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micropython-ulab/code/ndarray.c
Zoltán Vörös 4ddbd8b73f base code can be compiled
2020-07-30 20:49:14 +02:00

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/*
* 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 <unistd.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "py/runtime.h"
#include "py/binary.h"
#include "py/obj.h"
#include "py/objtuple.h"
#include "ndarray.h"
mp_uint_t ndarray_print_threshold = NDARRAY_PRINT_THRESHOLD;
mp_uint_t ndarray_print_edgeitems = NDARRAY_PRINT_EDGEITEMS;
//| """Manipulate numeric data similar to numpy
//| `ulab` is a numpy-like module for micropython, meant to simplify and
//| speed up common mathematical operations on arrays. The primary goal was to
//| implement a small subset of numpy that might be useful in the context of a
//| microcontroller. This means low-level data processing of linear (array) and
//| two-dimensional (matrix) data.
//| `ulab` is adapted from micropython-ulab, and the original project's
//| documentation can be found at
//| https://micropython-ulab.readthedocs.io/en/latest/
//| `ulab` is modeled after numpy, and aims to be a compatible subset where
//| possible. Numpy's documentation can be found at
//| https://docs.scipy.org/doc/numpy/index.html"""
//|
//| class array:
//| """1- and 2- dimensional array"""
//| def __init__(self, values, *, dtype=float):
//| """:param sequence values: Sequence giving the initial content of the array.
//| :param dtype: The type of array values, ``int8``, ``uint8``, ``int16``, ``uint16``, or ``float``
//|
//| The `values` sequence can either be another ~ulab.array, sequence of numbers
//| (in which case a 1-dimensional array is created), or a sequence where each
//| subsequence has the same length (in which case a 2-dimensional array is
//| created).
//| Passing a ~ulab.array and a different dtype can be used to convert an array
//| from one dtype to another.
//| In many cases, it is more convenient to create an array from a function
//| like `zeros` or `linspace`.
//| `ulab.array` implements the buffer protocol, so it can be used in many
//| places an `array.array` can be used."""
//| ...
//|
//| shape: tuple = ...
//| """The size of the array, a tuple of length 1 or 2"""
//|
//| size: int = ...
//| """The number of elements in the array"""
//|
//| itemsize: int = ...
//| """The number of elements in the array"""
//|
//| def flatten(self, *, order='C'):
//| """:param order: Whether to flatten by rows ('C') or columns ('F')
//|
//| Returns a new `ulab.array` object which is always 1 dimensional.
//| If order is 'C' (the default", then the data is ordered in rows;
//| If it is 'F', then the data is ordered in columns. "C" and "F" refer
//| to the typical storage organization of the C and Fortran languages."""
//| ...
//|
//| def sort(self, *, axis=1):
//| """:param axis: Whether to sort elements within rows (0), columns (1), or elements (None)"""
//| ...
//|
//| def transpose(self):
//| """Swap the rows and columns of a 2-dimensional array"""
//| ...
//|
//| def __add__(self):
//| """Adds corresponding elements of the two arrays, or adds a number to all
//| elements of the array. If both arguments are arrays, their sizes must match."""
//| ...
//|
//| def __sub__(self):
//| """Subtracts corresponding elements of the two arrays, or adds a number to all
//| elements of the array. If both arguments are arrays, their sizes must match."""
//| ...
//|
//| def __mul__(self):
//| """Multiplies corresponding elements of the two arrays, or multiplies
//| all elements of the array by a number. If both arguments are arrays,
//| their sizes must match."""
//| ...
//|
//| def __div__(self):
//| """Multiplies corresponding elements of the two arrays, or divides
//| all elements of the array by a number. If both arguments are arrays,
//| their sizes must match."""
//| ...
//|
//| def __pow__():
//| """Computes the power (x**y) of corresponding elements of the the two arrays,
//| or one number and one array. If both arguments are arrays, their sizes
//| must match."""
//| ...
//|
//| def __getitem__():
//| """Retrieve an element of the array."""
//| ...
//|
//| def __setitem__():
//| """Set an element of the array."""
//| ...
//|
//| int8 = ...
//| """Type code for signed integers in the range -128 .. 127 inclusive, like the 'b' typecode of `array.array`"""
//|
//| int16 = ...
//| """Type code for signed integers in the range -32768 .. 32767 inclusive, like the 'h' typecode of `array.array`"""
//|
//| float = ...
//| """Type code for floating point values, like the 'f' typecode of `array.array`"""
//|
//| uint8 = ...
//| """Type code for unsigned integers in the range 0 .. 255 inclusive, like the 'H' typecode of `array.array`"""
//|
//| uint16 = ...
//| """Type code for unsigned integers in the range 0 .. 65535 inclusive, like the 'h' typecode of `array.array`"""
//|
#ifdef OPENMV
void mp_obj_slice_indices(mp_obj_t self_in, mp_int_t length, mp_bound_slice_t *result) {
mp_obj_slice_t *self = MP_OBJ_TO_PTR(self_in);
mp_int_t start, stop, step;
if (self->step == mp_const_none) {
step = 1;
} else {
step = mp_obj_get_int(self->step);
if (step == 0) {
mp_raise_ValueError(translate("slice step can't be zero"));
}
}
if (step > 0) {
// Positive step
if (self->start == mp_const_none) {
start = 0;
} else {
start = mp_obj_get_int(self->start);
if (start < 0) {
start += length;
}
start = MIN(length, MAX(start, 0));
}
if (self->stop == mp_const_none) {
stop = length;
} else {
stop = mp_obj_get_int(self->stop);
if (stop < 0) {
stop += length;
}
stop = MIN(length, MAX(stop, 0));
}
} else {
// Negative step
if (self->start == mp_const_none) {
start = length - 1;
} else {
start = mp_obj_get_int(self->start);
if (start < 0) {
start += length;
}
start = MIN(length - 1, MAX(start, -1));
}
if (self->stop == mp_const_none) {
stop = -1;
} else {
stop = mp_obj_get_int(self->stop);
if (stop < 0) {
stop += length;
}
stop = MIN(length - 1, MAX(stop, -1));
}
}
result->start = start;
result->stop = stop;
result->step = step;
}
#endif
mp_float_t ndarray_get_float_value(void *data, uint8_t typecode, size_t index) {
if(typecode == NDARRAY_UINT8) {
return (mp_float_t)((uint8_t *)data)[index];
} else if(typecode == NDARRAY_INT8) {
return (mp_float_t)((int8_t *)data)[index];
} else if(typecode == NDARRAY_UINT16) {
return (mp_float_t)((uint16_t *)data)[index];
} else if(typecode == NDARRAY_INT16) {
return (mp_float_t)((int16_t *)data)[index];
} else {
return (mp_float_t)((mp_float_t *)data)[index];
}
}
void ndarray_fill_array_iterable(mp_float_t *array, mp_obj_t iterable) {
mp_obj_iter_buf_t x_buf;
mp_obj_t x_item, x_iterable = mp_getiter(iterable, &x_buf);
size_t i=0;
while ((x_item = mp_iternext(x_iterable)) != MP_OBJ_STOP_ITERATION) {
*array++ = (mp_float_t)mp_obj_get_float(x_item);
i++;
}
}
int32_t *strides_from_shape(size_t *shape, size_t n) {
// returns a strides array that corresponds to a dense array with the prescribed shape
int32_t *strides = m_new(int32_t, n);
strides[n-1] = 1;
for(uint8_t i=n-1; i > 0; i--) {
strides[i-1] = strides[i] * shape[i];
}
return strides;
}
size_t *ndarray_new_coords(uint8_t ndim) {
size_t *coords = m_new(size_t, ndim);
memset(coords, 0, ndim*sizeof(size_t));
return coords;
}
bool ndarray_object_is_nditerable(mp_obj_t o_in) {
if(MP_OBJ_IS_TYPE(o_in, &ulab_ndarray_type) ||
MP_OBJ_IS_TYPE(o_in, &mp_type_tuple) ||
MP_OBJ_IS_TYPE(o_in, &mp_type_list) ||
MP_OBJ_IS_TYPE(o_in, &mp_type_range)) {
return true;
}
return false;
}
void fill_array_iterable(mp_float_t *array, mp_obj_t iterable) {
mp_obj_iter_buf_t x_buf;
mp_obj_t x_item, x_iterable = mp_getiter(iterable, &x_buf);
size_t i=0;
while ((x_item = mp_iternext(x_iterable)) != MP_OBJ_STOP_ITERATION) {
array[i] = (mp_float_t)mp_obj_get_float(x_item);
i++;
}
}
mp_obj_t ndarray_set_printoptions(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_threshold, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_none} },
{ MP_QSTR_edgeitems, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_none} },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(n_args, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
if(args[0].u_rom_obj != mp_const_none) {
ndarray_print_threshold = mp_obj_get_int(args[0].u_rom_obj);
}
if(args[1].u_rom_obj != mp_const_none) {
ndarray_print_edgeitems = mp_obj_get_int(args[1].u_rom_obj);
}
return mp_const_none;
}
MP_DEFINE_CONST_FUN_OBJ_KW(ndarray_set_printoptions_obj, 0, ndarray_set_printoptions);
mp_obj_t ndarray_get_printoptions(void) {
mp_obj_t dict = mp_obj_new_dict(2);
mp_obj_dict_store(MP_OBJ_FROM_PTR(dict), MP_OBJ_NEW_QSTR(MP_QSTR_threshold), mp_obj_new_int(ndarray_print_threshold));
mp_obj_dict_store(MP_OBJ_FROM_PTR(dict), MP_OBJ_NEW_QSTR(MP_QSTR_edgeitems), mp_obj_new_int(ndarray_print_edgeitems));
return dict;
}
MP_DEFINE_CONST_FUN_OBJ_0(ndarray_get_printoptions_obj, ndarray_get_printoptions);
void ndarray_print_row(const mp_print_t *print, mp_obj_array_t *data, size_t n0, size_t n) {
mp_print_str(print, "[");
if((n <= ndarray_print_threshold) || (n <= 2*ndarray_print_edgeitems)) { // if the array is short, print everything
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0), PRINT_REPR);
for(size_t i=1; i < n; i++) {
mp_print_str(print, ", ");
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+i), PRINT_REPR);
}
} else {
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0), PRINT_REPR);
for(size_t i=1; i < ndarray_print_edgeitems; i++) {
mp_print_str(print, ", ");
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+i), PRINT_REPR);
}
mp_printf(print, ", ..., ");
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+n-ndarray_print_edgeitems), PRINT_REPR);
for(size_t i=1; i < ndarray_print_edgeitems; i++) {
mp_print_str(print, ", ");
mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+n-ndarray_print_edgeitems+i), PRINT_REPR);
}
}
mp_print_str(print, "]");
}
void ndarray_print(const mp_print_t *print, mp_obj_t self_in, mp_print_kind_t kind) {
(void)kind;
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
uint8_t print_extra = self->ndim;
size_t *coords = ndarray_new_coords(self->ndim);
int32_t last_stride = self->strides[self->ndim-1];
size_t offset = 0;
mp_print_str(print, "array(");
if(self->len == 0) mp_print_str(print, "[");
for(size_t i=0; i < self->len; i++) {
for(uint8_t j=0; j < print_extra; j++) {
mp_print_str(print, "[");
}
print_extra = 0;
if(!self->boolean) {
mp_obj_print_helper(print, mp_binary_get_val_array(self->dtype, self->array, offset), PRINT_REPR);
} else {
if(((uint8_t *)self->array)[offset]) {
mp_print_str(print, "True");
} else {
mp_print_str(print, "False");
}
}
offset += last_stride;
coords[self->ndim-1] += 1;
if(coords[self->ndim-1] != self->shape[self->ndim-1]) {
mp_print_str(print, ", ");
}
for(uint8_t j=self->ndim-1; j > 0; j--) {
if(coords[j] == self->shape[j]) {
offset -= self->shape[j] * self->strides[j];
offset += self->strides[j-1];
print_extra += 1;
coords[j] = 0;
coords[j-1] += 1;
mp_print_str(print, "]");
} else { // coordinates can change only, if the last coordinate changes
break;
}
}
if(print_extra && (i != self->len-1)) {
mp_print_str(print, "\n");
if(print_extra > 1) {
mp_print_str(print, "\n");
}
}
}
m_del(size_t, coords, self->ndim);
mp_print_str(print, "]");
if(self->boolean) {
mp_print_str(print, ", dtype=bool)");
} else if(self->dtype == NDARRAY_UINT8) {
mp_print_str(print, ", dtype=uint8)");
} else if(self->dtype == NDARRAY_INT8) {
mp_print_str(print, ", dtype=int8)");
} else if(self->dtype == NDARRAY_UINT16) {
mp_print_str(print, ", dtype=uint16)");
} else if(self->dtype == NDARRAY_INT16) {
mp_print_str(print, ", dtype=int16)");
} else if(self->dtype == NDARRAY_FLOAT) {
mp_print_str(print, ", dtype=float)");
}
}
void ndarray_assign_elements(ndarray_obj_t *ndarray, mp_obj_t iterable, uint8_t dtype, size_t *idx) {
// assigns a single row in the matrix
mp_obj_t item;
uint8_t *array = (uint8_t *)ndarray->array;
array += *idx;
if(ndarray->boolean) {
while ((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
// TODO: this might be wrong here: we have to check for the trueness of item
if(mp_obj_is_true(item)) {
*array = 1;
}
array++;
(*idx)++;
}
} else {
while ((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
mp_binary_set_val_array(dtype, ndarray->array, (*idx)++, item);
}
}
}
bool ndarray_is_dense(ndarray_obj_t *ndarray) {
// returns true, if the array is dense, false otherwise
// the array should dense, if the very first stride can be calculated from shape
int32_t stride = 1;
for(uint8_t i=0; i < ndarray->ndim; i++) {
stride *= ndarray->shape[i];
}
return stride == ndarray->strides[0] ? true : false;
}
ndarray_obj_t *ndarray_new_ndarray(uint8_t ndim, size_t *shape, int32_t *strides, uint8_t dtype) {
// Creates the base ndarray with shape, and initialises the values to straight 0s
// the function should work in the general n-dimensional case
ndarray_obj_t *ndarray = m_new_obj(ndarray_obj_t);
ndarray->base.type = &ulab_ndarray_type;
ndarray->dtype = dtype;
ndarray->ndim = ndim;
ndarray->len = 1;
for(uint8_t i=0; i < ndim; i++) {
ndarray->shape[i] = shape[i];
ndarray->strides[i] = strides[i];
ndarray->len *= shape[i];
}
if(dtype == NDARRAY_BOOL) {
dtype = NDARRAY_UINT8;
ndarray->boolean = NDARRAY_BOOLEAN;
} else {
ndarray->boolean = NDARRAY_NUMERIC;
}
uint8_t itemsize = mp_binary_get_size('@', dtype, NULL);
uint8_t *array = m_new(byte, itemsize*ndarray->len);
// this should set all elements to 0, irrespective of the of the dtype (all bits are zero)
// we could, perhaps, leave this step out, and initialise the array only, when needed
memset(array, 0, ndarray->len*itemsize);
ndarray->array = array;
return ndarray;
}
ndarray_obj_t *ndarray_new_dense_ndarray(uint8_t ndim, size_t *shape, uint8_t dtype) {
// creates a dense array, i.e., one, where the strides are derived directly from the shapes
// the function should work in the general n-dimensional case
int32_t *strides = m_new(int32_t, ndim);
strides[ndim-1] = 1;
for(size_t i=ndim-1; i > 0; i--) {
strides[i-1] = strides[i] * shape[i-1];
}
return ndarray_new_ndarray(ndim, shape, strides, dtype);
}
ndarray_obj_t *ndarray_new_ndarray_from_tuple(mp_obj_tuple_t *_shape, uint8_t dtype) {
// creates a dense array from a tuple
// the function should work in the general n-dimensional case
uint8_t ndim = _shape->len;
size_t *shape = m_new(size_t, ndim);
for(size_t i=0; i < ndim; i++) {
shape[i] = mp_obj_get_int(_shape->items[i]);
}
return ndarray_new_dense_ndarray(ndim, shape, dtype);
}
void ndarray_copy_array(ndarray_obj_t *source, ndarray_obj_t *target) {
// copies the content of source->array into a new dense void pointer
// it is assumed that the dtypes in source and target are the same
size_t *coords = ndarray_new_coords(source->ndim);
int32_t last_stride = source->strides[source->ndim-1];
uint8_t itemsize = mp_binary_get_size('@', source->dtype, NULL);
uint8_t *array = (uint8_t *)source->array;
uint8_t *new_array = (uint8_t *)target->array;
for(size_t i=0; i < source->len; i++) {
memcpy(new_array, array, itemsize);
new_array += itemsize;
array += last_stride*itemsize;
coords[source->ndim-1] += 1;
for(uint8_t j=source->ndim-1; j > 0; j--) {
if(coords[j] == source->shape[j]) {
array -= source->shape[j] * source->strides[j] * itemsize;
array += source->strides[j-1] * itemsize;
coords[j] = 0;
coords[j-1] += 1;
} else { // coordinates can change only, if the last coordinate changes
break;
}
}
}
m_del(size_t, coords, source->ndim);
}
ndarray_obj_t *ndarray_new_view(ndarray_obj_t *source, uint8_t ndim, size_t *shape, int32_t *strides, int32_t offset) {
// creates a new view from the input arguments
// the function should work in the n-dimensional case
ndarray_obj_t *ndarray = m_new_obj(ndarray_obj_t);
ndarray->base.type = &ulab_ndarray_type;
ndarray->boolean = source->boolean;
ndarray->dtype = source->dtype;
ndarray->ndim = ndim;
ndarray->len = 1;
for(uint8_t i=0; i < ndim; i++) {
ndarray->shape[i] = shape[i];
ndarray->strides[i] = strides[i];
ndarray->len *= shape[i];
}
uint8_t itemsize = mp_binary_get_size('@', source->dtype, NULL);
ndarray->array = (uint8_t *)source->array + offset * itemsize;
return ndarray;
}
ndarray_obj_t *ndarray_copy_view(ndarray_obj_t *source) {
// creates a one-to-one deep copy of the input ndarray or its view
// the function should work in the general n-dimensional case
// In order to make it dtype-agnostic, we copy the memory content
// instead of reading out the values
int32_t *strides = strides_from_shape(source->shape, source->ndim);
uint8_t dtype = source->dtype;
if(source->boolean) {
dtype = NDARRAY_BOOLEAN;
}
ndarray_obj_t *ndarray = ndarray_new_ndarray(source->ndim, source->shape, strides, dtype);
ndarray_copy_array(source, ndarray);
return ndarray;
}
ndarray_obj_t *ndarray_new_linear_array(size_t len, uint8_t dtype) {
size_t *shape = m_new(size_t, 1);
int32_t *strides = m_new(int32_t, 1);
shape[0] = len;
strides[0] = 1;
return ndarray_new_ndarray(1, shape, strides, dtype);
}
STATIC uint8_t ndarray_init_helper(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
{ MP_QSTR_dtype, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = NDARRAY_FLOAT } },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(n_args, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
uint8_t dtype = args[1].u_int;
return dtype;
}
STATIC mp_obj_t ndarray_make_new_core(const mp_obj_type_t *type, size_t n_args, size_t n_kw, const mp_obj_t *args, mp_map_t *kw_args) {
uint8_t dtype = ndarray_init_helper(n_args, args, kw_args);
mp_obj_t len_in = mp_obj_len_maybe(args[0]);
size_t i = 0, len1 = 0, len2 = 0;
if (len_in == MP_OBJ_NULL) {
mp_raise_ValueError(translate("first argument must be an iterable"));
} else {
// len1 is either the number of rows (for matrices), or the number of elements (row vectors)
len1 = MP_OBJ_SMALL_INT_VALUE(len_in);
}
ndarray_obj_t *self;
// TODO: this doesn't allow dtype conversion.
if(MP_OBJ_IS_TYPE(args[0], &ulab_ndarray_type)) {
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(args[0]);
self = ndarray_copy_view(ndarray);
return MP_OBJ_FROM_PTR(self);
}
// We have to figure out, whether the first element of the iterable is an iterable itself
// Perhaps, there is a more elegant way of handling this
mp_obj_iter_buf_t iter_buf1;
mp_obj_t item1, iterable1 = mp_getiter(args[0], &iter_buf1);
while ((item1 = mp_iternext(iterable1)) != MP_OBJ_STOP_ITERATION) {
len_in = mp_obj_len_maybe(item1);
if(len_in != MP_OBJ_NULL) { // indeed, this seems to be an iterable
// Next, we have to check, whether all elements in the outer loop have the same length
if(i > 0) {
if(len2 != (size_t)MP_OBJ_SMALL_INT_VALUE(len_in)) {
mp_raise_ValueError(translate("iterables are not of the same length"));
}
}
len2 = MP_OBJ_SMALL_INT_VALUE(len_in);
i++;
}
}
// By this time, it should be established, what the shape is, so we can now create the array
if(len2 == 0) {
self = ndarray_new_linear_array(len1, dtype);
} else {
size_t shape[2] = {len1, len2};
self = ndarray_new_dense_ndarray(2, shape, dtype);
}
size_t idx = 0;
iterable1 = mp_getiter(args[0], &iter_buf1);
if(len2 == 0) { // the first argument is a single iterable
ndarray_assign_elements(self, iterable1, dtype, &idx);
} else {
mp_obj_iter_buf_t iter_buf2;
mp_obj_t iterable2;
while ((item1 = mp_iternext(iterable1)) != MP_OBJ_STOP_ITERATION) {
iterable2 = mp_getiter(item1, &iter_buf2);
ndarray_assign_elements(self, iterable2, dtype, &idx);
}
}
return MP_OBJ_FROM_PTR(self);
}
#ifdef CIRCUITPY
mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, const mp_obj_t *args, mp_map_t *kw_args) {
(void) type;
mp_arg_check_num(n_args, kw_args, 1, 2, true);
size_t n_kw = 0;
if (kw_args != 0) {
n_kw = kw_args->used;
}
mp_map_init_fixed_table(kw_args, n_kw, args + n_args);
return ndarray_make_new_core(type, n_args, n_kw, args, kw_args);
}
#else
mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, size_t n_kw, const mp_obj_t *args) {
(void) type;
mp_arg_check_num(n_args, n_kw, 1, 2, true);
mp_map_t kw_args;
mp_map_init_fixed_table(&kw_args, n_kw, args + n_args);
return ndarray_make_new_core(type, n_args, n_kw, args, &kw_args);
}
#endif
#if 0
static size_t slice_length(mp_bound_slice_t slice) {
ssize_t len, correction = 1;
if(slice.step > 0) correction = -1;
len = (ssize_t)(slice.stop - slice.start + (slice.step + correction)) / slice.step;
if(len < 0) return 0;
return (size_t)len;
}
static size_t true_length(mp_obj_t bool_list) {
// returns the number of Trues in a Boolean list
// I wonder, wouldn't this be faster, if we looped through bool_list->items instead?
mp_obj_iter_buf_t iter_buf;
mp_obj_t item, iterable = mp_getiter(bool_list, &iter_buf);
size_t trues = 0;
while((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
if(!mp_obj_is_bool(item)) {
// numpy seems to be a little bit inconsistent in when an index is considered
// to be True/False. Bail out immediately, if the items are not True/False
mp_raise_TypeError(translate("wrong index type"));
}
if(mp_obj_is_true(item)) {
trues++;
}
}
return trues;
}
static mp_bound_slice_t generate_slice(mp_int_t n, mp_obj_t index) {
// micropython seems to have difficulties with negative steps
mp_bound_slice_t slice;
if(MP_OBJ_IS_TYPE(index, &mp_type_slice)) {
mp_obj_slice_indices(index, n, &slice);
} else if(MP_OBJ_IS_INT(index)) {
mp_int_t _index = mp_obj_get_int(index);
if(_index < 0) {
_index += n;
}
if((_index >= n) || (_index < 0)) {
mp_raise_msg(&mp_type_IndexError, translate("index is out of bounds"));
}
slice.start = _index;
slice.stop = _index + 1;
slice.step = 1;
} else {
mp_raise_msg(&mp_type_IndexError, translate("indices must be integers, slices, or Boolean lists"));
}
return slice;
}
static mp_bound_slice_t simple_slice(int16_t start, int16_t stop, int16_t step) {
mp_bound_slice_t slice;
slice.start = start;
slice.stop = stop;
slice.step = step;
return slice;
}
static void insert_binary_value(ndarray_obj_t *ndarray, size_t nd_index, ndarray_obj_t *values, size_t value_index) {
// there is probably a more elegant implementation...
mp_obj_t tmp = mp_binary_get_val_array(values->array->typecode, values->array->items, value_index);
if((values->array->typecode == NDARRAY_FLOAT) && (ndarray->array->typecode != NDARRAY_FLOAT)) {
// workaround: rounding seems not to work in the arm compiler
int32_t x = (int32_t)MICROPY_FLOAT_C_FUN(floor)(mp_obj_get_float(tmp)+MICROPY_FLOAT_CONST(0.5));
tmp = mp_obj_new_int(x);
}
mp_binary_set_val_array(ndarray->array->typecode, ndarray->array->items, nd_index, tmp);
}
static mp_obj_t insert_slice_list(ndarray_obj_t *ndarray, size_t m, size_t n,
mp_bound_slice_t row, mp_bound_slice_t column,
mp_obj_t row_list, mp_obj_t column_list,
ndarray_obj_t *values) {
if((m != values->m) && (n != values->n)) {
if(values->array->len != 1) { // not a single item
mp_raise_ValueError(translate("could not broadast input array from shape"));
}
}
size_t cindex, rindex;
// M, and N are used to manipulate how the source index is incremented in the loop
uint8_t M = 1, N = 1;
if(values->m == 1) {
M = 0;
}
if(values->n == 1) {
N = 0;
}
if(row_list == mp_const_none) { // rows are indexed by a slice
rindex = row.start;
if(column_list == mp_const_none) { // columns are indexed by a slice
for(size_t i=0; i < m; i++) {
cindex = column.start;
for(size_t j=0; j < n; j++) {
insert_binary_value(ndarray, rindex*ndarray->n+cindex, values, i*M*n+j*N);
cindex += column.step;
}
rindex += row.step;
}
} else { // columns are indexed by a Boolean list
mp_obj_iter_buf_t column_iter_buf;
mp_obj_t column_item, column_iterable;
for(size_t i=0; i < m; i++) {
column_iterable = mp_getiter(column_list, &column_iter_buf);
size_t j = 0;
cindex = 0;
while((column_item = mp_iternext(column_iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(column_item)) {
insert_binary_value(ndarray, rindex*ndarray->n+cindex, values, i*M*n+j*N);
j++;
}
cindex++;
}
rindex += row.step;
}
}
} else { // rows are indexed by a Boolean list
mp_obj_iter_buf_t row_iter_buf;
mp_obj_t row_item, row_iterable;
row_iterable = mp_getiter(row_list, &row_iter_buf);
size_t i = 0;
rindex = 0;
if(column_list == mp_const_none) { // columns are indexed by a slice
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(row_item)) {
cindex = column.start;
for(size_t j=0; j < n; j++) {
insert_binary_value(ndarray, rindex*ndarray->n+cindex, values, i*M*n+j*N);
cindex += column.step;
}
i++;
}
rindex++;
}
} else { // columns are indexed by a list
mp_obj_iter_buf_t column_iter_buf;
mp_obj_t column_item, column_iterable;
size_t j = 0;
cindex = 0;
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(row_item)) {
column_iterable = mp_getiter(column_list, &column_iter_buf);
while((column_item = mp_iternext(column_iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(column_item)) {
insert_binary_value(ndarray, rindex*ndarray->n+cindex, values, i*M*n+j*N);
j++;
}
cindex++;
}
i++;
}
rindex++;
}
}
}
return mp_const_none;
}
static mp_obj_t iterate_slice_list(ndarray_obj_t *ndarray, size_t m, size_t n,
mp_bound_slice_t row, mp_bound_slice_t column,
mp_obj_t row_list, mp_obj_t column_list,
ndarray_obj_t *values) {
if(values != NULL) {
return insert_slice_list(ndarray, m, n, row, column, row_list, column_list, values);
}
uint8_t _sizeof = mp_binary_get_size('@', ndarray->array->typecode, NULL);
ndarray_obj_t *out = create_new_ndarray(m, n, ndarray->array->typecode);
uint8_t *target = (uint8_t *)out->array->items;
uint8_t *source = (uint8_t *)ndarray->array->items;
size_t cindex, rindex;
if(row_list == mp_const_none) { // rows are indexed by a slice
rindex = row.start;
if(column_list == mp_const_none) { // columns are indexed by a slice
for(size_t i=0; i < m; i++) {
cindex = column.start;
for(size_t j=0; j < n; j++) {
memcpy(target+(i*n+j)*_sizeof, source+(rindex*ndarray->n+cindex)*_sizeof, _sizeof);
cindex += column.step;
}
rindex += row.step;
}
} else { // columns are indexed by a Boolean list
// TODO: the list must be exactly as long as the axis
mp_obj_iter_buf_t column_iter_buf;
mp_obj_t column_item, column_iterable;
for(size_t i=0; i < m; i++) {
column_iterable = mp_getiter(column_list, &column_iter_buf);
size_t j = 0;
cindex = 0;
while((column_item = mp_iternext(column_iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(column_item)) {
memcpy(target+(i*n+j)*_sizeof, source+(rindex*ndarray->n+cindex)*_sizeof, _sizeof);
j++;
}
cindex++;
}
rindex += row.step;
}
}
} else { // rows are indexed by a Boolean list
mp_obj_iter_buf_t row_iter_buf;
mp_obj_t row_item, row_iterable;
row_iterable = mp_getiter(row_list, &row_iter_buf);
size_t i = 0;
rindex = 0;
if(column_list == mp_const_none) { // columns are indexed by a slice
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(row_item)) {
cindex = column.start;
for(size_t j=0; j < n; j++) {
memcpy(target+(i*n+j)*_sizeof, source+(rindex*ndarray->n+cindex)*_sizeof, _sizeof);
cindex += column.step;
}
i++;
}
rindex++;
}
} else { // columns are indexed by a list
mp_obj_iter_buf_t column_iter_buf;
mp_obj_t column_item, column_iterable;
size_t j = 0;
cindex = 0;
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(row_item)) {
column_iterable = mp_getiter(column_list, &column_iter_buf);
while((column_item = mp_iternext(column_iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(column_item)) {
memcpy(target+(i*n+j)*_sizeof, source+(rindex*ndarray->n+cindex)*_sizeof, _sizeof);
j++;
}
cindex++;
}
i++;
}
rindex++;
}
}
}
return MP_OBJ_FROM_PTR(out);
}
static mp_obj_t ndarray_get_slice(ndarray_obj_t *ndarray, mp_obj_t index, ndarray_obj_t *values) {
mp_bound_slice_t row_slice = simple_slice(0, 0, 1), column_slice = simple_slice(0, 0, 1);
size_t m = 0, n = 0;
if(MP_OBJ_IS_INT(index) && (ndarray->m == 1) && (values == NULL)) {
// we have a row vector, and don't want to assign
column_slice = generate_slice(ndarray->n, index);
if(slice_length(column_slice) == 1) { // we were asked for a single item
// subscribe returns an mp_obj_t, if and only, if the index is an integer, and we have a row vector
return mp_binary_get_val_array(ndarray->array->typecode, ndarray->array->items, column_slice.start);
}
}
if(MP_OBJ_IS_INT(index) || MP_OBJ_IS_TYPE(index, &mp_type_slice)) {
if(ndarray->m == 1) { // we have a row vector
column_slice = generate_slice(ndarray->n, index);
row_slice = simple_slice(0, 1, 1);
} else { // we have a matrix
row_slice = generate_slice(ndarray->m, index);
column_slice = simple_slice(0, ndarray->n, 1); // take all columns
}
m = slice_length(row_slice);
n = slice_length(column_slice);
return iterate_slice_list(ndarray, m, n, row_slice, column_slice, mp_const_none, mp_const_none, values);
} else if(MP_OBJ_IS_TYPE(index, &mp_type_list)) {
n = true_length(index);
if(ndarray->m == 1) { // we have a flat array
// we might have to separate the n == 1 case
row_slice = simple_slice(0, 1, 1);
return iterate_slice_list(ndarray, 1, n, row_slice, column_slice, mp_const_none, index, values);
} else { // we have a matrix
return iterate_slice_list(ndarray, 1, n, row_slice, column_slice, mp_const_none, index, values);
}
}
else { // we certainly have a tuple, so let us deal with it
mp_obj_tuple_t *tuple = MP_OBJ_TO_PTR(index);
if(tuple->len != 2) {
mp_raise_msg(&mp_type_IndexError, translate("too many indices"));
}
if(!(MP_OBJ_IS_TYPE(tuple->items[0], &mp_type_list) ||
MP_OBJ_IS_TYPE(tuple->items[0], &mp_type_slice) ||
MP_OBJ_IS_INT(tuple->items[0])) ||
!(MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_list) ||
MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_slice) ||
MP_OBJ_IS_INT(tuple->items[1]))) {
mp_raise_msg(&mp_type_IndexError, translate("indices must be integers, slices, or Boolean lists"));
}
if(MP_OBJ_IS_TYPE(tuple->items[0], &mp_type_list)) { // rows are indexed by Boolean list
m = true_length(tuple->items[0]);
if(MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_list)) {
n = true_length(tuple->items[1]);
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
tuple->items[0], tuple->items[1], values);
} else { // the column is indexed by an integer, or a slice
column_slice = generate_slice(ndarray->n, tuple->items[1]);
n = slice_length(column_slice);
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
tuple->items[0], mp_const_none, values);
}
} else { // rows are indexed by a slice, or an integer
row_slice = generate_slice(ndarray->m, tuple->items[0]);
m = slice_length(row_slice);
if(MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_list)) { // columns are indexed by a Boolean list
n = true_length(tuple->items[1]);
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
mp_const_none, tuple->items[1], values);
} else { // columns are indexed by an integer, or a slice
column_slice = generate_slice(ndarray->n, tuple->items[1]);
n = slice_length(column_slice);
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
mp_const_none, mp_const_none, values);
}
}
}
}
mp_obj_t ndarray_subscr(mp_obj_t self_in, mp_obj_t index, mp_obj_t value) {
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
if (value == MP_OBJ_SENTINEL) { // return value(s)
return ndarray_get_slice(self, index, NULL);
} else { // assignment to slices; the value must be an ndarray, or a scalar
if(!MP_OBJ_IS_TYPE(value, &ulab_ndarray_type) &&
!MP_OBJ_IS_INT(value) && !mp_obj_is_float(value)) {
mp_raise_ValueError(translate("right hand side must be an ndarray, or a scalar"));
} else {
ndarray_obj_t *values = NULL;
if(MP_OBJ_IS_INT(value)) {
values = create_new_ndarray(1, 1, self->array->typecode);
mp_binary_set_val_array(values->array->typecode, values->array->items, 0, value);
} else if(mp_obj_is_float(value)) {
values = create_new_ndarray(1, 1, NDARRAY_FLOAT);
mp_binary_set_val_array(NDARRAY_FLOAT, values->array->items, 0, value);
} else {
values = MP_OBJ_TO_PTR(value);
}
return ndarray_get_slice(self, index, values);
}
}
return mp_const_none;
}
#endif
// itarray iterator
mp_obj_t ndarray_getiter(mp_obj_t o_in, mp_obj_iter_buf_t *iter_buf) {
return ndarray_new_ndarray_iterator(o_in, 0, iter_buf);
}
typedef struct _mp_obj_ndarray_it_t {
mp_obj_base_t base;
mp_fun_1_t iternext;
mp_obj_t ndarray;
size_t cur;
} mp_obj_ndarray_it_t;
mp_obj_t ndarray_iternext(mp_obj_t self_in) {
mp_obj_ndarray_it_t *self = MP_OBJ_TO_PTR(self_in);
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(self->ndarray);
size_t iter_end = ndarray->shape[0];
if(self->cur < iter_end) {
if(ndarray->ndim == 1) { // we have a linear array
// read the current value
self->cur++;
return mp_binary_get_val_array(ndarray->dtype, ndarray->array, self->cur-1);
} else { // we have a tensor, return the reduced view
int32_t offset = self->cur * ndarray->strides[0];
self->cur++;
ndarray_obj_t *value = ndarray_new_view(ndarray, ndarray->ndim-1, ndarray->shape+1, ndarray->strides+1, offset);
return MP_OBJ_FROM_PTR(value);
}
} else {
return MP_OBJ_STOP_ITERATION;
}
}
mp_obj_t ndarray_new_ndarray_iterator(mp_obj_t ndarray, size_t cur, mp_obj_iter_buf_t *iter_buf) {
assert(sizeof(mp_obj_ndarray_it_t) <= sizeof(mp_obj_iter_buf_t));
mp_obj_ndarray_it_t *o = (mp_obj_ndarray_it_t*)iter_buf;
o->base.type = &mp_type_polymorph_iter;
o->iternext = ndarray_iternext;
o->ndarray = ndarray;
o->cur = cur;
return MP_OBJ_FROM_PTR(o);
}
mp_obj_t ndarray_shape(mp_obj_t self_in) {
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
mp_obj_t *items = m_new(mp_obj_t, self->ndim);
for(uint8_t i=0; i < self->ndim; i++) {
items[i] = mp_obj_new_int(self->shape[i]);
}
mp_obj_t tuple = mp_obj_new_tuple(self->ndim, items);
m_del(mp_obj_t, items, self->ndim);
return tuple;
}
mp_obj_t ndarray_size(mp_obj_t self_in) {
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
return mp_obj_new_int(self->len);
}
mp_obj_t ndarray_itemsize(mp_obj_t self_in) {
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
return MP_OBJ_NEW_SMALL_INT(mp_binary_get_size('@', self->dtype, NULL));
}
/*
mp_obj_t ndarray_flatten(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_order, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_QSTR(MP_QSTR_C)} },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(n_args - 1, pos_args + 1, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
mp_obj_t self_copy = ndarray_copy(pos_args[0]);
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(self_copy);
GET_STR_DATA_LEN(args[0].u_obj, order, len);
if((len != 1) || ((memcmp(order, "C", 1) != 0) && (memcmp(order, "F", 1) != 0))) {
mp_raise_ValueError(translate("flattening order must be either 'C', or 'F'"));
}
// if order == 'C', we simply have to set m, and n, there is nothing else to do
if(memcmp(order, "F", 1) == 0) {
ndarray_obj_t *self = MP_OBJ_TO_PTR(pos_args[0]);
uint8_t _sizeof = mp_binary_get_size('@', self->array->typecode, NULL);
// get the data of self_in: we won't need a temporary buffer for the transposition
uint8_t *self_array = (uint8_t *)self->array->items;
uint8_t *array = (uint8_t *)ndarray->array->items;
size_t i=0;
for(size_t n=0; n < self->n; n++) {
for(size_t m=0; m < self->m; m++) {
memcpy(array+_sizeof*i, self_array+_sizeof*(m*self->n + n), _sizeof);
i++;
}
}
}
ndarray->n = ndarray->array->len;
ndarray->m = 1;
return self_copy;
}
*/
#if 0
// Binary operations
ndarray_obj_t *ndarray_from_mp_obj(mp_obj_t obj) {
// creates an ndarray from an micropython int or float
// if the input is an ndarray, it is returned
ndarray_obj_t *ndarray;
if(MP_OBJ_IS_INT(obj)) {
int32_t ivalue = mp_obj_get_int(obj);
if((ivalue > 0) && (ivalue < 256)) {
CREATE_SINGLE_ITEM(ndarray, uint8_t, NDARRAY_UINT8, ivalue);
} else if((ivalue > 255) && (ivalue < 65535)) {
CREATE_SINGLE_ITEM(ndarray, uint16_t, NDARRAY_UINT16, ivalue);
} else if((ivalue < 0) && (ivalue > -128)) {
CREATE_SINGLE_ITEM(ndarray, int8_t, NDARRAY_INT8, ivalue);
} else if((ivalue < -127) && (ivalue > -32767)) {
CREATE_SINGLE_ITEM(ndarray, int16_t, NDARRAY_INT16, ivalue);
} else { // the integer value clearly does not fit the ulab types, so move on to float
CREATE_SINGLE_ITEM(ndarray, mp_float_t, NDARRAY_FLOAT, ivalue);
}
} else if(mp_obj_is_float(obj)) {
mp_float_t fvalue = mp_obj_get_float(obj);
CREATE_SINGLE_ITEM(ndarray, mp_float_t, NDARRAY_FLOAT, fvalue);
} else if(MP_OBJ_IS_TYPE(obj, &ulab_ndarray_type)){
ndarray = MP_OBJ_TO_PTR(obj);
} else {
mp_raise_TypeError(translate("wrong operand type"));
}
return ndarray;
}
mp_obj_t ndarray_binary_op(mp_binary_op_t _op, mp_obj_t lhs, mp_obj_t rhs) {
// if the ndarray stands on the right hand side of the expression, simply swap the operands
ndarray_obj_t *ol, *or;
mp_binary_op_t op = _op;
if((op == MP_BINARY_OP_REVERSE_ADD) || (op == MP_BINARY_OP_REVERSE_MULTIPLY) ||
(op == MP_BINARY_OP_REVERSE_POWER) || (op == MP_BINARY_OP_REVERSE_SUBTRACT) ||
(op == MP_BINARY_OP_REVERSE_TRUE_DIVIDE)) {
ol = ndarray_from_mp_obj(rhs);
or = ndarray_from_mp_obj(lhs);
} else {
ol = ndarray_from_mp_obj(lhs);
or = ndarray_from_mp_obj(rhs);
}
if(op == MP_BINARY_OP_REVERSE_ADD) {
op = MP_BINARY_OP_ADD;
} else if(op == MP_BINARY_OP_REVERSE_MULTIPLY) {
op = MP_BINARY_OP_MULTIPLY;
} else if(op == MP_BINARY_OP_REVERSE_POWER) {
op = MP_BINARY_OP_POWER;
} else if(op == MP_BINARY_OP_REVERSE_SUBTRACT) {
op = MP_BINARY_OP_SUBTRACT;
} else if(op == MP_BINARY_OP_REVERSE_TRUE_DIVIDE) {
op = MP_BINARY_OP_TRUE_DIVIDE;
}
// One of the operands is a scalar
// TODO: conform to numpy with the upcasting
// TODO: implement in-place operators
// these are partial broadcasting rules: either the two arrays
// are of the same shape, or one of them is of length 1
if(((ol->m != or->m) || (ol->n != or->n))) {
if((ol->array->len != 1) && (or->array->len != 1)) {
if(op == MP_BINARY_OP_EQUAL) {
return mp_const_false;
} else if(op == MP_BINARY_OP_NOT_EQUAL) {
return mp_const_true;
}
mp_raise_ValueError(translate("operands could not be broadcast together"));
}
}
uint8_t linc = ol->array->len == 1 ? 0 : 1;
uint8_t rinc = or->array->len == 1 ? 0 : 1;
// do the partial broadcasting here
size_t m = MAX(ol->m, or->m);
size_t n = MAX(ol->n, or->n);
size_t len = MAX(ol->array->len, or->array->len);
if((ol->array->len == 0) || (or->array->len == 0)) {
len = 0;
}
switch(op) {
case MP_BINARY_OP_EQUAL:
case MP_BINARY_OP_NOT_EQUAL:
case MP_BINARY_OP_LESS:
case MP_BINARY_OP_LESS_EQUAL:
case MP_BINARY_OP_MORE:
case MP_BINARY_OP_MORE_EQUAL:
case MP_BINARY_OP_ADD:
case MP_BINARY_OP_SUBTRACT:
case MP_BINARY_OP_TRUE_DIVIDE:
case MP_BINARY_OP_MULTIPLY:
case MP_BINARY_OP_POWER:
// TODO: I believe, this part can be made significantly smaller (compiled size)
// by doing only the typecasting in the large ifs, and moving the loops outside
// These are the upcasting rules
// float always becomes float
// operation on identical types preserves type
// uint8 + int8 => int16
// uint8 + int16 => int16
// uint8 + uint16 => uint16
// int8 + int16 => int16
// int8 + uint16 => uint16
// uint16 + int16 => float
// The parameters of RUN_BINARY_LOOP are
// typecode of result, type_out, type_left, type_right, lhs operand, rhs operand, operator
if(ol->array->typecode == NDARRAY_UINT8) {
if(or->array->typecode == NDARRAY_UINT8) {
RUN_BINARY_LOOP(NDARRAY_UINT8, uint8_t, uint8_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT8) {
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, uint8_t, int8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_UINT16) {
RUN_BINARY_LOOP(NDARRAY_UINT16, uint16_t, uint8_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT16) {
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, uint8_t, int16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_FLOAT) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, uint8_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
}
} else if(ol->array->typecode == NDARRAY_INT8) {
if(or->array->typecode == NDARRAY_UINT8) {
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int8_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT8) {
RUN_BINARY_LOOP(NDARRAY_INT8, int8_t, int8_t, int8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_UINT16) {
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int8_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT16) {
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int8_t, int16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_FLOAT) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, int8_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
}
} else if(ol->array->typecode == NDARRAY_UINT16) {
if(or->array->typecode == NDARRAY_UINT8) {
RUN_BINARY_LOOP(NDARRAY_UINT16, uint16_t, uint16_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT8) {
RUN_BINARY_LOOP(NDARRAY_UINT16, uint16_t, uint16_t, int8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_UINT16) {
RUN_BINARY_LOOP(NDARRAY_UINT16, uint16_t, uint16_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT16) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, uint16_t, int16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_FLOAT) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, uint16_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
}
} else if(ol->array->typecode == NDARRAY_INT16) {
if(or->array->typecode == NDARRAY_UINT8) {
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int16_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT8) {
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int16_t, int8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_UINT16) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, int16_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT16) {
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int16_t, int16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_FLOAT) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, uint16_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
}
} else if(ol->array->typecode == NDARRAY_FLOAT) {
if(or->array->typecode == NDARRAY_UINT8) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT8) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, int8_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_UINT16) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_INT16) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, int16_t, ol, or, op, m, n, len, linc, rinc);
} else if(or->array->typecode == NDARRAY_FLOAT) {
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
}
}
// this instruction should never be reached, but we have to make the compiler happy
return MP_OBJ_NULL;
break;
default:
return MP_OBJ_NULL; // op not supported
break;
}
return MP_OBJ_NULL;
}
#endif
mp_obj_t ndarray_unary_op(mp_unary_op_t op, mp_obj_t self_in) {
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
uint8_t itemsize = mp_binary_get_size('@', self->dtype, NULL);
ndarray_obj_t *ndarray = NULL;
switch (op) {
case MP_UNARY_OP_LEN:
if(self->ndim > 1) {
return mp_obj_new_int(self->ndim);
} else {
return mp_obj_new_int(self->len);
}
break;
case MP_UNARY_OP_INVERT:
if(self->dtype == NDARRAY_FLOAT) {
mp_raise_ValueError(translate("operation is not supported for given type"));
}
// we can invert the content byte by byte, no need to distinguish between different dtypes
ndarray = ndarray_copy_view(self); // from this point, this is a dense copy
uint8_t *array = (uint8_t *)ndarray->array;
if(ndarray->boolean) {
for(size_t i=0; i < ndarray->len; i++, array++) *array = *array ^ 0x01;
} else {
for(size_t i=0; i < ndarray->len*itemsize; i++, array++) *array ^= 0xFF;
}
return MP_OBJ_FROM_PTR(ndarray);
break;
case MP_UNARY_OP_NEGATIVE:
ndarray = ndarray_copy_view(self); // from this point, this is a dense copy
if(self->dtype == NDARRAY_UINT8) {
uint8_t *array = (uint8_t *)ndarray->array;
for(size_t i=0; i < self->len; i++, array++) *array = -(*array);
} else if(self->dtype == NDARRAY_INT8) {
int8_t *array = (int8_t *)ndarray->array;
for(size_t i=0; i < self->len; i++, array++) *array = -(*array);
} else if(self->dtype == NDARRAY_UINT16) {
uint16_t *array = (uint16_t *)ndarray->array;
for(size_t i=0; i < self->len; i++, array++) *array = -(*array);
} else if(self->dtype == NDARRAY_INT16) {
int16_t *array = (int16_t *)ndarray->array;
for(size_t i=0; i < self->len; i++, array++) *array = -(*array);
} else {
mp_float_t *array = (mp_float_t *)ndarray->array;
for(size_t i=0; i < self->len; i++, array++) *array = -(*array);
}
return MP_OBJ_FROM_PTR(ndarray);
break;
case MP_UNARY_OP_POSITIVE:
return MP_OBJ_FROM_PTR(ndarray_copy_view(self));
case MP_UNARY_OP_ABS:
ndarray = ndarray_copy_view(self);
// if Booleam, NDARRAY_UINT8, or NDARRAY_UINT16, there is nothing to do
if(self->dtype == NDARRAY_INT8) {
int8_t *array = (int8_t *)ndarray->array;
for(size_t i=0; i < self->len; i++, array++) {
if(*array < 0) *array = -(*array);
}
} else if(self->dtype == NDARRAY_INT16) {
int16_t *array = (int16_t *)ndarray->array;
for(size_t i=0; i < self->len; i++, array++) {
if(*array < 0) *array = -(*array);
}
} else {
mp_float_t *array = (mp_float_t *)ndarray->array;
for(size_t i=0; i < self->len; i++, array++) {
if(*array < 0) *array = -(*array);
}
}
return MP_OBJ_FROM_PTR(ndarray);
break;
default: return MP_OBJ_NULL; // operator not supported
}
}
mp_obj_t ndarray_transpose(mp_obj_t self_in) {
// TODO: check, what happens to the offset here, if we have a view
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
size_t *shape = m_new(size_t, self->ndim);
int32_t *strides = m_new(int32_t, self->ndim);
for(uint8_t i=0; i < self->ndim; i++) {
shape[i] = self->shape[self->ndim-1-i];
strides[i] = self->strides[self->ndim-1-i];
}
// TODO: I am not sure ndarray_new_view is OK here...
// should be deep copy...
ndarray_obj_t *ndarray = ndarray_new_view(self, self->ndim, shape, strides, 0);
return MP_OBJ_FROM_PTR(ndarray);
}
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_transpose_obj, ndarray_transpose);
mp_obj_t ndarray_reshape(mp_obj_t oin, mp_obj_t _shape) {
// There is an error somewhere here: a = ulab.array(range(10)).reshape((2, 5)) won't work
ndarray_obj_t *ndarray_in = MP_OBJ_TO_PTR(oin);
if(!MP_OBJ_IS_TYPE(_shape, &mp_type_tuple)) {
mp_raise_TypeError(translate("shape must be a tuple"));
}
mp_obj_tuple_t *shape = MP_OBJ_TO_PTR(_shape);
if(shape->len > ULAB_MAX_DIMS) {
mp_raise_ValueError(translate("maximum number of dimensions is 4"));
}
size_t *new_shape = m_new(size_t, shape->len);
size_t new_length = 1;
for(uint8_t i=0; i < shape->len; i++) {
new_shape[i] = mp_obj_get_int(shape->items[i]);
new_length *= new_shape[i];
}
if(ndarray_in->len != new_length) {
mp_raise_ValueError(translate("input and output shapes are not compatible"));
}
ndarray_obj_t *ndarray;
if(ndarray_is_dense(ndarray_in)) {
// TODO: check if this is what numpy does
int32_t *new_strides = strides_from_shape(new_shape, shape->len);
ndarray = ndarray_new_view(ndarray_in, shape->len, new_shape, new_strides, 0);
} else {
ndarray = ndarray_new_ndarray_from_tuple(shape, ndarray_in->dtype);
ndarray_copy_array(ndarray_in, ndarray);
}
return MP_OBJ_FROM_PTR(ndarray);
}
MP_DEFINE_CONST_FUN_OBJ_2(ndarray_reshape_obj, ndarray_reshape);
mp_int_t ndarray_get_buffer(mp_obj_t self_in, mp_buffer_info_t *bufinfo, mp_uint_t flags) {
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
// buffer_p.get_buffer() returns zero for success, while mp_get_buffer returns true for success
return !mp_get_buffer(self->array, bufinfo, flags);
}
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