def kmeans_centers_plot(clus_center_dir):
kmeans_clus = AIF.pickle_load(op_join(clus_center_dir, 'kmeans.pickle'))
ccents = AIF.pickle_load(op_join(clus_center_dir, 'ccents.pickle'))
# export slices for visual inspection
if False:
for i in ccents:
auto.dsp_cub(ccents[i])
else:
clus_center_figure_dir = op_join(clus_center_dir, 'fig')
if os.path.isdir(clus_center_figure_dir):
shutil.rmtree(clus_center_figure_dir)
os.makedirs(clus_center_figure_dir)
# normalize across all images
min_t = N.min([ccents[_].min() for _ in ccents])
max_t = N.max([ccents[_].max() for _ in ccents])
assert max_t > min_t
ccents_t = {_: ((ccents[_] - min_t) / (max_t - min_t)) for _ in ccents}
ccents_t = ccents
for i in ccents_t:
clus_siz = len(kmeans_clus[i])
t = AIVU.cub_img(ccents_t[i])['im']
AIIO.save_png(t,
op_join(clus_center_figure_dir,
'%003d--%d.png' % (i, clus_siz)),
normalize=False)
def output_image(v, path):
TIIO.save_png(TIVU.cub_img(v)['im'], path)
def output_image(v, path):
"""
Saves the sliced image of model v to path
"""
AIIO.save_png(AIVU.cub_img(v)['im'], path)
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
TIF.put_mrc(vb, '/tmp/vb.mrc', overwrite=True)
TIF.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.tomominer.image.vol.util as TIVU
IIO.save_png(TIVU.cub_img(vb)['im'], "/tmp/vb.png")
IIO.save_png(TIVU.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]
vb_snr = 2 * vb_corr / (1 - vb_corr)
print('SNR', 'parameter', op['model']['SNR'], 'estimated',
vb_snr) # fsc = ssnr / (2.0 + ssnr)
import aitom.geometry.ang_loc as TGAL
import aitom.geometry.rotate as TGR
import random
import numpy as N
# randomly rotate and translate v
loc_proportion = 0.1
loc_max = N.array(v.shape, dtype=float) * loc_proportion
angle = TGAL.random_rotation_angle_zyz()
loc_r = (N.random.random(3) - 0.5) * loc_max
vr = TGR.rotate(v, angle=angle, loc_r=loc_r, default_val=0.0)
#--------------------------------
# align vr against v
import aitom.align.util as TAU
al = TAU.align_vols_no_mask(v, vr)
print('rigid transform of alignment', al)
vr_i = TGR.rotate(
vr, angle=al['angle'], loc_r=al['loc'], default_val=0.0
) # rotate vr according to the alignment, expected to produce an image similiar to v
# save images of the slices of the corresponding 3D iamges for visual inspection
import aitom.image.io as TIIO
import aitom.image.vol.util as TIVU
TIIO.save_png(TIVU.cub_img(v)['im'], "v.png")
TIIO.save_png(TIVU.cub_img(vr)['im'], "vr.png")
TIIO.save_png(TIVU.cub_img(vr_i)['im'], "vr_i.png")
a = IF.read_mrc_data("data/IS002_291013_005_subtomo000001.mrc")
# denoising using gaussian filter for visualization
from aitom.filter.gaussian import smooth
a = smooth(a, sigma=8)
# display image using image module
import aitom.image.vol.util as IVU
'''
a_im is a dict:
'im': image data, type of numpy.ndarray, elements in [0, 1]
'vt': volume data
'''
a_im = IVU.cub_img(a)
print(type(a_im['im']))
print(a_im['im'].shape)
print(a_im['im'][1][1])
import matplotlib.pyplot as plt
plt.imshow(a_im['im'], cmap='gray')
# save the figure into a png file
import aitom.image.io as IIO
IIO.save_png(m=a_im['im'], name='/tmp/a_im.png')
# display image using `image.util.dsp_cub`
IVU.dsp_cub(a)
# save slices of a into a png file
IIO.save_cub_img(v=a, name='/tmp/a.png')
def map2png(map, file):
import aitom.image.io as IIO
import aitom.image.vol.util as TIVU
IIO.save_png(TIVU.cub_img(map)['im'], file)
a_im = im_vol_util.cub_img(a) ''' a_im is a dict: 'im': image data 'vt': volume data image data is type of numpy.ndarray, elements $\in$ [0, 1] ''' print(type(a_im['im'])) print(a_im['im'].shape) print(a_im['im'][1][1]) import matplotlib.pyplot as plt #%matplotlib notebook plt.imshow(a_im['im'], cmap='gray') # save the figure into a png file import aitom.image.io as image_io image_io.save_png(m=a_im['im'], name='/tmp/a_im.png') # display image using `image.util.dsp_cub` im_vol_util.dsp_cub(a) # save slices of a into a png file image_io.save_cub_png(v=a, name='/tmp/a.png')
def visualize(img, path):
t = AIVU.cub_img(img)['im']
AIIO.save_png(t, path)
