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/* --------------------------------------------------------------------------------------------- *
* Libraries
* --------------------------------------------------------------------------------------------- */
pub mod timeseries;
pub mod frequencyseries;
pub mod spectrogram;
pub mod filters;
pub mod windows;
//pub mod plot;
use std::{
str::FromStr,
string::ToString,
//f64::consts::PI,
fmt::Display,
fs::File,
io::BufReader,
io::BufRead,
io::Write};
use crate::{
timeseries::TimeSeries,
frequencyseries::FrequencySeries,
spectrogram::Spectrogram,
filters::Filter,
windows::Window,
// plot::{RealPlot, ComplexPlot}
};
use rustfft::FftPlanner;
use num::{Complex, complex::ComplexFloat};
use std::ops::{AddAssign, SubAssign, MulAssign, DivAssign};
//use more_asserts as ma;
/* --------------------------------------------------------------------------------------------- *
* Constructors
* --------------------------------------------------------------------------------------------- */
/// Spectral analysis methods
impl<D> TimeSeries<D>
where D: ComplexFloat + AddAssign + SubAssign + MulAssign + DivAssign,
{
/// Compute the cross spectal density between two signals, using the Welch's method
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(20000, 1e3, 0f64, 1f64);
/// let mut signal_2: TimeSeries = signal_1.clone * 2.;
///
/// // compute the csd
/// let csd: FrequencySeries = signal_1.csd(&signal_2, &window);
///
/// ```
pub fn csd(
&self,
other: &TimeSeries<D>,
window: &Window) -> FrequencySeries {
assert_eq!(self.get_fs(), other.get_fs());
// initialize fft
let mut planner = FftPlanner::new();
let fft = planner.plan_fft_forward(window.get_size());
// compute the mean of each time series
let mut self_clone = &mut self.clone();
self_clone = self_clone - self.mean();
let mut other_clone = &mut other.clone();
other_clone = other_clone - other.mean();
// initialize frequency series
let mut temp_1: Vec<Complex<f64>>;
let mut temp_2: Vec<Complex<f64>>;
let f_max: f64 = self.get_fs() / 2.;
let mut output: &mut FrequencySeries = &mut FrequencySeries::from_vector(
f_max, vec![Complex{ re: 0.0f64, im: 0.0f64 }; window.get_size() / 2 + 1]);
let nb_fft: usize = window.nb_fft(self.get_size());
for i in 0..nb_fft {
// compute windowed data
temp_1 = window.get_windowed_data(self_clone.get_data(), i);
temp_2 = window.get_windowed_data(other_clone.get_data(), i);
// compute fft
fft.process(&mut temp_1); fft.process(&mut temp_2);
// compute product
output = output + &*(
&mut FrequencySeries::from_vector(
f_max,
temp_1[0..(window.get_size() / 2 + 1)].to_vec().clone()
).conj()
* &FrequencySeries::from_vector(
f_max,
temp_2[0..(window.get_size() / 2 + 1)].to_vec().clone()
)
);
}
output = output / (f_max * window.get_norm_factor() * nb_fft as f64);
output.clone()
}
/// Compute power spectral density using cross spectral density with itself
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
///
/// // compute the csd
/// let psd: FrequencySeries = signal_1.psd(&window);
///
/// ```
pub fn psd(
&self,
window: &Window) -> FrequencySeries {
// use csd
let self_copy: &TimeSeries<D> = &(self.clone());
self.csd(&self_copy, window)
}
/// Compute the amplitude spectral density of a signal. Uses the psd function
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
///
/// // compute the csd
/// let asd: FrequencySeries = signal_1.asd(&window);
///
/// ```
pub fn asd(
&self,
window: &Window) -> FrequencySeries {
self.psd(window).sqrt()
}
/// Compute the coherence between two signals.
/// `\gamma_{1,2}(f) = \frac{|csd_{1,2}(f)|}{psd_1(f) \cdot psd_2(f)}`
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
/// let mut signal_2: TimeSeries = signal_1.clone() * 2.;
///
/// // compute the csd
/// let coherence: FrequencySeries = signal_1.coherence(&signal_2, &window);
///
/// ```
pub fn coherence(
&self,
other: &TimeSeries<D>,
window: &Window) -> FrequencySeries {
let psd1: FrequencySeries = self.clone().psd(window);
let psd2: FrequencySeries = other.clone().psd(window);
let mut csd: FrequencySeries = self.csd(other, window).abs2();
((&mut csd / &psd1) / &psd2).clone()
}
/// Compute the transfer functions between two signals.
/// `\TF_{1,2}(f) = \frac{csd_{1,2}(f)}{psd_1(f)}`
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
/// let mut signal_2: TimeSeries = signal_1.clone() * 2.;
///
/// // compute the csd
/// let transfer_function: FrequencySeries = signal_1.transfer_function(&signal_2, &window);
///
/// ```
pub fn transfer_function(
&self,
other: &TimeSeries<D>,
window: &Window) -> FrequencySeries {
let psd: FrequencySeries = self.clone().psd(window);
let mut csd: FrequencySeries = self.csd(other, window);
(&mut csd / &psd).clone()
}
/* ----------------------------------------------------------------------------------------- *
* compute spectrogram
* ----------------------------------------------------------------------------------------- */
/// Compute the cross spectal density between two signals, using the Welch's method
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
/// let mut signal_2: TimeSeries = signal_1.clone() * 2.;
///
/// // compute the csd
/// let csd: Spectrogram = signal_1.time_csd(&signal_2, &window, 10.);
///
/// ```
pub fn time_csd(
&self,
other: &TimeSeries<D>,
window: &Window,
nb_fft: usize) -> Spectrogram {
let step: usize = window.get_size() - window.get_overlap();
let f_max: f64 = self.get_fs() / 2.;
// check if the number of fft is over 1 and below the maximum number of fft
assert!(nb_fft > 0);
assert!(nb_fft <= window.nb_fft(self.get_size()));
assert_eq!(self.get_fs(), other.get_fs());
// initialize fft
let mut planner = FftPlanner::new();
let fft = planner.plan_fft_forward(window.get_size());
// clone the time series and subtract their mean
let mut self_clone = &mut self.clone();
self_clone = self_clone - self.mean();
let mut other_clone = &mut other.clone();
other_clone = other_clone - other.mean();
// compute all frequency series and put them into a vector
let mut temp_1: Vec<Complex<f64>>;
let mut temp_2: Vec<Complex<f64>>;
let mut temp_series: &mut FrequencySeries = &mut FrequencySeries::from_vector(
f_max, vec![Complex{ re: 0.0f64, im: 0.0f64 }; window.get_size() / 2 + 1]);
let mut series_vec: Vec<FrequencySeries> = Vec::new();
for i in 0..window.nb_fft(self.get_size()) {
temp_series.set_to_zero();
// compute windowed data
temp_1 = window.get_windowed_data(self_clone.get_data(), i);
temp_2 = window.get_windowed_data(other_clone.get_data(), i);
// compute fft
fft.process(&mut temp_1); fft.process(&mut temp_2);
// compute product and add result into the frequency series vector
temp_series = temp_series +
&*( &mut FrequencySeries::from_vector(
f_max,
temp_1[0..(window.get_size() / 2 + 1)].to_vec().clone()
).conj()
* &FrequencySeries::from_vector(
f_max,
temp_2[0..(window.get_size() / 2 + 1)].to_vec().clone()
) / (f_max * window.get_norm_factor() * nb_fft as f64) );
series_vec.push(temp_series.clone());
}
// computes first value and push it into the output vector
let mut one_series: &mut FrequencySeries = &mut FrequencySeries::from_vector(
f_max, vec![Complex{ re: 0.0f64, im: 0.0f64 }; window.get_size() / 2 + 1]);
for i in 0..nb_fft {
one_series = one_series + &series_vec[i];
}
// initialize output data vector
let mut output: Vec<Vec<Complex<f64>>> = Vec::new();
output.push( one_series.get_data() );
// roll over the rest of the time series
for i in nb_fft..window.nb_fft(self.get_size()) {
one_series = one_series + &series_vec[i];
one_series = one_series - &series_vec[i-nb_fft];
output.push( one_series.get_data() );
}
Spectrogram::from_vector(
f_max,
((nb_fft - 1) * step + window.get_size()) as f64 / self.get_fs(),
step as f64 / self.get_fs(),
output)
}
/// Compute power spectral density using cross spectral density with itself
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
///
/// // compute the csd
/// let psd: FrequencySeries = signal_1.time_psd(&window, 10);
///
/// ```
pub fn time_psd(
&self,
window: &Window,
nb_fft: usize) -> Spectrogram {
// use csd
let self_copy: &TimeSeries<D> = &(self.clone());
self.time_csd(&self_copy, window, nb_fft)
}
/// Compute the amplitude spectral density of a signal. Uses the psd function
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
///
/// // compute the csd
/// let asd: FrequencySeries = signal_1.time_asd(&window, 10);
///
/// ```
pub fn time_asd(
&self,
window: &Window,
nb_fft: usize) -> Spectrogram {
self.time_psd(window, nb_fft).sqrt()
}
/// Compute the coherence between two signals.
/// `\gamma_{1,2}(f) = \frac{|csd_{1,2}(f)|}{psd_1(f) \cdot psd_2(f)}`
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
/// let mut signal_2: TimeSeries = signal_1.clone() * 2.;
///
/// // compute the csd
/// let coherence: FrequencySeries = signal_1.time_cohe(&signal_2, &window, 10);
///
/// ```
pub fn time_cohe(
&self,
other: &TimeSeries<D>,
window: &Window,
nb_fft: usize) -> Spectrogram {
let psd1: Spectrogram = self.clone().time_psd(window, nb_fft);
let psd2: Spectrogram = other.clone().time_psd(window, nb_fft);
let mut csd: Spectrogram = self.time_csd(other, window, nb_fft).abs2();
((&mut csd / &psd1) / &psd2).clone()
}
/// Compute the transfer functions between two signals.
/// `\TF_{1,2}(f) = \frac{csd_{1,2}(f)}{psd_1(f)}`
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// frequencyseries::*,
/// windows::*,
/// };
///
/// // creates two white noise signals
/// let window: Window = hann(1., 0.5, 1e3);
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(2000000, 1e3, 0f64, 1f64);
/// let mut signal_2: TimeSeries = signal_1.clone() * 2.;
///
/// // compute the csd
/// let transfer_function: FrequencySeries = signal_1.time_tf(&signal_2, &window 10);
///
/// ```
pub fn time_tf(
&self,
other: &TimeSeries<D>,
window: &Window,
nb_fft: usize) -> Spectrogram {
let psd: Spectrogram = self.clone().time_psd(window, nb_fft);
let mut csd: Spectrogram = self.time_csd(other, window, nb_fft);
(&mut csd / &psd).clone()
}
}
/* --------------------------------------------------------------------------------------------- */
/// Implement signal filtering
impl<D> TimeSeries<D>
where D: ComplexFloat + AddAssign + SubAssign + MulAssign + DivAssign,
{
/// The following method apply an IIR filter to a time series
/// modify the original time series object.
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// filter::*,
/// };
///
/// // creates two white noise signals
/// let fs: f64 = 1e3;
/// let mut signal_1: TimeSeries = TimeSeries::white_noise(20000, f64, 1.);
///
/// // generates an 8th butterworth lowpass filter at 10 Hz
/// let butter: Filter::butterworth(8, BType::LowType(10.), fs);
///
/// // apply the filter to the signal
/// let mut signal_2: TimeSeries = signal_1.apply_filter(butter);
///
/// ```
pub fn apply_filter(&mut self, input_filter: &Filter) {
assert!((1. - self.get_fs() / input_filter.get_fs()).abs() < 1e-10);
let mut flt: Filter = input_filter.clone();
// warp frequencies
flt.adapt_frequencies(true);
// compute bilinear transform of the filter, the filter is now in the z-space
flt.bilinear_transform();
// compute the polynomial coefficiants of the z-transform of the filter
let (mut b, mut a): (Vec<f64>, Vec<f64>) = flt.polezero_to_coef();
// complete a or b with 0. so that the two vectors have the same size
if a.len() < b.len() {
a.append(&mut vec![0.; b.len()-a.len()]);
}
else if a.len() > b.len() {
b.append(&mut vec![0.; a.len()-b.len()]);
}
// number of pole and zeros
let n: usize = a.len();
// apply filter to the data vector
let mut x: Vec<D> = vec![self[0]; n-1];
x.append(&mut self.get_data());
let mut y: Vec<D> = x.clone();
for i in 0..self.get_size() {
let mut temp_y: D = D::zero();
for j in 0..b.len() {
temp_y += x[i + b.len()-1 - j] * D::from(b[j]).unwrap();
}
for j in 1..a.len() {
temp_y -= y[i + a.len()-1 - j] * D::from(a[j]).unwrap();
}
temp_y /= D::from(a[0]).unwrap();
self[i] = temp_y;
y[i+a.len()-1] = temp_y;
}
}
}
/* --------------------------------------------------------------------------------------------- *
* SeriesIO trait
* --------------------------------------------------------------------------------------------- */
/// This trait is for dedug purpose only.
/// It provides a function to print some samples of the time/frequency series and to print it into a csv file.
pub trait SeriesIO {
///
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// Series_IO,
/// };
///
/// // creates a white noise signals
/// let fs: f64 = 1e3;
/// let mut signal: TimeSeries = TimeSeries::white_noise(20000, fs, 1.);
///
/// // print the 10 first values of the time series
/// signal.print(0, 10);
///
/// ```
fn print(&self, n1: usize, n2: usize);
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// filter::*,
/// };
///
/// // creates a white noise signals
/// let fs: f64 = 1e3;
/// let mut signal: TimeSeries = TimeSeries::white_noise(20000, fs, 1.);
///
/// // Write csv files
/// signal.write_csv("TimeSeries.csv");
/// ```
fn write_csv(&self, file_name: &str);
/// # Example
/// ```
/// use gw_signal::{
/// timeseries::*,
/// filter::*,
/// };
///
/// // Read time series from csv files
/// let mut signal: TimeSeries<f64> = TimeSeries::<f64>::read_csv("TimeSeries.csv");
/// ```
fn read_csv(file_name: &str) -> Self;
}
impl<D: ComplexFloat + ToString + FromStr + Display> SeriesIO for TimeSeries<D> {
fn print(&self, n1: usize, n2: usize) {
let mut time: f64;
for i in n1..n2 {
// compute time
time = self.get_t0() + (i as f64) / self.get_fs();
println!("t = {:.3} s: {:.6}", time, self[i]);
}
}
fn write_csv(&self, file_name: &str) {
let mut w = File::create(file_name).unwrap();
writeln!(&mut w, "time,value").unwrap();
let mut time: f64;
let mut i: f64 = 0.;
for value in self.get_data().iter() {
// compute time
time = self.get_t0() + i / self.get_fs();
writeln!(&mut w, "{},{}", time, value.to_string()).unwrap();
i += 1.;
}
}
fn read_csv(file_name: &str) -> Self {
println!("Read file: {}", file_name);
// read file
let r = File::open(file_name).expect("The file is not found!");
let buffer = BufReader::new(r);
// initialize time and data vectors
let (mut time, mut data): (Vec<f64>, Vec<D>) = (Vec::new(), Vec::new());
// make iterator over lines and read file header
let mut line_iter = buffer.lines();
// read line, split it over the "," character and make a vector of strings
let mut line_str = line_iter.next().unwrap().unwrap();
let mut line_vec: Vec<&str> = line_str.split(",").collect();
assert_eq!(line_vec[0], "time");
for line in line_iter {
line_str = line.expect("Unable to read line");
line_vec = line_str.split(",").collect();
time.push(f64::from_str(&line_vec[0]).expect("Unable to read time value"));
let read_data = D::from_str(&line_vec[1]);
match read_data {
Ok(x) => data.push(x),
Err(_) => panic!("Unable to read data value"),
}
}
let frequency: f64 = (time.len()-1) as f64 / (time[time.len()-1] - time[0]);
TimeSeries::from_vector(frequency, time[0], data)
}
}
impl SeriesIO for FrequencySeries {
fn print(&self, n1: usize, n2: usize) {
let mut freq: f64;
for i in n1..n2 {
// compute time
freq = self.get_f_max() * (i as f64) / ((self.get_size()-1) as f64);
println!("f = {:.3} Hz: {:.6} + {:.6}i", freq, self[i].re, self[i].im);
}
}
fn write_csv(&self, file_name: &str) {
let mut w = File::create(file_name).unwrap();
writeln!(&mut w, "frequency,value").unwrap();
let mut freq: f64;
let mut i: f64 = 0.;
for value in self.get_data().iter() {
// compute time
freq = self.get_f_max() * i / ((self.get_size()-1) as f64);
writeln!(&mut w, "{},{}", freq, value.to_string()).unwrap();
i += 1.;
}
}
fn read_csv(file_name: &str) -> Self {
println!("Read file: {}", file_name);
// read file
let r = File::open(file_name).expect("The file is not found!");
let buffer = BufReader::new(r);
// initialize time and data vectors
let (mut freq, mut data): (Vec<f64>, Vec<Complex<f64>>) = (Vec::new(), Vec::new());
// make iterator over lines and read file header
let mut line_iter = buffer.lines();
// read line, split it over the "," character and make a vector of strings
let mut line_str = line_iter.next().unwrap().unwrap();
let mut line_vec: Vec<&str> = line_str.split(",").collect();
assert_eq!(line_vec[0], "frequency");
for line in line_iter {
line_str = line.expect("Unable to read line");
line_vec = line_str.split(",").collect();
freq.push(f64::from_str(&line_vec[0]).expect("Unable to read frequency value"));
let read_data = Complex::from_str(&line_vec[1]);
match read_data {
Ok(x) => data.push(x),
Err(_) => panic!("Unable to read data value"),
}
}
FrequencySeries::from_vector(freq[freq.len()-1], data)
}
}
impl SeriesIO for Spectrogram {
fn print(&self, n1: usize, n2: usize){
println!("Print the first frequency series of the spectrogram");
let frequency_series = &self[0];
let mut freq: f64;
for i in n1..n2 {
// compute time
freq = self.get_f_max() * (i as f64) / ((self.get_size().1-1) as f64);
println!("f = {:.3} Hz: {:.6} + {:.6}i",
freq, frequency_series[i].re, frequency_series[i].im);
}
}
fn write_csv(&self, file_name: &str) {
let (_size_time, size_freq) = self.get_size();
// create file
let mut w = File::create(file_name).unwrap();
// write first line with frequency values
let mut line = String::from("time\\frequency");
let mut freq: f64;
for i in 0..size_freq {
// compute frequency
freq = self.get_f_max() * (i as f64) / (size_freq - 1) as f64;
line.push_str(",");
line.push_str(&freq.to_string());
}
//line.push_str("\n");
// write firsst line
writeln!(&mut w, "{}", line).unwrap();
// write data vector
let mut time: f64 = self.get_t0();
for frequency_series in self.get_data().iter() {
// compute time
line = time.to_string();
for value in frequency_series.iter() {
line.push_str(",");
line.push_str(&value.to_string());
}
//line.push_str("\n");
writeln!(&mut w, "{}", line).unwrap();
time += self.get_dt();
}
}
fn read_csv(file_name: &str) -> Self {
println!("Read file: {}", file_name);
// read file
let r = File::open(file_name).expect("The file is not found!");
let buffer = BufReader::new(r);
// make iterator over lines and read file header
let mut line_iter = buffer.lines();
// read line, split it over the "," character and make a vector of strings
let mut line_str = line_iter.next().unwrap().unwrap();
let mut line_vec: Vec<&str> = line_str.split(",").collect();
// read last value of the frequency vector
let f_max: f64 = f64::from_str(line_vec[line_vec.len() - 1])
.expect("Unable to read maximum frequency");
// initialize data vector and time vector
let mut time: Vec<f64> = Vec::new();
let mut data: Vec<Vec<Complex<f64>>> = Vec::new();
let mut is_time: bool;
// fill the vector
for line in line_iter {
line_str = line.expect("Unable to read line");
line_vec = line_str.split(",").collect();
is_time = true;
let mut line_vector: Vec<Complex<f64>> = Vec::new();
for str_value in line_vec.iter() {
if is_time {
time.push(f64::from_str(str_value).expect("unable to read time value"));
} else {
let read_data = Complex::from_str(str_value);
match read_data {
Ok(x) => line_vector.push(x),
Err(_) => panic!("Unable to read data value"),
}
}
is_time = false;
}
data.push(line_vector);
}
Spectrogram::from_vector(f_max, time[0], time[1]-time[0], data)
}
}
/* --------------------------------------------------------------------------------------------- *
* Plot time series
* --------------------------------------------------------------------------------------------- */
/*
impl RealPlot {
/// Add one time series to the plot
pub fn add_timeseries<D, F>(&mut self, series: &TimeSeries<D>)
where D: ComplexFloat<Real = F>, F: Float,
{
// build time axis
let mut time: Vec<f64> = Vec::new();
for i in 0..series.get_size() {
time.push(series.get_t0() + (i as f64) / series.get_fs());
}
self.add_data_vector(time, series.to_f64().get_data());
}
pub fn add_frequencyseries(&mut self, series: &FrequencySeries) {
// build time axis
let mut time: Vec<f64> = Vec::new();
let mut real_data: Vec<f64> = Vec::new();
for i in 1..series.get_size() {
time.push((i as f64) / ((series.get_size() - 1) as f64) * series.get_f_max());
real_data.push(series[i].re);
}
self.add_data_vector(time, real_data);
self.set_x_scale_to_log(true);
}
}
impl ComplexPlot {
/// Add one time series to the plot
pub fn add_timeseries<D, F>(&mut self, series: &TimeSeries<D>)
where D: ComplexFloat<Real = F>, F: Float,
{
// build time axis
let mut time: Vec<f64> = Vec::new();
for i in 0..series.get_size() {
time.push(series.get_t0() + (i as f64) / series.get_fs());
}
self.add_data_vector(time, series.to_c64().get_data());
}
pub fn add_frequencyseries(&mut self, series: &FrequencySeries) {
// build time axis
let mut time: Vec<f64> = Vec::new();
let mut data: Vec<Complex<f64>> = Vec::new();
for i in 1..series.get_size() {
time.push((i as f64) / ((series.get_size() - 1) as f64) * series.get_f_max());
data.push(series[i]);
}
self.add_data_vector(time, data);
self.set_x_scale_to_log(true);
self.set_y_scale_to_log(true);
}
}
*/
/* --------------------------------------------------------------------------------------------- *
* Filter methods
* --------------------------------------------------------------------------------------------- */
///
impl Filter {
/// Compute frequency response of the filter
pub fn frequency_response(&self, size: usize) -> FrequencySeries {
// warp frequencies
let mut clone_filter = self.clone();
clone_filter.adapt_frequencies(false);
// initialize frequency series
let mut response: FrequencySeries = FrequencySeries::from_vector(
clone_filter.get_fs()/2., vec![Complex{re: clone_filter.get_gain(), im: 0.}; size]);
let mut frequency: f64;
for i in 0..size {
frequency = clone_filter.get_fs() / 2. * (i as f64) / ((size-1) as f64);
// apply zeros
for z in clone_filter.get_zeros().iter() {
response[i] *= Complex{re: 0., im: frequency} - z
}
// apply poles
for p in clone_filter.get_poles().iter() {
response[i] /= Complex{re: 0., im: frequency} - p
}
}
response
}
}