arrakis.utils.fitting

Fitting utilities

Functions

best_aic_func(→ Tuple[float, int, int])	Find the best AIC for a set of AICs using Occam's razor.
chi_squared(→ float)	Calculate chi squared.
curved_power_law(→ numpy.ndarray)	A curved power law model.
fit_pl(→ dict)	Perform a power law fit to a spectrum.
fitted_mean(→ float)	Calculate the mean of a distribution.
fitted_std(→ float)	Calculate the standard deviation of a distribution.
flat_power_law(→ numpy.ndarray)	A flat power law model.
power_law(→ numpy.ndarray)	A power law model.

Module Contents

arrakis.utils.fitting.best_aic_func(aics: numpy.ndarray, n_param: numpy.ndarray) → Tuple[float, int, int]

Find the best AIC for a set of AICs using Occam’s razor.

arrakis.utils.fitting.chi_squared(model: numpy.ndarray, data: numpy.ndarray, error: numpy.ndarray) → float

Calculate chi squared.

Parameters:

• model (np.ndarray) – Model flux.

• data (np.ndarray) – Data flux.

• error (np.ndarray) – Data error.

Returns:

Chi squared.

Return type:

np.ndarray

arrakis.utils.fitting.curved_power_law(nu: numpy.ndarray, norm: float, alpha: float, beta: float, ref_nu: float) → numpy.ndarray

A curved power law model.

Parameters:

• nu (np.ndarray) – Frequency array.

• norm (float) – Reference flux.

• alpha (float) – Spectral index.

• beta (float) – Spectral curvature.

• ref_nu (float) – Reference frequency.

Returns:

Model flux.

Return type:

np.ndarray

arrakis.utils.fitting.fit_pl(freq: numpy.ndarray, flux: numpy.ndarray, fluxerr: numpy.ndarray, nterms: int) → dict

Perform a power law fit to a spectrum.

Parameters:

• freq (np.ndarray) – Frequency array.

• flux (np.ndarray) – Flux array.

• fluxerr (np.ndarray) – Error array.

• nterms (int) – Number of terms to use in the fit.

Returns:

Best fit parameters.

Return type:

dict

arrakis.utils.fitting.fitted_mean(data: numpy.ndarray, axis: int | None = None) → float

Calculate the mean of a distribution.

Parameters:

data (np.ndarray) – Data array.

Returns:

Mean.

Return type:

float

arrakis.utils.fitting.fitted_std(data: numpy.ndarray, axis: int | None = None) → float

Calculate the standard deviation of a distribution.

Parameters:

data (np.ndarray) – Data array.

Returns:

Standard deviation.

Return type:

float

arrakis.utils.fitting.flat_power_law(nu: numpy.ndarray, norm: float, ref_nu: float) → numpy.ndarray

A flat power law model.

Parameters:

• nu (np.ndarray) – Frequency array.

• norm (float) – Reference flux.

• ref_nu (float) – Reference frequency.

Returns:

Model flux.

Return type:

np.ndarray

arrakis.utils.fitting.power_law(nu: numpy.ndarray, norm: float, alpha: float, ref_nu: float) → numpy.ndarray

A power law model.

Parameters:

• nu (np.ndarray) – Frequency array.

• norm (float) – Reference flux.

• alpha (float) – Spectral index.

• ref_nu (float) – Reference frequency.

Returns:

Model flux.

Return type:

np.ndarray