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import numpy as np
from ctypes import *
libloss = np.ctypeslib.load_library("lossdistribgomez", "/home/share/CorpCdos/code/R")
libloss.fitprob.restype = None
libloss.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')]
libloss.stochasticrecov.restype = None
libloss.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')]
libgq = np.ctypeslib.load_library("GHquad", ".")
libgq.GHquad.restype = None
libgq.GHquad.argtypes = [c_int, np.ctypeslib.ndpointer('double', ndim=1, flags='F'),
np.ctypeslib.ndpointer('double', ndim=1, flags='F')]
def GHquad(n):
Z = np.zeros(n, dtype='double')
w = np.zeros(n, dtype='double')
libgq.GHquad(n, Z, w)
return Z, w
def stochasticrecov(R, Rtilde, Z, w, rho, porig, pmod):
q = np.zeros_like(Z)
libloss.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)
libloss.fitprob(Z, w, byref(c_int(Z.size)), byref(c_double(rho)), byref(c_double(p0)), result)
return result
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