Generated by Cython 0.22
Raw output: psparse.c
+001: # psparse.pyx
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002:
003: #-----------------------------------------------------------------------------
004: # Imports
005: #-----------------------------------------------------------------------------
006: cimport cython
007: cimport numpy as np
+008: import numpy as np
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+009: import scipy.sparse
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010: from libc.stddef cimport ptrdiff_t
011: from cython.parallel import parallel, prange
012:
013: #-----------------------------------------------------------------------------
014: # Headers
015: #-----------------------------------------------------------------------------
016:
017: ctypedef int csi
018:
019: ctypedef struct cs:
020: # matrix in compressed-column or triplet form
021: csi nzmax # maximum number of entries
022: csi m # number of rows
023: csi n # number of columns
024: csi *p # column pointers (size n+1) or col indices (size nzmax)
025: csi *i # row indices, size nzmax
026: double *x # numerical values, size nzmax
027: csi nz # # of entries in triplet matrix, -1 for compressed-col
028:
029: cdef extern csi cs_gaxpy (cs *A, double *x, double *y) nogil
030: cdef extern csi cs_print (cs *A, csi brief) nogil
031:
032: cdef extern csi cs_multiply (cs *A, cs *B) nogil
033:
+034: assert sizeof(csi) == 4
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#endif
035:
036: #-----------------------------------------------------------------------------
037: # Functions
038: #-----------------------------------------------------------------------------
039:
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043:
044: cdef cs csX
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048:
049: # Pack the scipy data into the CSparse struct. This is just copying some
050: # pointers.
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__pyx_t_10 = -1;
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__pyx_t_9 += __pyx_pybuffernd_Xindptr.diminfo[0].shape;
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__Pyx_RaiseBufferIndexError(__pyx_t_10);
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 54; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_t_11 = 0;
__pyx_t_10 = -1;
if (__pyx_t_11 < 0) {
__pyx_t_11 += __pyx_pybuffernd_Xindices.diminfo[0].shape;
if (unlikely(__pyx_t_11 < 0)) __pyx_t_10 = 0;
} else if (unlikely(__pyx_t_11 >= __pyx_pybuffernd_Xindices.diminfo[0].shape)) __pyx_t_10 = 0;
if (unlikely(__pyx_t_10 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_10);
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 55; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
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__pyx_t_10 = -1;
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__pyx_t_12 += __pyx_pybuffernd_Xdata.diminfo[0].shape;
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__Pyx_RaiseBufferIndexError(__pyx_t_10);
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_v_csX.x = (&(*__Pyx_BufPtrCContig1d(double *, __pyx_pybuffernd_Xdata.rcbuffer->pybuffer.buf, __pyx_t_12, __pyx_pybuffernd_Xdata.diminfo[0].strides)));
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__pyx_v_csX.nz = -1;
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+064: csY.nzmax = Y.data.shape[0]
__pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_Y, __pyx_n_s_data); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 64; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_shape); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 64; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_GetItemInt(__pyx_t_3, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 1); if (unlikely(__pyx_t_1 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 64; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_8 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_8 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 64; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_csY.nzmax = __pyx_t_8;
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__pyx_t_16 += __pyx_pybuffernd_Yindptr.diminfo[0].shape;
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__Pyx_RaiseBufferIndexError(__pyx_t_10);
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 67; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_RaiseBufferIndexError(__pyx_t_10);
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__Pyx_RaiseBufferIndexError(__pyx_t_10);
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071:
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074:
075: @cython.boundscheck(False)
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/* Python wrapper */
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static char __pyx_doc_7psparse_2pmultiply[] = "Multiply a sparse CSC matrix by a dense matrix\n\n Parameters\n ----------\n X : scipy.sparse.csc_matrix\n A sparse matrix, of size N x M\n W : np.ndarray[dtype=float564, ndim=2, mode='fortran']\n A dense matrix, of size M x P. Note, W must be contiguous and in\n fortran (column-major) order. You can ensure this using\n numpy's `asfortranarray` function.\n\n Returns\n -------\n A : np.ndarray[dtype=float64, ndim=2, mode='fortran']\n A dense matrix, of size N x P, the result of multiplying X by W.\n\n Notes\n -----\n This function is parallelized over the columns of W using OpenMP. You\n can control the number of threads at runtime using the OMP_NUM_THREADS\n environment variable. The internal sparse matrix code is from CSPARSE, \n a Concise Sparse matrix package. Copyright (c) 2006, Timothy A. Davis.\n http://www.cise.ufl.edu/research/sparse/CSparse, licensed under the\n GNU LGPL v2.1+.\n\n References\n ----------\n .. [1] Davis, Timothy A., \"Direct Methods for Sparse Linear Systems\n (Fundamentals of Algorithms 2),\" SIAM Press, 2006. ISBN: 0898716136\n ";
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__Pyx_RaiseArgtupleInvalid("pmultiply", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 76; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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077: """Multiply a sparse CSC matrix by a dense matrix
078:
079: Parameters
080: ----------
081: X : scipy.sparse.csc_matrix
082: A sparse matrix, of size N x M
083: W : np.ndarray[dtype=float564, ndim=2, mode='fortran']
084: A dense matrix, of size M x P. Note, W must be contiguous and in
085: fortran (column-major) order. You can ensure this using
086: numpy's `asfortranarray` function.
087:
088: Returns
089: -------
090: A : np.ndarray[dtype=float64, ndim=2, mode='fortran']
091: A dense matrix, of size N x P, the result of multiplying X by W.
092:
093: Notes
094: -----
095: This function is parallelized over the columns of W using OpenMP. You
096: can control the number of threads at runtime using the OMP_NUM_THREADS
097: environment variable. The internal sparse matrix code is from CSPARSE,
098: a Concise Sparse matrix package. Copyright (c) 2006, Timothy A. Davis.
099: http://www.cise.ufl.edu/research/sparse/CSparse, licensed under the
100: GNU LGPL v2.1+.
101:
102: References
103: ----------
104: .. [1] Davis, Timothy A., "Direct Methods for Sparse Linear Systems
105: (Fundamentals of Algorithms 2)," SIAM Press, 2006. ISBN: 0898716136
106: """
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109:
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{
#ifdef WITH_THREAD
PyThreadState *_save;
Py_UNBLOCK_THREADS
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if (1 == 0) abort();
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#if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))
#undef likely
#undef unlikely
#define likely(x) (x)
#define unlikely(x) (x)
#endif
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if (__pyx_t_21 > 0)
{
#ifdef _OPENMP
#pragma omp parallel
#endif /* _OPENMP */
{
#ifdef _OPENMP
#pragma omp for firstprivate(__pyx_v_i) lastprivate(__pyx_v_i)
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for (__pyx_t_20 = 0; __pyx_t_20 < __pyx_t_21; __pyx_t_20++){
{
__pyx_v_i = 0 + 1 * __pyx_t_20;
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Py_BLOCK_THREADS
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goto __pyx_L6;
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129: # X is in fortran format, so we can get quick access to each of its
130: # columns
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cs_gaxpy((&__pyx_v_csX), (&(*__Pyx_BufPtrFortranContig2d(__pyx_t_5numpy_float64_t *, __pyx_pybuffernd_W.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_W.diminfo[0].strides, __pyx_t_15, __pyx_pybuffernd_W.diminfo[1].strides))), (&(*__Pyx_BufPtrFortranContig2d(double *, __pyx_pybuffernd_result.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_result.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_result.diminfo[1].strides))));
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#if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))
#undef likely
#undef unlikely
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#define unlikely(x) __builtin_expect(!!(x), 0)
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132:
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