add circuitpython code

1 files changed, 102 insertions, 0 deletions

diff --git a/circuitpython/extmod/ulab/code/numpy/fft/fft.c b/circuitpython/extmod/ulab/code/numpy/fft/fft.c
new file mode 100644
index 0000000..27cb79c
--- /dev/null
+++ b/circuitpython/extmod/ulab/code/numpy/fft/fft.c
@@ -0,0 +1,102 @@
+/*
+ * This file is part of the micropython-ulab project,
+ *
+ * https://github.com/v923z/micropython-ulab
+ *
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2019-2021 Zoltán Vörös
+ *               2020 Scott Shawcroft for Adafruit Industries
+ *               2020 Taku Fukada
+*/
+
+#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 "../carray/carray_tools.h"
+#include "fft.h"
+
+//| """Frequency-domain functions"""
+//|
+//| import ulab.numpy
+
+
+//| def fft(r: ulab.numpy.ndarray, c: Optional[ulab.numpy.ndarray] = None) -> Tuple[ulab.numpy.ndarray, ulab.numpy.ndarray]:
+//|     """
+//|     :param ulab.numpy.ndarray r: A 1-dimension array of values whose size is a power of 2
+//|     :param ulab.numpy.ndarray c: An optional 1-dimension array of values whose size is a power of 2, giving the complex part of the value
+//|     :return tuple (r, c): The real and complex parts of the FFT
+//|
+//|     Perform a Fast Fourier Transform from the time domain into the frequency domain
+//|
+//|     See also ~ulab.extras.spectrum, which computes the magnitude of the fft,
+//|     rather than separately returning its real and imaginary parts."""
+//|     ...
+//|
+#if ULAB_SUPPORTS_COMPLEX & ULAB_FFT_IS_NUMPY_COMPATIBLE
+static mp_obj_t fft_fft(mp_obj_t arg) {
+    return fft_fft_ifft_spectrogram(arg, FFT_FFT);
+}
+
+MP_DEFINE_CONST_FUN_OBJ_1(fft_fft_obj, fft_fft);
+#else
+static mp_obj_t fft_fft(size_t n_args, const mp_obj_t *args) {
+    if(n_args == 2) {
+        return fft_fft_ifft_spectrogram(n_args, args[0], args[1], FFT_FFT);
+    } else {
+        return fft_fft_ifft_spectrogram(n_args, args[0], mp_const_none, FFT_FFT);
+    }
+}
+
+MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_fft_obj, 1, 2, fft_fft);
+#endif
+
+//| def ifft(r: ulab.numpy.ndarray, c: Optional[ulab.numpy.ndarray] = None) -> Tuple[ulab.numpy.ndarray, ulab.numpy.ndarray]:
+//|     """
+//|     :param ulab.numpy.ndarray r: A 1-dimension array of values whose size is a power of 2
+//|     :param ulab.numpy.ndarray c: An optional 1-dimension array of values whose size is a power of 2, giving the complex part of the value
+//|     :return tuple (r, c): The real and complex parts of the inverse FFT
+//|
+//|     Perform an Inverse Fast Fourier Transform from the frequeny domain into the time domain"""
+//|     ...
+//|
+
+#if ULAB_SUPPORTS_COMPLEX & ULAB_FFT_IS_NUMPY_COMPATIBLE
+static mp_obj_t fft_ifft(mp_obj_t arg) {
+    return fft_fft_ifft_spectrogram(arg, FFT_IFFT);
+}
+
+MP_DEFINE_CONST_FUN_OBJ_1(fft_ifft_obj, fft_ifft);
+#else
+static mp_obj_t fft_ifft(size_t n_args, const mp_obj_t *args) {
+    NOT_IMPLEMENTED_FOR_COMPLEX()
+    if(n_args == 2) {
+        return fft_fft_ifft_spectrogram(n_args, args[0], args[1], FFT_IFFT);
+    } else {
+        return fft_fft_ifft_spectrogram(n_args, args[0], mp_const_none, FFT_IFFT);
+    }
+}
+
+MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_ifft_obj, 1, 2, fft_ifft);
+#endif
+
+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 },
+};
+
+STATIC MP_DEFINE_CONST_DICT(mp_module_ulab_fft_globals, ulab_fft_globals_table);
+
+const mp_obj_module_t ulab_fft_module = {
+    .base = { &mp_type_module },
+    .globals = (mp_obj_dict_t*)&mp_module_ulab_fft_globals,
+};
+MP_REGISTER_MODULE(MP_QSTR_ulab_dot_fft, ulab_fft_module, MODULE_ULAB_ENABLED && CIRCUITPY_ULAB);