Split up filter in module parts
This commit is contained in:
parent
ebdb8a86a1
commit
b15e81409e
|
|
@ -29,8 +29,8 @@ num = "0.4.1"
|
|||
rayon = "1.8.0"
|
||||
|
||||
# Python bindings
|
||||
pyo3 = { version = "0.20", optional = true, features = ["extension-module", "anyhow"]}
|
||||
numpy = { version = "0.20", optional = true}
|
||||
pyo3 = { version = "0.21.2", optional = true, features = ["extension-module", "anyhow"]}
|
||||
numpy = { version = "0.21.0", optional = true}
|
||||
|
||||
# White noise etc
|
||||
rand = "0.8.5"
|
||||
|
|
@ -40,8 +40,8 @@ rand_distr = "0.4.3"
|
|||
cpal = { version = "0.15.3", optional = true }
|
||||
|
||||
# Nice enumerations
|
||||
strum = "0.25.0"
|
||||
strum_macros = "0.25.3"
|
||||
strum = "0.26.2"
|
||||
strum_macros = "0.26.2"
|
||||
|
||||
# Conditional compilation enhancements
|
||||
cfg-if = "1.0.0"
|
||||
|
|
|
|||
|
|
@ -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::*;
|
||||
|
||||
|
||||
/// 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
|
||||
pub type Cflt = Complex<Flt>;
|
||||
|
||||
use ndarray::{Array1, Array2};
|
||||
/// Vector of floating point values
|
||||
/// Complex unit sqrt(-1)
|
||||
pub const I: Cflt = Cflt::new(0., 1.);
|
||||
|
||||
/// (Owning) Vector of floating point values
|
||||
pub type Vd = Vec<Flt>;
|
||||
/// Vector of complex floating point values
|
||||
|
||||
/// (Owning) Vector of complex floating point values
|
||||
pub type Vc = Vec<Cflt>;
|
||||
|
||||
/// 1D array of floats
|
||||
pub type Dcol = Array1<Flt>;
|
||||
|
||||
/// 1D array of complex floats
|
||||
pub type Ccol = Array1<Cflt>;
|
||||
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ mod streamstatus;
|
|||
|
||||
// Module re-exports
|
||||
pub use daqconfig::{DaqChannel, DaqConfig};
|
||||
pub use datatype::*;
|
||||
pub use datatype::DataType;
|
||||
pub use deviceinfo::DeviceInfo;
|
||||
pub use qty::Qty;
|
||||
pub use streamhandler::StreamHandler;
|
||||
|
|
@ -31,13 +31,8 @@ pub use record::*;
|
|||
|
||||
use strum_macros::Display;
|
||||
|
||||
cfg_if::cfg_if! {
|
||||
if #[cfg(feature = "python-bindings")] {
|
||||
use pyo3::exceptions::PyValueError;
|
||||
use pyo3::prelude::*;
|
||||
use pyo3::{pymodule, pyclass, types::PyModule, PyResult};
|
||||
} else {} }
|
||||
|
||||
use crate::config::*;
|
||||
use super::*;
|
||||
/// Stream types that can be started
|
||||
///
|
||||
#[cfg_attr(feature = "python-bindings", pyclass)]
|
||||
|
|
|
|||
495
src/filter.rs
495
src/filter.rs
|
|
@ -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);
|
||||
}
|
||||
}
|
||||
|
|
@ -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);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,175 @@
|
|||
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())
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
//! # 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()
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,137 @@
|
|||
|
||||
/// 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
src/lib.rs
17
src/lib.rs
|
|
@ -2,29 +2,24 @@
|
|||
//!
|
||||
//! This crate contains structures and functions to perform acoustic measurements, interact with
|
||||
//! data acquisition devices and apply common acoustic analysis operations on them.
|
||||
|
||||
#![warn(missing_docs)]
|
||||
#![allow(non_snake_case)]
|
||||
|
||||
#![allow(non_upper_case_globals)]
|
||||
#![allow(unused_imports)]
|
||||
|
||||
mod config;
|
||||
pub mod filter;
|
||||
|
||||
mod config;
|
||||
use config::*;
|
||||
|
||||
pub use config::Flt;
|
||||
// pub mod window;
|
||||
// pub mod ps;
|
||||
pub mod filter;
|
||||
pub mod daq;
|
||||
pub mod siggen;
|
||||
|
||||
pub use config::*;
|
||||
pub use daq::*;
|
||||
|
||||
cfg_if::cfg_if! {
|
||||
if #[cfg(feature = "python-bindings")] {
|
||||
use pyo3::prelude::*;
|
||||
use pyo3::{pymodule, PyResult};
|
||||
} else {} }
|
||||
use filter::*;
|
||||
|
||||
|
||||
/// A Python module implemented in Rust.
|
||||
|
|
|
|||
Loading…
Reference in New Issue