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(__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