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#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2004-2015 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF by the Software group.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
#############################################################################*/
cimport cython
import numpy
cimport numpy
from polspl cimport polspl as _polspl
from bessel0 cimport j0Single, j0Multiple
def j0(x):
if hasattr(x, "__len__"):
return _besselMultiple(x)
else:
return _besselSingle(x)
def _besselMultiple(x):
result = numpy.array(x, copy=True, dtype=numpy.float64)
cdef double[:] c_x = result
cdef int c_npts = c_x.size
j0Multiple(&c_x[0], c_npts)
return result
def _besselSingle(double x):
return j0Single(x)
def polspl(x, y, w, npts, xl, xh, nr, nc):
c = numpy.zeros((36,), dtype=numpy.float64)
cdef double[:] c_c = c
cdef double[:] c_x = numpy.ascontiguousarray(x,
dtype=numpy.float64)
cdef double[:] c_y = numpy.ascontiguousarray(y,
dtype=numpy.float64)
cdef double[:] c_w = numpy.ascontiguousarray(w,
dtype=numpy.float64)
cdef int c_npts = npts
cdef double[:] c_xl = numpy.ascontiguousarray(xl,
dtype=numpy.float64)
cdef double[:] c_xh = numpy.ascontiguousarray(xh,
dtype=numpy.float64)
cdef int c_nr = nr
cdef int[:] c_nc = numpy.ascontiguousarray(nc,
dtype=numpy.int32)
cdef int c_sizeC = c_c.size
_polspl(&c_x[0], &c_y[0], &c_w[0], c_npts, \
&c_xl[0], &c_xh[0], &c_nc[0], c_nr, &c_c[0], c_sizeC)
return c
def polspl2(x,y,w,npts,xl0,xh0,nr,nc):
# ;
# ; few definitions
# ;
cdef numpy.ndarray[double, ndim=1, mode='c'] buffer_xl0 = \
numpy.ascontiguousarray(xl0, numpy.float64)
cdef double * xl = <double *> buffer_xl0.data
cdef numpy.ndarray[double, ndim=1, mode='c'] buffer_xh0 = \
numpy.ascontiguousarray(xh0, numpy.float64)
cdef double * xh = <double *> buffer_xh0.data
df = numpy.zeros(26)
a = numpy.zeros((36,37))
nbs = numpy.zeros(11,dtype=int)
cdef double[:] xk0 = numpy.zeros(10)
cdef double * xk = &xk0[0]
c = numpy.zeros(36)
cdef int j=0
cdef int i=0
ne_idl=0
n = 0
cdef int k = 0
cdef int ibl = 0
cdef int ns = 0
cdef int ns1 = 0
nbs[1]=1
for i in range(1,nr+1):
n=n+int(nc[i])
nbs[i+1]=n+1
if xl[i] < xh[i]:
pass
else:
t=xl[i]
xl[i]=xh[i]
xh[i]=t
n=n+2*(nr-1)
n1=n+1
xl[nr+1]=0.
xh[nr+1]=0.
# this loop ...
for ibl in range(1,nr+1):
xk[ibl]=.5*(xh[ibl]+xl[ibl+1])
if (xl[ibl] > xl[ibl+1]):
xk[ibl]=.5*(xl[ibl]+xh[ibl+1])
ns=nbs[ibl]
ne_idl=nbs[ibl+1]-1
for i in range(1, npts+1):
if((x[i] < xl[ibl]) or (x[i] > xh[ibl])):
pass
else:
df[ns]=1.0
ns1=ns+1
for j in range(ns1,ne_idl+1):
df[j]=df[j-1]*x[i]
for j in range(ns,ne_idl+1):
for k in range(j,ne_idl+1):
a[j,k]=a[j,k]+df[j]*df[k]*w[i]
a[j,n1]=a[j,n1]+df[j]*y[i]*w[i]
# ... has to be faster
ncol=nbs[nr+1]-1
nk=nr-1
if (nk == 0):
pass
else:
for ik in range(1,nk+1):
ncol=ncol+1
ns=nbs[ik]
ne_idl=nbs[ik+1]-1
a[ns,ncol]=-1.
ns=ns+1
for i in range(ns,ne_idl+1):
a[i,ncol]=a[i-1,ncol]*xk[ik]
ncol=ncol+1
a[ns,ncol]=-1.
ns=ns+1
if (ns > ne_idl):
pass
else:
for i in range(ns,ne_idl+1):
a[i,ncol]=(ns-i-2)*numpy.power(xk[ik],(i-ns+1))
ncol=ncol-1
ns=nbs[ik+1]
ne_idl=nbs[ik+2]-1
a[ns,ncol]=1.0
ns=ns+1
for i in range(ns,ne_idl+1):
a[i,ncol]=a[i-1,ncol]*xk[ik]
ncol=ncol+1
a[ns,ncol]=1.0
ns=ns+1
if (ns > ne_idl):
pass
else:
for i in range(ns,ne_idl+1):
a[i,ncol]=(i-ns+2)*numpy.power(xk[ik],(i-ns+1))
for i in range(1,n+1):
i1=i-1
for j in range(1,i1+1):
a[i,j]=a[j,i]
nm1=n-1
for i in range(1,nm1+1):
i1=i+1
m=i
t=numpy.abs(a[i,i])
for j in range(i1,n+1):
if (t >= numpy.abs(a[j,i])):
pass
else:
m=j
t=numpy.abs(a[j,i])
if (m == i):
pass
else:
for j in range(1,n1+1):
t=a[i,j]
a[i,j]=a[m,j]
a[m,j]=t
for j in range(i1,n+1):
t=a[j,i]/a[i,i]
for k in range(i1,n1+1):
a[j,k]=a[j,k]-t*a[i,k]
c[n]=a[n,n1]/a[n,n]
for i in range(1,nm1+1):
ni=n-i
t=a[ni,n1]
ni1=ni+1
for j in range(ni1,n+1):
t=t-c[j]*a[ni,j]
c[ni]=t/a[ni,ni]
return c
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