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 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 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
