def pre_process(self): # Apply dezing to dark and flat images inData = self.get_in_datasets()[0] dark = inData.data.dark() flat = inData.data.flat() self.data_size = inData.get_shape() pad_list = ((self.pad, self.pad), (0, 0), (0, 0)) # dezing the dark field print "*****in data shape in base filter", in_dataset[0].get_shape() if dark.size: (retval, self.warnflag, self.errflag) = dezing.setup_size(dark.shape, self.parameters['outlier_mu'], self.pad, mode=self.parameters['mode']) dark = self._dezing(np.pad(dark, pad_list, mode='edge')) inData.data.update_dark(dark[self.pad:-self.pad]) (retval, self.warnflag, self.errflag) = dezing.cleanup() # dezing the flat field if flat.size: (retval, self.warnflag, self.errflag) = dezing.setup_size(flat.shape, self.parameters['outlier_mu'], self.pad, mode=self.parameters['mode']) flat = self._dezing(np.pad(flat, pad_list, mode='edge')) inData.data.update_flat(flat[self.pad:-self.pad]) (retval, self.warnflag, self.errflag) = dezing.cleanup() # setup dezing for data self._dezing_setup(self.data_size)
def pre_process(self): # Apply dezing to dark and flat images inData = self.get_in_datasets()[0] dark = inData.data.dark() flat = inData.data.flat() pad_list = ((self.pad, self.pad), (0, 0), (0, 0)) # dezing the dark field (retval, self.warnflag, self.errflag) = dezing.setup_size( dark.shape, self.parameters['outlier_mu'], self.pad, mode=self.parameters['mode']) dark = self._dezing(np.pad(dark, pad_list, mode='edge')) (retval, self.warnflag, self.errflag) = dezing.cleanup() # dezing the flat field (retval, self.warnflag, self.errflag) = dezing.setup_size( flat.shape, self.parameters['outlier_mu'], self.pad, mode=self.parameters['mode']) flat = self._dezing(np.pad(flat, pad_list, mode='edge')) (retval, self.warnflag, self.errflag) = dezing.cleanup() # setup dezing for data (retval, self.warnflag, self.errflag) = \ dezing.setup_size(self.data_size, self.parameters['outlier_mu'], self.pad, mode=self.parameters['mode'])
def dezing_brickframe(pnum): mu=1.5 instack=read_image_set(pnum) outim=np.empty_like(instack) tifffile.imsave("/dls/science/users/kny48981/instack.tif",instack) dezing.setup(instack,outim,mu,2) dezing.run(instack,outim) dezing.cleanup(instack,outim) tifffile.imsave("/dls/science/users/kny48981/outstack.tif",outim)
def dezing_brickframe(pnum): mu = 1.5 instack = read_image_set(pnum) outim = np.empty_like(instack) tifffile.imsave("/dls/science/users/kny48981/instack.tif", instack) dezing.setup(instack, outim, mu, 2) dezing.run(instack, outim) dezing.cleanup(instack, outim) tifffile.imsave("/dls/science/users/kny48981/outstack.tif", outim)
def main(): #inim3,outim=get_image_array() inim3,outim=construct_test_array() tifffile.imsave("in.tif",inim3[:,:,0]) tifffile.imsave("inflop.tif",inim3[0,:,:]) print "in test_pymain: array shape is:",inim3.shape mu=float(sys.argv[1]) npad=2 dezing.setup_size(inim3.shape,mu,npad) dezing.run(inim3,outim) dezing.cleanup() tifffile.imsave("out%f.tif" % mu ,outim[10,:,:])
def main(): #inim3,outim=get_image_array() inim3, outim = construct_test_array() tifffile.imsave("in.tif", inim3[:, :, 0]) tifffile.imsave("inflop.tif", inim3[0, :, :]) print "in test_pymain: array shape is:", inim3.shape mu = float(sys.argv[1]) npad = 2 dezing.setup_size(inim3.shape, mu, npad) dezing.run(inim3, outim) dezing.cleanup() tifffile.imsave("out%f.tif" % mu, outim[10, :, :])
def pre_process(self): # Apply dezing to dark and flat images (data with image key only) inData = self.get_in_datasets()[0] dark = inData.data.dark() flat = inData.data.flat() (retval, self.warnflag, self.errflag) = dezing.setup_size( dark.shape, self.parameters['outlier_mu'], self.pad) pad_list = ((self.pad, self.pad), (0, 0), (0, 0)) dark = self._dezing(np.pad(dark, pad_list, mode='edge')) flat = self._dezing(np.pad(flat, pad_list, mode='edge')) inData.meta_data.set_meta_data( 'dark', dark[self.pad:-self.pad].mean(0)) inData.meta_data.set_meta_data( 'flat', flat[self.pad:-self.pad].mean(0)) (retval, self.warnflag, self.errflag) = dezing.cleanup() # setup dezing for data (retval, self.warnflag, self.errflag) = \ dezing.setup_size(self.data_size, self.parameters['outlier_mu'], self.pad)
def pre_process(self): # Apply dezing to dark and flat images (data with image key only) inData = self.get_in_datasets()[0] dark = inData.data.dark() flat = inData.data.flat() (retval, self.warnflag, self.errflag) = dezing.setup_size(dark.shape, self.parameters['outlier_mu'], self.pad) pad_list = ((self.pad, self.pad), (0, 0), (0, 0)) dark = self._dezing(np.pad(dark, pad_list, mode='edge')) flat = self._dezing(np.pad(flat, pad_list, mode='edge')) inData.meta_data.set_meta_data('dark', dark[self.pad:-self.pad].mean(0)) inData.meta_data.set_meta_data('flat', flat[self.pad:-self.pad].mean(0)) (retval, self.warnflag, self.errflag) = dezing.cleanup() # setup dezing for data (retval, self.warnflag, self.errflag) = \ dezing.setup_size(self.data_size, self.parameters['outlier_mu'], self.pad)
def post_process(self): logging.debug("Running Dezing Cleanup") dezing.cleanup() logging.debug("Finished Dezing Cleanup")
def post_process(self): (retval, self.warnflag, self.errflag) = dezing.cleanup()
def post_process(self): dezing.cleanup()