functions_example.rst

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Functions Example

This example will demonstrate how to use diffpy.labpdfproc.functions module independently to apply absorption correction to your 1D diffraction data.

1. First, you will need to prepare and load your input diffraction data. For example, if you want to load data from zro2_mo.xy in the example_data directory, you can write:

from diffpy.utils.parsers.loaddata import loadData
from diffpy.utils.diffraction_objects import DiffractionObject

filepath = "../example_data/zro2_mo.xy"
xarray, yarray = loadData(filepath, unpack=True)
input_pattern = DiffractionObject(
    xarray=xarray,
    yarray=yarray,
    xtype="tth",
    wavelength=0.7,
    scat_quantity="x-ray",
    name="input diffraction data",
    metadata={"beamline": "28ID-2"},
)

For the full tutorial, please refer to https://www.diffpy.org/diffpy.utils/examples/diffraction_objects_example.html.

2. Assume you have created your input_pattern and specified mu*D value as muD (e.g., muD=2). You can now compute the absorption correction (cve) for the given mu*D using the compute_cve function, apply it to your input pattern, and save the corrected file.

from diffpy.labpdfproc.functions import apply_corr, compute_cve
absorption_correction = compute_cve(input_pattern, muD) # compute cve, default method is "polynomial_interpolation"
corrected_pattern = apply_corr(input_pattern, absorption_correction) # apply cve correction
corrected_data.dump("corrected pattern.chi", xtype="tth") # save the corrected pattern

If you want to use brute-force computation instead, you can replace the first line with:

absorption_correction = compute_cve(input_pattern, muD, method="brute_force")

3. Now, you can visualize the effect of the absorption correction by plotting the original and corrected diffraction patterns.

import matplotlib.pyplot as plt
plt.plot(input_pattern.xarray, input_pattern.yarray, label="Original Intensity")
plt.plot(corrected_pattern.xarray, corrected_pattern.yarray, label="Corrected Intensity")
plt.xlabel("tth (degrees)")
plt.ylabel("Intensity")
plt.legend()
plt.title("Original vs. Corrected Intensity")
plt.show()

4. You can modify the global parameters N_POINTS_ON_DIAMETER (the number of points on each diameter to sample the circle) and TTH_GRID (the range of angles) when using the brute-force method.

To speed up computation, you can reduce the range of TTH_GRID. You can also increase N_POINTS_ON_DIAMETER for better accuracy, but keep in mind that this will increase computation time. For optimal results, we recommend setting it to an even number.

Currently, the interpolation coefficients were computed using N_POINTS_ON_DIAMETER=2000, which ensures good accuracy within the mu*D range of 0.5 to 6. This value also provides flexibility if we decide to extend the interpolation range in the future.