Librariesbasics.lib
A library of basic elements. Its official prefix is ba.
References
Conversion Tools
(ba.)samp2sec
Converts a number of samples to a duration in seconds at the current sampling rate (see ma.SR).
samp2sec is a standard Faust function.
Usage
samp2sec(n) : _
Where:
n: number of samples
(ba.)sec2samp
Converts a duration in seconds to a number of samples at the current sampling rate (see ma.SR).
samp2sec is a standard Faust function.
Usage
sec2samp(d) : _
Where:
d: duration in seconds
(ba.)db2linear
dB-to-linear value converter. It can be used to convert an amplitude in dB to a linear gain ]0-N].
db2linear is a standard Faust function.
Usage
db2linear(l) : _
Where:
l: amplitude in dB
(ba.)linear2db
linea-to-dB value converter. It can be used to convert a linear gain ]0-N] to an amplitude in dB.
linear2db is a standard Faust function.
Usage
linear2db(g) : _
Where:
g: a linear gain
(ba.)lin2LogGain
Converts a linear gain (0-1) to a log gain (0-1).
Usage
lin2LogGain(n) : _
Where:
n: the linear gain
(ba.)log2LinGain
Converts a log gain (0-1) to a linear gain (0-1).
Usage
log2LinGain(n) : _
Where:
n: the log gain
(ba.)tau2pole
Returns a real pole giving exponential decay.
Note that t60 (time to decay 60 dB) is ~6.91 time constants.
tau2pole is a standard Faust function.
Usage
_ : smooth(tau2pole(tau)) : _
Where:
tau: time-constant in seconds
(ba.)pole2tau
Returns the time-constant, in seconds, corresponding to the given real,
positive pole in (0-1).
pole2tau is a standard Faust function.
Usage
pole2tau(pole) : _
Where:
pole: the pole
(ba.)midikey2hz
Converts a MIDI key number to a frequency in Hz (MIDI key 69 = A440).
midikey2hz is a standard Faust function.
Usage
midikey2hz(mk) : _
Where:
mk: the MIDI key number
(ba.)hz2midikey
Converts a frequency in Hz to a MIDI key number (MIDI key 69 = A440).
hz2midikey is a standard Faust function.
Usage
hz2midikey(freq) : _
Where:
freq: frequency in Hz
(ba.)semi2ratio
Converts semitones in a frequency multiplicative ratio.
semi2ratio is a standard Faust function.
Usage
semi2ratio(semi) : _
Where:
semi: number of semitone
(ba.)ratio2semi
Converts a frequency multiplicative ratio in semitones.
ratio2semi is a standard Faust function.
Usage
ratio2semi(ratio) : _
Where:
ratio: frequency multiplicative ratio
(ba.)cent2ratio
Converts cents in a frequency multiplicative ratio.
Usage
cent2ratio(cent) : _
Where:
cent: number of cents
(ba.)ratio2cent
Converts a frequency multiplicative ratio in cents.
Usage
ratio2cent(ratio) : _
Where:
ratio: frequency multiplicative ratio
(ba.)pianokey2hz
Converts a piano key number to a frequency in Hz (piano key 49 = A440).
Usage
pianokey2hz(pk) : _
Where:
pk: the piano key number
(ba.)hz2pianokey
Converts a frequency in Hz to a piano key number (piano key 49 = A440).
Usage
hz2pianokey(freq) : _
Where:
freq: frequency in Hz
Counters and Time/Tempo Tools
(ba.)counter
Starts counting 0, 1, 2, 3..., and raise the current integer value at each upfront of the trigger.
Usage
counter(trig) : _
Where:
trig: the trigger signal, each upfront will move the counter to the next integer
(ba.)countdown
Starts counting down from n included to 0. While trig is 1 the output is n.
The countdown starts with the transition of trig from 1 to 0. At the end
of the countdown the output value will remain at 0 until the next trig.
countdown is a standard Faust function.
Usage
countdown(n,trig) : _
Where:
n: the starting point of the countdowntrig: the trigger signal (1: start atn; 0: decrease until 0)
(ba.)countup
Starts counting up from 0 to n included. While trig is 1 the output is 0.
The countup starts with the transition of trig from 1 to 0. At the end
of the countup the output value will remain at n until the next trig.
countup is a standard Faust function.
Usage
countup(n,trig) : _
Where:
n: the maximum count valuetrig: the trigger signal (1: start at 0; 0: increase untiln)
(ba.)sweep
Counts from 0 to period-1 repeatedly, generating a
sawtooth waveform, like os.lf_rawsaw,
starting at 1 when run transitions from 0 to 1.
Outputs zero while run is 0.
Usage
sweep(period,run) : _
(ba.)time
A simple timer that counts every samples from the beginning of the process.
time is a standard Faust function.
Usage
time : _
(ba.)ramp
A linear ramp with a slope of '(+/-)1/n' samples to reach the next value.
Usage
_ : ramp(n) : _
Where:
n: number of samples to increment/decrement the value by one
(ba.)line
A linear ramp to reach a next value in 'n' samples. Note that the interpolation process is restarted every time the desired output value changes, the interpolation time is sampled only then.
Usage
_ : line(n) : _
Where:
n: number of samples to reach the next value
(ba.)tempo
Converts a tempo in BPM into a number of samples.
Usage
tempo(t) : _
Where:
t: tempo in BPM
(ba.)period
Basic sawtooth wave of period p.
Usage
period(p) : _
Where:
p: period as a number of samples
(ba.)pulse
Pulses (like 10000) generated at period p.
Usage
pulse(p) : _
Where:
p: period as a number of samples
(ba.)pulsen
Pulses (like 11110000) of length n generated at period p.
Usage
pulsen(n,p) : _
Where:
n: pulse length as a number of samplesp: period as a number of samples
(ba.)cycle
Split nonzero input values into n cycles.
Usage
_ : cycle(n) : si.bus(n)
Where:
n: the number of cycles/output signals
(ba.)beat
Pulses at tempo t.
beat is a standard Faust function.
Usage
beat(t) : _
Where:
t: tempo in BPM
(ba.)pulse_countup
Starts counting up pulses. While trig is 1 the output is counting up, while trig is 0 the counter is reset to 0.
Usage
_ : pulse_countup(trig) : _
Where:
trig: the trigger signal (1: start at next pulse; 0: reset to 0)
(ba.)pulse_countdown
Starts counting down pulses. While trig is 1 the output is counting down, while trig is 0 the counter is reset to 0.
Usage
_ : pulse_countdown(trig) : _
Where:
trig: the trigger signal (1: start at next pulse; 0: reset to 0)
(ba.)pulse_countup_loop
Starts counting up pulses from 0 to n included. While trig is 1 the output is counting up, while trig is 0 the counter is reset to 0. At the end of the countup (n) the output value will be reset to 0.
Usage
_ : pulse_countup_loop(n,trig) : _
Where:
n: the highest number of the countup (included) before reset to 0trig: the trigger signal (1: start at next pulse; 0: reset to 0)
(ba.)pulse_countdown_loop
Starts counting down pulses from 0 to n included. While trig is 1 the output is counting down, while trig is 0 the counter is reset to 0. At the end of the countdown (n) the output value will be reset to 0.
Usage
_ : pulse_countdown_loop(n,trig) : _
Where:
n: the highest number of the countup (included) before reset to 0trig: the trigger signal (1: start at next pulse; 0: reset to 0)
(ba.)resetCtr
Function that lets through the mth impulse out of
each consecutive group of n impulses.
Usage
_ : resetCtr(n,m) : _
Where:
n: the total number of impulses being splitm: index of impulse to allow to be output
Array Processing/Pattern Matching
(ba.)count
Count the number of elements of list l.
count is a standard Faust function.
Usage
count(l)
count((10,20,30,40)) -> 4
Where:
l: list of elements
(ba.)take
Take an element from a list.
take is a standard Faust function.
Usage
take(P,l)
take(3,(10,20,30,40)) -> 30
Where:
P: position (int, known at compile time, P > 0)l: list of elements
(ba.)subseq
Extract a part of a list.
Usage
subseq(l, P, N)
subseq((10,20,30,40,50,60), 1, 3) -> (20,30,40)
subseq((10,20,30,40,50,60), 4, 1) -> 50
Where:
l: listP: start point (int, known at compile time, 0: begin of list)N: number of elements (int, known at compile time)
Note:
Faust doesn't have proper lists. Lists are simulated with parallel compositions and there is no empty list.
Function tabulation
The purpose of function tabulation is to speed up the computation of heavy functions over an interval, so that the computation at runtime can be faster than directly using the function. Two techniques are implemented:
-
tabulatecomputes the function in a table and read the points using interpolation -
tabulate_chebychevuses Chebyshev polynomial approximation
Comparison program example
process = line(50000, r0, r1) <: FX-tb,FX-ch : par(i, 2, maxerr)
with {
C = 0;
FX = sin;
NX = 50;
CD = 3;
r0 = 0;
r1 = ma.PI;
tb(x) = ba.tabulate(C, FX, NX*(CD+1), r0, r1, x).cub;
ch(x) = ba.tabulate_chebychev(C, FX, NX, CD, r0, r1, x);
maxerr = abs : max ~ _;
line(n, x0, x1) = x0 + (ba.time%n)/n * (x1-x0);
};
(ba.)tabulate
Tabulate a 1D function over the range [r0, r1] for access via nearest-value, linear, cubic interpolation. In other words, the uniformly tabulated function can be evaluated using interpolation of order 0 (none), 1 (linear), or 3 (cubic).
Usage
tabulate(C, FX, S, r0, r1, x).(val|lin|cub) : _
C: whether to dynamically force thexvalue to the range [r0, r1]: 1 forces the check, 0 deactivates it (constant numerical expression)FX: unary function Y=F(X) with one output (scalar function of one variable)S: size of the table in samples (constant numerical expression)r0: minimum value of argument xr1: maximum value of argument x
tabulate(C, FX, S, r0, r1, x).val uses the value in the table closest to x
tabulate(C, FX, S, r0, r1, x).lin evaluates at x using linear interpolation between the closest stored values
tabulate(C, FX, S, r0, r1, x).cub evaluates at x using cubic interpolation between the closest stored values
Example test program
midikey2hz(mk) = ba.tabulate(1, ba.midikey2hz, 512, 0, 127, mk).lin;
process = midikey2hz(ba.time), ba.midikey2hz(ba.time);
(ba.)tabulate_chebychev
Tabulate a 1D function over the range [r0, r1] for access via Chebyshev polynomial approximation.
In contrast to (ba.)tabulate, which interpolates only between tabulated samples, (ba.)tabulate_chebychev
stores coefficients of Chebyshev polynomials that are evaluated to provide better approximations in many cases.
Two new arguments controlling this are NX, the number of segments into which [r0, r1] is divided, and CD,
the maximum Chebyshev polynomial degree to use for each segment. A rdtable of size NX*(CD+1) is internally used.
Note that processing r1 the last point in the interval is not safe. So either be sure the input stays in [r0, r1[
or use C = 1.
Usage
_ : tabulate_chebychev(C, FX, NX, CD, r0, r1) : _
C: whether to dynamically force the value to the range [r0, r1]: 1 forces the check, 0 deactivates it (constant numerical expression)FX: unary function Y=F(X) with one output (scalar function of one variable)NX: number of segments for uniformly partitioning [r0, r1] (constant numerical expression)CD: maximum polynomial degree for each Chebyshev polynomial (constant numerical expression)r0: minimum value of argument xr1: maximum value of argument x
Example test program
midikey2hz_chebychev(mk) = ba.tabulate_chebychev(1, ba.midikey2hz, 100, 4, 0, 127, mk);
process = midikey2hz_chebychev(ba.time), ba.midikey2hz(ba.time);
Selectors (Conditions)
(ba.)if
if-then-else implemented with a select2. WARNING: since select2 is strict (always evaluating both branches),
the resulting if does not have the usual "lazy" semantic of the C if form, and thus cannot be used to
protect against forbidden computations like division-by-zero for instance.
Usage
if(cond, then, else) : _
Where:
cond: conditionthen: signal selected while cond is trueelse: signal selected while cond is false
(ba.)selector
Selects the ith input among n at compile time.
Usage
selector(I,N)
_,_,_,_ : selector(2,4) : _ // selects the 3rd input among 4
Where:
I: input to select (int, numbered from 0, known at compile time)N: number of inputs (int, known at compile time, N > I)
There is also cselector for selecting among complex input signals of the form (real,imag).
(ba.)select2stereo
Select between 2 stereo signals.
Usage
_,_,_,_ : select2stereo(bpc) : _,_
Where:
bpc: the selector switch (0/1)
(ba.)selectn
Selects the ith input among N at run time.
Usage
selectn(N,i)
_,_,_,_ : selectn(4,2) : _ // selects the 3rd input among 4
Where:
N: number of inputs (int, known at compile time, N > 0)i: input to select (int, numbered from 0)
Example test program
N = 64;
process = par(n, N, (par(i,N,i) : selectn(N,n)));
(ba.)selectmulti
Selects the ith circuit among N at run time (all should have the same number of inputs and outputs) with a crossfade.
Usage
selectmulti(n,lgen,id)
Where:
n: crossfade in sampleslgen: list of circuitsid: circuit to select (int, numbered from 0)
Example test program
process = selectmulti(ma.SR/10, ((3,9),(2,8),(5,7)), nentry("choice", 0, 0, 2, 1));
process = selectmulti(ma.SR/10, ((_*3,_*9),(_*2,_*8),(_*5,_*7)), nentry("choice", 0, 0, 2, 1));
(ba.)selectoutn
Route input to the output among N at run time.
Usage
_ : selectoutn(N, i) : si.bus(N)
Where:
N: number of outputs (int, known at compile time, N > 0)i: output number to route to (int, numbered from 0) (i.e. slider)
Example test program
process = 1 : selectoutn(3, sel) : par(i, 3, vbargraph("v.bargraph %i", 0, 1));
sel = hslider("volume", 0, 0, 2, 1) : int;
Other
(ba.)latch
Latch input on positive-going transition of trig:"records" the input when trig switches from 0 to 1, outputs a frozen values everytime else.
Usage
_ : latch(trig) : _
Where:
trig: hold trigger (0 for hold, 1 for bypass)
(ba.)sAndH
Sample And Hold: "records" the input when trig is 1, outputs a frozen value when trig is 0.
sAndH is a standard Faust function.
Usage
_ : sAndH(trig) : _
Where:
trig: hold trigger (0 for hold, 1 for bypass)
(ba.)downSample
Down sample a signal. WARNING: this function doesn't change the
rate of a signal, it just holds samples...
downSample is a standard Faust function.
Usage
_ : downSample(freq) : _
Where:
freq: new rate in Hz
(ba.)peakhold
Outputs current max value above zero.
Usage
_ : peakhold(mode) : _
Where:
mode means:
0 - Pass through. A single sample 0 trigger will work as a reset.
1 - Track and hold max value.
(ba.)peakholder
While peak-holder functions are scarcely discussed in the literature (please do send me an email if you know otherwise), common sense tells that the expected behaviour should be as follows: the absolute value of the input signal is compared with the output of the peak-holder; if the input is greater or equal to the output, a new peak is detected and sent to the output; otherwise, a timer starts and the current peak is held for N samples; once the timer is out and no new peaks have been detected, the absolute value of the current input becomes the new peak.
Usage
_ : peakholder(holdTime) : _
Where:
holdTime: hold time in samples
(ba.)impulsify
Turns a signal into an impulse with the value of the current sample
(0.3,0.2,0.1 becomes 0.3,0.0,0.0). This function is typically used with a
button to turn its output into an impulse. impulsify is a standard Faust
function.
Usage
button("gate") : impulsify;
(ba.)automat
Record and replay in a loop the successives values of the input signal.
Usage
hslider(...) : automat(t, size, init) : _
Where:
t: tempo in BPMsize: number of items in the loopinit: init value in the loop
(ba.)bpf
bpf is an environment (a group of related definitions) that can be used to create break-point functions. It contains three functions:
start(x,y)to start a break-point functionend(x,y)to end a break-point functionpoint(x,y)to add intermediate points to a break-point function
A minimal break-point function must contain at least a start and an end point:
f = bpf.start(x0,y0) : bpf.end(x1,y1);
A more involved break-point function can contains any number of intermediate points:
f = bpf.start(x0,y0) : bpf.point(x1,y1) : bpf.point(x2,y2) : bpf.end(x3,y3);
In any case the x_{i} must be in increasing order (for all i, x_{i} < x_{i+1}).
For example the following definition:
f = bpf.start(x0,y0) : ... : bpf.point(xi,yi) : ... : bpf.end(xn,yn);
implements a break-point function f such that:
f(x) = y_{0}whenx < x_{0}f(x) = y_{n}whenx > x_{n}f(x) = y_{i} + (y_{i+1}-y_{i})*(x-x_{i})/(x_{i+1}-x_{i})whenx_{i} <= xandx < x_{i+1}
bpf is a standard Faust function.
(ba.)listInterp
Linearly interpolates between the elements of a list.
Usage
index = 1.69; // range is 0-4
process = listInterp((800,400,350,450,325),index);
Where:
index: the index (float) to interpolate between the different values. The range ofindexdepends on the size of the list.
(ba.)bypass1
Takes a mono input signal, route it to e and bypass it if bpc = 1.
When bypassed, e is feed with zeros so that its state is cleanup up.
bypass1 is a standard Faust function.
Usage
_ : bypass1(bpc,e) : _
Where:
bpc: bypass switch (0/1)e: a mono effect
(ba.)bypass2
Takes a stereo input signal, route it to e and bypass it if bpc = 1.
When bypassed, e is feed with zeros so that its state is cleanup up.
bypass2 is a standard Faust function.
Usage
_,_ : bypass2(bpc,e) : _,_
Where:
bpc: bypass switch (0/1)e: a stereo effect
(ba.)bypass1to2
Bypass switch for effect e having mono input signal and stereo output.
Effect e is bypassed if bpc = 1.When bypassed, e is feed with zeros
so that its state is cleanup up.
bypass1to2 is a standard Faust function.
Usage
_ : bypass1to2(bpc,e) : _,_
Where:
bpc: bypass switch (0/1)e: a mono-to-stereo effect
(ba.)bypass_fade
Bypass an arbitrary (N x N) circuit with 'n' samples crossfade.
Inputs and outputs signals are faded out when 'e' is bypassed,
so that 'e' state is cleanup up.
Once bypassed the effect is replaced by par(i,N,_).
Bypassed circuits can be chained.
Usage
_ : bypass_fade(n,b,e) : _
or
_,_ : bypass_fade(n,b,e) : _,_
n: number of samples for the crossfadeb: bypass switch (0/1)e: N x N circuit
Example test program
process = bypass_fade(ma.SR/10, checkbox("bypass echo"), echo);
process = bypass_fade(ma.SR/10, checkbox("bypass reverb"), freeverb);
(ba.)toggle
Triggered by the change of 0 to 1, it toggles the output value between 0 and 1.
Usage
_ : toggle : _
Example test program
button("toggle") : toggle : vbargraph("output", 0, 1)
(an.amp_follower(0.1) > 0.01) : toggle : vbargraph("output", 0, 1) // takes audio input
(ba.)on_and_off
The first channel set the output to 1, the second channel to 0.
Usage
_,_ : on_and_off : _
Example test program
button("on"), button("off") : on_and_off : vbargraph("output", 0, 1)
(ba.)bitcrusher
Produce distortion by reduction of the signal resolution.
Usage
_ : bitcrusher(nbits) : _
Where:
nbits: the number of bits of the wanted resolution
Sliding Reduce
Provides various operations on the last n samples using a high order
slidingReduce(op,n,maxN,disabledVal,x) fold-like function:
slidingSum(n): the sliding sum of the last n input samples, CPU-lightslidingSump(n,maxN): the sliding sum of the last n input samples, numerically stable "forever"slidingMax(n,maxN): the sliding max of the last n input samplesslidingMin(n,maxN): the sliding min of the last n input samplesslidingMean(n): the sliding mean of the last n input samples, CPU-lightslidingMeanp(n,maxN): the sliding mean of the last n input samples, numerically stable "forever"slidingRMS(n): the sliding RMS of the last n input samples, CPU-lightslidingRMSp(n,maxN): the sliding RMS of the last n input samples, numerically stable "forever"
Working Principle
If we want the maximum of the last 8 values, we can do that as:
simpleMax(x) =
(
(
max(x@0,x@1),
max(x@2,x@3)
) :max
),
(
(
max(x@4,x@5),
max(x@6,x@7)
) :max
)
:max;
max(x@2,x@3) is the same as max(x@0,x@1)@2 but the latter re-uses a
value we already computed,so is more efficient. Using the same trick for
values 4 trough 7, we can write:
efficientMax(x)=
(
(
max(x@0,x@1),
max(x@0,x@1)@2
) :max
),
(
(
max(x@0,x@1),
max(x@0,x@1)@2
) :max@4
)
:max;
We can rewrite it recursively, so it becomes possible to get the maximum at have any number of values, as long as it's a power of 2.
recursiveMax =
case {
(1,x) => x;
(N,x) => max(recursiveMax(N/2,x), recursiveMax(N/2,x)@(N/2));
};
What if we want to look at a number of values that's not a power of 2?
For each value, we will have to decide whether to use it or not.
If n is bigger than the index of the value, we use it, otherwise we replace
it with (0-(ma.MAX)):
variableMax(n,x) =
max(
max(
(
(x@0 : useVal(0)),
(x@1 : useVal(1))
):max,
(
(x@2 : useVal(2)),
(x@3 : useVal(3))
):max
),
max(
(
(x@4 : useVal(4)),
(x@5 : useVal(5))
):max,
(
(x@6 : useVal(6)),
(x@7 : useVal(7))
):max
)
)
with {
useVal(i) = select2((n>=i) , (0-(ma.MAX)),_);
};
Now it becomes impossible to re-use any values. To fix that let's first look
at how we'd implement it using recursiveMax, but with a fixed n that is not
a power of 2. For example, this is how you'd do it with n=3:
binaryMaxThree(x) =
(
recursiveMax(1,x)@0, // the first x
recursiveMax(2,x)@1 // the second and third x
):max;
n=6
binaryMaxSix(x) =
(
recursiveMax(2,x)@0, // first two
recursiveMax(4,x)@2 // third trough sixth
):max;
Note that recursiveMax(2,x) is used at a different delay then in
binaryMaxThree, since it represents 1 and 2, not 2 and 3. Each block is
delayed the combined size of the previous blocks.
n=7
binaryMaxSeven(x) =
(
(
recursiveMax(1,x)@0, // first x
recursiveMax(2,x)@1 // second and third
):max,
(
recursiveMax(4,x)@3 // fourth trough seventh
)
):max;
To make a variable version, we need to know which powers of two are used, and at which delay time.
Then it becomes a matter of:
- lining up all the different block sizes in parallel:
sequentialOperatorParOut() - delaying each the appropriate amount:
sumOfPrevBlockSizes() - turning it on or off:
useVal() - getting the maximum of all of them:
parallelOp()
In Faust, we can only do that for a fixed maximum number of values: maxN, known at compile time.
(ba.)slidingReduce
Fold-like high order function. Apply a commutative binary operation op to
the last n consecutive samples of a signal x. For example :
slidingReduce(max,128,128,0-(ma.MAX)) will compute the maximum of the last
128 samples. The output is updated each sample, unlike reduce, where the
output is constant for the duration of a block.
Usage
_ : slidingReduce(op,n,maxN,disabledVal) : _
Where:
n: the number of values to processmaxN: the maximum number of values to process (int, known at compile time, maxN > 0)op: the operator. Needs to be a commutative one.disabledVal: the value to use when we want to ignore a value.
In other words, op(x,disabledVal) should equal to x. For example,
+(x,0) equals x and min(x,ma.MAX) equals x. So if we want to
calculate the sum, we need to give 0 as disabledVal, and if we want the
minimum, we need to give ma.MAX as disabledVal.
(ba.)slidingSum
The sliding sum of the last n input samples.
It will eventually run into numerical trouble when there is a persistent dc component.
If that matters in your application, use the more CPU-intensive ba.slidingSump.
Usage
_ : slidingSum(n) : _
Where:
n: the number of values to process
(ba.)slidingSump
The sliding sum of the last n input samples.
It uses a lot more CPU than ba.slidingSum, but is numerically stable "forever" in return.
Usage
_ : slidingSump(n,maxN) : _
Where:
n: the number of values to processmaxN: the maximum number of values to process (int, known at compile time, maxN > 0)
(ba.)slidingMax
The sliding maximum of the last n input samples.
Usage
_ : slidingMax(n,maxN) : _
Where:
n: the number of values to processmaxN: the maximum number of values to process (int, known at compile time, maxN > 0)
(ba.)slidingMin
The sliding minimum of the last n input samples.
Usage
_ : slidingMin(n,maxN) : _
Where:
n: the number of values to processmaxN: the maximum number of values to process (int, known at compile time, maxN > 0)
(ba.)slidingMean
The sliding mean of the last n input samples.
It will eventually run into numerical trouble when there is a persistent dc component.
If that matters in your application, use the more CPU-intensive ba.slidingMeanp.
Usage
_ : slidingMean(n) : _
Where:
n: the number of values to process
(ba.)slidingMeanp
The sliding mean of the last n input samples.
It uses a lot more CPU than ba.slidingMean, but is numerically stable "forever" in return.
Usage
_ : slidingMeanp(n,maxN) : _
Where:
n: the number of values to processmaxN: the maximum number of values to process (int, known at compile time, maxN > 0)
(ba.)slidingRMS
The root mean square of the last n input samples.
It will eventually run into numerical trouble when there is a persistent dc component.
If that matters in your application, use the more CPU-intensive ba.slidingRMSp.
Usage
_ : slidingRMS(n) : _
Where:
n: the number of values to process
(ba.)slidingRMSp
The root mean square of the last n input samples.
It uses a lot more CPU than ba.slidingRMS, but is numerically stable "forever" in return.
Usage
_ : slidingRMSp(n,maxN) : _
Where:
n: the number of values to processmaxN: the maximum number of values to process (int, known at compile time, maxN > 0)
Parallel Operators
Provides various operations on N parallel inputs using a high order
parallelOp(op,N,x) function:
parallelMax(N): the max of n parallel inputsparallelMin(N): the min of n parallel inputsparallelMean(N): the mean of n parallel inputsparallelRMS(N): the RMS of n parallel inputs
(ba.)parallelOp
Apply a commutative binary operation op to N parallel inputs.
usage
si.bus(N) : parallelOp(op,N) : _
where:
N: the number of parallel inputs known at compile timeop: the operator which needs to be commutative
(ba.)parallelMax
The maximum of N parallel inputs.
Usage
si.bus(N) : parallelMax(N) : _
Where:
N: the number of parallel inputs known at compile time
(ba.)parallelMin
The minimum of N parallel inputs.
Usage
si.bus(N) : parallelMin(N) : _
Where:
N: the number of parallel inputs known at compile time
(ba.)parallelMean
The mean of N parallel inputs.
Usage
si.bus(N) : parallelMean(N) : _
Where:
N: the number of parallel inputs known at compile time
(ba.)parallelRMS
The RMS of N parallel inputs.
Usage
si.bus(N) : parallelRMS(N) : _
Where:
N: the number of parallel inputs known at compile time