Source code for tomopy.prep.phase

#!/usr/bin/env python
# -*- coding: utf-8 -*-

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"""
Module for phase retrieval.
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import numpy as np
from tomopy.util.misc import (fft2, ifft2)


import tomopy.util.mproc as mproc
import logging

logger = logging.getLogger(__name__)


__author__ = "Doga Gursoy"
__credits__ = "Mark Rivers, Xianghui Xiao"
__copyright__ = "Copyright (c) 2015, UChicago Argonne, LLC."
__docformat__ = 'restructuredtext en'
__all__ = ['retrieve_phase']


BOLTZMANN_CONSTANT = 1.3806488e-16  # [erg/k]
SPEED_OF_LIGHT = 299792458e+2  # [cm/s]
PI = 3.14159265359
PLANCK_CONSTANT = 6.58211928e-19  # [keV*s]


def _wavelength(energy):
    return 2 * PI * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy


[docs]def retrieve_phase( tomo, pixel_size=1e-4, dist=50, energy=20, alpha=1e-3, pad=True, ncore=None, nchunk=None): """ Perform single-step phase retrieval from phase-contrast measurements :cite:`Paganin:02`. Parameters ---------- tomo : ndarray 3D tomographic data. pixel_size : float, optional Detector pixel size in cm. dist : float, optional Propagation distance of the wavefront in cm. energy : float, optional Energy of incident wave in keV. alpha : float, optional Regularization parameter. pad : bool, optional If True, extend the size of the projections by padding with zeros. ncore : int, optional Number of cores that will be assigned to jobs. nchunk : int, optional Chunk size for each core. Returns ------- ndarray Approximated 3D tomographic phase data. """ # New dimensions and pad value after padding. py, pz, val = _calc_pad(tomo, pixel_size, dist, energy, pad) # Compute the reciprocal grid. dx, dy, dz = tomo.shape w2 = _reciprocal_grid(pixel_size, dy + 2 * py, dz + 2 * pz) # Filter in Fourier space. phase_filter = np.fft.fftshift( _paganin_filter_factor(energy, dist, alpha, w2)) prj = np.full((dy + 2 * py, dz + 2 * pz), val, dtype='float32') arr = mproc.distribute_jobs( tomo, func=_retrieve_phase, args=(phase_filter, py, pz, prj, pad), axis=0, ncore=ncore, nchunk=nchunk) return arr
def _retrieve_phase(tomo, phase_filter, px, py, prj, pad): dx, dy, dz = tomo.shape num_jobs = tomo.shape[0] normalized_phase_filter = phase_filter / phase_filter.max() for m in range(num_jobs): prj[px:dy + px, py:dz + py] = tomo[m] prj[:px] = prj[px] prj[-px:] = prj[-px-1] prj[:, :py] = prj[:, py][:, np.newaxis] prj[:, -py:] = prj[:, -py-1][:, np.newaxis] fproj = fft2(prj, extra_info=num_jobs) fproj *= normalized_phase_filter proj = np.real(ifft2(fproj, extra_info=num_jobs, overwrite_input=True)) if pad: proj = proj[px:dy + px, py:dz + py] tomo[m] = proj def _calc_pad(tomo, pixel_size, dist, energy, pad): """ Calculate new dimensions and pad value after padding. Parameters ---------- tomo : ndarray 3D tomographic data. pixel_size : float Detector pixel size in cm. dist : float Propagation distance of the wavefront in cm. energy : float Energy of incident wave in keV. pad : bool If True, extend the size of the projections by padding with zeros. Returns ------- int Pad amount in projection axis. int Pad amount in sinogram axis. float Pad value. """ dx, dy, dz = tomo.shape wavelength = _wavelength(energy) py, pz, val = 0, 0, 0 if pad: val = _calc_pad_val(tomo) py = _calc_pad_width(dy, pixel_size, wavelength, dist) pz = _calc_pad_width(dz, pixel_size, wavelength, dist) return py, pz, val def _paganin_filter_factor(energy, dist, alpha, w2): return 1 / (_wavelength(energy) * dist * w2 / (4 * PI) + alpha) def _calc_pad_width(dim, pixel_size, wavelength, dist): pad_pix = np.ceil(PI * wavelength * dist / pixel_size ** 2) return int((pow(2, np.ceil(np.log2(dim + pad_pix))) - dim) * 0.5) def _calc_pad_val(tomo): return np.mean((tomo[..., 0] + tomo[..., -1]) * 0.5) def _reciprocal_grid(pixel_size, nx, ny): """ Calculate reciprocal grid. Parameters ---------- pixel_size : float Detector pixel size in cm. nx, ny : int Size of the reciprocal grid along x and y axes. Returns ------- ndarray Grid coordinates. """ # Sampling in reciprocal space. indx = _reciprocal_coord(pixel_size, nx) indy = _reciprocal_coord(pixel_size, ny) np.square(indx, out=indx) np.square(indy, out=indy) return np.add.outer(indx, indy) def _reciprocal_coord(pixel_size, num_grid): """ Calculate reciprocal grid coordinates for a given pixel size and discretization. Parameters ---------- pixel_size : float Detector pixel size in cm. num_grid : int Size of the reciprocal grid. Returns ------- ndarray Grid coordinates. """ n = num_grid - 1 rc = np.arange(-n, num_grid, 2, dtype = np.float32) rc *= 0.5 / (n * pixel_size) return rc