nabu.estimation.translation module

class nabu.estimation.translation.DetectorTranslationAlongBeam(vert_fft_width=False, horz_fft_width=False, verbose=False, logger=None, data_type=<class 'numpy.float32'>, extra_options=None, cor_options=None)[source]

Bases: AlignmentBase

Alignment basic functions.

Parameters:

  • vert_fft_width (boolean, optional) –

    If True, restrict the vertical size to a power of 2:

    >>> new_v_dim = 2 ** math.floor(math.log2(v_dim))

  • horz_fft_width (boolean, optional) –

    If True, restrict the horizontal size to a power of 2:

    >>> new_h_dim = 2 ** math.floor(math.log2(h_dim))

  • verbose (boolean, optional) – When True it will produce verbose output, including plots.

  • data_type (numpy.float32) – Computation data type.

find_shift(img_stack: ndarray, img_pos: array, roi_yxhw=None, median_filt_shape=None, padding_mode=None, peak_fit_radius=1, high_pass=None, low_pass=None, return_shifts=False, use_adjacent_imgs=False)[source]

Find the vertical and horizontal shifts for translations of the detector along the beam direction.

These shifts are in pixels-per-unit-translation, and they are due to the misalignment of the translation stage, with respect to the beam propagation direction.

To compute the vertical and horizontal tilt angles from the obtained shift_pix:

>>> tilt_deg = np.rad2deg(np.arctan(shift_pix * pixel_size))

where pixel_size and and the input parameter img_pos have to be expressed in the same units.

Parameters:

  • img_stack (numpy.ndarray) – A stack of images (usually 4) at different distances

  • img_pos (numpy.ndarray) – Position of the images along the translation axis

  • roi_yxhw ((2, ) or (4, ) numpy.ndarray, tuple, or array, optional) – 4 elements vector containing: vertical and horizontal coordinates of first pixel, plus height and width of the Region of Interest (RoI). Or a 2 elements vector containing: plus height and width of the centered Region of Interest (RoI). Default is None -> deactivated.

  • median_filt_shape ((2, ) numpy.ndarray, tuple, or array, optional) – Shape of the median filter window. Default is None -> deactivated.

  • padding_mode (str in numpy.pad's mode list, optional) –

    Padding mode, which determines the type of convolution. If None or ‘wrap’ are passed, this resorts to the traditional circular convolution. If ‘edge’ or ‘constant’ are passed, it results in a linear convolution. Default is the circular convolution. All options are:

    None | ‘constant’ | ‘edge’ | ‘linear_ramp’ | ‘maximum’ | ‘mean’ | ‘median’ | ‘minimum’ | ‘reflect’ | ‘symmetric’ |’wrap’

  • peak_fit_radius (int, optional) – Radius size around the max correlation pixel, for sub-pixel fitting. Minimum and default value is 1.

  • low_pass (float or sequence of two floats) – Low-pass filter properties, as described in nabu.misc.fourier_filters.

  • high_pass (float or sequence of two floats) – High-pass filter properties, as described in nabu.misc.fourier_filters.

  • return_shifts (boolean, optional) – Adds a third returned argument, containing the pixel shifts of each image with respect to the first one in the stack. Defaults to False.

  • use_adjacent_imgs (boolean, optional) – Compute correlation between adjacent images. It can be used when dealing with large shifts, to avoid overflowing the shift. This option allows to replicate the behavior of the reference function alignxc.m However, it is detrimental to shift fitting accuracy. Defaults to False.

Returns:

  • coeff_v (float) – Estimated vertical shift in pixel per unit-distance of the detector translation.

  • coeff_h (float) – Estimated horizontal shift in pixel per unit-distance of the detector translation.

  • shifts_vh (list, optional) – The pixel shifts of each image with respect to the first image in the stack. Activated if return_shifts is True.

Examples

The following example creates a stack of shifted images, and retrieves the computed shift. Here we use a high-pass filter, due to the presence of some low-frequency noise component.

>>> import numpy as np
... import scipy as sp
... import scipy.ndimage
... from nabu.preproc.alignment import  DetectorTranslationAlongBeam
...
... tr_calc = DetectorTranslationAlongBeam()
...
... stack = np.zeros([4, 512, 512])
...
... # Add low frequency spurious component
... for i in range(4):
...     stack[i, 200 - i * 10, 200 - i * 10] = 1
... stack = sp.ndimage.filters.gaussian_filter(stack, [0, 10, 10.0]) * 100
...
... # Add the feature
... x, y = np.meshgrid(np.arange(stack.shape[-1]), np.arange(stack.shape[-2]))
... for i in range(4):
...     xc = x - (250 + i * 1.234)
...     yc = y - (250 + i * 1.234 * 2)
...     stack[i] += np.exp(-(xc * xc + yc * yc) * 0.5)
...
... # Image translation along the beam
... img_pos = np.arange(4)
...
... # Find the shifts from the features
... shifts_v, shifts_h = tr_calc.find_shift(stack, img_pos, high_pass=1.0)
... print(shifts_v, shifts_h)
>>> ( -2.47 , -1.236 )

and the following commands convert the shifts in angular tilts:

>>> tilt_v_deg = np.rad2deg(np.arctan(shifts_v * pixel_size))
>>> tilt_h_deg = np.rad2deg(np.arctan(shifts_h * pixel_size))

To enable the legacy behavior of alignxc.m (correlation between adjacent images):

>>> shifts_v, shifts_h = tr_calc.find_shift(stack, img_pos, use_adjacent_imgs=True)

To plot the correlation shifts and the fitted straight lines for both directions:

>>> tr_calc = DetectorTranslationAlongBeam(verbose=True)
... shifts_v, shifts_h = tr_calc.find_shift(stack, img_pos)