def export_avgs(avgs, out_dir):
if not os.path.isdir(out_dir): os.makedirs(out_dir)
for k in avgs:
AIF.put_mrc(
avgs[k]['v'],
os.path.join(out_dir, '%03d--%s.mrc' % (avgs[k]['pass_i'], k)))
def peak__partition__single_job(v,
s1,
s2,
base=None,
find_maxima=None,
partition_id=None,
save_vg=False):
assert find_maxima is not None
# vg = FG.dog_smooth__large_map(v, s1=s1, s2=s2) 'dog_smooth' seems to perform better
vg = FG.dog_smooth(v, s1=s1, s2=s2)
# save the smoothed partition for inspection
if save_vg:
IOF.put_mrc(v,
'/tmp/%d-%d-%d--v.mrc' %
(partition_id[0], partition_id[1], partition_id[2]),
overwrite=True)
del v
if find_maxima:
# print 'local_maxima()'
# sys.stdout.flush()
x = FL.local_maxima(vg)
else:
# print 'local_minima()'
# sys.stdout.flush()
x = FL.local_minima(vg)
p = vg[x]
x = N.array(x).T
if base is not None:
assert base.shape[0] == x.shape[1]
assert base.shape[1] == 2
for dim_i in range(x.shape[1]):
x[:, dim_i] += base[dim_i, 0]
ps = []
for i in range(len(p)):
ps.append({
'val': float(p[i]),
'x': [_ for _ in x[i, :]],
'uuid': str(uuid.uuid4())
})
if save_vg:
# save the smoothed partition for inspection
IOF.put_mrc(vg,
'/tmp/%d-%d-%d--vg.mrc' %
(partition_id[0], partition_id[1], partition_id[2]),
overwrite=True)
del vg
GC.collect()
return {'ps': ps, 'partition_id': partition_id}
def convert(op):
from . import situs_pdb2vol as SP
import aitom.io.file as IF
r = SP.convert(op)
v = r['map']
if 'out_map_size' in op:
import aitom.image.vol.util as IVU
v = IVU.resize_center(v=v, s=op['out_map_size'], cval=0.0)
IF.put_mrc(v, op['out_file'])
def convert(op):
import aitom.structure.pdb.situs_pdb2vol as SP
r = SP.convert(op)
v = r['map']
if 'out_map_size' in op:
import tomominer.image.vol.util as IVU
v = IVU.resize_center(v=v, s=op['out_map_size'], cval=0.0)
import aitom.io.file as IF
IF.put_mrc(v, op['out_file'])
im = IF.read_mrc(op['vol_file'])
# voxel spacing in nm unit
voxel_spacing = (im['header']['MRC']['xlen'] /
im['header']['MRC']['nx']) * 0.1
print('voxel_spacing', voxel_spacing)
v = im['value']
if 'debug' in op:
se = N.array(op['debug']['start_end'])
v = v[se[0, 0]:se[0, 1], se[1, 0]:se[1, 1], se[2, 0]:se[2, 1]]
v = v.astype(N.float)
v -= v.mean()
IF.put_mrc(v, '/tmp/v.mrc', overwrite=True)
if op['inverse_intensity']:
v = -v
print('intensity distribution of v', 'max', v.max(), 'min', v.min(),
'mean', v.mean())
print('# gaussian smoothing')
vg = FG.smooth(v, sigma=float(op['sigma']) / voxel_spacing)
print('intensity distribution of vg', 'max', vg.max(), 'min', vg.min(),
'mean', vg.mean())
IF.put_mrc(vg, '/tmp/vg.mrc', overwrite=True)
v = MU.generate_toy_model(dim_siz=64) # generate a pseudo density map
print(v.shape)
# randomly rotate and translate v
loc_proportion = 0.1
loc_max = N.array(v.shape, dtype=float) * loc_proportion
angle = GAL.random_rotation_angle_zyz()
loc_r = (N.random.random(3) - 0.5) * loc_max
vr = GR.rotate(v, angle=angle, loc_r=loc_r, default_val=0.0)
# generate simulated subtomogram vb from v
vb = TSRSC.do_reconstruction(vr, op, verbose=True)
print('vb', 'mean', vb.mean(), 'std', vb.std(), 'var', vb.var())
# save v and vb as 3D grey scale images
IF.put_mrc(vb, '/tmp/vb.mrc', overwrite=True)
IF.put_mrc(v, '/tmp/v.mrc', overwrite=True)
# save images of the slices of the corresponding 3D iamges for visual inspection
import aitom.image.io as IIO
import aitom.image.vol.util as IVU
IIO.save_png(IVU.cub_img(vb)['im'], "/tmp/vb.png")
IIO.save_png(IVU.cub_img(v)['im'], "/tmp/v.png")
if True:
# verify the correctness of SNR estimation
vb_rep = TSRSC.do_reconstruction(vr, op, verbose=True)
import scipy.stats as SS
# calculate SNR
vb_corr = SS.pearsonr(vb.flatten(), vb_rep.flatten())[0]
