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Fourier Low-Pass Filtering

Source: R/smooth.R
step_measure_filter_fourier.Rd

step_measure_filter_fourier() creates a specification of a recipe step that applies Fourier-domain low-pass filtering to remove high-frequency noise.

Usage

step_measure_filter_fourier(
  recipe,
  measures = NULL,
  cutoff = 0.1,
  type = c("lowpass", "highpass"),
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_filter_fourier")
)

Arguments

recipe

A recipe object.

measures

An optional character vector of measure column names.

cutoff

The cutoff frequency as a fraction of the Nyquist frequency (0 to 0.5). Default is 0.1. Frequencies above this are attenuated. Tunable via fourier_cutoff().

type

Type of filter: "lowpass" (default) keeps low frequencies, "highpass" keeps high frequencies.

role

Not used.

trained

Logical indicating if the step has been trained.

skip

Logical. Should the step be skipped when baking?

id

Unique step identifier.

Value

An updated recipe with the new step added.

Details

Fourier filtering transforms the spectrum to the frequency domain using FFT, applies a frequency mask, and transforms back. This is effective for:

  • Removing periodic noise

  • Smoothing with precise frequency control

  • Removing high-frequency detector noise

The cutoff is specified as a fraction of the Nyquist frequency. A cutoff of 0.1 keeps only the lowest 10% of frequencies.

See also

Other measure-smoothing: step_measure_despike(), step_measure_savitzky_golay(), step_measure_smooth_gaussian(), step_measure_smooth_ma(), step_measure_smooth_median(), step_measure_smooth_wavelet()

Examples

library(recipes)

rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_filter_fourier(cutoff = 0.1) |>
  prep()

bake(rec, new_data = NULL)
#> # A tibble: 215 × 5
#>       id water   fat protein .measures
#>    <int> <dbl> <dbl>   <dbl>    <meas>
#>  1     1  60.5  22.5    16.7 [100 × 2]
#>  2     2  46    40.1    13.5 [100 × 2]
#>  3     3  71     8.4    20.5 [100 × 2]
#>  4     4  72.8   5.9    20.7 [100 × 2]
#>  5     5  58.3  25.5    15.5 [100 × 2]
#>  6     6  44    42.7    13.7 [100 × 2]
#>  7     7  44    42.7    13.7 [100 × 2]
#>  8     8  69.3  10.6    19.3 [100 × 2]
#>  9     9  61.4  19.9    17.7 [100 × 2]
#> 10    10  61.4  19.9    17.7 [100 × 2]
#> # ℹ 205 more rows