`VolterraBasis`.Estimator_gle¶

class VolterraBasis.Estimator_gle(xva_arg, model_class, basis, trunc=1.0, L_obs=None, saveall=True, prefix='', verbose=True, n_jobs=1, **kwargs)[source]¶

The main class for the position dependent memory extraction holding all data.

Create an instance of the Pos_gle class.

Parameters:

xva_argxarray dataset ([‘time’, ‘x’, ‘v’, ‘a’]) or list of datasets.: Use compute_va() or see its output for format details. The timeseries to analyze. It should be either a xarray timeseries or a listlike collection of them.
basisscikit-learn transformer to get the element of the basis: This class should implement, basis() and deriv() function and deal with periodicity of the data. If a fit() method is defined, it will be called at initialization
saveallbool, default=True: Whether to save all output functions.
prefixstr: Prefix for the saved output functions.
verbosebool, default=True: Set verbosity.
truncfloat, default=1.0: Truncate all correlation functions and the memory kernel after this time value.
L_obs: str, default given by the model: Name of the column containing the time derivative of the observable

__init__(xva_arg, model_class, basis, trunc=1.0, L_obs=None, saveall=True, prefix='', verbose=True, n_jobs=1, **kwargs)[source]¶

Create an instance of the Pos_gle class.

Parameters:

xva_argxarray dataset ([‘time’, ‘x’, ‘v’, ‘a’]) or list of datasets.: Use compute_va() or see its output for format details. The timeseries to analyze. It should be either a xarray timeseries or a listlike collection of them.
basisscikit-learn transformer to get the element of the basis: This class should implement, basis() and deriv() function and deal with periodicity of the data. If a fit() method is defined, it will be called at initialization
saveallbool, default=True: Whether to save all output functions.
prefixstr: Prefix for the saved output functions.
verbosebool, default=True: Set verbosity.
truncfloat, default=1.0: Truncate all correlation functions and the memory kernel after this time value.
L_obs: str, default given by the model: Name of the column containing the time derivative of the observable

check_volterra_inversion(return_diff=False)[source]¶

For checking if the volterra equation is correctly inversed Compute the integral in volterra equation using trapezoidal rule. This only check the volterra of the first kind

Parameters:

return_diffbool, default = False: Indicate if you want the result of the intégral or the difference between the result and the expected value

compute_basis_mean(basis_type='force')[source]¶: Compute mean value of the basis function

compute_corrs(large=False, rank_tol=None, **kwargs)[source]¶

Compute correlation functions.

Parameters:

largebool, default=False

When large is true, it use a slower way to compute correlation that is less demanding in memory

rank_tol: float, default=None

Tolerance for rank computation in case of projection onto the range of the basis

second_order_method:bool, default = True: If set to False do less computation but prevent to use second_order method in Volterra

compute_effective_mass()[source]¶: Return average effective mass computed from equipartition with the velocity.

compute_gram_kernel()[source]¶: Return gram matrix of the kernel part of the basis.

compute_kernel(method='rectangular', k0=None)[source]¶

Computes the memory kernel.

Parameters:

method{“rectangular”, “midpoint”, “midpoint_w_richardson”,”trapz”,”second_kind_rect”,”second_kind_trapz”}, default=rectangular: Choose numerical method of inversion of the volterra equation
k0float, default=0.: If you give a nonzero value for k0, this is used at time zero for the trapz and second kind method. If set to None, the F-routine will calculate k0 from the second kind memory equation.