wavesongs.optimizer

A collection of functions to solve the minimization problem.

Functions

all_optimal_gammas(bird)
optimal(syllable[, params, method, Ns, ...])
optimal_a(syllable[, params, method, Ns, ...])
optimal_bs(syllable[, params, method, Ns, ...])
optimal_gamma(syllable[, params, method, ...])
optimal_params(syllable[, params, method, ...])
optimal_params_general(syllable[, params, ...])
residual(z, *params)
residual_correlation(z, *params)
residual_ff(z, *params)
residual_ff_b02(z, *params)
residual_ff_b1(z, *params)
residual_sci(z, *params)
residual_sci_a0(z, *params)

wavesongs.optimizer.residual(z: List[float], *params: Tuple) → ndarray

Parameters:

• z (list [a0,b0,b1,b2])

• paramvs (tuple)

Returns:

SCIFF – Fundamental Frequency and Spectral Content Index scores

Return type:

np.ndarray

Examples

>>>

wavesongs.optimizer.residual_sci(z: List[float], *params: Tuple) → ndarray

Parameters:

• z (list [a0,b0,b1,b2])

• paramvs (tuple)

Returns:

SCIFF – Fundamental Frequency and Spectral Content Index scores

Return type:

np.ndarray

Examples

>>>

wavesongs.optimizer.residual_sci_a0(z: List[float], *params: Tuple) → ndarray

Parameters:

• z (list)

• params (tuple)

Returns:

SCIFF – Fundamental Frequency and Spectral Content Index scores

Return type:

np.ndarray

Examples

>>>

wavesongs.optimizer.residual_ff(z: List[float], *params: Tuple) → ndarray

Parameters:

• z (list)

• params (tuple)

Returns:

SCIFF – Fundamental Frequency and Spectral Content Index scores

Return type:

np.ndarray

Examples

>>>

wavesongs.optimizer.residual_ff_b02(z: List[float], *params: Tuple) → ndarray

Parameters:

• z (list)

• params (tuple)

Returns:

SCIFF – Fundamental Frequency and Spectral Content Index scores

Return type:

np.ndarray

Examples

>>>

wavesongs.optimizer.residual_ff_b1(z: List[float], *params: Tuple) → ndarray

Parameters:

• z (list)

• params (tuple)

Returns:

SCIFF – Fundamental Frequency and Spectral Content Index scores

Return type:

np.ndarray

Examples

>>>

wavesongs.optimizer.residual_correlation(z: List[float], *params: Tuple) → ndarray

Parameters:

• z (list)

• params (tuple)

Returns:

SCIFF – Fundamental Frequency and Spectral Content Index scores

Return type:

np.ndarray

Examples

>>>

wavesongs.optimizer.optimal(syllable, params: Dict = {'C': 343, 'Ch': 1.43e-10, 'L': 0.025, 'MB': 10000.0, 'MG': 20, 'RB': 5000000.0, 'Rh': 24000.0, 'gm': 40000.0, 'r': 0.65}, method: AnyStr = 'brute', Ns: int = 20, full_output: bool = True, disp: bool = True, workers: int = -1) → Dict

Parameters:

• syllable (Syllable)

• params (dict)

• method (str = "brute")

• Ns (int, optional = 20)

• full_output (bool, optional = False)

• disp (bool, optional = False)

• workers (int, optional = 1)

Returns:

parameters

Return type:

Dict

Examples

>>>

wavesongs.optimizer.optimal_bs(syllable, params: Dict = {'C': 343, 'Ch': 1.43e-10, 'L': 0.025, 'MB': 10000.0, 'MG': 20, 'RB': 5000000.0, 'Rh': 24000.0, 'gm': 40000.0, 'r': 0.65}, method: AnyStr = 'brute', Ns: int = 20, full_output: bool = True, disp: bool = True, workers: int = -1) → Dict

Parameters:

• syllable (Syllable)

• params (dict)

• method (str = "brute")

• Ns (int, optional = 20)

• full_output (bool, optional = False)

• disp (bool, optional = False)

• workers (int, optional = 1)

Returns:

params

Return type:

Dict

Examples

>>>

wavesongs.optimizer.optimal_a(syllable, params: Dict = {'C': 343, 'Ch': 1.43e-10, 'L': 0.025, 'MB': 10000.0, 'MG': 20, 'RB': 5000000.0, 'Rh': 24000.0, 'gm': 40000.0, 'r': 0.65}, method: AnyStr = 'brute', Ns: int = 20, full_output: bool = True, disp: bool = True, workers: int = -1) → Dict

Parameters:

• syllable (Syllable)

• params (dict)

• method (str = "brute")

• Ns (int, optional = 20)

• full_output (bool, optional = False)

• disp (bool, optional = False)

• workers (int, optional = 1)

Returns:

params

Return type:

Dict

Examples

>>>

wavesongs.optimizer.optimal_gamma(syllable, params: Dict = {'C': 343, 'Ch': 1.43e-10, 'L': 0.025, 'MB': 10000.0, 'MG': 20, 'RB': 5000000.0, 'Rh': 24000.0, 'gm': 40000.0, 'r': 0.65}, method: AnyStr = 'brute', Ns: int = 20, full_output: bool = True, disp: bool = True, workers: int = -1) → Dict

Parameters:

• syllable (Syllable)

• params (dict)

• method (str = "brute")

• Ns (int, optional = 20)

• full_output (bool, optional = False)

• disp (bool, optional = False)

• workers (int, optional = 1)

Returns:

parameters

Return type:

Dict

Examples

>>>

wavesongs.optimizer.optimal_params(syllable, params: Dict = {'C': 343, 'Ch': 1.43e-10, 'L': 0.025, 'MB': 10000.0, 'MG': 20, 'RB': 5000000.0, 'Rh': 24000.0, 'gm': 40000.0, 'r': 0.65}, method: AnyStr = 'brute', Ns: int = 20, full_output: bool = True, disp: bool = True, workers: int = -1) → Dict

Parameters:

• syllable (Syllable)

• params (dict)

• method (str = "brute")

• Ns (int, optional = 20)

• full_output (bool, optional = False)

• disp (bool, optional = False)

• workers (int, optional = 1)

Returns:

parameters

Return type:

Dict

Examples

>>>

wavesongs.optimizer.optimal_params_general(syllable, params: Dict = {'C': 343, 'Ch': 1.43e-10, 'L': 0.025, 'MB': 10000.0, 'MG': 20, 'RB': 5000000.0, 'Rh': 24000.0, 'gm': 40000.0, 'r': 0.65}, method: AnyStr = 'brute', Ns: int = 20, full_output: bool = True, disp: bool = True, workers: int = -1) → Dict

Parameters:

• syllable (Syllable)

• params (dict)

• method (str = "brute")

• Ns (int, optional = 20)

• full_output (bool, optional = False)

• disp (bool, optional = False)

• workers (int, optional = 1)

Returns:

parameters

Return type:

Dict

Examples

>>>

wavesongs.optimizer.all_optimal_gammas(bird)