def processAndSaveFFT(self, imgdata, fftpath):
if os.path.isfile(fftpath):
print "FFT file found"
if fftpath in self.freqdict.keys():
print "Freq found"
return False
print "Freq not found"
print "creating FFT file: ", fftpath
### downsize and filter leginon image
if self.params['uncorrected']:
imgarray = imagefilter.correctImage(imgdata, params)
else:
imgarray = imgdata['image']
### calculate power spectra
apix = apDatabase.getPixelSize(imgdata)
fftarray, freq = ctfpower.power(imgarray, apix, mask_radius=0.5, fieldsize=self.params['fieldsize'])
#fftarray = imagefun.power(fftarray, mask_radius=1)
fftarray = ndimage.median_filter(fftarray, 2)
## preform a rotational average and remove peaks
rotfftarray = ctftools.rotationalAverage2D(fftarray)
stdev = rotfftarray.std()
rotplus = rotfftarray + stdev*4
fftarray = numpy.where(fftarray > rotplus, rotfftarray, fftarray)
### save to jpeg
self.freqdict[fftpath] = freq
mrc.write(fftarray, fftpath)
self.saveFreqFile()
return True
def processAndSaveFFT(self, imgdata, fftpath):
if os.path.isfile(fftpath):
print "FFT file found"
if fftpath in self.freqdict.keys():
print "Freq found"
return False
print "Freq not found"
print "creating FFT file: ", fftpath
### downsize and filter leginon image
if self.params['uncorrected']:
imgarray = imagefilter.correctImage(imgdata, params)
else:
imgarray = imgdata['image']
### calculate power spectra
apix = apDatabase.getPixelSize(imgdata)
fftarray, freq = ctfpower.power(imgarray,
apix,
mask_radius=0.5,
fieldsize=self.params['fieldsize'])
#fftarray = imagefun.power(fftarray, mask_radius=1)
fftarray = ndimage.median_filter(fftarray, 2)
## preform a rotational average and remove peaks
rotfftarray = ctftools.rotationalAverage2D(fftarray)
stdev = rotfftarray.std()
rotplus = rotfftarray + stdev * 4
fftarray = numpy.where(fftarray > rotplus, rotfftarray, fftarray)
### save to jpeg
self.freqdict[fftpath] = freq
mrc.write(fftarray, fftpath)
self.saveFreqFile()
return True
def CTFpowerspec(self, imgdata, ctfdata, fftpath=None, fftfreq=None, twod=True):
"""
Make a nice looking powerspectra with lines for location of Thon rings
inputs:
imgdata - sinedon AcquistionImage table row
ctfdata - sinedon apCtfData table row
amplitude constrast - ( a cos + sqrt(1-a^2) sin format)
defocus1 > defocus2
angle - in degrees, positive x-axis is zero
outerbound is now set by self.outerAngstrom1D (in Angstroms)
outside this radius is trimmed away
"""
outerbound = self.outerAngstrom1D * 1e-10
### setup initial parameters for image
self.imgname = imgdata['filename']
if self.debug is True:
print apDisplay.short(self.imgname)
self.powerspecfile = apDisplay.short(self.imgname)+"-powerspec.jpg"
### get peak of CTF
self.cs = ctfdata['cs']*1e-3
self.volts = imgdata['scope']['high tension']
self.ampcontrast = ctfdata['amplitude_contrast']
### process power spectra
self.apix = apDatabase.getPixelSize(imgdata)
if self.debug is True:
print "Pixelsize (A/pix)", self.apix
apDisplay.printMsg("Reading image...")
image = imgdata['image']
self.initfreq = 1./(self.apix * image.shape[0])
self.origimageshape = image.shape
### get correct data
self.convertDefociToConvention(ctfdata)
if self.debug is True:
for key in ctfdata.keys():
if ctfdata[key] is not None and not isinstance(ctfdata[key], dict):
print " ", key, "--", ctfdata[key]
if fftpath is not None and fftfreq is not None and os.path.isfile(fftpath):
powerspec = mrc.read(fftpath).astype(numpy.float64)
self.trimfreq = fftfreq
else:
powerspec, self.trimfreq = ctftools.powerSpectraToOuterResolution(image,
self.outerAngstrom1D, self.apix)
self.trimapix = 1.0/(self.trimfreq * powerspec.shape[0])
#print "Median filter image..."
#powerspec = ndimage.median_filter(powerspec, 2)
apDisplay.printMsg("Preform a rotational average and remove spikes...")
rotfftarray = ctftools.rotationalAverage2D(powerspec)
stdev = rotfftarray.std()
rotplus = rotfftarray + stdev*4
powerspec = numpy.where(powerspec > rotplus, rotfftarray, powerspec)
#print "Light Gaussian blur image..."
#powerspec = ndimage.gaussian_filter(powerspec, 3)
if self.debug is True:
print "\torig pixel %.3f freq %.3e"%(self.apix, self.initfreq)
print "\ttrim pixel %.3f freq %.3e"%(self.trimapix, self.trimfreq)
### more processing
normpowerspec = self.normalizeCtf(powerspec, twod=twod)
if normpowerspec is None:
return None
if twod is True:
self.drawPowerSpecImage(normpowerspec)
ctfdisplaydict = {
'powerspecfile': self.powerspecfile,
'plotsfile': self.plotsfile,
'conf3010': self.conf3010,
'conf5peak': self.conf5peak,
'overconf3010': self.overconf3010,
'overconf5peak': self.overconf5peak,
'res80': self.res80,
'res50': self.res50,
'overres80': self.overres80,
'overres50': self.overres50,
}
return ctfdisplaydict
