pyIntensityFeatures.proc.fitting
================================

.. py:module:: pyIntensityFeatures.proc.fitting

.. autoapi-nested-parse::

   Functions used for fitting functions to intensity data.

   ..
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Functions
---------

.. autoapisummary::

   pyIntensityFeatures.proc.fitting.estimate_peak_widths
   pyIntensityFeatures.proc.fitting.gauss_quad_err
   pyIntensityFeatures.proc.fitting.get_fitting_params
   pyIntensityFeatures.proc.fitting.get_gaussian_func_fit


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

.. py:function:: estimate_peak_widths(data, data_loc, peak_inds, peak_mags)

   
   Estimate the full width at half max of the peaks in a curve.


   :Parameters:

       **data** : array-like
           Input data (y-axis)

       **data_loc** : array-like
           Input data location (x-axis)

       **peak_inds** : array-like
           Indexes of the peaks

       **peak_mags** : array-like
           Magnitude of the peaks



   :Returns:

       **peak_sigmas** : list-like
           FWHM values for each peak, or the closest estimate possible








   .. rubric:: Notes

   Differs from Longden, et al. (2010) function by not assuming a fixed data
   location increment of 1.0 degrees.



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.. py:function:: gauss_quad_err(params, xvals, yvals, weights)

   
   Calculate the error of a quadriatic Gaussian fit.


   :Parameters:

       **params** : set
           Set of parameters used by `mult_gauss_quad`

       **xvals** : float or array-like
           Location values

       **yvals** : float or array-like
           Intensity values

       **weights** : float or array-like
           Weights for each data value



   :Returns:

       **err** : float or array-like
           Weighted error of the fit defined by `params`







   .. seealso::

       
       :obj:`utils.distributions.mult_gauss_quad`
           ..
       



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.. py:function:: get_fitting_params(mlat_bins, ilats, ilt, mean_intensity, num_gauss=3)

   
   Find the parameters for a Gaussian fit with a quadratic background.


   :Parameters:

       **mlat_bins** : np.array
           Magnetic latitude of output bin centres

       **ilats** : np.array
           Indices for magnetic latitudes with valid intensity values

       **ilt** : int
           Index for the magnetic local time

       **mean_intensity** : np.array
           2D array of mean intensity values

       **num_gauss** : int
           Maximum number of Gaussians to fit (default=3)



   :Returns:

       **params** : list
           List of the parameters needed to form a mult-Gaussian fit with a
           quadratic background.

       **ipeaks** : list
           List containing indexes of the normalized intensity peaks for the
           2D `mean_intesity` array down-selected by [`ilats`, `ilt`].











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.. py:function:: get_gaussian_func_fit(mlat_bins, mlt_bins, mean_intensity, std_intensity, num_intensity, num_gauss=3, min_num=3, min_intensity=0.0, min_lat_perc=70.0)

   
   Fit intensity data using least-squares minimization in MLT bins.


   :Parameters:

       **mlat_bins** : np.array
           Magnetic latitude of output bin centres

       **mlt_bins** : np.array
           Magnetic local time of output bin centres

       **mean_intensity** : np.array
           2D array of mean intensity values

       **std_intensity** : np.array
           2D array of intensity standard deviations

       **num_intensity** : np.array
           2D array of intensity counts per bin

       **num_gauss** : int
           Maximum number of Gaussians to fit (default=3)

       **min_num** : int
           Minimum number of samples contributing to the intensity mean
           (default=3)

       **min_intensity** : float
           Minimum intensity magnitude in Rayleighs (default=0.0)

       **min_lat_perc** : int
           Minimum percentage of latitude bins needed to define an intensity
           function (default=70.0)



   :Returns:

       **func_params** : list-like
           Set of parameters used by `mult_gauss_quad` for each MLT bin that
           defines the mean intensity as a function of magnetic latitude or set
           to a string output with a reason if no successful fit was performed.

       **fit_cov** : list-like
           Covariance matrix for each fit

       **fit_pearsonr** : list-like
           Pearson r-value for correlation between the observations and the fit

       **fit_pearsonp** : list-like
           Pearson p-value for correlation between the observations and the fit

       **num_peaks** : list-like
           Number of peaks used in the fit







   .. seealso::

       
       :obj:`utils.distributions.mult_gauss_quad`
           ..
       



   ..
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