/* * This file is part of the micropython-ulab project, * * https://github.com/v923z/micropython-ulab * * The MIT License (MIT) * * Copyright (c) 2019-2020 Zoltán Vörös */ #include #include #include #include #include "py/runtime.h" #include "py/builtin.h" #include "py/binary.h" #include "py/obj.h" #include "py/objarray.h" #include "ndarray.h" #include "fft.h" #if ULAB_FFT_MODULE enum FFT_TYPE { FFT_FFT, FFT_IFFT, FFT_SPECTRUM, }; void fft_kernel(mp_float_t *real, mp_float_t *imag, int n, int isign) { // This is basically a modification of four1 from Numerical Recipes // The main difference is that this function takes two arrays, one // for the real, and one for the imaginary parts. int j, m, mmax, istep; mp_float_t tempr, tempi; mp_float_t wtemp, wr, wpr, wpi, wi, theta; j = 0; for(int i = 0; i < n; i++) { if (j > i) { SWAP(mp_float_t, real[i], real[j]); SWAP(mp_float_t, imag[i], imag[j]); } m = n >> 1; while (j >= m && m > 0) { j -= m; m >>= 1; } j += m; } mmax = 1; while (n > mmax) { istep = mmax << 1; theta = -2.0*isign*MP_PI/istep; wtemp = MICROPY_FLOAT_C_FUN(sin)(0.5 * theta); wpr = -2.0 * wtemp * wtemp; wpi = MICROPY_FLOAT_C_FUN(sin)(theta); wr = 1.0; wi = 0.0; for(m = 0; m < mmax; m++) { for(int i = m; i < n; i += istep) { j = i + mmax; tempr = wr * real[j] - wi * imag[j]; tempi = wr * imag[j] + wi * real[j]; real[j] = real[i] - tempr; imag[j] = imag[i] - tempi; real[i] += tempr; imag[i] += tempi; } wtemp = wr; wr = wr*wpr - wi*wpi + wr; wi = wi*wpr + wtemp*wpi + wi; } mmax = istep; } } mp_obj_t fft_fft_ifft_spectrum(size_t n_args, mp_obj_t arg_re, mp_obj_t arg_im, uint8_t type) { if(!MP_OBJ_IS_TYPE(arg_re, &ulab_ndarray_type)) { mp_raise_NotImplementedError(translate("FFT is defined for ndarrays only")); } if(n_args == 2) { if(!MP_OBJ_IS_TYPE(arg_im, &ulab_ndarray_type)) { mp_raise_NotImplementedError(translate("FFT is defined for ndarrays only")); } } // Check if input is of length of power of 2 ndarray_obj_t *re = MP_OBJ_TO_PTR(arg_re); uint16_t len = re->array->len; if((len & (len-1)) != 0) { mp_raise_ValueError(translate("input array length must be power of 2")); } ndarray_obj_t *out_re = create_new_ndarray(1, len, NDARRAY_FLOAT); mp_float_t *data_re = (mp_float_t *)out_re->array->items; if(re->array->typecode == NDARRAY_FLOAT) { // By treating this case separately, we can save a bit of time. // I don't know if it is worthwhile, though... memcpy((mp_float_t *)out_re->array->items, (mp_float_t *)re->array->items, re->bytes); } else { for(size_t i=0; i < len; i++) { *data_re++ = ndarray_get_float_value(re->array->items, re->array->typecode, i); } data_re -= len; } ndarray_obj_t *out_im = create_new_ndarray(1, len, NDARRAY_FLOAT); mp_float_t *data_im = (mp_float_t *)out_im->array->items; if(n_args == 2) { ndarray_obj_t *im = MP_OBJ_TO_PTR(arg_im); if (re->array->len != im->array->len) { mp_raise_ValueError(translate("real and imaginary parts must be of equal length")); } if(im->array->typecode == NDARRAY_FLOAT) { memcpy((mp_float_t *)out_im->array->items, (mp_float_t *)im->array->items, im->bytes); } else { for(size_t i=0; i < len; i++) { *data_im++ = ndarray_get_float_value(im->array->items, im->array->typecode, i); } data_im -= len; } } if((type == FFT_FFT) || (type == FFT_SPECTRUM)) { fft_kernel(data_re, data_im, len, 1); if(type == FFT_SPECTRUM) { for(size_t i=0; i < len; i++) { *data_re = MICROPY_FLOAT_C_FUN(sqrt)(*data_re * *data_re + *data_im * *data_im); data_re++; data_im++; } } } else { // inverse transform fft_kernel(data_re, data_im, len, -1); // TODO: numpy accepts the norm keyword argument for(size_t i=0; i < len; i++) { *data_re++ /= len; *data_im++ /= len; } } if(type == FFT_SPECTRUM) { return MP_OBJ_TO_PTR(out_re); } else { mp_obj_t tuple[2]; tuple[0] = out_re; tuple[1] = out_im; return mp_obj_new_tuple(2, tuple); } } mp_obj_t fft_fft(size_t n_args, const mp_obj_t *args) { if(n_args == 2) { return fft_fft_ifft_spectrum(n_args, args[0], args[1], FFT_FFT); } else { return fft_fft_ifft_spectrum(n_args, args[0], mp_const_none, FFT_FFT); } } MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_fft_obj, 1, 2, fft_fft); mp_obj_t fft_ifft(size_t n_args, const mp_obj_t *args) { if(n_args == 2) { return fft_fft_ifft_spectrum(n_args, args[0], args[1], FFT_IFFT); } else { return fft_fft_ifft_spectrum(n_args, args[0], mp_const_none, FFT_IFFT); } } MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_ifft_obj, 1, 2, fft_ifft); mp_obj_t fft_spectrum(size_t n_args, const mp_obj_t *args) { if(n_args == 2) { return fft_fft_ifft_spectrum(n_args, args[0], args[1], FFT_SPECTRUM); } else { return fft_fft_ifft_spectrum(n_args, args[0], mp_const_none, FFT_SPECTRUM); } } MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_spectrum_obj, 1, 2, fft_spectrum); STATIC const mp_rom_map_elem_t ulab_fft_globals_table[] = { { MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_fft) }, { MP_OBJ_NEW_QSTR(MP_QSTR_fft), (mp_obj_t)&fft_fft_obj }, { MP_OBJ_NEW_QSTR(MP_QSTR_ifft), (mp_obj_t)&fft_ifft_obj }, { MP_OBJ_NEW_QSTR(MP_QSTR_spectrum), (mp_obj_t)&fft_spectrum_obj }, }; STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_fft_globals, ulab_fft_globals_table); mp_obj_module_t ulab_fft_module = { .base = { &mp_type_module }, .globals = (mp_obj_dict_t*)&mp_module_ulab_fft_globals, }; #endif