Trait basic_dsp_vector::TimeToFrequencyDomainOperations [−] [src]

pub trait TimeToFrequencyDomainOperations<S, T>: ToFreqResult where S: ToSliceMut<T>, T: RealNumber {
    fn plain_fft<B>(self, buffer: &mut B) -> Self::FreqResult where B: Buffer<S, T>;
    fn fft<B>(self, buffer: &mut B) -> Self::FreqResult where B: Buffer<S, T>;
    fn windowed_fft<B>(self,
                   buffer: &mut B,
                   window: &WindowFunction<T>)
                   -> Self::FreqResult where B: Buffer<S, T>;
}

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

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!(1.0, 0.0, -0.5, 0.8660254, -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 = &[0.0, 0.0, 3.0, 0.0, 0.0, 0.0];
assert_eq!(actual.len(), expected.len());
for i in 0..actual.len() {
       assert!(f32::abs(actual[i] - expected[i]) < 1e-4);
}

Performs a Fast Fourier Transformation transforming a time domain vector into a frequency domain vector.

Unstable

FFTs of real vectors are unstable.

Example

use std::f32;
use basic_dsp_vector::*;
let vector = vec!(1.0, 0.0, -0.5, 0.8660254, -0.5, -0.8660254).to_complex_time_vec();
let mut buffer = SingleBuffer::new();
let result = vector.fft(&mut buffer);
let actual = &result[..];
let expected = &[0.0, 0.0, 0.0, 0.0, 3.0, 0.0];
assert_eq!(actual.len(), expected.len());
for i in 0..actual.len() {
       assert!(f32::abs(actual[i] - expected[i]) < 1e-4);
}

Applies a FFT window and performs a Fast Fourier Transformation transforming a time domain vector into a frequency domain vector.

Implementors