find_clipping

Builtin index · Signal processing

result = find_clipping(x, [threshold=], [full_scale=], [tol=], [min_run=3])

Input: x, threshold, full_scale, tol, min_run · Output: result

What it is

Same detection as is_clipped, same keywords, but returns *where*.

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = find_clipping(x, [threshold=], [full_scale=], [tol=], [min_run=3])`	`x` —not documented yet `threshold` Model, Num, or Vec, optionalReturns a `Model` of kind `"find_clipping"` (the named-field multi-return `findpeaks`/`qr`/`svd` already use, since Qu has no tuple unpacking) with fields `count` (a `Num`, how many runs), `starts`, `ends` (`Vec`s of run bounds, inclusive at both ends like every Qu range), `lengths` (a `Vec`), `values` (a `Vec`, the level each run is held at — signed, so the positive rail is distinguishable from the negative one), `samples` (a `Num`, total samples inside runs), `fraction` (a `Num`, `samples / N`), and `threshold`, `tol`, `min_run` (`Num`s, echoing back the parameters actually used — when the threshold was inferred, a caller reading only `starts` has no way to know what level the answer is relative to). `full_scale` optionalnot documented yet `tol` Model, Num, or Vec, optionalReturns a `Model` of kind `"find_clipping"` (the named-field multi-return `findpeaks`/`qr`/`svd` already use, since Qu has no tuple unpacking) with fields `count` (a `Num`, how many runs), `starts`, `ends` (`Vec`s of run bounds, inclusive at both ends like every Qu range), `lengths` (a `Vec`), `values` (a `Vec`, the level each run is held at — signed, so the positive rail is distinguishable from the negative one), `samples` (a `Num`, total samples inside runs), `fraction` (a `Num`, `samples / N`), and `threshold`, `tol`, `min_run` (`Num`s, echoing back the parameters actually used — when the threshold was inferred, a caller reading only `starts` has no way to know what level the answer is relative to). `min_run` Model, Num, or Vec, optional (default `3`)Returns a `Model` of kind `"find_clipping"` (the named-field multi-return `findpeaks`/`qr`/`svd` already use, since Qu has no tuple unpacking) with fields `count` (a `Num`, how many runs), `starts`, `ends` (`Vec`s of run bounds, inclusive at both ends like every Qu range), `lengths` (a `Vec`), `values` (a `Vec`, the level each run is held at — signed, so the positive rail is distinguishable from the negative one), `samples` (a `Num`, total samples inside runs), `fraction` (a `Num`, `samples / N`), and `threshold`, `tol`, `min_run` (`Num`s, echoing back the parameters actually used — when the threshold was inferred, a caller reading only `starts` has no way to know what level the answer is relative to).	`count` Numa `Num`, how many runs `ends` Vec`Vec`s of run bounds, inclusive at both ends like every Qu range `lengths` Veca `Vec` `values` Veca `Vec`, the level each run is held at — signed, so the positive rail is distinguishable from the negative one `samples` Numa `Num`, total samples inside runs `fraction` Numa `Num`, `samples / N` `min_run` Num`Num`s, echoing back the parameters actually used — when the threshold was inferred, a caller reading only `starts` has no way to know what level the answer is relative to	Returns a `Model` of kind `"find_clipping"` (the named-field multi-return `findpeaks`/`qr`/`svd` already use, since Qu has no tuple unpacking) with fields `count` (a `Num`, how many runs), `starts`, `ends` (`Vec`s of run bounds, inclusive at both ends like every Qu range), `lengths` (a `Vec`), `values` (a `Vec`, the level each run is held at — signed, so the positive rail is distinguishable from the negative one), `samples` (a `Num`, total samples inside runs), `fraction` (a `Num`, `samples / N`), and `threshold`, `tol`, `min_run` (`Num`s, echoing back the parameters actually used — when the threshold was inferred, a caller reading only `starts` has no way to know what level the answer is relative to).

Examples

No documentation example calls this form yet. The Signal processing chapter is where one belongs.

How it works

Returns a Value::Model, the named-field multi-return this engine already uses for findpeaks/qr/svd -- Qu has no tuple unpacking, and clipping is never one number. threshold/tol/min_run are echoed back on purpose: when the threshold was inferred from the record, a caller reading only starts has no way to know what level the answer is relative to.

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.