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 powerSpectraToOuterResolution(image, outerresolution, apix): """ apix and outerresolution must have same units (e.g., Anstroms or meters) """ if debug is True: print "Computing power spectra..." fieldsize = ctfpower.getFieldSize(image.shape) binning = max(image.shape)/fieldsize #data = imagefun.power(image) data, freq = ctfpower.power(image, apix, fieldsize) #data = numpy.exp(data) data = data.astype(numpy.float64) powerspec = trimPowerSpectraToOuterResolution(data, outerresolution, freq) return powerspec, freq
def powerSpectraToOuterResolution(image, outerresolution, apix):
"""
apix and outerresolution must have same units (e.g., Anstroms or meters)
"""
if debug is True:
print "Computing power spectra..."
fieldsize = ctfpower.getFieldSize(image.shape)
binning = max(image.shape) / fieldsize
#data = imagefun.power(image)
data, freq = ctfpower.power(image, apix, fieldsize)
#data = numpy.exp(data)
data = data.astype(numpy.float64)
powerspec = trimPowerSpectraToOuterResolution(data, outerresolution, freq)
return powerspec, freq
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
