286510416273, -0.963805291282899, -0.999286510416273, -0.573061561512667, 0.799934516166400],
[-0.995184514498978, 0.626977423649552, 0.799934516166400, 0.004814348317439, -0.298096205735759, 0.004814348317439, 0.799934516166400, 0.626977423649552]
])**2
))
def setUp(self):
sim = Simulation(n=1024,
filters=SourceFilter([
RadialCTFFilter(defocus=d)
for d in np.linspace(1.5e4, 2.5e4, 7)
],
n=1024))
basis = FBBasis3D((8, 8, 8))
self.estimator = MeanEstimator(sim, basis, preconditioner='none')
self.estimator_with_preconditioner = MeanEstimator(
sim, basis, preconditioner='circulant')
def testRadialCTFSourceFilter(self):
source_filter = SourceFilter(
[RadialCTFFilter(defocus=d) for d in np.linspace(1.5e4, 2.5e4, 7)],
n=42
)
result = source_filter.evaluate_grid(8)
self.assertEqual(result.shape, (8, 8, 7))
# Just check the value of the last filter for now
self.assertTrue(np.allclose(
result[:, :, -1],
np.array([
[ 0.461755701877834, -0.995184514498978, 0.063120922443392, 0.833250206225063, 0.961464660252150, 0.833250206225063, 0.063120922443392, -0.995184514498978],
[-0.995184514498978, 0.626977423649552, 0.799934516166400, 0.004814348317439, -0.298096205735759, 0.004814348317439, 0.799934516166400, 0.626977423649552],
[ 0.063120922443392, 0.799934516166400, -0.573061561512667, -0.999286510416273, -0.963805291282899, -0.999286510416273, -0.573061561512667, 0.799934516166400],
[ 0.833250206225063, 0.004814348317439, -0.999286510416273, -0.633095739808868, -0.368890743119366, -0.633095739808868, -0.999286510416273, 0.004814348317439],
[ 0.961464660252150, -0.298096205735759, -0.963805291282899, -0.368890743119366, -0.070000000000000, -0.368890743119366, -0.963805291282899, -0.298096205735759],
[ 0.833250206225063, 0.004814348317439, -0.999286510416273, -0.633095739808868, -0.368890743119366, -0.633095739808868, -0.999286510416273, 0.004814348317439],
[ 0.063120922443392, 0.799934516166400, -0.573061561512667, -0.999286510416273, -0.963805291282899, -0.999286510416273, -0.573061561512667, 0.799934516166400],
[-0.995184514498978, 0.626977423649552, 0.799934516166400, 0.004814348317439, -0.298096205735759, 0.004814348317439, 0.799934516166400, 0.626977423649552]
])
))
def setUpClass(cls):
cls.sim = Simulation(n=1024,
filters=SourceFilter([
RadialCTFFilter(defocus=d)
for d in np.linspace(1.5e4, 2.5e4, 7)
],
n=1024))
basis = FBBasis3D((8, 8, 8))
cls.noise_variance = 0.0030762743633643615
cls.mean_estimator = MeanEstimator(cls.sim, basis)
cls.mean_est = np.load(os.path.join(DATA_DIR, 'mean_8_8_8.npy'))
# Passing in a mean_kernel argument to the following constructor speeds up some calculations
cls.covar_estimator = CovarianceEstimator(
cls.sim,
basis,
mean_kernel=cls.mean_estimator.kernel,
preconditioner='none')
cls.covar_estimator_with_preconditioner = CovarianceEstimator(
cls.sim,
basis,
mean_kernel=cls.mean_estimator.kernel,
preconditioner='circulant')
parser.add_argument('--num_volumes', default=2, type=int)
parser.add_argument('--image_size', default=8, type=int)
parser.add_argument('--num_images', default=1024, type=int)
parser.add_argument('--num_eigs', default=16, type=int)
with parser.parse_args() as args:
C = args.num_volumes
L = args.image_size
n = args.num_images
sim = Simulation(
n=n,
C=C,
filters=SourceFilter(
[RadialCTFFilter(defocus=d) for d in np.linspace(1.5e4, 2.5e4, 7)],
n=n
)
)
basis = FBBasis3D((L, L, L))
noise_estimator = WhiteNoiseEstimator(sim, batchSize=500)
# Estimate the noise variance. This is needed for the covariance estimation step below.
noise_variance = noise_estimator.estimate()
print(f'Noise Variance = {noise_variance}')
"""
Estimate the mean. This uses conjugate gradient on the normal equations for the least-squares estimator of the mean
volume. The mean volume is represented internally using the basis object, but the output is in the form of an
L-by-L-by-L array.
"""