signal

Builtin index · Plotting

result = signal(x, fs)

Input: x, fs · Output: result

What it is

Tags Vec x (numbers) with sample rate fs (number, Hz), so later calls (like rfft) do not have to be told it again.

Overloads

One call shape — this function is not overloaded.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

sig = signal([1, 5, 2, 8], 100)
print(threshold(sig, 4))   # signal(Fs=100) [0, 1, 0, 1]

signal(Fs=100) [0, 1, 0, 1]

from images

double(v) := v * 2
sig = signal([1, 2, 3, 4], 8)
s = sig.apply("double")
print("signal: {s}")   # signal: signal(Fs=8) [2, 4, 6, 8]

signal: signal(Fs=8) [2, 4, 6, 8]

from signal-processing

double(v) := v * 2
sig = signal([1, 2, 3, 4], 8)
out = sig.apply("double")     # same Fs, genuinely transformed

print("apply(double): {out}")

apply(double): signal(Fs=8) [2, 4, 6, 8]

from signal-processing

s = signal(0 to 9, 10)          # 10 samples at 10 Hz, t = 0.0, 0.1, ..., 0.9s

trimmed = cut(s, 0.5, 100.0)    # end clamps past the signal's duration

print("trimmed: {trimmed}")
@s.cut(0.2, 0.7)                 # self-mutating form: s = cut(s, 0.2, 0.7)

print("s after @cut: {s}")

trimmed: signal(Fs=10) [5, 6, 7, 8, 9]
s after @cut: signal(Fs=10) [2, 3, 4, 5, 6, 7]

from signal-processing

How it works

§41.2 sampled-signal constructor/destructor. signal(data, Fs) (canonical spelling) and Signal(data, Fs) (capitalized alias) both work; array(x)/Array(x) unwraps back to a plain vector (a no-op on anything that already is one).

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