fastgoertzel 
A Python implementation of the Goertzel algorithm built using Rust for improved run time and efficiency on large datasets and loops.
To-Do:
- Improved speed.
- Implement benchmarking for speed comparison
- Add IIR and k-th FTT implementation of Goertzel.
- Add support for sampling rate.
Installation
You can install using two methods:
Using pip install:
$ pip install fastgoertzelUsing maturin after cloning repository:
$ git clone git://github.com/0zean/fastgoertzel.git
$ cd fastgoertzel
$ maturin developUsage
import numpy as np
import pandas as pd
import fastgoertzel as G
def wave(amp, freq, phase, x):
return amp * np.sin(2*np.pi * freq * x + phase)
x = np.arange(0, 512)
y = wave(1, 1/128, 0, x)
amp, phase = G.goertzel(y, 1/128)
print(f'Goertzel Amp: {amp:.4f}, phase: {phase:.4f}')
# Compared to max amplitude FFT output
ft = np.fft.fft(y)
FFT = pd.DataFrame()
FFT['amp'] = np.sqrt(ft.real**2 + ft.imag**2) / (len(y) / 2)
FFT['freq'] = np.fft.fftfreq(ft.size, d=1)
FFT['phase'] = np.arctan2(ft.imag, ft.real)
max_ = FFT.iloc[FFT['amp'].idxmax()]
print(f'FFT amp: {max_["amp"]:.4f}, '
f'phase: {max_["phase"]:.4f}, '
f'freq: {max_["freq"]:.4f}')
