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()))
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):
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):
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):
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
