def testCentroid(self):
print 'Centroid testing'
a = np.arange(12.)
x = np.arange(12.) + 2
ca = np.centroid(a)
self.assertEquals(ca[0], 539./66)
ca = np.centroid(a, x)
self.assertEquals(ca[0], 539./66 + 1.5)
a.shape = (3,4)
ca = np.centroid(a)
self.assertEquals(ca[0], 131./66)
self.assertEquals(ca[1], 147./66)
x = np.arange(3.) + 2
y = np.arange(4.) + 3
ca = np.centroid(a, [x,y])
self.assertEquals(ca[0], 131./66 + 1.5)
self.assertEquals(ca[1], 312./66) #147./66 + 2.5)
def testCentroid(self):
print 'Centroid testing'
a = np.arange(12.)
x = np.arange(12.) + 2
ca = np.centroid(a)
self.assertEquals(ca[0], 539. / 66)
ca = np.centroid(a, x)
self.assertEquals(ca[0], 539. / 66 + 1.5)
a.shape = (3, 4)
ca = np.centroid(a)
self.assertEquals(ca[0], 131. / 66)
self.assertEquals(ca[1], 147. / 66)
x = np.arange(3.) + 2
y = np.arange(4.) + 3
ca = np.centroid(a, [x, y])
self.assertEquals(ca[0], 131. / 66 + 1.5)
self.assertEquals(ca[1], 312. / 66) #147./66 + 2.5)
def analyseData(dd):
#ss = dd.sum(0)
#dnp.plot.image(ss)
##ss is the flat field
print "Calculating median for the dataset ..."
med = dnp.median(dd, 0)
print "Median calculated"
if plotData: dnp.plot.image(med)
Sleep.sleep(2000)
#flatFieldCorrectedSum = ss/med
#dnp.plot.image(flatFieldCorrectedSum)
xVals = []
yVals = []
##
print "Calculating centroid ..."
centroids = []
for i in range(18):
#dnp.plot.image(dd[i, :, :] / med)
im = dd[i, :, :] / med
im = javaImage.medianFilter(im, [3, 3])
im = dnp.array(im)
threshold = 0.85
if plotData: dnp.plot.image(im)
if plotData: Sleep.sleep(1000)
if plotData: dnp.plot.image(im < threshold)
if plotData: Sleep.sleep(1000)
cent = dnp.centroid(im < threshold)
yVals.append(cent[0])
xVals.append(cent[1])
centroids.append(cent)
print "y:" + `yVals`
print('')
print "x:" + `xVals`
#help(dnp.fit.ellipsefit)
if plotData: dnp.plot.line(dnp.array(xVals), dnp.array(yVals))
ellipseFitValues = dnp.fit.ellipsefit(xVals, yVals)
print "major: " + `ellipseFitValues[0]`
print "minor semi-axis: " + `ellipseFitValues[1]`
print "major axis angle: " + `ellipseFitValues[2]`
print "centre co-ord 1: " + `ellipseFitValues[3]`
print "centre co-ord 2: " + `ellipseFitValues[4]`
print "centroids:" + `centroids`
xOffset = getXOffset(ellipseFitValues[1], ellipseFitValues[0])
zOffset = getZOffset(ellipseFitValues[2])
return xOffset, zOffset, centroids, ellipseFitValues
def analyseData(dd):
#ss = dd.sum(0)
#dnp.plot.image(ss)
##ss is the flat field
print "Calculating median for the dataset ..."
med = dnp.median(dd, 0)
print "Median calculated"
if plotData: dnp.plot.image(med)
sleep(2)
#flatFieldCorrectedSum = ss/med
#dnp.plot.image(flatFieldCorrectedSum)
xVals = []
yVals = []
##
print "Calculating centroid ..."
centroids = []
for i in range(18):
#dnp.plot.image(dd[i, :, :] / med)
im = dd[i, :, :] / med
im = javaImage.medianFilter(im, [3, 3])
im = dnp.array(im)
threshold = 0.85
if plotData: dnp.plot.image(im)
if plotData: sleep(1)
if plotData: dnp.plot.image(im < threshold)
if plotData: sleep(1)
cent = dnp.centroid(im < threshold)
yVals.append(cent[0])
xVals.append(cent[1])
centroids.append(cent)
print "y:" + ` yVals `
print('')
print "x:" + ` xVals `
#help(dnp.fit.ellipsefit)
if plotData: dnp.plot.line(dnp.array(xVals), dnp.array(yVals))
ellipseFitValues = dnp.fit.ellipsefit(xVals, yVals)
print "major: " + ` ellipseFitValues[0] `
print "minor semi-axis: " + ` ellipseFitValues[1] `
print "major axis angle: " + ` ellipseFitValues[2] `
print "centre co-ord 1: " + ` ellipseFitValues[3] `
print "centre co-ord 2: " + ` ellipseFitValues[4] `
print "centroids:" + ` centroids `
xOffset = getXOffset(ellipseFitValues[1], ellipseFitValues[0])
zOffset = getZOffset(ellipseFitValues[2])
return xOffset, zOffset, centroids, ellipseFitValues
def sphere_alignment(threshold=0.55,
x_stage=ss1_x,
flat_field_position=-50,
projection_position=2.0,
theta_stage=ss1_theta,
exposure_time=0.06,
max_tries=15,
z_tilt_stage=ss1_rz,
x_tilt_stage=ss1_rx,
doscan=True,
working_directory='/dls/i12/data/2014/cm4963-2/rawdata',
flatfield_file=36466,
projection_file=36467):
print "Running sphere alignment"
# scan flat field
# Run the scan, then call sphere_alignmet_processing(flatfilename, projectionfilename)
if (doscan == True):
print " Moving to flatfield position" # this should be an optional step as it may not be possible to do this move without disrupting user equipment
#pos ss1.x -200
pos(x_stage, flat_field_position)
print "Collecting flat field image"
scan(ix, 1, 1, 1, pco4000_dio_hdf, exposure_time)
time.sleep(5)
working_dir = wd()
current_file = cfn()
filename = '%s/%d.nxs' % (working_dir, current_file)
print "Loading flat field image to data processing pipeline"
ff = None
try_number = 0
while (ff == None and try_number < max_tries):
try_number += 1
try:
data = dnp.io.load(filename)
ff = data['entry1']['pco4000_dio_hdf']['data'][...].mean(0)
except:
print("Failed to load %i, will try again in 5 seconds" %
(try_number))
time.sleep(5)
dnp.plot.image(ff)
print "Moving to sample position"
pos(x_stage, projection_position)
print "Collecting projections"
scan(theta_stage, 0, 360, 20, pco4000_dio_hdf, exposure_time)
working_dir = wd()
current_file = cfn()
filename = '%s/%d.nxs' % (working_dir, current_file)
time.sleep(5)
print "Loading projections to data processing pipeline"
#data = dnp.io.load(filename)
dd = None
try_number = 0
while (dd == None and try_number < max_tries):
try_number += 1
try:
data = dnp.io.load(filename)
dd = data['entry1']['pco4000_dio_hdf']['data']
except:
print("Failed to load %i, will try again in 5 seconds" %
(try_number))
time.sleep(5)
else:
print "Not Moving to flatfield position" # this should be an optional step as it may not be possible to do this move without disrupting user equipment
print "Not Collecting flat field image"
time.sleep(5)
working_dir = working_directory
current_file = flatfield_file
filename = '%s/%d.nxs' % (working_dir, current_file)
print "Loading flat field image to data processing pipeline"
ff = None
try_number = 0
while (ff == None and try_number < max_tries):
try_number += 1
try:
data = dnp.io.load(filename)
ff = data['entry1']['pco4000_dio_hdf']['data'][...].mean(0)
except:
print("Failed to load %i, will try again in 2 seconds" %
(try_number))
time.sleep(2)
dnp.plot.image(ff)
print "Not Moving to sample position"
print "Not Collecting projections"
working_dir = working_directory
current_file = projection_file
filename = '%s/%d.nxs' % (working_dir, current_file)
time.sleep(5)
print "Loading projections into data processing pipeline"
data = dnp.io.load(filename)
dd = data['entry1']['pco4000_dio_hdf']['data']
print "Processing data"
# apply any filtering
# exclude spheres which are not completely in the field of view
print "slow look at all the data"
preview = dd[:, :, :].mean(0) * 1.0 / ff
dnp.plot.image(preview[10:-1, :])
time.sleep(2)
print "Generating centroid data"
thresholdOK = False
xs = []
ys = []
while not thresholdOK:
xs = []
ys = []
for i in range(dd.shape[0]):
cor = dd[i, :, :] * 1.0 / ff
cor = cor[10:-1, :] < threshold
dnp.plot.clear()
#time.sleep(0.1)
dnp.plot.image(cor)
#time.sleep(.1)
x, y = dnp.centroid(cor)
xs.append(x)
ys.append(y)
xxx = dnp.array(ys)
yyy = dnp.array(xs)
responce = requestInput(
"Current threshold is %f, y for ok, otherwise enter a new threshold value "
% (threshold))
if responce == 'y':
thresholdOK = True
else:
try:
threshold = float(responce)
except:
print "could not interpret %s as a float" % responce
print "Fitting ellipse to centroid data"
ellipseFitValues = dnp.fit.ellipsefit(xxx, yyy)
t = dnp.arange(100) * dnp.pi / 50.
fitplot = dnp.fit.makeellipse(ellipseFitValues, t._jdataset())
dnp.plot.line(fitplot[0], fitplot[1])
time.sleep(1)
dnp.plot.addline(xxx, yyy)
#print("XXX is:")
#print (type(xxx))
#print(xxx)
print("ys is:")
type(xs)
print(xs)
print "Results of ellipse fitting routine:"
print " Major: %f" % (ellipseFitValues[0])
print " Minor semi-axis: %f" % (ellipseFitValues[1])
print " Major axis angle: %f" % (ellipseFitValues[2])
print " Centre co-ord 1: %f" % (ellipseFitValues[3])
print " Centre co-ord 2: %f" % (ellipseFitValues[4])
print "Calculating stage tilts"
xangle = math.fabs(math.atan2(ellipseFitValues[1], ellipseFitValues[0]))
#find out which direction
npoints = len(xs)
firstquarter = int(math.floor(npoints / 4.0))
lastquarter = int(math.floor(3.0 * npoints / 4.0))
print(" firstquarter %i %f lastquarter %i %f" %
(firstquarter, xs[firstquarter], lastquarter, xs[lastquarter]))
if (xs[firstquarter] > xs[lastquarter]):
xangle = -math.degrees(xangle)
print(" negative xangle %f" % xangle)
else:
xangle = math.degrees(xangle)
print(" positive xangle %f" % xangle)
#xangle = math.degrees(xangle) * -1.0
# This needs to be calculated correctly
zangle = math.degrees(ellipseFitValues[2])
if zangle > 90.0:
zangle -= 180.0
print xangle
print zangle
responce = requestInput("Would you like to rotate the X tilt stage by %f" %
(xangle))
if responce == 'y':
print "Moving x tilt stage"
pos(x_tilt_stage)
inc(x_tilt_stage, xangle)
pos(x_tilt_stage)
print "In position."
responce = requestInput("Would you like to rotate the Z tilt stage by %f" %
(zangle))
if responce == 'y':
print "Moving z tilt stage"
pos(z_tilt_stage)
inc(z_tilt_stage, zangle)
pos(z_tilt_stage)
print "In position."
print "sphere_alignment finished"
def get_image_data(
previewmean=False,
preview_frames=True,
visit_directory="/dls/i12/data/2014/cm4963-2/",
flatfile=37384,
projectionfile=37385,
threshold=0.5,
max_tries=15,
prompt=False,
request_input_command=request_DAWN_Input,
):
working_directory = "%s/rawdata" % visit_directory
flatfilename = "%s/%i.nxs" % (working_directory, flatfile)
projectionfilename = "%s/%i.nxs" % (working_directory, projectionfile)
# TODO put a unique time stamp on the otuput
# TODO make a folder for the outputs
print ("FlatFilename = %s" % flatfilename)
print ("projectionfilename = %s" % projectionfilename)
print "Loading flat field image %s\n\t to data processing pipeline" % flatfilename
ff = None
try_number = 0
while ff == None and try_number < max_tries:
try_number += 1
try:
print ("Trying to open the file %s" % (flatfilename))
data = dnp.io.load(flatfilename)
ff = data["entry1"]["pco4000_dio_hdf"]["data"][...].mean(0)
except:
print ("ff is not null is it %s" % ff)
print ("ff is not null is of type %s" % type(ff))
print ("Failed to load %i, will try again in 5 seconds" % (try_number))
time.sleep(5)
ff = dnp.array(javaImage.medianFilter(ff._jdataset(), [3, 3]))
dnp.plot.image(ff)
time.sleep(5)
print "Loading projections %s into data processing pipeline" % projectionfilename
data = dnp.io.load(projectionfilename)
try:
dd = data["entry1"]["pco4000_dio_hdf"]["data"]
except:
print "Couldn not find the data in %s" % projectionfilename
raise
print "Processing data"
# apply any filtering
# exclude spheres which are not completely in the field of view
if previewmean:
print "slow look at all the data"
preview = dd[:, :, :].mean(0) * 1.0 / ff
dnp.plot.image(preview[10:-1, :])
time.sleep(2)
print "Generating centroid data"
thresholdOK = False
xs = []
ys = []
while not thresholdOK:
xs = []
ys = []
for i in range(dd.shape[0]):
cor = dd[i, :, :] * 1.0 / ff
# print "Applying median filter"
cor = dnp.array(javaImage.medianFilter(cor._jdataset(), [3, 3]))
cor = cor[10:-1, :] < threshold
if preview_frames:
dnp.plot.clear()
time.sleep(0.01)
dnp.plot.image(cor)
time.sleep(0.2)
y, x = dnp.centroid(cor)
xs.append(x)
ys.append(y)
if prompt == False:
thresholdOK = True
else:
response = request_input_command(
"Current threshold is %f, y for ok, otherwise enter a new threshold value " % (threshold)
)
if response == "y":
thresholdOK = True
else:
try:
threshold = float(response)
except:
print "could not interpret %s as a float" % response
return (xs, ys)
def sphere_alignment(threshold=0.55, x_stage=ss1_x, flat_field_position=-50, projection_position=2.0, theta_stage=ss1_theta, exposure_time=0.06, max_tries=15, z_tilt_stage=ss1_rz, x_tilt_stage=ss1_rx,doscan=True, working_directory='/dls/i12/data/2014/cm4963-2/rawdata', flatfield_file=36466, projection_file=36467):
print "Running sphere alignment"
# scan flat field
# Run the scan, then call sphere_alignmet_processing(flatfilename, projectionfilename)
if (doscan == True):
print " Moving to flatfield position" # this should be an optional step as it may not be possible to do this move without disrupting user equipment
#pos ss1.x -200
pos(x_stage,flat_field_position)
print "Collecting flat field image"
scan(ix,1,1,1,pco4000_dio_hdf,exposure_time)
time.sleep(5)
working_dir = wd()
current_file = cfn()
filename = '%s/%d.nxs' % (working_dir, current_file)
print "Loading flat field image to data processing pipeline"
ff = None
try_number = 0
while (ff == None and try_number < max_tries):
try_number += 1
try:
data = dnp.io.load(filename)
ff = data['entry1']['pco4000_dio_hdf']['data'][...].mean(0)
except:
print("Failed to load %i, will try again in 5 seconds" % (try_number))
time.sleep(5)
dnp.plot.image(ff)
print "Moving to sample position"
pos(x_stage,projection_position)
print "Collecting projections"
scan(theta_stage,0,360,20,pco4000_dio_hdf,exposure_time)
working_dir = wd()
current_file = cfn()
filename = '%s/%d.nxs' % (working_dir, current_file)
time.sleep(5)
print "Loading projections to data processing pipeline"
#data = dnp.io.load(filename)
dd = None
try_number = 0
while (dd == None and try_number < max_tries):
try_number += 1
try:
data = dnp.io.load(filename)
dd = data['entry1']['pco4000_dio_hdf']['data']
except:
print("Failed to load %i, will try again in 5 seconds" % (try_number))
time.sleep(5)
else:
print "Not Moving to flatfield position" # this should be an optional step as it may not be possible to do this move without disrupting user equipment
print "Not Collecting flat field image"
time.sleep(5)
working_dir = working_directory
current_file=flatfield_file
filename = '%s/%d.nxs' % (working_dir, current_file)
print "Loading flat field image to data processing pipeline"
ff = None
try_number = 0
while (ff == None and try_number < max_tries):
try_number += 1
try:
data = dnp.io.load(filename)
ff = data['entry1']['pco4000_dio_hdf']['data'][...].mean(0)
except:
print("Failed to load %i, will try again in 2 seconds" % (try_number))
time.sleep(2)
dnp.plot.image(ff)
print "Not Moving to sample position"
print "Not Collecting projections"
working_dir = working_directory
current_file=projection_file
filename = '%s/%d.nxs' % (working_dir, current_file)
time.sleep(5)
print "Loading projections into data processing pipeline"
data = dnp.io.load(filename)
dd = data['entry1']['pco4000_dio_hdf']['data']
print "Processing data"
# apply any filtering
# exclude spheres which are not completely in the field of view
print "slow look at all the data"
preview = dd[:,:,:].mean(0)*1.0/ff
dnp.plot.image(preview[10:-1,:])
time.sleep(2)
print "Generating centroid data"
thresholdOK = False
xs = []
ys = []
while not thresholdOK:
xs = []
ys = []
for i in range(dd.shape[0]):
cor = dd[i,:,:]*1.0/ff
cor = cor[10:-1,:]<threshold
dnp.plot.clear()
#time.sleep(0.1)
dnp.plot.image(cor)
#time.sleep(.1)
x,y = dnp.centroid(cor)
xs.append(x)
ys.append(y)
xxx = dnp.array(ys)
yyy = dnp.array(xs)
responce = requestInput("Current threshold is %f, y for ok, otherwise enter a new threshold value " % (threshold))
if responce == 'y' :
thresholdOK = True
else :
try :
threshold = float(responce)
except :
print "could not interpret %s as a float" % responce
print "Fitting ellipse to centroid data"
ellipseFitValues = dnp.fit.ellipsefit(xxx, yyy)
t = dnp.arange(100)*dnp.pi/50.
fitplot = dnp.fit.makeellipse(ellipseFitValues, t._jdataset())
dnp.plot.line(fitplot[0], fitplot[1])
time.sleep(1)
dnp.plot.addline(xxx,yyy)
#print("XXX is:")
#print (type(xxx))
#print(xxx)
print ("ys is:")
type (xs)
print(xs)
print "Results of ellipse fitting routine:"
print " Major: %f" % (ellipseFitValues[0])
print " Minor semi-axis: %f" % (ellipseFitValues[1])
print " Major axis angle: %f" % (ellipseFitValues[2])
print " Centre co-ord 1: %f" % (ellipseFitValues[3])
print " Centre co-ord 2: %f" % (ellipseFitValues[4])
print "Calculating stage tilts"
xangle = math.fabs( math.atan2(ellipseFitValues[1], ellipseFitValues[0]))
#find out which direction
npoints=len(xs)
firstquarter=int(math.floor(npoints/4.0))
lastquarter=int(math.floor(3.0*npoints/4.0))
print(" firstquarter %i %f lastquarter %i %f"%(firstquarter,xs[firstquarter],lastquarter,xs[lastquarter]) )
if (xs[firstquarter] > xs[lastquarter]):
xangle=-math.degrees(xangle)
print(" negative xangle %f"%xangle)
else:
xangle=math.degrees(xangle)
print(" positive xangle %f"%xangle)
#xangle = math.degrees(xangle) * -1.0
# This needs to be calculated correctly
zangle = math.degrees(ellipseFitValues[2])
if zangle > 90.0:
zangle -= 180.0
print xangle
print zangle
responce = requestInput("Would you like to rotate the X tilt stage by %f" % (xangle))
if responce == 'y' :
print "Moving x tilt stage"
pos(x_tilt_stage)
inc(x_tilt_stage,xangle)
pos(x_tilt_stage)
print "In position."
responce = requestInput("Would you like to rotate the Z tilt stage by %f" % (zangle))
if responce == 'y' :
print "Moving z tilt stage"
pos(z_tilt_stage)
inc(z_tilt_stage,zangle)
pos(z_tilt_stage)
print "In position."
print "sphere_alignment finished"
def get_image_data(previewmean=False, preview_frames=True,visit_directory='/dls/i12/data/2014/cm4963-2/',flatfile=37384, projectionfile=37385, threshold=0.5, max_tries=15,prompt=False, request_input_command=request_DAWN_Input):
working_directory="%s/rawdata"%visit_directory
flatfilename="%s/%i.nxs"%(working_directory,flatfile)
projectionfilename="%s/%i.nxs"%(working_directory,projectionfile)
#TODO put a unique time stamp on the otuput
#TODO make a folder for the outputs
print("FlatFilename = %s"% flatfilename)
print("projectionfilename = %s"% projectionfilename)
print "Loading flat field image %s\n\t to data processing pipeline" % flatfilename
ff = None
try_number = 0
while (ff == None and try_number < max_tries):
try_number += 1
try:
print("Trying to open the file %s" % (flatfilename))
data = dnp.io.load(flatfilename)
ff = data['entry1']['pco4000_dio_hdf']['data'][...].mean(0)
except:
print("ff is not null is it %s" % ff)
print("ff is not null is of type %s" % type(ff))
print("Failed to load %i, will try again in 5 seconds" % (try_number))
time.sleep(5)
ff = dnp.array(javaImage.medianFilter(ff._jdataset(), [3, 3]))
dnp.plot.image(ff)
time.sleep(5)
print "Loading projections %s into data processing pipeline"% projectionfilename
data = dnp.io.load(projectionfilename)
try:
dd = data['entry1']['pco4000_dio_hdf']['data']
except:
print"Couldn not find the data in %s"%projectionfilename
raise
print "Processing data"
# apply any filtering
# exclude spheres which are not completely in the field of view
if (previewmean):
print "slow look at all the data"
preview = dd[:,:,:].mean(0)*1.0/ff
dnp.plot.image(preview[10:-1,:])
time.sleep(2)
print "Generating centroid data"
thresholdOK = False
xs = []
ys = []
while not thresholdOK:
xs = []
ys = []
for i in range(dd.shape[0]):
cor = dd[i,:,:]*1.0/ff
#print "Applying median filter"
cor = dnp.array(javaImage.medianFilter(cor._jdataset(), [3, 3]))
cor = cor[10:-1,:]<threshold
if (preview_frames):
dnp.plot.clear()
time.sleep(0.01)
dnp.plot.image(cor)
time.sleep(.2)
y,x = dnp.centroid(cor)
xs.append(x)
ys.append(y)
if (prompt==False):
thresholdOK=True
else:
response = request_input_command("Current threshold is %f, y for ok, otherwise enter a new threshold value " % (threshold))
if response == 'y' :
thresholdOK = True
else :
try :
threshold = float(response)
except :
print "could not interpret %s as a float" % response
return(xs,ys)
#res = javaImage.medianFilter(res, [3, 3])
#
dnp.plot.image(res)
res = res > 0.5
dnp.plot.image(res)
print "Calculting coms"
points = []
for i in range(res.shape[1]):
a = res[:,i:i+1].mean(1)
points.append(dnp.centroid(a))
coms = dnp.array(points)
coms.squeeze()
dnp.plot.line(coms)
p = dnp.fit.polyfit(dnp.arange(coms.shape[0]), coms, 1, full=True)
p[1].plot()
rotation = math.degrees(math.atan2(p[0][0],1.))
responce = requestInput("Would you like to rotate the camera stage by %f" % (rotation))
if responce == 'y' :
print "Moving cam1.roll"
