def main(args):
log = logging.getLogger(__name__)
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
data = {}
hdr = {}
for i, inp in enumerate(args.input[1:]):
d, h = read(inp, inc_header=True)
if args.normalize:
d = vop.normalize(d)
data[ascii_lowercase[i]] = d
hdr[ascii_lowercase[i]] = h
if args.eval:
final = eval(args.input[0], globals(), data)
else:
final = ne.evaluate(args.input[0], local_dict=data)
if args.apix is None:
args.apix = hdr[ascii_lowercase[0]]['xlen'] / hdr[
ascii_lowercase[0]]['nx']
write(args.output, final.astype(np.single), psz=args.apix)
return 0
def main(args):
if args.threshold is None:
print("Please provide a binarization threshold")
return 1
data, hdr = read(args.input, inc_header=True)
mask = binarize_volume(data, args.threshold, minvol=args.minvol, fill=args.fill)
if args.base_map is not None:
base_map = read(args.base_map, inc_header=False)
base_mask = binarize_volume(base_map, args.threshold, minvol=args.minvol, fill=args.fill)
total_width = args.extend + args.edge_width
excl_mask = binary_dilate(mask, total_width, strel=args.relion)
base_mask = binary_dilate(base_mask, args.extend, strel=args.relion)
base_mask = base_mask &~ excl_mask
if args.overlap > 0:
incl_mask = binary_dilate(base_mask, args.overlap, strel=args.relion) & excl_mask
base_mask = base_mask | incl_mask
mask = base_mask
elif args.extend > 0:
mask = binary_dilate(mask, args.extend, strel=args.relion)
if args.close:
se = binary_sphere(args.extend, False)
mask = binary_closing(mask, structure=se, iterations=1)
final = mask.astype(np.single)
if args.edge_width != 0:
dt = distance_transform_edt(~mask) # Compute *outward* distance transform of mask.
idx = (dt <= args.edge_width) & (dt > 0) # Identify edge points by distance from mask.
x = np.arange(1, args.edge_width + 1) # Domain of the edge profile.
if "sin" in args.edge_profile:
y = np.sin(np.linspace(np.pi/2, 0, args.edge_width + 1)) # Range of the edge profile.
f = interp1d(x, y[1:])
final[idx] = f(dt[idx]) # Insert edge heights interpolated at distance transform values.
write(args.output, final, psz=hdr["xlen"] / hdr["nx"])
return 0
def main(args):
if args.threshold is None:
print("Please provide a binarization threshold")
return 1
data, hdr = read(args.input, inc_header=True)
mask = data >= args.threshold
if args.minvol is not None:
mask = binary_volume_opening(mask, args.minvol)
if args.fill:
mask = binary_fill_holes(mask)
if args.extend is not None:
se = binary_sphere(args.extend, False)
mask = binary_dilation(mask, structure=se, iterations=1)
if args.close:
se = binary_sphere(args.extend, False)
mask = binary_closing(mask, structure=se, iterations=1)
final = mask.astype(np.single)
if args.edge_width is not None:
dt = distance_transform_edt(
~mask) # Compute *outward* distance transform of mask.
idx = (dt <= args.edge_width) & (
dt > 0) # Identify edge points by distance from mask.
x = np.arange(1, args.edge_width + 1) # Domain of the edge profile.
if "sin" in args.edge_profile:
y = np.sin(np.linspace(np.pi / 2, 0, args.edge_width +
1)) # Range of the edge profile.
f = interp1d(x, y[1:])
final[idx] = f(
dt[idx]
) # Insert edge heights interpolated at distance transform values.
write(args.output, final, psz=hdr["xlen"] / hdr["nx"])
return 0
def main(args):
x = mrc.read(args.input[0])
sigma = np.zeros(x.shape)
mu = x.copy()
for i, f in enumerate(args.input[1:]):
x = mrc.read(f)
olddif = x - mu
mu += (x - mu) / (i + 1)
sigma += olddif * (x - mu)
sigma_sq = np.power(sigma, 2)
mrc.write(args.output, sigma_sq)
if args.mean is not None:
mrc.write(args.mean, mu)
return 0
def main(args):
x = mrc.read(args.input[0])
m2 = np.zeros(x.shape)
mu = x.copy()
for i, f in enumerate(args.input[1:]):
x = mrc.read(f)
olddif = x - mu
mu += (x - mu) / (i + 1)
m2 += olddif * (x - mu)
var = m2 / len(args.input)
mrc.write(args.output, var)
if args.mean is not None:
mrc.write(args.mean, mu)
return 0
def main(args):
log = logging.getLogger(__name__)
hdlr = logging.StreamHandler(sys.stdout)
log.addHandler(hdlr)
log.setLevel(logging.getLevelName(args.loglevel.upper()))
data, hdr = read(args.input, inc_header=True)
if args.half2 is not None:
half2, hdr_half2 = read(args.input, inc_header=True)
if data.shape == half2.shape:
data += half2
else:
log.error("--half2 map is not the same shape as input map!")
return 1
final = None
box = np.array([hdr[a] for a in ["nx", "ny", "nz"]])
center = box // 2
if args.fft:
if args.final_mask is not None:
final_mask = read(args.final_mask)
data *= final_mask
data_ft = vop.vol_ft(data.T, pfac=args.pfac, threads=args.threads)
np.save(args.output, data_ft)
return 0
if args.transpose is not None:
try:
tax = [np.int64(a) for a in args.transpose.split(",")]
data = np.transpose(data, axes=tax)
except:
log.error(
"Transpose axes must be comma-separated list of three integers"
)
return 1
if args.flip is not None:
if args.flip.isnumeric():
args.flip = int(args.flip)
else:
args.flip = vop.label_to_axis(args.flip)
data = np.flip(data, axis=args.flip)
if args.apix is None:
args.apix = hdr["xlen"] / hdr["nx"]
log.info("Using computed pixel size of %f Angstroms" % args.apix)
if args.normalize:
if args.diameter is not None:
if args.diameter > 1.0:
args.diameter /= args.apix * 2 # Convert Angstrom diameter to pixel radius.
if args.reference is not None:
ref, refhdr = read(args.reference, inc_header=True)
final, mu, sigma = vop.normalize(data,
ref=ref,
return_stats=True,
rmask=args.diameter)
else:
final, mu, sigma = vop.normalize(data,
return_stats=True,
rmask=args.diameter)
log.info("Mean: %f, Standard deviation: %f" % (mu, sigma))
if args.apix_out is not None:
if args.scale is not None:
log.warn("--apix-out supersedes --scale")
args.scale = args.apix / args.apix_out
elif args.scale is not None:
args.apix_out = args.apix / args.scale
elif args.boxsize is not None:
args.scale = box[0] / np.double(args.boxsize)
if args.apix_out is None:
args.apix_out = args.apix
if args.boxsize is None:
if args.scale is None:
args.boxsize = box[0]
args.scale = 1
else:
args.boxsize = np.int(box[0] * args.scale)
log.info("Volume will be scaled by %f to size %d @ %f A/px" %
(args.scale, args.boxsize, args.apix_out))
if args.target and args.transform:
log.warn(
"Target pose transformation will be applied after explicit matrix")
if args.euler is not None and (args.target is not None
or args.transform is not None):
log.warn(
"Euler transformation will be applied after target pose transformation"
)
if args.translate is not None and (args.euler is not None
or args.target is not None
or args.transform is not None):
log.warn("Translation will be applied after other transformations")
if args.origin is not None:
try:
args.origin = np.array(
[np.double(tok) for tok in args.origin.split(",")]) / args.apix
assert np.all(args.origin < box)
except:
log.error(
"Origin must be comma-separated list of x,y,z coordinates and lie within the box"
)
return 1
else:
args.origin = center
log.info("Origin set to box center, %s" % (args.origin * args.apix))
if not (args.target is None and args.euler is None and args.transform is None and args.boxsize is None) \
and vop.ismask(data) and args.spline_order != 0:
log.warn(
"Input looks like a mask, --spline-order 0 (nearest neighbor) is recommended"
)
if args.transform is not None:
try:
args.transform = np.array(json.loads(args.transform))
except:
log.error("Transformation matrix must be in JSON/Numpy format")
return 1
r = args.transform[:, :3]
if args.transform.shape[1] == 4:
t = args.transform[:, -1] / args.apix
t = r.dot(args.origin) + t - args.origin
t = -r.T.dot(t)
else:
t = 0
log.debug("Final rotation: %s" % str(r).replace("\n", "\n" + " " * 16))
log.debug("Final translation: %s (%f px)" %
(str(t), np.linalg.norm(t)))
data = vop.resample_volume(data,
r=r,
t=t,
ori=None,
order=args.spline_order,
invert=args.invert)
if args.target is not None:
try:
args.target = np.array(
[np.double(tok) for tok in args.target.split(",")]) / args.apix
except:
log.error(
"Standard pose target must be comma-separated list of x,y,z coordinates"
)
return 1
args.target -= args.origin
args.target = np.where(np.abs(args.target) < 1, 0, args.target)
ori = None if args.origin is center else args.origin - center
r = vec2rot(args.target)
t = np.linalg.norm(args.target)
log.info("Euler angles are %s deg and shift is %f px" %
(np.rad2deg(rot2euler(r)), t))
log.debug("Final rotation: %s" % str(r).replace("\n", "\n" + " " * 16))
log.debug("Final translation: %s (%f px)" %
(str(t), np.linalg.norm(t)))
data = vop.resample_volume(data,
r=r,
t=args.target,
ori=ori,
order=args.spline_order,
invert=args.invert)
if args.euler is not None:
try:
args.euler = np.deg2rad(
np.array([np.double(tok) for tok in args.euler.split(",")]))
except:
log.error(
"Eulers must be comma-separated list of phi,theta,psi angles")
return 1
r = euler2rot(*args.euler)
offset = args.origin - 0.5
offset = offset - r.T.dot(offset)
data = affine_transform(data,
r.T,
offset=offset,
order=args.spline_order)
if args.translate is not None:
try:
args.translate = np.array(
[np.double(tok)
for tok in args.translate.split(",")]) / args.apix
except:
log.error(
"Translation vector must be comma-separated list of x,y,z coordinates"
)
return 1
args.translate -= args.origin
data = shift(data, -args.translate, order=args.spline_order)
if final is None:
final = data
if args.final_mask is not None:
final_mask = read(args.final_mask)
final *= final_mask
if args.scale != 1 or args.boxsize != box[0]:
final = vop.resample_volume(final,
scale=args.scale,
output_shape=args.boxsize,
order=args.spline_order)
write(args.output, final, psz=args.apix_out)
return 0
def main(args):
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
hdlr = logging.StreamHandler(sys.stdout)
if args.quiet:
hdlr.setLevel(logging.ERROR)
elif args.verbose:
hdlr.setLevel(logging.INFO)
else:
hdlr.setLevel(logging.WARN)
log.addHandler(hdlr)
data, hdr = read(args.input, inc_header=True)
final = None
box = np.array([hdr[a] for a in ["nx", "ny", "nz"]])
center = box // 2
if args.fft:
data_ft = vop.vol_ft(data.T, threads=args.threads)
np.save(args.output, data_ft)
return 0
if args.transpose is not None:
try:
tax = [np.int64(a) for a in args.transpose.split(",")]
data = np.transpose(data, axes=tax)
except:
log.error(
"Transpose axes must be comma-separated list of three integers"
)
return 1
if args.normalize:
if args.reference is not None:
ref, refhdr = read(args.reference, inc_header=True)
final, mu, sigma = vop.normalize(data, ref=ref, return_stats=True)
else:
final, mu, sigma = vop.normalize(data, return_stats=True)
final = (data - mu) / sigma
if args.verbose:
log.info("Mean: %f, Standard deviation: %f" % (mu, sigma))
if args.apix is None:
args.apix = hdr["xlen"] / hdr["nx"]
log.info("Using computed pixel size of %f Angstroms" % args.apix)
if args.target and args.matrix:
log.warn(
"Target pose transformation will be applied after explicit matrix")
if args.euler is not None and (args.target is not None
or args.matrix is not None):
log.warn(
"Euler transformation will be applied after target pose transformation"
)
if args.translate is not None and (args.euler is not None or args.target
is not None or args.matrix is not None):
log.warn("Translation will be applied after other transformations")
if args.origin is not None:
try:
args.origin = np.array(
[np.double(tok) for tok in args.origin.split(",")]) / args.apix
assert np.all(args.origin < box)
except:
log.error(
"Origin must be comma-separated list of x,y,z coordinates and lie within the box"
)
return 1
else:
args.origin = center
log.info("Origin set to box center, %s" % (args.origin * args.apix))
if not (args.target is None and args.euler is None and args.matrix is None and args.boxsize is None) \
and vop.ismask(data) and args.spline_order != 0:
log.warn(
"Input looks like a mask, --spline-order 0 (nearest neighbor) is recommended"
)
if args.matrix is not None:
try:
r = np.array(json.loads(args.matrix))
except:
log.error("Matrix format is incorrect")
return 1
data = vop.resample_volume(data,
r=r,
t=None,
ori=None,
order=args.spline_order)
if args.target is not None:
try:
args.target = np.array(
[np.double(tok) for tok in args.target.split(",")]) / args.apix
except:
log.error(
"Standard pose target must be comma-separated list of x,y,z coordinates"
)
return 1
args.target -= args.origin
args.target = np.where(np.abs(args.target) < 1, 0, args.target)
ori = None if args.origin is center else args.origin - args.center
r = vec2rot(args.target)
t = np.linalg.norm(args.target)
log.info("Euler angles are %s deg and shift is %f px" %
(np.rad2deg(rot2euler(r)), t))
data = vop.resample_volume(data,
r=r,
t=args.target,
ori=ori,
order=args.spline_order,
invert=args.target_invert)
if args.euler is not None:
try:
args.euler = np.deg2rad(
np.array([np.double(tok) for tok in args.euler.split(",")]))
except:
log.error(
"Eulers must be comma-separated list of phi,theta,psi angles")
return 1
r = euler2rot(*args.euler)
offset = args.origin - 0.5
offset = offset - r.T.dot(offset)
data = affine_transform(data,
r.T,
offset=offset,
order=args.spline_order)
if args.translate is not None:
try:
args.translate = np.array(
[np.double(tok)
for tok in args.translate.split(",")]) / args.apix
except:
log.error(
"Translation vector must be comma-separated list of x,y,z coordinates"
)
return 1
args.translate -= args.origin
data = shift(data, -args.translate, order=args.spline_order)
if args.boxsize is not None:
args.boxsize = np.double(args.boxsize)
data = zoom(data, args.boxsize / box, order=args.spline_order)
args.apix = args.apix * box[0] / args.boxsize
if final is None:
final = data
if args.final_mask is not None:
final_mask = read(args.final_mask)
final *= final_mask
write(args.output, final, psz=args.apix)
return 0