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 getResolution(self, defocus, raddata, PSD, lowerBoundIndex, show=True):
ctffitdata = genctf.generateCTF1d(
raddata * 1e10,
focus=defocus,
cs=self.ctfvalues['cs'],
volts=self.ctfvalues['volts'],
ampconst=self.ctfvalues['amplitude_contrast'],
failParams=False)
peaks = ctftools.getCtfExtrema(defocus,
self.freq * 1e10,
self.ctfvalues['cs'],
self.ctfvalues['volts'],
self.ctfvalues['amplitude_contrast'],
numzeros=25,
zerotype="peak")
### get the confidence
confraddata, confdata = ctfres.getCorrelationProfile(
raddata, PSD, ctffitdata, peaks, self.freq)
res5 = None
res8 = None
if confdata is not None and confdata.max() > self.maxAmpCon:
res5 = ctfres.getResolutionFromConf(confraddata,
confdata,
limit=0.5)
res8 = ctfres.getResolutionFromConf(confraddata,
confdata,
limit=0.8)
if res8 is None:
res8 = 100
if res5 is None:
res5 = 100
if (res8 + res5) < self.bestres and self.minAmpCon < self.ctfvalues[
'amplitude_contrast'] < self.maxAmpCon:
apDisplay.printColor(
"Congrats! Saving best resolution values %.3e and %.2f" %
(defocus, self.ctfvalues['amplitude_contrast']), "green")
self.bestres = (res8 + res5)
self.bestvalues = copy.deepcopy(self.ctfvalues)
self.bestvalues['defocus'] = defocus
self.bestellipse = copy.deepcopy(self.ellipseParams)
elif show is True:
if (res8 + res5) >= self.bestres:
print(
"not saving values %.2f, need an average better than %.2f"
% ((res8 + res5), self.bestres))
elif not (self.minAmpCon < self.ctfvalues['amplitude_contrast'] <
self.maxAmpCon):
print(
"not saving values amplitude contrast %.4f out of range (%.4f <> %.4f)"
% (self.ctfvalues['amplitude_contrast'], self.minAmpCon,
self.maxAmpCon))
else:
apDisplay.printError("Something went wrong")
## normalize the data
PSD -= (PSD[lowerBoundIndex:]).min()
PSD /= numpy.abs(PSD[lowerBoundIndex:]).max()
if self.debug is True and show is True:
### Show the data
raddatasq = raddata**2
confraddatasq = confraddata**2
peakradii = ctftools.getCtfExtrema(
defocus,
self.freq * 1e10,
self.ctfvalues['cs'],
self.ctfvalues['volts'],
self.ctfvalues['amplitude_contrast'],
numzeros=2,
zerotype="peaks")
firstpeak = peakradii[0]
from matplotlib import pyplot
pyplot.clf()
### raw powerspectra data
pyplot.plot(raddatasq[lowerBoundIndex:],
PSD[lowerBoundIndex:],
'-',
color="red",
alpha=0.5,
linewidth=1)
### ctf fit data
pyplot.plot(raddatasq[lowerBoundIndex:],
ctffitdata[lowerBoundIndex:],
'-',
color="black",
alpha=0.5,
linewidth=1)
### confidence profile
pyplot.plot(confraddatasq,
confdata,
'.',
color="blue",
alpha=0.9,
markersize=10)
pyplot.plot(confraddatasq,
confdata,
'-',
color="blue",
alpha=0.9,
linewidth=2)
pyplot.axvline(x=1 / res8**2, linewidth=2, color="gold")
pyplot.axvline(x=1 / res5**2, linewidth=2, color="red")
pyplot.title("Resolution values of %.3fA at 0.8 and %.3fA at 0.5" %
(res8, res5))
pyplot.xlim(xmin=raddatasq[lowerBoundIndex - 1],
xmax=raddatasq.max())
pyplot.ylim(ymin=-0.05, ymax=1.05)
pyplot.subplots_adjust(
wspace=0.05,
hspace=0.05,
bottom=0.05,
left=0.05,
top=0.95,
right=0.95,
)
pyplot.show()
apDisplay.printColor(
"Resolution values of %.4fA at 0.8 and %.4fA at 0.5" %
(res8, res5), "magenta")
return (res8 + res5) / 2.0
def fullTriSectionNormalize(self, raddata, PSD, defocus):
t0 = time.time()
###
### PART 1: BACKGROUND NOISE SUBTRACTION
###
# skip the center
valleys = ctftools.getCtfExtrema(defocus,
self.freq * 1e10,
self.ctfvalues['cs'],
self.ctfvalues['volts'],
self.ctfvalues['amplitude_contrast'],
numzeros=250,
zerotype="valleys")
firstvalley = valleys[0]
valleyradii = numpy.array(valleys, dtype=numpy.float64) * self.freq
firstvalleyindex = numpy.searchsorted(raddata, self.freq * firstvalley)
apDisplay.printColor(
"First valley: %.1f -> %d (1/%.1f A)" %
(firstvalley, firstvalleyindex, 1 / (firstvalley * self.freq)),
"yellow")
### split the function up in first 3/5 and last 3/5 of data with 1/5 overlap
numpoints = len(raddata) - firstvalleyindex
part1start = firstvalleyindex
part1end = int(firstvalleyindex + numpoints * 6 / 10.)
part2start = int(firstvalleyindex + numpoints * 5 / 10.)
part2end = int(firstvalleyindex + numpoints * 9 / 10.)
part3start = int(firstvalleyindex + numpoints * 8 / 10.)
part3end = len(raddata)
CtfNoise = ctfnoise.CtfNoise()
if valleyradii is None:
valleydata = ctfnoise.peakExtender(raddata, PSD, valleyradii,
"below")
else:
valleydata = ndimage.minimum_filter(PSD, 4)
## first part data
noisefitparams1 = CtfNoise.modelCTFNoise(
raddata[part1start:part1end], valleydata[part1start:part1end],
"below")
noisedata1 = CtfNoise.noiseModel(noisefitparams1, raddata)
## second part data
noisefitparams2 = CtfNoise.modelCTFNoise(
raddata[part2start:part2end], valleydata[part2start:part2end],
"below")
noisedata2 = CtfNoise.noiseModel(noisefitparams2, raddata)
## third part data
noisefitparams3 = CtfNoise.modelCTFNoise(
raddata[part3start:part3end], valleydata[part3start:part3end],
"below")
noisedata3 = CtfNoise.noiseModel(noisefitparams3, raddata)
## merge data
scale = numpy.arange(part1end - part2start, dtype=numpy.float32)
scale /= scale.max()
overlapdata1 = noisedata1[part2start:part1end] * (
1 - scale) + noisedata2[part2start:part1end] * scale
scale = numpy.arange(part2end - part3start, dtype=numpy.float32)
scale /= scale.max()
overlapdata2 = noisedata2[part3start:part2end] * (
1 - scale) + noisedata3[part3start:part2end] * scale
mergedata = numpy.hstack((noisedata1[:part2start], overlapdata1,
noisedata2[part1end:part3start],
overlapdata2, noisedata3[part2end:]))
noisedata = mergedata
### DO THE SUBTRACTION
normexpPSD = numpy.exp(PSD) - numpy.exp(noisedata)
normlogPSD = numpy.log(numpy.where(normexpPSD < 1, 1, normexpPSD))
###
### PART 2: ENVELOPE NORMALIZATION
###
# high pass filter the center
peaks = ctftools.getCtfExtrema(defocus,
self.freq * 1e10,
self.ctfvalues['cs'],
self.ctfvalues['volts'],
self.ctfvalues['amplitude_contrast'],
numzeros=250,
zerotype="peaks")
firstpeak = peaks[0]
peakradii = numpy.array(peaks, dtype=numpy.float64) * self.freq
firstpeakindex = numpy.searchsorted(raddata, firstpeak * self.freq)
apDisplay.printColor(
"First peak: %.1f (1/%.1f A)" %
(firstpeakindex, 1 / (firstpeak * self.freq)), "yellow")
### split the function up in first 3/5 and last 3/5 of data with 1/5 overlap
numpoints = len(raddata) - firstpeakindex
part1start = firstpeakindex
part1end = int(firstpeakindex + numpoints * 6 / 10.)
part2start = int(firstpeakindex + numpoints * 5 / 10.)
part2end = int(firstpeakindex + numpoints * 9 / 10.)
part3start = int(firstpeakindex + numpoints * 8 / 10.)
part3end = len(raddata)
CtfNoise = ctfnoise.CtfNoise()
if peakradii is None:
peakdata = ctfnoise.peakExtender(raddata, normlogPSD, peakradii,
"above")
else:
peakdata = ndimage.maximum_filter(normlogPSD, 4)
## first part data
envelopfitparams1 = CtfNoise.modelCTFNoise(
raddata[part1start:part1end], peakdata[part1start:part1end],
"above")
envelopdata1 = CtfNoise.noiseModel(envelopfitparams1, raddata)
## second part data
envelopfitparams2 = CtfNoise.modelCTFNoise(
raddata[part2start:part2end], peakdata[part2start:part2end],
"above")
envelopdata2 = CtfNoise.noiseModel(envelopfitparams2, raddata)
## third part data
envelopfitparams3 = CtfNoise.modelCTFNoise(
raddata[part3start:part3end], peakdata[part3start:part3end],
"above")
envelopdata3 = CtfNoise.noiseModel(envelopfitparams3, raddata)
## merge data
scale = numpy.arange(part1end - part2start, dtype=numpy.float32)
scale /= scale.max()
overlapdata1 = envelopdata1[part2start:part1end] * (
1 - scale) + envelopdata2[part2start:part1end] * scale
scale = numpy.arange(part2end - part3start, dtype=numpy.float32)
scale /= scale.max()
overlapdata2 = envelopdata2[part3start:part2end] * (
1 - scale) + envelopdata3[part3start:part2end] * scale
mergedata = numpy.hstack((envelopdata1[:part2start], overlapdata1,
envelopdata2[part1end:part3start],
overlapdata2, envelopdata3[part2end:]))
envelopdata = mergedata
normnormexpPSD = normexpPSD / numpy.exp(envelopdata)
if self.debug is True:
from matplotlib import pyplot
pyplot.clf()
pyplot.subplot(3, 1, 1)
raddatasq = raddata**2
pyplot.plot(
raddatasq,
normnormexpPSD,
'k.',
)
a = pyplot.plot(raddatasq, PSD, 'k-', alpha=0.5)
a = pyplot.plot(raddatasq, valleydata, 'k-', alpha=0.5)
b = pyplot.plot(raddatasq[firstvalleyindex:],
noisedata[firstvalleyindex:],
'--',
color="purple",
linewidth=2)
c = pyplot.plot(raddatasq[part1start:part1end],
noisedata1[part1start:part1end],
'b-',
alpha=0.5,
linewidth=2)
d = pyplot.plot(raddatasq[part2start:part2end],
noisedata2[part2start:part2end],
'r-',
alpha=0.5,
linewidth=2)
e = pyplot.plot(raddatasq[part3start:part3end],
noisedata3[part3start:part3end],
'-',
alpha=0.5,
linewidth=2,
color="green")
pyplot.legend([a, b, c, d, e],
["data", "merge", "part 1", "part 2", "part 3"])
pyplot.xlim(xmax=raddatasq.max())
pyplot.ylim(ymin=noisedata.min(), ymax=PSD[part1start:].max())
pyplot.subplot(3, 1, 2)
a = pyplot.plot(
raddatasq,
normlogPSD,
'k.',
)
a = pyplot.plot(raddatasq, normlogPSD, 'k-', alpha=0.5)
a = pyplot.plot(raddatasq, peakdata, 'k-', alpha=0.5)
b = pyplot.plot(raddatasq,
mergedata,
'--',
color="purple",
linewidth=2)
c = pyplot.plot(raddatasq[part1start:part1end],
envelopdata1[part1start:part1end],
'b-',
alpha=0.5,
linewidth=2)
d = pyplot.plot(raddatasq[part2start:part2end],
envelopdata2[part2start:part2end],
'r-',
alpha=0.5,
linewidth=2)
e = pyplot.plot(raddatasq[part3start:part3end],
envelopdata3[part3start:part3end],
'-',
alpha=0.5,
linewidth=2,
color="green")
pyplot.legend([a, b, c, d, e],
["data", "merge", "part 1", "part 2", "part 3"])
pyplot.xlim(xmax=raddatasq.max())
pyplot.ylim(ymin=normlogPSD[part1start:].min(),
ymax=envelopdata.max())
pyplot.subplot(3, 1, 3)
pyplot.plot(
raddatasq,
normnormexpPSD,
'k.',
)
pyplot.plot(raddatasq, normnormexpPSD, 'k-', alpha=0.5)
pyplot.xlim(xmax=raddatasq.max())
pyplot.subplots_adjust(
wspace=0.05,
hspace=0.05,
bottom=0.05,
left=0.05,
top=0.95,
right=0.95,
)
pyplot.show()
apDisplay.printColor(
"TriSection complete in %s" %
(apDisplay.timeString(time.time() - t0)), "cyan")
return normnormexpPSD
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 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 getResolution(self, defocus, raddata, PSD, lowerBoundIndex, show=True):
ctffitdata = genctf.generateCTF1d(raddata*1e10, focus=defocus, cs=self.ctfvalues['cs'],
volts=self.ctfvalues['volts'], ampconst=self.ctfvalues['amplitude_contrast'], failParams=False)
peaks = ctftools.getCtfExtrema(defocus, self.freq*1e10, self.ctfvalues['cs'],
self.ctfvalues['volts'], self.ctfvalues['amplitude_contrast'], numzeros=25, zerotype="peak")
### get the confidence
confraddata, confdata = ctfres.getCorrelationProfile(raddata, PSD, ctffitdata, peaks, self.freq)
res5 = None
res8 = None
if confdata is not None and confdata.max() > self.maxAmpCon:
res5 = ctfres.getResolutionFromConf(confraddata, confdata, limit=0.5)
res8 = ctfres.getResolutionFromConf(confraddata, confdata, limit=0.8)
if res8 is None:
res8 = 100
if res5 is None:
res5 = 100
if (res8+res5) < self.bestres and 0.0 < self.ctfvalues['amplitude_contrast'] < self.maxAmpCon:
apDisplay.printColor("Congrats! Saving best resolution values %.3e and %.2f"
%(defocus, self.ctfvalues['amplitude_contrast']), "green")
self.bestres = (res8+res5)
self.bestvalues = copy.deepcopy(self.ctfvalues)
self.bestvalues['defocus'] = defocus
self.bestellipse = copy.deepcopy(self.ellipseParams)
elif show is True:
print "not saving values %.2f, need an average better than %.2f"%((res8+res5), self.bestres)
## normalize the data
PSD -= (PSD[lowerBoundIndex:]).min()
PSD /= numpy.abs(PSD[lowerBoundIndex:]).max()
if self.debug is True and show is True:
### Show the data
raddatasq = raddata**2
confraddatasq = confraddata**2
peakradii = ctftools.getCtfExtrema(defocus, self.freq*1e10,
self.ctfvalues['cs'], self.ctfvalues['volts'], self.ctfvalues['amplitude_contrast'],
numzeros=2, zerotype="peaks")
firstpeak = peakradii[0]
from matplotlib import pyplot
pyplot.clf()
### raw powerspectra data
pyplot.plot(raddatasq[lowerBoundIndex:], PSD[lowerBoundIndex:], '-', color="red", alpha=0.5, linewidth=1)
### ctf fit data
pyplot.plot(raddatasq[lowerBoundIndex:], ctffitdata[lowerBoundIndex:], '-', color="black", alpha=0.5, linewidth=1)
### confidence profile
pyplot.plot(confraddatasq, confdata, '.', color="blue", alpha=0.9, markersize=10)
pyplot.plot(confraddatasq, confdata, '-', color="blue", alpha=0.9, linewidth=2)
pyplot.axvline(x=1/res8**2, linewidth=2, color="gold")
pyplot.axvline(x=1/res5**2, linewidth=2, color="red")
pyplot.title("Resolution values of %.3fA at 0.8 and %.3fA at 0.5"%(res8,res5))
pyplot.xlim(xmin=raddatasq[lowerBoundIndex-1], xmax=raddatasq.max())
pyplot.ylim(ymin=-0.05, ymax=1.05)
pyplot.subplots_adjust(wspace=0.05, hspace=0.05,
bottom=0.05, left=0.05, top=0.95, right=0.95, )
pyplot.show()
apDisplay.printColor("Resolution values of %.4fA at 0.8 and %.4fA at 0.5"
%(res8,res5), "magenta")
return (res8+res5)/2.0
def fullTriSectionNormalize(self, raddata, PSD, defocus):
t0 = time.time()
###
### PART 1: BACKGROUND NOISE SUBTRACTION
###
# skip the center
valleys = ctftools.getCtfExtrema(defocus, self.freq*1e10,
self.ctfvalues['cs'], self.ctfvalues['volts'], self.ctfvalues['amplitude_contrast'],
numzeros=250, zerotype="valleys")
firstvalley = valleys[0]
valleyradii = numpy.array(valleys, dtype=numpy.float64)*self.freq
firstvalleyindex = numpy.searchsorted(raddata, self.freq*firstvalley)
apDisplay.printColor("First valley: %.1f -> %d (1/%.1f A)"
%(firstvalley, firstvalleyindex, 1/(firstvalley*self.freq)), "yellow")
### split the function up in first 3/5 and last 3/5 of data with 1/5 overlap
numpoints = len(raddata) - firstvalleyindex
part1start = firstvalleyindex
part1end = int(firstvalleyindex + numpoints*6/10.)
part2start = int(firstvalleyindex + numpoints*5/10.)
part2end = int(firstvalleyindex + numpoints*9/10.)
part3start = int(firstvalleyindex + numpoints*8/10.)
part3end = len(raddata)
CtfNoise = ctfnoise.CtfNoise()
if valleyradii is None:
valleydata = ctfnoise.peakExtender(raddata, PSD, valleyradii, "below")
else:
valleydata = ndimage.minimum_filter(PSD, 4)
## first part data
noisefitparams1 = CtfNoise.modelCTFNoise(raddata[part1start:part1end],
valleydata[part1start:part1end], "below")
noisedata1 = CtfNoise.noiseModel(noisefitparams1, raddata)
## second part data
noisefitparams2 = CtfNoise.modelCTFNoise(raddata[part2start:part2end],
valleydata[part2start:part2end], "below")
noisedata2 = CtfNoise.noiseModel(noisefitparams2, raddata)
## third part data
noisefitparams3 = CtfNoise.modelCTFNoise(raddata[part3start:part3end],
valleydata[part3start:part3end], "below")
noisedata3 = CtfNoise.noiseModel(noisefitparams3, raddata)
## merge data
scale = numpy.arange(part1end-part2start, dtype=numpy.float32)
scale /= scale.max()
overlapdata1 = noisedata1[part2start:part1end]*(1-scale) + noisedata2[part2start:part1end]*scale
scale = numpy.arange(part2end-part3start, dtype=numpy.float32)
scale /= scale.max()
overlapdata2 = noisedata2[part3start:part2end]*(1-scale) + noisedata3[part3start:part2end]*scale
mergedata = numpy.hstack((noisedata1[:part2start], overlapdata1,
noisedata2[part1end:part3start], overlapdata2,
noisedata3[part2end:]))
noisedata = mergedata
### DO THE SUBTRACTION
normexpPSD = numpy.exp(PSD) - numpy.exp(noisedata)
normlogPSD = numpy.log(numpy.where(normexpPSD<1, 1, normexpPSD))
###
### PART 2: ENVELOPE NORMALIZATION
###
# high pass filter the center
peaks = ctftools.getCtfExtrema(defocus, self.freq*1e10,
self.ctfvalues['cs'], self.ctfvalues['volts'], self.ctfvalues['amplitude_contrast'],
numzeros=250, zerotype="peaks")
firstpeak = peaks[0]
peakradii = numpy.array(peaks, dtype=numpy.float64)*self.freq
firstpeakindex = numpy.searchsorted(raddata, firstpeak*self.freq)
apDisplay.printColor("First peak: %.1f (1/%.1f A)"%
(firstpeakindex, 1/(firstpeak*self.freq)), "yellow")
### split the function up in first 3/5 and last 3/5 of data with 1/5 overlap
numpoints = len(raddata) - firstpeakindex
part1start = firstpeakindex
part1end = int(firstpeakindex + numpoints*6/10.)
part2start = int(firstpeakindex + numpoints*5/10.)
part2end = int(firstpeakindex + numpoints*9/10.)
part3start = int(firstpeakindex + numpoints*8/10.)
part3end = len(raddata)
CtfNoise = ctfnoise.CtfNoise()
if peakradii is None:
peakdata = ctfnoise.peakExtender(raddata, normlogPSD, peakradii, "above")
else:
peakdata = ndimage.maximum_filter(normlogPSD, 4)
## first part data
envelopfitparams1 = CtfNoise.modelCTFNoise(raddata[part1start:part1end],
peakdata[part1start:part1end], "above")
envelopdata1 = CtfNoise.noiseModel(envelopfitparams1, raddata)
## second part data
envelopfitparams2 = CtfNoise.modelCTFNoise(raddata[part2start:part2end],
peakdata[part2start:part2end], "above")
envelopdata2 = CtfNoise.noiseModel(envelopfitparams2, raddata)
## third part data
envelopfitparams3 = CtfNoise.modelCTFNoise(raddata[part3start:part3end],
peakdata[part3start:part3end], "above")
envelopdata3 = CtfNoise.noiseModel(envelopfitparams3, raddata)
## merge data
scale = numpy.arange(part1end-part2start, dtype=numpy.float32)
scale /= scale.max()
overlapdata1 = envelopdata1[part2start:part1end]*(1-scale) + envelopdata2[part2start:part1end]*scale
scale = numpy.arange(part2end-part3start, dtype=numpy.float32)
scale /= scale.max()
overlapdata2 = envelopdata2[part3start:part2end]*(1-scale) + envelopdata3[part3start:part2end]*scale
mergedata = numpy.hstack((envelopdata1[:part2start], overlapdata1,
envelopdata2[part1end:part3start], overlapdata2,
envelopdata3[part2end:]))
envelopdata = mergedata
normnormexpPSD = normexpPSD / numpy.exp(envelopdata)
if self.debug is True:
from matplotlib import pyplot
pyplot.clf()
pyplot.subplot(3,1,1)
raddatasq = raddata**2
pyplot.plot(raddatasq, normnormexpPSD, 'k.',)
a = pyplot.plot(raddatasq, PSD, 'k-', alpha=0.5)
a = pyplot.plot(raddatasq, valleydata, 'k-', alpha=0.5)
b = pyplot.plot(raddatasq[firstvalleyindex:], noisedata[firstvalleyindex:], '--', color="purple", linewidth=2)
c = pyplot.plot(raddatasq[part1start:part1end],
noisedata1[part1start:part1end], 'b-', alpha=0.5, linewidth=2)
d = pyplot.plot(raddatasq[part2start:part2end],
noisedata2[part2start:part2end], 'r-', alpha=0.5, linewidth=2)
e = pyplot.plot(raddatasq[part3start:part3end],
noisedata3[part3start:part3end], '-', alpha=0.5, linewidth=2, color="green")
pyplot.legend([a, b, c, d, e], ["data", "merge", "part 1", "part 2", "part 3"])
pyplot.xlim(xmax=raddatasq.max())
pyplot.ylim(ymin=noisedata.min(), ymax=PSD[part1start:].max())
pyplot.subplot(3,1,2)
a = pyplot.plot(raddatasq, normlogPSD, 'k.',)
a = pyplot.plot(raddatasq, normlogPSD, 'k-', alpha=0.5)
a = pyplot.plot(raddatasq, peakdata, 'k-', alpha=0.5)
b = pyplot.plot(raddatasq, mergedata, '--', color="purple", linewidth=2)
c = pyplot.plot(raddatasq[part1start:part1end],
envelopdata1[part1start:part1end], 'b-', alpha=0.5, linewidth=2)
d = pyplot.plot(raddatasq[part2start:part2end],
envelopdata2[part2start:part2end], 'r-', alpha=0.5, linewidth=2)
e = pyplot.plot(raddatasq[part3start:part3end],
envelopdata3[part3start:part3end], '-', alpha=0.5, linewidth=2, color="green")
pyplot.legend([a, b, c, d, e], ["data", "merge", "part 1", "part 2", "part 3"])
pyplot.xlim(xmax=raddatasq.max())
pyplot.ylim(ymin=normlogPSD[part1start:].min(), ymax=envelopdata.max())
pyplot.subplot(3,1,3)
pyplot.plot(raddatasq, normnormexpPSD, 'k.',)
pyplot.plot(raddatasq, normnormexpPSD, 'k-', alpha=0.5)
pyplot.xlim(xmax=raddatasq.max())
pyplot.subplots_adjust(wspace=0.05, hspace=0.05,
bottom=0.05, left=0.05, top=0.95, right=0.95, )
pyplot.show()
apDisplay.printColor("TriSection complete in %s"
%(apDisplay.timeString(time.time()-t0)), "cyan")
return normnormexpPSD
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 findEllipseEdge(self, fftarray, mindef=None, maxdef=None): if mindef is None: mindef = self.params['mindef'] if maxdef is None: maxdef = self.params['maxdef'] minpeaks = ctftools.getCtfExtrema(maxdef, self.freq*1e10, self.ctfvalues['cs'], self.ctfvalues['volts'], 0.0, numzeros=3, zerotype="peaks") minEdgeRadius = minpeaks[0] maxvalleys = ctftools.getCtfExtrema(mindef, self.freq*1e10, self.ctfvalues['cs'], self.ctfvalues['volts'], 0.0, numzeros=3, zerotype="valleys") minEdgeRadius = (maxvalleys[0]+minpeaks[0])/2 maxEdgeRadius = maxvalleys[2] minCircum = math.ceil(2 * minEdgeRadius * math.pi) minEdges = int(minCircum) maxCircum = math.ceil(2 * maxEdgeRadius * math.pi) maxEdges = 2*int(maxCircum) dograd = (minpeaks[1] - minpeaks[0])/3.0 dogarray = apDog.diffOfGauss(fftarray, dograd, k=1.2) #dogarray = fftarray print "Edge range: ", minEdges, maxEdges edges = canny.canny_edges(dogarray, low_thresh=0.01, minEdgeRadius=minEdgeRadius, maxEdgeRadius=maxEdgeRadius, minedges=minEdges, maxedges=maxEdges) #minedges=2500, maxedges=15000, invedges = numpy.fliplr(numpy.flipud(edges)) self.edgeMap = numpy.logical_and(edges, invedges) edges = self.edgeMap * (dogarray.max()/self.edgeMap.max()) imagedata = dogarray.copy() + edges imagefile.arrayToJpeg(imagedata, "edges.jpg") edgeThresh = 3 ellipseParams = ransac.ellipseRANSAC(self.edgeMap, edgeThresh, maxRatio=self.maxRatio) if ellipseParams is None: return None fitEllipse1 = ransac.generateEllipseRangeMap2(ellipseParams, edgeThresh, self.edgeMap.shape) fitEllipse2 = ransac.generateEllipseRangeMap2(ellipseParams, edgeThresh*3, self.edgeMap.shape) outlineEllipse = fitEllipse2 - fitEllipse1 # image is draw upside down #outlineEllipse = numpy.flipud(outlineEllipse) imagedata = imagedata + 0.5*outlineEllipse*imagedata.max() if self.debug is True: from matplotlib import pyplot pyplot.clf() pyplot.imshow(imagedata) pyplot.gray() pyplot.show() self.ellipseParams = ellipseParams #self.convertEllipseToCtf(node=3) return ellipseParams
def drawPowerSpecImage(self, origpowerspec, maxsize=1200):
origpowerspec = ctftools.trimPowerSpectraToOuterResolution(origpowerspec, self.plotlimit2DAngstrom, self.trimfreq)
if self.debug is True:
print "origpowerspec shape", origpowerspec.shape
#compute elliptical average and merge with original image
pixelrdata, rotdata = ctftools.ellipticalAverage(origpowerspec, self.ellipratio, self.angle,
self.ringwidth*3, 1, full=True)
ellipavgpowerspec = ctftools.unEllipticalAverage(pixelrdata, rotdata,
self.ellipratio, self.angle, origpowerspec.shape)
halfshape = origpowerspec.shape[1]/2
halfpowerspec = numpy.hstack( (origpowerspec[:,:halfshape] , ellipavgpowerspec[:,halfshape:] ) )
if halfpowerspec.shape != origpowerspec.shape:
apDisplay.printError("Error in power spectra creation")
if max(halfpowerspec.shape) > maxsize:
scale = maxsize/float(max(halfpowerspec.shape))
#scale = math.sqrt((random.random()+random.random()+random.random())/3.0)
apDisplay.printMsg( "Scaling final powerspec image by %.3f"%(scale))
powerspec = imagefilter.scaleImage(halfpowerspec, scale)
else:
scale = 1280./float(max(halfpowerspec.shape))
powerspec = imagefilter.scaleImage(halfpowerspec, scale)
#scale = 1.0
#powerspec = halfpowerspec.copy()
self.scaleapix = self.trimapix
self.scalefreq = self.trimfreq/scale
if self.debug is True:
print "orig pixel", self.apix
print "trim pixel", self.trimapix
print "scale pixel", self.scaleapix
numzeros = 13
radii1 = ctftools.getCtfExtrema(self.defocus1, self.scalefreq*1e10,
self.cs, self.volts, self.ampcontrast, numzeros=numzeros, zerotype="valley")
radii2 = ctftools.getCtfExtrema(self.defocus2, self.scalefreq*1e10,
self.cs, self.volts, self.ampcontrast, numzeros=numzeros, zerotype="valley")
#smallest of two defocii
firstpeak = radii2[0]
###
### PART 9: DRAW THE 2D POWERSPEC IMAGE
###
center = numpy.array(powerspec.shape, dtype=numpy.float)/2.0
foundzeros = min(len(radii1), len(radii2))
"""
pyplot.clf()
ax = pyplot.subplot(1,1,1)
pyplot.xticks([], [])
pyplot.yticks([], [])
pyplot.imshow(powerspec)
pyplot.gray()
for i in range(foundzeros):
# because |def1| < |def2| ==> firstzero1 > firstzero2
major = radii1[i]*2
minor = radii2[i]*2
ell = Ellipse(xy=center, width=major, height=minor, angle=self.angle+90,
fill=False, edgecolor="yellow", antialiased=True, linewidth=0.5)
ax.add_artist(ell)
pyplot.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0, top=1, right=1, )
self.newpowerspecfile = apDisplay.short(self.imgname)+"-powerspec-new.png"
pyplot.savefig(self.newpowerspecfile, format="png", dpi=150, pad_inches=0.0)
"""
###
### PART 9: DRAW THE 2D POWERSPEC IMAGE
###
apDisplay.printColor("PART 9: DRAW THE 2D POWERSPEC IMAGE", "magenta")
center = numpy.array(powerspec.shape, dtype=numpy.float)/2.0
originalimage = imagefile.arrayToImage(powerspec)
originalimage = originalimage.convert("RGB")
pilimage = originalimage.copy()
draw = ImageDraw.Draw(pilimage)
#########
## draw astig axis line, if astig > 5%
#########
perdiff = 2*abs(self.defocus1-self.defocus2)/abs(self.defocus1+self.defocus2)
if self.debug is True:
print "Percent Difference %.1f"%(perdiff*100)
if perdiff > 0.05:
#print self.angle, radii2[0], center
x = 1*firstpeak*math.cos(math.radians(self.angle))
y = firstpeak*math.sin(math.radians(self.angle))
#print x,y
xy = (x+center[0], y+center[1], -x+center[0], -y+center[1])
#print xy
draw.line(xy, fill="#f23d3d", width=10)
elif perdiff > 1e-6:
#print self.angle, radii2[0], center
x = 1*firstpeak*math.cos(math.radians(self.angle))
y = firstpeak*math.sin(math.radians(self.angle))
#print x,y
xy = (x+center[0], y+center[1], -x+center[0], -y+center[1])
#print xy
draw.line(xy, fill="#f23d3d", width=2)
#########
## draw colored CTF Thon rings
#########
foundzeros = min(len(radii1), len(radii2))
#color="#3d3dd2" #blue
color="#ffd700" #gold
for i in range(foundzeros):
# because |def1| < |def2| ==> firstzero1 > firstzero2
major = radii1[i]
minor = radii2[i]
if self.debug is True:
print "major=%.1f, minor=%.1f, angle=%.1f"%(major, minor, self.angle)
if minor > powerspec.shape[0]/math.sqrt(3):
# this limits how far we draw out the ellipses sqrt(3) to corner, just 2 inside line
break
width = int(math.ceil(math.sqrt(numzeros - i)))*2
### determine color of circle
currentres = 1.0/(major*self.scalefreq)
if currentres > self.res80:
ringcolor = "green"
elif currentres > self.res50:
ringcolor = "gold"
else:
ringcolor = "red"
### determine number of points to use to draw ellipse, minimize distance btw points
#isoceles triangle, b: radius ot CTF ring, a: distance btw points
#a = 2 * b sin (theta/2)
#a / 2b = sin(theta/2)
#theta = 2 * asin (a/2b)
#numpoints = 2 pi / theta
## define a to be 5 pixels
a = 40
theta = 2.0 * math.asin (a/(2.0*major))
skipfactor = 2
numpoints = int(math.ceil(2.0*math.pi/theta/skipfactor))*skipfactor + 1
#print "numpoints", numpoints
points = ellipse.generate_ellipse(major, minor,
math.radians(self.angle), center, numpoints, None, "step", True)
x = points[:,0]
y = points[:,1]
## wrap around to end
x = numpy.hstack((x, [x[0],]))
y = numpy.hstack((y, [y[0],]))
## convert image
numsteps = int(math.floor((len(x)-2)/skipfactor))
for j in range(numsteps):
k = j*skipfactor
xy = (x[k], y[k], x[k+1], y[k+1])
draw.line(xy, fill=ringcolor, width=width)
#########
## draw blue resolution ring
#########
# 1/res = freq * pixrad => pixrad = 1/(res*freq)
maxrad = (max(powerspec.shape)-1)/2.0 - 3
maxres = 1.0/(self.scalefreq*maxrad)
bestres = math.ceil(maxres)
pixrad = 1.0/(self.scalefreq*bestres)
if self.debug is True:
print "bestres %d Angstroms (max: %.3f)"%(bestres, maxres)
print "pixrad %d (max: %.3f)"%(pixrad, maxrad)
if pixrad > maxrad:
apDisplay.printError("Too big of outer radius to draw")
outpixrad = math.ceil(pixrad)+1
inpixrad = math.floor(pixrad)-1
for i in numpy.arange(-4.0,4.01,0.01):
r = pixrad + i
blackxy = numpy.array((center[0]-r,center[1]-r,
center[0]+r,center[1]+r), dtype=numpy.float64)
draw.ellipse(tuple(blackxy), outline="black")
for i in numpy.arange(-1.50,1.51,0.01):
r = pixrad + i
whitexy = numpy.array((center[0]-r,center[1]-r,
center[0]+r,center[1]+r), dtype=numpy.float64)
draw.ellipse(tuple(whitexy), outline="#0BB5FF")
#########
## setup font to add text
#########
fontpath = "/usr/share/fonts/liberation/LiberationSans-Regular.ttf"
from PIL import ImageFont
if os.path.isfile(fontpath):
fontsize = int(math.ceil( 48/2. * min(powerspec.shape)/float(maxsize))*2)
font = ImageFont.truetype(fontpath, fontsize)
else:
font = ImageFont.load_default()
#########
## add resolution ring text
#########
angrad = maxrad/math.sqrt(2) + 1
coord = (angrad+maxrad, angrad+maxrad)
for i in [-2,2]:
for j in [-2,2]:
draw.text((coord[0]+i,coord[1]+j), "%.1f A"%(bestres), font=font, fill="black")
draw.text(coord, "%.1f A"%(bestres), font=font, fill="#0BB5FF")
#########
## add defocus value text
#########
meandef = abs(self.defocus1+self.defocus2)/2.0
deftext = "%.2f um"%(meandef*1e6)
tsize = draw.textsize(deftext, font=font)
coord = (powerspec.shape[0]-4-tsize[0], powerspec.shape[0]-4-tsize[1])
for i in [-2,2]:
for j in [-2,2]:
draw.text((coord[0]+i,coord[1]+j), deftext, font=font, fill="black")
draw.text(coord, deftext, font=font, fill="#AB82FF")
#########
## add text about what sides of powerspec are:
## left - raw data; right - elliptical average data
#########
leftcoord = (4, 4)
for i in [-3, -1, 0, 1, 3]:
for j in [-3, -1, 0, 1, 3]:
draw.text((leftcoord[0]+i,leftcoord[1]+j) , "Raw CTF Data", font=font, fill="black")
draw.text(leftcoord, "Raw CTF Data", font=font, fill="#00BFFF")
tsize = draw.textsize("Elliptical Average", font=font)
xdist = powerspec.shape[0] - 4 - tsize[0]
rightcoord = (xdist, 4)
for i in [-2,2]:
for j in [-2,2]:
draw.text((rightcoord[0]+i,rightcoord[1]+j), "Elliptical Average", font=font, fill="black")
draw.text(rightcoord, "Elliptical Average", font=font, fill="#00BFFF")
#########
## create an alpha blend effect
#########
originalimage = Image.blend(originalimage, pilimage, 0.95)
apDisplay.printMsg("Saving 2D powerspectra to file: %s"%(self.powerspecfile))
#pilimage.save(self.powerspecfile, "JPEG", quality=85)
originalimage.save(self.powerspecfile, "JPEG", quality=85)
if not os.path.isfile(self.powerspecfile):
apDisplay.printWarning("power spec file not created")
if self.debug is True:
#powerspecjpg = Image.open(self.powerspecfile)
#powerspecjpg.show()
pass
return
def normalizeCtf(self, zdata2d, twod=True):
"""
inner cut radius - radius for number of pixels to clip in the center of image
"""
###
### PART 1: SETUP PARAMETERS AND ELLIPTICAL AVERAGE
###
apDisplay.printColor("PART 1: SETUP PARAMETERS AND ELLIPTICAL AVERAGE", "magenta")
meandefocus = math.sqrt(self.defocus1*self.defocus2)
if meandefocus < 0.6e-6:
self.ringwidth = 3.0
elif meandefocus < 1.0e-6:
self.ringwidth = 2.0
elif meandefocus > 5.0e-6:
self.ringwidth = 0.5
### get all peak (not valley)
peak = ctftools.getCtfExtrema(meandefocus, self.trimfreq*1e10, self.cs, self.volts,
self.ampcontrast, numzeros=250, zerotype="peak")
apDisplay.printMsg("Number of available peaks is %d"%(len(peak)))
if len(peak) < 6:
apDisplay.printWarning("Too few peaks to work with, probably bad defocus estimate")
return None
firstpeak = peak[0]
peakradii = numpy.array(peak, dtype=numpy.float64)*self.trimfreq
### get all valley (not peak)
valley = ctftools.getCtfExtrema(meandefocus, self.trimfreq*1e10, self.cs, self.volts,
self.ampcontrast, numzeros=250, zerotype="valley")
firstvalley = valley[0]
valleyradii = numpy.array(valley, dtype=numpy.float64)*self.trimfreq
### do the elliptical average
if self.ellipratio is None:
return None
pixelrdata, rotdata = ctftools.ellipticalAverage(zdata2d, self.ellipratio, self.angle,
self.ringwidth, firstpeak, full=False)
raddata = pixelrdata*self.trimfreq
if self.debug is True:
print "Elliptical CTF limits %.1f A -->> %.1fA"%(1./raddata.min(), 1./raddata.max())
apDisplay.printMsg("Determine and subtract noise model")
CtfNoise = ctfnoise.CtfNoise()
###
### PART 2: BACKGROUND NOISE SUBTRACTION
###
apDisplay.printColor("PART 2: BACKGROUND NOISE SUBTRACTION", "magenta")
### split the function up in first 3/5 and last 3/5 of data with 1/5 overlap
firstvalleyindex = numpy.searchsorted(raddata, self.trimfreq*firstvalley)
numpoints = len(raddata) - firstvalleyindex
# require at least 10 points past first peak of CTF to perform estimation
if numpoints < 10:
apDisplay.printWarning("Not enough points past first peak (n=%d < 10) to do background subtraction"
%(numpoints))
return None
npart1start = firstvalleyindex
npart1end = int(firstvalleyindex + numpoints*6/10.)
npart2start = int(firstvalleyindex + numpoints*5/10.)
npart2end = int(firstvalleyindex + numpoints*9/10.)
npart3start = int(firstvalleyindex + numpoints*8/10.)
npart3end = len(raddata)
svalleydata = ctfnoise.peakExtender(raddata, rotdata, valleyradii, "below")
### fit function below log(CTF), i.e., noise model
## first part data
noisefitparams1 = CtfNoise.modelCTFNoise(raddata[npart1start:npart1end],
svalleydata[npart1start:npart1end], "below")
noisedata1 = CtfNoise.noiseModel(noisefitparams1, raddata)
## second part data
noisefitparams2 = CtfNoise.modelCTFNoise(raddata[npart2start:npart2end],
rotdata[npart2start:npart2end], "below")
noisedata2 = CtfNoise.noiseModel(noisefitparams2, raddata)
## third part data
#noisefitparams3 = CtfNoise.modelCTFNoise(raddata[npart3start:npart3end],
# svalleydata[npart3start:npart3end], "below")
noisefitparams3 = CtfNoise.modelCTFNoise(raddata[npart3start:npart3end],
rotdata[npart3start:npart3end], "below")
noisedata3 = CtfNoise.noiseModel(noisefitparams3, raddata)
## debug only
if self.debug is True:
singlenoisefitparams = CtfNoise.modelCTFNoise(raddata[npart1start:npart3end],
svalleydata[npart1start:npart3end], "below")
singlenoisedata = CtfNoise.noiseModel(singlenoisefitparams, raddata)
## merge data
scale = numpy.arange(npart1end-npart2start, dtype=numpy.float32)
scale /= scale.max()
overlapdata1 = noisedata1[npart2start:npart1end]*(1-scale) + noisedata2[npart2start:npart1end]*scale
scale = numpy.arange(npart2end-npart3start, dtype=numpy.float32)
scale /= scale.max()
overlapdata2 = noisedata2[npart3start:npart2end]*(1-scale) + noisedata3[npart3start:npart2end]*scale
mergedata = numpy.hstack((noisedata1[:npart2start], overlapdata1,
noisedata2[npart1end:npart3start], overlapdata2,
noisedata3[npart2end:]))
noisedata = mergedata
### DO THE SUBTRACTION
normexprotdata = numpy.exp(rotdata) - numpy.exp(noisedata)
### CUT OUT ANY NEGATIVE VALUES FOR DISPLAY AND FITTING PURPOSES ONLY
minval = -1
mindata = ndimage.maximum_filter(normexprotdata, 2)
count = 0
while minval < 3 and count < 10:
count += 1
mindata = ndimage.maximum_filter(mindata, 2)
minval = mindata.min()
if self.debug is True:
apDisplay.printMsg("Minimum value for normalization: %.3f"%(minval))
if minval < 3:
minval = 3
normlogrotdata = numpy.log(numpy.where(normexprotdata<minval, minval, normexprotdata))
if numpy.isnan(normlogrotdata).any() is True:
apDisplay.printError("Error in log normalization of CTF data")
###
### PART 3: ENVELOPE NORMALIZATION
###
apDisplay.printColor("PART 3: ENVELOPE NORMALIZATION", "magenta")
### split the function up in first 3/5 and last 3/5 of data with 1/5 overlap
firstpeakindex = numpy.searchsorted(raddata, firstpeak*self.trimfreq)
numpoints = len(raddata) - firstpeakindex
epart1start = firstpeakindex
epart1end = int(firstpeakindex + numpoints*6/10.)
epart2start = int(firstpeakindex + numpoints*5/10.)
epart2end = int(firstpeakindex + numpoints*9/10.)
epart3start = int(firstpeakindex + numpoints*8/10.)
epart3end = len(raddata)
peakdata = ctfnoise.peakExtender(raddata, normlogrotdata, peakradii, "above")
## first part data
envelopfitparams1 = CtfNoise.modelCTFNoise(raddata[epart1start:epart1end],
peakdata[epart1start:epart1end], "above")
envelopdata1 = CtfNoise.noiseModel(envelopfitparams1, raddata)
## second part data
envelopfitparams2 = CtfNoise.modelCTFNoise(raddata[epart2start:epart2end],
peakdata[epart2start:epart2end], "above")
envelopdata2 = CtfNoise.noiseModel(envelopfitparams2, raddata)
## third part data
envelopfitparams3 = CtfNoise.modelCTFNoise(raddata[epart3start:epart3end],
peakdata[epart3start:epart3end], "above")
envelopdata3 = CtfNoise.noiseModel(envelopfitparams3, raddata)
## merge data
scale = numpy.arange(epart1end-epart2start, dtype=numpy.float32)
scale /= scale.max()
overlapdata1 = envelopdata1[epart2start:epart1end]*(1-scale) + envelopdata2[epart2start:epart1end]*scale
scale = numpy.arange(epart2end-epart3start, dtype=numpy.float32)
scale /= scale.max()
overlapdata2 = envelopdata2[epart3start:epart2end]*(1-scale) + envelopdata3[epart3start:epart2end]*scale
mergedata = numpy.hstack((envelopdata1[:epart2start], overlapdata1,
envelopdata2[epart1end:epart3start], overlapdata2,
envelopdata3[epart2end:]))
envelopdata = mergedata
normnormexprotdata = normexprotdata / numpy.exp(envelopdata)
###
### PART 4: PEAK EXTENSION
###
apDisplay.printColor("PART 4: PEAK EXTENSION", "magenta")
### Subtract fit valley locations
valleydata = ctfnoise.peakExtender(raddata, normnormexprotdata, valleyradii, "below")
valleydata = ndimage.gaussian_filter1d(valleydata, 1)
normvalleydata = normnormexprotdata - valleydata
### Normalize fit peak locations
peakdata = ctfnoise.peakExtender(raddata, normvalleydata, peakradii, "above")
peakdata = ndimage.gaussian_filter1d(peakdata, 1)
normpeakdata = normvalleydata / peakdata
###
### PART 5: CTF FIT AND CONFIDENCE
###
apDisplay.printColor("PART 5: CTF FIT AND CONFIDENCE", "magenta")
### everything in mks units, because rdata is 1/A multiply be 1e10 to get 1/m
ctffitdata = genctf.generateCTF1d(raddata*1e10, focus=meandefocus, cs=self.cs,
volts=self.volts, ampconst=self.ampcontrast, failParams=False)
#ctffitdata2 = genctf.generateCTF1dACE2(raddata*1e10, focus=meandefocus, cs=self.cs,
# volts=self.volts, ampconst=self.ampcontrast, failParams=False)
overctffitdata = genctf.generateCTF1d(raddata*1e10, focus=meandefocus, cs=self.cs,
volts=self.volts, ampconst=self.ampcontrast, failParams=False, overfocus=True)
ind30 = numpy.searchsorted(raddata, 1/30.)
ind10 = numpy.searchsorted(raddata, 1/10.)
self.conf3010 = scipy.stats.pearsonr(normpeakdata[ind30:ind10], ctffitdata[ind30:ind10])[0]
self.overconf3010 = scipy.stats.pearsonr(normpeakdata[ind30:ind10], overctffitdata[ind30:ind10])[0]
apDisplay.printColor("1/30A - 1/10A confidence is %.3f (overfocus %.3f)"%(self.conf3010, self.overconf3010), "green")
if self.overconf3010 > self.conf3010*1.1:
apDisplay.printWarning("Image is possibly over-focused")
ind5peak1 = numpy.searchsorted(raddata, peakradii[0])
ind5peak2 = numpy.searchsorted(raddata, peakradii[5])
self.conf5peak = scipy.stats.pearsonr(normpeakdata[ind5peak1:ind5peak2], ctffitdata[ind5peak1:ind5peak2])[0]
self.overconf5peak = scipy.stats.pearsonr(normpeakdata[ind5peak1:ind5peak2], overctffitdata[ind5peak1:ind5peak2])[0]
apDisplay.printColor("5 peak confidence is %.3f (overfocus %.3f)"%(self.conf5peak, self.overconf5peak), "green")
if self.overconf5peak > self.conf5peak*1.1:
apDisplay.printWarning("Image is possibly over-focused")
###
### PART 6: CTF RESOLUTION LIMITS
###
apDisplay.printColor("PART 6: CTF RESOLUTION LIMITS", "magenta")
confraddata, confdata = ctfres.getCorrelationProfile(raddata,
normpeakdata, ctffitdata, peak, self.trimfreq)
overconfraddata, overconfdata = ctfres.getCorrelationProfile(raddata,
normpeakdata, overctffitdata, peak, self.trimfreq)
self.res80 = ctfres.getResolutionFromConf(confraddata, confdata, limit=0.8)
if self.res80 is None:
self.res80 = 100.0
self.overres80 = ctfres.getResolutionFromConf(overconfraddata, overconfdata, limit=0.8)
if self.overres80 is None:
self.overres80 = 100.0
self.res50 = ctfres.getResolutionFromConf(confraddata, confdata, limit=0.5)
if self.res50 is None:
self.res50 = 100.0
res50max = min(raddata.max(), 1/10.)
elif self.res50 > 15.0:
res50max = min(raddata.max(), 1/10.)
else:
res50max = min(raddata.max(), 1.5/self.res50)
self.overres50 = ctfres.getResolutionFromConf(overconfraddata, overconfdata, limit=0.5)
if self.overres50 is None:
self.overres50 = 100.0
apDisplay.printColor("Resolution limit is %.2f at 0.8 and %.2f at 0.5"
%(self.res80, self.res50), "green")
###
### PART 7: MAKE 1D PLOT SUMMARY FIGURE
###
apDisplay.printColor("PART 7: MAKE 1D PLOT SUMMARY FIGURE", "magenta")
titlefontsize=8
axisfontsize=7
raddatasq = raddata**2
confraddatasq = confraddata**2
valleyradiisq = valleyradii**2
peakradiisq = peakradii**2
fpi = firstpeakindex
pyplot.clf()
if 'subplot2grid' in dir(pyplot):
pyplot.subplot2grid((3,2), (0,0))
else:
pyplot.subplot(2,2,1) # 2 rows, 2 columns, plot 1
pyplot.title("Background Noise Subtraction", fontsize=titlefontsize)
pyplot.ylabel("Log(PSD)", fontsize=axisfontsize)
pyplot.plot(raddata[fpi:], rotdata[fpi:],
'-', color="blue", alpha=0.5, linewidth=0.5)
pyplot.plot(raddata[fpi:], rotdata[fpi:],
'.', color="blue", alpha=0.75, markersize=2.0)
pyplot.plot(raddata[npart1start:npart1end], noisedata1[npart1start:npart1end],
'-', color="magenta", alpha=0.5, linewidth=2)
pyplot.plot(raddata[npart2start:npart2end], noisedata2[npart2start:npart2end],
'-', color="red", alpha=0.5, linewidth=2)
pyplot.plot(raddata[npart3start:npart3end], noisedata3[npart3start:npart3end],
'-', color="orange", alpha=0.5, linewidth=2)
pyplot.plot(raddata[fpi:], noisedata[fpi:],
'--', color="purple", alpha=1.0, linewidth=1)
self.setPyPlotXLabels(raddata, valleyradii=valleyradii, maxloc=res50max)
pyplot.ylim(ymin=noisedata.min())
if 'subplot2grid' in dir(pyplot):
pyplot.subplot2grid((3,2), (0,1))
else:
pyplot.subplot(2,2,2) # 2 rows, 2 columns, plot 2
pyplot.title("Envelope Normalization", fontsize=titlefontsize)
pyplot.ylabel("Log(PSD-Noise)", fontsize=axisfontsize)
pyplot.plot(raddata[fpi:], normlogrotdata[fpi:],
'-', color="blue", alpha=0.5, linewidth=0.5)
pyplot.plot(raddata[fpi:], normlogrotdata[fpi:],
'.', color="blue", alpha=0.75, markersize=2.0)
pyplot.plot(raddata[epart1start:epart1end], envelopdata1[epart1start:epart1end],
'-', color="magenta", alpha=0.5, linewidth=2)
pyplot.plot(raddata[epart2start:epart2end], envelopdata2[epart2start:epart2end],
'-', color="red", alpha=0.5, linewidth=2)
pyplot.plot(raddata[epart3start:epart3end], envelopdata3[epart3start:epart3end],
'-', color="orange", alpha=0.5, linewidth=2)
pyplot.plot(raddata[fpi:], envelopdata[fpi:],
'--', color="purple", alpha=1.0, linewidth=1)
self.setPyPlotXLabels(raddata, peakradii=peakradii, maxloc=res50max)
pyplot.ylim(ymax=envelopdata.max())
if 'subplot2grid' in dir(pyplot):
pyplot.subplot2grid((3,2), (1,0), colspan=2)
else:
pyplot.subplot(2,2,3) # 2 rows, 2 columns, plot 3
pyplot.title("Fit of CTF data (30-10A %.3f / 5-peak %.3f) Def1= %.3e / Def2= %.3e"
%(self.conf3010, self.conf5peak, self.defocus1, self.defocus2), fontsize=titlefontsize)
pyplot.ylabel("Norm PSD", fontsize=titlefontsize)
pyplot.plot(raddatasq[fpi:], ctffitdata[fpi:],
'-', color="black", alpha=0.5, linewidth=1)
#pyplot.plot(raddatasq[fpi:], overctffitdata[fpi:],
# '-', color="red", alpha=0.75, linewidth=1)
pyplot.plot(raddatasq[fpi:], normpeakdata[fpi:],
'-', color="blue", alpha=0.5, linewidth=0.5)
pyplot.plot(raddatasq[fpi:], normpeakdata[fpi:],
'.', color="blue", alpha=0.75, markersize=2.0)
self.setPyPlotXLabels(raddatasq, maxloc=1.0/self.outerAngstrom1D**2, square=True)
pyplot.grid(True, linestyle=':', )
pyplot.ylim(-0.05, 1.05)
"""
pyplot.subplot2grid((3,2), (1,1))
tenangindex = numpy.searchsorted(raddata, 1/10.)-1
pyplot.title("Defocus1= %.3e / Defocus2= %.3e"
%(self.defocus1, self.defocus2), fontsize=titlefontsize)
pyplot.ylabel("Norm PSD", fontsize=titlefontsize)
pyplot.plot(raddatasq[tenangindex:], ctffitdata[tenangindex:],
'-', color="black", alpha=0.5, linewidth=1)
pyplot.plot(raddatasq[tenangindex:], normpeakdata[tenangindex:],
'-', color="blue", alpha=0.5, linewidth=0.5)
pyplot.plot(raddatasq[tenangindex:], normpeakdata[tenangindex:],
'.', color="blue", alpha=0.75, markersize=2.0)
self.setPyPlotXLabels(raddatasq[tenangindex:], maxloc=1/7.**2, square=True)
pyplot.grid(True, linestyle=':', )
pyplot.ylim(-0.05, 1.05)
"""
if 'subplot2grid' in dir(pyplot):
pyplot.subplot2grid((3,2), (2,0), colspan=2)
else:
pyplot.subplot(2,2,4) # 2 rows, 2 columns, plot 4
pyplot.title("Resolution limits: %.2fA at 0.8 and %.2fA at 0.5"
%(self.res80, self.res50), fontsize=titlefontsize)
pyplot.ylabel("Correlation", fontsize=titlefontsize)
pyplot.plot(raddata[fpi:], ctffitdata[fpi:],
'-', color="black", alpha=0.2, linewidth=1)
pyplot.plot(raddata[fpi:], normpeakdata[fpi:],
'-', color="blue", alpha=0.2, linewidth=1)
#pyplot.plot(raddata[fpi:], normpeakdata[fpi:],
# '.', color="black", alpha=0.25, markersize=1.0)
pyplot.axvline(x=1.0/self.res80, linewidth=2, color="gold", alpha=0.95, ymin=0, ymax=0.8)
pyplot.axvline(x=1.0/self.res50, linewidth=2, color="red", alpha=0.95, ymin=0, ymax=0.5)
res80index = numpy.searchsorted(confraddata, 1.0/self.res80)
pyplot.plot(confraddata[:res80index+1], confdata[:res80index+1],
'-', color="green", alpha=1, linewidth=2)
res50index = numpy.searchsorted(confraddata, 1.0/self.res50)
pyplot.plot(confraddata[res80index-1:res50index+1], confdata[res80index-1:res50index+1],
'-', color="orange", alpha=1, linewidth=2)
pyplot.plot(confraddata[res50index-1:], confdata[res50index-1:],
'-', color="red", alpha=1, linewidth=2)
self.setPyPlotXLabels(raddata, maxloc=res50max)
pyplot.grid(True, linestyle=':', )
if self.res80 < 99:
pyplot.ylim(-0.05, 1.05)
elif self.res50 < 99:
pyplot.ylim(-0.25, 1.05)
else:
pyplot.ylim(-0.55, 1.05)
pyplot.subplots_adjust(wspace=0.22, hspace=0.50,
bottom=0.08, left=0.07, top=0.95, right=0.965, )
self.plotsfile = apDisplay.short(self.imgname)+"-plots.png"
apDisplay.printMsg("Saving 1D graph to file %s"%(self.plotsfile))
pyplot.savefig(self.plotsfile, format="png", dpi=300, orientation='landscape', pad_inches=0.0)
if self.debug is True:
### write a 1d profile dat files
f = open(apDisplay.short(self.imgname)+"-noise_fit.dat", "w")
for i in range(npart1start, npart3end):
f.write("%.16f\t%.16f\t%.16f\t%.16f\n"%(raddata[i], rotdata[i], singlenoisedata[i], noisedata[i]))
f.write("&\n")
for i in range(npart1start, npart1end):
f.write("%.16f\t%.16f\n"%(raddata[i], noisedata1[i]))
f.write("&\n")
for i in range(npart2start, npart2end):
f.write("%.16f\t%.16f\n"%(raddata[i], noisedata2[i]))
f.write("&\n")
for i in range(npart3start, npart3end):
f.write("%.16f\t%.16f\n"%(raddata[i], noisedata3[i]))
f.write("&\n")
f.close()
#smallrotdata = numpy.where(rotdata-singlenoisedata>0.19, 0.19, rotdata-singlenoisedata)
noiseexp = numpy.exp(singlenoisedata)
smallrotdata = numpy.exp(rotdata) - noiseexp
minval = 3
smallrotdata = numpy.log(numpy.where(smallrotdata<minval, minval, smallrotdata))
smallnoise = numpy.exp(noisedata) - noiseexp
smallnoise = numpy.log(numpy.where(smallnoise<minval, minval, smallnoise))
smallnoise1 = numpy.exp(noisedata1) - noiseexp
smallnoise1 = numpy.log(numpy.where(smallnoise1<minval, minval, smallnoise1))
smallnoise2 = numpy.exp(noisedata2) - noiseexp
smallnoise2 = numpy.log(numpy.where(smallnoise2<minval, minval, smallnoise2))
smallnoise3 = numpy.exp(noisedata3) - noiseexp
smallnoise3 = numpy.log(numpy.where(smallnoise3<minval, minval, smallnoise3))
f = open(apDisplay.short(self.imgname)+"-noisesubt_fit.dat", "w")
for i in range(len(ctffitdata)):
f.write("%.16f\t%.16f\n"%(raddata[i], smallrotdata[i]))
f.write("&\n")
for i in range(npart1start, npart3end):
f.write("%.16f\t%.16f\t%.16f\t%.16f\n"%(raddata[i], smallrotdata[i], smallnoise[i], 0))
f.write("&\n")
for i in range(npart1start, npart1end):
f.write("%.16f\t%.16f\n"%(raddata[i], smallnoise1[i]))
f.write("&\n")
for i in range(npart2start, npart2end):
f.write("%.16f\t%.16f\n"%(raddata[i], smallnoise2[i]))
f.write("&\n")
for i in range(npart3start, npart3end):
f.write("%.16f\t%.16f\n"%(raddata[i], smallnoise3[i]))
f.write("&\n")
f.close()
f = open(apDisplay.short(self.imgname)+"-ctf_fit.dat", "w")
for i in range(len(ctffitdata)):
f.write("%.16f\t%.16f\t%.16f\n"%(raddata[i], normpeakdata[i], ctffitdata[i]))
f.close()
#sys.exit(1)
if self.debug is True:
print "Showing results"
#pyplot.show()
#plotspng = Image.open(self.plotsfile)
#plotspng.show()
pyplot.clf()
if twod is False:
return zdata2d
###
### PART 8: NORMALIZE THE 2D IMAGE
###
apDisplay.printColor("PART 8: NORMALIZE THE 2D IMAGE", "magenta")
### Convert 1D array into 2D array by un-elliptical average
noise2d = ctftools.unEllipticalAverage(pixelrdata, noisedata,
self.ellipratio, self.angle, zdata2d.shape)
envelop2d = ctftools.unEllipticalAverage(pixelrdata, envelopdata,
self.ellipratio, self.angle, zdata2d.shape)
valley2d = ctftools.unEllipticalAverage(pixelrdata, valleydata,
self.ellipratio, self.angle, zdata2d.shape)
peak2d = ctftools.unEllipticalAverage(pixelrdata, peakdata,
self.ellipratio, self.angle, zdata2d.shape)
### Do the normalization on the 2d data
#blur2d = ndimage.gaussian_filter(zdata2d, 2)
normal2d = numpy.exp(zdata2d) - numpy.exp(noise2d)
normal2d = normal2d / numpy.exp(envelop2d)
normal2d = normal2d - valley2d
normal2d = normal2d / peak2d
normal2d = numpy.where(normal2d < -0.2, -0.2, normal2d)
normal2d = numpy.where(normal2d > 1.2, 1.2, normal2d)
return normal2d
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
