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Data Range Selection

Set the left and right boundaries of the data you want to keep by moving the yellow cursors. On the left, preferably pick a starting point before the peak (time < 0), which helps in more accurate zero point determination later and doubles the number of points for better results at short distances. On the right, truncate whatever looks weird. You can also move the right cursor leftward and directly observe the deterioration of the Tikhonov fit due to data truncation (this is e.g. nicely visible with the default test grid loaded). Instead of moving the cursors, you can also manually pick how many points to trim from each end. By default, loading a new file will cause a reset to the default limits. Check “retain limits on load” if you want to keep the same number of trim points for the next data files.

Phase correction

The raw data is of complex datatype, but the analysis is done on plain arrays after the phase has been corrected. You can select between magnitude, real, or imaginary data to be used for processing. By default the program fits the magnitude, so phase is not a problem. (This works because correctly phased, the imaginary part is ~zero for DEER data. However, for poor S/N, the phase cannot be set accurately in the instrument and part of the data will end up in the imaginary channel, requiring phase correction.

If the Real part is used, the complex data needs to be rotated in the complex plane first. Automatic phase correction is typically very stable and accurate and there should never be a reason to adjust it manually, even though you can do it using the slider. Temporarily picking the imaginary data is useful when manually adjusting the phase (not recommended).

For data that sometimes goes negative after phase correction (e.g. data without background), the magnitude data cannot be used. The program will automatically determine if the REAL part should be used instead.

Guidelines

The text in the lower left of this tab page shows the estimated accuracy limits above which the distribution shape and distance position become no longer reliable, based on the time duration of the data trace. The limits are defined according to Jeschke. The same limits are also shown on most distance graphs with dotted cursors of the same color.

Denoising (Experimental feature*)

Denoising is based on the somewhat controversial assumption that it is possible to surgically separate data from noise, thus improving the analysis quality. Recent efforts By M. Srivastava have focused on improving wavelet base denoising with some promising results.

At the moment, the available denoising methods are more primitive.

    • No Denoise is the recommended default.
    • Savitsky-Golay Denoise is using a well known algorithm for filtering. The two parameters are "order" and "side points" and the defaults (1,2) are reasonable.
  • Wavelet Denoise is using the classic wavelet denoising methods with tons of options. Feel free to play around with noisy data.

Note that denoising will reduce the variance in the data and thus typically cause an increase in the relative chi2.

*To enable experimental features, go to the "Settings" tab and check the box.

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