comet_maths.interpolation.interpolation.interpolate_1d_along_example#

Method for interpolating between datapoints by following an example. The example can come from either models or higher-resolution observations. Here the example is assumed to have an unknown normalisation or poor absolute calibration, yet the low resolution data has a more precise calibration (and can thus be used to constrain the high-resolution model).

Parameters:

x_i – Independent variable quantity x for the low resolution data
y_i – measured variable quantity y for the low resolution data
x_hr – Independent variable quantity x for the high resolution data
y_hr – measured variable quantity y for the high resolution data
x – Independent variable quantity x for which we are trying to obtain the measurand y
relative – Boolean to indicate whether a relative normalisation (True) or absolute normalisation (False) should be used. Defaults to True.
method – Sting to indicate which interpolation method should be used to interpolate between normalised data (core interpolation step within the approach). Defaults to Gaussian Progress Regression.
method_hr – String to indicate which interpolation method should be used to interpolate between high resolution measurements. Defaults to cubic spline interpolation.
unc_methods – interpolation methods to use in the calculation of the model error for interpolation between normalised data. Not used for gpr. Defaults to None, in which case a standard list is used for each interpolation method.
unc_methods_hr – interpolation methods to use in the calculation of the model error for interpolation between high resolution measurements. Not used for gpr. Defaults to None, in which case a standard list is used for each interpolation method.
u_y_i – uncertainties on y_i, defaults to None
corr_y_i – error correlation matrix for u_y_i (can be “rand” for random, “syst” for systematic, or a custom 2D error correlation matrix), defaults to None
u_y_hr – uncertainties on y_hr, defaults to None
corr_y_hr – error correlation matrix for u_y_hr (can be “rand” for random, “syst” for systematic, or a custom 2D error correlation matrix), defaults to None
min_scale – minimum bound on the scale parameter in the gaussian process regression. Only used if gpr is selected as method. Defaults to 0.3
extrapolate – extrapolation method, which can be set to “extrapolate” (in which case extrapolation is used using interpolation method defined in “method”), “nearest” (in which case nearest values are used for extrapolation), or “linear” (in which case linear extrapolation is used). Defaults to “extrapolate”.
return_uncertainties – Boolean to indicate whether interpolation uncertainties should be calculated and returned. Defaults to False
return_corr – Boolean to indicate whether interpolation error-correlation matrix should be calculated and returned. Defaults to False
include_model_uncertainties – Boolean to indicate whether model uncertainties should be added to output uncertainties to account for interpolation uncertainties. Not used for gpr. Defaults to True
add_model_error – Boolean to indicate whether model error should be added to interpolated values to account for interpolation errors (useful in Monte Carlo approaches). Defaults to False
plot_residuals – Boolean to indicate whether a plot of the residuals should be made (and stored as residuals.png). Defaults to False
MCsteps – number of MC iterations. Defaults to 100
parallel_cores – number of CPU to be used in parallel processing. Defaults to 4

Returns:

The measurand y evaluated at the values x (interpolated values), optionally with correlation and uncertainties if specified

comet_maths.interpolation.interpolation.interpolate_1d_along_example

Contents

comet_maths.interpolation.interpolation.interpolate_1d_along_example#