Usage¶

Start by importing healing_scattering_image.

import healing_scattering_image

Functions¶

healing_scattering_image.heal_scatter_image.heal_scatter_image(im, mask, xcenter, ycenter, r_min, r_max, angle_resolution, delta_width, bkgd_threshold, peaks_threshold, bins=30, lambda_threshold_1=1.0, lambda_threshold_2=1.0, bkgd_fit_bias=4.0, fold_bias=4.0, fittness_threshold=1.0, extreme_fitness=0.001, two_fold_apply=True, fittness_prior=True, down_sample=0.1, fitting_shift=True)[source]¶

Heal Scattering Image basing on Physical features. See paper: Liu, Jiliang, et al. “Healing X-ray scattering images.” IUCrJ 4.4 (2017): 455-465.

Due to qphi image will have very low azimuth resolution at low angle region, if there is pattern very close beam center, which pixel distance less than 40 pixel, we would like to up size sample first. Usually 500x500 or 1000x1000 size image could be well processedself. This is how default parameters or paratmeters in example determinedself.

Parameters:

im: 2D numpy.array: input scattering image with gaps and defects,
mask: 2D numpy.array: the area want to mask in scattering image,, like beamstop, gaps,
xcenter: pixel float: the beamcenter column,
ycenter: pixel float: the beam center row,
r_min: pixel float: the start of healing,
r_max: pixel float: the end of healing,
angle_resolution: float: angle resolution of qphi map,
delta_width: float: delta q for standard deviation calultion of every q in qphi map
bkgd_threshold: float: threshold to classify scattering by the histogram of local standard devation verse global deviation. larger threshold will be less senstive to
peaks_threshold: float: threshold determines histogram of global standard deviation larger threshold means only sharp peaks are distinguishable,
bins: float: bins for histogram of standard deviation
lambda_threshold_1: float: lower threshold will allow more data point classified to patterns including anisotropic structrue information,
lambda_threshold_2: float: lower threshold will allow more data point classified to patterns including anisotropic structrue information,
bkgd_fit_bias: float: determine the bias to symmtry judgement of diffuse peaks,
fold_bias: float: determine the bias to symmtry judgement of sharp peaks,
fittness_threshold: float: increase error tolerance for diffuse reflections,
extreme_fitness: float: tolerance of error of symmetr model,
two_fold_apply: Boolean: automatical applied two fold symmetry, usualy true for SAXS,
fittness_prior: Boolean: True will enable algorithm to choose larger symmetry fold with fitting residual less than extreme_fitness,
down_sample: float: between (0,1),
fitting_shift: Boolean: allow small shift when heal the symmetrical patterns.

Returns:

im: 2D numpy.array: healed scattering image,
aniso_judge: Boolean: identify peaks with symmertical folds,
sym_record: float: symmetrical fold,
iso_judge_global: 1D numpy.array: global standard deviation,
iso_local_vs_global: 1D nummpy.array: local vs global standard deviation,
qphi_image_6: 2D numpy.array: healed qphi map,
I: 1D numpy array: circular averaged intensity.

Example¶

In [1]: import numpy as np

In [2]: from skimage.draw import polygon

In [3]: import matplotlib.pyplot as plt

In [4]: from scipy.io import loadmat

In [5]: from scipy import ndimage

In [6]: from skimage.filters import median

In [7]: from skimage.transform import resize

In [8]: import healing_scattering_image

In [9]: from healing_scattering_image.heal_scatter_image import heal_scatter_image

In [10]: import os

In [11]: from pkg_resources import resource_filename

In [12]: path = resource_filename('healing_scattering_image',\
   ....:                          'example_data/example_1.npz')
   ....: 

In [13]: sample_name = path

In [14]: im = np.double(np.load(sample_name)['im'])

In [15]: im += 1.

In [16]: mask = np.load(sample_name)['mask']

In [17]: from skimage.transform import resize

#im = resize(im,(int(np.shape(im)[0]/2),int(np.shape(im)[1]/2)))
#mask = resize(mask,(int(np.shape(mask)[0]/2),int(np.shape(mask)[1]/2)))
In [18]: mask = mask.astype(bool)

In [19]: im[mask] = np.nan

In [20]: xcenter = (np.load(sample_name)['xcenter'])#/2

In [21]: ycenter = (np.load(sample_name)['ycenter'])#/2

In [22]: r_min = 10

In [23]: r_max = np.max(im.shape)#int(np.max(np.shape(im)))

In [24]: angle_resolution = 360

In [25]: delta_width = 2

In [26]: bkgd_threshold  = 0.3

In [27]: peaks_threshold = 4

In [28]: healed_im, aniso_place, sym_record,\

In [28]: iso_judge_global , iso_local_vs_global,\

In [28]: qphi_image_6, I = heal_scatter_image(im,mask,
   ....:                                       xcenter,
   ....:                                       ycenter,
   ....:                                       r_min,
   ....:                                       r_max,
   ....:                                       angle_resolution,
   ....:                                       delta_width,
   ....:                                       bkgd_threshold,
   ....:                                       peaks_threshold,
   ....:                                       bins = 30,
   ....:                                       lambda_threshold_1 = 1.,
   ....:                                       lambda_threshold_2 = 1.,
   ....:                                       bkgd_fit_bias = 4.,
   ....:                                       fold_bias = 6.,
   ....:                                       fittness_threshold = 1.,
   ....:                                       extreme_fitness = 1e-3,
   ....:                                       two_fold_apply = True,
   ....:                                       fittness_prior=True,
   ....:                                       down_sample = 0.1,)
   ....: 

Plots¶