Remove eigency
Fan Jiang
parent
6604b4f45b
commit
8a130c1a54
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@ -1,42 +0,0 @@
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include(GtsamCythonWrap)
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# Copy eigency's sources to the build folder
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# so that the cython-generated header "conversions_api.h" can be found when cythonizing eigency's core
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# and eigency's cython pxd headers can be found when cythonizing gtsam
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file(COPY "." DESTINATION ".")
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set(OUTPUT_DIR "${GTSAM_CYTHON_INSTALL_PATH}/gtsam_eigency")
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set(EIGENCY_INCLUDE_DIR ${OUTPUT_DIR})
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# This is to make the build/cython/gtsam_eigency folder a python package
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configure_file(__init__.py.in ${OUTPUT_DIR}/__init__.py)
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# include eigency headers
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include_directories(${EIGENCY_INCLUDE_DIR})
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# Cythonize and build eigency
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message(STATUS "Cythonize and build eigency")
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# Important trick: use "../gtsam_eigency/conversions.pyx" to let cython know that the conversions module is
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# a part of the gtsam_eigency package and generate the function call import_gtsam_eigency__conversions()
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# in conversions_api.h correctly!
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cythonize(cythonize_eigency_conversions "../gtsam_eigency/conversions.pyx" "conversions"
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"${OUTPUT_DIR}" "${EIGENCY_INCLUDE_DIR}" "" "" "")
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cythonize(cythonize_eigency_core "../gtsam_eigency/core.pyx" "core"
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${OUTPUT_DIR} "${EIGENCY_INCLUDE_DIR}" "" "" "")
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# Include Eigen headers:
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target_include_directories(cythonize_eigency_conversions PUBLIC
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$<BUILD_INTERFACE:${GTSAM_EIGEN_INCLUDE_FOR_BUILD}>
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$<INSTALL_INTERFACE:${GTSAM_EIGEN_INCLUDE_FOR_INSTALL}>
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)
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target_include_directories(cythonize_eigency_core PUBLIC
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$<BUILD_INTERFACE:${GTSAM_EIGEN_INCLUDE_FOR_BUILD}>
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$<INSTALL_INTERFACE:${GTSAM_EIGEN_INCLUDE_FOR_INSTALL}>
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)
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add_dependencies(cythonize_eigency_core cythonize_eigency_conversions)
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add_custom_target(cythonize_eigency)
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add_dependencies(cythonize_eigency cythonize_eigency_conversions cythonize_eigency_core)
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if(TARGET ${python_install_target})
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add_dependencies(${python_install_target} cythonize_eigency)
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endif()
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@ -1,20 +0,0 @@
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Copyright (c) 2016 Wouter Boomsma
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Permission is hereby granted, free of charge, to any person obtaining
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a copy of this software and associated documentation files (the
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"Software"), to deal in the Software without restriction, including
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without limitation the rights to use, copy, modify, merge, publish,
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distribute, sublicense, and/or sell copies of the Software, and to
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permit persons to whom the Software is furnished to do so, subject to
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the following conditions:
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The above copyright notice and this permission notice shall be
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included in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
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LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
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OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
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WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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@ -1,13 +0,0 @@
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import os
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import numpy as np
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__eigen_dir__ = "${GTSAM_EIGEN_INCLUDE_FOR_INSTALL}"
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def get_includes(include_eigen=True):
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root = os.path.dirname(__file__)
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parent = os.path.join(root, "..")
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path = [root, parent, np.get_include()]
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if include_eigen:
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path.append(os.path.join(root, __eigen_dir__))
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return path
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@ -1,62 +0,0 @@
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cimport numpy as np
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cdef api np.ndarray[double, ndim=2] ndarray_double_C(double *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[double, ndim=2] ndarray_double_F(double *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[double, ndim=2] ndarray_copy_double_C(const double *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[double, ndim=2] ndarray_copy_double_F(const double *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[float, ndim=2] ndarray_float_C(float *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[float, ndim=2] ndarray_float_F(float *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[float, ndim=2] ndarray_copy_float_C(const float *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[float, ndim=2] ndarray_copy_float_F(const float *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[long, ndim=2] ndarray_long_C(long *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[long, ndim=2] ndarray_long_F(long *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[long, ndim=2] ndarray_copy_long_C(const long *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[long, ndim=2] ndarray_copy_long_F(const long *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned long, ndim=2] ndarray_ulong_C(unsigned long *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned long, ndim=2] ndarray_ulong_F(unsigned long *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned long, ndim=2] ndarray_copy_ulong_C(const unsigned long *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned long, ndim=2] ndarray_copy_ulong_F(const unsigned long *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[int, ndim=2] ndarray_int_C(int *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[int, ndim=2] ndarray_int_F(int *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[int, ndim=2] ndarray_copy_int_C(const int *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[int, ndim=2] ndarray_copy_int_F(const int *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned int, ndim=2] ndarray_uint_C(unsigned int *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned int, ndim=2] ndarray_uint_F(unsigned int *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned int, ndim=2] ndarray_copy_uint_C(const unsigned int *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned int, ndim=2] ndarray_copy_uint_F(const unsigned int *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[short, ndim=2] ndarray_short_C(short *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[short, ndim=2] ndarray_short_F(short *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[short, ndim=2] ndarray_copy_short_C(const short *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[short, ndim=2] ndarray_copy_short_F(const short *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned short, ndim=2] ndarray_ushort_C(unsigned short *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned short, ndim=2] ndarray_ushort_F(unsigned short *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned short, ndim=2] ndarray_copy_ushort_C(const unsigned short *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned short, ndim=2] ndarray_copy_ushort_F(const unsigned short *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[signed char, ndim=2] ndarray_schar_C(signed char *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[signed char, ndim=2] ndarray_schar_F(signed char *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[signed char, ndim=2] ndarray_copy_schar_C(const signed char *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[signed char, ndim=2] ndarray_copy_schar_F(const signed char *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned char, ndim=2] ndarray_uchar_C(unsigned char *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned char, ndim=2] ndarray_uchar_F(unsigned char *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned char, ndim=2] ndarray_copy_uchar_C(const unsigned char *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[unsigned char, ndim=2] ndarray_copy_uchar_F(const unsigned char *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[np.complex128_t, ndim=2] ndarray_complex_double_C(np.complex128_t *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[np.complex128_t, ndim=2] ndarray_complex_double_F(np.complex128_t *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[np.complex128_t, ndim=2] ndarray_copy_complex_double_C(const np.complex128_t *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[np.complex128_t, ndim=2] ndarray_copy_complex_double_F(const np.complex128_t *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[np.complex64_t, ndim=2] ndarray_complex_float_C(np.complex64_t *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[np.complex64_t, ndim=2] ndarray_complex_float_F(np.complex64_t *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[np.complex64_t, ndim=2] ndarray_copy_complex_float_C(const np.complex64_t *data, long rows, long cols, long outer_stride, long inner_stride)
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cdef api np.ndarray[np.complex64_t, ndim=2] ndarray_copy_complex_float_F(const np.complex64_t *data, long rows, long cols, long outer_stride, long inner_stride)
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cimport cython
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import numpy as np
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from numpy.lib.stride_tricks import as_strided
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@cython.boundscheck(False)
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cdef np.ndarray[double, ndim=2] ndarray_double_C(double *data, long rows, long cols, long row_stride, long col_stride):
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cdef double[:,:] mem_view = <double[:rows,:cols]>data
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dtype = 'double'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[double, ndim=2] ndarray_double_F(double *data, long rows, long cols, long row_stride, long col_stride):
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cdef double[::1,:] mem_view = <double[:rows:1,:cols]>data
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dtype = 'double'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[double, ndim=2] ndarray_copy_double_C(const double *data, long rows, long cols, long row_stride, long col_stride):
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cdef double[:,:] mem_view = <double[:rows,:cols]>data
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dtype = 'double'
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cdef int itemsize = np.dtype(dtype).itemsize
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return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
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@cython.boundscheck(False)
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cdef np.ndarray[double, ndim=2] ndarray_copy_double_F(const double *data, long rows, long cols, long row_stride, long col_stride):
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cdef double[::1,:] mem_view = <double[:rows:1,:cols]>data
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dtype = 'double'
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cdef int itemsize = np.dtype(dtype).itemsize
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return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
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@cython.boundscheck(False)
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cdef np.ndarray[float, ndim=2] ndarray_float_C(float *data, long rows, long cols, long row_stride, long col_stride):
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cdef float[:,:] mem_view = <float[:rows,:cols]>data
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dtype = 'float'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[float, ndim=2] ndarray_float_F(float *data, long rows, long cols, long row_stride, long col_stride):
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cdef float[::1,:] mem_view = <float[:rows:1,:cols]>data
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dtype = 'float'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[float, ndim=2] ndarray_copy_float_C(const float *data, long rows, long cols, long row_stride, long col_stride):
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cdef float[:,:] mem_view = <float[:rows,:cols]>data
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dtype = 'float'
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cdef int itemsize = np.dtype(dtype).itemsize
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return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
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@cython.boundscheck(False)
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cdef np.ndarray[float, ndim=2] ndarray_copy_float_F(const float *data, long rows, long cols, long row_stride, long col_stride):
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cdef float[::1,:] mem_view = <float[:rows:1,:cols]>data
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dtype = 'float'
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cdef int itemsize = np.dtype(dtype).itemsize
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return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
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@cython.boundscheck(False)
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cdef np.ndarray[long, ndim=2] ndarray_long_C(long *data, long rows, long cols, long row_stride, long col_stride):
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cdef long[:,:] mem_view = <long[:rows,:cols]>data
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dtype = 'int_'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[long, ndim=2] ndarray_long_F(long *data, long rows, long cols, long row_stride, long col_stride):
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cdef long[::1,:] mem_view = <long[:rows:1,:cols]>data
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dtype = 'int_'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[long, ndim=2] ndarray_copy_long_C(const long *data, long rows, long cols, long row_stride, long col_stride):
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cdef long[:,:] mem_view = <long[:rows,:cols]>data
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dtype = 'int_'
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cdef int itemsize = np.dtype(dtype).itemsize
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return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
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@cython.boundscheck(False)
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cdef np.ndarray[long, ndim=2] ndarray_copy_long_F(const long *data, long rows, long cols, long row_stride, long col_stride):
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cdef long[::1,:] mem_view = <long[:rows:1,:cols]>data
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dtype = 'int_'
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cdef int itemsize = np.dtype(dtype).itemsize
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return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
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@cython.boundscheck(False)
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cdef np.ndarray[unsigned long, ndim=2] ndarray_ulong_C(unsigned long *data, long rows, long cols, long row_stride, long col_stride):
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cdef unsigned long[:,:] mem_view = <unsigned long[:rows,:cols]>data
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dtype = 'uint'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[unsigned long, ndim=2] ndarray_ulong_F(unsigned long *data, long rows, long cols, long row_stride, long col_stride):
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cdef unsigned long[::1,:] mem_view = <unsigned long[:rows:1,:cols]>data
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dtype = 'uint'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[unsigned long, ndim=2] ndarray_copy_ulong_C(const unsigned long *data, long rows, long cols, long row_stride, long col_stride):
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cdef unsigned long[:,:] mem_view = <unsigned long[:rows,:cols]>data
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dtype = 'uint'
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cdef int itemsize = np.dtype(dtype).itemsize
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return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
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@cython.boundscheck(False)
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cdef np.ndarray[unsigned long, ndim=2] ndarray_copy_ulong_F(const unsigned long *data, long rows, long cols, long row_stride, long col_stride):
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cdef unsigned long[::1,:] mem_view = <unsigned long[:rows:1,:cols]>data
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dtype = 'uint'
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cdef int itemsize = np.dtype(dtype).itemsize
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return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
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@cython.boundscheck(False)
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cdef np.ndarray[int, ndim=2] ndarray_int_C(int *data, long rows, long cols, long row_stride, long col_stride):
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cdef int[:,:] mem_view = <int[:rows,:cols]>data
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dtype = 'int'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
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@cython.boundscheck(False)
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cdef np.ndarray[int, ndim=2] ndarray_int_F(int *data, long rows, long cols, long row_stride, long col_stride):
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cdef int[::1,:] mem_view = <int[:rows:1,:cols]>data
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dtype = 'int'
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cdef int itemsize = np.dtype(dtype).itemsize
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return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
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||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[int, ndim=2] ndarray_copy_int_C(const int *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef int[:,:] mem_view = <int[:rows,:cols]>data
|
||||
dtype = 'int'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[int, ndim=2] ndarray_copy_int_F(const int *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef int[::1,:] mem_view = <int[:rows:1,:cols]>data
|
||||
dtype = 'int'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned int, ndim=2] ndarray_uint_C(unsigned int *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned int[:,:] mem_view = <unsigned int[:rows,:cols]>data
|
||||
dtype = 'uint'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned int, ndim=2] ndarray_uint_F(unsigned int *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned int[::1,:] mem_view = <unsigned int[:rows:1,:cols]>data
|
||||
dtype = 'uint'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned int, ndim=2] ndarray_copy_uint_C(const unsigned int *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned int[:,:] mem_view = <unsigned int[:rows,:cols]>data
|
||||
dtype = 'uint'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned int, ndim=2] ndarray_copy_uint_F(const unsigned int *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned int[::1,:] mem_view = <unsigned int[:rows:1,:cols]>data
|
||||
dtype = 'uint'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[short, ndim=2] ndarray_short_C(short *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef short[:,:] mem_view = <short[:rows,:cols]>data
|
||||
dtype = 'short'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[short, ndim=2] ndarray_short_F(short *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef short[::1,:] mem_view = <short[:rows:1,:cols]>data
|
||||
dtype = 'short'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[short, ndim=2] ndarray_copy_short_C(const short *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef short[:,:] mem_view = <short[:rows,:cols]>data
|
||||
dtype = 'short'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[short, ndim=2] ndarray_copy_short_F(const short *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef short[::1,:] mem_view = <short[:rows:1,:cols]>data
|
||||
dtype = 'short'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned short, ndim=2] ndarray_ushort_C(unsigned short *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned short[:,:] mem_view = <unsigned short[:rows,:cols]>data
|
||||
dtype = 'ushort'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned short, ndim=2] ndarray_ushort_F(unsigned short *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned short[::1,:] mem_view = <unsigned short[:rows:1,:cols]>data
|
||||
dtype = 'ushort'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned short, ndim=2] ndarray_copy_ushort_C(const unsigned short *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned short[:,:] mem_view = <unsigned short[:rows,:cols]>data
|
||||
dtype = 'ushort'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned short, ndim=2] ndarray_copy_ushort_F(const unsigned short *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned short[::1,:] mem_view = <unsigned short[:rows:1,:cols]>data
|
||||
dtype = 'ushort'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[signed char, ndim=2] ndarray_schar_C(signed char *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef signed char[:,:] mem_view = <signed char[:rows,:cols]>data
|
||||
dtype = 'int8'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[signed char, ndim=2] ndarray_schar_F(signed char *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef signed char[::1,:] mem_view = <signed char[:rows:1,:cols]>data
|
||||
dtype = 'int8'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[signed char, ndim=2] ndarray_copy_schar_C(const signed char *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef signed char[:,:] mem_view = <signed char[:rows,:cols]>data
|
||||
dtype = 'int8'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[signed char, ndim=2] ndarray_copy_schar_F(const signed char *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef signed char[::1,:] mem_view = <signed char[:rows:1,:cols]>data
|
||||
dtype = 'int8'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned char, ndim=2] ndarray_uchar_C(unsigned char *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned char[:,:] mem_view = <unsigned char[:rows,:cols]>data
|
||||
dtype = 'uint8'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned char, ndim=2] ndarray_uchar_F(unsigned char *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned char[::1,:] mem_view = <unsigned char[:rows:1,:cols]>data
|
||||
dtype = 'uint8'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned char, ndim=2] ndarray_copy_uchar_C(const unsigned char *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned char[:,:] mem_view = <unsigned char[:rows,:cols]>data
|
||||
dtype = 'uint8'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[unsigned char, ndim=2] ndarray_copy_uchar_F(const unsigned char *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef unsigned char[::1,:] mem_view = <unsigned char[:rows:1,:cols]>data
|
||||
dtype = 'uint8'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[np.complex128_t, ndim=2] ndarray_complex_double_C(np.complex128_t *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef np.complex128_t[:,:] mem_view = <np.complex128_t[:rows,:cols]>data
|
||||
dtype = 'complex128'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[np.complex128_t, ndim=2] ndarray_complex_double_F(np.complex128_t *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef np.complex128_t[::1,:] mem_view = <np.complex128_t[:rows:1,:cols]>data
|
||||
dtype = 'complex128'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[np.complex128_t, ndim=2] ndarray_copy_complex_double_C(const np.complex128_t *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef np.complex128_t[:,:] mem_view = <np.complex128_t[:rows,:cols]>data
|
||||
dtype = 'complex128'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[np.complex128_t, ndim=2] ndarray_copy_complex_double_F(const np.complex128_t *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef np.complex128_t[::1,:] mem_view = <np.complex128_t[:rows:1,:cols]>data
|
||||
dtype = 'complex128'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[np.complex64_t, ndim=2] ndarray_complex_float_C(np.complex64_t *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef np.complex64_t[:,:] mem_view = <np.complex64_t[:rows,:cols]>data
|
||||
dtype = 'complex64'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[np.complex64_t, ndim=2] ndarray_complex_float_F(np.complex64_t *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef np.complex64_t[::1,:] mem_view = <np.complex64_t[:rows:1,:cols]>data
|
||||
dtype = 'complex64'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize])
|
||||
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[np.complex64_t, ndim=2] ndarray_copy_complex_float_C(const np.complex64_t *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef np.complex64_t[:,:] mem_view = <np.complex64_t[:rows,:cols]>data
|
||||
dtype = 'complex64'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="C"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
@cython.boundscheck(False)
|
||||
cdef np.ndarray[np.complex64_t, ndim=2] ndarray_copy_complex_float_F(const np.complex64_t *data, long rows, long cols, long row_stride, long col_stride):
|
||||
cdef np.complex64_t[::1,:] mem_view = <np.complex64_t[:rows:1,:cols]>data
|
||||
dtype = 'complex64'
|
||||
cdef int itemsize = np.dtype(dtype).itemsize
|
||||
return np.copy(as_strided(np.asarray(mem_view, dtype=dtype, order="F"), strides=[row_stride*itemsize, col_stride*itemsize]))
|
||||
|
||||
|
|
@ -1,917 +0,0 @@
|
|||
cimport cython
|
||||
cimport numpy as np
|
||||
|
||||
ctypedef signed char schar;
|
||||
ctypedef unsigned char uchar;
|
||||
|
||||
ctypedef fused dtype:
|
||||
uchar
|
||||
schar
|
||||
short
|
||||
int
|
||||
long
|
||||
float
|
||||
double
|
||||
|
||||
ctypedef fused DenseType:
|
||||
Matrix
|
||||
Array
|
||||
|
||||
ctypedef fused Rows:
|
||||
_1
|
||||
_2
|
||||
_3
|
||||
_4
|
||||
_5
|
||||
_6
|
||||
_7
|
||||
_8
|
||||
_9
|
||||
_10
|
||||
_11
|
||||
_12
|
||||
_13
|
||||
_14
|
||||
_15
|
||||
_16
|
||||
_17
|
||||
_18
|
||||
_19
|
||||
_20
|
||||
_21
|
||||
_22
|
||||
_23
|
||||
_24
|
||||
_25
|
||||
_26
|
||||
_27
|
||||
_28
|
||||
_29
|
||||
_30
|
||||
_31
|
||||
_32
|
||||
Dynamic
|
||||
|
||||
ctypedef Rows Cols
|
||||
ctypedef Rows StrideOuter
|
||||
ctypedef Rows StrideInner
|
||||
|
||||
ctypedef fused DenseTypeShort:
|
||||
Vector1i
|
||||
Vector2i
|
||||
Vector3i
|
||||
Vector4i
|
||||
VectorXi
|
||||
RowVector1i
|
||||
RowVector2i
|
||||
RowVector3i
|
||||
RowVector4i
|
||||
RowVectorXi
|
||||
Matrix1i
|
||||
Matrix2i
|
||||
Matrix3i
|
||||
Matrix4i
|
||||
MatrixXi
|
||||
Vector1f
|
||||
Vector2f
|
||||
Vector3f
|
||||
Vector4f
|
||||
VectorXf
|
||||
RowVector1f
|
||||
RowVector2f
|
||||
RowVector3f
|
||||
RowVector4f
|
||||
RowVectorXf
|
||||
Matrix1f
|
||||
Matrix2f
|
||||
Matrix3f
|
||||
Matrix4f
|
||||
MatrixXf
|
||||
Vector1d
|
||||
Vector2d
|
||||
Vector3d
|
||||
Vector4d
|
||||
VectorXd
|
||||
RowVector1d
|
||||
RowVector2d
|
||||
RowVector3d
|
||||
RowVector4d
|
||||
RowVectorXd
|
||||
Matrix1d
|
||||
Matrix2d
|
||||
Matrix3d
|
||||
Matrix4d
|
||||
MatrixXd
|
||||
Vector1cf
|
||||
Vector2cf
|
||||
Vector3cf
|
||||
Vector4cf
|
||||
VectorXcf
|
||||
RowVector1cf
|
||||
RowVector2cf
|
||||
RowVector3cf
|
||||
RowVector4cf
|
||||
RowVectorXcf
|
||||
Matrix1cf
|
||||
Matrix2cf
|
||||
Matrix3cf
|
||||
Matrix4cf
|
||||
MatrixXcf
|
||||
Vector1cd
|
||||
Vector2cd
|
||||
Vector3cd
|
||||
Vector4cd
|
||||
VectorXcd
|
||||
RowVector1cd
|
||||
RowVector2cd
|
||||
RowVector3cd
|
||||
RowVector4cd
|
||||
RowVectorXcd
|
||||
Matrix1cd
|
||||
Matrix2cd
|
||||
Matrix3cd
|
||||
Matrix4cd
|
||||
MatrixXcd
|
||||
Array22i
|
||||
Array23i
|
||||
Array24i
|
||||
Array2Xi
|
||||
Array32i
|
||||
Array33i
|
||||
Array34i
|
||||
Array3Xi
|
||||
Array42i
|
||||
Array43i
|
||||
Array44i
|
||||
Array4Xi
|
||||
ArrayX2i
|
||||
ArrayX3i
|
||||
ArrayX4i
|
||||
ArrayXXi
|
||||
Array2i
|
||||
Array3i
|
||||
Array4i
|
||||
ArrayXi
|
||||
Array22f
|
||||
Array23f
|
||||
Array24f
|
||||
Array2Xf
|
||||
Array32f
|
||||
Array33f
|
||||
Array34f
|
||||
Array3Xf
|
||||
Array42f
|
||||
Array43f
|
||||
Array44f
|
||||
Array4Xf
|
||||
ArrayX2f
|
||||
ArrayX3f
|
||||
ArrayX4f
|
||||
ArrayXXf
|
||||
Array2f
|
||||
Array3f
|
||||
Array4f
|
||||
ArrayXf
|
||||
Array22d
|
||||
Array23d
|
||||
Array24d
|
||||
Array2Xd
|
||||
Array32d
|
||||
Array33d
|
||||
Array34d
|
||||
Array3Xd
|
||||
Array42d
|
||||
Array43d
|
||||
Array44d
|
||||
Array4Xd
|
||||
ArrayX2d
|
||||
ArrayX3d
|
||||
ArrayX4d
|
||||
ArrayXXd
|
||||
Array2d
|
||||
Array3d
|
||||
Array4d
|
||||
ArrayXd
|
||||
Array22cf
|
||||
Array23cf
|
||||
Array24cf
|
||||
Array2Xcf
|
||||
Array32cf
|
||||
Array33cf
|
||||
Array34cf
|
||||
Array3Xcf
|
||||
Array42cf
|
||||
Array43cf
|
||||
Array44cf
|
||||
Array4Xcf
|
||||
ArrayX2cf
|
||||
ArrayX3cf
|
||||
ArrayX4cf
|
||||
ArrayXXcf
|
||||
Array2cf
|
||||
Array3cf
|
||||
Array4cf
|
||||
ArrayXcf
|
||||
Array22cd
|
||||
Array23cd
|
||||
Array24cd
|
||||
Array2Xcd
|
||||
Array32cd
|
||||
Array33cd
|
||||
Array34cd
|
||||
Array3Xcd
|
||||
Array42cd
|
||||
Array43cd
|
||||
Array44cd
|
||||
Array4Xcd
|
||||
ArrayX2cd
|
||||
ArrayX3cd
|
||||
ArrayX4cd
|
||||
ArrayXXcd
|
||||
Array2cd
|
||||
Array3cd
|
||||
Array4cd
|
||||
ArrayXcd
|
||||
|
||||
ctypedef fused StorageOrder:
|
||||
RowMajor
|
||||
ColMajor
|
||||
|
||||
ctypedef fused MapOptions:
|
||||
Aligned
|
||||
Unaligned
|
||||
|
||||
cdef extern from "eigency_cpp.h" namespace "eigency":
|
||||
|
||||
cdef cppclass _1 "1":
|
||||
pass
|
||||
|
||||
cdef cppclass _2 "2":
|
||||
pass
|
||||
|
||||
cdef cppclass _3 "3":
|
||||
pass
|
||||
|
||||
cdef cppclass _4 "4":
|
||||
pass
|
||||
|
||||
cdef cppclass _5 "5":
|
||||
pass
|
||||
|
||||
cdef cppclass _6 "6":
|
||||
pass
|
||||
|
||||
cdef cppclass _7 "7":
|
||||
pass
|
||||
|
||||
cdef cppclass _8 "8":
|
||||
pass
|
||||
|
||||
cdef cppclass _9 "9":
|
||||
pass
|
||||
|
||||
cdef cppclass _10 "10":
|
||||
pass
|
||||
|
||||
cdef cppclass _11 "11":
|
||||
pass
|
||||
|
||||
cdef cppclass _12 "12":
|
||||
pass
|
||||
|
||||
cdef cppclass _13 "13":
|
||||
pass
|
||||
|
||||
cdef cppclass _14 "14":
|
||||
pass
|
||||
|
||||
cdef cppclass _15 "15":
|
||||
pass
|
||||
|
||||
cdef cppclass _16 "16":
|
||||
pass
|
||||
|
||||
cdef cppclass _17 "17":
|
||||
pass
|
||||
|
||||
cdef cppclass _18 "18":
|
||||
pass
|
||||
|
||||
cdef cppclass _19 "19":
|
||||
pass
|
||||
|
||||
cdef cppclass _20 "20":
|
||||
pass
|
||||
|
||||
cdef cppclass _21 "21":
|
||||
pass
|
||||
|
||||
cdef cppclass _22 "22":
|
||||
pass
|
||||
|
||||
cdef cppclass _23 "23":
|
||||
pass
|
||||
|
||||
cdef cppclass _24 "24":
|
||||
pass
|
||||
|
||||
cdef cppclass _25 "25":
|
||||
pass
|
||||
|
||||
cdef cppclass _26 "26":
|
||||
pass
|
||||
|
||||
cdef cppclass _27 "27":
|
||||
pass
|
||||
|
||||
cdef cppclass _28 "28":
|
||||
pass
|
||||
|
||||
cdef cppclass _29 "29":
|
||||
pass
|
||||
|
||||
cdef cppclass _30 "30":
|
||||
pass
|
||||
|
||||
cdef cppclass _31 "31":
|
||||
pass
|
||||
|
||||
cdef cppclass _32 "32":
|
||||
pass
|
||||
|
||||
cdef cppclass PlainObjectBase:
|
||||
pass
|
||||
|
||||
cdef cppclass Map[DenseTypeShort](PlainObjectBase):
|
||||
Map() except +
|
||||
Map(np.ndarray array) except +
|
||||
|
||||
cdef cppclass FlattenedMap[DenseType, dtype, Rows, Cols]:
|
||||
FlattenedMap() except +
|
||||
FlattenedMap(np.ndarray array) except +
|
||||
|
||||
cdef cppclass FlattenedMapWithOrder "eigency::FlattenedMap" [DenseType, dtype, Rows, Cols, StorageOrder]:
|
||||
FlattenedMapWithOrder() except +
|
||||
FlattenedMapWithOrder(np.ndarray array) except +
|
||||
|
||||
cdef cppclass FlattenedMapWithStride "eigency::FlattenedMap" [DenseType, dtype, Rows, Cols, StorageOrder, MapOptions, StrideOuter, StrideInner]:
|
||||
FlattenedMapWithStride() except +
|
||||
FlattenedMapWithStride(np.ndarray array) except +
|
||||
|
||||
cdef np.ndarray ndarray_view(PlainObjectBase &)
|
||||
cdef np.ndarray ndarray_copy(PlainObjectBase &)
|
||||
cdef np.ndarray ndarray(PlainObjectBase &)
|
||||
|
||||
|
||||
cdef extern from "eigency_cpp.h" namespace "Eigen":
|
||||
|
||||
cdef cppclass Dynamic:
|
||||
pass
|
||||
|
||||
cdef cppclass RowMajor:
|
||||
pass
|
||||
|
||||
cdef cppclass ColMajor:
|
||||
pass
|
||||
|
||||
cdef cppclass Aligned:
|
||||
pass
|
||||
|
||||
cdef cppclass Unaligned:
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass VectorXd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector1i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector2i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector3i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector4i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass VectorXi(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector1i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector2i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector3i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector4i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVectorXi(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix1i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix2i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix3i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix4i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass MatrixXi(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector1f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector2f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector3f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector4f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass VectorXf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector1f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector2f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector3f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector4f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVectorXf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix1f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix2f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix3f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix4f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass MatrixXf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector1d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector2d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector3d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector4d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass VectorXd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector1d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector2d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector3d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector4d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVectorXd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix1d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix2d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix3d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix4d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass MatrixXd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector1cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector2cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector3cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector4cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass VectorXcf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector1cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector2cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector3cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector4cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVectorXcf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix1cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix2cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix3cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix4cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass MatrixXcf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector1cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector2cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector3cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Vector4cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass VectorXcd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector1cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector2cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector3cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVector4cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass RowVectorXcd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix1cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix2cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix3cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Matrix4cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass MatrixXcd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array22i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array23i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array24i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2Xi(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array32i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array33i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array34i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3Xi(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array42i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array43i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array44i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4Xi(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX2i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX3i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX4i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXXi(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4i(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXi(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array22f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array23f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array24f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2Xf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array32f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array33f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array34f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3Xf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array42f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array43f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array44f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4Xf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX2f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX3f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX4f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXXf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4f(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array22d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array23d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array24d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2Xd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array32d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array33d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array34d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3Xd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array42d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array43d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array44d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4Xd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX2d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX3d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX4d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXXd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4d(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array22cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array23cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array24cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2Xcf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array32cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array33cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array34cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3Xcf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array42cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array43cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array44cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4Xcf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX2cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX3cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX4cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXXcf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4cf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXcf(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array22cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array23cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array24cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2Xcd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array32cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array33cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array34cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3Xcd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array42cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array43cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array44cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4Xcd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX2cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX3cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayX4cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXXcd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array2cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array3cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass Array4cd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
cdef cppclass ArrayXcd(PlainObjectBase):
|
||||
pass
|
||||
|
||||
|
||||
|
|
@ -1 +0,0 @@
|
|||
|
||||
|
|
@ -1,504 +0,0 @@
|
|||
#include <Eigen/Dense>
|
||||
|
||||
#include <iostream>
|
||||
#include <stdexcept>
|
||||
#include <complex>
|
||||
|
||||
typedef ::std::complex< double > __pyx_t_double_complex;
|
||||
typedef ::std::complex< float > __pyx_t_float_complex;
|
||||
|
||||
#include "conversions_api.h"
|
||||
|
||||
#ifndef EIGENCY_CPP
|
||||
#define EIGENCY_CPP
|
||||
|
||||
namespace eigency {
|
||||
|
||||
template<typename Scalar>
|
||||
inline PyArrayObject *_ndarray_view(Scalar *, long rows, long cols, bool is_row_major, long outer_stride=0, long inner_stride=0);
|
||||
template<typename Scalar>
|
||||
inline PyArrayObject *_ndarray_copy(const Scalar *, long rows, long cols, bool is_row_major, long outer_stride=0, long inner_stride=0);
|
||||
|
||||
// Strides:
|
||||
// Eigen and numpy differ in their way of dealing with strides. Eigen has the concept of outer and
|
||||
// inner strides, which are dependent on whether the array/matrix is row-major of column-major:
|
||||
// Inner stride: denotes the offset between succeeding elements in each row (row-major) or column (column-major).
|
||||
// Outer stride: denotes the offset between succeeding rows (row-major) or succeeding columns (column-major).
|
||||
// In contrast, numpy's stride is simply a measure of how fast each dimension should be incremented.
|
||||
// Consequently, a switch in numpy storage order from row-major to column-major involves a switch
|
||||
// in strides, while it does not affect the stride in Eigen.
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<double>(double *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major) {
|
||||
// Eigen row-major mode: row_stride=outer_stride, and col_stride=inner_stride
|
||||
// If no stride is given, the row_stride is set to the number of columns.
|
||||
return ndarray_double_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
} else {
|
||||
// Eigen column-major mode: row_stride=outer_stride, and col_stride=inner_stride
|
||||
// If no stride is given, the cow_stride is set to the number of rows.
|
||||
return ndarray_double_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<double>(const double *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_double_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_double_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<float>(float *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_float_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_float_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<float>(const float *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_float_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_float_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<long>(long *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_long_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_long_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<long>(const long *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_long_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_long_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<unsigned long>(unsigned long *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_ulong_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_ulong_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<unsigned long>(const unsigned long *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_ulong_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_ulong_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<int>(int *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_int_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_int_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<int>(const int *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_int_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_int_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<unsigned int>(unsigned int *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_uint_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_uint_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<unsigned int>(const unsigned int *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_uint_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_uint_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<short>(short *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_short_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_short_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<short>(const short *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_short_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_short_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<unsigned short>(unsigned short *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_ushort_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_ushort_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<unsigned short>(const unsigned short *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_ushort_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_ushort_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<signed char>(signed char *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_schar_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_schar_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<signed char>(const signed char *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_schar_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_schar_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<unsigned char>(unsigned char *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_uchar_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_uchar_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<unsigned char>(const unsigned char *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_uchar_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_uchar_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<std::complex<double> >(std::complex<double> *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_complex_double_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_complex_double_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<std::complex<double> >(const std::complex<double> *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_complex_double_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_complex_double_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_view<std::complex<float> >(std::complex<float> *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_complex_float_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_complex_float_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
template<>
|
||||
inline PyArrayObject *_ndarray_copy<std::complex<float> >(const std::complex<float> *data, long rows, long cols, bool is_row_major, long outer_stride, long inner_stride) {
|
||||
if (is_row_major)
|
||||
return ndarray_copy_complex_float_C(data, rows, cols, outer_stride>0?outer_stride:cols, inner_stride>0?inner_stride:1);
|
||||
else
|
||||
return ndarray_copy_complex_float_F(data, rows, cols, inner_stride>0?inner_stride:1, outer_stride>0?outer_stride:rows);
|
||||
}
|
||||
|
||||
|
||||
template <typename Derived>
|
||||
inline PyArrayObject *ndarray(Eigen::PlainObjectBase<Derived> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_view(m.data(), m.rows(), m.cols(), m.IsRowMajor);
|
||||
}
|
||||
// If C++11 is available, check if m is an r-value reference, in
|
||||
// which case a copy should always be made
|
||||
#if __cplusplus >= 201103L
|
||||
template <typename Derived>
|
||||
inline PyArrayObject *ndarray(Eigen::PlainObjectBase<Derived> &&m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_copy(m.data(), m.rows(), m.cols(), m.IsRowMajor);
|
||||
}
|
||||
#endif
|
||||
template <typename Derived>
|
||||
inline PyArrayObject *ndarray(const Eigen::PlainObjectBase<Derived> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_copy(m.data(), m.rows(), m.cols(), m.IsRowMajor);
|
||||
}
|
||||
template <typename Derived>
|
||||
inline PyArrayObject *ndarray_view(Eigen::PlainObjectBase<Derived> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_view(m.data(), m.rows(), m.cols(), m.IsRowMajor);
|
||||
}
|
||||
template <typename Derived>
|
||||
inline PyArrayObject *ndarray_view(const Eigen::PlainObjectBase<Derived> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_view(const_cast<typename Derived::Scalar*>(m.data()), m.rows(), m.cols(), m.IsRowMajor);
|
||||
}
|
||||
template <typename Derived>
|
||||
inline PyArrayObject *ndarray_copy(const Eigen::PlainObjectBase<Derived> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_copy(m.data(), m.rows(), m.cols(), m.IsRowMajor);
|
||||
}
|
||||
|
||||
template <typename Derived, int MapOptions, typename Stride>
|
||||
inline PyArrayObject *ndarray(Eigen::Map<Derived, MapOptions, Stride> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_view(m.data(), m.rows(), m.cols(), m.IsRowMajor, m.outerStride(), m.innerStride());
|
||||
}
|
||||
template <typename Derived, int MapOptions, typename Stride>
|
||||
inline PyArrayObject *ndarray(const Eigen::Map<Derived, MapOptions, Stride> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
// Since this is a map, we assume that ownership is correctly taken care
|
||||
// of, and we avoid taking a copy
|
||||
return _ndarray_view(const_cast<typename Derived::Scalar*>(m.data()), m.rows(), m.cols(), m.IsRowMajor, m.outerStride(), m.innerStride());
|
||||
}
|
||||
template <typename Derived, int MapOptions, typename Stride>
|
||||
inline PyArrayObject *ndarray_view(Eigen::Map<Derived, MapOptions, Stride> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_view(m.data(), m.rows(), m.cols(), m.IsRowMajor, m.outerStride(), m.innerStride());
|
||||
}
|
||||
template <typename Derived, int MapOptions, typename Stride>
|
||||
inline PyArrayObject *ndarray_view(const Eigen::Map<Derived, MapOptions, Stride> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_view(const_cast<typename Derived::Scalar*>(m.data()), m.rows(), m.cols(), m.IsRowMajor, m.outerStride(), m.innerStride());
|
||||
}
|
||||
template <typename Derived, int MapOptions, typename Stride>
|
||||
inline PyArrayObject *ndarray_copy(const Eigen::Map<Derived, MapOptions, Stride> &m) {
|
||||
import_gtsam_eigency__conversions();
|
||||
return _ndarray_copy(m.data(), m.rows(), m.cols(), m.IsRowMajor, m.outerStride(), m.innerStride());
|
||||
}
|
||||
|
||||
|
||||
template <typename MatrixType,
|
||||
int _MapOptions = Eigen::Unaligned,
|
||||
typename _StrideType=Eigen::Stride<0,0> >
|
||||
class MapBase: public Eigen::Map<MatrixType, _MapOptions, _StrideType> {
|
||||
public:
|
||||
typedef Eigen::Map<MatrixType, _MapOptions, _StrideType> Base;
|
||||
typedef typename Base::Scalar Scalar;
|
||||
|
||||
MapBase(Scalar* data,
|
||||
long rows,
|
||||
long cols,
|
||||
_StrideType stride=_StrideType())
|
||||
: Base(data,
|
||||
// If both dimensions are dynamic or dimensions match, accept dimensions as they are
|
||||
((Base::RowsAtCompileTime==Eigen::Dynamic && Base::ColsAtCompileTime==Eigen::Dynamic) ||
|
||||
(Base::RowsAtCompileTime==rows && Base::ColsAtCompileTime==cols))
|
||||
? rows
|
||||
// otherwise, test if swapping them makes them fit
|
||||
: ((Base::RowsAtCompileTime==cols || Base::ColsAtCompileTime==rows)
|
||||
? cols
|
||||
: rows),
|
||||
((Base::RowsAtCompileTime==Eigen::Dynamic && Base::ColsAtCompileTime==Eigen::Dynamic) ||
|
||||
(Base::RowsAtCompileTime==rows && Base::ColsAtCompileTime==cols))
|
||||
? cols
|
||||
: ((Base::RowsAtCompileTime==cols || Base::ColsAtCompileTime==rows)
|
||||
? rows
|
||||
: cols),
|
||||
stride
|
||||
) {}
|
||||
|
||||
MapBase &operator=(const MatrixType &other) {
|
||||
Base::operator=(other);
|
||||
return *this;
|
||||
}
|
||||
|
||||
virtual ~MapBase() { }
|
||||
};
|
||||
|
||||
|
||||
template <template<class,int,int,int,int,int> class EigencyDenseBase,
|
||||
typename Scalar,
|
||||
int _Rows, int _Cols,
|
||||
int _Options = Eigen::AutoAlign |
|
||||
#if defined(__GNUC__) && __GNUC__==3 && __GNUC_MINOR__==4
|
||||
// workaround a bug in at least gcc 3.4.6
|
||||
// the innermost ?: ternary operator is misparsed. We write it slightly
|
||||
// differently and this makes gcc 3.4.6 happy, but it's ugly.
|
||||
// The error would only show up with EIGEN_DEFAULT_TO_ROW_MAJOR is defined
|
||||
// (when EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION is RowMajor)
|
||||
( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
|
||||
// EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION contains explicit namespace since Eigen 3.1.19
|
||||
#if EIGEN_VERSION_AT_LEAST(3,2,90)
|
||||
: !(_Cols==1 && _Rows!=1) ? EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION
|
||||
#else
|
||||
: !(_Cols==1 && _Rows!=1) ? Eigen::EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION
|
||||
#endif
|
||||
: ColMajor ),
|
||||
#else
|
||||
( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
|
||||
: (_Cols==1 && _Rows!=1) ? Eigen::ColMajor
|
||||
// EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION contains explicit namespace since Eigen 3.1.19
|
||||
#if EIGEN_VERSION_AT_LEAST(3,2,90)
|
||||
: EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
|
||||
#else
|
||||
: Eigen::EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
|
||||
#endif
|
||||
#endif
|
||||
int _MapOptions = Eigen::Unaligned,
|
||||
int _StrideOuter=0, int _StrideInner=0,
|
||||
int _MaxRows = _Rows,
|
||||
int _MaxCols = _Cols>
|
||||
class FlattenedMap: public MapBase<EigencyDenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > {
|
||||
public:
|
||||
typedef MapBase<EigencyDenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > Base;
|
||||
|
||||
FlattenedMap()
|
||||
: Base(NULL, 0, 0),
|
||||
object_(NULL) {}
|
||||
|
||||
FlattenedMap(Scalar *data, long rows, long cols, long outer_stride=0, long inner_stride=0)
|
||||
: Base(data, rows, cols,
|
||||
Eigen::Stride<_StrideOuter, _StrideInner>(outer_stride, inner_stride)),
|
||||
object_(NULL) {
|
||||
}
|
||||
|
||||
FlattenedMap(PyArrayObject *object)
|
||||
: Base((Scalar *)((PyArrayObject*)object)->data,
|
||||
// : Base(_from_numpy<Scalar>((PyArrayObject*)object),
|
||||
(((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[0] : 1,
|
||||
(((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[1] : ((PyArrayObject*)object)->dimensions[0],
|
||||
Eigen::Stride<_StrideOuter, _StrideInner>(_StrideOuter != Eigen::Dynamic ? _StrideOuter : (((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[0] : 1,
|
||||
_StrideInner != Eigen::Dynamic ? _StrideInner : (((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[1] : ((PyArrayObject*)object)->dimensions[0])),
|
||||
object_(object) {
|
||||
|
||||
if (((PyObject*)object != Py_None) && !PyArray_ISONESEGMENT(object))
|
||||
throw std::invalid_argument("Numpy array must be a in one contiguous segment to be able to be transferred to a Eigen Map.");
|
||||
|
||||
Py_XINCREF(object_);
|
||||
}
|
||||
FlattenedMap &operator=(const FlattenedMap &other) {
|
||||
if (other.object_) {
|
||||
new (this) FlattenedMap(other.object_);
|
||||
} else {
|
||||
// Replace the memory that we point to (not a memory allocation)
|
||||
new (this) FlattenedMap(const_cast<Scalar*>(other.data()),
|
||||
other.rows(),
|
||||
other.cols(),
|
||||
other.outerStride(),
|
||||
other.innerStride());
|
||||
}
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
operator Base() const {
|
||||
return static_cast<Base>(*this);
|
||||
}
|
||||
|
||||
operator Base&() const {
|
||||
return static_cast<Base&>(*this);
|
||||
}
|
||||
|
||||
operator EigencyDenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>() const {
|
||||
return EigencyDenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>(static_cast<Base>(*this));
|
||||
}
|
||||
|
||||
virtual ~FlattenedMap() {
|
||||
Py_XDECREF(object_);
|
||||
}
|
||||
|
||||
private:
|
||||
PyArrayObject * const object_;
|
||||
};
|
||||
|
||||
|
||||
template <typename MatrixType>
|
||||
class Map: public MapBase<MatrixType> {
|
||||
public:
|
||||
typedef MapBase<MatrixType> Base;
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
|
||||
enum {
|
||||
RowsAtCompileTime = Base::Base::RowsAtCompileTime,
|
||||
ColsAtCompileTime = Base::Base::ColsAtCompileTime
|
||||
};
|
||||
|
||||
Map()
|
||||
: Base(NULL,
|
||||
(RowsAtCompileTime == Eigen::Dynamic) ? 0 : RowsAtCompileTime,
|
||||
(ColsAtCompileTime == Eigen::Dynamic) ? 0 : ColsAtCompileTime),
|
||||
object_(NULL) {
|
||||
}
|
||||
|
||||
Map(Scalar *data, long rows, long cols)
|
||||
: Base(data, rows, cols),
|
||||
object_(NULL) {}
|
||||
|
||||
Map(PyArrayObject *object)
|
||||
: Base((PyObject*)object == Py_None? NULL: (Scalar *)object->data,
|
||||
// ROW: If array is in row-major order, transpose (see README)
|
||||
(PyObject*)object == Py_None? 0 :
|
||||
(!PyArray_IS_F_CONTIGUOUS(object)
|
||||
? ((object->nd == 1)
|
||||
? 1 // ROW: If 1D row-major numpy array, set to 1 (row vector)
|
||||
: object->dimensions[1])
|
||||
: object->dimensions[0]),
|
||||
// COLUMN: If array is in row-major order: transpose (see README)
|
||||
(PyObject*)object == Py_None? 0 :
|
||||
(!PyArray_IS_F_CONTIGUOUS(object)
|
||||
? object->dimensions[0]
|
||||
: ((object->nd == 1)
|
||||
? 1 // COLUMN: If 1D col-major numpy array, set to length (column vector)
|
||||
: object->dimensions[1]))),
|
||||
object_(object) {
|
||||
|
||||
if (((PyObject*)object != Py_None) && !PyArray_ISONESEGMENT(object))
|
||||
throw std::invalid_argument("Numpy array must be a in one contiguous segment to be able to be transferred to a Eigen Map.");
|
||||
Py_XINCREF(object_);
|
||||
}
|
||||
|
||||
Map &operator=(const Map &other) {
|
||||
if (other.object_) {
|
||||
new (this) Map(other.object_);
|
||||
} else {
|
||||
// Replace the memory that we point to (not a memory allocation)
|
||||
new (this) Map(const_cast<Scalar*>(other.data()),
|
||||
other.rows(),
|
||||
other.cols());
|
||||
}
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
Map &operator=(const MatrixType &other) {
|
||||
MapBase<MatrixType>::operator=(other);
|
||||
return *this;
|
||||
}
|
||||
|
||||
operator Base() const {
|
||||
return static_cast<Base>(*this);
|
||||
}
|
||||
|
||||
operator Base&() const {
|
||||
return static_cast<Base&>(*this);
|
||||
}
|
||||
|
||||
operator MatrixType() const {
|
||||
return MatrixType(static_cast<Base>(*this));
|
||||
}
|
||||
|
||||
virtual ~Map() {
|
||||
Py_XDECREF(object_);
|
||||
}
|
||||
|
||||
private:
|
||||
PyArrayObject * const object_;
|
||||
};
|
||||
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue