def setModel(modName, md):
global IntXVals, IntYVals, IntZVals, dx, dy, dz
#FIXME - use load_psf() instead
mf = open(getFullExistingFilename(modName), 'rb')
mod, voxelsize = pickle.load(mf)
mf.close()
mod = resizePSF(mod, interpModel().shape)
#if not voxelsize.x == md.voxelsize.x:
# raise RuntimeError("PSF and Image voxel sizes don't match")
IntXVals = 1e3 * voxelsize.x * mgrid[-(mod.shape[0] / 2.):(mod.shape[0] /
2.)]
IntYVals = 1e3 * voxelsize.y * mgrid[-(mod.shape[1] / 2.):(mod.shape[1] /
2.)]
IntZVals = 1e3 * voxelsize.z * mgrid[-(mod.shape[2] / 2.):(mod.shape[2] /
2.)]
dx = voxelsize.x * 1e3
dy = voxelsize.y * 1e3
dz = voxelsize.z * 1e3
#interpModel = mod
#interpModel = np.maximum(mod/mod.max(), 0) #normalise to 1
interpModel_by_chan[0] = np.maximum(
mod / mod[:, :, len(IntZVals) / 2].sum(), 0) #normalise to 1 and clip
def setModel(modName, md):
global IntXVals, IntYVals, IntZVals, interpModel, dx, dy, dz
mf = open(getFullExistingFilename(modName), 'rb')
mod, voxelsize = pickle.load(mf)
mf.close()
mod = resizePSF(mod, interpModel.shape)
#if not voxelsize.x == md.voxelsize.x:
# raise RuntimeError("PSF and Image voxel sizes don't match")
IntXVals = 1e3*voxelsize.x*mgrid[-(mod.shape[0]/2.):(mod.shape[0]/2.)]
IntYVals = 1e3*voxelsize.y*mgrid[-(mod.shape[1]/2.):(mod.shape[1]/2.)]
IntZVals = 1e3*voxelsize.z*mgrid[-(mod.shape[2]/2.):(mod.shape[2]/2.)]
dx = voxelsize.x*1e3
dy = voxelsize.y*1e3
dz = voxelsize.z*1e3
#interpModel = mod
#interpModel = np.maximum(mod/mod.max(), 0) #normalise to 1
interpModel = np.maximum(mod/mod[:,:,len(IntZVals)/2].sum(), 0) #normalise to 1 and clip
def setModel(modName, md):
global IntXVals, IntYVals, IntZVals, dx, dy, dz
from PYME.IO import load_psf
mod, vs_nm = load_psf.load_psf(modName)
mod = resizePSF(mod, interpModel().shape)
IntXVals = vs_nm.x * np.mgrid[-(mod.shape[0] / 2.):(mod.shape[0] / 2.)]
IntYVals = vs_nm.y * np.mgrid[-(mod.shape[1] / 2.):(mod.shape[1] / 2.)]
IntZVals = vs_nm.z * np.mgrid[-(mod.shape[2] / 2.):(mod.shape[2] / 2.)]
dx, dy, dz = vs_nm
#interpModel = np.maximum(mod/mod.max(), 0) #normalise to 1
interpModel_by_chan[0] = np.maximum(
mod / mod[:, :, len(IntZVals) / 2].sum(), 0) #normalise to 1 and clip