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