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 main(args):
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
if args.boxsize is None:
log.error("Please specify box size")
return 1
df = star.parse_star(args.input, keep_index=False)
if args.cls is not None:
df = star.select_classes(df, args.cls)
if args.apix is None:
args.apix = star.calculate_apix(df)
nside = 2**args.healpix_order
angular_sampling = np.sqrt(3 / np.pi) * 60 / nside
theta, phi = pix2ang(nside, np.arange(12 * nside**2))
phi = np.pi - phi
hp = np.column_stack((np.sin(theta) * np.cos(phi),
np.sin(theta) * np.sin(phi), np.cos(theta)))
kdtree = cKDTree(hp)
st = np.sin(np.deg2rad(df[star.Relion.ANGLETILT]))
ct = np.cos(np.deg2rad(df[star.Relion.ANGLETILT]))
sp = np.sin(np.deg2rad(df[star.Relion.ANGLEROT]))
cp = np.cos(np.deg2rad(df[star.Relion.ANGLEROT]))
ptcls = np.column_stack((st * cp, st * sp, ct))
_, idx = kdtree.query(ptcls)
cnts = np.bincount(idx, minlength=theta.size)
frac = cnts / np.max(cnts).astype(np.float64)
mu = np.mean(frac)
sigma = np.std(frac)
color_scale = (frac - mu) / sigma
color_scale[color_scale > 5] = 5
color_scale[color_scale < -1] = -1
color_scale /= 6
color_scale += 1 / 6.
r = args.boxsize * args.apix / 2
rp = np.reshape(r + r * frac * args.height_scale, (-1, 1))
base1 = hp * r
base2 = hp * rp
base1 = base1[:, [0, 1, 2]] + np.array([r] * 3)
base2 = base2[:, [0, 1, 2]] + np.array([r] * 3)
height = np.squeeze(np.abs(rp - r))
idx = np.where(height >= 0.01)[0]
width = args.width_scale * np.pi * r * angular_sampling / 360
bild = np.hstack((base1, base2, np.ones((base1.shape[0], 1)) * width))
fmt_color = ".color %f 0 %f\n"
fmt_cyl = ".cylinder %f %f %f %f %f %f %f\n"
with open(args.output, "w") as f:
for i in idx:
f.write(fmt_color % (color_scale[i], 1 - color_scale[i]))
f.write(fmt_cyl % tuple(bild[i]))
return 0
def main(args):
""" Main denoising CNN function """
# Load STAR file and neural network
star_file = load_star(args.input_micrographs)
num_mics = len(star_file)
apix = star.calculate_apix(star_file)
cutoff_frequency = 1. / args.max_resolution
nn = load_trained_model(args.model)
suffix = args.output_suffix
phaseflip = args.phaseflip
flipback = args.flipback
merge_noisy = args.merge_noisy
merge_freq1 = 1. / (args.merge_resolution + args.merge_width)
merge_freq2 = 1. / args.merge_resolution
# Main denoising loop
for i, metadata in tqdm(star_file.iterrows(),
desc="Denoising",
total=num_mics):
mic_file = metadata[star.Relion.MICROGRAPH_NAME]
# Pre-calculate frequencies, angles, and soft mask
if not i:
first_mic = load_mic(mic_file)
freqs, angles = get_mic_freqs(first_mic, apix, angles=True)
softmask = 1. - smoothstep(merge_freq1, merge_freq2, freqs)
merge_band = (softmask < 1) * (softmask > 0)
new_mic = process(nn,
mic_file,
metadata,
freqs,
angles,
apix,
cutoff_frequency,
softmask,
merge_band,
phaseflip=phaseflip,
flipback=flipback,
merge_noisy=merge_noisy)
new_mic_file = mic_file.replace(".mrc", "{0}.mrc".format(suffix))
save_mic(new_mic, new_mic_file)
return
def main(args):
pyfftw.interfaces.cache.enable()
refmap = mrc.read(args.key, compat="relion")
df = star.parse_star(args.input, keep_index=False)
star.augment_star_ucsf(df)
refmap_ft = vop.vol_ft(refmap, threads=args.threads)
apix = star.calculate_apix(df)
sz = refmap_ft.shape[0] // 2 - 1
sx, sy = np.meshgrid(rfftfreq(sz), fftfreq(sz))
s = np.sqrt(sx**2 + sy**2)
r = s * sz
r = np.round(r).astype(np.int64)
r[r > sz // 2] = sz // 2 + 1
a = np.arctan2(sy, sx)
def1 = df["rlnDefocusU"].values
def2 = df["rlnDefocusV"].values
angast = df["rlnDefocusAngle"].values
phase = df["rlnPhaseShift"].values
kv = df["rlnVoltage"].values
ac = df["rlnAmplitudeContrast"].values
cs = df["rlnSphericalAberration"].values
xshift = df["rlnOriginX"].values
yshift = df["rlnOriginY"].values
score = np.zeros(df.shape[0])
# TODO parallelize
for i, row in df.iterrows():
xcor = particle_xcorr(row, refmap_ft)
if args.top is None:
args.top = df.shape[0]
top = df.iloc[np.argsort(score)][:args.top]
star.simplify_star_ucsf(top)
star.write_star(args.output, top)
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.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('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):
if args.info:
args.input.append(args.output)
df = pd.concat(
(star.parse_star(inp, augment=args.augment) for inp in args.input),
join="inner")
dfaux = None
if args.cls is not None:
df = star.select_classes(df, args.cls)
if args.info:
if star.is_particle_star(df) and star.Relion.CLASS in df.columns:
c = df[star.Relion.CLASS].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 iteritems(c.sort_index())
]))
elif star.is_particle_star(df):
print("%s particles" % "{:,}".format(df.shape[0]))
if star.Relion.MICROGRAPH_NAME in df.columns:
mgraphcnt = df[star.Relion.MICROGRAPH_NAME].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)" %
(star.calculate_apix(df), "{:,.0f}".format(
df[star.Relion.MAGNIFICATION][0])))
except KeyError:
pass
if len(df.columns.intersection(star.Relion.ORIGINS3D)) > 0:
print("Largest shift is %f pixels" % np.max(
np.abs(df[df.columns.intersection(
star.Relion.ORIGINS3D)].values)))
return 0
if args.drop_angles:
df.drop(star.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[star.Relion.GROUPNUMBER] += args.offset_group
if args.restack is not None:
if not args.augment:
star.augment_star_ucsf(df, inplace=True)
star.set_original_fields(df, inplace=True)
df[star.UCSF.IMAGE_PATH] = args.restack
df[star.UCSF.IMAGE_INDEX] = np.arange(df.shape[0])
if args.subsample_micrographs is not None:
if args.bootstrap is not None:
print("Only particle sampling allows bootstrapping")
return 1
mgraphs = df[star.Relion.MICROGRAPH_NAME].unique()
if args.subsample_micrographs < 1:
args.subsample_micrographs = np.int(
max(np.round(args.subsample_micrographs * len(mgraphs)), 1))
else:
args.subsample_micrographs = np.int(args.subsample_micrographs)
ind = np.random.choice(len(mgraphs),
size=args.subsample_micrographs,
replace=False)
mask = df[star.Relion.MICROGRAPH_NAME].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 = star.parse_star(args.copy_angles, augment=args.augment)
df = star.smart_merge(df,
angle_star,
fields=star.Relion.ANGLES,
key=args.merge_key)
if args.copy_alignments is not None:
align_star = star.parse_star(args.copy_alignments,
augment=args.augment)
df = star.smart_merge(df,
align_star,
fields=star.Relion.ALIGNMENTS,
key=args.merge_key)
if args.copy_reconstruct_images is not None:
recon_star = star.parse_star(args.copy_reconstruct_images,
augment=args.augment)
df[star.Relion.RECONSTRUCT_IMAGE_NAME] = recon_star[
star.Relion.IMAGE_NAME]
if args.transform is not None:
if args.transform.count(",") == 2:
r = geom.euler2rot(
*np.deg2rad([np.double(s) for s in args.transform.split(",")]))
else:
r = np.array(json.loads(args.transform))
df = star.transform_star(df, r, inplace=True)
if args.invert_hand:
df = star.invert_hand(df, inplace=True)
if args.copy_paths is not None:
path_star = star.parse_star(args.copy_paths)
star.set_original_fields(df, inplace=True)
df[star.Relion.IMAGE_NAME] = path_star[star.Relion.IMAGE_NAME]
if args.copy_ctf is not None:
ctf_star = pd.concat((star.parse_star(inp, augment=args.augment)
for inp in glob.glob(args.copy_ctf)),
join="inner")
df = star.smart_merge(df,
ctf_star,
star.Relion.CTF_PARAMS,
key=args.merge_key)
if args.copy_micrograph_coordinates is not None:
coord_star = pd.concat(
(star.parse_star(inp, augment=args.augment)
for inp in glob.glob(args.copy_micrograph_coordinates)),
join="inner")
df = star.smart_merge(df,
coord_star,
fields=star.Relion.MICROGRAPH_COORDS,
key=args.merge_key)
if args.scale is not None:
star.scale_coordinates(df, args.scale, inplace=True)
star.scale_origins(df, args.scale, inplace=True)
star.scale_magnification(df, args.scale, inplace=True)
if args.scale_particles is not None:
star.scale_origins(df, args.scale_particles, inplace=True)
star.scale_magnification(df, args.scale_particles, inplace=True)
if args.scale_coordinates is not None:
star.scale_coordinates(df, args.scale_coordinates, inplace=True)
if args.scale_origins is not None:
star.scale_origins(df, args.scale_origins, inplace=True)
if args.scale_magnification is not None:
star.scale_magnification(df, args.scale_magnification, inplace=True)
if args.scale_apix is not None:
star.scale_apix(df, args.scale_apix, inplace=True)
if args.recenter:
df = star.recenter(df, inplace=True)
if args.zero_origins:
df = star.zero_origins(df, inplace=True)
if args.pick:
df.drop(df.columns.difference(star.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):
star.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:
df = star.to_micrographs(df)
if args.micrograph_range:
df.set_index(star.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.micrograph_path is not None:
df = star.replace_micrograph_path(df,
args.micrograph_path,
inplace=True)
if args.min_separation is not None:
gb = df.groupby(star.Relion.MICROGRAPH_NAME)
dupes = []
for n, g in gb:
nb = algo.query_connected(
g[star.Relion.COORDS].values - g[star.Relion.ORIGINS],
args.min_separation / star.calculate_apix(df))
dupes.extend(g.index[~np.isnan(nb)])
dfaux = df.loc[dupes]
df.drop(dupes, inplace=True)
if args.merge_source is not None:
if args.merge_fields is not None:
if "," in args.merge_fields:
args.merge_fields = args.merge_fields.split(",")
else:
args.merge_fields = [args.merge_fields]
else:
print("Merge fields must be specified using --merge-fields")
return 1
if args.merge_key is not None:
if "," in args.merge_key:
args.merge_key = args.merge_key.split(",")
if args.by_original:
args.by_original = star.original_field(args.merge_key)
else:
args.by_original = args.merge_key
merge_star = star.parse_star(args.merge_source, augment=args.augment)
df = star.smart_merge(df,
merge_star,
fields=args.merge_fields,
key=args.merge_key,
left_key=args.by_original)
if args.revert_original:
df = star.revert_original(df, inplace=True)
if args.set_optics is not None:
tok = args.set_optics.split(",")
df = star.set_optics_groups(df,
sep=tok[0],
idx=int(tok[1]),
inplace=True)
df.dropna(axis=0, how="any", inplace=True)
if args.drop_optics_group is not None:
idx = df[star.Relion.OPTICSGROUP].isin(args.drop_optics_group)
if not np.any(idx):
idx = df[star.Relion.OPTICSGROUPNAME].isin(args.drop_optics_group)
if not np.any(idx):
print("No group found to drop")
return 1
df = df.loc[~idx]
if args.split_micrographs:
dfs = star.split_micrographs(df)
for mg in dfs:
star.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:
if not args.relion2:
df = star.remove_deprecated_relion2(dfaux, inplace=True)
star.write_star(args.output,
df,
resort_records=args.sort,
simplify=args.augment_output,
optics=True)
else:
df = star.remove_new_relion31(dfaux, inplace=True)
star.write_star(args.output,
df,
resort_records=args.sort,
simplify=args.augment_output,
optics=False)
if args.output is not None:
if not args.relion2: # Relion 3.1 style output.
df = star.remove_deprecated_relion2(df, inplace=True)
star.write_star(args.output,
df,
resort_records=args.sort,
simplify=args.augment_output,
optics=True)
else:
df = star.remove_new_relion31(df, inplace=True)
star.write_star(args.output,
df,
resort_records=args.sort,
simplify=args.augment_output,
optics=False)
return 0
def main(args):
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
dfs = [metadata.parse_fx_par(fn) for fn in args.input]
n = dfs[0].shape[0]
if not np.all(np.array([df.shape[0] for df in dfs]) == n):
log.error("Input files are not aligned!")
return 1
df = pd.concat(dfs, axis=0, ignore_index=True)
df["CLASS"] = np.repeat(np.arange(1, len(dfs) + 1), n)
if args.min_occ:
df = df[df["OCC"] >= args.min_occ]
df = df.sort_values(by="OCC")
df = df.drop_duplicates("C", keep="last")
df = df.sort_values(by="C")
df.reset_index(inplace=True)
if args.min_score is not None:
if args.min_score < 1:
args.min_score = np.percentile(df["SCORE"],
(1 - args.min_score) * 100)
df = df.loc[df["SCORE"] >= args.min_score]
if args.merge is not None:
dfo = star.parse_star(args.merge)
args.apix = star.calculate_apix(dfo)
args.cs = dfo.iloc[0][star.Relion.CS]
args.ac = dfo.iloc[0][star.Relion.AC]
args.voltage = dfo.iloc[0][star.Relion.VOLTAGE]
df = metadata.par2star(df,
data_path=args.stack,
apix=args.apix,
cs=args.cs,
ac=args.ac,
kv=args.voltage,
invert_eulers=args.invert_eulers)
if args.stack is None:
df[star.UCSF.IMAGE_INDEX] = dfo[star.UCSF.IMAGE_INDEX]
df[star.UCSF.IMAGE_PATH] = dfo[star.UCSF.IMAGE_PATH]
key = [star.UCSF.IMAGE_INDEX, star.UCSF.IMAGE_PATH]
fields = star.Relion.MICROGRAPH_COORDS + [
star.UCSF.IMAGE_ORIGINAL_INDEX, star.UCSF.IMAGE_ORIGINAL_PATH
] + [star.Relion.OPTICSGROUP
] + star.Relion.OPTICSGROUPTABLE + [star.Relion.RANDOMSUBSET]
df = star.smart_merge(df, dfo, fields=fields, key=key)
if args.revert_original:
df = star.revert_original(df, inplace=True)
else:
df = metadata.par2star(df,
data_path=args.stack,
apix=args.apix,
cs=args.cs,
ac=args.ac,
kv=args.voltage,
invert_eulers=args.invert_eulers)
if args.cls is not None:
df = star.select_classes(df, args.cls)
df = star.check_defaults(df, inplace=True)
df = star.compatible(df, relion2=args.relion2, inplace=True)
star.write_star(args.output, df, optics=(not args.relion2))
return 0
def main(args):
if args.info:
args.input.append(args.output)
df = pd.concat(
(star.parse_star(inp, augment=args.augment) for inp in args.input),
join="inner")
dfaux = None
if args.cls is not None:
df = star.select_classes(df, args.cls)
if args.info:
if star.is_particle_star(df) and star.Relion.CLASS in df.columns:
c = df[star.Relion.CLASS].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 star.is_particle_star(df):
print("%s particles" % "{:,}".format(df.shape[0]))
if star.Relion.MICROGRAPH_NAME in df.columns:
mgraphcnt = df[star.Relion.MICROGRAPH_NAME].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)" %
(star.calculate_apix(df), "{:,.0f}".format(
df[star.Relion.MAGNIFICATION][0])))
except KeyError:
pass
return 0
if args.drop_angles:
df.drop(star.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[star.Relion.GROUPNUMBER] += 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[star.Relion.MICROGRAPH_NAME].unique()
if args.subsample_micrographs < 1:
args.subsample_micrographs = np.int(
max(np.round(args.subsample_micrographs * len(mgraphs)), 1))
else:
args.subsample_micrographs = np.int(args.subsample_micrographs)
ind = np.random.choice(len(mgraphs),
size=args.subsample_micrographs,
replace=False)
mask = df[star.Relion.MICROGRAPH_NAME].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 = star.parse_star(args.copy_angles, augment=args.augment)
df = star.smart_merge(df, angle_star, fields=star.Relion.ANGLES)
if args.transform is not None:
if args.transform.count(",") == 2:
r = star.euler2rot(
*np.deg2rad([np.double(s) for s in args.transform.split(",")]))
else:
r = np.array(json.loads(args.transform))
df = star.transform_star(df, r, inplace=True)
if args.invert_hand:
df[star.Relion.ANGLEROT] = -df[star.Relion.ANGLEROT]
df[star.Relion.ANGLETILT] = 180 - df[star.Relion.ANGLETILT]
if args.copy_paths is not None:
path_star = star.parse_star(args.copy_paths)
df[star.Relion.IMAGE_NAME] = path_star[star.Relion.IMAGE_NAME]
if args.copy_ctf is not None:
ctf_star = pd.concat((star.parse_star(inp, augment=args.augment)
for inp in glob.glob(args.copy_ctf)),
join="inner")
df = star.smart_merge(df, ctf_star, star.Relion.CTF_PARAMS)
if args.copy_micrograph_coordinates is not None:
coord_star = pd.concat(
(star.parse_star(inp, augment=args.augment)
for inp in glob.glob(args.copy_micrograph_coordinates)),
join="inner")
df = star.smart_merge(df,
coord_star,
fields=star.Relion.MICROGRAPH_COORDS)
if args.scale is not None:
star.scale_coordinates(df, args.scale, inplace=True)
star.scale_origins(df, args.scale, inplace=True)
star.scale_magnification(df, args.scale, inplace=True)
if args.scale_particles is not None:
star.scale_origins(df, args.scale, inplace=True)
star.scale_magnification(df, args.scale, inplace=True)
if args.scale_coordinates is not None:
star.scale_coordinates(df, args.scale_coordinates, inplace=True)
if args.scale_origins is not None:
star.scale_origins(df, args.scale_origins, inplace=True)
if args.scale_magnification is not None:
star.scale_magnification(df, args.scale_magnfication, inplace=True)
if args.recenter:
df = star.recenter(df, inplace=True)
if args.zero_origins:
df = star.zero_origins(df, inplace=True)
if args.pick:
df.drop(df.columns.difference(star.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):
star.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(star.Relion.MICROGRAPH_NAME)
mu = gb.mean()
df = mu[[
c for c in star.Relion.CTF_PARAMS + star.Relion.MICROSCOPE_PARAMS +
[star.Relion.MICROGRAPH_NAME] if c in mu
]].reset_index()
if args.micrograph_range:
df.set_index(star.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.micrograph_path is not None:
df = star.replace_micrograph_path(df,
args.micrograph_path,
inplace=True)
if args.min_separation is not None:
gb = df.groupby(star.Relion.MICROGRAPH_NAME)
dupes = []
for n, g in gb:
nb = algo.query_connected(
g[star.Relion.COORDS],
args.min_separation / star.calculate_apix(df))
dupes.extend(g.index[~np.isnan(nb)])
dfaux = df.loc[dupes]
df.drop(dupes, inplace=True)
if args.merge_source is not None:
if args.merge_fields is not None:
if "," in args.merge_fields:
args.merge_fields = args.merge_fields.split(",")
else:
args.merge_fields = [args.merge_fields]
else:
print("Merge fields must be specified using --merge-fields")
return 1
if args.merge_key is not None:
if "," in args.merge_key:
args.merge_key = args.merge_key.split(",")
merge_star = star.parse_star(args.merge_source, augment=args.augment)
df = star.smart_merge(df,
merge_star,
fields=args.merge_fields,
key=args.merge_key)
if args.split_micrographs:
dfs = star.split_micrographs(df)
for mg in dfs:
star.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:
star.write_star(args.auxout, dfaux, simplify=args.augment)
if args.output is not None:
star.write_star(args.output, df, simplify=args.augment)
return 0
def main(args):
"""
Projection subtraction program entry point.
:param args: Command-line arguments parsed by ArgumentParser.parse_args()
:return: Exit status
"""
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
if args.dest is None and args.suffix == "":
args.dest = ""
args.suffix = "_subtracted"
log.info("Reading particle .star file")
df = star.parse_star(args.input, keep_index=False)
star.augment_star_ucsf(df)
if not args.original:
df[star.UCSF.IMAGE_ORIGINAL_PATH] = df[star.UCSF.IMAGE_PATH]
df[star.UCSF.IMAGE_ORIGINAL_INDEX] = df[star.UCSF.IMAGE_INDEX]
df.sort_values(star.UCSF.IMAGE_ORIGINAL_PATH,
inplace=True,
kind="mergesort")
gb = df.groupby(star.UCSF.IMAGE_ORIGINAL_PATH)
df[star.UCSF.IMAGE_INDEX] = gb.cumcount()
df[star.UCSF.IMAGE_PATH] = df[star.UCSF.IMAGE_ORIGINAL_PATH].map(
lambda x: os.path.join(
args.dest, args.prefix + os.path.basename(x).replace(
".mrcs", args.suffix + ".mrcs")))
if args.submap_ft is None:
log.info("Reading volume")
submap = mrc.read(args.submap, inc_header=False, compat="relion")
if args.submask is not None:
log.info("Masking volume")
submask = mrc.read(args.submask, inc_header=False, compat="relion")
submap *= submask
log.info("Preparing 3D FFT of volume")
submap_ft = vop.vol_ft(submap,
pfac=args.pfac,
threads=min(args.threads, cpu_count()))
log.info("Finished 3D FFT of volume")
else:
log.info("Loading 3D FFT from %s" % args.submap_ft)
submap_ft = np.load(args.submap_ft)
log.info("Loaded 3D FFT from %s" % args.submap_ft)
sz = (submap_ft.shape[0] - 3) // args.pfac
maxshift = np.round(np.max(np.abs(df[star.Relion.ORIGINS].values)))
if args.crop is not None and sz < 2 * maxshift + args.crop:
log.error("Some shifts are too large to crop (maximum crop is %d)" %
(sz - 2 * maxshift))
return 1
sx, sy = np.meshgrid(np.fft.rfftfreq(sz), np.fft.fftfreq(sz))
s = np.sqrt(sx**2 + sy**2)
r = s * sz
r = np.round(r).astype(np.int64)
r[r > sz // 2] = sz // 2 + 1
nr = np.max(r) + 1
a = np.arctan2(sy, sx)
if args.refmap is not None:
coefs_method = 1
if args.refmap_ft is None:
refmap = mrc.read(args.refmap, inc_header=False, compat="relion")
refmap_ft = vop.vol_ft(refmap,
pfac=args.pfac,
threads=min(args.threads, cpu_count()))
else:
log.info("Loading 3D FFT from %s" % args.refmap_ft)
refmap_ft = np.load(args.refmap_ft)
log.info("Loaded 3D FFT from %s" % args.refmap_ft)
else:
coefs_method = 0
refmap_ft = np.empty(submap_ft.shape, dtype=submap_ft.dtype)
apix = star.calculate_apix(df)
log.info("Computed pixel size is %f A" % apix)
log.debug("Grouping particles by output stack")
gb = df.groupby(star.UCSF.IMAGE_PATH)
iothreads = threading.BoundedSemaphore(args.io_thread_pairs)
qsize = args.io_queue_length
fftthreads = args.fft_threads
def init():
global tls
tls = threading.local()
log.info("Instantiating thread pool with %d workers" % args.threads)
pool = Pool(processes=args.threads, initializer=init)
threads = []
log.info("Performing projection subtraction")
try:
for fname, particles in gb:
log.debug("Instantiating queue")
queue = Queue.Queue(maxsize=qsize)
log.debug("Create producer for %s" % fname)
prod = threading.Thread(target=producer,
args=(pool, queue, submap_ft, refmap_ft,
fname, particles, sx, sy, s, a, apix,
coefs_method, r, nr, fftthreads,
args.crop, args.pfac))
log.debug("Create consumer for %s" % fname)
cons = threading.Thread(target=consumer,
args=(queue, fname, apix, iothreads))
threads.append((prod, cons))
iothreads.acquire()
log.debug("iotheads at %d" % iothreads._Semaphore__value)
log.debug("Start consumer for %s" % fname)
cons.start()
log.debug("Start producer for %s" % fname)
prod.start()
except KeyboardInterrupt:
log.debug("Main thread wants out!")
for pair in threads:
for thread in pair:
try:
thread.join()
except RuntimeError as e:
log.debug(e)
pool.close()
pool.join()
pool.terminate()
log.info("Finished projection subtraction")
log.info("Writing output .star file")
if args.crop is not None:
df = star.recenter(df, inplace=True)
star.simplify_star_ucsf(df)
star.write_star(args.output, df)
return 0
def main(args):
""" Main SNR-measuring function """
# Load STAR file and neural network
star_file = load_star(args.input_micrographs)
num_mics = len(star_file)
apix = star.calculate_apix(star_file)
cutoff_frequency = 1. / args.max_resolution
nn = load_trained_model(args.model)
orig, even, odd = args.even_odd_suffix.split(",")
phaseflip = args.phaseflip
augment = args.augment
SNR_df = pd.DataFrame(columns=[
"MicrographName", "var_S", "var_N_noisy", "var_N_denoised", "var_B",
"frequencies", "svar_S", "svar_N_noisy", "svar_N_denoised", "svar_B"
])
# Main denoising loop
for i, metadata in tqdm(star_file.iterrows(),
desc="Denoising",
total=num_mics):
mic_file = metadata[star.Relion.MICROGRAPH_NAME]
# Pre-calculate frequencies and angles
if not i:
first_mic = load_mic(mic_file)
freqs, angles = get_mic_freqs(first_mic, apix, angles=True)
# Bin and denoise the even and odd micrographs
even_mic_file = mic_file.replace(orig, even)
Re, De = process_snr(nn,
even_mic_file,
metadata,
freqs,
angles,
apix,
cutoff_frequency,
phaseflip=phaseflip,
augment=augment)
odd_mic_file = mic_file.replace(orig, odd)
Ro, Do = process_snr(nn,
odd_mic_file,
metadata,
freqs,
angles,
apix,
cutoff_frequency,
phaseflip=phaseflip,
augment=augment)
# Calculate variances and spectral variances for plotting
var_S, var_N_noisy, var_N_denoised, var_B = get_variances(
Re, Ro, De, Do)
frequencies, svar_S, svar_N_noisy, svar_N_denoised, svar_B = \
get_spectral_variances(Re, Ro, De, Do, apix=apix)
SNR_df.loc[i] = [
mic_file, var_S, var_N_noisy, var_N_denoised, var_B, frequencies,
svar_S, svar_N_noisy, svar_N_denoised, svar_B
]
SNR_df.to_pickle(args.output_dataframe)
return
def generate_training_data(training_mics,
cutoff,
training_data,
suffixes,
window=192,
phaseflip=True):
""" Generate the training data given micrographs and their CTF information
Keyword arguments:
training_mics -- Micrograph STAR file with CTF information for each image
cutoff -- Spatial frequency for Fourier cropping an image
training_data -- Filename for the HDF file that is created
It is presumed that all images have the same shape and pixel size.
By default, phase-flipping is performed to correct for the CTF.
"""
star_file = load_star(training_mics)
apix = star.calculate_apix(star_file)
n_mics = len(star_file)
dset_file = File(training_data, "w")
dset_shape, n_patches, mic_freqs, mic_angles = get_dset_shape(
star_file, window, apix, cutoff)
even_dset = dset_file.create_dataset("even", dset_shape, dtype="float32")
odd_dset = dset_file.create_dataset("odd", dset_shape, dtype="float32")
orig, even, odd = suffixes.split(",")
if len(suffixes.split(",")) != 3:
raise Exception("Improperly formatted suffixes for even/odd mics!")
for i, metadata in tqdm(star_file.iterrows(),
desc="Pre-processing",
total=n_mics):
mic_file = metadata[star.Relion.MICROGRAPH_NAME]
even_file = mic_file.replace(orig, even)
odd_file = mic_file.replace(orig, odd)
mic_even_patches, apix_bin = process(metadata,
cutoff,
window,
even_file,
mic_freqs,
mic_angles,
phaseflip=phaseflip)
mic_odd_patches, apix_bin = process(metadata,
cutoff,
window,
odd_file,
mic_freqs,
mic_angles,
phaseflip=phaseflip)
even_dset[i * n_patches:(i + 1) * n_patches] = mic_even_patches
odd_dset[i * n_patches:(i + 1) * n_patches] = mic_odd_patches
even_dset.attrs['apix'] = apix_bin
even_dset.attrs['phaseflip'] = phaseflip
odd_dset.attrs['apix'] = apix_bin
odd_dset.attrs['phaseflip'] = phaseflip
dset_file.close()
return
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 main(args):
log = logging.getLogger(__name__)
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
if args.target is None and args.sym is None and args.transform is None and args.euler is None:
log.error("At least a target, transformation matrix, Euler angles, or a symmetry group must be provided")
return 1
elif (args.target is not None or args.transform 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.apix is None:
df = star.parse_star(args.input, nrows=1)
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.Relion.DETECTORPIXELSIZE] = 1.0
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.euler is not None:
try:
args.euler = np.deg2rad(np.array([np.double(tok) for tok in args.euler.split(",")]))
args.transform = np.zeros((3, 4))
args.transform[:, :3] = geom.euler2rot(*args.euler)
if args.target is not None:
args.transform[:, -1] = args.target
except:
log.error("Euler angles must be comma-separated list of rotation, tilt, skew in degrees")
return 1
if args.transform is not None and not hasattr(args.transform, "dtype"):
if args.target is not None:
log.warn("--target supersedes --transform")
try:
args.transform = np.array(json.loads(args.transform))
except:
log.error("Transformation matrix must be in JSON/Numpy format")
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(",")])
args.origin /= args.apix
except:
log.error("Origin must be comma-separated list of x,y,z coordinates")
return 1
elif args.boxsize is not None:
args.origin = np.ones(3) * args.boxsize / 2
if args.sym is not None:
args.sym = util.relion_symmetry_group(args.sym)
df = star.parse_star(args.input)
if star.calculate_apix(df) != args.apix:
log.warn("Using specified pixel size of %f instead of calculated size %f" %
(args.apix, star.calculate_apix(df)))
if args.cls is not None:
df = star.select_classes(df, args.cls)
if args.target is not None:
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
r = geom.euler2rot(*np.array([np.arctan2(ax[1], ax[0]), np.arccos(ax[2]), np.deg2rad(args.psi)]))
d = -d
elif args.transform is not None:
r = args.transform[:, :3]
if args.transform.shape[1] == 4:
d = args.transform[:, -1] / args.apix
d = r.dot(args.origin) + d - args.origin
else:
d = 0
elif args.sym is not None:
r = np.identity(3)
d = -args.displacement / args.apix
else:
log.error("At least a target or symmetry group must be provided via --target or --sym")
return 1
log.debug("Final rotation: %s" % str(r).replace("\n", "\n" + " " * 16))
ops = [op.dot(r.T) for op in args.sym] if args.sym is not None else [r.T]
log.debug("Final translation: %s (%f px)" % (str(d), np.linalg.norm(d)))
dfs = list(subparticle_expansion(df, ops, d, rotate=args.shift_only, invert=args.invert, adjust_defocus=args.adjust_defocus))
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]
df = star.compatible(df, relion2=args.relion2, inplace=True)
star.write_star(args.output, df, optics=(not args.relion2))
else:
for i, s in enumerate(dfs):
s = star.compatible(s, relion2=args.relion2, inplace=True)
star.write_star(os.path.join(args.output, args.suffix + "_%d" % i), s, optics=(not args.relion2))
return 0
def main(args):
"""
Projection subtraction program entry point.
:param args: Command-line arguments parsed by ArgumentParser.parse_args()
:return: Exit status
"""
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
log.debug("Reading particle .star file")
df = parse_star(args.input, keep_index=False)
df.reset_index(inplace=True)
df["rlnImageOriginalName"] = df["rlnImageName"]
df["ucsfOriginalParticleIndex"], df["ucsfOriginalImagePath"] = \
df["rlnImageOriginalName"].str.split("@").str
df["ucsfOriginalParticleIndex"] = pd.to_numeric(
df["ucsfOriginalParticleIndex"])
df.sort_values("rlnImageOriginalName", inplace=True, kind="mergesort")
gb = df.groupby("ucsfOriginalImagePath")
df["ucsfParticleIndex"] = gb.cumcount() + 1
df["ucsfImagePath"] = df["ucsfOriginalImagePath"].map(
lambda x: os.path.join(
args.dest, args.prefix + os.path.basename(x).replace(
".mrcs", args.suffix + ".mrcs")))
df["rlnImageName"] = df["ucsfParticleIndex"].map(
lambda x: "%.6d" % x).str.cat(df["ucsfImagePath"], sep="@")
log.debug("Read particle .star file")
if args.submap_ft is None:
submap = mrc.read(args.submap, inc_header=False, compat="relion")
submap_ft = vol_ft(submap, threads=min(args.threads, cpu_count()))
else:
log.debug("Loading %s" % args.submap_ft)
submap_ft = np.load(args.submap_ft)
log.debug("Loaded %s" % args.submap_ft)
sz = submap_ft.shape[0] // 2 - 1
sx, sy = np.meshgrid(np.fft.rfftfreq(sz), np.fft.fftfreq(sz))
s = np.sqrt(sx**2 + sy**2)
r = s * sz
r = np.round(r).astype(np.int64)
r[r > sz // 2] = sz // 2 + 1
nr = np.max(r) + 1
a = np.arctan2(sy, sx)
if args.refmap is not None:
coefs_method = 1
if args.refmap_ft is None:
refmap = mrc.read(args.refmap, inc_header=False, compat="relion")
refmap_ft = vol_ft(refmap, threads=min(args.threads, cpu_count()))
else:
log.debug("Loading %s" % args.refmap_ft)
refmap_ft = np.load(args.refmap_ft)
log.debug("Loaded %s" % args.refmap_ft)
else:
coefs_method = 0
refmap_ft = np.empty(submap_ft.shape, dtype=submap_ft.dtype)
apix = calculate_apix(df)
log.debug("Constructing particle metadata references")
# npart = df.shape[0]
idx = df["ucsfOriginalParticleIndex"].values
stack = df["ucsfOriginalImagePath"].values.astype(np.str, copy=False)
def1 = df["rlnDefocusU"].values
def2 = df["rlnDefocusV"].values
angast = df["rlnDefocusAngle"].values
phase = df["rlnPhaseShift"].values
kv = df["rlnVoltage"].values
ac = df["rlnAmplitudeContrast"].values
cs = df["rlnSphericalAberration"].values
az = df["rlnAngleRot"].values
el = df["rlnAngleTilt"].values
sk = df["rlnAnglePsi"].values
xshift = df["rlnOriginX"].values
yshift = df["rlnOriginY"].values
new_idx = df["ucsfParticleIndex"].values
new_stack = df["ucsfImagePath"].values.astype(np.str, copy=False)
log.debug("Grouping particles by output stack")
gb = df.groupby("ucsfImagePath")
iothreads = threading.BoundedSemaphore(args.io_thread_pairs)
qsize = args.io_queue_length
fftthreads = args.fft_threads
# pyfftw.interfaces.cache.enable()
log.debug("Instantiating worker pool")
pool = Pool(processes=args.threads)
threads = []
try:
for fname, particles in gb.indices.iteritems():
log.debug("Instantiating queue")
queue = Queue.Queue(maxsize=qsize)
log.debug("Create producer for %s" % fname)
prod = threading.Thread(
target=producer,
args=(pool, queue, submap_ft, refmap_ft, fname, particles, idx,
stack, sx, sy, s, a, apix, def1, def2, angast, phase, kv,
ac, cs, az, el, sk, xshift, yshift, new_idx, new_stack,
coefs_method, r, nr, fftthreads))
log.debug("Create consumer for %s" % fname)
cons = threading.Thread(target=consumer,
args=(queue, fname, apix, fftthreads,
iothreads))
threads.append((prod, cons))
iothreads.acquire()
log.debug("iotheads at %d" % iothreads._Semaphore__value)
log.debug("Start consumer for %s" % fname)
cons.start()
log.debug("Start producer for %s" % fname)
prod.start()
except KeyboardInterrupt:
log.debug("Main thread wants out!")
for pair in threads:
for thread in pair:
try:
thread.join()
except RuntimeError as e:
log.debug(e)
pool.close()
pool.join()
pool.terminate()
df.drop([c for c in df.columns if "ucsf" in c or "eman" in c],
axis=1,
inplace=True)
df.set_index("index", inplace=True)
df.sort_index(inplace=True, kind="mergesort")
write_star(args.output, df, reindex=True)
return 0
