circuitpython-ulab/code/fft.c
2020-02-27 08:56:07 -06:00

199 lines
6.2 KiB
C

/*
* 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 <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#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