def project(f3d,
p,
s,
sx,
sy,
a,
apply_ctf=False,
size=None,
flip_phase=False):
orient = util.euler2rot(np.deg2rad(p[star.Relion.ANGLEROT]),
np.deg2rad(p[star.Relion.ANGLETILT]),
np.deg2rad(p[star.Relion.ANGLEPSI]))
pshift = np.exp(
-2 * np.pi * 1j *
(-p[star.Relion.ORIGINX] * sx + -p[star.Relion.ORIGINY] * sy))
f2d = vop.interpolate_slice_numba(f3d, orient, size=size)
f2d *= pshift
if apply_ctf or flip_phase:
apix = star.calculate_apix(p)
c = ctf.eval_ctf(s / apix,
a,
p[star.Relion.DEFOCUSU],
p[star.Relion.DEFOCUSV],
p[star.Relion.DEFOCUSANGLE],
p[star.Relion.PHASESHIFT],
p[star.Relion.VOLTAGE],
p[star.Relion.AC],
p[star.Relion.CS],
bf=0,
lp=2 * apix)
if flip_phase:
c = np.sign(c)
f2d *= c
return f2d
def transform_star(df,
r,
t=None,
inplace=False,
rots=None,
invert=False,
rotate=True,
adjust_defocus=False):
"""
Transform particle angles and origins according to a rotation
matrix (in radians) and an optional translation vector.
The translation may also be given as the 4th column of a 3x4 matrix,
or as a scalar distance to be applied along the axis of rotation.
"""
assert (r.shape[0] == 3)
if r.shape[1] == 4 and t is None:
t = r[:, -1]
r = r[:, :3]
assert (r.shape == (3, 3))
assert t is None or np.isscalar(t) or len(t) == 3
if inplace:
newstar = df
else:
newstar = df.copy()
if rots is None:
rots = [
euler2rot(*np.deg2rad(row[1]))
for row in df[Relion.ANGLES].iterrows()
]
if invert:
r = r.T
newrots = [ptcl.dot(r) for ptcl in rots]
if rotate:
angles = [np.rad2deg(rot2euler(q)) for q in newrots]
newstar[Relion.ANGLES] = angles
if t is not None and np.linalg.norm(t) > 0:
if np.isscalar(t):
if invert:
tt = -np.vstack([np.squeeze(q[:, 2]) * t for q in rots])
else:
tt = np.vstack([np.squeeze(q[:, 2]) * t for q in newrots])
else:
if invert:
tt = -np.vstack([q.dot(t) for q in rots])
else:
tt = np.vstack([q.dot(t) for q in newrots])
newshifts = df[Relion.ORIGINS] + tt[:, :-1]
newstar[Relion.ORIGINS] = newshifts
if adjust_defocus:
newstar[Relion.DEFOCUSU] += tt[:, -1] * calculate_apix(df)
newstar[Relion.DEFOCUSV] += tt[:, -1] * calculate_apix(df)
newstar[Relion.DEFOCUSANGLE] = np.rad2deg(
np.arctan2(newstar[Relion.DEFOCUSV], newstar[Relion.DEFOCUSV]))
return newstar
def subparticle_expansion(s, ops=[np.eye(3)], dists=None, rots=None):
if rots is None:
rots = [euler2rot(*np.deg2rad(r[1])) for r in s[ANGLES].iterrows()]
if dists is not None:
if np.isscalar(dists):
dists = [dists] * len(ops)
for i in range(len(ops)):
yield transform_star(s, ops[i], dists[i], rots=rots)
else:
for op in ops:
yield transform_star(s, op, rots=rots)
def particle_xcorr(ptcl, refmap_ft):
r = util.euler2rot(*np.deg2rad(ptcl[star.Relion.ANGLES]))
proj = vop.interpolate_slice_numba(refmap_ft, r)
c = ctf.eval_ctf(s / apix,
a,
def1[i],
def2[i],
angast[i],
phase[i],
kv[i],
ac[i],
cs[i],
bf=0,
lp=2 * apix)
pshift = np.exp(-2 * np.pi * 1j * (-xshift[i] * sx + -yshift * sy))
proj_ctf = proj * pshift * c
with mrc.ZSliceReader(ptcl[star.Relion.IMAGE_NAME]) as f:
exp_image_fft = rfft2(fftshift(f.read(i)))
xcor_fft = exp_image_fft * proj_ctf
xcor = fftshift(irfft2(xcor_fft))
return xcor
def main(args):
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
hdlr = logging.StreamHandler(sys.stdout)
if args.quiet:
hdlr.setLevel(logging.WARNING)
else:
hdlr.setLevel(logging.INFO)
log.addHandler(hdlr)
if args.target is None and args.sym is None:
log.error(
"At least a target or symmetry group must be provided via --target or --sym"
)
return 1
elif args.target is not None and args.boxsize is None and args.origin is None:
log.error("An origin must be provided via --boxsize or --origin")
return 1
if args.target is not None:
try:
args.target = np.array(
[np.double(tok) for tok in args.target.split(",")])
except:
log.error(
"Target must be comma-separated list of x,y,z coordinates")
return 1
if args.origin is not None:
if args.boxsize is not None:
log.warn("--origin supersedes --boxsize")
try:
args.origin = np.array(
[np.double(tok) for tok in args.origin.split(",")])
except:
log.error(
"Origin must be comma-separated list of x,y,z coordinates")
return 1
if args.sym is not None:
args.sym = util.relion_symmetry_group(args.sym)
df = star.parse_star(args.input)
if args.apix is None:
args.apix = star.calculate_apix(df)
if args.apix is None:
log.warn(
"Could not compute pixel size, default is 1.0 Angstroms per pixel"
)
args.apix = 1.0
df[star.Relion.MAGNIFICATION] = 10000
df[star.DETECTORPIXELSIZE] = 1.0
if args.cls is not None:
df = star.select_classes(df, args.cls)
if args.target is not None:
if args.origin is not None:
args.origin /= args.apix
elif args.boxsize is not None:
args.origin = np.ones(3) * args.boxsize / 2
args.target /= args.apix
c = args.target - args.origin
c = np.where(np.abs(c) < 1, 0, c) # Ignore very small coordinates.
d = np.linalg.norm(c)
ax = c / d
cm = util.euler2rot(*np.array(
[np.arctan2(ax[1], ax[0]),
np.arccos(ax[2]),
np.deg2rad(args.psi)]))
ops = [op.dot(cm) for op in args.sym] if args.sym is not None else [cm]
dfs = [
star.transform_star(df,
op.T,
-d,
rotate=args.shift_only,
invert=args.target_invert,
adjust_defocus=args.adjust_defocus)
for op in ops
]
elif args.sym is not None:
dfs = list(
subparticle_expansion(df, args.sym,
-args.displacement / args.apix))
else:
log.error(
"At least a target or symmetry group must be provided via --target or --sym"
)
return 1
if args.recenter:
for s in dfs:
star.recenter(s, inplace=True)
if args.suffix is None and not args.skip_join:
if len(dfs) > 1:
df = util.interleave(dfs)
else:
df = dfs[0]
star.write_star(args.output, df)
else:
for i, s in enumerate(dfs):
star.write_star(os.path.join(args.output, args.suffix + "_%d" % i),
s)
return 0
def main(args):
log = logging.getLogger(__name__)
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
data, hdr = read(args.input, inc_header=True)
if args.half2 is not None:
half2, hdr_half2 = read(args.input, inc_header=True)
if data.shape == half2.shape:
data += half2
else:
log.error("--half2 map is not the same shape as input map!")
return 1
final = None
box = np.array([hdr[a] for a in ["nx", "ny", "nz"]])
center = box // 2
if args.fft:
if args.final_mask is not None:
final_mask = read(args.final_mask)
data *= final_mask
data_ft = vop.vol_ft(data.T, pfac=args.pfac, threads=args.threads)
np.save(args.output, data_ft)
return 0
if args.transpose is not None:
try:
tax = [np.int64(a) for a in args.transpose.split(",")]
data = np.transpose(data, axes=tax)
except:
log.error(
"Transpose axes must be comma-separated list of three integers"
)
return 1
if args.flip is not None:
if args.flip.isnumeric():
args.flip = int(args.flip)
else:
args.flip = vop.label_to_axis(args.flip)
data = np.flip(data, axis=args.flip)
if args.apix is None:
args.apix = hdr["xlen"] / hdr["nx"]
log.info("Using computed pixel size of %f Angstroms" % args.apix)
if args.normalize:
if args.diameter is not None:
if args.diameter > 1.0:
args.diameter /= args.apix * 2 # Convert Angstrom diameter to pixel radius.
if args.reference is not None:
ref, refhdr = read(args.reference, inc_header=True)
final, mu, sigma = vop.normalize(data,
ref=ref,
return_stats=True,
rmask=args.diameter)
else:
final, mu, sigma = vop.normalize(data,
return_stats=True,
rmask=args.diameter)
log.info("Mean: %f, Standard deviation: %f" % (mu, sigma))
if args.apix_out is not None:
if args.scale is not None:
log.warn("--apix-out supersedes --scale")
args.scale = args.apix / args.apix_out
elif args.scale is not None:
args.apix_out = args.apix / args.scale
elif args.boxsize is not None:
args.scale = box[0] / np.double(args.boxsize)
if args.apix_out is None:
args.apix_out = args.apix
if args.boxsize is None:
if args.scale is None:
args.boxsize = box[0]
args.scale = 1
else:
args.boxsize = np.int(box[0] * args.scale)
log.info("Volume will be scaled by %f to size %d @ %f A/px" %
(args.scale, args.boxsize, args.apix_out))
if args.target and args.transform:
log.warn(
"Target pose transformation will be applied after explicit matrix")
if args.euler is not None and (args.target is not None
or args.transform is not None):
log.warn(
"Euler transformation will be applied after target pose transformation"
)
if args.translate is not None and (args.euler is not None
or args.target is not None
or args.transform is not None):
log.warn("Translation will be applied after other transformations")
if args.origin is not None:
try:
args.origin = np.array(
[np.double(tok) for tok in args.origin.split(",")]) / args.apix
assert np.all(args.origin < box)
except:
log.error(
"Origin must be comma-separated list of x,y,z coordinates and lie within the box"
)
return 1
else:
args.origin = center
log.info("Origin set to box center, %s" % (args.origin * args.apix))
if not (args.target is None and args.euler is None and args.transform is None and args.boxsize is None) \
and vop.ismask(data) and args.spline_order != 0:
log.warn(
"Input looks like a mask, --spline-order 0 (nearest neighbor) is recommended"
)
if args.transform is not None:
try:
args.transform = np.array(json.loads(args.transform))
except:
log.error("Transformation matrix must be in JSON/Numpy format")
return 1
r = args.transform[:, :3]
if args.transform.shape[1] == 4:
t = args.transform[:, -1] / args.apix
t = r.dot(args.origin) + t - args.origin
t = -r.T.dot(t)
else:
t = 0
log.debug("Final rotation: %s" % str(r).replace("\n", "\n" + " " * 16))
log.debug("Final translation: %s (%f px)" %
(str(t), np.linalg.norm(t)))
data = vop.resample_volume(data,
r=r,
t=t,
ori=None,
order=args.spline_order,
invert=args.invert)
if args.target is not None:
try:
args.target = np.array(
[np.double(tok) for tok in args.target.split(",")]) / args.apix
except:
log.error(
"Standard pose target must be comma-separated list of x,y,z coordinates"
)
return 1
args.target -= args.origin
args.target = np.where(np.abs(args.target) < 1, 0, args.target)
ori = None if args.origin is center else args.origin - center
r = vec2rot(args.target)
t = np.linalg.norm(args.target)
log.info("Euler angles are %s deg and shift is %f px" %
(np.rad2deg(rot2euler(r)), t))
log.debug("Final rotation: %s" % str(r).replace("\n", "\n" + " " * 16))
log.debug("Final translation: %s (%f px)" %
(str(t), np.linalg.norm(t)))
data = vop.resample_volume(data,
r=r,
t=args.target,
ori=ori,
order=args.spline_order,
invert=args.invert)
if args.euler is not None:
try:
args.euler = np.deg2rad(
np.array([np.double(tok) for tok in args.euler.split(",")]))
except:
log.error(
"Eulers must be comma-separated list of phi,theta,psi angles")
return 1
r = euler2rot(*args.euler)
offset = args.origin - 0.5
offset = offset - r.T.dot(offset)
data = affine_transform(data,
r.T,
offset=offset,
order=args.spline_order)
if args.translate is not None:
try:
args.translate = np.array(
[np.double(tok)
for tok in args.translate.split(",")]) / args.apix
except:
log.error(
"Translation vector must be comma-separated list of x,y,z coordinates"
)
return 1
args.translate -= args.origin
data = shift(data, -args.translate, order=args.spline_order)
if final is None:
final = data
if args.final_mask is not None:
final_mask = read(args.final_mask)
final *= final_mask
if args.scale != 1 or args.boxsize != box[0]:
final = vop.resample_volume(final,
scale=args.scale,
output_shape=args.boxsize,
order=args.spline_order)
write(args.output, final, psz=args.apix_out)
return 0
def main(args):
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
hdlr = logging.StreamHandler(sys.stdout)
if args.quiet:
hdlr.setLevel(logging.ERROR)
elif args.verbose:
hdlr.setLevel(logging.INFO)
else:
hdlr.setLevel(logging.WARN)
log.addHandler(hdlr)
data, hdr = read(args.input, inc_header=True)
final = None
box = np.array([hdr[a] for a in ["nx", "ny", "nz"]])
center = box // 2
if args.fft:
data_ft = vop.vol_ft(data.T, threads=args.threads)
np.save(args.output, data_ft)
return 0
if args.transpose is not None:
try:
tax = [np.int64(a) for a in args.transpose.split(",")]
data = np.transpose(data, axes=tax)
except:
log.error(
"Transpose axes must be comma-separated list of three integers"
)
return 1
if args.normalize:
if args.reference is not None:
ref, refhdr = read(args.reference, inc_header=True)
final, mu, sigma = vop.normalize(data, ref=ref, return_stats=True)
else:
final, mu, sigma = vop.normalize(data, return_stats=True)
final = (data - mu) / sigma
if args.verbose:
log.info("Mean: %f, Standard deviation: %f" % (mu, sigma))
if args.apix is None:
args.apix = hdr["xlen"] / hdr["nx"]
log.info("Using computed pixel size of %f Angstroms" % args.apix)
if args.target and args.matrix:
log.warn(
"Target pose transformation will be applied after explicit matrix")
if args.euler is not None and (args.target is not None
or args.matrix is not None):
log.warn(
"Euler transformation will be applied after target pose transformation"
)
if args.translate is not None and (args.euler is not None or args.target
is not None or args.matrix is not None):
log.warn("Translation will be applied after other transformations")
if args.origin is not None:
try:
args.origin = np.array(
[np.double(tok) for tok in args.origin.split(",")]) / args.apix
assert np.all(args.origin < box)
except:
log.error(
"Origin must be comma-separated list of x,y,z coordinates and lie within the box"
)
return 1
else:
args.origin = center
log.info("Origin set to box center, %s" % (args.origin * args.apix))
if not (args.target is None and args.euler is None and args.matrix is None and args.boxsize is None) \
and vop.ismask(data) and args.spline_order != 0:
log.warn(
"Input looks like a mask, --spline-order 0 (nearest neighbor) is recommended"
)
if args.matrix is not None:
try:
r = np.array(json.loads(args.matrix))
except:
log.error("Matrix format is incorrect")
return 1
data = vop.resample_volume(data,
r=r,
t=None,
ori=None,
order=args.spline_order)
if args.target is not None:
try:
args.target = np.array(
[np.double(tok) for tok in args.target.split(",")]) / args.apix
except:
log.error(
"Standard pose target must be comma-separated list of x,y,z coordinates"
)
return 1
args.target -= args.origin
args.target = np.where(np.abs(args.target) < 1, 0, args.target)
ori = None if args.origin is center else args.origin - args.center
r = vec2rot(args.target)
t = np.linalg.norm(args.target)
log.info("Euler angles are %s deg and shift is %f px" %
(np.rad2deg(rot2euler(r)), t))
data = vop.resample_volume(data,
r=r,
t=args.target,
ori=ori,
order=args.spline_order,
invert=args.target_invert)
if args.euler is not None:
try:
args.euler = np.deg2rad(
np.array([np.double(tok) for tok in args.euler.split(",")]))
except:
log.error(
"Eulers must be comma-separated list of phi,theta,psi angles")
return 1
r = euler2rot(*args.euler)
offset = args.origin - 0.5
offset = offset - r.T.dot(offset)
data = affine_transform(data,
r.T,
offset=offset,
order=args.spline_order)
if args.translate is not None:
try:
args.translate = np.array(
[np.double(tok)
for tok in args.translate.split(",")]) / args.apix
except:
log.error(
"Translation vector must be comma-separated list of x,y,z coordinates"
)
return 1
args.translate -= args.origin
data = shift(data, -args.translate, order=args.spline_order)
if args.boxsize is not None:
args.boxsize = np.double(args.boxsize)
data = zoom(data, args.boxsize / box, order=args.spline_order)
args.apix = args.apix * box[0] / args.boxsize
if final is None:
final = data
if args.final_mask is not None:
final_mask = read(args.final_mask)
final *= final_mask
write(args.output, final, psz=args.apix)
return 0
def main(args):
if args.info:
args.input.append(args.output)
df = pd.concat((parse_star(inp, keep_index=False) for inp in args.input),
join="inner")
dfaux = None
if args.cls is not None:
df = select_classes(df, args.cls)
if args.info:
if is_particle_star(df) and "rlnClassNumber" in df.columns:
c = df["rlnClassNumber"].value_counts()
print("%s particles in %d classes" %
("{:,}".format(df.shape[0]), len(c)))
print(" ".join([
'%d: %s (%.2f %%)' % (i, "{:,}".format(s), 100. * s / c.sum())
for i, s in c.sort_index().iteritems()
]))
elif is_particle_star(df):
print("%s particles" % "{:,}".format(df.shape[0]))
if "rlnMicrographName" in df.columns:
mgraphcnt = df["rlnMicrographName"].value_counts()
print(
"%s micrographs, %s +/- %s particles per micrograph" %
("{:,}".format(len(mgraphcnt)), "{:,.3f}".format(
np.mean(mgraphcnt)), "{:,.3f}".format(np.std(mgraphcnt))))
try:
print("%f A/px (%sX magnification)" %
(calculate_apix(df), "{:,.0f}".format(
df["rlnMagnification"][0])))
except KeyError:
pass
return 0
if args.drop_angles:
df.drop(Relion.ANGLES, axis=1, inplace=True, errors="ignore")
if args.drop_containing is not None:
containing_fields = [
f for q in args.drop_containing for f in df.columns if q in f
]
if args.invert:
containing_fields = df.columns.difference(containing_fields)
df.drop(containing_fields, axis=1, inplace=True, errors="ignore")
if args.offset_group is not None:
df["rlnGroupNumber"] += args.offset_group
if args.subsample_micrographs is not None:
if args.bootstrap is not None:
print("Only particle sampling allows bootstrapping")
return 1
mgraphs = df["rlnMicrographName"].unique()
if args.subsample_micrographs < 1:
args.subsample_micrographs = np.int(
max(np.round(args.subsample_micrographs * len(mgraphs)), 1))
ind = np.random.choice(len(mgraphs),
size=args.subsample_micrographs,
replace=False)
mask = df["rlnMicrographName"].isin(mgraphs[ind])
if args.auxout is not None:
dfaux = df.loc[~mask]
df = df.loc[mask]
if args.subsample is not None and args.suffix == "":
if args.subsample < 1:
args.subsample = np.int(
max(np.round(args.subsample * df.shape[0]), 1))
else:
args.subsample = np.int(args.subsample)
ind = np.random.choice(df.shape[0], size=args.subsample, replace=False)
mask = df.index.isin(ind)
if args.auxout is not None:
dfaux = df.loc[~mask]
df = df.loc[mask]
if args.copy_angles is not None:
angle_star = parse_star(args.copy_angles, keep_index=False)
df = smart_merge(df, angle_star, fields=Relion.ANGLES)
if args.transform is not None:
if args.transform.count(",") == 2:
r = euler2rot(
*np.deg2rad([np.double(s) for s in args.transform.split(",")]))
else:
r = np.array(json.loads(args.transform))
df = transform_star(df, r, inplace=True)
if args.copy_paths is not None:
path_star = parse_star(args.copy_paths, keep_index=False)
df[Relion.IMAGE_NAME] = path_star[Relion.IMAGE_NAME]
if args.copy_ctf is not None:
ctf_star = pd.concat(
(parse_star(inp, keep_index=False) for inp in glob(args.copy_ctf)),
join="inner")
df = smart_merge(df, ctf_star, Relion.CTF_PARAMS)
if args.copy_micrograph_coordinates is not None:
coord_star = pd.concat(
(parse_star(inp, keep_index=False)
for inp in glob(args.copy_micrograph_coordinates)),
join="inner")
df = smart_merge(df, coord_star, fields=Relion.MICROGRAPH_COORDS)
if args.scale_coordinates is not None:
df[Relion.COORDS] = df[Relion.COORDS] * args.scale_coordinates
if args.scale_origins:
df[Relion.ORIGINS] = df[Relion.ORIGINS] * args.scale_origins
df["rlnMagnification"] = df["rlnMagnification"] * args.scale_origins
if args.recenter:
df = recenter(df, inplace=True)
if args.zero_origins:
df = zero_origins(df, inplace=True)
if args.pick:
df.drop(df.columns.difference(Relion.PICK_PARAMS),
axis=1,
inplace=True,
errors="ignore")
if args.subsample is not None and args.suffix != "":
if args.subsample < 1:
print("Specific integer sample size")
return 1
nsamplings = args.bootstrap if args.bootstrap is not None else df.shape[
0] / np.int(args.subsample)
inds = np.random.choice(df.shape[0],
size=(nsamplings, np.int(args.subsample)),
replace=args.bootstrap is not None)
for i, ind in enumerate(inds):
write_star(
os.path.join(
args.output,
os.path.basename(args.input[0])[:-5] + args.suffix +
"_%d" % (i + 1)), df.iloc[ind])
if args.to_micrographs:
gb = df.groupby(Relion.MICROGRAPH_NAME)
mu = gb.mean()
df = mu[[
c for c in Relion.CTF_PARAMS + Relion.MICROSCOPE_PARAMS +
[Relion.MICROGRAPH_NAME] if c in mu
]].reset_index()
if args.micrograph_range:
df.set_index(Relion.MICROGRAPH_NAME, inplace=True)
m, n = [int(tok) for tok in args.micrograph_range.split(",")]
mg = df.index.unique().sort_values()
outside = list(range(0, m)) + list(range(n, len(mg)))
dfaux = df.loc[mg[outside]].reset_index()
df = df.loc[mg[m:n]].reset_index()
if args.min_separation is not None:
gb = df.groupby(Relion.MICROGRAPH_NAME)
dupes = []
for n, g in gb:
nb = query_connected(g[Relion.COORDS],
args.min_separation / calculate_apix(df))
dupes.extend(g.index[~np.isnan(nb)])
dfaux = df.loc[dupes]
df.drop(dupes, inplace=True)
if args.split_micrographs:
dfs = split_micrographs(df)
for mg in dfs:
write_star(
os.path.join(args.output,
os.path.basename(mg)[:-4]) + args.suffix, dfs[mg])
return 0
if args.auxout is not None and dfaux is not None:
write_star(args.auxout, dfaux)
if args.output is not None:
write_star(args.output, df)
return 0
def main(args):
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
hdlr = logging.StreamHandler(sys.stdout)
if args.quiet:
hdlr.setLevel(logging.WARNING)
else:
hdlr.setLevel(logging.INFO)
log.addHandler(hdlr)
if args.markers is None and args.target is None and args.sym is None:
log.error(
"A marker or symmetry group must be provided via --target, --markers, or --sym"
)
return 1
elif args.sym is None and args.markers is None and args.boxsize is None and args.origin is None:
log.error(
"An origin must be provided via --boxsize, --origin, or --markers")
return 1
elif args.sym is not None and args.markers is None and args.target is None and \
(args.boxsize is not None or args.origin is not None):
log.warn("Symmetry expansion alone will ignore --target or --origin")
if args.target is not None:
try:
args.target = np.array(
[np.double(tok) for tok in args.target.split(",")])
except:
log.error(
"Target must be comma-separated list of x,y,z coordinates")
return 1
if args.origin is not None:
if args.boxsize is not None:
logger.warn("--origin supersedes --boxsize")
try:
args.origin = np.array(
[np.double(tok) for tok in args.origin.split(",")])
except:
log.error(
"Origin must be comma-separated list of x,y,z coordinates")
return 1
if args.marker_sym is not None:
args.marker_sym = relion_symmetry_group(args.marker_sym)
star = parse_star(args.input, keep_index=False)
if args.apix is None:
args.apix = calculate_apix(star)
if args.apix is None:
logger.warn(
"Could not compute pixel size, default is 1.0 Angstroms per pixel"
)
args.apix = 1.0
if args.cls is not None:
star = select_classes(star, args.cls)
cmms = []
if args.markers is not None:
cmmfiles = glob.glob(args.markers)
for cmmfile in cmmfiles:
for cmm in parse_cmm(cmmfile):
cmms.append(cmm / args.apix)
if args.target is not None:
cmms.append(args.target / args.apix)
stars = []
if len(cmms) > 0:
if args.origin is not None:
args.origin /= args.apix
elif args.boxsize is not None:
args.origin = np.ones(3) * args.boxsize / 2
else:
log.warn("Using first marker as origin")
if len(cmms) == 1:
log.error(
"Using first marker as origin, expected at least two markers"
)
return 1
args.origin = cmms[0]
cmms = cmms[1:]
markers = [cmm - args.origin for cmm in cmms]
if args.marker_sym is not None and len(markers) == 1:
markers = [op.dot(markers[0]) for op in args.marker_sym]
elif args.marker_sym is not None:
log.error(
"Exactly one marker is required for symmetry-derived subparticles"
)
return 1
rots = [euler2rot(*np.deg2rad(r[1])) for r in star[ANGLES].iterrows()]
#origins = star[ORIGINS].copy()
for m in markers:
d = np.linalg.norm(m)
ax = m / d
op = euler2rot(
*np.array([np.arctan2(ax[1], ax[0]),
np.arccos(ax[2]), 0.]))
stars.append(transform_star(star, op.T, -d, rots=rots))
if args.sym is not None:
args.sym = relion_symmetry_group(args.sym)
if len(stars) > 0:
stars = [
se for se in subparticle_expansion(
s, args.sym, -args.displacement / args.apix) for s in stars
]
else:
stars = list(
subparticle_expansion(star, args.sym,
-args.displacement / args.apix))
if args.recenter:
for s in stars:
recenter(s, inplace=True)
if args.suffix is None and not args.skip_join:
if len(stars) > 1:
star = interleave(stars)
else:
star = stars[0]
write_star(args.output, star)
else:
for i, star in enumerate(stars):
write_star(os.path.join(args.output, args.suffix + "_%d" % i),
star)
return 0
def test_euler2rot(self):
r1test = util.euler2rot(*e1)
self.assertTrue(np.array_equal(r1, r1test))
r2test = util.euler2rot(*e3)
self.assertTrue(np.allclose(r3, r2test))
