rm_lite.utils.fitting¶

Attributes¶

GAUSSIAN_SIGMA_TO_FWHM

Classes¶

`FDFFitResult`	Results of a Gaussian FDF fit
`FitResult`	Results of a Stokes I fit
`StokesIModel`	Base class for protocol classes.

Functions¶

`best_aic_func`(→ tuple[float, int, int])	Find the best AIC for a set of AICs using Occam's razor.
`dynamic_fit`(→ FitResult)
`fit_fdf`(→ FDFFitResult)
`fit_rmsf`(→ float)
`fit_stokes_i_model`(→ FitResult)
`fwhm_to_sigma`(→ float)
`gaussian`(→ numpy.typing.NDArray[numpy.float64])
`gaussian_integrand`(→ float)
`polynomial`(→ StokesIModel)
`power_law`(→ StokesIModel)
`sigma_to_fwhm`(→ float)
`static_fit`(→ FitResult)
`unit_centred_gaussian`(...)
`unit_gaussian`(→ numpy.typing.NDArray[numpy.float64])

Module Contents¶

class rm_lite.utils.fitting.FDFFitResult¶

Bases: NamedTuple

Results of a Gaussian FDF fit

amplitude_fit: float¶: Amplitude of the best fit model

mean_fit: float¶: Mean (Faraday depth) of the best fit model

stddev_fit: float¶: Standard deviation (Faraday depth) of the best fit model

class rm_lite.utils.fitting.FitResult¶

Bases: NamedTuple

Results of a Stokes I fit

aic: float¶: Akaike Information Criterion of the fit

pcov: numpy.typing.ArrayLike¶: Covariance matrix of the fit

popt: numpy.typing.ArrayLike¶: Best fit parameters

stokes_i_model_func: StokesIModel¶: Function of the best fit model

class rm_lite.utils.fitting.StokesIModel¶

Bases: Protocol

Base class for protocol classes.

Protocol classes are defined as:

class Proto(Protocol):
    def meth(self) -> int:
        ...

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing).

For example:

class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check

See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as:

class GenProto[T](Protocol):
    def meth(self) -> T:
        ...

__call__(x: numpy.typing.NDArray[numpy.float64], *params) → numpy.typing.NDArray[numpy.float64]¶

rm_lite.utils.fitting.best_aic_func(aics: numpy.typing.NDArray[numpy.float64], n_param: numpy.typing.NDArray[numpy.int32]) → tuple[float, int, int]¶: Find the best AIC for a set of AICs using Occam’s razor.

rm_lite.utils.fitting.dynamic_fit(freq_arr_hz: numpy.typing.NDArray[numpy.float64], ref_freq_hz: float, stokes_i_arr: numpy.typing.NDArray[numpy.float64], stokes_i_error_arr: numpy.typing.NDArray[numpy.float64], fit_order: int = 2, fit_type: Literal['log', 'linear'] = 'log') → FitResult¶

rm_lite.utils.fitting.fit_fdf(fdf_to_fit_arr: numpy.typing.NDArray[numpy.float64], phi_arr_radm2: numpy.typing.NDArray[numpy.float64], fwhm_fdf_radm2: float) → FDFFitResult¶

rm_lite.utils.fitting.fit_rmsf(rmsf_to_fit_arr: numpy.typing.NDArray[numpy.float64], phi_double_arr_radm2: numpy.typing.NDArray[numpy.float64], fwhm_rmsf_radm2: float) → float¶

rm_lite.utils.fitting.fit_stokes_i_model(freq_arr_hz: numpy.typing.NDArray[numpy.float64], ref_freq_hz: float, stokes_i_arr: numpy.typing.NDArray[numpy.float64], stokes_i_error_arr: numpy.typing.NDArray[numpy.float64], fit_order: int = 2, fit_type: Literal['log', 'linear'] = 'log') → FitResult¶

rm_lite.utils.fitting.fwhm_to_sigma(fwhm: float) → float¶

rm_lite.utils.fitting.gaussian(x: numpy.typing.NDArray[numpy.float64], amplitude: float | complex, mean: float, stddev: float | None = None, fwhm: float | None = None) → numpy.typing.NDArray[numpy.float64]¶

rm_lite.utils.fitting.gaussian_integrand(amplitude: float, stddev: float | None = None, fwhm: float | None = None) → float¶

rm_lite.utils.fitting.polynomial(order: int) → StokesIModel¶

rm_lite.utils.fitting.power_law(order: int) → StokesIModel¶

rm_lite.utils.fitting.sigma_to_fwhm(sigma: float) → float¶

rm_lite.utils.fitting.static_fit(freq_arr_hz: numpy.typing.NDArray[numpy.float64], ref_freq_hz: float, stokes_i_arr: numpy.typing.NDArray[numpy.float64], stokes_i_error_arr: numpy.typing.NDArray[numpy.float64], fit_order: int = 2, fit_type: Literal['log', 'linear'] = 'log') → FitResult¶

rm_lite.utils.fitting.unit_centred_gaussian(x: numpy.typing.NDArray[numpy.float64], stddev: float | None = None, fwhm: float | None = None) → numpy.typing.NDArray[numpy.float64]¶

rm_lite.utils.fitting.unit_gaussian(x: numpy.typing.NDArray[numpy.float64], mean: float, stddev: float | None = None, fwhm: float | None = None) → numpy.typing.NDArray[numpy.float64]¶

rm_lite.utils.fitting.GAUSSIAN_SIGMA_TO_FWHM¶