fastgoertzel 1.0.2

High-performance Goertzel algorithm implementation

fastgoertzel

A Python implementation of the Goertzel algorithm built using C++ for improved run time and efficiency on large datasets and loops.

To-Do:

• Improved speed. (Significantly increased speed by using numpy arrays).
• Implement benchmarking for speed comparison. (fastgoertzel is ~75 times faster than native python)
• Implement batch processing for multiple frequencies.
• 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 fastgoertzel

Using setup.py after cloning repository:

$ git clone git://github.com/0zean/fastgoertzel.git
$ cd fastgoertzel
$ python -m build

Usage

import numpy as np
import pandas as pd

import fastgoertzel as fg


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 = fg.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}')