def genTheoreticalModel(md, zernikes={}, **kwargs):
from PYME.Analysis.PSFGen import fourierHNA
global IntXVals, IntYVals, IntZVals, dx, dy, dz
if True: #not dx == md.voxelsize.x*1e3 or not dy == md.voxelsize.y*1e3 or not dz == md.voxelsize.z*1e3:
vs = md.voxelsize_nm
IntXVals = vs.x * np.mgrid[-150:150]
IntYVals = vs.y * np.mgrid[-150:150]
IntZVals = vs.z * np.mgrid[-30:30]
dx, dy, dz = vs
P = np.arange(0, 1.01, .01)
#interpModel = genWidefieldPSF(IntXVals, IntYVals, IntZVals, P ,1e3, 0, 0, 0, 2*pi/525, 1.47, 10e3).astype('f')
im = fourierHNA.GenZernikeDPSF(IntZVals,
zernikes,
X=IntXVals,
Y=IntYVals,
dx=vs.x,
**kwargs)
#print('foo')
#print((interpModel.strides, interpModel.shape))
for i in range(1, len(interpModel_by_chan)):
interpModel_by_chan[i] = None
interpModel_by_chan[0] = np.maximum(
im / im[:, :, int(len(IntZVals) / 2)].sum(),
0) #normalise to 1 and clip
def OnPSFFromZernikeModes(self, event):
import numpy as np
#import pylab
from PYME.Analysis.PSFGen import fourierHNA
from PYME.IO.image import ImageStack
from PYME.DSView import ViewIm3D
self.configure_traits(kind='modal')
z_ = np.arange(self.sizeZ)*float(self.zSpacing)
z_ -= z_.mean()
#if self.vectorial:
# ps = fourierHNA.PsfFromPupilVect(self.image.data[:,:], z_, self.image.mdh['voxelsize.x']*1e3, self.wavelength, apodization=self.apodization, NA=self.NA)#, shape = [self.sizeX, self.sizeX])
# #ps = abs(ps*np.conj(ps))
#else:
# ps = fourierHNA.PsfFromPupil(self.image.data[:,:], z_, self.image.mdh['voxelsize.x']*1e3, self.wavelength, apodization=self.apodization, NA=self.NA)#, shape = [self.sizeX, self.sizeX])
ps = fourierHNA.GenZernikeDPSF(z_, dx = self.image.voxelsize_nm.x,
zernikeCoeffs = self.dsviewer.zernModes, lamb=self.wavelength,
n=1.51, NA = self.NA, ns=1.51, beadsize=0,
vect=self.vectorial, apodization=self.apodization)
#ps = ps/ps[:,:,self.sizeZ/2].sum()
ps = ps/ps.max()
im = ImageStack(ps, titleStub = 'Generated PSF')
im.mdh.copyEntriesFrom(self.image.mdh)
im.mdh['Parent'] = self.image.filename
#im.mdh['Processing.CropROI'] = roi
mode = 'psf'
dv = ViewIm3D(im, mode=mode, glCanvas=self.dsviewer.glCanvas, parent=wx.GetTopLevelParent(self.dsviewer))
def fitallA(r2, mdh, zCoeffs, ns=1.51, axialShift=200.):
mdh = MetaDataHandler.NestedClassMDHandler(mdh)
mdh['Analysis.AxialShift'] = axialShift
voxelsize = mdh.voxelsize_nm
voxelsize.z = 50.
zs = 50. * np.arange(-30., 31)
p1 = fourierHNA.GenZernikeDPSF(zs, 70, zCoeffs, ns=ns)
interpolator.setModel('foo', p1, voxelsize)
estimator.splines.clear()
estimator.calibrate(interpolator, mdh)
sp_all = [
startEstROI(r2[j], mdh, interpolator, estimator,
mdh['Analysis.ColourRatio'], mdh['Analysis.AxialShift'])
for j in range(len(r2))
]
fr = np.array([(fitROI(r2[j], mdh, interpolator, sp_all[j],
mdh['Analysis.ColourRatio'],
mdh['Analysis.AxialShift'])[0])
for j in range(len(r2))])
#return mf[mf < (median(mf)+ 2*std(mf))].mean()
return fr
def misfallA(r2,
mdh,
zCoeffs,
ns=1.51,
axialShift=None,
colourRatio=None,
beadSize=0):
mdh = MetaDataHandler.NestedClassMDHandler(mdh)
if not axialShift == None:
mdh['Analysis.AxialShift'] = axialShift
if not colourRatio == None:
mdh['Analysis.ColourRatio'] = colourRatio
voxelsize = mdh.voxelsize_nm
voxelsize.z = 50.
zs = 50. * np.arange(-30., 31)
p1 = fourierHNA.GenZernikeDPSF(zs, 70, zCoeffs, ns=ns)
interpolator.setModel('foo', p1, voxelsize)
estimator.splines.clear()
estimator.calibrate(interpolator, mdh)
sp_all = [
startEstROI(r2[j], mdh, interpolator, estimator,
mdh['Analysis.ColourRatio'], mdh['Analysis.AxialShift'])
for j in range(len(r2))
]
mf = np.array([(fitROI(r2[j], mdh, interpolator, sp_all[j],
mdh['Analysis.ColourRatio'],
mdh['Analysis.AxialShift'])[1]).sum()
for j in range(len(r2))])
#return mf[mf < (median(mf)+ 2*std(mf))].mean()
return np.sqrt(mf).mean()
def genTheoreticalModelZernike(self,
md,
zmodes={},
nDesign=1.51,
nSample=1.51,
NA=1.47,
wavelength=700):
from PYME.Analysis.PSFGen import fourierHNA
zs = arange(-1e3, 1e3, 50)
voxelsize = dummy()
voxelsize.x = 1e3 * md['voxelsize.x']
voxelsize.y = 1e3 * md['voxelsize.x']
voxelsize.z = 1e3 * .05
ps = fourierHNA.GenZernikeDPSF(zs,
voxelsize.x,
zmodes,
lamb=wavelength,
NA=NA,
n=nDesign,
ns=nSample)
psc = self.centre2d(ps) #FIXME: why is this needed / useful?
return psc, voxelsize
def genTheoreticalModelZernike(self,
md,
zmodes={},
nDesign=1.51,
nSample=1.51,
NA=1.47,
wavelength=700):
from PYME.Analysis.PSFGen import fourierHNA
from PYME.IO import MetaDataHandler
zs = arange(-1e3, 1e3, 50)
voxelsize = MetaDataHandler.VoxelSize(md.voxelsize_nm)
voxelsize.z = 50.
ps = fourierHNA.GenZernikeDPSF(zs,
voxelsize.x,
zmodes,
lamb=wavelength,
NA=NA,
n=nDesign,
ns=nSample)
psc = self.centre2d(ps) #FIXME: why is this needed / useful?
return psc, voxelsize
def GenMultiChanZernikeDPSF(Z,
zerns2,
wavelengths,
dx=70.,
output_shape=[61, 61, 61],
**kwargs):
from PYME.Analysis.PSFGen import fourierHNA
psfs = list()
for i in range(len(zerns2)):
psf = fourierHNA.GenZernikeDPSF(Z,
dx=dx,
zernikeCoeffs=zerns2[i],
lamb=wavelengths[i],
output_shape=output_shape,
**kwargs)
psf /= psf.sum(1).sum(0).max() #, 1)).max()
psfs.append(psf)
psfs = np.concatenate(psfs, 0)
return psfs
