def set_dataset(self,cryodata):
self.cryodata = cryodata
self.fspace_stack = FourierStack(self.cryodata.imgstack,
caching = self.fspace_premult_stack_caching)
self.quad_domain_RI = None
self.quad_domain_R = None
self.quad_domain_I = None
self.quad_domain_S = None
self.N = self.cryodata.N
self.N_D = self.cryodata.N_D
self.N_D_Train = self.cryodata.N_D_Train
self.outlier_model = None
def dataset_loading_test(params, visualize=False):
imgpath = params['inpath']
psize = params['resolution']
imgstk = MRCImageStack(imgpath, psize)
# if params.get('float_images', True):
# imgstk.float_images()
ctfpath = params['ctfpath']
mscope_params = params['microscope_params']
ctfstk = CTFStack(ctfpath, mscope_params)
cryodata = CryoDataset(imgstk, ctfstk)
cryodata.compute_noise_statistics()
# if params.get('window_images',True):
# imgstk.window_images()
cryodata.divide_dataset(params['minisize'], params['test_imgs'],
params['partition'], params['num_partitions'], params['random_seed'])
# cryodata.set_datasign(params.get('datasign', 'auto'))
# if params.get('normalize_data',True):
# cryodata.normalize_dataset()
# voxel_size = cryodata.pixel_size
N = cryodata.imgstack.get_num_pixels()
fspace_stack = FourierStack(cryodata.imgstack,
caching = True, zeropad=1)
premult = cryoops.compute_premultiplier(N + 2 * int(1 * (N/2)), 'lanczos', 8)
premult = premult.reshape((-1,1)) * premult.reshape((1,-1))
fspace_stack.set_transform(premult, 1)
if visualize:
rad = 0.99
coords = geometry.gencoords(N, 2).reshape((N**2, 2))
Cs = np.sum(coords**2, axis=1).reshape((N, N)) > (rad * N / 2.0 - 1.5)**2
idx = np.random.randint(cryodata.imgstack.num_images)
normalized = cryodata.imgstack.get_image(1)
f_normalized = fspace_stack.get_image(1)
plot_noise_histogram(normalized, f_normalized,
rmask=~Cs, fmask=None, plot_unmask=False)
plt.show()
return cryodata, fspace_stack
