Sine properties estimation

Estimating sine properties from 1D array of raw data

Let say you have a noisy record of a sine, and you want to know its properties.
Frequency estimation done by polynomial interpolation on fft values.
Estimating amp, phase and offset using least square on trigonometric sine identity.
Probably useful for DSP (signal processing) and spectral analysis.

usage example:
    input:
        import numpy as np
        
        from sine_properties_estimation import calc_sine_properties

        # sine with random noise
        samples = 1000
        seconds = 10
        amp_mv = 340
        phase_rad = 2.2
        offset_mv = 60
        freq_ghz = 1.834
    
        t = np.linspace(0, seconds, samples)
        signal = amp_mv * np.sin(2 * np.pi * freq_ghz * t + phase_rad) + offset_mv + np.random.normal(0, 50, samples)
    
        estimation = calc_sine_properties(signal, seconds)
        print(f'estimated freq is {estimation.est_freq} Hz')
        print(f'estimated sine amp is {estimation.est_sine_amp}')
        estimation.print_estimation()
        
    output:
    
        estimate freq is 1.8372033171227042 Hz
        estimated sine amp is 338.5901422611014
        
               samples : 1000
         total_seocnds : 10
                  freq : 1.8372033171227042
              sine_amp : 338.5901422611014
             phase_rad : 2.0986036955592624
                offset : 63.01041660377115
        --------------------------------------------------