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DIVAnd_cv.py
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DIVAnd_cv.py
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"""
Copy the method of calling DIVAnd Julia from
https://github.com/gher-ulg/DIVAnd.py/blob/master/DIVAnd/DIVAnd.py
"""
import numpy as np
import julia
from julia import DIVAnd as D
def DIVAnd_cv(mask, pmn, xi, x, f, corlen, epsilon2, nl, ne, method=0):
"""
Performs a cross validation to estimate the analysis parameters
(correlation length and signal-to-noise ratio).
https://github.com/gher-ulg/DIVAnd.jl/blob/master/src/DIVAnd_cv.jl
:param mask: binary mask delimiting the domain. true is inside and false outside
:param pmn: scale factor of the grid. pmn is a tuple with n elements. Every element
represents the scale factor of the corresponding dimension. Its inverse is the local
resolution of the grid in a particular dimension.
:param xi: tuple with n elements. Every element represents a coordinate of the final
grid on which the observations are interpolated
:param x: tuple with n elements. Every element represents a coordinate of the observations
:param f: value of the observations minus the background estimate (m-by-1 array)
:param corlen: correlation length
:param epsilon2: error variance of the observations (normalized by the error variance of
the background field). epsilon2 can be a scalar (all observations have the same error
variance and their errors are decorrelated), a vector (all observations can have a
difference error variance and their errors are decorrelated) or a matrix (all observations
can have a difference error variance and their errors can be correlated). If epsilon2 is a
scalar, it is thus the inverse of the signal-to-noise ratio
:param nl: number of testing points around the current value of L. 1 means one additional
point on both sides of the current L. 0 is allowed and means the parameter is not optimised
:param ne: number of testing points around the current value of epsilon2. 0 is allowed as
for nl
:param method: cross validation estimator method 1: full CV; 2: sampled CV; 3: GCV;
0: automatic choice between the three possible ones, default value
:return bestfactorl: best estimate of the multiplication factor to apply to len
:return bestfactore: best estimate of the multiplication factor to apply to epsilon2
:return cvvales: the cross validation values calculated
:return factors: the tested multiplication factors
:return cvinter: the interpolated cv values for final optimisation
:returns X2Data, Y2Data: coordinates of sampled cross validation in L,epsilon2 space.
Normally only used for debugging or plotting
:returns Xi2D, Yi2d: coordinates of interpolated estimator. Normally only used for
debugging or plotting
"""
bestfactorl, bestfactore, cvval, cvvalues, x2Ddata, y2Ddata, cvinter, xi2D, yi2D = D.DIVAnd_cv(
np.transpose(mask), tuple([np.transpose(_) for _ in pmn]),
tuple([np.transpose(_) for _ in xi]), x, f, corlen, epsilon2, nl, ne, method)
return bestfactorl, bestfactore, cvval, cvvalues, x2Ddata, y2Ddata, cvinter, xi2D, yi2D