| Larch: Data Analysis Tools for X-ray Spectroscopy

# 4.3. XASViewer

The XASViewer GUI uses Larch to read and display XAFS spectra. This application is still in active development, with more features planned with special emphasis on helping users with XANES analysis. Current features (as of June, 2018, Larch version 0.9.38) include:

> - read XAFS spectra from simple data column files or Athena Project Files.
> - XAFS pre-edge removal and normalization.
> - visualization of normalization steps.
> - data merging
> - data deglitching, and energy recalibration
> - data smoothing, rebinning, and deconvolution.
> - over-absorption corrections (for XANES)
> - linear combination analysis of spectra.
> - pre-edge peak fitting.
> - saving of data to Athena Project files.
> - saving of data to CSV files.

The XAS Viewer GUI includes a simple form for basic pre-edge subtraction, and normalization of XAFS spectra. Figure 4.3.1 shows the main window for the XAS Viewer program. The left-hand portion contains a list of files (or data groups) that have been read into the program either from individual ASCII column files or Athena Project files. Clicking on the file or group name makes that “the current data group”, while checking the boxes next to each name will select multiple files or group. Buttons at the top of the list of files can be used to “Select All” or “Select None”. In addition, right-clicking on the file list will pop up a menu that allows more detailed selecting of data sets.

Figure 4.3.1 XASViewer showing the File/Group list on the left-hand side and the the XAFS pre-edge subtraction and normalization panel on the right.

The right-hand portion of the XAS Viewer window shows multiple forms for more specialized XAFS data processing tasks, each on a separate Notebook tab. These will be covered in more detail in sections below. The main panel is for pre-edge subtraction and Normalization (4.3.3), with other available tabs for fitting pre-edge peaks (4.3.4), Linear Combinaton Analysis (4.3.5), Principal Component Analysis (4.3.6), and EXAFS Analysis (4.3.7 and 4.3.8).

There are a few important general notes to mention about XAS Viewer before going into more detail about how to use it. First, XAS Viewer is still very new and in active development. If you find problems with it or unexpected or missing functionality, please let us know. Second, XAS Viewer has many features and functionality in common with Athena and Sixpack. This is partly intentional, as we expect that XAS Viewer may be a useful alternative to these that may be better supported and maintained, especially on macOS. That also means that if you find things that you think are missing or different from how Athena or Sixpack work, let us know.

As a GUI, XAS Viewer is intended to make data processing analysis easy and intuitive. As a Larch application it is also intended to enable more complex analysis, batch processing, and scripting of analysis. To do this, essentially all the real processing work in XAS Viewer is done through the Larch Buffer (as shown in 4.1) which records the commands that it executes. If, at any point you want to know exactly what XAS Viewer is “really doing”, you can open the Larch Buffer and see. You can also copy the code from the Larch buffer to reproduce the analysis steps, or modify into procedures for batch processing.

XAS Viewer will display many different datasets as 2-d line plots. As with all such plots made with Larch (see 7), these are highly interactive, customizable, and can produce publication-quality images. Larch plots can be zoomed in an out, and configured to change the colors, linestyles, margins, text for labels, and more. From any plot window you can use Ctrl-C to copy the image to the clipboard, Ctrl-S to Save the image (as PNG) to a file, or Ctrl-P to print the image. Ctrl-K will bring up a window to configure the colors, text, and so on. These and a few other common options are available from the File and Options menu.

In particular for XAS Viewer, clicking on the legend for any labeled curve on a plot will toggle whether that curve is displayed. This allows us to draw plot components as you can turn them on or off interactively. Also, note that many of the entries for numbers on the form panels in XAS Viewer have a button with a ‘pin’ icon . Clicking anywhere on the plot window will remember the energy value of the last point clicked, and show the value in the middle section of the status bar, just below the plot itself. Clicking on any of these ‘pin’ buttons will insert that “most recent energy” value into the corresponding field.

## 4.3.1. Reading Data into XAS Viewer

Data groups can be read from plain ASCII data files using a GUI form to help build \(\mu(E)\), or from Athena Project files, as shown in Figure 4.3.2 and Figure 4.3.3. Multiple data groups can be read in, compared, and merged. These datasets can then be exported to Athena Project files, or to CSV files.

Figure 4.3.2 ASCII data file importer.

Figure 4.3.3 Athena Project importer.

When reading in raw data, the data importer shown in Figure 4.3.2 will help you build XAFS \(\mu(E)\) from the various columns in your data file. This form wraps some of the routines described in Chapter 6. Currently, this form is somewhat limited in being able to process all data formats, but should be useful for many data sets. If you have trouble reading in your data with XAS Viewer, contact us and we will try to help.

## 4.3.2. Common XAS Processing Dialogs

There are several dialogs for interacting with data groups and for doing data corrections. These include speciality dialogs for:

> - copying, removing, and renaming data grouos.
> - merging of data groups – summing spectra.
> - de-glitching spectra.
> - recalibrating spectra.
> - smoothing of noisy spectra.
> - rebinning of spectra onto a “normal” XAFS energy grid.
> - de-convolving spectra.
> - correcting over-absorption in fluorescence XANES spectra.

Screenshots of some of these dialogs are shown in the images below.

Figure 4.3.4 Energy calibration dialog.

Figure 4.3.5 Deglitching dialog

Figure 4.3.6 Energy smoothing dialog.

Figure 4.3.7 Deglitching dialog

Figure 4.3.8 Energy rebinning dialog.

Figure 4.3.9 Over-absortion correction dialog

## 4.3.3. Pre-edge subtraction and Normalization

As above, Figure 4.3.1 shows the main window for the XAS Viewer program with the right hand side showing the “XAS Normalization” Panel. This panel helps you do pre-edge subtraction and normalization of XAFS data using the `pre_edge()` function. This processing step is important for getting normalized XAFS spectra that is used for further analysis of both XANES and EXAFS.

From the form, you can plot the data for the current selected group in several ways: Raw \(\mu(E)\), normalized \(\mu(E)\), the derivative \(d\mu(E)/dE\), flattened \(\mu(E)\), or the raw \(\mu(E)\) with the pre-edge line and post-edge normalization curve. You can also set the parameters like \(E_0\) and the edge step and ranges for the pre-edge line and normalization curve. Consult with `pre_edge()` function for more details on these parameters. You can also plot several selected groups together, and copy processing parameters from one group to another.

## 4.3.4. Pre-edge peak fitting

The “Pre-edge Peak Fit” tab (show in Figure 4.3.10) provides a form for fitting pre-edge peaks to line shapes such as Gaussian, Lorentzian, or Voigt functions. This provides an easy-to-use wrapper around lmfit and the `minimize()` function for curve-fitting with the ability to constrain fitting Parameters.

To do fitting of pre-edge peaks with the interface, one begins by fitting a “baseline” to account for the main absorption edge. This baseline is modeled as a Lorentzian curve plus a line. Fitting a baseline requires identifying energy ranges for both the main spectrum to be fitted and the pre-edge peaks – the part of the spectrum where the baseline should not be fitted. This is illustrated in Figure 4.3.10 and Figure 4.3.11. Note that there are separate ranges for the “fit range” and the “pre-edge peak” range (illustrated with grey lines and blue ‘+’ signs on the plot). The “pre-edge peak” range should be inside the fit range.

Clicking “Fit baseline” will fit a baseline function and display the results. The initial fit may have poorly guessed ranges for the pre-edge peaks and fit range and may require some adjustment.

Figure 4.3.10 Pre-edge peak Window of XASViewer, showing how select regions of pre-edge peaks for fitting a baseline.

Figure 4.3.11 Plot of pre-edge peaks with baseline. Note that the grey vertical lines show the fit range, the blue crosses show the pre-edge peak range, and the pink line shows the centroid of the pre-edge peaks after removal of the baseline.

Once the pre-edge baseline is satisfactory, you can add functions to model the pre-edge peaks themselves. Select one of the “Peak Models” (typically Gaussian, Lorentzian, or Voigt), which will show a new tab in the “model components area” in the lower part of the form. Note that the baseline will consist of a Lorentzian and linear model component, so that there will be at least 3 tabs for the 3 or more components of the pre-edge peak model. This is shown in Figure 4.3.12, which shows the form for 1 Gaussian peak, and the baseline. You can include multiple peaks by repeatedly selecting the peak type from the drop-down menu.

After selecting a peak type, click on the “Pick Values from Data” button, and then pick two points on the plot to help give initial ranges for that peak. The points you pick do not have to be very accurate, and the initial values selected for the amplitude, center, and sigma parameters can be modified. Note that you can place bounds on any of these parameters – it is probably a good idea to enforce the amplitude and sigma to be positive. If using multiple peaks, it is often helpful to give realistic energy bounds for the center of each peak, so that they do not overlap.

Figure 4.3.12 Pre-edge peak Window of XASViewer, showing how select regions of pre-edge peaks for fitting a baseline.

Figure 4.3.13 Pre-edge peak Window of XASViewer, showing how select regions of pre-edge peaks for fitting a baseline.

Upon doing a fit, the plot is updated to show the data, best-fit, and each of the components used in the fit (Figure 4.3.13). Fit statistics and best-fit parameter values, their uncertainties, and correlations are shown as a report in a separate window, with an example shown in Figure 4.3.14. Note that for peaks such as Gaussian, Lorentzian, and Voigt, not only are amplitude (that is, area under the peak), sigma, and center shown but so are fwhm (full width of peak at half the maximum height) and height (the maximum height of the peak).

Figure 4.3.14 Fit result frame for Pre-edge peak fit.

Figure 4.3.15 Pre-edge Peak fit with residual.

Though the plot of the fit in Figure 4.3.13 looks good, plotting the fit along with the residual (by selecting “Data+Residual” in the drop-down menu of “Plot:” choices) as shown in Figure 4.3.15 reveals a systematic mis-fit. That is, the data-fit for this model clearly shows some spectral structure beyond just the noise in the data. Adding a second Gaussian (and maybe even a third) will greatly help this fit. To do that, add another Gaussian peak component to the fit model using the drop-down menu of “Add component:”, select initial values for that Gaussian, and re-fit the model. We’ll leave that as an exercise for the reader.

Fit results can be saved in two different ways, using the “PreEdge Peaks” menu. First, the model to set up the fit can be saved to a .modl file and then re-read later and used for other fits. This model file can also be read in and used with the lmfit python module for complete scripting control. Secondly, a fit can be exported to an ASCII file that will include the text of the fit report and columns including data, best-fit, and each of the components of the model.

## 4.3.5. Linear Combination Analysis

Linear Combination Analysis is useful for modeling a XANES spectrum as a combination of other spectra.

## 4.3.6. Principal Component and Non-negative Factor Analysis

Principal Component Analysis (PCA) is one of a family of numerical techniques to reduce the number of variable components in a set of data. There are many related techniques and procedures, and quite a bit of nomeclature and jargon around the methods.

In essence, all these methods are aimed at taking a large set of similar data and trying to determine how many independent components make up that larger dataset. That is, the only question PCA and related methods can ever really answer is:

    how many indepedent spectra make up my collection of spectra?

It is important to note that PCA cannot tell you what those independent spectra represent or even what they look like. However, you can also use the results of PCA to ask:

    is this *other* spectrum made up of the same components as make up my collection?

## 4.3.7. EXAFS Processing: Background Subtraction

## 4.3.8. EXAFS Processing: Fourier Transforms