import numpy as np
from ctypes import *

lib = np.ctypeslib.load_library("lossdistribgomez", "/home/share/CorpCdos/code/R")
lib.fitprob.restype = None
lib.fitprob.argtypes = [np.ctypeslib.ndpointer('double', ndim=1, flags='F'),
                        np.ctypeslib.ndpointer('double', ndim=1, flags='F'),
                        POINTER(c_int),
                        POINTER(c_double),
                        POINTER(c_double),
                        np.ctypeslib.ndpointer('double', ndim=1, flags='F,writeable')]
lib.stochasticrecov.restype = None
lib.stochasticrecov.argtypes = [POINTER(c_double),
                                 POINTER(c_double),
                                 np.ctypeslib.ndpointer('double', ndim=1, flags='F'),
                                 np.ctypeslib.ndpointer('double', ndim=1, flags='F'),
                                 POINTER(c_int),
                                 POINTER(c_double),
                                 POINTER(c_double),
                                 POINTER(c_double),
                                 np.ctypeslib.ndpointer('double', ndim=1, flags='F,writeable')]

def stochasticrecov(R, Rtilde, Z, w, rho, porig, pmod):
    q = np.zeros_like(Z)
    lib.stochasticrecov(byref(c_double(R)), byref(c_double(Rtilde)), Z, w, byref(c_int(Z.size)),
                        byref(c_double(rho)), byref(c_double(porig)), byref(c_double(pmod)), q)
    return q


def lossdistZ(p, w, S, N, defaultflag= False, rho, Z, wZ):
    q = np.zeros_like(Z)
    lib.lossdistrib_Z(byref())

def fitprob(Z, w, rho, p0):
    result = np.empty_like(Z)
    lib.fitprob(Z, w, byref(c_int(Z.size)), byref(c_double(rho)), byref(c_double(p0)), result)
    return result