3. Description of usage

Setting up

Defining the experimental geometry

To use pygix one must first create and instance of the Transform class. You will interact with this object, to set the experimental details and reduce your data. Here is an example of creating an instance of the Transform class, setting the detector geometry with a .poni file:

import pygix
    
pg = pygix.Transform()
pg.load('detector_calibration.poni')

Alternatively one can set the detector geometry by passing the values directly when instantiating the class:

pg = pygix.Transform(dist=0.2076, poni1=0.0943, poni2=0.1157,
                     rot1=0.00859, rot2=0.00174, rot3=0.0,
                     wavelength=9.5373, detector='Eiger4M')

The sample geometry parameters are set as follows:

pg.sample_orientation = 2  # a vertical geometry
pg.incident_angle = 0.2
pg.tilt_angle = -1.46

By default, sample_orientation = 1 for the standard horizontal geometry, and incident_angle and tilt_angle are zero.

Setting up correction files

A number of additional files can be specified to be considered when processing data. These include mask files (image arrays of the same size as the detector, where pixels to be ignored have a value of 1 and valid pixels have a value of 0), flat-field and dark-current images. If the detector is specified, a mask (if relevant) will be used, e.g., for the dead regions between modules in a Pilatus detector. However, one may wish to also create a separate mask file to mask off other irrelevant regions of data, such as beam stops, flight tubes, sample holder etc. Masks can be made with the pyFAI-drawmask command line tool. These correction files can be passed as follows:

# passing paths to the images
pg.maskfile = '/data/correction_files/detector_mask.edf'
pg.flatfiles = '/data/correction_files/flat_0001.edf'
pg.darkfiles = '/data/correction_files/dark_0001.edf'

Note that for the dark current and flat field, one can pass a list of files. All of the supplied files will be averaged and the resulting array will be stored in as class attributes. Alternatively, one can pass numpy arrays directly as follows:

# passing arrays
pg.mask = mask_array
pg.flatfield = flat_array
pg.darkcurrent = dark_array

Processing data

2D image transformations

Transforming into reciprocal space:

import fabio  # library for loading X-ray images

data = fabio.open('datafile_0000.edf').data
intensity, qxy, qz = pg.transform_reciprocal(data, filename='datafile_0000_rsm.edf')

Transforming into angular coordinates:

intensity, alp_f, tth_f = pg.transform_angular(data)

Transforming into polar coordinates:

intensity, q_abs, chi = pg.transform_polar(data, npt=(1000, 720))

1D line profiles

Sector integration:

i, q = pg.profile_sector(data, npt=1000, chi_pos=0, chi_width=20,
                         radial_range=(0, 20), unit='q_nm^-1')

Chi integration:

i, chi = pg.profile_chi(data, npt=200, radial_pos=7.7, radial_width=1.0,
                        chi_range=(-60, 60))

Out-of-plane box integration:

i, qz = pg.profile_op_box(data, npt=1000, ip_pos=13.23, ip_width=0.2,
                          op_range=(0, 32))

In-plane box integration:

i, qxy = pg.profile_ip_box(data, npt=1000, op_pos=1.2, op_width=1.0,
                           ip_range=(0, 28))

Plotting data

There are some tools for easy plotting GIXS data built into pygix.plotting for 2D and 1D data. These handle all the default plotting options like axis labels etc.

2D plotting

from pygix import plotting as ppl

i, qxy, qz = pg.transform_reciprocal(data)
ppl.implot(i, qxy, qz, mode='rsm')

1D plotting

i, qz = pg.profile_sector(data, 1000, chi_pos=0, chi_width=20,
                          radial_range=(0, 20))
ppl.plot(i, qz)