Interpolate

The interpolate command resamples data onto a new uniform grid using cubic spline interpolation. This is a destructive operation — the original axis and data values are replaced.

(Note that the original data file remains unchanged; only the in-memory dataset is modified.)

Basic Usage 

interpolate <range> <--interval <step> | --number <n>> [options]

where:

  • <range> defines the new axis limits
  • Either --interval or --number specifies grid spacing (exactly one required)

Command Options

Option Description
<range> Range for the new interpolated axis (required). See range specification for details.
--interval <step> Fixed step size for the new axis (mutually exclusive with --number). Must include units.
--number <n> Number of evenly-spaced points in the new axis (mutually exclusive with --interval).
--axis <axis> Axis column for interpolation (auto-inferred from range units if omitted).
--domain <domain> Domain in which to interpolate (auto-inferred if omitted). Options: reciprocal, fourier.

Interpolation Method

EstraPy uses cubic spline interpolation (degree 3) via scipy.interpolate.make_interp_spline. This provides:

  • Smooth, continuous curves through existing data points
  • C² continuity (smooth first and second derivatives)
  • Minimal oscillation between points

Examples 

# Interpolate to 0.5 eV steps over 8000-9000 eV
interpolate 8000eV 9000eV --interval 0.5eV

# Interpolate to exactly 500 points in k-space
interpolate 3k 15k --number 500

# Fine r-space grid with 0.01 Å spacing
interpolate 0A 6A --interval 0.01A --domain fourier

# Explicit axis specification
interpolate 0 1000 --number 2000 --axis E --domain reciprocal

Behavior

The command:

  1. Creates a new uniform axis within [range[0], range[1]] with specified spacing or count
  2. Interpolates all data columns onto this new axis using cubic splines
  3. Replaces the domain’s data with interpolated values

Destructive operation: Original axis values and data are replaced with interpolated results. The original data file remains unchanged; only the in-memory dataset is modified. Interpolation cannot add information — it only resamples existing data, and may introduce noise or artifacts if the original data is sparse or noisy.

Use Cases

  • Uniform grids: Convert irregularly-spaced data to fixed intervals
  • Alignment: Resample multiple scans to identical grids before averaging
  • Upsampling: Increase point density for smoother plots (cosmetic only)
  • Downsampling: Reduce point count while preserving overall shape

Tips and Best Practices

  1. Avoid excessive upsampling: Interpolation does not improve resolution or add real information
  2. Preserve Nyquist limits: When downsampling, ensure the new interval captures all spectral features
  3. Check endpoint behavior: Cubic splines may oscillate near range boundaries if data is noisy
  4. Pre-filter noise: Consider using rebin instead if your goal is noise reduction

Comparison with Rebin

Feature interpolate rebin
Method Cubic spline fitting Binning + averaging
Noise reduction No Yes (averages reduce noise)
Speed Moderate Fast
Use case Resampling to new grid Noise reduction + downsampling

See also: