Data cleaning

Data cleaning removes non-physical signal features that can bias normalization, Fourier transforms and fitting. In EstraPy, this section focuses on two operations:

  1. glitch removal - Detects and corrects isolated spikes or drops (glitches)
  2. multiple edges - Corrects secondary edge-like contributions in the scanned range

When to Use Data Cleaning

Apply data cleaning when your spectra show one or more of the following:

  • Sudden, narrow spikes inconsistent with neighboring points
  • Sharp drops caused by detector or monochromator artifacts
  • Extra step-like features from secondary excitations
  • Distortions that create artificial oscillations after background removal

Data cleaning is typically performed after setting the edge and before final normalization/Fourier analysis.

Typical Workflow

In a typical workflow, you would first perform data cleaning

# 1. Remove isolated glitches
glitch-removal ...

# 2. Correct secondary edge contributions
multiple-edges ...

# 3. Continue with normalization / Fourier workflow
preedge ...
postedge ...
normalize ...

See Also

Next: Start with Glitch Removal for local spike cleanup

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