epdfsuite.snr

Functions

compute_SNR(r, g[, r_cut])

Compute the signal-to-noise ratio of a PDF curve G(r).

compute_SNR_from_file(file[, r_cut])

Load a G(r) file and compute its signal-to-noise ratio.
epdfsuite.snr.compute_SNR(r, g, r_cut=0.75)[source]

Compute the signal-to-noise ratio of a PDF curve G(r).

The signal is defined as the maximum of G(r). The noise is estimated as the standard deviation of G(r) in the high-r tail, where the PDF is expected to converge to zero.

Parameters:

  • r (ndarray) – Real-space distance axis in Å.

  • g (ndarray) – Reduced pair distribution function G(r).

  • r_cut (float, optional) – Fraction of r.max() above which the signal is considered noise. Default is 0.75 (i.e. the top 25 % of the r range is used).

Returns:

snr (float) – Signal-to-noise ratio: max(G) / std(G[r > r_cut * r_max]).

epdfsuite.snr.compute_SNR_from_file(file, r_cut=0.75)[source]

Load a G(r) file and compute its signal-to-noise ratio.

The file is expected to be a two-column text file (r, G) with 27 header lines, as produced by ePDFsuite.

Parameters:

  • file (str) – Path to the G(r) text file.

  • r_cut (float, optional) – Fraction of r.max() used to define the noise tail. See compute_SNR(). Default is 0.75.

Returns:

snr (float) – Signal-to-noise ratio of the loaded G(r) curve.