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 __getattr__(self, name):
"""Lazy attributes instantiated on first-access"""
if name == 'mean_kernel':
mean_kernel = self.mean_kernel = MeanEstimator(
self.src, self.basis).kernel
return mean_kernel
return super(CovarianceEstimator, self).__getattr__(name)
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')
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.
"""
mean_estimator = MeanEstimator(sim, basis)
mean_est = mean_estimator.estimate()
# Passing in a mean_kernel argument to the following constructor speeds up some calculations
covar_estimator = CovarianceEstimator(sim, basis, mean_kernel=mean_estimator.kernel)
covar_est = covar_estimator.estimate(mean_est, noise_variance)
"""
Number of eigenvectors to estimate. This is typically (C-1), since this is
the expected rank of a covariance matrix obtained from C distinct volumes.
In experimental settings, however, this is of course not known in advance
and a larger value must be selected. Here, we select num_eigs = 16 to
get an idea of the spectrum of the estimated covariance matrix.
"""
num_eigs = args.num_eigs
parser.add_argument('--pixel_size', default=1, type=float)
parser.add_argument('--ignore_missing_files', action='store_true')
parser.add_argument('--max_rows', default=None, type=int)
parser.add_argument('-L', default=16, type=int)
with parser.parse_args() as args:
source = Starfile(args.starfile,
pixel_size=args.pixel_size,
ignore_missing_files=args.ignore_missing_files,
max_rows=args.max_rows)
L = args.L
source.set_max_resolution(L)
source.cache()
source.whiten()
basis = FBBasis3D((L, L, L))
mean_estimator = MeanEstimator(source, basis, batch_size=8192)
mean_est = mean_estimator.estimate()
noise_estimator = WhiteNoiseEstimator(source, 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}')
# Passing in a mean_kernel argument to the following constructor speeds up some calculations
covar_estimator = CovarianceEstimator(
source, basis, mean_kernel=mean_estimator.kernel)
covar_estimator.estimate(mean_est, noise_variance)
class MeanEstimatorTestCase(TestCase):
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 tearDown(self):
pass
def testEstimate(self):
estimate = self.estimator.estimate()
self.assertTrue(
np.allclose(
estimate[:, :, 4],
[[
+0.00000000, +0.00000000, +0.00000000, +0.00000000,
-0.00000000, +0.00000000, +0.00000000, +0.00000000
],
[
+0.00000000, +0.00000000, +0.02446793, +0.05363505,
+0.21988572, +0.19513786, +0.01174418, +0.00000000
],
[
+0.00000000, -0.06168774, +0.13178457, +0.36011154,
+0.88632372, +0.92307694, +0.45524491, +0.15142541
],
[
+0.00000000, -0.09108749, +0.19564009, +0.78325885,
+2.34527692, +2.44817345, +1.41268619, +0.53634876
],
[
+0.00000000, +0.07150180, +0.38347393, +1.70868980,
+3.78134981, +3.03582139, +1.49942724, +0.52104809
],
[
+0.00000000, +0.00736866, +0.19239950, +1.71596036,
+3.59823119, +2.64081679, +1.08514933, +0.24995637
],
[
+0.00000000, +0.11075829, +0.43197553, +0.82667320,
+1.51163241, +1.25342639, +0.36478594, -0.00464912
],
[
+0.00000000, +0.00000000, +0.43422818, +0.64440739,
+0.44137408, +0.25311494, +0.00011242, +0.00000000
]],
atol=1e-5))
def testAdjoint(self):
mean_b_coeff = self.estimator.src_backward().squeeze()
self.assertTrue(
np.allclose(mean_b_coeff, [
1.07338590e-01, 1.23690941e-01, 6.44482039e-03,
-5.40484306e-02, -4.85304586e-02, 1.09852144e-02,
3.87838396e-02, 3.43796455e-02, -6.43284705e-03,
-2.86677145e-02, -1.42313328e-02, -2.25684091e-03,
-3.31840727e-02, -2.59706174e-03, -5.91919887e-04,
-9.97433028e-03, 9.19123928e-04, 1.19891589e-03,
7.49154982e-03, 6.18865229e-03, -8.13265715e-04,
-1.30715655e-02, -1.44160603e-02, 2.90379956e-03,
2.37066082e-02, 4.88805735e-03, 1.47870707e-03, 7.63376018e-03,
-5.60619559e-03, 1.05165081e-02, 3.30510143e-03,
-3.48652120e-03, -4.23228797e-04, 1.40484061e-02,
1.42914291e-03, -1.28129504e-02, 2.19868825e-03,
-6.30835037e-03, 1.18524223e-03, -2.97855052e-02,
1.15491057e-03, -8.27947006e-03, 3.45442781e-03,
-4.72868856e-03, 2.66615329e-03, -7.87929790e-03,
8.84126590e-04, 1.59402808e-03, -9.06854048e-05,
-8.79119004e-03, 1.76449039e-03, -1.36414673e-02,
1.56793855e-03, 1.44708445e-02, -2.55974802e-03,
5.38506357e-03, -3.24188673e-03, 4.81582945e-04,
7.74260101e-05, 5.48772082e-03, 1.92058500e-03,
-4.63538896e-03, -2.02735133e-03, 3.67592386e-03,
7.23486969e-04, 1.81838422e-03, 1.78793284e-03,
-8.01474060e-03, -8.54007528e-03, 1.96353845e-03,
-2.16254252e-03, -3.64243996e-05, -2.27329863e-03,
1.11424393e-03, -1.39389189e-03, 2.57787159e-04,
3.66918811e-03, 1.31477774e-03, 6.82220128e-04, 1.41822851e-03,
-1.89476924e-03, -6.43966255e-05, -7.87888465e-04,
-6.99459279e-04, 1.08918981e-03, 2.25264584e-03,
-1.43651015e-04, 7.68377620e-04, 5.05955256e-04,
2.66936132e-06, 2.24934884e-03, 6.70529439e-04, 4.81121742e-04,
-6.40789745e-05, -3.35915672e-04, -7.98651783e-04,
-9.82705453e-04, 6.46337066e-05
]))
def testOptimize1(self):
mean_b_coeff = np.array([
1.07338590e-01, 1.23690941e-01, 6.44482039e-03, -5.40484306e-02,
-4.85304586e-02, 1.09852144e-02, 3.87838396e-02, 3.43796455e-02,
-6.43284705e-03, -2.86677145e-02, -1.42313328e-02, -2.25684091e-03,
-3.31840727e-02, -2.59706174e-03, -5.91919887e-04, -9.97433028e-03,
9.19123928e-04, 1.19891589e-03, 7.49154982e-03, 6.18865229e-03,
-8.13265715e-04, -1.30715655e-02, -1.44160603e-02, 2.90379956e-03,
2.37066082e-02, 4.88805735e-03, 1.47870707e-03, 7.63376018e-03,
-5.60619559e-03, 1.05165081e-02, 3.30510143e-03, -3.48652120e-03,
-4.23228797e-04, 1.40484061e-02, 1.42914291e-03, -1.28129504e-02,
2.19868825e-03, -6.30835037e-03, 1.18524223e-03, -2.97855052e-02,
1.15491057e-03, -8.27947006e-03, 3.45442781e-03, -4.72868856e-03,
2.66615329e-03, -7.87929790e-03, 8.84126590e-04, 1.59402808e-03,
-9.06854048e-05, -8.79119004e-03, 1.76449039e-03, -1.36414673e-02,
1.56793855e-03, 1.44708445e-02, -2.55974802e-03, 5.38506357e-03,
-3.24188673e-03, 4.81582945e-04, 7.74260101e-05, 5.48772082e-03,
1.92058500e-03, -4.63538896e-03, -2.02735133e-03, 3.67592386e-03,
7.23486969e-04, 1.81838422e-03, 1.78793284e-03, -8.01474060e-03,
-8.54007528e-03, 1.96353845e-03, -2.16254252e-03, -3.64243996e-05,
-2.27329863e-03, 1.11424393e-03, -1.39389189e-03, 2.57787159e-04,
3.66918811e-03, 1.31477774e-03, 6.82220128e-04, 1.41822851e-03,
-1.89476924e-03, -6.43966255e-05, -7.87888465e-04, -6.99459279e-04,
1.08918981e-03, 2.25264584e-03, -1.43651015e-04, 7.68377620e-04,
5.05955256e-04, 2.66936132e-06, 2.24934884e-03, 6.70529439e-04,
4.81121742e-04, -6.40789745e-05, -3.35915672e-04, -7.98651783e-04,
-9.82705453e-04, 6.46337066e-05
])
x = self.estimator.conj_grad(mean_b_coeff)
self.assertTrue(
np.allclose(x, [
1.24325149e+01, 4.06481998e+00, 1.19149607e+00,
-3.31414200e+00, -1.23897783e+00, 1.53987018e-01,
2.50221093e+00, 9.18131863e-01, 4.09624945e-02,
-1.81129255e+00, -2.58832135e-01, -7.21149988e-01,
-1.00909836e+00, 5.72232366e-02, -3.90701966e-01,
-3.65655187e-01, 2.33601017e-01, 1.75039197e-01,
2.52945224e-01, 3.29783105e-01, 7.85601834e-02,
-3.96439884e-01, -8.56255814e-01, 7.35131473e-03,
1.10704423e+00, 7.35615877e-02, 5.61772211e-01, 2.60428522e-01,
-5.41932165e-01, 4.29851425e-01, 3.86300956e-01,
-8.90168838e-02, -1.02959264e-01, 6.03104058e-01,
1.85286462e-01, -4.16434930e-01, 2.11092135e-01,
-1.85514653e-01, 9.80712710e-02, -8.98429489e-01,
-9.54759574e-02, -1.17952459e-01, 1.41721715e-01,
-1.36184702e-01, 3.23733962e-01, -2.68721792e-01,
-1.42064052e-01, 1.41909797e-01, -2.24251300e-03,
-4.27538724e-01, 1.28441757e-01, -5.57623000e-01,
-1.54801935e-01, 6.51729903e-01, -2.15567768e-01,
1.95041528e-01, -4.18334680e-01, 3.26735913e-02,
6.35474331e-02, 3.06828631e-01, 1.43149180e-01,
-2.34377520e-01, -1.54299735e-01, 2.82627560e-01,
9.60630473e-02, 1.47687304e-01, 1.38157247e-01,
-4.25581692e-01, -5.62236939e-01, 2.09287213e-01,
-1.14280315e-01, 2.70617650e-02, -1.19705716e-01,
1.68350236e-02, -1.20459065e-01, 6.03971532e-02,
3.21465643e-01, 1.82032297e-01, -2.95991444e-02,
1.53711400e-01, -1.30594319e-01, -4.71412485e-02,
-1.35301477e-01, -2.36292616e-01, 1.95728111e-01,
2.54618329e-01, -1.81663289e-03, 2.77960420e-02,
3.58816749e-02, -2.50138365e-02, 2.54103161e-01,
9.82534014e-02, 9.00807559e-02, 3.71458771e-02,
-7.86838200e-02, -1.03837231e-01, -1.26116949e-01,
9.82006976e-02
],
atol=1e-4))
def testOptimize2(self):
mean_b_coeff = np.array([
1.07338590e-01, 1.23690941e-01, 6.44482039e-03, -5.40484306e-02,
-4.85304586e-02, 1.09852144e-02, 3.87838396e-02, 3.43796455e-02,
-6.43284705e-03, -2.86677145e-02, -1.42313328e-02, -2.25684091e-03,
-3.31840727e-02, -2.59706174e-03, -5.91919887e-04, -9.97433028e-03,
9.19123928e-04, 1.19891589e-03, 7.49154982e-03, 6.18865229e-03,
-8.13265715e-04, -1.30715655e-02, -1.44160603e-02, 2.90379956e-03,
2.37066082e-02, 4.88805735e-03, 1.47870707e-03, 7.63376018e-03,
-5.60619559e-03, 1.05165081e-02, 3.30510143e-03, -3.48652120e-03,
-4.23228797e-04, 1.40484061e-02, 1.42914291e-03, -1.28129504e-02,
2.19868825e-03, -6.30835037e-03, 1.18524223e-03, -2.97855052e-02,
1.15491057e-03, -8.27947006e-03, 3.45442781e-03, -4.72868856e-03,
2.66615329e-03, -7.87929790e-03, 8.84126590e-04, 1.59402808e-03,
-9.06854048e-05, -8.79119004e-03, 1.76449039e-03, -1.36414673e-02,
1.56793855e-03, 1.44708445e-02, -2.55974802e-03, 5.38506357e-03,
-3.24188673e-03, 4.81582945e-04, 7.74260101e-05, 5.48772082e-03,
1.92058500e-03, -4.63538896e-03, -2.02735133e-03, 3.67592386e-03,
7.23486969e-04, 1.81838422e-03, 1.78793284e-03, -8.01474060e-03,
-8.54007528e-03, 1.96353845e-03, -2.16254252e-03, -3.64243996e-05,
-2.27329863e-03, 1.11424393e-03, -1.39389189e-03, 2.57787159e-04,
3.66918811e-03, 1.31477774e-03, 6.82220128e-04, 1.41822851e-03,
-1.89476924e-03, -6.43966255e-05, -7.87888465e-04, -6.99459279e-04,
1.08918981e-03, 2.25264584e-03, -1.43651015e-04, 7.68377620e-04,
5.05955256e-04, 2.66936132e-06, 2.24934884e-03, 6.70529439e-04,
4.81121742e-04, -6.40789745e-05, -3.35915672e-04, -7.98651783e-04,
-9.82705453e-04, 6.46337066e-05
])
x = self.estimator_with_preconditioner.conj_grad(mean_b_coeff)
self.assertTrue(
np.allclose(x, [
1.24325149e+01, 4.06481998e+00, 1.19149607e+00,
-3.31414200e+00, -1.23897783e+00, 1.53987018e-01,
2.50221093e+00, 9.18131863e-01, 4.09624945e-02,
-1.81129255e+00, -2.58832135e-01, -7.21149988e-01,
-1.00909836e+00, 5.72232366e-02, -3.90701966e-01,
-3.65655187e-01, 2.33601017e-01, 1.75039197e-01,
2.52945224e-01, 3.29783105e-01, 7.85601834e-02,
-3.96439884e-01, -8.56255814e-01, 7.35131473e-03,
1.10704423e+00, 7.35615877e-02, 5.61772211e-01, 2.60428522e-01,
-5.41932165e-01, 4.29851425e-01, 3.86300956e-01,
-8.90168838e-02, -1.02959264e-01, 6.03104058e-01,
1.85286462e-01, -4.16434930e-01, 2.11092135e-01,
-1.85514653e-01, 9.80712710e-02, -8.98429489e-01,
-9.54759574e-02, -1.17952459e-01, 1.41721715e-01,
-1.36184702e-01, 3.23733962e-01, -2.68721792e-01,
-1.42064052e-01, 1.41909797e-01, -2.24251300e-03,
-4.27538724e-01, 1.28441757e-01, -5.57623000e-01,
-1.54801935e-01, 6.51729903e-01, -2.15567768e-01,
1.95041528e-01, -4.18334680e-01, 3.26735913e-02,
6.35474331e-02, 3.06828631e-01, 1.43149180e-01,
-2.34377520e-01, -1.54299735e-01, 2.82627560e-01,
9.60630473e-02, 1.47687304e-01, 1.38157247e-01,
-4.25581692e-01, -5.62236939e-01, 2.09287213e-01,
-1.14280315e-01, 2.70617650e-02, -1.19705716e-01,
1.68350236e-02, -1.20459065e-01, 6.03971532e-02,
3.21465643e-01, 1.82032297e-01, -2.95991444e-02,
1.53711400e-01, -1.30594319e-01, -4.71412485e-02,
-1.35301477e-01, -2.36292616e-01, 1.95728111e-01,
2.54618329e-01, -1.81663289e-03, 2.77960420e-02,
3.58816749e-02, -2.50138365e-02, 2.54103161e-01,
9.82534014e-02, 9.00807559e-02, 3.71458771e-02,
-7.86838200e-02, -1.03837231e-01, -1.26116949e-01,
9.82006976e-02
],
atol=1e-4))