API reference

This page provides an auto-generated summary of comet_maths’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.

Interpolation

interpolation.interpolation.Interpolator([...])	Class to provide a set of interpolation methods for the interpolation of data.
interpolation.interpolation.Interpolator.interpolate_1d(...)	Interpolates 1D data to defined coordinates x in 1D
interpolation.interpolation.Interpolator.interpolate_1d_along_example(...)	Method for interpolating between datapoints by following an example.
interpolation.interpolation.interpolate_1d(...)	Interpolates 1D data to defined coordinates x in 1D
interpolation.interpolation.default_unc_methods(method)	Function providing for each analytical interpolation method, the default methods that are compared to determine the model uncertainty for this interpolation method.
interpolation.interpolation.model_error_analytical_methods(...)	Function to calculate the interpolation model uncertainty by calculating the standard deviation between various interpolation methods.
interpolation.interpolation.gaussian_process_regression(...)	Function to perform interpolation using Gaussian process regression
interpolation.interpolation.gpr_basics(x_i, ...)	Function to perform basic gaussian process regression
interpolation.interpolation.interpolate_1d_along_example(...)	Method for interpolating between datapoints by following an example.

Linear Algebra

linear_algebra.matrix_calculation.calculate_Jacobian(fun, x)	Calculate the local Jacobian of function y=f(x) for a given value of x
linear_algebra.matrix_calculation.calculate_corr(MC_y)	Calculate the correlation matrix between the MC-generated samples of output quantities.
linear_algebra.matrix_calculation.nearestPD_cholesky(A)	Find the nearest positive-definite matrix
linear_algebra.matrix_calculation.isPD(B)	Returns true when input is positive-definite, via Cholesky
linear_algebra.matrix_conversion.correlation_from_covariance(...)	Convert covariance matrix to correlation matrix
linear_algebra.matrix_conversion.uncertainty_from_covariance(...)	Convert covariance matrix to uncertainty
linear_algebra.matrix_conversion.convert_corr_to_cov(corr, u)	Convert correlation matrix to covariance matrix
linear_algebra.matrix_conversion.convert_cov_to_corr(cov, u)	Convert covariance matrix to correlation matrix
linear_algebra.matrix_conversion.calculate_flattened_corr(...)	Combine correlation matrices for different input quantities, with a correlation matrix that gives the correlation between the input quantities into a full (flattened) correlation matrix combining the two.
linear_algebra.matrix_conversion.separate_flattened_corr(...)	Separate a full (flattened) correlation matrix into a list of correlation matrices for each output variable and a correlation matrix between the output variables.
linear_algebra.matrix_conversion.expand_errcorr_dims(...)	Function to expand the provided correlation matrix (which defines the correlation along 1 or more dimensions), to higher dimensions, so that the total correlation matrix can be calculated.
linear_algebra.matrix_conversion.change_order_errcorr_dims(...)	Function to flip the order of the underlying dimensions for an err_corr for matrices that describe the combination of 2 dimensions

Generating MC Samples

generate_sample.generate_sample.generate_sample(...)	Generate correlated MC sample of input quantity with given uncertainties and correlation matrix.
generate_sample.generate_sample.generate_error_sample(...)	Generate the errors of a correlated MC sample of input quantity with given uncertainties and correlation matrix.
generate_sample.generate_sample.generate_sample_shape(...)	function to determine the shape of the Monte Carlo (MC) sample
generate_sample.generate_sample.generate_sample_same(...)	Generate MC sample of input quantity with zero uncertainties.
generate_sample.generate_sample.generate_sample_random(...)	Generate MC sample of input quantity with random uncertainties.
generate_sample.generate_sample.generate_sample_systematic(...)	Generate correlated MC sample of input quantity with systematic uncertainties.
generate_sample.generate_sample.generate_sample_correlated(...)	Generate correlated MC sample of input quantity with given uncertainties and correlation matrix.
generate_sample.generate_sample.generate_sample_corr(...)	Generate correlated MC sample of input quantity with a given covariance matrix.
generate_sample.generate_sample.generate_sample_cov(...)	Generate correlated MC sample of input quantity with a given covariance matrix.
generate_sample.generate_sample.correlate_sample_corr(...)	Method to correlate independent sample of input quantities using correlation matrix and Cholesky decomposition.
generate_sample.probability_density_function.generate_sample_pdf(...)	Function to generate samples from standard probability functions (with zero as mean and 1 as width)