pub trait TimeToFrequencyDomainOperations<S, T>: ToFreqResultwhere
S: ToSliceMut<T>,
T: RealNumber,{
fn plain_fft<B>(self, buffer: &mut B) -> Self::FreqResult
where
B: for<'a> Buffer<'a, S, T>;
fn fft<B>(self, buffer: &mut B) -> Self::FreqResult
where
B: for<'a> Buffer<'a, S, T>;
fn windowed_fft<B>(
self,
buffer: &mut B,
window: &dyn WindowFunction<T>
) -> Self::FreqResult
where
B: for<'a> Buffer<'a, S, T>;
}
Expand description
Defines all operations which are valid on DataVecs
containing time domain data.
Failures
All operations in this trait set self.len()
to 0
if the vector isn’t in time domain.
Required Methods
sourcefn plain_fft<B>(self, buffer: &mut B) -> Self::FreqResultwhere
B: for<'a> Buffer<'a, S, T>,
fn plain_fft<B>(self, buffer: &mut B) -> Self::FreqResultwhere
B: for<'a> Buffer<'a, S, T>,
Performs a Fast Fourier Transformation transforming a time domain vector into a frequency domain vector.
This version of the FFT neither applies a window nor does it scale the vector.
Example
use std::f32;
use basic_dsp_vector::*;
let vector = vec!(Complex::new(1.0, 0.0), Complex::new(-0.5, 0.8660254), Complex::new(-0.5, -0.8660254)).to_complex_time_vec();
let mut buffer = SingleBuffer::new();
let result = vector.plain_fft(&mut buffer);
let actual = &result[..];
let expected = &[Complex::new(0.0, 0.0), Complex::new(3.0, 0.0), Complex::new(0.0, 0.0)];
assert_eq!(actual.len(), expected.len());
for i in 0..actual.len() {
assert!((actual[i] - expected[i]).norm() < 1e-4);
}
sourcefn fft<B>(self, buffer: &mut B) -> Self::FreqResultwhere
B: for<'a> Buffer<'a, S, T>,
fn fft<B>(self, buffer: &mut B) -> Self::FreqResultwhere
B: for<'a> Buffer<'a, S, T>,
Performs a Fast Fourier Transformation transforming a time domain vector into a frequency domain vector.
Example
use std::f32;
use basic_dsp_vector::*;
let vector = vec!(Complex::new(1.0, 0.0), Complex::new(-0.5, 0.8660254), Complex::new(-0.5, -0.8660254)).to_complex_time_vec();
let mut buffer = SingleBuffer::new();
let result = vector.fft(&mut buffer);
let actual = &result[..];
let expected = &[Complex::new(0.0, 0.0), Complex::new(0.0, 0.0), Complex::new(3.0, 0.0)];
assert_eq!(actual.len(), expected.len());
for i in 0..actual.len() {
assert!((actual[i] - expected[i]).norm() < 1e-4);
}
sourcefn windowed_fft<B>(
self,
buffer: &mut B,
window: &dyn WindowFunction<T>
) -> Self::FreqResultwhere
B: for<'a> Buffer<'a, S, T>,
fn windowed_fft<B>(
self,
buffer: &mut B,
window: &dyn WindowFunction<T>
) -> Self::FreqResultwhere
B: for<'a> Buffer<'a, S, T>,
Applies a FFT window and performs a Fast Fourier Transformation transforming a time domain vector into a frequency domain vector.