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()))
# 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")):
log.error("Only .star, .mrc, and .mrcs 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, keep_index=False)
star.augment_star_ucsf(df)
df = df.sort_values([
star.UCSF.IMAGE_ORIGINAL_PATH,
star.UCSF.IMAGE_ORIGINAL_INDEX
])
gb = df.groupby(star.UCSF.IMAGE_ORIGINAL_PATH)
for name, g in gb:
with mrc.ZSliceReader(name) as reader:
for i in g[star.UCSF.IMAGE_ORIGINAL_INDEX].values:
writer.write(reader.read(i))
else:
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
if args.star is not None:
df[star.UCSF.IMAGE_INDEX] = np.arange(first_ptcl,
first_ptcl + df.shape[0])
df[star.UCSF.IMAGE_PATH] = writer.path
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")
star.write_star(args.star, 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):
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('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):
"""
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):
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('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)
if args.map is not None:
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
else:
print("Please supply a map")
return 1
f3d = vop.vol_ft(vol, pfac=args.pfac, threads=args.threads)
sz = f3d.shape[0] // 2 - 1
sx, sy = np.meshgrid(np.fft.rfftfreq(sz), np.fft.fftfreq(sz))
s = np.sqrt(sx**2 + sy**2)
a = np.arctan2(sy, sx)
ift = None
with mrc.ZSliceWriter(args.output) as zsw:
for i, p in df.iterrows():
f2d = project(f3d,
p,
s,
sx,
sy,
a,
apply_ctf=args.ctf,
size=args.size)
if ift is None:
ift = irfft2(f2d.copy(),
threads=cpu_count(),
planner_effort="FFTW_ESTIMATE",
auto_align_input=True,
auto_contiguous=True)
proj = fftshift(
ift(f2d.copy(), np.zeros(vol.shape[:-1], dtype=vol.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
zsw.write(proj)
log.info(
"%d@%s: %d/%d" %
(p["ucsfImageIndex"], p["ucsfImagePath"], i + 1, df.shape[0]))
if args.star is not None:
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
