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la-kitchen

A historical collection of Pure Data abstractions from La Kitchen, including sensor-data utilities such as automatic calibration, extrema detection, and retaining recent input values.

Contributors

People who contribute to this project.

Objects

  • amplitude_n amplitude_n returns the amplitude covered by the most recent values, using a history of size n.
  • autocal autocal is an abstraction for calibrating sensor input values by learning their minimum, maximum, and center, then producing a calibrated output over a configurable range.
  • catch_extremum catch_extremum returns the most recent local minimum and maximum from incoming data.
  • catch_extremum2 catch_extremum2 returns the most recent local minimum and maximum from its input data.
  • change_n change_n examines the last n boolean inputs and outputs 0 if they are all the same, otherwise 1.
  • count_n count_n counts from 0 through n−1.
  • debounce_b debounce_b suppresses subsequent bangs after receiving one, blocking them until the specified duration has elapsed.
  • fir_filter fir_filter computes a finite impulse response (FIR) filter.
  • fir_hip_n fir_hip_n computes a finite impulse response (FIR) high-pass filter of order n.
  • fir_mean_n fir_mean_n computes a finite impulse response (FIR) low-pass mean filter of order n, for smoothing input data.
  • iir_hip iir_hip computes an infinite impulse response high-pass filter for its input.
  • iir_lop iir_lop computes an infinite impulse response low-pass filter.
  • last_n last_n stores the last n incoming data values in a list.
  • last_n_matrix last_n_matrix saves the last n incoming data items in a column matrix, providing a rolling history of recent values.
  • list2matcol list2matcol converts an input list into a column matrix.
  • max_n max_n returns the maximum among the last n input values, providing a rolling maximum.
  • min_n min_n returns the minimum among the last n input values, providing a rolling minimum over recent data.
  • one_n one_n accepts Boolean input (0 or 1) and returns 1 when the last n data values were nonzero; otherwise, it returns 0.
  • seuil_n seuil_n compares the current input sample with the sample from n samples earlier and returns 1 when their difference is up to a threshold value.
  • zero_n zero_n outputs 1 if the last n input values were all 0; otherwise, it outputs 0.