pysnr

Submodules

pysnr.sfdr module

pysnr.sfdr.sfdr_power_spectral_density(pxx, frequencies, msd=0)

SFDR from input signal.

This function computes the SFDR for an input signal from its density-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • pxx (numpy ndarray) – The power spectral density of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

  • msd (int) – Minimum number of discrete Fourier bins to ignore for the SFDR computation

Returns

  • float – The computed SFDR

  • float – The spurious power magnitude

pysnr.sfdr.sfdr_power_spectrum(sxx, frequencies, msd=0)

SFDR from input signal.

This function computes the SFDR for an input signal from its spectrum-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • sxx (numpy ndarray) – The power spectrum of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

  • msd (int) – Minimum number of discrete Fourier bins to ignore for the SFDR computation

Returns

  • float – The computed SFDR

  • float – The spurious power magnitude

pysnr.sfdr.sfdr_signal(signal, fs=1.0, msd=0)

SFDR from input signal.

This function computes the SFDR for an input signal. It assumes the fundamental frequency to be the desired signal.

Parameters

  • signal (numpy ndarray) – The true signal

  • fs (float) – Sampling Frequency. Defaults to 1.0.

  • msd (int) – Minimum number of discrete Fourier bins to ignore for the SFDR computation

Returns

  • float – The computed SFDR

  • float – The spurious power magnitude

pysnr.sinad module

pysnr.sinad.sinad_power_spectral_density(pxx, frequencies)

SINAD from input signal.

This function computes the SINAD for an input signal from its density-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • pxx (numpy ndarray) – The power spectral density of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

Returns

  • float – The computed SINAD

  • float – The total noise and harmonic power magnitude

pysnr.sinad.sinad_power_spectrum(sxx, frequencies, rbw)

SINAD from input signal.

This function computes the SINAD for an input signal from its spectrum-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • sxx (numpy ndarray) – The power spectrum of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

  • rbw (float) – Resolution Bandwidth computed from the window and the sampling frequency

Returns

  • float – The computed SINAD

  • float – The total noise and harmonic power magnitude

pysnr.sinad.sinad_signal(signal, fs=1.0)

SINAD from input signal.

This function computes the SINAD for an input signal. It assumes the fundamental frequency to be the desired signal. Uses a Kaiser window with beta set to 38 to compute the periodogram.

Parameters

  • signal (numpy ndarray) – The true signal

  • fs (float) – Sampling Frequency. Defaults to 1.0.

Returns

  • float – The computed SINAD

  • float – The total noise and harmonic power magnitude

pysnr.snr module

pysnr.snr.snr_power_spectral_density(pxx, frequencies, n=6, aliased=False)

SNR from input signal.

This function computes the SNR for a signal where the noise is not known from its density-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • pxx (numpy ndarray) – The power spectral density of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

  • n (int) – Number of harmonics to use (including the fundamental frequency)

  • aliased (bool) – If True, converts the harmonics that are aliased into the Nyquist frequency

Returns

  • float – The computed SNR

  • float – The noise power magnitude

pysnr.snr.snr_power_spectrum(sxx, frequencies, rbw, n=6, aliased=False)

SNR from input signal.

This function computes the SNR for a signal where the noise is not known from its spectrum-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • sxx (numpy ndarray) – The power spectrum of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

  • rbw (float) – Resolution Bandwidth computed from the window and the sampling frequency

  • n (int) – Number of harmonics to use (including the fundamental frequency)

  • aliased (bool) – If True, converts the harmonics that are aliased into the Nyquist frequency

Returns

  • float – The computed SNR

  • float – The noise power magnitude

pysnr.snr.snr_signal(signal, fs=1.0, n=6, aliased=False)

SNR from input signal.

This function computes the SNR for a signal where the noise is not known. It assumes the fundamental frequency to be the desired signal. Uses a Kaiser window with beta set to 38 to compute the periodogram.

Parameters

  • signal (numpy ndarray) – The true signal

  • fs (float) – Sampling Frequency. Defaults to 1.0.

  • n (int) – Number of harmonics to use (including the fundamental frequency)

  • aliased (bool) – If True, converts the harmonics that are aliased into the Nyquist frequency

Returns

  • float – The computed SNR

  • float – The noise power magnitude

pysnr.snr.snr_signal_noise(signal, noise)

SNR from input signal and known noise.

This function computes the SNR for a signal and a known noise.

Parameters

  • signal (numpy ndarray) – The true signal

  • noise (numpy ndarray) – The noise

Returns

  • float – The computed SNR

  • float – The noise magnitude

pysnr.thd module

pysnr.thd.thd_power_spectral_density(pxx, frequencies, n=6, aliased=False)

THD from input signal.

This function computes the THD for an input signal from its density-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • pxx (numpy ndarray) – The power spectral density of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

  • n (int) – Number of harmonics to use (including the fundamental frequency)

  • aliased (bool) – If True, converts the harmonics that are aliased into the Nyquist frequency

Returns

  • float – The computed THD

  • float – The harmonic power magnitude

pysnr.thd.thd_power_spectrum(sxx, frequencies, rbw, n=6, aliased=False)

THD from input signal.

This function computes the THD for an input signal from its spectrum-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • sxx (numpy ndarray) – The power spectrum of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

  • rbw (float) – Resolution Bandwidth computed from the window and the sampling frequency

  • n (int) – Number of harmonics to use (including the fundamental frequency)

  • aliased (bool) – If True, converts the harmonics that are aliased into the Nyquist frequency

Returns

  • float – The computed THD

  • float – The harmonic power magnitude

pysnr.thd.thd_signal(signal, fs=1.0, n=6, aliased=False)

THD from input signal.

This function computes the THD for an input signal. It assumes the fundamental frequency to be the desired signal. Uses a Kaiser window with beta set to 38 to compute the periodogram.

Parameters

  • signal (numpy ndarray) – The true signal

  • fs (float) – Sampling Frequency. Defaults to 1.0.

  • n (int) – Number of harmonics to use (including the fundamental frequency)

  • aliased (bool) – If True, converts the harmonics that are aliased into the Nyquist frequency

Returns

  • float – The computed THD

  • float – The harmonic power magnitude

pysnr.toi module

pysnr.toi.toi_power_spectral_density(pxx, frequencies)

TOI from input signal.

This function computes the TOI for an input signal from its density-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • pxx (numpy ndarray) – The power spectral density of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

Returns

  • float – The computed TOI

  • np.ndarray – The powers contained in the two fundamental sinusoids of the signal

  • np.ndarray – The power contained in the lower and upper intermodulation products of the signal

pysnr.toi.toi_power_spectrum(sxx, frequencies, rbw)

TOI from input signal.

This function computes the TOI for an input signal from its spectrum-periodogram. The function assumes the fundamental frequency to be the desired signal.

Parameters

  • sxx (numpy ndarray) – The power spectrum of the signal

  • frequencies (numpy ndarray) – The frequencies corresponding to the power spectral density

  • rbw (float) – Resolution Bandwidth computed from the window and the sampling frequency

Returns

  • float – The computed TOI

  • np.ndarray – The powers contained in the two fundamental sinusoids of the signal

  • np.ndarray – The power contained in the lower and upper intermodulation products of the signal

pysnr.toi.toi_signal(signal, fs=1.0)

TOI from input signal.

This function computes the TOI for an input signal. It assumes the fundamental frequency to be the desired signal. Uses a Kaiser window with beta set to 38 to compute the periodogram.

Parameters

  • signal (numpy ndarray) – The true signal

  • fs (float) – Sampling Frequency. Defaults to 1.0.

Returns

  • float – The computed TOI

  • np.ndarray – The powers contained in the two fundamental sinusoids of the signal

  • np.ndarray – The power contained in the lower and upper intermodulation products of the signal

pysnr.utils module

pysnr.utils.bandpower(pxx, f)

Computes the equivalent noise bandwidth

Parameters

  • pxx (numpy ndarray) – The periodogram values in the power spectral density form

  • f (numpy ndarray) – The frequencies corresponding to the periodogram

Returns

The computed value

Return type

float

pysnr.utils.enbw(window, Fs=None)

Computes the equivalent noise bandwidth

Parameters

  • window (numpy ndarray) – The window as an array of floats.

  • Fs (float) – Sampling frequency of original signal

Returns

The computed value

Return type

float

pysnr.utils.mag2db(data, scaling=10)

Converts the magnitude to decibels

Parameters

  • data (flaot or numpy ndarray) – Item to be converted

  • scaling (int) – Value by which the log is to be scaled

Returns

The converted items

Return type

float or numpy ndarray

pysnr.utils.periodogram(data, Fs, window, method='welch', scaling='density')

Computes the periodogram from signal.

This function computes the periodogram using one of two techniques - Welch method or FFT method By default, it is set to Welch method.

Parameters

  • data (numpy ndarray) – The signal whose periodogram is to be computed

  • Fs (numpy ndarray) – Sampling Freqeuncy of input signal

  • window (str or tuple or array_like) – Desired window to use. This is passed as an input to scipy’s get_window() function

  • method (str) – Decides which method to use for computing the periodogram. Can be welch or ‘fft’

  • scaling (str) – Decides whether to compute the power spectral density or the power spectrum. Can be ‘density’ or ‘spectrum’

Returns

  • numpy ndarray – List of frequencies

  • numpy ndarray – The periodogram

pysnr.utils.rssq(data)

Computes the root of sum of squares.

Parameters

data (numpy ndarray) – Array of numbers

Returns

The computed value

Return type

float

Module contents