def ffp_dax(filename,xmlname):
print "ffpdax started at " + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + " on file: " + filename
#read in the settings in the .xml file using hazen's Parameter Class
par = params.Parameters(xmlname+'.xml') #par is an object of type Parameters, defined in sa_library
#to access parameters, use par.parameter name. eg par.start_frame
#note these values can be manually changed: par.frameset = 200 replaces whatever was there.
par = fixpar.fix_par(par,filename,'ffpdax') #function to 'fix' par by reading in needed stuff from setup file,
#and making other changes which might be needed based on the settings -
#eg round frame numbers to mutiple of 4 if alternating laser on STORM2
print "x pixels: %d. y pixels: %d" %(par.dimx,par.dimy)
if par.emchs == 2: #read in mapping files
Pr2l, Qr2l = mapcoords.readmapping(par,'r2l')
Pl2r, Ql2r = mapcoords.readmapping(par,'l2r')
#generate mapx and mapy for l-->r map (transforms right ch onto left)
mapxl2r,mapyl2r = mapcoords.genmapxy(par,Pl2r,Ql2r) #does not include the dimx/2 offset
fileptr = open(filename+'.dax','rb')
#open the dax file
#print "Filename: ", fileptr.name
if par.d3peaks == 1:
no_sets = long(math.floor(float(par.max_frame - par.start_frame + 1) / float(par.frameset)))
#arrays for the pks data
active = np.zeros((3,50000))
complete = np.zeros((4,50000))
#keep track of how many active, complete
no_a = 0 #number active
no_com = 0#number complete
#start reading in frame sets
currset_st = long(par.start_frame) #start frame of the current set of interest
frames = np.zeros((par.dimy,par.dimx,par.frameset)) #note: x value is column, y value is row.
print "number sets: %i" %no_sets
for i in range(0,no_sets): #goes from 0 to no_sets -1
if i % 1000 == 0: print "working on %i" %i #keep track of progress
for k in range(0,par.frameset):
frame = loadframe.load_frame(fileptr,currset_st+k,par)
#im =Image.fromarray(frame)
#im.show()
frame.astype(float) #change to float
frames[:,:,k] = frame
#if ALEX4 =1, pick out subset of frames to be used, based on pickcol. otherwise, use all frames
if par.ALEX4 ==1:
if par.pickcol == 0:
rframes = np.zeros((par.dimy,par.dimx,(par.frameset)/4))
for k in range(0,par.frameset,4):
rframes[:,:,k/4] = (frames[:,:,k] + frames[:,:,k+1])/2
elif par.pickcol ==1:
rframes = np.zeros((par.dimy,par.dimx,par.frameset/4))
for k in range(2,par.frameset,4):
rframes[:,:,(k-2)/4] = (frames[:,:,k] + frames[:,:,k+1])/2
elif par.pickcol ==2:
rframes = np.zeros((par.dimy,par.dimx,par.frameset/2))
for k in range(0,par.frameset,2):
rframes[:,:,k/2] = (frames[:,:,k]+frames[:,:,k+1])/2
else:
print "That's not a pickcol option!"
break
else:
rframes = frames
#next, median filter the frames in the frameset
medimg = np.median(rframes, axis = 2)
#background for medimg
fr_bk = smbkgr.sm_bkgr(medimg,par.bksize) #seems okay; check once showing images added
medimg = medimg-fr_bk
#deal with multiple emission channels -- either use one, or combine after mapping.
if par.emchs ==1:
pass #one channel - no subset is picked out.
elif par.emchs ==2:
if par.pickchan ==0: #'left' channel
medimg = medimg[:,0:par.dimx/2]
elif par.pickchan ==1: #'right' channel; still find peaks in left coords so we get both.
src = np.zeros((par.dimy,par.dimx/2,3))
src[:,:,0] = medimg[:,par.dimx/2:par.dimx]
mapyl2rCV = mapyl2r.astype(np.float32)
mapxl2rCV = mapxl2r.astype(np.float32)
rightmapped = cv2.remap(src,mapxl2rCV,mapyl2rCV,interpolation = cv2.INTER_CUBIC)
medimg = rightmapped[:,:,0]
elif par.pickchan ==2: #combine channels after warping. Using the 'left' channel coordinates
print 'warping is lightly tested. Appears to work.'
#medimg = medimg[:,0:par.dimx/2] + transform.warp(medimg[:,par.dimx/2:par.dimx],tformr2l)
#medimg = medimg[:,0:par.dimx/2] + medimg[:,par.dimx/2:par.dimx] #FOR TESTING ONLY. NOT WARPED
#templeft = medimg[:,0:par.dimx/2]
#tempright = medimg[:,par.dimx/2:par.dimx]
src = np.zeros((par.dimy,par.dimx/2,3))
src[:,:,0] = medimg[:,par.dimx/2:par.dimx]
mapyl2rCV = mapyl2r.astype(np.float32)
mapxl2rCV = mapxl2r.astype(np.float32)
rightmapped = cv2.remap(src,mapxl2rCV,mapyl2rCV,interpolation = cv2.INTER_CUBIC)
rightmapped = cv2.remap(src,mapxl2rCV,mapyl2rCV,interpolation = cv2.INTER_CUBIC)
#medimg = medimg[:,0:par.dimx/2] + cv2.remap(rightch,mapyl2r,mapxl2r,interpolation = cv2.INTER_CUBIC)
medimg = medimg[:,0:par.dimx/2] + rightmapped[:,:,0]
#medimg = rightmapped[:,:,0]
else:
print "Not set up for more than two emission channel"
#scale the med_img for display. for convenience, also find peaks in the scaled image
medimg=255.0*(medimg+par.disp_off)/par.disp_fact
#requires scaling factors to be known ahead of time. tricky to pick out of the data -often nothing present at the begining
#ready to go find peaks!
sliceresult = ffpslice.ffp_slice(medimg,currset_st,par) #these are all 'raw' results in medimg frame. Wait until the very end to map, if needed
current = sliceresult[0]
#fixme: if number ch > 1, somewhere, output all channels with picked peaks circled
#add current peaks to active unless they are already present
no_cu = current.shape[1]
#print "number current %d" %no_cu
for c in range(0,no_cu):
xc = current[0,c]
yc = current[1,c]
ID = 0 #has it been found?
if xc > 0.1: #real peaks aren't at zero
for a in range(0,no_a):
distance = ((xc - active[0,a])**2 + (yc - active[1,a])**2)**0.5
#print distance
if distance < par.dist_thr:
ID = 1 #this peak is already in active.
break #can stop looking for it
if ID == 0:
active[:,no_a] = [xc,yc,float(currset_st)]
no_a +=1
#move peaks from active to complete if they aren't found in keep
n_t = 0 #number moved
temp_active = np.zeros((3,50000))
if par.keeptype == 0:
for a in range(0,no_a): #for each active peak, look through keep to decide whether to keep it active
keep = sliceresult[1]
xa = active[0,a]
ya = active[1,a]
ID = 0
for c in range(0,no_cu):
distance = ((xa - keep[0,c])**2 + (ya - keep[1,c])**2)**0.5
if distance < dist_thr :
ID = 1
break
if ID ==1:
temp_active[:,n_t] = active[:,a]
n_t = n_t + 1
else: #event over. move to complete, but only if long enough but not too long
if((currset_st - active[2,a]) > par.length_thr) and ((currset_st - active[2,a]) < par.max_len):
complete[0:3,no_com] = active[:,a]
complete[3,no_com] = float(currset_st-1)
no_com +=1
else: #keeptype = 1
keep = ffpslice.ffp_keep(medimg,currset_st,par,active[:,0:no_a])
for a in range(0,no_a):
if keep[a] == 0: #event a is done
if((currset_st - active[2,a]) > par.length_thr) and ((currset_st - active[2,a]) < par.max_len):
complete[0:3,no_com] = active[:,a]
complete[3,no_com] = float(currset_st-1)
no_com +=1
else: #keep on active list
temp_active[:,n_t] = active[:,a]
n_t +=1
active = temp_active
no_a = n_t
currset_st += par.frameset
#end of analysis for this frameset
#once we're done flipping through sets, move everything from active to complete, assuming long enough
#print 'num active: %d' %no_a
for a in range(0,no_a):
if (((par.max_frame - active[2,a]) > par.length_thr) and ((par.max_frame - active[2,a]) < par.max_len)) :
complete[0:3,no_com] = active[:,a]
complete[3,no_com] = float(par.max_frame)
no_com += 1
if no_com > 0:
print "there were %d events" %no_com
times = complete[3,0:no_com] - complete[2,0:no_com] + 1
print 'average event length: %f' %float(np.mean(times))
print 'median event length: %f' %float(np.median(times))
else:
times = np.zeros((1,1))
#now, if appropriate, map the values
if par.emchs ==1:
pass
elif par.emchs == 2:
#mapping - have 'left' coordinates; need 'right' channel coordinates too (**this is true even if picking on the right side, since it is mapped)
#first, expand complete to have space for more coordinates.
bigcomplete = np.zeros((6,no_com))
bigcomplete[0:2,:] = complete[0:2,0:no_com]
bigcomplete[4:6,:] = complete[2:4,0:no_com]
for a in range(0,no_com):
bigcomplete[2:4,a]=mapcoords.map_coords(bigcomplete[0,a],bigcomplete[1,a],Pl2r,Ql2r)
#bigcomplete[2:4,a] = complete[0:2,a] #testing only. for null mapping
bigcomplete[2,a] += par.dimx/2
bigcomplete[2,a] = 0.5*round(bigcomplete[2,a]*2,0)
bigcomplete[3,a] =0.5*round(bigcomplete[3,a]*2,0)
complete = bigcomplete
#then, need to make sure all the mapped positions are also acceptable . if not, remove that peak!
#compare to par.frameborder
filtcomplete = np.zeros((6,no_com))
no_filt = 0
for a in range(0,no_com):
okay =1
if(complete[0,a]-par.frameborder < 0 or complete[0,a]+par.frameborder >par.dimx/2):
okay = 0
if(complete[1,a] -par.frameborder<0 or complete[1,a] +par.frameborder>par.dimy):
okay = 0
if(complete[2,a]-par.frameborder<par.dimx/2 or complete[2,a]+par.frameborder>par.dimx):
okay = 0
if(complete[3,a]-par.frameborder<0 or complete[3,a]+par.frameborder>par.dimy):
okay = 0
if(okay==1):
filtcomplete[:,no_filt] = complete[:,a]
no_filt += 1
complete = filtcomplete
no_com = no_filt
#save output as text file
c_tosave = np.zeros((complete.shape[0]+1,no_com))
c_tosave[1:complete.shape[0]+1,:] = complete[:,0:no_com]
for i in range(0,no_com):
c_tosave[0,i] =i
c_tosave = np.transpose(c_tosave)
format = ['%- i','%-.1f','%-.1f','%-.1f','%-.1f']
if par.emchs ==2:
format = ['%- i','%-.1f','%-.1f','%-.1f','%-.1f','%-.1f','%-.1f']
np.savetxt(filename+'.pks3d',c_tosave,fmt=format,delimiter='\t')
#make and save a histogram of the times
#hbinnum = np.round((times.max()-times.min())/par.frameset)
p95 = np.percentile(times,95)#make histogram look reasonable - don't display the very long tail
hbinnum = np.round((p95 - times.min())/par.frameset)
if hbinnum == 0: hbinnum =1
hist,binedges = np.histogram(times,bins=hbinnum,range = (times.min(),p95))
plt.plot(binedges[0:-1],hist)
plt.xlabel('duration,frames')
#plt.show()
plt.savefig(filename+'durationhist.jpeg')
else:
print "not set up for this yet. use IDL code or add here"
#save par object as an xml file. mostly the same as the input file, but some things are changed by fixpar.
outxml = filename + "ffpdaxOUT.xml"
writexml.write_xml(par,outxml,'ffpdax',filename,[no_com,np.mean(times),np.median(times)])
#close the dax file
fileptr.close()
print "done at " + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if par.autocont==1:
print 'automatically calling apdax'
#print 'apdax.py'
from apdax import ap_dax
ap_dax(filename,xmlname)
def ap_dax(filename, xmlname):
print "apdax started at " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " on file: " + filename
# read in the settings in the .xml file using hazen's Parameter Class
par = params.Parameters(xmlname + ".xml")
par = fixpar.fix_par(par, filename, "apdax") # function to 'fix' par by reading in needed stuff from setup file,
# and making other changes which might be needed based on the settings -
# eg round frame numbers to mutiple of 4 if alternating laser on STORM2
if par.emchs == 2: # read in mapping files
Pr2l, Qr2l = mapcoords.readmapping(par, "r2l")
Pl2r, Ql2r = mapcoords.readmapping(par, "l2r")
# for now, not using CMOS calibration. FIXME
if par.hcam_cal == 1:
print "Not set up for CMOS calibration"
# for now, only set up for .trdir output and .pks3d input
if par.pks_type == 0:
print "Not set up for .pks file. Only .pks3d"
if par.outtype == 0:
print "Not set up for .traces file. only trdir"
if par.mt == 1: # gaussian mask
g_peaks = gengauss.gen_gauss()
# read in peaks info
if par.pks_type == 0:
print "not set up for pks file"
elif par.pks_type == 1:
pkfile = filename + ".pks3d"
peaks = loadpeaks.load_peaks(pkfile, par) # loads and processes (adds buffer) peaks file
else:
print "format not recognized"
no_peaks = peaks.shape[0]
peaks_dim = peaks.shape[1] # useful since since of peaks depends on analysis type
# make list of arrays to hold data. allows unequal lengths for different traces
time_tr = [] # for intensities. always need.
crds_tr = [] # for fitting. sometimes need.
for p in range(0, no_peaks):
curlen = int(peaks[p, peaks_dim - 1] - peaks[p, peaks_dim - 2]) + 1
if par.ALEX4 == 1:
curlen = curlen / 2 # note this rounds down
# note colors are interleaved. emchs is based on emission path
time_tr.append(np.zeros((par.emchs, curlen)))
crds_tr.append(np.zeros((par.emchs, curlen, 8)))
# for holding coordinates (x,y, x_stdev, y_stdev, fitting flag, quality metric, tilt angle, fit height)
# keep track of which position in the array next frame's info will be added to.
addfr = np.zeros(no_peaks)
# fixme: use peak position to decide where to center gaussian mask. see idl code.
# start going through the frames.
incr = 1
if par.ALEX4 == 1:
incr = 2 # go through frames 2 at a time
fileptr = open(filename + ".dax", "rb")
# if using constant background subtraction, determine that now:
if par.bst == 0:
# load first 10 frames to determine background
frs = loadframe.load_frame(fileptr, 0, par)
for i in range(1, 10):
frs += loadframe.load_frame(fileptr, i, par)
frs = frs / 10
fr_bkgd = smbkgr.sm_bkgr(frs, par.bksize)
# fitting stuff. fixme: choose fit type in xml file? for now, don't allow tilt
fitfunc = lambda p, x, y: p[0] + p[1] * np.exp(-((x - p[2]) / p[4]) ** 2 - ((y - p[3]) / p[5]) ** 2)
errfunc = lambda p, x, y, data: np.ravel(fitfunc(p, x, y) - data)
fitxval = np.zeros((par.fit_box * 2 + 1, par.fit_box * 2 + 1)) # independent variables for fit
fityval = np.zeros((par.fit_box * 2 + 1, par.fit_box * 2 + 1))
for i in range(0, par.fit_box * 2 + 1): # fill arrays with row or column number
fitxval[:, i] = i
fityval[i, :] = i
fitxval1D = np.ravel(fitxval)
fityval1D = np.ravel(fityval)
# fixme: adjust error function to allow weighting
for i in range(par.apst_fr, par.apmax_fr + 1, incr): # i is always a real camera frame number
if i % 1000 == 0:
print "working on : " + str(i) + " " + str(par.apmax_fr) + "at " + datetime.datetime.now().strftime(
"%Y-%m-%d %H:%M:%S"
)
if par.ALEX4 == 0:
frame = loadframe.load_frame(fileptr, i, par)
if par.ALEX4 == 1:
frame1 = loadframe.load_frame(fileptr, i, par)
frame2 = loadframe.load_frame(fileptr, i + 1, par)
frame = frame1 + frame2
# either way, now work with frame array
# if set to determine background on each frame, do that
if par.bst == 1:
fr_bkgd = smbkgr.sm_bkgr(frame, par.bksize)
# analyze each trace
for j in range(0, no_peaks):
if peaks[j, peaks_dim - 1] >= i and peaks[j, peaks_dim - 2] <= i: # this event is happening now!
for ch in range(0, par.emchs):
# determine intensity
curx = math.floor(peaks[j, 1 + 2 * ch])
cury = math.floor(peaks[j, 2 + 2 * ch])
if par.mt == 0: # simple integration
local = (
frame[cury - par.sibs : cury + par.sibs + 1, curx - par.sibs : curx + par.sibs + 1]
- fr_bkgd[cury - par.sibs : cury + par.sibs + 1, curx - par.sibs : curx + par.sibs + 1]
)
time_tr[j][ch, addfr[j]] = np.sum(local)
elif par.mt == 1: # gaussian masking
print "not set up for gaussian masking yet"
# if appropriate, fit
if time_tr[j][ch, addfr[j]] > par.fit_thr:
# use least squares optimization
# loc = np.ravel(frame[cury-par.fit_box:cury+par.fit_box+1,curx-par.fit_box:curx+par.fit_box+1])
loc = frame[
cury - par.fit_box : cury + par.fit_box + 1, curx - par.fit_box : curx + par.fit_box + 1
]
[yguess, xguess] = np.unravel_index(loc.argmax(), loc.shape)
p0 = np.array(
[float(fr_bkgd[cury, curx]), float(loc[yguess, xguess]), xguess, yguess, 1.1, 1.1]
) # a smarter initial guess
loc = np.ravel(loc)
# p0 = np.array([float(fr_bkgd[cury,curx]), float(frame[cury,curx]), par.fit_box,par.fit_box,1.0,1.0]) #initial guess
[p1, cov_x, infodict, mesg, success] = optimize.leastsq(
errfunc, p0, args=(fitxval1D, fityval1D, loc), full_output=1, xtol=par.fitxtol
)
# if fit successful and center is within box, store result
if (
success > 0
and success < 5
and p1[2] > 0
and p1[2] < (2 * par.fit_box + 1)
and p1[3] > 0
and p1[3] < (2 * par.fit_box + 1)
):
xfitpos = p1[2] + curx - par.fit_box
yfitpos = p1[3] + cury - par.fit_box
if ch == 1: # map right channel onto the left.
xfitpos -= par.dimx / 2 # mapping takes relative coordinate - within own channel.
[xfitpos, yfitpos] = mapcoords.map_coords(xfitpos, yfitpos, Pr2l, Qr2l)
crds_tr[j][ch, addfr[j], :] = [
xfitpos,
yfitpos,
abs(p1[4]),
abs(p1[5]),
1.0,
0.0,
0.0,
p1[1],
]
# for coordinates (x,y, x_stdev, y_stdev, fitting flag, quality metric, tilt angle, fit height)
# fixme: not using any metric of quality.
addfr[j] = addfr[j] + 1
if i % par.outputpartial == 0 and i != 0:
savetrdir.save_trdir(filename, par, peaks, time_tr, crds_tr, 0)
fileptr.close()
# print "saving data at" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if par.outtype == 0:
print "not set up to save .traces file"
elif par.outtype == 1:
savetrdir.save_trdir(filename, par, peaks, time_tr, crds_tr, 1)
# output xml file with actual settings in par.
outxml = filename + "apdaxOUT.xml"
writexml.write_xml(par, outxml, "apdax", filename, [])
print "apdax done at " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " on file: " + filename
