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Split up filter in module parts

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This commit is contained in:
Anne de Jong 2024-04-19 14:09:32 +02:00
parent ebdb8a86a1
commit b15e81409e
9 changed files with 564 additions and 521 deletions
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8
Cargo.toml
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@ -29,8 +29,8 @@ num = "0.4.1"
rayon = "1.8.0" rayon = "1.8.0"
# Python bindings # Python bindings
pyo3 = { version = "0.20", optional = true, features = ["extension-module", "anyhow"]} pyo3 = { version = "0.21.2", optional = true, features = ["extension-module", "anyhow"]}
numpy = { version = "0.20", optional = true} numpy = { version = "0.21.0", optional = true}
# White noise etc # White noise etc
rand = "0.8.5" rand = "0.8.5"
@ -40,8 +40,8 @@ rand_distr = "0.4.3"
cpal = { version = "0.15.3", optional = true } cpal = { version = "0.15.3", optional = true }
# Nice enumerations # Nice enumerations
strum = "0.25.0" strum = "0.26.2"
strum_macros = "0.25.3" strum_macros = "0.26.2"
# Conditional compilation enhancements # Conditional compilation enhancements
cfg-if = "1.0.0" cfg-if = "1.0.0"

33
src/config.rs
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@ -20,18 +20,45 @@ cfg_if::cfg_if! {
} }
} }
cfg_if::cfg_if! {
if #[cfg(feature = "python-bindings")] {
pub use numpy::ndarray::{ArrayD, ArrayViewD, ArrayViewMutD};
pub use numpy::ndarray::prelude::*;
pub use numpy::{IntoPyArray, PyArray1, PyArrayDyn, PyArrayLike1, PyReadonlyArrayDyn};
pub use pyo3::exceptions::PyValueError;
pub use pyo3::prelude::*;
pub use pyo3::{pymodule, types::PyModule, PyResult};
} else {
pub use ndarray::prelude::*;
pub use ndarray::{Array1, Array2, ArrayView1};
} }
// pub use num::complex::i;
use num::complex::*; use num::complex::*;
/// View into 1D array of floats
pub type VdView<'a> = ArrayView1<'a, Flt>;
/// View into 1D array of complex floats
pub type VcView<'a> = ArrayView1<'a, Cflt>;
/// Complex number floating point /// Complex number floating point
pub type Cflt = Complex<Flt>; pub type Cflt = Complex<Flt>;
use ndarray::{Array1, Array2}; /// Complex unit sqrt(-1)
/// Vector of floating point values pub const I: Cflt = Cflt::new(0., 1.);
/// (Owning) Vector of floating point values
pub type Vd = Vec<Flt>; pub type Vd = Vec<Flt>;
/// Vector of complex floating point values
/// (Owning) Vector of complex floating point values
pub type Vc = Vec<Cflt>; pub type Vc = Vec<Cflt>;
/// 1D array of floats /// 1D array of floats
pub type Dcol = Array1<Flt>; pub type Dcol = Array1<Flt>;
/// 1D array of complex floats /// 1D array of complex floats
pub type Ccol = Array1<Cflt>; pub type Ccol = Array1<Cflt>;

11
src/daq/mod.rs
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@ -18,7 +18,7 @@ mod streamstatus;
// Module re-exports // Module re-exports
pub use daqconfig::{DaqChannel, DaqConfig}; pub use daqconfig::{DaqChannel, DaqConfig};
pub use datatype::*; pub use datatype::DataType;
pub use deviceinfo::DeviceInfo; pub use deviceinfo::DeviceInfo;
pub use qty::Qty; pub use qty::Qty;
pub use streamhandler::StreamHandler; pub use streamhandler::StreamHandler;
@ -31,13 +31,8 @@ pub use record::*;
use strum_macros::Display; use strum_macros::Display;
cfg_if::cfg_if! { use crate::config::*;
if #[cfg(feature = "python-bindings")] { use super::*;
use pyo3::exceptions::PyValueError;
use pyo3::prelude::*;
use pyo3::{pymodule, pyclass, types::PyModule, PyResult};
} else {} }
/// Stream types that can be started /// Stream types that can be started
/// ///
#[cfg_attr(feature = "python-bindings", pyclass)] #[cfg_attr(feature = "python-bindings", pyclass)]

495
src/filter.rs
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@ -1,495 +0,0 @@
//! # Filter implemententations for biquads, series of biquads and banks of series of biquads.
//!
//! Contains [Biquad], [SeriesBiquad], and [BiquadBank]. These are all constructs that work on
//! blocks of input data, and apply filters on it. Todo: implement FIR filter.
#![allow(non_snake_case)]
use super::config::*;
use anyhow::{bail, Result};
use cfg_if::cfg_if;
use rayon::prelude::*;
cfg_if! {
if #[cfg(feature = "python-bindings")] {
use numpy::ndarray::{ArrayD, ArrayViewD, ArrayViewMutD};
use numpy::{IntoPyArray, PyArray1, PyArrayDyn, PyArrayLike1, PyReadonlyArrayDyn};
use pyo3::exceptions::PyValueError;
use pyo3::prelude::*;
use pyo3::{pymodule, types::PyModule, PyResult};
} else {} }
pub trait Filter: Send {
//! The filter trait is implemented by Biquad, SeriesBiquad, and BiquadBank
/// Filter input to generate output. A vector of output floats is generated with the same
/// length as input.
fn filter(&mut self, input: &[Flt]) -> Vd;
/// Reset the filter state(s). In essence, this makes sure that all memory of the past is
/// forgotten.
fn reset(&mut self);
/// Required method for cloning a BiquadBank, such that arbitrary filter types can be used as
/// their 'channels'.
fn clone_dyn(&self) -> Box<dyn Filter>;
}
impl Clone for Box<dyn Filter> {
fn clone(&self) -> Self {
self.clone_dyn()
}
}
/// # A biquad is a second order recursive filter structure.
#[cfg_attr(feature = "python-bindings", pyclass)]
#[derive(Clone, Copy, Debug)]
pub struct Biquad {
// State parameters
w1: Flt,
w2: Flt,
// Filter coefficients - forward
b0: Flt,
b1: Flt,
b2: Flt,
// Filter coefficients - recursive
// a0: Flt, // a0 is assumed one, not used
a1: Flt,
a2: Flt,
}
#[cfg(feature = "python-bindings")]
#[cfg_attr(feature = "python-bindings", pymethods)]
impl Biquad {
#[new]
/// Create new biquad filter. See [Biquad::new()]
///
pub fn new_py<'py>(coefs: PyReadonlyArrayDyn<Flt>) -> PyResult<Self> {
Ok(Biquad::new(coefs.as_slice()?)?)
}
#[pyo3(name = "unit")]
#[staticmethod]
/// See: [Biquad::unit()]
pub fn unit_py() -> Biquad {
Biquad::unit()
}
#[pyo3(name = "firstOrderHighPass")]
#[staticmethod]
/// See: [Biquad::firstOrderHighPass()]
pub fn firstOrderHighPass_py(fs: Flt, cuton_Hz: Flt) -> PyResult<Biquad> {
Ok(Biquad::firstOrderHighPass(fs, cuton_Hz)?)
}
#[pyo3(name = "filter")]
/// See: [Biquad::filter()]
pub fn filter_py<'py>(
&mut self,
py: Python<'py>,
input: PyArrayLike1<Flt>,
) -> PyResult<&'py PyArray1<Flt>> {
Ok(self.filter(input.as_slice()?).into_pyarray(py))
}
}
impl Biquad {
/// Create new biquad filter from given filter coeficients
///
/// # Args
///
/// - coefs: Filter coefficients.
///
pub fn new(coefs: &[Flt]) -> Result<Self> {
match coefs {
[b0, b1, b2, a0, a1, a2] => {
if *a0 != 1.0 {
bail!("Coefficient a0 should be equal to 1.0")
}
Ok(Biquad { w1: 0., w2: 0., b0: *b0, b1: *b1, b2: *b2, a1: *a1, a2: *a2})
},
_ => bail!("Could not initialize biquad. Please make sure that the coefficients contain 6 terms")
}
}
/// Create unit impulse response biquad filter. Input = output
fn unit() -> Biquad {
let filter_coefs = &[1., 0., 0., 1., 0., 0.];
Biquad::new(filter_coefs).unwrap()
}
/// Initialize biquad as first order high pass filter.
///
///
/// * fs: Sampling frequency in \[Hz\]
/// * cuton_Hz: -3 dB cut-on frequency in \[Hz\]
///
pub fn firstOrderHighPass(fs: Flt, cuton_Hz: Flt) -> Result<Biquad> {
if fs <= 0. {
bail!("Invalid sampling frequency: {} [Hz]", fs);
}
if cuton_Hz <= 0. {
bail!("Invalid cuton frequency: {} [Hz]", cuton_Hz);
}
if cuton_Hz >= 0.98 * fs / 2. {
bail!(
"Invalid cuton frequency. We limit this to 0.98* fs / 2. Given value {} [Hz]",
cuton_Hz
);
}
let tau: Flt = 1. / (2. * pi * cuton_Hz);
let facnum = 2. * fs * tau / (1. + 2. * fs * tau);
let facden = (1. - 2. * fs * tau) / (1. + 2. * fs * tau);
let coefs = [
facnum, // b0
-facnum, // b1
0., // b2
1., // a0
facden, // a1
0., // a2
];
Ok(Biquad::new(&coefs).unwrap())
}
fn filter_inout(&mut self, inout: &mut [Flt]) {
for sample in inout.iter_mut() {
let w0 = *sample - self.a1 * self.w1 - self.a2 * self.w2;
let yn = self.b0 * w0 + self.b1 * self.w1 + self.b2 * self.w2;
self.w2 = self.w1;
self.w1 = w0;
*sample = yn;
}
// println!("{:?}", inout);
}
}
impl Filter for Biquad {
fn filter(&mut self, input: &[Flt]) -> Vec<Flt> {
let mut out = input.to_vec();
self.filter_inout(&mut out);
// println!("{:?}", out);
out
}
fn reset(&mut self) {
self.w1 = 0.;
self.w2 = 0.;
}
fn clone_dyn(&self) -> Box<dyn Filter> {
Box::new(*self)
}
}
/// Series of biquads that filter sequentially on an input signal
///
/// # Examples
///
/// See (tests)
/// ```
#[derive(Clone, Debug)]
#[cfg_attr(feature = "python-bindings", pyclass)]
pub struct SeriesBiquad {
biqs: Vec<Biquad>,
}
#[cfg(feature = "python-bindings")]
#[cfg_attr(feature = "python-bindings", pymethods)]
impl SeriesBiquad {
#[new]
/// Create new series filter set. See [SeriesBiquad::new()]
///
pub fn new_py<'py>(coefs: PyReadonlyArrayDyn<Flt>) -> PyResult<Self> {
Ok(SeriesBiquad::new(coefs.as_slice()?)?)
}
#[pyo3(name = "unit")]
#[staticmethod]
/// See: [Biquad::unit()]
pub fn unit_py() -> SeriesBiquad {
SeriesBiquad::unit()
}
#[pyo3(name = "filter")]
/// See: [SeriesBiquad::filter()]
pub fn filter_py<'py>(
&mut self,
py: Python<'py>,
input: PyArrayLike1<Flt>,
) -> PyResult<&'py PyArray1<Flt>> {
Ok(self.filter(input.as_slice()?).into_pyarray(py))
}
#[pyo3(name = "reset")]
/// See: [SeriesBiquad::reset()]
pub fn reset_py(&mut self) {
self.reset();
}
}
impl SeriesBiquad {
/// Create a new series biquad, having an arbitrary number of biquads.
///
/// # Arguments
///
/// * `filter_coefs` - Vector of biquad coefficients, stored in a single array. The first six
/// for the first biquad, and so on.
///
///
pub fn new(filter_coefs: &[Flt]) -> Result<SeriesBiquad> {
if filter_coefs.len() % 6 != 0 {
bail!(
"filter_coefs should be multiple of 6, given: {}.",
filter_coefs.len()
);
}
let nfilters = filter_coefs.len() / 6;
let mut biqs: Vec<Biquad> = Vec::with_capacity(nfilters);
for coefs in filter_coefs.chunks(6) {
let biq = Biquad::new(coefs)?;
biqs.push(biq);
}
if biqs.is_empty() {
bail!("No filter coefficients given!");
}
Ok(SeriesBiquad { biqs })
}
/// Unit impulse response series biquad. Input = output
pub fn unit() -> SeriesBiquad {
let filter_coefs = &[1., 0., 0., 1., 0., 0.];
SeriesBiquad::new(filter_coefs).unwrap()
}
fn clone_dyn(&self) -> Box<dyn Filter> {
Box::new(self.clone())
}
}
impl Filter for SeriesBiquad {
//! Filter input by applying all biquad filters in series on each input sample, to obtain the
//! output samples.
//!
fn filter(&mut self, input: &[Flt]) -> Vd {
let mut inout = input.to_vec();
for biq in self.biqs.iter_mut() {
biq.filter_inout(&mut inout);
}
inout
}
fn reset(&mut self) {
self.biqs.iter_mut().for_each(|f| f.reset());
}
fn clone_dyn(&self) -> Box<dyn Filter> {
Box::new(self.clone())
}
}
#[cfg_attr(feature = "python-bindings", pyclass)]
#[derive(Clone)]
/// Multiple biquad filter that operate in parallel on a signal, and can apply a gain value to each
/// of the returned values. The BiquadBank can be used to decompose a signal by running it through
/// parallel filters, or it can directly be used to eq a signal. For the latter process, also a
/// gain can be applied when the output is made as the sum of the filtered inputs for each biquad.
///
/// # Detailed description
///
/// Below is an example of the signal flow is for the case of three SeriesBiquad filters, `h1`,
/// `h2` and `h3`:
///
/// ```markdown
///
/// analysis() gain application sum()
///
/// +------+ +-----+ +------------+
/// +-----+ h1 |---+ g1 +-------------+ | |
/// | +------+ +-----+ ++ | +------ |
/// | +-----| ++ |
/// Input | +------+ +-----+ | ++ | Output of filter()
///--------|>--+-----+ h2 |---+ g2 |--------------------| +-+ +----------------|>
/// | ---+---+ +-----+ | +-+ |
/// | +-----| ++ |
/// | +------+ +-----+ ++ | ++ |
/// +-----+ h3 |---+ g3 |-------------+ | +------ |
/// +------+ +-----+ | |
/// | +------------+
/// |
/// | Output of analysis() method (optional)
/// +-------------|>
/// ```
pub struct BiquadBank {
biqs: Vec<Box<dyn Filter>>,
gains: Vec<Flt>,
}
#[cfg(feature = "python-bindings")]
#[cfg_attr(feature = "python-bindings", pymethods)]
/// Methods to wrap it in Python
impl BiquadBank {
#[new]
/// Create new biquadbank filter set. See [BiquadBank::new()]
///
pub fn new_py<'py>(coefs: PyReadonlyArrayDyn<Flt>) -> PyResult<Self> {
let mut filts = vec![];
for col in coefs.as_array().columns() {
match col.as_slice() {
Some(colslice) => {
let new_ser = SeriesBiquad::new(colslice)?;
filts.push(new_ser.clone_dyn());
}
None => {
return Err(PyValueError::new_err("Error generating column"));
}
}
}
Ok(BiquadBank::new(filts))
}
#[pyo3(name = "filter")]
/// See: [BiquadBank::filter()]
pub fn filter_py<'py>(
&mut self,
py: Python<'py>,
input: PyArrayLike1<Flt>,
) -> PyResult<&'py PyArray1<Flt>> {
Ok(self.filter(input.as_slice()?).into_pyarray(py))
}
#[pyo3(name = "reset")]
/// See: [BiquadBank::reset()]
pub fn reset_py(&mut self) {
self.reset();
}
#[pyo3(name = "set_gains")]
/// See: [BiquadBank::set_gains()]
pub fn set_gains_py<'py>(&mut self, gains: PyArrayLike1<Flt>) -> PyResult<()> {
if gains.len() != self.len() {
return Err(PyValueError::new_err("Invalid number of provided gains"));
}
self.set_gains(gains.as_slice()?);
Ok(())
}
#[pyo3(name = "set_gains_dB")]
/// See: [BiquadBank::set_gains_dB()]
pub fn set_gains_dB_py<'py>(&mut self, gains_dB: PyArrayLike1<Flt>) -> PyResult<()> {
if gains_dB.len() != self.len() {
return Err(PyValueError::new_err("Invalid number of provided gains"));
}
self.set_gains_dB(gains_dB.as_slice()?);
Ok(())
}
#[pyo3(name = "len")]
/// See: [BiquadBank::len()]
pub fn len_py(&self) -> usize {
self.len()
}
}
impl BiquadBank {
/// Create new biquad bank. Initialized from given vector of series biquads.
pub fn new(biqs: Vec<Box<dyn Filter>>) -> BiquadBank {
let gains = vec![1.0; biqs.len()];
BiquadBank { biqs, gains }
}
/// Return the number of parallel filters installed.
pub fn len(&self) -> usize {
self.biqs.len()
}
/// Set the gains for each of the biquad. The gains are not used in the analyisis phase, but in
/// the reconstruction phase, so when BiquadBank::filter() is run on an input signal.
///
/// # Panics
///
/// When gains_dB.len() != to the number of filters.
pub fn set_gains_dB(&mut self, gains_dB: &[Flt]) {
if gains_dB.len() != self.gains.len() {
panic!("Invalid gains size!");
}
self.gains
.iter_mut()
.zip(gains_dB)
.for_each(|(g, gdB)| *g = Flt::powf(10., gdB / 20.));
}
/// Set linear gain values for each biquad. Same comments hold as for
/// [BiquadBank::set_gains_dB()].
pub fn set_gains(&mut self, gains: &[Flt]) {
if gains.len() != self.gains.len() {
panic!("Invalid gains size!");
}
// This could be done more efficient, but it does not matter. How often would you change
// the gain values?
self.gains.clone_from(&gains.to_vec());
}
/// Analysis step. Runs input signal through all filters. Outputs a vector of output results,
/// one for each filter in the bank.
pub fn analysis(&mut self, input: &[Flt]) -> Vec<Vd> {
// Filtered output for each filter in biquad bank
let filtered_out: Vec<Vd> = self
.biqs
.par_iter_mut()
// .iter_mut()
.map(|biq| biq.filter(input))
.collect();
filtered_out
}
}
impl Filter for BiquadBank {
fn filter(&mut self, input: &[Flt]) -> Vd {
// Sum of filter output times gains
let filtered_out = self.analysis(input);
let mut out: Vd = vec![0.; input.len()];
for (f, g) in filtered_out.iter().zip(&self.gains) {
for (outi, fi) in out.iter_mut().zip(f) {
// Sum and multiply by gain value
*outi += g * fi;
}
}
out
}
fn reset(&mut self) {
self.biqs.iter_mut().for_each(|b| b.reset());
}
fn clone_dyn(&self) -> Box<dyn Filter> {
Box::new(self.clone())
}
}
#[cfg(test)]
mod test {
use super::*;
#[test]
fn test_biquad1() {
let mut ser = Biquad::unit();
let inp = vec![1., 0., 0., 0., 0., 0.];
let filtered = ser.filter(&inp);
assert_eq!(&filtered, &inp);
}
#[test]
#[should_panic]
fn test_biquad2() {
// A a0 coefficient not in the right place, meaning we panic on unwrap
let filter_coefs = vec![1., 0., 0., 0., 0., 0.];
let mut ser = SeriesBiquad::new(&filter_coefs).unwrap();
let inp = vec![1., 0., 0., 0., 0., 0.];
let filtered = ser.filter(&inp);
assert_eq!(&filtered, &inp);
}
#[test]
fn test_biquad3() {
let filter_coefs = vec![0.5, 0.5, 0., 1., 0., 0.];
let mut ser = SeriesBiquad::new(&filter_coefs).unwrap();
let mut inp = vec![1., 0., 0., 0., 0., 0.];
let filtered = ser.filter(&inp);
// Change input to see match what should come out of output
inp[0] = 0.5;
inp[1] = 0.5;
assert_eq!(&inp, &filtered);
}
#[test]
fn test_biquadbank1() {
//! Creates two unit filters with gains of 0.5. Runs the input signal through these filters
//! in parallel and check if input == output.
let ser = Biquad::unit();
let mut biq = BiquadBank::new(vec![ser.clone_dyn(), ser.clone_dyn()]);
biq.set_gains(&[0.5, 0.5]);
let inp = vec![1., 0., 0., 0., 0., 0.];
let out = biq.filter(&inp);
assert_eq!(&out, &inp);
}
}

164
src/filter/biquad.rs Normal file
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@ -0,0 +1,164 @@
use super::*;
use ndarray::prelude::*;
use anyhow::{Result, bail};
use num::Complex;
#[cfg_attr(feature = "python-bindings", pyclass)]
#[derive(Clone, Copy, Debug)]
/// # A biquad is a second order recursive filter structure.
///
///
pub struct Biquad {
// State parameters
w1: Flt,
w2: Flt,
// Filter coefficients - forward
b0: Flt,
b1: Flt,
b2: Flt,
// Filter coefficients - recursive
// a0: Flt, // a0 is assumed one, not used
a1: Flt,
a2: Flt,
}
#[cfg(feature = "python-bindings")]
#[cfg_attr(feature = "python-bindings", pymethods)]
impl Biquad {
#[new]
/// Create new biquad filter. See [Biquad::new()]
///
pub fn new_py<'py>(coefs: PyReadonlyArrayDyn<Flt>) -> PyResult<Self> {
Ok(Biquad::new(coefs.as_slice()?)?)
}
#[pyo3(name = "unit")]
#[staticmethod]
/// See: [Biquad::unit()]
pub fn unit_py() -> Biquad {
Biquad::unit()
}
#[pyo3(name = "firstOrderHighPass")]
#[staticmethod]
/// See: [Biquad::firstOrderHighPass()]
pub fn firstOrderHighPass_py(fs: Flt, cuton_Hz: Flt) -> PyResult<Biquad> {
Ok(Biquad::firstOrderHighPass(fs, cuton_Hz)?)
}
#[pyo3(name = "filter")]
/// See: [Biquad::filter()]
pub fn filter_py<'py>(
&mut self,
py: Python<'py>,
input: PyArrayLike1<Flt>,
) -> PyResult<&'py PyArray1<Flt>> {
Ok(self.filter(input.as_slice()?).into_pyarray(py))
}
}
impl Biquad {
/// Create new biquad filter from given filter coeficients
///
/// # Args
///
/// - coefs: Filter coefficients.
///
pub fn new(coefs: &[Flt]) -> Result<Self> {
match coefs {
[b0, b1, b2, a0, a1, a2] => {
if *a0 != 1.0 {
bail!("Coefficient a0 should be equal to 1.0")
}
Ok(Biquad { w1: 0., w2: 0., b0: *b0, b1: *b1, b2: *b2, a1: *a1, a2: *a2})
},
_ => bail!("Could not initialize biquad. Please make sure that the coefficients contain 6 terms")
}
}
/// Create unit impulse response biquad filter. Input = output
fn unit() -> Biquad {
let filter_coefs = &[1., 0., 0., 1., 0., 0.];
Biquad::new(filter_coefs).unwrap()
}
/// Initialize biquad as first order high pass filter.
///
///
/// * fs: Sampling frequency in \[Hz\]
/// * cuton_Hz: -3 dB cut-on frequency in \[Hz\]
///
pub fn firstOrderHighPass(fs: Flt, cuton_Hz: Flt) -> Result<Biquad> {
if fs <= 0. {
bail!("Invalid sampling frequency: {} [Hz]", fs);
}
if cuton_Hz <= 0. {
bail!("Invalid cuton frequency: {} [Hz]", cuton_Hz);
}
if cuton_Hz >= 0.98 * fs / 2. {
bail!(
"Invalid cuton frequency. We limit this to 0.98* fs / 2. Given value {} [Hz]",
cuton_Hz
);
}
let tau: Flt = 1. / (2. * pi * cuton_Hz);
let facnum = 2. * fs * tau / (1. + 2. * fs * tau);
let facden = (1. - 2. * fs * tau) / (1. + 2. * fs * tau);
let coefs = [
facnum, // b0
-facnum, // b1
0., // b2
1., // a0
facden, // a1
0., // a2
];
Ok(Biquad::new(&coefs).unwrap())
}
pub fn filter_inout(&mut self, inout: &mut [Flt]) {
for sample in inout.iter_mut() {
let w0 = *sample - self.a1 * self.w1 - self.a2 * self.w2;
let yn = self.b0 * w0 + self.b1 * self.w1 + self.b2 * self.w2;
self.w2 = self.w1;
self.w1 = w0;
*sample = yn;
}
// println!("{:?}", inout);
}
}
impl Filter for Biquad {
fn filter(&mut self, input: &[Flt]) -> Vec<Flt> {
let mut out = input.to_vec();
self.filter_inout(&mut out);
// println!("{:?}", out);
out
}
fn reset(&mut self) {
self.w1 = 0.;
self.w2 = 0.;
}
fn clone_dyn(&self) -> Box<dyn Filter> {
Box::new(*self)
}
}
impl TransferFunction for Biquad {
fn tf(&self, fs: Flt, freq: VdView) -> Ccol {
let res = freq.mapv(|f| {
let z = Complex::exp(I * 2. * pi * f / fs);
let num = self.b0 + self.b1 / z + self.b2 / z / z;
let den = 1. + self.a1 / z + self.a2 / z / z;
num / den
});
res
// re
}
}
#[cfg(test)]
mod test {
use super::*;
#[test]
fn test_biquad1() {
let mut ser = Biquad::unit();
let inp = vec![1., 0., 0., 0., 0., 0.];
let filtered = ser.filter(&inp);
assert_eq!(&filtered, &inp);
}
}

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use super::*;
use super::seriesbiquad::*;
use rayon::prelude::*;
#[cfg_attr(feature = "python-bindings", pyclass)]
#[derive(Clone)]
/// Multiple biquad filter that operate in parallel on a signal, and can apply a gain value to each
/// of the returned values. The BiquadBank can be used to decompose a signal by running it through
/// parallel filters, or it can directly be used to eq a signal. For the latter process, also a
/// gain can be applied when the output is made as the sum of the filtered inputs for each biquad.
///
/// # Detailed description
///
/// Below is an example of the signal flow is for the case of three SeriesBiquad filters, `h1`,
/// `h2` and `h3`:
///
/// ```markdown
///
/// analysis() gain application sum()
///
/// +------+ +-----+ +------------+
/// +-----+ h1 |---+ g1 +-------------+ | |
/// | +------+ +-----+ ++ | +------ |
/// | +-----| ++ |
/// Input | +------+ +-----+ | ++ | Output of filter()
///--------|>--+-----+ h2 |---+ g2 |--------------------| +-+ +----------------|>
/// | ---+---+ +-----+ | +-+ |
/// | +-----| ++ |
/// | +------+ +-----+ ++ | ++ |
/// +-----+ h3 |---+ g3 |-------------+ | +------ |
/// +------+ +-----+ | |
/// | +------------+
/// |
/// | Output of analysis() method (optional)
/// +-------------|>
/// ```
pub struct BiquadBank {
biqs: Vec<Box<dyn Filter>>,
gains: Vec<Flt>,
}
#[cfg(feature = "python-bindings")]
#[cfg_attr(feature = "python-bindings", pymethods)]
/// Methods to wrap it in Python
impl BiquadBank {
#[new]
/// Create new biquadbank filter set. See [BiquadBank::new()]
///
pub fn new_py<'py>(coefs: PyReadonlyArrayDyn<Flt>) -> PyResult<Self> {
let mut filts = vec![];
for col in coefs.as_array().columns() {
match col.as_slice() {
Some(colslice) => {
let new_ser = SeriesBiquad::new(colslice)?;
filts.push(new_ser.clone_dyn());
}
None => {
return Err(PyValueError::new_err("Error generating column"));
}
}
}
Ok(BiquadBank::new(filts))
}
#[pyo3(name = "filter")]
/// See: [BiquadBank::filter()]
pub fn filter_py<'py>(
&mut self,
py: Python<'py>,
input: PyArrayLike1<Flt>,
) -> PyResult<&'py PyArray1<Flt>> {
Ok(self.filter(input.as_slice()?).into_pyarray(py))
}
#[pyo3(name = "reset")]
/// See: [BiquadBank::reset()]
pub fn reset_py(&mut self) {
self.reset();
}
#[pyo3(name = "set_gains")]
/// See: [BiquadBank::set_gains()]
pub fn set_gains_py<'py>(&mut self, gains: PyArrayLike1<Flt>) -> PyResult<()> {
if gains.len()? != self.len() {
return Err(PyValueError::new_err("Invalid number of provided gains"));
}
self.set_gains(gains.as_slice()?);
Ok(())
}
#[pyo3(name = "set_gains_dB")]
/// See: [BiquadBank::set_gains_dB()]
pub fn set_gains_dB_py<'py>(&mut self, gains_dB: PyArrayLike1<Flt>) -> PyResult<()> {
if gains_dB.len()? != self.len() {
return Err(PyValueError::new_err("Invalid number of provided gains"));
}
self.set_gains_dB(gains_dB.as_slice()?);
Ok(())
}
#[pyo3(name = "len")]
/// See: [BiquadBank::len()]
pub fn len_py(&self) -> usize {
self.len()
}
}
impl BiquadBank {
/// Create new biquad bank. Initialized from given vector of series biquads.
pub fn new(biqs: Vec<Box<dyn Filter>>) -> BiquadBank {
let gains = vec![1.0; biqs.len()];
BiquadBank { biqs, gains }
}
/// Return the number of parallel filters installed.
pub fn len(&self) -> usize {
self.biqs.len()
}
/// Set the gains for each of the biquad. The gains are not used in the analyisis phase, but in
/// the reconstruction phase, so when BiquadBank::filter() is run on an input signal.
///
/// # Panics
///
/// When gains_dB.len() != to the number of filters.
pub fn set_gains_dB(&mut self, gains_dB: &[Flt]) {
if gains_dB.len() != self.gains.len() {
panic!("Invalid gains size!");
}
self.gains
.iter_mut()
.zip(gains_dB)
.for_each(|(g, gdB)| *g = Flt::powf(10., gdB / 20.));
}
/// Set linear gain values for each biquad. Same comments hold as for
/// [BiquadBank::set_gains_dB()].
pub fn set_gains(&mut self, gains: &[Flt]) {
if gains.len() != self.gains.len() {
panic!("Invalid gains size!");
}
// This could be done more efficient, but it does not matter. How often would you change
// the gain values?
self.gains.clone_from(&gains.to_vec());
}
/// Analysis step. Runs input signal through all filters. Outputs a vector of output results,
/// one for each filter in the bank.
pub fn analysis(&mut self, input: &[Flt]) -> Vec<Vd> {
// Filtered output for each filter in biquad bank
let filtered_out: Vec<Vd> = self
.biqs
.par_iter_mut()
// .iter_mut()
.map(|biq| biq.filter(input))
.collect();
filtered_out
}
}
impl Filter for BiquadBank {
fn filter(&mut self, input: &[Flt]) -> Vd {
// Sum of filter output times gains
let filtered_out = self.analysis(input);
let mut out: Vd = vec![0.; input.len()];
for (f, g) in filtered_out.iter().zip(&self.gains) {
for (outi, fi) in out.iter_mut().zip(f) {
// Sum and multiply by gain value
*outi += g * fi;
}
}
out
}
fn reset(&mut self) {
self.biqs.iter_mut().for_each(|b| b.reset());
}
fn clone_dyn(&self) -> Box<dyn Filter> {
Box::new(self.clone())
}
}

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//! # Filter implemententations for biquads, series of biquads and banks of series of biquads.
//!
//! Contains [Biquad], [SeriesBiquad], and [BiquadBank]. These are all constructs that work on
//! blocks of input data, and apply filters on it. Todo: implement FIR filter.
#![allow(non_snake_case)]
pub use super::config::*;
mod biquad;
mod biquadbank;
mod seriesbiquad;
pub use biquad::Biquad;
pub use biquadbank::BiquadBank;
pub use seriesbiquad::SeriesBiquad;
/// Implementations of this trait are able to DSP-filter input data.
pub trait Filter: Send {
//! The filter trait is implemented by Biquad, SeriesBiquad, and BiquadBank
/// Filter input to generate output. A vector of output floats is generated with the same
/// length as input.
fn filter(&mut self, input: &[Flt]) -> Vd;
/// Reset the filter state(s). In essence, this makes sure that all memory of the past is
/// forgotten.
fn reset(&mut self);
/// Required method for cloning a BiquadBank, such that arbitrary filter types can be used as
/// their 'channels'.
fn clone_dyn(&self) -> Box<dyn Filter>;
}
/// Implementations are able to generate transfer functions of itself
pub trait TransferFunction: Send {
/// Compute frequency response (i.e. transfer function from input to output)
///
/// Args
fn tf(&self, fs: Flt, freq: VdView) -> Ccol;
}
impl Clone for Box<dyn Filter> {
fn clone(&self) -> Self {
self.clone_dyn()
}
}

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/// Series of biquads that filter sequentially on an input signal
///
/// # Examples
///
/// See (tests)
///
use super::*;
use super::biquad::Biquad;
use anyhow::{bail, Result};
#[derive(Clone, Debug)]
#[cfg_attr(feature = "python-bindings", pyclass)]
pub struct SeriesBiquad {
biqs: Vec<Biquad>,
}
#[cfg(feature = "python-bindings")]
#[cfg_attr(feature = "python-bindings", pymethods)]
impl SeriesBiquad {
#[new]
/// Create new series filter set. See [SeriesBiquad::new()]
///
pub fn new_py<'py>(coefs: PyReadonlyArrayDyn<Flt>) -> PyResult<Self> {
Ok(SeriesBiquad::new(coefs.as_slice()?)?)
}
#[pyo3(name = "unit")]
#[staticmethod]
/// See: [Biquad::unit()]
pub fn unit_py() -> SeriesBiquad {
SeriesBiquad::unit()
}
#[pyo3(name = "filter")]
/// See: [SeriesBiquad::filter()]
pub fn filter_py<'py>(
&mut self,
py: Python<'py>,
input: PyArrayLike1<Flt>,
) -> PyResult<&'py PyArray1<Flt>> {
Ok(self.filter(input.as_slice()?).into_pyarray(py))
}
#[pyo3(name = "reset")]
/// See: [SeriesBiquad::reset()]
pub fn reset_py(&mut self) {
self.reset();
}
}
impl SeriesBiquad {
/// Create a new series biquad, having an arbitrary number of biquads.
///
/// # Arguments
///
/// * `filter_coefs` - Vector of biquad coefficients, stored in a single array. The first six
/// for the first biquad, and so on.
///
///
pub fn new(filter_coefs: &[Flt]) -> Result<SeriesBiquad> {
if filter_coefs.len() % 6 != 0 {
bail!(
"filter_coefs should be multiple of 6, given: {}.",
filter_coefs.len()
);
}
let nfilters = filter_coefs.len() / 6;
let mut biqs: Vec<Biquad> = Vec::with_capacity(nfilters);
for coefs in filter_coefs.chunks(6) {
let biq = Biquad::new(coefs)?;
biqs.push(biq);
}
if biqs.is_empty() {
bail!("No filter coefficients given!");
}
Ok(SeriesBiquad { biqs })
}
/// Unit impulse response series biquad. Input = output
pub fn unit() -> SeriesBiquad {
let filter_coefs = &[1., 0., 0., 1., 0., 0.];
SeriesBiquad::new(filter_coefs).unwrap()
}
fn clone_dyn(&self) -> Box<dyn Filter> {
Box::new(self.clone())
}
}
impl Filter for SeriesBiquad {
//! Filter input by applying all biquad filters in series on each input sample, to obtain the
//! output samples.
//!
fn filter(&mut self, input: &[Flt]) -> Vd {
let mut inout = input.to_vec();
for biq in self.biqs.iter_mut() {
biq.filter_inout(&mut inout);
}
inout
}
fn reset(&mut self) {
self.biqs.iter_mut().for_each(|f| f.reset());
}
fn clone_dyn(&self) -> Box<dyn Filter> {
Box::new(self.clone())
}
}
#[cfg(test)]
mod test {
use super::*;
#[test]
#[should_panic]
fn test_biquad2() {
// A a0 coefficient not in the right place, meaning we panic on unwrap
let filter_coefs = vec![1., 0., 0., 0., 0., 0.];
let mut ser = SeriesBiquad::new(&filter_coefs).unwrap();
let inp = vec![1., 0., 0., 0., 0., 0.];
let filtered = ser.filter(&inp);
assert_eq!(&filtered, &inp);
}
#[test]
fn test_biquad3() {
let filter_coefs = vec![0.5, 0.5, 0., 1., 0., 0.];
let mut ser = SeriesBiquad::new(&filter_coefs).unwrap();
let mut inp = vec![1., 0., 0., 0., 0., 0.];
let filtered = ser.filter(&inp);
// Change input to see match what should come out of output
inp[0] = 0.5;
inp[1] = 0.5;
assert_eq!(&inp, &filtered);
}
}

17
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//! //!
//! This crate contains structures and functions to perform acoustic measurements, interact with //! This crate contains structures and functions to perform acoustic measurements, interact with
//! data acquisition devices and apply common acoustic analysis operations on them. //! data acquisition devices and apply common acoustic analysis operations on them.
#![warn(missing_docs)] #![warn(missing_docs)]
#![allow(non_snake_case)] #![allow(non_snake_case)]
#![allow(non_upper_case_globals)] #![allow(non_upper_case_globals)]
#![allow(unused_imports)] #![allow(unused_imports)]
mod config;
pub mod filter;
mod config;
use config::*;
pub use config::Flt;
// pub mod window; // pub mod window;
// pub mod ps; // pub mod ps;
pub mod filter;
pub mod daq; pub mod daq;
pub mod siggen; pub mod siggen;
pub use config::*; use filter::*;
pub use daq::*;
cfg_if::cfg_if! {
if #[cfg(feature = "python-bindings")] {
use pyo3::prelude::*;
use pyo3::{pymodule, PyResult};
} else {} }
/// A Python module implemented in Rust. /// A Python module implemented in Rust.