def main(args):
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
if args.input.endswith(".cs"):
log.debug("Detected CryoSPARC 2+ .cs file")
cs = np.load(args.input)
try:
df = metadata.parse_cryosparc_2_cs(cs,
passthrough=args.passthrough,
minphic=args.minphic)
except (KeyError, ValueError) as e:
log.error(e.message)
log.error(
"A passthrough file may be required (check inside the cryoSPARC 2+ job directory)"
)
log.debug(e, exc_info=True)
return 1
else:
log.debug("Detected CryoSPARC 0.6.5 .csv file")
meta = metadata.parse_cryosparc_065_csv(
args.input) # Read cryosparc metadata file.
df = metadata.cryosparc_065_csv2star(meta, args.minphic)
if args.cls is not None:
df = star.select_classes(df, args.cls)
if args.copy_micrograph_coordinates is not None:
coord_star = pd.concat(
(star.parse_star(inp, keep_index=False)
for inp in glob(args.copy_micrograph_coordinates)),
join="inner")
star.augment_star_ucsf(coord_star)
star.augment_star_ucsf(df)
key = star.merge_key(df, coord_star)
log.debug("Coordinates merge key: %s" % key)
if args.cached or key == star.Relion.IMAGE_NAME:
fields = star.Relion.MICROGRAPH_COORDS
else:
fields = star.Relion.MICROGRAPH_COORDS + [
star.UCSF.IMAGE_INDEX, star.UCSF.IMAGE_PATH
]
df = star.smart_merge(df, coord_star, fields=fields, key=key)
star.simplify_star_ucsf(df)
if args.micrograph_path is not None:
df = star.replace_micrograph_path(df,
args.micrograph_path,
inplace=True)
if args.transform is not None:
r = np.array(json.loads(args.transform))
df = star.transform_star(df, r, inplace=True)
# Write Relion .star file with correct headers.
star.write_star(args.output, df, reindex=True)
log.info("Output fields: %s" % ", ".join(df.columns))
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 = metadata.par2star(df,
data_path=args.stack,
apix=args.apix,
cs=args.cs,
ac=args.ac,
kv=args.voltage,
invert_eulers=args.relion)
if args.cls is not None:
df = star.select_classes(df, args.cls)
star.write_star(args.output, df, reindex=True)
return 0
def main(args):
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
if args.input[0].endswith(".cs"):
log.debug("Detected CryoSPARC 2+ .cs file")
cs = np.load(args.input[0])
try:
df = metadata.parse_cryosparc_2_cs(cs, passthroughs=args.input[1:], minphic=args.minphic,
boxsize=args.boxsize, swapxy=args.swapxy,
invertx=args.invertx, inverty=args.inverty)
except (KeyError, ValueError) as e:
log.error(e, exc_info=True)
log.error("Required fields could not be mapped. Are you using the right input file(s)?")
return 1
else:
log.debug("Detected CryoSPARC 0.6.5 .csv file")
if len(args.input) > 1:
log.error("Only one file at a time supported for CryoSPARC 0.6.5 .csv format")
return 1
meta = metadata.parse_cryosparc_065_csv(args.input[0]) # Read cryosparc metadata file.
df = metadata.cryosparc_065_csv2star(meta, args.minphic)
if args.cls is not None:
df = star.select_classes(df, args.cls)
if args.copy_micrograph_coordinates is not None:
df = star.augment_star_ucsf(df, inplace=True)
coord_star = pd.concat(
(star.parse_star(inp, keep_index=False, augment=True) for inp in
glob(args.copy_micrograph_coordinates)), join="inner")
key = star.merge_key(df, coord_star)
log.debug("Coordinates merge key: %s" % key)
if args.cached or key == star.Relion.IMAGE_NAME:
fields = star.Relion.MICROGRAPH_COORDS
else:
fields = star.Relion.MICROGRAPH_COORDS + [star.UCSF.IMAGE_INDEX, star.UCSF.IMAGE_PATH]
df = star.smart_merge(df, coord_star, fields=fields, key=key)
star.simplify_star_ucsf(df)
if args.micrograph_path is not None:
df = star.replace_micrograph_path(df, args.micrograph_path, inplace=True)
if args.transform is not None:
r = np.array(json.loads(args.transform))
df = star.transform_star(df, r, inplace=True)
df = star.check_defaults(df, inplace=True)
if args.relion2:
df = star.remove_new_relion31(df, inplace=True)
star.write_star(args.output, df, resort_records=True, optics=False)
else:
df = star.remove_deprecated_relion2(df, inplace=True)
star.write_star(args.output, df, resort_records=True, optics=True)
log.info("Output fields: %s" % ", ".join(df.columns))
return 0
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):
if args.input.endswith(".cs"):
cs = np.load(args.input)
if args.passthrough is None:
if u"blob/path" not in cs.dtype.names:
print(
"A passthrough file is required (found inside the cryoSPARC 2+ job directory)"
)
return 1
df = metadata.parse_cryosparc_2_cs(cs,
passthrough=args.passthrough,
minphic=args.minphic)
else:
meta = metadata.parse_cryosparc_065_csv(
args.input) # Read cryosparc metadata file.
df = metadata.cryosparc_065_csv2star(meta, args.minphic)
if args.cls is not None:
df = star.select_classes(df, args.cls)
if args.copy_micrograph_coordinates is not None:
coord_star = pd.concat(
(star.parse_star(inp, keep_index=False)
for inp in glob(args.copy_micrograph_coordinates)),
join="inner")
df = star.smart_merge(df,
coord_star,
fields=star.Relion.MICROGRAPH_COORDS)
if args.transform is not None:
r = np.array(json.loads(args.transform))
df = star.transform_star(df, r, inplace=True)
# Write Relion .star file with correct headers.
star.write_star(args.output, df, reindex=True)
return 0
def main(args):
meta = parse_metadata(args.input) # Read cryosparc metadata file.
meta["data_input_idx"] = [
"%.6d" % (i + 1) for i in meta["data_input_idx"]
] # Reformat particle idx for Relion.
if "data_input_relpath" not in meta.columns:
if args.data_path is None:
print(
"Data path missing, use --data-path to specify particle stack path"
)
return 1
meta["data_input_relpath"] = args.data_path
meta["data_input_relpath"] = meta["data_input_idx"].str.cat(
meta["data_input_relpath"], sep="@") # Construct _rlnImageName field.
# Take care of trivial mappings.
rlnheaders = [
general[h] for h in meta.columns
if h in general and general[h] is not None
]
star = meta[[
h for h in meta.columns if h in general and general[h] is not None
]].copy()
star.columns = rlnheaders
if "rlnRandomSubset" in star.columns:
star["rlnRandomSubset"] = star["rlnRandomSubset"].apply(
lambda x: ord(x) - 64)
if "rlnPhaseShift" in star.columns:
star["rlnPhaseShift"] = np.rad2deg(star["rlnPhaseShift"])
# general class assignments and other model parameters.
phic = meta[[h for h in meta.columns if "phiC" in h
]] # Posterior probability over class assignments.
if len(phic.columns) > 0: # Check class assignments exist in input.
# phic.columns = [int(h[21]) for h in meta.columns if "phiC" in h]
phic.columns = range(len(phic.columns))
cls = phic.idxmax(axis=1)
for p in model:
if model[p] is not None:
pspec = p.split("model")[1]
param = meta[[h for h in meta.columns if pspec in h]]
if len(param.columns) > 0:
param.columns = phic.columns
star[model[p]] = param.lookup(param.index, cls)
star[
"rlnClassNumber"] = cls + 1 # Compute most probable classes and add one for Relion indexing.
else:
for p in model:
if model[p] is not None and p in meta.columns:
star[model[p]] = meta[p]
star["rlnClassNumber"] = 1
if args.cls is not None:
star = select_classes(star, args.cls)
# Convert axis-angle representation to Euler angles (degrees).
if star.columns.intersection(angles).size == len(angles):
star[angles] = np.rad2deg(star[angles].apply(
lambda x: rot2euler(expmap(x)), axis=1, raw=True, broadcast=True))
if args.minphic is not None:
mask = np.all(phic < args.minphic, axis=1)
if args.drop_bad:
star.drop(star[mask].index,
inplace=True) # Delete low-confidence particles.
else:
star.loc[
mask,
"rlnClassNumber"] = 0 # Set low-confidence particles to dummy class.
if args.transform is not None:
r = np.array(json.loads(args.transform))
star = transform_star(star, r, inplace=True)
# Write Relion .star file with correct headers.
write_star(args.output, star, reindex=True)
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):
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):
log = logging.getLogger('root')
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
# apix = args.apix = hdr["xlen"] / hdr["nx"]
for fn in args.input:
if not (fn.endswith(".star") or fn.endswith(".mrcs") or
fn.endswith(".mrc") or fn.endswith(".par")):
log.error("Only .star, .mrc, .mrcs, and .par files supported")
return 1
first_ptcl = 0
dfs = []
with mrc.ZSliceWriter(args.output) as writer:
for fn in args.input:
if fn.endswith(".star"):
df = star.parse_star(fn, augment=True)
if args.cls is not None:
df = star.select_classes(df, args.cls)
star.set_original_fields(df, inplace=True)
if args.resort:
df = df.sort_values([star.UCSF.IMAGE_ORIGINAL_PATH,
star.UCSF.IMAGE_ORIGINAL_INDEX])
for idx, row in df.iterrows():
if args.stack_path is not None:
input_stack_path = os.path.join(args.stack_path, row[star.UCSF.IMAGE_ORIGINAL_PATH])
else:
input_stack_path = row[star.UCSF.IMAGE_ORIGINAL_PATH]
with mrc.ZSliceReader(input_stack_path) as reader:
i = row[star.UCSF.IMAGE_ORIGINAL_INDEX]
writer.write(reader.read(i))
elif fn.endswith(".par"):
if args.stack_path is None:
log.error(".par file input requires --stack-path")
return 1
df = metadata.par2star(metadata.parse_fx_par(fn), data_path=args.stack_path)
# star.set_original_fields(df, inplace=True) # Redundant.
star.augment_star_ucsf(df)
elif fn.endswith(".csv"):
return 1
elif fn.endswith(".cs"):
return 1
else:
if fn.endswith(".mrcs"):
with mrc.ZSliceReader(fn) as reader:
for img in reader:
writer.write(img)
df = pd.DataFrame(
{star.UCSF.IMAGE_ORIGINAL_INDEX: np.arange(reader.nz)})
df[star.UCSF.IMAGE_ORIGINAL_PATH] = fn
else:
print("Unrecognized input file type")
return 1
if args.star is not None:
df[star.UCSF.IMAGE_INDEX] = np.arange(first_ptcl,
first_ptcl + df.shape[0])
if args.abs_path:
df[star.UCSF.IMAGE_PATH] = writer.path
else:
df[star.UCSF.IMAGE_PATH] = os.path.relpath(writer.path, os.path.dirname(args.star))
df["index"] = df[star.UCSF.IMAGE_INDEX]
star.simplify_star_ucsf(df)
dfs.append(df)
first_ptcl += df.shape[0]
if args.star is not None:
df = pd.concat(dfs, join="inner")
# df = pd.concat(dfs)
# df = df.dropna(df, axis=1, how="any")
if not args.relion2: # Relion 3.1 style output.
df = star.remove_deprecated_relion2(df, inplace=True)
star.write_star(args.star, df, resort_records=False, optics=True)
else:
df = star.remove_new_relion31(df, inplace=True)
star.write_star(args.star, df, resort_records=False, optics=False)
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 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):
meta = parse_metadata(args.input) # Read cryosparc metadata file.
meta["data_input_idx"] = [
"%.6d" % (i + 1) for i in meta["data_input_idx"]
] # Reformat particle idx for Relion.
if "data_input_relpath" not in meta.columns:
if args.data_path is None:
print(
"Data path missing, use --data-path to specify particle stack path"
)
return 1
meta["data_input_relpath"] = args.data_path
meta["data_input_relpath"] = meta["data_input_idx"].str.cat(
meta["data_input_relpath"], sep="@") # Construct rlnImageName field.
# Take care of trivial mappings.
rlnheaders = [
general[h] for h in meta.columns
if h in general and general[h] is not None
]
df = meta[[
h for h in meta.columns if h in general and general[h] is not None
]].copy()
df.columns = rlnheaders
if "rlnRandomSubset" in df.columns:
df["rlnRandomSubset"] = df["rlnRandomSubset"].apply(
lambda x: ord(x) - 64)
if "rlnPhaseShift" in df.columns:
df["rlnPhaseShift"] = np.rad2deg(df["rlnPhaseShift"])
# Class assignments and other model parameters.
phic = meta[[h for h in meta.columns if "phiC" in h
]] # Posterior probability over class assignments.
if len(phic.columns) > 0: # Check class assignments exist in input.
# phic.columns = [int(h[21]) for h in meta.columns if "phiC" in h]
phic.columns = range(len(phic.columns))
cls = phic.idxmax(axis=1)
for p in model:
if model[p] is not None:
pspec = p.split("model")[1]
param = meta[[h for h in meta.columns if pspec in h]]
if len(param.columns) > 0:
param.columns = phic.columns
df[model[p]] = param.lookup(param.index, cls)
df["rlnClassNumber"] = cls + 1 # Add one for Relion indexing.
else:
for p in model:
if model[p] is not None and p in meta.columns:
df[model[p]] = meta[p]
df["rlnClassNumber"] = 1
if args.cls is not None:
df = star.select_classes(df, args.cls)
# Convert axis-angle representation to Euler angles (degrees).
if df.columns.intersection(star.Relion.ANGLES).size == len(
star.Relion.ANGLES):
df[star.Relion.ANGLES] = np.rad2deg(df[star.Relion.ANGLES].apply(
lambda x: rot2euler(expmap(x)), axis=1, raw=True, broadcast=True))
if args.minphic is not None:
mask = np.all(phic < args.minphic, axis=1)
if args.keep_bad:
df.loc[mask, "rlnClassNumber"] = 0
else:
df.drop(df[mask].index, inplace=True)
if args.copy_micrograph_coordinates is not None:
coord_star = pd.concat(
(star.parse_star(inp, keep_index=False)
for inp in glob(args.copy_micrograph_coordinates)),
join="inner")
df = star.smart_merge(df,
coord_star,
fields=star.Relion.MICROGRAPH_COORDS)
if args.transform is not None:
r = np.array(json.loads(args.transform))
df = star.transform_star(df, r, inplace=True)
# Write Relion .star file with correct headers.
star.write_star(args.output, df, reindex=True)
return 0
