def getAverageAzimuthFromSeries(imgtree):
# this somewhat duplicates the getDefaultAzimuthFromLegionon but is slightly different
predict1 = apDatabase.getPredictionDataForImage(imgtree[0])
predict2 = apDatabase.getPredictionDataForImage(imgtree[-1])
try:
try:
phi1 = predict1[0]['predicted position']['phi'] * 180 / math.pi
phi2 = predict2[0]['predicted position']['phi'] * 180 / math.pi
except: #Tilt-series was uploaded
phi1 = phi2 = imgtree[0]['scope']['stage position']['phi']
except: #Phi was not recorded
apDisplay.printWarning(
"Azimuth was not recorded. Setting azimuth to -90 degrees, relative to the x-axis."
)
phi1 = phi2 = 0
###Azimuth is determined from phi. In protomo tilt axis is measured from x where phi is from y
###Note there is a mirror between how Leginon reads images vs how protomo does
azimuth = -(
90 - ((phi1 + phi2) / 2)
) # Made negative because now images are y-flipped because Protomo
apDisplay.printMsg(
("Azimuth is %f (relative to y-flipped images)" % azimuth))
return azimuth
def getAverageAzimuthFromSeries(imgtree):
# this somewhat duplicates the getDefaultAzimuthFromLegionon but is slightly different
predict1=apDatabase.getPredictionDataForImage(imgtree[0])
predict2=apDatabase.getPredictionDataForImage(imgtree[-1])
phi1=predict1[0]['predicted position']['phi']*180/math.pi
phi2=predict2[0]['predicted position']['phi']*180/math.pi
###Azimuth is determined from phi. In protomo tilt axis is measured from x where phi is from y
###Note there is a mirror between how Leginon reads images vs how protomo does
azimuth=90-((phi1+phi2)/2)
apDisplay.printMsg(("Azimuth is %f" % azimuth))
return azimuth
def getAverageAzimuthFromSeries(imgtree):
# this somewhat duplicates the getDefaultAzimuthFromLegionon but is slightly different
predict1 = apDatabase.getPredictionDataForImage(imgtree[0])
predict2 = apDatabase.getPredictionDataForImage(imgtree[-1])
phi1 = predict1[0]['predicted position']['phi'] * 180 / math.pi
phi2 = predict2[0]['predicted position']['phi'] * 180 / math.pi
###Azimuth is determined from phi. In protomo tilt axis is measured from x where phi is from y
###Note there is a mirror between how Leginon reads images vs how protomo does
azimuth = 90 - ((phi1 + phi2) / 2)
apDisplay.printMsg(("Azimuth is %f" % azimuth))
return azimuth
def getAverageAzimuthFromSeries(imgtree):
# this somewhat duplicates the getDefaultAzimuthFromLegionon but is slightly different
predict1 = apDatabase.getPredictionDataForImage(imgtree[0])
predict2 = apDatabase.getPredictionDataForImage(imgtree[-1])
try:
try:
phi1 = predict1[0]["predicted position"]["phi"] * 180 / math.pi
phi2 = predict2[0]["predicted position"]["phi"] * 180 / math.pi
except: # Tilt-series was uploaded
phi1 = phi2 = imgtree[0]["scope"]["stage position"]["phi"]
except: # Phi was not recorded
apDisplay.printWarning("Azimuth was not recorded. Setting azimuth to -90 degrees, relative to the x-axis.")
phi1 = phi2 = 0
###Azimuth is determined from phi. In protomo tilt axis is measured from x where phi is from y
###Note there is a mirror between how Leginon reads images vs how protomo does
azimuth = -(90 - ((phi1 + phi2) / 2)) # Made negative because now images are y-flipped because Protomo
apDisplay.printMsg(("Azimuth is %f (relative to y-flipped images)" % azimuth))
return azimuth
zerotilts=[]
for n in range(len(imgtree)):
print "determining parameters for", imgtree[n]['filename']
imdict={}
imdictkey=n+1
#assign parameters to ptdict
tilt=imgtree[n]['scope']['stage position']['a']*180/math.pi
imdict['tilt']=tilt
origx=imgtree[n]['camera']['dimension']['x']/2
origy=imgtree[n]['camera']['dimension']['y']/2
imdict['rotation']=0
#determine shifts for each image from correlation during data collection
predictdata=apDatabase.getPredictionDataForImage(imgtree[n])
neworigx=origx-predictdata[0]['correlation']['x']
neworigy=origy+predictdata[0]['correlation']['y']
imdict['x']=neworigx
imdict['y']=neworigy
#set up names
imgpath=imgtree[n]['session']['image path']
presetname=imgtree[n]['preset']['name']
imgprefix=presetname+imgtree[n]['filename'].split(presetname)[-1]
imgname=imgprefix+'.img'
imdict['filename']=imgprefix
#create symlinks to files
if not os.path.exists(os.path.join(rawdir,imgname)):
os.symlink(os.path.join(imgpath,imgtree[n]['filename']+'.mrc'),os.path.join(rawdir,imgname))
zerotilts = []
for n in range(len(imgtree)):
print "determining parameters for", imgtree[n]['filename']
imdict = {}
imdictkey = n + 1
#assign parameters to ptdict
tilt = imgtree[n]['scope']['stage position']['a'] * 180 / math.pi
imdict['tilt'] = tilt
origx = imgtree[n]['camera']['dimension']['x'] / 2
origy = imgtree[n]['camera']['dimension']['y'] / 2
imdict['rotation'] = 0
#determine shifts for each image from correlation during data collection
predictdata = apDatabase.getPredictionDataForImage(imgtree[n])
neworigx = origx - predictdata[0]['correlation']['x']
neworigy = origy + predictdata[0]['correlation']['y']
imdict['x'] = neworigx
imdict['y'] = neworigy
#set up names
imgpath = imgtree[n]['session']['image path']
presetname = imgtree[n]['preset']['name']
imgprefix = presetname + imgtree[n]['filename'].split(presetname)[-1]
imgname = imgprefix + '.img'
imdict['filename'] = imgprefix
#create symlinks to files
if not os.path.exists(os.path.join(rawdir, imgname)):
os.symlink(os.path.join(imgpath, imgtree[n]['filename'] + '.mrc'),