pyIntensityFeatures.proc.fitting

Functions used for fitting functions to intensity data.

Functions

`estimate_peak_widths`(data, data_loc, peak_inds, peak_mags)	Estimate the full width at half max of the peaks in a curve.
`gauss_quad_err`(params, xvals, yvals, weights)	Calculate the error of a quadriatic Gaussian fit.
`get_fitting_params`(mlat_bins, ilats, ilt, mean_intensity)	Find the parameters for a Gaussian fit with a quadratic background.
`get_gaussian_func_fit`(mlat_bins, mlt_bins, ...[, ...])	Fit intensity data using least-squares minimization in MLT bins.

Module Contents

pyIntensityFeatures.proc.fitting.estimate_peak_widths(data, data_loc, peak_inds, peak_mags)[source]

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

Parameters:

dataarray-like: Input data (y-axis)
data_locarray-like: Input data location (x-axis)
peak_indsarray-like: Indexes of the peaks
peak_magsarray-like: Magnitude of the peaks

Returns:

peak_sigmaslist-like: FWHM values for each peak, or the closest estimate possible

Notes

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

pyIntensityFeatures.proc.fitting.gauss_quad_err(params, xvals, yvals, weights)[source]

Calculate the error of a quadriatic Gaussian fit.

Parameters:

paramsset: Set of parameters used by mult_gauss_quad
xvalsfloat or array-like: Location values
yvalsfloat or array-like: Intensity values
weightsfloat or array-like: Weights for each data value

Returns:

errfloat or array-like: Weighted error of the fit defined by params

See also

utils.distributions.mult_gauss_quad

pyIntensityFeatures.proc.fitting.get_fitting_params(mlat_bins, ilats, ilt, mean_intensity, num_gauss=3)[source]

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

Parameters:

mlat_binsnp.array: Magnetic latitude of output bin centres
ilatsnp.array: Indices for magnetic latitudes with valid intensity values
iltint: Index for the magnetic local time
mean_intensitynp.array: 2D array of mean intensity values
num_gaussint: Maximum number of Gaussians to fit (default=3)

Returns:

paramslist: List of the parameters needed to form a mult-Gaussian fit with a quadratic background.
ipeakslist: List containing indexes of the normalized intensity peaks for the 2D mean_intesity array down-selected by [ilats, ilt].

pyIntensityFeatures.proc.fitting.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)[source]

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

Parameters:

mlat_binsnp.array: Magnetic latitude of output bin centres
mlt_binsnp.array: Magnetic local time of output bin centres
mean_intensitynp.array: 2D array of mean intensity values
std_intensitynp.array: 2D array of intensity standard deviations
num_intensitynp.array: 2D array of intensity counts per bin
num_gaussint: Maximum number of Gaussians to fit (default=3)
min_numint: Minimum number of samples contributing to the intensity mean (default=3)
min_intensityfloat: Minimum intensity magnitude in Rayleighs (default=0.0)
min_lat_percint: Minimum percentage of latitude bins needed to define an intensity function (default=70.0)

Returns:

func_paramslist-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_covlist-like: Covariance matrix for each fit
fit_pearsonrlist-like: Pearson r-value for correlation between the observations and the fit
fit_pearsonplist-like: Pearson p-value for correlation between the observations and the fit
num_peakslist-like: Number of peaks used in the fit