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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
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