def boxerRotate(imgfile, parttree, outstack, boxsize):
"""
boxes the particles with expanded size,
applies a rotation to particle,
reduces boxsize to requested size,
and saves them to a imagic file
"""
# size needed is sqrt(2)*boxsize, using 1.5 to be extra safe
bigboxsize = int(math.ceil(1.5*boxsize))
imgarray = mrc.read(imgfile)
bigboxedparticles = boxerMemory(imgarray, parttree, bigboxsize)
boxedparticles = []
boxshape = (boxsize,boxsize)
apDisplay.printMsg("Rotating particles...")
for i in range(len(bigboxedparticles)):
if i % 10 == 0:
sys.stderr.write(".")
bigboxpart = bigboxedparticles[i]
partdict = parttree[i]
### add 90 degrees because database angle is from x-axis not y-axis
angle = partdict['angle']+90.0
rotatepart = ndimage.rotate(bigboxpart, angle=angle, reshape=False, order=1)
boxpart = imagefilter.frame_cut(rotatepart, boxshape)
boxedparticles.append(boxpart)
sys.stderr.write("done\n")
apImagicFile.writeImagic(boxedparticles, outstack)
return True
def getEllipticalDistanceArray(ellipratio, ellipangle, shape):
## ellip angle is positive toward y-axis
if ellipratio < 1:
ellipratio = 1.0/ellipratio
ellipangle += 90
while ellipangle > 180:
ellipangle -= 180
while ellipangle < 0:
ellipangle += 180
if debug is True:
apDisplay.printColor("ellipangle = %.3f"%(ellipangle), "cyan")
bigshape = numpy.array(numpy.array(shape)*math.sqrt(2)/2., dtype=numpy.int)*2
xhalfshape = bigshape[0]/2.0
x = numpy.arange(-xhalfshape, xhalfshape, 1) + 0.5
yhalfshape = bigshape[1]/2.0
y = numpy.arange(-yhalfshape, yhalfshape, 1) + 0.5
xx, yy = numpy.meshgrid(x, y)
### apply ellipse ratio
yy = ellipratio*yy
radial = xx**2 + yy**2
### apply ellipse rotation
## ellip angle is positive toward y-axis, which is clockwise, so negative angle
radial = scipy.ndimage.interpolation.rotate(radial, angle=-ellipangle,
reshape=False, mode='wrap', order=1)
radial = imagefilter.frame_cut(radial, shape)
if debug is True:
print "minimal radial distance", radial.min()
radial = numpy.sqrt(radial)
return radial
def getEllipticalDistanceArray(ellipratio, ellipangle, shape):
## ellip angle is positive toward y-axis
if ellipratio < 1:
ellipratio = 1.0/ellipratio
ellipangle += 90
while ellipangle > 180:
ellipangle -= 180
while ellipangle < 0:
ellipangle += 180
if debug is True:
apDisplay.printColor("ellipangle = %.3f"%(ellipangle), "cyan")
bigshape = numpy.array(numpy.array(shape)*math.sqrt(2)/2., dtype=numpy.int)*2
xhalfshape = bigshape[0]/2.0
x = numpy.arange(-xhalfshape, xhalfshape, 1) + 0.5
yhalfshape = bigshape[1]/2.0
y = numpy.arange(-yhalfshape, yhalfshape, 1) + 0.5
xx, yy = numpy.meshgrid(x, y)
### apply ellipse ratio
yy = ellipratio*yy
radial = xx**2 + yy**2
### apply ellipse rotation
## ellip angle is positive toward y-axis, which is clockwise, so negative angle
radial = scipy.ndimage.interpolation.rotate(radial, angle=-ellipangle,
reshape=False, mode='wrap', order=1)
radial = imagefilter.frame_cut(radial, shape)
if debug is True:
print "minimal radial distance", radial.min()
radial = numpy.sqrt(radial)
return radial
def trimPowerSpectraToOuterResolution(powerspec, outerresolution, freq):
"""
freq and outerresolution must have same units (e.g., Anstroms or meters)
resolution = (# columns) * apix / (pixel distance from center)
therefore:
pixel distance from center = (# columns) * apix / resolution
"""
if debug is True:
print "trimPowerSpectraToOuterResolution()"
imagewidth = powerspec.shape[0]
initmaxres = 2.0 / (freq * imagewidth)
if debug is True:
print "__Image shape %d x %d" % (powerspec.shape[0],
powerspec.shape[1])
print "__Frequeny %.3e" % (freq)
print "__Resolution request %.3f" % (outerresolution)
print "__Init max resolution %.3f" % (initmaxres)
if initmaxres > outerresolution:
apDisplay.printWarning(
"Requested resolution (%.3f) is not available (%.3f)" %
(outerresolution, initmaxres))
outerresolution = initmaxres
pixellimitradius = min(powerspec.shape[0], powerspec.shape[1]) / 2
else:
pixellimitradius = int(math.ceil(1. / (freq * outerresolution)))
goodpixellimitradius = primefactor.getNextEvenPrime(pixellimitradius)
# If outerresolution input is larger than initmaxres, the way goodpixellimitradius
# is calculated above may make the result larger than half the image dimension.
while goodpixellimitradius > min(powerspec.shape[0],
powerspec.shape[1]) / 2:
pixellimitradius -= 2
if debug is True:
print "__Recalculate pixel limit dimension with: ", pixellimitradius
goodpixellimitradius = primefactor.getNextEvenPrime(pixellimitradius)
finalres = 1. / (freq * goodpixellimitradius)
if debug is True:
print "__Pixel limit dimension: ", goodpixellimitradius
print "__Final max resolution %.3f" % (finalres)
### convert to diameter and trim
newshape = (goodpixellimitradius * 2, goodpixellimitradius * 2)
if debug is True:
print "__Trimming image"
trimpowerspec = imagefilter.frame_cut(powerspec, newshape)
if newshape != trimpowerspec.shape:
apDisplay.printError(
"shape mismatch for frame_cut (%d,%d) --> (%d,%d) = (%d,%d)" %
(powerspec.shape[0], powerspec.shape[1], newshape[0], newshape[1],
trimpowerspec.shape[0], trimpowerspec.shape[1]))
if debug is True:
print "original image size %d x %d" % (powerspec.shape)
print "trimmed image size %d x %d" % (trimpowerspec.shape)
return trimpowerspec
def trimPowerSpectraToOuterResolution(powerspec, outerresolution, freq):
"""
freq and outerresolution must have same units (e.g., Anstroms or meters)
resolution = (# columns) * apix / (pixel distance from center)
therefore:
pixel distance from center = (# columns) * apix / resolution
"""
if debug is True:
print "trimPowerSpectraToOuterResolution()"
imagewidth = powerspec.shape[0]
initmaxres = 2.0/(freq*imagewidth)
if debug is True:
print "__Image shape %d x %d"%(powerspec.shape[0], powerspec.shape[1])
print "__Frequeny %.3e"%(freq)
print "__Resolution request %.3f"%(outerresolution)
print "__Init max resolution %.3f"%(initmaxres)
if initmaxres > outerresolution:
apDisplay.printWarning("Requested resolution (%.3f) is not available (%.3f)"
%(outerresolution, initmaxres))
outerresolution = initmaxres
pixellimitradius = min(powerspec.shape[0],powerspec.shape[1]) / 2
else:
pixellimitradius = int(math.ceil(1./(freq * outerresolution)))
goodpixellimitradius = primefactor.getNextEvenPrime(pixellimitradius)
# If outerresolution input is larger than initmaxres, the way goodpixellimitradius
# is calculated above may make the result larger than half the image dimension.
while goodpixellimitradius > min(powerspec.shape[0],powerspec.shape[1])/2:
pixellimitradius -= 2
if debug is True:
print "__Recalculate pixel limit dimension with: ", pixellimitradius
goodpixellimitradius = primefactor.getNextEvenPrime(pixellimitradius)
finalres = 1./(freq * goodpixellimitradius)
if debug is True:
print "__Pixel limit dimension: ", goodpixellimitradius
print "__Final max resolution %.3f"%(finalres)
### convert to diameter and trim
newshape = (goodpixellimitradius*2, goodpixellimitradius*2)
if debug is True:
print "__Trimming image"
trimpowerspec = imagefilter.frame_cut(powerspec, newshape)
if newshape != trimpowerspec.shape:
apDisplay.printError("shape mismatch for frame_cut (%d,%d) --> (%d,%d) = (%d,%d)"
%(powerspec.shape[0],powerspec.shape[1],
newshape[0],newshape[1],
trimpowerspec.shape[0],trimpowerspec.shape[1]))
if debug is True:
print "original image size %d x %d"%(powerspec.shape)
print "trimmed image size %d x %d"%(trimpowerspec.shape)
return trimpowerspec
def mergeImageStackIntoBigStack(self, imgstackfile, imgdata):
t0 = time.time()
apDisplay.printMsg("filtering particles and adding to stack")
# if applying a boxmask, write to a temporary file before adding to main stack
bigimgstack = os.path.join(self.params['rundir'], self.params['single'])
if self.params['boxmask'] is not None:
bigimgstack = os.path.splitext(imgstackfile)[0]+"-premask.hed"
### here is the craziness
### step 1: read imgstackfile into memory
imgstackmemmap = imagic.read(imgstackfile)
### when only particle is read it defaults to a 2D array instead of 3D array
if len(imgstackmemmap.shape) < 3:
imgstackmemmap = imgstackmemmap.reshape(1, imgstackmemmap.shape[0], imgstackmemmap.shape[1])
if self.params['debug'] is True:
print "imgstackmemmap.shape", imgstackmemmap.shape
apix = self.params['apix'] #apDatabase.getPixelSize(imgdata)
boxshape = (self.boxsize, self.boxsize)
processedParticles = []
for particle in imgstackmemmap:
### step 2: filter particles
### high / low pass filtering
#if self.params['pixlimit']:
# particle = imagefilter.pixelLimitFilter(particle, self.params['pixlimit'])
if self.params['lowpass']:
particle = imagefilter.lowPassFilter(particle, apix=apix, radius=self.params['lowpass'])
if self.params['highpass']:
particle = imagefilter.highPassFilter2(particle, self.params['highpass'], apix=apix)
### unless specified, invert the images
if self.params['inverted'] is True:
particle = -1.0 * particle
if particle.shape != boxshape:
if self.boxsize <= particle.shape[0] and self.boxsize <= particle.shape[1]:
particle = imagefilter.frame_cut(particle, boxshape)
else:
apDisplay.printError("particle shape (%dx%d) is smaller than boxsize (%d)"
%(particle.shape[0], particle.shape[1], self.boxsize))
### step 3: normalize particles
#self.normoptions = ('none', 'boxnorm', 'edgenorm', 'rampnorm', 'parabolic') #normalizemethod
if self.params['normalizemethod'] == 'boxnorm':
particle = imagenorm.normStdev(particle)
elif self.params['normalizemethod'] == 'edgenorm':
particle = imagenorm.edgeNorm(particle)
elif self.params['normalizemethod'] == 'rampnorm':
particle = imagenorm.rampNorm(particle)
elif self.params['normalizemethod'] == 'parabolic':
particle = imagenorm.parabolicNorm(particle)
### step 4: decimate/bin particles if specified
### binning is last, so we maintain most detail and do not have to deal with binned apix
if self.params['bin'] > 1:
particle = imagefun.bin2(particle, self.params['bin'])
#from scipy.misc import toimage
#toimage(particle).show()
processedParticles.append(particle)
### step 5: merge particle list with larger stack
apImagicFile.appendParticleListToStackFile(processedParticles, bigimgstack,
msg=self.params['debug'])
#remove original image stack from memory
del imgstackmemmap
del processedParticles
t0 = time.time()
# if applying boxmask, now mask the particles & append to stack
if self.params['boxmask'] is not None:
# normalize particles before boxing, since zeros in mask
# can affect subsequent processing if not properly normalized
apEMAN.executeEmanCmd("proc2d %s %s edgenorm inplace"%(bigimgstack,bigimgstack),showcmd=False)
imgstack = apImagicFile.readImagic(bigimgstack, msg=False)
maskstack = apImagicFile.readImagic(self.params['boxmaskf'],msg=False)
for i in range(len(imgstack['images'])):
imgstack['images'][i]*=maskstack['images'][i]
maskedpartstack = os.path.splitext(imgstackfile)[0]+"-aftermask.hed"
apImagicFile.writeImagic(imgstack['images'], maskedpartstack)
bigimgstack = os.path.join(self.params['rundir'], self.params['single'])
apEMAN.executeEmanCmd("proc2d %s %s flip"%(maskedpartstack,bigimgstack))
### count particles
bigcount = apFile.numImagesInStack(bigimgstack, self.boxsize/self.params['bin'])
imgcount = apFile.numImagesInStack(imgstackfile, self.boxsize)
### append to particle log file
partlogfile = os.path.join(self.params['rundir'], self.timestamp+"-particles.info")
f = open(partlogfile, 'a')
for i in range(imgcount):
partnum = self.particleNumber + i + 1
line = str(partnum)+'\t'+os.path.join(imgdata['session']['image path'], imgdata['filename']+".mrc")
f.write(line+"\n")
f.close()
self.mergestacktimes.append(time.time()-t0)
return bigcount
def runRefineCTF(self, imgdata, fftpath):
### reset important values
self.bestres = 1e10
self.bestellipse = None
self.bestvalues = {}
self.volts = imgdata['scope']['high tension']
self.wavelength = ctftools.getTEMLambda(self.volts)
## get value in meters
self.cs = apInstrument.getCsValueFromSession(
self.getSessionData()) * 1e-3
self.ctfvalues = {
'volts': self.volts,
'wavelength': self.wavelength,
'cs': self.cs,
}
### need to get FFT file open and freq of said file
fftarray = mrc.read(fftpath).astype(numpy.float64)
self.freq = self.freqdict[fftpath]
### convert resolution limit into pixel distance
fftwidth = fftarray.shape[0]
maxres = 2.0 / (self.freq * fftwidth)
if maxres > self.params['reslimit']:
apDisplay.printWarning(
"Cannot get requested res %.1fA higher than max Nyquist resolution %.1fA"
% (self.params['reslimit'], maxres))
self.params['reslimit'] = math.ceil(maxres)
limitwidth = int(math.ceil(2.0 /
(self.params['reslimit'] * self.freq)))
limitwidth = primefactor.getNextEvenPrime(limitwidth)
requestres = 2.0 / (self.freq * limitwidth)
if limitwidth > fftwidth:
apDisplay.printError("Cannot get requested resolution" + (
" request res %.1fA higher than max res %.1fA for new widths %d > %d"
% (requestres, maxres, limitwidth, fftwidth)))
apDisplay.printColor(
"Requested resolution OK: " +
(" request res %.1fA less than max res %.1fA with fft widths %d < %d"
% (requestres, maxres, limitwidth, fftwidth)), "green")
newshape = (limitwidth, limitwidth)
fftarray = imagefilter.frame_cut(fftarray, newshape)
### spacing parameters
self.mfreq = self.freq * 1e10
fftwidth = min(fftarray.shape)
self.apix = 1.0 / (fftwidth * self.freq)
self.ctfvalues['apix'] = self.apix
### print message
bestDbValues = ctfdb.getBestCtfByResolution(imgdata)
if bestDbValues is None:
apDisplay.printColor(
"SKIPPING: No CTF values for image %s" %
(apDisplay.short(imgdata['filename'])), "red")
self.badprocess = True
return
### skip if resolution > 90.
if bestDbValues['resolution_50_percent'] > 90.:
apDisplay.printColor(
"SKIPPING: No decent CTF values for image %s" %
(apDisplay.short(imgdata['filename'])), "yellow")
self.badprocess = True
return
self.ctfvalues['amplitude_contrast'] = bestDbValues[
'amplitude_contrast']
lowerrad1 = ctftools.getCtfExtrema(
bestDbValues['defocus1'], self.mfreq, self.cs, self.volts,
self.ctfvalues['amplitude_contrast'], 1, "valley")
lowerrad2 = ctftools.getCtfExtrema(
bestDbValues['defocus2'], self.mfreq, self.cs, self.volts,
self.ctfvalues['amplitude_contrast'], 1, "valley")
meanRad = (lowerrad1[0] + lowerrad2[0]) / 2.0
self.ellipseParams = {
'a': lowerrad1[0],
'b': lowerrad2[0],
'alpha': math.radians(bestDbValues['angle_astigmatism']),
}
ellipratio = self.ellipseParams['a'] / self.ellipseParams['b']
defratio = bestDbValues['defocus2'] / bestDbValues['defocus1']
print "ellr=%.3f, defr=%.3f, sqrt(defr)=%.3f" % (ellipratio, defratio,
math.sqrt(defratio))
self.bestvalues['defocus'] = (bestDbValues['defocus1'] +
bestDbValues['defocus2']) / 2.0
raddata, PSDarray = self.from2Dinto1D(fftarray)
lowerbound = numpy.searchsorted(raddata, meanRad * self.freq)
upperbound = numpy.searchsorted(raddata, 1 / 10.)
###
# This is the start of the actual program
###
self.refineEllipseLoop(fftarray, lowerbound, upperbound)
##==================================
## FINISH UP
##==================================
apDisplay.printColor("Finishing up using best found CTF values",
"blue")
self.printBestValues()
### take best values and use them
self.ctfvalues = self.bestvalues
self.ellipseParams = self.bestellipse
### stupid fix, get value in millimeters
self.ctfvalues['cs'] = apInstrument.getCsValueFromSession(
self.getSessionData())
### translate ellipse into ctf values
if self.ellipseParams is not None:
self.ctfvalues['angle_astigmatism'] = math.degrees(
self.ellipseParams['alpha'])
ellipratio = self.ellipseParams['a'] / self.ellipseParams['b']
phi = math.asin(self.ctfvalues['amplitude_contrast'])
#note: a > b then def1 < def2
#major axis
self.ctfvalues['defocus1'] = self.ctfvalues['defocus'] / ellipratio
#minor axis
self.ctfvalues['defocus2'] = self.ctfvalues['defocus'] * ellipratio
defdiff = 1.0 - 2 * self.ctfvalues['defocus'] / (
self.ctfvalues['defocus1'] + self.ctfvalues['defocus2'])
print "%.3e --> %.3e,%.3e" % (self.ctfvalues['defocus'],
self.ctfvalues['defocus2'],
self.ctfvalues['defocus1'])
print defdiff * 100
if defdiff * 100 > 1:
apDisplay.printWarning("Large astigmatism")
#sys.exit(1)
else:
ellipratio = 1.0
self.ctfvalues['angle_astigmatism'] = 0.0
self.ctfvalues['defocus1'] = self.ctfvalues['defocus']
self.ctfvalues['defocus2'] = self.ctfvalues['defocus']
self.badprocess = True
return
try:
if self.ctfvalues['amplitude_contrast'] < self.minAmpCon:
self.ctfvalues['amplitude_contrast'] = self.minAmpCon
if self.ctfvalues['amplitude_contrast'] > self.maxAmpCon:
self.ctfvalues['amplitude_contrast'] = self.maxAmpCon
except KeyError:
pass
if abs(self.ctfvalues['defocus1']) > abs(self.ctfvalues['defocus2']):
# incorrect, need to shift angle by 90 degrees
apDisplay.printWarning("|def1| > |def2|, flipping defocus axes")
tempdef = self.ctfvalues['defocus1']
self.ctfvalues['defocus1'] = self.ctfvalues['defocus2']
self.ctfvalues['defocus2'] = tempdef
self.ctfvalues['angle_astigmatism'] += 90
# get astig_angle within range -90 < angle <= 90
while self.ctfvalues['angle_astigmatism'] > 90:
self.ctfvalues['angle_astigmatism'] -= 180
while self.ctfvalues['angle_astigmatism'] < -90:
self.ctfvalues['angle_astigmatism'] += 180
avgres = self.getResolution(self.ctfvalues['defocus'], raddata,
PSDarray, lowerbound)
apDisplay.printColor(
"Final defocus values %.3e -> %.3e, %.3e; ac=%.2f, res=%.1f" %
(self.ctfvalues['defocus'], self.ctfvalues['defocus1'],
self.ctfvalues['defocus2'], self.ctfvalues['amplitude_contrast'],
avgres / 2.0), "green")
for i in range(10):
print "===================================="
print "PREVIOUS VALUES"
ctfdb.getBestCtfByResolution(imgdata)
print "CURRENT VALUES"
defocusratio = self.ctfvalues['defocus2'] / self.ctfvalues['defocus1']
apDisplay.printColor(
"def1: %.2e | def2: %.2e | angle: %.1f | ampcontr %.2f | defratio %.3f"
% (self.ctfvalues['defocus1'], self.ctfvalues['defocus2'],
self.ctfvalues['angle_astigmatism'],
self.ctfvalues['amplitude_contrast'], defocusratio), "blue")
self.printBestValues()
#ellipratio = self.ellipseParams['a']/self.ellipseParams['b']
print "ellr=%.3f, defr=%.3f, sqrt(defr)=%.3f" % (
ellipratio, defocusratio, math.sqrt(defocusratio))
print "===================================="
return
def findAstigmatism(fftarray, freq, defocus, resolution, ctfvalues, peakNum=1): """ find the astigmatism from a radial 1D estimate of the defocus """ #searchError = resolution/100. extrema = ctftools.getCtfExtrema(defocus, freq*1e10, ctfvalues['cs'], ctfvalues['volts'], ctfvalues['amplitude_contrast'], numzeros=peakNum*2+1, zerotype="all") if len(extrema) < 2*peakNum: return None minEdgeRadius = int(math.ceil(extrema[peakNum-1])) #first peak maxEdgeRadius = int(math.ceil(extrema[peakNum])) #second peak newshape = (maxEdgeRadius*2, maxEdgeRadius*2) print "newshape",newshape fftcenter = copy.deepcopy(imagefilter.frame_cut(fftarray, newshape)) showImage(fftcenter) shape = fftcenter.shape ## create a grid of distance from the center xhalfshape = shape[0]/2.0 x = numpy.arange(-xhalfshape, xhalfshape, 1) + 0.5 yhalfshape = shape[1]/2.0 y = numpy.arange(-yhalfshape, yhalfshape, 1) + 0.5 xx, yy = numpy.meshgrid(x, y) radialArray = xx**2 + yy**2 - 0.5 #radialArray = numpy.sqrt(radial) maxVal = fftcenter.max()*2 minVal = fftcenter.min() if debug is True: fftcenter = numpy.where(radialArray > maxEdgeRadius**2, maxVal, fftcenter) showImage(fftcenter) fftcenter = numpy.where(radialArray < minEdgeRadius**2, maxVal, fftcenter) showImage(fftcenter) angleArray = numpy.arctan2(-yy,xx) numSteps = 360 angleArray += math.pi angleArray /= 2*math.pi angleArray *= numSteps angleArray = numpy.asarray(angleArray, dtype=numpy.uint16) showImage(angleArray) dataIntegers = numpy.array(range(numSteps)) xyData = numpy.array( scipy.ndimage.measurements.minimum_position( fftcenter, angleArray, dataIntegers)) if debug is True: fftcenter[xyData[:,0], xyData[:,1]] = maxVal*3 showImage(fftcenter) ellipseParams = ellipse.totalLeastSquareEllipse(xyData, center=(xhalfshape, yhalfshape)) if ellipseParams is None: return None ellipse.printParamsDict(ellipseParams) if debug is True: numPoints = int(math.pi*(ellipseParams['a']+ellipseParams['b'])) ellPoints = ellipse.generate_ellipse(ellipseParams['a'], ellipseParams['b'], ellipseParams['alpha'], center=ellipseParams['center'], numpoints=numPoints, integers=True) fftcenter[ellPoints[:,0], ellPoints[:,1]] += maxVal showImage(fftcenter) return ellipseParams
def runRefineCTF(self, imgdata, fftpath):
### reset important values
self.bestres = 1e10
self.bestellipse = None
self.bestvalues = {}
self.volts = imgdata['scope']['high tension']
self.wavelength = ctftools.getTEMLambda(self.volts)
## get value in meters
self.cs = apInstrument.getCsValueFromSession(self.getSessionData())*1e-3
self.ctfvalues = {
'volts': self.volts,
'wavelength': self.wavelength,
'cs': self.cs,
}
### need to get FFT file open and freq of said file
fftarray = mrc.read(fftpath).astype(numpy.float64)
self.freq = self.freqdict[fftpath]
### convert resolution limit into pixel distance
fftwidth = fftarray.shape[0]
maxres = 2.0/(self.freq*fftwidth)
if maxres > self.params['reslimit']:
apDisplay.printError("Cannot get requested res %.1fA higher than max res %.1fA"
%(maxres, self.params['reslimit']))
limitwidth = int(math.ceil(2.0/(self.params['reslimit']*self.freq)))
limitwidth = primefactor.getNextEvenPrime(limitwidth)
requestres = 2.0/(self.freq*limitwidth)
if limitwidth > fftwidth:
apDisplay.printError("Cannot get requested resolution"
+(" request res %.1fA higher than max res %.1fA for new widths %d > %d"
%(requestres, maxres, limitwidth, fftwidth)))
apDisplay.printColor("Requested resolution OK: "
+(" request res %.1fA less than max res %.1fA with fft widths %d < %d"
%(requestres, maxres, limitwidth, fftwidth)), "green")
newshape = (limitwidth, limitwidth)
fftarray = imagefilter.frame_cut(fftarray, newshape)
### spacing parameters
self.mfreq = self.freq*1e10
fftwidth = min(fftarray.shape)
self.apix = 1.0/(fftwidth*self.freq)
self.ctfvalues['apix'] = self.apix
### print message
bestDbValues = ctfdb.getBestCtfByResolution(imgdata)
if bestDbValues is None:
apDisplay.printColor("SKIPPING: No CTF values for image %s"
%(apDisplay.short(imgdata['filename'])), "red")
self.badprocess = True
return
### skip if resolution > 90.
if bestDbValues['resolution_50_percent'] > 90.:
apDisplay.printColor("SKIPPING: No decent CTF values for image %s"
%(apDisplay.short(imgdata['filename'])), "yellow")
self.badprocess = True
return
self.ctfvalues['amplitude_contrast'] = bestDbValues['amplitude_contrast']
lowerrad1 = ctftools.getCtfExtrema(bestDbValues['defocus1'], self.mfreq, self.cs, self.volts,
self.ctfvalues['amplitude_contrast'], 1, "valley")
lowerrad2 = ctftools.getCtfExtrema(bestDbValues['defocus2'], self.mfreq, self.cs, self.volts,
self.ctfvalues['amplitude_contrast'], 1, "valley")
meanRad = (lowerrad1[0] + lowerrad2[0])/2.0
self.ellipseParams = {
'a': lowerrad1[0],
'b': lowerrad2[0],
'alpha': math.radians(bestDbValues['angle_astigmatism']),
}
ellipratio = self.ellipseParams['a']/self.ellipseParams['b']
defratio = bestDbValues['defocus2']/bestDbValues['defocus1']
print "ellr=%.3f, defr=%.3f, sqrt(defr)=%.3f"%(ellipratio, defratio, math.sqrt(defratio))
self.bestvalues['defocus'] = (bestDbValues['defocus1']+bestDbValues['defocus2'])/2.0
raddata, PSDarray = self.from2Dinto1D(fftarray)
lowerbound = numpy.searchsorted(raddata, meanRad*self.freq)
upperbound = numpy.searchsorted(raddata, 1/10.)
###
# This is the start of the actual program
###
self.refineEllipseLoop(fftarray, lowerbound, upperbound)
##==================================
## FINISH UP
##==================================
apDisplay.printColor("Finishing up using best found CTF values", "blue")
self.printBestValues()
### take best values and use them
self.ctfvalues = self.bestvalues
self.ellipseParams = self.bestellipse
### stupid fix, get value in millimeters
self.ctfvalues['cs'] = apInstrument.getCsValueFromSession(self.getSessionData())
### translate ellipse into ctf values
if self.ellipseParams is not None:
self.ctfvalues['angle_astigmatism'] = math.degrees(self.ellipseParams['alpha'])
ellipratio = self.ellipseParams['a']/self.ellipseParams['b']
phi = math.asin(self.ctfvalues['amplitude_contrast'])
#note: a > b then def1 < def2
#major axis
self.ctfvalues['defocus1'] = self.ctfvalues['defocus']/ellipratio
#minor axis
self.ctfvalues['defocus2'] = self.ctfvalues['defocus']*ellipratio
defdiff = 1.0 - 2*self.ctfvalues['defocus']/(self.ctfvalues['defocus1']+self.ctfvalues['defocus2'])
print "%.3e --> %.3e,%.3e"%(self.ctfvalues['defocus'],
self.ctfvalues['defocus2'], self.ctfvalues['defocus1'])
print defdiff*100
if defdiff*100 > 1:
apDisplay.printWarning("Large astigmatism")
#sys.exit(1)
else:
ellipratio = 1.0
self.ctfvalues['angle_astigmatism'] = 0.0
self.ctfvalues['defocus1'] = self.ctfvalues['defocus']
self.ctfvalues['defocus2'] = self.ctfvalues['defocus']
if self.ctfvalues['amplitude_contrast'] < 0.0:
self.ctfvalues['amplitude_contrast'] = 0.0
if self.ctfvalues['amplitude_contrast'] > self.maxAmpCon:
self.ctfvalues['amplitude_contrast'] = self.maxAmpCon
if abs(self.ctfvalues['defocus1']) > abs(self.ctfvalues['defocus2']):
# incorrect, need to shift angle by 90 degrees
apDisplay.printWarning("|def1| > |def2|, flipping defocus axes")
tempdef = self.ctfvalues['defocus1']
self.ctfvalues['defocus1'] = self.ctfvalues['defocus2']
self.ctfvalues['defocus2'] = tempdef
self.ctfvalues['angle_astigmatism'] += 90
# get astig_angle within range -90 < angle <= 90
while self.ctfvalues['angle_astigmatism'] > 90:
self.ctfvalues['angle_astigmatism'] -= 180
while self.ctfvalues['angle_astigmatism'] < -90:
self.ctfvalues['angle_astigmatism'] += 180
avgres = self.getResolution(self.ctfvalues['defocus'], raddata, PSDarray, lowerbound)
apDisplay.printColor("Final defocus values %.3e -> %.3e, %.3e; ac=%.2f, res=%.1f"
%(self.ctfvalues['defocus'], self.ctfvalues['defocus1'], self.ctfvalues['defocus2'],
self.ctfvalues['amplitude_contrast'], avgres/2.0), "green")
for i in range(10):
print "===================================="
print "PREVIOUS VALUES"
ctfdb.getBestCtfByResolution(imgdata)
print "CURRENT VALUES"
defocusratio = self.ctfvalues['defocus2']/self.ctfvalues['defocus1']
apDisplay.printColor("def1: %.2e | def2: %.2e | angle: %.1f | ampcontr %.2f | defratio %.3f"
%(self.ctfvalues['defocus1'], self.ctfvalues['defocus2'], self.ctfvalues['angle_astigmatism'],
self.ctfvalues['amplitude_contrast'], defocusratio), "blue")
self.printBestValues()
#ellipratio = self.ellipseParams['a']/self.ellipseParams['b']
print "ellr=%.3f, defr=%.3f, sqrt(defr)=%.3f"%(ellipratio, defocusratio, math.sqrt(defocusratio))
print "===================================="
return
def runPhasorCTF(self, imgdata, fftpath):
### reset important values
self.bestres = 1e10
self.bestellipse = None
self.bestvalues = None
self.ellipseParams = None
self.volts = imgdata['scope']['high tension']
self.wavelength = ctftools.getTEMLambda(self.volts)
## get value in meters
self.cs = apInstrument.getCsValueFromSession(self.getSessionData())*1e-3
self.ctfvalues = {
'volts': self.volts,
'wavelength': self.wavelength,
'cs': self.cs,
}
if self.params['astig'] is False:
self.maxRatio=1.3
else:
self.maxRatio=2.0
### need to get FFT file open and freq of said file
fftarray = mrc.read(fftpath).astype(numpy.float64)
self.freq = self.freqdict[fftpath]
### print message
ctfdb.getBestCtfByResolution(imgdata)
### convert resolution limit into pixel distance
fftwidth = fftarray.shape[0]
maxres = 2.0/(self.freq*fftwidth)
if maxres > self.params['reslimit']:
apDisplay.printError("Cannot get requested res %.1fA higher than max res %.1fA"
%(maxres, self.params['reslimit']))
limitwidth = int(math.ceil(2.0/(self.params['reslimit']*self.freq)))
limitwidth = primefactor.getNextEvenPrime(limitwidth)
requestres = 2.0/(self.freq*limitwidth)
if limitwidth > fftwidth:
apDisplay.printError("Cannot get requested resolution"
+(" request res %.1fA higher than max res %.1fA for new widths %d > %d"
%(requestres, maxres, limitwidth, fftwidth)))
apDisplay.printColor("Requested resolution OK: "
+(" request res %.1fA less than max res %.1fA with fft widths %d < %d"
%(requestres, maxres, limitwidth, fftwidth)), "green")
newshape = (limitwidth, limitwidth)
fftarray = imagefilter.frame_cut(fftarray, newshape)
### spacing parameters
self.mfreq = self.freq*1e10
fftwidth = min(fftarray.shape)
self.apix = 1.0/(fftwidth*self.freq)
self.ctfvalues['apix'] = self.apix
if self.params['sample'] is 'stain':
self.ctfvalues['amplitude_contrast'] = 0.14
else:
self.ctfvalues['amplitude_contrast'] = 0.07
###
# This is the start of the actual program
###
### this is either simple rotational average,
### or complex elliptical average with edge find and RANSAC
raddata, PSDarray = self.from2Dinto1D(fftarray)
lowerrad = ctftools.getCtfExtrema(self.params['maxdef'], self.mfreq, self.cs, self.volts,
self.ctfvalues['amplitude_contrast'], 1, "valley")
lowerbound = numpy.searchsorted(raddata, lowerrad[0]*self.freq)
if self.params['sample'] is 'stain':
upperbound = numpy.searchsorted(raddata, 1/8.)
else:
upperbound = numpy.searchsorted(raddata, 1/12.)
### fun way to get initial defocus estimate
fitData = self.fitLinearPlus(raddata, PSDarray)
#lowessFit = lowess.lowess(raddata**2, PSDarray, smoothing=0.6666)
if self.debug is True:
from matplotlib import pyplot
pyplot.clf()
pyplot.plot(raddata**2, PSDarray)
pyplot.plot(raddata**2, fitData)
pyplot.show()
flatPSDarray = PSDarray-fitData
flatPSDarray /= numpy.abs(flatPSDarray[lowerbound:upperbound]).max()
defocus = self.gridSearch(raddata, flatPSDarray, lowerbound)
#defocus = findroots.estimateDefocus(raddata[lowerbound:upperbound], flatPSDarray[lowerbound:upperbound],
# cs=self.cs, wavelength=self.wavelength, amp_con=self.ctfvalues['amplitude_contrast'],
# mindef=self.params['mindef'], maxdef=self.params['maxdef'], volts=self.volts)
amplitudecontrast = sinefit.refineAmplitudeContrast(raddata[lowerbound:upperbound]*1e10, defocus,
flatPSDarray[lowerbound:upperbound], self.ctfvalues['cs'], self.wavelength, msg=self.debug)
if amplitudecontrast is not None:
print "amplitudecontrast", amplitudecontrast
self.ctfvalues['amplitude_contrast'] = amplitudecontrast
defocus = self.defocusLoop(defocus, raddata, flatPSDarray, lowerbound, upperbound)
#lowerrad = ctftools.getCtfExtrema(defocus, self.mfreq, self.cs, self.volts,
# self.ctfvalues['amplitude_contrast'], 1, "valley")
#lowerbound = numpy.searchsorted(raddata, lowerrad[0]*self.freq)
normPSDarray = self.fullTriSectionNormalize(raddata, PSDarray, defocus)
defocus = self.defocusLoop(defocus, raddata, normPSDarray, lowerbound, upperbound)
self.findEllipseLoop(fftarray, lowerbound, upperbound)
self.refineEllipseLoop(fftarray, lowerbound, upperbound)
##==================================
## FINISH UP
##==================================
apDisplay.printColor("Finishing up using best found CTF values", "blue")
self.printBestValues()
if self.bestvalues is None:
apDisplay.printColor("No CTF estimate found for this image", "red")
self.badprocess = True
return
### take best values and use them
self.ctfvalues = self.bestvalues
self.ellipseParams = self.bestellipse
### stupid fix, get value in millimeters
self.ctfvalues['cs'] = apInstrument.getCsValueFromSession(self.getSessionData())
### translate ellipse into ctf values
if self.ellipseParams is not None:
self.ctfvalues['angle_astigmatism'] = math.degrees(self.ellipseParams['alpha'])
ellipratio = self.ellipseParams['a']/self.ellipseParams['b']
phi = math.asin(self.ctfvalues['amplitude_contrast'])
#note: a > b then def1 < def2
#major axis
self.ctfvalues['defocus1'] = self.ctfvalues['defocus']/ellipratio
#minor axis
self.ctfvalues['defocus2'] = self.ctfvalues['defocus']*ellipratio
defdiff = 1.0 - 2*self.ctfvalues['defocus']/(self.ctfvalues['defocus1']+self.ctfvalues['defocus2'])
print "%.3e --> %.3e,%.3e"%(self.ctfvalues['defocus'],
self.ctfvalues['defocus2'], self.ctfvalues['defocus1'])
print defdiff*100
if defdiff*100 > 1:
apDisplay.printWarning("Large astigmatism")
#sys.exit(1)
else:
self.ctfvalues['angle_astigmatism'] = 0.0
self.ctfvalues['defocus1'] = self.ctfvalues['defocus']
self.ctfvalues['defocus2'] = self.ctfvalues['defocus']
if self.ctfvalues['amplitude_contrast'] < 0.0:
self.ctfvalues['amplitude_contrast'] = 0.0
if self.ctfvalues['amplitude_contrast'] > self.maxAmpCon:
self.ctfvalues['amplitude_contrast'] = self.maxAmpCon
if abs(self.ctfvalues['defocus1']) > abs(self.ctfvalues['defocus2']):
# incorrect, need to shift angle by 90 degrees
apDisplay.printWarning("|def1| > |def2|, flipping defocus axes")
tempdef = self.ctfvalues['defocus1']
self.ctfvalues['defocus1'] = self.ctfvalues['defocus2']
self.ctfvalues['defocus2'] = tempdef
self.ctfvalues['angle_astigmatism'] += 90
# get astig_angle within range -90 < angle <= 90
while self.ctfvalues['angle_astigmatism'] > 90:
self.ctfvalues['angle_astigmatism'] -= 180
while self.ctfvalues['angle_astigmatism'] < -90:
self.ctfvalues['angle_astigmatism'] += 180
avgres = self.getResolution(self.ctfvalues['defocus'], raddata, PSDarray, lowerbound)
apDisplay.printColor("Final defocus values %.3e -> %.3e, %.3e; ac=%.2f, res=%.1f"
%(self.ctfvalues['defocus'], self.ctfvalues['defocus1'], self.ctfvalues['defocus2'],
self.ctfvalues['amplitude_contrast'], avgres/2.0), "green")
for i in range(10):
print "===================================="
print "PREVIOUS VALUES"
ctfdb.getBestCtfByResolution(imgdata)
print "CURRENT VALUES"
defocusratio = self.ctfvalues['defocus2']/self.ctfvalues['defocus1']
apDisplay.printColor("def1: %.2e | def2: %.2e | angle: %.1f | ampcontr %.2f | defratio %.3f"
%(self.ctfvalues['defocus1'], self.ctfvalues['defocus2'], self.ctfvalues['angle_astigmatism'],
self.ctfvalues['amplitude_contrast'], defocusratio), "blue")
self.printBestValues()
print "===================================="
return
def findAstigmatism(fftarray, freq, defocus, resolution, ctfvalues, peakNum=1):
"""
find the astigmatism from a radial 1D estimate of the defocus
"""
#searchError = resolution/100.
extrema = ctftools.getCtfExtrema(defocus, freq*1e10,
ctfvalues['cs'], ctfvalues['volts'], ctfvalues['amplitude_contrast'],
numzeros=peakNum*2+1, zerotype="all")
if len(extrema) < 2*peakNum:
return None
minEdgeRadius = int(math.ceil(extrema[peakNum-1])) #first peak
maxEdgeRadius = int(math.ceil(extrema[peakNum])) #second peak
newshape = (maxEdgeRadius*2, maxEdgeRadius*2)
print "newshape",newshape
fftcenter = copy.deepcopy(imagefilter.frame_cut(fftarray, newshape))
showImage(fftcenter)
shape = fftcenter.shape
## create a grid of distance from the center
xhalfshape = shape[0]/2.0
x = numpy.arange(-xhalfshape, xhalfshape, 1) + 0.5
yhalfshape = shape[1]/2.0
y = numpy.arange(-yhalfshape, yhalfshape, 1) + 0.5
xx, yy = numpy.meshgrid(x, y)
radialArray = xx**2 + yy**2 - 0.5
#radialArray = numpy.sqrt(radial)
maxVal = fftcenter.max()*2
minVal = fftcenter.min()
if debug is True:
fftcenter = numpy.where(radialArray > maxEdgeRadius**2, maxVal, fftcenter)
showImage(fftcenter)
fftcenter = numpy.where(radialArray < minEdgeRadius**2, maxVal, fftcenter)
showImage(fftcenter)
angleArray = numpy.arctan2(-yy,xx)
numSteps = 360
angleArray += math.pi
angleArray /= 2*math.pi
angleArray *= numSteps
angleArray = numpy.asarray(angleArray, dtype=numpy.uint16)
showImage(angleArray)
dataIntegers = numpy.array(range(numSteps))
xyData = numpy.array(
scipy.ndimage.measurements.minimum_position(
fftcenter, angleArray, dataIntegers))
if debug is True:
fftcenter[xyData[:,0], xyData[:,1]] = maxVal*3
showImage(fftcenter)
ellipseParams = ellipse.totalLeastSquareEllipse(xyData, center=(xhalfshape, yhalfshape))
if ellipseParams is None:
return None
ellipse.printParamsDict(ellipseParams)
if debug is True:
numPoints = int(math.pi*(ellipseParams['a']+ellipseParams['b']))
ellPoints = ellipse.generate_ellipse(ellipseParams['a'], ellipseParams['b'],
ellipseParams['alpha'], center=ellipseParams['center'], numpoints=numPoints, integers=True)
fftcenter[ellPoints[:,0], ellPoints[:,1]] += maxVal
showImage(fftcenter)
return ellipseParams
%(fieldsize, count, apDisplay.timeString(time.time()-t0)))
return poweravg, freq
#===================
#===================
#===================
if __name__ == "__main__":
a = mrc.read("/home/vosslab/test.mrc")
a = imagefilter.planeRegression(a)
fullpower = imagefun.power(a)
#imagestat.printImageInfo(a)
t0 = time.time()
x = numpy.arange(6, 13)
N = 2**x
print N
for n in N:
print "====================================="
b = power(a, n)
b = imagefilter.frame_cut(b, numpy.array(b.shape)/2)
imagefile.arrayToPng(b, "%04d-field.png"%(n))
imagestat.printImageInfo(b)
bin = int(round(2**12/n))
b = imagefun.bin2(fullpower, bin)
b = imagefilter.frame_cut(b, numpy.array(b.shape)/2)
imagefile.arrayToPng(b, "%04d-binned.png"%(n))
imagestat.printImageInfo(b)
print "complete in %s"%(apDisplay.timeString(time.time()-t0))
#imagestat.printImageInfo(b)
