Qu

is_stable

Builtin index · Signal processing

result = is_stable(filt)

Input: filt · Output: result

What it is

filt: a Model (kind "filter").

Overloads

One call shape — this function is not overloaded.

CodeInputOutputDescription
result = is_stable(filt)
filt Modelnot documented yet
result boolean
Returns a boolean, true iff every pole lies strictly inside the unit circle — the standard causal-LTI stability criterion. An FIR filter (poles at the origin) is always stable.

Examples

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

fs     = 1000
t      = (0 to 999) / fs
signal = sin(2 * pi * 50 * t) + 0.5 * sin(2 * pi * 220 * t) + 0.1 * randn(1000, seed = 2)
lp = butter(4, "low", 100, fs)             # 4th-order lowpass, 100 Hz cutoff

notch = butter(2, "notch", [45, 55], fs)   # removes 45-55 Hz mains hum


filtered = sosfilt(lp, signal)             # causal, one pass

zero_phase = filtfilt(lp, signal)          # zero-phase, double effective order

print("lp is stable: {is_stable(lp)}, notch is stable: {is_stable(notch)}")

H = freqz(lp, 512)
plot(linspace(0, fs/2, 512), mag2db(abs(H)))
output
lp is stable: true, notch is stable: true
· plot: figure 3 (512 points)
-200 -100 0 100 200 300 400 500 -200 0 200 400 600 800 x=0.21, y=0 x=0.21, y=0 x=0.21, y=0 x=0.21, y=0 x=0.21, y=0 x=0.21, y=0 x=0.21, y=$10^{-9}$ x=0.21, y=$1.1\times10^{-9}$ x=0.21, y=$1.2\times10^{-9}$ x=0.21, y=$1.3\times10^{-9}$ x=0.21, y=$1.4\times10^{-9}$ x=0.21, y=$1.5\times10^{-9}$ x=0.21, y=$1.7\times10^{-9}$ x=0.21, y=$1.8\times10^{-9}$ x=0.21, y=$2\times10^{-9}$ x=0.21, y=$2.1\times10^{-9}$ x=0.21, y=$2.3\times10^{-9}$ x=0.21, y=$2.5\times10^{-9}$ x=0.21, y=$2.7\times10^{-9}$ x=0.21, y=$2.9\times10^{-9}$ x=0.21, y=$3.2\times10^{-9}$ x=0.21, y=$3.4\times10^{-9}$ x=0.21, y=$3.7\times10^{-9}$ x=0.21, y=$4\times10^{-9}$ x=0.21, y=$4.4\times10^{-9}$ x=0.21, y=$4.8\times10^{-9}$ x=0.21, y=$5.2\times10^{-9}$ x=0.21, y=$5.6\times10^{-9}$ x=0.21, y=$6.1\times10^{-9}$ x=0.21, y=$6.6\times10^{-9}$ x=0.21, y=$7.1\times10^{-9}$ x=0.21, y=$7.7\times10^{-9}$ x=0.21, y=$8.4\times10^{-9}$ x=0.21, y=$9.1\times10^{-9}$ x=0.21, y=$9.9\times10^{-9}$ x=0.21, y=$1.1\times10^{-8}$ x=0.21, y=$1.2\times10^{-8}$ x=0.21, y=$1.3\times10^{-8}$ x=0.21, y=$1.4\times10^{-8}$ x=0.21, y=$1.5\times10^{-8}$ x=0.21, y=$1.6\times10^{-8}$ x=0.21, y=$1.7\times10^{-8}$ x=0.21, y=$1.9\times10^{-8}$ x=0.21, y=$2\times10^{-8}$ x=0.21, y=$2.2\times10^{-8}$ x=0.21, y=$2.4\times10^{-8}$ x=0.21, y=$2.6\times10^{-8}$ x=0.21, y=$2.8\times10^{-8}$ x=0.21, y=$3.1\times10^{-8}$ x=0.21, y=$3.3\times10^{-8}$ x=0.21, y=$3.6\times10^{-8}$ x=0.21, y=$3.9\times10^{-8}$ x=0.21, y=$4.2\times10^{-8}$ x=0.21, y=$4.6\times10^{-8}$ x=0.21, y=$5\times10^{-8}$ x=0.21, y=$5.4\times10^{-8}$ x=0.21, y=$5.9\times10^{-8}$ x=0.21, y=$6.4\times10^{-8}$ x=0.21, y=$6.9\times10^{-8}$ x=0.21, y=$7.5\times10^{-8}$ x=0.21, y=$8.1\times10^{-8}$ x=0.21, y=$8.8\times10^{-8}$ x=0.21, y=$9.5\times10^{-8}$ x=0.21, y=$10^{-7}$ x=0.21, y=$1.1\times10^{-7}$ x=0.21, y=$1.2\times10^{-7}$ x=0.21, y=$1.3\times10^{-7}$ x=0.21, y=$1.4\times10^{-7}$ x=0.21, y=$1.5\times10^{-7}$ x=0.21, y=$1.7\times10^{-7}$ x=0.21, y=$1.8\times10^{-7}$ x=0.21, y=$2\times10^{-7}$ x=0.21, y=$2.1\times10^{-7}$ x=0.21, y=$2.3\times10^{-7}$ x=0.21, y=$2.5\times10^{-7}$ x=0.21, y=$2.7\times10^{-7}$ x=0.21, y=$3\times10^{-7}$ x=0.21, y=$3.2\times10^{-7}$ x=0.21, y=$3.5\times10^{-7}$ x=0.21, y=$3.8\times10^{-7}$ x=0.21, y=$4.1\times10^{-7}$ x=0.21, y=$4.4\times10^{-7}$ x=0.21, y=$4.8\times10^{-7}$ x=0.21, y=$5.2\times10^{-7}$ x=0.21, y=$5.7\times10^{-7}$ x=0.21, y=$6.1\times10^{-7}$ x=0.21, y=$6.7\times10^{-7}$ x=0.21, y=$7.2\times10^{-7}$ x=0.21, y=$7.8\times10^{-7}$ x=0.21, y=$8.5\times10^{-7}$ x=0.21, y=$9.2\times10^{-7}$ x=0.21, y=$10^{-6}$ x=0.21, y=$1.1\times10^{-6}$ x=0.21, y=$1.2\times10^{-6}$ x=0.21, y=$1.3\times10^{-6}$ x=0.21, y=$1.4\times10^{-6}$ x=0.21, y=$1.5\times10^{-6}$ x=0.21, y=$1.6\times10^{-6}$ x=0.21, y=$1.8\times10^{-6}$ x=0.21, y=$1.9\times10^{-6}$ x=0.21, y=$2.1\times10^{-6}$ x=0.21, y=$2.2\times10^{-6}$ x=0.21, y=$2.4\times10^{-6}$ x=0.21, y=$2.6\times10^{-6}$ x=0.21, y=$2.9\times10^{-6}$ x=0.21, y=$3.1\times10^{-6}$ x=0.21, y=$3.4\times10^{-6}$ x=0.21, y=$3.6\times10^{-6}$ x=0.21, y=$4\times10^{-6}$ x=0.21, y=$4.3\times10^{-6}$ x=0.21, y=$4.7\times10^{-6}$ x=0.21, y=$5\times10^{-6}$ x=0.21, y=$5.5\times10^{-6}$ x=0.21, y=$5.9\times10^{-6}$ x=0.21, y=$6.4\times10^{-6}$ x=0.21, y=$7\times10^{-6}$ x=0.21, y=$7.6\times10^{-6}$ x=0.21, y=$8.2\times10^{-6}$ x=0.21, y=$8.9\times10^{-6}$ x=0.21, y=$9.6\times10^{-6}$ x=0.21, y=$10^{-5}$ x=0.21, y=$1.1\times10^{-5}$ x=0.21, y=$1.2\times10^{-5}$ x=0.21, y=$1.3\times10^{-5}$ x=0.21, y=$1.4\times10^{-5}$ x=0.21, y=$1.6\times10^{-5}$ x=0.21, y=$1.7\times10^{-5}$ x=0.21, y=$1.8\times10^{-5}$ x=0.21, y=$2\times10^{-5}$ x=0.21, y=$2.2\times10^{-5}$ x=0.21, y=$2.3\times10^{-5}$ x=0.21, y=$2.5\times10^{-5}$ x=0.21, y=$2.8\times10^{-5}$ x=0.21, y=$3\times10^{-5}$ x=0.21, y=$3.2\times10^{-5}$ x=0.21, y=$3.5\times10^{-5}$ x=0.21, y=$3.8\times10^{-5}$ x=0.21, y=$4.1\times10^{-5}$ x=0.21, y=$4.5\times10^{-5}$ x=0.21, y=$4.9\times10^{-5}$ x=0.21, y=$5.3\times10^{-5}$ x=0.21, y=$5.7\times10^{-5}$ x=0.21, y=$6.2\times10^{-5}$ x=0.21, y=$6.7\times10^{-5}$ x=0.21, y=$7.3\times10^{-5}$ x=0.21, y=$7.9\times10^{-5}$ x=0.21, y=$8.6\times10^{-5}$ x=0.21, y=$9.3\times10^{-5}$ x=0.21, y=$10^{-4}$ x=0.21, y=$1.1\times10^{-4}$ x=0.21, y=$1.2\times10^{-4}$ x=0.21, y=$1.3\times10^{-4}$ x=0.21, y=$1.4\times10^{-4}$ x=0.21, y=$1.5\times10^{-4}$ x=0.21, y=$1.6\times10^{-4}$ x=0.21, y=$1.8\times10^{-4}$ x=0.21, y=$1.9\times10^{-4}$ x=0.21, y=$2.1\times10^{-4}$ x=0.21, y=$2.3\times10^{-4}$ x=0.21, y=$2.5\times10^{-4}$ x=0.21, y=$2.7\times10^{-4}$ x=0.21, y=$2.9\times10^{-4}$ x=0.21, y=$3.1\times10^{-4}$ x=0.21, y=$3.4\times10^{-4}$ x=0.21, y=$3.7\times10^{-4}$ x=0.21, y=$4\times10^{-4}$ x=0.21, y=$4.3\times10^{-4}$ x=0.21, y=$4.7\times10^{-4}$ x=0.21, y=$5.1\times10^{-4}$ x=0.21, y=$5.5\times10^{-4}$ x=0.21, y=$6\times10^{-4}$ x=0.21, y=$6.5\times10^{-4}$ x=0.21, y=$7\times10^{-4}$ x=0.21, y=$7.6\times10^{-4}$ x=0.21, y=$8.2\times10^{-4}$ x=0.21, y=$8.9\times10^{-4}$ x=0.21, y=$9.7\times10^{-4}$ x=0.21, y=0.001 x=0.21, y=0.001 x=0.21, y=0.001 x=0.21, y=0.001 x=0.21, y=0.001 x=0.21, y=0.002 x=0.21, y=0.002 x=0.21, y=0.002 x=0.21, y=0.002 x=0.21, y=0.002 x=0.209, y=0.002 x=0.209, y=0.002 x=0.209, y=0.003 x=0.209, y=0.003 x=0.209, y=0.003 x=0.209, y=0.003 x=0.209, y=0.003 x=0.209, y=0.004 x=0.208, y=0.004 x=0.208, y=0.004 x=0.208, y=0.004 x=0.207, y=0.004 x=0.207, y=0.005 frequency (Hz)

from signal-processing

fs = 1000
ep = ellip(4, 1, 40, "low", 100, fs)
c1 = cheby1(4, 1, "low", 100, fs)
c2 = cheby2(4, 40, "low", 100, fs)
print("ellip stable: {is_stable(ep)}, cheby1 stable: {is_stable(c1)}, cheby2 stable: {is_stable(c2)}")

fir = fir1(6, 100, kind="low", window="hamming", fs=fs)
b = fir.b
a = [1]
n = 0:1:255
t = n / fs
x = sin(2*pi*40*t) + sin(2*pi*300*t)
y  = filter_ba(b, a, x)
y2 = lfilter(b, a, x)               # lfilter is the same builtin under another name

print("filter_ba/lfilter agree: {max(abs(y - y2)) < 1e-12}")

lsq = firls(20, [0, 0.2, 0.3, 1.0], [1, 1, 0, 0])   # low-pass band edges/response

print("firls taps: {length(lsq.b)}")

gd = group_delay(fir, 256)
print("FIR group delay (should be ~order/2=3): {gd[10]}")
print("ellip filter has {length(poles(ep))} poles")

ep.fvtool()   # 2x2 magnitude/phase/group-delay/impulse-response figure

pzplot(ep)    # pole-zero plot with the unit circle and stability verdict
output
ellip stable: true, cheby1 stable: true, cheby2 stable: true
filter_ba/lfilter agree: true
firls taps: 21
FIR group delay (should be ~order/2=3): 3
ellip filter has 4 poles
-80 -60 -40 -20 0 0 150 300 450 Magnitude Response Frequency (Hz) Magnitude (dB) -150 -100 -50 0 50 100 150 0 150 300 450 Phase Response Frequency (Hz) Phase (deg) -250 -200 -150 -100 -50 0 0 150 300 450 Group Delay Frequency (Hz) Delay (samples) -20 -10 0 10 20 0 20 40 60 x=-0.135, y=0.991 x=-0.135, y=-0.991 x=0.57, y=0.821 x=0.57, y=-0.821 Pole-Zero Plot (stable) Real part Imaginary part

from signal-processing

How it works

is_stable(filt) — true iff every pole lies strictly inside the unit circle, the standard causal-LTI stability criterion. An FIR filter (all poles at the origin) is always stable, which falls out of this one general definition automatically rather than needing a special case.

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