def ccfs(ref,stack):
"compute and center CCFs between ref and each member of stack"
ret=[i.calc_ccf(ref) for i in stack]
f=old_div(ref["nx"],4)
for i in ret: i.process_inplace("xform.phaseorigin.tocenter")
ret=[i.get_clip(EMAN2.Region(f,f,f*2,f*2)) for i in ret]
return ret
def _build(self, index):
'''Build a single image in the file.'''
# print "...index:", index
header = {}
# Read the header
img = EMAN2.EMData()
img.read_image(self.workfile, 0, True)
h = img.get_attr_dict()
# Copy basic header information
header['nx'] = h['nx']
header['ny'] = h['ny']
header['nz'] = h['nz']
header['slices'] = []
if header['nz'] == 1:
# 2D Image
img2 = EMAN2.EMData()
img2.read_image(self.workfile, index, False)
img2.process_inplace("normalize")
if self.nimg > 1:
# ... stack of 2D images.
header['slices'].append(
self.build_slice(img2, index=index, fixed=[128, 512,
1024]))
else:
# regular old 2D image -- also generate power spectrum and tiles.
header['slices'].append(
self.build_slice(img2,
index=index,
tile=True,
pspec=True,
fixed=[128, 512, 1024]))
else:
# 3D Image -- read region for each Z slice
for i in range(header['nz']):
region = EMAN2.Region(0, 0, i, header['nx'], header['ny'], 1)
img2 = EMAN2.EMData()
img2.read_image(self.workfile, 0, False, region)
header['slices'].append(
self.build_slice(img2,
index=index,
nz=i,
fixed=[128, 512, 1024]))
return header
def build_tiles(self, img, nz=1, index=0, tilesize=256):
'''Build tiles for a 2D slice.'''
# Work with a copy of the EMData
img2 = img.copy()
# Calculate the number of zoom levels based on the tile size
levels = math.ceil(
math.log(max(img.get_xsize(), img.get_ysize()) / tilesize) /
math.log(2.0))
# Tile header
header = img.get_attr_dict()
tile_dict = {}
# Step through shrink range creating tiles
for level in range(int(levels)):
scale = 2**level
rmin = img2.get_attr("mean") - img2.get_attr("sigma") * 3.0
rmax = img2.get_attr("mean") + img2.get_attr("sigma") * 3.0
for x in range(0, img2.get_xsize(), tilesize):
for y in range(0, img2.get_ysize(), tilesize):
i = img2.get_clip(EMAN2.Region(x, y, tilesize, tilesize),
fill=rmax)
i.set_attr("render_min", rmin)
i.set_attr("render_max", rmax)
i.set_attr("jpeg_quality", 80)
# Write output
fsp = "tile.index-%d.scale-%d.z-%d.x-%d.y-%d.jpg" % (
index, scale, nz, x / tilesize, y / tilesize)
fsp = os.path.join(self.tmpdir, fsp)
i.write_image(fsp)
tile_dict[(scale, x / tilesize, y / tilesize)] = [
fsp, None, 'jpg', tilesize, tilesize
]
# Shrink by 2 for next round.
img2.process_inplace("math.meanshrink", {"n": 2})
return tile_dict
invert = True
norm = False
coords = numpy.asarray([[int(val) for val in line.split()]
for line in open(partcoords, 'r')])
#get_trans
#get_rotation_transform
for i in xrange(len(coords)):
print i + 1, len(coords)
x, y, z = coords[i]
x = round(x * cshrink)
y = round(y * cshrink)
z = round(z * cshrink)
r = EMAN2.Region((2 * x - boxsize) / 2, (2 * y - boxsize) / 2,
(2 * z - boxsize) / 2, boxsize, boxsize, boxsize)
e = EMAN2.EMData()
e.read_image(tomogram, 0, False, r)
if invert:
e = e * -1
if norm:
e.process_inplace('normalize', {})
e['origin_x'] = 0
e['origin_y'] = 0
e['origin_z'] = 0
e.write_image(output, i)
# norm, intervt, threshold
#ptcl.process_inplace("xform",{"transform":ptcl_parms[0]["xform.align3d"]})
def build_pspec(self, img, tilesize=512, nz=1, index=0):
"""Build a 2D FFT and 1D rotationally averaged power spectrum of a 2D EMData."""
# Return dictionaries
pspec_dict = {}
pspec1d_dict = {}
# Output files
outfile = "pspec.index-%d.z-%d.size-%d.png" % (index, nz, tilesize)
outfile = os.path.join(self.tmpdir, outfile)
outfile1d = "pspec1d.index-%d.z-%d.size-%d.json" % (index, nz,
tilesize)
outfile1d = os.path.join(self.tmpdir, outfile1d)
# Create a new image to hold the 2D FFT
nx, ny = img.get_xsize() / tilesize, img.get_ysize() / tilesize
a = EMAN2.EMData()
a.set_size(tilesize, tilesize)
# Image isn't big enough..
if (ny < 2 or nx < 2):
return pspec_dict, pspec1d_dict
# Create FFT
for y in range(1, ny - 1):
for x in range(1, nx - 1):
c = img.get_clip(
EMAN2.Region(x * tilesize, y * tilesize, tilesize,
tilesize))
c.process_inplace("normalize")
c.process_inplace("math.realtofft")
c.process_inplace("math.squared")
a += c
# Reset the center value
a.set_value_at(tilesize / 2, tilesize / 2, 0, .01)
# Adjust brightness
a -= a.get_attr("minimum") - a.get_attr("sigma") * .01
a.process_inplace("math.log")
a.set_attr("render_min",
a.get_attr("minimum") - a.get_attr("sigma") * .1)
a.set_attr("render_max",
a.get_attr("mean") + a.get_attr("sigma") * 4.0)
# Write out the PSpec png
a.write_image(outfile)
# Add to dictionary
pspec_dict[tilesize] = [
outfile, None, 'png',
a.get_xsize(), a.get_ysize()
]
# Calculate
t = (tilesize / 2) - 1
y = a.calc_radial_dist(t, 1, 1, 0) # radial power spectrum (log)
f = open(outfile1d, "w")
json.dump(y, f)
f.close()
pspec1d_dict[tilesize] = [outfile1d, None, 'json', t]
# Return both the 2D and 1D files
return pspec_dict, pspec1d_dict
