API Reference#

Classes

`Model`	Represents a model.
`Parameter`	Represents a model parameter or a single parameter vector.
`Penalty`	Represents a penalty term of the objective function.
`FitResult`	Represents the results of a fit.
`UQResult`	Represents the uncertainty quantification of fit results.

Functions

`fit`	Fit the model(s) to the dataset(s)
`merge`	Create a multi-response model from multiple individual models.
`link`	Create equality relationships between parameters
`lincombine`	Create model whose response is a linear combination of multiple individual model responses.
`relate`	Create functional relationships between model parameters.
`regoperator`	Computes the discrete approximation to the derivative operators used as regularization operators.
`selregparam`	Selection of optimal regularization parameter based on a selection criterion.
`noiselevel`	Estimate the noise level in a dataset.
`whitegaussnoise`	Generates a vector of white Gaussian (normal) noise
`diststats`	Computes statistical quantities for the location, spread, and shape of a distance distribution, with or without their corresponding uncertainties.
`correctphase`	Phase correction of complex-valued data.
`bootstrap_analysis`	Bootstrap analysis for uncertainty quantification
`profile_analysis`	Profile likelihood analysis for uncertainty quantification
`snlls`	Separable non-linear least squares (SNLLS) solver
`fnnls`	Fast non-negative least-squares (NNLS) solver.
`cvxnnls`	Non-negative least-squares (NNLS) via the CVXOPT package.

Dipolar EPR functions

`dipolarmodel`	Generate a dipolar EPR signal model.
`dipolarpenalty`	Construct penalties based on the distance distribution.
`deerload`	Load file in BES3T format (Bruker EPR Standard for Spectrum Storage and Transfer)
`dipolarkernel`	Compute the (multi-pathway) dipolar kernel operator which enables the linear transformation from distance-domain to time-domain data.
`dipolarbackground`	Calculate multi-pathway dipolar background decays.
`fftspec`	Fast-Fourier transform spectrum
`distancerange`	Empirical distance range given a DEER time axis

Utility functions

All functions in deerlab.utils are private functions used internally by other functions. Stable functionality is not guaranteed.

`store_pickle`	Save/export an object to a `.pkl` file serialized as bytes.
`read_pickle`	Load a pickled object file `.pkl` and deserialize the bytes into a Python object.
`hccm`	Heteroscedasticity Consistent Covariance Matrix (HCCM)
`Jacobian`	Finite difference Jacobian estimation
`nearest_psd`	Find the nearest positive semi-definite matrix
`movmean`	Moving mean filter
`ovl`	Overlap index
`der_snr`	DER-SNR noise estimation

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