margin_fits_utils.py

import numpy as np
import rpy2.robjects as ro

from extreme_estimator.extreme_models.result_from_fit import ResultFromIsmev
from extreme_estimator.extreme_models.utils import r, get_null, safe_run_r_estimator

"""
These two functions are “extremely light” functions to fit the GEV/GPD. These functions are mainlyuseful 
to compute starting values for the Schlather and Smith mode
If more refined (univariate) analysis have to be performed, users should use more specialised pack-ages
 - e.g. POT, evd, ismev, . . . .
"""


def spatial_extreme_gevmle_fit(x_gev):
    res = r.gevmle(x_gev, method="Nelder")
    return dict(zip(res.names, res))


def spatial_extreme_gpdmle_fit(x_gev, threshold):
    res = r.gpdmle(x_gev, threshold, method="Nelder")
    return dict(zip(res.names, res))


# todo: define more robust function gevmle_fit/gpdmle_fit

"""
IsMev fit functions
"""


def ismev_gev_fit(x_gev, y, mul):
    """
    For non-stationary fitting it is recommended that the covariates within the generalized linear models are
    (at least approximately) centered and scaled (i.e.the columns of ydat should be approximately centered and scaled).
    """
    xdat = ro.FloatVector(x_gev)
    gev_fit = r('gev.fit')
    y = y if y is not None else get_null()
    mul = mul if mul is not None else get_null()
    res = gev_fit(xdat, y, mul)

    # r.assign('python_wrapping', True)
    # r.source(file="""/home/erwan/Documents/projects/spatiotemporalextremes/extreme_estimator/margin_fits/gev/wrapper_ismev_gev_fit.R""")
    # y = np.arange(1, len(x_gev), 1).reshape((-11, 1))
    # res = r.gev_fit(data=xdat, y=y, trend=1)
    return dict(zip(res.names, res))

# if __name__ == '__main__':
#     a = np.array([2, 2])
#     v = ro.vectors.FloatVector((1, 2, 3, 4, 5))
#     ro.globalenv['a'] = a
#     print(r('class(a)'))