pyIntensityFeatures.utils.checks
Functions for testing the outcome of various functions.
Attributes
Functions
|
Test the robustness of the quadratic Gaussian fitting results. |
|
Evaluate the Pearson correlation coefficient and p-value. |
|
Evaluate the dayglow level across a range of locations. |
|
Compare different boundaries and choose the best values. |
|
Evaluate different Pearson correlation coefficients and choose the best. |
|
Evaluate boundary consistency with local time. |
Module Contents
- pyIntensityFeatures.utils.checks.evaluate_gauss_quad(npeaks, fit_coeff, rvalue, pvalue, po_bound, un_bound_po, eq_bound, un_bound_eq, min_mlat, max_mlat, un_threshold=1.25)[source]
Test the robustness of the quadratic Gaussian fitting results.
- Parameters:
- npeaksint
Number of Gaussian peaks in the fit
- fit_coeffarray-like
List or array containing Guassian constant, quadratic multiplier for x, quadratic multiplier for x^2, and Gaussian amplitudes, x offsets, and exponential scalers in that order.
- rvaluefloat
Pearson correlation coefficient
- pvaluefloat
Pearson p-value
- po_boundfloat
Polar auroral boundary location in degrees latitude
- un_bound_pofloat
Uncertainty of the polar auroral boundary location in degrees latitude
- eq_boundfloat
Equatorward auroral boundary location in degrees latitude
- un_bound_eqfloat
Uncertainty of the equatorward auroral boundary location in degrees lat
- min_mlatfloat
Minimum latitude in intensity slice in degrees
- max_mlatfloat
Maximum latitude in intensity slice in degrees
- un_thresholdfloat
Maximum acceptable uncertainty value in degrees (default=1.25)
- Returns:
- bool
True if all quality criteria are met, False if any fails
Notes
Differs from Longden, et al. (2010) by using Pearson correlation coefficients.
References
Longden, N. S., et al. (2010) Estimating the location of the open-closed magnetic field line boundary from auroral images, 28 (9), p 1659-1678, doi:10.5194/angeo-28-1659-2010.
- pyIntensityFeatures.utils.checks.evaluate_pearson(rvalue, pvalue, rthresh=0.9, pmax=0.0001)[source]
Evaluate the Pearson correlation coefficient and p-value.
- Parameters:
- rvaluefloat
Pearson correlation coefficient
- pvaluefloat
Pearson p-value for testing non-correlation
- rthreshfloat
Minimum acceptable correlation coefficient (default=0.8)
- pmaxfloat
Maximum acceptable p-value (default=1.0e-4)
- Returns:
- bool
True if thresholds pass, False if they fail
- pyIntensityFeatures.utils.checks.evaluate_dayglow(fit_coeff, locations, thresh=300.0)[source]
Evaluate the dayglow level across a range of locations.
- Parameters:
- fit_coeffarray-like
Coefficients, the first three of which are the quadratic constant, x-term, and x-squared-term.
- locationsarray-like
Locations at which the dayglow level will be evaluated.
- threshfloat
Maximum allowable background intensity value in Rayleighs (default=300)
- Returns:
- goodbool
True if the background level is low across all desired locations, False if any location is too high.
- pyIntensityFeatures.utils.checks.compare_boundaries(rvalue, pvalue, eq_bounds, eq_uncert, po_bounds, po_uncert, min_mlat, max_mlat, max_uncert=3.0)[source]
Compare different boundaries and choose the best values.
- Parameters:
- rvaluelist-like
Pearson correlation coefficient
- pvaluelist-like
Pearson p-value for testing non-correlation
- eq_boundsfloat
Equatorward auroral boundary location in degrees latitude
- eq_uncertfloat
Equatorward auroral boundary uncertainty in degrees latitude
- po_boundsfloat
Polar auroral boundary location in degrees latitude
- po_uncertfloat
Polar auroral boundary uncertainty in degrees latitude
- min_mlatfloat
Minimum latitude in intensity slice in degrees
- max_mlatfloat
Maximum latitude in intensity slice in degrees
- max_uncertfloat
Maximum allowable boundary uncertainty in degrees (default=3.0)
- Returns:
- igood_eqint or NoneType
Index corresponding to the best equatorial index, or None
- igood_point or NoneType
Index corresponding to the best polar index, or None
- pyIntensityFeatures.utils.checks.compare_pearson(rvalue, pvalue)[source]
Evaluate different Pearson correlation coefficients and choose the best.
- Parameters:
- rvaluelist-like
Pearson correlation coefficient
- pvaluelist-like
Pearson p-value for testing non-correlation
- Returns:
- igoodint or NoneType
Index correspeonding to the best fit, None if no fit is good
- pyIntensityFeatures.utils.checks.evaluate_boundary_in_mlt(bound_data, eq_key, po_key, lt_key, ut_key, lt_bin=5.0, max_iqr=1.5)[source]
Evaluate boundary consistency with local time.
- Parameters:
- bound_dataxr.Dataset
Boundary data stored in an xarray Dataset
- eq_keystr
Data key for the equatorward boundary data
- po_keystr
Data key for the poleward boundary data
- lt_keystr
Coordinate key for the MLT data
- ut_keystr
Coordinate key for the UT data
- lt_binfloat
Size of local time bin in hours over which the data will be evaluated (default=5.0)
- max_iqrfloat
Maximum multiplier for the interquartile range (IQR) used to identify outliers above or below the upper or lower quartile (default=1.5)
- Returns:
- bdataxr.Dataset
Dataset with only good boundaries