def get_image(number, training):
if training:
L = train_list
else:
L = eval_list
adv_path=L[number]
with mrcfile.open(adv_path) as mrc:
adv = mrc.data
with mrcfile.open(locate_gt(adv_path)) as mrc:
gt = mrc.data
return gt, adv
def get_image(noise_level, methode, eval_data):
if eval_data:
d = eval_dic
else:
d = train_dic
l = d[noise_level][methode]
adv_path = random.choice(l)
with mrcfile.open(adv_path) as mrc:
adv = mrc.data
with mrcfile.open(locate_gt(adv_path)) as mrc:
gt = mrc.data
return gt, adv
def get_image(noise_level, method, data_dict, eval_data):
data_list = data_dict[noise_level][method]
adv_path = random.choice(data_list)
if method == 'div':
# print('adv_path', adv_path)
# raise Exception
star_file = load_star(adv_path)
with mrcfile.open(
cleanStarPath(
adv_path, star_file['external_reconstruct_general']
['rlnExtReconsDataReal'])) as mrc:
data_real = mrc.data
with mrcfile.open(
cleanStarPath(
adv_path, star_file['external_reconstruct_general']
['rlnExtReconsDataImag'])) as mrc:
data_im = mrc.data
with mrcfile.open(
cleanStarPath(
adv_path, star_file['external_reconstruct_general']
['rlnExtReconsWeight'])) as mrc:
kernel = mrc.data
adv = np.divide(data_real + 1j * data_im, kernel + 1e-3)
adv = irfft(adv)
else:
with mrcfile.open(adv_path) as mrc:
adv = mrc.data
with mrcfile.open(locate_gt(adv_path, noise_level,
eval_data=eval_data)) as mrc:
gt = mrc.data
# print(locate_gt(adv_path, eval_data=eval_data))
# print(star_file)
# raise Exception
return gt, adv
# In[19]:
file = load_star(path)
# In[20]:
with mrcfile.open(
file['external_reconstruct_general']['rlnExtReconsDataReal']) as mrc:
data_real = mrc.data.copy()
with mrcfile.open(
file['external_reconstruct_general']['rlnExtReconsDataImag']) as mrc:
data_im = mrc.data.copy()
with mrcfile.open(
file['external_reconstruct_general']['rlnExtReconsWeight']) as mrc:
kernel = mrc.data.copy()
with mrcfile.open(locate_gt(PDB_ID, full_path=False)) as mrc:
ground_truth = mrc.data.copy()
with mrcfile.open(
file['external_reconstruct_general']['rlnExtReconsResult']) as mrc:
naive_recon = mrc.data.copy()
ground_truth = unify_form(ground_truth)
r_gt = Rescaler(ground_truth)
r_gt.normalize(ground_truth)
complex_data = data_real + 1j * data_im
# In[23]:
REG = 0.03