def scaleTemplate(templatearray, scalefactor=1.0, boxsize=None):
if(templatearray.shape[0] != templatearray.shape[1]):
apDisplay.printWarning("template shape is NOT square, this may cause errors")
if abs(scalefactor - 1.0) > 0.01:
apDisplay.printMsg("scaling template by a factor of "+str(scalefactor))
templatearray = apImage.scaleImage(templatearray, scalefactor)
#make sure the box size is divisible by 16
if boxsize is not None or (templatearray.shape[0] % 16 != 0):
edgeavg = apImage.meanEdgeValue(templatearray)
origsize = templatearray.shape[0]
if boxsize is None:
# minimal padisize is 16
padsize = max(int(math.floor(float(origsize)/16)*16),16)
else:
padsize = boxsize
padshape = numpy.array([padsize,padsize])
apDisplay.printMsg("changing box size from "+str(origsize)+" to "+str(padsize))
if origsize > padsize:
#shrink image
templatearray = apImage.frame_cut(templatearray, padshape)
else:
#grow image
templatearray = apImage.frame_constant(templatearray, padshape, cval=edgeavg)
if templatearray.shape[0] < 20 or templatearray.shape[1] < 20:
apDisplay.printWarning("template is only "+str(templatearray.shape[0])+" pixels wide\n"+\
" and may only correlation noise in the image")
return templatearray
def projectFullZ(processdir,
runname,
seriesname,
bin=1,
rotx=True,
flipyz=False):
"""
# Command for projecting full tomogram to z-axis
#
# Tomography reconstruction y and z are usually flipped
# full tomogram mrc file axes [x,z,y]
# clip average command need [x,y,z]
clip flipyz 09feb18c_002_full.rec temp.mrc
clip avg -2d -iz 0-199 temp.mrc projection.mrc
"""
inputparams = {
'recon': os.path.join(processdir, seriesname + "_full.rec"),
'temp': os.path.join(processdir, "temp.rec"),
'project': os.path.join(processdir, seriesname + "_zproject.mrc"),
}
fullshape = apFile.getMrcFileShape(inputparams['recon'])
commands = []
if rotx or flipyz:
op = ''
if rotx:
op += ' rotx'
if flipyz:
op += ' flipyz'
lookup = {'y': 0, 'z': 1}
inputparams['3d'] = inputparams['temp']
commands.append("$clip" + op + " %s %s" % (
inputparams['recon'],
inputparams['temp'],
))
else:
lookup = {'y': 1, 'z': 0}
inputparams['3d'] = inputparams['recon']
# limit slices for projection to 200 to save time.
zcenter = int(fullshape[lookup['z']] / 2)
zstart = max(0, zcenter - 100)
zend = min(fullshape[lookup['z']] - 1, zcenter + 99)
commands.append(
"$clip avg -2d -iz %d-%d %s %s" % (
zstart,
zend,
inputparams['3d'],
inputparams['project'],
), )
writeCommandAndRun(
processdir, 'projectZ', commands,
[inputparams['temp'], inputparams['project'], 'projectZ.log'])
if bin > 1:
# unbin the projection
a = mrc.read(inputparams['project'])
b = apImage.scaleImage(a, bin)
mrc.write(b, inputparams['project'])
return inputparams['project']
def array2jpg(pictpath,im,imin=None,imax=None,size=512):
jpgpath = pictpath+'.jpg'
imshape = im.shape
scale = float(size)/imshape[1]
im = apImage.scaleImage(im,scale)
stats = arraystats.all(im)
if imin is not None and imax is not None:
range = (imin,imax)
else:
range = stats['mean']-3*stats['std'],stats['mean']+3*stats['std']
numpil.write(im,jpgpath, format = 'JPEG', limits=range)
def array2jpg(pictpath, im, imin=None, imax=None, size=512):
jpgpath = pictpath + '.jpg'
imshape = im.shape
scale = float(size) / imshape[1]
im = apImage.scaleImage(im, scale)
stats = arraystats.all(im)
if imin is not None and imax is not None:
range = (imin, imax)
else:
range = stats['mean'] - 3 * stats['std'], stats[
'mean'] + 3 * stats['std']
numpil.write(im, jpgpath, format='JPEG', limits=range)
def projectFullZ(processdir, runname, seriesname,bin=1,rotx=True,flipyz=False):
"""
# Command for projecting full tomogram to z-axis
#
# Tomography reconstruction y and z are usually flipped
# full tomogram mrc file axes [x,z,y]
# clip average command need [x,y,z]
clip flipyz 09feb18c_002_full.rec temp.mrc
clip avg -2d -iz 0-199 temp.mrc projection.mrc
"""
inputparams = {
'recon': os.path.join(processdir, seriesname+"_full.rec"),
'temp': os.path.join(processdir, "temp.rec"),
'project': os.path.join(processdir, seriesname+"_zproject.mrc"),
}
fullshape = apFile.getMrcFileShape(inputparams['recon'])
commands = []
if rotx or flipyz:
op = ''
if rotx:
op += ' rotx'
if flipyz:
op += ' flipyz'
lookup = {'y':0,'z':1}
inputparams['3d'] = inputparams['temp']
commands.append(
"$clip"+op+" %s %s"
% (inputparams['recon'],inputparams['temp'],
)
)
else:
lookup = {'y':1,'z':0}
inputparams['3d'] = inputparams['recon']
# limit slices for projection to 200 to save time.
zcenter = int(fullshape[lookup['z']] / 2)
zstart = max(0,zcenter - 100)
zend = min(fullshape[lookup['z']]-1,zcenter + 99)
commands.append(
"$clip avg -2d -iz %d-%d %s %s"
% (zstart,zend,
inputparams['3d'],inputparams['project'],
),
)
writeCommandAndRun(processdir,'projectZ',commands,[inputparams['temp'],inputparams['project'],'projectZ.log'])
if bin > 1:
# unbin the projection
a = mrc.read(inputparams['project'])
b = apImage.scaleImage(a,bin)
mrc.write(b,inputparams['project'])
return inputparams['project']
def scaleTemplate(templatearray, scalefactor=1.0, boxsize=None):
if (templatearray.shape[0] != templatearray.shape[1]):
apDisplay.printWarning(
"template shape is NOT square, this may cause errors")
if abs(scalefactor - 1.0) > 0.01:
apDisplay.printMsg("scaling template by a factor of " +
str(scalefactor))
templatearray = apImage.scaleImage(templatearray, scalefactor)
#make sure the box size is divisible by 16
if boxsize is not None or (templatearray.shape[0] % 16 != 0):
edgeavg = apImage.meanEdgeValue(templatearray)
origsize = templatearray.shape[0]
if boxsize is None:
# minimal padisize is 16
padsize = max(int(math.floor(float(origsize) / 16) * 16), 16)
else:
padsize = boxsize
padshape = numpy.array([padsize, padsize])
apDisplay.printMsg("changing box size from " + str(origsize) + " to " +
str(padsize))
if origsize > padsize:
#shrink image
templatearray = apImage.frame_cut(templatearray, padshape)
else:
#grow image
templatearray = apImage.frame_constant(templatearray,
padshape,
cval=edgeavg)
if templatearray.shape[0] < 20 or templatearray.shape[1] < 20:
apDisplay.printWarning("template is only "+str(templatearray.shape[0])+" pixels wide\n"+\
" and may only correlation noise in the image")
return templatearray
