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resample_linear

Resample a spectrum onto a uniform wavelength grid by linear interpolation.
Category Resampling
Backend numpy - implemented here on top of numpy primitives
Version 1.0.1
Reads ctx.spectrum (a Spectrum1D)
Writes spectrum

Linear interpolation is fast and adequate when the target sampling is similar to the source. For large changes in resolution prefer a flux-conserving method.

Parameters

Parameter Default Required Description
wavelength_min None - Lower bound of the new grid (Å); null keeps the input minimum.
wavelength_max None - Upper bound of the new grid (Å); null keeps the input maximum.
step None - Constant sampling step (Å). Mutually exclusive with n_points.
n_points None - Number of samples in the new grid. Mutually exclusive with step.

Use it

from spectro_kernel import run_algorithm

output = run_algorithm("resample_linear", ctx, {
    "wavelength_min": None,
    "wavelength_max": None,
    "step": None,
    "n_points": None
})

spectro run resample_linear --input spectrum.fits \
  --param wavelength_min=none \
  --param wavelength_max=none \
  --param step=none \
  --param n_points=none

{
  "tool": "resample_linear",
  "arguments": {
    "session_id": "<session_id>",
    "params": {
      "wavelength_min": null,
      "wavelength_max": null,
      "step": null,
      "n_points": null
    }
  }
}

Every algorithm is an MCP tool of the same name; describe_algorithm returns this page's metadata as JSON.

References

  • numpy.interp — piecewise-linear interpolation
  • For large resolution changes, prefer flux-conserving resampling (specutils.manipulation.FluxConservingResampler)