rm_lite.utils.fitting
=====================

.. py:module:: rm_lite.utils.fitting


Attributes
----------

.. autoapisummary::

   rm_lite.utils.fitting.GAUSSIAN_SIGMA_TO_FWHM


Classes
-------

.. autoapisummary::

   rm_lite.utils.fitting.FDFFitResult
   rm_lite.utils.fitting.FitResult
   rm_lite.utils.fitting.StokesIModel


Functions
---------

.. autoapisummary::

   rm_lite.utils.fitting.best_aic_func
   rm_lite.utils.fitting.dynamic_fit
   rm_lite.utils.fitting.fit_fdf
   rm_lite.utils.fitting.fit_rmsf
   rm_lite.utils.fitting.fit_stokes_i_model
   rm_lite.utils.fitting.fwhm_to_sigma
   rm_lite.utils.fitting.gaussian
   rm_lite.utils.fitting.gaussian_integrand
   rm_lite.utils.fitting.polynomial
   rm_lite.utils.fitting.power_law
   rm_lite.utils.fitting.sigma_to_fwhm
   rm_lite.utils.fitting.static_fit
   rm_lite.utils.fitting.unit_centred_gaussian
   rm_lite.utils.fitting.unit_gaussian


Module Contents
---------------

.. py:class:: FDFFitResult

   Bases: :py:obj:`NamedTuple`


   Results of a Gaussian FDF fit


   .. py:attribute:: amplitude_fit
      :type:  float

      Amplitude of the best fit model


   .. py:attribute:: mean_fit
      :type:  float

      Mean (Faraday depth) of the best fit model


   .. py:attribute:: stddev_fit
      :type:  float

      Standard deviation (Faraday depth) of the best fit model


.. py:class:: FitResult

   Bases: :py:obj:`NamedTuple`


   Results of a Stokes I fit


   .. py:attribute:: aic
      :type:  float

      Akaike Information Criterion of the fit


   .. py:attribute:: pcov
      :type:  numpy.typing.ArrayLike

      Covariance matrix of the fit


   .. py:attribute:: popt
      :type:  numpy.typing.ArrayLike

      Best fit parameters


   .. py:attribute:: stokes_i_model_func
      :type:  StokesIModel

      Function of the best fit model


.. py:class:: StokesIModel

   Bases: :py:obj:`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:
               ...


   .. py:method:: __call__(x: numpy.typing.NDArray[numpy.float64], *params) -> numpy.typing.NDArray[numpy.float64]


.. py:function:: 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.


.. py:function:: 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

.. py:function:: fit_fdf(fdf_to_fit_arr: numpy.typing.NDArray[numpy.float64], phi_arr_radm2: numpy.typing.NDArray[numpy.float64], fwhm_fdf_radm2: float) -> FDFFitResult

.. py:function:: 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

.. py:function:: 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

.. py:function:: fwhm_to_sigma(fwhm: float) -> float

.. py:function:: 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]

.. py:function:: gaussian_integrand(amplitude: float, stddev: float | None = None, fwhm: float | None = None) -> float

.. py:function:: polynomial(order: int) -> StokesIModel

.. py:function:: power_law(order: int) -> StokesIModel

.. py:function:: sigma_to_fwhm(sigma: float) -> float

.. py:function:: 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

.. py:function:: unit_centred_gaussian(x: numpy.typing.NDArray[numpy.float64], stddev: float | None = None, fwhm: float | None = None) -> numpy.typing.NDArray[numpy.float64]

.. py:function:: unit_gaussian(x: numpy.typing.NDArray[numpy.float64], mean: float, stddev: float | None = None, fwhm: float | None = None) -> numpy.typing.NDArray[numpy.float64]

.. py:data:: GAUSSIAN_SIGMA_TO_FWHM