Generators#

multi_base(x: ndarray, distribution_func: Type[BaseDistribution], params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate data based on a combination of distributions with optional noise.

Parameters:

xnp.ndarray: Input array of values.
distribution_func: Type[BaseDistribution]: The distribution function to be used to generate data.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_chi_squared(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-ChiSquareDistribution data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_exponential(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-ExponentialDistribution data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_folded_normal(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-FoldedNormalDistribution data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_gamma_sr(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-GammaDistributionSR data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_gamma_ss(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-GammaDistributionSS data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_gaussian(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-GaussianDistribution data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_half_normal(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-HalfNormalDistribution data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_laplace(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-LaplaceDistribution data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_log_normal(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-LogNormalDistribution data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multi_skew_normal(x: ndarray, params: List[Tuple[float, ...]] | ndarray, noise_level: float = 0.0, normalize: bool = False) → ndarray[source]#

Generate multi-SkewNormalDistribution data with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for the required distribution.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data. Defaults to 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.

multiple_models(x: ndarray, params: List[Tuple[float, ...]] | ndarray, model_list, noise_level=0.0, normalize: bool = False) → ndarray[source]#

Generate data based on a combination of different models with optional noise.

Parameters:

xnp.ndarray: Input array of values.
paramslistOfTuplesOrArray: List of tuples containing the parameters for each model.
model_listlist: A list of model names corresponding to the models to be used.
noise_levelfloat, optional: Standard deviation of the noise to be added to the data, by default 0.0.
normalizebool, optional: If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Returns:

np.ndarray: Array of the same shape as \(x\), containing the evaluated values.