669 lines
27 KiB
Cython
669 lines
27 KiB
Cython
# Cythonized parts of QPMS here
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# -----------------------------
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import numpy as np
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cimport numpy as np
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cimport cython
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from cython.parallel cimport parallel, prange
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#cimport openmp
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#openmp.omp_set_dynamic(1)
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## Auxillary function for retrieving the "meshgrid-like" indices; inc. nmax
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@cython.boundscheck(False)
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def get_mn_y(int nmax):
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"""
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Auxillary function for retreiving the 'meshgrid-like' indices from the flat indexing;
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inc. nmax.
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('y to mn' conversion)
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Parameters
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----------
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nmax : int
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The maximum order to which the VSWFs / Legendre functions etc. will be evaluated.
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Returns
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-------
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output : (m, n)
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Tuple of two arrays of type np.array(shape=(nmax*nmax + 2*nmax), dtype=np.int),
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where [(m[y],n[y]) for y in range(nmax*nmax + 2*nma)] covers all possible
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integer pairs n >= 1, -n <= m <= n.
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"""
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cdef Py_ssize_t nelems = nmax * nmax + 2 * nmax
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cdef np.ndarray[np.int_t,ndim=1] m_arr = np.empty([nelems], dtype=np.int)
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cdef np.ndarray[np.int_t,ndim=1] n_arr = np.empty([nelems], dtype=np.int)
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cdef Py_ssize_t i = 0
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cdef np.int_t n, m
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for n in range(1,nmax+1):
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for m in range(-n,n+1):
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m_arr[i] = m
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n_arr[i] = n
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i = i + 1
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return (m_arr, n_arr)
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def get_nelem(unsigned int lMax):
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return lMax * (lMax + 2)
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def get_y_mn_unsigned(int nmax):
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"""
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Auxillary function for mapping 'unsigned m', n indices to the flat y-indexing.
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For use with functions as scipy.special.lpmn, which have to be evaluated separately
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for positive and negative m.
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Parameters
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----------
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nmax : int
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The maximum order to which the VSWFs / Legendre functions etc. will be evaluated.
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output : (ymn_plus, ymn_minus)
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Tuple of two arrays of shape (nmax+1,nmax+1), containing the flat y-indices corresponding
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to the respective (m,n) and (-m,n). The elements for which |m| > n are set to -1.
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(Therefore, the caller must not use those elements equal to -1.)
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"""
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cdef np.ndarray[np.intp_t, ndim=2] ymn_plus = np.full((nmax+1,nmax+1),-1, dtype=np.intp)
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cdef np.ndarray[np.intp_t, ndim=2] ymn_minus = np.full((nmax+1,nmax+1),-1, dtype=np.intp)
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cdef Py_ssize_t i = 0
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cdef np.int_t n, m
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for n in range(1,nmax+1):
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for m in range(-n,0):
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ymn_minus[-m,n] = i
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i = i + 1
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for m in range(0,n+1):
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ymn_plus[m,n] = i
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i = i + 1
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return(ymn_plus, ymn_minus)
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cdef int q_max(int m, int n, int mu, int nu):
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return min(n,nu,(n+nu-abs(m+mu)//2))
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"""
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Now we generate our own universal functions to be used with numpy.
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Good way to see how this is done is to look at scipy/scipy/special/generate_ufuncs.py
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and scipy/scipy/special/generate_ufuncs.py
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In simple words, it works like this:
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- Let's have a single element function. This can be function which returns or a "subroutine".
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- Then we need a loop function; this is a wrapper that gets bunch of pointers from numpy and
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has to properly call the single element function.
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- From those two, we build a python object using PyUFunc_FromFuncAndData.
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* If the ufunc is supposed to work on different kinds of input/output types,
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then a pair of single-element and loop functions is o be provided for
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each combination of types. However, the single-element function can be reused if
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the corresponding loop functions do the proper casting.
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"""
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## as in scipy/special/_ufuncs_cxx.pyx
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##-------------------------------------
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#cdef extern from "numpy/ufuncobject.h":
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# int PyUFunc_getfperr() nogil
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#
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#cdef public int wrap_PyUFunc_getfperr() nogil:
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# """
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# Call PyUFunc_getfperr in a context where PyUFunc_API array is initialized;
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#
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# """
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# return PyUFunc_getfperr()
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#
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#cimport sf_error
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#-------------------------------------
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ctypedef double complex cdouble
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cdef void loop_D_iiiidddii_As_D_lllldddbl(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) nogil:
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cdef np.npy_intp i, n = dims[0]
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cdef void *func = (<void**>data)#[0]
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#cdef char *func_name= <char*>(<void**>data)[1] # i am not using this, nor have I saved func_name to data
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cdef char *ip0 = args[0]
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cdef char *ip1 = args[1]
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cdef char *ip2 = args[2]
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cdef char *ip3 = args[3]
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cdef char *ip4 = args[4]
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cdef char *ip5 = args[5]
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cdef char *ip6 = args[6]
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cdef char *ip7 = args[7]
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cdef char *ip8 = args[8]
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cdef char *op0 = args[9]
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cdef cdouble ov0
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for i in range(n): # iterating over dimensions
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ov0 = (<double complex(*)(int, int, int, int, double, double, double, int, int) nogil>func)(
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<int>(<np.npy_long*>ip0)[0],
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<int>(<np.npy_long*>ip1)[0],
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<int>(<np.npy_long*>ip2)[0],
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<int>(<np.npy_long*>ip3)[0],
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<double>(<np.npy_double*>ip4)[0],
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<double>(<np.npy_double*>ip5)[0],
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<double>(<np.npy_double*>ip6)[0],
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<int>(<np.npy_bool*>ip7)[0],
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<int>(<np.npy_long*>ip8)[0],
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)
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(<cdouble *>op0)[0] = <cdouble>ov0
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ip0 += steps[0]
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ip1 += steps[1]
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ip2 += steps[2]
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ip3 += steps[3]
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ip4 += steps[4]
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ip5 += steps[5]
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ip6 += steps[6]
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ip7 += steps[7]
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ip8 += steps[8]
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op0 += steps[9]
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# FIXME ERROR HANDLING!!! requires correct import and different data passed (see scipy's generated ufuncs)
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# sf_error.check_fpe(func_name)
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#cdef extern from "numpy/arrayobject.h":
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# cdef enum NPY_TYPES:
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# NPY_DOUBLE
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# NPY_CDOUBLE # complex double
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# NPY_LONG # int
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# ctypedef int npy_intp
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cdef extern from "translations.h":
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cdouble qpms_trans_single_A_Taylor_ext(int m, int n, int mu, int nu,
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double r, double th, double ph, int r_ge_d, int J) nogil
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cdouble qpms_trans_single_B_Taylor_ext(int m, int n, int mu, int nu,
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double r, double th, double ph, int r_ge_d, int J) nogil
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struct qpms_trans_calculator:
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int lMax
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size_t nelem
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cdouble** A_multipliers
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cdouble** B_multipliers
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enum qpms_normalization_t:
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pass
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qpms_trans_calculator* qpms_trans_calculator_init(int lMax, int nt) # should be qpms_normalization_t
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void qpms_trans_calculator_free(qpms_trans_calculator* c)
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cdouble qpms_trans_calculator_get_A_ext(const qpms_trans_calculator* c,
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int m, int n, int mu, int nu, double kdlj_r, double kdlj_th, double kdlj_phi,
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int r_ge_d, int J) nogil
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cdouble qpms_trans_calculator_get_B_ext(const qpms_trans_calculator* c,
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int m, int n, int mu, int nu, double kdlj_r, double kdlj_th, double kdlj_phi,
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int r_ge_d, int J) nogil
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int qpms_trans_calculator_get_AB_p_ext(const qpms_trans_calculator* c,
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cdouble *Adest, cdouble *Bdest,
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int m, int n, int mu, int nu, double kdlj_r, double kdlj_th, double kdlj_phi,
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int r_ge_d, int J) nogil
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int qpms_trans_calculator_get_AB_arrays_ext(const qpms_trans_calculator *c,
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cdouble *Adest, cdouble *Bdest,
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size_t deststride, size_t srcstride,
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double kdlj_r, double kdlj_theta, double kdlj_phi,
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int r_ge_d, int J) nogil
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int qpms_cython_trans_calculator_get_AB_arrays_loop(qpms_trans_calculator *c,
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int J, int resnd,
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int daxis, int saxis,
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char *A_data, np.npy_intp *A_shape, np.npy_intp *A_strides,
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char *B_data, np.npy_intp *B_shape, np.npy_intp *B_strides,
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char *r_data, np.npy_intp *r_shape, np.npy_intp *r_strides,
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char *theta_data, np.npy_intp *theta_shape, np.npy_intp *theta_strides,
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char *phi_data, np.npy_intp *phi_shape, np.npy_intp *phi_strides,
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char *r_ge_d_data, np.npy_intp *phi_shape, np.npy_intp *phi_strides) nogil
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# Module initialisation
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# ---------------------
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np.import_array() # not sure whether this is really needed
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np.import_ufunc()
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# Arrays passed to PyUFunc_FromFuncAndData()
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# ------------------------------------------
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# BTW, aren't there anonymous arrays in cython?
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cdef np.PyUFuncGenericFunction trans_X_taylor_loop_func[1]
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cdef void *trans_A_taylor_elementwise_funcs[1]
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cdef void *trans_B_taylor_elementwise_funcs[1]
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trans_X_taylor_loop_func[0] = loop_D_iiiidddii_As_D_lllldddbl
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# types to be used for all of the single-type translation
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# coefficient retrieval ufuncs called like
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# coeff = func(m, n, mu, nu, r, theta, phi, r_ge_d, J)
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# currently supported signatures: (D_lllldddbl)
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cdef char ufunc__get_either_trans_coeff_types[10]
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ufunc__get_either_trans_coeff_types[0] = np.NPY_LONG
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ufunc__get_either_trans_coeff_types[1] = np.NPY_LONG
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ufunc__get_either_trans_coeff_types[2] = np.NPY_LONG
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ufunc__get_either_trans_coeff_types[3] = np.NPY_LONG
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ufunc__get_either_trans_coeff_types[4] = np.NPY_DOUBLE
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ufunc__get_either_trans_coeff_types[5] = np.NPY_DOUBLE
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ufunc__get_either_trans_coeff_types[6] = np.NPY_DOUBLE
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ufunc__get_either_trans_coeff_types[7] = np.NPY_BOOL
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ufunc__get_either_trans_coeff_types[8] = np.NPY_LONG
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ufunc__get_either_trans_coeff_types[9] = np.NPY_CDOUBLE
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# types to be used for all of the both-type translation
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# coefficient retrieval ufuncs called like
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# errval = func(m, n, mu, nu, r, theta, phi, r_ge_d, J, &A, &B)
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# currently supported signatures: (lllldddbl_DD)
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cdef char ufunc__get_both_coeff_types[11]
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ufunc__get_both_coeff_types[0] = np.NPY_LONG
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ufunc__get_both_coeff_types[1] = np.NPY_LONG
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ufunc__get_both_coeff_types[2] = np.NPY_LONG
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ufunc__get_both_coeff_types[3] = np.NPY_LONG
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ufunc__get_both_coeff_types[4] = np.NPY_DOUBLE
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ufunc__get_both_coeff_types[5] = np.NPY_DOUBLE
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ufunc__get_both_coeff_types[6] = np.NPY_DOUBLE
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ufunc__get_both_coeff_types[7] = np.NPY_BOOL
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ufunc__get_both_coeff_types[8] = np.NPY_LONG
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ufunc__get_both_coeff_types[9] = np.NPY_CDOUBLE
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ufunc__get_both_coeff_types[10] = np.NPY_CDOUBLE
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trans_A_taylor_elementwise_funcs[0] = <void*> qpms_trans_single_A_Taylor_ext
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trans_B_taylor_elementwise_funcs[0] = <void*> qpms_trans_single_B_Taylor_ext
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trans_A_Taylor = np.PyUFunc_FromFuncAndData(
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trans_X_taylor_loop_func, # func
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trans_A_taylor_elementwise_funcs, #data
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ufunc__get_either_trans_coeff_types, # types
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1, # ntypes: number of supported input types
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9, # nin: number of input args
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1, # nout: number of output args
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0, # identity element, unused
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"trans_A_Taylor", # name
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"""
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TODO computes the E-E or M-M translation coefficient in Taylor's normalisation
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""", # doc
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0 # unused, for backward compatibility of numpy c api
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)
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trans_B_Taylor = np.PyUFunc_FromFuncAndData(
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trans_X_taylor_loop_func,
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trans_B_taylor_elementwise_funcs,
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ufunc__get_either_trans_coeff_types,
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1, # number of supported input types
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9, # number of input args
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1, # number of output args
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0, # identity element, unused
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"trans_B_Taylor",
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"""
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TODO computes the E-E or M-M translation coefficient in Taylor's normalisation
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""",
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0 # unused
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)
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# ---------------------------------------------
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# Wrapper for the qpms_trans_calculator "class"
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# ---------------------------------------------
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ctypedef struct trans_calculator_get_X_data_t:
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qpms_trans_calculator* c
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void* cmethod
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cdef void trans_calculator_loop_D_Ciiiidddii_As_D_lllldddbl(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) nogil:
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cdef np.npy_intp i, n = dims[0]
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cdef void *func = (<trans_calculator_get_X_data_t*>data)[0].cmethod
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#cdef cdouble (*func)(qpms_trans_calculator*, int, int, int, int, double, double, double, int, int) nogil = (<trans_calculator_get_X_data_t*>data)[0].cmethod
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cdef qpms_trans_calculator* c = (<trans_calculator_get_X_data_t*>data)[0].c
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#cdef char *func_name= <char*>(<void**>data)[1] # i am not using this, nor have I saved func_name to data
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cdef char *ip0 = args[0]
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cdef char *ip1 = args[1]
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cdef char *ip2 = args[2]
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cdef char *ip3 = args[3]
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cdef char *ip4 = args[4]
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cdef char *ip5 = args[5]
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cdef char *ip6 = args[6]
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cdef char *ip7 = args[7]
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cdef char *ip8 = args[8]
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cdef char *op0 = args[9]
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cdef cdouble ov0
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for i in range(n): # iterating over dimensions
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#ov0 = func(
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ov0 = (<double complex(*)(qpms_trans_calculator*, int, int, int, int, double, double, double, int, int) nogil>func)(
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c,
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<int>(<np.npy_long*>ip0)[0],
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<int>(<np.npy_long*>ip1)[0],
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<int>(<np.npy_long*>ip2)[0],
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<int>(<np.npy_long*>ip3)[0],
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<double>(<np.npy_double*>ip4)[0],
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<double>(<np.npy_double*>ip5)[0],
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<double>(<np.npy_double*>ip6)[0],
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<int>(<np.npy_bool*>ip7)[0],
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<int>(<np.npy_long*>ip8)[0],
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)
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(<cdouble *>op0)[0] = <cdouble>ov0
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ip0 += steps[0]
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ip1 += steps[1]
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ip2 += steps[2]
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ip3 += steps[3]
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ip4 += steps[4]
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ip5 += steps[5]
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ip6 += steps[6]
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ip7 += steps[7]
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ip8 += steps[8]
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op0 += steps[9]
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# FIXME ERROR HANDLING!!! requires correct import and different data passed (see scipy's generated ufuncs)
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# sf_error.check_fpe(func_name)
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cdef void trans_calculator_loop_E_C_DD_iiiidddii_As_lllldddbl_DD(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) nogil:
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# E stands for error value (int), C for qpms_trans_calculator*
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cdef np.npy_intp i, n = dims[0]
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cdef void *func = (<trans_calculator_get_X_data_t*>data)[0].cmethod
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#cdef complex double (*func)(qpms_trans_calculator*, double complex *, double complex *, int, int, int, int, double, double, double, int, int) nogil = (<trans_calculator_get_X_data_t*>data)[0].cmethod
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cdef qpms_trans_calculator* c = (<trans_calculator_get_X_data_t*>data)[0].c
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#cdef char *func_name= <char*>(<void**>data)[1] # i am not using this, nor have I saved func_name to data
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cdef char *ip0 = args[0]
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cdef char *ip1 = args[1]
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cdef char *ip2 = args[2]
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cdef char *ip3 = args[3]
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cdef char *ip4 = args[4]
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cdef char *ip5 = args[5]
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cdef char *ip6 = args[6]
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cdef char *ip7 = args[7]
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cdef char *ip8 = args[8]
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cdef char *op0 = args[9]
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cdef char *op1 = args[10]
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cdef cdouble ov0
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cdef int errval
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for i in range(n): # iterating over dimensions
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#errval = func(
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errval = (<int(*)(qpms_trans_calculator*, double complex *, double complex *, int, int, int, int, double, double, double, int, int) nogil>func)(
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c,
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<cdouble *> op0,
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<cdouble *> op1,
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<int>(<np.npy_long*>ip0)[0],
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<int>(<np.npy_long*>ip1)[0],
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<int>(<np.npy_long*>ip2)[0],
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<int>(<np.npy_long*>ip3)[0],
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<double>(<np.npy_double*>ip4)[0],
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<double>(<np.npy_double*>ip5)[0],
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<double>(<np.npy_double*>ip6)[0],
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<int>(<np.npy_bool*>ip7)[0],
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<int>(<np.npy_long*>ip8)[0],
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)
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ip0 += steps[0]
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ip1 += steps[1]
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ip2 += steps[2]
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ip3 += steps[3]
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ip4 += steps[4]
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ip5 += steps[5]
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ip6 += steps[6]
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ip7 += steps[7]
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ip8 += steps[8]
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op0 += steps[9]
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op1 += steps[10]
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# TODO if (errval != 0): ...
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# FIXME ERROR HANDLING!!! requires correct import and different data passed (see scipy's generated ufuncs)
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# sf_error.check_fpe(func_name)
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef void trans_calculator_parallel_loop_E_C_DD_iiiidddii_As_lllldddbl_DD(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) nogil:
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# E stands for error value (int), C for qpms_trans_calculator*
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cdef np.npy_intp i, n = dims[0]
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cdef void *func = (<trans_calculator_get_X_data_t*>data)[0].cmethod
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#cdef complex double (*func)(qpms_trans_calculator*, double complex *, double complex *, int, int, int, int, double, double, double, int, int) nogil = (<trans_calculator_get_X_data_t*>data)[0].cmethod
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cdef qpms_trans_calculator* c = (<trans_calculator_get_X_data_t*>data)[0].c
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#cdef char *func_name= <char*>(<void**>data)[1] # i am not using this, nor have I saved func_name to data
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cdef char *ip0
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cdef char *ip1
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cdef char *ip2
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cdef char *ip3
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cdef char *ip4
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cdef char *ip5
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cdef char *ip6
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cdef char *ip7
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cdef char *ip8
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cdef char *op0
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cdef char *op1
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cdef int errval
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for i in prange(n): # iterating over dimensions
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ip0 = args[0] + i * steps[0]
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ip1 = args[1] + i * steps[1]
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ip2 = args[2] + i * steps[2]
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ip3 = args[3] + i * steps[3]
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ip4 = args[4] + i * steps[4]
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ip5 = args[5] + i * steps[5]
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ip6 = args[6] + i * steps[6]
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ip7 = args[7] + i * steps[7]
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ip8 = args[8] + i * steps[8]
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op0 = args[9] + i * steps[9]
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op1 = args[10] + i * steps[10]
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#errval = func(
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errval = (<int(*)(qpms_trans_calculator*, double complex *, double complex *, int, int, int, int, double, double, double, int, int) nogil>func)(
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c,
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<cdouble *> op0,
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<cdouble *> op1,
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<int>(<np.npy_long*>ip0)[0],
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<int>(<np.npy_long*>ip1)[0],
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<int>(<np.npy_long*>ip2)[0],
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<int>(<np.npy_long*>ip3)[0],
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<double>(<np.npy_double*>ip4)[0],
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<double>(<np.npy_double*>ip5)[0],
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<double>(<np.npy_double*>ip6)[0],
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<int>(<np.npy_bool*>ip7)[0],
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<int>(<np.npy_long*>ip8)[0],
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)
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# TODO if (errval != 0): ...
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# FIXME ERROR HANDLING!!! requires correct import and different data passed (see scipy's generated ufuncs)
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# sf_error.check_fpe(func_name)
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cdef np.PyUFuncGenericFunction trans_calculator_get_X_loop_funcs[1]
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trans_calculator_get_X_loop_funcs[0] = trans_calculator_loop_D_Ciiiidddii_As_D_lllldddbl
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cdef np.PyUFuncGenericFunction trans_calculator_get_AB_loop_funcs[1]
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#trans_calculator_get_AB_loop_funcs[0] = trans_calculator_parallel_loop_E_C_DD_iiiidddii_As_lllldddbl_DD
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trans_calculator_get_AB_loop_funcs[0] = trans_calculator_loop_E_C_DD_iiiidddii_As_lllldddbl_DD
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cdef void *trans_calculator_get_AB_elementwise_funcs[1]
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trans_calculator_get_AB_elementwise_funcs[0] = <void *>qpms_trans_calculator_get_AB_p_ext
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'''
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cdef extern from "numpy/ndarrayobject.h":
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struct PyArrayInterface:
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int itemsize
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np.npy_uintp *shape
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np.npy_uintp *strides
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void *data
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'''
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from libc.stdlib cimport malloc, free, calloc, abort
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cdef class trans_calculator:
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cdef qpms_trans_calculator* c
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cdef trans_calculator_get_X_data_t get_A_data[1]
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cdef trans_calculator_get_X_data_t* get_A_data_p[1]
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cdef trans_calculator_get_X_data_t get_B_data[1]
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cdef trans_calculator_get_X_data_t* get_B_data_p[1]
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cdef trans_calculator_get_X_data_t get_AB_data[1]
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cdef trans_calculator_get_X_data_t* get_AB_data_p[1]
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cdef public: # TODO CHECK FOR CORRECT REFERENCE COUNTING AND LEAKS
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# have to be cdef public in order that __init__ can set these attributes
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object get_A, get_B, get_AB
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def __cinit__(self, int lMax, int normalization = 1):
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if (lMax <= 0):
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raise ValueError('lMax has to be greater than 0.')
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self.c = qpms_trans_calculator_init(lMax, normalization)
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if self.c is NULL:
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raise MemoryError
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def __init__(self, int lMax, int normalization = 1):
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if self.c is NULL:
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raise MemoryError()
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self.get_A_data[0].c = self.c
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self.get_A_data[0].cmethod = <void *>qpms_trans_calculator_get_A_ext
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self.get_A_data_p[0] = &(self.get_A_data[0])
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self.get_A = <object>np.PyUFunc_FromFuncAndData(# TODO CHECK FOR CORRECT REFERENCE COUNTING AND LEAKS
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trans_calculator_get_X_loop_funcs, # func
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<void **>self.get_A_data_p, #data
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ufunc__get_either_trans_coeff_types, #types
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|
1, # ntypes: number of supported input types
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9, # nin: number of input args
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1, # nout: number of output args
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0, # identity element, unused
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"get_A", #name
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"""
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TODO doc
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""", # doc
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0 # unused
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)
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self.get_B_data[0].c = self.c
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self.get_B_data[0].cmethod = <void *>qpms_trans_calculator_get_B_ext
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self.get_B_data_p[0] = &(self.get_B_data[0])
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self.get_B = <object>np.PyUFunc_FromFuncAndData(# TODO CHECK FOR CORRECT REFERENCE COUNTING AND LEAKS
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trans_calculator_get_X_loop_funcs, # func
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<void **>self.get_B_data_p, #data
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ufunc__get_either_trans_coeff_types, #types
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1, # ntypes: number of supported input types
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9, # nin: number of input args
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1, # nout: number of output args
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0, # identity element, unused
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"get_B", #name
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"""
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TODO doc
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""", # doc
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0 # unused
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)
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self.get_AB_data[0].c = self.c
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self.get_AB_data[0].cmethod = <void *>qpms_trans_calculator_get_AB_p_ext
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self.get_AB_data_p[0] = &(self.get_AB_data[0])
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self.get_AB = <object>np.PyUFunc_FromFuncAndData(# TODO CHECK FOR CORRECT REFERENCE COUNTING AND LEAKS
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trans_calculator_get_AB_loop_funcs, # func
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<void **>self.get_AB_data_p, #data
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ufunc__get_both_coeff_types, #types
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|
1, # ntypes: number of supported input types
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9, # nin: number of input args
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2, # nout: number of output args
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0, # identity element, unused
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"get_AB", #name
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|
"""
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|
TODO doc
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|
""", # doc
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|
0 # unused
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|
)
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def __dealloc__(self):
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|
if self.c is not NULL:
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qpms_trans_calculator_free(self.c)
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|
# TODO Reference counts to get_A, get_B, get_AB?
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def lMax(self):
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return self.c[0].lMax
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def nelem(self):
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return self.c[0].nelem
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|
def get_AB_arrays(self, r, theta, phi, r_ge_d, int J,
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destaxis=None, srcaxis=None, expand=True):
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|
"""
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|
Returns arrays of translation coefficients, inserting two new nelem-sized axes
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|
(corresponding to the destination and source axes of the translation matrix,
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|
respectively).
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|
|
|
By default (expand==True), it inserts the new axes. or it can be provided with
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|
the resulting shape (with the corresponding axes dimensions equal to one).
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|
The provided axes positions are for the resulting array.
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|
|
|
If none axis positions are provided, destaxis and srcaxis will be the second-to-last
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and last, respectively.
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"""
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|
# TODO CHECK (and try to cast) INPUT ARRAY TYPES (now is done)
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|
# BIG FIXME: make skalars valid arguments, now r, theta, phi, r_ge_d have to be ndarrays
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|
cdef:
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|
int daxis, saxis, smallaxis, bigaxis, resnd, i, j, d, ax, errval
|
|
np.npy_intp sstride, dstride, longi
|
|
int *local_indices
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|
char *r_p
|
|
char *theta_p
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|
char *phi_p
|
|
char *r_ge_d_p
|
|
char *a_p
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|
char *b_p
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|
# Process the array shapes
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|
baseshape = np.broadcast(r,theta,phi,r_ge_d).shape # nope, does not work as needed
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|
'''
|
|
cdef int r_orignd = r.ndim if hasattr(r, "ndim") else 0
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|
cdef int theta_orignd = theta.ndim if hasattr(theta, "ndim") else 0
|
|
cdef int phi_orignd = phi.ndim if hasattr(phi, "ndim") else 0
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|
cdef int r_ge_d_orignd = r_ge_d.ndim if hasattr(r_ge_d, "__len__") else 0
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|
cdef int basend = max(r_orignd, theta_orignd, phi_orignd, r_ge_d_orignd)
|
|
baseshape = list()
|
|
for d in range(basend):
|
|
baseshape.append(max(
|
|
r.shape[d+r_orignd-basend] if d+r_orignd-basend >= 0 else 1,
|
|
theta.shape[d+theta_orignd-basend] if d+theta_orignd-basend >= 0 else 1,
|
|
phi.shape[d+phi_orignd-basend] if d+phi_orignd-basend >= 0 else 1,
|
|
r_ge_d.shape[d+r_ge_d_orignd-basend] if d+r_ge_d_orignd-basend >= 0 else 1,
|
|
))
|
|
baseshape = tuple(baseshape)
|
|
'''
|
|
if not expand:
|
|
resnd = len(baseshape)
|
|
if resnd < 2:
|
|
raise ValueError('Translation matrix arrays must have at least 2 dimensions!')
|
|
daxis = (resnd-2) if destaxis is None else destaxis
|
|
saxis = (resnd-1) if srcaxis is None else srcaxis
|
|
if daxis < 0:
|
|
daxis = resnd + daxis
|
|
if saxis < 0:
|
|
saxis = resnd + saxis
|
|
if daxis < 0 or saxis < 0 or daxis >= resnd or saxis >= resnd or daxis == saxis:
|
|
raise ValueError('invalid axes provided (destaxis = %d, srcaxis = %d, # of axes: %d'
|
|
% (daxis, saxis, resnd))
|
|
if baseshape[daxis] != 1 or baseshape[saxis] != 1:
|
|
raise ValueError('dimension mismatch (input argument dimensions have to be 1 both at'
|
|
'destaxis (==%d) and srcaxis (==%d) but are %d and %d' %
|
|
(daxis, saxis, baseshape[daxis], baseshape[saxis]))
|
|
resultshape = list(baseshape)
|
|
else:
|
|
resnd = len(baseshape)+2
|
|
daxis = (resnd-2) if destaxis is None else destaxis
|
|
saxis = (resnd-1) if srcaxis is None else srcaxis
|
|
if daxis < 0:
|
|
daxis = resnd + daxis
|
|
if saxis < 0:
|
|
saxis = resnd + saxis
|
|
if daxis < 0 or saxis < 0 or daxis >= resnd or saxis >= resnd or daxis == saxis:
|
|
raise ValueError('invalid axes provided') # TODO better error formulation
|
|
resultshape = list(baseshape)
|
|
if daxis > saxis:
|
|
smallaxis = saxis
|
|
bigaxis = daxis
|
|
else:
|
|
smallaxis = daxis
|
|
bigaxis = saxis
|
|
resultshape.insert(smallaxis,1)
|
|
resultshape.insert(bigaxis,1)
|
|
r = np.expand_dims(np.expand_dims(r.astype(np.float_, copy=False), smallaxis), bigaxis)
|
|
theta = np.expand_dims(np.expand_dims(theta.astype(np.float_, copy=False), smallaxis), bigaxis)
|
|
phi = np.expand_dims(np.expand_dims(phi.astype(np.float_, copy=False), smallaxis), bigaxis)
|
|
r_ge_d = np.expand_dims(np.expand_dims(r_ge_d.astype(np.bool_, copy=False), smallaxis), bigaxis)
|
|
|
|
resultshape[daxis] = self.c[0].nelem
|
|
resultshape[saxis] = self.c[0].nelem
|
|
cdef np.ndarray r_c = np.broadcast_to(r,resultshape)
|
|
cdef np.ndarray theta_c = np.broadcast_to(theta,resultshape)
|
|
cdef np.ndarray phi_c = np.broadcast_to(phi,resultshape)
|
|
cdef np.ndarray r_ge_d_c = np.broadcast_to(r_ge_d, resultshape)
|
|
cdef np.ndarray a = np.empty(resultshape, dtype=complex)
|
|
cdef np.ndarray b = np.empty(resultshape, dtype=complex)
|
|
dstride = a.strides[daxis]
|
|
sstride = a.strides[saxis]
|
|
with nogil:
|
|
errval = qpms_cython_trans_calculator_get_AB_arrays_loop(
|
|
self.c, J, resnd,
|
|
daxis, saxis,
|
|
a.data, a.shape, a.strides,
|
|
b.data, b.shape, b.strides,
|
|
r_c.data, r_c.shape, r_c.strides,
|
|
theta_c.data, theta_c.shape, theta_c.strides,
|
|
phi_c.data, phi_c.shape, phi_c.strides,
|
|
r_ge_d_c.data, r_ge_d_c.shape, r_ge_d_c.strides
|
|
)
|
|
return a, b
|
|
|
|
# TODO make possible to access the attributes (to show normalization etc)
|