def subtract_outer(p1r, ptcl, submap_ft, refmap_ft, sx, sy, s, a, apix, coefs_method, r, nr, **kwargs):
log = logging.getLogger('root')
log.info("%d@%s Exp %f +/- %f" % (ptcl[star.UCSF.IMAGE_ORIGINAL_INDEX], ptcl[star.UCSF.IMAGE_ORIGINAL_PATH], np.mean(p1r), np.std(p1r)))
ft = getattr(tls, 'ft', None)
if ft is None:
ft = rfft2(fftshift(p1r.copy()), threads=kwargs["fftthreads"],
planner_effort="FFTW_ESTIMATE",
overwrite_input=False,
auto_align_input=True,
auto_contiguous=True)
tls.ft = ft
if coefs_method >= 1:
p1 = ft(p1r.copy(), np.zeros(ft.output_shape, dtype=ft.output_dtype)).copy()
else:
p1 = np.empty(ft.output_shape, ft.output_dtype)
p1s = subtract(p1, submap_ft, refmap_ft, sx, sy, s, a, apix,
ptcl[star.Relion.DEFOCUSU], ptcl[star.Relion.DEFOCUSV], ptcl[star.Relion.DEFOCUSANGLE],
ptcl[star.Relion.PHASESHIFT], ptcl[star.Relion.VOLTAGE], ptcl[star.Relion.AC], ptcl[star.Relion.CS],
ptcl[star.Relion.ANGLEROT], ptcl[star.Relion.ANGLETILT], ptcl[star.Relion.ANGLEPSI],
ptcl[star.Relion.ORIGINX], ptcl[star.Relion.ORIGINY], coefs_method, r, nr)
ift = getattr(tls, 'ift', None)
if ift is None:
ift = irfft2(p1s.copy(), threads=kwargs["fftthreads"],
planner_effort="FFTW_ESTIMATE",
auto_align_input=True,
auto_contiguous=True)
tls.ift = ift
p1sr = fftshift(ift(p1s.copy(), np.zeros(ift.output_shape, dtype=ift.output_dtype)).copy())
log.info("%d@%s Exp %f +/- %f, Sub %f +/- %f" % (ptcl[star.UCSF.IMAGE_ORIGINAL_INDEX], ptcl[star.UCSF.IMAGE_ORIGINAL_PATH], np.mean(p1r), np.std(p1r), np.mean(p1sr), np.std(p1sr)))
new_image = p1r - p1sr
return new_image
def subtract_outer(p1r, p1rmask, ptcl, submap_ft, refmap_ft, sx, sy, s, a, apix, coefs_method, r, nr, **kwargs):
log = logging.getLogger('root')
log.debug("%d@%s Exp %f +/- %f" % (ptcl[star.UCSF.IMAGE_ORIGINAL_INDEX], ptcl[star.UCSF.IMAGE_ORIGINAL_PATH], np.mean(p1r), np.std(p1r)))
ft = getattr(tls, 'ft', None)
# Clip p1r to match to map dimensions
p1r_clipped = clip_img(p1r, p1rmask.shape[0])*p1rmask
if ft is None:
ft = rfft2(fftshift(p1r_clipped.copy()), threads=kwargs["fftthreads"],
planner_effort="FFTW_ESTIMATE",
overwrite_input=False,
auto_align_input=True,
auto_contiguous=True)
tls.ft = ft
if coefs_method >= 1:
p1 = ft(p1r_clipped.copy(), np.zeros(ft.output_shape, dtype=ft.output_dtype)).copy()
else:
p1 = np.empty(ft.output_shape, ft.output_dtype)
p1s = subtract(p1, submap_ft, refmap_ft, sx, sy, s, a, apix,
ptcl[star.Relion.DEFOCUSU], ptcl[star.Relion.DEFOCUSV], ptcl[star.Relion.DEFOCUSANGLE],
ptcl[star.Relion.PHASESHIFT], ptcl[star.Relion.VOLTAGE], ptcl[star.Relion.AC], ptcl[star.Relion.CS],
ptcl[star.Relion.ANGLEROT], ptcl[star.Relion.ANGLETILT], ptcl[star.Relion.ANGLEPSI],
ptcl[star.Relion.ORIGINX], ptcl[star.Relion.ORIGINY], coefs_method, r, nr, kwargs["pfac"])
ift = getattr(tls, 'ift', None)
if ift is None:
ift = irfft2(p1s.copy(), threads=kwargs["fftthreads"],
planner_effort="FFTW_ESTIMATE",
auto_align_input=True,
auto_contiguous=True)
tls.ift = ift
p1sr = fftshift(ift(p1s.copy(), np.zeros(ift.output_shape, dtype=ift.output_dtype)).copy())
# Zero pad p1sr
p1sr = zero_pad(p1sr, p1r.shape[0])
log.debug("%d@%s Exp %f +/- %f, Sub %f +/- %f" % (ptcl[star.UCSF.IMAGE_ORIGINAL_INDEX], ptcl[star.UCSF.IMAGE_ORIGINAL_PATH], np.mean(p1r), np.std(p1r), np.mean(p1sr), np.std(p1sr)))
new_image = p1r - p1sr
if kwargs["crop"] is not None:
orihalf = new_image.shape[0] // 2
newhalf = kwargs["crop"] // 2
x = orihalf - np.int(np.round(ptcl[star.Relion.ORIGINX]))
y = orihalf - np.int(np.round(ptcl[star.Relion.ORIGINY]))
new_image = new_image[y - newhalf:y + newhalf, x - newhalf:x + newhalf]
return new_image
def calculateProjection( my_ndimage, my_rotation ):
my_rotation = iseccFFT_v2.pyquat2scipy(my_rotation)
my_rotmatrix = R.from_quat(my_rotation)
""" Get random rotation for testing """
my_rotmatrix = R.random()
my_rotmatrix = my_rotmatrix.as_matrix()
#my_ndimage = iseccFFT_v2.swapAxes_ndimage( my_ndimage )
#my_2dsection = iseccFFT_v2.get2dsection( my_ndimage, my_rotation )
f3d = vop.vol_ft(my_ndimage, pfac=2 )
f2d = vop.interpolate_slice_numba(f3d, my_rotmatrix, pfac=2)
ift = irfft2(f2d.copy(),
planner_effort="FFTW_ESTIMATE",
auto_align_input=True,
auto_contiguous=True)
proj = fftshift(ift(f2d.copy(), np.zeros(ift.output_shape, dtype=ift.output_dtype)))
#iseccFFT_v2.plot2dimage( np.fft.ifftn(f2d).astype(np.float) )
#iseccFFT_v2.plot2dimage( proj )
return proj
def main(args):
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
df = star.parse_star(args.input, keep_index=False)
star.augment_star_ucsf(df)
maxshift = np.round(np.max(np.abs(df[star.Relion.ORIGINS].values)))
if args.map is not None:
if args.map.endswith(".npy"):
log.info("Reading precomputed 3D FFT of volume")
f3d = np.load(args.map)
log.info("Finished reading 3D FFT of volume")
if args.size is None:
args.size = (f3d.shape[0] - 3) // args.pfac
else:
vol = mrc.read(args.map, inc_header=False, compat="relion")
if args.mask is not None:
mask = mrc.read(args.mask, inc_header=False, compat="relion")
vol *= mask
if args.size is None:
args.size = vol.shape[0]
if args.crop is not None and args.size // 2 < maxshift + args.crop // 2:
log.error(
"Some shifts are too large to crop (maximum crop is %d)" %
(args.size - 2 * maxshift))
return 1
log.info("Preparing 3D FFT of volume")
f3d = vop.vol_ft(vol, pfac=args.pfac, threads=args.threads)
log.info("Finished 3D FFT of volume")
else:
log.error("Please supply a map")
return 1
sz = (f3d.shape[0] - 3) // args.pfac
apix = star.calculate_apix(df) * np.double(args.size) / sz
sx, sy = np.meshgrid(np.fft.rfftfreq(sz), np.fft.fftfreq(sz))
s = np.sqrt(sx**2 + sy**2)
a = np.arctan2(sy, sx)
log.info("Projection size is %d, unpadded volume size is %d" %
(args.size, sz))
log.info("Effective pixel size is %f A/px" % apix)
if args.subtract and args.size != sz:
log.error("Volume and projections must be same size when subtracting")
return 1
if args.crop is not None and args.size // 2 < maxshift + args.crop // 2:
log.error("Some shifts are too large to crop (maximum crop is %d)" %
(args.size - 2 * maxshift))
return 1
ift = None
with mrc.ZSliceWriter(args.output, psz=apix) as zsw:
for i, p in df.iterrows():
f2d = project(f3d,
p,
s,
sx,
sy,
a,
pfac=args.pfac,
apply_ctf=args.ctf,
size=args.size,
flip_phase=args.flip)
if ift is None:
ift = irfft2(f2d.copy(),
threads=args.threads,
planner_effort="FFTW_ESTIMATE",
auto_align_input=True,
auto_contiguous=True)
proj = fftshift(
ift(f2d.copy(),
np.zeros(ift.output_shape, dtype=ift.output_dtype)))
log.debug("%f +/- %f" % (np.mean(proj), np.std(proj)))
if args.subtract:
with mrc.ZSliceReader(p["ucsfImagePath"]) as zsr:
img = zsr.read(p["ucsfImageIndex"])
log.debug("%f +/- %f" % (np.mean(img), np.std(img)))
proj = img - proj
if args.crop is not None:
orihalf = args.size // 2
newhalf = args.crop // 2
x = orihalf - np.int(np.round(p[star.Relion.ORIGINX]))
y = orihalf - np.int(np.round(p[star.Relion.ORIGINY]))
proj = proj[y - newhalf:y + newhalf, x - newhalf:x + newhalf]
zsw.write(proj)
log.debug(
"%d@%s: %d/%d" %
(p["ucsfImageIndex"], p["ucsfImagePath"], i + 1, df.shape[0]))
if args.star is not None:
log.info("Writing output .star file")
if args.crop is not None:
df = star.recenter(df, inplace=True)
if args.subtract:
df[star.UCSF.IMAGE_ORIGINAL_PATH] = df[star.UCSF.IMAGE_PATH]
df[star.UCSF.IMAGE_ORIGINAL_INDEX] = df[star.UCSF.IMAGE_INDEX]
df[star.UCSF.IMAGE_PATH] = args.output
df[star.UCSF.IMAGE_INDEX] = np.arange(df.shape[0])
star.simplify_star_ucsf(df)
star.write_star(args.star, df)
return 0
def main(args):
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
df = star.parse_star(args.input, keep_index=False)
star.augment_star_ucsf(df)
if args.map is not None:
vol = mrc.read(args.map, inc_header=False, compat="relion")
if args.mask is not None:
mask = mrc.read(args.mask, inc_header=False, compat="relion")
vol *= mask
else:
print("Please supply a map")
return 1
f3d = vop.vol_ft(vol, pfac=args.pfac, threads=args.threads)
sz = f3d.shape[0] // 2 - 1
sx, sy = np.meshgrid(np.fft.rfftfreq(sz), np.fft.fftfreq(sz))
s = np.sqrt(sx**2 + sy**2)
a = np.arctan2(sy, sx)
ift = None
with mrc.ZSliceWriter(args.output) as zsw:
for i, p in df.iterrows():
f2d = project(f3d,
p,
s,
sx,
sy,
a,
apply_ctf=args.ctf,
size=args.size)
if ift is None:
ift = irfft2(f2d.copy(),
threads=cpu_count(),
planner_effort="FFTW_ESTIMATE",
auto_align_input=True,
auto_contiguous=True)
proj = fftshift(
ift(f2d.copy(), np.zeros(vol.shape[:-1], dtype=vol.dtype)))
log.debug("%f +/- %f" % (np.mean(proj), np.std(proj)))
if args.subtract:
with mrc.ZSliceReader(p["ucsfImagePath"]) as zsr:
img = zsr.read(p["ucsfImageIndex"])
log.debug("%f +/- %f" % (np.mean(img), np.std(img)))
proj = img - proj
zsw.write(proj)
log.info(
"%d@%s: %d/%d" %
(p["ucsfImageIndex"], p["ucsfImagePath"], i + 1, df.shape[0]))
if args.star is not None:
if args.subtract:
df[star.UCSF.IMAGE_ORIGINAL_PATH] = df[star.UCSF.IMAGE_PATH]
df[star.UCSF.IMAGE_ORIGINAL_INDEX] = df[star.UCSF.IMAGE_INDEX]
df[star.UCSF.IMAGE_PATH] = args.output
df[star.UCSF.IMAGE_INDEX] = np.arange(df.shape[0])
star.simplify_star_ucsf(df)
star.write_star(args.star, df)
return 0
