Peaks Extension#

EEG_harmonics_mult(peaks, n_harmonics, n_oct_up=0)[source]#

Computes the harmonics of a list of frequency peaks. Given a list of frequency peaks, this function computes the desired number of harmonics for each peak. The harmonics are calculated using the formula x, 2x, 3x …, nx, where x is the frequency of the peak and n_harmonics is the number of harmonics to compute.

Parameters

  • peaks (list of float) – The frequency peaks, represented as local maxima in a spectrum.

  • n_harmonics (int) – The number of harmonics to compute for each peak.

  • n_oct_up (int, default=0) – The number of octaves by which to shift the peaks before computing the harmonics.

Returns

multi_harmonics (numpy.ndarray) – An array of shape (n_peaks, n_harmonics+1), where n_peaks is the number of frequency peaks and each row represents the computed harmonics for the corresponding peak.

Examples

>>> peaks = [10.0, 20.0, 30.0]
>>> n_harmonics = 3
>>> n_oct_up = 1
>>> EEG_harmonics_mult(peaks, n_harmonics, n_oct_up)
array([[ 20.,  40.,  60.,  80.],
    [ 40.,  80., 120., 160.],
    [ 60., 120., 180., 240.]])
EEG_harmonics_div(peaks, n_harmonics, n_oct_up=0, mode='div')[source]#

Computes the sub-harmonics of a list of frequency peaks using division. Given a list of frequency peaks, this function computes the desired number of sub-harmonics for each peak using division. The sub-harmonics are calculated using the formulas x, x/2, x/3 …, x/n or x, (x+x/2), (x+x/3), … (x+x/n), depending on the specified mode, where x is the frequency of the peak and n_harmonics is the number of sub-harmonics to compute.

Parameters

  • peaks (list of float) – The frequency peaks, represented as local maxima in a spectrum.

  • n_harmonics (int) – The number of sub-harmonics to compute for each peak.

  • n_oct_up (int, default=0) – The number of octaves by which to shift the peaks before computing the sub-harmonics.

  • mode (str, default=’div’) – The mode to use for computing the sub-harmonics. Possible values are ‘div’ for x, x/2, x/3 …, x/n and ‘div_add’ for x, (x+x/2), (x+x/3), … (x+x/n).

Returns

  • div_harmonics (numpy.ndarray) – An array of shape (n_peaks, n_harmonics+1), where n_peaks is the number of frequency peaks and each row represents the computed sub-harmonics for the corresponding peak, in Hz.

  • div_harmonics_bounded (numpy.ndarray) – An array of shape (n_peaks, n_harmonics+1), where n_peaks is the number of frequency peaks and each row represents the computed sub-harmonics for the corresponding peak, bounded between unison (1) and octave (2), in Hz.

Examples

>>> peaks = [10.0, 20.0, 30.0]
>>> n_harmonics = 3
>>> n_oct_up = 1
>>> mode = 'div'
>>> EEG_harmonics_div(peaks, n_harmonics, n_oct_up, mode)
(array([[10.        ,  5.        ,  3.33333333,  2.5       ],
        [20.        , 10.        ,  6.66666667,  5.        ],
        [30.        , 15.        , 10.        ,  7.5       ]]),
array([[1.        , 1.        , 1.        , 1.        ],
        [2.        , 2.        , 1.5       , 1.25      ],
        [2.        , 2.        , 1.5       , 1.25      ]]))
harmonic_fit(peaks, n_harm=10, bounds=1, function='mult', div_mode='div', n_common_harms=5)[source]#

Compute harmonics of a list of peaks and compare the lists of harmonics pairwise to find fitting between the harmonic series.

Parameters

  • peaks (list of float) – Spectral peaks representing local maximum in a spectrum

  • n_harm (int, default=10) – Number of harmonics to compute.

  • bounds (int, default=1) – Minimum distance (in Hz) between two frequencies to consider a fit.

  • function (str, default=’mult’) – Type of harmonic function to use. Possible values are:

    • ‘mult’ will use natural harmonics.

    • ‘div’ will use natural sub-harmonics.

    • ‘exp’ will use exponentials.

  • div_mode (str, default=’div’) – Mode of the natural sub-harmonic function when function=’div’. See EEG_harmonics_div function.

  • n_common_harms (int, default=5) – Number of harmonic positions to be sent to the most_common_harmonics output.

Returns

  • harm_fit (list) – Frequencies of the harmonics that match.

  • harmonics_pos (list) – Positions of the harmonics that match.

  • most_common_harmonics (list) – Harmonics that are present at least ‘n_common_harms’ times.

  • matching_positions (list of lists) – Each sublist corresponds to an harmonic fit, the first number is the frequency and the two others are harmonic positions.

Examples

>>> from biotuner.peaks_extension import harmonic_fit
>>> peaks = [3, 9, 12]
>>> harm_fit, harmonics_pos, _, _ = harmonic_fit(peaks, n_harm=5, bounds=0.1, function="mult")
>>> print(harm_fit)
>>> harm_fit, harmonics_pos, _, _ = harmonic_fit(peaks, n_harm=10, bounds=0.1, function="div")
>>> print(harm_fit)
[9.0, 18.0, 12.0, 36.0]
[0.784, 1.5, 1.045, 3.0, 1.0, 1.757, 1.31, 1.243, 1.162, 1.108]
multi_consonance(cons_pairs, n_freqs=5)[source]#

Function that keeps the frequencies that are the most consonant with others Takes pairs of frequencies that are consonant as input (output of the ‘compute consonance’ function).

Parameters

  • cons_pairs (List of lists (float)) – list of lists of each pairs of consonant peaks

  • n_freqs (int) – maximum number of consonant freqs to keep

Returns

freqs_related (List (float)) – peaks that are consonant with at least two other peaks, starting with the peak that is consonant with the maximum number of other peaks