Generated by Cython 0.29.23
Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.
Raw output: pyfftw.c
+0001: # cython: language_level=3
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0002: #
0003: # Copyright 2015 Knowledge Economy Developments Ltd
0004: # Copyright 2014 David Wells
0005:
0006: # Henry Gomersall
0007: # heng@kedevelopments.co.uk
0008: #
0009: # All rights reserved.
0010: #
0011: # Redistribution and use in source and binary forms, with or without
0012: # modification, are permitted provided that the following conditions are met:
0013: #
0014: # * Redistributions of source code must retain the above copyright notice, this
0015: # list of conditions and the following disclaimer.
0016: #
0017: # * Redistributions in binary form must reproduce the above copyright notice,
0018: # this list of conditions and the following disclaimer in the documentation
0019: # and/or other materials provided with the distribution.
0020: #
0021: # * Neither the name of the copyright holder nor the names of its contributors
0022: # may be used to endorse or promote products derived from this software without
0023: # specific prior written permission.
0024: #
0025: # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
0026: # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
0027: # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
0028: # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
0029: # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
0030: # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
0031: # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
0032: # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
0033: # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
0034: # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
0035: # POSSIBILITY OF SUCH DAMAGE.
0036: #
+0037: import numpy as np
__pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 37, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 37, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
0038: cimport numpy as np
0039: from libc.stdlib cimport calloc, malloc, free
0040: from libc.stdint cimport intptr_t, int64_t
0041: from libc cimport limits
+0042: import warnings
__pyx_t_1 = __Pyx_Import(__pyx_n_s_warnings, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_warnings, __pyx_t_1) < 0) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+0043: import threading
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0044:
+0045: cdef int _simd_alignment = simd_alignment()
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0046:
0047: #: A tuple of simd alignments that make sense for this cpu
+0048: if _simd_alignment == 16:
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/* … */
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case 32:
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0056:
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0058: '''byte_align(array, n=None, dtype=None)
0059:
0060: Function that takes a numpy array and checks it is aligned on an n-byte
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0239: # do not attempt to plan simultaneously.
+0240: cdef object plan_lock = threading.Lock()
__Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_threading); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 240, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_Lock); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 240, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_CallNoArg(__pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 240, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XGOTREF(__pyx_v_6pyrost_3bin_6pyfftw_plan_lock); __Pyx_DECREF_SET(__pyx_v_6pyrost_3bin_6pyfftw_plan_lock, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0;
0241:
0242: # Function wrappers
0243: # =================
0244: # All of these have the same signature as the fftw_generic functions
0245: # defined in the .pxd file. The arguments and return values are
0246: # cast as required in order to call the actual fftw functions.
0247: #
0248: # The wrapper function names are simply the fftw names prefixed
0249: # with a single underscore.
0250:
0251: # Planners
0252: # ========
0253: #
0254: # Complex double precision
+0255: cdef void* _fftw_plan_guru_dft(
static void *__pyx_f_6pyrost_3bin_6pyfftw__fftw_plan_guru_dft(int __pyx_v_rank, fftw_iodim *__pyx_v_dims, int __pyx_v_howmany_rank, fftw_iodim *__pyx_v_howmany_dims, void *__pyx_v__in, void *__pyx_v__out, int *__pyx_v_direction, unsigned int __pyx_v_flags) {
void *__pyx_r;
/* … */
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
0256: int rank, fftw_iodim *dims,
0257: int howmany_rank, fftw_iodim *howmany_dims,
0258: void *_in, void *_out,
0259: int *direction, unsigned flags) nogil:
0260:
+0261: return <void *>fftw_plan_guru_dft(rank, dims,
__pyx_r = ((void *)fftw_plan_guru_dft(__pyx_v_rank, __pyx_v_dims, __pyx_v_howmany_rank, __pyx_v_howmany_dims, ((__pyx_t_6pyrost_3bin_6pyfftw_cdouble *)__pyx_v__in), ((__pyx_t_6pyrost_3bin_6pyfftw_cdouble *)__pyx_v__out), (__pyx_v_direction[0]), __pyx_v_flags)); goto __pyx_L0;
0262: howmany_rank, howmany_dims,
0263: <cdouble *>_in, <cdouble *>_out,
0264: direction[0], flags)
0265:
0266: # real to complex double precision
+0267: cdef void* _fftw_plan_guru_dft_r2c(
static void *__pyx_f_6pyrost_3bin_6pyfftw__fftw_plan_guru_dft_r2c(int __pyx_v_rank, fftw_iodim *__pyx_v_dims, int __pyx_v_howmany_rank, fftw_iodim *__pyx_v_howmany_dims, void *__pyx_v__in, void *__pyx_v__out, CYTHON_UNUSED int *__pyx_v_direction, unsigned int __pyx_v_flags) {
void *__pyx_r;
/* … */
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
0268: int rank, fftw_iodim *dims,
0269: int howmany_rank, fftw_iodim *howmany_dims,
0270: void *_in, void *_out,
0271: int *direction, unsigned flags) nogil:
0272:
+0273: return <void *>fftw_plan_guru_dft_r2c(rank, dims,
__pyx_r = ((void *)fftw_plan_guru_dft_r2c(__pyx_v_rank, __pyx_v_dims, __pyx_v_howmany_rank, __pyx_v_howmany_dims, ((double *)__pyx_v__in), ((__pyx_t_6pyrost_3bin_6pyfftw_cdouble *)__pyx_v__out), __pyx_v_flags)); goto __pyx_L0;
0274: howmany_rank, howmany_dims,
0275: <double *>_in, <cdouble *>_out,
0276: flags)
0277:
0278: # complex to real double precision
+0279: cdef void* _fftw_plan_guru_dft_c2r(
static void *__pyx_f_6pyrost_3bin_6pyfftw__fftw_plan_guru_dft_c2r(int __pyx_v_rank, fftw_iodim *__pyx_v_dims, int __pyx_v_howmany_rank, fftw_iodim *__pyx_v_howmany_dims, void *__pyx_v__in, void *__pyx_v__out, CYTHON_UNUSED int *__pyx_v_direction, unsigned int __pyx_v_flags) {
void *__pyx_r;
/* … */
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
0280: int rank, fftw_iodim *dims,
0281: int howmany_rank, fftw_iodim *howmany_dims,
0282: void *_in, void *_out,
0283: int *direction, unsigned flags) nogil:
0284:
+0285: return <void *>fftw_plan_guru_dft_c2r(rank, dims,
__pyx_r = ((void *)fftw_plan_guru_dft_c2r(__pyx_v_rank, __pyx_v_dims, __pyx_v_howmany_rank, __pyx_v_howmany_dims, ((__pyx_t_6pyrost_3bin_6pyfftw_cdouble *)__pyx_v__in), ((double *)__pyx_v__out), __pyx_v_flags)); goto __pyx_L0;
0286: howmany_rank, howmany_dims,
0287: <cdouble *>_in, <double *>_out,
0288: flags)
0289:
0290: # real to real double precision
+0291: cdef void* _fftw_plan_guru_r2r(
static void *__pyx_f_6pyrost_3bin_6pyfftw__fftw_plan_guru_r2r(int __pyx_v_rank, fftw_iodim *__pyx_v_dims, int __pyx_v_howmany_rank, fftw_iodim *__pyx_v_howmany_dims, void *__pyx_v__in, void *__pyx_v__out, int *__pyx_v_direction, int __pyx_v_flags) {
void *__pyx_r;
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_fftw_plan_guru_r2r", 0);
/* … */
/* function exit code */
__pyx_L0:;
__Pyx_RefNannyFinishContext();
return __pyx_r;
}
0292: int rank, fftw_iodim *dims,
0293: int howmany_rank, fftw_iodim *howmany_dims,
0294: void *_in, void *_out,
0295: int *direction, int flags):
0296:
+0297: return <void *>fftw_plan_guru_r2r(rank, dims,
__pyx_r = ((void *)fftw_plan_guru_r2r(__pyx_v_rank, __pyx_v_dims, __pyx_v_howmany_rank, __pyx_v_howmany_dims, ((double *)__pyx_v__in), ((double *)__pyx_v__out), __pyx_v_direction, __pyx_v_flags)); goto __pyx_L0;
0298: howmany_rank, howmany_dims,
0299: <double *>_in, <double *>_out,
0300: direction, flags)
0301:
0302: # Executors
0303: # =========
0304: #
0305: # Complex double precision
+0306: cdef void _fftw_execute_dft(void *_plan, void *_in, void *_out) nogil:
static void __pyx_f_6pyrost_3bin_6pyfftw__fftw_execute_dft(void *__pyx_v__plan, void *__pyx_v__in, void *__pyx_v__out) {
/* … */
/* function exit code */
}
0307:
+0308: fftw_execute_dft(<fftw_plan>_plan,
fftw_execute_dft(((fftw_plan)__pyx_v__plan), ((__pyx_t_6pyrost_3bin_6pyfftw_cdouble *)__pyx_v__in), ((__pyx_t_6pyrost_3bin_6pyfftw_cdouble *)__pyx_v__out));
0309: <cdouble *>_in, <cdouble *>_out)
0310:
0311: # real to complex double precision
+0312: cdef void _fftw_execute_dft_r2c(void *_plan, void *_in, void *_out) nogil:
static void __pyx_f_6pyrost_3bin_6pyfftw__fftw_execute_dft_r2c(void *__pyx_v__plan, void *__pyx_v__in, void *__pyx_v__out) {
/* … */
/* function exit code */
}
0313:
+0314: fftw_execute_dft_r2c(<fftw_plan>_plan,
fftw_execute_dft_r2c(((fftw_plan)__pyx_v__plan), ((double *)__pyx_v__in), ((__pyx_t_6pyrost_3bin_6pyfftw_cdouble *)__pyx_v__out));
0315: <double *>_in, <cdouble *>_out)
0316:
0317: # complex to real double precision
+0318: cdef void _fftw_execute_dft_c2r(void *_plan, void *_in, void *_out) nogil:
static void __pyx_f_6pyrost_3bin_6pyfftw__fftw_execute_dft_c2r(void *__pyx_v__plan, void *__pyx_v__in, void *__pyx_v__out) {
/* … */
/* function exit code */
}
0319:
+0320: fftw_execute_dft_c2r(<fftw_plan>_plan,
fftw_execute_dft_c2r(((fftw_plan)__pyx_v__plan), ((__pyx_t_6pyrost_3bin_6pyfftw_cdouble *)__pyx_v__in), ((double *)__pyx_v__out));
0321: <cdouble *>_in, <double *>_out)
0322:
0323: # real to real double precision
+0324: cdef void _fftw_execute_r2r(void *_plan, void *_in, void *_out) nogil:
static void __pyx_f_6pyrost_3bin_6pyfftw__fftw_execute_r2r(void *__pyx_v__plan, void *__pyx_v__in, void *__pyx_v__out) {
/* … */
/* function exit code */
}
0325:
+0326: fftw_execute_r2r(<fftw_plan>_plan, <double *>_in, <double *>_out)
fftw_execute_r2r(((fftw_plan)__pyx_v__plan), ((double *)__pyx_v__in), ((double *)__pyx_v__out));
0327:
0328: # Destroyers
0329: # ==========
0330: #
0331: # Double precision
+0332: cdef void _fftw_destroy_plan(void *_plan):
static void __pyx_f_6pyrost_3bin_6pyfftw__fftw_destroy_plan(void *__pyx_v__plan) {
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_fftw_destroy_plan", 0);
/* … */
/* function exit code */
__Pyx_RefNannyFinishContext();
}
0333:
+0334: fftw_destroy_plan(<fftw_plan>_plan)
fftw_destroy_plan(((fftw_plan)__pyx_v__plan));
0335:
0336: # Function lookup tables
0337: # ======================
0338: # Planner table (of size the number of planners).
0339: cdef fftw_generic_plan_guru planners[4]
0340:
+0341: cdef fftw_generic_plan_guru * _build_planner_list():
static __pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_guru *__pyx_f_6pyrost_3bin_6pyfftw__build_planner_list(void) {
__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_guru *__pyx_r;
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_build_planner_list", 0);
/* … */
/* function exit code */
__pyx_r = 0;
__Pyx_RefNannyFinishContext();
return __pyx_r;
}
+0342: planners[0] = <fftw_generic_plan_guru>&_fftw_plan_guru_dft
(__pyx_v_6pyrost_3bin_6pyfftw_planners[0]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_guru)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_plan_guru_dft));
+0343: planners[1] = <fftw_generic_plan_guru>&_fftw_plan_guru_dft_r2c
(__pyx_v_6pyrost_3bin_6pyfftw_planners[1]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_guru)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_plan_guru_dft_r2c));
+0344: planners[2] = <fftw_generic_plan_guru>&_fftw_plan_guru_dft_c2r
(__pyx_v_6pyrost_3bin_6pyfftw_planners[2]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_guru)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_plan_guru_dft_c2r));
+0345: planners[3] = <fftw_generic_plan_guru>&_fftw_plan_guru_r2r
(__pyx_v_6pyrost_3bin_6pyfftw_planners[3]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_guru)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_plan_guru_r2r));
0346:
0347: # Executor table (of size the number of executors)
0348: cdef fftw_generic_execute executors[4]
0349:
+0350: cdef fftw_generic_execute * _build_executor_list():
static __pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_execute *__pyx_f_6pyrost_3bin_6pyfftw__build_executor_list(void) {
__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_execute *__pyx_r;
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_build_executor_list", 0);
/* … */
/* function exit code */
__pyx_r = 0;
__Pyx_RefNannyFinishContext();
return __pyx_r;
}
+0351: executors[0] = <fftw_generic_execute>&_fftw_execute_dft
(__pyx_v_6pyrost_3bin_6pyfftw_executors[0]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_execute)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_execute_dft));
+0352: executors[1] = <fftw_generic_execute>&_fftw_execute_dft_r2c
(__pyx_v_6pyrost_3bin_6pyfftw_executors[1]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_execute)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_execute_dft_r2c));
+0353: executors[2] = <fftw_generic_execute>&_fftw_execute_dft_c2r
(__pyx_v_6pyrost_3bin_6pyfftw_executors[2]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_execute)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_execute_dft_c2r));
+0354: executors[3] = <fftw_generic_execute>&_fftw_execute_r2r
(__pyx_v_6pyrost_3bin_6pyfftw_executors[3]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_execute)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_execute_r2r));
0355:
0356: # Destroyer table (of size the number of destroyers)
0357: cdef fftw_generic_destroy_plan destroyers[1]
0358:
+0359: cdef fftw_generic_destroy_plan * _build_destroyer_list():
static __pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_destroy_plan *__pyx_f_6pyrost_3bin_6pyfftw__build_destroyer_list(void) {
__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_destroy_plan *__pyx_r;
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_build_destroyer_list", 0);
/* … */
/* function exit code */
__pyx_r = 0;
__Pyx_RefNannyFinishContext();
return __pyx_r;
}
+0360: destroyers[0] = <fftw_generic_destroy_plan>&_fftw_destroy_plan
(__pyx_v_6pyrost_3bin_6pyfftw_destroyers[0]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_destroy_plan)(&__pyx_f_6pyrost_3bin_6pyfftw__fftw_destroy_plan));
0361:
0362: # nthreads plan setters table
0363: cdef fftw_generic_plan_with_nthreads nthreads_plan_setters[1]
0364:
+0365: cdef fftw_generic_plan_with_nthreads * _build_nthreads_plan_setters_list():
static __pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_with_nthreads *__pyx_f_6pyrost_3bin_6pyfftw__build_nthreads_plan_setters_list(void) {
__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_with_nthreads *__pyx_r;
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_build_nthreads_plan_setters_list", 0);
/* … */
/* function exit code */
__pyx_r = 0;
__Pyx_RefNannyFinishContext();
return __pyx_r;
}
+0366: nthreads_plan_setters[0] = (<fftw_generic_plan_with_nthreads>&fftw_plan_with_nthreads)
(__pyx_v_6pyrost_3bin_6pyfftw_nthreads_plan_setters[0]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_with_nthreads)(&fftw_plan_with_nthreads));
0367:
0368: # Set planner timelimits
0369: cdef fftw_generic_set_timelimit set_timelimit_funcs[1]
0370:
+0371: cdef fftw_generic_set_timelimit * _build_set_timelimit_funcs_list():
static __pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_set_timelimit *__pyx_f_6pyrost_3bin_6pyfftw__build_set_timelimit_funcs_list(void) {
__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_set_timelimit *__pyx_r;
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_build_set_timelimit_funcs_list", 0);
/* … */
/* function exit code */
__pyx_r = 0;
__Pyx_RefNannyFinishContext();
return __pyx_r;
}
+0372: set_timelimit_funcs[0] = (<fftw_generic_set_timelimit>&fftw_set_timelimit)
(__pyx_v_6pyrost_3bin_6pyfftw_set_timelimit_funcs[0]) = ((__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_set_timelimit)(&fftw_set_timelimit));
0373:
0374: # Data validators table
0375: cdef validator validators[2]
0376:
+0377: cdef validator * _build_validators_list():
static __pyx_t_6pyrost_3bin_6pyfftw_validator *__pyx_f_6pyrost_3bin_6pyfftw__build_validators_list(void) {
__pyx_t_6pyrost_3bin_6pyfftw_validator *__pyx_r;
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_build_validators_list", 0);
/* … */
/* function exit code */
__pyx_r = 0;
__Pyx_RefNannyFinishContext();
return __pyx_r;
}
+0378: validators[0] = &_validate_r2c_arrays
(__pyx_v_6pyrost_3bin_6pyfftw_validators[0]) = (&__pyx_f_6pyrost_3bin_6pyfftw__validate_r2c_arrays);
+0379: validators[1] = &_validate_c2r_arrays
(__pyx_v_6pyrost_3bin_6pyfftw_validators[1]) = (&__pyx_f_6pyrost_3bin_6pyfftw__validate_c2r_arrays);
0380:
0381: # Validator functions
0382: # ===================
+0383: cdef bint _validate_r2c_arrays(np.ndarray input_array,
static int __pyx_f_6pyrost_3bin_6pyfftw__validate_r2c_arrays(PyArrayObject *__pyx_v_input_array, PyArrayObject *__pyx_v_output_array, int64_t *__pyx_v_axes, int64_t *__pyx_v_not_axes, int64_t __pyx_v_axes_length) {
npy_intp *__pyx_v_in_shape;
npy_intp *__pyx_v_out_shape;
int64_t __pyx_v_n;
int __pyx_r;
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_validate_r2c_arrays", 0);
/* … */
/* function exit code */
__pyx_L0:;
__Pyx_RefNannyFinishContext();
return __pyx_r;
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0384: np.ndarray output_array, int64_t *axes, int64_t *not_axes,
0385: int64_t axes_length):
0386: ''' Validates the input and output array to check for
0387: a valid real to complex transform.
0388: '''
0389: # We firstly need to confirm that the dimenions of the arrays
0390: # are the same
+0391: if not (input_array.ndim == output_array.ndim):
__pyx_t_1 = ((!((__pyx_v_input_array->nd == __pyx_v_output_array->nd) != 0)) != 0);
if (__pyx_t_1) {
/* … */
}
+0392: return False
__pyx_r = 0;
goto __pyx_L0;
0393:
+0394: in_shape = input_array.shape
__pyx_t_2 = __pyx_v_input_array->dimensions; __pyx_v_in_shape = __pyx_t_2;
+0395: out_shape = output_array.shape
__pyx_t_2 = __pyx_v_output_array->dimensions; __pyx_v_out_shape = __pyx_t_2;
0396:
+0397: for n in range(axes_length - 1):
__pyx_t_3 = (__pyx_v_axes_length - 1);
__pyx_t_4 = __pyx_t_3;
for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
__pyx_v_n = __pyx_t_5;
+0398: if not out_shape[axes[n]] == in_shape[axes[n]]:
__pyx_t_1 = ((!(((__pyx_v_out_shape[(__pyx_v_axes[__pyx_v_n])]) == (__pyx_v_in_shape[(__pyx_v_axes[__pyx_v_n])])) != 0)) != 0);
if (__pyx_t_1) {
/* … */
}
}
+0399: return False
__pyx_r = 0;
goto __pyx_L0;
0400:
0401: # The critical axis is the last of those over which the
0402: # FFT is taken.
+0403: if not (out_shape[axes[axes_length-1]]
__pyx_t_1 = ((!(((__pyx_v_out_shape[(__pyx_v_axes[(__pyx_v_axes_length - 1)])]) == (((__pyx_v_in_shape[(__pyx_v_axes[(__pyx_v_axes_length - 1)])]) / 2) + 1)) != 0)) != 0);
if (__pyx_t_1) {
/* … */
}
0404: == in_shape[axes[axes_length-1]]//2 + 1):
+0405: return False
__pyx_r = 0;
goto __pyx_L0;
0406:
+0407: for n in range(input_array.ndim - axes_length):
__pyx_t_3 = (__pyx_v_input_array->nd - __pyx_v_axes_length);
__pyx_t_4 = __pyx_t_3;
for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
__pyx_v_n = __pyx_t_5;
+0408: if not out_shape[not_axes[n]] == in_shape[not_axes[n]]:
__pyx_t_1 = ((!(((__pyx_v_out_shape[(__pyx_v_not_axes[__pyx_v_n])]) == (__pyx_v_in_shape[(__pyx_v_not_axes[__pyx_v_n])])) != 0)) != 0);
if (__pyx_t_1) {
/* … */
}
}
+0409: return False
__pyx_r = 0;
goto __pyx_L0;
0410:
+0411: return True
__pyx_r = 1; goto __pyx_L0;
0412:
+0413: cdef bint _validate_c2r_arrays(np.ndarray input_array,
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0480: # that is different to the length of the input array).
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0522: else:
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0525:
0526: # Set the cleanup routine
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__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("_cleanup", 0);
/* … */
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fftw_cleanup();
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fftw_cleanup_threads();
0530:
0531: # Initialize the module
0532:
0533: # Define the functions
+0534: _build_planner_list()
(void)(__pyx_f_6pyrost_3bin_6pyfftw__build_planner_list());
+0535: _build_destroyer_list()
(void)(__pyx_f_6pyrost_3bin_6pyfftw__build_destroyer_list());
+0536: _build_executor_list()
(void)(__pyx_f_6pyrost_3bin_6pyfftw__build_executor_list());
+0537: _build_nthreads_plan_setters_list()
(void)(__pyx_f_6pyrost_3bin_6pyfftw__build_nthreads_plan_setters_list());
+0538: _build_validators_list()
(void)(__pyx_f_6pyrost_3bin_6pyfftw__build_validators_list());
+0539: _build_set_timelimit_funcs_list()
(void)(__pyx_f_6pyrost_3bin_6pyfftw__build_set_timelimit_funcs_list());
0540:
+0541: fftw_init_threads()
fftw_init_threads();
0542:
+0543: Py_AtExit(_cleanup)
(void)(Py_AtExit(__pyx_f_6pyrost_3bin_6pyfftw__cleanup));
0544:
0545: # Helper functions
+0546: cdef void make_axes_unique(int64_t *axes, int64_t axes_length,
static void __pyx_f_6pyrost_3bin_6pyfftw_make_axes_unique(int64_t *__pyx_v_axes, int64_t __pyx_v_axes_length, int64_t **__pyx_v_unique_axes, int64_t **__pyx_v_not_axes, int64_t __pyx_v_dimensions, int64_t *__pyx_v_unique_axes_length) {
int64_t __pyx_v_unique_axes_count;
int64_t __pyx_v_holding_offset;
int64_t *__pyx_v_axes_holding;
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0550: the unique array in unique_axes. It also creates and fills another
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0552:
0553: unique_axes_length is updated with the length of unique_axes.
0554:
0555: dimensions is the number of dimensions to which the axes array
0556: might refer.
0557:
0558: It is the responsibility of the caller to free unique_axes and not_axes.
0559: '''
0560:
+0561: cdef int64_t unique_axes_count = 0
__pyx_v_unique_axes_count = 0;
+0562: cdef int64_t holding_offset = 0
__pyx_v_holding_offset = 0;
0563:
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__pyx_v_axes_holding = ((int64_t *)calloc(__pyx_v_dimensions, (sizeof(int64_t))));
0566: cdef int64_t *axes_holding_offset = (
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__pyx_v_axes_holding_offset = ((int64_t *)calloc(__pyx_v_dimensions, (sizeof(int64_t))));
0568:
+0569: for n in range(dimensions):
__pyx_t_1 = __pyx_v_dimensions;
__pyx_t_2 = __pyx_t_1;
for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
__pyx_v_n = __pyx_t_3;
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(__pyx_v_axes_holding[__pyx_v_n]) = -1L; }
0571:
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0573: # been stored (this keeps one and only one and the first index to axes
0574: # i.e. storing the unique set of entries).
0575: #
0576: # axes_holding_offset holds the shift due to repeated axes
+0577: for n in range(axes_length):
__pyx_t_1 = __pyx_v_axes_length;
__pyx_t_2 = __pyx_t_1;
for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
__pyx_v_n = __pyx_t_3;
+0578: if axes_holding[axes[n]] == -1:
__pyx_t_4 = (((__pyx_v_axes_holding[(__pyx_v_axes[__pyx_v_n])]) == -1L) != 0);
if (__pyx_t_4) {
/* … */
goto __pyx_L7;
}
+0579: axes_holding[axes[n]] = n
(__pyx_v_axes_holding[(__pyx_v_axes[__pyx_v_n])]) = __pyx_v_n;
+0580: axes_holding_offset[axes[n]] = holding_offset
(__pyx_v_axes_holding_offset[(__pyx_v_axes[__pyx_v_n])]) = __pyx_v_holding_offset;
+0581: unique_axes_count += 1
__pyx_v_unique_axes_count = (__pyx_v_unique_axes_count + 1);
0582: else:
+0583: holding_offset += 1
/*else*/ {
__pyx_v_holding_offset = (__pyx_v_holding_offset + 1);
}
__pyx_L7:;
}
0584:
+0585: unique_axes[0] = <int64_t *>malloc(
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0586: unique_axes_count * sizeof(int64_t))
0587:
+0588: not_axes[0] = <int64_t *>malloc(
(__pyx_v_not_axes[0]) = ((int64_t *)malloc(((__pyx_v_dimensions - __pyx_v_unique_axes_count) * (sizeof(int64_t)))));
0589: (dimensions - unique_axes_count) * sizeof(int64_t))
0590:
0591: # Now we need to write back the unique axes to a tmp axes
+0592: cdef int64_t not_axes_count = 0
__pyx_v_not_axes_count = 0;
0593:
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__pyx_t_1 = __pyx_v_dimensions;
__pyx_t_2 = __pyx_t_1;
for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
__pyx_v_n = __pyx_t_3;
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__pyx_t_4 = (((__pyx_v_axes_holding[__pyx_v_n]) != -1L) != 0);
if (__pyx_t_4) {
/* … */
goto __pyx_L10;
}
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((__pyx_v_unique_axes[0])[((__pyx_v_axes_holding[__pyx_v_n]) - (__pyx_v_axes_holding_offset[__pyx_v_n]))]) = (__pyx_v_axes[(__pyx_v_axes_holding[__pyx_v_n])]);
0597: axes[axes_holding[n]])
0598:
0599: else:
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/*else*/ {
((__pyx_v_not_axes[0])[__pyx_v_not_axes_count]) = __pyx_v_n;
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__pyx_v_not_axes_count = (__pyx_v_not_axes_count + 1);
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__pyx_L10:;
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free(__pyx_v_axes_holding);
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free(__pyx_v_axes_holding_offset);
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0607:
0608:
0609: # The External Interface
0610: # ======================
0611: #
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struct __pyx_obj_6pyrost_3bin_6pyfftw_FFTW {
PyObject_HEAD
struct __pyx_vtabstruct_6pyrost_3bin_6pyfftw_FFTW *__pyx_vtab;
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__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_execute _fftw_execute;
__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_destroy_plan _fftw_destroy;
__pyx_t_6pyrost_3bin_6pyfftw_fftw_generic_plan_with_nthreads _nthreads_plan_setter;
void *_plan;
PyArrayObject *_input_array;
PyArrayObject *_output_array;
int *_direction;
unsigned int _flags;
int _simd_allowed;
int _input_array_alignment;
int _output_array_alignment;
int _use_threads;
PyObject *_input_item_strides;
PyObject *_input_strides;
PyObject *_output_item_strides;
PyObject *_output_strides;
PyObject *_input_shape;
PyObject *_output_shape;
PyObject *_input_dtype;
PyObject *_output_dtype;
PyObject *_flags_used;
double _normalisation_scaling;
double _sqrt_normalisation_scaling;
int _rank;
__pyx_t_6pyrost_3bin_6pyfftw__fftw_iodim *_dims;
int _howmany_rank;
__pyx_t_6pyrost_3bin_6pyfftw__fftw_iodim *_howmany_dims;
int64_t *_axes;
int64_t *_not_axes;
int64_t _total_size;
int _normalise_idft;
int _ortho;
};
struct __pyx_vtabstruct_6pyrost_3bin_6pyfftw_FFTW {
PyObject *(*update_arrays)(struct __pyx_obj_6pyrost_3bin_6pyfftw_FFTW *, PyObject *, PyObject *, int __pyx_skip_dispatch);
PyObject *(*_update_arrays)(struct __pyx_obj_6pyrost_3bin_6pyfftw_FFTW *, PyArrayObject *, PyArrayObject *);
PyObject *(*execute)(struct __pyx_obj_6pyrost_3bin_6pyfftw_FFTW *, int __pyx_skip_dispatch);
};
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0613: '''
0614: FFTW is a class for computing a variety of discrete Fourier
0615: transforms of multidimensional, strided arrays using the FFTW
0616: library. The interface is designed to be somewhat pythonic, with
0617: the correct transform being inferred from the dtypes of the passed
0618: arrays.
0619:
0620: The exact scheme may be either directly specified with the
0621: ``direction`` parameter or inferred from the dtypes and relative
0622: shapes of the input arrays. Information on which shapes and dtypes
0623: imply which transformations is available in the :ref:`FFTW schemes
0624: <scheme_table>`. If a match is found, the plan corresponding to that
0625: scheme is created, operating on the arrays that are passed in. If no
0626: scheme can be created then a ``ValueError`` is raised.
0627:
0628: The actual transformation is performed by calling the
0629: :meth:`~pyfftw.FFTW.execute` method.
0630:
0631: The arrays can be updated by calling the
0632: :meth:`~pyfftw.FFTW.update_arrays` method.
0633:
0634: The created instance of the class is itself callable, and can perform
0635: the execution of the FFT, both with or without array updates, returning
0636: the result of the FFT. Unlike calling the :meth:`~pyfftw.FFTW.execute`
0637: method, calling the class instance will also optionally normalise the
0638: output as necessary. Additionally, calling with an input array update
0639: will also coerce that array to be the correct dtype.
0640:
0641: See the documentation on the :meth:`~pyfftw.FFTW.__call__` method
0642: for more information.
0643: '''
0644: # Each of these function pointers simply
0645: # points to a chosen fftw wrapper function
0646: cdef fftw_generic_plan_guru _fftw_planner
0647: cdef fftw_generic_execute _fftw_execute
0648: cdef fftw_generic_destroy_plan _fftw_destroy
0649: cdef fftw_generic_plan_with_nthreads _nthreads_plan_setter
0650:
0651: # The plan is typecast when it is created or used
0652: # within the wrapper functions
0653: cdef void *_plan
0654:
0655: cdef np.ndarray _input_array
0656: cdef np.ndarray _output_array
0657: cdef int *_direction
0658: cdef unsigned _flags
0659:
0660: cdef bint _simd_allowed
0661: cdef int _input_array_alignment
0662: cdef int _output_array_alignment
0663: cdef bint _use_threads
0664:
0665: cdef object _input_item_strides
0666: cdef object _input_strides
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0668: cdef object _output_strides
0669: cdef object _input_shape
0670: cdef object _output_shape
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0672: cdef object _output_dtype
0673: cdef object _flags_used
0674:
0675: cdef double _normalisation_scaling
0676: cdef double _sqrt_normalisation_scaling
0677:
0678: cdef int _rank
0679: cdef _fftw_iodim *_dims
0680: cdef int _howmany_rank
0681: cdef _fftw_iodim *_howmany_dims
0682:
0683: cdef int64_t *_axes
0684: cdef int64_t *_not_axes
0685:
0686: cdef int64_t _total_size
0687:
0688: cdef bint _normalise_idft
0689: cdef bint _ortho
0690:
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case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5);
CYTHON_FALLTHROUGH;
case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4);
CYTHON_FALLTHROUGH;
case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
CYTHON_FALLTHROUGH;
case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
CYTHON_FALLTHROUGH;
case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
CYTHON_FALLTHROUGH;
case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
CYTHON_FALLTHROUGH;
case 0: break;
}
kw_args = PyDict_Size(__pyx_kwds);
switch (pos_args) {
case 0:
if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_input_array)) != 0)) kw_args--;
else goto __pyx_L5_argtuple_error;
CYTHON_FALLTHROUGH;
case 1:
if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_output_array)) != 0)) kw_args--;
else {
__Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 9, 1); __PYX_ERR(0, 864, __pyx_L3_error)
}
CYTHON_FALLTHROUGH;
case 2:
if (kw_args > 0) {
PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_axes);
if (value) { values[2] = value; kw_args--; }
}
CYTHON_FALLTHROUGH;
case 3:
if (kw_args > 0) {
PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_direction);
if (value) { values[3] = value; kw_args--; }
}
CYTHON_FALLTHROUGH;
case 4:
if (kw_args > 0) {
PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_flags);
if (value) { values[4] = value; kw_args--; }
}
CYTHON_FALLTHROUGH;
case 5:
if (kw_args > 0) {
PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_threads);
if (value) { values[5] = value; kw_args--; }
}
CYTHON_FALLTHROUGH;
case 6:
if (kw_args > 0) {
PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_planning_timelimit);
if (value) { values[6] = value; kw_args--; }
}
CYTHON_FALLTHROUGH;
case 7:
if (kw_args > 0) {
PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_normalise_idft);
if (value) { values[7] = value; kw_args--; }
}
CYTHON_FALLTHROUGH;
case 8:
if (kw_args > 0) {
PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_ortho);
if (value) { values[8] = value; kw_args--; }
}
}
if (unlikely(kw_args > 0)) {
const Py_ssize_t used_pos_args = (pos_args < 9) ? pos_args : 9;
if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, __pyx_v_kwargs, values, used_pos_args, "__cinit__") < 0)) __PYX_ERR(0, 864, __pyx_L3_error)
}
} else {
switch (PyTuple_GET_SIZE(__pyx_args)) {
default:
case 9: values[8] = PyTuple_GET_ITEM(__pyx_args, 8);
CYTHON_FALLTHROUGH;
case 8: values[7] = PyTuple_GET_ITEM(__pyx_args, 7);
CYTHON_FALLTHROUGH;
case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6);
CYTHON_FALLTHROUGH;
case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5);
CYTHON_FALLTHROUGH;
case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4);
CYTHON_FALLTHROUGH;
case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
CYTHON_FALLTHROUGH;
case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
CYTHON_FALLTHROUGH;
case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
break;
case 1:
case 0:
goto __pyx_L5_argtuple_error;
}
}
__pyx_v_input_array = values[0];
__pyx_v_output_array = values[1];
__pyx_v_axes = values[2];
__pyx_v_direction = values[3];
__pyx_v_flags = values[4];
if (values[5]) {
__pyx_v_threads = __Pyx_PyInt_As_unsigned_int(values[5]); if (unlikely((__pyx_v_threads == (unsigned int)-1) && PyErr_Occurred())) __PYX_ERR(0, 866, __pyx_L3_error)
} else {
__pyx_v_threads = ((unsigned int)1);
}
__pyx_v_planning_timelimit = values[6];
if (values[7]) {
__pyx_v_normalise_idft = __Pyx_PyObject_IsTrue(values[7]); if (unlikely((__pyx_v_normalise_idft == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 867, __pyx_L3_error)
} else {
+0867: bint normalise_idft=True, bint ortho=False,
__pyx_v_normalise_idft = ((int)1);
}
if (values[8]) {
__pyx_v_ortho = __Pyx_PyObject_IsTrue(values[8]); if (unlikely((__pyx_v_ortho == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 867, __pyx_L3_error)
} else {
__pyx_v_ortho = ((int)0);
}
}
goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:;
__Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 9, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 864, __pyx_L3_error)
__pyx_L3_error:;
__Pyx_DECREF(__pyx_v_args); __pyx_v_args = 0;
__Pyx_DECREF(__pyx_v_kwargs); __pyx_v_kwargs = 0;
__Pyx_AddTraceback("pyrost.bin.pyfftw.FFTW.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
__Pyx_RefNannyFinishContext();
return -1;
__pyx_L4_argument_unpacking_done:;
__pyx_r = __pyx_pf_6pyrost_3bin_6pyfftw_4FFTW_34__cinit__(((struct __pyx_obj_6pyrost_3bin_6pyfftw_FFTW *)__pyx_v_self), __pyx_v_input_array, __pyx_v_output_array, __pyx_v_axes, __pyx_v_direction, __pyx_v_flags, __pyx_v_threads, __pyx_v_planning_timelimit, __pyx_v_normalise_idft, __pyx_v_ortho, __pyx_v_args, __pyx_v_kwargs);
0868: *args, **kwargs):
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__pyx_t_1 = PyUnicode_Check(__pyx_v_direction);
__pyx_t_2 = (__pyx_t_1 != 0);
if (__pyx_t_2) {
/* … */
goto __pyx_L3;
}
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__pyx_t_3 = PyList_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 871, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_direction); __Pyx_GIVEREF(__pyx_v_direction); PyList_SET_ITEM(__pyx_t_3, 0, __pyx_v_direction); __pyx_v_given_directions = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0;
0872: else:
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/*else*/ {
__pyx_t_3 = PySequence_List(__pyx_v_direction); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 873, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_3);
__pyx_v_given_directions = ((PyObject*)__pyx_t_3);
__pyx_t_3 = 0;
}
__pyx_L3:;
0874:
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__pyx_v_self->_dims = NULL;
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__pyx_v_self->_howmany_dims = NULL;
0879:
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__pyx_v_self->_axes = NULL;
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__pyx_v_self->_not_axes = NULL;
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__pyx_v_self->_direction = NULL;
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__pyx_v_self->_normalise_idft = __pyx_v_normalise_idft;
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__pyx_v_self->_ortho = __pyx_v_ortho;
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__pyx_t_1 = (__pyx_v_self->_ortho != 0);
if (__pyx_t_1) {
} else {
__pyx_t_2 = __pyx_t_1;
goto __pyx_L5_bool_binop_done;
}
__pyx_t_1 = (__pyx_v_self->_normalise_idft != 0);
__pyx_t_2 = __pyx_t_1;
__pyx_L5_bool_binop_done:;
if (unlikely(__pyx_t_2)) {
/* … */
}
+0887: raise ValueError('Invalid options: '
__pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 887, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(0, 887, __pyx_L1_error) /* … */ __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_u_Invalid_options_ortho_and_normal); if (unlikely(!__pyx_tuple__6)) __PYX_ERR(0, 887, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__6); __Pyx_GIVEREF(__pyx_tuple__6);
0888: 'ortho and normalise_idft cannot both be True.')
0889:
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__pyx_t_3 = PySequence_List(__pyx_v_flags); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 890, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF_SET(__pyx_v_flags, __pyx_t_3); __pyx_t_3 = 0;
0891:
0892: cdef double _planning_timelimit
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__pyx_t_2 = (__pyx_v_planning_timelimit == Py_None);
__pyx_t_1 = (__pyx_t_2 != 0);
if (__pyx_t_1) {
/* … */
goto __pyx_L7;
}
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__pyx_v__planning_timelimit = FFTW_NO_TIMELIMIT;
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/*else*/ {
{
/*try:*/ {
/* … */
}
__Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
__Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
goto __pyx_L13_try_end;
__pyx_L8_error:;
__Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
/* … */
__Pyx_XGIVEREF(__pyx_t_4);
__Pyx_XGIVEREF(__pyx_t_5);
__Pyx_XGIVEREF(__pyx_t_6);
__Pyx_ExceptionReset(__pyx_t_4, __pyx_t_5, __pyx_t_6);
goto __pyx_L1_error;
__pyx_L13_try_end:;
}
}
__pyx_L7:;
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__pyx_t_7 = __pyx_PyFloat_AsDouble(__pyx_v_planning_timelimit); if (unlikely((__pyx_t_7 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 897, __pyx_L8_error) __pyx_v__planning_timelimit = __pyx_t_7;
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__pyx_t_8 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_TypeError); if (__pyx_t_8) { __Pyx_AddTraceback("pyrost.bin.pyfftw.FFTW.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_3, &__pyx_t_9, &__pyx_t_10) < 0) __PYX_ERR(0, 898, __pyx_L10_except_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GOTREF(__pyx_t_9); __Pyx_GOTREF(__pyx_t_10);
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__pyx_t_11 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 899, __pyx_L10_except_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_Raise(__pyx_t_11, 0, 0, 0); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __PYX_ERR(0, 899, __pyx_L10_except_error) } goto __pyx_L10_except_error; __pyx_L10_except_error:; /* … */ __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_u_Invalid_planning_timelimit_The_p); if (unlikely(!__pyx_tuple__7)) __PYX_ERR(0, 899, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__7); __Pyx_GIVEREF(__pyx_tuple__7);
0900: 'The planning timelimit needs to be a float.')
0901:
+0902: if not isinstance(input_array, np.ndarray):
__pyx_t_1 = __Pyx_TypeCheck(__pyx_v_input_array, __pyx_ptype_5numpy_ndarray);
__pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
if (unlikely(__pyx_t_2)) {
/* … */
}
+0903: raise ValueError('Invalid input array: '
__pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 903, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(0, 903, __pyx_L1_error) /* … */ __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_u_Invalid_input_array_The_input_ar); if (unlikely(!__pyx_tuple__8)) __PYX_ERR(0, 903, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__8); __Pyx_GIVEREF(__pyx_tuple__8);
0904: 'The input array needs to be an instance '
0905: 'of numpy.ndarray')
0906:
+0907: if not isinstance(output_array, np.ndarray):
__pyx_t_2 = __Pyx_TypeCheck(__pyx_v_output_array, __pyx_ptype_5numpy_ndarray);
__pyx_t_1 = ((!(__pyx_t_2 != 0)) != 0);
if (unlikely(__pyx_t_1)) {
/* … */
}
+0908: raise ValueError('Invalid output array: '
__pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__9, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 908, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(0, 908, __pyx_L1_error) /* … */ __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_u_Invalid_output_array_The_output); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(0, 908, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__9); __Pyx_GIVEREF(__pyx_tuple__9);
0909: 'The output array needs to be an instance '
0910: 'of numpy.ndarray')
0911:
+0912: try:
{
/*try:*/ {
/* … */
}
__Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
__Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
goto __pyx_L23_try_end;
__pyx_L18_error:;
__Pyx_XDECREF(__pyx_t_10); __pyx_t_10 = 0;
__Pyx_XDECREF(__pyx_t_11); __pyx_t_11 = 0;
__Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
__Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0;
/* … */
__Pyx_XGIVEREF(__pyx_t_6);
__Pyx_XGIVEREF(__pyx_t_5);
__Pyx_XGIVEREF(__pyx_t_4);
__Pyx_ExceptionReset(__pyx_t_6, __pyx_t_5, __pyx_t_4);
goto __pyx_L1_error;
__pyx_L23_try_end:;
}
+0913: input_dtype = input_array.dtype
__pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_v_input_array, __pyx_n_s_dtype); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 913, __pyx_L18_error) __Pyx_GOTREF(__pyx_t_10); __pyx_v_input_dtype = __pyx_t_10; __pyx_t_10 = 0;
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__pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_v_output_array, __pyx_n_s_dtype); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 914, __pyx_L18_error) __Pyx_GOTREF(__pyx_t_10); __pyx_v_output_dtype = __pyx_t_10; __pyx_t_10 = 0;
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__pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 915, __pyx_L18_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_INCREF(__pyx_v_input_dtype); __Pyx_GIVEREF(__pyx_v_input_dtype); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_v_input_dtype); __Pyx_INCREF(__pyx_v_output_dtype); __Pyx_GIVEREF(__pyx_v_output_dtype); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_v_output_dtype); __pyx_t_9 = __Pyx_PyObject_GetItem(__pyx_v_6pyrost_3bin_6pyfftw_fftw_schemes, __pyx_t_10); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 915, __pyx_L18_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_scheme = __pyx_t_9; __pyx_t_9 = 0;
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__pyx_t_8 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_KeyError); if (__pyx_t_8) { __Pyx_AddTraceback("pyrost.bin.pyfftw.FFTW.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_9, &__pyx_t_10, &__pyx_t_3) < 0) __PYX_ERR(0, 916, __pyx_L20_except_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GOTREF(__pyx_t_10); __Pyx_GOTREF(__pyx_t_3);
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0918: 'The output array and input array dtypes '
0919: 'do not correspond to a valid fftw scheme.')
0920:
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__Pyx_INCREF(__pyx_v_input_dtype); __Pyx_GIVEREF(__pyx_v_input_dtype); __Pyx_GOTREF(__pyx_v_self->_input_dtype); __Pyx_DECREF(__pyx_v_self->_input_dtype); __pyx_v_self->_input_dtype = __pyx_v_input_dtype;
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__Pyx_INCREF(__pyx_v_output_dtype); __Pyx_GIVEREF(__pyx_v_output_dtype); __Pyx_GOTREF(__pyx_v_self->_output_dtype); __Pyx_DECREF(__pyx_v_self->_output_dtype); __pyx_v_self->_output_dtype = __pyx_v_output_dtype;
0923:
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__Pyx_GetModuleGlobalName(__pyx_t_10, __pyx_n_s_scheme_functions); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 924, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_10))) { __pyx_t_9 = PyMethod_GET_SELF(__pyx_t_10); if (likely(__pyx_t_9)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_10); __Pyx_INCREF(__pyx_t_9); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_10, function); } } __pyx_t_3 = (__pyx_t_9) ? __Pyx_PyObject_Call2Args(__pyx_t_10, __pyx_t_9, __pyx_v_scheme) : __Pyx_PyObject_CallOneArg(__pyx_t_10, __pyx_v_scheme); __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 924, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_functions = __pyx_t_3; __pyx_t_3 = 0;
0925:
+0926: self._fftw_planner = planners[functions['planner']]
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__pyx_t_3 = __Pyx_PyObject_Dict_GetItem(__pyx_v_functions, __pyx_n_u_generic_precision); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 928, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_self->_fftw_destroy = (__pyx_v_6pyrost_3bin_6pyfftw_destroyers[__pyx_t_12]);
0929:
+0930: self._nthreads_plan_setter = (
__pyx_v_self->_nthreads_plan_setter = (__pyx_v_6pyrost_3bin_6pyfftw_nthreads_plan_setters[__pyx_t_12]);
+0931: nthreads_plan_setters[functions['generic_precision']])
__pyx_t_3 = __Pyx_PyObject_Dict_GetItem(__pyx_v_functions, __pyx_n_u_generic_precision); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 931, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
0932:
0933: cdef fftw_generic_set_timelimit set_timelimit_func = (
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__pyx_t_3 = __Pyx_PyObject_Dict_GetItem(__pyx_v_functions, __pyx_n_u_generic_precision); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 934, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 934, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_set_timelimit_func = (__pyx_v_6pyrost_3bin_6pyfftw_set_timelimit_funcs[__pyx_t_12]);
0935:
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__pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_input_array, __pyx_n_s_real); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 940, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_dtype); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 940, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_alignment); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 940, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_8 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_8 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 940, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_natural_input_alignment = __pyx_t_8;
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__pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_output_array, __pyx_n_s_real); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 941, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_dtype); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 941, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_alignment); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 941, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_8 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_8 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 941, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_natural_output_alignment = __pyx_t_8;
0942:
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__pyx_t_1 = (__Pyx_PySequence_ContainsTF(__pyx_n_u_FFTW_UNALIGNED, __pyx_v_flags, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 946, __pyx_L1_error) __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* … */ goto __pyx_L26; }
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__pyx_v_self->_simd_allowed = 0;
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__pyx_v_self->_input_array_alignment = __pyx_v_natural_input_alignment;
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__pyx_v_self->_output_array_alignment = __pyx_v_natural_output_alignment;
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0951: else:
0952:
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/*else*/ {
__pyx_v_self->_input_array_alignment = -1;
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__pyx_v_self->_output_array_alignment = -1;
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__Pyx_GetModuleGlobalName(__pyx_t_3, __pyx_n_s_valid_simd_alignments); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 956, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (likely(PyList_CheckExact(__pyx_t_3)) || PyTuple_CheckExact(__pyx_t_3)) { __pyx_t_10 = __pyx_t_3; __Pyx_INCREF(__pyx_t_10); __pyx_t_12 = 0; __pyx_t_13 = NULL; } else { __pyx_t_12 = -1; __pyx_t_10 = PyObject_GetIter(__pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 956, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_13 = Py_TYPE(__pyx_t_10)->tp_iternext; if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 956, __pyx_L1_error) } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; for (;;) { if (likely(!__pyx_t_13)) { if (likely(PyList_CheckExact(__pyx_t_10))) { if (__pyx_t_12 >= PyList_GET_SIZE(__pyx_t_10)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyList_GET_ITEM(__pyx_t_10, __pyx_t_12); __Pyx_INCREF(__pyx_t_3); __pyx_t_12++; if (unlikely(0 < 0)) __PYX_ERR(0, 956, __pyx_L1_error) #else __pyx_t_3 = PySequence_ITEM(__pyx_t_10, __pyx_t_12); __pyx_t_12++; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 956, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); #endif } else { if (__pyx_t_12 >= PyTuple_GET_SIZE(__pyx_t_10)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyTuple_GET_ITEM(__pyx_t_10, __pyx_t_12); __Pyx_INCREF(__pyx_t_3); __pyx_t_12++; if (unlikely(0 < 0)) __PYX_ERR(0, 956, __pyx_L1_error) #else __pyx_t_3 = PySequence_ITEM(__pyx_t_10, __pyx_t_12); __pyx_t_12++; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 956, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); #endif } } else { __pyx_t_3 = __pyx_t_13(__pyx_t_10); if (unlikely(!__pyx_t_3)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(0, 956, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_3); } __Pyx_XDECREF_SET(__pyx_v_each_alignment, __pyx_t_3); __pyx_t_3 = 0; /* … */ } __pyx_L28_break:; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
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if (!(likely(((__pyx_v_input_array) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_input_array, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 957, __pyx_L1_error) __pyx_t_3 = PyInt_FromSsize_t(((intptr_t)PyArray_DATA(((PyArrayObject *)__pyx_v_input_array)))); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 957, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* … */ __pyx_t_9 = PyNumber_Remainder(__pyx_t_3, __pyx_v_each_alignment); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 957, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* … */ if (__pyx_t_2) { /* … */ }
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__pyx_t_3 = __Pyx_PyInt_EqObjC(__pyx_t_9, __pyx_int_0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 958, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 958, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L30_bool_binop_done; }
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if (!(likely(((__pyx_v_output_array) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_output_array, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 959, __pyx_L1_error) __pyx_t_3 = PyInt_FromSsize_t(((intptr_t)PyArray_DATA(((PyArrayObject *)__pyx_v_output_array)))); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 959, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* … */ __pyx_t_9 = PyNumber_Remainder(__pyx_t_3, __pyx_v_each_alignment); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 959, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
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__pyx_t_3 = __Pyx_PyInt_EqObjC(__pyx_t_9, __pyx_int_0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 960, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 960, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_2 = __pyx_t_1; __pyx_L30_bool_binop_done:;
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__pyx_t_8 = __Pyx_PyInt_As_int(__pyx_v_each_alignment); if (unlikely((__pyx_t_8 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 965, __pyx_L1_error) __pyx_v_self->_output_array_alignment = __pyx_t_8;
0966:
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goto __pyx_L28_break;
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if (!__pyx_t_1) {
} else {
__pyx_t_2 = __pyx_t_1;
goto __pyx_L33_bool_binop_done;
}
/* … */
if (__pyx_t_2) {
/* … */
}
}
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__pyx_t_2 = __pyx_t_1;
__pyx_L33_bool_binop_done:;
0971:
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__pyx_v_self->_simd_allowed = 0;
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__pyx_v_self->_input_array_alignment = __pyx_v_natural_input_alignment;
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__pyx_v_self->_output_array_alignment = __pyx_v_natural_output_alignment;
0977: natural_output_alignment)
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__pyx_t_14 = __Pyx_PyObject_Append(__pyx_v_flags, __pyx_n_u_FFTW_UNALIGNED); if (unlikely(__pyx_t_14 == ((int)-1))) __PYX_ERR(0, 978, __pyx_L1_error)
0979:
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if (!(likely(((__pyx_v_input_array) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_input_array, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 980, __pyx_L1_error) /* … */ __pyx_t_2 = ((!(((((intptr_t)PyArray_DATA(((PyArrayObject *)__pyx_v_input_array))) % __pyx_v_self->_input_array_alignment) == 0) != 0)) != 0); /* … */ if (unlikely(__pyx_t_2)) { /* … */ }
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0983: 'The input array is expected to lie on a %d '
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__pyx_t_10 = __Pyx_PyInt_From_int(__pyx_v_self->_input_array_alignment); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 984, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_3 = PyUnicode_Format(__pyx_kp_u_Invalid_input_alignment_The_inpu, __pyx_t_10); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 984, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
0985:
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if (!(likely(((__pyx_v_output_array) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_output_array, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 986, __pyx_L1_error) /* … */ __pyx_t_2 = ((!(((((intptr_t)PyArray_DATA(((PyArrayObject *)__pyx_v_output_array))) % __pyx_v_self->_output_array_alignment) == 0) != 0)) != 0); /* … */ if (unlikely(__pyx_t_2)) { /* … */ }
0987: % self._output_array_alignment == 0)):
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__pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 988, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(0, 988, __pyx_L1_error)
0989: 'The output array is expected to lie on a %d '
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__pyx_t_10 = __Pyx_PyInt_From_int(__pyx_v_self->_output_array_alignment); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 990, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_3 = PyUnicode_Format(__pyx_kp_u_Invalid_output_alignment_The_out, __pyx_t_10); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 990, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
0991:
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__pyx_t_10 = __pyx_v_given_directions; __Pyx_INCREF(__pyx_t_10); __pyx_t_12 = 0; for (;;) { if (__pyx_t_12 >= PyList_GET_SIZE(__pyx_t_10)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyList_GET_ITEM(__pyx_t_10, __pyx_t_12); __Pyx_INCREF(__pyx_t_3); __pyx_t_12++; if (unlikely(0 < 0)) __PYX_ERR(0, 992, __pyx_L1_error) #else __pyx_t_3 = PySequence_ITEM(__pyx_t_10, __pyx_t_12); __pyx_t_12++; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 992, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); #endif __Pyx_DECREF_SET(__pyx_v_direction, __pyx_t_3); __pyx_t_3 = 0; /* … */ } __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
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__pyx_t_3 = __Pyx_PyObject_GetItem(__pyx_v_6pyrost_3bin_6pyfftw_scheme_directions, __pyx_v_scheme); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 993, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = (__Pyx_PySequence_ContainsTF(__pyx_v_direction, __pyx_t_3, Py_NE)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(0, 993, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_1 = (__pyx_t_2 != 0); if (unlikely(__pyx_t_1)) { /* … */ }
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__pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(0, 994, __pyx_L1_error) /* … */ __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_u_Invalid_direction_The_direction); if (unlikely(!__pyx_tuple__11)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__11); __Pyx_GIVEREF(__pyx_tuple__11);
0995: 'The direction is not valid for the scheme. '
0996: 'Try setting it explicitly if it is not already.')
0997:
+0998: self._direction = <int *>malloc(len(axes)*sizeof(int))
__pyx_t_12 = PyObject_Length(__pyx_v_axes); if (unlikely(__pyx_t_12 == ((Py_ssize_t)-1))) __PYX_ERR(0, 998, __pyx_L1_error) __pyx_v_self->_direction = ((int *)malloc((__pyx_t_12 * (sizeof(int)))));
0999:
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__pyx_v_real_transforms = 1;
1001: cdef int i
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__Pyx_INCREF(PyList_GET_ITEM(__pyx_v_given_directions, 0)); __pyx_t_10 = PyList_GET_ITEM(__pyx_v_given_directions, 0); __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_t_10, __pyx_n_u_FFTW_FORWARD, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(0, 1002, __pyx_L1_error) if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L41_bool_binop_done; } __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_t_10, __pyx_n_u_FFTW_BACKWARD, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(0, 1002, __pyx_L1_error) __pyx_t_1 = __pyx_t_2; __pyx_L41_bool_binop_done:; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* … */ goto __pyx_L40; }
+1003: self._direction[0] = directions[given_directions[0]]
__pyx_t_10 = __Pyx_PyObject_GetItem(__pyx_v_6pyrost_3bin_6pyfftw_directions, PyList_GET_ITEM(__pyx_v_given_directions, 0)); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1003, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_8 = __Pyx_PyInt_As_int(__pyx_t_10); if (unlikely((__pyx_t_8 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1003, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; (__pyx_v_self->_direction[0]) = __pyx_t_8;
+1004: real_transforms = False
__pyx_v_real_transforms = 0;
1005: else:
+1006: if len(axes) != len(given_directions):
/*else*/ {
__pyx_t_12 = PyObject_Length(__pyx_v_axes); if (unlikely(__pyx_t_12 == ((Py_ssize_t)-1))) __PYX_ERR(0, 1006, __pyx_L1_error)
__pyx_t_15 = PyList_GET_SIZE(__pyx_v_given_directions); if (unlikely(__pyx_t_15 == ((Py_ssize_t)-1))) __PYX_ERR(0, 1006, __pyx_L1_error)
__pyx_t_2 = ((__pyx_t_12 != __pyx_t_15) != 0);
if (unlikely(__pyx_t_2)) {
/* … */
}
+1007: raise ValueError('For real-to-real transforms, there must '
__pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1007, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(0, 1007, __pyx_L1_error) /* … */ __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_u_For_real_to_real_transforms_ther); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(0, 1007, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__12); __Pyx_GIVEREF(__pyx_tuple__12);
1008: 'be exactly one specified transform for each '
1009: 'transformed axis.')
+1010: for i in range(len(axes)):
__pyx_t_15 = PyObject_Length(__pyx_v_axes); if (unlikely(__pyx_t_15 == ((Py_ssize_t)-1))) __PYX_ERR(0, 1010, __pyx_L1_error) __pyx_t_12 = __pyx_t_15; for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_12; __pyx_t_8+=1) { __pyx_v_i = __pyx_t_8;
+1011: if given_directions[0] in ['FFTW_FORWARD', 'FFTW_BACKWARD']:
__Pyx_INCREF(PyList_GET_ITEM(__pyx_v_given_directions, 0)); __pyx_t_10 = PyList_GET_ITEM(__pyx_v_given_directions, 0); __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_t_10, __pyx_n_u_FFTW_FORWARD, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 1011, __pyx_L1_error) if (!__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L47_bool_binop_done; } __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_t_10, __pyx_n_u_FFTW_BACKWARD, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 1011, __pyx_L1_error) __pyx_t_2 = __pyx_t_1; __pyx_L47_bool_binop_done:; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_1 = (__pyx_t_2 != 0); if (unlikely(__pyx_t_1)) { /* … */ }
+1012: raise ValueError('Heterogeneous transforms cannot be '
__pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1012, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(0, 1012, __pyx_L1_error) /* … */ __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_u_Heterogeneous_transforms_cannot); if (unlikely(!__pyx_tuple__13)) __PYX_ERR(0, 1012, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__13); __Pyx_GIVEREF(__pyx_tuple__13);
1013: 'assigned with \'FFTW_FORWARD\' or '
1014: '\'FFTW_BACKWARD\'.')
1015: else:
+1016: self._direction[i] = directions[given_directions[i]]
/*else*/ {
__pyx_t_10 = __Pyx_PyObject_GetItem(__pyx_v_6pyrost_3bin_6pyfftw_directions, PyList_GET_ITEM(__pyx_v_given_directions, __pyx_v_i)); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1016, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_10);
__pyx_t_16 = __Pyx_PyInt_As_int(__pyx_t_10); if (unlikely((__pyx_t_16 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1016, __pyx_L1_error)
__Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
(__pyx_v_self->_direction[__pyx_v_i]) = __pyx_t_16;
}
}
}
__pyx_L40:;
1017:
+1018: self._input_shape = input_array.shape
__pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_v_input_array, __pyx_n_s_shape); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1018, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_10); __Pyx_GOTREF(__pyx_v_self->_input_shape); __Pyx_DECREF(__pyx_v_self->_input_shape); __pyx_v_self->_input_shape = __pyx_t_10; __pyx_t_10 = 0;
+1019: self._output_shape = output_array.shape
__pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_v_output_array, __pyx_n_s_shape); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1019, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_10); __Pyx_GOTREF(__pyx_v_self->_output_shape); __Pyx_DECREF(__pyx_v_self->_output_shape); __pyx_v_self->_output_shape = __pyx_t_10; __pyx_t_10 = 0;
1020:
+1021: self._input_array = input_array
if (!(likely(((__pyx_v_input_array) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_input_array, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 1021, __pyx_L1_error) __pyx_t_10 = __pyx_v_input_array; __Pyx_INCREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_10); __Pyx_GOTREF(__pyx_v_self->_input_array); __Pyx_DECREF(((PyObject *)__pyx_v_self->_input_array)); __pyx_v_self->_input_array = ((PyArrayObject *)__pyx_t_10); __pyx_t_10 = 0;
+1022: self._output_array = output_array
if (!(likely(((__pyx_v_output_array) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_output_array, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 1022, __pyx_L1_error) __pyx_t_10 = __pyx_v_output_array; __Pyx_INCREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_10); __Pyx_GOTREF(__pyx_v_self->_output_array); __Pyx_DECREF(((PyObject *)__pyx_v_self->_output_array)); __pyx_v_self->_output_array = ((PyArrayObject *)__pyx_t_10); __pyx_t_10 = 0;
1023:
+1024: self._axes = <int64_t *>malloc(len(axes)*sizeof(int64_t))
__pyx_t_15 = PyObject_Length(__pyx_v_axes); if (unlikely(__pyx_t_15 == ((Py_ssize_t)-1))) __PYX_ERR(0, 1024, __pyx_L1_error) __pyx_v_self->_axes = ((int64_t *)malloc((__pyx_t_15 * (sizeof(int64_t)))));
+1025: for n in range(len(axes)):
__pyx_t_15 = PyObject_Length(__pyx_v_axes); if (unlikely(__pyx_t_15 == ((Py_ssize_t)-1))) __PYX_ERR(0, 1025, __pyx_L1_error) __pyx_t_12 = __pyx_t_15; for (__pyx_t_17 = 0; __pyx_t_17 < __pyx_t_12; __pyx_t_17+=1) { __pyx_v_n = __pyx_t_17;
+1026: self._axes[n] = axes[n]
__pyx_t_10 = __Pyx_GetItemInt(__pyx_v_axes, __pyx_v_n, Py_ssize_t, 1, PyInt_FromSsize_t, 0, 0, 0); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1026, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_18 = __Pyx_PyInt_As_int64_t(__pyx_t_10); if (unlikely((__pyx_t_18 == ((int64_t)-1)) && PyErr_Occurred())) __PYX_ERR(0, 1026, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; (__pyx_v_self->_axes[__pyx_v_n]) = __pyx_t_18; }
1027:
1028: # Set the negative entries to their actual index (use the size
1029: # of the shape array for this)
+1030: cdef int64_t array_dimension = len(self._input_shape)
__pyx_t_10 = __pyx_v_self->_input_shape; __Pyx_INCREF(__pyx_t_10); __pyx_t_15 = PyObject_Length(__pyx_t_10); if (unlikely(__pyx_t_15 == ((Py_ssize_t)-1))) __PYX_ERR(0, 1030, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_array_dimension = __pyx_t_15;
1031:
+1032: for n in range(len(axes)):
__pyx_t_15 = PyObject_Length(__pyx_v_axes); if (unlikely(__pyx_t_15 == ((Py_ssize_t)-1))) __PYX_ERR(0, 1032, __pyx_L1_error) __pyx_t_12 = __pyx_t_15; for (__pyx_t_17 = 0; __pyx_t_17 < __pyx_t_12; __pyx_t_17+=1) { __pyx_v_n = __pyx_t_17;
+1033: if self._axes[n] < 0:
__pyx_t_1 = (((__pyx_v_self->_axes[__pyx_v_n]) < 0) != 0);
if (__pyx_t_1) {
/* … */
}
+1034: self._axes[n] = self._axes[n] + array_dimension
(__pyx_v_self->_axes[__pyx_v_n]) = ((__pyx_v_self->_axes[__pyx_v_n]) + __pyx_v_array_dimension);
1035:
+1036: if self._axes[n] >= array_dimension or self._axes[n] < 0:
__pyx_t_2 = (((__pyx_v_self->_axes[__pyx_v_n]) >= __pyx_v_array_dimension) != 0);
if (!__pyx_t_2) {
} else {
__pyx_t_1 = __pyx_t_2;
goto __pyx_L55_bool_binop_done;
}
__pyx_t_2 = (((__pyx_v_self->_axes[__pyx_v_n]) < 0) != 0);
__pyx_t_1 = __pyx_t_2;
__pyx_L55_bool_binop_done:;
if (unlikely(__pyx_t_1)) {
/* … */
}
}
+1037: raise IndexError('Invalid axes: '
__pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_IndexError, __pyx_tuple__14, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1037, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(0, 1037, __pyx_L1_error) /* … */ __pyx_tuple__14 = PyTuple_Pack(1, __pyx_kp_u_Invalid_axes_The_axes_list_canno); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(0, 1037, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__14); __Pyx_GIVEREF(__pyx_tuple__14);
1038: 'The axes list cannot contain invalid axes.')
1039:
1040: cdef int64_t unique_axes_length
1041: cdef int64_t *unique_axes
1042: cdef int64_t *not_axes
1043:
+1044: make_axes_unique(self._axes, len(axes), &unique_axes,
__pyx_t_15 = PyObject_Length(__pyx_v_axes); if (unlikely(__pyx_t_15 == ((Py_ssize_t)-1))) __PYX_ERR(0, 1044, __pyx_L1_error) /* … */ __pyx_f_6pyrost_3bin_6pyfftw_make_axes_unique(__pyx_v_self->_axes, __pyx_t_15, (&__pyx_v_unique_axes), (&__pyx_v_not_axes), __pyx_v_array_dimension, (&__pyx_v_unique_axes_length));
1045: ¬_axes, array_dimension, &unique_axes_length)
1046:
1047: # and assign axes and not_axes to the filled arrays
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free(__pyx_v_self->_axes);
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__pyx_v_self->_axes = __pyx_v_unique_axes;
+1050: self._not_axes = not_axes
__pyx_v_self->_not_axes = __pyx_v_not_axes;
1051:
+1052: total_N = 1
__Pyx_INCREF(__pyx_int_1);
__pyx_v_total_N = __pyx_int_1;
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__pyx_t_18 = __pyx_v_unique_axes_length;
__pyx_t_19 = __pyx_t_18;
for (__pyx_t_15 = 0; __pyx_t_15 < __pyx_t_19; __pyx_t_15+=1) {
__pyx_v_n = __pyx_t_15;
+1054: if self._input_shape[self._axes[n]] == 0:
__pyx_t_10 = __Pyx_GetItemInt(__pyx_v_self->_input_shape, (__pyx_v_self->_axes[__pyx_v_n]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1054, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_3 = __Pyx_PyInt_EqObjC(__pyx_t_10, __pyx_int_0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1054, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 1054, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(__pyx_t_1)) { /* … */ }
+1055: raise ValueError('Zero length array: '
__pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1055, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(0, 1055, __pyx_L1_error) /* … */ __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_u_Zero_length_array_The_input_arra); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(0, 1055, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__15); __Pyx_GIVEREF(__pyx_tuple__15);
1056: 'The input array should have no zero length'
1057: 'axes over which the FFT is to be taken')
1058:
+1059: if real_transforms:
__pyx_t_1 = (__pyx_v_real_transforms != 0);
if (__pyx_t_1) {
/* … */
goto __pyx_L60;
}
+1060: if self._direction[n] == FFTW_RODFT00:
__pyx_t_1 = (((__pyx_v_self->_direction[__pyx_v_n]) == __pyx_e_6pyrost_3bin_6pyfftw_FFTW_RODFT00) != 0);
if (__pyx_t_1) {
/* … */
goto __pyx_L61;
}
+1061: total_N *= 2*(self._input_shape[self._axes[n]] + 1)
__pyx_t_3 = __Pyx_GetItemInt(__pyx_v_self->_input_shape, (__pyx_v_self->_axes[__pyx_v_n]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyInt_AddObjC(__pyx_t_3, __pyx_int_1, 1, 0, 0); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = PyNumber_Multiply(__pyx_int_2, __pyx_t_10); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = PyNumber_InPlaceMultiply(__pyx_v_total_N, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF_SET(__pyx_v_total_N, __pyx_t_10); __pyx_t_10 = 0;
+1062: elif self._direction[n] == FFTW_REDFT00:
__pyx_t_1 = (((__pyx_v_self->_direction[__pyx_v_n]) == __pyx_e_6pyrost_3bin_6pyfftw_FFTW_REDFT00) != 0);
if (__pyx_t_1) {
/* … */
goto __pyx_L61;
}
+1063: if (self._input_shape[self._axes[n]] < 2):
__pyx_t_10 = __Pyx_GetItemInt(__pyx_v_self->_input_shape, (__pyx_v_self->_axes[__pyx_v_n]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1063, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_3 = PyObject_RichCompare(__pyx_t_10, __pyx_int_2, Py_LT); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1063, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 1063, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(__pyx_t_1)) { /* … */ }
+1064: raise ValueError('FFTW_REDFT00 (also known as DCT-1) is'
__pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__16, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1064, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(0, 1064, __pyx_L1_error) /* … */ __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_u_FFTW_REDFT00_also_known_as_DCT_1); if (unlikely(!__pyx_tuple__16)) __PYX_ERR(0, 1064, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__16); __Pyx_GIVEREF(__pyx_tuple__16);
1065: ' not defined for inputs of length less than two.')
+1066: total_N *= 2*(self._input_shape[self._axes[n]] - 1)
__pyx_t_3 = __Pyx_GetItemInt(__pyx_v_self->_input_shape, (__pyx_v_self->_axes[__pyx_v_n]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1066, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyInt_SubtractObjC(__pyx_t_3, __pyx_int_1, 1, 0, 0); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1066, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = PyNumber_Multiply(__pyx_int_2, __pyx_t_10); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1066, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = PyNumber_InPlaceMultiply(__pyx_v_total_N, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1066, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF_SET(__pyx_v_total_N, __pyx_t_10); __pyx_t_10 = 0;
1067: else:
+1068: total_N *= 2*self._input_shape[self._axes[n]]
/*else*/ {
__pyx_t_10 = __Pyx_GetItemInt(__pyx_v_self->_input_shape, (__pyx_v_self->_axes[__pyx_v_n]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1068, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_10);
__pyx_t_3 = PyNumber_Multiply(__pyx_int_2, __pyx_t_10); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1068, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_3);
__Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
__pyx_t_10 = PyNumber_InPlaceMultiply(__pyx_v_total_N, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1068, __pyx_L1_error)
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if (__pyx_t_1) {
/* … */
goto __pyx_L63;
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1078:
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+1082: if functions['validator'] == -1:
__pyx_t_10 = __Pyx_PyObject_Dict_GetItem(__pyx_v_functions, __pyx_n_u_validator); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1082, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = __Pyx_PyInt_EqObjC(__pyx_t_10, __pyx_int_neg_1, -1L, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1082, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 1082, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; if (__pyx_t_1) { /* … */ goto __pyx_L64; }
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__pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_output_array, __pyx_n_s_shape); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1083, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_v_input_array, __pyx_n_s_shape); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1083, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_3 = PyObject_RichCompare(__pyx_t_9, __pyx_t_10, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1083, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 1083, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_2 = ((!__pyx_t_1) != 0); if (unlikely(__pyx_t_2)) { /* … */ }
+1084: raise ValueError('Invalid shapes: '
__pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__17, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1084, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(0, 1084, __pyx_L1_error) /* … */ __pyx_tuple__17 = PyTuple_Pack(1, __pyx_kp_u_Invalid_shapes_The_output_array); if (unlikely(!__pyx_tuple__17)) __PYX_ERR(0, 1084, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__17); __Pyx_GIVEREF(__pyx_tuple__17);
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1087: else:
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/*else*/ {
__pyx_t_3 = __Pyx_PyObject_Dict_GetItem(__pyx_v_functions, __pyx_n_u_validator); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1088, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_3);
__pyx_t_15 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_15 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 1088, __pyx_L1_error)
__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
__pyx_v__validator = (__pyx_v_6pyrost_3bin_6pyfftw_validators[__pyx_t_15]);
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1090: self._axes, self._not_axes, unique_axes_length):
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__pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__18, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1091, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(0, 1091, __pyx_L1_error) /* … */ __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_u_Invalid_shapes_The_input_array_a); if (unlikely(!__pyx_tuple__18)) __PYX_ERR(0, 1091, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__18); __Pyx_GIVEREF(__pyx_tuple__18);
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__pyx_v_self->_rank = __pyx_v_unique_axes_length;
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__pyx_v_self->_howmany_rank = (__pyx_v_self->_input_array->nd - __pyx_v_unique_axes_length);
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__pyx_v_self->_flags = 0;
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{
/*try:*/ {
/* … */
}
__Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
__Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
goto __pyx_L76_try_end;
__pyx_L69_error:;
__Pyx_XDECREF(__pyx_t_10); __pyx_t_10 = 0;
__Pyx_XDECREF(__pyx_t_11); __pyx_t_11 = 0;
__Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0;
/* … */
__Pyx_XGIVEREF(__pyx_t_4);
__Pyx_XGIVEREF(__pyx_t_5);
__Pyx_XGIVEREF(__pyx_t_6);
__Pyx_ExceptionReset(__pyx_t_4, __pyx_t_5, __pyx_t_6);
goto __pyx_L1_error;
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__pyx_t_10 = __Pyx_PyInt_From_unsigned_int(__pyx_v_self->_flags); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1102, __pyx_L69_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = __Pyx_PyObject_GetItem(__pyx_v_6pyrost_3bin_6pyfftw_flag_dict, __pyx_v_each_flag); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1102, __pyx_L69_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_11 = PyNumber_InPlaceOr(__pyx_t_10, __pyx_t_9); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 1102, __pyx_L69_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_20 = __Pyx_PyInt_As_unsigned_int(__pyx_t_11); if (unlikely((__pyx_t_20 == (unsigned int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1102, __pyx_L69_error) __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_v_self->_flags = __pyx_t_20;
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__pyx_t_14 = __Pyx_PyObject_Append(__pyx_v_self->_flags_used, __pyx_v_each_flag); if (unlikely(__pyx_t_14 == ((int)-1))) __PYX_ERR(0, 1103, __pyx_L69_error)
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__pyx_t_21 = PyNumber_Add(__pyx_kp_u_Invalid_flag, __pyx_v_each_flag); if (unlikely(!__pyx_t_21)) __PYX_ERR(0, 1105, __pyx_L71_except_error) __Pyx_GOTREF(__pyx_t_21); /* … */ __pyx_t_21 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_22); if (unlikely(!__pyx_t_21)) __PYX_ERR(0, 1105, __pyx_L71_except_error) __Pyx_GOTREF(__pyx_t_21); __Pyx_DECREF(__pyx_t_22); __pyx_t_22 = 0; __Pyx_Raise(__pyx_t_21, 0, 0, 0); __Pyx_DECREF(__pyx_t_21); __pyx_t_21 = 0; __PYX_ERR(0, 1105, __pyx_L71_except_error) } goto __pyx_L71_except_error; __pyx_L71_except_error:;
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__pyx_t_22 = PyNumber_Add(__pyx_t_21, __pyx_kp_u_is_not_a_valid_planner_flag); if (unlikely(!__pyx_t_22)) __PYX_ERR(0, 1106, __pyx_L71_except_error) __Pyx_GOTREF(__pyx_t_22); __Pyx_DECREF(__pyx_t_21); __pyx_t_21 = 0;
1107:
1108:
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__pyx_t_1 = (__Pyx_PySequence_ContainsTF(__pyx_n_u_FFTW_DESTROY_INPUT, __pyx_v_flags, Py_NE)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 1109, __pyx_L1_error) __pyx_t_23 = (__pyx_t_1 != 0); if (__pyx_t_23) { } else { __pyx_t_2 = __pyx_t_23; goto __pyx_L80_bool_binop_done; } /* … */ if (__pyx_t_2) { /* … */ }
+1110: (scheme[0] != 'c2r') or not self._rank > 1):
__pyx_t_3 = __Pyx_GetItemInt(__pyx_v_scheme, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1110, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_23 = (__Pyx_PyUnicode_Equals(__pyx_t_3, __pyx_n_u_c2r, Py_NE)); if (unlikely(__pyx_t_23 < 0)) __PYX_ERR(0, 1110, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (!__pyx_t_23) { } else { __pyx_t_2 = __pyx_t_23; goto __pyx_L80_bool_binop_done; } __pyx_t_23 = ((!((__pyx_v_self->_rank > 1) != 0)) != 0); __pyx_t_2 = __pyx_t_23; __pyx_L80_bool_binop_done:;
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1114:
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1118: self._rank * sizeof(_fftw_iodim))
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__pyx_v_self->_howmany_dims = ((__pyx_t_6pyrost_3bin_6pyfftw__fftw_iodim *)malloc((__pyx_v_self->_howmany_rank * (sizeof(__pyx_t_6pyrost_3bin_6pyfftw__fftw_iodim)))));
1120: self._howmany_rank * sizeof(_fftw_iodim))
1121:
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__pyx_t_23 = ((__pyx_v_self->_dims == NULL) != 0);
if (!__pyx_t_23) {
} else {
__pyx_t_2 = __pyx_t_23;
goto __pyx_L84_bool_binop_done;
}
__pyx_t_23 = ((__pyx_v_self->_howmany_dims == NULL) != 0);
__pyx_t_2 = __pyx_t_23;
__pyx_L84_bool_binop_done:;
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/* … */
}
1123: # Not much else to do than raise an exception
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PyErr_NoMemory(); __PYX_ERR(0, 1124, __pyx_L1_error)
1125:
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{ /* enter inner scope */
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__Pyx_GOTREF(__pyx_v_self->_output_item_strides);
__Pyx_DECREF(__pyx_v_self->_output_item_strides);
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1151:
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__pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_INCREF(__pyx_kp_u_Strides_of_the_output_array_must); __Pyx_GIVEREF(__pyx_kp_u_Strides_of_the_output_array_must); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_kp_u_Strides_of_the_output_array_must); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_10, NULL); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_Raise(__pyx_t_9, 0, 0, 0); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __PYX_ERR(0, 1153, __pyx_L1_error)
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__Pyx_INCREF(__pyx_t_9);
__pyx_v_fft_shape = __pyx_t_9;
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__Pyx_INCREF(__pyx_v_fft_shape_lookup);
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{
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__Pyx_INCREF(__pyx_v_input_array);
__Pyx_GIVEREF(__pyx_v_input_array);
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__Pyx_INCREF(__pyx_v_output_array);
__Pyx_GIVEREF(__pyx_v_output_array);
PyTuple_SET_ITEM(__pyx_t_3, 1+__pyx_t_8, __pyx_v_output_array);
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__Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
__pyx_v_fft_shape = __pyx_t_9;
__pyx_t_9 = 0;
}
__pyx_L106:;
1161:
1162: # Fill in the stride and shape information
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__pyx_t_9 = __pyx_v_self->_input_item_strides;
__Pyx_INCREF(__pyx_t_9);
__pyx_v_input_strides_array = __pyx_t_9;
__pyx_t_9 = 0;
+1164: output_strides_array = self._output_item_strides
__pyx_t_9 = __pyx_v_self->_output_item_strides;
__Pyx_INCREF(__pyx_t_9);
__pyx_v_output_strides_array = __pyx_t_9;
__pyx_t_9 = 0;
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__pyx_t_8 = __pyx_v_self->_rank;
__pyx_t_16 = __pyx_t_8;
for (__pyx_t_24 = 0; __pyx_t_24 < __pyx_t_16; __pyx_t_24+=1) {
__pyx_v_i = __pyx_t_24;
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__pyx_t_9 = __Pyx_GetItemInt(__pyx_v_fft_shape, (__pyx_v_self->_axes[__pyx_v_i]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1166, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_25 = __Pyx_PyInt_As_int(__pyx_t_9); if (unlikely((__pyx_t_25 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1166, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; (__pyx_v_self->_dims[__pyx_v_i])._n = __pyx_t_25;
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__pyx_t_9 = __Pyx_GetItemInt(__pyx_v_input_strides_array, (__pyx_v_self->_axes[__pyx_v_i]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1167, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_25 = __Pyx_PyInt_As_int(__pyx_t_9); if (unlikely((__pyx_t_25 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1167, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; (__pyx_v_self->_dims[__pyx_v_i])._is = __pyx_t_25;
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__pyx_t_9 = __Pyx_GetItemInt(__pyx_v_output_strides_array, (__pyx_v_self->_axes[__pyx_v_i]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1168, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_25 = __Pyx_PyInt_As_int(__pyx_t_9); if (unlikely((__pyx_t_25 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1168, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; (__pyx_v_self->_dims[__pyx_v_i])._os = __pyx_t_25; }
1169:
+1170: for i in range(0, self._howmany_rank):
__pyx_t_8 = __pyx_v_self->_howmany_rank;
__pyx_t_16 = __pyx_t_8;
for (__pyx_t_24 = 0; __pyx_t_24 < __pyx_t_16; __pyx_t_24+=1) {
__pyx_v_i = __pyx_t_24;
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__pyx_t_9 = __Pyx_GetItemInt(__pyx_v_fft_shape, (__pyx_v_self->_not_axes[__pyx_v_i]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1171, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_25 = __Pyx_PyInt_As_int(__pyx_t_9); if (unlikely((__pyx_t_25 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1171, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; (__pyx_v_self->_howmany_dims[__pyx_v_i])._n = __pyx_t_25;
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__pyx_t_9 = __Pyx_GetItemInt(__pyx_v_input_strides_array, (__pyx_v_self->_not_axes[__pyx_v_i]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1172, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_25 = __Pyx_PyInt_As_int(__pyx_t_9); if (unlikely((__pyx_t_25 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1172, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; (__pyx_v_self->_howmany_dims[__pyx_v_i])._is = __pyx_t_25;
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__pyx_t_9 = __Pyx_GetItemInt(__pyx_v_output_strides_array, (__pyx_v_self->_not_axes[__pyx_v_i]), int64_t, 1, __Pyx_PyInt_From_int64_t, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_25 = __Pyx_PyInt_As_int(__pyx_t_9); if (unlikely((__pyx_t_25 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; (__pyx_v_self->_howmany_dims[__pyx_v_i])._os = __pyx_t_25; }
1174:
1175: # parallel execution
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__pyx_v_self->_use_threads = (__pyx_v_threads > 1);
1177:
1178: ## Point at which FFTW calls are made
1179: ## (and none should be made before this)
1180:
1181: # noop if threads library not available
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__pyx_v_self->_nthreads_plan_setter(__pyx_v_threads);
1183:
1184: # Set the timelimit
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__pyx_v_set_timelimit_func(__pyx_v__planning_timelimit);
1186:
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1188: # (so that only one python thread can plan at a time, as the FFTW
1189: # planning functions are not thread-safe)
1190:
1191: # no self-lookups allowed in nogil block, so must grab all these first
1192: cdef void *plan
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__pyx_t_8 = __pyx_v_self->_rank; __pyx_v_rank = __pyx_t_8;
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__pyx_v_dims = ((fftw_iodim *)__pyx_v_self->_dims);
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__pyx_t_8 = __pyx_v_self->_howmany_rank; __pyx_v_howmany_rank = __pyx_t_8;
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__pyx_v_howmany_dims = ((fftw_iodim *)__pyx_v_self->_howmany_dims);
+1198: cdef void *_in = <void *>np.PyArray_DATA(self._input_array)
__pyx_t_9 = ((PyObject *)__pyx_v_self->_input_array); __Pyx_INCREF(__pyx_t_9); __pyx_v__in = ((void *)PyArray_DATA(((PyArrayObject *)__pyx_t_9))); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
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__pyx_t_9 = ((PyObject *)__pyx_v_self->_output_array); __Pyx_INCREF(__pyx_t_9); __pyx_v__out = ((void *)PyArray_DATA(((PyArrayObject *)__pyx_t_9))); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
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__pyx_t_20 = __pyx_v_self->_flags; __pyx_v_c_flags = __pyx_t_20;
1201:
+1202: with plan_lock, nogil:
/*with:*/ {
__pyx_t_6 = __Pyx_PyObject_LookupSpecial(__pyx_v_6pyrost_3bin_6pyfftw_plan_lock, __pyx_n_s_exit); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 1202, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_6);
__pyx_t_10 = __Pyx_PyObject_LookupSpecial(__pyx_v_6pyrost_3bin_6pyfftw_plan_lock, __pyx_n_s_enter); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1202, __pyx_L111_error)
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if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_10))) {
__pyx_t_3 = PyMethod_GET_SELF(__pyx_t_10);
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PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_10);
__Pyx_INCREF(__pyx_t_3);
__Pyx_INCREF(function);
__Pyx_DECREF_SET(__pyx_t_10, function);
}
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__pyx_t_9 = (__pyx_t_3) ? __Pyx_PyObject_CallOneArg(__pyx_t_10, __pyx_t_3) : __Pyx_PyObject_CallNoArg(__pyx_t_10);
__Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
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/*try:*/ {
{
/*try:*/ {
{
#ifdef WITH_THREAD
PyThreadState *_save;
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/*try:*/ {
/* … */
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/*normal exit:*/{
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Py_BLOCK_THREADS
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/*normal exit:*/{
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__pyx_t_27 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_tuple__19, NULL);
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goto __pyx_L1_error;
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/* … */
__pyx_tuple__19 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(0, 1202, __pyx_L1_error)
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__Pyx_GIVEREF(__pyx_tuple__19);
+1203: plan = fftw_planner(rank, dims, howmany_rank, howmany_dims,
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}
1204: _in, _out, self._direction, c_flags)
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__pyx_v_self->_plan = __pyx_v_plan;
1206:
+1207: if self._plan == NULL:
__pyx_t_2 = ((__pyx_v_self->_plan == NULL) != 0);
if (__pyx_t_2) {
/* … */
}
+1208: if 'FFTW_WISDOM_ONLY' in flags:
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+1209: raise RuntimeError('No FFTW wisdom is known for this plan.')
__pyx_t_9 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1209, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_Raise(__pyx_t_9, 0, 0, 0); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __PYX_ERR(0, 1209, __pyx_L1_error) /* … */ __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_u_No_FFTW_wisdom_is_known_for_this); if (unlikely(!__pyx_tuple__20)) __PYX_ERR(0, 1209, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__20); __Pyx_GIVEREF(__pyx_tuple__20);
1210: else:
+1211: raise RuntimeError('The data has an uncaught error that led '+
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__Pyx_Raise(__pyx_t_9, 0, 0, 0);
__Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
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/* … */
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__Pyx_GIVEREF(__pyx_tuple__21);
1212: 'to the planner returning NULL. This is a bug.')
1213:
+1214: def __init__(self, input_array, output_array, axes=(-1,),
/* Python wrapper */
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static char __pyx_doc_6pyrost_3bin_6pyfftw_4FFTW_36__init__[] = "\n **Arguments**:\n\n * ``input_array`` and ``output_array`` should be numpy arrays.\n The contents of these arrays will be destroyed by the planning\n process during initialisation. Information on supported\n dtypes for the arrays is :ref:`given below <scheme_table>`.\n\n * ``axes`` describes along which axes the DFT should be taken.\n This should be a valid list of axes. Repeated axes are\n only transformed once. Invalid axes will raise an ``IndexError``\n exception. This argument is equivalent to the same\n argument in :func:`numpy.fft.fftn`, except for the fact that\n the behaviour of repeated axes is different (``numpy.fft``\n will happily take the fft of the same axis if it is repeated\n in the ``axes`` argument). Rudimentary testing has suggested\n this is down to the underlying FFTW library and so unlikely\n to be fixed in these wrappers.\n\n * The ``direction`` parameter describes what sort of\n transformation the object should compute. This parameter is\n poorly named for historical reasons: older versions of pyFFTW\n only supported forward and backward transformations, for which\n this name made sense. Since then pyFFTW has been expanded to\n support real to real transforms as well and the name is not\n quite as descriptive.\n\n ``direction`` should either be a string, or, in the case of\n multiple real transforms, a list of strings. The two values\n corresponding to the DFT are\n\n * ``'FFTW_FORWARD'``, which is the forward discrete Fourier\n transform, and\n * ``'FFTW_BACKWARD'``, which is the backward discrete Fourier\n transform.\n\n Note that, for the two above options, only the Complex schemes\n allow a free choice for ``direction``. The direction *must*\n agree with the"" the :ref:`table below <scheme_table>` if a Real\n scheme is used, otherwise a ``ValueError`` is raised.\n\n\n Alternatively, if you are interested in one of the real to real\n transforms, then pyFFTW supports four different discrete cosine\n transforms:\n\n * ``'FFTW_REDFT00'``,\n * ``'FFTW_REDFT01'``,\n * ``'FFTW_REDFT10'``, and\n * ``'FFTW_REDFT01'``,\n\n and four discrete sine transforms:\n\n * ``'FFTW_RODFT00'``,\n * ``'FFTW_RODFT01'``,\n * ``'FFTW_RODFT10'``, and\n * ``'FFTW_RODFT01'``.\n\n pyFFTW uses the same naming convention for these flags as FFTW:\n the ``'REDFT'`` part of the name is an acronym for 'real even\n discrete Fourier transform, and, similarly, ``'RODFT'`` stands\n for 'real odd discrete Fourier transform'. The trailing ``'0'``\n is notation for even data (in terms of symmetry) and the\n trailing ``'1'`` is for odd data.\n\n Unlike the plain discrete Fourier transform, one may specify a\n different real to real transformation over each axis: for example,\n\n .. code-block:: none\n a = pyfftw.empty_aligned((128,128,128))\n b = pyfftw.empty_aligned((128,128,128))\n directions = ['FFTW_REDFT00', 'FFTW_RODFT11']\n transform = pyfftw.FFTW(a, b, axes=(0, 2), direction=directions)\n\n will create a transformation across the first and last axes\n with a discrete cosine transform over the first and a discrete\n sine transform over the last.\n\n Unfortunately, since this class is ultimately just a wrapper\n for various transforms implemented in FFTW, one cannot combine\n real transformations with real to complex transformations in a\n single object.\n\n .. _FFTW_flags:\n\n * ``flags`` is a list of strings and is a subset of the\n "" flags that FFTW allows for the planners:\n\n * ``'FFTW_ESTIMATE'``, ``'FFTW_MEASURE'``, ``'FFTW_PATIENT'`` and\n ``'FFTW_EXHAUSTIVE'`` are supported. These describe the\n increasing amount of effort spent during the planning\n stage to create the fastest possible transform.\n Usually ``'FFTW_MEASURE'`` is a good compromise. If no flag\n is passed, the default ``'FFTW_MEASURE'`` is used.\n * ``'FFTW_UNALIGNED'`` is supported.\n This tells FFTW not to assume anything about the\n alignment of the data and disabling any SIMD capability\n (see below).\n * ``'FFTW_DESTROY_INPUT'`` is supported.\n This tells FFTW that the input array can be destroyed during\n the transform, sometimes allowing a faster algorithm to be\n used. The default behaviour is, if possible, to preserve the\n input. In the case of the 1D Backwards Real transform, this\n may result in a performance hit. In the case of a backwards\n real transform for greater than one dimension, it is not\n possible to preserve the input, making this flag implicit\n in that case. A little more on this is given\n :ref:`below<scheme_table>`.\n * ``'FFTW_WISDOM_ONLY'`` is supported.\n This tells FFTW to raise an error if no plan for this transform\n and data type is already in the wisdom. It thus provides a method\n to determine whether planning would require additional effort or the\n cached wisdom can be used. This flag should be combined with the\n various planning-effort flags (``'FFTW_ESTIMATE'``,\n ``'FFTW_MEASURE'``, etc.); if so, then an error will be raised if\n wisdom derived from that level of planning effort (or higher) is\n not present. If no planning-effort flag is used, the default of\n ``""'FFTW_ESTIMATE'`` is assumed.\n Note that wisdom is specific to all the parameters, including the\n data alignment. That is, if wisdom was generated with input/output\n arrays with one specific alignment, using ``'FFTW_WISDOM_ONLY'``\n to create a plan for arrays with any different alignment will\n cause the ``'FFTW_WISDOM_ONLY'`` planning to fail. Thus it is\n important to specifically control the data alignment to make the\n best use of ``'FFTW_WISDOM_ONLY'``.\n\n The `FFTW planner flags documentation\n <http://www.fftw.org/fftw3_doc/Planner-Flags.html#Planner-Flags>`_\n has more information about the various flags and their impact.\n Note that only the flags documented here are supported.\n\n * ``threads`` tells the wrapper how many threads to use\n when invoking FFTW, with a default of 1. If the number\n of threads is greater than 1, then the GIL is released\n by necessity.\n\n * ``planning_timelimit`` is a floating point number that\n indicates to the underlying FFTW planner the maximum number of\n seconds it should spend planning the FFT. This is a rough\n estimate and corresponds to calling of ``fftw_set_timelimit()``\n (or an equivalent dependent on type) in the underlying FFTW\n library. If ``None`` is set, the planner will run indefinitely\n until all the planning modes allowed by the flags have been\n tried. See the `FFTW planner flags page\n <http://www.fftw.org/fftw3_doc/Planner-Flags.html#Planner-Flags>`_\n for more information on this.\n\n .. _fftw_schemes:\n\n **Schemes**\n\n The currently supported full (so not discrete sine or discrete\n cosine) DFT schemes are as follows:\n\n .. _scheme_table:\n\n +----------------+-----------------------+------------------------+-----------+""\n | Type | ``input_array.dtype`` | ``output_array.dtype`` | Direction |\n +================+=======================+========================+===========+\n | Complex | ``complex64`` | ``complex64`` | Both |\n +----------------+-----------------------+------------------------+-----------+\n | Complex | ``complex128`` | ``complex128`` | Both |\n +----------------+-----------------------+------------------------+-----------+\n | Complex | ``clongdouble`` | ``clongdouble`` | Both |\n +----------------+-----------------------+------------------------+-----------+\n | Real | ``float32`` | ``complex64`` | Forwards |\n +----------------+-----------------------+------------------------+-----------+\n | Real | ``float64`` | ``complex128`` | Forwards |\n +----------------+-----------------------+------------------------+-----------+\n | Real | ``longdouble`` | ``clongdouble`` | Forwards |\n +----------------+-----------------------+------------------------+-----------+\n | Real\\ :sup:`1` | ``complex64`` | ``float32`` | Backwards |\n +----------------+-----------------------+------------------------+-----------+\n | Real\\ :sup:`1` | ``complex128`` | ``float64`` | Backwards |\n +----------------+-----------------------+------------------------+-----------+\n | Real\\ :sup:`1` | ``clongdouble`` | ``longdouble`` | Backwards |\n +----------------+-----------------------+------------------------+-----------+\n\n \\ :sup:`1` Note that the Backwards Real transform for the case\n in which the dimensionality of the transform is greater than 1\n will destroy the input array. This is inherent to FFTW and ""the only\n general work-around for this is to copy the array prior to\n performing the transform. In the case where the dimensionality\n of the transform is 1, the default is to preserve the input array.\n This is different from the default in the underlying library, and\n some speed gain may be achieved by allowing the input array to\n be destroyed by passing the ``'FFTW_DESTROY_INPUT'``\n :ref:`flag <FFTW_flags>`.\n\n The discrete sine and discrete cosine transforms are supported\n for all three real types.\n\n ``clongdouble`` typically maps directly to ``complex256``\n or ``complex192``, and ``longdouble`` to ``float128`` or\n ``float96``, dependent on platform.\n\n The relative shapes of the arrays should be as follows:\n\n * For a Complex transform, ``output_array.shape == input_array.shape``\n * For a Real transform in the Forwards direction, both the following\n should be true:\n\n * ``output_array.shape[axes][-1] == input_array.shape[axes][-1]//2 + 1``\n * All the other axes should be equal in length.\n\n * For a Real transform in the Backwards direction, both the following\n should be true:\n\n * ``input_array.shape[axes][-1] == output_array.shape[axes][-1]//2 + 1``\n * All the other axes should be equal in length.\n\n In the above expressions for the Real transform, the ``axes``\n arguments denotes the unique set of axes on which we are taking\n the FFT, in the order passed. It is the last of these axes that\n is subject to the special case shown.\n\n The shapes for the real transforms corresponds to those\n stipulated by the FFTW library. Further information can be\n found in the FFTW documentation on the `real DFT\n <http://www.fftw.org/fftw3_doc/Guru-Real_002ddata-DFTs.html>`_.\n\n The actual arrangement in memory is arbitrary a""nd the scheme\n can be planned for any set of strides on either the input\n or the output. The user should not have to worry about this\n and any valid numpy array should work just fine.\n\n What is calculated is exactly what FFTW calculates.\n Notably, this is an unnormalized transform so should\n be scaled as necessary (fft followed by ifft will scale\n the input by N, the product of the dimensions along which\n the DFT is taken). For further information, see the\n `FFTW documentation\n <http://www.fftw.org/fftw3_doc/What-FFTW-Really-Computes.html>`_.\n\n The FFTW library benefits greatly from the beginning of each\n DFT axes being aligned on the correct byte boundary, enabling\n SIMD instructions. By default, if the data begins on such a\n boundary, then FFTW will be allowed to try and enable\n SIMD instructions. This means that all future changes to\n the data arrays will be checked for similar alignment. SIMD\n instructions can be explicitly disabled by setting the\n FFTW_UNALIGNED flags, to allow for updates with unaligned\n data.\n\n :func:`~pyfftw.byte_align` and\n :func:`~pyfftw.empty_aligned` are two methods\n included with this module for producing aligned arrays.\n\n The optimum alignment for the running platform is provided\n by :data:`pyfftw.simd_alignment`, though a different alignment\n may still result in some performance improvement. For example,\n if the processor supports AVX (requiring 32-byte alignment) as\n well as SSE (requiring 16-byte alignment), then if the array\n is 16-byte aligned, SSE will still be used.\n\n It's worth noting that just being aligned may not be sufficient\n to create the fastest possible transform. For example, if the\n array is not contiguous (i.e. certain axes are displaced in\n memory), it may be ""faster to plan a transform for a contiguous\n array, and then rely on the array being copied in before the\n transform (which :class:`pyfftw.FFTW` will handle for you when\n accessed through :meth:`~pyfftw.FFTW.__call__`).\n ";
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1218: '''
1219: **Arguments**:
1220:
1221: * ``input_array`` and ``output_array`` should be numpy arrays.
1222: The contents of these arrays will be destroyed by the planning
1223: process during initialisation. Information on supported
1224: dtypes for the arrays is :ref:`given below <scheme_table>`.
1225:
1226: * ``axes`` describes along which axes the DFT should be taken.
1227: This should be a valid list of axes. Repeated axes are
1228: only transformed once. Invalid axes will raise an ``IndexError``
1229: exception. This argument is equivalent to the same
1230: argument in :func:`numpy.fft.fftn`, except for the fact that
1231: the behaviour of repeated axes is different (``numpy.fft``
1232: will happily take the fft of the same axis if it is repeated
1233: in the ``axes`` argument). Rudimentary testing has suggested
1234: this is down to the underlying FFTW library and so unlikely
1235: to be fixed in these wrappers.
1236:
1237: * The ``direction`` parameter describes what sort of
1238: transformation the object should compute. This parameter is
1239: poorly named for historical reasons: older versions of pyFFTW
1240: only supported forward and backward transformations, for which
1241: this name made sense. Since then pyFFTW has been expanded to
1242: support real to real transforms as well and the name is not
1243: quite as descriptive.
1244:
1245: ``direction`` should either be a string, or, in the case of
1246: multiple real transforms, a list of strings. The two values
1247: corresponding to the DFT are
1248:
1249: * ``'FFTW_FORWARD'``, which is the forward discrete Fourier
1250: transform, and
1251: * ``'FFTW_BACKWARD'``, which is the backward discrete Fourier
1252: transform.
1253:
1254: Note that, for the two above options, only the Complex schemes
1255: allow a free choice for ``direction``. The direction *must*
1256: agree with the the :ref:`table below <scheme_table>` if a Real
1257: scheme is used, otherwise a ``ValueError`` is raised.
1258:
1259:
1260: Alternatively, if you are interested in one of the real to real
1261: transforms, then pyFFTW supports four different discrete cosine
1262: transforms:
1263:
1264: * ``'FFTW_REDFT00'``,
1265: * ``'FFTW_REDFT01'``,
1266: * ``'FFTW_REDFT10'``, and
1267: * ``'FFTW_REDFT01'``,
1268:
1269: and four discrete sine transforms:
1270:
1271: * ``'FFTW_RODFT00'``,
1272: * ``'FFTW_RODFT01'``,
1273: * ``'FFTW_RODFT10'``, and
1274: * ``'FFTW_RODFT01'``.
1275:
1276: pyFFTW uses the same naming convention for these flags as FFTW:
1277: the ``'REDFT'`` part of the name is an acronym for 'real even
1278: discrete Fourier transform, and, similarly, ``'RODFT'`` stands
1279: for 'real odd discrete Fourier transform'. The trailing ``'0'``
1280: is notation for even data (in terms of symmetry) and the
1281: trailing ``'1'`` is for odd data.
1282:
1283: Unlike the plain discrete Fourier transform, one may specify a
1284: different real to real transformation over each axis: for example,
1285:
1286: .. code-block:: none
1287: a = pyfftw.empty_aligned((128,128,128))
1288: b = pyfftw.empty_aligned((128,128,128))
1289: directions = ['FFTW_REDFT00', 'FFTW_RODFT11']
1290: transform = pyfftw.FFTW(a, b, axes=(0, 2), direction=directions)
1291:
1292: will create a transformation across the first and last axes
1293: with a discrete cosine transform over the first and a discrete
1294: sine transform over the last.
1295:
1296: Unfortunately, since this class is ultimately just a wrapper
1297: for various transforms implemented in FFTW, one cannot combine
1298: real transformations with real to complex transformations in a
1299: single object.
1300:
1301: .. _FFTW_flags:
1302:
1303: * ``flags`` is a list of strings and is a subset of the
1304: flags that FFTW allows for the planners:
1305:
1306: * ``'FFTW_ESTIMATE'``, ``'FFTW_MEASURE'``, ``'FFTW_PATIENT'`` and
1307: ``'FFTW_EXHAUSTIVE'`` are supported. These describe the
1308: increasing amount of effort spent during the planning
1309: stage to create the fastest possible transform.
1310: Usually ``'FFTW_MEASURE'`` is a good compromise. If no flag
1311: is passed, the default ``'FFTW_MEASURE'`` is used.
1312: * ``'FFTW_UNALIGNED'`` is supported.
1313: This tells FFTW not to assume anything about the
1314: alignment of the data and disabling any SIMD capability
1315: (see below).
1316: * ``'FFTW_DESTROY_INPUT'`` is supported.
1317: This tells FFTW that the input array can be destroyed during
1318: the transform, sometimes allowing a faster algorithm to be
1319: used. The default behaviour is, if possible, to preserve the
1320: input. In the case of the 1D Backwards Real transform, this
1321: may result in a performance hit. In the case of a backwards
1322: real transform for greater than one dimension, it is not
1323: possible to preserve the input, making this flag implicit
1324: in that case. A little more on this is given
1325: :ref:`below<scheme_table>`.
1326: * ``'FFTW_WISDOM_ONLY'`` is supported.
1327: This tells FFTW to raise an error if no plan for this transform
1328: and data type is already in the wisdom. It thus provides a method
1329: to determine whether planning would require additional effort or the
1330: cached wisdom can be used. This flag should be combined with the
1331: various planning-effort flags (``'FFTW_ESTIMATE'``,
1332: ``'FFTW_MEASURE'``, etc.); if so, then an error will be raised if
1333: wisdom derived from that level of planning effort (or higher) is
1334: not present. If no planning-effort flag is used, the default of
1335: ``'FFTW_ESTIMATE'`` is assumed.
1336: Note that wisdom is specific to all the parameters, including the
1337: data alignment. That is, if wisdom was generated with input/output
1338: arrays with one specific alignment, using ``'FFTW_WISDOM_ONLY'``
1339: to create a plan for arrays with any different alignment will
1340: cause the ``'FFTW_WISDOM_ONLY'`` planning to fail. Thus it is
1341: important to specifically control the data alignment to make the
1342: best use of ``'FFTW_WISDOM_ONLY'``.
1343:
1344: The `FFTW planner flags documentation
1345: <http://www.fftw.org/fftw3_doc/Planner-Flags.html#Planner-Flags>`_
1346: has more information about the various flags and their impact.
1347: Note that only the flags documented here are supported.
1348:
1349: * ``threads`` tells the wrapper how many threads to use
1350: when invoking FFTW, with a default of 1. If the number
1351: of threads is greater than 1, then the GIL is released
1352: by necessity.
1353:
1354: * ``planning_timelimit`` is a floating point number that
1355: indicates to the underlying FFTW planner the maximum number of
1356: seconds it should spend planning the FFT. This is a rough
1357: estimate and corresponds to calling of ``fftw_set_timelimit()``
1358: (or an equivalent dependent on type) in the underlying FFTW
1359: library. If ``None`` is set, the planner will run indefinitely
1360: until all the planning modes allowed by the flags have been
1361: tried. See the `FFTW planner flags page
1362: <http://www.fftw.org/fftw3_doc/Planner-Flags.html#Planner-Flags>`_
1363: for more information on this.
1364:
1365: .. _fftw_schemes:
1366:
1367: **Schemes**
1368:
1369: The currently supported full (so not discrete sine or discrete
1370: cosine) DFT schemes are as follows:
1371:
1372: .. _scheme_table:
1373:
1374: +----------------+-----------------------+------------------------+-----------+
1375: | Type | ``input_array.dtype`` | ``output_array.dtype`` | Direction |
1376: +================+=======================+========================+===========+
1377: | Complex | ``complex64`` | ``complex64`` | Both |
1378: +----------------+-----------------------+------------------------+-----------+
1379: | Complex | ``complex128`` | ``complex128`` | Both |
1380: +----------------+-----------------------+------------------------+-----------+
1381: | Complex | ``clongdouble`` | ``clongdouble`` | Both |
1382: +----------------+-----------------------+------------------------+-----------+
1383: | Real | ``float32`` | ``complex64`` | Forwards |
1384: +----------------+-----------------------+------------------------+-----------+
1385: | Real | ``float64`` | ``complex128`` | Forwards |
1386: +----------------+-----------------------+------------------------+-----------+
1387: | Real | ``longdouble`` | ``clongdouble`` | Forwards |
1388: +----------------+-----------------------+------------------------+-----------+
1389: | Real\ :sup:`1` | ``complex64`` | ``float32`` | Backwards |
1390: +----------------+-----------------------+------------------------+-----------+
1391: | Real\ :sup:`1` | ``complex128`` | ``float64`` | Backwards |
1392: +----------------+-----------------------+------------------------+-----------+
1393: | Real\ :sup:`1` | ``clongdouble`` | ``longdouble`` | Backwards |
1394: +----------------+-----------------------+------------------------+-----------+
1395:
1396: \ :sup:`1` Note that the Backwards Real transform for the case
1397: in which the dimensionality of the transform is greater than 1
1398: will destroy the input array. This is inherent to FFTW and the only
1399: general work-around for this is to copy the array prior to
1400: performing the transform. In the case where the dimensionality
1401: of the transform is 1, the default is to preserve the input array.
1402: This is different from the default in the underlying library, and
1403: some speed gain may be achieved by allowing the input array to
1404: be destroyed by passing the ``'FFTW_DESTROY_INPUT'``
1405: :ref:`flag <FFTW_flags>`.
1406:
1407: The discrete sine and discrete cosine transforms are supported
1408: for all three real types.
1409:
1410: ``clongdouble`` typically maps directly to ``complex256``
1411: or ``complex192``, and ``longdouble`` to ``float128`` or
1412: ``float96``, dependent on platform.
1413:
1414: The relative shapes of the arrays should be as follows:
1415:
1416: * For a Complex transform, ``output_array.shape == input_array.shape``
1417: * For a Real transform in the Forwards direction, both the following
1418: should be true:
1419:
1420: * ``output_array.shape[axes][-1] == input_array.shape[axes][-1]//2 + 1``
1421: * All the other axes should be equal in length.
1422:
1423: * For a Real transform in the Backwards direction, both the following
1424: should be true:
1425:
1426: * ``input_array.shape[axes][-1] == output_array.shape[axes][-1]//2 + 1``
1427: * All the other axes should be equal in length.
1428:
1429: In the above expressions for the Real transform, the ``axes``
1430: arguments denotes the unique set of axes on which we are taking
1431: the FFT, in the order passed. It is the last of these axes that
1432: is subject to the special case shown.
1433:
1434: The shapes for the real transforms corresponds to those
1435: stipulated by the FFTW library. Further information can be
1436: found in the FFTW documentation on the `real DFT
1437: <http://www.fftw.org/fftw3_doc/Guru-Real_002ddata-DFTs.html>`_.
1438:
1439: The actual arrangement in memory is arbitrary and the scheme
1440: can be planned for any set of strides on either the input
1441: or the output. The user should not have to worry about this
1442: and any valid numpy array should work just fine.
1443:
1444: What is calculated is exactly what FFTW calculates.
1445: Notably, this is an unnormalized transform so should
1446: be scaled as necessary (fft followed by ifft will scale
1447: the input by N, the product of the dimensions along which
1448: the DFT is taken). For further information, see the
1449: `FFTW documentation
1450: <http://www.fftw.org/fftw3_doc/What-FFTW-Really-Computes.html>`_.
1451:
1452: The FFTW library benefits greatly from the beginning of each
1453: DFT axes being aligned on the correct byte boundary, enabling
1454: SIMD instructions. By default, if the data begins on such a
1455: boundary, then FFTW will be allowed to try and enable
1456: SIMD instructions. This means that all future changes to
1457: the data arrays will be checked for similar alignment. SIMD
1458: instructions can be explicitly disabled by setting the
1459: FFTW_UNALIGNED flags, to allow for updates with unaligned
1460: data.
1461:
1462: :func:`~pyfftw.byte_align` and
1463: :func:`~pyfftw.empty_aligned` are two methods
1464: included with this module for producing aligned arrays.
1465:
1466: The optimum alignment for the running platform is provided
1467: by :data:`pyfftw.simd_alignment`, though a different alignment
1468: may still result in some performance improvement. For example,
1469: if the processor supports AVX (requiring 32-byte alignment) as
1470: well as SSE (requiring 16-byte alignment), then if the array
1471: is 16-byte aligned, SSE will still be used.
1472:
1473: It's worth noting that just being aligned may not be sufficient
1474: to create the fastest possible transform. For example, if the
1475: array is not contiguous (i.e. certain axes are displaced in
1476: memory), it may be faster to plan a transform for a contiguous
1477: array, and then rely on the array being copied in before the
1478: transform (which :class:`pyfftw.FFTW` will handle for you when
1479: accessed through :meth:`~pyfftw.FFTW.__call__`).
1480: '''
1481:
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1504: '''__call__(input_array=None, output_array=None, normalise_idft=True,
1505: ortho=False)
1506:
1507: Calling the class instance (optionally) updates the arrays, then
1508: calls :meth:`~pyfftw.FFTW.execute`, before optionally normalising
1509: the output and returning the output array.
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1511: It has some built-in helpers to make life simpler for the calling
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1513: calling :meth:`~pyfftw.FFTW.execute`).
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1562:
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1565:
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