def makefft(img,pad=0):
d=darc.Control("SH")
return correlation.transformPSF(img,3,[256,256,128],[128]*3,[98,98,49],d.Get("subapLocation"),d.Get("subapFlag"),pad)
dmflag = tel.Pupil(8, 4, 0).fn.ravel()
numpy.put(tmp, dmflag.nonzero()[0], numpy.arange(52))
corrClip = 1
corrNStore = 20
corrThresh = 0.0
corrThreshType = 2 #CORR_FRAC_SUB
corrUpdateGain = 0.
centroidMode = "CorrelationCoG"
psf = FITS.Read(fname.strip("\0"))[1].mean(0).ravel() - bgImage
psf = numpy.where(psf < 0, 0, psf)
import correlation
corrdata = correlation.transformPSF(
psf, ncam, npxlx, npxly, nsub, subapLocation, subapFlag, pad=8
) #Note - to get identical slope estimates when doing auto correlation update and using the correlation result for shift and add (corrUpdateToCoG=0), probably need pad to be larger, to avoid wrapping. But in practice, the difference in results is small.
corrFFTPattern = corrdata["corrFFTPattern"]
corrSubapLoc = corrdata["corrSubapLoc"]
corrNpxlx = corrdata["corrNpxlx"]
corrNpxlCum = corrdata["corrNpxlCum"]
#Now populate the control structure - this is what gets used.
control = {
"switchRequested":
0, #this is the only item in a currently active buffer that can be changed...
"pause": 0,
"go": 1,
"maxClipped": nacts,
"refCentroids": None,
"mirrorName":
"dmcSocket",
"mirrorOpen":
0,
"frameno":
0,
"switchTime":
numpy.zeros((1, ), "d")[0],
"adaptiveWinGain":
0.5,
"corrThreshType":
0,
"corrThresh":
0.,
"corrFFTPattern":
correlation.transformPSF(correlationPSF, ncam, npxlx, npxly, nsub,
subapLocation, subapFlag),
# "correlationPSF":correlationPSF,
"nsubapsTogether":
1,
"nsteps":
0,
}
#set the gain array
control["gain"][:2] = 0.5
# Note, gain is NOT used by the reconstructor - here, we assume that rows of the reconstructor have already been multiplied by the appropriate gain. Similarly, the rows of E have been multiplied by 1-gain. This multiplication is handled transparently by the GUI.
# set up the pxlCnt array - number of pixels to wait until each subap is ready. Here assume identical for each camera.
for k in range(ncam):
# tot=0#reset for each camera
for i in range(nsuby[k]):
for j in range(nsubx[k]):
#plt.show()
nacts = 140 #The total number of actuators for the system.
ncam = 1 #This is the number of camera objects in the system
npxly = numpy.zeros((ncam,), numpy.int32)#An array of length ncam, specifying the number of pixels in a vertical direction with an entry for each frame grabber.
npxly[:] = 1080#An array of length ncam, specifying the number of pixels in a vertical direction with an entry for each frame grabber.
npxlx = npxly.copy()#An array of length ncam, specifying the number of pixels in a horizontal direction with an entry for each frame grabber.
npxlx[:] = 1920#An array of length ncam, specifying the number of pixels in a horizontal direction with an entry for each frame grabber.
nsuby = npxly.copy()
nsuby[:] = 1#this is science, so only one subap required...
nsubx = nsuby.copy()
nsub = nsubx*nsuby #An array with ncam entries, specifying the number of sub-apertures for each frame grabber.
nsubaps = (nsuby*nsubx).sum() #nsubaps is the total number of sub-apertures
subapFlag = numpy.ones((nsubaps,), "i") #An array of size equal to the total number of sub-apertures, with a flag value for each, specifying whether this sub-aperture should be used.
# correlation.transformPSF(img,ncam,npxlx,npxly,nsub, subapLocation,d.Get("subapFlag"),pad)
correlation.transformPSF(img,ncam,npxlx,npxly,nsub, d.Get("subapLocation"),d.Get("subapFlag"),pad,savespace=1)
#def transformPSF(psf,ncam,npxlx,npxly,nsub,subapLocation,subflag,pad=None,savespace=0):
# """Function to transform psf into a form that will be usable by the RTC.
# psf is eg the LGS spot elongation pattern. ncam is number of cameras
# npxlx/y is array of size ncam with number of pixels for each camera nsubx/y
# is array size ncam with number of subaps for each camera subapLocation is
# array containing subap location details, one set of entries per
# subaperture. If pad is set, it will pad the fft pattern, and also return
# lots of other things required by darc. If savespace is also set (as per
# default), the returned fft pattern subaperture locations will bear no
# resemblance to the wavefront sensors.
(slopes, usenow) = getSlopes(img)
print slopes
print usenow
print slopes.size
